B.Sc. Information System Technology (PO 2015)€¦ · B.Sc. Information System Technology (PO 2015) Module manual Date: 01.03.2020 StudyAreaInformationSystemTechnol-ogy

B.Sc. Information SystemTechnology(PO 2015)Module manualDate: 01.03.2020

Study Area Information System Technol-ogy

Module manual: B.Sc. Information System Technology (PO 2015)

Date: 01.03.2020

Study Area Information System TechnologyEmail: [email protected]

I

Contents

1 Fundamentals 1

1.1 Fundamentals of Mathematics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Mathematics I (Electrical Engineering) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Mathematics II (Electrical Engineering) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2Mathematics III (Electrical Engineering) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Numerical and Statistical Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.2 Fundamentals of Electrical Engineering and Information Technology . . . . . . . . . . . . . . . . . . . . 51.2.1 Electrical Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Introductionary Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5Electrical Engineering and Information Technology I . . . . . . . . . . . . . . . . . . . . . . . . . . 6Electrical Engineering and Information Technology Lab I . . . . . . . . . . . . . . . . . . . . . . . 7Electrical Engineering and Information Technology II . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.2.2 Information Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Deterministic Signals and Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Fundamentals of Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Electronics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

1.3 Foundations of Computer Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171.3.1 Programming Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Functional and Object-oriented Programming Concepts . . . . . . . . . . . . . . . . . . . . . . . . 17Algorithms and Data Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

1.3.2 Digital Design / Logic Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21Digital Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21Logic Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

1.3.3 Computer Systems / Computer Organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24Computer Organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24Computer Systems I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

1.3.4 Systems and Parallel Programming & Operating Systems . . . . . . . . . . . . . . . . . . . . . . . 26System and Parallel Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26Operating Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

1.3.5 Software-Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29Software Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29Software Engineering - Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

2 Options - Optionals 32

2.1 Optional Subjects CTS: Communication Technology and Communication Systems . . . . . . . . . . . . 32Advances in Digital Signal Processing: Imaging and Image Processing . . . . . . . . . . . . . . . . . . . . 32Communication Technology II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34Digital Signal Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36Information Theory II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37Communication Networks IV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38Mobile Communications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40Speech and Audio Signal Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42Digital Signal Processing Lab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44Multimedia Communications Lab II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45Multimedia Communications Project Seminar II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47Project Seminar Wireless Communications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49Multimedia Communications Seminar II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

II

Adaptive Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51Advanced Topics in Statistical Signal Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53Antennas and Adaptive Beamforming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54Fundamentals of Signal Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56Microwave Engineering I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58Information Theory I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59Convex Optimization in Signal Processing and Communications . . . . . . . . . . . . . . . . . . . . . . . 60Microwave Engineering II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61MIMO - Communication and Space-Time-Coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62Laboratory Communication and Sensor Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63Multimedia Communications Project II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64Project Seminar Communication and Sensor Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66Project Seminar Communication and Sensor Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67Project Seminar Communication and Sensor Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68Project Seminar Communication and Sensor Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69Projekt Seminar Advanced Algorithms for Smart Antenna Systems . . . . . . . . . . . . . . . . . . . . . . 70Advanced Seminar on Networking, Security, Mobility, and Wireless Communications . . . . . . . . . . 71Wireless Network for Emergency Response: Fundamentals, Design, and Build-up from Scratch . . . . 73Internet - Practical Course Telecooperation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75Communication Networks I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76Mobile Networking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78Peer-to-Peer Middleware Lab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80Multimedia Communications Lab I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81Practical Project Telecooperation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83Multimedia Communications Project I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84Network, Traffic and Quality Management for Internet Services . . . . . . . . . . . . . . . . . . . . . . . 86Resilient Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87Multimedia Communications Seminar I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89Seminar Smart City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90Software Defined Networking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91Seminar Telecooperation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92TK1: Distributed Systems and Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93Ubiquitous computing in business processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95Radar Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97Computer Networks and Distributed Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98TK2: Human Computer Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100Microwave Measurement Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102Sensor Array Processing and Adaptive Beamforming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104Machine Learning in Information and Communication Technology (ICT) . . . . . . . . . . . . . . . . . . 106Robust Signal Processing With Biomedical Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108Robust and Biomedical Signal Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110Acoustics I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112TK3: Ubiquitous / Mobile Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113Optical Communications 1 – Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115International Summer School ’Microwaves and Lightwaves’ . . . . . . . . . . . . . . . . . . . . . . . . . . 117IoT and wireless protocols in embedded systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118Data Science I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

2.2 Optional Subjects SES: System on Chip and Embedded Systems . . . . . . . . . . . . . . . . . . . . . . . 121High-Level Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121Low-Level Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122Microprocessor Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123Verification Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124Labs on Adaptive Computing Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125Advanced Integrated Circuit Design Lab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127HDL Lab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

Contents III

Project Seminar Design for Testability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129Projektseminar Rekonfigurable Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130Seminar Integrated Electronic Systems Design A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131Seminar on Computer Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132Computer Aided Design for SoCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134Analog Integrated Circuit Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135Digital Design Lab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136Industrial Colloquium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137Labs on Computer Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138Printed Electronics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139Processor Microarchitecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140Project Seminar Integrated Electronic Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142Project Seminar Computer Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143Embedded Systems Hands-On 1: Design and Implementation of Hardware-Software Systems . . . . . 144Embedded Systems Hands-On 2: Designing Hardware Accelerators for Systems-on-Chip . . . . . . . . 145Sensor Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146Advanced Topics in Embedded Systems and Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148Mastering Modern Embedded System Processors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150

2.3 Optional Subjects SWE: Software-Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151Advanced Compiler Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151Optimizing Compiler Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153Seminar Software System Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154C/C++ Programming Lab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155Real-Time Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156Formal Principles of Computer Science III . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157Concepts of Programming Languages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158Compiler Construction Lab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160Programming parallel computer architectures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161Autonomous Driving Lab I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163Autonomous Driving Lab II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165Projektseminar Software Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166Software Engineering - Projectmanagement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168Software Engineering in industrial practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169Modeling, Specification and Semantics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170Introduction to Compiler Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172Static and Dynamic Program Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173

3 Applications 174

3.1 Optional Subjects AIS-AS: Automotive Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174Ride and Handling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174Automotive Mechatronics and Assistance Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176System Dynamics and Automatic Control Systems I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178Technical Thermodynamics I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179Automotive Development Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181ADP (4 CP) Internal Combustion Engines and Powertrain Systems . . . . . . . . . . . . . . . . . . . . . . 182ADP (6 CP) Automotive Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183Tutorial Automotive Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184Research Seminar Automotive Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185Optical Technologies in Car Lighting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186Avionics System Safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187Combustion Engines I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188Technical Mechanics for Electrical Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190Motor Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192Laboratory Control Engineering I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193Laboratory Matlab/Simulink I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194

Contents IV

Project seminar Applications of Lighting Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195Project seminar Advanced Applications of Lighting Engineering . . . . . . . . . . . . . . . . . . . . . . . . 196Project seminar Special Applications of Lighting Engineering . . . . . . . . . . . . . . . . . . . . . . . . . 197Fundamentals of Navigation I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198Space Debris – Risks, Surveillance and Mitigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199Tutorial Advanced Cax Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201

3.2 Optional Subjects AIS-IA: Intelligent Systems and Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . 202Natural Language Processing and the Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202Web-Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204Practical Lab Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207Practical Course in Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208Data Mining and Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209Seminar Data Mining and Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211Knowledge Engineering and Learning in Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212Statistical Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213Deep Learning for Natural Language Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215Project Lab Deep Learning in Computer Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216Learning and Educational Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217Algorithmic Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219Efficient Graph Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220Practical Lab Advanced Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222Fuzzy Logic, Neural Networks and Evolutionary Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . 223Concepts and Technologies for Distributed Systems and Big Data Processing . . . . . . . . . . . . . . . . 224Foundations of Language Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226Bioinformatics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228Optimization Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229Information Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230Ambient Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232Data Science Practical Course . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234Automata, Formal Languages and Decidability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235Propositional Logic and Predicate Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236Extended Seminar - Systems and Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238Deep Learning: Architectures & Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239Reinforcement Learning: From Foundations to Deep Approaches . . . . . . . . . . . . . . . . . . . . . . . 241Text Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242Matrix Analysis and Computations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243Algorithmic modeling for creating schedules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245Machine Learning and Deep Learning for Automation Systems . . . . . . . . . . . . . . . . . . . . . . . . 246

3.3 Optional Subjects AIS-IE: Information Processing in Electrical Power Engineering . . . . . . . . . . . . 247Advanced Power Electronics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247Real Time Applications and Communication with Microcontrollers and programmable Logic Devices 249Power Systems II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251Energy Converters - CAD and System Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252Power Laboratory I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254Power Laboratory II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255Application, Simulation and Control of Power Electronic Systems . . . . . . . . . . . . . . . . . . . . . . 256Energy Management and Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257Electromagnetic Compatibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259Electrical Power Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260Large Generators and High Power Drives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262High Voltage Measuring Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263Overvoltage Protection and Insulation Coordination in Power System . . . . . . . . . . . . . . . . . . . . 265Power Systems I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267Electrical Machines and Drives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268Power Electronics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270

Contents V

High Voltage Switchgear and Substations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272High Voltage Technology I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273High Voltage Technology II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275New Technologies of Electrical Energy Converters and Actuators . . . . . . . . . . . . . . . . . . . . . . . 277Design of Electrical Machines and Actuators with Numerical Field Calculation . . . . . . . . . . . . . . 279Physics and Technology of Accelerators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280Machine Learning & Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281Electric Railways . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283

3.4 Optional Subjects AIS-MT: Medical Technics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284Fuzzy Logic, Neural Networks and Evolutionary Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . 284Visualization in Medicine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286Current Trends in Medical Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287Computer-aided planning and navigation in medicine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289Microsystem Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291Sensor Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292Evolutionary Systems - From Biology to Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294Computational Modeling for the IGEM Competition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295Medical Image Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 296Signal Detection and Parameter Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297Technology of Microsystems Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299Sensor Signal Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 300Measuring Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301Bioinformatics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303Analysis and Synthesis of Human Movements I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304Deep Learning for Medical Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305

3.5 Optional Subjects AIS-CSR: Control Systems and Robotics . . . . . . . . . . . . . . . . . . . . . . . . . . . 306Digital Control Systems I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306Foundations of Robotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307Modeling and Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309System Dynamics and Automatic Control Systems II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310System Dynamics and Automatic Control Systems III . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311Integrated Robotics Project 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312Integrated Robotics Project 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313Robot Learning: Integrated Project - Part 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314Robot Learning: Integrated Project - Part 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315Laboratory Control Engineering II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316Laboratory Matlab/Simulink II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317Project Course Control Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318Project Seminar Robotics and Computational Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . 319Robust Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320Computational Engineering and Robotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321Practical Training with Drives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323Acceleration of Charged Particles in Electromagnetic Fields . . . . . . . . . . . . . . . . . . . . . . . . . . 324Control of Drives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325Digital Control Systems II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327Technical Mechanics for Electrical Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 328System Dynamics and Automatic Control Systems I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 330Measuring Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331Electromechanical Systems I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333Introduction to Electrodynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334Railway Vehicle Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335Identification of Dynamic Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336Robot Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338Controller Design for Multivariable Systems in State Space . . . . . . . . . . . . . . . . . . . . . . . . . . 340New Technologies of Electrical Energy Converters and Actuators . . . . . . . . . . . . . . . . . . . . . . . 341

Contents VI

Design of Electrical Machines and Actuators with Numerical Field Calculation . . . . . . . . . . . . . . 343Physics and Technology of Accelerators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344Actuators for Mechatronic Systems Lab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345Electromechanical Systems Lab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346Laboratory Matlab/Simulink I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347Laboratory Control Engineering I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 348Planning and Application of Electrical Drives (Drives for Electric Vehicles) . . . . . . . . . . . . . . . . . 349Project Seminar Automatic Control Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350Project Course Practical Application of Mechatronics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351Computational Electromagnetics and Applications III . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 352Micro Actuators and Small Motors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353Optimization of static and dynamic systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354Computer Aided Design (CAD) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 356Fundamentals of Navigation I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357Machine Learning and Deep Learning for Automation Systems . . . . . . . . . . . . . . . . . . . . . . . . 358Tutorial Advanced Cax Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359

3.6 Optional Subjects AIS-SS: Secure Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 360Embedded System Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 360IT Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 362Physical Layer Security in Wireless Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364Secure Mobile Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366Lab Exercise on Secure Mobile Networking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368Project on Secure Mobile Networking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 370Introduction to Cryptography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 372Computer Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374Electronic Voting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376Cryptography, Privacy and Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378Formal Methods for Information Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380IT Security Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382Cryptography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383Network Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385Cryptographic Gems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387Public Key Infrastructures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 388Secure, Trusted and Trustworthy Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391Security of Critical Infrastructures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393Seminar on Networking, Security, Mobility, and Wireless Communications . . . . . . . . . . . . . . . . . 394Security in Multimedia Systems and Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396Safty of railway signaling systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398Blockchain Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399Secure Computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400Real World Cryptography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401Practical Lab on System and IoT Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402Cybersecurity Lab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403Protection in Networked Systems - Trust, Resilience, and Privacy . . . . . . . . . . . . . . . . . . . . . . . 404Protection in Infrastructures and Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 406Cryptographic Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407Mobile Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 408Lab Peace-, Security and Crisis Informatics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 410Crisis, Security and Peace Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411Safe and secure critical infrastructure using railway systems as an example . . . . . . . . . . . . . . . . 413Lab Blockchain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415

3.7 Optional Subjects AIS-VC: Visual Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 416Capturing Reality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 416Computer Vision I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 418Computer Vision II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 420

Contents VII

Computer Graphics I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 422Geometric Methods of CAE/CAD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424Programming Massively Parallel Processors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 426Advanced Visual Computing Lab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427Virtual and Augmented Reality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 428Hardware Design for Video Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4303D Animation & Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431Ambient Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432Image Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434Computer Vision in Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435Visual Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437Computer Graphics II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439Principles of CAE/CAD I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441Information Visualization and Visual Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 442Probabilistic Graphical Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 444Visual Computing Lab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446Scale Space and PDE methods in image analysis and processing . . . . . . . . . . . . . . . . . . . . . . . 447Serious Games Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449Serious Games Seminar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 450Visual Analytics: Interactive Visualization of Very Large Data . . . . . . . . . . . . . . . . . . . . . . . . . 451Visualization and Animation of Algorithms and Data Structures . . . . . . . . . . . . . . . . . . . . . . . 452Serious Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 453Applied Topics in Computer Graphics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455User-Centered Design in Visual Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456Advanced User Interfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 458

3.8 Optional Subjects AIS-EC: Economics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459Introduction to Business Administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459Introduction to Economics (V) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461Financial and Management Accounting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 462Accounting and Finance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 464Management and Marketing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 466Operations Research / Production and Supply Chain Management . . . . . . . . . . . . . . . . . . . . . . 468Macroeconomics I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 470Economic and Financial Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 471Economic Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 472Management Accounting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474IT Project Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 476Technology and Innovation Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 478Introduction to Innovation Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 480

3.9 Optional Subjects AIS-EI: Entrepreneurship and Management . . . . . . . . . . . . . . . . . . . . . . . . . 481Introduction to Entrepreneurship . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481Technology and Innovation Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483Introduction to Innovation Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485Introduction to Business Administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 486Introduction to Law . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 488Digital Product and Service Marketing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489Leadership and Human Resource Management Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491German and International Law of Business Transactions and Corporation Law I . . . . . . . . . . . . . . 493Masterseminar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495Project Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 496Introduction to Project Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 498

3.10 Optional Subjects AIS-TE: Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499Product Development Methodology I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499Product Development Methodology II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 500Product Development Methodology III . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501

Contents VIII

Product Development Methodology IV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 502Technology of Micro- and Precision Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503Sensor Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 504Introduction 3D-Printing and Additive Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 506Tutorial in 3D-Printing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 507Lighting Technology I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 508Advanced Lighting Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 509

4 Studium Generale 510Mentoring (für iST) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 510

Contents IX

1 Fundamentals

1.1 Fundamentals of Mathematics

Module nameMathematics I (Electrical Engineering)

Module Nr. Credit Points Workload Self study Duration Cycle offered04-00-0108 8 CP 240 h 150 h 1 Every 2. Sem.

Language Module ownerGerman Apl. Prof. Dr. rer. nat. Steffen Roch

1 ContentBasics, real and complex numbers, real funktions, continuity, differential andintegral calculus in one variable, vector spaces, linear mappings, systems oflinear equations

2 Learning objectives / Learning Outcomes

3 Recommended prerequisite for participation

4 Form of examinationModule Final Examination:

• Module Examination (Technical Examination, Technical Examination, Standard Grading System)

5 GradingModule Final Examination:

• Module Examination (Technical Examination, Technical Examination, Weighting: 100 %)

6 Usability of this module

7 Grade bonus compliant to §25 (2)

8 References

Courses

Course Nr. Course name04-00-0126-vu Mathematics I (Electical Engineering)

Instructor Type SWSApl. Prof. Dr. rer. nat. Steffen Roch Lecture & Prac-

tice6

1

Module nameMathematics II (Electrical Engineering)



1 ContentDeterminants, eigenvalues, quadratic forms, sequences and series of functions,Taylor and Fourier series, differentiala calculus in Rn , extrema, inverseand implicit functions, path integrals, integration in Rn









8 References

Courses

Course Nr. Course name04-00-0079-vu Mathematics II (Electrical Engineering)


tice6

1.1 Fundamentals of Mathematics 2

Module nameMathematics III (Electrical Engineering)



1 Contentintegral calculus: surface integrals, integral theorems; ordinary differentialequations: linear and non-linear differential equations, existence and uniquenessof solutions, elementary techniques, linear systems with constantcoefficients, Laplace transform; Complex Analysis: complex functions, complexdifferentiation, Cauchy’s integral formula, power series and Laurentseries, residues, residue theorem









8 References

Courses

Course Nr. Course name04-00-0127-vu Mathematics III (Electrical Engineering)


tice4


Module nameNumerical and Statistical Methods


Language Module ownerGerman Prof. Dr. rer. nat. Stefan Ulbrich

1 ContentNumerical Analysis: linear equations, interpolation, numerical integration,systems of nonlinear equations, initial value problems for ODEs, numericalmethods for eigenvalue problemsStatistics: basic concepts of statistics and probability theory, regression,multivariate distributions, methods of estimation, confidence intervals, testsfor normally distributed random variables, robust statistics









8 References

Courses

Course Nr. Course name04-00-0081-vu Numerical and Statistical Methods

Instructor Type SWSProf. Dr. rer. nat. Stefan Ulbrich Lecture & Prac-

tice6


1.2 Fundamentals of Electrical Engineering and Information Technology

1.2.1 Electrical Engineering

Module nameIntroductionary Project

Module Nr. Credit Points Workload Self study Duration Cycle offered18-de-1010 2 CP 60 h 30 h 1 WiSe

Language Module ownerGerman Prof. Dr. rer. nat. Andreas Schürr

1 ContentBased on a complex technical problem students will get to know an idea of the diversity of electrical andinformation enginering. The introductionary project gives a perspective of the upcoming course of studies.It gives an introduction in engineering thinking and working. Groups of students will work in teams forone complete week. Each group of students will be accompanied by a team- and a technical tutor.

2 Learning objectives / Learning OutcomesStudents get to know problem analysis, information acquisition, team work, project management, andpresentation of results.



• Module Examination (Study Achievement, Oral Examination, Duration: 15 min, Pass/Fail GradingSystem)


• Module Examination (Study Achievement, Oral Examination, Weighting: 100 %)

6 Usability of this moduleBSc ETiT, BSc MEC, BSc iST


8 Referenceslecture notes (will be handed out)

Courses

Course Nr. Course name18-de-1010-pj Introductionary Project (Project Week)

Instructor Type SWSProf. Dr. rer. nat. Andreas Schürr Project 2

1.2 Fundamentals of Electrical Engineering and Information Technology 5

Module nameElectrical Engineering and Information Technology I

Module Nr. Credit Points Workload Self study Duration Cycle offered18-hs-1070 7 CP 210 h 135 h 1 WiSe

Language Module ownerGerman Prof. Dr.-Ing. Jutta Hanson

1 ContentUnits and Equations: Unit systems, equation writing.Basic definitions: Charge, current, voltage, resistance, energy and power.Currents and voltages in electrical circuits: Ohmic law, node and mesh equations, parallel and seriesconnections, current and voltage measurement, linear and nonlinear elements, superposition method, star-delta-transformation, node and mesh analysis in linear circuits, controlled sources.AC systems: Time-dependent currents and voltages, steady-state mode sinusoidal currents and voltagesin linear RLC-circuits, phasor diagrams, resonances in RLC circuits, AC power, locus diagrams, two-portnetworks, transformer, polyphase systems.

2 Learning objectives / Learning OutcomesStudents will be able after visiting this lecture* to utilize the basic equations in electrical engineering,* to determine the currents and voltages in linear and nonlinear circuits,* to analyze DC and AC systems,* to calculate simple filter and resonant circuits,* to apply the complex calculation in electrical AC systems.



• Module Examination (Technical Examination, Written Examination, Duration: 90 min, StandardGrading System)


• Module Examination (Technical Examination, Written Examination, Weighting: 100 %)

6 Usability of this moduleBSc. ETiT, BSc iST, BSc MEC, BSc. Wi-ETiT, BSc CE, LA Physik/Mathematik


8 ReferencesFrohne, H. u.a. Moeller Grundlagen der ElektrotechnikClausert, H. u.a. Grundgebiete der Elektrotechnik 1 + 2

Courses

Course Nr. Course name18-hs-1070-vl Electrical Engineering and Information Technology I

Instructor Type SWSProf. Dr.-Ing. Jutta Hanson Lecture 3

Course Nr. Course name18-hs-1070-ue Electrical Engineering and Information Technology I

Instructor Type SWSProf. Dr.-Ing. Jutta Hanson Practice 2


Module nameElectrical Engineering and Information Technology Lab I

Module Nr. Credit Points Workload Self study Duration Cycle offered18-kn-1040 4 CP 120 h 0 h 2 WiSe

Language Module ownerGerman Prof. Dr. Mario Kupnik

1 ContentAfter a safety instruction for electrical equipment, students dolab experiments covering foundations ofelectrical engineering by using theoretical and experimental instructions to improve basic electrical under-standing. Building up a test set autonomously and performing of measurements and evaluations in theform of logs to confirm the theoretical knowledge and lead to independent work in practice.The following experiments are performed:

• Investigate real behavior of ohmic resistors• Investigate real behavior of capacitors and inductors• Calculate impedances of basic two-terminal circuits using network theory• Measure of electrical power in AC circuits and investigate in the real behaviour of transformers• DC technology, capacity and inductors, AC technology - Impedances and two-terminal circuits,

transformer & power;

2 Learning objectives / Learning OutcomesAfter preparing the afternoons independently and self-implementing the measurement setup and measure-ment tasks by active participation in the practical group and by thorough preparation of the associatedmeasurement protocols, you should be able to:

• Perform the measurement of basic electrical parameters of DC and AC circuits, independently and incompliance with safety rules

• measuring the frequency response of passive electrical networks and resonant circuits, and electricpower measurement

• the measurement of circuits for the determination of magnetic, electro-thermal and high-frequency.You have to be able to build and run your own measurements

• interpretations of the measurement results in terms of its technical meaning, but also their accuracyand error sources safely.

3 Recommended prerequisite for participationParallel attending the lectures and exercises, “Electrical Engineering I and II”


• Module Examination (Study Achievement, Optional, Standard Grading System)


• Module Examination (Study Achievement, Optional, Weighting: 100 %)

6 Usability of this moduleBSc ETiT


8 Referencesdetailed script with instructions for the experiments; Clausert, H. / Wiesemann, G.: Grundgebiete derElektrotechnik, Oldenbourg,1999

Courses


Course Nr. Course name18-kn-1040-pr Electrical Engineering and Information Technology Lab I A

Instructor Type SWSProf. Dr. Mario Kupnik Internship 2

Course Nr. Course name18-kn-1041-pr Electrical Engineering and Information Technology Lab I B


Course Nr. Course name18-kn-1040-tt Electrical Engineering and Information Technology I, Safety instructions and rules

Instructor Type SWSProf. Dr. Mario Kupnik Tutorial 0


Module nameElectrical Engineering and Information Technology II

Module Nr. Credit Points Workload Self study Duration Cycle offered18-gt-1020 7 CP 210 h 135 h 1 SoSe

Language Module ownerGerman Prof. Dr.-Ing. Gerd Griepentrog

1 ContentElectrostatic fields; stationary electrical flow fields; stationary magnetic fields; temporally variable mag-netic fields; capacitor networks, transmission lines

2 Learning objectives / Learning OutcomesThe students have detached themselves from the conception that all electrical procedures are line- bound;they have a clear idea of the field term, can read and interpret field plots and also design simple field plotsthemselves; they understand the difference between a curl and a divergence field, can describe this differ-ence mathematically and are able to recognize the field type from a mathematical description, respectively;they are able to calculate field distributions for simple rotationally symmetric arrangements analytically;they can deal surely with the definitions of the electrostatic, the electrical quasi-static, the magnetostaticand the magneto-electric field; they have recognized the connection and dualism of electricity and mag-netism; they control the mathematical apparatus necessary for their description and can apply it to simpleexamples; they can calculate with nonlinear magnetic circuits; they can compute inductance, capacity andresistance of simple geometrical arrangements and understand them now as physical characteristics of therespective arrangement; they have recognized, how different forms of energy can be transferred into eachother and are thereby already able to solve simple scientific engineering problems; they have understoodthe underlying physical backgrounds for many applications of electrical engineering and are able to de-scribe them mathematically, develop it further in a simple way and apply it to other examples; they arefamiliar with the system of Maxwell’s equations and can transfer them from the integral into the differen-tial form; they have a first idea of the importance of Maxwell’s equations for all conceptual formulations ofelectrical engineering and they understand the propagation of electromagnetic waves in the free space andon transmission lines

3 Recommended prerequisite for participationElectrical Engineering and Information Technology I





6 Usability of this moduleBSc ETiT, BSc MEC, BSc Wi-ETiT, LA Physik/Mathematik, BSc CE, BSc iST

7 Grade bonus compliant to §25 (2)Notenverbesserung entsprechend §25 (2) APB TU Darmstadt

8 References• Downloadable slides• Clausert, Wiesemann, Hinrichsen, Stenzel: „Grundgebiete der Elektrotechnik I und II“; ISBN 978-3-

486-59719-6• Prechtl, A.: „Vorlesungen über die Grundlagen der Elektrotechnik – Band 2“ ISBN: 978-3-211-

72455-2

Courses


Course Nr. Course name18-gt-1020-vl Electrical Engineering and Information Technology II

Instructor Type SWSProf. Dr.-Ing. Gerd Griepentrog Lecture 3

Course Nr. Course name18-gt-1020-ue Electrical Engineering and Information Technology II

Instructor Type SWSProf. Dr.-Ing. Gerd Griepentrog Practice 2


1.2.2 Information Technology

Module nameDeterministic Signals and Systems

Module Nr. Credit Points Workload Self study Duration Cycle offered18-kl-1010 7 CP 210 h 135 h 1 WiSe

Language Module ownerGerman Prof. Dr.-Ing. Anja Klein

1 ContentFourier Series: Motivation; Fourier series with real coefficients; Fourier series with complex coeffi-cients;examples and applicationsFourier Transform: Motivation - Derviation from Fourier series - Dirichlet conditions - delta function - stepfunction - properties of F-transform - special cases - examples and applications - transmissions systems-expansion into partial fractionsConvolution: Time invariant systems - convolution in frequency domain- Parseval’s theorem - properties-examples and applicationsSystems and Signals: Bandlimited and time limited systems - systems with only one energy store - examplesand applicationsLaplace Transform: Motivation - single sided L-transform - inverse L-transform - theorems of L- transform- examples and applicationsLinear differential equations: Time invariant systems - rules - general differentiation - linear passive elec-trical networks - equivalent circuits for passive electrical elements - examples and applicationsz-Transform: motivation - sampling - numerical order - definition - examples - transfer function - samplingtheorem - examples and applicationsDiscrete Fourier Transform: motivation, derivation sampling, examples and applications

2 Learning objectives / Learning OutcomesThe student should understand the principles of integral transformations. He should apply them for thesolution of physical problems. The techniques of this lecture are essential tools which will be needed inmany follow-up lectures and exercises.

3 Recommended prerequisite for participationElektrotechnik und Informationstechnik I und Elektrotechnik und Informationstechnik II





6 Usability of this moduleBSc ETiT, BSc MEC, BSc Wi-ETiT, LA Physik/Mathematik, BSc CE, BSc iST


8 References


A script of the lecture or slides respectively, will be provided in electronic form.Basic Literature:Wolfgang Preuss, “Funktionaltransformationen”, Carl Hanser Verlag, 2002; Klaus-Eberhard Krueger "Trans-formationen", Vieweg Verlag, 2002;H. Clausert, G. Wiesemann "Grundgebiete der Elektrotechnik 2", Oldenbourg, 1993; Otto Föllinger"Laplace-, Fourier- und z-Transformation", Hüthig, 2003;T. Frey, M. Bossert, Signal- und Systemtheorie, Teubner Verlag, 2004Further Literature:Dieter Mueller-Wichards "Transformationen und Signale", Teubner Verlag, 1999Exercises:Hwei Hsu "Signals and Systems", Schaum’s Outlines, 1995

Courses

Course Nr. Course name18-kl-1010-vl Deterministic Signals and Systems

Instructor Type SWSProf. Dr.-Ing. Anja Klein Lecture 3

Course Nr. Course name18-kl-1010-ue Deterministic Signals and Systems

Instructor Type SWSProf. Dr.-Ing. Anja Klein Practice 2


Module nameFundamentals of Communication

Module Nr. Credit Points Workload Self study Duration Cycle offered18-jk-1010 6 CP 180 h 120 h 1 SoSe

Language Module ownerGerman Prof. Dr.-Ing. Rolf Jakoby

1 ContentPart 1: Chap. 1 will be a brief introduction in “Electrical Information- and Communication Engineering”,presenting signals as carrier of information, classifying electrical signals and describing elements of com-munication systems. Then, Chap. 2 introduces various line-conducted and wireless transmission media,power budget calculations for both media types, basics of antenna radiation and parameters etc., whichwill be emphasized by application examples like TV-satellite reception and mobile communication chan-nels.Part 2: Chap. 3 is focused on signal distortions and interferences, especially thermal noise, consideringnoisy two-port devices and its concatenations, lossy networks, antenna noise temperature and the impactof noise on analog and digital signals. This chap. ends with basics of information theory and channelcapacity for AWGN-channels. In contrast, chap 4 deals with noise-reduction and distortion-compensationmethods.Part 3: Chap. 5 introduces sampling of band-limited signals and analog modulation of a pulse carrier(pulse-amplitude- pulse-duration- and pulse-angle-modulation), which will be extended on digital modu-lation in the baseband by means of pulse-code modulation (PCM), focusing on signal quantizing, analog-digital conversion, minimum bandwidth, bit error rate and error probability of a PCM word. At least,PCM-time-division multiplex and –systems will be discussed.Part 4: Chap. 7 deals with fundamentals of multiplex- and RF-modulation schemes as well as with fre-quency conversion, frequency multiplication and mixing strategies. Then, receiver principles and imagefrequency problems of heterodyne-receivers as well as amplitude modulation of a sinus carrier will closethis chapter. Chap. 8 introduces digital modulation of a harmonic carrier, including band-limited inter-symbol interference-free transmission, matched filtering and binary shift keying of a sinusoidal carrier inamplitude (ASK), phase (PSK) or frequency (FSK). From this follows higher-order modulation schemes likeM-PSK or M-QAM. A brief outlook on the functionality of channel coding and interleaving in chap. 9 willend up the lecture.

2 Learning objectives / Learning OutcomesAim of the Lecture: To teach the fundamentals of communications (physical layer), primarily the transmis-sion of signals from a source to a sink, possible modulation and access methods as well as signal distortionand noise.The introduction of communications is a basement for further lectures like Communication Technology,Laboratories of Communication Technology (NTP A, B), Microwave Eng., Optical Communications, MobileCommunications and Terrestrial and satellite-based radio systems.

3 Recommended prerequisite for participationDeterministic Signals and Systems





6 Usability of this moduleBSc ETiT, Wi-ETiT



8 ReferencesComplete Script and Literature: Pehl, E.: Digitale und analoge Nachrichtenübertragung, Hüthig, 1998;Meyer, Martin: Kommunikationstechnik, Vieweg, 1999; Stanski, B.: Kommunikationstechnik; Kammeyer,K.D.: Nachrichtenübertragung. B.G. Teubner 1996; Mäusl, R.: Digitale Modulationsverfahren. Hüthig Ver-lag 1995; Haykin, S.: Communication Systems. John Wiley 1994; Proakis, J., Salehi M.: CommunicationSystems Engineering. Prentice Hall 1994; Ziemer, R., Peterson, R.: Digital Communication. Prentice Hall2001; Cheng, D.: Field and Wave Electromagnetics, Addision-Wesley 1992.

Courses

Course Nr. Course name18-jk-1010-vl Fundamentals of Communications

Instructor Type SWSProf. Dr.-Ing. Rolf Jakoby Lecture 3

Course Nr. Course name18-jk-1010-ue Fundamentals of Communications

Instructor Type SWSProf. Dr.-Ing. Rolf Jakoby Practice 1


Module nameElectronics

Module Nr. Credit Points Workload Self study Duration Cycle offered18-ho-1011 7 CP 210 h 135 h 1 WiSe

Language Module ownerGerman Prof. Dr.-Ing. Klaus Hofmann

1 Content18-ho-1011-vl bzw. –ue:Semiconductor Elements: Diode, MOSFET, Bipolartransistor. Electronic Circuit Design; Basic Analog Cir-cuits and their properties, Behavior and properties of operational amplifiers, circuit simulation with SPICE,small signal amplification, single stage amplifiers, frequency response; digital circuits: CMOS-logic18-ho-1011-pr:Practical experiments in the fields:

• digital circuits: FPGA-programming• analog circuits: basic building blocks, amplifiers, operational amplifiers, filters and demodulators

2 Learning objectives / Learning OutcomesA student is after successful attending the lecture able to

• analyse the behavior of diodes, MOS- and Bipolartransistors in simple circuits,• assess the properties of single-transistor amplifiers (MOSFET and BJT), such as small signal behavior,

input- and output-resistance;• design inverting and non-inverting operational amplifiers with passive components and knows the

ideal and non-ideal properties;• calculate the frequency response of simple transistor circuits;• knows the different circuit techniques (CMOS, NMOS) of logical gates and knows the basic functions

(inverter, NAND, NOR).

A student is after successful attending the lab able to• perform measurements in time and frequency domain using an oscilloscope on simple operational

amplifiers;• design and realize a traffic light controller based on a finite state machine using a FPGA as the target

implementation;• mount passive and active components on a PCB (including preparation of components, soldering)

and put the system to function,• simulate a circuit (filter) using SPICE and perform measurements on the realization.

3 Recommended prerequisite for participationBasics of Electrical Engineering



Module Eccompanying Examination:• [18-ho-1011-pr] (Study Achievement, Optional, Standard BWS)


• Module Examination (Technical Examination, Written Examination, Weighting: 4)Module Eccompanying Examination:

• [18-ho-1011-pr] (Study Achievement, Optional, Weighting: 3)

6 Usability of this moduleBSc ETiT, BSc Wi-ETiT, BSc iST, BEd



8 References

Courses

Course Nr. Course name18-ho-1011-vl Electronics

Instructor Type SWSProf. Dr.-Ing. Klaus Hofmann Lecture 2

Course Nr. Course name18-ho-1011-pr Electronics Lab

Instructor Type SWSProf. Dr.-Ing. Klaus Hofmann Internship 2

Course Nr. Course name18-ho-1011-ue Electronics

Instructor Type SWSProf. Dr.-Ing. Klaus Hofmann Practice 1


1.3 Foundations of Computer Science

1.3.1 Programming Concepts

Module nameFunctional and Object-oriented Programming Concepts


Language Module ownerGerman Prof. Dr. phil. nat. Marc Fischlin

1 ContentBasic competences in science-based, problem-oriented development of software systems. Introduction tobasic terms and principles of computer science. Development of essential programming skills. Understand-ing the role of abstraction and modeling in the field of computer science.The main topics are:- Basic concepts of programming languages- Foundations of functional programming languages- Foundations of object-oriented programming languages- Design and implementation of small software systems- Basic type systems- Fundamental data structures and algorithms and their complexity- Recursion- Simple I/O- Basics of testing- Documenting source code

2 Learning objectives / Learning OutcomesAfter successfully completing the course, the students are familiar with the foundations of functional andobject-oriented programming languages and they are able to perform the following tasks:- systematically solve small programming tasks using functional and/or object-oriented programming lan-guage concepts;- perform quality assurance using basic (unit) tests;- understand the complexity of algorithms and data structures and assess their suitability for solving spe-cific tasks;- document source code using standard tools.


4 Form of examinationModule Eccompanying Examination:

• [20-00-0004-iv] (Study Achievement, Written/Oral Examination, BWS b/nb)• [20-00-0004-iv] (Technical Examination, Written/Oral Examination, Standard BWS)

5 GradingModule Eccompanying Examination:

• [20-00-0004-iv] (Study Achievement, Written/Oral Examination, Weighting: 0 %)• [20-00-0004-iv] (Technical Examination, Written/Oral Examination, Weighting: 100 %)


1.3 Foundations of Computer Science 17

B.Sc. InformatikB.Sc. WirtschaftsinformatikB.Sc. Psychologie in ITJoint B.A. InformatikB.Sc. Sportwissenschaft und InformatikB.Sc. Computational EngineeringB.Sc. InformationssystemtechnikMay be used in other degree programs.

7 Grade bonus compliant to §25 (2)In dieser Vorlesung findet eine Anrechnung von vorlesungsbegleitenden Leistungen statt, die lt. §25 (2)der 5. Novelle der APB und den vom FB 20 am 30.3.2017 beschlossenen Anrechnungsregeln zu einerNotenverbesserung um bis zu 1.0 führen kann.

8 References- How to Design Programs; M. Felleisen et al.; The MIT Press Cambridge- Structure and Interpretation of Computer Programs; H. Abelson et al.; Springer- Thinking in Java; B. Eckel; Prentice Hall- Christian Ullenboom: Java ist auch eine Insel; Galileo Computing

Courses

Course Nr. Course name20-00-0004-iv Functional and Object-oriented Programming Concepts

Instructor Type SWSIntegratedCourse

8


Module nameAlgorithms and Data Structures



1 Content- data structures: array, list, binary search tree, b-tree, graph representation, hash table, heaps- algorithms: sorting algorithmgs, string matching, graph traversal, insertion, search, and deletion onparticular data structures, shortest path search, minimal spanning trees- asymptotic complexity- NP completeness- algorithmic strategies: Divide-and-Conquer, dynamic programming, brute-force, greedy, backtracking,meta heuristics

2 Learning objectives / Learning OutcomesIn this course students get to know fundamental data structures and algorithms and the complexity classesP, NP, and NPC. They acquire the abilities to apply fundamental principles of algorithmics and to assessand determine asymptotic complexity. Furthermore, they understand major algorithmic strategies and canapply them.

3 Recommended prerequisite for participationRecommended: Funktionale und objektorientierte Programmierkonzepte


• [20-00-0005-iv] (Technical Examination, Written/Oral Examination, Standard BWS)• [20-00-0005-iv] (Study Achievement, Written/Oral Examination, BWS b/nb)


• [20-00-0005-iv] (Technical Examination, Written/Oral Examination, Weighting: 100 %)• [20-00-0005-iv] (Study Achievement, Written/Oral Examination, Weighting: 0 %)

6 Usability of this moduleB.Sc. InformatikB.Sc. WirtschaftsinformatikB.Sc. Psychologie in ITJoint B.A. InformatikB.Sc. Sportwissenschaft und InformatikB.Sc. Computational EngineeringB.Sc. InformationssystemtechnikMay be used in other degree programs.


8 ReferencesWill be appointed in lecture.

Courses


Course Nr. Course name20-00-0005-iv Algorithms and data structures


8


1.3.2 Digital Design / Logic Design

Module nameDigital Design



1 Content- Digital Design: digital abstraction and its technological realization, number systems, logic gates, MOSFETtransistors and CMOS gates, power consumption- Combinational Logic Design: boolean equations and algebra, mapping equations to gates, multi-levellogic circuits, four-valued logic (0,1,X,Z), logic minimization, combinational building blocks, timing- Sequential Logic Design: latches, flip-flops, synchronous logic design, finite-state machines, timing, par-allelism- Hardware Description Languages: modeling of combinational and sequential circuits, structural model-ing, modeling of finite-state machines, data types, parametrized modules, testbenches- Digital Building Blocks: arithmetic circuits, fixed-/floating-point representations, sequential buildingblocks, memory arrays, logic arrays

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students are familiar with the concepts and basic building blocksof digital logic and their technological realization. They can independently apply this knowledge to designcombinational and sequential circuits having specific behaviors and are able to implement them usinga hardware description language. They can analyze digital circuits with regard to functional and non-functional characteristics.






6 Usability of this moduleB.Sc. InformatikB.Sc. InformationssystemtechnikMay be used in other degree programs.


8 ReferencesLiterature recommenations will be updated regularly, an example might be:Harris/Harris: Digital Design and Computer Architecture

Courses


Course Nr. Course name20-00-0900-iv Digital Design

Instructor Type SWSProf. Dr.-Ing. Andreas Koch Integrated

Course3


Module nameLogic Design

Module Nr. Credit Points Workload Self study Duration Cycle offered18-hb-1010 6 CP 180 h 120 h 1 WiSe

Language Module ownerGerman Prof. Dr.-Ing. Christian Hochberger

1 ContentBoolean algebra, logic gates, hardware description languages, flipflops, sequential circuits, state-diagramsand -tables, technology mapping, programmable logic circuits

2 Learning objectives / Learning OutcomesBy this module, Students will be enabled to

• rewrite boolean expressions and transform them into circuits of logic gates• analyze and synthesize digital circuits• describe digital circuits in a hardware description language• extract finite state machines from informal descriptions and implement them with synchronous

circuits






6 Usability of this moduleBSc ETiT, BSc MEC, BSc Wi-ETiT


8 ReferencesR.H. Katz: Contemporary Logic Design

Courses

Course Nr. Course name18-hb-1010-vl Logic Design

Instructor Type SWSProf. Dr.-Ing. Christian Hochberger Lecture 3

Course Nr. Course name18-hb-1010-ue Logic Design

Instructor Type SWSProf. Dr.-Ing. Christian Hochberger Practice 1


1.3.3 Computer Systems / Computer Organisation

Module nameComputer Organisation



1 Content- Architecture of Microprocessors: programming in assembly and machine language, addressing modes,tool flows, run-time environment- Microarchitecture: instruction set and architectural state, performance analysis, microarchitectures withsingle-cycle/multi-cycle/pipelined execution, exception handling, advanced microarchitectures- Memory and I/O-Systems: performance analysis, caches, virtual memory, I/O techniques, standard inter-faces

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students understand the concepts of machine-level programmingand can implement algorithms in Assembler. They are familiar with different techniques to independentlyrealize processor architectures as microarchitectures in digital logic. They understand the structure andthe operation of memory- and I/O systems and know the basics of standard interfaces. They can evaluatethe quality of different realizations in multiple performance characteristics.








8 ReferencesLiterature recommendations will be updated regularly, an example might be:Harris/Harris: Digital Design and Computer Architecture

Courses

Course Nr. Course name20-00-0902-iv Computer Organisation


Course3


Module nameComputer Systems I

Module Nr. Credit Points Workload Self study Duration Cycle offered18-hb-1020 6 CP 180 h 120 h 1 SoSe


1 ContentTypes of instruction sets, memory organization and its impact on the runtime, pipelining, instruction levelparallelism, superscalar processors, VLIW processors, floating point numbers and operations, memory sub-system, cache types, virtual address spaces, benchmarking and performance prediction, system architectureand bus systems, peripheral devices

2 Learning objectives / Learning OutcomesSuccessful students can analyze and evaluate processors, memory systems and bus systems. They cantransform structures of high-level programming languages like subroutine calls into sequences of machineinstructions. They are able to measure the performance of computers. They know how instructions areexecuted in modern processors and thus, they can predict the influence of a specific memory hierarchyonto the execution time of a given program. They know how internal and external bus systems work andcan define the essential parameters for their dimension and operation.

3 Recommended prerequisite for participationBasic knowledge of digital design as it can be obtained by the lecture “Logic Design”.





6 Usability of this moduleBSc ETiT, BSc Wi-ETiT


8 ReferencesHennessy/Patterson: Computer architecture - a quantitative approach

Courses

Course Nr. Course name18-hb-1020-vl Computer Systems I


Course Nr. Course name18-hb-1020-ue Computer Systems I



1.3.4 Systems and Parallel Programming & Operating Systems

Module nameSystem and Parallel Programming



1 Content- programming languages for systems programming- foundations of parallel systems- parallel architectures, multi-core and many-core systems, clusters- programming paradigms and models for parallel computing- parallel algorithms- significant practical programming exercises covering the above topics

2 Learning objectives / Learning OutcomesAfter successfully attending this course, students understand the foundations of parallel systems and oftechniques for their efficiently programming. They can develop and analyze basic applications using sys-tems and/or parallel programming techniques on selected platforms.



• [20-00-0905-iv] (Study Achievement, Written/Oral Examination, Standard BWS)


• [20-00-0905-iv] (Study Achievement, Written/Oral Examination, Weighting: 100 %)



8 ReferencesTo be appointed in lecture.

Courses

Course Nr. Course name20-00-0905-iv System and Parallel Programming


Course3


Module nameOperating Systems



1 Content- Introduction to Operating Systems (OS) - Role, purpose and design issues- Processes and Threads - OS structures, process control, abstractions, kernel/user modes and operations,context switching, interrupts- Inter-Process Communication - Message passing IPC, RPC, layers, interfaces, hierarchies- Coordination: Deadlocks - Process coordination, critical sections, deadlock characterization, deadlockdetection and recovery, deadlock avoidance- Scheduling/Resource Management - Task ordering, preemptive and non-preemptive scheduling, sched-ulers and policies, OS implementations- Concurrency: Races, Mutual Exclusions - Critical sections, races, spin locks, synchronization- Programming Abstractions: Semaphores - Semaphores, Monitors- Memory Management - Storage structures, management/replacements approaches, virtual memory, pag-ing, caching, segmentation- I/O - Device management, drivers, segmentation, interrupt handling, DMA- File systems - File systems requirements, design and implementation, file structures, directories, naming,partitions, virtual file systems- Fault Tolerance/Resilience - Fault types, fault handling approaches, reliable message delivery, OS reliabil-ity and availability, security issues- Embedded/RT OS - Memory/disk/performance management, recovery, fault-tolerances, real-time aspects- Distributed OS - Distributed computation and communication abstractions, synchronization, coordina-tion, consistency- Virtual Machines - Purpose and types of virtualization, virtual file systems, Hypervisors

2 Learning objectives / Learning OutcomesStudents will gain an overview on fundamental Operating System concepts consequent to their succesfulcourse attendance. Students are able to discuss approaches to different concepts regarding various techni-cal requirements such as fault tolerance, security and performance. Moreover, students acquire techniquesfor the creation of operating systems.

3 Recommended prerequisite for participationRecommended:“Algorithmen und Datenstrukturen”, “Funktionale und objektorientierte Programmierung”, “Rechnerorgan-isation”


• [20-00-0903-iv] (Technical Examination, Written/Oral Examination, Standard BWS)


• [20-00-0903-iv] (Technical Examination, Written/Oral Examination, Weighting: 100 %)



8 References


- Modern Operating Systems; A. Tanenbaum, Prentice Hall, ISBN 0-13-813459-6- Operating System Concepts; Silberschatz et al, John Wiley and Sons, ISBN 0-470-23399-3

Courses

Course Nr. Course name20-00-0903-iv Operating Systems


Course3


1.3.5 Software-Engineering

Module nameSoftware Engineering



1 ContentProviding an overview of the main areas of software engineering and the skills necessary for modeling andimplementing small software systems.The main topics are:- Software Project Management- Software Process Models- Requirements Engineering- Software Development Tools- Software Quality; in particular:- Test Processes (automated testing, test coverage metrics, debugging)- Software Metrics- Object-oriented Analysis and Design- Modeling using UML- Software Design Patterns

2 Learning objectives / Learning OutcomesAfter successfully completing the lecture, the students are able to perform the following tasks:- name and classify the areas of Software Engineering in the context of software development projects;- effectively use standard software development tools;- perform basic quality assurance using automated tests;- design and implement object-oriented systems using UML and design patterns.

3 Recommended prerequisite for participationRecommended:Funktionale und Objektorientierte ProgrammierkonzepteAlgorithmen und Datenstrukturen





6 Usability of this moduleB.Sc. InformatikB.Sc. WirtschaftsinformatikB.Sc. Psychologie in ITJoint B.A. InformatikB.Sc. Sportwissenschaft und InformatikM.Sc. Sportwissenschaft und InformatikB.Sc. Computational EngineeringB.Sc. InformationssystemtechnikMay be used in other degree programs.



In dieser Vorlesung findet eine Anrechnung von vorlesungsbegleitenden Leistungen statt, die lt. §25 (2)der 5. Novelle der APB und den vom FB 20 am 30.3.2017 beschlossenen Anrechnungsregeln zu einerNotenverbesserung um bis zu 1.0 führen kann.

8 References- Lehrbuch der Softwaretechnik: Softwaremanagement; H. Balzert; Springer- Design Patterns - Elements of Reusable Object-Oriented Software; E. Gamma, R. Helm, R. Johnson, J.Vlissides; Prentice Hall- Software Qualität - Testen, Analysieren und Verifizieren von Software; P. Liggesmeyer; Springer- WHY PROGRAMS FAIL: A Guide to Systematic Debugging; A. Zeller; Morgan Kaufmann- Writing Effective Use Cases; A. Cockburn; Pearson

Courses

Course Nr. Course name20-00-0017-iv Software Engineering


3


Module nameSoftware Engineering - Introduction

Module Nr. Credit Points Workload Self study Duration Cycle offered18-su-1010 6 CP 180 h 120 h 1 WiSe


1 ContentThe lecture gives an introduction to the broad discipline of software engineering. All major topics of thefield - as entitled e.g. by the IEEE’s “Guide to the Software Engi-neering Body of Knowledge” - get addressedin the indicated depth. Main emphasis is laid upon requirements elicitation techniques (software analysis)and the design of soft-ware architectures (software design). UML (2.0) is introduced and used throughoutthe course as the favored modeling language. This requires the attendees to have a sound knowledge of atleast one object-oriented programming language (preferably Java).During the exercises, a running example (embedded software in a technical gadget or device) is utilizedand a team-based elaboration of the tasks is encouraged. Exercises cover tasks like the elicitation of re-quirements, definition of a design and eventually the implementation of executable (proof-of-concept)code.

2 Learning objectives / Learning OutcomesThis lecture aims to introduce basic software engineering techniques - with recourse to a set of best-practiceapproaches from the engineering of software systems - in a practice-oriented style and with the help of onerunning example.After attending the lecture students should be able to uncover, collect and document essential requirementswith respect to a software system in a systematic manner using a model-driven/centric approach. Further-more, at the end of the course a variety of means to acquiring insight into a software system’s design(architecture) should be at the student’s disposal.

3 Recommended prerequisite for participationsound knowledge of an object-oriented programming language (preferably Java)





6 Usability of this moduleBSc ETiT, BSc iST, BSc Wi-ETiT


8 Referenceswww.es.tu-darmstadt.de/lehre/se-i-v/

Courses

Course Nr. Course name18-su-1010-vl Software Engineering - Introduction

Instructor Type SWSProf. Dr. rer. nat. Andreas Schürr Lecture 3

Course Nr. Course name18-su-1010-ue Software Engineering - Introduction

Instructor Type SWSProf. Dr. rer. nat. Andreas Schürr Practice 1


http://www.es.tu-darmstadt.de/lehre/se-i-v/

2 Options - Optionals

2.1 Optional Subjects CTS: Communication Technology and Communication Systems

Module nameAdvances in Digital Signal Processing: Imaging and Image Processing

Module Nr. Credit Points Workload Self study Duration Cycle offered18-zo-2080 5 CP 150 h 90 h 1 SoSe

Language Module ownerEnglish Prof. Dr.-Ing. Abdelhak Zoubir

1 Content• Basics

– Detection, Estimation and Classification

• Imaging– Radar Signal Processing– Array Signal Processing– Image formation– Applications of Imaging

• Image Processing– Random fields– Image segmentation– Image reconstruction– Image classification

• Project work

2 Learning objectives / Learning OutcomesAfter attending the lecture, a student understands the basic principles of imaging systems with radar andsonar. He also is capable of applying image formation with sensor arrays as well as image processingtechniques such as segmentation, image reconstruction and classification.

3 Recommended prerequisite for participationDSP


• Module Examination (Technical Examination, Optional, Standard Grading System)


• Module Examination (Technical Examination, Optional, Weighting: 100 %)

6 Usability of this moduleBSc/MSc ETiT, MSc Wi/ETiT, BSc/MSc iST, MSc iCE, BSc/MSc MEC


8 References

32

• Mark Richards, Principles of Modern Radar: Basic Principles. SciTech Publishing 2010• Didier Massonnet and Jean-Claude Souyris, Imaging with Synthetic Aperture Radar. EPFL Press,

2008• Gerhard Winkler, Image Analysis, Random Fields and Markov Chain Monte Carlo Methods, 2nd

edition, Springer Verlag 2003

Courses

Course Nr. Course name18-zo-2080-vl Advances in Digital Signal Processing: Imaging and Image Processing

Instructor Type SWSDr.-Ing. Christian Debes Lecture 2

Course Nr. Course name18-zo-2080-ue Advances in Digital Signal Processing: Imaging and Image Processing

Instructor Type SWSDr.-Ing. Christian Debes Practice 2

2.1 Optional Subjects CTS: Communication Technology and Communication Systems 33

Module nameCommunication Technology II

Module Nr. Credit Points Workload Self study Duration Cycle offered18-kl-2010 4 CP 120 h 75 h 1 WiSe

Language Module ownerEnglish Prof. Dr.-Ing. Anja Klein

1 Contentlinear and nonlinear digital modulation schemes, optimum receivers for AWGN channels, error proba-bility, channel capacity, channel models, channel estimation and data detection for multipath channels,multicarrier schemes, OFDM

2 Learning objectives / Learning OutcomesAfter completion of the lecture, students possess:

• the ability of comparing, evaluating, classifying an analyzing linear and nonlinear modulationschemes by means of signal space representations;

• the ability to understand, describe and analyze the influence of AWGN on the signal;• the ability to understand and derive optimum receivers in case of AWGN channels;• the ability to understand, describe and analyze the influence of multipath propagation on the signal;• the ability to describe the influence of a multipath channel mathematically (channel model) and

estimate the multipath channel at the receiver;• the knowledge of equalizing the received signal in order to undo the influence of multipath propa-

gation, as well as the ability to derive and design several equalizer structures;• the ability to analyze and evaluate the properties and application areas of multicarrier transmission

systems, e.g. OFDM-systems;• the ability to design and evaluate the system parameters of multicarrier schemes for the application

in realistic mobile radio scenarios;

3 Recommended prerequisite for participationElectrical Engineering I and II, Deterministische Signale und Systeme, Stochastische Signale und Systeme,Communication Technology I, Basics of Telecommunication, Mathematics I to IV





6 Usability of this moduleMSc ETIT, MSc Wi-ETiT, MSc CE, MSc iCE, MSc iST, MSc MEC


8 Referenceswill be announced in the lecture

Courses

Course Nr. Course name18-kl-2010-vl Communication Technology II



Course Nr. Course name18-kl-2010-ue Communication Technology II



Module nameDigital Signal Processing

Module Nr. Credit Points Workload Self study Duration Cycle offered18-zo-2060 6 CP 180 h 120 h 1 WiSe


1 Content1) Discrete-Time Signals and Linear Systems – Sampling and Reconstruction of Analog Signals2) Digital Filter Design – Filter Design Principles; Linear Phase Filters; Finite Impulse Response Filters;Infinite Impulse Response Filters; Implementations3) Digital Spectral Analysis - Random Signals; Nonparametric Methods for Spectrum Estimation; Paramet-ric Spectrum Estimation; Applications;4) Kalman Filter

2 Learning objectives / Learning OutcomesStudents will understand basic concepts of signal processing and analysis in time and frequency of deter-ministic and stochastic signals. They will have first experience with the standard software tool MATLAB.

3 Recommended prerequisite for participationDeterministic signals and systems theory







8 ReferencesCourse manuscriptAdditional References:

• A. Oppenheim, W. Schafer: Discrete-time Signal Processing, 2nd ed.• J.F. Böhme: Stochastische Signale, Teubner Studienbücher, 1998

Courses

Course Nr. Course name18-zo-2060-vl Digital Signal Processing

Instructor Type SWSProf. Dr.-Ing. Abdelhak Zoubir Lecture 3

Course Nr. Course name18-zo-2060-ue Digital Signal Processing

Instructor Type SWSProf. Dr.-Ing. Abdelhak Zoubir Practice 1


Module nameInformation Theory II

Module Nr. Credit Points Workload Self study Duration Cycle offered18-pe-2010 6 CP 180 h 120 h 1 SoSe

Language Module ownerEnglish Prof. Dr.-Ing. Marius Pesavento

1 ContentThis lecture course is devoted to advances of network information theory. Outline: overview of Shannoncapacity, outage and ergodic capacity, capacity of channels with state, capacity of Gaussian vector channels,capacity regions of multi-user channels, capacity regions of multiple-access and broadcast fading channels,interference channel, relay channel, multiuser bounds, multi-user diversity., wiretap channel, secrecy rateand physical layer security.

2 Learning objectives / Learning OutcomesStudents will understand advanced concepts and strategies in networkinformation theory.

3 Recommended prerequisite for participationKnowledge of basic communication theory





6 Usability of this moduleMSc ETiT, BSc iST, MSc Wi-ETiT, MSc iCE, BSc/MSc CE


8 References1. Abbas El Gamal and Young-Han Kim, Network Information Theory, Cambrige, 2011.2.. T.M. Cover and J.A. Thomas, Elements of Information Theory, Wiley Sons, 1991.3.. D. Tse and P. Vishwanath, Fundamentals of Wireless Communications, Cambridge University Press,2005.

Courses

Course Nr. Course name18-pe-2010-vl Information Theory II

Instructor Type SWSProf. Dr.-Ing. Marius Pesavento Lecture 3

Course Nr. Course name18-pe-2010-ue Information Theory II

Instructor Type SWSProf. Dr.-Ing. Marius Pesavento Practice 1


Module nameCommunication Networks IV

Module Nr. Credit Points Workload Self study Duration Cycle offered18-sm-2030 3 CP 90 h 60 h 1 WiSe

Language Module ownerEnglish Prof. Dr.-Ing. Ralf Steinmetz

1 ContentThe lecture communication networks IV deals with modelling and performance evaluation of computernetworks and communication systems. The emphasis is on current analytical approaches. Owing to thesemethods a fundamental understanding of major performance related aspects in networking is achievedand basic knowledge for planning, optimization and advancement of communications networks is pro-vided. The relevance and implications of individual theories are illustrated using examples which aredrawn mainly from the Internet. Apart from analytical methods the lecture gives an introduction to sim-ulation of communication networks as well as measuring in real or prototypical systems and testbeds. Inaddition to well-known methods and their applications selected aspects of current research questions willbe elaborated on.Topics of the lecture are:

• Introduction to performance evaluation and applications• Leaky bucket traffic regulators, deterministic traffic models, deterministic and empirical envelopes• Scheduling, generalized processor sharing• Network calculus, min-plus systems theory, deterministic performance bounds• Poisson processes, Markov-chains, classical queuing theory, M|M|1 and M|G|1 models• Modeling of packet data traffic, self-similarity• Effective bandwidths, moment generating functions, statistical multiplexing• Statistical network calculus, effective envelopes, effective performance bounds• Simulation, generation of random numbers, distributions, confidence intervals• Instrumentation, measurements, bandwidth estimation in the Internet

2 Learning objectives / Learning OutcomesStudents attending this lecture obtain an overview on the impact, fundamental methods, and importantapplications of performance evaluation of communication networks. They are acquainted with character-istic mechanisms and scheduling algorithms used in quality of service networks and are able to explaintheir functionality in terms of network calculus and the framework of min-plus systems theory. In additionto basic queuing theory the students acquire sound knowledge of the theory of effective bandwidths andthus exhibit a theoretically founded understanding of statistical multiplexing. Beyond analytical methods,the students gain insight into simulation as well as selected measurement methods and tools used in realnetworks. They are able to define the scope of individual theories and methods, select suitable, problemtailored techniques, apply these to typical problems, and draw relevant conclusions.

3 Recommended prerequisite for participationBasic courses of the first 4 semesters are required. Knowledge of lectures Communication Networks I andII are recommended.


• Module Examination (Technical Examination, Oral Examination, Duration: 30 min, Standard Grad-ing System)


• Module Examination (Technical Examination, Oral Examination, Weighting: 100 %)

6 Usability of this moduleWi-CS, Wi-ETiT, BSc/MSc CS, MSc ETiT, MSc iST



8 ReferencesAusgewählte Kapitel aus folgenden Büchern:

• J.-Y. Le Boudec, P. Thiran: “Network Calculus: A Theory of Deterministic Queuing Systems for theInternet”, Springer LNCS 2050, http://ica1www.epfl.ch/PS_files/netCalBookv4.pdf, 2004.

• A. Kumar, D. Manjunath, J. Kuri: "Communication Networking: An Analytical Approach", MorganKaufmann, 2004.

• A. M. Law, W. D. Kelton: "Simulation, Modeling and Analysis", McGraw Hill, 3rd Ed., 2000.• Selected Journal Articles and Conference Papers

Courses

Course Nr. Course name18-sm-2030-vl Communication Netzworks IV: Performance Evaluation of Communication Networks

Instructor Type SWSDr.-Ing. Amr Rizk Lecture 2


http://ica1www.epfl.ch/PS_files/netCalBookv4.pdf

Module nameMobile Communications

Module Nr. Credit Points Workload Self study Duration Cycle offered18-kl-2020 6 CP 180 h 120 h 1 SoSe


1 ContentThe lecture covers aspects of mobile communication systems with particular focus on the physical layer.Mobile radio systems, services, market, standardizationduplex and multiple access techniques, cellular conceptmobile radio channel, deterministic and stochastic descriptionmodulation schemescode division multiple access (CDMA)orthogonal frequency division multiplexing (OFDM)optimum and suboptimum receiver techniquescellular radio capacity and spectrum efficiencydiversity methodsmultiple input multiple output (MIMO) systemspower control and handoverarchitecture of mobile radio systems

2 Learning objectives / Learning OutcomesAfter completion of the lecture, students possess

• a profound understanding of physical layer aspects ,e.g., transmission schemes, multiple accessschemes of mobile communication systems, duplex schemes, multi carrier schemes, receiver tech-niques, multi antenna schemes

• a profound understanding of signal propagation in mobile radio systems (mobile radio channel)• the ability to understand and solve problems of the field of the physical layer• the ability to compare, analyse and evaluate different system concepts• knowledge on modelling of the transmission properties of the mobile radio channel

3 Recommended prerequisite for participationElectrical Engineering I and II, Deterministic Signals and Systems, Communication Technology I, Mathe-matics I to IV





6 Usability of this moduleMSc ETIT, MSc Wi-ETiT, MSc CE, MSc iCE, MSc iST, MSc MEC


8 Referenceswill be announced in the lecture

Courses


Course Nr. Course name18-kl-2020-vl Mobile Communications


Course Nr. Course name18-kl-2020-ue Mobile Communications



Module nameSpeech and Audio Signal Processing


Language Module ownerGerman Prof. Dr.-Ing. Abdelhak Zoubir

1 ContentAlgorithms of speech and audio signal processing: Introduction to the models of speech and audio signalsand basic methods of audio signal processing. Procedures of codebook based processing and audio coding.Beamforming for spatial filtering and noise reduction for spectral filtering. Cepstral filtering and funda-mental frequency estimation. Mel-filterind cepstral coefficients (MFCCs) as basis for speaker detection andspeech recognition. Classification methods based on GMM (Gaussian mixture models) and speech recog-nition with HMM (Hidden markov models). Introduction to the methods of music signal processing, e.g.Shazam-App or beat detection.

2 Learning objectives / Learning OutcomesBased on the lecture you acquire an advanced knowledge of digital audio signal processing mainly withthe help of the analysis of speech signals. You learn about different basic and advanced methods of audiosignal processing, to range from the theory to practical applications. You will acquire knowledge aboutalgorithms such as they are applied in mobile telephones, hearing aids, hands-free telephones, and man-machine-interfaces (MMI). The exercise will be organized as a talk given by each student with one self-selected topic of speech and audio processing. This will allow you to acquire the know-how to read andunderstand scientific literature, familiarize with an unknown topic and present your knowledge, such as itwill be certainly required from you in your professional life as an engineer.

3 Recommended prerequisite for participationKnowlegde about satistical signal processing is required (lecture „Digital Signal Processing“). Desired– but not mandatory – is knowledge about adaptive filters.


• Module Examination (Technical Examination, Written/Oral Examination, Duration: 20 min, Stan-dard Grading System)

Seminar presentation: Scientific talk about a topic in the field of “Speech and Audio Signal Processing”,single (duration 10-15 min) or in groups of two students (15-20 min)


• Module Examination (Technical Examination, Written/Oral Examination, Weighting: 100 %)

6 Usability of this moduleMSc ETiT, MSc iCE


8 ReferencesSlides (for further details see homepage of the lecture)

Courses

Course Nr. Course name18-zo-2070-vl Speech and Audio Signal Processing

Instructor Type SWSProf. Dr.-Ing. Henning Puder Lecture 2

Course Nr. Course name18-zo-2070-ue Speech and Audio Signal Processing

Instructor Type SWSProf. Dr.-Ing. Henning Puder Practice 1


Course Nr. Course name18-zo-2070-se

Instructor Type SWSProf. Dr.-Ing. Henning Puder Seminar 1


Module nameDigital Signal Processing Lab

Module Nr. Credit Points Workload Self study Duration Cycle offered18-zo-2030 6 CP 180 h 135 h 1 WiSe/SoSe


1 Content1) Introduction to MATLAB2) Discrete-Time Signals and Systems3) Frequency-Domain Analysis using the DFT4) Digital FIR Filter Design5) IIR Filter Design using Analog Prototypes6) Nonparametric Spectrum Estimation7) Parametric Spectrum Estimation.

2 Learning objectives / Learning OutcomesThe students are able to apply skills acquired in the course Digital Signal Processing. These include thedesign of digital FIR and IIR filters as well as non-parametric and parametric spectrum estimation. Studentslearn how MATLAB is used to apply theoretical concepts and to demonstrate signal processing techniquesby using hands-on application examples.

3 Recommended prerequisite for participationDeterministic signals and systems theory


• Module Examination (Study Achievement, Written Examination, Duration: 120 min, Standard Grad-ing System)


• Module Examination (Study Achievement, Written Examination, Weighting: 100 %)

6 Usability of this moduleMSc ETiT, MSc iCE


8 ReferencesLab manual

Courses

Course Nr. Course name18-zo-2030-pr Digital Signal Processing Lab

Instructor Type SWSProf. Dr.-Ing. Abdelhak Zoubir Internship 3


Module nameMultimedia Communications Lab II

Module Nr. Credit Points Workload Self study Duration Cycle offered18-sm-2070 6 CP 180 h 135 h 1 WiSe/SoSe

Language Module ownerGerman and English Prof. Dr.-Ing. Ralf Steinmetz

1 ContentThe course deals with cutting edge development topics in the area of multimedia communication systems.Besides a general overview it provides a deep insight into a special development topic. The topics areselected according to the specific working areas of the participating researchers and convey technical andbasic scientific competences in one or more of the following topics:

• Network planning and traffic analysis• Performance evaluation of network applications• Discrete event simulation for network services• Protocols for mobile ad hoc networks / sensor networks• Infrastructure networks for mobile communication / mesh networks• Context-aware communication and services• Peer-to-peer systems and architectures• Content distribution and management systems for multimedia / e-learning• Multimedia authoring and re-authoring tools• Web service technologies and service-oriented architectures• Applications for distributed workflows

2 Learning objectives / Learning OutcomesThe ability to solve and evaluate problems in the area of design and development of future multimediacommunication networks and applications shall be acquired. Acquired competences are:

• Design of complex communication applications and protocols• Implementing and testing of software components for distributed systems• Application of object-oriented analysis and design techniques• Acquisition of project management techniques for small development teams• Writing of software documentation and project reports• Presentation of project advances and outcomes

3 Recommended prerequisite for participationKeen interest to explore challenging topics which are cutting edge in technology and research. Further weexpect:

• Solid experience in programming Java and/or C# (C/C++)• Solid knowledge in object oriented analysis and design• Solid knowledge in computer communication networks are recommended• Lectures in Communication Networks I (II, III, or IV) are an additional plus





6 Usability of this moduleMSc ETiT, MSc iCE, BSc/MSc iST, Wi-ETiT, BSc/MSc CS, Wi-CS,



8 ReferencesEach topic is covered by a selection of papers and articles. In addition we recommend reading of selectedchapters from following books:

• Andrew Tanenbaum: “Computer Networks”. Prentice Hall PTR (ISBN 0130384887)• Christian Ullenboom: "Java ist auch eine Insel: Programmieren mit der Java Standard Edition Version

5 / 6" (ISBN-13: 978-3898428385)• Joshua Bloch: "Effective Java Programming Language Guide" (ISBN-13: 978-0201310054)• Erich Gamma, Richard Helm, Ralph E. Johnson: "Design Patterns: Objects of Reusable Object Ori-

ented Software" (ISBN 0-201-63361-2)• Kent Beck: "Extreme Programming Explained - Embrace Changes" (ISBN-13: 978-0321278654)

Courses

Course Nr. Course name18-sm-2070-pr Multimedia Communications Lab II

Instructor Type SWSProf. Dr.-Ing. Ralf Steinmetz Internship 3


Module nameMultimedia Communications Project Seminar II



1 ContentThe course deals with cutting edge scientific and development topics in the area of multimedia communica-tion systems. Besides a general overview it provides a deep insight into a special scientific topic. The topicsare selected according to the specific working areas of the participating researchers and convey technicaland scientific competences in one or more of the following topics:

• Network planning and traffic analysis• Performance evaluation of network applications• Discrete event simulation for network services• Protocols for mobile ad hoc networks / sensor networks• Infrastructure networks for mobile communication / mesh networks• Context-aware communication and services• Peer-to-peer systems and architectures• Content distribution and management systems for multimedia / e-learning• Multimedia authoring and re-authoring tools• Web service technologies and service-oriented architectures• Applications for distributed workflows

2 Learning objectives / Learning OutcomesThe ability to solve and evaluate technical and scientific problems in the area of design and development offuture multimedia communication networks and applications using state of the art scientific methods shallbe acquired. Acquired competences are:

• Searching and reading of project relevant literature• Design of complex communication applications and protocols• Implementing and testing of software components for distributed systems• Application of object-oriented analysis and design techniques• Acquisition of project management techniques for small development teams• Systematic evaluation and analyzing of technical and scientific experiments• Writing of software documentation and project reports• Presentation of project advances and outcomes

3 Recommended prerequisite for participationKeen interest to develop and explore challenging solutions and applications in cutting edge multimediacommunications systems using scientific methods. Further we expect:

• Solid experience in programming Java and/or C (C/C++)• Solid knowledge in object oriented analysis and design• Basic knowledge of design patterns, refactoring and project management• Solid knowledge in computer communication networks are recommended• Lectures in Communication Networks I (II, III, or IV) are an additional plus






6 Usability of this moduleWi-CS, Wi-ETiT, BSc/MSc CS, MSc ETiT, MSc iST



• Andrew Tanenbaum: “Computer Networks”. Prentice Hall PTR (ISBN 0130384887)• Raj Jain: "The Art of Computer Systems Performance Analysis: Techniques for Experimental Design,

Measurement, Simulation, and Modeling" (ISBN 0-471-50336-3)• Joshua Bloch: "Effective Java - Programming Language Guide" (ISBN-13: 978-0201310054)• Erich Gamma, Richard Helm, Ralph E. Johnson: "Design Patterns: Objects of Reusable Object Ori-

ented Software" (ISBN 0-201-63361-2)• Martin Fowler: "Refactorings - Improving the Design of Existing Code" (ISBN-13: 978-0201485677)• Kent Beck: "Extreme Programming Explained - Embrace Changes" (ISBN-13: 978-0321278654)

Courses

Course Nr. Course name18-sm-2080-pj Multimedia Communications Project Seminar II

Instructor Type SWSProf. Dr.-Ing. Ralf Steinmetz Project Seminar 3


Module nameProject Seminar Wireless Communications

Module Nr. Credit Points Workload Self study Duration Cycle offered18-kl-2040 8 CP 240 h 180 h 1 SoSe


1 ContentSolving special Problems concerning mobile communications (problems concerning signal transmissionand processing as well as problems concerning the network are possible, topics will be defined out of thecurrent research topics of the lab),working on the project in teams together (2-3 students)organizing and structuring of a projectdealing with scientific publications, reading up the theoretical background of the taskpractical work on a complex taskscientific presentation of the results (report/presentation)defending the work in an oral discussion including an audience

2 Learning objectives / Learning OutcomesAfter completion of the course, students possess

• the ability to classify and analyze special problems concerning mobile communications,• the knowledge to plan and organize projects with temporal limitation,• the capability to setup and test methodologies for analysis and simulation- environments,• skills to evaluate and present achieved results and achieved conclusions.

3 Recommended prerequisite for participationPrevious knowledge in digital communications, signal processing, mobile radio


• Module Examination (Study Achievement, Oral Examination, Duration: 20 min, Standard GradingSystem)



6 Usability of this moduleMSc ETiT, MSc Wi-ETiT, MSc CE, MSc iCE, MSc iST, MSc MEC


8 ReferencesLecture documentation will be provided and specific literature will be announced during the course.

Courses

Course Nr. Course name18-kl-2040-pj Project Seminar Wireless Communications

Instructor Type SWSProf. Dr.-Ing. Anja Klein Project Seminar 4


Module nameMultimedia Communications Seminar II



1 ContentThis seminar deals with current and upcoming trends relevant to the future development of multimediacommunication systems. The educational objective of this seminar is to gain knowledge about futureresearch trends in different areas. To this aim, an extensive literature research will be performed, as wellas the writing-up of a report and the presentation of selected, high-quality research topics from currentleading magazines, newspapers and conferences in the web technologies research area.Some potential topics are:

• Knowledge & Educational Technologies• Self organizing Systems & Overlay Communication• Mobile Systems & Sensor Networking• Service-oriented Computing• Multimedia Technologies & Serious Games

2 Learning objectives / Learning OutcomesStudents shall acquire profound knowledge from current scientific publications, standards and literatureon multimedia communication systems and applications which will build the future Internet. In so doing,the students will develop the following competencies:

• Search for and review relevant scientific literature.• Analyse and evaluate complex technical and scientific information.• Write technical and scientific abstracts and summary reports.• Present technical and scientific information.

3 Recommended prerequisite for participationSolid knowledge in computer communication networks. Lectures in Communication Networks I and II arerecommended.





6 Usability of this moduleCS, Wi-CS, ETiT, Wi-ETiT, MSc CS, MSc ETiT, MSc iST


8 ReferencesDepending on specific topic (selected articles of journals, magazines, and conferences).

Courses

Course Nr. Course name18-sm-2090-se Multimedia Communications Seminar II

Instructor Type SWSProf. Dr.-Ing. Ralf Steinmetz Seminar 2


Module nameAdaptive Filters


Language Module ownerGerman and English Prof. Dr.-Ing. Abdelhak Zoubir

1 ContentTheory:1) Derivation of optimal filters for stochastic processes, e.g. Wiener filter or linear prediction filter basedon suitable cost functions.2) Elaboration of adaptive procedures, which allow to iteratively approach the optimal solution for non-stationary signals in non-stationary environments. Here, the adaptive procedures such as NLMS adaptation,affine projection, and the RLS algorithm are derived and extensively analysed.3) Analysis of the adaptation behaviour and control procedures of adaptive filters based on the NLMSprocedure.4) Derivation and analysis of the Kalman filter as optimal filter for non-stationary input signals.5) Procedures for the decomposition of signals into sub-bands for the realization of optimal filters in thefrequency domain, e.g. noise reduction procedures.Applications:Parallel to the theory, practical applications are explained. As an example for the Weiner filter, the acousticnoise reduction procedures are explained. Acoustic echo cancellation and feedback cancellation are givenas examples for adaptive filters. Furthermore beamforming approaches are introduced.It is planned to offer an excursion to Siemens Audiology Engineering Group in Erlangen.In the 4 to 5 exercises, some content of the lecture will be implemented in MATLAB which allows thestudents to get familiar with practical realizations of the theoretical procedures.

2 Learning objectives / Learning OutcomesDuring the lecture, basics of adaptive filters are taught. The necessary algorithms are derived, interpretedand applied to examples of speech, audio and video processing.Based on the content of the lecture you are able to apply adaptive filters to real practical applications.For the admission to the exam you give a talk about a topic in the domain of adaptive filters chosen byyou. This will allow you to acquire the know-how to read and understand scientific literature, familiarizeyourself with an unknown topic and present your knowledge, such as it will be certainly required from youin your professional life as an engineer.

3 Recommended prerequisite for participationDigital Signal Processing





6 Usability of this moduleMSc ETiT


8 References


Slides of the lecture.Literature:

• E. Hänsler, G. Schmidt: Acoustic Echo and Noise Control, Wiley, 2004 (Textbook of this course);• S. Haykin: Adaptive Filter Theory, Prentice Hall, 2002;• A. Sayed: Fundamentals of Adaptive Filtering, Wiley, 2004;• P. Vary, U. Heute, W. Hess: Digitale Sprachsignalverarbeitung, Teubner, 1998 (in German)

Courses

Course Nr. Course name18-zo-2010-vl Adaptive Filters

Instructor Type SWSProf. Dr.-Ing. Henning Puder Lecture 3

Course Nr. Course name18-zo-2010-ue Adaptive Filters

Instructor Type SWSProf. Dr.-Ing. Henning Puder Practice 1


Module nameAdvanced Topics in Statistical Signal Processing



1 ContentThis course extends the signal processing fundamentals taught in DSP towards advanced topics that are thesubject of current research. It is aimed at those with an interest in signal processing and a desire to extendtheir knowledge of signal processing theory in preparation for future project work (e.g. Diplomarbeit) andtheir working careers. This course consists of a series of five lectures followed by a supervised researchseminar during two months approximately. The final evaluation includes students seminar presentationsand a final exam.The main topics of the Seminar are:

• Estimation Theory• Detection Theory• Robust Estimation Theory• Seminar projects: e.g. Microphone array beamforming, Geolocation and Tracking, Radar Imaging,

Ultrasound Imaging, Acoustic source localization, Number of sources detection.

2 Learning objectives / Learning OutcomesStudents obtain advanced knowledge in signal processing based on the fundamentals taught in DSP andETiT 4. They will study advanced topics in statistical signal processing that are subject to current research.The acquired skills will be useful for their future research projects and professional careers.

3 Recommended prerequisite for participationDSP, general interest in signal processing is desirable.





6 Usability of this moduleMSc ETiT, BSc/MSc iST, MSc iCE, Wi-ETiT


8 References• L. L. Scharf, Statistical Signal Processing: Detection, Estimation, and Time Series Analysis (New

York: Addison-Wesley Publishing Co., 1990).• S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory (Book 1), Detection

Theory (Book 2).• R. A. Maronna, D. R. Martin, V. J. Yohai, Robust Statistics: Theory and Methods, 2006.

Courses

Course Nr. Course name18-zo-2040-se Advanced Topics in Statistical Signal Processing

Instructor Type SWSProf. Dr.-Ing. Abdelhak Zoubir Seminar 4


Module nameAntennas and Adaptive Beamforming

Module Nr. Credit Points Workload Self study Duration Cycle offered18-jk-2020 6 CP 180 h 120 h 1 WiSe

Language Module ownerEnglish Prof. Dr.-Ing. Rolf Jakoby

1 ContentOverview of most important antenna parameters types as well as their applications. Fundamental theories:Fourier transform for far-field pattern calculations, antenna modeling techniques, antenna synthesis meth-ods, image theory, determination of field regions of line sources, of the average radiated power densityand power, directivity and gain. Antennas as key elements in power budgets of radio links, introducing theeffective aperture of an antenna, deriving the relation between gain and effective aperture. Array anten-nas are a key hardware for beamforming and smart antenna systems: fundamentals of phased-scanningarrays, non-uniformly excited, equally spaced linear arrays, multi-dimensional planar arrays and mutualcoupling effects. Wire antennas: still the most prevalent of all antenna forms, relatively simple in concept,easy to construct, very inexpensive. Antenna radiation fields and antenna parameters for different types ofantennas are derived from Maxwell´s equations, applied for aperture antennas (horns, lenses or reflectorantennas) and printed antennas (microstrip-patch and coplanar-slot antennas) Some basic numerical calcu-lation methods: integral equation methods in the time and frequency domain, physical optics and uniformtheory of diffraction are briefly summarized and compared for antennas and scattering problems. Smartantennas in communication and radar systems, with focus on beam steering and adaptive beamforming.

2 Learning objectives / Learning OutcomesStudents will know basic antenna parameters: pattern, gain, directivity, half-power beamwidth, sidelobe-level, efficiency and input impedance to compare, assess and evaluate different antennas for various appli-cations and operating frequencies. The antenna field regions, reactive near-field, near-field and far-field,can be differentiated and the far-field pattern of an antenna can be determined from given current dis-tributions along the antenna by using Fourier transformation or integral solutions with distributed idealdipoles as basic elements (antenna analysis). To assess in general physical requirements, constrains andlimitations of antennas, students can use fundamental antenna theory: impedance matching techniques,antenna modeling and far-field pattern analysis, antenna synthesis, image theory and fundamental limits ofelectrically small antennas. After being incorporated into the different adaptive beamforming techniques,the array theory enables the student to design antenna systems that are assembled of a certain number ofseparate elements, feeding network, beamforming network etc. for phased-scanning or smart antennas incommunications and sensing. Moreover, students are able to determine, analyze and evaluate the mostimportant classes of antennas in wireless technology for many applications, operating frequencies, desiredrequirements or practical constrains: (1.) wire- dipole antennas, (2.) planar antennas (microstrip, dipoleand slot antennas), (3.) aperture antennas (horn antennas, parabolic reflector antennas, lens antennas,Cassegrain and Gregorian double-reflector configurations), (4.) broadband and frequency-independentantennas (V antennas, biconical antennas, helical antennas, spiral and log-periodic antennas).

3 Recommended prerequisite for participationFundamentals of Communications, Microwave Engineering 1





6 Usability of this moduleBSc ETiT, MSc ETiT, MSc iCE, Wi-ETiT



8 ReferencesJakoby, Skriptum Antennas and Adaptive Beamforming, wird am Beginn der Vorlesung verkauft und kanndanach im FG-Sekretariat erworben werden

Courses

Course Nr. Course name18-jk-2020-vl Antennas and Adaptive Beamforming


Course Nr. Course name18-jk-2020-ue Antennas and Adaptive Beamforming



Module nameFundamentals of Signal Processing


Language Module ownerGerman Prof. Dr.-Ing. Abdelhak Zoubir

1 ContentThe course covers the following topics:

• The basic concepts of stochastic• The sampling theorem• Discrete-time noise processes and their properties• Description of noise processes in the frequency domain• Linear time-invariant systems: FIR and IIR filters• Filtering of noise processes: AR, MA, and ARMA models• The Matched filter• The Wiener filter• Properties of estimators• The method of least squares

2 Learning objectives / Learning OutcomesThe course covers basic concepts of signal processing, and illustrates them with practical examples. Itserves as an introductory course for advanced lectures in digital signal processing, adaptive filtering, com-munications, and control theory.




In general, the examination takes place in form of a written exam (duration: 120 minutes). If up to 10students register in semesters in which the lecture does not take place, there will will be an oral examination(duration: 30 min.). The type of examination will be announced within one working week after the end ofthe examination registration phase.



6 Usability of this moduleBSc ETiT, BSc MEC


8 References


Lecture notes and slides can be downloaded here:• http://www.spg.tu-darmstadt.de• Moodle platform

Further reading:• A. Papoulis: Probability, Random Variables and Stochastic Processes. McGraw-Hill, Inc., third edition,

1991.• P. Z. Peebles, Jr.: Probability, Random Variables and Random Signal Principles. McGraw-Hill, Inc.,

fourth edition, 2001.• E. Hänsler: Statistische Signale; Grundlagen und Anwendungen. Springer Verlag, 3. Auflage, 2001.• J. F. Böhme: Stochastische Signale. Teubner Studienbücher, 1998.• A. Oppenheim, W. Schafer: Discrete-time Signal Processing. Prentice Hall Upper Saddle River,1999.

Courses

Course Nr. Course name18-zo-1030-vl Fundamentals of Signal Processing

Instructor Type SWSProf. Dr.-Ing. Abdelhak Zoubir Lecture 3

Course Nr. Course name18-zo-1030-ue Fundamentals of Signal Processing

Instructor Type SWSProf. Dr.-Ing. Abdelhak Zoubir Practice 1


http://www.spg.tu-darmstadt.de

Module nameMicrowave Engineering I



1 ContentElectromagnetic spectrum, kinds of transmission media, frequency ranges, bit rates, applications; Radio-Frequency (RF) and Microwave Circuits, Components and Modules, Passive RF Circuits with R-, L- andC-Lumped Elements: Resonant and Equivalent RLC Circuits, Graphical Representation of RF Circuits withthe Smith Chart, Lumped-Element Impedance Matching; Theory and Applications of Transmission Lines:General Transmission-Line Equations, Lossless Transmission Lines as Circuit Elements, Line Terminations,Transmission-Line devices; Scattering-Matrix Formulation of N-Port RF Devices: Characterization of Mi-crowave Networks, Concatenation of Two S-Matrixes, Applications of S-Parameters; Passive microwavecomponents: waveguide splitter, circulator, directional coupler, filter, attenuator, matching network; An-tennas: Antenna performance parameter, Ideal dipole with uniform current distribution, Antenna arraysof ideal dipoles, Image theory, Antenna modelling, Transmission Factor and Power Budget of Radio Links:Friis transmission equation, Gain and effective aperture of antennas, Radar equation, System noise tem-perature, Antenna noise temperature, Power budget of radio links, Basic propagation effects: reflection,transmission, scattering, diffraction; The radio channel: The two-ray propagation model, Doppler shiftMultipath propagation, Stochastic behaviour of the mobile radio channel


3 Recommended prerequisite for participationNachrichtentechnik, Grundlagen der Technischen Elektrodynamik







8 ReferencesScript will be hand out; Literature will be recommended in first lecture

Courses

Course Nr. Course name18-jk-1020-vl Microwave Engineering I


Course Nr. Course name18-jk-1020-ue Microwave Engineering I



Module nameInformation Theory I

Module Nr. Credit Points Workload Self study Duration Cycle offered18-kp-1010 6 CP 180 h 120 h 1 WiSe

Language Module ownerEnglish Prof. Dr. techn. Heinz Köppl

1 ContentThis lecture course introduces the fundamentals of information and network information theory.Outline:information, uncertainty, entropy, mutual information, capacity, differential entropy, typical sequences,Gaussian channels, basics of source and channel coding, linear block codes, Shannon’s source coding theo-rem, Shannon’s channel coding theorem, capacity of Gaussian channels, capacity of bandlimited channels,Shannon’s bound, bandwidth efficiency, capacity of multiple parallel channels and waterfilling, Gaussianvector channel, Multiple Access Channel, Broadcast Channel, rate region..

2 Learning objectives / Learning OutcomesStudents will understand the fundamentals of classic information theory.

3 Recommended prerequisite for participationKnowledge of basic communication theory und probability theory





6 Usability of this moduleBSc ETiT, BSc iST, MSc iCE, BSc Wi-ETiT, BSc/MSc CE


8 References1. T.M. Cover and J.A. Thomas, Elements of Information Theory, Wiley & Sons, 1991.2. Abbas El Gamal and Young-Han Kim, Network Information Theory, Cambrige, 2011.3. S. Haykin, Communication Systems, Wiley & Sons, 2001.

Courses

Course Nr. Course name18-kp-1010-vl Information Theory I

Instructor Type SWSProf. Dr. techn. Heinz Köppl Lecture 3

Course Nr. Course name18-kp-1010-ue Information Theory I

Instructor Type SWSProf. Dr. techn. Heinz Köppl Practice 1


Module nameConvex Optimization in Signal Processing and Communications



1 ContentThis graduate course introduces the basic theory of convex optimization and illustrates its use with manyrecent applications in communication systems and signal processing.Outline: Introduction, convex sets and convex functions, convex problems and classes of convex problems(LP, QP, SOCP, SDP, GP), Lagrange duality and KKT conditions, basics of numerical algorithms and interiorpoint methods, optimization tools, convex inner and outer approximations for non convex problems, sparseoptimization, distributed optimization, mixted integer linear and non-linear programming, applications.

2 Learning objectives / Learning OutcomesStudents will learn the basic theory of convex optimization and its applications.

3 Recommended prerequisite for participationKnowledge in linear algebra and the basic concepts of signal processing and communications.







8 References1. S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004. (online Verfüg-bar: http://www.stanford.edu/~boyd/cvxbook/)2. D. P. Bertsekas, Nonlinear Programming, Athena Scientific, Belmont, Massachusetts, 2nd Ed., 1999.3. Daniel P. Palomar and Yonina C. Eldar, Convex Optimization in Signal Processing and Communications,Cambridge University Press, 2009.

Courses

Course Nr. Course name18-pe-2020-vl Convex Optimization in Signal Processing and Communications


Course Nr. Course name18-pe-2020-ue Convex Optimization in Signal Processing and Communications


Course Nr. Course name18-pe-2020-pr Convex Optimization in Signal Processing and Communications Lab

Instructor Type SWSProf. Dr.-Ing. Marius Pesavento Internship 1


http://www.stanford.edu/~boyd/cvxbook/

Module nameMicrowave Engineering II

Module Nr. Credit Points Workload Self study Duration Cycle offered18-ku-2040 6 CP 180 h 120 h 1 WiSe

Language Module ownerEnglish PD Dr.-Ing. Oktay Yilmazoglu

1 ContentPart 1 Passive microwave components:Calculation of the properties of simple passive components (microstrip line, filter, resonator, capacitor,inductance) for MMICsPart 2 Active microwave components:* Semiconductor material systems: properties, fabrication and requirements* Contacts to semiconductor devices: properties and characteristics* Charge carrier transport: characteristics and scattering processes* Field Effect Transistor (FET) and heterostructure transistors (HEMTs)Part 3 Active microwave circuits (main part):* Wave parameter and S-parameter* FET amplifier: operation, equivalent circuit, gain, matching circuit, stability and circuit implementation* Oscillator design* Mixer designApplications of these circuits range from communication systems such as cell phones to satellite transceiversas well as high-frequency souces up to Terahertz.

2 Learning objectives / Learning OutcomesStudents will gain knowledge on the physics of microwave waveguides, resonators, microwave components(passive and active) as well as microwave circuits.

3 Recommended prerequisite for participationDesirable: Introduction to Electrodynamics, Microwave Engineering I





6 Usability of this moduleMSc ETiT, MSc iCE, MSc IST, Wi-ETiT


8 ReferencesScript and slides will be handed out. Literature will be recommended in the lecture.

Courses

Course Nr. Course name18-ku-2040-vl Microwave Engineering II

Instructor Type SWSPD Dr.-Ing. Oktay Yilmazoglu Lecture 3

Course Nr. Course name18-ku-2040-ue Microwave Engineering II

Instructor Type SWSPD Dr.-Ing. Oktay Yilmazoglu Practice 1


Module nameMIMO - Communication and Space-Time-Coding

Module Nr. Credit Points Workload Self study Duration Cycle offered18-pe-2030 4 CP 120 h 75 h 1 WiSe


1 ContentThis lecture course introduces the principles of space-time and multiple-input multiple-output (MIMO)communications.Outline: Motivation and background; overview of space-time and MIMO communications; fading MIMOchannel models, MIMO information theory, receive and transmit diversity; channel estimation, MIMOdetectors, Alamouti space-time block code, orthogonal space-time block codes; linear dispersion codes;coherent and non-coherent decoders, differential space-time block coding; MIMO with limited feedback,Multiantenna- and multiuser diversity, BER performance analysis, MIMO in moden wireless communicationnetworks, multicell and multiuser MIMO (coordinated multipoint).

2 Learning objectives / Learning OutcomesStudents will understand modern MIMO communications and existing space-time coding techniques.

3 Recommended prerequisite for participationKnowledge of basic communication theory and basic information theory.







8 References1. A.B.Gershman and N.D.Sidiropoulos, Editors, Space-Time Processing for MIMO Communications, Wileyand Sons, 2005.2. E.G.Larsson and P.Stoica, Space-Time Block Coding for Wireless Communications, Cambridge UniversityPress, 2003;3. A.Paulraj, R.Nabar, and D.Gore, Introduction to Space-Time Wireless Communications, Cambridge Uni-versity Press, 2003.4. Lin Bai and Jinho Choi, Low Complexity MIMO detectors, Springer, 2012.5. Howard Huang, Constantinos B. Papadias, and Sivarama Venkatesan, MIMO Communication for CellularNetworks, Springer, 2012.

Courses

Course Nr. Course name18-pe-2030-vl MIMO - Communication and Space-Time-Coding


Course Nr. Course name18-pe-2030-ue MIMO - Communication and Space-Time-Coding



Module nameLaboratory Communication and Sensor Systems


Language Module ownerGerman and English Prof. Dr.-Ing. Rolf Jakoby

1 ContentThe student communications lab consist of 7 fundamental experiments outof the field of Communication Engineering:Mobile Radio Channel + Diversity (SW)Signal Detection and Parameter Estimation (Matlab)Digital Modulation (HW)Coding (SW)Parasitic Effects in Passive RF Devices (SW)RF FET Amplifier (HW)Polarization of Light (HW)Antennas:Fields and Impedance (HW)

2 Learning objectives / Learning OutcomesThe students are guided to acquaint themselves with given topics. Theylearn to perform prepared experiments within a defined frame and minute,analyze and discuss the results. In this training the fundamentals of freescientific work are practiced.

3 Recommended prerequisite for participationFundamentals of:

• Communications• Microwave Engineering• Digital Signal Processing





6 Usability of this moduleMSc ETiT, MSc iCE, Wi-ETiT


8 ReferencesA description of experiments is offered. It can be bought from Mr.Ziemann (S306/409) or being loaded from the WEB page.

Courses

Course Nr. Course name18-jk-2050-pr Laboratory Communication and Sensor Systems

Instructor Type SWSProf. Dr.-Ing. Rolf Jakoby Internship 3


Module nameMultimedia Communications Project II



1 ContentThe course deals with cutting edge scientific and development topics in the area of multimedia communica-tion systems. Besides a general overview it provides a deep insight into a special scientific topic. The topicsare selected according to the specific working areas of the participating researchers and convey technicaland scientific competences in one or more of the following topics:

• Network planning and traffic analysis• Performance evaluation of network applications• Discrete event simulation for network services• Protocols for mobile ad hoc networks / sensor networks• Infrastructure networks for mobile communication / mesh networks• Context-aware communication and services• Peer-to-peer systems and architectures• Content distribution and management systems for multimedia / e-learning• Multimedia authoring and re-authoring tools• Web service technologies and service-oriented architectures• Resource-based Learning

2 Learning objectives / Learning OutcomesThe ability to solve and evaluate technical and scientific problems in the area of design and development offuture multimedia communication networks and applications using state of the art scientific methods shallbe acquired. Acquired competences are:

• Searching and reading of project relevant literature• Design of complex communication applications and protocols• Implementing and testing of software components for distributed systems• Application of object-oriented analysis and design techniques• Acquisition of project management techniques for small development teams• Systematic evaluation and analyzing of technical and scientific experiments• Writing of software documentation and project reports• Presentation of project advances and outcomes

3 Recommended prerequisite for participationKeen interest to develop and explore challenging solutions and applications in cutting edge multimediacommunications systems using scientific methods. Further we expect:

• Solid experience in programming Java and/or C# (C/C++).• Solid knowledge in object oriented analysis and design.• Basic knowledge of design patterns, refactoring and project management.• Solid knowledge in computer communication networks is recommended.• Lectures in “Communication Networks I” and “Communication Networks II” are recommended






6 Usability of this moduleMSc Wi-ETiT, BSc/MSc CS, MSc Wi-CS, MSc ETiT, MSc iST




Measurement, Simulation, and Modeling" (ISBN 0-471-50336-3)• Joshua Bloch: "Effective Java - Programming Language Guide" (ISBN-13: 978-0201310054)• Erich Gamma, Richard Helm, Ralph E. Johnson: "Design Patterns: Objects of Reusable Object Ori-

ented Software" (ISBN 0-201-63361-2)• Martin Fowler: "Refactorings - Improving the Design of Existing Code" (ISBN-13: 978-0201485677)• Kent Beck: "Extreme Programming Explained - Embrace Changes" (ISBN-13: 978-0321278654)

Courses

Course Nr. Course name18-sm-2130-pr Multimedia Communications Project II



Module nameProject Seminar Communication and Sensor Systems

Module Nr. Credit Points Workload Self study Duration Cycle offered18-pe-1041 8 CP 240 h 180 h 1 WiSe/SoSe

Language Module ownerGerman and English Prof. Dr.-Ing. Marius Pesavento

1 ContentInvestigating and solving specific problems concerning communication and sensor systems (Problems con-cerning communications engineering, microwave technology, signal processing, sensor networks etc. arepossible, topics will be defined out of the recent research topics of the involved labs), working on a a giventask by one’s own, organizing and structuring of a seminar task, searching and analyzing of scientific ref-erence publications for a given task, summarizing achieved results and conclusions by means of a writtenreport, presenting achieved results and conclusions and defending them in an oral discussion includingaudience.

2 Learning objectives / Learning OutcomesAfter completion of the course, students possess:

• the ability to apply methods of communication and sensor systems to practical problems• deep and special knowledge in a particular field of communication and sensor systems (communica-

tions engineering), RF technology, signal processing, sensor networks• the skills to find, analyze and evaluate scientific reference papers for a particular topic• the capability to summarize the achieved scientific findings in the form of a concise report• the ability to present and discuss achieved results in the form of a presentation in front of an audience

3 Recommended prerequisite for participationPrevious knowledge in chosen discipline, e.g. communication technology, signal processing, microwavetechnology, sensor networks





6 Usability of this moduleBSc ETiT, BSc Wi-ETiT, BSc CE, BSc iST, BSc MEC


8 ReferencesWill be announced in the lecture

Courses

Course Nr. Course name18-pe-1041-pj Project Seminar Communication and Sensor Systems

Instructor Type SWSProf. Dr.-Ing. Marius Pesavento Project Seminar 4



Module Nr. Credit Points Workload Self study Duration Cycle offered18-zo-1041 8 CP 240 h 180 h 1 WiSe/SoSe

Language Module ownerGerman and English Prof. Dr.-Ing. Abdelhak Zoubir













Courses

Course Nr. Course name18-zo-1041-pj Project Seminar Communication and Sensor Systems

Instructor Type SWSProf. Dr.-Ing. Abdelhak Zoubir Project Seminar 4



Module Nr. Credit Points Workload Self study Duration Cycle offered18-jk-1041 8 CP 240 h 180 h 1 WiSe/SoSe














Courses

Course Nr. Course name18-jk-1041-pj Project Seminar Communication and Sensor Systems

Instructor Type SWSProf. Dr.-Ing. Rolf Jakoby Project Seminar 4



Module Nr. Credit Points Workload Self study Duration Cycle offered18-kl-1041 8 CP 240 h 180 h 1 WiSe/SoSe

Language Module ownerGerman and English Prof. Dr.-Ing. Anja Klein













Courses

Course Nr. Course name18-kl-1041-pj Project Seminar Communication and Sensor Systems

Instructor Type SWSProf. Dr.-Ing. Anja Klein Project Seminar 4


Module nameProjekt Seminar Advanced Algorithms for Smart Antenna Systems



1 ContentThis project-seminar course introduces the basics of the theory and applications of smart antennas includingspace-time and multiple-input multiple-output communications, direction-of-arrival estimation and sourcelocalization in antenna arrays, and adaptive multiantenna techniques for interference suppression, adaptivetransmit and receive beamforming, concensus and defusion algorithms for wireless sensor networks.

2 Learning objectives / Learning OutcomesStudents will understand theory, algorithms and applications of smart antennas.

3 Recommended prerequisite for participationKnowledge of basic communication theory





6 Usability of this moduleMSc ETiT, MSc Wi-ETiT, MSc iCE


8 References1. Daniel P. Palomar and Yonina C. Eldar, Convex Optimization in Signal Processing and Communications,Cambridge University Press, 2009.2. Harry L. Van Trees, Optimum Array Processing: Part IV of Detection, Estimation, and Modulation Theory,John Wiley & Sons, 2002.3. Y. Hua, A.B. Gershman and Q. Cheng (Editors), High-Resolution and Robust Signal Processing, MarcelDekker, NY, 2004.4. A.B. Gershman and N.D. Sidiropoulos (Editors), Space-Time Processing for MIMO Communications,Wiley & Sons, 2005.

Courses

Course Nr. Course name18-pe-2040-pj Projekt Seminar Advanced Algorithms for Smart Antenna Systems

Instructor Type SWSProf. Dr.-Ing. Marius Pesavento Project Seminar 4


Module nameAdvanced Seminar on Networking, Security, Mobility, and Wireless Communications


Language Module ownerGerman and English Prof. Dr. rer. nat. Karsten Weihe

1 ContentThe Advanced Seminar on Networking, Security, Mobility, and Wireless Communications covers currentresearch that is considered highly relevant for the future development of the given topic areas. Goal of theseminar is to explore the aforementioned research area by studying, critically analyzing and discussing,summarizing, and presenting selected first-rate research articles. Deliverables are a short presentation, afinal presentation, and a seminar paper.The prospective topics for the advanced seminar will be derived from the current research topics of theSEEMOO group.Course contents:- Indepentent exploration of a topic in the area of networking, security, mobility, and wireless communica-tions (typically in english)- Own, enhanced literature study- Interpretation and classification of the literature study- Preparation of an introductory talk as well as a final talk including presentation slides- Presentation of both talks for a heterogenous audience (experts/non-experts)- Technical discussion after the talks- Feedback to the speakers and the talks (including presentation skills) and technical content- Understanding the process of scientific work as well as of scientific publications

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students are able to independently explore new topics in a scien-tific manner. They have aquired detailed knowledge on selected mechanisms, methodologies as well asapplications for the investigated topic area. Techniques such as thoroughly surveying literature, criticaldiscussion and analysis of scientific articles, and the presentation of the obtained results are demonstratedby the students. Students can defend their work against a critical technical audience.

3 Recommended prerequisite for participationSuccessfull participation of an lecture of SEEMOO


• [20-00-0549-se] (Study Achievement, Written/Oral Examination, Standard BWS)


• [20-00-0549-se] (Study Achievement, Written/Oral Examination, Weighting: 100 %)

6 Usability of this moduleB.Sc. InformatikM.Sc. InformatikM.Sc. WirtschaftsinformatikB.Sc. Psychologie in ITJoint B.A. InformatikB.Sc. Sportwissenschaft und InformatikCan be used in other degree programs.


8 ReferencesWill be announced in seminar.


Courses

Course Nr. Course name20-00-0549-se Advanced Seminar on Networking, Security, Mobility, and Wireless Communications

Instructor Type SWSProf. Dr.-Ing. Matthias Hollick Seminar 3


Module nameWireless Network for Emergency Response: Fundamentals, Design, and Build-up from Scratch


Language Module ownerGerman Prof. Dr. rer. nat. Eberhard Max Mühlhäuser

1 ContentThe communication capabilities among the population is of utmost importance to respond to crises. Thiscourse will discuss how to build wireless communication systems from scratch, i.e. under the assumptionthat no communication infrastructure is left intact as a result of the crisis. The course introduces thetheoretical basis from the fields of amateur radio as well as communication systems. It deepens thesefields with the knowledge to design and build communication networks for times of crisis. The discussedtechnologies will span from local to global wireless communications without need of further infrastructure.Theoretical exercises as well as experimentation, the design and building of electrical circuits and theanalysis of wireless technology under laboratory conditions deepen the understanding of the subject.Course contents:- Signals, signal propagation, antennas, basics of electrical engineering- Modulation schemes in analog and digital systems (OFDM, ATV/SSTV, Packet Radio, SSB, ...)- System aspects for communication in times of crisis- Design and practical realization from scratch of wireless communication systems

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students have theoretical and practical knowledge in the area ofwireless and infrastructureless communication for emergency response. They understand the most im-portant physical and electrotechnical basics of wireless communications and know wireless transmissionmechanisms in theory and practice. They are able to build a wireless communication system from scratchand operate it. The students acquire competences in the area of amateur radio and software defined radiotechnology.








8 ReferencesSelected and given in lecture.

Courses


Course Nr. Course name20-00-0780-iv Wireless Network for Emergency Response: Fundamentals, Design, and Build-up from

Scratch

Instructor Type SWSProf. Dr. rer. nat. Eberhard Max Mühlhäuser Integrated

Course3


Module nameInternet - Practical Course Telecooperation


Language Module ownerGerman and English Prof. Dr. rer. nat. Eberhard Max Mühlhäuser

1 ContentThe Praktikum is divided into three parts. In each of the parts, there will be one lecture for reviewingthe basic concepts in that part and for introducing new material. After the lecture, the students will haveroughly 4 weeks to implement the assignment given in the lecture. Each of the assignments will be gradedseparately and all of the grades will be used to determine the total grade for the Praktikum.Relevant topics are:- Introduction to Java network programming and HTTP- Peer-to-peer technologies- Web caching- Internet standards

2 Learning objectives / Learning OutcomesStudents will have aquired knowledge on currently evolving technologies. Thus, students will have usedthese building blocks of future-generation Internet services practically and will have gathered experienceswith using, developing and integrating those technologies.

3 Recommended prerequisite for participationNet Centric Systems


• [20-00-0131-pr] (Study Achievement, Written/Oral Examination, Standard BWS)


• [20-00-0131-pr] (Study Achievement, Written/Oral Examination, Weighting: 100 %)

6 Usability of this moduleB.Sc. InformatikM.Sc. InformatikM.Sc. WirtschaftsinformatikB.Sc. Psychologie in ITJoint B.A. InformatikB.Sc. Sportwissenschaft und InformatikM.Sc. Sportwissenschaft und InformatikMay be used in other degree programs.


8 ReferencesHandbook of Research: Ubiquitous Computing Technology for Real Time Enterprises edited by Prof. Dr.Max Mühlhäuser, Dr. Iryna Gurevych, 2008, Information Science Reference, ISBN-10: 1599048329

Courses

Course Nr. Course name20-00-0131-pr Internet - Practical Course Telecooperation

Instructor Type SWSInternship 4


Module nameCommunication Networks I

Module Nr. Credit Points Workload Self study Duration Cycle offered18-sm-1010 6 CP 180 h 120 h 1 SoSe

Language Module ownerEnglish Prof. Dr.-Ing. Ralf Steinmetz

1 ContentIn this class the technologies that make today‘s communication networks work are introduced and dis-cussed.This lecture covers basic knowledge about communication networks and discusses in detail the physicallayer, the data link layer, the network layer and parts of the transport layer.The physical layer, which is responsible for an adequate transmission across a channel, is discussed briefly.Next, error control, flow control and medium access mechanisms of the data link layer are presented. Thenthe network layer is discussed. It comprises mainly routing and congestion control algorithms. After thatbasic functionalities of the transport layer are discussed. This includes UDP and TCP. The Internet is thor-oughly studied throughout the class.Detailed Topics are:

• ISO-OSI and TCP/IP layer models• Tasks and properties of the physical layer• Physical layer coding techniques• Services and protocols of the data link layer• Flow control (sliding window)• Applications: LAN, MAN, High-Speed LAN, WAN• Services of the network layer• Routing algorithms• Broadcast and Multicast routing• Congestion Control• Addressing• Internet protocol (IP)• Internetworking• Mobile networking• Services and protocols of the transport layer• TCP, UDP

2 Learning objectives / Learning OutcomesThis lecture teaches about basic functionalities, services, protocols, algorithms and standards of networkcommunication systems. Competencies aquired are basic knowledge about the lower four ISO-OSI lay-ers: physical layer, datalink layer, network layer and transport layer; Furthermore, basic knowledge aboutcommunication networks is taught. Attendants will learn about the functionality of today’s network tech-nologies and the Internet.






6 Usability of this moduleWi-CS, Wi-ETiT, BSc CS, BSc ETiT, BSc iST


7 Grade bonus compliant to §25 (2)A bonus of 0.3 or 0.7 can be obtained.For 0.3 bonus: 7 out of 9 exercises are to be solved to the best of your knowledge. That is, every questionneeds to be answered. However, not every question needs to be answered correctly. Additionally, at leastone wiki article or applet concerning a topic of the lecture has to be provided (written).For the 0.7 bonus: Additionally, present one exercise and write at least three wiki articles, or write at least5 wiki articles.An oral exam (“Fachgespräch”) is mandatory in order to receive the bonus. The bonus can only be appliedif the exam grade is 4.0 or better.

8 References• Andrew S. Tanenbaum: Computer Networks, 5th Edition, Prentice Hall, 2010• Andrew S. Tanenbaum: Computernetzwerke, 3. Auflage, Prentice Hall, 1998• Larry L. Peterson, Bruce S. Davie: Computer Networks: A System Approach, 2nd Edition, Morgan

Kaufmann Publishers, 1999• Larry L. Peterson, Bruce S. Davie: Computernetze, Ein modernes Lehrbuch, 2. Auflage, Dpunkt

Verlag, 2000• James F. Kurose, Keith W. Ross: Computer Networking: A Top-Down Approach Featuring the Internet,

2nd Edition, Addison Wesley-Longman, 2002• Jean Walrand: Communication Networks: A First Course, 2nd Edition, McGraw-Hill, 1998

Courses

Course Nr. Course name18-sm-1010-vl Communication Networks I

Instructor Type SWSDr.-Ing. Amr Rizk Lecture 3

Course Nr. Course name18-sm-1010-ue Communication Networks I

Instructor Type SWSDr.-Ing. Amr Rizk Practice 1


Module nameMobile Networking


Language Module ownerEnglish Prof. Dr.-Ing. Thorsten Strufe

1 ContentMobile communications and wireless networking technology has seen a thriving development in recentyears. The integrated course addresses the characteristics/principles of mobile networks in detail, andpractical solutions are presented. Hereby our focus is on the network layer, which is often regarded asthe glue of communication systems. In addition to describing the state of the art in technology we discussactual research problems and learn about methodologies to approach such problems systematically. Thecontents of the course will be deepended by exercises.Course contents:- Introduction to mobile and wireless communications: Applications, history, market vision- Overview of wireless transmission: frequencies & regulations, signals, antennas, signal propagation, mul-tiplexing, modulation, spread spectrum, cellular systems- Medium access control in the wireless domain: SDMA, FDMA, CDMA TDMA (fixed, Aloha, CSMA, DAMA,PRMA, MACA, collision avoidance, polling)- Wireless local area networks: IEEE 802.11 standard including physical layer, MAC layer and accessschemes, quality of service and power management- Wireless metropolitan area networks: Wireless mesh networks, IEEE 802.16 standard including modes ofoperation, medium access control, quality of service and scheduling- Mobility at network layer: Concepts to support mobility on various layers, Mobile IP- Ad hoc networks: Terminology, basics and applications, characteristics of ad hoc communication, ad hocrouting paradigms and protocols- Performance evaluation of mobile networks: Overview of performance evaluation, systematic approach /common mistakes and how to avoid them, experimental design and analysis- Mobility at transport layer: Variants of TCP (indirect TCP, snoop TCP, mobile TCP, wireless TCP)- Mobility at application layer. Outlook: Applications for mobile networks and wireless sensor networks

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students have an in-deep knowledge on the working of mobilecommunication networks. They have gained insight into media access control mechanisms dedicated towireless communication and have a thorough understanding of mechanisms based on the network andthe transport layers, with a focus on ad hoc and mesh networks. Moreover, the students have acquiredknowledge about the connections between the different protocol layers and are able to apply the acquiredknowledge on methodological analysis of real communication systems. The students are therefore beconversant with the characteristics and basic principles of wireless and mobile communications in theoryand practice. The exercise-parts of the integrated course deepen the theoretical foundations by means ofexercises, which consist of literature, calculation as well as practical implementation/application examples.

3 Recommended prerequisite for participationBasic courses in Communication Networks are recommended.







B.Sc. InformatikM.Sc. InformatikM.Sc. WirtschaftsinformatikB.Sc. Psychologie in ITJoint B.A. InformatikB.Sc. Sportwissenschaft und InformatikM.Sc. Sportwissenschaft und InformatikCan be used in other degree programs.


8 ReferencesSelected literature, details are given in lecture.

Courses

Course Nr. Course name20-00-0748-iv Mobile Networking

Instructor Type SWSProf. Dr.-Ing. Thorsten Strufe Integrated

Course4


Module namePeer-to-Peer Middleware Lab


Language Module ownerEnglish Prof. Dr. rer. nat. Eberhard Max Mühlhäuser

1 Content- Independently solive a Peer-to-Peer middleware problem in a small group- Implementing the own solution- Working in a small group- Presenting the own work including the progress- Iterative creation of a lab report- Performance analysis of the implementation- Evaluation of the system according to different quality metrics

2 Learning objectives / Learning OutcomesAfter successfully attending the lab, students are able to solve independently in a small group problemsfrom the Peer-to-Peer Middleware domain. They are able to implement their solution, analyze its perfor-mance, and evaluate it according to different quality metrics. The students are able to present their workinkluding including intermediate results as well as desribing it in a written lab report.

3 Recommended prerequisite for participationComputer-Netzwerke und verteilte Systeme/Einführung in Net Centric SystemsAlgorithmen und Datenstrukturen







8 ReferencesWill be announced in course.

Courses

Course Nr. Course name20-00-0269-pr Middleware Lab (Peer-to-Peer Infrastructures)



Module nameMultimedia Communications Lab I



1 ContentThe course deals with cutting edge development topics in the area of multimedia communication systems.Beside a general overview it provides a deep insight into a special development topic. The topics areselected according to the specific working areas of the participating researchers and convey technical andbasic scientific competences in one or more of the following topics:

• Network planning and traffic analysis• Performance evaluation of network applications• Discrete event simulation for network services• Protocols for mobile ad hoc networks / sensor networks• Infrastructure networks for mobile communication / mesh networks• Context-aware communication and services• Peer-to-peer systems and architectures• Content distribution and management systems for multimedia/e-learning• Multimedia authoring and re-authoring tools• Web service technologies and service-oriented architectures• Applications for distributed workflows• Resource-based Learning

2 Learning objectives / Learning OutcomesThe ability to solve simple problems in the area of multimedia communication shall be acquired. Acquiredcompetences are:

• Design of simple communication applications and protocols• Implementing and testing of software components for distributed systems• Application of object-oriented analysis and design techniques• Presentation of project advances and outcomes

3 Recommended prerequisite for participationKeen interest to explore basic topics of cutting edge communication and multimedia technologies. Furtherwe expect:

• Basic experience in programming Java/C# (C/C++).• Knowledge in computer communication networks. Lectures in Communication Networks I and/or

Net Centric Systems are recommended.





6 Usability of this moduleBSc ETiT, BSc/MSc iST, MSc MEC, Wi-CS, Wi-ETiT, BSc/MSc CS


8 References


Each topic is covered by a selection of papers and articles. In addition we recommend reading of selectedchapters from following books:

• Andrew Tanenbaum: “Computer Networks”. Prentice Hall PTR (ISBN 0130384887)• Christian Ullenboom: "Java ist auch eine Insel: Programmieren mit der Java Standard Edition Version

5 / 6" (ISBN-13: 978-3898428385)• Kent Beck: "Extreme Programming Explained - Embrace Changes" (ISBN-13: 978-0321278654)

Courses

Course Nr. Course name18-sm-1020-pr Multimedia Communications Lab I



Module namePractical Project Telecooperation



1 ContentResearch-related project.The students will learn to conduct their own research given an individual research project. The topics willbe defined together with the adviser.Possible research topics:* Multimodale Interaction* Multitouch* Proactive Support System* Sensor Fusion

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students are familiar with the process of conducting research fromfirst idea to written paper. They understand how to break down complex research questions into sub-problems and solve these problems. They are able to evaluate their system and write a report based ontheir findings.






6 Usability of this moduleB.Sc. InformatikM.Sc. InformatikM.Sc. WirtschaftsinformatikB.Sc. Psychologie in ITJoint B.A. InformatikB.Sc. Sportwissenschaft und InformatikM.Sc. Sportwissenschaft und InformatikCan be used in other degree programs.


8 ReferencesVarious

Courses

Course Nr. Course name20-00-0485-pr Practical Project Telecooperation

Instructor Type SWSProf. Dr. rer. nat. Eberhard Max Mühlhäuser Internship 6


Module nameMultimedia Communications Project I



1 ContentThe course deals with cutting edge scientific and development topics in the area of multimedia communica-tion systems. Besides a general overview, it provides a deep insight into a special scientific topic. The topicsare selected according to the specific working areas of the participating researchers and convey technicaland scientific competences in one or more of the following topics:

• Network planning and traffic analysis• Performance evaluation of network applications• Discrete event simulation for network services• Protocols for mobile ad hoc networks / sensor networks• Infrastructure networks for mobile communication / mesh networks• Context-aware communication and services• Peer-to-peer systems and architectures• Content distribution and management systems for multimedia/e-learning• Multimedia authoring and re-authoring tools• Web service technologies and service-oriented architectures• Applications for distributed workflows• Resource-based Learning

2 Learning objectives / Learning OutcomesThe ability to solve and evaluate technical problems in the area of design and development of futuremultimedia communication networks and applications using state of the art scientific methods. Acquiredcompetences are among the following:

• Searching and reading of project relevant literature• Design of communication applications and protocols• Implementing and testing of software components• Application of object-orient analysis and design techniques• Acquisition of project management techniques for small development teams• Evaluation and analyzing of technical scientific experiments• Writing of software documentation and project reports• Presentation of project advances and outcomes

3 Recommended prerequisite for participationKeen interest to develop and explore challenging solutions and applications in cutting edge multimediacommunication systems. Further we expect:

• Basic experience in programming Java/C# (C/C++).• Basic knowledge in Object oriented analysis and design.• Knowledge in computer communication networks. Lectures in Communication Networks I and/or

Net Centric Systems are recommended.






6 Usability of this moduleBSc ETiT, BSc/MSc iST, MSc MEC, Wi-CS, Wi-ETiT, BSc/MSc CS




Measurement, Simulation, and Modeling" (ISBN 0-471-50336-3)• Erich Gamma, Richard Helm, Ralph E. Johnson: "Design Patterns: Objects of Reusable Object Ori-

ented Software" (ISBN 0-201-63361-2)• Kent Beck: "Extreme Programming Explained - Embrace Changes" (ISBN-13: 978-0321278654)

Courses

Course Nr. Course name18-sm-1030-pj Multimedia Communications Project I

Instructor Type SWSProf. Dr.-Ing. Ralf Steinmetz Project Seminar 4


Module nameNetwork, Traffic and Quality Management for Internet Services



1 ContentIntroduction into management of Internet service provider (ISP-)networks for integrating IP service plat-forms with their quality and traffic profiles

2 Learning objectives / Learning OutcomesCourse Content:Demands and measures for ensuring Quality-of-Service (QoS)?Criteria from the applications & users view (QoE: Quality of Experience)?IP QoS Architecture: Differentiated & Integrated Services?QoS support & impact per application in the current IP traffic mix(Video streaming, VoIP, web browsing, downloads, social networking etc.)Quality support for IP services within ISP network infrastructures?Impact of network and transport layerRouting (OSPF, BGP), Multiprotocol Label Switching (MPLS), TCPincl. failure handling and resilience?Measurement, monitoring and optimization of IP traffic regarding QoS criteriaQuality support in service overlays and on application layer?Content Delivery Networks (CDN), clouds and Peer-to-Peer networks (P2P)incl. distributed caches, optimization of transport paths, scalability and?IETF Standardization (CDN Interconnection, ALTO: Appl. Layer Traffic Opt.)

3 Recommended prerequisite for participationPrerequisites: Basic knowledge in computer science and Internet applications is required. The courses onKommunikationsnetze I and II are recommended.


• [20-00-0056-vl] (Technical Examination, Written/Oral Examination, Standard BWS)


• [20-00-0056-vl] (Technical Examination, Written/Oral Examination, Weighting: 100 %)



8 ReferencesWill be given in lecture.

Courses

Course Nr. Course name20-00-0056-vl Network, traffic and quality management for Internet services

Instructor Type SWSLecture 2


Module nameResilient Networks


Language Module ownerEnglish Prof. Dr.-Ing. Thorsten Strufe

1 ContentThe lecture resilient networking provides an overview on the basics of secure networks as well as on currentthreats and respective countermeasures. Especially bandwidth-depleting Denial of Service attacks representa serious threat. Moreover, over the last years the number of targeted and highly sophisticated attacks oncompany and governmental networks increased. To make it worse, as a new trend at the moment, theinterconnection of the Internet with cyber physical systems takes place. Such systems, e.g., the energynetwork (smart grid), transportation systems and large industrial facilities, are critical infrastructures withsevere results in case of their failure. Thus, the Internet that interconnects these systems has evolved to acritical infrastructure as well.The lecture introduces the current state-of-the-art in the research towards resilient networks. Resilience-enhancing techniques can be generally classified in proactive and reactive methods. Proactive techniquesare redundancy and compartmentalization. Redundancy allows tolerating attacks to a certain extent, whilecompartmentalization attempts to restrict the attack locally and preventing its expansion across the wholesystem. Reactive techniques follow a three step approach by comprising the phases of detecting an attack,mitigate its impacts, and finally restore a system’s usual operation.Based upon this categorization of resilience strategies the lecture will give an excursus to graph theoryand will introduce generic strategies to increase the resilience of networks, e.g., proactively establishingbackup routes and fast restoration strategies. Furthermore, the lecture will provide an overview on BGProuting and the Domain Name Service, as two essential Internet services. Both services are presentedand current attacks as well as corresponding countermeasures are described. Moreover, Denial of Serviceattacks and their mitigation are observed in detail as well as mechanism for increasing the resilience ofP2P networks. Finally, Intrusion Detection systems are covered as mechanisms to mitigate the impacts ofsuccessful attacks.

2 Learning objectives / Learning OutcomesThe main goal of the lecture is to give a survey of resilience-enhancing measures for networks and toincrease the awareness for the security problems arising in networked environments. Participants obtain acomprehensive overview on generic mechanisms to enhance the resilience of networks. Constitutive, basicservices of the Internet are discussed and countermeasures against sophisticated attacks are observed.The lecture is completed by a reading group, in which participants have to deal with recent research papersin the core areas of the lecture.

3 Recommended prerequisite for participationFoundations of net centric systems. Basic knowledge in P2P systems and IT security is recommended.









8 ReferencesLiterature recommendations will be updated regularly, an example might be:1. G. Schäfer, M. Roßberg - Netzsicherheit, dpunkt.verlag, 676 pages, August 20142. Michal Pioro and Deepankar Medhi - Routing, Flow, and Capacity Design in Communication and Com-puter Networks, The Morgan Kaufmann Series in Networking, 800 pages, 20043. Network Analysis: Methodological Foundations, Springer: Lecture Notes in Computer Science / Theo-retical Computer Science and General Issues, 484 pages,20054. Andrew S. Tanenbaum and Maarten Van Steen, Distributed Systems: Principles and Paradigms (2ndEdition). 2006. Prentice Hall. ISBN: 978-0132392273

Courses

Course Nr. Course name20-00-0710-iv Resilient Networks

Instructor Type SWSProf. Dr.-Ing. Thorsten Strufe Integrated

Course4


Module nameMultimedia Communications Seminar I



1 ContentThe seminar investigates current and upcoming topics in multimedia communication systems, which areexpected to be of utmost importance for the future evolution of the Internet and information technolgyin goal. The goal is to learn more about multimedia communication systems by studying, summarizing,and presenting top quality papers from recent high quality networking research journals, magazines, orconferences. The selection of topics corresponds to the research area of participating researchers.Possible topics are:

• Knowledge & Educational Technologies• Self organizing Systems & Overlay Communication• Mobile Systems & Sensor Networking• Service-oriented Computing• Multimedia Technologies & Serious Games

2 Learning objectives / Learning OutcomesThe students are actively studying cutting edge scientific articles, standards, and books about multimediacommunication systems and applications, which are expected to be of utmost important for the future ofthe Internet.Students acquire competences in the following areas:

• Searching and reviewing of relevant scientific literature• Analysis and evaluation of complex technical and scientific information• Writing of technical and scientific summaries and short papers• Presentation of complex technical and scientific information






6 Usability of this moduleCS, WiCS, ETiT, Wi-ETiT, BSc/MSc iST


8 ReferencesDepending on specific topic (selected articles of journals, magazines, and conferences).

Courses

Course Nr. Course name18-sm-2300-se Multimedia Communications Seminar I

Instructor Type SWSProf. Dr.-Ing. Ralf Steinmetz Seminar 3


Module nameSeminar Smart City


Language Module ownerEnglish

1 ContentRapid urbanization presents cities with complex challenges, from socio-economic and environmental prob-lems to issues involving infrastructure and governance. In the seminar students will have a look at differenttechnical approaches to cope with these different challenges, e.g. for traffic prediction, analysis of environ-mental data or disaster preparation and management.

2 Learning objectives / Learning OutcomesThe students learn fundamentals of scientific work when interacting with existing Smart City literature.Furthermore, the students get a good overview of the topic smart city.








8 ReferencesVarious

Courses

Course Nr. Course name20-00-0619-se Seminar Smart City

Instructor Type SWSProf. Dr. rer. nat. Eberhard Max Mühlhäuser Seminar 2


Module nameSoftware Defined Networking

Module Nr. Credit Points Workload Self study Duration Cycle offered18-sm-2280 6 CP 180 h 120 h 1 WiSe


1 ContentThe course deals with topics in the area of software defined networking:

• SDN Data Plane• SDN Control Plane• SDN Application Plane• Network Function Virtualization• Network Virtualization and Slicing• QoS and QoE in Software Defined Networks

2 Learning objectives / Learning OutcomesStudents will get a deep insight into Software Defined Networking as well as underlying technologies andapplications.

3 Recommended prerequisite for participationBasic courses of the first 4 semesters are required. Knowledge of lectures Communication Networks I andII are recommended.





6 Usability of this moduleMSc ETiT, BSc/MSc iST, MSc Wi-ETiT, CS, Wi-CS


8 ReferencesTextbooks as indicated.Slides and paper copies as necessary.

Courses

Course Nr. Course name18-sm-2280-vl Software Defined Networking

Instructor Type SWSPh.D. Boris Koldehofe Lecture 2

Course Nr. Course name18-sm-2280-ue Software Defined Networking

Instructor Type SWSPh.D. Boris Koldehofe Practice 2


Module nameSeminar Telecooperation



1 ContentThe TK Seminar is cycle of seminar where students are given the chance to read and analyze currentscientific publications.

2 Learning objectives / Learning OutcomesAfter participation in the seminar Telekooperation, studentshave been introduced to the research area of their seminar topic- are able to critically read and analyze scientific papers- can document and present the evaluation of such critical analysis in both written and spoken form

3 Recommended prerequisite for participationGeneral knowledge within Computer Science based on Bachelor.







8 ReferencesW. Strunk, E. B. White. The Elements of Style, Pearson, ISBN 0-321-24861-9

Courses

Course Nr. Course name20-00-0130-se Seminar Telecooperation

Instructor Type SWSSeminar 2


Module nameTK1: Distributed Systems and Algorithms



1 ContentObjectives:- Comprehensive overview about the fundamental problems and approaches in distributed computing- In-depth methodical knowledge about classical distributed algorithms and programming paradigms- Applied knowledge of current developments and standardsCourse Content:- Introduction- Recap of and addition to the first chapter of the Net Centric Computing lecture- Distributed algorithms- Distributed programming

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students are familiar with the concepts of distributed algorithmsand programming. They understand the fundamental issues of distributed systems and the classical dis-tributed algorithms and programming paradigms. They are able to apply these classical and current stan-dards of distributed programming to given problems.

3 Recommended prerequisite for participationComputer Networks and Distributed Systems







8 References


Literature recommendations will be updated regularly, an example might be:- George Coulouris, Jean Dollimore, Tim Kindberg: Distributed Systems. Concepts and Design (GebundeneAusgabe) 832 Seiten, Addison Wesley; Auflage: 4th (14. Juni 2005), ISBN: 0321263545- M. Boger: Java in verteilten Systemen, 1999, dpunkt-Verlag, Heidelberg, ISBN: 3932588320- G. Tel: Introduction to Distributed Algorithms, 2nd Ed 2001, Cambridge University Press, ISBN:0521794838- A. Tanenbaum, M.v.Steen, Verteilte Systeme: Grundlagen und Paradigmen, Pearson Studium 2003, ISBN:3827370574- A. Tanenbaum: Computernetzwerke. 4te Auflage. Pearson Studium 2003, ISBN-10: 3827370469- J. Kurose, K. Ross: Computer Networking, 1. Ed. 2000, Adison-Wesley. ISBN: 0201477114- L. Peterson, B. Davie, Computernetze, 1. Aufl. 2000, dpunkt Heidelberg, ISBN: 393258869X- Hammerschall, U.: Verteilte Systeme und Anwendungen. Pearson, München 2005, ISBN: 3827370965

Courses

Course Nr. Course name20-00-0065-iv TK1: Distributed Systems and Algorithms


4


Module nameUbiquitous computing in business processes



1 Content- Learning how state-of-the-art ubiquitous computing technologies can be utilized in enterprise businessprocesses and in the context of smart city services- Identifying technologies’ economic potential for business processes and in the context of smart cities- Understanding underlying technologies, their benefits, challenges, and corresponding business cases- Technologies considered will be RFID technology and its integration with business processes, other smartitems (e.g., smart shelfs), etc.- Demonstration of how integration works between the real world and the virtual world as it is representedin enterprise software systems today- Hands-on experience and live demonstrations

2 Learning objectives / Learning OutcomesAfter participation in this course, students will have aquired knowledge about implications of ubiquitouscomputing on business to business processes and in the context of smart city services in conjunction withbasic concepts.








8 References- Mühlhäuser, M.; Gurevych, I. (Eds.): Ubiquitous Computing Technology for Real Time Enterprises Infor-mation Science Reference, Dezember, 2007- Finkenzeller, K: RFID-Handbuch. Grundlagen und praktische Anwendungen von Transpondern, kontakt-losen Chipkarten und NFC. Hanser Fachbuch; Auflage: 5., aktual. u. erw. Aufl. (1. Oktober 2008)- Fleisch, E.; Mattern, F. (Hrsg.): Das Internet der Dinge: Ubiquitous Computing und RFID in der Praxis,Springer, Berlin, Heidelberg, New York 2005- Österle, H.; Fleisch, E.; Alt, R.: Business Networking – Shaping Collaboration between Enterprises,Springer- Callaway, E.H.: Wireless Sensor Networks: Architectures and Protocols, Auerbach Publications

Courses


Course Nr. Course name20-00-0121-vl Ubiquitous computing in business processes



Module nameRadar Techniques



1 ContentFirst, there will be an introduction of different radar techniques, describing their concepts and principles,their applications and the operating frequency ranges. In a historical survey, the radar ranges and propa-gation effects will be dealt with. In the second part, various primary and secondary radar techniques willbe investigated in detail, including specific techniques of radar signal processing and -analysis.

2 Learning objectives / Learning OutcomesStudents will know about concepts and principles to detect objects as well as to determine the angularposition and range of objects. They learn about the functional principles of various radar systems, includingsignal processing. They will understand the major physical propagation effects.

3 Recommended prerequisite for participationFundamentals of Communications, Microwave Engineering I





6 Usability of this moduleMSc ETiT, MSc iCE, MSc Wi-ETiT


8 ReferencesSlides, Latest Publications and Books

Courses

Course Nr. Course name18-jk-2040-vl Radar Techniques

Instructor Type SWSDr.-Ing. Holger Maune Lecture 2


Module nameComputer Networks and Distributed Systems



1 ContentOverview of Net-Centric Computing (NCC), a basic element of modern computer science. Fundamentalnetwork concepts of modeling, planning and evaluating net-centric systems- Foundations: Service, protococols, connection, layer model- protocol mechanisms for media access, routing, broad-/multicast- Multimedia Data Handling- Aspects of continuous data streams and their processing- Quality of service: definition and mechanisms- Multimedia - Synchronisation: Basics- Compression procedures;

2 Learning objectives / Learning Outcomes- Overview knowledge of relevant areas and basic problems of net-centric computing (NCC)- Reproducible comprehension of selected, elementary algorithms, protocols and procedures used in theinternet- Applicable methodological knowledge of widely applied elements of the modeling and engineering ofNCC-systemsNCC is, in this context, understood as “internet technology in the broadcast sense”. It covers, in particular,themes of the "classical areas" constituted by computer networks, distributed systems, multimedia andmobile communication/ mobile computing, as those from "modern areas", such as ubiquitous/pervasivecomputing, peer-to-peer-computing or ambient intelligence. The canonical lecture "Introduction to NCS"focusses on the area of computer networks, the understanding of which is fundamental for all other listedareas; the latter will be the subject matter of advanced lectures in the area of NCS.

3 Recommended prerequisite for participationRecommended:Funktionale und objektorientierte Programmierkonzepte“, „Algorithmen und Datenstrukturen“, „Betrieb-ssysteme“, „Einführung in den Compilerbau“, „Rechnerorganisation“ und „Systemnahe and parallele Pro-grammierung“.





6 Usability of this moduleB.Sc. InformatikB.Sc. WirtschaftsinformatikB.Sc. Psychologie in ITJoint B.A. InformatikB.Sc. Sportwissenschaft und InformatikM.Sc. Sportwissenschaft und InformatikB.Sc. InformationssystemtechnikMay be used in other degree programs.




8 ReferencesMain literature:- A. Tanenbaum, D. Wetherall: Computernetzwerke, 5te Aufl., Pearson Studium 2012- (englisch: Computer Networks, 5th Ed., Prentics Hall 2010)- J. Kurose, K. Ross: Computernetzwerke; Pearson Studium 2012 (also in english by Prentice Hall)Selected chapters of:- G. Coulouris, J. Dollimore, T. Kindberg: Distributed Systems – Concept and Design, Pearson Studium- G. Krüger, D. Reschke: „Lehr- und Übungsbuch Telematik“- L. Kleinrock: Queueing Systems, vol. 1 (Wiley)- W.R. Stevens: Unix Network Programming, Volume 1: The Sockets Networking API (Addison Wesley)

Courses

Course Nr. Course name20-00-0016-iv Computer Networks and distributed Systems


3


Module nameTK2: Human Computer Interaction


Language Module ownerGerman and English Prof. Dr. Bernt Schiele

1 ContentThe course presents fundamental concepts, models, and theories in the area of Human Computer Interac-tion (HCI). More specifically, it contains the following topics:- Theoretical foundation on psychology and interaction design as basis for the design of intuitive user in-terfaces- Overview of the different types of user interfaces- Command line interfaces- Graphical user interfaces (MacOS, Windows, . . . )- Interactive surfaces (Tabletops, Multitouch, . . . )- Mobile user interfaces (iOS, Android, . . . )- Pen-based user interfaces (electronic pens)- Tangible user interfaces, organic user interfaces- Speech-based user interfaces- Evaluation, measurement and assessment of user interfaces- User studies- Quantitative evaluation- Qualitative evaluation- User-centric software development

2 Learning objectives / Learning OutcomesAfter perticipation in this course, students will have- an understanding of the psychologic foundations of the design of user interfaces- know methods of the user-centric design process- aquired an overview on common UI concepts- learnt to know and how to use techniques for the evaluation of user interfaces








8 References


Literature recommendations will be updated regularly, an example might be:Selected chapters out of:* Donald Norman: The Design of Everyday Things* Alan Dix, Janet Finlay, Gregory Abowd and Russel Beale: Human-Computer Interaction* Jenny Preece , Yvonne Rogers and Helen Sharp: Interaction Design: Beyond Human-Computer Interac-tion

Courses

Course Nr. Course name20-00-0535-iv TK2: Human Computer Interaction


2


Module nameMicrowave Measurement Technologies

Module Nr. Credit Points Workload Self study Duration Cycle offered18-jk-2090 6 CP 180 h 120 h 1 SoSe


1 ContentIntroduction to microwave measurement technoloies, high frequency components and their poperties: rfpower measurement, spectrum analysis, vertor network analysis (s-parameter, x-parameter, calibrationtechniques), on-wafer measurements, load/source-pull, material characterization

2 Learning objectives / Learning OutcomesBy this module, Students will be enabled to undetstand the basic principles of microwave measuementtechnologies. They are able to use them in measurement applications. The following objectives are linkedto the lecture:

• The students understand the basic features of the power measurements and the effects of a mismatchor pulsed signals and can independently carry out and interpret measurements.

• The students understand the basics of spectrum analysis and can carry out and interpret measure-ments independently.

• The students understand the basics of s-parameter measurements and calibration of network analyz-ers and can carry out and interpret measurements independently

• Students are familiar with various methods for material characterization

3 Recommended prerequisite for participationRecommended: Grundlagen der Nachrichtentechnik, Hochfrequenztechnik I





6 Usability of this moduleMSc etit, MSc WI-etit, MSc iCE, MSc iST


8 References

Courses

Course Nr. Course name18-jk-2090-vl Microwave Measurement Technologies

Instructor Type SWSDr.-Ing. Holger Maune Lecture 2

Course Nr. Course name18-jk-2090-ue Microwave Measurement Technologies

Instructor Type SWSDr.-Ing. Holger Maune Practice 1


Course Nr. Course name18-jk-2090-pr Microwave Measurement Technologies Lab

Instructor Type SWSDr.-Ing. Holger Maune Internship 1


Module nameSensor Array Processing and Adaptive Beamforming



1 ContentThis lecture course introduces the principles of modern sensor array processing and adaptive beamforming.Outline: Motivation and background; applications, narrowband and wideband signal modelDirection-of-arrival estimation (DoA):traditional methods based on beamforming, super resolution methods, Maximum-Likelihood methods,Subspace based methods, MUSIC, ESPRIT, MODE, root-MUSIC, multidimensional source localization,beamspace processing, array interpolation, partly calibrated arrays, wideband DOA estimation, spatialsmoothing, forward-backward averaging, redundancy averaging, correlated sources, minimum redundancyarrays, compressed sensing and sparse reconstruction based DoA estimation, performance boundsAdaptive beamforming:Point-source model, covariance model, Wiener-Hopf equation, Minimum Variance Distortionless Response(MVDR) beamformer, Capon Beamformer, sample matrix inversion, signal self-nulling effect, robustadaptive beamforming, Hung-Turner projection beamformer, Generalized Sidelobe canceller beamformer,Eigenspace-based beamformer, non-stationary environments, modern convex optimization based beam-forming, worst-case based beamforming, multiuser beamforming.

2 Learning objectives / Learning OutcomesStudents will standard and modern sensor array processing techniques for source localization and trans-mit/receive beamforming

3 Recommended prerequisite for participationKnowledge in linear algebra.





6 Usability of this moduleBSc / MSc etit, BSc / MSc WI-etit, MSc MEC, MSc iST, MSc iCE


8 References• Academic Press Library in Signal Processing: Volume 3 Array and Statistical Signal Processing Edited

by Rama Chellappa and Sergios Theodoridis, Section 2, Edited by Mats Viberg, Pages 457-967 (2014)– Chapter 12 - Adaptive and Robust Beamforming, Sergiy A. Vorobyov, Pages 503-552– Chapter 14 - DOA Estimation Methods and Algorithms, Pei-Jung Chung, Mats Viberg, Jia Yu,

Pages 599-650– Chapter 15 - Subspace Methods and Exploitation of Special Array Structures, Martin Haardt,

Marius Pesavento, Florian Roemer, Mohammed Nabil El Korso, Pages 651-717

• Spectral Analysis of Signals, Petre Stoica, Randolph Moses, Prentice Hall, April 2005Optimum ArrayProcessing: Part IV of Detection, Estimation, and Modulation Theory, Harry L. Van Trees, WileyOnline, 2002.

Courses


Course Nr. Course name18-pe-2060-vl Sensor Array Processing and Adaptive Beamforming


Course Nr. Course name18-pe-2060-ue Sensor Array Processing and Adaptive Beamforming



Module nameMachine Learning in Information and Communication Technology (ICT)

Module Nr. Credit Points Workload Self study Duration Cycle offered18-kp-2110 6 CP 180 h 120 h 1 SoSe


1 ContentThe module provides an introduction to the emerging field of machine learning from an engineering per-spective. Important models and learning methods are presented and exemplified through problems frominformation and communication technology.

• Fundamentals of probability theory and multivariate statistics• Taxonomy of machine learning problems and models (supervised, unsupervised, generative, discrim-

inative)• Regression and classification: theory, methods and ICT applications• Dimensionality reduction, clustering and big data analytics: methods and application in communi-

cations and signal processing• Probabilistic graphical models: categories, inference and parameter estimation• Fundamentals of Bayesian inference, Monte Carlo methods, Bayesian non-parametrics• Fundamentals of convex optimization: Solution methods and application in communications• Approximate algorithms for scalable Bayesian inference; application in signal processing and infor-

mation theory (e.g. decoding of LDPC codes)• Hidden Markov models (HMM): Theory, Algorithms and ICT applications (e.g. Viterbi decoding of

convolutional codes)• High-dimensional statistics (“large p small n” setting), learning dependency structure in high-

dimensional data, learning causality relations from observational data.• Sparse estimation, random projections, compressive sensing: Theory and applications in signal pro-

cessing• Deep neural networks (deep learning): Models, learning algorithms, libraries and ICT applications

2 Learning objectives / Learning OutcomesStudents are able to interpret and categorize specific engineering problems from the ICT domain in termsof machine learning problems.They are able to reduce such problems to standard machine learning problems and are able to determinesuitable solution methods for them.They are able to implement all necessary algorithms from scratch, but they are also familiar with the state-of-the-art libraries in machine learning.They are able to determine the involved computational complexity of a method and choose an appropriatesolution algorithms based on application constraints.They are able to apply the acquired methods to other domains, such as data analysis in biomedical engi-neering, analysis of social network data, etc.

3 Recommended prerequisite for participationGood command of Matlab (for instance knowledge from course 18-st-2030 Matlab Grundkurs) and engi-neering mathematics





6 Usability of this moduleMSc etit, BSc/MSc iST, MSc iCE, MSc CE



8 References• Kevin P. Murphy. Machine Learning – A probabilistic perspective, MIT Press, 2012• Christopher M. Bishop. Pattern recognition and Machine Learning, Springer, 2006• Peter Bühlmann und Sara van de Geer. Statistics of high-dimensional data – Methods, theory and

applications, Springer, 2011

Courses

Course Nr. Course name18-kp-2110-vl Machine Learning in Information and Communication Technology (ICT)

Instructor Type SWSProf. Dr. techn. Heinz Köppl Lecture 2

Course Nr. Course name18-kp-2110-pr Machine Learning in Information and Communication Technology (ICT) Lab

Instructor Type SWSProf. Dr. techn. Heinz Köppl Internship 1

Course Nr. Course name18-kp-2110-ue Machine Learning in Information and Communication Technology (ICT)

Instructor Type SWSProf. Dr. techn. Heinz Köppl Practice 1


Module nameRobust Signal Processing With Biomedical Applications


Language Module ownerEnglish Dr.-Ing. Michael Muma

1 Content1. Robust Signal Processing and Learning

• Measuring robustness• Robust estimation of the mean and the variance• Robust regression models• Robust filtering• Robust location and covariance estimation• Robust clustering and classification• Robust time-series and spectral analysis

2. Biomedical Applications• Body-worn sensing of physiological parameters• Electrocardiogram (ECG)• Photoplethysmogram (PPG)• Eye research• Intracranial Pressure (ICP)• Algorithms for cardiac activity monitoring

The lecture covers fundamental topics and recent developments in robust signal processing. Unlike classicalsignal processing, which relies strongly on the normal (Gaussian) distribution, robust methods can tolerateimpulsive noise, outliers and artifacts that are frequently encountered in biomedical applications. Robustsignal processing and biomedical application lectures alternate. Exercises revise the theory and applyrobust signal processing algorithms to real world data.


3 Recommended prerequisite for participationFundamental knowledge of statistical signal processing





6 Usability of this moduleMSc ETiT, MSc Wi-ETiT, MSc iCE, MSc iST


8 References


A manuscript and lecture slides can be downloaded via Moodle. Further reading• Zoubir, A. M. and Koivunen, V. and Ollila, E. and Muma, M.: Robust Statistics for Signal Processing.

Cambridge University Press, 2018.• Zoubir, A. M. and Koivunen, V. and Chackchoukh J, and Muma, M. Robust Estimation in Signal

Processing: A Tutorial-Style Treatment of Fundamental Concepts. IEEE Signal Proc. Mag. Vol. 29,No. 4, 2012, pp. 61-80.

• Huber, P. J. and Ronchetti, E. M.: Robust Statistics. Wiley Series in Probability and Statistics, 2009.• Maronna, R. A. and Martin, R. D. and Yohai, V. J.: Robust Statistics: Theory and Methods. Wiley

Series in Probability and Statistics, 2006.

Courses

Course Nr. Course name18-zo-2090-vl Robust Signal Processing With Biomedical Applications

Instructor Type SWSDr.-Ing. Michael Muma Lecture 3

Course Nr. Course name18-zo-2090-ue Robust Signal Processing With Biomedical Applications

Instructor Type SWSDr.-Ing. Michael Muma Practice 1


Module nameRobust and Biomedical Signal Processing



1 ContentA series of 3 lectures provides the necessary background on robust signal processing and machine learning:

• Background on robust signal processing• Robust regression and robust filters for artifact cancellation• Robust location and covariance estimation and classification

They are followed by two lectures on selected biomedical applications, such as:• Body-worn sensing of physiological parameters• Optical heart rate sensing (PPG)• Signal processing for the electrocardiogram (ECG)• Biomedical image processing

Students then work in groups to apply robust signal processing algorithms to real-world biomedical data.Depending on the application, the data is either recorded by the students, or provided to them. The groupresults are presented during a 20-minute presentation. The final assessment is based on the presentationand an oral examination.


3 Recommended prerequisite for participationFundamental knowledge of statistical signal processing







8 References• Slides can be downloaded via Moodle.

Further reading:• Zoubir, A. M. and Koivunen, V. and Ollila, E. and Muma, M.: Robust Statistics for Signal Processing.

Cambridge University Press, 2018.• Zoubir, A. M. and Koivunen, V. and Chackchoukh J, and Muma, M. Robust Estimation in Signal

Processing: A Tutorial-Style Treatment of Fundamental Concepts. IEEE Signal Proc. Mag. Vol. 29,No. 4, 2012, pp. 61-80.

• Huber, P. J. and Ronchetti, E. M.: Robust Statistics. Wiley Series in Probability and Statistics, 2009.• Maronna, R. A. and Martin, R. D. and Yohai, V. J.: Robust Statistics: Theory and Methods. Wiley

Series in Probability and Statistics, 2006.


Courses

Course Nr. Course name18-zo-2100-se Robust and Biomedical Signal Processing

Instructor Type SWSDr.-Ing. Michael Muma Seminar 4


Module nameAcoustics I

Module Nr. Credit Points Workload Self study Duration Cycle offered18-se-2010 3 CP 90 h 60 h 1 WiSe

Language Module ownerGerman Prof. (em.) Dr. Gerhard Sessler

1 Content1.Basic concepts of vibrations; impedance; electromechanical analogues,2.sound field: wave equation; plane waves; sound absorption and dispersion; room absorption,3.sound radiation: spherical, dipole, and cardioid source; linear arrays;circular piston membrane,4.physiological and psychological acoustics: hearing organ; acoustic perception; speech production and-speech intelligibility,5. electroacoustic transducers; reciprocity relations; electrostatic, piezoelectric, electrodynamic, and othertransducers; directional microphones; microphone calibration,6. acoustic measuring methods: measurements of fundamental acoustic quantities; acoustic testing cham-bers; vibration measurements,7 analogical and digital sound recording: digital and analogical disc and magnetic tape methods; moviesound,8. ultrasound and hypersound: generation and detection; applications

2 Learning objectives / Learning OutcomesAfter completion of the lecture, students possess:

• the understanding of basic phenomena of generation, propagation, reception, storage and reproduc-tion of sound;

• the ability to analyze acoustic components and systems;• the ability to judge and design applications in the audio and ultrasonic frequency ranges.

3 Recommended prerequisite for participationElectrical Engineering I and II, Mathematics I to IV, Physics, Basics of Telecommunication







8 ReferencesH. Kuttruft, Akustik (Hilzel 2004); M. Zollner u. E. Zwicker, Elektroakustik, 3. Auflage (Springer, correctedreprint 1998); H. Fastl, E. Zwicker, Psychoacoustics (Springer 2005); J. Blauert, Communication Acoustics(Springer 2005); R.Lerch, G. Sessler u. D. Wolf, Technische Akustik (Springer 2009)

Courses

Course Nr. Course name18-se-2010-vl Acoustics I

Instructor Type SWSProf. (em.) Dr. Gerhard Sessler Lecture 2


Module nameTK3: Ubiquitous / Mobile Computing



1 ContentObjectives:- Knowledge of technical basics of the mobile communication- Knowledge of important challenges of the Ubiquitous Computing- Methodic knowledge about current approaches to these challengesCourse Content:- Introduction to Ubiquitous Computing- Mobile Communication- Internet of Things: RFID and Smart Items- Service Discovery & Cloudlets- Context- and Location-aware Computing- Human Computer Interaction- Privacy and Trust in Ubiquitous Computing

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students are familiar with the technical basis of mobile communica-tion. They understand the fundamental challenge of ubiquitous computing. They know current approachesto solve these challenges. They are able to apply their knowledge to build ubiquitous computing systems.

3 Recommended prerequisite for participationComputer Netzwerke and Distributed Systems







8 References


Literature recommendations will be updated regularly, an example might be:A Primary Literature:Handbook of Research: Ubiquitous Computing Technology for Real Time Enterprises edited by Prof. Dr.Max Mühlhäuser, Dr. Iryna Gurevych, 2008, Information Science Reference, ISBN-10: 1599048329B Secondary Literature:1. F. Adelstein, S. Gupta et al.: Fundamentals of Mobile & Pervasive Computing McGraw Hill 2004,2. Stefan Poslad: Ubiquitous Computing, Wiley 2009, ISBN 978-0-470-03560-33. Kapitel Mobilkommunikation: M. Sauter: Grundkurs Mobile Kommunikationssysteme: UMTS, HSDPAund LTE, GSM, GPRS und Wireless LAN; Vieweg-Teubner Studium 20104. J. Krumm (Ed.): Ubiquitous Computing Fundamentals, CRC Press 2010D. Cook, S. Das (Ed.): Smart Environments, Wiley 2005

Courses

Course Nr. Course name20-00-0120-iv TK3: Ubiquitous / Mobile Computing


4


Module nameOptical Communications 1 – Components

Module Nr. Credit Points Workload Self study Duration Cycle offered18-ku-1060 6 CP 180 h 120 h 1 SoSe

Language Module ownerEnglish Prof. Dr. rer. nat. Thomas Kusserow

1 ContentOptical telecommunication and data networksOptical transmisison systemsThe nature of light / wave-partical dualismWave equation / planar wavePolarizationAbsorption, transmission, reflexion, refractionConnectors and splicesMirrors, HR-/AR coatingsFilm waveguidesFiber-optic waveguidesAttenuation, modes, dispersionFiber typesDispersion and dispersion compensationKerr nonlinearity and self-phase modulationOptical filtersWavelength division multiplexersMagneto-optical effect / optical isolator / circulatorLasers / basics, concepts, typesErbium-doped fiber lasers / amplifiers (EDFL / EDFA)Optical semiconductor laser / amplifier (laser diode)Electro-optic modulatorOther selected components and devices

2 Learning objectives / Learning OutcomesStudents understand concepts, basics of physics, design criteria and system requirements (component spec-ifications) of the most important passive and active components of optical communications.

3 Recommended prerequisite for participationET 1-4, Physics





6 Usability of this moduleBSc ETiT, MSc ETiT, MSc iCE


8 ReferencesLecture slidesTextbook (M. Cvijetic, I. B. Djordjevic: „Advanced Optical Communication Systems and Networks“)

Courses


Course Nr. Course name18-ku-1060-vl Optical Communications 1 – Components

Instructor Type SWSProf. Dr. rer. nat. Sascha Preu Lecture 3

Course Nr. Course name18-ku-1060-ue Optical Communications 1 – Components

Instructor Type SWSProf. Dr. rer. nat. Sascha Preu Practice 1


Module nameInternational Summer School ’Microwaves and Lightwaves’

Module Nr. Credit Points Workload Self study Duration Cycle offered18-pr-2020 4 CP 120 h 90 h 1 SoSe

Language Module ownerEnglish Prof. Dr. rer. nat. Sascha Preu

1 ContentThis lecture covers the fundamentals and the latest deveolpments of microwave electronics, THz technol-ogy, and optical communication systems with particular focus on the physical concepts involved.

2 Learning objectives / Learning OutcomesStudents understand

• the background of microwave enginering, THz engineering, and optical communications and• of related electronics, and• the influence of the relevant properties of materials and of waveguides on signal processing.

They gain inside into the latest developments in these fields.






6 Usability of this moduleBSc ETiT, MSc ETiT


8 ReferencesA script (English) will be distributed and English slides can be downloaded.

Courses

Course Nr. Course name18-pr-2020-se International Summer School “Microwaves and Lightwaves”

Instructor Type SWSProf. Dr. rer. nat. Sascha Preu Seminar 2


Module nameIoT and wireless protocols in embedded systems



1 ContentAs part of the internship, students become acquainted with IoT and radio protocols and independently carryout a project with embedded hardware. In addition, aspects of IT security are also taken into account.The main focus is on Bluetooth LE, Bluetooth Mesh, LoRaWAN and communication via OOB channels.Depending on the selected project topic, hardware (microcontrollers, FPGAs, RF transceivers, softwaredefined radio, etc.) as well as laboratory environment (logic analyzers, RF analyzers, oscilloscopes, etc.)are provided.

2 Learning objectives / Learning OutcomesAt the end of the course, students will be able to deal with complex specifications of radio protocols andtransfer them into practice. Furthermore, the practical handling of embedded systems and laboratoryequipment is taught.

3 Recommended prerequisite for participationPrevious knowledge in computer networks (compulsory lecture “Computer Networks and Distributed Sys-tems) and Embedded Systems (compulsory lectures Computer Organization and / or Data Engineering)Knowledge of the programming language C and basic knowledge of electrical engineering are helpful, aswell as knowledge from relevant lectures in the field” Networks and Systems " Distributed systems "such asTK3, mobile networks or KN1.


• [20-00-1064-pr] (Study Achievement, Written/Oral Examination, Standard BWS)Pass exam (100%)



6 Usability of this moduleB.Sc. InformatikM.Sc. InformatikMay be used in other degree programs.


8 References

Courses

Course Nr. Course name20-00-1064-pr IoT and wireless protocols in embedded systems



Module nameData Science I



1 ContentThe course covers the following topics:

• Python programming basics• Data science introduction• Data storage and formats• Data exploration and visualization• Statistical methods and inference

– Descriptive statistics (uni & bivariate)– Inferential statistics

• Feature extraction– Time Series Data– Image data– Audio data

• Statistical learning– Cross-validation, overfitting, annotation– Regression– Classification

2 Learning objectives / Learning OutcomesThe course provides a full introduction to data science with an emphasis on hands-on examples. Studentswill acquire relevant knowledge of the whole data science chain: From storage/acquisition to statisticalinference to visualization. It also serves as an introductory course to the Data Science project seminar.




In general, the examination takes place in form of a written exam (duration: 90 minutes). If up to 15students register, there will will be an oral examination (duration: 45 min.). The type of examination willbe announced within one working week after the end of the examination registration phase.




7 Grade bonus compliant to §25 (2)Yes

8 References


• Lecture notes and slides can be downloaded here:– http://www.spg.tu-darmstadt.de– moodle

• Further reading:– Wes McKinney: Python for Data Analysis, O’Reilly, 2017– Christopher M. Bishop: Pattern Recognition and Machine Learning, 2011– James, Witten, Hastie and Tibshirani, Introduction to Statistical Learning, Springer, 2017

Courses

Course Nr. Course name18-zo-2110-vl Data Science I

Instructor Type SWSDr.-Ing. Christian Debes Lecture 2

Course Nr. Course name18-zo-2110-ue Data Science I

Instructor Type SWSDr.-Ing. Christian Debes Practice 2


http://www.spg.tu-darmstadt.de

2.2 Optional Subjects SES: System on Chip and Embedded Systems

Module nameHigh-Level Synthesis

Module Nr. Credit Points Workload Self study Duration Cycle offered18-hb-2020 6 CP 180 h 120 h 1 WiSe

Language Module ownerEnglish Prof. Dr.-Ing. Christian Hochberger

1 Content• Mapping of behavioral descriptions (e.g. in the form of program fragments) on FPGA and CGRA

structures• Sub-tasks allocation, scheduling, binding• Exact or heuristic solutions• Design principles of heuristic solutions

2 Learning objectives / Learning OutcomesStudents that have completed this module know alternative approaches for all of the tasks of the high levelsynthesis and can select appropriate ones for specific applications. They can evaluate the memory and timecomplexity of the given algorithms. They are enabled to adapt the algorithms for new constraints and newtarget technologies.

3 Recommended prerequisite for participationKnowledge of hardware synthesis on the basis of at least one hardware description language is required(e.g. Reese/Thornton: Introduction to Logic Synthesis Using Verilog Hdl oder Brown/Vranesic: Fundamen-tals of Digital Logic with VHDL Design). The student should have basic knowledge of at least one objectoriented programming language, preferably Java





6 Usability of this moduleMSc ETiT, BSc/MSc iST, MSc iCE


8 ReferencesEnglish slides can be obtained through Moodle.

Courses

Course Nr. Course name18-hb-2020-vl High-Level Synthesis


Course Nr. Course name18-hb-2020-ue High-Level Synthesis


2.2 Optional Subjects SES: System on Chip and Embedded Systems 121

Module nameLow-Level Synthesis



1 ContentThe module deals with synthesis steps on all abstraction layers below the register transfer level focusing onapproaches suitable for FPGAs. At the logic level different types of minimization are explained (exact andheuristic two level minimizations, exact and heuristic multi level logic minimizations). The transition tothe technology level is achieved by different decomposition and structural mapping techniques (FlowMap).Place&Route add geometric information to the technology mapped circuit. Analytical and heuristic placersare discussed (Simulated Annealing, Genetic Placers) and routing is illustrated through the PathFinderalgorithm.

2 Learning objectives / Learning OutcomesAfter completion of the module, students are enabled to investigate synthesis approaches for low levelsynthesis tasks. They can evaluate these approaches regarding their time and space complexity, as well asregarding their applicability to specific implementation technologies.Students can apply these approaches to new architectures and technologies.

3 Recommended prerequisite for participationKnowledge of hardware synthesis on the basis of at least one hardware description language is required(e.g. Reese/Thornton: Introduction to Logic Synthesis Using Verilog Hdl oder Brown/Vranesic: Fundamen-tals of Digital Logic with VHDL Design). The student should have basic knowledge of at least one objectoriented programming language, preferably Java





6 Usability of this moduleMSc ETiT, MSc iCE, MSc iST


8 ReferencesA script of the lecture (in German) and English foils can be obtained from here: http://www.rs.tu-darmstadt.de/

Courses

Course Nr. Course name18-hb-2010-vl Low-Level Synthesis


Course Nr. Course name18-hb-2010-ue Low-Level Synthesis



http://www.rs.tu-darmstadt.de/

http://www.rs.tu-darmstadt.de/

Module nameMicroprocessor Systems

Module Nr. Credit Points Workload Self study Duration Cycle offered18-ho-2040 4 CP 120 h 75 h 1 SoSe

Language Module ownerEnglish Prof. Dr.-Ing. Klaus Hofmann

1 ContentMicroprocessor Architectures, DSP Architectures and Hardware related Programming

2 Learning objectives / Learning OutcomesA student is, after successful completion of this module, able to1. gain the overview on the fundamentals of computer architecture and the different processor classes(RISC, CISC, Mikrocontroller, CPU, DSP),2. understand the central building blocks of a CPU3. understand the major properties of the required semiconductor memories, I/O blocks and data busses(USB, PCI, RS232),4. understand the most commonly used Interrupt- and Trap-handling algorithms,5. know the common software development methodologies for microcontrollers (assembler, pseudoopera-tions, makros, subprograms and subroutines),6. understand the most important fundamentals of hardware oriented programming using C.

3 Recommended prerequisite for participationBasics of Computer Architectures





6 Usability of this moduleMSc ETiT, MSc Wi-ETiT, MSc iCE, MSc iST, MSc MEC, MSc EPE


8 ReferencesSlide Copies

Courses

Course Nr. Course name18-ho-2040-vl Microprocessor Systems

Instructor Type SWSDr.-Ing. Matthias Rychetsky Lecture 2

Course Nr. Course name18-ho-2040-ue Microprocessor Systems

Instructor Type SWSDr.-Ing. Matthias Rychetsky Practice 1


Module nameVerification Technology

Module Nr. Credit Points Workload Self study Duration Cycle offered18-ev-2020 6 CP 180 h 120 h 1 WiSe

Language Module ownerEnglish Prof. Dr. Hans Eveking

1 ContentDecision diagrams, Satisfiability checking, Symbolic state-space traversal, Reachability analysis, Semanticsof temporal logics (CTL, LTL), Symbolic and bounded model-checking, Property specification languages(PSL, ITL)

2 Learning objectives / Learning OutcomesStudents understand the verification problem of complex systems and the basic principles and algorithmsof modern verification tools and techniques. They are able to assess the limitations of verification toolsand techniques, and are able to consider these limitations in the verification of systems. They can specifytemporal properties of a system in the languages of temporal logics or in formal property specificationlanguages like PSL.

3 Recommended prerequisite for participationBasic knowledge of digital circuits







8 ReferencesTh. Kropf: Introduction to formal hardware verification.W.K. Lam: Hardware design verification.

Courses

Course Nr. Course name18-ev-2020-vl Verification Technology

Instructor Type SWSProf. Dr. Hans Eveking Lecture 3

Course Nr. Course name18-ev-2020-ue Verification Technology

Instructor Type SWSProf. Dr. Hans Eveking Practice 1


Module nameLabs on Adaptive Computing Systems


Language Module ownerGerman and English Prof. Dr.-Ing. Andreas Koch

1 Content- Independently solve a posed problem by a software implementation on an embedded system based on areconfigurable system-on-chip- Evaluate the performance of the implementation according to a number of quality metrics- Design an application-specific computing architecture to relieve performance bottlenecks- Implement a hardware accelerator for the computing architecture in a hardware description language- Integrate the accelerator into the system at the hardware level- Integrate the accelerator into the system at the operating system level- Modify the original software to access the accelerator- Evaluate the complete heterogeneous system according to a number of quality metrics

2 Learning objectives / Learning OutcomesAfter successfully attending the course, the students can independently design, realize, and evaluate hard-ware and software on embedded systems, and evaluate them according to different quality metrics. Thestudents can interpret the results and construct application-specific computing architectures to address lim-itations in one or more quality metrics. They can apply hardware design methods and industrial designautomation software tools to implement the computing architecture and integrate the resulting acceleratorinto a real embedded system, at both hardware and software levels.

3 Recommended prerequisite for participationRecommended:Participation in Lectures „Digitaltechnik“, „Rechnerorganisation“ and „Architektur und Entwurf von Rech-nersystemen“, respectively according knowledge.





6 Usability of this moduleB.Sc. InformatikM.Sc. InformatikB.Sc. Computational EngineeringM.Sc. Computational EngineeringM.Sc. WirtschaftsinformatikB.Sc. Psychologie in ITJoint B.A. InformatikB.Sc. Sportwissenschaft und InformatikM.Sc. Sportwissenschaft und InformatikMay be used in other degree programs.


8 ReferencesWill be given according to topic.

Courses


Course Nr. Course name20-00-0274-pr Labs on Adaptive Computing Systems



Module nameAdvanced Integrated Circuit Design Lab



1 ContentPractical Design Tasks in Full Custom Design of Digital or Analog Ciruits using State-of-the-Art CommercialCAD Tools

2 Learning objectives / Learning OutcomesA student is, after successful completion of this module, able to 1. develop and verify transistor circuitryusing Cadence 2. simulate logic and analog circuits (Pre- and Postlayout) 3. draw, verify and extract layout

3 Recommended prerequisite for participationLecture “Advanced Digital Integrated Circuit Design” or “Analog Integrated Circuit Design”







8 ReferencesADIC Lecture Slide Copies; John P. Uyemura: Fundamentals of MOS Digital Integrated Circuits; Neil Westeet al.: Principles of CMOS VLSI Design

Courses

Course Nr. Course name18-ho-2120-pr Advanced Integrated Circuit Design Lab



Module nameHDL Lab



1 ContentRealisation of a VHDL- or Verilog-based VLSI System Design Project in a Team with industrial constraints

2 Learning objectives / Learning OutcomesA student is, after successful completion of this module, able to 1. design, optimize and verify a complexdigital system (e.g. a pipelined CPU or signal processor) using Verilog or VHDL, 2. synthesize the HDLdescription using commercial CAD software to a gate level description

3 Recommended prerequisite for participationMandatory Prerequisite: Lecture Computer Aided Design for System on Chips,At least one high-level Programming Language, Basic Know-How Linux/Unix, Computer Architectures





6 Usability of this moduleBSc/MSc ETiT, BSc/MSc Wi-ETiT, MSc iCE, BSc/MSc iST, BSc/MSc MEC, MSc EPE


8 ReferencesLecture slides „HDL: Verilog and VHDL“

Courses

Course Nr. Course name18-ho-1090-pr HDL Lab



Module nameProject Seminar Design for Testability



1 ContentLearning advanced Methods for Testing Microchips after Manufacturing and Practical Application in smallDesign Scenarios, Final Presentation

2 Learning objectives / Learning OutcomesLearning advanced Methods for Testing Microchips after Manufacturing and Practical Application in smallDesign Scenarios, Final Presentation

3 Recommended prerequisite for participationLecture “Advanced Digital Integrated Circuit Design”








Courses

Course Nr. Course name18-ho-2130-pj Project Seminar Design for Testability

Instructor Type SWSProf. Dr.-Ing. Klaus Hofmann Project Seminar 3


Module nameProjektseminar Rekonfigurable Systems

Module Nr. Credit Points Workload Self study Duration Cycle offered18-hb-2040 6 CP 180 h 135 h 1 WiSe/SoSe


1 ContentStudents will work in small groups in this course. Topics and application context will be defined individu-ally for each group. All projects will follow the same approach. At first, the given problem will be describedin a programmatic way. Following, it will be implemented by a reconfigurable system. Depending on thenature of the application, either predefined architectures will be used, parameterizable architectures will beadapted to the needs of the application or new architectures may be designed. The programmatic descrip-tion will now be mapped (semi-)automatically to the chosen architecture with the help of the supportingtools. Usually, this requires to rewrite the programmatic description to better suit the tools. Finally, thesolution will be evaluated using some benchmark data sets.

2 Learning objectives / Learning OutcomesSuccessful students will know how to use reconfigurable systems within a given application context. Theycan use tools to program these systems and know how to map an application onto a given reconfigurablearchitecture. They are capable to evaluate the performance critical parts of an application. They understandthe implications of different coding styles for a particular task.

3 Recommended prerequisite for participation• Knowledge of reconfigurable devices (cf. course computer systems II)• Knowledge of computer architecture (cf. course computer systems I)• Solid programming skills (either in C or Java depending on the application scenario).





6 Usability of this moduleMSc ETiT, MSc iST, MSc Informatik, MSc iCE


8 ReferencesWill be made available through the Moodle page for this course.

Courses

Course Nr. Course name18-hb-2040-pj Projektseminar Rekonfigurable Systems

Instructor Type SWSProf. Dr.-Ing. Christian Hochberger Project Seminar 3


Module nameSeminar Integrated Electronic Systems Design A

Module Nr. Credit Points Workload Self study Duration Cycle offered18-ho-2160 4 CP 120 h 90 h 1 WiSe/SoSe


1 ContentResearch oriented Formulation of a Topic within the area of Microelectronics System Design; Creation of awritten Documentation and Presentation; Team Work

2 Learning objectives / Learning OutcomesA student is, after successful completion of this module, able to 1. gain a deep understanding of the chosenresearch subject in the field of integrated electronic systems, 2. write an essay on the chosen subject in acomprehesive form and present the outcome to an audience

3 Recommended prerequisite for participationAdvanced Digital Integrated Circuit Design, CAD Methods, Computer Architectures, Programming Know-How





6 Usability of this moduleMSc ETiT, MSc Wi-ETiT, MSc iCE, MSc iST, MSc MEC


8 ReferencesTopic-oriented Materials will be provided

Courses

Course Nr. Course name18-ho-2160-se Seminar Integrated Electronic Systems Design A

Instructor Type SWSProf. Dr.-Ing. Klaus Hofmann Seminar 2


Module nameSeminar on Computer Engineering



1 Content- Participants independently familiarize themselves with the assigned seminar topic by working with theprovided seed scientific papers (usually English-language texts)- Deeper and/or wider library research originating from the seed papers, assisted by the advisor- Classification and interpretation of the gathered research results, together with the advisor- Preparation of a shorter introductory and a longer main talk on the subject (including slide presentations),with feedback from the advisor- Giving both presentations in front of an audience with mixed prior knowledge on the topics- Interactive discussions after the presentations- Feedback on the talks themselves (rhetoric, presentation techniques)

2 Learning objectives / Learning OutcomesSuccessful participation in the course enables students to become acquainted with an unfamiliar subjectby working with scientific papers. The students are proficient in different techniques of library research(including accessing special databases). They can compare and contrast research results across multiplepublications and perform an overarching evaluation of these results. Students recognize the essentialaspects of the examined works and are able to concisely present them to an audience with mixed priorexperience on the subject, effectively applying a number of presentation techniques in the process. Thestudents are able to actively participate in a scientific discussion on the presented topics.

3 Recommended prerequisite for participationParticipation of lectures:- Digitaltechnik- Rechnerorganisation- Architektur und Entwurf von Rechnersystemenrespectiv according knowledge. Depending on topic other knowledge may be helpful.





6 Usability of this moduleB.Sc. InformatikM.Sc. InformatikB.Sc. Computational EngineeringM.Sc. Computational EngineeringM.Sc. WirtschaftsinformatikB.Sc. Psychologie in ITJoint B.A. InformatikB.Sc. Sportwissenschaft und InformatikM.Sc. Sportwissenschaft und InformatikCan be used in other degree programs.


8 References


Will be given in seminar.

Courses

Course Nr. Course name20-00-0653-se Seminar on Computer Engineering

Instructor Type SWSProf. Dr.-Ing. Andreas Koch Seminar 2


Module nameComputer Aided Design for SoCs



1 ContentCAD-Concepts for the design and simulation of integrated system-on-chips

2 Learning objectives / Learning OutcomesA student is, after successful completion of this module, able to understand

• The most important design and verification abstractions as well as the design flow for the design ofintegrated electronic systems,

• Selected algorithms for optimization, simulation and solving of design tasks,• Advanced methods for the design and simulation of analog integrated circuits in modern CMOS

technologies,• Advanced concepts of hardware description languages and their concepts (Verilog, VHDL, Verilog-A,

Verilog-AMS, System-Verilog)

3 Recommended prerequisite for participationLecture "Advanced Digital Integrated Circuit Design" (can be attended in parallel) and „Analog IntegratedCircuit Design" and "Logic Design"





6 Usability of this moduleMSc ETiT, MSc iST, MSc MEC, MSc Wi-ETiT, MSc iCE



Courses

Course Nr. Course name18-ho-2200-vl Computer Aided Design for SoCs


Course Nr. Course name18-ho-2200-ue Computer Aided Design for SoCs


Course Nr. Course name18-ho-2200-pr Computer Aided Design for SoCs



Module nameAnalog Integrated Circuit Design



1 ContentBasic analog Building Blocks: Current Mirrors, Reference Circuits; Multi Stage Amplifier, internal Struc-ture and Properties of Differential and Operational Amplifiers, Feedback Techniques, Frequency Response,Oscillators

2 Learning objectives / Learning OutcomesA student is, after successful completion of this module, able to 1. derive the fundamental properties ofthe MOS-Transistors from knowledge of the layout or fabrication process, 2. derive fundamental MOSFET-circuits (current source, current mirror, switch, active resistors, inverting amplifiers, differential ampli-fiers, output amplifiers, operational amplifiers, comparators) and knows their fundamental properties(y-Parameters, DC- and AC-properties), 3. understands simulation methods for analog circuits on tran-sistor level using SPICE, 4. analyse feedback amplifiers regarding frequency gain, stability, bandwidth, rootlocus, amplitude and phase-margin, 5. derive and calculate the analog propierties of digital logic gates

3 Recommended prerequisite for participationLecture “Electronics”





6 Usability of this moduleBSc ETiT, BSc Wi-ETiT, MSc iCE, BSc/MSc iST, BSc/MSc MEC, MSc EPE


8 ReferencesLecture Slide Copies; Richard Jaeger: Microelectronic Circuit Design

Courses

Course Nr. Course name18-ho-1020-vl Analog Integrated Circuit Design


Course Nr. Course name18-ho-1020-ue Analog Integrated Circuit Design



Module nameDigital Design Lab



1 Content• Introduction to the MP3 encoding standard for audio signals• Analysis of the individual steps of the decoding process wrt. the used algo-rithms• Analysis of the individual steps of the decoding process wrt. the storage of in-termediate results• Design and configuration of the datapath to realize the individual process steps• Simulation on functional level and with timing annotation• Check, whether the design meets all restrictions• Test of the final HW design with all relevant MP3 variants (short and long frames)

2 Learning objectives / Learning OutcomesStudents are able to manually map complex problems onto a digital target architecture. They are proficientin using the design tools to implement their solution on FPGAs. They know strategies to systematically finderrors in their design. They can explore de-signs by simulation.

3 Recommended prerequisite for participationBasic knowledge of digital design





6 Usability of this moduleBSc ETiT, BSc iST


8 References

Courses

Course Nr. Course name18-hb-1030-pr Digital Design Lab

Instructor Type SWSProf. Dr.-Ing. Christian Hochberger Internship 3


Module nameIndustrial Colloquium

Module Nr. Credit Points Workload Self study Duration Cycle offered18-sm-2290 2 CP 60 h 30 h 1 SoSe

Language Module ownerGerman Prof. Dr. rer. nat. Florian Steinke

1 ContentTo get an idea about current trends in industry. In addition, to give a glimpse of job opportunities theindustry will provide after graduation. Acquired competences are:

• Active knowledge about industry trends and applications in multimedia communications• Build contact with persons from various important companies• Presentation skills improvement

2 Learning objectives / Learning OutcomesToday, the Internet is much more than just a browser window on your desktop-PC. It is a part of our every-day life and has become ubiquitous thanks to smartphones, tablet-PCs and laptops. This pervasiveness ofthe Internet requires tremendous effort on the provider side. This is due to the fact that the Internet itselfis a communication system with a vast number of mechanisms running on different functional layers. Withthe rapid increase of mobile devices, traffic consumption, and the sheer number of users, many of thosemechanisms reach their limits. This problem becomes visible to the end user, if, for example, large crowdsof people suddenly overload the mobile communication infrastructure.With the recently established collaborative research center MAKI (Multi-Mechanismen-Adaption für daskünftige Internet) scientists of TU Darmstadt study the possibilities of coordinated and automated transi-tions between different mechanisms of a communication system. Thereby, the Future Internet will be ableto react to changes by, for example, switching from the mobile communication infrastructure to a localad-hoc network between users if the demand by users exceeds the resources of the available infrastructure.In this year’s industrial colloquium, partners from the industry present their visions, challenges and so-lutions regarding the Future Internet. Additionally, researches from TU Darmstadt provide insights intocurrent scientific work in the context of the collaborative research center MAKI.

3 Recommended prerequisite for participationMandatory: Basic knowledge in Information Systems and Commubnication Systems. The sutdent has to becapeable to understand the technical aspects and to summerize them in a written report as a short paper.





6 Usability of this moduleMSc ETiT, MSc iST, MSc iCE


8 References

Courses

Course Nr. Course name18-sm-2290-ko Industrial Colloquium

Instructor Type SWSProf. Dr. rer. nat. Florian Steinke Colloquy 2


Module nameLabs on Computer Engineering



1 ContentParticipants independently solve alone or in a small group an individually posed problem from the area ofComputer Engineering. The problems are usually programming or hardware development tasks inspiredby the research performed at the Embedded Systems and Applications Group.

2 Learning objectives / Learning OutcomesAfter successfully completing the labs, the participant/s is/are able to independently solve a complex prob-lem in the field of Computer Engineering. They can evaluate the quality of their solution and compare andcontrast it with other existing solutions.

3 Recommended prerequisite for participationDepending on topic.







8 ReferencesDepending on topic.

Courses

Course Nr. Course name20-00-0647-pr Practical Lab in Technical Foundations of Computer Science

Instructor Type SWSProf. Dr.-Ing. Andreas Koch Internship 4


Module namePrinted Electronics


Language Module ownerGerman Prof. Dr. Edgar Dörsam

1 ContentPrinting technologies for functional printing (printing methods and systems); Design and materials forprinted electronics (aerial, OFET, RFID); Activities for quality assurance; Examples of application (aerial,RFID, OFET, photovoltaic, batteries, lab on a chip).

2 Learning objectives / Learning OutcomesOn successful completion of this module, students should be able to:

• Describe the printing technologies that are applicable for “Printed Electronics”.• Name materials that are appropriate to printing processes and to describe the impact of the materials

on the design e.g. of antennas and OFETs.• Classify and rate different activites for quality assurance.• Explain basic functions, configurations, materials, and specific properties of printed antennas, RFIDs,

photovoltaics and batteries.• Describe “Printed Electronics” as a multidisciplinary task that consists of electrical engineering,

material science, and mechanical engineering.

3 Recommended prerequisite for participationMechanical components and Mechatronics I and II recommended


• Module Examination (Technical Examination, Technical Examination, Standard Grading System)Oral exam 30 min



6 Usability of this moduleWPB Master MPE III (Wahlfächer aus Natur- und Ingenieurwissenschaft)WPB Master PST III (Fächer aus Natur- und Ingenieurwissenschaft für Papiertechnik)Master ETiT IMNT; Master Mechatronik


8 ReferencesThe current lecture notes can be downloaded from the web pages of the institute while the semester is insession.

Courses

Course Nr. Course name16-17-5110-vl Printed Electronics



Module nameProcessor Microarchitecture



1 ContentLectures (each block takes 3 * 90 minutes)1. Processor execution. Sources of performance loss, latency. Possible techniques to improve performance.Simultaneous multi-threading as an established solution. Motivation for multi-threading – p-threads as amodel of execution in SW, micro-threading as a model of execution in HW.2. Definition of micro-threading, its requirements on the microarchitecture. Microthreaded assembly in-structions, design alternatives for extended instruction sets. Required support in micro-architecture – self-synchronizing register file, cache controllers, thread scheduler.3. Execution in the micro-threaded pipeline. Interaction between cache controllers, register file, threadscheduler, integer pipeline. Data dependences between threads and its influence on execution (embarrass-ingly parallel vs. sequential programs). Interaction with legacy code, execution modes, OS support.4. Developing for the real world: Writing testbenches. Performance profiling. Indicators of efficient siliconuse.5. Microthreading in multi-core architectures. Big issues: Scalability, sufficient parallelism, trade-off be-tween clock frequency and access latencyLabs:1. Set up the utgrlib VHDL sources in the home directory. Set up the utbinutils in the home directory.Compilation of introductory examples.2.-3. Analysis of execution traces for introductory examples. Design of a FIR filter in micro-threaded as-sembly. Compilation, execution, analysis of pipeline efficiency.4.-9. Re-design of existing blocks (choose from dcache, icache, regfile). Preparation of a TLM testbench.Coding and testing of the block in a stand-alone testbench.10.-15. Integration of the block in UTLEON3, execution of micro-threaded programs, evaluation of perfor-mance analysis (% performance gain over the original block, % decreased resource requirements).

2 Learning objectives / Learning OutcomesAfter completion of the module, students will be able to design a customized microarchitecture of a modernRISC processor and analyze its performance.The course will be taught using a VHDL implementation of an existing micro-threaded processor UTLEON3in an FPGA, nevertheless the knowledge gained in the lecture will be applicable to other HDLs, differentprocessor architectures and other implementation technologies.

3 Recommended prerequisite for participationHands-on experience with at least one of Verilog or VHDL is expected. Basic understanding of FPGAtechnology and thorough knowledge of digital circuit design and computer architecture. Several toolsused throughout the labs might require additional programming languages and tools (Perl, C, bash). Thisknowledge can be obtained during the labs.





6 Usability of this moduleMSc ETiT, MSc iCE, MSc iST



8 ReferencesA script is available as a published book and English slides can be obtained through moodle.

Courses

Course Nr. Course name18-hb-2050-vl Processor Microarchitecture

Instructor Type SWSPh.D. Martin Danek Lecture 2

Course Nr. Course name18-hb-2050-pr Processor Microarchitecture

Instructor Type SWSPh.D. Martin Danek Internship 2


Module nameProject Seminar Integrated Electronic Systems

Module Nr. Credit Points Workload Self study Duration Cycle offered18-ho-1060 9 CP 270 h 210 h 1 WiSe/SoSe


1 ContentResearch-oriented project in the domain of Integrated Electronic Systems or Microelectronic System Design,Final Report and Presentation of Results in a Team

2 Learning objectives / Learning OutcomesAfter attending this projectseminar, a student is able to fulfill/implement a given task or project in thedomain of Integrated Electronic System design (optionally in a group of students), write a final report andpresent the results to an audience.

3 Recommended prerequisite for participationLecture Analog Integrated Circuit Design





6 Usability of this moduleBSc ETiT, Wi ETiT


8 ReferencesMaterial on the subject will be handed out

Courses

Course Nr. Course name18-ho-1060-pj Project Seminar Integrated Electronic Systems



Module nameProject Seminar Computer Systems

Module Nr. Credit Points Workload Self study Duration Cycle offered18-hb-1040 9 CP 270 h 210 h 1 WiSe/SoSe


1 ContentStudents elaborate on a research-oriented subject in the area of computer-systems. They present a writtendocumentation and a presentation of the acquired advanced knowledge. They provide a set of alternativesolutions to a given problem.

2 Learning objectives / Learning OutcomesStudents are able to systematically develop design alternatives to a given problem. They learn to acquirethe necessary fundamental knowledge in terms of references and terminology.

3 Recommended prerequisite for participationBasic knowledge of digital design





6 Usability of this moduleBSc ETiT, BSc/MSc iST


8 References

Courses

Course Nr. Course name18-hb-1040-pj Project Seminar Computer Systems

Instructor Type SWSProf. Dr.-Ing. Christian Hochberger Project Seminar 4


Module nameEmbedded Systems Hands-On 1: Design and Implementation of Hardware-Software Systems


Language Module ownerGerman and English Prof. Dr. rer. nat. Oskar Stryk

1 ContentThese labs are intended for students interested in obtaining hands-on practical experience with the designand implementation of embedded systems.The labs will begin by introducing fundamentals such as- basic electrical engineering- using lab test and measurement instruments- design and fabrication of electronic circuits- acquiring and processing data from sensors- bus protocols in embedded systems- programming and debugging heterogeneous embedded systems- the use of the Linux kernel as an operating system in an embedded contextThe lab core then has the participants implement a concrete embedded system. A number of possibleprojects will be offered, each with a different focus (e.g., hardware or software) to match student interest.

2 Learning objectives / Learning OutcomesAfter successful completion, students are familiar with the practical techniques and tools required fordesigning, implementing and bringing-up embedded hardware/software systems.This includes basic knowledge of electrical engineering, the use of lab test and measurement instruments,the use of languages and EDA/CAD tools for hardware design. They are able to program and debugsoftware in an embedded systems context as well as employ Linux as an operating system here.

3 Recommended prerequisite for participationRecommended: Successful completion of „Digital Design“, „Computer Organisation“, „Architecture andDesign of Computer Systems“, „Operating Systems“ and „System-level and Parallel Programming“ or similarcompetencies obtained in other study programmes







8 References

Courses

Course Nr. Course name20-00-0959-pr Embedded Systems Hands-On 1: Design and Implementation of Hardware-Software

Systems

Instructor Type SWSProf. Dr. rer. nat. Oskar Stryk Internship 4


Module nameEmbedded Systems Hands-On 2: Designing Hardware Accelerators for Systems-on-Chip


Language Module ownerGerman Prof. Dr.-Ing. Andreas Koch

1 ContentThese practical labs are intended for students interested in learning how to design hardware acceleratorsfor systems-on-chips.It covers a wide range of topics, including- OS drivers for accelerators- design and interfacing of accelerators in Bluespec SystemVerilog- Design flows and tool chains for hardware/software co-developmentThe actual accelerators covered are inspired by typical applications, e.g., image processing or stereovisioncomputations.

2 Learning objectives / Learning OutcomesAcquire skills in using the knowledge and techniques taught in prior classes to actually perform a completehardware/software co-design of an application in an embedded systems context.

3 Recommended prerequisite for participationBasic knowledge using Linux on embedded Systems (e.g., acquired in ESHO1). Knowledge of the BluespecSystemVerilog hardware description language (e.g., as taught in Architecture and Design of ComputingSystems).







8 References

Courses

Course Nr. Course name20-00-0968-pr Embedded Systems Hands-On 2: Designing Hardware Accelerators for Systems-on-Chip



Module nameSensor Technique



1 ContentThe module provides basic principles of different sensors and the necessary skills for proper applicationof sensors. In terms of measuring chain, the focus of the event is located in the forming of any generallynon-electric variable in an electrically evaluable signal.Resistive, capacitive, inductive, piezoelectric, optical and magnetic measuring principles are treated in thelectures, in order to convey measuring of important values such as force, torque, pressure, acceleration,velocity, and flow.In addition to the phenomenological description of the principles and resulting technical description, itshould be traced an understood the main elements of the primary and secondary electronic for each prin-ciple.In addition to the measuring principles, the errors description will be treated.Thereby in addition to static and dynamic errors also error in the signal processing and error analysis ofthe entire measuring chain will be discussed.

2 Learning objectives / Learning OutcomesThe Students acquire knowledge of the different measuring methods and their advantages and disadvan-tages. They can understand error in data sheets and descriptions interpret in relation to the applicationand are thus able to select a suitable sensor for applications in electronics and information, as well processtechnology and to apply them correctly.

3 Recommended prerequisite for participationMeasuring Technique





6 Usability of this moduleMSc ETiT, MSc WI-ETiT, MSc MEC


8 References• Slide set of lecture• Script of lecture• Textbook Tränkler „Sensortechnik“, Springer• Exercise script

Courses

Course Nr. Course name18-kn-2120-vl Sensor Technique

Instructor Type SWSProf. Dr. Mario Kupnik Lecture 2


Course Nr. Course name18-kn-2120-ue Sensor Technique

Instructor Type SWSProf. Dr. Mario Kupnik Practice 1


Module nameAdvanced Topics in Embedded Systems and Applications



1 ContentThe course covers current topics in research and development of computing systems and programmingtools, including focused ones in the areas of embedded and application-specific architectures. The subjectsare determined by current research efforts in the ESA group and are intended to guide students towardsacquiring technical as well as introductory scientific skills, for example, including one or more of thefollowing domains:- Computing systems architecture at the processor and systems-level- Design of digital electronic circuits and hardware systems- Use of Field-Programmable Gate ArraysHardware/Software design and programming tools- Operating systems and low-level programmingHardware/Software Co-DesignApplication-specific architectures and techniques- Design and/or programming of compute accelerators- Debugging and analysis techniques for hardware/software-systems

2 Learning objectives / Learning OutcomesParticipants are intended to acquire the skills necessary to quickly become familiar with a new domainand then solve a complex practical problem within that domain. These skills can include studies of sci-entific literature, surveying existing code-bases from the hardware/software domains, and the practicalimplementation of hardware and/or software systems. The final talk should show proficiency with basicpresentation techniques.

3 Recommended prerequisite for participationAn interest to develop high-quality solutions in the assigned problem domain. For different domains,different pre-requisites will be required. These can include digital design, compiler construction, system-level and parallel programming. Such skills can be acquired by successfully completing the appropriatelectures.


• [20-00-1001-pp] (Study Achievement, Written/Oral Examination, Standard BWS)


• [20-00-1001-pp] (Study Achievement, Written/Oral Examination, Weighting: 100 %)

6 Usability of this moduleB.Sc. InformatjkM.Sc InformatikMay be used in other degree programs.


8 References

Courses


Course Nr. Course name20-00-1001-pp Advanced Topics in Embedded Systems and Applications

Instructor Type SWSProf. Dr.-Ing. Andreas Koch Project 6


Module nameMastering Modern Embedded System Processors



1 Content* Processor architectures in embedded systems* ARM instruction set and microarchitecture* ARM compiler and simulator* ARM bootloading and (realtime) operating systems* ARM debugging, profiling and tracing* ARM peripheral control* ARM power management* ARM application scenarios (Cortex-M/-A/-R)* Future development of embedded processors* Recent research results

2 Learning objectives / Learning OutcomesAfter successful participation, students are able to* outline the essential components and functionality of embedded processors,* differentiate the advantages and disadvantages of different processor architectures,* use relevant development tools for embedded processors,* examine the functionality and efficiency of existing source code,* develop efficient source code for specific applications,* assess recent embedded systems research results.

3 Recommended prerequisite for participationSuccessful participation in “Rechnerorganisation” or similar







8 References

Courses

Course Nr. Course name20-00-1004-iv Mastering Modern Embedded System Processors


Course3


2.3 Optional Subjects SWE: Software-Engineering

Module nameAdvanced Compiler Construction



1 Content- Compilation and run-time environment for object-oriented programming languages- Control flow graphs as intermediate representations- Static dataflow analysis- Static single-assignment form- Eliminating total and partial redundancy- Scalar optimization- Register allocation- Scheduling- Loop optimization- Structure and organization of real compilers (e.g., phases, intermediate representations, compfile flow)

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students understand techniques for the compilation and executionof object-oriented programs at the machine-level. The can apply static dataflow analysis to control flowgraphs and are practiced using their SSA form. They are familiar with optimizing techniques for a numberof problems as well as fundamental algorithms for register allocation. They know the internal structure ofreal production-grade compilers.

3 Recommended prerequisite for participationSuccessfull participation of “Einführung in den Compilerbau”







8 References

2.3 Optional Subjects SWE: Software-Engineering 151

Literature recommendations will be updated regularly, an example might be:Cooper/Torczon: Engineering a CompilerMuchnick: Advanced Compiler Design and ImplementationAho/Lam/Sethi/Ullman: Compilers - Principles, Techniques, and Tools

Courses

Course Nr. Course name20-00-0701-vl Advanced Compiler Construction

Instructor Type SWSProf. Dr.-Ing. Andreas Koch Lecture 3


Module nameOptimizing Compiler Project


Language Module ownerGerman Prof. Dr.-Ing. Andreas Koch

1 Content- Compiler implementation in Java - Modification of an existing compiler - Extension by a new intermediaterepresentation - scalar optimizations on new IR


3 Recommended prerequisite for participationThe lectures Optimizing Compilers in the same term.







8 References

Courses

Course Nr. Course name20-00-0498-pr



Module nameSeminar Software System Technology

Module Nr. Credit Points Workload Self study Duration Cycle offered18-su-2080 4 CP 120 h 90 h 1 SoSe


1 ContentIn this course, the students produce scientific reports from changing subject areas. Each student has toexplore a subject related to IT system development and produce a written report as well as a final talk with apresentation. A list of the subjects of the current semester is available at www.es.tu-darmstadt.de/lehre/sst.

2 Learning objectives / Learning OutcomesAfter a successful participation, the students will be able to explore an unknown topic under scientificaspects. The students learn to support the exploration by a literature research and to analyze the subjectcritically. They achieve the skills to present a definite subject in a written report as well as in an oralpresentation.

3 Recommended prerequisite for participationBasic knowledge in software engineering and programming languages





6 Usability of this moduleBSc iST, BSc Informatik, MSc ETiT


8 Referenceswww.es.tu-darmstadt.de/lehre/sst

Courses

Course Nr. Course name18-su-2080-se Seminar Software System Technology

Instructor Type SWSProf. Dr. rer. nat. Andreas Schürr Seminar 2


http://www.es.tu-darmstadt.de/lehre/sst

http://www.es.tu-darmstadt.de/lehre/sst

Module nameC/C++ Programming Lab



1 ContentThe six-day programming lab is divided into two sections.In the first four days, the programming languages C and C++ are taught with practical tasks and lectures.All covered aspects are extensively practiced under supervision. Based on the fundamental basics of C++,manual memory management and dynamic data structures are handled from a procedural as well as froman object-oriented perspective. Object orientation with C++ is extensively addressed by treating multipleinheritance, polymorphism and parametric polymorphism.The last two days are dedicated to microcontroller programming in C including the opportunity of pro-gramming of a distributed application (via a CAN-bus).

2 Learning objectives / Learning OutcomesDuring the lab, the students acquire a fundamental understanding of the programming languages C andC++ with emphasis not only on procedural but also on object-oriented characteristics. The students gainhands-on experience with applying C++ and discover the challenges of using C++ safely and properlyespecially in the context of embedded system software development.

3 Recommended prerequisite for participationJava skills





6 Usability of this moduleBSc ETiT, BSc MEC, BSc iST, BSc Wi-ETiT


8 Referenceshttp://www.es.tu-darmstadt.de/lehre/aktuelle-veranstaltungen/c-und-c-p

Courses

Course Nr. Course name18-su-1030-pr C/C++ Programming Lab

Instructor Type SWSProf. Dr. rer. nat. Andreas Schürr Internship 3


http://www.es.tu-darmstadt.de/lehre/aktuelle-veranstaltungen/c-und-c-p

Module nameReal-Time Systems



1 ContentThe lecture basically covers a model-driven software engineering process which is specially customizedfor real-time systems. This process is more deeply explored in the exercise using an automotive example.A focus is laid on object-oriented techniques. In this context, a real-time specific state-of-the-art CASEtool is introduced and used. Furthermore, fundamental characteristics of real-time systems and systemarchitectures are introduced. Scheduling algorithms are discussed to get insights into real-time operatingsystems. Finally, a comparison between the Java programming language and its expansion for real-timeoperating systems (RT Java) will conclude the lecture.

2 Learning objectives / Learning OutcomesStudents, who have successfully attended this lecture have acquired skills needed for the model-driven andobject-oriented development of embedded real-time systems. This includes a deeper understanding of thefollowing topics:

• classification of real-time systems• create and analyze executable models• application of real-time scheduling algorithms• evaluation and comparison of pros/cons of real-time programming languages as well as real-time

operating systems

3 Recommended prerequisite for participationBasic knowledge of software engineering techniques and excellent knowledge of at least one object-oriented programming language (preferably Java)





6 Usability of this moduleMSc ETiT, BSc iST, MSc Wi-ETiT, BSc Informatik


8 Referenceswww.es.tu-darmstadt.de/lehre/es/

Courses

Course Nr. Course name18-su-2020-vl Real-Time Systems

Instructor Type SWSProf. Dr. rer. nat. Andreas Schürr Lecture 3

Course Nr. Course name18-su-2020-ue Real-Time Systems

Instructor Type SWSProf. Dr. rer. nat. Andreas Schürr Practice 1


http://www.es.tu-darmstadt.de/lehre/es/

Module nameFormal Principles of Computer Science III



1 ContentThe course is given in German

2 Learning objectives / Learning OutcomesThe course is given in German

3 Recommended prerequisite for participationThe course is given in German


• [20-00-0901-iv] (Technical Examination, Written/Oral Examination, Standard BWS)• [20-00-0901-iv] (Study Achievement, Written/Oral Examination, BWS b/nb)


• [20-00-0901-iv] (Technical Examination, Written/Oral Examination, Weighting: 100 %)• [20-00-0901-iv] (Study Achievement, Written/Oral Examination, Weighting: 0 %)



8 References

Courses

Course Nr. Course name20-00-0901-iv Formale Methods in Software Design


Course3


Module nameConcepts of Programming Languages


Language Module ownerEnglish Prof. Dr.-Ing. Ermira Mezini

1 ContentFundamental concepts of programming languages. In particular, we identify various basic concepts ofprogramming languages and discuss them in detail, for example:- role of syntax- functions- meta-interpreters- recursion- lazy evaluation- state and side effects- continuations- domain-specific languages and macros- object-oriented programming

2 Learning objectives / Learning OutcomesAfter the successful completion of the lecture, students will be able to perform the following tasks:- they will be able to identify the defining features of programming languages;- they will be familiar with fundamental theoretical concepts of programming languages;- they will be able to implement simple programming languages using different implementation techniques;- students will understand the influence of different programming languages on the solution space ofvarious software development problems;- students will be able to overcome stereotypical categorizations of programming languages.

3 Recommended prerequisite for participationFunktionale und Objektorientierte Programmierkonzepte







8 References


- S. Krishnamurthi: Programming Languages - Application and Interpretation- M. Scott: Programming Language Pragmatics, Morgan Kaufmann- D. Friedman et al.: Programming Language Essentials, MIT Press

Courses

Course Nr. Course name20-00-0072-iv Concepts of Programming Languages


4


Module nameCompiler Construction Lab


Language Module ownerGerman and English Prof. Dr.-Ing. Ermira Mezini

1 ContentIndependently implement a compiler or extend an existing compile flow (e.g., realize new optimizationpasses or back-ends).

2 Learning objectives / Learning OutcomesAfter successfully completing the labs, students are able to independently implement core parts of a moderncompiler, either from scratch or integrating them into an existing compiler framework. In this process, theycan apply and improve their knowledge both of compiler technology (e.g, use of different intermediaterepresentations), as well as of general implementation techniques (e.g., applying design patterns).

3 Recommended prerequisite for participationRecommended:Participation of lecture „Rechnerorganisation“, „Einführung in den Compilerbau“ and „FortgeschrittenerCompilerbau“, respectively according knowledge.





6 Usability of this moduleB.Sc. InformatikM.Sc. InformatikB.Sc. Computational EngineeringM.Sc. Computational EngineeringM.Sc. WirtschaftsinformatikB.Sc. Psychologie in ITJoint B.A. InformatikB.Sc. Sportwissenschaft und InformatikM.Sc. Sportwissenschaft und InformatikM.Sc. InformationssystemtechnikMay be used in other degree programs.


8 ReferencesWill be given to actual topic.

Courses

Course Nr. Course name20-00-0911-pr Compiler Construction Lab

Instructor Type SWSProf. Dr.-Ing. Ermira Mezini Internship 4


Module nameProgramming parallel computer architectures



1 Content- Foundations of parallel programming- Parallel architectures, i.e. multi- and many-core computers with shared and distributed memory- Message-Passing Interface (MPI), OpenMP, OpenCL programming standards- Building blocks of parallel computations- Criteria for the design of parallel algorithms

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students can design parallel programs employing the current paral-lel language standards. They understand the foundations of parallel programming as well as fundamentalbuilding blocks of parallel programming. They can evaluate the suitability of algorithms for parallel archi-tectures.

3 Recommended prerequisite for participationBasics in programming (C/C++, Fortran, Java, etc.).





6 Usability of this moduleB.Sc. InformatikM.Sc. InformatikB.Sc. Computational EngineeringM.Sc. Computational EngineeringM.Sc. WirtschaftsinformatikB.Sc. Psychologie in ITJoint B.A. InformatikB.Sc. Sportwissenschaft und InformatikM.Sc. Sportwissenschaft und InformatikCan be used in other degree programs


8 References- Using OpenMP: Portable Shared Memory Parallel Programming, Volume 10“”, Barbara Chapman, GabrieleJost and Ruud Van Der Pas, MIT Press, 2007- Parallel programming in C with MPI and OpenMP"", Michael J. Quinn, McGraw-Hill, 2004- Parallele Programmierung"", T. Rauber and G. Rünger, Springer, 2007- Intel Xeon Phi Coprocessor High-Performance Programming"", J Jeffers und J. Reinders, Morgan Kaufman,2013- Heterogeneous Computing With OpenCL"", B. R. Gaster, Elsevier, 2011- Programming Massively Parallel Processors: A Hands-On Approach"", D. B. Kirk, W. W. Hwu, MorganKaufmann, 2012


Courses

Course Nr. Course name20-00-0626-iv


4


Module nameAutonomous Driving Lab I

Module Nr. Credit Points Workload Self study Duration Cycle offered18-su-2070 6 CP 180 h 135 h 1 WiSe


1 Content• Hands-on programming experience with C++ in the development of embedded software systems

for autonomous driving based on a model car• Application of control methods from the area of autonomous driving• Application of software engineering techniques (design, documentation, test, ...) of a non-trivial

embedded software system with hard real-time requirements and limited resources (memory, ...)• Use of a given software framework and further libraries including a modular (real-time) operating

system• Hands-on experience using source code management systems, time management and other project

management tools• Presentations of the project results

2 Learning objectives / Learning OutcomesDuring this project seminar students gain practical experience in software development for embeddedsystems in the field of autonomous driving using a model car. In teamwork, they learn to cope with anextensive task. In order to solve this task they practice to use the theoretical knowledge available in thegroup (from other courses such as real-time systems, software engineering - introduction, C++ lab, digitalcontrol systems).Students that have successfully participated in this project seminar are able to organize and set-up a non-trivial software project in an interdisciplinary team according to a given problem independently. Theparticipants acquire the following skills in detail:

• Independent familiarization with a given software framework and ready-made libraries• Transfer of theoretic knowledge into a software system• Extensive use of tools for version, configuration, and change management• Realistic time and resource management (project management)• Development of hardware/software systems with C++ considering important limitations of embed-

ded systems• Planning and implementation of extensive quality assurance measures• Collaboration and communication in and between teams

3 Recommended prerequisite for participationRecommended prerequisites are:

• ETiT/DT, iST, Informatik, WI-ET/DT: Basic software technology knowledge and advanced knowledgeof object-oriented programming languages (especially C++)

Additionally desired:• Basic knowledge of the development of real-time systems or image processing• ETiT/AUT, MEC: Basic knowledge in control engineering including state space control design, some

additional basic knowledge in digital control design may be helpful



5 Grading


Module Final Examination:• Module Examination (Study Achievement, Oral Examination, Weighting: 100 %)

6 Usability of this moduleMSc ETiT, BSc iST


8 Referenceshttps://www.es.tu-darmstadt.de/lehre/aktuelle-veranstaltungen/ps-af-i/ and Moodle

Courses

Course Nr. Course name18-su-2070-pj Autonomous Driving Lab I

Instructor Type SWSProf. Dr. rer. nat. Andreas Schürr Project Seminar 3


https://www.es.tu-darmstadt.de/lehre/aktuelle-veranstaltungen/ps-af-i/

Module nameAutonomous Driving Lab II


Language Module ownerGerman and English Prof. Dr. rer. nat. Andreas Schürr

1 Content

2 Learning objectives / Learning OutcomesStudents learn to independently develop, implement and present new concepts and algorithms in the fieldof autonomous driving. Realistic problems from the Carolo Cup are solved with existing knowledge andskills practically and the implementation is ensured by quality assurance measures.Students who have successfully participated in this project seminar are able to independently analyze andsolve a complex and realistic task in the field of autonomous driving. The participants acquire the followingskills in detail:

• Further development and optimization of an existing software system and the used algorithms inde-pendently

• Solving and implementation of non-trivial, realistic control engineering challenges• Extensive use of tools for version, configuration, change, and quality assurance management• Realistic time planning and resource allocation (project management)• Further development and optimization of complex hardware/software systems under realistic envi-

ronmental conditions• Planning and implementation of extensive quality assurance measures• Collaboration, communication and organization within the team








8 Referenceshttps://www.es.tu-darmstadt.de/lehre/aktuelle-veranstaltungen/ps-af-ii und Moodle

Courses

Course Nr. Course name18-su-2100-pj Autonomous Driving Lab II



https://www.es.tu-darmstadt.de/lehre/aktuelle-veranstaltungen/ps-af-ii

Module nameProjektseminar Software Systems

Module Nr. Credit Points Workload Self study Duration Cycle offered18-su-1060 9 CP 270 h 210 h 1 WiSe/SoSe


1 ContentThe course deals with various development and research topics in the area of model-driven engineeringand object-oriented software engineering. Besides a general overview, it provides a deep insight into aspecial scientific topic. The topics are selected according to the specific working areas of the participatingresearchers and convey technical and scientific competences in one or more of the following topics:

• Model-Driven Enginnering and Model Synchronization• Model Transformation• Object-Oriented Refactorings• Program Variability (Software Product Lines)• Feature Model Analysis

Additional information and topic description for the current semester: http://www.es.tu-darmstadt.de/lehre/aktuelle-veranstaltungen/projektseminar-softwaresysteme/

2 Learning objectives / Learning OutcomesThe student gains practical experience in development (reengineering and maintenance) of complex soft-ware systems. He/She learns to work and function in a team, and to analyze and solve a non- trivial task.Moreover, students exercise using theoretical knowledge in the group (e.g. from lectures like softwareengineering – introduction / Design / Maintenance & Quality Assurance) to solve a concrete and practicalproblem.Students that have successfully completed this seminar are able to independently organize and set-up anon-trivial software project and function to analyze and solve a certain task. Attendees gain the followingskills in detail:

• realistic time and resource management (project management)• experience with tools for version control and change management• usage of CASE tools for model- based software development• planning and execution of quality assurance measures

3 Recommended prerequisite for participationMandatory: Basic software technology knowledge and advanced knowledge of object-oriented program-ming languages





6 Usability of this moduleBSc ETiT, MSc ETiT, BSc iST


8 ReferencesEach topic is covered by a specific selection of papers and articles.

Courses


http://www.es.tu-darmstadt.de/lehre/aktuelle-veranstaltungen/projektseminar-softwaresysteme/

http://www.es.tu-darmstadt.de/lehre/aktuelle-veranstaltungen/projektseminar-softwaresysteme/

Course Nr. Course name18-su-1060-pj Projektseminar Software Systems



Module nameSoftware Engineering - Projectmanagement


Language Module ownerGerman Prof. Dr.-Ing. Ermira Mezini

1 Content


3 Recommended prerequisite for participationFoundations of Software Technology







8 References

Courses

Course Nr. Course name20-00-0178-vl Software Engineering - Projectmanagement



Module nameSoftware Engineering in industrial practice


Language Module ownerGerman Prof. Dr.-Ing. Ermira Mezini

1 Content


3 Recommended prerequisite for participationExperience in proramming (language irrelevant) and Software Engineering







8 References

Courses

Course Nr. Course name20-00-0317-vl Software Engineering in industrial practice



Module nameModeling, Specification and Semantics



1 Content- introduction to modeling using predicate logic and algebraic concepts- interpretation and faithfulness of formal models- systematic construction of models and making of design decisions- abstraction, refinement, composition, and decomposition of models- syntax and operational semantics of programming languages- elementary proof techniques and their use- introduction to specification languages- syntax and denotational semantics of specification languages- modeling communication and coordination in concurrent systems- taxonomy of system properties

2 Learning objectives / Learning OutcomesAfter successfully participating in this course, students know basic concepts in the areas modeling, spec-ification, and semantics. They are able to use predicate logic and algebraic concepts to formalize given,informally described scenarios. They are able to develop formal models in a systematic fashion, to makenecessary design decisions, and to employ informal notation and graphics to facilitate the construction offormal models. They know selected formal specfication languages and are able to apply at least one suchlanguage. They understand the distinction between the syntax and semantics of formal languages and areable to prove propositions about expressions as well as simple meta-properties about the languages them-selves. They are able to formalize basic system requirements as predicates and can assess the faithfulnessof such formalizations.

3 Recommended prerequisite for participationRecommended:Participation of lecture “Automaten, formale Sprachen und Entscheidbarkeit” and “Aussagen- undPrädikatenlogik”, respective according knowledge.





6 Usability of this moduleB.Sc. InformatikB.Sc. WirtschaftsinformatikB.Sc. Psychologie in ITJoint B.A. InformatikB.Sc. Sportwissenschaft und InformatikMay be used in other degree programs.


8 References


U. Kastens, H. Kleine Büning: Modellierung - Grundlagen und formale Methoden, HanserG. Winskel: The Formal Semantics of Programming Languages, MIT PressC. A. R. Hoare: Communicating Sequential Processes, Prentice-HallLiterature recommendations will be updated regularly.

Courses

Course Nr. Course name20-00-0013-iv Modellierung, Spezifikation und Semantik


3


Module nameIntroduction to Compiler Construction



1 Content- Structure of compilers- Context-free grammars for the description of language syntax- Lexing and parsing techniques- Intermediate representations- Semantic analysis- Run-time organisation- Code generation- Software tools for compiler constructions- Implementation techniques for compilers

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students are familiar with the structure of compilers. They under-stand formal concepts for the description of syntax and semantics of programming languages. They cancombine these concepts with algorithmic techniques to independently construct a compiler that maps aspecified programming language to a given target machine. They know software tools supporting the con-struction of compilers and can apply these together with manual techniques to implement the compilers.

3 Recommended prerequisite for participationRecommended:Participation of lecture “Algorithmen und Datenstrukturen”, “Funktionale und objektorientierte Program-mierung” and “Rechnerorganisation”, respectively according knowledge.


• [20-00-0904-iv] (Study Achievement, Written/Oral Examination, Standard BWS)


• [20-00-0904-iv] (Study Achievement, Written/Oral Examination, Weighting: 100 %)



8 ReferencesLiterature recommendations will be updated regularly, an example might be:Watt/Brown: Programming Language Processors in Java

Courses

Course Nr. Course name20-00-0904-iv Introduction to Compiler Construction


Course3


Module nameStatic and Dynamic Program Analysis


Language Module ownerEnglish Prof. Dr.-Ing. Heiko Mantel

1 Content- operational semantics for sequential and parallel programs- overview of techniques for static and dynamic program analysis- abstract interpretation- data flow analysis- slicing techniques- type-based program analysis- concepts of runtime monitoring- techniques for implementing runtime monitoring- language-based security- soundness and precision of program analysis

2 Learning objectives / Learning OutcomesAfter successfully participating in this course the students will know a range of different program analyses.The students will understand the functionality of each program analysis and the difference between each ofthe considered program analyses. Furthermore, the students will be able to judge which program analysis issuitable for a specific problem, and they will be able to apply the different program analyses. The studentswill also be able to judge the precision and soundness of program analyses. Finally, the students will beable to implement and define the considered program analyses and variants of them.

3 Recommended prerequisite for participationKnowledge of Computer Science and Mathematics equivalent to the first four semesters in the ComputerScience Bachelor program, in particular basic knowledge about logic and the ability to understand formalcalculi.







8 References

Courses


Instructor Type SWSProf. Dr.-Ing. Heiko Mantel Integrated

Course4


3 Applications

3.1 Optional Subjects AIS-AS: Automotive Systems

Module nameRide and Handling


Language Module ownerGerman Prof. Dr. rer. nat. Hermann Winner

1 ContentLongitudinal and lateral dynamics; tyre influence on vehicle dynamics; vehicle dynamics control; suspen-sion and kinematics; noise vibration harshness. Modelling of tyre, wheel, quarter car as well as longitudinaland lateral vehicle dynamics.


• Derive vehicle longitudinal dynamics (achievable acceleration, deceleration and maximum velocity)from driving and frictional conditions as well as from the design of the power train and the brakesystem.

• Employ the basic equations of lateral dynamics with the fundamental motion and force dimensionsof the single-track model and describe and assess vehicle behaviour at steady state skidpad testingas well as at load changes during curve-driving.

• Discuss measures which influence a vehicle’s self-steering properties.• Explain the transmission of lateral forces between the road and tyre and discuss the interaction

between longitudinal and lateral forces.• Locate the significance of tyres to vehicle vertical dynamics.• Substantiate the principal ESP estimation and control processes as well as to explain their meaning

regarding to vehicle dynamics control.• Explain the effects of the kinematics of the wheel suspension on the vehicle handling, describe the

axle kinematics, determine the position of the instantaneous centres of rotation for the vehicle’s pitchand rolling axis, and sketch the distribution of the forces in a vehicle’s suspension.

• Describe the vibrations which can occur in a vehicle and name its respective sources as well as therelevance of its resonance frequencies.

• List comfort measures and its assessing standards.• List steady and unsteady state road trials for handling and assessment and refer to results of road

trials for making conclusions about handling characteristics.• Derive a modell of tyres, wheels, quarter car as well as longitudinal and lateral vehicle dynamics

and technically discuss the simulation results.

3 Recommended prerequisite for participationFundamentals of automotive engineering, basic knowledge of technical mechanics (force diagram, equa-tions of motion), basic knowledge of thermodynamics, basic knowledge of vibrations


• Module Examination (Technical Examination, Optional, Standard Grading System)Written Exam 90 min or oral Exam 50 min

5 Grading

174

Module Final Examination:• Module Examination (Technical Examination, Optional, Weighting: 100 %)

6 Usability of this moduleWPB Master MPE II (Kernlehrveranstaltungen aus dem Maschinenbau)WPB Master PST III (Fächer aus Natur- und Ingenieurwissenschaft für Papiertechnik)WI/MB, MSc Traffic&Transport, (Vertiefungsmodul FB16, ggf. Auflage), Master Mechatronik, MSc. Infor-matik (Anwendungsfach Fahrzeugtechnik, Spezialisierung)


8 Referencesmanuscript, e-Learning Materials via Moodle

Courses

Course Nr. Course name16-27-5020-vl Ride and Handling


Course Nr. Course name16-27-5020-ue Ride and Handling

Instructor Type SWSPractice 2

3.1 Optional Subjects AIS-AS: Automotive Systems 175

Module nameAutomotive Mechatronics and Assistance Systems



1 ContentElectric power supply and hybrid systems; drivetrain, brake and steering mechatronics; driver and driverassistance models; measurement techniques of sensors; vehicle dynamics sensors; surrounding sensors; in-frastructure depending sensors; actuators for engine, brakes, and steering; longitudinal control assistance;lateral control assistance; information and warning systems; active collision protection systems, safety,navigation and telematics; future assistance systems.


• List the requirements for a vehicle’s electrical power supply system and explain the structure andprinciples of its main components.

• Illustrate different types of hybrid-electric power trains and the mode of operation of a fuel cell.• Conduct a competent discussion about the future power train concepts as well as future power supply

systems.• Illustrate the operating mode of active and mechatronical suspension, power train, brake, and steer-

ing components.• Classify driver assistance systems according to their category and operating mode.• Indicate special difficulties at recognising the vehicle’s surrounding field and describe the conse-

quences of these difficulties for the system utilisation.• Explain the effect chain of the sensors from detection over perception up to surrounding field repre-

sentation for ultrasonic, radar, lidar, and video.• Describe the basic functions and the function limits of automatically acting driver assistance systems

and collision mitigation systems.• Evaluate the benefits and modes of action of vehicle safety systems and illustrate the course of an

accident and describe a crash test.• Illustrate the function of the modules necessary in the vehicle for navigation and conduct a

competent discussion about the state of the art and the prospects of traffic telematics systems andassistance systems.

3 Recommended prerequisite for participationFundamentals of automotive engineering


• Module Examination (Technical Examination, Technical Examination, Standard Grading System)Written exam 90 min or oral exam 45 min



6 Usability of this moduleWPB Master MPE II (Kernlehrveranstaltungen aus dem Maschinenbau)WPB Master PST III (Fächer aus Natur- und Ingenieurwissenschaft für Papiertechnik)WI/MB, MSc Traffic&Transport, (Vertiefungsmodul FB16, ggf. Auflage), Master Mechatronik, MSc. Infor-matik (Anwendungsfach Fahrzeugtechnik, Spezialisierung)


8 References


Manuscript; e-Learning Materials via Moodle

Courses

Course Nr. Course name16-27-5040-vl Automotive Mechatronics and Assistance Systems


Course Nr. Course name16-27-5040-ue Automotive Mechatronics and Assistance Systems



Module nameSystem Dynamics and Automatic Control Systems I

Module Nr. Credit Points Workload Self study Duration Cycle offered18-ko-1010 6 CP 180 h 120 h 1 WiSe

Language Module ownerGerman Prof. Dr.-Ing. Ulrich Konigorski

1 ContentDescription and classification of dynamic systems; Linearization around an equilibrium point; Stabilityof dynamic systems; Frequency response; Linear time-invariant closed-loop systems; Controller design;Control structure optimization

2 Learning objectives / Learning OutcomesStudents will know how to describe and classify different dynamic systems. They will be able to analysethe dynamic behaviour in time and frequency domain. The students will be able to design controllers forlinear time invariant systems.






6 Usability of this moduleBSc ETiT, BSc MEC, MSc Informatik


8 ReferencesSkript Konigorski: “Systemdynamik und Regelungstechnik I”, Aufgabensammlung zur Vorlesung, Lunze:"Regelungstechnik 1: Systemtheoretische Grundlagen, Analyse und Entwurf einschleifiger Regelungen",Föllinger: "Regelungstechnik: Einführung in die Methoden und ihre Anwendungen",Unbehauen: "Regelungstechnik I:Klassische Verfahren zur Analyse und Synthese linearer kontinuierlicherRegelsysteme, Fuzzy-Regelsysteme", Föllinger: "Laplace-, Fourier- und z-Transformation",Jörgl: "Repetitorium Regelungstechnik",Merz, Jaschke: "Grundkurs der Regelungstechnik: Einführung in die praktischen und theoretischen Meth-oden",Horn, Dourdoumas: "Rechnergestützter Entwurf zeitkontinuierlicher und zeitdiskreter Regelkreise",Schneider: "Regelungstechnik für Maschinenbauer",Weinmann: "Regelungen. Analyse und technischer Entwurf: Band 1: Systemtechnik linearer und lin-earisierter Regelungen auf anwendungsnaher Grundlage"

Courses

Course Nr. Course name18-ko-1010-vl System Dynamics and Automatic Control Systems I

Instructor Type SWSProf. Dr.-Ing. Ulrich Konigorski Lecture 3

Course Nr. Course name18-ko-1010-tt System Dynamics and Automatic Control Systems I- Auditorium Exercise

Instructor Type SWSProf. Dr.-Ing. Ulrich Konigorski Tutorial 1


Module nameTechnical Thermodynamics I


Language Module ownerGerman Prof. Dr.-Ing. Peter Christian Stephan

1 ContentFundamental terms of thermodynamics; thermodynamic equilibrium and temperature; different forms ofenergy (internal energy, heat, work, enthalpy); properties and equations of state for gases and incompress-ible substances; first law of thermodynamics and energy balances for technical systems; second law ofthermodynamics and entropy balances for technical systems; exergy analysis; thermodynamic behaviourduring phase change; the carnot cycle for power generation or refrigeration; energy efficiency and coef-ficient of performance; cyclic processes for gas turbines, combustion engines, power plants, refrigeratorsand heat pumps.


• Explain the relationships between thermodynamic properties and the thermodynamic state of a sys-tem and apply them within calculations of thermal system behaviour.

• Distinguish between different types of energy (e.g. work, heat, internal energy, enthalpy) and definethem.

• Analyse technical systems and processes using energy balances and equations of state.• Assess energy conversion processes by means of an entropy balance or an exergy analysis.• Characterise the thermal behaviour of gases, liquids and solids and corresponding phase change

processes.• Apply this basic knowledge (1.-5.) to examine machines (turbines, pumps etc.) and processes for

energy conversion (combustion engine, power plants, refrigerators, heat pumps).

3 Recommended prerequisite for participationNone


• Module Examination (Technical Examination, Technical Examination, Standard Grading System)Written exam 150 min



6 Usability of this moduleBachelor MPE PflichtBachelor WI-MBMaster ETiT MFT, Bachelor Mechatronik


8 ReferencesP. Stephan; K. Schaber; K. Stephan; F. Mayinger: Thermodynamik, Band 1: Einstoffsysteme, SpringerVerlag.Further material (slides, collection of exercises, table of fomulas etc.) is available through the Moodlesystem of TU Darmstadt.

Courses


Course Nr. Course name16-14-5010-vl Technical Thermodynamics I


Course Nr. Course name16-14-5010-hü Technical Thermodynamics I

Instructor Type SWSLecture HallPractice

1

Course Nr. Course name16-14-5010-gü Technical Thermodynamics I - Group Exercise

Instructor Type SWSGroup Practice 1


Module nameAutomotive Development Trends

Module Nr. Credit Points Workload Self study Duration Cycle offered16-27-5030 4 CP 120 h 90 h 1 Every Sem.

Language Module ownerGerman and English Prof. Dr. rer. nat. Hermann Winner

1 ContentGlobal mobility; development trends; current research activities: system and function development onadvanced driver assistance systems, vehicle dynamics control, motorcycles research, testing requirementsand functional safety, brake system development, driving Simulators.


• Competently report and discuss about present and forward-looking technologies in the fields of chas-sis systems and components, driver assistance systems, motorcycles, functional safety, brake systemdevelopment as well as driving simulators.

• State current developments.• Evaluate possibilities and limitations of distinct approaches.

3 Recommended prerequisite for participationAdvanced knowledge of automotive engineering as e.g. provided in “Ride and Handling” or "AutomotiveMechatronics and Assistance Systems"


• Module Examination (Technical Examination, Technical Examination, Standard Grading System)Written exam 90 min or oral exam 30 min



6 Usability of this moduleWPB Master MPE III (Wahlfächer aus Natur- und Ingenieurwissenschaft)WPB Master PST III (Fächer aus Natur- und Ingenieurwissenschaft für Papiertechnik)Master Mechatronik, MSc. Informatik (Anwendungsfach Fahrzeugtechnik, Spezialisierung), MSc Traf-fic&Transport, (Vertiefungsmodul FB16, ggf. Auflage)


8 Referencesmanuscript, e-Learning Materials via Moodle

Courses

Course Nr. Course name16-27-5030-vl Trends in Automotive Engineering



Module nameADP (4 CP) Internal Combustion Engines and Powertrain Systems

Module Nr. Credit Points Workload Self study Duration Cycle offered16-03-a041 4 CP 120 h 120 h 1 Every Sem.

Language Module ownerGerman Prof. Dr. techn. Christian Beidl

1 ContentCurrent research topic from the general area of the administering institute.

2 Learning objectives / Learning OutcomesThe students become acquainted with teamwork and are able to take over responsibility for leading taskswithin the team. They learn to assess divergent positions and the necessity of common agreements ininterpersonal relationships as well as typical engineering challenges in a positive manner. They are able torecognize and specify complex problems and to distinguish between different solutions. They also studyhow to valuate the importance of an exact time and work schedule positively.

3 Recommended prerequisite for participationPossible prerequisites will be prescribed by the individual institute supervising the project.







8 Referenceswill depend on topic; available upon announcement

Courses


Module nameADP (6 CP) Automotive Engineering

Module Nr. Credit Points Workload Self study Duration Cycle offered16-27-a061 6 CP 180 h 180 h 1 Every Sem.



2 Learning objectives / Learning OutcomesThe students become acquainted with teamwork and are able to take over responsibility for leading taskswithin the team. They learn to assess divergent positions and the necessity of common agreements ininterpersonal relationships as well as typical engineering challenges in a positive manner. They are able torecognize and specify complex problems and to distinguish between different solutions. They also studyhow to valuate the importance of an exact time and work schedule positively.









Courses


Module nameTutorial Automotive Engineering



1 ContentThe Automotive Engineering Tutorium deepens special topics from the courses Motor Vehicles I+II on thebasis of practically performed experiments. The selection of the experiments follows the availability oftesting vehicles or current problems.

2 Learning objectives / Learning OutcomesYou are able to make independent experiments with vehicles for a given problem. This comprises thedefinition of test procedures and measuring devices. Test parameters are definied and varied. You are ableto make use of the theoretical knowledge from Motor Vehicles I and II.

3 Recommended prerequisite for participationFundamentals of automotive engineering







8 Referencesmaterials are handed out to participants

Courses

Course Nr. Course name16-27-5080-tt Tutorial Automotive Engineering

Instructor Type SWSTutorial 4


Module nameResearch Seminar Automotive Engineering




2 Learning objectives / Learning OutcomesStudents who have attended this course have a command of basic scientific methodology. They can inde-pendently familiarize themselves with a new topic and know where to find relevant scientific literature indatabases, libraries and third-party sources. The students can structure a given task and organise a realistictime schedule. Furthermore, the students can formulate the results in written and oral form in an acceptedscientific manner. Finally, they are capable of conducting a critical scientific discourse and debate withother participants of the course.









Courses

Course Nr. Course name16-27-5100-fs Research Seminar Automotive Engineering

Instructor Type SWSResearch Semi-nar

0


Module nameOptical Technologies in Car Lighting

Module Nr. Credit Points Workload Self study Duration Cycle offered18-kh-2041 4 CP 120 h 75 h 1 SoSe

Language Module ownerGerman Prof. Dr.-Ing. Khanh Quoc Tran

1 ContentHistory and standardisation of car lithing. Description of the oused lighting sources and the function ofthese(lowbeam, highbeam, bending light, stop lamp, daytime running light. . . ), visuell perception, glare,detection, traffic infrastructure, traffic elements, interior lighting, driver assistance systems(GPS, Radar,Lidar. . . ),methods of psychophysics, lighting application concepts in future automated vehicles.Voluntary trip planed to an automobile manufacturer

2 Learning objectives / Learning OutcomesTo describe the basics and deepening knowledge of car lighting, understanding of the light distribution ofhead and rear lamps, to learn the basics of standardisation, enlarge glare and detection skills, know thetraffic elements, as well as the driver assistance systems

3 Recommended prerequisite for participationLighting technology 1(desireable)





6 Usability of this moduleMSc ETiT, MSc WI-ETiT, MSc iST, MSc MEC, MSc MPE, MSc Physik


8 ReferencesLecture slides, Automotive Lighting and Human Vision, Handbuch Fahrassistenzsysteme

Courses

Course Nr. Course name18-kh-2041-vl Optical Technologies in Car Lighting

Instructor Type SWSProf. Dr.-Ing. Khanh Quoc Tran Lecture 2

Course Nr. Course name18-kh-2041-pr

Instructor Type SWSProf. Dr.-Ing. Khanh Quoc Tran Internship 1


Module nameAvionics System Safety


Language Module ownerGerman Prof. Dr.-Ing. Uwe Klingauf

1 ContentOperational requirements for flight guidance systems, structure of flight guidance systems, architecturesand design of safe systems, pilot assistance systems in the cockpit, human factors.


• Elucidate the basics of automated flight and human-machine interfaces on the flight deck of modernaircraft.

• Explain basic concepts and methods in the design of safety critical systems for flight guidance.• Differentiate between system architecture concepts.• Discuss the critical relations between technical systems, operations requirements and the human

operator within the scope of avionics systems.

3 Recommended prerequisite for participationNone; recommended: Flugmechanik I, Grundlagen der Navigation I, Flugverkehrsmanagement undFlugsicherung, Systemzuverlässigkeit im Maschinenbau


• Module Examination (Technical Examination, Technical Examination, Standard Grading System)Oral exam 20 min



6 Usability of this moduleWPB Master MPE II (Kernlehrveranstaltungen aus dem Maschinenbau)WPB Master PST III (Fächer aus Natur- und Ingenieurwissenschaft für Papiertechnik)


8 ReferencesC.C. Rodriges, S.K. Cusick: Commercial Aviation Safety, McGraw Hill 2011Messerschmidt, Bölkow, Blohm (Hrsg.): Technische Zuverlässigkeit, Springer VerlagA. Meyna, G. Pauli: Zuverlässigkeitstechnik: Quantitative Bewertungsverfahren, Hanser 2. Auflage 2010

Courses

Course Nr. Course name16-23-5110-vl Avionics System Safety



Module nameCombustion Engines I


Language Module ownerGerman Prof. Dr. techn. Christian Beidl

1 ContentIntroduction: Historic review, economic and ecological aspects, classification of engines.Fundamentals of the thermodynamic process: Carnot cycle, constant-volume cycle, constant-pressure cycle,Seiliger cycle.Fundamentals of engine construction: Crank shaft, con-rod, bearing, piston, piston rings, piston pin, liner,cylinder head gasket, cylinder head, charge cycle.Parameters: Mean pressure, power, torque, fuel consumption, efficiency, cylinder charge, air fuel ratio,kinematics of the crank mechanism, compression ratio, characteristic diagrams, main dimensions.Fuel: Chemical configuration, characteristics, heat value, characteristics of ignition, production, alternativefuels.Basics of carburation: Spark-ignition engines, diesel engines, spreading, conditioning.Carburation of spark-ignition engines: Carburator, electronic fuel injection, HCCI (Homogeneous ChargeCompression Ignition).Ignition of spark-ignition engines: Requirements, spark plug, ignition systems, magnetic systems, knockcontrol systems.Mixture formation of diesel engines: basics, classification of different methods, mixturedistribution and mixture formation, injection systems


• Explain the principles and the construction of combustion engines (ranging from small two-strokemodels to the marine diesel engine).

• Explain the physical principles of combustion engines.• Develop the essential parameters and apply these to characterise engines.• Explain the economic and ecological relevance of combustion engines.• Apply the thermodynamic basics of combustion engines to develop new drive concepts.• Describe the basics of the engine construction.• Analyse and evaluate the interdependency of fuel, mixture formation, and combustion.• Explain the difference by mixture formation and ignition process of spark ignited engines and diesel

engines.• Explain the ignition and ignition systems of the spark ignited engine.



• Module Examination (Technical Examination, Technical Examination, Standard Grading System)Written or oral exam (optional) [written: 1 h 30 min; oral: 1 h 30 min (per group with 4 people]



6 Usability of this moduleWP Bachelor MPEBachelor Mechatronik


8 References


VKM I - script, available at the secretariat

Courses

Course Nr. Course name16-03-5010-vl Combustion Engines I



Module nameTechnical Mechanics for Electrical Engineering


Language Module ownerGerman Prof. Dr.-Ing. Tobias Melz

1 ContentStatics: force, moment (torque), free body diagram, equilibrium equations, center of gravity, truss, beams,adhesion and friction.Mechanics of elastic bodies: stress and deformation, tension, torsion, bending.Kinematics: point and rigid body movement.Kinetics: dynamic force and moment equilibrium equations, energy and work, linear oscillators, momen-tum and angular momentum conservation laws, impact.

2 Learning objectives / Learning OutcomesIn this course the students will learn the basic concepts of technical mechanics. They should be able toanalyze the statics of simple statically determinate planar systems, to carry out elementary elastomechan-ical calculations of statically determinate and statically indeterminate structures, to describe and analyzemovements, and to solve planar motion problems, oscillation and shock phenomena with the laws ofkinetics.



• Module Examination (Technical Examination, Written Examination, Standard Grading System)





8 ReferencesMarkert, Norrick: Einführung in die Technische Mechanik, ISBN 978-3-8440-3228-4Exercises are embodied in the book.Further reading:Markert: Statik – Aufgaben, Übungs- und Prüfungsaufgaben mit Lösungen, ISBN 978-3-8440-3279-6Markert: Elastomechanik – Aufgaben, Übungs- und Prüfungsaufgaben mit Lösungen, ISBN 978-3-8440-3280-2Markert: Dynamik – Aufgaben, Übungs- und Prüfungsaufgaben mit Lösungen, ISBN 978-3-8440-2200-1Gross, Hauger, Schröder, Wall: Technische Mechanik 1 - 3. Springer-Verlag Berlin (2012-2014).Hagedorn: Technische Mechanik, Band 1 - 3. Verlag Harri Deutsch Frankfurt.

Courses

Course Nr. Course name16-26-6400-vl Technical Mechanics for Electrical Engineering



Course Nr. Course name16-26-6400-ue Technical Mechanics for Electrical Engineering



Module nameMotor Vehicles



1 ContentLayout and function of vehicle components (engine, transmission, drivetrain, tires); driving performance;steering and steering systems; brakes and brake systems; springs and shock absorbers; axle construction.


• List the influencing factors on a vehicle’s fuel consumption and estimate the fuel consumption.• Name measures on vehicle design which lead to a reduction of consumption and can indicate driver

operation characteristics which contribute to minimising fuel consumption.• Explain and evaluate the main requirements, function principles, and the basic constitution of com-

ponents like tires, power train, brakes and steering.• List different types of spring-damper-systems and explain their basic construction.• Explain the functionality and discuss the main properties of diverse axle-concepts.

3 Recommended prerequisite for participationBasic knowledge of technical mechanics (force diagram, equations of motion) and basic knowledge ofthermodynamics


• Module Examination (Technical Examination, Optional, Standard Grading System)Written exam 90 min or oral exam 45 min



6 Usability of this moduleWP Bachelor MPEBachelor MechatronikMSc. Informatik (Anwendungsfach Fahrzeugtechnik, Spezialisierung)


8 Referencesmanuscript, CD-ROM (can be purchased at the department’s office), internet download

Courses

Course Nr. Course name16-27-5010-vl Motor Vehicles


Course Nr. Course name16-27-5010-ue Motor Vehicles



Module nameLaboratory Control Engineering I

Module Nr. Credit Points Workload Self study Duration Cycle offered18-ko-1020 4 CP 120 h 60 h 1 SoSe


1 Content• Control of a 2-tank system.• Control of pneumatic and hydraulic servo-drives.• Control of a 3 mass oscillator.• Position control of a MagLev system.• Control of a discrete transport process with electro-pneumatic components.• Microcontroller-based control of an electrically driven throttle valve.• Identification of a 3 mass oscillator.• Process control using PLC.

2 Learning objectives / Learning OutcomesAfter this lab tutorial the students will be able to practically apply the modelling and design techniquesfor different dynamic systems presented in the lecture ”System dynamics and control systems I” to real labexperiments and to bring them into operation at the lap setup.

3 Recommended prerequisite for participationSystem Dynamics and Control Systems I







8 ReferencesLab handouts will be given to students

Courses

Course Nr. Course name18-ko-1020-pr Laboratory Control Engineering I

Instructor Type SWSProf. Dr.-Ing. Ulrich Konigorski Internship 4


Module nameLaboratory Matlab/Simulink I

Module Nr. Credit Points Workload Self study Duration Cycle offered18-ko-1030 3 CP 90 h 45 h 1 WiSe/SoSe


1 ContentIn this lab tutorial, an introduction to the software tool MatLab/Simulink will be given. The lab is splitinto two parts. First the fundamentals of programming in Matlab are introduced and their application todifferent problems is trained. In addition, an introduction to the Control System Toolbox will be given. Inthe second part, the knowledge gained in the first part is applied to solve a control engineering specificproblem with the software tools.

2 Learning objectives / Learning OutcomesFundamentals in the handling of Matlab/Simulink and the application to control engineering tasks.

3 Recommended prerequisite for participationThe lab should be attended in parallel or after the lecture “System Dynamics and Control Systems I”





6 Usability of this moduleBSc ETiT; BSc MEC


8 ReferencesLecture notes for the lab tutorial can be obtained at the secretariatLunze; Regelungstechnik IDorp; Bishop: Moderne RegelungssystemeMoler: Numerical Computing with MATLAB

Courses

Course Nr. Course name18-ko-1030-pr Laboratory Matlab/Simulink I



Module nameProject seminar Applications of Lighting Engineering

Module Nr. Credit Points Workload Self study Duration Cycle offered18-kh-2051 5 CP 150 h 105 h 1 WiSe/SoSe

Language Module ownerGerman and English Prof. Dr.-Ing. Khanh Quoc Tran

1 ContentThe project seminar deals with the following subjects: automotive lighting, interior lighting, exteriorlighting; generation, perception and cognition of the visual stimulus (luminaires, displays, projection);LED/OLED technology; physical and psychophysical light measurement; illuminating engineering, colorperception.

2 Learning objectives / Learning OutcomesThe objective of this project seminar is the practice oriented implementation of the material learned duringthe lectures in form of a project work. Via communication of the interdisciplinary way of thinking of thelighting engineer, students should carry out autonomous project work on their own or in a team.

3 Recommended prerequisite for participationLighting Technology I-II (desireable)





6 Usability of this moduleMSc ETiT, MSc iST, MSc WI-ETiT, MSc MEC, MSc MPE, MSc Phys


8 ReferencesLecture notes of Lighting Technology I (Khanh); Lecture slides of our Laboratory; Book “LED Lighting:Technology and Perception” (Khanh et al., Wiley); Book „Farbwiedergabe“ (Khanh et al., Pflaum-Verlag);specific literature depending on the topic, publications.

Courses

Course Nr. Course name18-kh-2051-pj Project seminar Applications of Lighting Engineering

Instructor Type SWSProf. Dr.-Ing. Khanh Quoc Tran Project Seminar 3


Module nameProject seminar Advanced Applications of Lighting Engineering



1 ContentFor the project seminar a question from the following topics can be worked on: automotive lighting,light for the autonomous car, interior lighting, exterior lighting; smart lighting, human centric lighting(hcl); horticultural lighting; generation, perception and cognition of the visual stimulus (luminaires, dis-plays, projection); LED/OLED technology; physical and psychophysical light measurement; illuminatingengineering, color perception, virtual reality tests for light-simulation.

2 Learning objectives / Learning OutcomesThe objective of this project seminar is the practical implementation of the knowledge acquired duringthe study in the form of a project work. Students participate on their own or in a team. In this projectseminar, students learn to plan, implement and validate lighting issues. The basics of the lecture and theproject seminar ‘Applications of Lighting Engineering’ are applied and deepened. This usually includes theselection of suitable illuminants, the development of electronic hardware as well as the use of photometricmeasuring instruments. In addition, the students learn how to abstract questions, communicate project-dependent information as well as present and discuss results.

3 Recommended prerequisite for participationLighting Technology I-II (desireable), Project seminar Applications of Lighting Engineering


• Module Examination (Study Achievement, Written/Oral Examination, Standard Grading System)To conclude the project, every student has to hold a presentation with a short round of questions and an-swers and also to deliver a written report about the work and the results.The presentation with exam and the report will be graded according to the fixed guidelines of our Labora-tory.


• Module Examination (Study Achievement, Written/Oral Examination, Weighting: 100 %)



8 ReferencesLecture notes of Lighting Technology I (Khanh); Lecture slides of our Laboratory; Book "LED Lighting:Technology and Perception" (Khanh et al., Wiley); Book „Farbwiedergabe" (Khanh et al., Pflaum-Verlag);specific literature depending on the topic, publications.

Courses

Course Nr. Course name18-kh-2052-pj Project seminar Advanced Applications of Lighting Engineering



Module nameProject seminar Special Applications of Lighting Engineering


Language Module ownerGerman and English Prof. Dr.-Ing. Khanh Quoc Tran

1 ContentFor the project seminar a question from the following subject areas can be worked on: Automotive lighting,light for autonomous cars, interior lighting, exterior lighting; smart lighting; human centric lighting (HCL);horticulture lighting; generation, perception and cognition of visual stimuli (luminaires, displays, projec-tion); LED/OLED technology; physical and psychophysical light measurement; illuminating engineering,color perception, virtual reality tests for light-simulation.

2 Learning objectives / Learning OutcomesThe objective of this project seminar is the practical implementation of the knowledge acquired during thestudy in the form of research or project work in an interdisciplinary context, which also takes up topicsbeyond the lectures. Students participate on their own or in a team. In this project seminar, the studentslearn the approach, implementation and validation or investigation of inter-disciplinary lighting issues.This requires an introduction into topics that go beyond the subject area of the lectures.Usually, this includes the selection of suitable illuminants, the development of electronic hardware, the useof photometric measuring instruments as well as the conception, execution and evaluation of studies. Inaddition, students learn to abstract questions, to develop research questions, to com-municate informationdepending on the project, and to present and discuss results.

3 Recommended prerequisite for participationLighting Technology I-II (desireable), Project seminar Applications of Lighting Engineering (recommended)


• Module Examination (Study Achievement, Written/Oral Examination, Standard Grading System)At the beginning of the project, a short introductory presentation has to be held followed by a technicaldiscussion. Each student involved in the project has to conclude the project with a presen-tation followedby a short question and answer session. Every student has to deliver a written re-port about the work andthe results.The final presentation with exam and the report will be graded according to the fixed guidelines of theinstitute.


• Module Examination (Study Achievement, Written/Oral Examination, Weighting: 100 %)



8 ReferencesLecture notes of Lighting Technology I (Khanh); Lecture slides of our Laboratory; Book "LED Lighting:Technology and Perception" (Khanh et al., Wiley); Book „Farbwiedergabe" (Khanh et al., Pflaum-Verlag);specific literature depending on the topic, publications.

Courses

Course Nr. Course name18-kh-2053-pj Project seminar Special Applications of Lighting Engineering



Module nameFundamentals of Navigation I


Language Module ownerGerman Prof. Dr.-Ing. Jürgen Beyer

1 ContentNavigation principles, Earth models, Coordinate systems, Radio navigation, Basics and instruments (ADF,VOR, DME, ILS), dead reckoning, functional principles and error analysis, satellite navigation, Introductioninto GPS, signal description and measurement principles, Dilution of Precision (DoP), Differential GPS,Augmentation systems (RAIM, GIC, WAAS, LAAS, EGNOS).


• Explain the physics associated with the navigation of the earth.• Classify common coordinate systems and map projections.• Judge the methods of radio, coupling, and satellite navigation with respect to performance and

applications.

3 Recommended prerequisite for participationRecommanded: Control Engineering



Oral exam (in a group with 3 students) 60 min



6 Usability of this moduleWPB Master MPE III (Wahlfächer aus Natur- und Ingenieurwissenschaft)WPB Master PST III (Fächer aus Natur- und Ingenieurwissenschaft für Papiertechnik)Master Mechatronik


8 ReferencesCourse notes available.

Courses

Course Nr. Course name16-23-5050-vl Fundamentals of Navigation I


Course Nr. Course name16-23-5050-ue Fundamentals of Navigation I



Module nameSpace Debris – Risks, Surveillance and Mitigation


Language Module ownerGerman

1 ContentThis lecture will provide the scientific, technical and operational background in relation to the sources,surveillance and mitigation of space debris.This covers risk assessment aspects: source and sink terms, particle flux models, aerodynamics andaerothermal aspects during atmospheric re-entry and related on-ground risk assessments;all major aspects of space surveillance: ground-based radar and telescope systems, orbit determinationmethods (batch least square, Levenberg-Marquardt, Kalmanfilter), residuals, covariances, operational col-lision avoidance;As well as space debris mitigation aspects: long-term environment projection models, international guide-lines, passivation methods, shielding concepts, methods for post mission disposal and verification of mea-sures;

2 Learning objectives / Learning OutcomesOn successful completion of this module, students should be able to:1.name the sources of space debris and describe the human-made particle environment and the conse-quences of particle impacts;2.analyse and determine the risks to a space mission due the natural and human-made particle environ-ment and limit this this risk by suitable technical measures;3.determine the on-ground risk caused by the atmospheric re-entry of a space object;4.lay-out a space mission according to applicable space debris mitigation guidelines and verify the resultingsetup along with international standards;5.perform the main tasks of flight dynamics in operations (orbit determination and manoeuvre-planning)and explain the operational processes in the context of collision avoidance;6.Present the main technical aspects of space surveillance, lay-out the required sensor systems and applythe related computational methods;

3 Recommended prerequisite for participationknowledge of the content of „Space Flight Mechanics” (module no. 16-25-5130) is an asset but not apre-requisite.







8 ReferencesKlinkrad: Space Debris – Models and Risk Analysis, Springer Springer Praxis Books Astronautical Engineer-ing, 2006, ISBN 978-3-540-37674-3

Courses


Course Nr. Course name16-23-3164-vl Space Debris - Risks, Surveillance and Mitigation



Module nameTutorial Advanced Cax Methods


Language Module ownerGerman Prof. Dr. Reiner Anderl

1 ContentStudents gain knowledge of advanced CA Methods through the analysis of recent industrial examples. Thiscourse builds on the basic course ’Einführung in das rechnerunterstützte Konstruieren (CAD)’.

2 Learning objectives / Learning OutcomesThe students will be familiar with advanced CA Methods. They are able to recognise, execute and plan thegeneric workflow of CA Processes. Furthermore they are able to transfer their theoretical knowledge intoindustrial practice.

3 Recommended prerequisite for participationEinführung in das rechnergestützte Konstruieren (CAD)Virtuelle Produktentwicklung A, B, C







8 References

Courses

Course Nr. Course name16-07-5100-tt Tutorial Advanced CAx Methods



3.2 Optional Subjects AIS-IA: Intelligent Systems and Algorithms

Module nameNatural Language Processing and the Web


Language Module ownerGerman and English Prof. Dr. techn. Johannes Fürnkranz

1 ContentThe Web contains more than 10 billion indexable web pages, which can be retrieved via keyword searchqueries. The lecture will present natural language processing (NLP) methods to automatically process largeamounts of unstructured text from the web and analyze the use of web data as a resource for other NLPtasks.Key topics:- Processing unstructured web content- NLP basics: tokenization, part-of-speech tagging, stemming, lemmatization, chunking- UIMA: principles and applications- Web contents and their characteristics, incl. diverse genres such as personal web sites, news sites, blogs,forums, wikis- The web as a corpus – innovative use of the web as a very large, distributed, interlinked, growing, andmultilingual corpus- NLP applications for the web- Introduction to information retrieval- Web information retrieval and natural language interfaces- Web-based question answering- Mining Web 2.0 sites such as Wikipedia, Wiktionary- Quality assessment of web contents- Multilingualism- Internet of services: service retrieval- Sentiment analysis and community mining- Paraphrases, synonyms, semantic relatedness

2 Learning objectives / Learning OutcomesAfter attending this course, students are in a position to- understand and differentiate between methods and approaches for processing unstructured text,- reconstruct and explicate the principle of operation of web search engines,- construct and analyze exemplary NLP applications for web data,- analyze and evaluate the potential of using web contents to enhance NLP applications.

3 Recommended prerequisite for participationBasic knowledge in Algorithms and Data StructureProgramming in Java






3.2 Optional Subjects AIS-IA: Intelligent Systems and Algorithms 202



8 References- Kai-Uwe Carstensen, Christian Ebert, Cornelia Endriss, Susanne Jekat, Ralf Klabunde: Computerlinguistikund Sprachtechnologie. Eine Einführung. 3. Auflage. Heidelberg: Spektrum, 2009. ISBN: 978-3-8274-20123-7.- http://www.linguistics.rub.de/CLBuch/- T. Götz, O. Suhre: Design and implementation of the UIMA Common Analysis System, IBM Systems Jour-nal 43(3): 476–489, 2004.- Adam Kilgarriff, Gregory Grefenstette: Introduction to the Special Issue on the Web as Corpus, Computa-tional Linguistics 29(3): 333–347, 2003.- Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze: Introduction to Information Retrieval,Cambridge: Cambridge University Press, 2008. ISBN: 978-0-521-86571-5. http://nlp.stanford.edu/IR-book/

Courses



4


http://nlp.stanford.edu/IR-book/

http://nlp.stanford.edu/IR-book/

Module nameWeb-Mining



1 Content


The World-Wide Web provides every internet uses access to an ever-growing plentitude of information,which can not be processed without appropriate support. Web Mining is the research area that is triesto solve this problem with machine learning and data mining techniques. In this course, we will discussfoundations of information retrieval and text classification, as well as consider the pecularities of webdocuments (i.e. their document and graph structure).- Introduction- Web Mining Overview- The Web, HTTP, HTML, DOM, XPath- Data Mining Overview- Structured, Semi-Structured and Unstructured Data- Sample Web Mining Tasks- Information Retrieval on the Web- search engines & web crawlers- document indexing- the vector space model- inverted index- performance measures (recall & precision)- relevance feedback- estimating the size of the web- Text Mining- text classification- document representation- induction of classifiers (k-NN, Naive Bayes, SVMs, Rule Learners)- Overfitting Avoidance- Evaluation of Classifiers- Multi-Label Classification- feature engineering- stop words- feature subset selection- n-grams- stemming- phrases- latent semantic indexing- semi- and unsupervised learning- clustering (k-means, bottom-up agglomerative)- semi-supervised learning (active learning, self-training, co-training)- Structure mining- the Web as a graph- hyperlink-based relevance ranking (hubs and authorities, page rank)- hypertext classification (Naive Method, HyperClass, hyperlink ensembles)- Information Extraction & Wrapper Induction- conventional information extraction (AutoSlog)- structured text (LR-Wrappers)- semi-structured text (SoftMealy, WHISK, SRV, RAPIER)- Web Usage Mining- recommender systems- memory-based collaborative filtering- model-based collaborative filtering- web log mining

2 Learning objectives / Learning OutcomesAfter attending this course, students are in a position to- understand and explain fundemental techniques of information retrieval and web mining- apply practical information retrieval and web mining systems and understand their strengths and limita-tions- critically judge new developments in this area









8 References- Soumen Chakrabarti: Mining the Web - Discovering Knowledge from Hypertext Data. Morgan KaufmannPublishers, 2003.- Christopher D. Manning, P. Raghavan and H. Schütze, Introduction to Information Retrieval, CambridgeUniversity Press. 2008.

Courses

Course Nr. Course name20-00-0101-iv Web Mining


4


Module namePractical Lab Algorithms


Language Module ownerGerman Prof. Dr.-Ing. Heiko Mantel

1 ContentSolution of an algorithmic problem form practice and its implementation in software.

2 Learning objectives / Learning OutcomesIn this course students acquire expertise in solving algorithmic problems from practice and skill to imple-ment efficient algorithms

3 Recommended prerequisite for participation- Knowledge in program language (e.g. Java / C++)- Knowledge about basic algorithms and data structure








Courses

Course Nr. Course name20-00-0189-pr Practical Lab Algorithms



Module namePractical Course in Artificial Intelligence



1 ContentStudents have to work on a concrete practical problem in the area of artificial intelligence and solve it withthe help of tools and techniques that they developed on their own or that are already publicly available.Note the announcements on the homepage of the KE group regarding this course(http://www.ke.informatik.tu-darmstadt.de/lehre/)!In semesters, where this course is not announced on the above pages, there is often the possibility ofindividual projects (please ask).

2 Learning objectives / Learning OutcomesAfter completion of this practical course, students should be able torecognize potential uses of artificial intelligence tools- select appropriate tools for a given task and apply them to this task- evaluate and measure the success of the use of such tools

3 Recommended prerequisite for participationBasic knowledge in artificial intelligence







8 References

Courses

Course Nr. Course name20-00-0412-pr Practical Course in Artificial Intelligence



Module nameData Mining and Machine Learning



1 ContentWith the rapid development of information technology bigger and bigger amounts of data are available.These often contain implicit knowledge, which, if it were known, could have significant commercial orscientific value. Data Mining is a research area that is concerned with the search for potentially usefulknowledge in large data sets, and machine learning is one of the key techniques in this area.This course offers an introduction into the area of machine learning from the angle of data mining. Differ-ent techniques from various paradigms of machine learning will be introduced with exemplary applications.To operationalize this knowledge, a practical part of the course is concerned with the use of data miningtools in applications.- Introduction (Foundation, Learning problems, Concepts, Examples, Representation)- Rule Learning- Learning of indivicual rules (generalization vs. specialization, structured hypothesis spaces, versionspaces)- Learning of rule sets (covering strategy, evaluation measures for rules, pruning, multi-class problems)- Evaluation and cost-sensitive Learning (Accuracy, X-Val, ROC Curves, Cost-Sensitive Learning)- Instance-Based Learning (kNN, IBL, NEAR, RISE)- Decision Tree Learning (ID3, C4.5, etc.)- Ensemble Methods (Bias/Variance, Bagging, Randomization, Boosting, Stacking, ECOCs)- Pre-Processing (Feature Subset Selection, Discretization, Sampling, Data Cleaning)- Clustering and Learning of Association Rules (Apriori)"

2 Learning objectives / Learning OutcomesAfter a successful completion of this course, students are in a position to- understand and explain fundemental techniques of data mining and machine learning- apply practical data mining systems and understand their strengths and limitations- critically judge new developments in this area






6 Usability of this module“B.Sc. InformatikM.Sc. InformatikM.Sc. WirtschaftsinformatikB.Sc. Psychologie in ITJoint B.A. InformatikB.Sc. Sportwissenschaft und InformatikM.Sc. Sportwissenschaft und InformatikKann im Rahmen fachübergreifender Angebote auch in anderenStudiengängen verwendet werden.”




8 References- Mitchell: Machine Learning, McGraw-Hill, 1997- Ian H. Witten and Eibe Frank: Data Mining: Practical Machine Learning Tools and Techniques with JavaImplementations, Morgan-Kaufmann, 1999

Courses

Course Nr. Course name20-00-0052-iv Machine Learning and Data Mining


4


Module nameSeminar Data Mining and Machine Learning



1 ContentThis seminar serves the purpose of discussing new research papers in the areas of data mining and machinelearning. Every participant will present one paper, which will be subsequently discussed by all participants.Grades are based on the preparation and presentation of the paper, as well as the participation in thediscussion, in some cases also a written report.The papers will typically recent publications in relevant journals such as “Data Mining and KnowledgeDiscovery”, “”Machine Learning"", as well as ""Journal of Machine Learning Research"". Students may alsopropose their own topics if they fit the theme of the seminar.Please note current announcements to this course at http://www.ke.informatik.tu-darmstadt.de/lehre.

2 Learning objectives / Learning OutcomesAfter this seminar, students should be able to- understand an unknown text in the area of machine learning- work out a presentation for an audience proficient in this field- make useful contributions in a scientific discussion in the area of machine learning

3 Recommended prerequisite for participationBasic knowledge in Machine Learning and Data Mining







8 References

Courses

Course Nr. Course name20-00-0102-se Seminar Data Mining and Machine Learning



http://www.ke.informatik.tu-darmstadt.de/lehre

Module nameKnowledge Engineering and Learning in Games



1 ContentIn the course of this seminar we will discuss knowledge-based approaches to intelligent computer gameplaying. Emphasis will be on machine learning, but typically other topics are available as well. The focusof the topics will change regularly.Please note current announcements to this course at http://www.ke.informatik.tu-darmstadt.de/lehre.

2 Learning objectives / Learning OutcomesAfter this seminar, students should be able to- understand an unknown text in the area of intelligent game playing- work out a presentation for an audience proficient in this field- make useful contributions in a scientific discussion in this area

3 Recommended prerequisite for participationBasic knowledge in artificial intelligence







8 References

Courses

Course Nr. Course name20-00-0228-se Knowledge Engineering and Learning in Games



http://www.ke.informatik.tu-darmstadt.de/lehre

Module nameStatistical Machine Learning


Language Module ownerEnglish Prof. Dr. rer. nat. Kristian Kersting

1 Content- Statistical Methods for Machine Learning- Refreshers on Statistics, Optimization and Linear Algebra- Bayes Decision Theory- Probability Density Estimation- Non-Parametric Models- Mixture Models and EM-Algorithms- Linear Models for Classification and Regression- Statistical Learning Theory- Kernel Methods for Classification and Regression

2 Learning objectives / Learning OutcomesThe lecture gives a systematic introduction to statistical methods for machine learning. Upon success-ful completion of this lecture, students will understand the most important methods and approaches ofstatistical machine learning. They can apply machine learning to solve various new problems.








8 References


1. C.M. Bishop, Pattern Recognition and Machine Learning (2006), Springer2. K.P. Murphy, Machine Learning: a Probabilistic Perspective (expected 2012), MIT Press3. D. Barber, Bayesian Reasoning and Machine Learning (2012), Cambridge University Press4. T. Hastie, R. Tibshirani, and J. Friedman (2003), The Elements of Statistical Learning, Springer Verlag5. D. MacKay, Information Theory, Inference, and Learning Algorithms (2003), Cambridge University Press6. R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification (2nd ed. 2001), Willey-Interscience7. T.M. Mitchell, Machine Learning (1997), McGraw-Hill

Courses

Course Nr. Course name20-00-0358-iv Statistical Machine Learning

Instructor Type SWSProf. Dr. rer. nat. Kristian Kersting Integrated

Course4


Module nameDeep Learning for Natural Language Processing


Language Module ownerGerman Prof. Dr. phil. Iryna Gurevych

1 ContentThe lecture provides an introduction to the foundational concepts of deep learning and their application toproblems in the area of natural language processing (NLP)Main content:- foundations of deep learning (e.g. feed-forward networks, hidden layers, backpropagation, activationfunctions, loss functions)- word embeddings: theory, different approaches and models, application as features for machine learning- different architectures of neuronal networks (e.g. recurrent NN, recursive NN, convolutional NN) andtheir application for groups of NLP problems such as document classification (e.g. spam detection), se-quence labeling (e.g. POS-tagging, Named Entity Recognition) and more complex structure prediction(e.g. Chunking, Parsing, Semantic Role Labeling)

2 Learning objectives / Learning OutcomesAfter completion of the lecture, the students are able toexplain the basic concepts of neural networks and deep learning.- explain the concept of word embeddings, train word embeddings and use them for solving NLP problems.- understand and describe neural network architectures that are used to tackle classical NLP problems suchas classification, sequence prediction, structure prediction.- implement neural networks for NLP problems using existing libraries in Python.

3 Recommended prerequisite for participationBasic knowledge of mathematics and programming







8 References

Courses

Course Nr. Course name20-00-0947-iv Deep Learning for Natural Language Processing

Instructor Type SWSProf. Dr. phil. Iryna Gurevych Integrated

Course4


Module nameProject Lab Deep Learning in Computer Vision


Language Module ownerGerman and English Prof. Dr.-Ing. Michael Gösele

1 ContentIn this project lab groups of students will work on selected topics in deep learning (deep neural networks)for problems in computer vision. This includes the practical implementation with modern deep learningframeworks. Results will be presented in a talk at the end of the lab. Concrete topics follow the currentstate of the art and change from term to term.

2 Learning objectives / Learning OutcomesThrough their successful participation, students acquire in-depth knowledge on deep neural networks andtheir applications in computer vision. They are able to analyze, modify, and apply state-of-the-art tech-niques in this area. Moreover, they practice their abilities for presenting their results and for collaborationin teams.

3 Recommended prerequisite for participation* Solid programming skills in C/C++ or Python or Lua* Prior or concurrent registration for “Computer Vision I”







8 References

Courses

Course Nr. Course name20-00-0980-pp Project Lab Deep Learning in Computer Vision

Instructor Type SWSProf. Dr.-Ing. Michael Gösele Internship 6


Module nameLearning and Educational Technologies



1 ContentDigital applications and the Internet are changing the way we learn. If digital teaching and learningapplications are designed appropriately, they offer a wide range of possibilities. The module aims to impartbasic knowledge about the most important aspects of system design and about technologies needed formodern, web-based and mobile learning applications. Important theoretical foundations for the design oflearning applications are learning theories. Therefore, learning theories are briefly discussed in the contextof this module. The focus of the module is on adaptive learning applications. Different methods for therealization of adaptive learning applications will be presented. Frequently, Natural Language Processingand Artificial Intelligence methods are used for this purpose. In this context, current research work isconsidered. The module also focuses on the design of learning applications for individual and cooperativelearning in various fields of application (e.g. school, university, vocational education and lifelong learning).Examples from current research projects as well as teaching/learning practice are presented. In addition,methods for the evaluation of learning applications are considered.

2 Learning objectives / Learning OutcomesAfter completion of the module, students will be able to analyze and design applications for knowledgeacquisition and learning based on different design patterns and technologies. They will be able to de-cide on information representation (data level), design of functionalities (application level), and selec-tion/configuration of algorithms to support platform users concerning challenges in the learning process.Students are capable to consider techniques of adaptation to learners needs and will know appropriateevaluation methods to measure the qualities and effects of learning applications and the algorithms andmethods used in the learning applications.

3 Recommended prerequisite for participationBasic knowledge of Machine Learning and Natural Language Processing is desirable but not a prerequi-site. For students who do not meet these requirements, we offer short learning modules that allow anunderstanding of the application-specific mechanisms.





6 Usability of this moduleB.Sc. InformatikM.Sc. InformatikKann in anderen Studiengängen verwendet werden.


8 References

Courses


Course Nr. Course name20-00-0773-iv Learning and Educational Technologies

Instructor Type SWSProf. Dr. rer. nat. Eberhard Max Mühlhäuser Integrated

Course4


Module nameAlgorithmic Modelling



1 Content- Algorithmic modeling languages like OPL and eclipse- modeling problems as (integer) linear programming problems- modelling as combinatorial optimization problems- complex case studies: e.g. applications in logistics and manufacturing; deterministic and stochasticscheduling

2 Learning objectives / Learning OutcomesAfter successfully attending the course,- students know modelling strategies for decision, construction, and optimization problems- students can apply two algorithmic modelling languages- student can adequately model complex problems

3 Recommended prerequisite for participationGrundzüge III der Informatik oder vergleichbar (Einführung in Foundations of Computing wäre ebenfallswünschenswert).







8 ReferencesWill be appointed in lecture.

Courses

Course Nr. Course name20-00-0113-iv Algorithmic Modelling


4


Module nameEfficient Graph Algorithms



1 Content- Efficient Data- Efficient Algorithms for Graph Scanning and Connectivity- Optimal Trees and Branchings- Network Flow Problems- Matching and Assignment- Planar Graphs.- Theory, Generic Approaches, Improvement by means of Speedup Techniques and Structures

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students- know fundamental algorithms- know techniques to improve efficiency- can analyse graph algorithms- know methods to exploit particular characteristics (planarity, sparseness)- can judge practical efficiency of techniques








8 ReferencesWill be appointed in lecture

Courses


Course Nr. Course name20-00-0110-iv Efficient Graph Algorithms


4


Module namePractical Lab Advanced Algorithms



1 ContentSolution of an advanced algorithmic problem form practice and its implementation in software.

2 Learning objectives / Learning OutcomesIn this course students enhance their expertise in solving algorithmic problems from practice and theirskills to implement efficient algorithms

3 Recommended prerequisite for participationBased on Lab Algorithms







8 ReferencesWill be given in lab.

Courses

Course Nr. Course name20-00-0276-pr Advanced Algorithms



Module nameFuzzy Logic, Neural Networks and Evolutionary Algorithms

Module Nr. Credit Points Workload Self study Duration Cycle offered18-ad-2020 4 CP 120 h 75 h 1 WiSe

Language Module ownerGerman Prof. Dr.-Ing. Jürgen Adamy

1 ContentFuzzy systems: basics, rule based fuzzy logic, design methods, decision making, fuzzy control, patternrecognition, diagnosis; Neural networks: basics, multilayer perceptrons, radial basis functions, patternrecognition, identification, control, interpolation and approximation, Neuro-fuzzy: optimization of fuzzysystems, data driven rule generation; Evolutionary algorithms: optimization problems, evolutionary strate-gies and their applications, genetic programming and its applications

2 Learning objectives / Learning OutcomesAfter attending the lecture, a student is capable of:

• recalling the elements and set-up of standardized fuzzy-logic, neural networks and evolutionaryalgorithms,

• discussing the pros and cons of certain setups of systems from computational intelligence for solvinga given problem,

• recognizing situations in which tools taken from computational intelligence can be applied for prob-lem solving,

• creating programs from algorithms taught in the lecture, and• extending the learned standard procedures in order to solve new problems.






6 Usability of this moduleBSc iST, MSc ETiT, MSc MEC, MSc WI-ETiT, MSc iCE, MSc EPE, MSc CE, MSc Informatik


8 ReferencesAdamy: Fuzzy Logik, Neuronale Netze und Evolutionäre Algorithmen, Shaker Verlag (available for pur-chase at the FG office)www.rtr.tu-darmstadt.de (optionales Material)

Courses

Course Nr. Course name18-ad-2020-vl Fuzzy Logic, Neuronal Networks and Evolutionary Algorithms

Instructor Type SWSProf. Dr.-Ing. Jürgen Adamy Lecture 2

Course Nr. Course name18-ad-2020-ue Fuzzy Logic, Neuronal Networks and Evolutionary Algorithms

Instructor Type SWSProf. Dr.-Ing. Jürgen Adamy Practice 1


http://www.rtr.tu-darmstadt.de

Module nameConcepts and Technologies for Distributed Systems and Big Data Processing


Language Module ownerEnglish Dr.-Ing. Michael Eichberg

1 ContentThe course provides an overview of recent advances in distributedsystems for Big Data processing. The course starts presentingcomputational models for high throughput batch processing likeMapReduce. Next, we will introduce software engineering techniques fordistributed systems such as REST and component-based architectures. We will then cover low latency realtime stream processing and complexevent processing. Finally, we will present advanced topics indistributed data-intensive systems, such as geodistribution andsecurity.The course focuses both on the fundamental concepts as wellas on the concrete technologies and applications of the aforementionedtechniques to real-world case studies.

2 Learning objectives / Learning Outcomes- The students are familiar with basic concepts and technologies ondistributed systems and big data and are able to implement basic cloudbased/distributed applications.- The students are familiar with the fundamental computational modelsbehind recent advances in distributed systems, such as models forbatch processing of massive data amounts, stream processing andcomplex event processing.- The students are familiar with selected advanced topics on big data,including security and geolocalization.- The students know about real-world case studies that apply theconcepts and the technologies presented during the course.

3 Recommended prerequisite for participationThis course is targeted at master students.







8 References

Courses


Course Nr. Course name20-00-0951-iv Concepts and Technologies for Distributed Systems and Big Data Processing

Instructor Type SWSDr.-Ing. Michael Eichberg Integrated

Course2


Module nameFoundations of Language Technology


Language Module ownerGerman Prof. Dr. techn. Johannes Fürnkranz

1 ContentThis lecture provides an introduction into the fundamental perspectives, problems, methods, and tech-niques of text technology and natural language processing using the example of the Python programminglanguage.Key topics:- Natural language processing (NLP)- Tokenization- Segmentation- Part-of-speech tagging- Corpora- Statistical analysis- Machine Learning- Categorization and classification- Information extraction- Introduction to Python- Data structures- Structured programming- Working with files- Usage of libraries- NLTK libraryThe course is based on the Python programming language together with an open-source library calledthe Natural Language Toolkit (NLTK). NLTK allows explorative and problem-solving learning of theoreticalconcepts without the requirement of extensive programming knowledge.

2 Learning objectives / Learning OutcomesAfter attending this course, students are in a position to- define the fundamental terminology of the language technology field,- specify and explain the central questions and challenges of this field,- explicate and implement simple Python programs,- transfer the learned techniques and methods to practical application scenarios of text understanding, aswell as- critically assess their merits and limitations.










8 ReferencesSteven Bird, Ewan Klein, Edward Loper: Natural Language Processing with Python, O’Reilly, 2009. ISBN:978-0596516499. http://www.nltk.org/book/

Courses


Instructor Type SWSProf. Dr. phil. Iryna Gurevych Integrated

Course4


Module nameBioinformatics



1 Content




• Module Examination (Technical Examination, Oral Examination, Standard Grading System)





8 References

Courses

Course Nr. Course name10-01-0036-vl Bio Informatics-Lecture


Course Nr. Course name10-01-0036-se Bio Informatics-Exercise



Module nameOptimization Algorithms


Language Module ownerGerman Prof. Dr. rer. nat. Karsten Weihe

1 ContentAlgorithmic standard approaches to complex discrete optimization problems; for example, evolution strate-gies, dynamic programming, branch-and-bound, etc.

2 Learning objectives / Learning OutcomesIn this course students acquire systematic knowledge of generic algorithmic approaches in discrete opti-mization and the abitiliy to tackle complex discrete optimization problems algorithmically.

3 Recommended prerequisite for participationFunktionale und objektorientierte Programmierkonzepte, Algorithmen und Datenstrukturen or similar.








Courses

Course Nr. Course name20-00-0667-iv Optimization Algorithms

Instructor Type SWSProf. Dr. rer. nat. Karsten Weihe Integrated

Course4


Module nameInformation Management



1 ContentInformation Management Concepts:Information systems conceptsInformation storage/retrieval, searching, browsing, navigational vs. declarative accessQuality issues: consistency, scalability, availability, reliabilityData Modeling:Conceptual data models (ER/UML structure diagr.)Conceptual designOperational models (relational model)Mapping from conceptual to operational modelRelational Model:OperatorsRelational algebraRelational calculusImplications on query languages derived from RA and RCDesign theory, normalizationQuery LanguagesSQL (in detail)QBE, Xpath, rdf (high level)Storage mediaRAID, SSDsBuffering, cachingImplementation of relational operators:Implementation algorithmsCost functionsQuery optimization:Heuristic query optimizationCost based query optimizationTransaction processing (concurrency control and recovery):Flat transactionsConcurrency control, correctness criteria: serializability, recoverability, ACA, strictnessIsolation levelsLock-based schedulers, 2PLMultiversion concurrency controlOptimistic schedulersLoggingCheckpointingRecovery/restartNew trends in data managementMain memory databasesColumn storesNoSQL



After successfully attending the course, students are familiar with the fundamental concepts of informationmanagement. They understand the techniques for realizing information management systems and canapply the models, algorithms and languages to independently use and (partially) implement informationmanagement systems that fulfill the given requirements. They are able to evaluate such systens in a numberof quality metrics.

3 Recommended prerequisite for participationRecommended:Participation of lecture „Funktionale und Objektorientierte Programmierkonzepte“ and „Algorithmen undDatenstrukturen“, respective according knowledge.





6 Usability of this moduleB.Sc. InformatikB.Sc. WirtschaftsinformatikB.Sc. Psychologie in ITJoint B.A. InformatikB.Sc. Sportwissenschaft und InformatikM.Sc. Sportwissenschaft und InformatikMay be used in other degree programs.


8 ReferencesWill be updated regularly, an example might be:Haerder, Rahm, “Datenbanksysteme - Konzepte und Techniken der Implementierung”, Springer 1999Elmasri, R., Navathe, S. B.: Fundamentals of Database Systems, 3rd. ed., Redwood City, CA: Ben-jamin/CummingsUllman, J. D.: Principles of Database and Knowledge-Base Systems, Vol. 1 Computer Science

Courses

Course Nr. Course name20-00-0015-iv Information Management


3


Module nameAmbient Intelligence


Language Module ownerGerman Prof. Dr. Bernt Schiele

1 ContentThe course will provide an overview of a new vision for Human-Computer-Interaction (HCI) in which peo-ple are surrounded by intelligent and intuitive interfaces embedded in the everyday objects around them.In specific the course addresses the emergence of Ambient Mobility and the ubiquitous, pervasive informa-tion access, retrieval and display on mobile devices. It will focus on understanding enabling technologiesand studying applications and experiments, and, to lesser extent, it will adress the sociocultural impact.Additional topics of the lecture include system architectures for distributed systems, context awarenessand management, user models and their implications, sensing and interaction in smart environments. Thelecture discusses recent topics and research projects in the domain of Ambient Intelligence.

2 Learning objectives / Learning OutcomesAfter successfully attending the lecture, the students will be able to describe technology trends and researchresults in the domain of Ambient Intelligence. The most important concepts to create smart environments- intelligent networks and objects, technologies for mobile, augmented reality, ubiquitous and pervasiveinformation spaces, nomadic communications, real-time communication and related middle ware, embed-ded systems, sensor networks and wearable computing - can be discussed and classified. After completingthe practical part, students will be able to plan and realize the different project phases required to developan Ambient-Intelligence solution.

3 Recommended prerequisite for participationMaster-StudentsParticipation in lecture “Visual Computing“ and „Multimodale Interaktion mit intelligenten Umgebungen“







8 ReferencesWill be given according to actual topics.

Courses


Course Nr. Course name20-00-0390-iv Ambient Intelligence


4


Module nameData Science Practical Course



1 ContentThe goal of the practical course is to gain experience in the use of machine learning and data mining toolsfor real-world problems. Students will work together in groups in order to create an end-to-end solutionto a data science problem from the industry. The course is held in cooperation with business informaticsdepartments. For up-to-date information on the course please visit http://www.ke.tu-darmstadt.de/lehre

2 Learning objectives / Learning OutcomesAfter completion of this course the students are able torecognize problems in industry and commerce that can be solved with data mining and machine learningtechniques- develop problem-specific solutions in data mining and machine learning and put them in use

3 Recommended prerequisite for participationFoundations in machine learning and data mining







8 References

Courses

Course Nr. Course name20-00-1005-pr Data Science Practical Course

Instructor Type SWSProf. Dr. techn. Johannes Fürnkranz Internship 4


Module nameAutomata, Formal Languages and Decidability

Module Nr. Credit Points Workload Self study Duration Cycle offered04-10-0120/de 5 CP 150 h 105 h 1 Every 2. Sem.


1 Contentintroduction: transition systems, words, languages; basic mathematical methods and proof patterns; finiteautomata and regular languages; determinism and nondeterminism, closure properties and automata con-structions, Kleene Theorem, Myhill-Nerode Theorem, pumping lemma;grammars and the Chomsky hierrachy, context-free languages, pumping lemma, CYK algorithm;models of computation: PDA and Turing machines; decidability and recursive enumerability in the Chom-sky hierarchy

2 Learning objectives / Learning OutcomesSchöning: Theoretische Informatik – kurz gefasst

Hopcroft, Motwani, Ullman: Einführung in die Automatentheorie, formale Sprachen und Komplex-itätstheorie

Wegener: Theoretische Informatik – eine algorithmenorientierte Einführung

Skript (elektronisch unter www.mathematik.tu-darmstadt.de/~otto)

3 Recommended prerequisite for participationnone


• Module Examination (Technical Examination, Written/Oral Examination, Standard Grading System)• Module Examination (Study Achievement, Written/Oral Examination, Pass/Fail Grading System)


• Module Examination (Technical Examination, Written/Oral Examination, Weighting: 100 %)• Module Examination (Study Achievement, Written/Oral Examination, Weighting: 0 %)



8 References

Courses

Course Nr. Course name04-00-0091-vu Automata, Formal Languages and Decidability

Instructor Type SWSProf. Dr. rer. nat. Martin Otto Lecture & Prac-

tice3


Module namePropositional Logic and Predicate Logic

Module Nr. Credit Points Workload Self study Duration Cycle offered04-10-0121/de 5 CP 150 h 105 h 1 Every 2. Sem.


1 Contentsyntax and semantics of propositional logic,functional completeness and normal forms, compactness, complete proof calculi: resolution and a sequentcalculus;

syntax and semantics of first-order logic,structures and assignments, normal forms, Skolemization, Herbrand theorem, compactness, completeproof calculi: (ground) resolution and a sequent calculus,Gödel’s Completeness Theorem;undecidability of first-order logic;

optional: digressions on expressiveness and model checking




• Module Examination (Technical Examination, Written/Oral Examination, Standard Grading System)• Module Examination (Study Achievement, Written/Oral Examination, Pass/Fail Grading System)


• Module Examination (Technical Examination, Written/Oral Examination, Weighting: 100 %)• Module Examination (Study Achievement, Written/Oral Examination, Weighting: 0 %)



8 ReferencesBurris: Logic for Mathematics and Computer Science

Schöning: Logik für Informatiker

Boolos, Burgess, Jeffrey: Computability and Logic

Skript (2 Teile, elektronisch unter www.mathematik.tu-darmstadt.de/~otto)

Courses


Course Nr. Course name04-00-0090-vu Propositional Logic and Predicate Logic

Instructor Type SWSProf. Dr. rer. nat. Martin Otto Lecture & Prac-

tice3


Module nameExtended Seminar - Systems and Machine Learning


Language Module ownerEnglish Prof. Dr. techn. Johannes Fürnkranz

1 ContentThis seminar serves the purpose of discussing new research papers in the intersection ofhardware/software-systems and machine learning. The seminar aims to elicit new connections amongstthese fields and discusses important topics regarding systems questions machine learning including topicssuch as hardware accelerators for ML, distributed scalable ML systems, novel programming paradigms forML, Automated ML approaches, as well as using ML for systems.Every participant will present one research paper, which will be subsequently discussed by all participants.In addition, summary papers will be written in groups and submitted to a peer review process. The paperswill typically be recent publications in relevant research venues and journals.The seminar will be offered as a block seminar. Further information can be found at: http://binnig.name

2 Learning objectives / Learning OutcomesAfter this seminar, the students should be able to- understand a new research contribution in the areas of the seminar- prepare a written report and present the results of such a paper in front of an audience- participate in a discussion in the areas of the seminar- to peer-review the results of other students

3 Recommended prerequisite for participationBasic knowledge in Machine Learning, Data Management, and Hardware-/Software-Systems.





6 Usability of this moduleB. Sc InformatikM.Sc. InformatikMay be used in other degree programs.


8 References

Courses

Course Nr. Course name20-00-1057-se Extended Seminar - Systems and Machine Learning

Instructor Type SWSProf. Dr. techn. Johannes Fürnkranz Seminar 3


Module nameDeep Learning: Architectures & Methods


Language Module ownerEnglish Prof. Dr. techn. Johannes Fürnkranz

1 Content* Review of machine learning background* Deep Feedforward Networks* Regularization for Deep Learning* Optimization for Training Deep Models* Convolutional Networks* Sequence Modeling: Recurrent and Recursive Nets* Linear Factor Models* Autoencoders* Representation Learning* Structured Probabilistic Models for Deep Learning* Monte Carlo Methods* Approximate Inference* Deep Generative Models* Deep Reinforcement Learning* Deep Learning in Vision* Deep Learning in NLP

2 Learning objectives / Learning OutcomesThis course provides students with the required advanced background on machine learning the knowledgeto independently carry out research projects on the hot topic of deep learning, e.g. within the scope ofa Bachelor’s or Master’s thesis. In particular, this class aims at providing the students with fundamentalunderstanding of deep learning algorithms and the architecture of deep networks.

3 Recommended prerequisite for participation20-00-0358-iv Statistical Machine Learning20-00-0052-iv Data Mining and Machine Learning







8 References

Courses


Course Nr. Course name20-00-1034-iv Deep Learning: Architectures & Methods

Instructor Type SWSProf. Dr. techn. Johannes Fürnkranz Integrated

Course4


Module nameReinforcement Learning: From Foundations to Deep Approaches



1 Content* Review of machine learning background* Black box Reinforcement Learning* Modeling as bandit, Markov Decision Processes and Partially Observable Markov Decision Processes* Optimal control* System identification* Learning value functions* Policy search* Deep value functions methods* Deep policy search methods* Exploration vs exploitation* Hierarchical reinforcement learning* Intrinsic motivation

2 Learning objectives / Learning OutcomesThis course provides students with the required basic background on machine learning the knowledge toindependently carry out research projects on the hot topic of reinforcement learning, e.g. within the scopeof a Bachelor’s or Master’s thesis. In particular, this class aims at providing the students with fundamentalunderstanding of reinforcement learning algorithms and the application within deep learning.

3 Recommended prerequisite for participationGood programming in Python.Lecture Statistical Machine Learning is helpful but not mandatory.







8 References

Courses

Course Nr. Course name20-00-1047-iv Reinforcement Learning: From Foundations to Deep Approaches

Instructor Type SWSProf. Dr. rer. nat. Oskar Stryk Integrated

Course4


Module nameText Analytics


Language Module ownerGerman and English Prof. Dr. phil. Iryna Gurevych

1 ContentThe seminar introduces current topics in natural language processing. It provides a thorough introductioninto state-of-the-art technology in text analytics. The main focus of the seminar changes each semester.Further information: https://www.ukp.tu-darmstadt.de/teaching/courses/regular-seminar/

2 Learning objectives / Learning OutcomesAfter attending this course, students are in a position to- name and explain state-of-the-art research questions in the area of the seminar,- understand, critically assess, and discuss scientific publications,- independently comprehend and work out a research topic andpresent it to the group and react on questions and discussion threads.








8 ReferencesWill be given in seminar.

Courses

Course Nr. Course name20-00-0596-se

Instructor Type SWSProf. Dr. phil. Iryna Gurevych Seminar 2


https://www.ukp.tu-darmstadt.de/teaching/courses/regular-seminar/

Module nameMatrix Analysis and Computations



1 ContentThis graduate course is a foundation class on matrix analysis and computations, which are widelyused in many different fields, e.g., machine learning, computer vision, systems and control, signal andimage processing, communications, networks, optimization, and many more. . .Apart from the theory this course will also cover the design of efficient algorithm and it considers manydifferent examples from the aforementioned fields including examples from social media and big dataanalysis, image processing and medical imaging, communication network optimization, and written textclassification.Specific topics: (i) basic matrix concepts, subspace, norms, (ii) linear least squares (iii) eigendecompo-sition, singular value decomposition, positive semidenite matrices, (iv) linear system of equations, LUdecomposition, Cholesky decomposition (v) pseudo-inverse, QR decomposition (vi) advanced tensor de-composition, advanced matrix calculus, compressive sensing, structured matrix factorization

2 Learning objectives / Learning OutcomesStudents will learn matrix analysis and computations at an advanced or research level.

3 Recommended prerequisite for participationBasic knowledge in linear algebra.







8 References1.Gene H. Golub and Charles F. van Loan, Matrix Computations (Fourth Edition), John Hopkins UniversityPress, 2013.2.Roger A. Horn and Charles R. Johnson, Matrix Analysis (Second Edition), Cambridge University Press,2012.3.Jan R. Magnus and Heinz Neudecker, Matrix Differential Calculus with Applications in Statistics andEconometrics (Third Edition), John Wiley and Sons, New York, 2007.4.Giuseppe Calaore and Laurent El Ghaoui, Optimization Models, Cambridge University Press, 2014.ECE 712 Course Notes by Prof. Jim Reilly, McMaster University, Canada (friendly notes for engineers)http://www.ece.mcmaster.ca/faculty/reilly/ece712/course_notes.htm

Courses

Course Nr. Course name18-pe-2070-vl Matrix Analysis and Computations



Course Nr. Course name18-pe-2070-ue Matrix Analysis and Computations



Module nameAlgorithmic modeling for creating schedules



1 Content- Modeling of periodic schedules especially for railways- Respecting infrastructural constrains in schedule creation- robustness of schedules- timetable information systems

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students have gained skills in algorithmic modelling in the field ofrailway optimization

3 Recommended prerequisite for participationAlgorithms and data structure








Courses

Course Nr. Course name20-00-0391-se Algorithmic modelling for creating schedules



Module nameMachine Learning and Deep Learning for Automation Systems

Module Nr. Credit Points Workload Self study Duration Cycle offered18-ad-2100 3 CP 90 h 60 h 1 SoSe


1 Content• Concepts of machine learning• Linear methods• Support vector machines• Trees and ensembles• Training and assessment• Unsupervised learning• Neural networks and deep learning• Convolutional neuronal networks (CNNs)• CNN applications• Recurrent neural networks (RNNs)

2 Learning objectives / Learning OutcomesStudents will get a broad and practical view on the field of machine learning. First, the most relevantalgorithm classes of supervised and unsupervised learning are discussed. After that, the course addressesdeep neural networks, which enable many of today’s applications in image and signal processing. Thefundamental characteristics of all algorithms are compiled and demonstrated by programming examples.Students will be able to assess the methods and apply them to practical tasks.

3 Recommended prerequisite for participationFundamental knowledge in linear algebra and statisticsPreferred: Lecture “Fuzzy logic, neural networks and evolutionary algorithms”







8 References• T. Hastie et al.: The Elements of Statistical Learning. 2. Aufl., Springer, 2008• I. Goodfellow et al.: Deep Learning. MIT Press, 2016• A. Géron: Hands-On Machine Learning with Scikit-Learn and TensorFlow. O’Reilly, 2017

Courses

Course Nr. Course name18-ad-2100-vl Machine Learning and Deep Learning for Automation Systems

Instructor Type SWSDr.-Ing. Michael Vogt Lecture 2


3.3 Optional Subjects AIS-IE: Information Processing in Electrical Power Engineering

Module nameAdvanced Power Electronics

Module Nr. Credit Points Workload Self study Duration Cycle offered18-gt-2010 5 CP 150 h 90 h 1 WiSe

Language Module ownerEnglish Prof. Dr.-Ing. Gerd Griepentrog

1 ContentSwitch mode power supplies (insulating DC/DC-converters) Realistic behavior of power semiconductors:Basics of semiconductor physics; Behavior of diode, bipolar transistor, SCR, GTO, MOSDFET and IGBT,Important circuits for switching real semiconductors with low lossesForced commutation of SCRs, Loss reducing snubbers, quasi- resonant circuits, resonant switching.Topologies and control strategies for multilevel converterThermal design and thermo mechanical aging of power electronics systems

2 Learning objectives / Learning OutcomesAfter an active participation in the lecture, especially by asking all questions on topics which you did notfully understand as well by solving all exercises prior to the respective tutorial (i.e. not just shortly beforethe examination) you should be able to1.) Explain und understand the cross sectional layers and the basic modes of operation for power semicon-ductors (diode, thyristor, GTO. Mosfet and IGBT). Describe the steady state and dynamic behavior of thesedevices.2.) Identify the circuit diagrams for isolating DC/DC converters, especially for use in switched mode powersupplies. Calculate the currents and voltages in these circuits using defined simplifications.3.) Describe the functions of gate dive-circuits for ITGBTs.4.) Calculate the thermal behavior and design the cooling equipment for a voltage source inverter equippedwith IGBT modules.5.) Describe the stress reliving circuits to reduce switching losses in IGBTs.6.) Calculate the current and voltage characteristics in quasi-resonant and resonant circuits used in powerelectronics.7.) Explain multilevel converters such as 3L-NPC and MMC8.) Know the main concepts for cooling of power electronics incl. the ability to design a cooling conceptand should know main aspects which influence lifetime

3 Recommended prerequisite for participationBSc ETiT or equivalent, especially Power Electronics and Basics of Semiconductors





6 Usability of this moduleMSc ETiT, MSc EPE, Wi-ETiT


8 References

3.3 Optional Subjects AIS-IE: Information Processing in Electrical Power Engineering 247

Script available in Moodle for downloadLiterature:

• Schröder, D.: “Leistungselektronische Schaltungen”, Springer-Verlag, 1997• Mohan, Undeland, Robbins: Power Electronics: Converters, Applications and Design; John Wiley

Verlag; New York; 2003• Luo, Ye: “Power Electronics, Advanced Conversion Technologies”, Taylor and Francis, 2010

Courses

Course Nr. Course name18-gt-2010-vl Advanced Power Electronics


Course Nr. Course name18-gt-2010-ue Advanced Power Electronics



Module nameReal Time Applications and Communication with Microcontrollers and programmable Logic Devices

Module Nr. Credit Points Workload Self study Duration Cycle offered18-gt-2040 4 CP 120 h 75 h 1 WiSe/SoSe


1 ContentMicrocontroller and programmable logic devices are being used for a variety of control tasks for industrialand residential products and systems. For the control of drives and power electronics, those devices areused for the control of frequency converters or DC/DC converters.In most of these applications, real time requirements have to be met. Simultaneously a communicationinterface has to be served.The module will impart knowledge and expertise on how to realize successfully control task.More in detail, the following content will be taught:

• Architecture of microcontroller• Structure and function of FPGAs, tools and programming languages• Typical peripheral components for microcontrollers• Capture & Compare, PWM, A/D-converter• I2C, SPI, CAN, Ethernet• Programming of microcontrollers in C• Software: real-time properties, interrupt handling, interrupt latency• Control of inductive components• Basic of circuit design for power electronics, Power-MOSFETS, IGBTsNumerical methods

2 Learning objectives / Learning OutcomesStudents will be able to:

• Separate a digital control task into HW and SW parts• Specify the HW-content in a HW description language and implement the SW by means of a micro-

controller• Evaluate the real-time capabilities of a program and to determine upper limits for the response time

of the systemTransfer the developed solution to the target system by means of a development kitand debug the software onto the target system.

3 Recommended prerequisite for participationBasic knowledge in programmig language C (syntax, operators, pointer)





6 Usability of this moduleMSc MEC, MSc ETiT


8 ReferencesScript, Instruction for practical lab courses, ppt-Slides; either in hard-copy or for download; User Manualsof the used devices and development kits

Courses


Course Nr. Course name18-gt-2040-vl Real Time Applications and Communication with Microcontrollers and programmable

Logic Devices


Course Nr. Course name18-gt-2040-pr Real Time Applications and Communication with Microcontrollers and programmable

Logic Devices

Instructor Type SWSProf. Dr.-Ing. Gerd Griepentrog Internship 2


Module namePower Systems II

Module Nr. Credit Points Workload Self study Duration Cycle offered18-hs-2030 5 CP 150 h 90 h 1 WiSe


1 ContentThis lecture covers the essential aspects of the operation and analysis of power systems. The followingtopics will be covered:

• Operation of synchronous generators (steady-state operation, power chart, steady-state stability,transient stability, transient behavior)

• Calculation of short-circuit currents (Decaying three-phase short-circuit currents)• Neutral grounding in MV- and HV-Systems (Systems with isolated neutrals, resonant grounding and

solidly grounded neutrals)• Network Protection

2 Learning objectives / Learning OutcomesAt the end of the lecture, the student should have a profound understanding of synchronous generatorbehavior, decaying short-circuit currents and their calculation and a basic understandning of neutral pointtreatment and network protection. The different types of power system stability are known.

3 Recommended prerequisite for participationKnowledge comparable to “Energieversorgung I” or basic knowledge of power system equipment and cal-culations using symmetrical components.





6 Usability of this moduleMSc ETiT, MSc EPE, MSc Wi-ETiT


8 ReferencesA script of the lecture, tutorials and past exams are available via Moodle.

Courses

Course Nr. Course name18-hs-2030-vl Power Systems II


Course Nr. Course name18-hs-2030-ue Power Systems II



Module nameEnergy Converters - CAD and System Dynamics

Module Nr. Credit Points Workload Self study Duration Cycle offered18-bi-2010 7 CP 210 h 135 h 1 WiSe

Language Module ownerEnglish Prof. Dr. techn. Dr.h.c. Andreas Binder

1 ContentDesign of cage-rotor and wound-rotor induction machines: Calculation of forces, torque, losses, efficiency,cooling and temperature rise. Transient machine performance of converter-fed dc machines and line-fedand inverter-fed ac machines. Theory is illustrated by examples: Sudden short circuit, load step, run up. Forcontrol design transfer functions of machines are derived. In the exercise lessons demonstration examplesof power transformer and induction motor design are given. The students design one induction machine insmall groups by themselves. Transient performance calculation is trained by using Laplace-Transformationand MATLAB.

2 Learning objectives / Learning OutcomesWith active collaboration during lectures by asking questions related to those parts, which have not beencompletely understood by you, as well as by independent solving of examples ahead of the tutorial (not aslate as during preparation for examination) you should be able to:

• do and explain the electromagnetic design of an induction machine both analytically and with useof computer program,

• understand and predict the thermal performance of electrical drives in a simplified way,• calculate the instationary performance of separately excited DC drives• to predict the dynamical performance of AC polyphase machines with space vector theory and use

the MATLAB/Simulink package for this purpose.

3 Recommended prerequisite for participationBachelor of Science in Electrical Engineering, Power Engineering or similar





6 Usability of this moduleMSc ETiT, MSc MEC, MSc EPE


8 ReferencesDetailed textbook and collection of exercises; Complete set of PowerPoint presentationLeonhard, W.: Control of electrical drives, Springer, 1996Fitzgerald, A.; Kingsley, C.: Kusko, A.: Electric machinery, McGraw-Hill, 1971McPherson, G.: An Introduction to Electrical Machines and Transformers, Wiley, 1981Say, M.: Alternating Current Machines, Wiley, 1983Say, M.; Taylor, E.: Direct Current Machines, Pitman, 1983Vas, P.: Vector control of ac machines, Oxford Univ. Press, 1990Novotny, D,; Lipo, T.: Vector control and dynamics of ac drives, Clarendon, 1996

Courses


Course Nr. Course name18-bi-2010-vl Energy Converters - CAD and System Dynamics

Instructor Type SWSProf. Dr. techn. Dr.h.c. Andreas Binder Lecture 3

Course Nr. Course name18-bi-2010-ue Energy Converters - CAD and System Dynamics

Instructor Type SWSProf. Dr. techn. Dr.h.c. Andreas Binder Practice 2


Module namePower Laboratory I


Language Module ownerGerman and English Prof. Dr. techn. Dr.h.c. Andreas Binder

1 ContentSafety instructions for laboratory;Topic of experiments:

• Electrical energy conversion• Power electronics• High voltage technology• Electrical energy supply• Renewable energies

2 Learning objectives / Learning OutcomesPractical knowledge is gained in measuring and operating electrical devices and apparatus of electricalpower engineering in small groups of students.

3 Recommended prerequisite for participationPower Engineering or similar





6 Usability of this moduleMSc ETiT, MSc MEC, MSc WI-ETiT


8 ReferencesBinder, A. et al.: Textbook with detailed description of experiments; Hindmarsh, J.: Electrical Machinesand their Application, Pergamon Press, 1991Nasar, S.A.: Electric Power systems. Schaum’s OutlinesMohan, N. et al: Power Electronics, Converters, Applications and Design, John Wiley & Sons, 1995Kind, D., Körner, H.: High-Voltage Insualtion Technology, Friedr. Vieweg & Sohn, Braunschweig Wiesbaden,1985, ISBN 3-528-08599-1

Courses

Course Nr. Course name18-bi-2091-pr Power Laboratory I

Instructor Type SWSProf. Dr. techn. Dr.h.c. Andreas Binder Internship 3

Course Nr. Course name18-bi-2090-tt Laboratory Briefing

Instructor Type SWSProf. Dr. techn. Dr.h.c. Andreas Binder Tutorial 0


Module namePower Laboratory II

Module Nr. Credit Points Workload Self study Duration Cycle offered18-bi-2092 4 CP 120 h 75 h 1 SoSe


1 ContentPractical course on power engineering - Distribution and Application. About 50% of the units are devoted topower distribution and high voltage engineering; About 50% are dealing with application in drive systems,concerning “field-oriented control” of variable speed drives, encoder sytems, linear permanent magnet andswitched reluctance machines.

2 Learning objectives / Learning OutcomesPractical knowledge is gained in measuring and operating electrical devices and apparatus of electricalpower engineering in small groups of students.

3 Recommended prerequisite for participationMaster program: Power Lab 1







8 ReferencesText book with detailed laboratory instructions

Courses

Course Nr. Course name18-bi-2092-pr Power Laboratory II





Module nameApplication, Simulation and Control of Power Electronic Systems

Module Nr. Credit Points Workload Self study Duration Cycle offered18-gt-2030 8 CP 240 h 180 h 1 WiSe/SoSe

Language Module ownerGerman and English Prof. Dr.-Ing. Gerd Griepentrog

1 ContentIn an introductory meeting topics according to power electronics and control of drives are given to thestudents. During the seminary problems can be treated concerning the following topics:

• Simulation of power electronic systems plus analysis and evaluation of the models• Implementing and startup of power electronic systems, test stand development plus measurement of

characteristic parameters• Modeling and simulation in the field of control of electrical drives• Implementing and startup of controlled drive systems• Suggested topics from the students are welcome

The students are working autonomous on the chosen problem. The results are documented in a writtenreport and at the end of the module, a presentation about the problem must be held.

2 Learning objectives / Learning OutcomesThe Competences are:

• Autonomous familiarization with a given problem• Selection and evaluation of appropriate development tools• Familiarization with the used development tools• Practical experience in power electronics and control of drives• Logical presentation of the results in a report• Presentation skills

3 Recommended prerequisite for participationLecture „Leistungselektronik 1“ or „Einführung Energietechnik“ and ggf. „Regelungstechnik I“ or similar





6 Usability of this moduleMSc ETiT, MSc Wi-ETiT, MSc MEC


8 ReferencesDefinition of project task

Courses

Course Nr. Course name18-gt-2030-se Application, Simulation and Control of Power Electronic Systems

Instructor Type SWSProf. Dr.-Ing. Gerd Griepentrog Seminar 4


Module nameEnergy Management and Optimization

Module Nr. Credit Points Workload Self study Duration Cycle offered18-st-2010 6 CP 180 h 120 h 1 SoSe


1 ContentThe lecture reviews the different levels of energy management. It then focuses on economic dispatch anddiscusses its different use cases like optimization of self-consumption, virtual power plants, electric vehicleload management or multi-modal neighborhood optimization. Relevant knowledge about the componentsto be controlled as well as the markets to be addressed is explained.After this introduction to economic dispatch‘s application environment, the lecture focuses on the meth-ods employed. The underlying mathematical formulations as different types of optimization problems (LP,MILP, QP, stochastic optimization) are reviewed. In parallel, a practical introduction to numerical optimiza-tion is given (descent algorithms, convergence, convexity, programming languages for the formulation ofoptimization problems). Moreover, an introduction into simple methods for the prognosis of future values(linear regression) is provided.All methodological learning is accompanied by hands-on exercises using theMatlab/Octave and the GAMS/AMPL software environments.

2 Learning objectives / Learning OutcomesStudents know the different use cases and formulations of economic dispatch. They have a basic under-standing of the typically employed optimization methods and are able to judge the quality of the achievedresults.Moreover, students are independently able to formulate (energy) optimization problems and solve themwith the tool GAMS/AMPL.

3 Recommended prerequisite for participationStandard knowledge of linear algebra and multivariate analysis as well as basic knowledge in the use ofMatlab/Octave is required. Knowledge of the modules „Kraftwerke & EE“ or „Energiewirtschaft“ is helpfulbut not necessarry.





6 Usability of this moduleMSc ETiT, MSc iST, MSc Wi-ETiT, MSc CE

7 Grade bonus compliant to §25 (2)Improvement of grades up to 0.4 compliant to APB §25(2) through bonus system for re-gular attention ofexercises and practical courses

8 ReferencesBoyd, Vandenberghe: Convex Optimization, Cambridge University Press, 2004A GAMS Tutorial by RichardE. Rosenthal, https://www.gams.com/24.8/docs/userguides/userguide/_u_g__tutorial.html

Courses

Course Nr. Course name18-st-2010-vl Energy Management and Optimization



Course Nr. Course name18-st-2010-pr Energy Management and Optimization Lab


Course Nr. Course name18-st-2010-ue Energy Management and Optimization



Module nameElectromagnetic Compatibility

Module Nr. Credit Points Workload Self study Duration Cycle offered18-hi-2060 4 CP 120 h 75 h 1 WiSe

Language Module ownerGerman Prof. Dr.-Ing. Volker Hinrichsen

1 ContentFundamentals of Electromagnetic Compatibility, sources of emission, coupling mechanisms and countermeasures, components for noise suppression, electromagnetic shields, EMC measuring and test techniques,excursion to VDE Offenbach

2 Learning objectives / Learning OutcomesThe students know that from every electromagnetic system a interaction is possible and that every electro-magnetic (and also biological) system can be effected; they can differ between typical interference sourcesand sinks; they know the typical coupling paths und can identify and describe them mathematically; theyknow the basic methods to avoid interference at the source side and can derive their own actions againstinterference from this basic understanding; they know the basic actions to avoid interference at the sinkside and can also derive actions to avoid interference; they have the ability to recognize coupling paths andcan systematically influence or interrupt them completely; they know the situation of the EMC standard-ization and know basically which requirements have to be fulfilled and how to do this (also i.e. how togive a device a CE-label); they have learned the most important EMC testing and measurement techniquestheoretically and practically know on the field trip.

3 Recommended prerequisite for participationBSc





6 Usability of this moduleMSc ETiT, MSc MEC, MSc Wi-ETiT


8 References• All lecture slides (ca. 500 pcs.) available for download• Adolf J. Schwab: Elektromagnetische Verträglichkeit, Springer-Verlag• Clayton R. Paul: Introduction to Electromagnetic Compatibility, Wiley & Sons

Courses

Course Nr. Course name18-hi-2060-vl Electromagnetic Compatibility

Instructor Type SWSProf. Dr.-Ing. Volker Hinrichsen Lecture 2

Course Nr. Course name18-hi-2060-ue Electromagnetic Compatibility

Instructor Type SWSProf. Dr.-Ing. Volker Hinrichsen Practice 1


Module nameElectrical Power Engineering


Language Module ownerGerman Prof. Dr. techn. Dr.h.c. Andreas Binder

1 ContentThe lecture gives an introduction to the technical processes for the use of energy for the human civilizationin general and to the basic tasks and challenges of the electrical energy in particular. Biochemical energyprocesses such as the human metabolism are therefore not subject of the course.First, the physical basics of the term “energy” are repeated and the different forms of energy (mechanical,thermal, electromagnetic, chemical and nuclear) are explained in terms of the technical use of energy asheat, mechanical movement and electricity.Then, an overview of the energy resources is given, starting from the solar radiation and its direct andindirect impact, such as the solar heat and the motion of air mass, surface water and sea waves. Next, theenergy source of biomass due to solar radiation and the fossil energy sources oil, natural gas and coal willbe discussed. The energy sources of nuclear fission (uranium deposits) and nuclear fusion (heavy water),and geothermal energy due to nuclear effects in the Earth’s interior are explained as well as the tidal effectscaused by planetary motion. The increasing energy demand of the rapidly growing world population andthe geographic distribution of energy sources (deposits, acreage, solar radiation, wind maps, tidal currents,...) are described.The resulting energy flows on transport routes such as pipelines, waterways, ..., are briefly presented. Inanother section, energy conversion processes (direct and indirect methods) are illustrated. Large-scale pro-cesses such as thermal cycles or hydraulic processes in power plants are discussed mainly, but also marginalprocesses such as thermionic converters are addressed.Afterwards, a specialization takes place on the sub-ject of electric power supply with respect to the increasing proportion of the electric power applications.The chain from the electric generator to the consumer with an overview of the required resources, thehiring electrical load flow and its stability is addressed. The storage of energy and in particular of electricalenergy by converting into other forms of energy will be discussed. Finally, questions for the contemporaryuse of energy resources in regard to sustainability are mentioned.

2 Learning objectives / Learning OutcomesStudents know the physically based energy basics and have an overview of the energy resources of ourplanet Earth.They understand the fundamental energy conversion processes on the technical use of energy in the formof heat as well as mechanical and electrical work.They have acquired basic knowledge of electrical engineering in the chain of effects from electric powerproducer to the consumer and are able to educate themselves about current issues of energy use and itsfuture development.They are able to perform basic calculations for energy content, energy conversion, efficiencies, storage, andfor conversion and transportation losses.They are prepared for advanced lectures on energy componentsand systems, energy industry, and on future forms of energy supply.

3 Recommended prerequisite for participationBasic knowledge of physics (mechanics, thermodynamics, electrical engineering, structure of matter) andchemistry (binding energy) are desirable and facilitate understanding of the energetic processes.






6 Usability of this moduleBSc ETiT, BSc WI-ETiT, BSc MEC, BSc iST, BSc CE, MSc ESE


8 ReferencesLecture notes (slides)Practice documents (examples, solutions)Additional and more detailed literature:Grothe/Feldhusen: Dubbel-Taschenbuch für den Maschinenbau, Springer, Berlin, 2007, 22. Aufl.; beson-ders: Kapitel „Energietechnik und Wirtschaft“; Sterner/Stadler: Energiespeicher – Bedarf, Technolo-gien, Integration, Springer-Vieweg, Berlin, 2011; Rummich: Energiespeicher, expert-verlag, Rennin-gen, 2015, 2. Aufl.; Strauß: Kraftwerkstechnik zur Nutzung fossiler, nuklearer und regenerativer En-ergiequellen, Springer, Berlin, 2006, 5. Aufl.; Hau: Windkraftanlagen –Grundlagen, Technik, Einsatz,Wirtschaftlichkeit, Springer-Vieweg, Berlin, 2014, 5. Aufl.; Heuck/Dettmann/Schulz: Elektrische En-ergieversorgung, Springer-Vieweg, Berlin, 2014, 9. Aufl.;Quaschning: Regenerative Energiesystem, Hanser,München, 2001, 7. Aufl.

Courses

Course Nr. Course name18-bi-1010-vl Electrical Power Engineering


Course Nr. Course name18-bi-1010-ue Electrical Power Engineering



Module nameLarge Generators and High Power Drives



1 ContentDesign of large electric generators: Special cooling methods with air, hydrogen and water, loss evaluation,especially eddy current losses, and measures to reduce the additional losses. Design of big hydrogeneratorsup to 800 MVA and turbo generators up to 2000 MVA with desing examples. Application of power elec-tronics in large variable speed drives with synchronous motors: Synchronous converter and cyclo-converter.Numerous photographs to illustrate applications, excursion with students to special firms or plants.

2 Learning objectives / Learning OutcomesExpert knowledge in design of generators, large drives, their cooling systems and operational performanceis acquired.

3 Recommended prerequisite for participationPhysics, Electrical Machines and Drives, Electrical Power Engineering





6 Usability of this moduleMSc EPE, MSc ETiT, MSc MEC, MSc WI-ETiT


8 ReferencesDetailed textbook with calculated examples; Vas, P.: Parameter estimation, condition monitoring, anddiagnosis of electrical machines, Clarendon Press, 1993Fitzgerald, A.; Kingsley, C.; Kusko, A.: Electric machinery, McGraw-Hill, 2003Leonhard, W.: Control of electrical drives, Springer, 1996

Courses

Course Nr. Course name18-bi-2020-vl Large Generators and High Power Drives

Instructor Type SWSDr. techn. Georg Traxler-Samek Lecture 2

Course Nr. Course name18-bi-2020-ue Large Generators and High Power Drives

Instructor Type SWSDr. techn. Georg Traxler-Samek Practice 1


Module nameHigh Voltage Measuring Techniques



1 Content1. Measurement of High DC Voltages1.1 Resistor Voltage Dividers1.2 Electrostatic Voltmeters1.3 Generating Voltmeters1.4 Rod/Rod Gaps1.5 DKD-Calibration of a 1500 kV-DC-Measuring System2. Measurement of High AC Voltages in High VoltageTransmission Systems2.1 Inductive Voltage Transformers with Oil and with SF6-Insulation2.2 Capacitor Voltage Transformers2.3 Electronic Voltage Transformers2.4 Electro-Optical Voltage Transformers2.5 Calibration of Voltage Transformers3. Measurement of High AC Voltages in the Laboratory3.1 Resistor Voltage Dividers3.2 Capacitor Voltage Dividers3.3 Measuring Sphere Gap3.4 Electronic Peak Voltmeter3.5 DKD-Calibration of a 1200 kV AC-Measuring System4. Measurement of High Impulse Voltages4.1 Standard Impulse Voltages and their Normalized Amplitude Frequency Spectra4.2 Designs of R- , C- and RC-Dividers4.3 Computation of the Step Response of Impulse Voltage Dividers4.4 Analytical Calculation of the Response Time of a Divider with a Lead4.5 EMTP-Calculation of the Divider Output Voltage for Lightning Impulse Voltages4.6 DKD-Calibration of a 3 MV Lightning Impulse Measuring System4.7 DKD-Calibration of a 2 MV Switching Impulse Measuring System



The students learn the fundamentals, the dimensions, the application and the operation of voltage dividersup to 1,5 MV DC, 1,2 MV AC, 3,2 MV LI and 2 MV Si. They know and have understood the standardsIEC 60060-2 High-Voltage Measuring systems and the Calibration Procedures of the German CalibrationService (DKD) for High-Voltage Measuring Systems which show that the uncertainty of the High-VoltageMeasuring results of approved measuring systems are lower than the maximal permissible uncertainty forType tests in an accredited High-Voltage Test Laboratory.They know, how the material of the resistors and the Isolationsystem influence the measuring uncertainty,the costs and the the level of the maximal DC Voltage.The students know and understand the equivalent circuits for power frequency of inductive and capacitivevoltage transformer and are able to deduce the measuring errors and their dependency of the dimensioningof the magnetic and electric components.The students learn and understand, why an ohmic resistor voltage divider in contrary to a capacitor voltagedivider is not applicable for the measurement of high AC voltages. They are able to calculate the influenceof the distance between a wall and the capacitor voltage divider with oil-paper insulation on the measuringerror.The students know, why capacitor voltage dividers without serious resistors are not applicable to the mea-surement of lightning impulse voltages. They can describe the advantages and the disadvantages of a lowdamped capacitor voltage divider as front capacitor of a lightning impulse voltage generator and as voltagedivider. They know, why at LI-voltage measurements the test object must be situated between the generatorand the divider. The students are able to reduce the interference of steep currents in the walls and in theground of a high voltage test lab on the secondary voltage signal in the measuring cable from the dividerto the recorder.

3 Recommended prerequisite for participationBSc ETiT, BSc Wi-ETiT





6 Usability of this moduleMSc ETiT, MSc Wi-ETiT


8 References• Breilmann, W.: Skriptum zur Vorlesung “ Messverfahren der Hochspannungstechnik” im WS

2014/2015• Kuffel, E.; Zaengl, W.S.: High Voltage Engineering, Fundamentals ISBN-13:987-0750636346; But-

terworth Heinemann; July 2000. 539 Seiten; 81,20 Euro.• VDE 0432: Hochspannungs-Prüftechnik: Teil 1: Allgemeine Begriffe und Prüfbedingungen; (2011-

10) : 78 Euro• VDE 0432: Hochspannungs-Prüftechnik: Teil 2: Messsysteme (2011-10) : 78 Euro• Schon, K.: Stoßspannungs- und Stoßstrommesstechnik ISBN 978-3-642-13117-2; Springer Heidel-

berg; September 2010, 285 Seiten; 88 Euro

Courses

Course Nr. Course name18-hi-2050-vl High Voltage Measuring Techniques

Instructor Type SWSDr. Ing. Wolfgang Breilmann Lecture 2


Module nameOvervoltage Protection and Insulation Coordination in Power System


Language Module ownerEnglish Prof. Dr.-Ing. Volker Hinrichsen

1 Content• Introduction, basics and overview• Determination of representative overvoltages

– Origin and classification of overvoltages– Normal distribution of overvoltage probability and derivated variables– Operating voltage and temporary overvoltages– Slow front overvoltages– Fast front overvoltages– Characteristics of overvoltage protective devices– Operation and design of metal-oxide surge arresters– Travelling wave effect and protective distance of surge arresters– Representative voltage and overvoltages in the case of using surge arresters

• Determination of coordination withstand voltage– Insulation strength for different voltage shapes and geometric configurations (gap factors)– Performance criterion– Insulation coordination procedure

• Determination of required withstand voltage– General remarks– Atmospheric correction– Safety factor for internal and external insulations

• Standard withstand voltage and testing procedures– General remarks– Test conversion factors– Determination and verification of insulation withstand by type tests– Table of test voltages and required clearances

2 Learning objectives / Learning OutcomesThe student have understood the main procedures of insulation coordination based on the relevant IECstandard (and the main difference with related IEEE standard procedure) which leads to selection of theelectric strength of equipment in relation to the voltages which can appear on the system. In addition, theyhave learned the origin of different type of overvoltages as well as the protection of equipment againstthem. The operation and design of surge arresters as an important instrument of insulation coordinationin power systems have been understood. The theoretical knowledge about the procedure of insulationcoordination has been confirmed and expanded by practical case studies. The students are finally be ableto carry out the insulation coordination independetly in any application.

3 Recommended prerequisite for participationHigh Voltage Technology I and II



5 Grading


Module Final Examination:• Module Examination (Technical Examination, Written Examination, Weighting: 100 %)

6 Usability of this moduleMSc ETiT, MSc EPE, MSc Wi-ETiT


8 ReferencesThe related IEC standard can be borrowed during the lecture time. Lecture notes (in English) and otherhelpful materials can be downloaded from HST homepage: www.hst.tu-darmstadt.de.

Courses

Course Nr. Course name18-hi-2030-vl Overvoltage Protection and Insulation Coordination in Power System


Course Nr. Course name18-hi-2030-ue Overvoltage Protection and Insulation Coordination in Power System



http://www.hst.tu-darmstadt.de

Module namePower Systems I

Module Nr. Credit Points Workload Self study Duration Cycle offered18-hs-1010 5 CP 150 h 90 h 1 SoSe


1 ContentThree-phase network and symmetrical components; overhead lines; cables; transformers; calculation ofshort-circuit currents; switch equipment; switchgears

2 Learning objectives / Learning OutcomesThe education goals are

• Presentation of components of power system• Functional elaboration of equipment• Calculation of the component rating• Impact on the electrical power system

3 Recommended prerequisite for participationContents of the lecture Electrical Power Engineering





6 Usability of this moduleBSc ETiT, BSc/MSc WI-ET, BSc EPE, BSc/MSc CE, BSc/MSc iST, MSc Informatik


8 ReferencesScript, lecture slides, guiding questions, excercises

Courses

Course Nr. Course name18-hs-1010-vl Power Systems I


Course Nr. Course name18-hs-1010-ue Power Systems I



Module nameElectrical Machines and Drives



1 ContentConstruction and function of induction machine, synchronous machine, direct current machine. Electro-magnetic field within machines, armature windings, steady-state performance as motor/generator, appli-cation as line-fed and inverter-fed drives. Significance for electric power generation, both to the grid andin stand-alone version.

2 Learning objectives / Learning OutcomesWith active collaboration during lectures by asking questions related to those parts, which have not beencompletely understood by you, as well as by independent solving of examples ahead of the tutorial (not aslate as during preparation for examination) you should be able to:

• calculate and explain the stationary operation performance of the three basic types of electric ma-chine sin motor and generator mode,

• understand the application of electrical machines in modern drive systems and to design simple driveapplications by yourself,

• understand and explain the function and physical background of the components of electrical ma-chines

• understand and explain the impact of basic electromagnetic field and force theory on the basicfunction of electrical machines.

3 Recommended prerequisite for participationMathematics I to III, Electrical Engineering I and II, Physics, Mechanical Engineering





6 Usability of this moduleBSc ETiT, BSc/MSc Wi-ETiT, BEd


8 ReferencesDetailed textbook and collection of exercices; Complete set of PowerPoint presentationsL.Matsch: Electromagnetic and electromechanical machines, Int.Textbook, 1972A.Fitzgerald et al: Electric machinery, McGraw-Hill, 1971S.Nasar et al: Electromechanics and electric machines, Wiley&Sons, 1995R.Fischer: Elektrische Maschinen, C.Hanser-Verlag, 2004

Courses

Course Nr. Course name18-bi-1020-vl Electrical Machines and Drives



Course Nr. Course name18-bi-1020-ue Electrical Machines and Drives



Module namePower Electronics

Module Nr. Credit Points Workload Self study Duration Cycle offered18-gt-1010 5 CP 150 h 90 h 1 WiSe


1 ContentPower electronic devices convert the energy from the distribution network to the form required by theload. This conversion does not wear out, can be controlled very fast and has a high efficiency. In lecture“Power Electronics“ the most important circuits required for the energy conversion are treated, using idealswitches.The main chapters areI.) Line commutated converters in order to understand the basic concepts of power electronic systems.II.) Self- commutated converters (one, two and four quadrant converters, 3-phase- VSI)

2 Learning objectives / Learning OutcomesAfter an active participation in the lecture, as well as by solving all exercises prior to the respective tutorialstudents should be able to:

• Understand the ideal concept of power semiconductors• Calculate and sketch the time-characteristics of all currents and voltages in a line-commutated con-

verter using defined simplifications as well as represent the behavior of currents and voltages duringcommutation in line-commutated converters for center –tapped as well as for bridge circuits.

• Specify the basic circuit diagrams for one, two and four quadrant DC/DC converters and calculatethe characteristics of voltages and currents in these circuits.

• Explain the function of single-phase and three-phase voltage source inverters and calculate the cur-rents and voltages in these circuits using defined simplifications.

• Understannd the concept und operation of HVDC transmission

3 Recommended prerequisite for participationMathe I und II, ETiT I und II, Energietechnik





6 Usability of this moduleMSc ETiT, MSc MEC, Wi-ETiT


8 ReferencesLecture notes, instructions for exercises are available for download in Moodle.Literature:Probst U.: „Leistungselektronik für Bachelors: Grundlagen und praktische Anwendungen“, Carl HanserVerlag GmbH & Co. KG, 2011Jäger, R.: „Leistungselektronik: Grundlagen und Anwendungen“, VDE-Verlag; Auflage 2011Heumann, K.: „Grundlagen der Leistungselektronik“; Teubner; Stuttgart; 1985Lappe, R.: „Leistungselektronik“; Springer-Verlag; 1988Mohan, Undeland, Robbins: Power Electronics: Converters, Applications and Design; John Wiley Verlag;New York; 2003


Courses

Course Nr. Course name18-gt-1010-vl Power Electronics


Course Nr. Course name18-gt-1010-ue Power Electronics



Module nameHigh Voltage Switchgear and Substations

Module Nr. Credit Points Workload Self study Duration Cycle offered18-hi-2020 3 CP 90 h 60 h 1 SoSe


1 ContentThis lecture covers the basic designs of high voltage substations as well as the design and working principlesof high voltage switchgear:

• Tyes of switching and stresses induced by switching• Arc behaviour in air, SF6 and vacuum• Types of switchgear: earthing switches, disconnectors and circuit breakers• Design and working principles of earthing switches and disconnectors in air and SF6• Design and working principles of circuit breakers: vacuum breakers, pressured air and SF6 breakers

(thermal blast and self-blast chambers)• Stresses of earthing switches and disconnectors by short circuit conditions• Testing of Switchgear• Reliability of Switchgear• Future developments: Intelligent control of switchgear, static switches, superconducting switchgear

2 Learning objectives / Learning OutcomesThe student should understand the purpose and working principles of high voltage switchgear as well astheir usage in high voltage substations.

3 Recommended prerequisite for participationPrior attendance of the lectures High Voltage Technology I and II is recommended





6 Usability of this moduleMSc ETiT, BSc/MSc iST, MSc Wi-ETiT, MSc EPE


8 ReferencesA script of the lecture (in German) can be obtained from here: http://www.hst.tu-darmstadt.de/index.php?id=30

Courses

Course Nr. Course name18-hi-2020-vl High Voltage Switchgear and Substations

Instructor Type SWSProf. Dr. Claus Neumann Lecture 2


http://www.hst.tu-darmstadt.de/index.php?id=30

http://www.hst.tu-darmstadt.de/index.php?id=30

Module nameHigh Voltage Technology I



1 ContentChoice of Voltage Level, Generation of High AC Voltage, Generation of High DC Voltage, Generation ofImpulse Voltages, Measurement of High AC/DC/Impulse Voltages, Electrical Fields, Two excursions tomanufacturers of high voltage products

2 Learning objectives / Learning OutcomesThe students know why electrical energy is transported and distributed at high voltages and what is theoptimal voltage level for different purposes; they are able to identify different basic kinds of electricalstress in the system; they know how to generate and to measure high test voltages in the laboratory;they have understood the requirements in the test standards and why standards are so important at all;they are able to interpret and correctly apply the standards; they know the basic test circuits for generatingalternating, direct and impulse voltages, and they can extend and adopt them for special purposes; they areaware of the particular problems of high-voltage measuring techniques and are able to apply high-voltagemeasuring systems and optimize them for particular tasks; thus, in sum they are basically prepared to plan,erect and operate a high-voltage test laboratory; they can analytically solve the electrical field equations forbasic electrode configurations and make use of them for optimizing configurations with regard to dielectricstrength; they know about surge propagation on lines and are aware that this is also relevant for impulsemeasuring techniques and how to handle related problems.








8 References• All lecture slides (ca. 600 pcs.) available for download• Kind, Feser: High-voltage test techniques, SBA publications• Kind, Kärner: High-voltage insulation technology, Vieweg

Courses

Course Nr. Course name18-hi-1020-vl High Voltage Technology I



Course Nr. Course name18-hi-1020-ue High Voltage Technology I



Module nameHigh Voltage Technology II

Module Nr. Credit Points Workload Self study Duration Cycle offered18-hi-2010 4 CP 120 h 75 h 1 SoSe


1 ContentLayered Dielectrics, Methods of Field Control and Potential Control, Breakdown in Gases (air and SF6),Breakdown in Vacuum, Surface Discharges, Lightnings and Lightning Protection, Travelling Waves on Con-ductors; Excursion to a substation

2 Learning objectives / Learning OutcomesThe students are now able to optimize insulation systems also by choice of the dielectrics, by capacitive, re-fractive or resistive internal grading systems or by external geometrical/capacitive grading elements; theyhave understood why equipment is designed as it is and how and where it can or has to be optimized ifrequirements from service are changing; they have understood the physical phenomena behind the dielec-tric breakdown of gases and do know which are the main influencing parameters; they know the effectof strongly inhomogeneous electrode configurations and of extremely large gaps; they know the time de-pendencies of a dielectric breakdown and their impact on dielectric strength under impulse voltage stress;they are able to identify critical surface discharge configurations, know about the problems under severeexternal pollution of insulators and how to solve them; they are thus qualified to predict the dielectricstrength of any electrode configuration under any kind of voltage stress and to design a particular requireddielectric strength of equipment; they are particularly enabled to realize the demands of emerging UHVsystems and to manage them; they have understood the mechanism of thunderstorms and lightning flashesand are able to derive protective measures for buildings, substations and overhead lines; they are skilledto calculate travelling wave effects and their effect on fast-front overvoltages and to develop adequatecountermeasures.

3 Recommended prerequisite for participationHigh Voltage Technology I





6 Usability of this moduleMSc ETiT, MSc Wi-ETiT


8 References• all lecture slides (ca. 460 pcs.) available for download• Kind, Feser: High-voltage test techniques, SBA publications• Kind, Kärner: High-voltage insulation technology, Vieweg

Courses

Course Nr. Course name18-hi-2010-vl High Voltage Technology II



Course Nr. Course name18-hi-2010-ue High Voltage Technology II



Module nameNew Technologies of Electrical Energy Converters and Actuators



1 ContentGoal: The application of new technologies, i.e. super conduction, magnetic levitation techniques andmagneto-hydrodynamic converter principles, are introduced to the students. The physical operation modein principle, implemented prototypes and the current state of the development are described in detail.Content:Application of the superconductors for electrical energy converters:

• rotating electrical machines (motors and generators),• solenoid coils for the fusion research,• locomotive- and railway transformers,• magnetic bearings.

Active magnetic bearings (“magnetic levitation”):• basics of the magnetic levitation technique,• magnetic bearings for high speed drives in kW to MW range,• application for high-speed trains with linear drives.

Magneto-hydrodynamic energy conversion:• physical principle,• state of the art and perspectives.

Fusion research:• magnetic field arrangements for contactless plasma inclusion,• state of the current research.

2 Learning objectives / Learning OutcomesBasic knowledge in application of superconductivity in energy systems is understood as well as magneticlevitation, magnetohydrodynamics and fusion technology.








8 ReferencesDetailed textbook; Komarek, P.: Hochstromanwendungen der Supraleitung, Teubner, Stuttgart, 1995Buckel, W.: Supraleitung, VHS-Wiley, Weinheim, 1994Schweitzer, G.; Traxler, A.; Bleuler, H.: Magnetlager, Springer, Berlin, 1993Schmidt, E.: Unkonventionelle Energiewandler, Elitera, 1975


Courses

Course Nr. Course name18-bi-2040-vl New Technologies of Electrical Energy Converters and Actuators


Course Nr. Course name18-bi-2040-ue New Technologies of Electrical Energy Converters and Actuators



Module nameDesign of Electrical Machines and Actuators with Numerical Field Calculation



1 ContentIntroduction to Finite Element Method (FEM), Basic examples of electromagnetic devices designed in 2Dwith FEM, 2D electromagnetic Design of transformers, AC machines, permanent magnet devices; eddycurrent applications such as squirrel-cage machines (Example: Wind generator); Cooling systems andthermal design: Calculation of temperature distribution within power devices

2 Learning objectives / Learning OutcomesA good knowledge in applying FEMAG and ANSYS software package to basic field problems is gained.

3 Recommended prerequisite for participationStrongly recommended is the attendance of lecture and active co-operation in the tutorial “Energy Con-verters - CAD and System Dynamics”





6 Usability of this moduleMSc EPE, MSc ETiT, MSc MEC


8 ReferencesDetailed textbook; User manual FEMAG and ANSYS. Müller, C. Groth: FEM für Praktiker – Band 1: Grund-lagen, expert-Verlag, 5. Aufl., 2000

Courses

Course Nr. Course name18-bi-2110-se Design of Electrical Machines and Actuators with Numerical Field Calculation

Instructor Type SWSDr.-Ing. Bogdan Funieru Seminar 2


Module namePhysics and Technology of Accelerators



1 Content




• Module Examination (Study Achievement, Study Archievment, Pass/Fail Grading System)Module Eccompanying Examination:

• [05-25-6302-pr] (Study Achievement, Study Archievment, BWS b/nb)


• Module Examination (Study Achievement, Study Archievment, Weighting: 100 %)Module Eccompanying Examination:

• [05-25-6302-pr] (Study Achievement, Study Archievment, Weighting: 0 %)



8 References

Courses

Course Nr. Course name18-bf-2010-vl Accelerator Physics

Instructor Type SWSProf. Dr. Oliver Boine-Frankenheim Lecture 2

Course Nr. Course name05-21-2502-ku Introduction to Accelerator Physics

Instructor Type SWSCourse 2

Course Nr. Course name05-25-6302-pr Vocational Laboratory: Introduction to Accelerator Physics



Module nameMachine Learning & Energy

Module Nr. Credit Points Workload Self study Duration Cycle offered18-st-2020 6 CP 180 h 120 h 1 WiSe


1 ContentThe analysis and interpretation of data becomes ever more important, also for engineers. Digitalizationand Smart Grids are terms to describe a host of novel data-based services in the field of generation, distri-bution, consumption and marketing of (renewable) energy. The lecture presents the recent developmentsand their underlying principles of machine learning technology.For a start we will describe the different problem settings of machine learning in a structured way (clas-sification, regression, clustering, dimensionality reductions, time series models, . . . ) and present for eachsetting relevant applications from the energy sector (prediction of renewable energy or consumption inmultimodal energy systems, fault detection and prediction, data visualization, robust investments deci-sions, customer analysis, probabilistic load flow, . . . ).Thereafter we will briefly review necessary tools from optimization and probability theory, as well as in-troduce probabilistic graphical models. With these tools we will then study for each problem setting oneor more machine learning algorithms in detail, together with use cases from the energy domain. Classicalgorithms will be developed (e.g. linear regression, k-means, principal component analysis, . . . ) as wellas modern ones (e.g. SVMs, Deep Learning, Collaborative filtering, . . . ). Practical exercise with Matlabwill deepen the understanding and support student’s active knowledge.

2 Learning objectives / Learning OutcomesStudents understand important machine learning problem settings and some key algorithms for each task.They know common applications thereof in the energy domain. Moreover, the students are able to applyand adapt those methods independently to new applications (not only from the energy domain).

3 Recommended prerequisite for participation• Good knowledge of linear algebra and the foundations of numerical optimization (e.g. from the

course 18-st-2010 Energieanagement & Optimierung)• Using Matlab for programming the practical examples should pose no difficulty. A block tutorial on

the use of Matlab is offered as 18-st-2030 Matlab Grundkurs.





6 Usability of this moduleMSc etit, MSc iST, MSc Wi-etit, MSc CE

7 Grade bonus compliant to §25 (2)Notenverbesserungen bis zu 0,4 nach APB §25(2) durch Bonus für regelmäßig besuchte Übungs-/Praktikumstermine und mindestens einmaliges Vorrechnen in den Übungen

8 References• A Géron: Hands on Machine Learning with scikit-learn and Tensorflow, 2017• Friedman, Hastie, Tibshirani: The elements of statistical learning, 2001• Koller, Friedmann: Graphical Models, 2009

Courses


Course Nr. Course name18-st-2020-vl Machine Learning & Energy

Instructor Type SWSProf. Dr. rer. nat. Florian Steinke Lecture 2

Course Nr. Course name18-st-2020-pr Machine Learning & Energy Lab

Instructor Type SWSProf. Dr. rer. nat. Florian Steinke Internship 1

Course Nr. Course name18-st-2020-ue Machine Learning & Energy

Instructor Type SWSProf. Dr. rer. nat. Florian Steinke Practice 1


Module nameElectric Railways



1 Content• Mechanics of traction• Electrical part of traction vehicles• Converter and motors for electrical traction• Monitoring systems• Comparison of different power supply systems• DC- and AC- systems for light- and heavy rail• Problems of earthing and earth return currents• Sub stations, converters, power plants

2 Learning objectives / Learning OutcomesComprehension of the basic concepts of electric traction vehicles and power supply for electric railways

3 Recommended prerequisite for participationBasic knowledge in electrical machines and drives







8 ReferencesText book for the lecture. Bendel, H. u.a.: Die elektrische Lokomotive. Transpress, Berlin, 1994. Filipovic,Z: Elektrische Bahnen. Springer, Berlin, Heidelberg, 1995. Steimel, A.: Elektrische Triebfahrzeuge und ihreEnergieversorgung. Oldenburg Industrieverlag, 2006. Bäzold, D. u.a.: Elektrische Lokomotion deutscherEisenbahnen. Alba, Düsseldorf, 1993. Obermayer, H. J.: Internationaler Schnellverkehr. Franckh-Kosmos,Stuttgart, 1994; Guckow, A.; Kiessling, F.; Puschmann, R.: Fahrleitungen el. Bahnen. Teubner, Stuttgart,1997. Schaefer, H.: Elektrotechnische Anlagen für Bahnstrom. Eisenbahn-Fachverlag, Heidelberg, 1981

Courses

Course Nr. Course name18-bi-2140-vl Electric Railways

Instructor Type SWSProf. Harald Neudorfer Lecture 3


3.4 Optional Subjects AIS-MT: Medical Technics

Module nameFuzzy Logic, Neural Networks and Evolutionary Algorithms



1 ContentFuzzy systems: basics, rule based fuzzy logic, design methods, decision making, fuzzy control, patternrecognition, diagnosis; Neural networks: basics, multilayer perceptrons, radial basis functions, patternrecognition, identification, control, interpolation and approximation, Neuro-fuzzy: optimization of fuzzysystems, data driven rule generation; Evolutionary algorithms: optimization problems, evolutionary strate-gies and their applications, genetic programming and its applications


• recalling the elements and set-up of standardized fuzzy-logic, neural networks and evolutionaryalgorithms,

• discussing the pros and cons of certain setups of systems from computational intelligence for solvinga given problem,

• recognizing situations in which tools taken from computational intelligence can be applied for prob-lem solving,

• creating programs from algorithms taught in the lecture, and• extending the learned standard procedures in order to solve new problems.






6 Usability of this moduleBSc iST, MSc ETiT, MSc MEC, MSc WI-ETiT, MSc iCE, MSc EPE, MSc CE, MSc Informatik


8 ReferencesAdamy: Fuzzy Logik, Neuronale Netze und Evolutionäre Algorithmen, Shaker Verlag (available for pur-chase at the FG office)www.rtr.tu-darmstadt.de (optionales Material)

Courses

Course Nr. Course name18-ad-2020-vl Fuzzy Logic, Neuronal Networks and Evolutionary Algorithms


3.4 Optional Subjects AIS-MT: Medical Technics 284

http://www.rtr.tu-darmstadt.de

Course Nr. Course name18-ad-2020-ue Fuzzy Logic, Neuronal Networks and Evolutionary Algorithms



Module nameVisualization in Medicine



1 ContentMedical Image Data; Image Processing; Medical Visualization with VTK; Indirect Volume Visualization;Direct Volume Visualization; Transfer Functions; Interactive Volume Visualization; Illustrative Rendering;Example: Visualization of Tensor Image Data; Example: Visualization of Tree Structures; Example: VirtualEndoscopy; Image-guided Surgery

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students are familiar with volume visualization techniques. Theyunderstand the necessity of image enhancement for the visualization. They can use the “VisualizationToolkit” (VTK) to apply the techniques to implement computing systems for the visualization of medicalimage data for diagnosis, planning and therapy.

3 Recommended prerequisite for participationUseful but not mandatory: GDV I, (Medical) Image processing







8 ReferencesPreim, Botha: Visual Computing for Medicine

Courses

Course Nr. Course name20-00-0467-iv Medical Visualization


4


Module nameCurrent Trends in Medical Computing



1 Content- Participants independently familiarize themselves with a chosen seminar topic by working with the pro-vided initial scientific papers (usually English-language texts)- Deeper and/or wider library research originating from the initially provided papers- Critical discussion of the provided topic- Preparation of a presentation (written text and slides) about the topic- Giving a talk in front of a heterogenous (mixed prior knowledge) audience- Interactive discussion after the presentation- Medical application areas include oncology, orthopedics and navigated surgery.Learning about methods related to medical image processing: segmentation, registration, visualization,simulation, navigation, tracking and others.

2 Learning objectives / Learning OutcomesSuccessful participation in the course enables students to become acquainted with an unfamilar topic byworking with scientific papers. They recognize the essential aspects of the examined works and are able toconcisely present them to an audience with mixed prior knowledge on the subject. They apply a number ofpresentation techniques in the process. The students are able to actively guide and participate in a scientificdiscussion on the presented topic.

3 Recommended prerequisite for participationBachelor from 4. Semester or Master students.







8 ReferencesWill be announced in seminar.

Courses





Module nameComputer-aided planning and navigation in medicine


Language Module ownerGerman Prof. Dr. Georgios Sakas

1 Content- Participants independently familiarize themselves with a chosen seminar topic by working with the pro-vided initial scientific papers (usually English-language texts)- Deeper and/or wider library research originating from the initially provided papers- Critical discussion of the provided topic- Preparation of a presentation (written text and slides) about the topic- Giving a talk in front of a heterogenous (mixed prior knowledge) audience- Interactive discussion after the presentationLearning about methods related to planning and navigation are: segmentation, registration, visualization,simulation, navigation, tracking and others.

2 Learning objectives / Learning OutcomesSuccessful participation in the course enables students to become acquainted with an unfamilar topic byworking with scientific papers. They recognize the essential aspects of the examined works and are able toconcisely present them to an audience with mixed prior knowledge on the subject. They apply a number ofpresentation techniques in the process. The students are able to actively guide and participate in a scientificdiscussion on the presented topic.

3 Recommended prerequisite for participationBachelors: >=4th semesterMasters: >=1st semester








Courses


Course Nr. Course name20-00-0677-se Computer-aided planning and navigation in medicine

Instructor Type SWSProf. Dr. Georgios Sakas Seminar 2


Module nameMicrosystem Technology

Module Nr. Credit Points Workload Self study Duration Cycle offered18-bu-2010 4 CP 120 h 75 h 1 WiSe

Language Module ownerGerman Prof. Ph.D. Thomas Peter Burg

1 ContentIntroduction and definitions to micro system technology; definitions, basic aspects of materials in microsystem technology, basic principles of micro fabrication technologies, functional elements of microsystems,micro actuators, micro fluidic systems, micro sensors, integrated sensor-actuator systems, trends, economicaspects.

2 Learning objectives / Learning OutcomesTo explain the structure, function and fabrication processes of microsystems, including micro sensors,micro actuators, micro fluidic and micro-optic components, to explain fundamentals of material properties,to calculate simple microsystems.

3 Recommended prerequisite for participationBSc







8 ReferencesScript for lecture: Mikrosystemtechnik

Courses

Course Nr. Course name18-bu-2010-vl Microsystem Technology

Instructor Type SWSProf. Ph.D. Thomas Peter Burg Lecture 2

Course Nr. Course name18-bu-2010-ue Microsystem Technology

Instructor Type SWSProf. Ph.D. Thomas Peter Burg Practice 1















Courses







Module nameEvolutionary Systems - From Biology to Technology



1 Contenttheory of biological evolution, introduction to genetics, population genetics, population growth, evolution-ary algorithms, applications, DNA computing, artificial life, theory of evolutionary algorithms, optimiza-tion algorithms, multi-objective optimization, meta models, co-evolution, genetic coding, representationsof evolutionary algorithms, developmental processes, self-adaptation, evolution and learning

2 Learning objectives / Learning OutcomesAfter attending the lecture, a student is capable of: 1.understanding the basic principles of evolutionarybiology on a systems level, 2. transferring of this knowledge to the technical domain (evolutionary algo-rithms), 3. applying evolutionary algorithms to hard optimization problems, 4. gaining insight into thepotentials and challenges of interdisciplinary research (natural and engineering/computer science).

3 Recommended prerequisite for participationIntroductory courses mathematics. Basic computer skills.





6 Usability of this moduleMSc ETiT, MSc MEC, MSc iST, MSc WI-ETiT, MSc iCE, MSc EPE, MSc CE, MSc Informatik, Biotechnik


8 ReferencesD.J. Futuyama: Evolutionary Biology. W. Henning, Genetik, Springer Verlag; D.B. Fogel: EvolutionaryComputation, IEEE Press; I. Rechenberg: Evolutionsstrategie ‘94; H.-P. Schwefel: Evolution and OptimumSeeking

Courses

Course Nr. Course name18-ad-2050-vl Evolutionary Systems - From Biology to Technology

Instructor Type SWSDr. rer. nat. Bernhard Sendhoff Lecture 2


Module nameComputational Modeling for the IGEM Competition

Module Nr. Credit Points Workload Self study Duration Cycle offered18-kp-2100 4 CP 120 h 90 h 1 WiSe/SoSe

Language Module ownerEnglish Prof. Dr. techn. Heinz Köppl

1 ContentThe International Genetically Engineered Machine (IGEM) competition is a yearly international studentcompetition in the domain of synthetic biology, initiated and hosted by the Massachusetts Institute of Tech-nology (MIT), USA since 2004. In the past years teams from TU Darmstadt participated and were verysuccessfully in the competition. This seminar provides training for students and prospective IGEM teammembers in the domain of computational modeling of biomolecular circuits. The seminar aims at compu-tationally inclined students from all background, but in particular from electrical engineering, computerscience, physics and mathematics. Seminar participants that are interested to become IGEM team memberscould later team up with biologists and biochemists for the 2017 IGEM project of TU Darmstadt and beresponsible for the computational modeling part of the project.The seminar will cover basic modeling approaches but will focus on discussing and presenting recenthigh-impact synthetic biology research results and past IGEM projects in the domain of computationalmodeling.

2 Learning objectives / Learning OutcomesStudents that successfully passed that seminar should be able to perform practical modeling of biomolecularcircuits that are based on transcriptional and translational control mechanism of gene expression as usedin synthetic biology. This relies on the understanding of the following topics:

• Differential equation models of biomolecular processes• Markov chain models of biomolecular processes• Use of computational tools for the composition of genetic parts into circuits• Calibration methods of computational models from experimental measurement• Use of bioinformatics and database tools to select well-characterized genetic parts






6 Usability of this moduleBSc etit, MSc etit


8 References

Courses

Course Nr. Course name18-kp-2100-se Computational Modeling for the IGEM Competition

Instructor Type SWSProf. Dr. techn. Heinz Köppl Seminar 2


Module nameMedical Image Processing



1 ContentThe lecture consists of two parts. The first half of the lecture describes how devices that yield medicalimage data (CT, NMR, PET, SPECT, Ultrasound) work. The second half of the lecture covers various imageprocessing techniques that are typically applied to medical images.

2 Learning objectives / Learning OutcomesAfter successfully completing the course, students have an overview over the mechanisms used in and theabilities of modern medical image processing techniques. They are able to solve basic to medium levelproblems in medical image processing.

3 Recommended prerequisite for participationBasics within Mathematics are highly recommended.Participation in lecture “Bildverarbeitung”.







8 References1) Heinz Handels: Medizinische Bildverarbeitung2) 2) Gonzalez/Woods: Digital Image Processing (last edition)3) 3) Bernd Jähne: Digitale Bildverarbeitung. 6. überarbeitete und erweiterte Auflage. Springer, Berlin u.a. 2005, ISBN 3-540-24999-04) Kristian Bredies, Dirk Lorenz: Mathematische Bildverarbeitung. Einführung in Grundlagen und moderneTheorie. Vieweg+Teubner, Wiesbaden 2011, ISBN 978-3-8348-1037-3

Courses

Course Nr. Course name20-00-0379-vl Medical Image Processing



Module nameSignal Detection and Parameter Estimation



1 ContentSignal detection and parameter estimation are fundamental signal processing tasks. In fact, they appearin many common engineering operations under a variety of names. In this course, the theory behinddetection and estimation will be presented, allowing a better understanding of how (and why) to design“good” detection and estimation schemes.These lectures will cover: FundamentalsDetection Theory Hypothesis Testing Bayesian TestsIdeal Observer TestsNeyman-Pearson TestsReceiver Operating CharacteristicsUniformly Most Powerful TestsThe Matched Filter Estimation Theory Types of EstimatorsMaxmimum Likelihood EstimatorsSufficiency and the Fisher-Neyman/Factorisation CriterionUnbiasedness and Minimum varianceFisher Information and the CRBAsymptotic properties of the MLE

2 Learning objectives / Learning OutcomesStudents gain deeper knowledge in signal processing based on the fundamentals taught in DSP and EtiT 4.Thexy will study advanced topics of statistical signal processing in the area of detection and estimation.In a sequence of 4 lectures, the basics and important concepts of detection and estimation theory will betaught. These will be studied in dept by implementation of the methods in MATLAB for practical examples.In sequel, students will perform an independent literature research, i.e. choosing an original work indetection and estimation theory which they will illustrate in a final presentation.This will support the students with the ability to work themselves into a topic based on literature researchand to adequately present their knowledge. This is especially expected in the scope of the students’ futureresearch projects or in their professional carreer.

3 Recommended prerequisite for participationDSP, general interest in signal processing





6 Usability of this moduleMSc ETiT,MSc iST, MSc iCE, Wi-ETiT


8 References


• Lecture slides• Jerry D. Gibson and James L. Melsa. Introduction to Nonparametric Detection with Applications.

IEEE Press, 1996.• S. Kassam. Signal Detection in Non-Gaussian Noise. Springer Verlag, 1988.• S. Kay. Fundamentals of Statistical Signal Processing: Estimation Theory. Prentice Hall,

1993.• S. Kay. Fundamentals of Statistical Signal Processing: Detection Theory. Prentice Hall, 1998.• E. L. Lehmann. Testing Statistical Hypotheses. Springer Verlag, 2nd edition, 1997.• E. L. Lehmann and George Casella. Theory of Point Estimation. Springer Verlag, 2nd edition, 1999.• Leon-Garcia. Probability and Random Processes for Electrical Engineering. Addison Wesley, 2nd

edition, 1994.• P. Peebles. Probability, Random Variables, and Random Signal Principles. McGraw-Hill, 3rd edition,

1993.• H. Vincent Poor. An Introduction to Signal Detection and Estimation. Springer Verlag, 2nd edition,

1994.• Louis L. Scharf. Statistical Signal Processing: Detection, Estimation, and Time Series Analysis. Pear-

son Education POD, 2002.• Harry L. Van Trees. Detection, Estimation, and Modulation Theory, volume I,II,III,IV. John Wiley &

Sons, 2003.• A. M. Zoubir and D. R. Iskander. Bootstrap Techniques for Signal Processing. Cambridge University

Press, May 2004.

Courses

Course Nr. Course name18-zo-2050-se Signal Detection and Parameter Estimation

Instructor Type SWSProf. Dr.-Ing. Abdelhak Zoubir Seminar 4


Module nameTechnology of Microsystems Technology

Module Nr. Credit Points Workload Self study Duration Cycle offered18-bu-2020 4 CP 120 h 75 h 1 SoSe


1 ContentProvide insights into the various production and processing methods in micro- and precision engineeringand the influence of these methods on the development of devices and components.

2 Learning objectives / Learning OutcomesTo describe coating processes like powder coating, electrochemical and vacuum deposition and CVD. Toexplain manufacturing of glass conponents: glass production, optical components, glass fibres, glass ce-ramics. To describe microfabrication technologies: photolithography, etching, diffusion, silicon microma-chining, LIGA. To report manufacturing of electronic assemblies/modules and surface mount technologies(SMT).

3 Recommended prerequisite for participationTechnology of Micro and Precision Engineering (recommended)


• Module Examination (Technical Examination, Optional, Duration: 30 min, Standard Grading Sys-tem)





8 ReferencesScript for lecture: Technology of Microsystem Technology

Courses

Course Nr. Course name18-bu-2020-vl Technology of Microsystems Technology


Course Nr. Course name18-bu-2020-ue Technology of Microsystems Technology



Module nameSensor Signal Processing

Module Nr. Credit Points Workload Self study Duration Cycle offered18-kn-2130 3 CP 90 h 60 h 1 SoSe


1 ContentThe module provides knowledge in-depth about the measuring and processing of sensor signals. In thearea of primary electronics, some particular characteristics such as errors, noise and intrinsic compensationof bridges and amplifier circuits (carrier frequency amplifiers, chopper amplifiers, Low-drift amplifiers)in terms of error and energy aspects are discussed. Within the scope of the secondary electronic, theclassical and optimal filter circuits, modern AD conversion principles and the issues of redundancy anderror compensation will be discussed.

2 Learning objectives / Learning OutcomesThe Students acquire advanced knowledge on the structure of modern sensors and sensor proximity signalprocessing. They are able to select appropriate basic structure of modern primary and secondary electronicsand to consider the error characteristics and other application requirements.

3 Recommended prerequisite for participationMeasuring Technique, Sensor Technique, Electronic, Digital Signal Processing







8 References• Slide set of lecture• Skript of lecture• Textbook Tränkler „Sensortechnik“, Springer• Textbook Tietze/Schenk „Halbleiterschaltungstechnik“, Springer

Courses

Course Nr. Course name18-kn-2130-vl Sensor Signal Processing



Module nameMeasuring Technique



1 ContentThe module includes theoretical discussion and practical application of the measuring chain in detail onexample the electrical variables (current, voltage, impedance, power) and selected non-electrical variables(frequency, time, force, pressure and acceleration).In the lecture the following chapter will be thematically treated measuring signals and measuring equip-ment (oscilloscope, laboratory testing equipment), static measurement error and disturbance variables(especially temperature), basic measurement circuits, AD conversion principles and filtering, measurementmethod non-electrical variables and the statistics of measurements (distributions, statist safe tests).The topics of the lecture are discussed in the exercise of the module. Examples are analyzed and theirapplication in measurement scenarios are practiced.The practicum of the module consists of five experiments which are time closely matched in time to thelecture:

• Measuring of signals in the time range with digital storage oscilloscope, trigger conditions• Measuring of signals in the frequency range with digital storage oscilloscope, error of measurement

(aliasing / subsampling, leackage) and window functions• Measuring of mechanical dimensions with suitable primary sensors, sensor electronics / amplifier

circuits• computer-based measuring• Importing of sensor signals, whose processing and the resulting automated control of a process

using a programmable logic controller (PLC)

2 Learning objectives / Learning OutcomesThe students know the structure of the measuring chain and the specific properties of the correspondingelements. They know the structure of electronic measuring instruments and basic measuring circuits forelectrical and selected non-electrical variables and can apply them. They know the basics of capturing,processing, transferring and storage of measurement data and can describe error sources and quantifyingtheir influences.In the practicum, the students deepen the basis of the measurements with the oscilloscope, the understand-ing of the relationship between time and frequency range. Methodically they are able to document andevaluate the data during laboratory measuring.

3 Recommended prerequisite for participationBasics of ETiT I-III, Math I-III, Electronic



Module Eccompanying Examination:• [18-kn-1011-pr] (Study Achievement, Optional, Standard BWS)



• [18-kn-1011-pr] (Study Achievement, Optional, Weighting: 2)

6 Usability of this moduleBSc ETiT, BSc Wi-ETiT, BSc MEC



8 References• Slide set of lecture• Textbook and exercise book Lerch: „Elektrische Messtechnik“, Springer• Exercise documents• Practical experiment manuals

Courses

Course Nr. Course name18-kn-1011-vl Measuring Technique


Course Nr. Course name18-kn-1011-pr Measuring Technique Lab


Course Nr. Course name18-kn-1011-ue Measuring Technique



Module nameBioinformatics



1 Content




• Module Examination (Technical Examination, Oral Examination, Standard Grading System)





8 References

Courses

Course Nr. Course name10-01-0036-vl Bio Informatics-Lecture


Course Nr. Course name10-01-0036-se Bio Informatics-Exercise



Module nameAnalysis and Synthesis of Human Movements I


Language Module ownerGerman and English Prof. Dr. phil. André Seyfarth

1 Content




• [03-41-0580-se] (Study Achievement, Optional, Standard BWS)


• [03-41-0580-se] (Study Achievement, Optional, Weighting: 1)



8 References

Courses




Module nameDeep Learning for Medical Imaging


Language Module ownerEnglish Prof. Dr.-Ing. Michael Gösele

1 ContentFormulating Medical Image Segmentation, Computer Aided Diagnosis and Surgical Planning as MachineLearning Problems, Deep Learning for Medical Image Segmentation, Deep Learning for Computer AidedDiagnosis, Surgical Planning from pre-surgical images using Deep Learning, Tool presence detection andlocalization from endoscopic videos using Deep learning, Adversarial Examples for Medical Imaging, Gen-erative Adversarial Networks for Medical Imaging.

2 Learning objectives / Learning OutcomesAfter successful completion of the course, students should be able to understand all components of formu-lating a Medical Image Analysis problem as a Machine Learning problem. They should also be able to makeinformed decision of choosing a general purpose deep learning paradigm for given medical image analysisproblem.

3 Recommended prerequisite for participation- Programming skills- Understanding of Algorithmic design- Linear Algebra- Image Processing / Computer Vision I- Statistical Machine Learning







8 References

Courses

Course Nr. Course name20-00-1014-iv Deep Learning for Medical Imaging

Instructor Type SWSProf. Dr.-Ing. Michael Gösele Integrated

Course3


3.5 Optional Subjects AIS-CSR: Control Systems and Robotics

Module nameDigital Control Systems I



1 ContentTheoretical fundamentals of sampled control systems:Discrete-time functions, sample/hold element, z-transform, convolution sum, z-transfer function, stabilityof sampled systems, design of digital controllers, discrete PI-, PD-, and PID-controllers, compensation anddead-beat controller, anti-windup methods

2 Learning objectives / Learning OutcomesThe students know the fundamental analysis and design methods for digital feed-forward and feed-backcontrol systems. They know the fundamental differences between continuous-time and discrete-time con-trol systems and can design and analyze discrete-time control systems using different methods.

3 Recommended prerequisite for participationHelpful is knowledge of the Laplace- and Fourier-transforms as well as continuous-time control systems.These fundamentals are taught in the lecture “System Dynamics and Control Systems I”





6 Usability of this moduleBSc/MSc Wi-ETiT, MSc ETiT, BSc/MSc CE, MSc MEC, BSc/MSc iST, MSc iCE, MSc Informatik


8 ReferencesLecture notes Konigorski: “Digitale Regelungssysteme”Ackermann: "Abtastregelung"Aström, Wittenmark: "Computer-controlled Systems"Föllinger: "Lineare Abtastsysteme"Phillips, Nagle: "Digital control systems analysis and design"Unbehauen: "Regelungstechnik 2: Zustandsregelungen, digitale und nichtlineare Regelsysteme"

Courses

Course Nr. Course name18-ko-2020-vl Digital Control Systems I


Course Nr. Course name18-ko-2020-ue Digital Control Systems I

Instructor Type SWSProf. Dr.-Ing. Ulrich Konigorski Practice 1

3.5 Optional Subjects AIS-CSR: Control Systems and Robotics 306

Module nameFoundations of Robotics


Language Module ownerGerman Prof. Dr. rer. nat. Oskar Stryk

1 Content- spatial representation and transformations- kinematics of robot manipulators- kinematics of mobile robots- velocity kinematics and manipulator Jacobian- robot motion dynamics- robot actuators, internal and external sensors- basic robot controls- path planning- localization and navigation of mobile robots- case studies- theoretical and practical assignments as well as programming tasks for deepening of knowledge andmethodological skills

2 Learning objectives / Learning OutcomesThrough successful participation students acquire the basic technical knowledge and fundamental method-ological skills in modeling, kinematics, dynamics, control, path planning and navigation of robots neededfor fundamental investigations and engineering developments in robotics.

3 Recommended prerequisite for participationbasic mathematical knowledge and skills in linear algebra, multi-variable analysis and fundamentals ofordinary differential equations







8 ReferencesScript and films of lecture.

Courses


Course Nr. Course name20-00-0735-iv Foundations of Robotics

Instructor Type SWSProf. Dr. rer. nat. Oskar Stryk Integrated

Course6


Module nameModeling and Simulation



1 Contentaim of modeling, theoretical modeling by application of fundamental physical laws, generalized networkanalysis, modeling of distributed parameter systems, model reduction, linearization, order reduction, digi-tal simulation of linear systems, numerical integration methods

2 Learning objectives / Learning OutcomesThe students will know different techniques for the mathematical modeling of dynamic systems from var-ious domains. They will acquire the ability to digitally simulate the dynamic behavior of the modeledsystems and to systematically apply the available numerical integration methods.

3 Recommended prerequisite for participationBasic knowledge of continuous- and discrete-time control theory. Supplementary lectures are “SystemDynamics and Control Systems I and II” as well as “Digital Control Systems I and II”.





6 Usability of this moduleMSc ETiT, MSc MEC


8 ReferencesLecture notes Konigorski: “Modellbildung und Simulation”,Lunze: „Regelungstechnik 1 und 2“,Föllinger: „Regelungstechnik: Einführung in die Methoden und ihre Anwendung“

Courses

Course Nr. Course name18-ko-2010-vl Modeling and Simulation


Course Nr. Course name18-ko-2010-ue Modeling and Simulation



Module nameSystem Dynamics and Automatic Control Systems II



1 ContentMain topics covered are:

• Root locus method (construction and application),• State space representation of linear systems (representation, time solution, controllability, observ-

ability, observer- based controller design)

2 Learning objectives / Learning OutcomesAfter attending the lecture, a student is capable of: 1. constructing and evaluating the root locus ofgiven systems, 2. describing the concept and importance of the state space for linear systems, 3. definingcontrollability and observability for linear systems and being able to test given systems with respect to theseproperties, 4. stating controller design methods using the state space, and applying them to given systems,and 5. applying the method of linearization to non-linear systems with respect to a given operating point






6 Usability of this moduleBSc ETiT, MSc MEC, MSc iST, MSc WI-ETiT, MSc iCE, MSc EPE, MSc CE, MSc Informatik


8 ReferencesAdamy: Systemdynamik und Regelungstechnik II, Shaker Verlag (available for purchase at the FG office)http://www.rtr.tu-darmstadt.de/lehre/e-learning (optionales Material)

Courses

Course Nr. Course name18-ad-1010-vl System Dynamics and Automatic Control Systems II


Course Nr. Course name18-ad-1010-ue System Dynamics and Automatic Control Systems II



http://www.rtr.tu-darmstadt.de/lehre/e-learning

Module nameSystem Dynamics and Automatic Control Systems III



1 ContentTopics covered are:

• basic properties of non-linear systems,• limit cycles and stability criteria,• non-linear control of linear systems,• non-linear control of non-linear systems,• observer design for non-linear systems


• explaining the fundamental differences between linear and non-linear systems,• testing non-linear systems for limit cycles,• stating different definitions of stability and testing the stability of equilibria,• recalling the pros and cons of non-linear controllers for linear systems,• recalling and applying different techniques for controller design for non-linear systems,• designing observers for non-linear systems






6 Usability of this moduleMSc ETiT, MSc MEC, MSc iST, MSc WI-ETiT, MSc iCE, MSc EPE, MSc CE, MSc Informatik


8 ReferencesAdamy: Systemdynamik und Regelungstechnik III (available for purchase at the FG office)

Courses

Course Nr. Course name18-ad-2010-vl System Dynamics and Automatic Control Systems III


Course Nr. Course name18-ad-2010-ue System Dynamics and Automatic Control Systems III



Module nameIntegrated Robotics Project 1



1 Content- guided independent work on a concrete task from development and application of modern robotic systemsand, as far as possible, as member of a team of developers- becoming acquainted with the relevant state of research and technology- development of a solution approach and its implementation- application and evaluation based on robot experiments or simulations- documentation of task, approach, implementation and results in a final report and conduction of a finalpresentation

2 Learning objectives / Learning OutcomesThrough successful participation students acquire deepened knowledge in selected areas, subsystems andmethods of modern robotic systems as well as in-depth skills for development, implementation, and exper-imental evaluation. They train presentation skills and, as far as possible, team work.

3 Recommended prerequisite for participationbasic knowledge within Robotics as given in lecture “Grundlagen der Robotik”- programming skills depending on task








Courses

Course Nr. Course name20-00-0324-pr Integrated Robotics Project (Part 1)



Module nameIntegrated Robotics Project 2



1 Content- guided independent work on a concrete task from development and application of modern robotic systemsand, as far as possible, as member of a team of developers- becoming acquainted with the relevant state of research and technology- development of a solution approach and its implementation- application and evaluation based on robot experiments or simulations- documentation of task, approach, implementation and results in a final report and conduction of a finalpresentation

2 Learning objectives / Learning OutcomesThrough successful participation students acquire deepened knowledge in selected areas, subsystems andmethods of modern robotic systems as well as in-depth skills for development, implementation, and exper-imental evaluation. They train presentation skills and, as far as possible, team work.

3 Recommended prerequisite for participationbasic knowledge within Robotics as given in lecture “Grundlagen der Robotik”- programming skills depending on task- Participation in “Integriertes Robotik-Project 1”







8 ReferencesWill be given in course.

Courses

Course Nr. Course name20-00-0357-pr Integrated Project (Part 2)



Module nameRobot Learning: Integrated Project - Part 1


Language Module ownerEnglish Prof. Dr. rer. nat. Oskar Stryk

1 ContentIn “Robot Learning: Integrated Project, Part 1”, students will pose a current research problem in the domainof robot learning with assistance of their advisor. The students will select a robot learning topic to fit theirresearch interests, on which they will pursue in-depth literature studies. Using these results, they willdevelop a plan for their project, try out the algorithms of interest and implement a prototype in simulation.

2 Learning objectives / Learning OutcomesUpon successful completion of this course, students will be able to independently develop small researchprojects in the domain of robot learning and test first research ideas in simulation.

3 Recommended prerequisite for participationPrevious or concurrent participation in the lecture “Robot Learning”.


• [20-00-0753-pj] (Study Achievement, Written/Oral Examination, Standard BWS)


• [20-00-0753-pj] (Study Achievement, Written/Oral Examination, Weighting: 100 %)



8 References

Courses

Course Nr. Course name20-00-0753-pj Robot Learning: Integrated Project - Part 1

Instructor Type SWSProf. Dr. rer. nat. Oskar Stryk Project 4


Module nameRobot Learning: Integrated Project - Part 2


Language Module ownerEnglish Prof. Dr. rer. nat. Oskar Stryk

1 ContentIn “Robot Learning: Integrated Project, Part 2”, students will complete their approach to the researchproblem from Part 1 and apply it to a real robot. A scientific article on the research problem, methods andresults will be written and potentially submitted to a national or international scientific venue.

2 Learning objectives / Learning OutcomesUpon successful completion of this course, students will be able to independently develop small researchprojects in the domain of robot learning and test first research ideas in simulation.

3 Recommended prerequisite for participationPrevious or concurrent participation in the lecture “Robot Learning”.


• [20-00-0754-pj] (Study Achievement, Written/Oral Examination, Standard BWS)


• [20-00-0754-pj] (Study Achievement, Written/Oral Examination, Weighting: 100 %)



8 References

Courses

Course Nr. Course name20-00-0754-pj Robot Learning: Integrated Project - Part 2

Instructor Type SWSProf. Dr. rer. nat. Oskar Stryk Project 4


Module nameLaboratory Control Engineering II



1 ContentDuring the laboratory course the following experiments will be conducted: Coupling control of a helicopter,Non-linear control of a gyroscope, Nonlinear multivariable control of an aircraft, Servo control systems,Control of an overhead crane system, Programmable logic control of a stirring process

2 Learning objectives / Learning OutcomesAfter attending this laboratory course, a student is capable of:

• recalling the basics of the conducted experiments,• organize and comprehend background information for experiments,• assemble experimental set-ups based on manuals,• judge the relevance of experimental results by comparing them with theoretically predicted out-

comes,• present the results of the experiments

3 Recommended prerequisite for participationSystem Dynamics and Control Systems II, the attendance of the additional lecture “System Dynamics andControl Systems III” is recommended





6 Usability of this moduleMSc ETiT, MSc MEC, MSc iST, MSc Wi-ETiT, Biotechnik


8 ReferencesAdamy: Instruction manuals for the experiments (available during the kick-off meeting)

Courses

Course Nr. Course name18-ad-2060-pr Laboratory Control Engineering II

Instructor Type SWSProf. Dr.-Ing. Jürgen Adamy Internship 4


Module nameLaboratory Matlab/Simulink II



1 ContentThe lab is split into the two parts Simulink and Control Engineering II. First the fundamentals of the simu-lation tool Simulink are introduced and their application to problems from different fields of application istrained. In the second part, the knowledge gained in the first part is applied to autonomously solve severalcontrol design problems as well as simulation tasks.

2 Learning objectives / Learning OutcomesThe students will be able to work with the tool MatLab/Simulink on their own and can solve tasks fromthe areas of control engineering and numericial simulation. The students will know the different designmethods of the control system toolbox and the fundamental concepts of the simulation tool Simulink. Theycan practically apply the knowledge gathered in the lectures “System Dynamics and Control Systems I andII” and “Modelling and Simulation”.

3 Recommended prerequisite for participationThe lab should be attended in parallel or after the lectures “System Dynamics and Control Systems II” and“Modelling and Simulation”





6 Usability of this moduleMSc ETiT, MSC MEC


8 ReferencesLecture notes for the lab tutorial can be obtained at the secretariat

Courses

Course Nr. Course name18-ko-2070-pr Laboratory Matlab/Simulink II



Module nameProject Course Control Engineering



1 ContentTeams of 2 - 4 students work on different control engineering projects under the guidance of a projectcoordinator from the institute. The projects mainly cover the following subject areas:

• Modelling, analysis and design of multivariable control systems• Modelling, analysis and design of distributed parameter systems• Robust control design• System analysis, supervision and fault diagnosis• Modelling and identification

Application areas are machine tools, production lines, test benches, process control, automobiles.

2 Learning objectives / Learning OutcomesAfter completing the project the students will be familiar with the individual steps of investigating a controlengineering project. This includes in particular the compilation of a system specification as well as criticaldiscussions and systematic selection of appropriate control engineering solutions and their real technicalimplementation. Doing so the students learn the practical application of control engineering methodstaught in the lecture “System Dynamics and Control Systems I” to real world problems. Additionally, inthis project course the students are supposed to improve their professional skills. These skills include e.g.teamwork, presentation techniques and systematic information retrieval.

3 Recommended prerequisite for participationLecture “System Dynamics and Control Systems I”







8 ReferencesHandouts will be distributed at start of the project (e.g. Hints for writing a project documentation, etc.)

Courses

Course Nr. Course name18-ko-2090-pj Project Course Control Engineering

Instructor Type SWSProf. Dr.-Ing. Ulrich Konigorski Project Seminar 4


Module nameProject Seminar Robotics and Computational Intelligence



1 ContentThe following topics are taught in the lecture: 1. Industrial robots, 1a. Types and applications, 1b. Ge-ometry and kinematics, 1c. Dynamic model, 1d. Control of industrial robots, 2. Mobile robots, 2a. Typesand applications, 2b. Sensors, 2c. Environmental maps and map building, 2d. Trajectory planning. Groupprojects are arranged after the lectures in order to apply the taught material in practical exercises.

2 Learning objectives / Learning OutcomesAfter attending the lecture, a student is capable of: 1. recalling the basis elements of industrial robots, 2.recalling the dynamic equations of industrial robots and be able to apply them to describe the dynamics ofa given robot, 3. stating model problems and solutions to standard problems in mobile robotics, 4. planinga small project, 5. organizing the work load in a project team, 6. searching for additional backgroundinformation on a given project, 7. creating ideas on how to solve problems arising in the project, 8. writingan scientific report about the outcome of the project 8. presenting the results of the project.








8 ReferencesAdamy: Lecture notes (available for purchase at the FG office)

Courses

Course Nr. Course name18-ad-2070-pj Project Seminar Robotics and Computational Intelligence

Instructor Type SWSProf. Dr.-Ing. Jürgen Adamy Project Seminar 4


Module nameRobust Control



1 Content• Basics (SVD, norms, system representations)• Control design in the frequency domain

– Expressing control tasks as H2 and Hinf optimization problems– Design of H2 and Hinf optimal controllers

• Robust Control– Uncertainity representations (Additive und multiplicative uncertainities, multi model represen-

tations)– Analysis of robustness (Small-Gain-theorem, mu-analysis)– Robust control design in the frequency domainRobust control design by region-based pole

placement

2 Learning objectives / Learning OutcomesThe students are able to express control tasks as H2 and H8 optimization problems, to represent uncer-tainities of a system in a suitable form and to design a controller which ensures robust stability and robustperformance.

3 Recommended prerequisite for participationSystemdynamik und Regelungstechnik I und II







8 References• S. Skogestad, I. Postlethwaite, Multivariable Feedback Control,2. Auflage, 2005, Wiley• K. Zhou, Essentials of Robust Control, 1998, Prentice-Hall• O. Föllinger, Regelungstechnik, 11. Auflage, 2013, VDE Verlag

Courses

Course Nr. Course name18-ko-2140-vl Robust Control

Instructor Type SWSDr. Ing. Eric Lenz Lecture 2


Module nameComputational Engineering and Robotics



1 Content- Foundations of modelling and simulation- Problem specification and system description for computational engineering- Model generation for the example of mechanical systems- Model analysis for the example of mechanical systems- Implementations of simulations for the example of robots and other systems- Interpretation and validation using measurement data- Applications in simulation and control of robots as well as in physically based animation and computergames

2 Learning objectives / Learning OutcomesUpon successful completion of this class, students will be able to develop first models and simulations andcan perform first simulation studies within robotics. They know the necessary key steps needed to constructsimulations (problem specification, model generation, model analysis, implementation, and validation) andcan use them to construct first simulations to meet the specification requirements.






6 Usability of this moduleB.Sc. InformatikB.Sc. WirtschaftsinformatikB.Sc. Computational EngineeringB.Sc. Psychologie in ITJoint B.A. InformatikB.Sc. Sportwissenschaft und InformatikM.Sc. Sportwissenschaft und InformatikB.Sc. InformationssystemtechnikMay be used in other degree programs.


8 ReferencesF. Föllinger: Einführung in die Zustandsbeschreibung dynamischer Systeme (Oldenbourg, 1982)P. Corke: Robotics, Vision & Control, Springer, 2011F.L. Severance: System Modeling and Simulation: An Introduction, J. Wiley & Sons, 2001

Courses


Course Nr. Course name20-00-0011-iv Computational Engineering and Robotics


3


Module namePractical Training with Drives

Module Nr. Credit Points Workload Self study Duration Cycle offered18-bi-2100 4 CP 120 h 75 h 1 WiSe/SoSe


1 ContentThe purpose of this laboratory is gaining extented knowledge about realization and behaviour of drive sys-tems. An introduction in measurement problems concerning drives is given. The contents of the laboratoryis setting drives to work and investigating drive systems under laboratory conditions. Special attention ispaid to inverter-fed AC drives. The laboratory experiments are individually coordinated with the previousknowledge of the respective courses (ETiT or MEC).

2 Learning objectives / Learning OutcomesThe students get the ability of measurement for electrical motors, generators and transformers.

3 Recommended prerequisite for participationBachelor of Science in Electrical Engineering, Power Engineering or similar







8 ReferencesTextbook with lab instructions;Nürnberg, W.: Die Prüfung elektrischer Maschinen, Springer, 2000;Leonhard, W.: Control of electric drives, Springer, 2000;Textbook – Binder, A.: Motor Developement for Electrical Drive Systems; Lecture notes – Mutschler, P.:Control of Drives

Courses

Course Nr. Course name18-bi-2100-pr Practical Training with Drives





Module nameAcceleration of Charged Particles in Electromagnetic Fields

Module Nr. Credit Points Workload Self study Duration Cycle offered18-kb-2010 5 CP 150 h 90 h 1 SoSe

Language Module ownerGerman and English Prof. Dr.-Ing. Harald Klingbeil

1 ContentBasics of Tensor Analysis, Basics of Special Relativity, Covariant Form of Maxwell‘s Equations, Basics ofNonlinear Dynamics, Hamilton Formalism, Phase Space, Basics of Longitudinal Beam Dynamics, Liou-ville‘s Theorem, Beam Equations, Particle Tracking, Accelerating Cavities and Systems, Longitudinal BeamManipulations.

2 Learning objectives / Learning OutcomesThe lecture shows how different theories like electrodynamics, special relativity and nonlinear dynamicsmerge together in accelerator engineering for the motion of charged particles in electromagnetic fields. Thestudent will get a good understanding of these theories, and he will be able to understand more advancedliterature in the area of accelerator engineering and accelerator physics.

3 Recommended prerequisite for participationVector analysis, infinitesimal calculus, basics in differential equations, first contact with Maxwell‘s equa-tions.







8 ReferencesLecture slides. List of textbooks.

Courses

Course Nr. Course name18-kb-2010-vl Acceleration of Charged Particles in Electromagnetic Fields

Instructor Type SWSProf. Dr.-Ing. Harald Klingbeil Lecture 2

Course Nr. Course name18-kb-2010-ue Acceleration of Charged Particles in Electromagnetic Fields

Instructor Type SWSProf. Dr.-Ing. Harald Klingbeil Practice 2


Module nameControl of Drives

Module Nr. Credit Points Workload Self study Duration Cycle offered18-gt-2020 5 CP 150 h 90 h 1 SoSe

Language Module ownerEnglish Prof. Dr.-Ing. Gerd Griepentrog

1 ContentControl structures for drives; Design of controllers for drives; VSIs for drives; Space Vectors as basis ofmodelling AC-machines; Reference frames for description of AC-machines; Control oriented block diagramfor DC-drive; Structure and design of the controllers;Control oriented block diagram for Permanent Magnet Synchronous Machine (PMSM); Control orientedblock diagram for Induction machine (IM)Torque control for AC-machines using linear or switching controllers. Field Oriented Control and DirectTorque Control for PMSM and IM. Models and observers for rotor flux of IMSpeed control, including oscillatory load. Resolver and Encoder.

2 Learning objectives / Learning OutcomesAfter an active participation in the course including solving all exercises prior to the respective tutorialstudents should be able to:1.) develop the control-oriented block diagrams for the DC-machine operating in base speed range as wellas in field weakening range.2.) design the control loops for 1.) concerning the structure and the control parameters.3.) Understand and apply space vectors and master their application in different rotating frames of refer-ence.4.) Develop the dynamic equations of the permanent exited synchronous machine and the induction ma-chine and to simplify these equations by help of suitable rotating reference frames and represent theseequations as non-linear control-oriented block diagram.5.) Design the control loops according to 4.) especially the field-oriented control concerning the structureof the control loops and the control parameters.6.) Understand the deduction of equations given in the literature for machine types, which are not dis-cussed in this lecture, e.g. for the doubly fed induction machine.7.) Derive the models and the observers for the rotor flux for the induction machine in different frames ofreference and to apprise the benefits and drawbacks of the different solutions.8.) Design the control loops for the super-imposed speed controls even for mechanically oscillating loads.

3 Recommended prerequisite for participationBSc ETiT or equivalent, especially Control Theory and Electrical Machines / Drives





6 Usability of this moduleMSc ETiT, MSc EPE, MSc MEC, Wi-ETiT


8 References


Lecture notes, instructions for exercises are available in Moodle for download.Literature:

• Mohan, Ned: “Electric Drives and Machines”• De Doncker, Rik; et. al.: “Advanced Electrical Drives”• Schröder, Dierk: “Elektrische Antriebe – Regelung von Antriebssystemen”• Leonhard, W.: “Control of Electrical Drives”

Courses

Course Nr. Course name18-gt-2020-vl Control of Drives


Course Nr. Course name18-gt-2020-ue Control of Drives



Module nameDigital Control Systems II



1 ContentState space description of discrete-time systems, controllability, observability, state feedback controller, poleassignment, PI-state feedback controller, discrete state observers, modified Luenberger observer

2 Learning objectives / Learning OutcomesThe students know the state spacel description of sampled control systems and the corresponding analysisand design methods. They can design deadbeat controllers, state feedback controllers by pole assignmentand PI- state feedback controllers for single input systems and know how to implement state feedbackcontrollers together with a discrete- time observer.

3 Recommended prerequisite for participationKnowledge of the z-transform as well as the fundamentals of discrete-time control systems. These funda-mentals are taught in the lecture “Digital Control systems I”.





6 Usability of this moduleMSc ETiT, MSc Wi-ETiT, BSc/MSc iST, MSc MEC, MSc iCE


8 ReferencesLecture notes Konigorski: “Digitale Regelungssysteme”Ackermann: "Abtastregelung"Aström, Wittenmark: "Computer-controlled Systems"Föllinger: "Lineare Abtastsysteme"Phillips, Nagle: "Digital control systems analysis and design"Unbehauen: "Regelungstechnik 2: Zustandsregelungen, digitale und nichtlineare Regelsysteme"

Courses

Course Nr. Course name18-ko-2030-vl Digital Control Systems II


Course Nr. Course name18-ko-2030-ue Digital Control Systems II



Module nameTechnical Mechanics for Electrical Engineering


Language Module ownerGerman Prof. Dr.-Ing. Tobias Melz

1 ContentStatics: force, moment (torque), free body diagram, equilibrium equations, center of gravity, truss, beams,adhesion and friction.Mechanics of elastic bodies: stress and deformation, tension, torsion, bending.Kinematics: point and rigid body movement.Kinetics: dynamic force and moment equilibrium equations, energy and work, linear oscillators, momen-tum and angular momentum conservation laws, impact.

2 Learning objectives / Learning OutcomesIn this course the students will learn the basic concepts of technical mechanics. They should be able toanalyze the statics of simple statically determinate planar systems, to carry out elementary elastomechan-ical calculations of statically determinate and statically indeterminate structures, to describe and analyzemovements, and to solve planar motion problems, oscillation and shock phenomena with the laws ofkinetics.



• Module Examination (Technical Examination, Written Examination, Standard Grading System)





8 ReferencesMarkert, Norrick: Einführung in die Technische Mechanik, ISBN 978-3-8440-3228-4Exercises are embodied in the book.Further reading:Markert: Statik – Aufgaben, Übungs- und Prüfungsaufgaben mit Lösungen, ISBN 978-3-8440-3279-6Markert: Elastomechanik – Aufgaben, Übungs- und Prüfungsaufgaben mit Lösungen, ISBN 978-3-8440-3280-2Markert: Dynamik – Aufgaben, Übungs- und Prüfungsaufgaben mit Lösungen, ISBN 978-3-8440-2200-1Gross, Hauger, Schröder, Wall: Technische Mechanik 1 - 3. Springer-Verlag Berlin (2012-2014).Hagedorn: Technische Mechanik, Band 1 - 3. Verlag Harri Deutsch Frankfurt.

Courses

Course Nr. Course name16-26-6400-vl Technical Mechanics for Electrical Engineering



Course Nr. Course name16-26-6400-ue Technical Mechanics for Electrical Engineering



Module nameSystem Dynamics and Automatic Control Systems I



1 ContentDescription and classification of dynamic systems; Linearization around an equilibrium point; Stabilityof dynamic systems; Frequency response; Linear time-invariant closed-loop systems; Controller design;Control structure optimization

2 Learning objectives / Learning OutcomesStudents will know how to describe and classify different dynamic systems. They will be able to analysethe dynamic behaviour in time and frequency domain. The students will be able to design controllers forlinear time invariant systems.






6 Usability of this moduleBSc ETiT, BSc MEC, MSc Informatik


8 ReferencesSkript Konigorski: “Systemdynamik und Regelungstechnik I”, Aufgabensammlung zur Vorlesung, Lunze:"Regelungstechnik 1: Systemtheoretische Grundlagen, Analyse und Entwurf einschleifiger Regelungen",Föllinger: "Regelungstechnik: Einführung in die Methoden und ihre Anwendungen",Unbehauen: "Regelungstechnik I:Klassische Verfahren zur Analyse und Synthese linearer kontinuierlicherRegelsysteme, Fuzzy-Regelsysteme", Föllinger: "Laplace-, Fourier- und z-Transformation",Jörgl: "Repetitorium Regelungstechnik",Merz, Jaschke: "Grundkurs der Regelungstechnik: Einführung in die praktischen und theoretischen Meth-oden",Horn, Dourdoumas: "Rechnergestützter Entwurf zeitkontinuierlicher und zeitdiskreter Regelkreise",Schneider: "Regelungstechnik für Maschinenbauer",Weinmann: "Regelungen. Analyse und technischer Entwurf: Band 1: Systemtechnik linearer und lin-earisierter Regelungen auf anwendungsnaher Grundlage"

Courses

Course Nr. Course name18-ko-1010-vl System Dynamics and Automatic Control Systems I


Course Nr. Course name18-ko-1010-tt System Dynamics and Automatic Control Systems I- Auditorium Exercise

Instructor Type SWSProf. Dr.-Ing. Ulrich Konigorski Tutorial 1


Module nameMeasuring Technique



1 ContentThe module includes theoretical discussion and practical application of the measuring chain in detail onexample the electrical variables (current, voltage, impedance, power) and selected non-electrical variables(frequency, time, force, pressure and acceleration).In the lecture the following chapter will be thematically treated measuring signals and measuring equip-ment (oscilloscope, laboratory testing equipment), static measurement error and disturbance variables(especially temperature), basic measurement circuits, AD conversion principles and filtering, measurementmethod non-electrical variables and the statistics of measurements (distributions, statist safe tests).The topics of the lecture are discussed in the exercise of the module. Examples are analyzed and theirapplication in measurement scenarios are practiced.The practicum of the module consists of five experiments which are time closely matched in time to thelecture:

• Measuring of signals in the time range with digital storage oscilloscope, trigger conditions• Measuring of signals in the frequency range with digital storage oscilloscope, error of measurement

(aliasing / subsampling, leackage) and window functions• Measuring of mechanical dimensions with suitable primary sensors, sensor electronics / amplifier

circuits• computer-based measuring• Importing of sensor signals, whose processing and the resulting automated control of a process

using a programmable logic controller (PLC)

2 Learning objectives / Learning OutcomesThe students know the structure of the measuring chain and the specific properties of the correspondingelements. They know the structure of electronic measuring instruments and basic measuring circuits forelectrical and selected non-electrical variables and can apply them. They know the basics of capturing,processing, transferring and storage of measurement data and can describe error sources and quantifyingtheir influences.In the practicum, the students deepen the basis of the measurements with the oscilloscope, the understand-ing of the relationship between time and frequency range. Methodically they are able to document andevaluate the data during laboratory measuring.

3 Recommended prerequisite for participationBasics of ETiT I-III, Math I-III, Electronic



Module Eccompanying Examination:• [18-kn-1011-pr] (Study Achievement, Optional, Standard BWS)



• [18-kn-1011-pr] (Study Achievement, Optional, Weighting: 2)

6 Usability of this moduleBSc ETiT, BSc Wi-ETiT, BSc MEC



8 References• Slide set of lecture• Textbook and exercise book Lerch: „Elektrische Messtechnik“, Springer• Exercise documents• Practical experiment manuals

Courses

Course Nr. Course name18-kn-1011-vl Measuring Technique


Course Nr. Course name18-kn-1011-pr Measuring Technique Lab


Course Nr. Course name18-kn-1011-ue Measuring Technique



Module nameElectromechanical Systems I



1 ContentStructure and design methods of elektromechanical systems, mechanical, acoustical and thermal networks,transducers between mechanical and acoustical networks. Design and devices of electromechanical trans-ducers.

2 Learning objectives / Learning OutcomesComprehension, description, calculation and application of the most relevant electromechanical transduc-ers, comprising electrostatic transducer (e.g. microphone and accelerometer), piezoelectric transducers(e.g micro motors, micro sensors), electrodynamic transducer (loudspeaker, shaker), piezomagnetic trans-ducer (e.g. ultrasonic source). Design of complex electromechanical systems like sensors and actuatorsand their applications by applying the discrete element network method.

3 Recommended prerequisite for participationElectrical Engineering and Information Technology I





6 Usability of this moduleBSc ETiT, BSc WI-ETiT, MSc MEC


8 ReferencesBook: Electromechanical Systems in Microtechnic und Mechatronic, Springer 2012, Script for lecture Elec-tromechanical Systems I, Workbook

Courses

Course Nr. Course name18-kn-1050-vl Electromechanical Systems I


Course Nr. Course name18-kn-1050-ue Electromechanical Systems I



Module nameIntroduction to Electrodynamics

Module Nr. Credit Points Workload Self study Duration Cycle offered18-dg-1010 5 CP 150 h 90 h 1 SoSe

Language Module ownerGerman Prof. Dr.-Ing. Herbert De Gersem

1 ContentVector calculus, orthogonal coordinate systems, Maxwell’s equations, interface and boundary conditions,layered media, electrostatics, scalar potential, Coulomb integral, separation of variables, method of imagecharges, magnetostatics, vector potential, Biot-Savart law, stationary current fields, fields in matter, energyflow, skin effect, plane waves, polarization, TEM waves, reflection and multi-layer problems, multi con-ductor transmission lines (capacitance, inductance, and conductance matrix), velocity definitions, basics ofrectangular waveguides.

2 Learning objectives / Learning OutcomesStudents will be familiar with Maxwell’s equations in integral and differential form for static and dynamicfield problems. They will have a mental picture of wave phenomena in free space. They are able torecognice and interpret wave effects in the different areas of electrical engineering. They are able to derivethe wave effects from Maxwell’s equations and have a good understanding of the necessary mathematicaltools.

3 Recommended prerequisite for participationLecture notes. Further literature recommendations are given in the course.





6 Usability of this moduleBSc ETiT, BSc Wi-ETiT

7 Grade bonus compliant to §25 (2)Improvement by up to 0.4 due to bonus points which can be acquired by means of e-learning online tests.

8 ReferencesLecture notes. Further literature recommendations are given in the course.

Courses

Course Nr. Course name18-dg-1010-vl Introduction to Electrodynamics

Instructor Type SWSProf. Dr.-Ing. Herbert De Gersem Lecture 2

Course Nr. Course name18-dg-1010-ue Introduction to Electrodynamics

Instructor Type SWSProf. Dr.-Ing. Herbert De Gersem Practice 2


Module nameRailway Vehicle Engineering



1 ContentFrom the comprehensive and interdisciplinary domain of the railway technology (vehicle technology, sig-nal and safety technology, construction engineering and railway operating technology) the lecture picksout the domain of the automotive engineering with the emphasis of the mechanical part. It offers an in-terrelated introduction into selected chapters of the rail vehicle engineering with special emphasis in therailway-specific technical solutions and procedures. The lecture is divided into 7 chapters, whereby fourchapters the theoretical basic topics cover and three chapters the fundamental components of the rail ve-hicle present.In a one-day excursion, it is possible to gain insights into the production of modern rail vehicles. Participa-tion is voluntary.

2 Learning objectives / Learning OutcomesBasic understanding of mechanical parts of railways and their components.

3 Recommended prerequisite for participationBachelor in Electrical Engineering, Mechatronics or Mechanical Engineering



In general, the examination takes place in form of a written exam (duration: 90 minutes). If up to 20students register in semesters in which the lecture does not take place, there will be an oral examination(duration: 30 min.). The type of examination will be announced within one working week after the end ofthe examination registration phase.



6 Usability of this moduleMSc ETiT, MSc MEC, MSc EPE, MSc WI-ETiT


8 ReferencesReferences/Textbooks: Detailed textbook; Filipovic, Z: Elektrische Bahnen. Springer, Berlin, Heidelberg,1995. Obermayer, H.J.: Internationaler Schnellverkehr.Franckh-Kosmos, Stuttgart, 1994.

Courses

Course Nr. Course name18-bi-2050-vl Railway Vehicle Engineering



Module nameIdentification of Dynamic Systems



1 Content• Introduction into the determination of mathematical process models based on measured data• Theoretical and experimental modeling of dynamic systems• System identification using continuous time signals:

– Aperiodic signals

* Fourier analysis

* Evaluation of characteristic values (stepresponses)

– Periodic signals

* Frequency response analysis

* Correlation analysis

• System identification using discrete time signals:– Deterministic and stochastic signals– Basics in estimation theory– Correlation analysis

• Parameter estimation techniques:– Least-squares estimation– Model structure determination– Recursive estimation algorithms

• Kalman Filter and Extended Kalman Filter• Numerical Methods• Implementation under MatLab Numerous examples with real experimental data

2 Learning objectives / Learning OutcomesThe students are taught the fundamental methods in signal and system analysis. Furthermore, the studentsmaster methods such as Fourier analysis, correlation analysis and parameter estimation methods. Basedon this foundation, the students are able to assess and to apply the individual methods and can derivenon-parametric as well as parametric models from measured data.

3 Recommended prerequisite for participationMSc ETiT, MSc MEC





6 Usability of this moduleAll disciplines of Electrical Engineering and Information Technology and similar disciplines (Mechatronics,Mechanical and Process Engineering, . . . ), Master of Science


8 References


Pintelon, R.; Schoukens, J.: System Identification: A Frequency Domain Approach. IEEE Press, New York,2001.Ljung, L.: System Identification: Theory for the user. Prentice Hall information and systems sciences series.Prentice Hall PTR, Upper Saddle River NJ, 2. edition, 1999.

Courses

Course Nr. Course name18-ko-2040-vl Identification of Dynamic Systems

Instructor Type SWSDr. Ing. Eric Lenz Lecture 2

Course Nr. Course name18-ko-2040-ue Identification of Dynamic Systems

Instructor Type SWSDr. Ing. Eric Lenz Practice 1


Module nameRobot Learning



1 Content- Foundations from robotics and machine learning for robot learning- Learning of forward models- Representation of a policy, hierarchical abstraction wiith movement primitives- Imitation learning- Optimal control with learned forward models- Reinforcement learning and policy search- Inverse reinforcement learning

2 Learning objectives / Learning OutcomesUpon successful completion of this course, students are able to understand the relevant foundations ofmachine learning and robotics. They will be able to use machine learning approaches to empower robotsto learn new tasks. They will understand the foundations of optimal decision making and reinforcementlearning and can apply reinforcement learning algorithms to let a robot learn from interaction with its en-vironment. Students will understand the difference between Imitation Learning, Reinforcement Learning,Policy Search and Inverse Reinforcement Learning and can apply each of this approaches in the appropriatescenario.

3 Recommended prerequisite for participationGood programming in MatlabLecture Machine Learning 1 - Statistical Approaches is helpful but not mandatory.







8 References


Deisenroth, M. P.; Neumann, G.; Peters, J. (2013). A Survey on Policy Search for Robotics, Foundationsand Trends in RoboticsKober, J; Bagnell, D.; Peters, J. (2013). Reinforcement Learning in Robotics: A Survey, InternationalJournal of Robotics ResearchC.M. Bishop, Pattern Recognition and Machine Learning (2006),R. Sutton, A. Barto. Reinforcement Learning - an IntroductionNguyen-Tuong, D.; Peters, J. (2011). Model Learning in Robotics: a Survey

Courses

Course Nr. Course name20-00-0629-vl Robot Learning



Module nameController Design for Multivariable Systems in State Space



1 ContentPole assignment, Coupling and decoupling of linear multivarible systems, Optimal control, Design of stateobservers, Dynamic state feedback control, Structurally constrained state feedback

2 Learning objectives / Learning OutcomesThe students will be able to analyse and design linear time-invariant multivariable systems by means ofdifferent state space design methods.

3 Recommended prerequisite for participationBasic knowledge of linear control theory ("System Dynamics and Control Systems I and II”)







8 ReferencesSkript Konigorski: “Mehrgrößenregler im Zustandsraum”,Anderson, Moore: "Optimal Control: Linear Quadratic Methods", Föllinger:"Regelungstechnik: Einführungin die Methoden und ihre Anwendung", Föllinger: "Optimale Regelung und Steuerung: Eine Einführungfür Ingenieure", Roppenecker: "Zeitbereichsentwurf linearer Regelungen: Grundlegende Strukturen undeine Allgemeine Methodik ihrer Parametrierung",Unbehauen: "Regelungstechnik II:Zustandsregelungen, digitale und nichtlineare Regelungssysteme",Zurmühl: "Matrizen und ihre Anwendung: Für Angewandte Mathematiker, Physiker und Ingenieure. Teil1: Grundlagen"

Courses

Course Nr. Course name18-ko-2050-vl Controller Design for Multivariable Systems in State Space


Course Nr. Course name18-ko-2050-ue Controller Design for Multivariable Systems in State Space



Module nameNew Technologies of Electrical Energy Converters and Actuators



1 ContentGoal: The application of new technologies, i.e. super conduction, magnetic levitation techniques andmagneto-hydrodynamic converter principles, are introduced to the students. The physical operation modein principle, implemented prototypes and the current state of the development are described in detail.Content:Application of the superconductors for electrical energy converters:

• rotating electrical machines (motors and generators),• solenoid coils for the fusion research,• locomotive- and railway transformers,• magnetic bearings.

Active magnetic bearings (“magnetic levitation”):• basics of the magnetic levitation technique,• magnetic bearings for high speed drives in kW to MW range,• application for high-speed trains with linear drives.

Magneto-hydrodynamic energy conversion:• physical principle,• state of the art and perspectives.

Fusion research:• magnetic field arrangements for contactless plasma inclusion,• state of the current research.

2 Learning objectives / Learning OutcomesBasic knowledge in application of superconductivity in energy systems is understood as well as magneticlevitation, magnetohydrodynamics and fusion technology.








8 ReferencesDetailed textbook; Komarek, P.: Hochstromanwendungen der Supraleitung, Teubner, Stuttgart, 1995Buckel, W.: Supraleitung, VHS-Wiley, Weinheim, 1994Schweitzer, G.; Traxler, A.; Bleuler, H.: Magnetlager, Springer, Berlin, 1993Schmidt, E.: Unkonventionelle Energiewandler, Elitera, 1975


Courses

Course Nr. Course name18-bi-2040-vl New Technologies of Electrical Energy Converters and Actuators


Course Nr. Course name18-bi-2040-ue New Technologies of Electrical Energy Converters and Actuators



Module nameDesign of Electrical Machines and Actuators with Numerical Field Calculation



1 ContentIntroduction to Finite Element Method (FEM), Basic examples of electromagnetic devices designed in 2Dwith FEM, 2D electromagnetic Design of transformers, AC machines, permanent magnet devices; eddycurrent applications such as squirrel-cage machines (Example: Wind generator); Cooling systems andthermal design: Calculation of temperature distribution within power devices

2 Learning objectives / Learning OutcomesA good knowledge in applying FEMAG and ANSYS software package to basic field problems is gained.

3 Recommended prerequisite for participationStrongly recommended is the attendance of lecture and active co-operation in the tutorial “Energy Con-verters - CAD and System Dynamics”





6 Usability of this moduleMSc EPE, MSc ETiT, MSc MEC


8 ReferencesDetailed textbook; User manual FEMAG and ANSYS. Müller, C. Groth: FEM für Praktiker – Band 1: Grund-lagen, expert-Verlag, 5. Aufl., 2000

Courses

Course Nr. Course name18-bi-2110-se Design of Electrical Machines and Actuators with Numerical Field Calculation

Instructor Type SWSDr.-Ing. Bogdan Funieru Seminar 2


Module namePhysics and Technology of Accelerators



1 Content




• Module Examination (Study Achievement, Study Archievment, Pass/Fail Grading System)Module Eccompanying Examination:

• [05-25-6302-pr] (Study Achievement, Study Archievment, BWS b/nb)


• Module Examination (Study Achievement, Study Archievment, Weighting: 100 %)Module Eccompanying Examination:

• [05-25-6302-pr] (Study Achievement, Study Archievment, Weighting: 0 %)



8 References

Courses

Course Nr. Course name18-bf-2010-vl Accelerator Physics

Instructor Type SWSProf. Dr. Oliver Boine-Frankenheim Lecture 2

Course Nr. Course name05-21-2502-ku Introduction to Accelerator Physics

Instructor Type SWSCourse 2

Course Nr. Course name05-25-6302-pr Vocational Laboratory: Introduction to Accelerator Physics



Module nameActuators for Mechatronic Systems Lab



1 ContentSafety instructions; Practical experiments about electrical energy conversion and mechatronic actuators:

• Record preparation (one for each group) for every experiment.• One exam for all practical experiments at the end of the semester.• The mark for the students result from the practical experiments, the prepared records and the results

of the 2 short exams.

2 Learning objectives / Learning OutcomesThe use of mechanical actors is trained and knowledge in using the actors is acquired.

3 Recommended prerequisite for participationRecommended lecture “Elektrische Antriebe (MEC)” and "Maschinenelemente und Mechatronik 1"





6 Usability of this moduleBSc MEC


8 ReferencesDetailed textbook with description for the performance of the lab tests

Courses

Course Nr. Course name18-bi-1030-pr Actuators for Mechatronic Systems Lab





Module nameElectromechanical Systems Lab



1 ContentElectromechanical sensors, drives and actuators, electronic signal processing mechanisms, systems fromactuators, sensors and electronic signal processing mechanism.

2 Learning objectives / Learning OutcomesElaborating concrete examples of electromechanical systems, which are explained within the lectureEMS I+II.The Analyzing of these examples is needed to explain the mode of operation and to gather characteristicvalues. On this students are able to explain the derivative of proposals for the solution.The aim of the 6 laboratory experiments is to get to know the mode of operation of the electro- mechan-ical systems. The experimental analysis of the characteristic values leads to the derivation of proposedsolutions.

3 Recommended prerequisite for participationBachelor ETiT







8 ReferencesLaboratory script in Electromechanical Systems

Courses

Course Nr. Course name18-kn-2090-pr Electomechanical Systems Lab


Course Nr. Course name18-kn-2090-ev Electomechanical Systems Lab - Introduction

Instructor Type SWSProf. Dr. Mario Kupnik Introductory

Course0


Module nameLaboratory Matlab/Simulink I



1 ContentIn this lab tutorial, an introduction to the software tool MatLab/Simulink will be given. The lab is splitinto two parts. First the fundamentals of programming in Matlab are introduced and their application todifferent problems is trained. In addition, an introduction to the Control System Toolbox will be given. Inthe second part, the knowledge gained in the first part is applied to solve a control engineering specificproblem with the software tools.

2 Learning objectives / Learning OutcomesFundamentals in the handling of Matlab/Simulink and the application to control engineering tasks.

3 Recommended prerequisite for participationThe lab should be attended in parallel or after the lecture “System Dynamics and Control Systems I”





6 Usability of this moduleBSc ETiT; BSc MEC


8 ReferencesLecture notes for the lab tutorial can be obtained at the secretariatLunze; Regelungstechnik IDorp; Bishop: Moderne RegelungssystemeMoler: Numerical Computing with MATLAB

Courses

Course Nr. Course name18-ko-1030-pr Laboratory Matlab/Simulink I



Module nameLaboratory Control Engineering I



1 Content• Control of a 2-tank system.• Control of pneumatic and hydraulic servo-drives.• Control of a 3 mass oscillator.• Position control of a MagLev system.• Control of a discrete transport process with electro-pneumatic components.• Microcontroller-based control of an electrically driven throttle valve.• Identification of a 3 mass oscillator.• Process control using PLC.

2 Learning objectives / Learning OutcomesAfter this lab tutorial the students will be able to practically apply the modelling and design techniquesfor different dynamic systems presented in the lecture ”System dynamics and control systems I” to real labexperiments and to bring them into operation at the lap setup.








8 ReferencesLab handouts will be given to students

Courses

Course Nr. Course name18-ko-1020-pr Laboratory Control Engineering I



Module namePlanning and Application of Electrical Drives (Drives for Electric Vehicles)



1 ContentMono- and hybrid drive concepts, motor technology, DC and AC machines, drive systems, car dynamic,energy storage;Seminary work: simulation of car with electric drive train, presentation of seminary work

2 Learning objectives / Learning OutcomesKnowledge on design proceduces for electric modulation systems for electric and hybrid cars

3 Recommended prerequisite for participationBachelor in Electrical Engineering or Mechatronics, “Electrical Drives and Machines” and "Power electron-ics" recommended





6 Usability of this moduleMSc ETiT, MSc MEC, MSc EPE, MSc WI-ETiT


8 ReferencesTextbook; Binder, A.: Electric machines and drives I, Darmstadt Univ. of TechnologyMitschke, M.: Dynamik der Kraftfahrzeuge, Springer Verlag Berlin

Courses

Course Nr. Course name18-bi-2120-se Planning and application of electrical drives (Drives for electric vehicles)

Instructor Type SWSProf. Harald Neudorfer Seminar 2


Module nameProject Seminar Automatic Control Systems



1 ContentThe students work in small groups, supervised by a scientific staff member, on individual problems takenfrom the field of automatic control. A compulsory training course is part of the project course and willcover the topics 1. team work and project management, 2. professional presentation skills, and 3. scientificwriting skills.

2 Learning objectives / Learning OutcomesAfter attending the project course, a student is capable of: 1. planing a small project, 2. organizing the workwithin a project team, 3. searching for scientific background information on a given project, 4. creatingideas on how to solve problems arising in the project, 5. presenting the results in a scientific report, and 6.giving a talk on the results of the project.








8 ReferencesTraining course material

Courses

Course Nr. Course name18-ad-2080-pj Project Seminar Automatic Control Systems

Instructor Type SWSProf. Dr.-Ing. Jürgen Adamy Project Seminar 4


Module nameProject Course Practical Application of Mechatronics



1 ContentTeams of 2-4 students work on different mechatronic projects under the guidance of a project coordinatorfrom the institute.The projects mainly cover the following subject areas:

• Modeling, analysis, and design of mechatronic systems• Robust control design• System analysis, supervision and fault diagnosis• Modeling and identification

Application areas are mechatronic actuators, machine tools, production lines, test benches, automobiles,quadrocopters.

2 Learning objectives / Learning OutcomesAfter completing the project, the students will be familiar with the individual steps of investigating amechatronic project. This includes in particular the compilation of a system specification as well as criticaldiscussions and systematic selection of appropriate mechatronic solutions and their real technical imple-mentation. Doing so, the students learn the practical application of mechatronic methods taught in thelectures to real world problems. Additionally, in this project course, the students are supposed to im-prove their professional skills. These skills include e.g. teamwork, presentation techniques and systematicinformation retrieval.

3 Recommended prerequisite for participationLectures „System Dynamics and Automatic Control Systems I“, „System Dynamics and Automatic ControlSystems II“





6 Usability of this moduleMSc ETiT, MSc MEC, MSc iST


8 ReferencesHandouts will be distributed at start of the project (e.g. hints for writing project documentation, etc.)

Courses

Course Nr. Course name18-ko-2130-pj Project Course Practical Application of Mechatronics

Instructor Type SWSProf. Dr.-Ing. Ulrich Konigorski Project Seminar 4


Module nameComputational Electromagnetics and Applications III

Module Nr. Credit Points Workload Self study Duration Cycle offered18-dg-2020 3 CP 90 h 60 h 1 WiSe/SoSe

Language Module ownerGerman and English Prof. Dr.-Ing. Herbert De Gersem

1 ContentFinite Difference, Finite Volume and Finite Element Methods for the solution of Maxwell equations inthe time domain. High order Discontinuous Galerkin methods. Stability and convergence analysis. Highperformance computing. Particle based simulations for beams and plasmas.

2 Learning objectives / Learning OutcomesStudents lern the theoretical basis of advanced simulation techniques for time dependent electromagneticfields. Furthermore, the lecture mediates practical skills for the implementation, analysis and applicationof simulation codes for common problems of Electrical Engineering

3 Recommended prerequisite for participationMaxwell’s equations, infinitesimal calculus, vector calculus. Basics of differential equations and linearalgebra







8 ReferencesLecture slides, matlab scripts, various literature sources

Courses

Course Nr. Course name18-dg-2020-vl Computational Electromagnetics and Applications III

Instructor Type SWSPrivatdozent Dr. rer. nat. Erion Gjonaj Lecture 2


Module nameMicro Actuators and Small Motors

Module Nr. Credit Points Workload Self study Duration Cycle offered18-sl-2020 4 CP 120 h 75 h 1 WiSe

Language Module ownerGerman Prof. Dr.-Ing. Helmut Schlaak

1 ContentLinear and rotating movements, action of force, actuators with mechanical and electronic commutation aswell as alternating stator field, switched reluctance, stepping motors, micro actuators, piezoelectric motorsand special actuators, gears. Measurement and control in actuation systems, choosing electrical actuators.

2 Learning objectives / Learning OutcomesThe educational objective of the course is to teach the students to independently design an actuation systemin precision engineering. The students will be able to describe several actuator concepts and basic physicalprinciples and optimally choose an actuator for a specific task.

3 Recommended prerequisite for participationBSc ETiT







8 ReferencesScript for lecture: Small electromechanical actuators and motors

Courses

Course Nr. Course name18-sl-2020-vl Micro Actuators and Small Motors

Instructor Type SWSProf. Dr.-Ing. Helmut Schlaak Lecture 2

Course Nr. Course name18-sl-2020-ue Micro Actuators and Small Motors

Instructor Type SWSProf. Dr.-Ing. Helmut Schlaak Practice 1


Module nameOptimization of static and dynamic systems



1 Contentoptimization for static systems:- unconstrained and constrained nonlinear optimization, optimality conditions- numerical Newton type and SQP methods- nonlinear least squares- gradient free optimization methods- practical aspects like problem formulation, approximation of derivatives, method specific parameters, as-sessment of a computed solutionoptimization for dynamic systems:- parameter optimization and estimation problems- optimal control problem- maximum principle and optimality conditions- numerical methods for computing optimal trajectories- optimal feedback control- linear quadratic regulatorapplications and case studies from engineering sciences and roboticstheoretical and practical assignments as well as programming tasks for deepening of knowledge andmethodological skills

2 Learning objectives / Learning OutcomesThrough successful participation students acquire fundamental knowledge and methodological skills inconcepts, techniques and computational methods of optimization for static and dynamic systems and theirapplication for optimization problems in engineering sciences.

3 Recommended prerequisite for participationgrundlegende mathematische Kenntnisse und Fähigkeiten in Linearer Algebra, Analysis mehrerer Verän-derlicher und Grundlagen gewöhnlicher Differentialgleichungen









8 References- Script of Lecture- J. Nocedal, S.J. Wright: Numerical Optimization, Springer- C.T. Kelley: Iterative Methods for Optimization, SIAM Frontiers in Applied Mathematics- L.M. Rios, N.V. Sahinidis: Derivative-free optimization: a review of algorithms and comparison of softwareimplementations, Journal of Global Optimization (2013) 56:1247-1293- A.E. Bryson, Y.-C. Ho: Applied Optimal Control: Optimization, Estimation and Control, CRC Press- J.T. Betts: Practical Methods for Optimal Control and Estimation Using Nonlinear Programming, SIAMAdvances in Design and Control

Courses

Course Nr. Course name20-00-0186-iv Optimization of static and dynamic systems


6


Module nameComputer Aided Design (CAD)



1 ContentParametric 3D CAD systems, PDM systems, 3D hand sketching, geometric models, design of single partswith geometric elements, features and parametrics, assembly modeling, bill of materials, tolerances andsurface fits, technical product documentation, drawing standards, product development in teams


• Understand and apply parametric 3D CAD and PDM systems.• Design parametric single parts and complex assemblies.• Create engineering drawings for documentation.• Manage generated product data using PDM processes.• Work on and solve advanced tasks in virtual product development in teams.



• Module Examination (Technical Examination, Special Form, Standard Grading System)Continuous assessment procedure


• Module Examination (Technical Examination, Special Form, Weighting: 100 %)

6 Usability of this moduleBachelor MPE PflichtBachelor WI-MBBachelor Mechatronik


8 ReferencesLecture notes can be purchased in the institute’s secretarial office. Exercises and background theory areavailable on the website

Courses

Course Nr. Course name16-07-5020-vl Computer Aided Design (CAD)


Course Nr. Course name16-07-5020-tt Computer Aided Design (CAD)


Course Nr. Course name16-07-5020-ue Computer Aided Design (CAD)



Module nameFundamentals of Navigation I


Language Module ownerGerman Prof. Dr.-Ing. Jürgen Beyer

1 ContentNavigation principles, Earth models, Coordinate systems, Radio navigation, Basics and instruments (ADF,VOR, DME, ILS), dead reckoning, functional principles and error analysis, satellite navigation, Introductioninto GPS, signal description and measurement principles, Dilution of Precision (DoP), Differential GPS,Augmentation systems (RAIM, GIC, WAAS, LAAS, EGNOS).


• Explain the physics associated with the navigation of the earth.• Classify common coordinate systems and map projections.• Judge the methods of radio, coupling, and satellite navigation with respect to performance and

applications.

3 Recommended prerequisite for participationRecommanded: Control Engineering



Oral exam (in a group with 3 students) 60 min



6 Usability of this moduleWPB Master MPE III (Wahlfächer aus Natur- und Ingenieurwissenschaft)WPB Master PST III (Fächer aus Natur- und Ingenieurwissenschaft für Papiertechnik)Master Mechatronik


8 ReferencesCourse notes available.

Courses

Course Nr. Course name16-23-5050-vl Fundamentals of Navigation I


Course Nr. Course name16-23-5050-ue Fundamentals of Navigation I



Module nameMachine Learning and Deep Learning for Automation Systems



1 Content• Concepts of machine learning• Linear methods• Support vector machines• Trees and ensembles• Training and assessment• Unsupervised learning• Neural networks and deep learning• Convolutional neuronal networks (CNNs)• CNN applications• Recurrent neural networks (RNNs)

2 Learning objectives / Learning OutcomesStudents will get a broad and practical view on the field of machine learning. First, the most relevantalgorithm classes of supervised and unsupervised learning are discussed. After that, the course addressesdeep neural networks, which enable many of today’s applications in image and signal processing. Thefundamental characteristics of all algorithms are compiled and demonstrated by programming examples.Students will be able to assess the methods and apply them to practical tasks.

3 Recommended prerequisite for participationFundamental knowledge in linear algebra and statisticsPreferred: Lecture “Fuzzy logic, neural networks and evolutionary algorithms”







8 References• T. Hastie et al.: The Elements of Statistical Learning. 2. Aufl., Springer, 2008• I. Goodfellow et al.: Deep Learning. MIT Press, 2016• A. Géron: Hands-On Machine Learning with Scikit-Learn and TensorFlow. O’Reilly, 2017

Courses

Course Nr. Course name18-ad-2100-vl Machine Learning and Deep Learning for Automation Systems

Instructor Type SWSDr.-Ing. Michael Vogt Lecture 2


Module nameTutorial Advanced Cax Methods



1 ContentStudents gain knowledge of advanced CA Methods through the analysis of recent industrial examples. Thiscourse builds on the basic course ’Einführung in das rechnerunterstützte Konstruieren (CAD)’.

2 Learning objectives / Learning OutcomesThe students will be familiar with advanced CA Methods. They are able to recognise, execute and plan thegeneric workflow of CA Processes. Furthermore they are able to transfer their theoretical knowledge intoindustrial practice.

3 Recommended prerequisite for participationEinführung in das rechnergestützte Konstruieren (CAD)Virtuelle Produktentwicklung A, B, C







8 References

Courses

Course Nr. Course name16-07-5100-tt Tutorial Advanced CAx Methods



3.6 Optional Subjects AIS-SS: Secure Systems

Module nameEmbedded System Security


Language Module ownerGerman Prof. Dr.-Ing. Ahmad-Reza Sadeghi

1 ContentTrusted Computing- Authenticated Boot- Binding and Sealing- Integrity Measurement and Attestation- Direct Anonymous Attestation- Trusted Platform Modules (TPM/MTM)- On-board CredentialsMobile Security with focus on smartphones- Security Architectures- Selected Access Control and Permission Model Aspects- Context-based Security Policies- Selected Modern Attack TechniquesHardware-based Cryptography- Hardware-assisted Cryptographic Protocols- Introduction to Physical Unclonable Functions (PUFs)

2 Learning objectives / Learning OutcomesDuring this lecture students acquire detailed knowledge of selected aspects in system security (based onhardware and software).

3 Recommended prerequisite for participationGrundlagen der Kryptographie







8 References- Challener, David, VanDoorn, Leendert, Safford, David, Yoder, Kent, Catherman, Ryan “”A Practical Guideto Trusted Computing"", IBM Press, 2007- Smith, Sean W. ""Trusted Computing Platforms: Design and Applications"", Springer Verlag, 2005

Courses

3.6 Optional Subjects AIS-SS: Secure Systems 360


Instructor Type SWSProf. Dr.-Ing. Ahmad-Reza Sadeghi Integrated

Course4


Module nameIT Security


Language Module ownerGerman Dr.-Ing. Michael Kreutzer

1 ContentSelected concepts of IT-Security (cryptography, security models, authentication, access control, security innetworks; trusted computing, security engineering, privacy, web and browser security, information securitymanagement, IT forensic, cloud computing)

2 Learning objectives / Learning OutcomesAfter successful participation in the course, students are versant in common mechanisms and protocols toincrease security in modern it-systems. Students have broad knowledge of it-security, data protection andprivacy on the Internet.Students are familiar with modern information technology security concepts from the field of cryptography,identity management, web, browser and network security. Students are able to identify attack vectors init-systems and develop countermeasures.

3 Recommended prerequisite for participationParticipation of lecture Trusted Systems





6 Usability of this moduleB.Sc. InformatikM.Sc. InformatikM.Sc. WirtschaftsinformatikB.Sc. Psychologie in ITJoint B.A. InformatikB.Sc. Sportwissenschaft und InformatikMay be used in other degree programs.


8 References* C. Eckert: IT-Sicherheit, 3. Auflage, Oldenbourg Verlag, 2004* J. Buchmann, Einführung in die Kryptographie, 2.erw. Auflage, Springer Verlag, 2001* E. D. Zwicky, S. Cooper, B. Chapman: Building Internet Firewalls, 2. Auflage, O’Reilly, 2000* B. Schneier, Secrets & Lies: IT-Sicherheit in einer vernetzten Welt, dpunkt Verlag, 2000* W. Rankl und W. Effing: Handbuch der Chipkarten, Carl Hanser Verlag, 1999* S. Garfinkel und G. Spafford: Practical Unix & Internet Security, O’Reilly & Associates

Courses


Course Nr. Course name20-00-0219-iv IT Security


4


Module namePhysical Layer Security in Wireless Systems



1 ContentPhysical layer security techniques promise information theoretic security on the physical layer for wirelesscommunication. This integrated course discusses the theory and practice of physical layer security. Theunderlying theory is introduced and the application of these fundamentals towards practical solutions isdiscussed. Attacks against (practical) physical layer security techniques are presented. Theoretical andpractical exercises as well as the presentation of selected recent research results by seminar talks of stu-dents further deepen the understanding of the subject matter.Course contents:- Properties of the physical layer- Fundamentals of information theoretic security and delineation from cryptography- Physical layer security techniques (such as cooperative jamming, orthogonal blinding, zero-forcing, inter-ference alignment, key extraction)- Practical aspects of physical layer security techniques- Practical implementations of physical layer security techniques using software-defined radios- Selected current approaches to physical layer security

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students have a basic theoretical knowledge and an in-deep prac-tical knowledge in the area of physical layer security. They are able to describe the most importantinformation-theoretic basics as well as theory and practice of physical layer security techniques. Theyare able to analyze practical physical layer security techniques and describe their weaknesses. Studentshave competencies in the practical realization of physical layer security techniques using software-definedradios. They can independently acquire the current state of research on physical layer security and presentthe aquired knowledge in a comprehensible fashion.

3 Recommended prerequisite for participationBasics Mobile Networking







8 References


Selected literature, will be given in lecture.

Courses

Course Nr. Course name20-00-0745-iv Physical Layer Security in Wireless Systems

Instructor Type SWSDr.-Ing. Michael Kreutzer Integrated

Course3


Module nameSecure Mobile Systems


Language Module ownerGerman and English Prof. Dr.-Ing. Matthias Hollick

1 ContentThe integrated course Secure Mobile Systems covers the topic area of security in wireless and mobilenetworks and communication systems. Fundamental topics will be enriched by current research.Course contents:- Security analysis and modelling of security threats in mobile and wireless systems- Selected attacks and security mechanisms specific to mobile and wireless systems- Security in wireless sensor networks- Security in wireless mesh networks- Threats against privacy and privacy-preserving mechanisms in mobile and wireless systems- Security in cellular networks (GSM, UMTS, LTE)- Security on the physical layer in mobile and wireless systems- Selected research topisc in mobile and wireless systems

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students have a specialized knowledge in the domain of securitywith emphasis on mobile, distributed, wireless communication networks. Students are able to apply andtransfer the most important fundamentals from IT security, cryptography and traditional network securityto the field of mobile systems.Students obtain a thorough understanding of security mechanisms on the different network layers (ap-plication layer, transport layer, network layer, link layer, physical layer). As a result, they are able tothoroughly discuss the characteristics and principles in the area of mobile system security and exhibitdetailed theoretical and practical knowledge in this field.

3 Recommended prerequisite for participationGrundlagen der Netzsicherheit und der Mobilen Netze







8 References


Levente Buttyan, Jean-Pierre Hubaux: Security and Cooperation in Wireless Networks, Cambridge Univer-sity Press, 2008, ISBN: 978-0-521-87371-0 (book is available online for download).Ausgewählte Buchkapitel und ausgewählte wissenschaftliche Veröffentlichungen.

Courses

Course Nr. Course name20-00-0583-vl Secure Mobile Systems

Instructor Type SWSProf. Dr.-Ing. Matthias Hollick Lecture 2


Module nameLab Exercise on Secure Mobile Networking



1 ContentThe Lab Exercise on Secure Mobile Networking covers the applied software development as well ashardware-software development. Topic areas covered are communication networks, IT security, mobilenetworks and wireless communications as well as the combination of these. Goal is the solving of a givenproblem by implementation in software or hardware/software in a team.Course contents:- Solving of a problem in the area of communication networks, IT security, mobile networks and wirelesscommunications- Survey on solution alternatives and discussion of pros and cons- Conception of a software architecture or a combined hardware-software architecture- Software/hardware design for the target platform- Prototypical realization on the target platform- Evaluation of the system with respect to performance aspects- Documentation of the implemented solution

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students have aquired the ability to solve problems in the areaof secure mobile networking using software technology. The students have gained insight into the de-sign/implementation of complex protocols or applications in one/multiple of the areas of communicationnetworks, IT security, mobile netwokrs and wireless communications. They are able to implement the cho-sen protocols and application, and to test the functionality as well as to evaluate the performance. Studentsare able to document the developed software artefacts and to present the project progress and outcomes.

3 Recommended prerequisite for participationSuccessfull participation in an lecture of SEEMOO.








Courses


Course Nr. Course name20-00-0552-pr Secure Mobile Networking Lab

Instructor Type SWSProf. Dr.-Ing. Matthias Hollick Internship 4


Module nameProject on Secure Mobile Networking



1 ContentThe Project on Secure Mobile Networking covers the applied software development as well as hardware-software development. Topic areas covered are communication networks, IT security, mobile netwokrsand wireless communications as well as the combination of these. Goal is to independently carry out adevelopment project in a team.Course contents:- Independent solving of a development project in the area of communication networks, IT security, mobilenetwokrs and wireless communications- Project planning and project management- Survey on solution alternatives and discussion of pros and cons- Conception of a software architecture or a combined hardware-software architecture- Software/hardware design for the target platform- Prototypical realization on the target platform- Evaluation of the system with respect to performance aspects- Documentation of the implemented solution as well as extensive documentation of the project manage-ment

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students have aquired the ability to solve complex problems in thearea of secure mobile networking using software technology. To this end, the students are able to indepen-dently define, manage and carrry out a project.The students have gained insight into the design/implementation of complex protocols or applications inone/multiple of the areas of communication networks, IT security, mobile netwokrs and wireless commu-nications. They are able to implement the chosen protocols and application, and to test the functionalityas well as to evaluate the performance. The students are able to document the project planning andmanagement, the developed software artefacts and to present the project progress and outcomes.

3 Recommended prerequisite for participationSuccessfull participation of an lecture of SEEMOO.







8 References


Will be given in project.

Courses

Course Nr. Course name20-00-0553-pp Secure Mobile Networking Project

Instructor Type SWSProf. Dr.-Ing. Matthias Hollick Internship 6


Module nameIntroduction to Cryptography



1 ContentMathematical basic principles:- Calculations in congruence and residue class ringsBasic principles of encryption:- Symmetric vs. asymmetric cryptosystems- Block and stream ciphers, AES, DES- Cryptanalysis- Probability and perfect security- Public-key encryption- RSA, Diffie-Hellman, ElGamal- Factoring large numbers- Discrete logarithms- Cryptographic hash functions- Digital signatures- Identification

2 Learning objectives / Learning Outcomes- understanding the mathematical foundations of cryptography such as calculations in congruence andresidue class rings, factoring large numbers, probability theory and perfect security- understanding the principles of public and secret key encryption and relevant schemes including theirsecurity and efficiency- understanding the principles of digital signatures and the relevant schemes including their security andefficiency

3 Recommended prerequisite for participation- Linear Algebra for Computer Science- Funktionale und Objektorientierte Programmierkonzepte







8 References


- Johannes Buchmann: Einführung in die Kryptographie,5. Auflage, Springer-Verlag, 2010, 278 p. ISBN: 978-3-642-11185-3- Johannes Buchmann:Cryptographic Protocols. Vorlesungsskript(u.a. Undeniable, Fail-Stop und Blind Signatures)- Neal Koblitz:A Course in Number Theory and Cryptography, Springer Verlag, 1994- Alfred J. Menezes, Paul C. van Oorschot, Scot A. Vanstone:Handbook of Applied Cryptography, CRC Press, 1997 (erhältlich als PDF)- Bruce Schneier:Applied Cryptography, John Wiley & Sons, Inc., 1994- Douglas R. Stinson:Cryptography - Theory and Practice, CRC Press, 1995- Gustavus J. Simmons:Contemporary Cryptology - The Science of Information Integrity, IEEE Press, 1992

Courses

Course Nr. Course name20-00-0085-iv Introduction to Cryptography


4


Module nameComputer Security



1 ContentPart I: Cryptography- Background in Mathematics for cryptography- Security objectives: Confidentiality, Integrity, Authenticity- Symmetric and Asymmetric Cryptography- Hash functions and digital signatures- Protocols for key distributionPart II: IT-Security and Dependability- Basic concepts of IT security- Authentication and biometrics- Access control models and mechanisms- Basic concepts of network security- Basic concepts of software security- Dependable systems: error tolerance, redundancy, availability

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students are familiar with the basic concepts, methods and modelsin the areas of cryptography and computer security. They understand the most important methods thatallow to secure software and hardware systems against attackers and are able to apply this knowledge toconcrete application scenarios.






6 Usability of this moduleB.Sc. InformatikB.Sc. WirtschaftsinformatikB.Sc. Psychologie in ITJoint B.A. InformatikB.Sc. Sportwissenschaft und InformatikB.Sc. InformationssystemtechnikMay be used in other degree programs.


8 References- J. Buchmann, Einführung in die Kryptographie, Springer-Verlag, 2010- C. Eckert, IT-Sicherheit, Oldenbourg Verlag, 2013- M. Bishop, Computer Security: Art and Science, Addison Wesley, 2004

Courses


Course Nr. Course name20-00-0018-iv Computer Security


3


Module nameElectronic Voting



1 Content1. Introduction to e-voting- Different types of e-voting systems- Classifica-tion of voting systems- Postal voting vs. online voting- Advantages/disadvantages of e-voting- Factors of influence2. Requirements for e-voting systems- Legal issues and challenges- Existing ad hoc requirement documents- KORA: Konkretisierung rechtlicher Anforderungen- Security approach / security requirements- Other categories of requirements3. E-voting systems in use - description and analysis- Polyas system- Estonian remote electronic voting system- Digital election pen- Overview of remote electronic voting in Germany and e-voting in the world4. Internet voting specific challenges and solutions- Voter identification/authentication- Secrecy of the vote (Randomized Authentication Tokens, Benaloh Model, Separation of Duty, Blind Sig-natures, Mix-Net, Homomorphic Tallying)- Trustworthiness of voting clients- Uncontrolled environment- Availability5. Verifiability in e-voting- Introduction to verifiability- Paper audit trail- Bulletin Board- Receipt free / Coercion resistant- Helios and Civitas voting schemes- Universal verifiability mechanisms- Possible extensions to anonymization techniques (Randomized Authentication Tokens, Benaloh Model,Separation of Duty, Blind Signatures)6. Evaluation and certification of e-voting systems- Common Criteria- ISO 27001- k-resilience term

2 Learning objectives / Learning OutcomesStudents develop within this lecture an understanding for the different aspects of electronic voting. Theseaspects cover requirements for electronic voting systems, approaches for the deployment of these require-ments and approaches to evaluate electronic voting systems. After the successful completion of the lecture,students will be able to implement (partially contradicting) requirements for electronic voting systems inan adequate manner building upon tailored security mechanisms.

3 Recommended prerequisite for participationIntroduction to CryptographyComputer Security








8 References- Michael Ian Shamos: Electronic Voting Glossary- Melanie Volkamer: Evaluation of Electronic Voting- Laure Fouard, Mathilde Duclos, and Pascal Lafourcade: Survey on Electronic Voting Schemes- Chris Karlof, Naveen Sastry and David Wagner: Cryptographic Voting Protocols: A Systems Perspective- Warren D. Smith: Cryptography meets voting (2005)- Henk C. A. van Tilborg, “”Encyclopedia of Cryptography and Security"", ISBN-13: 978-0387234731- Common Criteria- IT Grundschutz / BSI

Courses

Course Nr. Course name20-00-0499-vl Electronic Voting

Instructor Type SWSDr.-Ing. Michael Kreutzer Lecture 2


Module nameCryptography, Privacy and Security


Language Module ownerGerman and English Prof. Dr. rer. nat. Johannes Buchmann

1 ContentSelected topics concerning applications in the electronic society. Main topics are privacy, eletronic votingand public-key infrastructures. Introduction to scientific research and publishing.Exemplary topics are:Public Key Infrastructures-Understanding and application of basic concepts of PKI-elaboration of the basics known from the lecture “Public Key Infrastructures”-PKI in practical application - e.g. SSL and Web Security, PKI for eID (Germany, Austria...)-Revocation-Trust models-Criticism and weaknesses-AlternativesElectronic Voting Systems-Electronic voting systems with verifiable evaluation of votes (solutions based on cryptography): Split-Ballot, Prêt à Voter, Scantegrity;-Requirements of electronic voting systems: End-to-end Verifiability, Universal Verifiability, Correctness,Computational Privacy, Everlasting Privacy;-Type of elections: Bundestag elections, local elections;-Evaluation of encrypted votes based on: Mix Nets, Homomorphic Tallying.Anonymous Communication Systems-Understanding and application of basic concepts of anonymous communication-Anonymous communication networks in use, understanding anonymous communication networks, e.g.Tor, I2P-Weaknesses / strengths-Criticism-Alternatives-Increments-Properties, e.g. censorship resistance-Application and combination of anonymous communication systems with other technologies - e.g. socialnetworks

2 Learning objectives / Learning OutcomesGoals: Understanding of the main problems concerning the selected topics, application of the relevantsecurity models, development of new and presentation of existing approaches, scientific writing and pre-sentation. Investigation and processing of information.The seminar will be held as a conference seminar. The topics will be presented and assigned in the kick offmeeting. (groups of 2-3 persons).The students practice the process of a real scientific conference:- Call for Papers (CfP)- submission- peer review- notification/feedback- submission of camera ready version- presentation of the paper, session chair

3 Recommended prerequisite for participationGrundkenntnisse in IT SicherheitEinführung in die KryptographiePublic-Key Infrastrukturen








8 ReferencesJ. Buchmann, E. Karatsiolis, and A. Wiesmaier. “Introduction to Public Key Infrastructures”, Springer-VerlagBerlin Heidelberg, 2013. ISBN: 978-3-642-40656-0 (Print) 978-3-642-40657-7 (Online)

Courses

Course Nr. Course name20-00-0672-se Cryptography, Privacy and Security

Instructor Type SWSProf. Dr. rer. nat. Johannes Buchmann Seminar 3


Module nameFormal Methods for Information Security


Language Module ownerEnglish Prof. Dr.-Ing. Heiko Mantel

1 Content- formal modeling of security-critical systems in predicate logic- theoretical foundations of access control and information-flow control- formal modeling of security properties in predicate logic- distinction between qualitative and quantitative security properties- decidability and complexity results for security properties- verification of security guarantees in distributed systems- impact of stepwise composition and refinement on security guarantees- formal languages for specifying security policies and their semantics- certification of security-critical systems

2 Learning objectives / Learning OutcomesAfter successfully participating in this course, students know relevant formal security models and analysistechniques. They understand the fundamental differences between various classes of security propertiesand the interplay between stepwise software development and security guarantees. They are able to modelsystems and security requirements formally and to analyze security aspects rigorously based on formalspecifications.

3 Recommended prerequisite for participationKnowledge within Computer Science and Mathematics according 1-4 semester of B.Sc. Computer Science.







8 References- M. Bishop: Computer Security, Addison-Wesley- J. Biskup: Security in Computing Systems, Springer-Verlag- C. P. Pfleeger, S. L. Pfleeger: Security in Computing, Prentice Hall- D. Denning: Cryptography and Data Security, Addison WesleyLiterature recommendations will be updatet regularly.

Courses


Course Nr. Course name20-00-0362-iv Formal Methods for Information Security


6


Module nameIT Security Management



1 Content

2 Learning objectives / Learning OutcomesThis lecture teaches the knowledge for initiating and implementing a continual IT security managementprocess and shows practical methods of solution.

3 Recommended prerequisite for participationKnowledge of IT Security







8 References

Courses

Course Nr. Course name20-00-0088-vl IT Security Management



Module nameCryptography


Language Module ownerGerman and English Dr.-Ing. Michael Kreutzer

1 ContentIn this lab, selected topics of public-key Infrastructure (PKI), cryptographic schemes and protocols areaddressed. For example, it is possible to choose:- Efficient implementation of ciphers, hash functions and signature schemes.- Integration of cryptographic primitives in applications.- Use of cryptographic hardware such as smart cards.

2 Learning objectives / Learning Outcomes- expanding knowledge in programming- collecting experience in software development- deeper understanding of security concepts and cryptography- collecting xperience in using various development tools"

3 Recommended prerequisite for participation- Kenntnisse der entsprechenden Programmiersprache bei Programmieraufgaben- Grundlagen der Kryptographie- Kenntnisse in IT-Sicherheit sind von Vorteil







8 References- Johannes Buchmann: Einführung in die Kryptographie, 5. Auflage, Springer-Verlag, 2010, 278 p. ISBN:978-3-642-11185-3- Johannes Buchmann: Cryptographic Protocols. Vorlesungsskript (u.a. Undeniable, Fail-Stop und BlindSignatures)- Neal Koblitz: A Course in Number Theory and Cryptography, Springer Verlag, 1994- Alfred J. Menezes, Paul C. van Oorschot, Scot A. Vanstone: Handbook of Applied Cryptography, CRCPress, 1997- Bruce Schneier: Applied Cryptography, John Wiley & Sons, Inc., 1994- Douglas R. Stinson: Cryptography - Theory and Practice, CRC Press, 1995- Gustavus J. Simmons: Contemporary Cryptology - The Science of Information Integrity, IEEE Press, 1992- Programmieren und Dokumentieren komponentenbasierter Software (Java, C, C++ UML, Java-Beans)

Courses


Course Nr. Course name20-00-0105-pr Efficient cryptography



Module nameNetwork Security


Language Module ownerEnglish Dr.-Ing. Michael Kreutzer

1 ContentThe integrated course Network Security covers the principles and practice of computer and telecommuni-cation network security with particular emphasis on Internet security. After transferring the fundamentalsof IT security and cryptography to the networking domain, we follow a top-down approach to networksecurity. Starting with the application layer, the course provides a detailed discussion of network securityprinciples and protocols. In addition to well known mechanisms, selected recent developments in the areaof network security will be examined.Course contents:- Network security: introduction, motivation, and challenges- Fundamentals: a reference model for network security, security standards for networks and the Internet,security threats, attacks, services, and mechanisms- Cryptographic foundations for networking security: symmetric crypto and its use in networks, public-keycrypto and its use in networks, support functions to implement network security- Application layer security- Transport layer security- Network layer security- Link layer security- Physical layer security and physical security- Operational network security: firewalls, intrusion detection systems- Selected topics in network security

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students have aquired an in-deep knowledge in the domain of com-munication network security with emphasis on Internet security. Students are able to apply and transferthe most important fundamentals from IT security and cryptography to the field of communication net-works. Students are able to distinguish the most important basic techniques for securing communicationnetworks. They have a thorough understanding of security mechanisms on the different network layers(application layer, transport layer, network layer, link layer, physical layer). As a result, they are able tothoroughly discuss the characteristics and principles in the area of network security and exhibit detailedtheoretical and practical knowledge in this field. Additionally, students are able to describe recent devel-opments in the area of network security (e.g. peer-to-peer security, mobile network security, etc.). Theexercise deepens the theoretical foundations by means of exercises, which consist of literature, calculationas well as practical implementation/application examples.

3 Recommended prerequisite for participationKnowledge in the area IT Security, Introduction to Cryptography and Communication Networks







B.Sc. InformatikM.Sc. InformatikM.Sc. WirtschaftsinformatikB.Sc. Psychologie in ITJoint B.A. InformatikB.Sc. Sportwissenschaft und InformatikCan be used in other degree programs.


8 ReferencesCharlie Kaufman, Radia Perlman, Mike Speciner: Network Security – Private Communication in a PublicWorld, 2nd Edition, Prentice Hall, 2002, ISBN: 978-0-14-046019-6; additional texts may be announced

Courses

Course Nr. Course name20-00-0512-iv Network Security


Course4


Module nameCryptographic Gems



1 ContentOutstanding results in cryptography like Fully Homomorphic Encryption, Leftover Hash Lemma, Non-Black-Box Constructions, Random Oracles, Multi-Party Computations, Zero-Knowledge

2 Learning objectives / Learning OutcomesAfter successful completion, participants understand prominent and important results and techniques incryptography. They are able to value their importance, and can apply them in other contexts.

3 Recommended prerequisite for participationIntroduction to Cryptography







8 References- Arora, Barak: Computational Complexity: A Modern Approach, 2007 (auch online erhältlich).- Balcazar, Diaz, Gabarro; Structural Complexity I und II, 1995 (nicht mehr als Hardcover verfügbar)- Katz, Lindell: Introduction to Modern Cryptography, 2007- Goldreich: Foundations of Cryptography, Volume I und II, 2001 und 2004 (als Online-Variante erhältlich)- Goldreich: Computational Complexity: A Conceptual Approach, 2006 (als Online-Variante erhältlich)

Courses

Course Nr. Course name20-00-0685-iv Cryptographic Gems

Instructor Type SWSProf. Dr. phil. nat. Marc Fischlin Integrated

Course4


Module namePublic Key Infrastructures



1 Content


1. Security Goals1. Confidentiality2. Integrity3. Authenticity of Data4. Entity Authentication/Identification5. Non-repudiation6. Availability7. Other Goals2. Public Key Cryptography1. Encryption (symmetric, assymetric, hybrid, cryptosystems, key exchange, performance, security, compu-tational problems)2. Cryptographic Hash Functions3. Message Authentication Codes4. Digital Signatures (performance, standards)3. Certificates1. X.509 Public Key Certificates (properties, content, extensions)2. PGP3. WAP Certificates4. Attribute Certificates4. Trust Models1. Direct Trust (fingerprints, examples of)2. Web of Trust (key legitimacy, owner trust, trusted introducers)3. Use of PGP4. Hierarchical Trust (trusted list, common root, cross-certification, bridge)5. Private Keys1. Software Personal Security Environments (PKCS#12, Java Keystore, application specific )2. Hardware Personal Security Environments (smart cards, hardware security modules, java cards)3. Private Key Life-cycle6. Revocation1. Revocation (reaons for, requirements, criteria)2. Certificate Revocation Lists3. Delta Certificate Revocation Lists4. Other Certificate Revocation Lists (over-issued, indirect, redirect)5. OCSP6. Other Revocation Mechanisms (NOVOMODO)7. Policies1. Certificate Life-cycle2. Certificate Policy and Certification Practice Statement3. Set of Provisions8. Validity Models1. Shell Model2. Modified Shell Model3. Chain Model9. Certification Path Validation10. Trust Center1. Registration Authority (registration protocols, proof-of-possession, extended validation certificates)2. Certification Authority3. Certificate Management Authority11. Certification Paths and Protocols1. Construction2. LDAP and other methods3. SCVP4. Timestamping5. Long Term Archiving Signatures



After attending the lecture Public Key Infrastructures, the students are able to- understand the IT security goals and the cryptographic primitives to realize these goals.- understand and explain the foundations of Public Key Infrastructures, in particular the different compo-nents (e.g., private keys, certificates, policies), actors (e.g., Trust centers, key owners) and processes (e.g.,certificate request, certificate issuance, validation of certificates, revocation).- understand, explain and apply the underlying theoretical models (e.g., trust models, validity models.- use Public Key Infrastructures in practice (e.g., for Email signing and encryption, validation of the au-thenticity of web sites).








8 References- J. Buchmann, E. Karatsiolis, and A. Wiesmaier. “”Introduction to Public Key Infrastructures"", Springer-Verlag Berlin Heidelberg, 2013. ISBN: 978-3-642-40656-0 (Print) 978-3-642-40657-7 (Online)- J. Buchmann, ""Einführung in die Kryptographie"", ISBN 3-540-41283-2- C. Adams / S. Lloyd, ""Understanding Public-Key Infrastructure"", ISBN 1-57870-166-X- Tom Austin, ""PKI / A Wiley Tech Brief"", ISBN 0-471-35380-9- R. Housley / T. Polk, ""Planning for PKI"", ISBN 0-471-39702-4- A. Nash / W. Duane / C. Joseph/ D. Brink, ""PKI Implementing and Managing E-Security"", ISBN 0-007-213123-3- Henk C.A. van Tilborg, ""Encyclopedia of Cryptography and Security"", ISBN-13: 978-0387234731

Courses

Course Nr. Course name20-00-0063-iv Public Key Infrastructures


4


Module nameSecure, Trusted and Trustworthy Computing



1 ContentBasics- Trust and Security Objectives- Trust ModelsTrusted Computing- Trustworthy Architectures and Applications- Introduction to the TCG-Approach (Terminology and Assumptions)- Introduction to Trusted Platform Module (TPM)Trusted Platform Module (TPM)- TPM Architecture and Key Hierarchy- Authentication and Authorization Protocols- Key Management and MaintenanceRuntime Attacks- Buffer Overflows- Return-Oriented Programming

2 Learning objectives / Learning OutcomesDuring this lecture students will gain an overview of fundamental concepts, methods and models in thearea of secure and trustworthy computing. Futhermore they acquire detailed knowledge about trustedcomputing technology in practice.

3 Recommended prerequisite for participationGrundlagen der Kryptographie







8 References- Challener, David, VanDoorn, Leendert, Safford, David, Yoder, Kent, Catherman, Ryan “”A Practical Guideto Trusted Computing"", IBM Press, 2007- Pei, Dingyi, Yung, Moti, Lin, Dongdai, Wu, Chuankun ""Information Security and Cryptology"", Springer,2007- Smith, Sean W. ""Trusted Computing Platforms: Design and Applications"", Springer Verlag, 2005- Müller, Thomas ""Trusted Computing Systeme"", Springer, 2008


Courses



4


Module nameSecurity of Critical Infrastructures



1 Content- Overview of critical infrastructures and associated security problems- Case study: smart grid- Case study: transportation sector and logistics- Case study: telecommunications- Case study: industrial automation systems

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students are familiar with the most important IT security problemsin critical infrastructures. They understand techniques that allow to secure critical infrastrures and are ableto apply them in different sectors (such as the smart grid, the transportation or the telecommunicationssectors).

3 Recommended prerequisite for participationComputersystemsicherheit








Courses

Course Nr. Course name20-00-0720-iv Critical Infrastructure Protection


Course2


Module nameSeminar on Networking, Security, Mobility, and Wireless Communications


Language Module ownerGerman and English Prof. Dr.-Ing. Matthias Hollick

1 ContentThe Seminar on Networking, Security, Mobility, and Wireless Communications covers current research inthe given topic areas. Under supervision of the tutors, the seminar imcludes studying, critically analyzingand discussing, summarizing, and presenting selected research articles. Deliverables are a short presenta-tion, a final presentation, and a seminar paper.Course contents:- Indepentent exploration of a topic in the area of networking, security, mobility, and wireless communica-tions (typically in english)- Own, enhanced literature study, gudided by tutor- Interpretation and classification of the literature study, gudided by tutor- Preparation of an introductory talk as well as a final talk including presentation slides, gudided by tutor- Presentation of both talks for a heterogenous audience (experts/non-experts)- Technical discussion after the talks- Feedback to the speakers and the talks (including presentation skills) and technical content

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students are able to work in a scientific manner under guidance.They know the fundamental techniques for scientific literature work and can apply them to a well-definedtopic area. They have aquired intermediate knowledge on selected mechanisms, methodologies as well asapplications for the investigated topic area. Students can present this aquired knowledge to a heteroge-neous audience and explain the technical details of the investigated topic.

3 Recommended prerequisite for participationSuccessfull participation in a lecture of SEEMOO.








Courses


Course Nr. Course name20-00-0582-se Seminar on Networking, Security, Mobility, and Wireless Communications

Instructor Type SWSProf. Dr.-Ing. Matthias Hollick Seminar 2


Module nameSecurity in Multimedia Systems and Applications



1 ContentThe students will gain an insight into the challenges of multimedia security and the established solutionapproaches. These include concepts such as media integrity, confidentiality and authenticity. The studentsalready know the procedures used in digital watermarking, robust hashing, partial encryption, multimediaforensics and DRM. Choosing the appropriate solution from a full panoply of solution mechanisms they willbe able to respond optimally to the multimedia security challenges depending on the respective require-ments.- Partial encoding procedures for video and audio to ensure confidentiality and authenticity- Digital watermarks for images and audio - areas of application, methods and procedures- Digital Rights Management and Copyright protection procedures- Visual cryptography Besides discussing different algorithms, i.e. their possibilities, limitations and weak-nesses, the commercial and societal aspects of protective measures will also be part of the lecture.

2 Learning objectives / Learning OutcomesThe students get an overview of the challenges posed by multimedia security as well as known solutions.This includes concepts of digital watermarks, robust hashes, partial encryption, multimedia forensics, andDRM. The student is able to address challenges of multimedia security by applying appropriate solutions.

3 Recommended prerequisite for participationBasic knowledge in Multimedia-Formats and IT Security.







8 References- Steinmetz: Multimedia-Technologie. Grundlagen, Komponenten und Systeme, ISBN: 3540673326,Springer, Heidelberg, 2000- Dittmann: Digitale Wasserzeichen, Springer Verlag, ISBN 3 - 540 - 66661 - 3, 2000- Cox, Miller, Bloom: Digital Watermarking, Academic Press, San Diego, USA, ISBN 1-55860-714-5, 2002- und spezifische Veröffentlichungen aus Tagungsbänden"

Courses


Course Nr. Course name20-00-0093-iv Security in Multimedia Systems and Applications


4


Module nameSafty of railway signaling systems



1 Content









8 References

Courses




Module nameBlockchain Technology


Language Module ownerGerman and English Prof. Dr. techn. Stefan Katzenbeisser

1 ContentIn this seminar we will have a scientific and practical view on the prominent blockchain technology, whichis considered to be an innovative technology for use in a great variety of industries such as the financialsector. Thus, we will consider various papers on how blockchains work, how they are implemented andwhat practical potentials it has.

2 Learning objectives / Learning OutcomesUnderstanding of the main problems concerning the selectedtopics, development of new and presentation of existing approaches,scientific writing and presentation.

3 Recommended prerequisite for participationIntroduction to Cryptography





6 Usability of this moduleB.Sc. InformatikM.Sc. InformatikMay be used in other degree programs


8 References

Courses

Course Nr. Course name20-00-0997-se Blockchain Technology

Instructor Type SWSProf. Dr. techn. Stefan Katzenbeisser Seminar 2


Module nameSecure Computation


Language Module ownerEnglish Prof. Dr. techn. Stefan Katzenbeisser

1 ContentSecure Computation has emerged as a central cryptographic tool to construct applications that handlesensitive data on untrusted platforms. The course gives an overview of the most common constructions,based on either homomorphic encryption or on “garbled circuits”. Furthermore, the lecture will discussprominent applications as well as programming paradigms for secure computation.

2 Learning objectives / Learning OutcomesBasic knowledge of the most important protocols of secure computation; knowledge on the possibilitiesand limits of the technology; application of secure computation to several application domains.

3 Recommended prerequisite for participationBasic knowledge of cryptographic primitives and protocols, e.g., obtained through attending the course“Introduction to Cryptography”







8 References

Courses

Course Nr. Course name20-00-0991-iv Secure Computation

Instructor Type SWSProf. Dr. techn. Stefan Katzenbeisser Integrated

Course2


Module nameReal World Cryptography


Language Module ownerGerman Prof. Dr. techn. Stefan Katzenbeisser

1 ContentKey derivation, key exchange, secure communication, credentials, crypto currencies (TLS, SSH, IPSec,Bitcoin,. . . ).

2 Learning objectives / Learning OutcomesAfter successful completion the participants understand the design choices and security guarantees of real-world cryptographic protocols, used in our daily lives. They learn to judge the significance and limitationsof security models and security proofs for practical purposes.

3 Recommended prerequisite for participationEinführung in die Kryptographie





6 Usability of this moduleB.Sc. InformatikM.Sc. InformatikMay be used in other degree progams.


8 References

Courses

Course Nr. Course name20-00-0993-iv Real World Cryptography


Course4


Module namePractical Lab on System and IoT Security



1 ContentIn this practical course, the students deal with different aspects of smartphone security. The project tasksspecifically target the open-source Android OS and comprise the following areas:- Design and implementation of selected software attacks (ethical hacking)- Design and implemenation of secure user apps- Modifications of the Android Middleware and Kernel to build security architectures- System programming in general

2 Learning objectives / Learning OutcomesAfter successfully completing this lab students will have gained knowledge and hands-on expercience withsecurity mechanisms in modern smartphone operating systems. Futhermore they gain experience in systemprogramming in general.

3 Recommended prerequisite for participation- Basics operating systems- Knowledge in C++ and Java








Courses

Course Nr. Course name20-00-0615-pr Practical Lab on System and IoT Security



Module nameCybersecurity Lab



1 ContentIn this lab we will learn the basic and advanced aspects of network security. We will review the foundationalprotocols, such as BGP and DNS, infrastructure modules, such as routers, switches and firewalls, and willalso discuss applications security. We will discuss and demonstrate attacks and defences. Each of thestudents will receive a specific topic on which they will work during the semester, and on which they willbe guided.

2 Learning objectives / Learning OutcomesAt the end of the course the students will acquire a good knowledge in Network security, and in particularin topics on which they will prepare projects. The grade is based on the quality of the submitted project.

3 Recommended prerequisite for participationThe students should have a background in networking and in operating systems – these are prerequisitecourses.







8 References

Courses

Course Nr. Course name20-00-1018-pr Cybersecurity Lab

Instructor Type SWSProf. Dr. techn. Stefan Katzenbeisser Internship 4


Module nameProtection in Networked Systems - Trust, Resilience, and Privacy



1 Content- Protection in Networked Systems: background, motivation, challenges- Trust (Computational Trust): models and mechanisms- Trust (Computational Trust): application in PKI, Cloud Computing, Reputation Systems, and Web Services- Trust: regret management and device comfort- Privacy: privacy definitions, models, data anonymity, communication anonymity- Privacy & Trust: privacy-preserving trust models, mechanisms, and application to IDM- Security & Economics- Resilience: models, network intrusion detection systems, collaborative intrusion detection systems, hon-eypots- Resilient networks

2 Learning objectives / Learning OutcomesThe integrated lecture Protection in Networked Systems ? Trust, Resilience, and Privacy covers the topicsof computational trust, resilient and anonymous networks, and collaborative defense mechanisms. Byattending this course, the students will be able to understand the problems and solutions in the context ofnetworked systems. The course content will consider the concept of End-to-End systems emphasizing onusers, devices, networks, and applications or services.

3 Recommended prerequisite for participation- Trust and Reputation for Service-Oriented Environments: Technologies For Building Business IntelligenceAnd Consumer Confidence, Elizabeth Chang, Tharam Dillon, and Farookh K. Hussain, 374 pages, 2006.ISBN: 978-0-470-01547-6- On anonymity in an electronic society: A survey of anonymous communication systems, Matthew Edmanand Bülent Yener, ACM Computing Surveys, Vol. 42, Issue 1, 2009.- Taxonomy and Survey of Collaborative Intrusion Detection, Emmanouil Vasilomanolakis, Shankar Karup-payah, Max Mühlhäuser, Mathias Fischer, ACM Computing Surveys, Vol. 47 Issue 4, 2015.- Selected book chapters and scientific publications







8 References

Courses


Course Nr. Course name20-00-0969-iv Protection in Networked Systems - Trust, Resilience, and Privacy


Course2


Module nameProtection in Infrastructures and Networks



1 ContentThe Seminar on Protection in Infrastructures and Networks is a cycle of seminars where students are giventhe chance to read, analyze and summarize current scientific publications. The topics are related to theareas of:- Trust- Privacy- Resiliencein the domain of infrastructures and networks.

2 Learning objectives / Learning OutcomesStudents participating in the seminar will have the opportunity to learn and conduct research in the direc-tion of these topics.Your task will be to understand state-of-the-art scientific publications in order to explain their contributions.Furthermore, you are expected to write a survey in relation to the topic assigned to you.

3 Recommended prerequisite for participationBasic knowledge about it-security and distributed systems.Lectures:Computersystemsicherheit (CSS)Computer-Netzwerke und verteilte Systeme (CNuvS)





6 Usability of this moduleB.Sc. InformatikM.Sc. InformatikMay be used in other degree programss


8 References

Courses

Course Nr. Course name20-00-1022-se Protection in Infrastructures and Networks



Module nameCryptographic Protocols



1 ContentCryptographic protocols allow parties with potentially conflicting interests to jointly perform certain tasks.This course covers basic and advanced constructions for cryptographic protocols and their applications,including Commitments, Secure Coin Flipping, Zero-Knowledge Proofs, Mixnets, Anonymous Credentials,Private Information Retrieval, Secure Multiparty Computation, and Hardware-assisted Cryptographic Pro-tocols.

2 Learning objectives / Learning OutcomesStudents know basic and advanced cryptographic protocols, can assess and compare their efficiency andsecurity, and know their basic applications.

3 Recommended prerequisite for participationBasic knowledge in cryptography is strongly recommended, e.g., by successfully completing the course“Introduction to Cryptography”.





6 Usability of this moduleB.Sc. InformatikM.Sc. InformatikM.Sc. IT Sicherheit


8 References

Courses

Course Nr. Course name20-00-1032-iv Cryptographic Protocols


Course4


Module nameMobile Security


Language Module ownerGerman and English Prof. Dr.-Ing. Ahmad-Reza Sadeghi

1 ContentIn this seminar different security aspects of mobile devices (especially smartphone) will be analyzed anddiscussed. Students will process, summarize and evaluate a number of current scientific publications for acertain topic in form of an essay. Additionally, each student will present his work in front of the group atthe end of the semester.Possible topics include:- Security models of current smartphone operating systems (e.g. Android, iOS, Windows Phone, MeeGo,Symbian, RIM)- Security analysis and comparison of current app store models- Usage of mobile devices in enterprises- Security extensions for Android- Kernel security- Application security (e.g. mobile malware and runtime attacks)- Privacy aspects in mobile devices- Security of mobile networks

2 Learning objectives / Learning OutcomesThis seminar covers several topics in the area of mobile security with the focus on smartphone security.Through this course students will gain detailed knowledge on the security and privacy aspects of mobileoperating systems, devices, infrastructures and end-user applications. Moreover, they learn to examinecurrent research in this area, to dive into a scientific topic and present their results in a short paper as wellas an oral presentation.

3 Recommended prerequisite for participationGrundlagen der Informatik








Courses


Course Nr. Course name20-00-0652-se Mobile Security

Instructor Type SWSProf. Dr.-Ing. Ahmad-Reza Sadeghi Seminar 2


Module nameLab Peace-, Security and Crisis Informatics



1 ContentThe lab course offers development projects relevant to the current research of the research group “Scienceand Technology for Peace and Security” (PEASEC). In addition to a broad overview, students will gaininsight in a specific area of development. These topics are linked to the specializations of the membersof the re-search group and provide the students with technical and scientific insight. The topics will beworked on in small groups.Examples for topics are available at www.peasec.de/lehre

2 Learning objectives / Learning OutcomesAcquiring the qualification for solving a practical task in a team or by oneself and presenting the results,such as:- Resolution of an issue in the domain of peace, security and crisis informatics- Requirements engineering and empirical studies- Research of solution alternatives and assessment of their (dis)advantages- Design, prototypical implementation and maintenance of innovative applications- Evaluation of existing applications with regard to different assessment criteria- Documentation of the developed solution

3 Recommended prerequisite for participationFoundations of Computer Science/Functional and Object-oriented Programming Concepts







8 ReferencesReuter, C. (2018) Sicherheitskritische Mensch-Computer-Interaktion: Interaktive Technologien und SozialeMedien im Krisen- und Sicherheitsmanagement, 660 S., Wiesbaden: Springer Vieweg – im DruckAltmann, J., Bernhardt, U., Nixdorff, K., Ruhmann, I., & Wöhrle, D. (2016). Naturwissenschaft - Rüstung -Frieden - Basiswissen für die Friedensforschung (Vol. 49), Wiesbaden: Springer Vieweg.Further literature will be provided in the course dependent on the selected topic.

Courses

Course Nr. Course name20-00-1020-pr Lab Peace-, Security and Crisis Informatics



Module nameCrisis, Security and Peace Technologies



1 ContentDuring the course of this seminar advanced theories and subjects from the field of “Science and Technologyfor Peace and Security” (PEASEC) will be developed. Based on the introduction and repetition of theprinci-ples in scientific research, contemporary project topics which are related to PEASEC research will beoffered by us and addressed by students applying scientific methods. During the semester scientific articles(“pa-pers”) will be developed and presented. As usual in scientific work, students will constructively revieweach other’s work in a peer-review process. Subsequently, the papers will be revised for the finalization andsub-mission.EXEMPLARY THEMATIC AREAS- Safety-critical human-computer interaction, social media and collaborative technologies in conflict andcrisis situations, usable security and privacy- Information technology for peace and security, information warfare, manipulation of opinions, fake news,cyber war, cyber peace, dual-use in computer science, conscientious digitalization, computer science andsociety- Resilient IT-based (critical) infrastructures, particularly communication, agriculture, energyTopics for the current semester are available at www.peasec.de/lehrePROCEDURE:- Technical introduction including the presentation and assignment of topics- Writing and submitting a short exposé- Methodological lecture- Short presentation of the own topic and constructive feedback- Submission of a first complete version of the paper- Assessment within a students‘ peer-review process- Final submission of the paper- GradingA mandatory introduction („kick-off“) is the first date, the mandatory methodological lecture is the seconddate. The assignment of topics and formation of groups will take place collaboratively during the kickoffand, if necessary, in the following week.

2 Learning objectives / Learning Outcomes- Autonomous familiarization with a topic in the field of peace, conflict and security research from theper-spective of computer science- Further autonomous literature research, interpretation and classification- Preparation and presentation of the topic to a heterogenous audience and subsequent expert discussion- Writing a scientific article- Assessment of scientific articles („peer-review“) with constructive feedback- Knowledge of the procedures of academic research and publication

3 Recommended prerequisite for participationPrinciples in one of the subjects: Computer Science, IT-Security, Human-Computer-Interaction or Peace andConflict Studies; basic knowledge in the topics of PEASEC








8 ReferencesReuter, C. (2018) Sicherheitskritische Mensch-Computer-Interaktion: Interaktive Technologien und SozialeMedien im Krisen- und Sicherheitsmanagement, 660 S., Wiesbaden: Springer Vieweg – im DruckAltmann, J., Bernhardt, U., Nixdorff, K., Ruhmann, I., & Wöhrle, D. (2016) Naturwissenschaft - Rüstung -Frieden - Basiswissen für die Friedensforschung (Vol. 49), Wiesbaden: Springer Vieweg.Flick, U. (2015) Introducing Research Methodology. Sage Publications LtdFurther literature will be provided in the course dependent on the selected topic.

Courses

Course Nr. Course name20-00-1019-se Crisis, Security and Peace Technologies



Module nameSafe and secure critical infrastructure using railway systems as an example



1 ContentFoundations of control systems, data networks and railway systems; safety; security; standardization; legalaspects and homologation; philosophical aspectsContent:Introduction- Overview of the topics of the course- Terminology safety and securityFoundations of control systems of critical infrastructure- Properties of critical infrastructure- Generic architecture of control systems, as they are used in railway systems and other critical infrastruc-ture- Role of softwareFoundations of data networks- OSI model with focus on layer 2- Bus systems (PROFINET)Foundations of railway systems- Inter-dependence of fixed installations, signaling systems and rolling stock- Railway operationSafety- Methods of safety engineering- Inter-dependence of hardware and software- Software safety (EN 50 128)- Failure Mode and Effects Analysis (FMEA)Security- Organizational requirements for operators of critical infrastructure- Technical requirements for product and system suppliers- Threat & Risk AnalysisStandardization- Details of the standards introduced in the chapters on safety and security- Purpose of standards- Inter-dependence of German (DKE), European (CENELEC) und world-wide (IEC) StandardizationLegal aspects and homologation- Homologation using the German railway agency (EBA) as an example- Application on the content developed in the safety part- Security: German law for protection of critical infrastructure (KRITIS) and European NIS directivePhilosophical aspects- Philosophical analysis of the terms safety and security- Human dignity vs. cost-effective provision of critical infrastructure- Challenges when certifying systems with artificial intelligence for critical infrastructureSummary and outlook- Summary of the course content- Open research questions



- Role of hardware, software and network technology in critical infrastructure- Overview of railway systems- Methods of proving safety for critical infrastructure- Methods to protect critical infrastructure against attackers- Processes of standardization- Getting to know legal and philosophical aspects of critical infrastructure

3 Recommended prerequisite for participationFoundations of computer science (embedded Systems, communication networks, software engineering, ITsecurity)







8 References

Courses

Course Nr. Course name20-00-1007-vl Safe and secure critical infrastructure using railway systems as an example

Instructor Type SWSProf. Dr. techn. Stefan Katzenbeisser Lecture 2


Module nameLab Blockchain



1 ContentThis course is aimed at students who have attended the lecture Cryptocurrencies and want to understandand examine some aspects of this topic in more detail. It provides a platform to check novel applicationsbased on Blockchain technology for their feasibility and usefulness.Complex cryptographic systems and ideas from the lecture Cryptocurrencies should be understood in teamwork and implemented in a decentralized system. The students are asked to develop a project plan andoutline, which should be implemented over the course of the semester.The students get first experiences with the implementation of a more complex development project.

2 Learning objectives / Learning OutcomesParticipants of this course will learn about the technical and practical implications of distributed crypto-graphic systems. These include first hands-on experiences in the following areas:* Development of smart contracts and distributed applications* Communication of systems through decentral peer-to-peer networks* Development of software using cryptographic building blocks* Security and anonymity of users of cryptographic currencies* Possible attacks on smart contracts and cryptocurrencies

3 Recommended prerequisite for participationThis course is directed at students that finished the cryptocurrencies lecture with good marks and program-ming skills.







8 References

Courses

Course Nr. Course name20-00-1031-pr Lab Blockchain



3.7 Optional Subjects AIS-VC: Visual Computing

Module nameCapturing Reality


Language Module ownerEnglish Prof. Dr. Bernt Schiele

1 ContentThis course covers a broad range of techniques to capture and model our world with a focus on applicationin computer graphics and computer vision. This includes:- basic tools and calibration techniques required in capturing applications- capturing and modeling techniques for various object properties (such as geometry and reflectance)- basic set of relevant mathematical modeling and optimization techniques- implementation and practical application of several techniques

2 Learning objectives / Learning OutcomesAfter successful completion of the course, students are able to analyze digitization and modeling problemsfor objects and scenes in computer graphics and computer vision as well as the underlying techniques.They are able to develop new setups, perform experiments and evaluate the results.

3 Recommended prerequisite for participationRecommended:Participation in lecture Graphische Datenverarbeitung I or Computer Vision IBasic knowledge in C/C++







8 References

3.7 Optional Subjects AIS-VC: Visual Computing 416

Noriko Kurachi: The Magic of Computer Graphics. A K Peters/CRC PressRichard Szeliski: Algorithms and Applications, SpringerMarcus Magnor, Oliver Grau, Olga Sorkine-Hornung, Christian Theobalt: Digital Representations of theReal World: How to Capture, Model, and Render Visual RealityWolfgang Förstner, Bernhard P. Wrobel: Photogrammetric Computer Vision - Geometry, Orientation andReconstruction

Courses

Course Nr. Course name20-00-0489-iv Capturing Reality

Instructor Type SWSProf. Dr. Bernt Schiele Integrated

Course4


Module nameComputer Vision I



1 Content- Basics of image formation- Linear and (simple) nonlinear image filtering- Foundations of multi-view geometry- Camera calibration and pose estimation- Foundations of 3D reconstruction- Foundations of motion estimation from video- Template and subspace methods for object recognition- Object classification with bag of words- Object detection- Basics of image segmentation

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students are familiar with the basics of computer vision. Theyunderstand fundamental techniques for the analysis of images and videos, can name their assumptions andmathematical formulations, as well as describe the resulting algorithms. They are able to implement thesetechniques in order to solve basic image analysis tasks on realistic imagery.

3 Recommended prerequisite for participationParticiation of lecture Visual Computing is recommended.







8 ReferencesLiterature recommendations will be updated regularly, an example might be:- R. Szeliski, “”Computer Vision: Algorithms and Applications"", Springer 2011- D. Forsyth, J. Ponce, ""Computer Vision – A Modern Approach"", Prentice Hall, 2002

Courses


Course Nr. Course name20-00-0157-iv Computer Vision


4


Module nameComputer Vision II



1 Content- Computer vision as (probabilistic) inference- Robust estimation and modeling- Foundations of Bayesian networks and Markov random fields- Basic inference and learning methods in computer vision- Image restoration- Stereo- Optical flow- Bayesian tracking of (articulated) objects- Semantic segmentation- Current research topics

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students have developed a more in-depth understanding of com-puter vision. They formulate image and video analysis tasks as inference problems, taking challenges ofreal applications into account, e.g. regarding robustness. They solve the inference problem using discreteor continuous inference algorithms, and apply these to realistic imagery. They quantitatively evaluate theapplication specific results.

3 Recommended prerequisite for participationParticipation of lecture Visual Computing and Computer Vision I is recommended.







8 ReferencesLiterature recommendations will be updated regularly, an example might be:- S. Prince, “Computer Vision: Models, Learning, and Inference”, Cambridge University Press, 2012- R. Szeliski, “”Computer Vision: Algorithms and Applications"", Springer 2011

Courses


Course Nr. Course name20-00-0401-iv Computer Vision II


4


Module nameComputer Graphics I



1 ContentIntroduction to basic principles of computer graphics, in particular input and output devices, renderingusing OpenGL, ray tracing, illumination modelling, ongoing development in computer grapics.

2 Learning objectives / Learning OutcomesAfter successful completion of the course, students are able to understand all components of the graphicpipeline and change variable parts (Vertex-Shader, Fragment-Shader, etc.). They are able to arrange,change and effectively store objects in the 3D-space, as well as appropriately choose the camera and theperspective, and utilize various shading-techniques and lighting-models to adapt all steps on the way tothe displayed 2D-Image.

3 Recommended prerequisite for participation- Programming- Basic algorithm and data structure- Linear algebra- Analysis- Topics of lecture Visual Computing







8 References- Real-Time Rendering: Tomas Akenine-Möller, Eric Haines, Naty Hoffman A.K. Peters Ltd., 3rd edition,ISBN 987-1-56881-424-7- Fundamentals of Computer Graphics: Peter Shirley, Steve Marschner, third edition, ISBN 979-1-56881-469-8- Additional literature will be given in the lecture.

Courses


Course Nr. Course name20-00-0040-iv Computer Graphics I


4


Module nameGeometric Methods of CAE/CAD



1 Content- parametric curve models- parametric surface models- topology and volumetric CAD models- CAD operations on surfaces- tesselation- approximation of curves and surfaces- finite element method and computational fluid dynamics- various applications from the area of CAD

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students understand the foundations of computer-aided methodsfor geometric modelling and simulation. They understand multiple parametric representations for curvesand surfaces and are able to analyze and compare them. They know classical data structures and algorithmsfrom computer aided design (CAD). They can use the presented techniques to model and visualize 3Dgeometry.

3 Recommended prerequisite for participationBasic knowledge in Computer Science.







8 ReferencesVorlesungsfolienLee: Principles of CAD / CAM / CAE Systems, Addison-Wesley.Piegl, Tiller: The NURBS Book, Springer Verlag.Farin: Kurven und Flächen im Computer Aided Geometric Design, viewegShah, Mäntylä: Parametric and Feature-based CAD/CAM, Wiley & Sons


Courses

Course Nr. Course name20-00-0140-iv Geometrical Methods of CAE/CAD


3


Module nameProgramming Massively Parallel Processors



1 Content- foundations of massively parallel processors with a focus on modern accelerator hardware- parallel algorithms- efficient programming of massively parallel systems- practical programming projects co-advised by domain scientists

2 Learning objectives / Learning OutcomesAfter successful completion of the course, students are able to analyze problems in the context of mas-sively parallel systems. They can develop novel applications and systematically improve their performance.They understand basic parallel algorithms and are able to independently understand and analyze currentliterature.

3 Recommended prerequisite for participationProgramming skills in C/C++Recommended: Systemnahe und Parallele Programmierung






7 Grade bonus compliant to §25 (2)In dieser Vorlesung findet eine Anrechnung von vorlesungsbegleitenden Leistungen statt, die lt. §25(2)der 5. Novelle der APB und den vom FB 20 am 02.10.2012 beschlossenen Anrechnungsregeln zu einerNotenverbesserung um bis zu 1.0 führen kann.

8 ReferencesWill be announced in lecture.

Courses

Course Nr. Course name20-00-0419-iv Programming Massively Parallel Processors


4


Module nameAdvanced Visual Computing Lab



1 ContentStudents work in this lab on selected advanced topics in the area of visual computing. Project results will bepresented in a talk at the end of the course. The specific topics addressed in the lab change every semesterand should be discussed directly with one of the instructors.

2 Learning objectives / Learning OutcomesAfter successful completion of this course, the students will be able to independently analyze and solve anadvanced problem in the area of visual computing and to evaluate the results.

3 Recommended prerequisite for participationProgramming skills, e.g. Java, C++Basic knowledge in Visula ComputingParticipation in at least one basic lectures and one lab in the are of Visual Computing.







8 ReferencesWill be announced in lecture.

Courses

Course Nr. Course name20-00-0537-pr Advanced Visual Computing Lab



Module nameVirtual and Augmented Reality



1 ContentThis course starts to detail the principal concepts of Augmented and Virtual Reality in relation to ComputerGraphics and Computer Vision. Starting from here basic principles, methods, algorithms as well as relevantstandards are discussed. This includes- VR/AR specific requirements and interfaces- Interaction technologies (e.g. interaction with range camera technologies)- Rendering technologies (in particular real-time rendering)- Web-based VR and AR- Computer-Vision-based Tracking- Augmented Reality with range camera technologies- Augmented Reality on smartphone platformsThe technologies will be illustrated and discussed with the results of actual research projects including inapplication fields „AR-maintenance support“ and „AR/VR based Cultural Heritage presentation“.

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students are familiar with the challenges and the requirements ofVirtual and Augmented reality applications. They know the standards used for the specification of VR/AR-applications. In particular, the students understand the potential of Computer Vision based tracking andthey can decide which methods can be applied in with environment.

3 Recommended prerequisite for participationGrundlagen der Graphischen Datenverarbeitung (GDV)







8 ReferencesDörner, R., Broll, W., Grimm, P., Jung, B. Virtual und Augmented Reality (VR / AR)

Courses


Course Nr. Course name20-00-0160-iv Virtual and Augmented Reality


4


Module nameHardware Design for Video Processing



1 ContentThese labs introduce the use of hardware accelerators to process realtime video streams. Such problemsare highly relevant in industrial use cases, the labs will cover them in context of autonomous cars, using aCorner Detector as an example.The hardware accelerators will be implemented in a hardware description language, validated in simula-tion, and then integrated into a real FPGA-based hardware system.In contrast to other teaching and research activities at the ESA group, these labs will use VHDL as a lan-guage and FPGA technology by Altera, especially the Quartus and Qsys design tools. The labs start withintroductory lectures covering the basics of VHDL and the tools, but participants are expected to becomefamiliar with the details by self-study of the background material provided.

2 Learning objectives / Learning OutcomesSuccessful participants are able to design hardware accelerators for realtime video processing in VHDL andvalidate them in simulation. They are able to map their designs to reconfigurable systems-on-chip usingEDA/CAD tools. They are able to evaluate the resulting systems and use the observed characteristics toguide further hardware development.

3 Recommended prerequisite for participationRecommended: Successful completion of „Digital Design“, „Computer Organisation“, and „Architectureand Design of Computer Systems“, ideally also „Visual Computing“ and „Computational Engineering andRobotics“ or similar competencies obtained in other study programmes







8 References

Courses

Course Nr. Course name20-00-0958-pr Hardware design for video processing



Module name3D Animation & Visualization



1 ContentThis seminar focuses on current research topics and the latest results in the areas of physically-basedsimulation, animation, real-time rendering and visualization.- participants independently familiarize themselves with the assigned seminar topic by working with theprovided scientific papers (usually texts written in English)- classification and interpretation of the gathered research results- preparation of a textual summary and a slide-based presentation on the subject- presentation in front of an audience with mixed prior knowledge on the topic and discussion

2 Learning objectives / Learning OutcomesSuccessful participation in the course enables students to get expertise by working with scientific papers.They can extract the essential aspects of the examined works and are able to concisely present them astextual form and presentation, targeting an audience with mixed prior experience on the subject. Thestudents are able to actively participate in a scientific discussion on the presented topics.

3 Recommended prerequisite for participationGDV I, (GDV II)







8 ReferencesSelected articles from ACM SIGGRAPH, EUROGRPAHICS, IEEE and similar Conferences. All articles arewritten in English.

Courses

Course Nr. Course name20-00-0216-se 3D Animation & Visualization



Module nameAmbient Intelligence



1 ContentThe course will provide an overview of a new vision for Human-Computer-Interaction (HCI) in which peo-ple are surrounded by intelligent and intuitive interfaces embedded in the everyday objects around them.In specific the course addresses the emergence of Ambient Mobility and the ubiquitous, pervasive informa-tion access, retrieval and display on mobile devices. It will focus on understanding enabling technologiesand studying applications and experiments, and, to lesser extent, it will adress the sociocultural impact.Additional topics of the lecture include system architectures for distributed systems, context awarenessand management, user models and their implications, sensing and interaction in smart environments. Thelecture discusses recent topics and research projects in the domain of Ambient Intelligence.

2 Learning objectives / Learning OutcomesAfter successfully attending the lecture, the students will be able to describe technology trends and researchresults in the domain of Ambient Intelligence. The most important concepts to create smart environments- intelligent networks and objects, technologies for mobile, augmented reality, ubiquitous and pervasiveinformation spaces, nomadic communications, real-time communication and related middle ware, embed-ded systems, sensor networks and wearable computing - can be discussed and classified. After completingthe practical part, students will be able to plan and realize the different project phases required to developan Ambient-Intelligence solution.

3 Recommended prerequisite for participationMaster-StudentsParticipation in lecture “Visual Computing“ and „Multimodale Interaktion mit intelligenten Umgebungen“







8 ReferencesWill be given according to actual topics.

Courses


Course Nr. Course name20-00-0390-iv Ambient Intelligence


4


Module nameImage Processing



1 ContentFundamentals of image processing:- Image properties- Image transformations- Simple and complex filtering- Image compression,- Segmentation- Classification

2 Learning objectives / Learning OutcomesAfter successfully completing the course, students have an overview over the mechanisms used in and theabilities of modern image processing techniques. They are able to solve basic to medium level problems inimage processing.








8 References- Gonzalez, R.C., Woods, R.E., “”Digital Image Processing"", Addison- Wesley Publishing Company, 1992- Haberaecker, P., ""Praxis der Digitalen Bildverarbeitung und Mustererkennung"", Carl Hanser Verlag, 1995- Jaehne, B., ""Digitale Bildverarbeitung"", Springer Verlag, 1997

Courses

Course Nr. Course name20-00-0155-iv Image Processing


2


Module nameComputer Vision in Engineering



1 ContentA Basics

• Scene Representation2D and 3D Geomtery• Image Acquisition

– Geometric Projections Camera Calibration

• Objective and Illumination• Discrete 2D signals

– Separability, Sampling– Transformation, Interpolation– Convolution, Correlation– Discrete Fourier Transformation

B Basics of Image Analysis• Filtering

– Basics2D Filter Design– Linear Filtering– Nichtlinear Filtering

• Image Decompositions– Multi-scale Representation– Pyramids– Filter Banks

• Image Features– Structure– Moments, Histograms

2 Learning objectives / Learning OutcomesThe lecture communicates mathematical basics needed to solve computer vision problems in the field ofengineering. The focus is on methods that are relevant for measuring and control tasks. Applications rangefrom visual quality inspection, visual robotics, photogrammetry, visual odometry up to visually guideddriver assistance etc.The students should obtain a good understanding for the relations between the three-dimensional worldand its two-dimensional projection onto the image plane of a camera. They also should learn about meth-ods that exist to infer knowledge from the world given image data. They should develop some feeling forthe different kinds of problems that arise in computer vision and how to choose an efficient solution interms of algorithms.




5 Grading


Module Final Examination:• Module Examination (Technical Examination, Optional, Weighting: 100 %)

6 Usability of this moduleMSc ETiT, MSc iST, MSc CE, MSc iST


8 ReferencesReferences / Textbooks: Lecture slides, exercise sheets and matlab-code.Further reading

• Yi Ma, Stefano Soatto, Jana Kosecka und Shankar S. Sastry, An Invitation to 3-D Vision - From Imagesto Geometric Models, Springer, 2003.

• Richard Hartley and Andrew Zisserman, Multiple View Geometry in Computer Vision, Second Edi-tion, Cambridge University Press, 2004.

• Karl Kraus, Photogrammetrie, Band 1 Geometrische Informationen aus Photographien und Laser-scanneraufnahmen 7. Auflage, de Gruyter Lehrbuch, 2004.

• Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer 2006.• Bernd Jähne, Digital Image Processing, 6. Auflage, 2005.

Courses

Course Nr. Course name18-ad-2090-vl Computer Vision in Engineering

Instructor Type SWSDr.-Ing. Volker Willert Lecture 2

Course Nr. Course name18-ad-2090-ue Computer Vision in Engineering

Instructor Type SWSDr.-Ing. Volker Willert Practice 1


Module nameVisual Computing



1 Content- Basics of perception- Basic Fourier transformation- Images, filtering, compression & processing- Basic object recognition- Geometric transformations- Basic 3D reconstruction- Surface and scene representations- Rendering algorithms- Color: Perception, spaces & models- Basic visualization

2 Learning objectives / Learning OutcomesAfter successful participation in the course students are able to describe the foundational concepts as well asthe basic models and methods of visual computing. They explain important approaches for image synthesis(computer graphics & visualization) and analysis (computer vision) and can solve basic image synthesisand analysis tasks.

3 Recommended prerequisite for participationRecommended:Participation of lecture “Mathematik I/II/III”.





6 Usability of this moduleB.Sc. InformatikB.Sc. WirtschaftsinformatikB.Sc. Psychologie in ITJoint B.A. InformatikB.Sc. Sportwissenschaft und InformatikM.Sc. Sportwissenschaft und InformatikB.Sc. Computational EngineeringB.Sc. InformationssystemtechnikMay be used in other degree programs.


8 ReferencesLiterature recommendations will be updated regularly, an example might be:- R. Szeliski, “Computer Vision: Algorithms and Applications”, Springer 2011- B. Blundell, “An Introduction to Computer Graphics and Creative 3D Environments”, Springer 2008

Courses


Course Nr. Course name20-00-0014-iv Visual Computing


3


Module nameComputer Graphics II



1 ContentFoundations of the various object- and surface-representations in computer graphics. Curves and surfaces(polynomials, splines, RBF) Interpolation and approximation, display techniques, algorithms: de Casteljau,de Boor, Oslo, etc. Volumes and implicit surfaces. visualization techniques, iso-surfaces, MLS, surface ren-dering, marching cubes. Meshes, mesh compression, mesh simplication, multiscale expansion, subdivision.Pointclouds: rendering techniques, surface reconstruction, voronoi-diagram and delaunay-triangulation.

2 Learning objectives / Learning OutcomesAfter successful completion of the course, students are able to handle various object- and surface-representations, i.e., to use, adapt, display (render), and effectively store these objects. This includesmathematical polynomial representations, iso-surfaces, volume representations, implicite surfaces, meshes,subdivision control meshes and pointclouds.

3 Recommended prerequisite for participation- Algorithmen und Datenstrukturen- Grundlagen aus der Höheren Mathematik- Graphische Datenverarbeitung I- C / C++







8 References- Real-Time Rendering: Tomas Akenine-Möller, Eric Haines, Naty Hoffman A.K. Peters Ltd., 3rd edition,ISBN 987-1-56881-424-7- Additional literature will be given in the lecture.

Courses


Course Nr. Course name20-00-0041-iv Computer Graphics II


4


Module namePrinciples of CAE/CAD I



1 ContentWithin the course and exercises, fundamental knowledge of 3D-CAD and computational tools are covered.The focus is directed towards feature modelling, principals of model transformation, building up a com-putation and results interpretation. Teamwork is encouraged for both the exercises and the examinationmodules, in which complex modeling assignments are to be solved.

2 Learning objectives / Learning OutcomesStudents acquire fundamental knowledge of computer aided product modelling and simulation. They areable to build an integrated CAE-process to functionally validate product properties. They are familiar withthe basic modelling techniques using 3D-CAD tools. Furthermore, they can transform the generated mastergeometry into different domains for further analysis and computation and can analyse these from variouspoints of view.






6 Usability of this moduleCore Elective Area D, MSc Maschinenbau - Mechanical and Process Engineering Core Course, BSc Compu-tational Engineering


8 ReferencesLecture notes can be purchased in the institute’s secretarial office. Lecture slides and exercise tutorials areavailable on the website. This lecture is designated an ’e-learning’ modul.

Courses


Module nameInformation Visualization and Visual Analytics



1 ContentThis lecture will give a detailed introduction to the scientific topics of information visualization and VisualAnalytics, and will cover current research areas as well as practical application scenarios of Visual Analytics.* Overview of information visualization and Visual Analytics (definitions, models, history)* Data representation and data transformation* Mapping of data to visual structures* Introduction to human cognition* Visual representations and interaction for bivariate and multivariate Data, time series, networks andgeographic data* Basic data mining techniques* Visual Analytics - Analytics reasoning - Data mining - Statistics Analytical techniques and scaling* Evaluation of Visual Analytics Systems

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students will be able to* use information visualization methods for specific data types* design interactive visualization systems for data from various application domains* couple visualization and automated methods to solve large-scale data analysis problems* apply knowledge about key characteristics of the human visual and cognitive system for informationvisualization and visual analyticschose evaluation methods are used for specific situations and scenarios

3 Recommended prerequisite for participationInteresse an Methoden der Computergrafik und VisualisierungDie Veranstaltung richtet sich an Informatiker, Wirtschaftsinformatiker, Mathematiker in Bachelor, Masterund Diplomstudiengänge und weiteren interessierten Kreisen (z.B. Biologen, Psychologen).









8 ReferencesWill be announced in lecture, an example might be:C. Ware: Information Visualization: Perception for DesignEllis et al: Mastering the Information Age

Courses

Course Nr. Course name20-00-0294-iv Information Visualization and Visual Analytics


4


Module nameProbabilistic Graphical Models


Language Module ownerEnglish Prof. Ph. D. Stefan Roth

1 Content- Refresher of probability & Bayesian decision theory- Directed and undirected models and their properties- Inference in tree graphs- Approximate inference in general graphs: Message passing and mean field- Learning of directed and undirected models- Sampling methods for learning and inference- Modeling in example applications, including topic models- Deep networks- Semi-supervised learning

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students have developed an in-depth understanding of probabilisticgraphical models. They describe and analyze properties of graphical models, and formulate suitable modelsfor concrete estimation and learning tasks. They understand inference algorithms, judge their suitabilityand apply them to graphical models in relevant applications. Moreover, they determine which learningalgorithms are suitable to estimate the model parameters from example data, and apply these.

3 Recommended prerequisite for participationRecommended: Participation in “Statistisches Maschinelles Lernen”.







8 ReferencesLiterature recommendations will be updated regularly, an example might be:- D. Barber: “Bayesian Reasoning and Machine Learning”, Cambridge University Press 2012- D. Koller, N. Friedman: “Probabilistic Graphical Models: Principles and Techniques”, MIT Press 2009

Courses


Course Nr. Course name20-00-0449-iv Probabilistic Graphical Models


4


Module nameVisual Computing Lab



1 ContentStudents work in this lab on selected topics in the area of visual computing. Project results will be presentedin a talk at the end of the course. The specific topics addressed in the lab change every semester and shouldbe discussed directly with one of the instructors.

2 Learning objectives / Learning OutcomesAfter successful completion of this course, the students will be able to independently analyze and solve aproblem in the area of visual computing and to evaluate the results.

3 Recommended prerequisite for participationPractical programming skills, e.g. Java, C++Basic knowledge or interest within Visual ComputingParticipation in one basic lecture within Visiual Computing







8 ReferencesWill be announced in course.

Courses

Course Nr. Course name20-00-0418-pr Lab Visual Computing



Module nameScale Space and PDE methods in image analysis and processing



1 ContentImage analysis & processing deals with the investigation of images and the application of specific tasks onthem, like enhancement, denoising, deblurring, and segmentation. In this course, mathematical methodsthat are commonly used are presented and discussed. The focus will be on the axiomatic choice for themodels, their mathematical properties, and their practical use.Some key words:- Filtering (Edge detection, enhancement, Wiener, Fourier, ...)- Images & Observations: Scale space, regularisation, distributions- Objects: Differential structure, invariants, feature detection- Deep structure: Catastrophes & multi-scale hierarchy- Variational Methods & Partial Differential Methods: Perona Malik, anisotropic diffusion, total variation,Mumford-Shah, Chan-Vese, geometric PDEs, level sets- Curve Evolution: Normal motion, mean curvature motion, Euclidean shortening flow.

2 Learning objectives / Learning OutcomesAfter successful participation in the course students are able to describe the foundational mathematicalconcepts as well as the basic models and methods of image analysis and processing. They explain impor-tant approaches for scale space and PDE methods and can evaluate, transfer, and explain representativetechnical papers.

3 Recommended prerequisite for participationDa Bildanalyse und -verarbeitung eine Mischung aus verschiedenen Disziplinen, wie Physik, Mathematik,Vision, Informatik und Engineering, ist, ist dieser Kurs gezielt auf ein breites Publikum zugeschnit-ten. Daher werden nur Grundkenntnisse in Analysis angenommen. Weitere notwendige mathematischeWerkzeuge werden in den Sitzungen skizziert.







8 References


Main:- B. M. ter Haar Romeny, Front-End Vision and Multi-scale Image Analysis, Dordrecht, Kluwer AcademicPublishers, 2003.Recommended:- T. Lindeberg: Scale-Space Theory in Computer Vision, Dordrecht, Kluwer Academic Publishers, 1994.- J. Weickert: Anisotropic Diffusion in Image Processing, Teubner-Verlag, Stuttgart, Germany, 1998.G. Aubert & P. Kornprobst: Mathematical problems in image processing: Partial Differential Equations andthe Calculus of Variations (second edition), Springer, Applied Mathematical Sciences, Vol 147, 2006.

Courses

Course Nr. Course name20-00-0469-se Scale space and PDE methods in image analysis and processing



Module nameSerious Games Project



1 ContentIn this project the students will design concepts and implement prototypes in the field of serious games(e.g. in education, health and sports).The topics relate to current research questions in the field, partly in cooperation with partners from thegames industry and/or Serious Games users.

2 Learning objectives / Learning OutcomesAfter successfully attending the course, the students can conceptualize and prototypically implement prac-tical tasks in the context of “Serious Games”. Additionally they acquire practical knowledge in the area ofproject management, which they can apply to their own topic as well as transfer it to future projects. Be-sides, the students are able to present their findings in front of an audience applying a number of differentpresentation techniques and to actively participate in a scientific discussion on their topic.

3 Recommended prerequisite for participationProgramming skills (the language will depended on the topic and may be chosen at will for certain topics).







8 References

Courses

Course Nr. Course name20-00-0649-pp Serious Games Project



Module nameSerious Games Seminar



1 ContentIn this seminar the students will analyze and discuss the current state of the art for serious games (e.g. ineducation, health and sports).The topics relate to current research questions in the field, partly in cooperation with partners from thegames industry and/or Serious Games users.

2 Learning objectives / Learning OutcomesAfter successfully completing this course the students are able to become acquainted with an unfamiliarsubject in the field of “Serious Games”. They are familiar with library research techniques for scientificpapers and industry sources. The techniques and results mentioned in these references can be summarized,assessed and compared to each other. Besides, the students are able to present their findings in front of anaudience applying a number of different presentation techniques and to actively participate in a scientificdiscussion on their topic.








8 References

Courses

Course Nr. Course name20-00-0328-se Serious Games Seminar

Instructor Type SWSProf. Dr.-Ing. Michael Gösele Seminar 2


Module nameVisual Analytics: Interactive Visualization of Very Large Data



1 ContentThis seminar is targeted at computer science students with an interest in information visualization, inparticular the visualization of extremely large data. Students will analyze and present a topic from visualanalytics. They will also write a paper about this topic.

2 Learning objectives / Learning OutcomesAfter successfully completing the course, students are able to analyze and understand a scientific problembased on the literature. Students are able to present and discuss the topic.

3 Recommended prerequisite for participationInteresse sich mit einer graphisch-analytischen Fragestellung bzw. Anwendung aus der aktuellen Fachlit-eratur zu befassen. Vorkenntnisse in Graphischer Datenverarbeitung, Informationssysteme oder Informa-tionsvisualisierung







8 References

Courses

Course Nr. Course name20-00-0268-se Visual Analytics: Interactive Visualization of very large amounts of data



Module nameVisualization and Animation of Algorithms and Data Structures



1 ContentThe students will be enabled to create animations of algorithms and data structures to enhance the learningprocess. The contents will be usable for studying the topics covered and can be used in the ICS / GdI 2lecture. The competencies gained especially include:* Becoming familiar with a complex software system for animating algorithms and data structures* Familiarization with a scripting language, a Java-based API and a framework for generators for creatinganimations.* Design and implementation of at least two generators for algorithm or data structure animations* Learning criteria for determining if animations support learning processes* Creation and provision of contents ready for use in teaching and self-study* Competent use of the CS learning platform for submitting feedback and finished tasks

2 Learning objectives / Learning OutcomesAfter taking part in this lab, students will be able to...- use the provided API for animating algorithms.- analyze a given algorithm with regard to its central elements.- construct one visualization each for the central elements of two chosen algorithms.- generalize the generated visualizations by an appropriate support of adjustable parameters.- critically reflect whether the created visualization will support the learning process of the viewer.

3 Recommended prerequisite for participationParticipants need good Java programming skills and should be familiar with the algorithms and data struc-tures taught in ICS 2.







8 References

Courses

Course Nr. Course name20-00-0344-pr Visualization and Animation of Algorithms and Data Structures



Module nameSerious Games



1 ContentIntroduction to the topic of “”Serious Games"": scientific and technical foundations, application areas andtrends. Individual lectures include:* Introduction to Serious Games* Game Development, Game Design* Game Technology, Tools and Engines* Personalization and Adaptation* Interactive Digital Storytelling* Authoring and Content Generation* Multiplayer Games* Game Interfaces and Sensor Technology* Effects, Affects and User Experience* Mobile Games* Serious Games Application Domains and Best Practice ExamplesThe exercise consists of theoretical and practical parts. Students are taught how to use a Game Engine.

2 Learning objectives / Learning OutcomesAfter successfully completing this course the students are able to explain the concept of “Serious Games”and can transfer it to different application domains (like education or health). They can describe thegeneral approach for developing computer games and can apply basic principles of game design, person-alisation / adaptation and interactive digital storytelling. Aside from that students are able to sketch outother current research questions regarding Serious Games as well as their solutions.








8 References


Will be given in lecture.

Courses

Course Nr. Course name20-00-0366-iv Serious Games


4


Module nameApplied Topics in Computer Graphics


Language Module ownerGerman Prof. Dr.-Ing. Michael Gösele

1 ContentSelected papers from the following fields of computer graphics:- Visualization / Rendering- Simulation- Geometry processing and modeling- Semantics and 3D

2 Learning objectives / Learning OutcomesAfter successfully completing the course, students know selected current topics in computer graphics. Theyare able to independently analyze the content of a scientific publications, to understand and to present theproblem as well as the proposed solution. Furthermore, they can analyze and present directions for furtherimprovements in the area.

3 Recommended prerequisite for participationPrior knowledge of GDV or geom. methods of CAD/CAE is advantageous








Courses

Course Nr. Course name20-00-0724-se Applied Topics in Computer Graphics

Instructor Type SWSProf. Dr.-Ing. Michael Gösele Seminar 2


Module nameUser-Centered Design in Visual Computing



1 ContentDeveloping user-centered software leads to a more efficient usage and increases the acceptance by thehuman user. The higher acceptance leads to a better dissemination and exploitation of the developedsolutions. The lecture “User Centered Design in Visual Computing” aims at enabling students from thedepartment of computer science to acquire knowledge about models, methods, and techniques for user-centered development of visualizations and interactive visual representations. This course will introducemethods that lead to designing more efficient solutions with higher acceptance. Furthermore, the lecturewill explain evaluation methods that allow measuring acceptance and efficiency. User Centered Designintroduces the mentioned topics with a special focus on visual computing and graphical user interfaces.Content:* Usability* User experience* Task analysis* User interfaces* Interaction design* Prototyping* Graphics design and information visualization* Evaluation during and after software development* Applications and examples

2 Learning objectives / Learning OutcomesAfter a successful participation, students will be able to:* Identify and argue about adequate methods for developing user-centered software* Apply techniques for user-centered visual interfaces* Identify and choose adequate evaluation methods for the chosen techniques in the different stages ofsoftware development* Recommend improvements for information acquisition and navigation based on studies and evaluations

3 Recommended prerequisite for participation* Basics of visual computing, as e.g. taught in the introductory course HCS and in the course GDV I





6 Usability of this moduleB.Sc. InformatikM.Sc. InformatikM.Sc. WirtschaftsinformatikB.Sc. Psychologie in ITM.Sc. Psychologie in ITJoint B.A. InformatikB.Sc. Sportwissenschaft und InformatikM.Sc. Sportwissenschaft und InformatikCan be used in other degree programs.




8 References

Courses

Course Nr. Course name20-00-0793-iv User-Centered Design in Visual Computing

Instructor Type SWSProf. Dr.-Ing. Michael Gösele Integrated

Course2


Module nameAdvanced User Interfaces



1 Content- Requirements analysis of a given problem- Design and presentation of a user interface concept- Implementation of a prototype

2 Learning objectives / Learning OutcomesStudents have been provided insights into the principles and methods to realize multimedial, collaborativeand adaptive user interfaces for a given problem.

3 Recommended prerequisite for participation- Interesse an neuen, innovativen Benutzungsschnittstellen- Wünschenswert sind Grundkenntnisse der Human Computer Interaction- gute Programmierkenntnisse (C#/WPF und/oder Java)








Courses

Course Nr. Course name20-00-0570-pr



3.8 Optional Subjects AIS-EC: Economics

Module nameIntroduction to Business Administration

Module Nr. Credit Points Workload Self study Duration Cycle offered01-10-1028/f 3 CP 90 h 60 h 1 Every Sem.

Language Module ownerGerman Prof. Dr. rer. pol. Dirk Schiereck

1 ContentThis course serves as an introduction into studies of business administration for students of other siences.The course will provide a broad spectrum of knowledge from the “birth” of business administration asan university science field until its fragmentation into many specialized disciplines. Core topics will in-clude basics of business administration (definitions and German legal forms), some Marketing concepts,introduction into Production Management (business process optimization and quality management), basicknowledge of organisational and personnel related topics, fundamental concepts of finance and investmentas well as internal and external reporting standards.

2 Learning objectives / Learning OutcomesThe couse encourages students who have not been confronted with business studies before to think eco-nomicially. Furthermore, it should enable students to better understand actions of managers and corpora-tions in general.After the course students are able to

• comprehend the development in the history of business administration,• apply essential marketing concepts,• use fundamental methods in production management,• economically valuate investment alternatives and• understand important interrelations in financial accounting.



• Module Examination (Technical Examination, Written/Oral Examination, Standard Grading System)





8 ReferencesThommen, J.-P. & Achleitner, A.-K. (2006): Allgemeine Betriebswirtschaftslehre, 5. Aufl., Wiesbaden.Domschke, W. & Scholl, A. (2008): Grundlagen der Betriebswirtschaftslehre, 3. Aufl., Heidelberg.Further literature will be announced in the lecture.

Courses

Course Nr. Course name01-10-0000-vl Introduction to Business Administration


3.8 Optional Subjects AIS-EC: Economics 459

Course Nr. Course name01-10-0000-tt

Instructor Type SWSProf. Dr. rer. pol. Dirk Schiereck Tutorial 0


Module nameIntroduction to Economics (V)


Language Module ownerGerman and English Prof. Dr. rer. pol. Michael Neugart

1 Content• Economic modeling• Supply and demand• Elasticities• Consumer and producer rent• Opportunity costs• Marginal analysis• Cost theory• Utility maximization• Macroeconomic aggregates• Long-run growth• Aggregate supply and aggregate demand

2 Learning objectives / Learning OutcomesStudents are introduced to the principles of economics and their application to selected fields of interest.






6 Usability of this modulenone


8 Referencesto be announced in course.

Courses

Course Nr. Course name01-60-0000-vl Introduction to Economics



Module nameFinancial and Management Accounting


Language Module ownerGerman and English Prof. Dr. rer. pol. Reiner Quick

1 ContentAccounting: Fundamentals of accounting and bookkeeping, inventory, balance sheet, recording of assetsand debt, recording of expenses and revenues, selected transactions (sales and purchases, non-currentassets, current assets, accruals, wage and salary, distribution of earnings), annual closing entry,Cost Accounting: Fundamentals of cost accounting, cost-type accounting, cost-centre accounting, cost-unitaccounting, operating income statement, direct costing, standard cost accounting, breakeven analysis

2 Learning objectives / Learning OutcomesAfter the courses the students are able to

• understand the core principles of accounting, inventory and preparation of the balance sheet.• book stocks and profit.• solve specific accounting problems in the fields of sales and purchases, non-current and current

assets, accruals, wage and salary, distribution of earnings.• understand the basic concepts and the purpose of cost accounting.• apply the steps of cost accounting (cost type accounting, cost-unit accounting and cost-centre ac-

counting).• deal with modern systems of cost accounting.








8 ReferencesQuick, R./Wurl, H.-J.: Doppelte Buchführung, Wiesbaden: Springer.Coenenberg, A. G./Fischer, T. M./Günther, T.: Kostenrechnung und Kostenanalyse, Stuttgart: Schäffer-Poeschel.Däumler, K. D./Grabe, J.: Kostenrechnung 1: Grundlagen, Herne/Berlin: NWB-Verlag.Further literature will be announced in the lecture.

Courses

Course Nr. Course name01-14-0002-vu Cost Accounting

Instructor Type SWSLecture & Prac-tice

3


Course Nr. Course name01-14-0001-vu Financial Accounting

Instructor Type SWSProf. Dr. rer. pol. Reiner Quick Lecture & Prac-

tice2

Course Nr. Course name01-14-0001-tt Financial Accounting

Instructor Type SWSProf. Dr. rer. pol. Reiner Quick Tutorial 1

Course Nr. Course name01-14-0002-tt Cost Accounting



Module nameAccounting and Finance



1 ContentAccounting: Fundamentals of accounting based on the rules of the German Commercial Code (HGB), ac-counting concepts, purpose of accounting, bookkeeping, inventory, recognition and measurement of assetsand liabilities, income statement, notes, management reportFinancing: Financial decision-making by corporations faces two broad financial questions: What invest-ments should the firm make? And how should it pay for those investments? The focus of the first questioninvolves spending money; the second involves raising it


• understand the steps prior to the preparation of annual financial statements according to the GermanCom-mercial Code (HGB).

• analysze the recognition and measurement of assets and liabilities.• understand income statements, notes and management reports.• solve accounting cases in the context of the German Commercial Code (HGB).• understand financial decision-making processes by individuals.• apply simple tools for rational financial decision-making.

3 Recommended prerequisite for participationBookkeeping







8 ReferencesQuick, R., Wolz, M.: Bilanzierung in FällenSchmidt, R.H., Terberger, E.: Grundzüge der Investitions- und FinanzierungstheorieBrealey, R.A. et al.: Principles of Corporate Finance

Courses

Course Nr. Course name01-16-0001-vl Investment and Finance

Instructor Type SWSProf. Dr. rer. pol. Dirk Schiereck Lecture 2

Course Nr. Course name01-14-0003-vu Financial Reporting

Instructor Type SWSProf. Dr. rer. pol. Reiner Quick Lecture 2


Course Nr. Course name01-14-0003-tt Financial Accounting

Instructor Type SWSProf. Dr. rer. pol. Reiner Quick Tutorial 2

Course Nr. Course name01-16-0001-ue Investment and Finance



Module nameManagement and Marketing


Language Module ownerGerman and English Prof. Dr. rer. pol. Ralf Elbert

1 ContentStudents will be taught in the field of management. In addition students will be teach in the usage ofmanagement instruments and methods. They will learn how to use the instruments in the broad field ofbusiness management. In marketing students will learn about basics, strategic basics, and perspectives ofmarketing. Furthermore, they will learn about the institutional perspective of marketing.

2 Learning objectives / Learning OutcomesAfter the course students are able to

• implement basic knowledge in the management of companies.• impart theoretical knowledge and possibilities of implementation.• connect the different fields of knowledge in management and marketing.• know about basic knowledge of marketing theories and concepts and to apply theories and concepts

on extant problems in marketing practice.

3 Recommended prerequisite for participationGrundlagen der Betriebswirtschaftslehre I und II







8 ReferencesHungenberg, H., Wulf, T.: Grundlagen der UnternehmensführungMarketing:Pflichtliteratur:Homburg, Ch. (2012), Grundlagen des Marketingmanagements: Ein-führung in Strategie, Instrumente,Umsetzung und Unternehmensführung, 3. Auflage, Wiesbaden: Kap. 1, Abschnitt 5.2.2.2, Kap. 6 - 14.Vertiefende Literatur:Esch, F.-R., Herrmann, A., Sattler, H. (2011), Marketing: Eine managementorientierte Einführung, 3. Au-flage, München.Homburg, Ch. (2012), Marketingmanagement: Strategie – Instrumente – Umsetzung – Un-ternehmensführung, 4. Auflage, Wiesbaden.Homburg, Ch. (2010), Übungsbuch Marketingmanagement, 1. Auflage, Wiesbaden.Homburg, Ch., Stock-Homburg, R. (2012), Der kundenorientierte Mitarbeiter, Bewerten, begeistern, be-wegen, 2. Auflage, Wiesbaden.Kotler, P., Armstrong, G. (2011), Principles of Marketing, 14. Auflage, Upper Saddle River.Meffert, H., Bruhn, M. (2009), Dienstleistungsmarketing: Grundlagen – Konzepte – Methoden, 6. Auflage,Wiesbaden.Zusatzliteratur (wird in der Vorlesung bekannt gegeben)

Courses


Course Nr. Course name01-12-0001-vu Management

Instructor Type SWSProf. Dr. rer. pol. Ralf Elbert Lecture 2

Course Nr. Course name01-17-0002-vu Marketing

Instructor Type SWSProf. Dr. rer. pol. Ruth Stock-Homburg Lecture 2


Module nameOperations Research / Production and Supply Chain Management


Language Module ownerGerman and English Prof. Dr. rer. pol. Christoph Glock

1 ContentOperations Research: Basic terms and development of operations research, optimization models, lin-ear programming (Simplex – algorithms, duality, transportation problems), fundamentals of graph theory,project planning, solution principles of mixed integer and combinatorial optimisation, dynamic optimiza-tion, OR and spreadsheet analysis (Excel), standard OR software, fundamentals of simulationProduction and Supply Chain Management: Introduction (basics, production and supply chain types,modeling, planning), strategic and tactical planning (planning concepts, design of production systemsand supply chains), mid-term operational planning (forecasting, aggregate production planning, masterplanning), short-term operational planning (material requirements planning, lot-sizing, job release andscheduling, inventory management)


• describe decision problems in terms of mathematical optimization models.• understand fundamental mathematical methods to solve such optimization models and to assess its

versatility to solve certain classes of optimization models.• to apply modern standard software of operations research.• to identify the most important concepts and problems for production planning.• to apply basic solution methods on their own.• describe computer-based concepts for production planning and control.• judge the possible application of commercial software such as Enterprise Resource Planning or

Advanced Planning Systems.

3 Recommended prerequisite for participationGrundlagen der Betriebswirtschaftslehre I und II







8 ReferencesDomschke, W., Drexl, A.: Einführung in Operations ResearchPrüfungsrelevant sind alle Kapitel des Buchs außer den Kapiteln 2.5.4 (Sensitivitätsanalyse), 2.6 (Simplexmit unteren und oberen Schranken für Variablen; revidierter Simplex-Algorithmus), 2.8 (Spieltheorie), 5(Netzplantechnik), 8 (Nichtlineare Optimierung) und 9 (Warteschlangentheorie).Domschke et al.: Übungen und Fallbeispiele zum Operations ResearchGlock, C.: Produktion und Supply Chain Management – Eine Einführung. B+G Wissenschaftsverlag,Würzburg 2014.

Courses


Course Nr. Course name01-11-0002-vl Production and Supply Chain Management

Instructor Type SWSProf. Dr. rer. pol. Christoph Glock Lecture 2

Course Nr. Course name01-13-0001-vl Operations Research


Course Nr. Course name01-13-0001-ue Operations Research


Course Nr. Course name01-11-0002-ue Production and Supply Chain Management

Instructor Type SWSProf. Dr. rer. pol. Christoph Glock Practice 1


Module nameMacroeconomics I

Module Nr. Credit Points Workload Self study Duration Cycle offered01-61-1B01/5 5 CP 150 h 90 h 1 Every Sem.


1 ContentMacroeconomic indicators, goods market, financial market, IS-LM model, labor market, Philipps curve,medium run, growth, production and saving, technological progress, open economies, exchange rateregimes, rule-based policy makingMacroeconomics (Recitation): The tutorials aim to reinforce and deepen understanding of key topics pre-sented in the lecture.


• identify core macroeconomic indicators and understand their meaning.• explain major drivers of the business cycles in the short and medium run, and identify and compare

policy interventions.• identify drivers of economic growth.• understand the interplay of economies in a global context via the exchange of goods and capital.• evaluate the scope and limits of policy interventions.

3 Recommended prerequisite for participationPrerequisites: nonePrevial Knowledge: see initial skills





6 Usability of this moduleB.Sc. Wirtschaftsingenieurwesen, B.Sc. Wirtschaftsinformatik


8 References• Blanchard, O. und G. Illing (2017): Makroökonomie. 7. Auflage, Pearson.

Further literature will be announced in the lecture.

Courses

Course Nr. Course name01-61-0002-vl Macroeconomics I

Instructor Type SWSProf. Dr. rer. pol. Michael Neugart Lecture 3

Course Nr. Course name01-61-0002-ue Macroeconomics I

Instructor Type SWSProf. Dr. rer. pol. Michael Neugart Practice 1


Module nameEconomic and Financial Policy



1 Content• Efficiency and equity as objectives for economic policy marketing,• Market failures and policies,• Taxation and income redistribution,• Rule-based versus discretionary monetary policy• Collective decisions,• Theory of economic policy reforms


• identify and compare economic policy objectives,• identify market failures and develop economic policy interventions,• understand simple collective decision making mechanisms and applied to the analysis of economic

policy reforms.

3 Recommended prerequisite for participationEinführung in die Volkswirtschaftslehre oder Grundlagen der Volkswirtschaftslehre







8 Referencesto be announced in course.

Courses

Course Nr. Course name01-63-0002-vl Economic and Financial Policy

Instructor Type SWSProf. Dr. rer. pol. Michael Neugart Lecture 2


Module nameEconomic Policy

Module Nr. Credit Points Workload Self study Duration Cycle offered01-63-0M02/6 6 CP 180 h 120 h 1 Every Sem.

Language Module ownerGerman Prof. Dr. rer. pol. Michael Neugart

1 ContentPublic Economics: Stylized facts of the public sector, theories of the public sector, public goods, club goods,externalities, cost-benefit analysis, fiscal federalism, fiscal competitionNeue Politische Ökonomie:Arrow paradox, median voter theorem, probabilistic voting models, interestgroups, agency, citizen-candidate models, legislative bargaining, Richard-Meltzer model, political economyof economic policy reforms, political economy of pension policies


• identify and compare goals of economic policy making.• understand the limits of markets in relation to the allocation problem of scarce resources and derive

and compare theory driven solutions.• explain major theories on economic policy making and apply them to selected areas.• explain and evaluate the outcomes of collective decisions in relation to efficiency and equity.

3 Recommended prerequisite for participationPrerequisites: nonePrevious Knowledge: see initial skills


• Module Examination (Technical Examination, Technical Examination, Standard Grading System)Supplement to Assessment MethodsOral/written: Type and duration of exam are announced by the beginning of the courseWritten: exam (duration 60 - 90 minutes)Oral: team or individual exam (duration 15 - 20 minutes per participant)



6 Usability of this moduleM.Sc. Wirtschaftsingenieurwesen, M.Sc. Wirtschaftsinformatik, M.Sc. Entrepreneurship and InnovationManagement, M.Sc. Logistics and Supply Chain Management


8 ReferencesHindriks, J. and G. D. Myles (2013): Intermediate Public Economics. 2nd edition, MIT Press, Persson, T.and G. Tabellini (2002): Political economics: explaining economic policy, MIT Press.Further literature will be announced in the lecture.

Courses

Course Nr. Course name01-63-2M02-vu Political Economics


2


Course Nr. Course name01-63-1M02-vu Public Economics


2


Module nameManagement Accounting

Module Nr. Credit Points Workload Self study Duration Cycle offered01-14-6200/6 6 CP 180 h 120 h 1 Every Sem.


1 ContentStrategic Management Accounting: Objectives of management accounting, overview of operational man-agement accounting, concept of strategic management accounting, instruments to prepare future prospects,instruments to analyze strategic business environment (e. g. PEST analysis, Porter’s Five Forces, SWOTanalysis), instruments to develop strategies for business segments (e. g. Porter’s generic strategies, portfo-lio analyses)Operational Management Accounting: Introduction: basic principles – budgeting and deviation analy-sis, activity based costing, innovation controlling: target costing, life cycle costing, controlling with keyperformance indicators and balanced scorecard


• understand the functions, objectives and challenges of management accounting.• evaluate the benefits and limitations of management accounting instruments.• evaluate the pros and cons of innovation management accounting.• explain different decisions of management accounting from a theoretical perspective and search for

solutions.

3 Recommended prerequisite for participationPrerequisites: nonePrevious Knowledge: see intial skills


• Module Examination (Technical Examination, Written/Oral Examination, Standard Grading System)Supplement to Assessment MethodsOral/written: Type and duration of exam are announced by the beginning of the courseWritten: exam (duration 60 - 90 minutes)Oral: team or individual exam (duration 15 - 20 minutes per participant)





8 ReferencesHorvath, P.: Controlling, Baum, H.-G., Coenenberg, A. G., Günther, T.: Strategisches Controlling, Küpper,H.-U.: Controlling, Schultz, V.: Basiswissen Controlling, Weber, J.; Schäffer, U.: Einführung in das Control-ling.Further literature will be announced in the lecture.

Courses


Course Nr. Course name01-14-0005-vu Strategic Management Accounting


2

Course Nr. Course name01-14-0012-vu

Instructor Type SWSapl. Prof. Dr. Anette Ahsen Lecture & Prac-

tice2


Module nameIT Project Management


Language Module ownerGerman and English Prof. Dr. rer. pol. Peter Buxmann

1 Content• Definition of the terms project and management• Project Management: Tasks and Methods• Reference Models for IT Projects• Organization of IT Projects• Project Controlling• Reports from Practice• Project Portfolio Management in the context of Information Management• Realization of an IT project in a team of students in cooperation with enterprises


• understand the tasks, methods and challenges of project management.• understand and apply methods for planning the work breakdown structure, for scheduling of activi-

ties, for risk management, for tracking project performance, and for project controlling.• understand challenges of portfolio management in the context of information management.• gain experience with the independent implementation of a software project in a real world scenario.• understand, evaluate and employ modern softwre development methods and tools.• conceive and implement a customer’s requirements.• take on responsibility in a team according to the agreed task allocation.• solve problems and conflicts in a team.• adequately present results.

3 Recommended prerequisite for participationPrerequisites: nonePrevious Knowledge: see initial skills and for IT-Project Management: General knowledge of businessstudies; IT-Project Management practical course: Basic Knowledge of process, data and object modeling(according to study plan), knowledge of at least one computer language.


• [01-15-0003-vl] (Technical Examination, Written/Oral Examination, Standard BWS)• [01-15-0004-pr] (Study Achievement, Written/Oral Examination, Standard BWS)


• [01-15-0003-vl] (Technical Examination, Written/Oral Examination, Weighting: 3)• [01-15-0004-pr] (Study Achievement, Written/Oral Examination, Weighting: 9)



8 References


Burghardt, M.: Einführung in Projektmanagement - Definition, Planung, Kontrolle, AbschlussFiedler, R.: Controlling von Projekten: Projektplanung, Projektsteuerung, ProjektkontrolleKezsbom, D. S., Edward, K. A.: Dynamic Project ManagementKlose, B.: ProjektabwicklungLitke, H.-D.: Projektmanagement: Methoden, Techniken, VerhaltensweisenPatzak von, G., Rattay, G.: ProjektmanagementProject Management Institute: An Introduction to the Project Management Body of KnowledgeRinza, P.: Projektmanagement - Planung, Überwachung und Steuerung von technischen und nichttechnis-chen VorhabenSteinbuch von, P. A.: Projektorganisation und ProjektmanagementWisocky, R., Beck, R., Crane, D.: Effective Project Management

Courses

Course Nr. Course name01-15-0003-vl IT Project Management (Lecture)

Instructor Type SWSProf. Dr. rer. pol. Peter Buxmann Lecture 2

Course Nr. Course name01-15-0004-pr IT Project Management (Practical Course)

Instructor Type SWSProf. Dr. rer. pol. Peter Buxmann Internship 6


Module nameTechnology and Innovation Management


Language Module ownerGerman and English Prof. Dr. Alexander Kock

1 ContentThe lecture Technology and Innovation Management is designed for the students to learn about the chal-lenges of managing innovation. Organizational change and innovation are the basic requirements forcompetitiveness and success of businesses. However, in most industries innovation is often paired withorganizational challenges and barriers. In this lecture, students get to know the fundamental conceptsand design of Innovation Management and the innovation process (form initiative to implementation), aswell as the interaction of central actors. Furthermore, this lecture provides insights into the specialisationsInnovation Behaviour and Strategic Technology and Innovation Management.

2 Learning objectives / Learning OutcomesAfter the course the students are able to

• identify and evaluate problems emerging from managing innovation.• explain, evaluate and apply theories of Technology and Innovation Management.• evaluate fundamental design factors of corporate innovation systems.• derive improvement procedures for innovation processes in firms.• apply tools of technology management.• make relevant recommendations for corporate practice.








8 ReferencesHauschildt, J., Salomo, S., Schultz. C., Kock, A. (2016): Innovationsmanagement, 6. Aufl. Vahlen Ver-lag, Tidd/Bessant (2013): Managing Innovation: Integrating Technological, Market and OrganizationalChange.Further literature will be announced in the lecture.

Courses


Course Nr. Course name01-10-1M01-vu Technology and Innovation Management

Instructor Type SWSProf. Dr. Alexander Kock Lecture & Prac-

tice4


Module nameIntroduction to Innovation Management

Module Nr. Credit Points Workload Self study Duration Cycle offered01-22-2B01 3 CP 90 h 90 h 1 Every Sem.


1 ContentThe lecture offers students an introduction to the topic of innovation management in companies. In timesof disruptive and radical innovations, well-founded knowledge in innovation management is an elementarycore competence of companies in order to stay competitive. After learning the conceptual basics, studentslearn about managing the different stages of the innovation process, from initiative to the adoption of an in-novation. In addition, strategic aspects and the human side of innovation management will be introduced.The lecture thus forms an excellent thematic orientation and introduction for undergraduate students forthe advanced courses of the master studies.

2 Learning objectives / Learning OutcomesAfter the event, students will be able to

• to give an overview of the components of the innovation process and management.• identify and evaluate problems that arise in the management of innovations.• to explain, evaluate and apply theories of technology and innovation management.• assess the basic design factors of a firm’s innovation system.• derive actions to improve innovation processes in companies.• apply the concepts to practice-relevant questions.

3 Recommended prerequisite for participationKenntnisse der Vorlesungen Grundlagen der Betriebswirtschaftslehre I und II.







8 ReferencesBasic literature:Hauschildt, J., Salomo, S., Schultz. C., Kock, A. (2016): Innovationsmanagement, 6. Aufl. Vahlen Verlag.Tidd/Bessant (2013): Managing Innovation: Integrating Technological, Market and OrganizationalChange.Further articles will be announced in the lecture.

Courses

Course Nr. Course name01-22-2B01-vl

Instructor Type SWSProf. Dr. Alexander Kock Lecture 0


3.9 Optional Subjects AIS-EI: Entrepreneurship and Management

Module nameIntroduction to Entrepreneurship


Language Module ownerGerman and English Prof. Dr. rer. pol. Carolin Bock

1 ContentThe course “Grundlagen des Entrepreneurship” (Introduction to Entrepreneurship), being part of themodule “Grundlagen Entrepreneurship” (Introduction to Entrepreneurship) is taught in German, containspartially quotes and illustrations in English however. The course includes the following topics: Definitionof entrepreneurship and its importance for the economic development, entrepreneurship at universities,entrepreneurial motivation and intentions, decision making under uncertainty, effectuation and causa-tion, entrepreneurial failure, entrepreneurial strategy and networks, business planning, growth models,strategic alliances of young ventures, human and social capital of entrepreneurs, social and sustainabilityentrepreneurship.

2 Learning objectives / Learning OutcomesAfter successfully completing the module, students are able to:

• define and describe basic concepts towards entrepreneurship,• understand the psychologically-related concepts of being an entrepreneur,• understand and describe the evolution from small firms to multinational enterprises,• describe special types of entrepreneurship,• understand basic concepts of entrepreneurial thinking towards idea- and business model creation,• realize business opportunities and build sustainable business models,• evaluate chances and risks of national and international markets as well choosing among various

market entry strategies,• incorporate stakeholder feedback into the business model.

3 Recommended prerequisite for participationnone







8 References• Hisrich, R. D., Peters, M. P., & Shepherd, D. A. (2010). Entrepreneurship (8th ed.). New York:

McGraw-Hill.• Read, S., Sarasvathy, S., Dew, N., Wiltbank, R. & Ohlsson, A.-V. (2010). Effectual Entrepreneurship.

New York: Routledge Chapman & Hall.• More literature will be provided within the course and distributed to the students accordingly.

Courses

3.9 Optional Subjects AIS-EI: Entrepreneurship and Management 481

Course Nr. Course name01-27-1B01-vl Introduction to Entrepreneurship

Instructor Type SWSProf. Dr. rer. pol. Carolin Bock Lecture 2


Module nameTechnology and Innovation Management



1 ContentThe lecture Technology and Innovation Management is designed for the students to learn about the chal-lenges of managing innovation. Organizational change and innovation are the basic requirements forcompetitiveness and success of businesses. However, in most industries innovation is often paired withorganizational challenges and barriers. In this lecture, students get to know the fundamental conceptsand design of Innovation Management and the innovation process (form initiative to implementation), aswell as the interaction of central actors. Furthermore, this lecture provides insights into the specialisationsInnovation Behaviour and Strategic Technology and Innovation Management.

2 Learning objectives / Learning OutcomesAfter the course the students are able to

• identify and evaluate problems emerging from managing innovation.• explain, evaluate and apply theories of Technology and Innovation Management.• evaluate fundamental design factors of corporate innovation systems.• derive improvement procedures for innovation processes in firms.• apply tools of technology management.• make relevant recommendations for corporate practice.








8 ReferencesHauschildt, J., Salomo, S., Schultz. C., Kock, A. (2016): Innovationsmanagement, 6. Aufl. Vahlen Ver-lag, Tidd/Bessant (2013): Managing Innovation: Integrating Technological, Market and OrganizationalChange.Further literature will be announced in the lecture.

Courses


Course Nr. Course name01-10-1M01-vu Technology and Innovation Management


tice4


Module nameIntroduction to Innovation Management



1 ContentThe lecture offers students an introduction to the topic of innovation management in companies. In timesof disruptive and radical innovations, well-founded knowledge in innovation management is an elementarycore competence of companies in order to stay competitive. After learning the conceptual basics, studentslearn about managing the different stages of the innovation process, from initiative to the adoption of an in-novation. In addition, strategic aspects and the human side of innovation management will be introduced.The lecture thus forms an excellent thematic orientation and introduction for undergraduate students forthe advanced courses of the master studies.

2 Learning objectives / Learning OutcomesAfter the event, students will be able to

• to give an overview of the components of the innovation process and management.• identify and evaluate problems that arise in the management of innovations.• to explain, evaluate and apply theories of technology and innovation management.• assess the basic design factors of a firm’s innovation system.• derive actions to improve innovation processes in companies.• apply the concepts to practice-relevant questions.

3 Recommended prerequisite for participationKenntnisse der Vorlesungen Grundlagen der Betriebswirtschaftslehre I und II.







8 ReferencesBasic literature:Hauschildt, J., Salomo, S., Schultz. C., Kock, A. (2016): Innovationsmanagement, 6. Aufl. Vahlen Verlag.Tidd/Bessant (2013): Managing Innovation: Integrating Technological, Market and OrganizationalChange.Further articles will be announced in the lecture.

Courses

Course Nr. Course name01-22-2B01-vl

Instructor Type SWSProf. Dr. Alexander Kock Lecture 0


Module nameIntroduction to Business Administration


Language Module ownerGerman Prof. Dr. rer. pol. Dirk Schiereck

1 ContentThis course serves as an introduction into studies of business administration for students of other siences.The course will provide a broad spectrum of knowledge from the “birth” of business administration asan university science field until its fragmentation into many specialized disciplines. Core topics will in-clude basics of business administration (definitions and German legal forms), some Marketing concepts,introduction into Production Management (business process optimization and quality management), basicknowledge of organisational and personnel related topics, fundamental concepts of finance and investmentas well as internal and external reporting standards.

2 Learning objectives / Learning OutcomesThe couse encourages students who have not been confronted with business studies before to think eco-nomicially. Furthermore, it should enable students to better understand actions of managers and corpora-tions in general.After the course students are able to

• comprehend the development in the history of business administration,• apply essential marketing concepts,• use fundamental methods in production management,• economically valuate investment alternatives and• understand important interrelations in financial accounting.








8 ReferencesThommen, J.-P. & Achleitner, A.-K. (2006): Allgemeine Betriebswirtschaftslehre, 5. Aufl., Wiesbaden.Domschke, W. & Scholl, A. (2008): Grundlagen der Betriebswirtschaftslehre, 3. Aufl., Heidelberg.Further literature will be announced in the lecture.

Courses

Course Nr. Course name01-10-0000-vl Introduction to Business Administration



Course Nr. Course name01-10-0000-tt



Module nameIntroduction to Law


Language Module ownerGerman and English Prof. Dr. jur. Janine Wendt

1 ContentThe lecture provides a broad insight into the most important legal fields of daily life - e.g.:

• The law of sales contracts• Tenancy law• Family law• Employment law• Corporate law etc.

These will be illustrated by means of practical cases. Important points of how to frame a contract will bediscussed.

2 Learning objectives / Learning OutcomesThe students will acquire knowledge of the basic principles of German civil law.








8 ReferencesBGB-Gesetzestext(z.B. Beck-Texte im dtv)Materialien zum Download auf der Homepage des Fachgebiets.

Courses

Course Nr. Course name01-40-0000-vl Introduction to Law

Instructor Type SWSProf. Dr. jur. Janine Wendt Lecture 2


Module nameDigital Product and Service Marketing


Language Module ownerGerman and English Prof. Dr. rer. pol. Ruth Stock-Homburg

1 ContentDigital Product and Service Marketing: Selected instruments of various phases of customer relationshipmanagement (analysis, strategic management, operations management, implementation, control) in theera of digitalization; challenge of digital marketing channels; potential of social media marketing and in-fluencer marketing; e-commerce; sustainability and ethical responsibility in digital marketing.Digital Innovation Marketing: Fundamentals and differences of B2B/B2C marketing; significance and fun-damentals of innovation management in the era of digitization; process and design elements of customer-oriented innovation management; digital innovations, user innovations and crowd-based innovations; sig-nificance of digital idea management; co-creation and role of the customer; innovative digital businessmodels.


• Evaluate approaches to analyzing customer relationships.• Explain different phases and tools for managing customer relationships.• Recognize the role of digitization for marketing and to estimate potentials.• Evaluate selected marketing management concepts in the B2B and B2C context.• Explain the process and the organizational design elements of a holistic and customer-oriented in-

novation management.• Recognize the potential of user innovations and crowd-based innovation and to reflect on the role of

the customer.• Critically reflect on ethical aspects of marketing.• Apply the concepts and instruments dealt with to practice-relevant questions in the form of case

studies.• Transfer the learned contents to business practice through guest lectures.








8 References


Digitales Produkt- und Dienstleistungsmarketing:Bruhn, M. (2012): Relationship Marketing, München, 3. Auflage, Homburg, C../Stock-Homburg, R.(2011): Theoretische Perspektiven der Kundenzufriedenheit, in: Homburg, C. (Hrsg.), Kundenzufrieden-heit: Konzepte, Methoden, Erfahrungen, Wiesbaden, 8. Auflage,Stock-Homburg, R. (2011), Der Zusammenhang zwischen Mitarbeiter- und Kundenzufriedenheit: Direkte,indirekte und moderierende Effekte, Wiesbaden, 5. Auflage, Stauss, B., Seidel, W. (2007), Beschwerdem-anagement: Unzufriedene Kunden als profitable Zielgruppe, München, 4. Auflage.Digital Innovation Marketing:Homburg, C. (2012), Marketingmanagement: Strategie – Instrumente – Umsetzung – Un-ternehmensführung, Wiesbaden, 4. Auflage, Szymanski, D. M., Kroff, M. W., Troy, L. C. (2007), Inno-vativeness and New Product Success: Insights from the Cumulative Evidence, Journal of the Academy ofMarketing Science, 35(1), 35-52.Hauser, J., Tellis, G. J., Griffin, A. (2006), Research on Innovation: AReview and Agenda for Marketing Science, Marketing Science, 25(6), 687-717, von Hippel, E. (2005),Democratizing Innovation, Cambridge, Kapitel 9-11.Further literature will be announced in the lecture.

Courses

Course Nr. Course name01-17-0007-vu Digital Innovation Marketing

Instructor Type SWSProf. Dr. rer. pol. Ruth Stock-Homburg Lecture & Prac-

tice2

Course Nr. Course name01-17-0005-vu Digital Product and Service Marketing


tice2


Module nameLeadership and Human Resource Management Systems


Language Module ownerGerman and English Prof. Dr. rer. pol. Ruth Stock-Homburg

1 ContentLeadership:

• Approaches, selected instruments and international aspects of employee and team leadership• Instruments for evaluating one’s own leadership potential and leadership style• Conceptual approaches and success factors of leadership• Leadership of the future• Special application areas of leadership (e.g. regional distributed or virtual leadership)

Future of Work:• Influence of new technologies and digitization on the world of work• Future development and design approaches in human resources management• Approaches to measuring the sustainability of companies and individuals• Special challenges of the future of work (e.g. work-life balance, electronic accessibility, working via

platforms)

2 Learning objectives / Learning OutcomesAfter the course students are able to,

• comprehend the main theoretical concepts of leading employees and teams.• apply the instruments and tools available for leading employees and teams.• apply learned concepts and instruments in case studies.• connect their knowledge to business cases in presentations of experienced practitioners.








8 ReferencesStock-Homburg, Ruth (2013): Personalmanagement: Theorien – Konzepte – Instrumente, Wiesbaden, 3.Auflage.Further literature will be announced in the lecture.


Courses

Course Nr. Course name01-17-0004-vu Leadership

Instructor Type SWSDr. rer. pol. Gisela Gerlach Lecture & Prac-

tice2

Course Nr. Course name01-17-0008-vu Future of Work


tice2


Module nameGerman and International Law of Business Transactions and Corporation Law I

Module Nr. Credit Points Workload Self study Duration Cycle offered01-42-1B01/4 4 CP 120 h 75 h 1 Every Sem.

Language Module ownerGerman and English Prof. Dr. jur. Janine Wendt

1 ContentGerman and International Corporate Law (Lecture): The lecture is divided into two parts: The first partis an introduction to commercial law. The aim is to understand the importance of contract drafting in acompany and to take into account the main aspects of commercial law regulations.The second part is devoted to company law, in particular the law of commercial partnerships and corpora-tions. It also deals with the basic issues of good corporate governance and the importance of compliance.European company law will also be introduced.German and International Corporate Law (Recitation): This course discusses practical cases concerningcommercial law and general company law. In preparation for the exam, sample cases will be discussed.


• recognise the conditions for the application of commercial law.• distinguish between the different commercial intermediaries.• understand the basic structures of the most important forms of partnerships and corporations as legal

entities for companies.• understand the importance of good corporate governance and the importance of compliance for

companies.• deal with different legal texts.• understand the significance of European legal developments for German law and in particular for

the protection of investors.• understand the context of legal regulations (e.g. sales law + commercial law + company law)• work on simple facts of the German commercial and company law, as well as the financial market

law by applying a legal approach and to compile answers to simple legal questions independently• generally recognise, assess and respond to the possibilities and risks of liability in legal matters.

3 Recommended prerequisite for participationGute Kenntnisse der Grundzüge des Allgemeinen Teils des BGB, insbesondere des Vertragsrechts, werdenvorausgesetzt.







8 References


• Wendt, J., Wendt, D. (2019): Finanzmarktrecht, 1. Aufl. De Gruyter Verlag.• Buck-Heeb, P. (2017): Kapitalmarktrecht, 9. Aufl. C.F. Müller Verlag• Poelzig, D. (2017): Kaptalmarktrecht, 1. Aufl. C.H. Beck Verlag• Brox/Henssler, Handelsrecht• Kindler, Grundkurs Handels- und Gesellschaftsrecht

Further literature will be announced in the lecture.

Courses

Course Nr. Course name01-42-0001-vl German and International Law of Business Transactions and Corporation Law I

Instructor Type SWSProf. Dr. jur. Janine Wendt Lecture 2

Course Nr. Course name01-42-0001-ue German and International Law of Business Transactions and Corporation Law I

Instructor Type SWSProf. Dr. jur. Janine Wendt Practice 1


Module nameMasterseminar

Module Nr. Credit Points Workload Self study Duration Cycle offered01-01-0M05 5 CP 150 h 150 h 1 Every Sem.

Language Module ownerGerman and English

1 ContentSpecial topics from of a field of specialization, conducted as a weekly seminar or in blocked form


• work on a scientific problem with scientific methods.• research, narrow down and evaluate the relevant literature (i.e. research literature in English lan-

guage).• structure the topic reasonably and develop the argument.• balance the pros and cons in a comprehensible way.• write down the results according to scientific rules.• present the topic and to discuss it with the group.



• [01-01-0M01-se] (Technical Examination, Written/Oral Examination, Standard BWS)


• [01-01-0M01-se] (Technical Examination, Written/Oral Examination, Weighting: 100 %)



8 ReferencesBänsch, A.: Wissenschaftliches Arbeiten: Seminar- und DiplomarbeitenTheissen, M.R.: Wissenschaftliches Arbeiten: Technik, Methodik, FormThomson, W.: A Guide for the Young Economist - Writing and Speaking Effectively about Economics

Courses

Course Nr. Course name01-01-0M01-se



Module nameProject Management


Language Module ownerGerman and English Prof. Dr. rer. pol. Andreas Pfnür

1 ContentProject management I: Basics of planning and decision making for projects, project goals, generation ofproject alternatives, separation basics in configuration management, project definition, program – portfo-lio, stake-holder management and communication, quality management, scope and change management,human re-sources management for projects / project managersProject management II: Strategic goals, separation and linking of projects; project portfolio planning; multiproject management; organizational structures of multi project management; tools to select project alterna-tives; tools for project controlling; project management as professional service.


• understand the strategic goals of project management, the methods of choosing realization alterna-tives and the methods of project controlling

• understand the various subsystems of project management (e.g. Configuration Management, HumanResource Management, Stakeholder Management, Risk Management)

• understand the principles, methods and organization of multi project management








8 ReferencesProject Management Institute (2013): A Guide to the Project Management Body of Knowledge (PMBOKGuide) 5th EditionFurther literature will be announced in the lecture.

Courses

Course Nr. Course name01-19-0003-vu Project Management II


tice2


Course Nr. Course name01-19-0001-vu Project Management I


2


Module nameIntroduction to Project Management


Language Module ownerGerman and English Prof. Dr. rer. pol. Andreas Pfnür

1 ContentConceptual fundamentals, project organisation, Project structure planning, Volume and cost estimation,Time, cost and capacity planning, Project monitoring/controlling, project risk management

2 Learning objectives / Learning OutcomesThe course shall provide a profound and critical understanding for problems and challenges of projectmanagement. Learnd skills and knowledge:

• Ability to understand and evaluate different alternatives of project management referring to the formof organization with their advantages and disadvantages. These include also the knowledge of dutiesand the establishment of an advisory board.

• Ability to understand, enhance, evaluate and apply methods of cost estimation.• Ability to understand, enhance, evaluate and apply state of the art models and methods referring to

time-, cost- and resource planning.• Ability to understand, enhance and apply the process of project controlling in specific situations.• Knowledge of different standard-software for project management and risk management and the

ability to evaluate and apply them.








8 ReferencesBurghardt, M. (2008): Projektmanagement. Leitfaden für die Planung, Überwachung und Steuerung vonProjekten (8., überarb. und erw. Aufl.). Erlangen: Publicis Corp. Publ.Kerzner, H. (2006): Project Management - A Systems Approach to Planning, Scheduling, and Controlling(9. Aufl.). Hoboken, NJ: Wiley.Madaus, B. (2000): Handbuch Projektmanagement (6., überarb. und erw. Aufl.). Stuttgart: Schäffer-Poeschel.

Courses

Course Nr. Course name01-19-5100-vu Introduction to Project Management

Instructor Type SWSProf. Dr. rer. pol. Andreas Pfnür Lecture & Prac-

tice2


3.10 Optional Subjects AIS-TE: Technology

Module nameProduct Development Methodology I



1 ContentPractical experience in the methods used for the development of technical products. Work in a projectteam.

2 Learning objectives / Learning OutcomesApplying the development methodology to a specific development project in a team. To do this, studentscan create a schedule, can analyze the state of the art, can compose a list of requirements, can abstractthe task, can work out the sub-problems, can seek solutions with different methods, can work out optimalsolutions using valuation methods, can set up a final concept, can derive the parameters needed by compu-tation and modeling, can create the production documentation with all necessary documents such aspartlists, technical drawings and circuit diagrams, can build up and investigate a laboratory prototype and canreflect their development in retrospect.

3 Recommended prerequisite for participationParallel attendance of Proseminar ETiT Option MPE





6 Usability of this moduleBSc ETiT, BSc WI-ETiT


8 ReferencesScript: Development Methodology (PEM)

Courses

Course Nr. Course name18-kn-1025-pj Product Development Methodology I

Instructor Type SWSProf. Dr. Mario Kupnik Project Seminar 3

3.10 Optional Subjects AIS-TE: Technology 499

Module nameProduct Development Methodology II



1 ContentPractical experiences by using methodical procedures in the development of technical products. In additionteamwork, verbal and written representation of results and the organization of development. Work in aproject team and organize the development process independently.

2 Learning objectives / Learning OutcomesApplying the development methodology to a specific development project in a team. To do this, studentscan create a schedule, can analyze the state of the art, can compose a list of requirements, can abstractthe task, can work out the sub-problems, can seek solutions with different methods, can work out optimalsolutions using valuation methods, can set up a final concept, can derive the parameters needed by compu-tation and modeling, can create the production documentation with all necessary documents such as billsof materials, technical drawings and circuit diagrams, can build up and investigate a laboratory prototypeand can reflect their development in retrospect.

3 Recommended prerequisite for participationProduct Development Methodology I





6 Usability of this moduleBSc ETiT, BSc WI-ETiT, MSc MEC



Courses

Course Nr. Course name18-ho-1025-pj Product Development Methodology II



Module nameProduct Development Methodology III

Module Nr. Credit Points Workload Self study Duration Cycle offered18-bu-2125 5 CP 150 h 105 h 1 WiSe


1 ContentPractical experiences by using methodical procedures in the development of technical products. In additionteamwork, verbal and written representation of results and the organisation of development. Work in aproject team and organize the development process independendly.

2 Learning objectives / Learning OutcomesApplying the development methodology to a specific development project in a team. To do this, studentscan create a schedule, can analyze the state of the art, can compose a list of requirements, can abstractthe task, can work out the sub-problems, can seek solutions with different methods, can work out optimalsolutions using valuation methods, can set up a final concept, can derive the parameters needed by compu-tation and modeling, can create the production documentation with all necessary documents such as billsof materials, technical drawings and circuit diagrams, can build up and investigate a laboratory prototypeand can reflect their development in retrospect.









Courses

Course Nr. Course name18-bu-2125-pj Product Development Methodology III

Instructor Type SWSProf. Ph.D. Thomas Peter Burg Project Seminar 3


Module nameProduct Development Methodology IV



1 ContentPractical experiences by using methodical procedures in the development of technical products. In additionteamwork, verbal and written representation of results and the organization of development. Work in aproject team and organize the development process independently.

2 Learning objectives / Learning OutcomesApplying the development methodology to a specific development project in a team. To do this, studentscan create a schedule, can analyze the state of the art, can compose a list of requirements, can abstractthe task, can work out the sub-problems, can seek solutions with different methods, can work out optimalsolutions using valuation methods, can set up a final concept, can derive the parameters needed by compu-tation and modeling, can create the production documentation with all necessary documents such as partlists, technical drawings and circuit diagrams, can build up and investigate a laboratory prototype and canreflect their development in retrospect.









Courses

Course Nr. Course name18-kh-2125-pj Product Development Methodology IV



Module nameTechnology of Micro- and Precision Engineering

Module Nr. Credit Points Workload Self study Duration Cycle offered18-bu-1010 4 CP 120 h 75 h 1 Every 2. Sem.


1 ContentProvide insights into the various production and processing methods in micro- and precision engineeringand the influence of these methods on the development of devices and components.

2 Learning objectives / Learning OutcomesTo explain production processes of parts like: casting, sintering of metal and ceramic parts, injectionmoulding, metal injection moulding, rapid prototyping, to describe manufacturing processes of parts like:forming processes, compression moulding, shaping, deep-drawing, fine cutting machines, ultrasonic treat-ment, laser manufacturing, machining by etching, to classify the joining of materials by: welding, bonding,soldering, sticking, to discuss modification of material properties by: tempering, annealing, compositematerials.






6 Usability of this moduleBSc ETiT, MSc MEC, MSc WI-ETiT


8 ReferencesScript for lecture: Technology of Micro- and Precision Engineering

Courses

Course Nr. Course name18-bu-1010-vl Technology of Micro- and Precision Engineering


Course Nr. Course name18-bu-1010-ue Technology of Micro- and Precision Engineering
















Courses







Module nameIntroduction 3D-Printing and Additive Manufacturing


Language Module ownerGerman Prof. Dr. Edgar Dörsam

1 Contentterminology, process chains, process types, industrial technologies, materials, design, engineering strength,data workflow and data models, potential


• Explain all terms of 3D-Printing and Additive Manufacturing.• Follow through with a systematic comparison of alternative production methods.• Analyze the influence of the materials on the quality of products.• Explain the design demands of 3D-parts.• Distinguish important aspects of CAD models and voxel models.• Show and discuss the potentials of Additive Manufacturing.

3 Recommended prerequisite for participationRecommended modules are: 16-09-5010 Production Technology, 16-08-3241/5251 Material Science &Engineering I and II, 16-07-3011 Information and Communication Technology in Mechanical Engineeringand 16-07-5020 Computer Aided Design.


• Module Examination (Technical Examination, Optional, Standard Grading System)facultative (written 90 min or oral exam 30 min)



6 Usability of this moduleBachelor MPE WPB


8 ReferencesThe current lecture notes can be downloaded from the moodle web pages while the semester is in session.

Courses

Course Nr. Course name16-17-3253-vl Introduction 3D-Printing and Additive Manufacturing



Module nameTutorial in 3D-Printing



1 ContentClassification of 3D printing in manufacturing technology; requirements; selection; workflow; independentrealization of products; justification of approach and chosen production process

2 Learning objectives / Learning OutcomesOn successful completion of this module, students should be able to:1. Identify application areas for 3D printing.2. Choose a suitable 3D printing production process to manufacture parts.3. Modify part geometry regarding the specific production process.4. Use common software from the 3D printing workflow.5. Analyze typical printing errors and modify print parameters.6. Justify the chosen approach in written form.

3 Recommended prerequisite for participationParticipation of lecture „Introduction 3D-Printing and Additive Manufacturing“


• Module Examination (Technical Examination, Special Form, Standard Grading System)


• Module Examination (Technical Examination, Special Form, Weighting: 100 %)



8 ReferencesGebhardt, Andreas. 3D-Drucken: Grundlagen und Anwendungen des Additive Manufacturing (AM). CarlHanser Verlag GmbH Co KG, 2014. http://www.hanser-elibrary.com/isbn/9783446442382

Courses

Course Nr. Course name16-17-3264-tt Tutorial in 3D-Printing



Module nameLighting Technology I

Module Nr. Credit Points Workload Self study Duration Cycle offered18-kh-2010 5 CP 150 h 90 h 1 WiSe


1 ContentStructure and functionality of the human eye, terms and unit in lighting technology, photometry, radio-metric and photometric properties of materials, filters, physiology of vision, colour theory, lighting, lightsources.Measurement of luminous flux, luminous intensity, illuminance, luminance, determination of the spectralresponsivity function of the human eye, colorimetry colour rendering, colour as traffic signals, measuringof optical material characteristics, LED properties

2 Learning objectives / Learning OutcomesTo list and connect terms, units and radiometric and photometric properties of materials in lighting tech-nology, to describe and understand structure and functionality of the human eye and the physiology ofvision, to illustrate basics of lighting, measuring methods and application.Being able to measure base items in lighting technology, applying knowlegde of lighting and enhance themwith experiments. Developing a better understanding for light and color.

3 Recommended prerequisite for participationMSc ETiT, MSc Wi-ETiT, MSc MEC







8 ReferencesScript for lecture: Lighting Technology IExcersisebook: laboratory: lighting technology I

Courses

Course Nr. Course name18-kh-2010-vl Lighting Technology I


Course Nr. Course name18-kh-2010-pr Lighting Technology I



Module nameAdvanced Lighting Technology



1 ContentChosen topics in lighting technology – current developments and applications: Street lighting, Physiology:Detektion / Glare / Lighing and Health, LED – Generation of white Light / State of the Art, Modern Methodsof Light Measurement, Interiour Lighting, Display Technologies, Non-visual Light Impacts,UV-Applications,Automotive Lighting, Solar Modules.

2 Learning objectives / Learning OutcomesTo know current developments and applications, list and connect terms, to illustrate special topics oflighting, measuring methods and application.Beeing able to measure base items in lighting technology, applying knowlegde of lighting and dedicatedapplications and further to enhance them with experiments. Developing a better understanding for light,color, perception and lighting situations.

3 Recommended prerequisite for participationLighting Technology I







8 ReferencesExcercisebook: laboratory: lighting technology II

Courses

Course Nr. Course name18-kh-2020-vl Advanced Lighting Technology


Course Nr. Course name18-kh-2020-pr Advanced Lighting Technology



4 Studium GeneraleModules for the Studium Generale can be found in a separate module handbook for the Studium Generale.

Module nameMentoring (für iST)

Module Nr. Credit Points Workload Self study Duration Cycle offered18-de-1031 1 CP 30 h 15 h 1 WiSe

Language Module ownerGerman PD Dr.-Ing. Oktay Yilmazoglu

1 ContentThe following learning content is taught in the Mentoring:* reflection of own study decision and situation,* basics of the working techniques,* learning techniques and time management methods.The mentoring consists of student-led tutorials in the scope of normally twelve units consist-ing of groupand one-on-one talks, as well as workshop elements and the simulation of an examination situation.For students without exam success in the first semester (WiSe) in an examination in the field of fundamen-tals (catalog 1 to 3) of the study and examination plan, the second semester (SoSe) takes place, usually inthe scope of three units consisting of one-to-one-talks and workshop elements.

2 Learning objectives / Learning OutcomesThrough the mentoring, the students were encouraged to reflect on their study decision and situation.Mentoring enables students to learn and to train working methods and learning methods. They realizethe importance of application of time management methods in learn-ing processes and acquire the abilityto implement them target-oriented for enhancement of learning success. Students reflect own actions inlearning processes and receive feedback from the mentor to gain a higher level of self-competence. Aftercompletion of this module students have the ability to optimize time management for learning success, todevelop the personal learning style and methods and apply learning methods adequate to the met situa-tion and conditions. Students have the ability to analyse reasons for personal problems of understandingand solve them by means of adequate actions and methods.



• Module Examination (Study Achievement, Optional, Pass/Fail Grading System)



6 Usability of this moduleBSc iST


8 References

510

* Kurt Landau, Arbeitstechniken für Studierende der Ingenieurswissenschaften; Verlag ergonomia oHG,Stuttgart, ISBN 3-935089-65-1* Kurt Landau, Besser studieren! Übungsbuch zum Werk Arbeitstechniken; Verlag er-gonomia oHG,Stuttgart, ISBN 3-935089-67-X* Other materials are provided in Moodle

Courses

Course Nr. Course name18-de-1031-tt Mentoring (for iST)

Instructor Type SWSPD Dr.-Ing. Oktay Yilmazoglu Lecture 1

511

Documents

B.Sc. Information System Technology (PO 2015)€¦ · B.Sc. Information System Technology (PO 2015) Module manual Date: 01.03.2020 StudyAreaInformationSystemTechnol-ogy