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1 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module Handbook
for the Master's program
Digital Engineering
at
Otto-von-Guericke University Magdeburg
Faculty of computer science
from 30.09.2012
2 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
The Master's program Digital Engineering (DigiEng)
Graduates of the Master's program in digital engineering are engineers with a strong knowledge of IT methods for the development, construction and operation of complex engineering products and systems, used in production engineering or in the automotive industry. The training qualifies the graduates for demanding activities and leadership roles in the planning and implementation of projects, on the use of modern IT solutions, such as virtual and augmented reality in application of engineering science, as well as in the field of industrial, semi-industrial and academic research. Through its multidisciplinary knowledge the graduates are capable of taking an interface function within interdisciplinary development. The course provides essential skills to perform academic research and advance industrial development. This is achieved through a combination of methods of computer science / engineering and domains. Special project work with objectives, contents and scopes over comparable offers prepare students optimally for the unique challenges of interdisciplinary research. In addition to the technical content of current technologies for the development and operation of engineering solutions, a strong focus lies on methodical knowledge, which is a necessary prerequisite for their successful application. The key skills taught in the course have a focus on interdisciplinary communication and project work. Selected content of the course will be offered in coordination and cooperation with partners in industry-related research.
3 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Table of Contents
1.Computer science fundamentals for engineers ....................................................................................................... 6
Computer Graphics I .................................................................................................................................. 7
Databases .................................................................................................................................................. 9
Introduction to Simulation ...................................................................................................................... 10
Principles of Computer Hardware ........................................................................................................... 11
Software and systems engineering ......................................................................................................... 13
2.Engineering for Computer Science ........................................................................................................................ 14
General Electrical Engineering ................................................................................................................ 15
Digital Information Processing ................................................................................................................ 16
Finite element method (FEM) * .............................................................................................................. 18
Concepts, methods and tools for product lifecycle management .......................................................... 20
Product data modeling ............................................................................................................................ 22
3.Human Factors ........................................................................................................................................................ 24
Human Factors ........................................................................................................................................ 25
Organizational and staff development, team work, problem solving in groups (Basic) ......................... 27
4.Methods of Digital Engineering ............................................................................................................................. 29
CAx applications (CAA) ............................................................................................................................ 30
CAx Management (CAM) ......................................................................................................................... 31
COMPUTER AIDED GEOMETRIC DESIGN ................................................................................................. 32
Computer tomography - theory and application .................................................................................... 34
Digital Planning in automation technology ............................................................................................. 36
Discrete event simulation and 3D visualization ...................................................................................... 38
Communication technology for digital engineering ................................................................................ 39
Machine learning for medical systems .................................................................................................... 41
Product modeling and visualization (PMV) ............................................................................................. 42
Virtual Commissioning ............................................................................................................................ 43
5.Methods of computer science ................................................................................................................................. 44
Data Mining ............................................................................................................................................. 45
Interactive Systems ................................................................................................................................. 47
Mobile Communication ........................................................................................................................... 49
4 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Secure Systems ........................................................................................................................................ 51
Visualization ............................................................................................................................................ 52
6.Interdisciplinary team project ............................................................................................................................... 54
Interdisciplinary team project * .............................................................................................................. 55
7.Specialization ........................................................................................................................................................... 56
Adaptronics * .......................................................................................................................................... 57
Advanced Database Models .................................................................................................................... 58
Advanced Machine Learning ................................................................................................................... 59
Advanced Topics in Databases ................................................................................................................ 60
Applied Discrete Modeling ...................................................................................................................... 62
Bayesian Networks .................................................................................................................................. 63
Image coding * ........................................................................................................................................ 65
Computational Fluid Dynamics ............................................................................................................... 66
Introduction to Data Warehousing ......................................................................................................... 68
Digital Filter * .......................................................................................................................................... 70
Distributed Data Management ............................................................................................................... 71
Introduction to Medical Imaging ............................................................................................................. 72
Embedded Networks ............................................................................................................................... 73
Advanced Programming Concepts for Tailor-made Data Management ................................................. 75
Flow Visualization .................................................................................................................................... 77
Fuzzy Systems .......................................................................................................................................... 78
Information and Coding Theory .............................................................................................................. 80
Data Mining for Changing Environments ................................................................................................ 81
Cognitive Systems * ................................................................................................................................. 83
Mesh Processing ...................................................................................................................................... 84
Modeling with population balances ........................................................................................................ 85
Multimedia Retrieval ............................................................................................................................... 87
Numerical Methods in Biomechanics * ................................................................................................... 89
Security of embedded systems * ............................................................................................................ 90
Speech processing * ................................................................................................................................ 91
Electromagnetic Theory .......................................................................................................................... 92
5 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Theory of electrical cables ....................................................................................................................... 93
THREE-DIMENSIONAL & ADVANCED INTERACTION ................................................................................ 95
Introduction to concurrency control ....................................................................................................... 97
Transport phenomena in granular, particulate and porous media ........................................................ 98
Uncertain knowledge .............................................................................................................................. 99
Distributed Real-Time Systems ............................................................................................................. 100
8.Digital Engineering Project .................................................................................................................................. 102
Digital Engineering Project * ................................................................................................................. 103
6 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
1.Computer science fundamentals for engineers
7 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Computer Graphics I
Module level, if applicable:
Abbrevation, if applicable:
Subheading, if applicable:
Classes, if applicable:
Semester:
Module coordinator: Professor of Visual Computing
Lecturer: Prof. Dr. Holger Theisel
Language: German
Classification within the curriculum:
CV-B compulsory section 2 Semester IngINF-B: Major: computer science techniques INF-B: Major: Computer Graphics / Image Processing
WIF-B: elective computer science / computer science economy
Teaching format / class hours per week during the semester:
Lecture, tutorial
Workload: Attendance time: 2 SWS lectures 2 SWS excercisess
Independent work: 94 h completion of exercises
Credit points: 5 Credit Points = 150 h = 4 h = 56h Attendance time + 94h independent work, grading scale according to examination regulations
Requirements under the examination regulations:
none
Recommended prerequisites:
Module: introduction to computer science
Targeted learning outcomes:
Learning outcomes and competences acquired:
Acquisition of basic knowledge about the most important algorithms in computer graphics
Recognize fundamental principles of computer graphics allows rapid incorporation into new graphics packages and graphics libraries
Ability to use graphical approaches for different applications of computer science
Content: Introduction, history, application areas of computer graphics
Modeling and acquisition of graphical data
Graphical application programming
Transformations
Clipping
Rasterization and anti-aliasing
Lighting
Radiosity
8 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Texturing
Visibility
Raytracing
Overview of modern concepts of computer graphics
Study / exam achievements: Services: - Successful processing of the exercises - Meet the OpenGL programming task
Examination: written, 2 hours
Note
Inputs according to the specification at the beginning of the semester
Forms of media:
Literature: J.D. Foley, A. van Dam, S.K. Feiner, J.F. Hughes: Computer Graphics – Principles and Practice (second Edition). Addison-Wesley Publishing Company, Inc., 1996
J. Encarnacao, W. Straßer, R. Klein: Gerätetechnik, Programmierung und Anwendung graphischer Systeme, Teil I und II. Oldenbourg, München, Wien, 1966, 1997
D. Salomon: Computer Graphics Geometric Modeling, Springer, 1999
A. Watt: 3D Computer Graphics. Addison-Wesley Publishing Company, Inc., 2000
9 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Databases
Module level, if applicable:
Abbrevation, if applicable: 100391
Subheading, if applicable: DB I
Classes, if applicable:
Semester: 3 IF IngIF, WIF
5 CV
Module coordinator: Professor of Practical computer science / information systems and databases
Lecturer: Prof. Gunter Saake
Language: German
Classification within the curriculum:
IF IngIF, CV: computer science 1
WIF: computer science
Teaching format / class hours per week during the semester:
Lecture, tutorial
Workload: Attendance time: 2 SWS lectures 2 SWS tutorial Independent work: Exercises and exam preparation
Credit points: 5 credit points = 150h = 4h = 56h Attendance time + 94h independent work
Grading scale according to examination regulations
Requirements under the examination regulations:
None
Recommended prerequisites:
None
Targeted learning outcomes:
Learning objectives and competences acquired: Basic understanding of database systems (terms, basic concepts) Ability to design a relational database
Knowledge of relational database languages Ability to develop database applications
Content: Properties of database systems Architectures Conceptual design of a relational database
Relational database model Mapping of ER schema to relations Database languages (relational algebra, SQL) Formal design criteria and normalization theory
Application programming
Other database concepts such as views, triggers, assignment of rights
Study / exam achievements: Check or bill: written
Forms of media:
Literature: See http://wwwiti.cs.uni-magdeburg.de/iti_db/lehre/db1/index.html
10 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Introduction to Simulation
Module level, if applicable:
Abbrevation, if applicable: ItS
Subheading, if applicable:
Classes, if applicable:
Semester: 5
Module coordinator: Professor of simulation
Lecturer: Graham Horton
Language: lecture:English / exercises: German and English
Classification within the curriculum:
B-CV: CV WPF FIN INF area
B-INF: WPF computer science consolidation (Applied computer science or computer engineering computer science systems) B IngINF: Compulsory
WIF-B: elective
Teaching format / class hours per week during the semester:
Lectures, exercises
Workload: Attendance time = 56 hours 2 SWS lecture
2 SWS excercises Independent work = 94 h
- Processing of homework & exam preparation
Credit points: 5 credit points
Requirements under the examination regulations:
-
Recommended prerequisites:
Mathematics I-III
Targeted learning outcomes:
Ability to carry out a semester-long project, using basics of simulation, event-oriented modeling and programming, abstract modeling and applications of computer science in other fields
Content: Event simulation, random variables, random number generation, statistical data analysis, ordinary differential equations, numerical integration, AnyLogic simulation system, stochastic Petri nets, queuing
Study / exam achievements: Rated: Written test, 120 min
Ungraded: Homework + 20 min discussion note
Forms of media:
Literature: Banks, Carson, Nelson, Nicol: Discrete-Event Simulation See www.sim.ovgu.de
Other Title during summer semester: "Modeling and Simulation"
11 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Principles of Computer Hardware
Module level, if applicable:
Abbrevation, if applicable: TI-I
Subheading, if applicable:
Classes, if applicable:
Semester: 1
Module coordinator: Chair of Technical computer science
Lecturer:
Language: German
Classification within the curriculum:
PF IF, B 1
PF IngINF, B 1
WPF CV, B 1-5
WIF WPF, B 1-5
Teaching format / class hours per week during the semester:
Lecture, exercises
Workload: Attendance time: 2 SWS lecture
2 SWS exercises Independent work: Processing of exercise and programming assignments & exam preparation
Credit points: 5 Credit Points = 150 h = 4 h = 56h Attendance time+ 94h independent work. Grading scale according Prufungsordnung
Requirements under the examination regulations:
none
Recommended prerequisites:
none
Targeted learning outcomes:
Learning objectives and competences acquired:
Ability to understand the basic structure of computers as a layered model of different levels of abstraction and describe
Competency, components of the digital logic level to design independently,
In-depth knowledge available via the machine level of a digital computer.
Understanding of the principles for performance improvement through pipelined and parallel processing
Content: - Combinational Switching Networks - Sequential derailleurs - Computer arithmetic
- Building a computer - Instruction set and addressing
- Pipelined and parallel processing
Study / exam achievements: Services: Processing of exercise and programming tasks
12 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Examination: written
Forms of media:
Literature: Will be announced in the lecture
13 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Software and systems engineering
Module level, if applicable
Abbrevation, if applicable: S & SE
Subheading, if applicable:
Classes, if applicable:
Semester: 1
Module coordinator: Professor of Computer Systems in engineering and other
Lecturer:
Language: English (German optional)
Classification within the curriculum:
Teaching format / class hours per week during the semester:
Lecture, exercises
Workload: Attendance time: 2 SWS lecture 2 SWS excercisess Independent work: Analyzing, modeling, presenting
Credit points: 6 Credit Points = 180 h = 4 h = 56h Attendance time + 124h independent work Grading scale according to examination regulations
Recommended prerequisites:
none
Recommended prerequisites:
none
Targeted learning outcomes:
Learning objectives and competences acquired: Understanding and application of process models and modeling languages for analysis and design Understanding and applying different approaches to requirements engineering Understanding and applying different methods for detection of fulfilling requirements of
Content: Process models, UML, SysML, programming languages/ restriction / technical boundary technical Systems, requierements modelling / acceptance / traceability
Study / exam achievements:
Forms of media: Lecture, student presentation, exercise
Literature:
14 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
2.Engineering for Computer Science
15 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Name of the module General Electrical Engineering
Contents and objectives of the module
Learning outcomes and competences acquired:
Convey basic knowledge of electrical engineering, electronics, electronic components, and the structure and performance of electrical machines and drives
Development of skills for independent solving electrical engineering tasks and
Aptitude for practical tests on electrical systems and components
Contents:
Fundamentals of Electrical Engineering
DC circuits
AC Technology
Electric field
Magnetic field
Electronics
Electrical Machines and Drives
Measurement of electrical quantities
Protection measures
Methods of Teaching Lecture, exercise, internship
Prerequisites for participation
Mathematics, Physics
Applicability of the module Creditability in consecutive courses: Compulsory and elective in different bachelor's degree programs for non-electrical engineers
Requirements for awarding credit points
Examination for admission to the internship in the summer semester, course certificate, exam without any aids at the end of the module
Credits and grades 8 credit points = 240h (96h Attendance time + 144h independent work) Grading scale according to examination regulations
Workload Attendance time: over 2 semesters 2 SWS lecture over 2 semesters 1 SWS excercises and internship
independent work: Reworking lecture, solve exercises, prepare laboratory experiments, exam preparation
Frequency of occurrence each year - first part in WS, second part in SS
Duration of module two semesters
Responsible Palis / Lindemann
16 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Modules Digital Information Processing
Objectives and contents Objectives:
The participant has to overview of basic problems and methods of digital signal processing.
The participant understands the functionality of a digital signal processing system and can Mathematically explain the mode of operation.
The participant can assess assurance applications in terms of stability and other markers. He / She can calculate the frequency response and reconstruction of analogue signal.
The participant can perform synthesis calculations and assessments as well on stochastically excited digital systems.
The participant can apply this knowledge in a field of specialization, egMedical Signal Analysis
Contents: 1. Digital signal and digital LTI system 2. Z-Transform and Difference Equations 3. Sampling and Reconstruction 4. Synthesis and analysis of search system 5. Discrete and Fast Fourier Transform 6. Processing of Stochastic signal by LTI system:
Correlation Techniques and model-based system (ARMA) 7. Selected Specialization topics, eg Medical Signal
Analysis
Teaching Lecture and exercises
Books used: Wendemuth, A (2004): “Grundlagen der Digitalen Signalverarbeitung”, 268 pages, Springer Verlag, Heidelberg. ISBN: 3-540-21885-8 Oppenheim, A; Schafer R (1975): “Digital Signal Processing” 784 pages, Prentice Hall, ISBN: 0132146355
Prerequisits Bachelor's degree in Electrical Engineering or related studies
Knowledge of signals and systems, analog Fourier transformations
Usability of the module Master Courses in the Faculty of Electrical Engineering and Information Technology, and other Master courses
Exam Mandatory participation in exercise classes, successful results in exercises / written exam at the and of the course
Credit points 4 credit points = 120 h (42 h time of attendance and 78 h autonomous work)
Work load Time of attendance 2 hours / week - lecture 1 hours / week - exercises
Autonomous work
17 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
postprocessing of lectures preparation of exercises and exam
Availability Winter semester, every year
Duration One semester
Responsibility Prof. Dr. A. Wendemuth, FEIT-IESK
18 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Name of the module Finite element method (FEM) *
Aims and Content of the
module
Aims and content
In this course, students will learnt to use the finite element method as
an approximation method for solving of practical tasks of engineering
(mechanical engineering, automotive, machine tool, aerospace).
The course focuses on problems of mechanics of solids by use of three-
dimensional models (solid and surface models). In the lectures the most important theoretical basis for understanding
the modeling and evaluation of the results (fault analysis, power
adaptation) are taught.
In the exercises, the material based on practical tasks is deepened, and
in the internship, the students independently solve a complex task
whose successful processing is a prerequisite for admission to the
examination.
Lecture series
1. Introduction to the course (including an overview of commercial software
tools) 2. Adapted problem modeling with volume and shell elements (shell models
versus 3D continuum models) 3. Finite volume elements (shape functions, isoparametric element concept,
numerical integration, and hourglass-locking phenomena, super
convergence) 4. Shell finite elements (Ahmad elements, Kirchhoff and Mindlin elements,
discrete Kirchhoff elements, patch test, item selection) 5. Coupling of shell elements with 3D solid elements (constraints, weak form of
coupling,) 6. Dynamic structure calculations (eigenvalues, model reduction by Gyan and
Craig-Bampton modal method, time integration, frequency domain methods,
model updating). 7. Overview of the FEM for solving general (coupled) field problems (heat
conduction, heat stress). 8. Summary and Outlook (Non-linear FEM, optimization)
Exercises (2h every 14 days)
Calculation of tasks on the computer using commercial FEM software
Internship (14 weeks 2 hours)
Independent preparation of an individual project (group project)
Methods of Teaching Lectures (2 SWS), exercises (1 SWS) internship (1 credit hour)
Prerequisites for participation TM, Computational Mechanics and FEM
Applicability of the module There are no interactions with other modules
Grade and credit points Oral examination
19 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Credits 5 ECTS
Workload Attendance time: 4 SWS (lectures, exercises, internship) Independent
Processing a Project
Frequency of occurrence annually
Duration of module one semester
Responsible Prof. U. Gabbert
20 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Concepts, methods and tools for product lifecycle management
Module level, if applicable
Abbrevation, if applicable: PLM
Subheading, if applicable:
Classes, if applicable:
Semester:
Module coordinator: Chair for Applied computer science / computer-aided engineering systems
Lecturer:
Language: German / if necessary English
Classification within the curriculum:
Teaching format / class hours per week during the semester:
Lecture, exercises, tutorials
Workload: Attendance time
2 SWS lecture
2 SWS excercises / tutorial
Ability to work independently
Solution of exercises including tutorial tasks
Exam Preparation
Credit points: 6 Credit Points = 180 h
(56 h Attendance time + 124 hours independent work) Grading scale according to examination regulations
Requirements under the examination regulations:
none
Recommended prerequisites: Knowledge of software development based on UML
Knowledge of document management
Knowledge in the design of data structures
Fundamentals in Mechanical Engineering
Fundamentals in CAD / CAE / CAM
Knowledge from the field of computer-assisted engineering systems
Targeted learning outcomes: Objectives & Competences to be Acquired
Acquisition of knowledge of concepts, methods, procedures and tools for PLM
Acquire an understanding of product data and their significance for the business processes of manufacturing companies
Acquisition of basic skills for the uniform production, processing and management of technical product data and documents
21 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Ability to solve individual problems for product data management in the context of specific PLM strategies
Ability to develop, design and implementation company-specific PLM strategies
Content: Topics of the lecture
Methodological fundamentals for product data management
Methodological fundamentals for PLM
Concepts and tools for analysis and modeling of integrated product models
Tools for PDM / PLM integration (CAD, CAE)
Organizational requirements of PDM / PLM introduction
Economic aspects of PDM / PLM introduction
PDM / PLM implementation strategies
System architectures for PDM / PLM
Concrete PDM - systems and their features and capabilities
Corporate realized as concrete solutions
Content exercise / tutorial
Exercises related to selected content of the lectures
Solution of a specific PLM project (example) for all phases in the context of a concrete example
Study / exam achievements: Services
Completion of exercises and the project with successful presentation in the exercises
Examination
Spoken
Forms of media:
Literature: R. Anderl, H. Grabowski, A. Polly: Integriertes Produktmodell. Entwicklungen zur Normung von CIM, Beuth-Verlag
M. Eigner, R. Stelzer: Produktdatenmanagement-Systeme: Ein Leitfaden für Product Development und Life Cycle Management, Springer-Verlag
V. Arnold, H. Dettmering, T. Engel, A. Karcher: Produkt Lifecycle Management, Springer-Verlag
A.-W. Scheer, M. Boczanski, M. Muth, W.-G. Schmitz, U. Segelbacher: Prozessorientiertes Product Lifecycle Management , Springer-Verlag
Own script
22 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Name of the module Product data modeling
Contents and objectives of the module
In this course the following content will be taught:
Classification of components of technical systems in terms of
their model characteristics
Mediation of the methodological basis for product data
description, including: feature systems, semantic networks
and forms of notation suchAs XML and class diagrams
Essential standards of performance in the field suchAs IEC
61360, ecl @ ss, ETIM, BMEcat PROLIST
Notion of a feature-based information model
mechanical, electrical and automation engineering application
examples
Contents:
In many areas of machine and plant automation technologies
and the efficient flow of information between different life cycle
phases, tools and operating engineers are becoming increasingly
important.There is a trend to gradually replace routine jobs of
engineering through automated or semi-automated technical
processes. To this unique and digitally available description of
the components of technical systems are required. The
descriptions will be referred to as the product data, which will be
merged as mechatronic models. This course provides the basics
for digital modeling of product data technology systems.
Methods of Teaching Lecture (2) + Exercise (1)
Prerequisites for participation
Basic knowledge in computer science and software development
Applicability of the module There is no interaction with other modules. Eligibility: elective master's degree in "Digital Engineering"
Requirements for awarding credit points
Participation in the courses
Examination at the end of the module, grade scale according to
examination regulations, award of points accourding to written or
oral examination
Credits and grades 5 Credit Points = 120 h (42 h Attendance time + 78 hours independent work) Grading scale according to examination regulations
Workload Attendance time: weekly lectures, 2 SWS weekly exercises 1 SWS
Independent work: Reworking the lecture Solution of exercises and exam preparation
Frequency of occurrence Every xxx - Semester
23 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Duration of module one semester
Responsible Prof. Dr. Christian Diedrich, FEIT-IFAT
24 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
3.Human Factors
25 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Human Factors
Module level, if applicable ---
Abbrevation, if applicable: ---
Subheading, if applicable: ---
Classes, if applicable: Ergonomics
Semester: 1
Module coordinator: Deml
Lecturer: Brennecke, Deml
Language: German, if necessary English
Classification within the curriculum:
Teaching format / class hours per week during the semester:
Lecture and lecture accompanying exercises
Workload: Attendance time:
Lecture: 2 SWS
Exercise: 1 SWS
Independent work:
Follow the lectures
Preparation of written examination
Credit points: 3 Credit Points = 75 h (42 h Attendance time + 33 hours independent work)
Requirements under the examination regulations:
Attending lectures
The written examination
Recommended prerequisites:
---
Targeted learning outcomes:
The aim of this lecture is to convey the relevant relationship between humans, technology and organization needed for engineering behavior. The participants should acquire methods and standards to make work humane. It will give the need to plan the relational structure-man-technology-organization and designed so that the human performance potencies can be optimally utilized and further refined and that no damaging or impairing effects on health and well-being of the people arise. In this way, the cost can be realized in unity with the humanity of the work. The courses offer work science basics and instructions or pulses for engineers, who are not working as specialists in the design work,
Content: Object, definition, objectives and elements of Industrial Science
Physiological and psychological foundations of the work
Workplace design
Design of computer work
Work environment design (noise, lighting)
Work organization
26 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Human information processing
Human-Machine Interaction
Human error and reliability
Time Management
Health and Safety
Study / exam achievements:
Written examination
Forms of media: PowerPoint
Literature: Is provided in the lecture
27 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Organizational and staff development, team work, problem solving in groups (Basic)
Module level, if applicable ---
Abbrevation, if applicable: OPE
Subheading, if applicable: ---
Classes, if applicable: ---
Semester: 1
Module coordinator: Dr.-Ing. Sonja Schmicker
Lecturer: Dr.-Ing. Schmicker, Dipl.-Kff. Silke Schröder
Language: German
Classification within the curriculum:
Teaching format / class hours per week during the semester:
Lecture with integrated exercise
Workload: Attendance time:
Lecture and integrated exercise: 4 SWS
Independent work:
Preparation and review of the lectures or Exercises
Preparation for the oral examination
Credit points: 4 Credit Points = 100h (56h Attendance time + 44h independent work)
Requirements under the examination regulations:
Attending lectures or Exercises
Passing the oral examination
Recommended prerequisites:
---
Targeted learning outcomes:
The aim of the event is to learn methods for facilitation of group work. For this purpose, theoretical knowledge and practical training in the areas of organizational and personal development, intra-and inter-personal communication, intra-and inter-group behavior, creativity and structured problem solving are taught and realised.
Content: Overview of tasks and functions of human resources and organizational development
current trends in personal and organizational development
Derivation of requirements for the competence development
Design, approaches to group and team work and employee participation in the economy
social and communicative skills in group work
Control of dynamic processes on the theme-centered interaction (TCI)
Application of creativity techniques in group work
systematic and methodical action in problem solving
Presentation of group work.
Study / exam Oral examination
28 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
achievements:
Forms of media: Multimedia (overhead projector, PowerPoint, flipchart, pin board, TV / video, etc.)
Literature: Is provided in the event
29 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
4.Methods of Digital Engineering
30 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Course: M.Sc.Integrated Design Engineering (IDE)
Module:
CAx applications (CAA)
Objectives of the module: Learning objectives and competences acquired:
Learn about different CAx applications and their correlations
Mastering the essential elements of product lifecycle management
Dominate Simple PDM applications
Learn and master simple simulation method
Contents:
Product Lifecycle Management
Process Modeling
Networks
CAPP and NC systems, CAM systems, Flexible manufacturing systems, handling systems
Simulation method
PDM applications and databases
Methods of Teaching: Lectures and exercises with corresponding scripts and excercises. Media: overhead projector, blackboard
Prerequisite for participation: Attend the lecture: introduction IDE
Workload: Attendance time: 42h Lectures: 2 SWS lectures, 2 SWS excercisess.
Working independently 108h: Wrapping up the lectures, preparation of exercises and the written examination
Assessment / Credits: Examination requirement: Participation in lectures and exercises (at least 75%). Written examination (120 min duration). Grading scale according to examination regulations.
5 credit points
Responsible for Module: Prof. Dr.-Ing. Dr, h.c. Sándor Vajna, FMB - LMI
Suggested Reading: Lecture notes and excercises and Vajna, We-ber, Bley, Zeman: CAx for engineers, Springer 2008
31 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Course: M.Sc.Integrated Design Engineering (IDE)
Module:
CAx Management (CAM)
Objectives of the module:
Awaken the understanding of the needs of the CAx management
Getting to know and applying relevant procedure-wise introduction to and migration of a CAx system
Getting to know and applying of methods for determining the efficiency of CAx systems and applications
Master the basic elements of the management of CAx systems
Exploring methods to predict costs of product costs in the various phases of the product life cycle
Contents: Methods and procedures to
Introduction and migration of CAx technology
Cost of CAx systems (including Costs, benefit, investment procedures of the business economics)
Evaluation of the benefits of new technologies in the product development process with the BAPM procedure
Product Lifecycle Costing
Efficient system management
Methods of Teaching: Lectures and exercises with corresponding scripts and excercises. media: beamer, overhead projector, blackboard
Prerequisite for participation: Attend the lecture: introduction IDE
Workload: Attendance time: 42h Lectures: 2 SWS lectures, 2 SWS excercisess.
Working independently 108h: Wrapping up the lectures, preparation of exercises and the written examination
Assessment / Credits: Examination requirement: Participation in lectures and exercises (at least 75%). Written examination (120 min duration). Grading scale according to examination regulations.
5 credit points
Responsible for Module: Prof. Dr.-Ing. Dr, hcSándor Vajna, FMB-IMK/LMI
Suggested Reading: Lecture notes and excercises and Vajna, Weber, Bley, Zeman: CAx für Ingenieure, Springer 2008
32 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: COMPUTER AIDED GEOMETRIC DESIGN
Module level, if applicable:
Abbrevation, if applicable: CAGD
Subheading, if applicable:
Classes, if applicable:
Semester:
Module coordinator: Chair for Applied computer science / Visual Computing
Lecturer: Prof. Dr. Holger Theisel
Language: German / English on demand
Classification within the curriculum:
WPF Bachelor CV: CV electives WPF Bachelor IF: consolidation AI / consolidation CG / BV
WPF Bachelor IngIF: elective computer science techniques
Bachelor WIF WPF: computer science electives
Teaching format / class hours per week during the semester:
Lecture and exercise / 4 SWS
Workload: Attendance time: 3 SWS lecture / 1 SWS exercise
Independent work: Reworking the lecture
Solve the exercises
Credit points: 5 Credit Points = 150 h (56h Attendance time + 94h independent work), grade scale according to examination regulations
Requirements under the examination regulations:
none
Recommended prerequisites: Fundamentals of computer graphics Mathematics I to III
Targeted learning outcomes: Learning objectives and competences acquired:
Learn the most important techniques for curve and surface modeling
Understanding of the underlying theoretical principles
Applying the approaches to other problems in computer science (data interpolation Datenapproximation, data extrapolation, numerical methods)
Content: Differential geometry of curves and surfaces Bezier curves Bezier spline curves B-spline curves Rational curves Polar forms Bezier and tensor product B-spline surfaces Bezier surfaces over triangles Surface interrogation and fairing Subdivision curves and surfaces
Study / exam achievements: Exam prerequisite: Successful Exercises processing
33 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Oral examination
Note
Inputs according to the specification at the beginning of the semester
Forms of media: PowerPoint, video, black board
Literature: G. Farin. Curves and Surfaces for Computer Aided Geometric Design. Morgan Kaufmann, 2002. Fourth edition.
G. Farin and D. Hansford. The Essentials of CAGD. AK Peters, 2000.
J. Hoschek and D. Lasser. Grundlagen der Geometrischen Datenverarbeitung. B.G. Teubner, Stuttgart, 1989. (English translation: Fundamentals of Computer Aided Geometric Design, AK Peters.)
G. Farin. NURB Curves and Surfaces. AK Peters, Wellesley, 1995.
34 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Name of the module Computer tomography - theory and application
Contents and objectives of the module
Objectives of the Module (Competencies):
Understanding of the systems theory of imaging systems Overview of the physics and operation of computer tomography Understanding of the mathematical method for tomographical reconstructing Overview of current research areas in tomographic imaging
Contents:
Starting with the system theory of imaging systems follows the treatment of the physical properties of the X-ray radiation and its interaction with matter.In the second part the X-ray-based imaging projection will be discussed. In the third part follows the study of the exact mathematical method of tomographic imaging and the treatment of various image reconstruction methods.The individual issues are:
System theory of imaging systems Physical Basics X-ray tubes and X-ray detectors Projection imaging Reconstruction techniques: Fourier-based methods, filtered back-projection, algebraic method, statistical method Geometries: parallel, fan-and cone-beam Implementation Issues Image artifacts and their corrections
Methods of Teaching Lecture, excercises
Prerequisites for participation Digital signal processing, principles of physics
Applicability of the module Creditable for all master programs of other departments whose academic regulations allow.
Requirements for awarding credit points
Oral examination
Credits and grades 5 credit points = 150h (42h + 108h independent work) Grading scale according to examination regulations
Workload Attendance time: 2 SWS lecture, 1 SWS exercise
independent work
Frequency of occurrence Every year in SS
Duration of module one semester
Responsible Prof. Dr. rer. nat. George Rose (FEIT-IESK)
35 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Data Management for Engineering Applications *
(Prof. Saake) Lecture (English) in preparation Module description will be given later
36 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Name of the module Digital Planning in automation technology
Contents and objectives of the module
Learning objectives and competencies acquired are divided into the following parts.
Planning process with the phases of project management
Support of engineering using modern CAE systems
Provision of specific requirements of the process and production technology
Information Technical description of the technical and organizational processes
Contents:
The configuration of process control systems (PLT) systems based on distributed process control systems is a complex knowledge and teaching area, which was placed in recent years on a sound economic knowledge base.Training objective of this lecture, is to provide these conceptual and methodological principles systematically. This is done from the point that the planning process and the resulting planning information and documents are increasingly created digitally, stored and reused.The individual phases and content of the continuous project management are described and the basics of computerized aided engineering are taught.In this way, students are capable of working with engineers from other disciplines, such as process engineers, plant engineers and other investment partners, cooperatively. The students should be able to critically deal with the concept of automation objects apart to formulate the automation goals and tasks and to influence the automation-based design of technological systems in order to enhance the effectivenes
Methods of Teaching Lecture (2) + Exercise (1)
Prerequisites for participation
The course is suitable for students of engineering science students from the 4th Semester.
Applicability of the module There is no interaction with other modules. Eligibility: elective master's degree in "Digital Engineering"
Requirements for awarding credit points
Mandatory participation in the exercises, successful execution of
the exercises
Credits and grades 4 Credit Points = 90 h (45 h Attendance time + 45 hours independent work) Grading scale according to examination regulations
Workload Attendance time: weekly lectures, 2 SWS weekly exercises 1 SWS
Independent work: Reworking the lecture Solution of exercises and exam preparation
37 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Frequency of occurrence Every year in xx-semester
Duration of module one semester
Responsible Prof. Dr. Christian Diedrich, FEIT-IFAT
38 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Discrete event simulation and 3D visualization
Module level, if applicable
Abbrevation, if applicable: DiSi3D
Subheading, if applicable:
Classes, if applicable:
Semester:
Module coordinator: Chair for Applied computer science
Lecturer: Prof. Dr. Thomas Schulze
Language:
Curriculum
Teaching format / class hours per week during the semester:
Lectures, frontal exercises and independent work
Workload: Attendance time: Weekly Lecture 2 SWS Weekly exercise 2 SWS Independent work: Exercises and exam preparation
Credit points: 6 Credit Points = 180 h (42 h Attendance time + 138 hours independent work) Grading scale according to examination regulations
Requirements under the examination regulations:
none
Recommended prerequisites: none
Targeted learning outcomes: Learning objectives and competences to be acquired:
Basic understanding of discrete simulation
Ability to implement discrete simulation systems
Methods and techniques in applications of discrete simulation
Content: Worldviews of the simulation and its implementation
Methods and techniques for validation and verification
Experiment design and management
Simulation and Optimization
Distributed Simulation
Study / exam achievements: Regular attendance at lectures and excercises; solve the exercises and successful presentation in the exercises Written or oral exam at the end of the module
Forms of media:
Literature: A. Law and D. Kelton (2003) Simulation Modeling and Analysis. New York , McGraw-Hill J. Banks, John S. Carson and Barry Nelson.(2003).Discrete-Event System Simulation Prentice Hall J. Banks (eds) (1998).Handbook of Simulation.John Wiley & Sons
39 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Name of the module Communication technology for digital engineering
Contents and objectives of the module
Learning outcomes and competences to be acquired: 1. Introduction to Communication Technology
Teaching the concepts information, information-bearing signals, modulation, noise, transmission channels, channel capacity, as well as source and channel coding
Development of mathematical models for the treatment of the above concepts Description and quantitative treatment of information transmission systems
Teaching of engineering scientific decision base for the design of information transmission systems
2. Information and Coding Theory
Teaching the concepts of information theory information content, entropy, redundancy, source coding, channel capacity, channel coding, Hamming space and Hamming distance.
Mathematical modeling for the above concepts.
A method for the treatment of selected source and channel coding.
Treatment of selected error-correcting decoding method. Contents: 1. Introduction to Communication Technology
Mathematical representation of the signals as an information carrier in the time and frequency domain (Fourier series and Fourier Transform)
The sampling theory and the digitization of the signals
Source coding and data compression
Mathematical description of the noise
Noise performance of the transmission channels; calculate the bit error rate
Treatment of selected digital transmission systems in the baseband (PCM, DPCM, ....)
Treatment of selected digital transmission systems in the passband (ASK, PSK, FSK, QAM, ....)
2. Information and Coding Theory
Information content and entropy of discrete information.
Redundancy, memory and source coding (Shannon-Fano and Huffman method).
Continuous sources.
Discrete and continuous channels, and channel capacity Kanalentropien
Channel coding and Hamming space
Linear block codes
Cyclic codes
Syndrome decoding
Methods of Teaching 2 lectures per 2 SWS + 2 exercises per 1 SWS
Prerequisites for participation Mathematics, Physics, Fundamentals of Electrical Engineering Literature: see script
Applicability of the module Digital Engineering
Requirements for awarding credit points
Examination
Credits and grades 8 credit points = 240 h (84 h Attendance time + 156 hours independent work) Grading scale according to examination regulations
Workload Attendance time: 6SWS Weekly lectures and excercises Independent work
Frequency of occurrence Once per year
40 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Duration of module 2 semesters
Responsible Prof. Omar, FEIT-IESK
41 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Machine learning for medical systems
Module level, if applicable
Abbrevation, if applicable:
Subheading, if applicable:
Classes, if applicable:
Semester:
Module coordinator: Chair for Information Retrieval
Lecturer:
Language: German
Curriculum
Teaching format / class hours per week during the semester:
Lecture, exercises
Workload: Attendance time: 2 SWS lecture 2 SWS excercises Independent work: Processing of exercise and programming tasks, follow the lecture
Credit points: 6 Credit Points = 180 h = 4 h = 56h Attendance time + 124h independent work Grading scale according to examination regulations
Requirements under the examination regulations:
none
Recommended prerequisites:
Algorithms and Data Structures
Targeted learning outcomes: Learning objectives and competences acquired: Foundations of learning theory and in-depth understanding of issues and concepts of machine learning methods Knowledge of fundamental data structures and algorithms of machine learning that enable students to apply these approaches to real data analysis problems.
Content: Term learning and version spaces; learning of decision trees, neural networks, Bayesian learning, instance-based learning and cluster analysis, association rules, amplifying learning; hypotheses evaluation
Study / exam achievements: Services: Processing of exercise and programming tasks and successful presentation of the results in the exercises Examination: oral
Forms of media:
Literature:
42 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Course: M.Sc.Integrated Design Engineering (IDE)
Module:
Product modeling and visualization (PMV)
Objectives of the module:
Understanding the need and role of a consistent product model for the product lifecycle
Get to know different strategies and options for product modeling and visualization of systems of different modeling philosophy
Relevant features of the product modeling
Meet the relevant functions of the optimization of components
Proficient use of the design data in a visualization system (VR)
Contents:
Integrated model with different partial models for product modeling and visualization
Basics of parametric and feature technology (standard and advanced features)
Basics of macro programming in CAx systems
Modelling strategies and techniques
Visualization strategies and techniques
Strength Analysis in CAx systems
Component optimization
Methods of Teaching: Lectures and exercises with the corresponding scripts and excercise manuals. media: beamer, overhead projector, blackboard
Prerequisite for participation: Attend the lecture: introduction IDE demonstrable knowledge in Siemens PLM NX CAx system
Workload: Attendance time: 42h Lectures: 2 SWS lectures, 2 SWS excercisess.
Working independently 108h: Wrapping up the lectures, preparation of exercises and the written examination
Assessment / Credits: Examination requirement: Participation in lectures and exercises (at least 75%). Written examination (120 min duration). Grading scale according to examination regulations.
5 credit points
Responsible for Module: Prof. Dr.-Ing. Dr, hcSándor Vajna, FMB-IMK/LMI
Suggested Reading: Lecture notes and excercises and Vajna, Weber, Bley, Zeman: CAx for engineers, Springer 2008
43 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Name of the module Virtual Commissioning
Contents and objectives of the module
Objectives of the course are:
Classification of machine and plant simulation with
an emphasis on virtual and hybrid operation in the
digital planning and operational life cycle phases
Mediation of the automation aspects of the virtual
commissioning
Teaching the basics of the model components
used in the virtual commissioning
Mediation of integration technologies in the PLM
Contents:
In the early planning and production phase simulation tools are
used in the engineering of technical systems for validation and
validation of the design, to test the control software as well as
training purposes for the user.The real non-existing system
components are treated by simulation and are therefore referred
to as virtual. Thus, a stepwise approach from fully virtual to real
and full functioning technical system is possible (hybrid
operation). The simulation is performed in an interdisciplinary
environment between mechanical, electrical and automation
engineering..
Methods of Teaching Lecture (2) + Exercise (1)
Prerequisites for participation
Basic knowledge in computer science and software development
Applicability of the module There is no interaction with other modules. Eligibility: elective master's degree in "Digital Engineering"
Requirements for awarding credit points
Participation in the courses
Examination at the end of the module, grade scale according to
examination regulations, award for points according after written
or oral exam
Credits and grades 5 Credit Points = 120 h (42 h Attendance time + 78 hours independent work) Grading scale according to examination regulations
Workload Attendance time: weekly lectures, 2 SWS weekly exercises 1 SWS
Independent work: Reworking the lecture Solution of exercises and exam preparation
Frequency of occurrence Every xxx - Semester
Duration of module one semester
Responsible Prof. Dr. Christian Diedrich, FEIT-IFAT
44 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
5.Methods of computer science
45 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Data Mining
Module level, if applicable: Bachelor, also: Master DKE
Abbrevation, if applicable: DM
Subheading, if applicable:
Classes, if applicable:
Semester: Bachelor: from 3 (course-related), Master: from 1
Module coordinator: Professor of Applied Computer science / business computer science II - KMD
Lecturer: Dr. Myra Spiliopoulou
Language: German
Classification within the curriculum:
Bachelor CV: WPF INF from 4 Semester
Bachelor INF: WPF INF from 4 Semester
Bachelor INGINF: INF from WPF 4 Semester
Bachelor WIF: WPF WIF 5th Semester, WPF INF WPF from 5 Semester
Master DKE: WPF "Methods I" from 1 Semester
Teaching format / class hours per week during the semester:
Lectures (2 SWS), exercise (2 hours)
Workload: Attendence time: 2 hrs Lecture + 2 hrs exercise Independent work:
Pre-and post-preparation of the lecture
Development of solutions to the exercises
Preparation for the final exam
Credit points: 5 Credit Points = 150 h = 4 h =
Attendance time 56h + 94h independent work
Grading scale according to examination regulations
Requirements under the examination regulations:
None
Recommended prerequisites: None
Targeted learning outcomes: Learning objectives and competences acquired:
Acquisition of basic knowledge about data mining
Application of data mining skills to solve real, simplified problems
Familiarity with data mining tools
Superior way of dealing with German-and English-language literature on the Subject
Content: Data and data preparation for data mining
Data mining methods: classification, clustering, association rules discovery of
Data mining tools and software suites
Case studies
Study / exam achievements: Examination: oral
Note
Preliminary work according to the specification at the beginning of the semester
46 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Forms of media:
Literature: Main Source: Pan-Ning Tan, Steinbach, Vipin Kumar."Introduction to Data Mining", Wiley, 2004: excerpts, from chapter 1-4, 6-8
Specific topics and examples from H. H. Hippner, U. Küsters, M. Meyer, K. Wilde (ed.) „Handbuch Data Mining im Marketing (Knowledge Discovery in Marketing Databases)“, Vieweg, 2001.
47 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Interactive Systems
Module level, if applicable:
Abbrevation, if applicable:
Subheading, if applicable:
Classes, if applicable:
Semester: 5, 6
Module coordinator: Chair for Applied computer science / visualization
Lecturer: Prof. Dr. Bernhard Preim
Language: German
Classification within the curriculum:
Teaching format / class hours per week during the semester:
Lecture, tutorial
Workload: Attendance time: 2 SWS lecture
2 SWS excercises Independent work:
Wrapping up the lecture
Solving exercises Project Development
Credit points: 5 Credit Points = 150 h = 4 h = 56h Attendance time + 94h independent work, grading scale according to examination regulations
Requirements for Examination regulations:
none
Recommended prerequisites: Algorithms and Data Structures
Targeted learning outcomes: Learning objectives and competences acquired:
Basic understanding of human-computer interaction
Application of knowledge about human perception in the design and evaluation of user interfaces
Tasks and user-dependent selection of interaction techniques
Ability to independently design, implementation and interpretation of user studies
Mastery of the usability engineering in compliance with conditions and resource constraints (systematic generating good usable systems)
Content: Technical foundations of human-computer interaction (window, menu and dialog systems)
Interaction techniques and interactive tasks
Cognitive foundations of human-computer interaction
Analysis of tasks and users
Prototype development and evaluation
Specification of user interfaces
Study / exam achievements: Examination requirements, see Course Examination: written, 2 hours
Forms of media:
48 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Literature: Preim / Dachselt: Interactive Systems. Springer 2010
49 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Mobile Communication
Module level, if applicable:
Abbrevation, if applicable: MOBKOM
Subheading, if applicable:
Classes, if applicable:
Semester:
Module coordinator: Chair of Technical computer science / systems and real-time communication
Lecturer: Edgar Nett
Language:
Classification within the curriculum:
Master CSE / IF / WIF: Applied computer science
Master CSE / CV: Technical computer science (TI) Master IF / WIF: Network Computing
Teaching format / class hours per week during the semester:
Lecture, practical and theoretical exercises, independent work
Workload: Presence time = 56 h
2 SWS lecture
2 SWS excercises Independent Work = 124 h
Processing of exercise and programming assignments & exam preparation
Credit points: 6 credit points
Requirements under the examination regulations:
none
Recommended prerequisites: Participation in introductory courses on distributed and embedded systems is recommended
Targeted learning outcomes: Learning objectives and competences acquired:
Comprehensive overview of requirements and principles of mobile communication
Ability to analyze the basic design alternatives and their inherent trade-offs and classify
Competence in the practical application of a WLAN
Content: Content
Technical Basics - Media access method
- Media access protocols (wired / wireless) - Wireless LANs (technologies, standards, applications) - Security issues - Network protocols (IP mobile, ad hoc networks, route selection) Transport Protocols / Mobile TCP
Study / exam achievements: Successful completion of the exercises and programming assignments
50 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Examination: Oral or written
Forms of media:
Literature: Literature data on the current web page for the module (http://euk.cs.ovgu.de/de/lehrveranstaltungen)
51 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Secure Systems
Module level, if applicable:
Abbrevation, if applicable: SISY
Subheading, if applicable:
Classes, if applicable:
Semester: from 4
Module coordinator: Jana Dittmann, FIN-ITI
Lecturer: Jana Dittmann, FIN-ITI
Language: German
Classification within the curriculum:
Mandatory: IngINF, B, INF, B and WIF; B
Elective: CV, B (as INF subject) DigiEng, M (as methods of computer science
Teaching format / class hours per week during the semester:
Lectures, excercises / 4 SWS
Workload: Presence time = 56h
2 SWS lecture
2 SWS excercises
Ability to work independently = 94h
Solution of exercises and exam preparation
Credit points: 5 Credit Points = 150 h = 4 h = 56h Attendance time + 94h independent work
Grading scale according to examination regulations
Requirements under the examination regulations:
"Algorithms and Data Structures", "Theoretical foundations of computer science"
Recommended prerequisites: Technical fundamentals of computer science
Targeted learning outcomes: Learning objectives and competences acquired:
Assess the reliability capabilities of IT security
Ability to generate threat analysis
Skills for selection and evaluation of security mechanisms and creation of IT security concepts
Content: IT security issues and IT security threats
Design principles of secure IT systems
Security Policies
Selected security mechanisms
Study / exam achievements: Regular attendance at lectures and excercises:
Rating: examination (written, 2h, no inputs)
Note: the announcement of the necessary inputs in the lecture
Forms of media:
Literature: Literature see http://wwwiti.cs.uni-magdeburg.de/iti_amsl/lehre/
52 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Visualization
Module level, if applicable:
Abbrevation, if applicable:
Subheading, if applicable:
Classes, if applicable:
Semester: 5
Module coordinator: Chair for Applied computer science / visualization
Lecturer:
Language: German
Classification within the curriculum:
CV-B: Mandatory 5 Sem
IngINF-B: Major: computer science techniques INF-B: Major: Applied computer science
INF-B: Major: Computer Graphics / Image Processing
WIF-B: elective computer science / computer science economy
Teaching format / class hours per week during the semester:
Lecture. Exercise
Workload: Attendance time: 2 SWS lecture
2 SWS excercises Independent work:
Edit the exercises and follow the lectures, exam preparation
Credit points: 5 Credit Points = 150 h = 4 h = 56h + 94h independent work time presence
Requirements for Examination regulations:
none
Recommended prerequisites: Computer Graphics I, Mathematics I to III
Targeted learning outcomes: Learning Objectives: This course provides basic knowledge about how large amounts of structured data, represented, visualized, and can be explored interactively. The focus is on meth diodes of 3D visualization. Competences to be acquired:
Assessment of visualization objectives, selection and Evaluation of visualization techniques
Application of fundamental principles in computer-based visualization
Use and adjustment of the fundamental algorithms Visualization to solve application problems
Evaluation of algorithms in terms of their cost and the quality of the results
Content: Visualization objectives and quality criteria
Foundations of visual perception
Data structures in the visualization
53 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Fundamental Algorithms (isolines, color illustrations, Interpolation approximation of gradients and Curvatures)
Direct and indirect visualization of volume data
Visualization of multi-parameter data
Flow visualization (static and dynamic visualization of vector fields, vector field topology)
Study / exam achievements: Examination requirements: see Course
Examination: written 2 hours
Forms of media:
Literature: P und M Keller (1994) Visual Cues, IEEE Computer Society Press
H. Schumann, W. Müller (2000) Visualisierung: Grundlagen und allgemeine Methoden, Springer Verlag, Heidelberg
W. Schroeder, K. Martin, B. Lorensen (2001) The Visualization Toolkit: An object-oriented approach to 3d graphics, 3. Aufl. Springer Verlag, Heidelberg
R S Wolff und L Yaeger (1993) Visualization of Natural Phenomena, Springer
A. Telea (2007) Data Visualization, AK Peters
54 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
6.Interdisciplinary team project
55 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Interdisciplinary team project *
Module description will be given later The aim of this "small" project is in addition to the depression reached in bases in the complementary field of science, especially the development of key competencies of interdisciplinary work on the basis of a defined task that will be processed by students in a team. The the organization and content will be supervised by two teachers from the areas of engineering and computer science
56 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
7.Specialization
57 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Name of the module Adaptronics *
Aims and Content of the
module
Aims and content Adaptronics creates a new class of technical, elastomechanical systems by using
new materials and activatable faster digital controller that can be automatically
adapted to different environmental conditions. Adaptronics has four target areas
of technical applications • Contour adjustment by elastic deformation • Vibration reduction by structure-borne noise interference • Noise reduction through active measures • Life increase by structurally integrated component monitoring
The students should learn and train, as is typical for the engineering profession
interdisciplinary thinking in engineering to hand the interdisciplinary research
field Adaptronics. Adaptronics combines materials science, mechanical, electrical
and control engineering knowledge and skills. The exercises are performed as
laboratory exercises. In the lab, the students independently solve complex tasks
whose successful processing is a prerequisite for admission to the examination.
Lecture series 9. Overview of Adaptroncis, applications from research 10. Integrable structure of sensors and actuators 11. Conformal structure integration of actuators and sensors 12. Target field contour adaptation: Methods of morphing. 13. Target field vibration suppression: structure-borne noise
interference, eradication, compensation 14. Target field sound reduction: Concepts of Active Noise
Reduction 15. Autonomous Systems - Concepts of Energy Harvesting 16. Concepts integrated component monitoring 17. Regulation 18. Reliability / robustness
Accompanying laboratory course
Carrying out experiments to Adaptronics measurements, analysis and
presentation of results
Methods of Teaching Lectures (2 SWS) internship (2 SWS)
Prerequisites for participation No special conditions are required, desirable: Principles of Adaptive
Systems (BA degree)
Applicability of the module There are no interactions with other modules
Grade and credit points Participation in the lab, oral examination
Credits 5 ECTS
Workload Attendance time: 2 SWS (lecture) and practical, Independent edit the
experiments, construction of test protocols, presentation of results
Frequency of occurrence Annually
Duration of module one semester
Responsible Professor Michael Sinapius, IFME
58 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Modulename: Advanced Database Models
Module level, if applicable:
Abbrevation, if applicable: 103805
Subheading, if applicable: ADBM
Classes, if applicable:
Semester:
Professor of Practical computer science / information systems and databases
Lecturer: Dr. Eike Schallehn
Language: English
Classification within the Classification within the curriculum:
Teaching format / class SWS during the semester:
Lectures (2 SWS) and exercises (2 hours)
Workload: Workload: 180h (56h attendences + 124 hours self-study)
Credit points: 6 credit points Degree Accor ding to the "Examination Regulations"
Examination requirements under the regulations:
none
Recommended prerequisites: Database introduction course
Targeted learning outcomes: Comprehension of different non-relational database models, their basic concepts, and their historical development
Comprehension of implications of non-relational data models for query processing and application development
Competence to use non-relational DBMS and based on their specific capabilities
Competence to develop databases and according applications using non-relational databases
Content: Overview and history of database models
NF2-, object-oriented, object-relational, and semi-structured database models
Application of the database models and design methodologies (extended ERM, UML, ODMG, XML Schema, etc.)
Foundations of query languages (OQL, SQL:2003, XPath/XQuery, etc.) and query processing for non-relational data models
Study / exam achievements: Participation and active involvement in the course and the exercises, successful realization of the exercises and final examination, oral exam (30 minutes)
Forms of Forms of media:
Literature:
59 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Advanced Machine Learning
Module level, if applicable
Abbrevation, if applicable: AML
Subheading, if applicable:
Classes, if applicable:
Semester:
Module coordinator: Professor of Data and Knowledge Engineering
Lecturer:
Language:
Curriculum
Teaching format / class hours per week during the semester:
Presentation of theory in the classroom, exercises and student projects
Workload: theory (2 hours per week) exercise in the lab and project work (2 hours per week) Homework (124 h): Study of the theoretical material Realization of the exercises and the student projects Preparation for the final examination
Credit points: 6 Credit Points = 180h (56 h Attendance time + 124 hours independent work) Grading scale according to examination regulations
Requirements under the examination regulations:
none
Recommended prerequisites: Basic knowledge in machine learning, data mining, or related fields.
Targeted learning outcomes: Learning objectives and competences acquired:
In recent years, machine learning has become one of the core disciplines in artificial intelligence research and related areas. This lecture is devoted to advanced methods and techniques of machine learning that go beyond the topics that are typically covered by introductory courses in the field.
A successful attendance of the course will enable the student to solve practical machine learning and data mining problems by state-of-the-art methods, to analyze and evaluate the results from a theoretical point of view, and to develop new, specialized approaches for particular problems whenever needed.
Content: Content - Introduction and overview of machine learning - Model assessment and selection - Ensemble Methods and Boosting - Variable and Feature Selection - ROC-Analysis - Kernel-based learning
Study / exam achievements: final examination
Forms of media:
Literature:
60 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Modulename: Advanced Topics in Databases
Module level, if applicable:
Abbrevation, if applicable: AdvDB
Subheading, if applicable:
Classes, if applicable:
Semester:
Professor of Practical computer science / databases and
Information Systems
Lecturer:
Language: English
Classification within the Classification within the curriculum:
Teaching format / class SWS during the semester:
Lectures (2 SWS) and exercises (2 hours)
Workload: Classes (2 hours per week) Exercises in the lab and project work (2 hours per week) Homework (124 h):
Further Studies
Realization of the exercises and the student projects
Preparation for the final examination
Credit points: 6 Credit Points = 180h (56h Attendance time + 124h self-study) Grades according to the ”Prüfungsordnung“
Examination requirements under the regulations:
None
Recommended prerequisites: Knowledge about database foundations and about principles of internal database operations
Targeted learning outcomes: In the lecture students will be made familiar with most recent technological developments in data management. The first goal is to enable the attendees to use these new technologies in their professional careers in industry. Furthermore, the lecture focuses on aspects currently addressed in scientific research being on the verge to wide usage in current applications, and this way, enabling students to participate in academic and industrial research.
Content: Topics of the lecture will frequently change in accordance with current research directions in the database community and represent cutting-edge aspects as for instance
Indexing and storage techniques for new applications and data types,
Data management for embedded devices and sensor networks,
Self-management capabilities of database management systems,etc.
Study / exam achievements: Participation and active involvement in the course and the exercises Successful realization of the exercises, student projects and final examination
61 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Oral Exam (30 Minutes)
Forms of Forms of media:
Literature: Cf. http://wwwiti.cs.uni-magdeburg.de/iti_db/lehre/advdb/
62 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Applied Discrete Modeling
Module level, if applicable
Abbrevation, if applicable: ADM
Subheading, if applicable: Applications of stochastic models, especially in CV, DKE and Digital Engineering
Classes, if applicable:
Semester:
Module coordinator: Professor of simulation
Lecturer:
Language: German, English when needed
Curriculum
Teaching format / class hours per week during the semester:
Lectures, exercises, project work
Workload: Lecture: 2 SWS
Exercise & Internship: 2 SWS
Homework and project work, self-study
Credit points: 6 Credit Points = 180h (56h attendance time + 124h self-study) Grading scale according to examination regulations
Requirements under the examination regulations:
None
Recommended prerequisites: Mathematics for Engineers Programming skills
Targeted learning outcomes: Participants learn about Markov chains and selected applications and solution methods
Participants learn about non-Markovian stochastic processes and can model them in different ways and simulate
The participants know hidden Markov and non-Markov processes
Participants learn about selected research topics of the Chair
The participants can implement the learned models and procedures and apply them to problems in the research areas of the university, particularly in the medical and engineering
Content: Discrete-time and continuous-time Markov chains
Applications and programming of calculation method for Markov chains
Method of additional variables
Proxel simulation and phase distributions
Modeling with latent models
Programming solution methods for various model classes
Modeling and solution of problems in medicine and engineering
Study / exam achievements: Project work and oral examination
Forms of media:
Literature: Selected recent scientific articles
63 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Bayesian Networks
Module level, if applicable: Master
Abbrevation, if applicable: BN
Subheading, if applicable:
Classes, if applicable:
Semester: 1
Module coordinator: Professor of Practical computer science / computational intelligence
Lecturer: Prof. Dr. Rudolf Kruse
Language: English
Classification within the curriculum:
WPF CMA, M 1-2
WPF CV, M 1-3
WPF DKE, M 1-3
WPF IF, M 1-2
WPF IngINF, M 1-2
PF IT, D IE 5
PF IT, D-TIF 5
WPF MS, M 1-3
WPF SPTE, D from 5
WPF Stat, M 1-3
WIF WPF, M 1-2
WPF WLO, D from 5
Teaching format / class hours per week during the semester:
Lecture and exercise / 4 SWS
Workload: Presence time = 56 hours
2 SWS lecture
2 SWS excercises Self-employed = 124 hours
Pre-and post-processing of lecture and exercise
Editing exercises and programming assignments
Credit points: 6 credit points of 180 hours of work
Requirements under the examination regulations:
None
Recommended prerequisites: Fundamentals of Probability and Statistics
Targeted learning outcomes: Provision of basic concepts and methods of Bayesian networks and related methods for decision support
The participant can apply techniques for the design of Bayesian networks
The participant can apply data analysis methods for problem solving
The participant knows and understands exemplary applications of Bayesian networks whose basic functioning
Content: Methods for representing uncertain knowledge
Dependency analysis
Learning process
Tools for designing Bayesian networks
64 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Propagation, updating, revision
Decision Support with Bayesian networks
Non-standard method for decision support such as Fuzzy Models
Case studies of industrial and medical applications
Study / exam achievements: Examination in oral form, length: 30 minutes required inputs: o Processing of two-thirds of the exercises o Successful presentation in the exercises
Note
o Processing of two-thirds of the exercises o Successful presentation in the exercises o Successful completion of the oral colloquium
Forms of media:
Literature: Christian Borgelt, Matthias Steinbrecher and Rudolf Kruse. Graphical Models: Representations for Learning, Reasoning and Data Mining
(2nd edition).John Wiley & Sons, Chiche-art, United Kingdom, 2009. Christian Borgelt, Heiko Timm und Rudolf Kruse. Unsicheres und vages Wissens. Kapitel 9 in Günther Görz, Claus-Rainer Rollinger, und Josef Schneeberger (ed.). Handbuch der künstlichen Intelligenz. Oldenbourg, München, 2000. Enrique del Castillo, Jose M. Gutierrez, Ali S. Hadi. Expert Systems and Probabilistic Network Models.Springer, New York, NY, USA, 1997. F inn V. Jensen.An Introduction to Bayesian Networks. UCL Press, London, United Kingdom, 1996. Judea Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (2nd edition). Morgan Kau fmann, San Mateo, CA, USA, 1992.
65 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Image coding *
(Dr. Gerald Krell)
Course Module description will be given later
66 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Computational Fluid Dynamics
Module level, if applicable
Abbrevation, if applicable: CFD
Subheading, if applicable:
Classes, if applicable:
Semester:
Module coordinator: Professor for Fluid Dynamics
Lecturer: Dr.-Ing. G. Janiga
Language: English
Curriculum
Teaching format / class hours per week during the semester:
Lectures, Exercises with computer hands-on
Workload: Presence: Weekly lecture 1 SWS Weekly exercises with computer hands-on 2 SWS Autonomous work: Complementary reading, final project work
Credit points: 3 Credit Points = 90h (42 h presence + 48 h autonomous work) Grades following official instructions
Requirements under the examination regulations:
Fluid Dynamics
Recommended prerequisites: Advanced Fluid Dynamics
Targeted learning outcomes: Aims and competences:
Students participating in this course will get both a solid theoretical knowledge of Computational Fluid Dynamics (CFD) as well as a practical experience of problem-solving on the computer.
Best-practice guidelines for CFD are discussed extensively.
CFD-code properties and structure are described and the students first realize their own, simple CFD-code, before considering different existing codes with advantages and drawbacks.
At the end of the module, the students are able to use CFD in an autonomous manner for solving a realistic test-case, including a critical check of the obtained solutions.
Content: Content
Introduction and organization, main discretization methods
Vector- and parallel computing, supercomputers, optimal computing loop.
Validation procedure, Best Practice Guidelines.
Linear systems of equations and iterative solution methods.
67 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Practical solution of unsteady problems, explicit and implicit methods, stability.
Gridding and grid independency.
Practical CFD, importance and choice of physical models.
Properties and computation of turbulent flows.
Properties and computation of Non-newtonian flows.
Properties and computation of multi-phase flows.
Preparation of final CFD project as teamwork
Study / exam achievements: Success: Oral defense of final CFD project Exam: oral
Forms of media:
Literature: Ferziger and Peric, “Computational Methods for Fluid Dynamics”, Springer (2002) Further literature given during first lecture
68 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Introduction to Data Warehousing
Module level, if applicable:
Abbrevation, if applicable: 102808
Subheading, if applicable: DWT
Classes, if applicable:
Semester:
Module coordinator: Professor of Practical computer science / information systems and databases
Lecturer: Veit Köppen
Language:
Classification within the curriculum:
Teaching format / class hours per week during the semester:
Lectures, excercises and practical exercises in the laboratory (including presentation to the exercise group) and independent work (solving exercises, literature study)
Workload: Attendance time: weekly lectures, 2 SWS
weekly exercises 2 SWS
Independent work:
Exercises and exam preparation
Credit points: 6 Credit Points = 180h (56h Attendance time in lectures and exercises + 124h independent work) Grading scale according to examination regulations
Requirements under the examination regulations:
None
Recommended prerequisites: Event "Databases I" and "Databases II"
Targeted learning outcomes: Learning objectives and competences acquired:
Understanding of the data warehousing approach
Understanding of database technologies in the field of Data Warehouses
Ability to use DW-specific DBMS functionality
Ability to design and development of a data warehouse application
Content: The data warehouse approach, distinction
Architecture
Extract-Transform-Load
OLAP and Multidimensional Data Model
Implementation in databases
Query processing and optimization
Index and storage structures
Business Intelligence
Study / exam achievements: Regular attendance of lectures and exercises Examination Admission requirements: To be determined by instructor
69 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Oral or written exam (depending on number of participants) at the end of the module
Forms of media:
Literature: http://wwwiti.cs.uni-magdeburg.de/iti_db/lehre/dw/index.html
70 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Digital Filter *
(2C +1 R)
Prof. Dr. Abbas Omar
Course Module description will be given later
71 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Modulename: Distributed Data Management
Module level, if applicable:
Abbrevation, if applicable: DDM
Subheading, if applicable:
Classes, if applicable:
Semester:
Professor of Practical computer science / information systems and databases
Lecturer: Dr. Eike Schallehn
Language: English
Classification within the Classification within the curriculum:
Teaching format / class SWS during the semester:
Lectures (2 SWS) and exercises (2 hours)
Workload: 180h (56 h contact hours + 124 h self-study)
Credit points: 6 Credit Points Grades according to the "Prüfungsordnung"
Examination requirements under the regulations:
none
Recommended prerequisites: Database introduction course
Targeted learning outcomes: Comprehension of basic principles and advantages of distributed data management
Competence to develop distributed databases
Comprehension of query and transaction processing in distributed and parallel databases
Competence to optimize the run-time performance and satisfy requirements regarding reliability and availability of distributed systems
Content: Overview and classification of distributed data management (distributed DBMS, parallel DBMS, fedrated DBMS, P2P)
Distributed DBMS: architecture, distribution design, distributed query processing and optimization, distributed transactions, and transactional replication
Parallel DBMS: fundamentals of parallel processing, types of parallelization in DBMS, parallel query processing
Study / exam achievements: Participation and active involvement in the course and the exercises, successful realization of the exercises and final examination, oral exam (30 minutes)
Forms of Forms of media:
Literature:
72 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Name of the module Introduction to Medical Imaging
Contents and objectives of the module
Learning outcomes and competences acquired: Imaging is the most important form of medical diagnostics today. In this course an overview of the modalities of modern medical imaging is given. Therefore the principle, the operation as well as the most important medical applications will be presented and the advantages and disadvantages in terms of image quality and patient risks will be discussed. Knowledge of the required data processing step, and further optional image processing are mediated to the participants . That knowledge is reinforced in the exercises and especially within an internship
Contents: Fluoroscopy Computed tomography Nuclear medical imaging (PET, SPECT) Ultrasound Imaging MRI
Methods of Teaching Lecture, excercises
Prerequisites for participation Mathematics, physics, fundamentals of electrical engineering, basic medical terms
Applicability of the module There is no interaction with other modules. Eligibility: Elective in the Bachelor study programs of the Faculty
Requirements for awarding credit points
Regular attendance of lectures, processing exercises, participation in the internship. Exam or oral exam or participation form
Credits and grades 5 Credit Points = 150 h (42 h Attendance time + 108 hours independent work) plus optional internship:
Workload Attendance time: weekly lectures: 1 semester * 2 SWS weekly exercises: 1 semester 1 credit hour *
Independent work: Nachbreitung of lectures, editing exercises, Preparation and review of laboratory experiments, PREPARATIONS for the exam
Frequency of occurrence each year in WS
Duration of module One or two semesters
Responsible Prof. G. Rose, FEIT, IESK
73 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Embedded Networks
Module level, if applicable:
Abbrevation, if applicable: EN
Subheading, if applicable:
Classes, if applicable:
Semester:
Module coordinator: Professor EOS
Lecturer: Prof. Dr. Jörg Kaiser
Language: German or English on request
Classification within the curriculum:
Master's programs
Teaching format / class hours per week during the semester:
Lecture, practical and theoretical exercises, independent work
Workload: 2 SWS lecture
2 SWS excercises Independent work:
Solving exercises and exam preparations
Credit points: 6 Credit Points = 180h (56h Attendance time + 124 hours self-study) Grading scale according to examination regulations
Requirements under the examination regulations:
Bachelor etc.
Recommended prerequisites: Participation in "Communication and Networks" and "principles and components of embedded systems" is recommended.
Targeted learning outcomes: Learning objectives and competences acquired:
Understanding of the special characteristics and problems in networks of industrial automation, automotive networks and wireless sensor networks.
Ability to grasp the far-reaching implications of quality properties in safety-critical and resource-constrained embedded networks to classify and evaluate them.
Skills for practical implementation of system properties and applications of an embedded network.
Content: Basics:
Reliability and fault tolerance
Time and clock synchronization
The physical layer transmission
Bandwidth and transmission capacity
Coding and synchronization
Embedded systems for safety-critical applications
Master-slave networks
74 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Time-Triggered Networks Token-based networks CSMA networks
Wireless Sensor Networks:
Protocols for wireless networks Energy saving concepts
Study / exam achievements: Services
Regular attendance and the lectures and exercises
Completion of exercises
Examination: oral (30 min)
Forms of media:
Literature: will be announced on the web page of the lecture
75 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Advanced Programming Concepts for Tailor-made Data Management
Module level, if applicable:
Abbrevation, if applicable: EPMD
Subheading, if applicable:
Classes, if applicable:
Semester: See below
Module coordinator: Professor of Practical computer science / information systems and databases
Lecturer: Norbert Siegmund
Language: German
Classification within the curriculum:
WPF CV, B from 5 - computer science WPF IF, B from 5 - computer science WPF IngINF, B from 5 - mathematics and computer science WIF WPF, B from 5 - computer science / computer science economy WPF CV, M 1-2 - Software Engineering and Algorithm WPF DigiEng, M 1-3 - Methods of computer science WPF DKE, M 1-3 - Fundamentals of theory and Pr computer science WPF IF, M 1-2 - Algorithms and Complexity WPF IngINF, M 1-2 - Software Engineering and Algorithm WIF WPF, M 1-2 - Algorithms and Complexity WPF CV, i - (Practical / Applied) computer science WPF IF, I - II computer science / theoretical computer science WPF INGIF; i - computer science I or II after election WIF WPF, i - computer science III
Teaching format / class hours per week during the semester:
2 hrs Lecture + 2 hrs exercise / Internship
Workload: 5 CP: 150h = 56h attendence time + 94h independent work 6 CP: 180h = 150h + 30h additional tasks
Credit points: 5 or 6 CP CP to choice
Requirements under the examination regulations:
Regular attendance of lectures and exercises. Oral exam at the end of the module and project work.
Recommended prerequisites: Fundamentals of Software Engineering are required ; Basic knowledge of compiler construction and concepts of Programming languages are recommended
Targeted learning outcomes: Understanding of the limitations of traditional programming paradigms regarding the development of information systems
Knowledge of modern, advanced programming paradigms with focus on the development of customized systems
Ability to evaluate, select and apply advanced programming techniques
Content: Introduction to the problem of customized systems using the example of embedded DBMS
Modeling and implementation of software
76 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
product lines
Introduction to basic concepts (including Separation of concerns, information hiding, modularization, structured programming and design)
Overview of advanced programming concepts, etc. Components, design patterns, meta-object protocols and aspect-oriented programming, feature-oriented programming and collaborations
Study / exam achievements: Lecture and lecture accompanying exercises with questionnaires including a programming internship at a selected topic of the lecture; Independent processing of the exercises and the selected topic as a prerequisite for the exam Exam / bill: oral
Forms of media:
Literature: See http://wwwiti.cs.uni-magdeburg.de/iti_db/lehre/epmd/
77 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Flow Visualization
Module level, if applicable
Abbrevation, if applicable: FlowVis
Subheading, if applicable:
Classes, if applicable:
Semester:
Module coordinator: Professor of Visual Computing
Lecturer:
Language:
Classification within the curriculum:
Teaching format / class hours per week during the semester:
Lectures, exercises
Workload: Lecture: 2 SWS
Exercise: 2 SWS
Homework, programming models example, self-study
Credit points: 6 Credit Points = 180h (56h attendance time + 124h self-study) Grading scale according to examination regulations
Requirements under the examination regulations:
none
Recommended prerequisites: degree computer graphics 1 neccessary
Targeted learning outcomes: Learning objectives and competences acquired:
Participants will acquire knowledge of the main methods for flow visualization
Some methods are independently implemented and evaluated in the exercises
Participants are able to independently analyze a simple flow data visually using the existing or self-designed tools.
Content: Mathematical foundations of vector and tensor fields
Obtaining stream data
Direct methods for flow visualization
Texture based methods for flow visualization
Geometry-based methods for flow visualization
Feature-based methods for flow visualization
Topological methods for flow visualization
Visualization of tensor fields
Study / exam achievements: Visual analysis of a given data flow rate
oral exam at the end of the semester
Forms of media:
Literature:
78 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Fuzzy Systems
Module level, if applicable: Master
Abbrevation, if applicable: FS
Subheading, if applicable:
Classes, if applicable:
Semester: 1
Module coordinator: Professor of Practical computer science / computational intelligence
Lecturer: Prof. Dr. Rudolf Kruse
Language: English
Classification within the curriculum:
WPF CMA, M 1-3
WPF CV, M 1-2
WPF DKE, M 1-3
WPF IF, M 1-2
WPF IngINF, M 1-2
PF IT, D IE 5
PF IT, D-5 from TIF
WPF MA, D-AFIF 5-8
WPF MS, M 2-3
WPF PH, D from 5
WPF SPTE, D from 5
WPF Stat, M 1-3
WIF WPF, M 1-2
Teaching format / class hours per week during the semester:
Lecture and exercise / 4 SWS
Workload: Presence time = 56 hours
2 SWS lecture
2 SWS excercises
Self-employed = 124 hours
Pre-and post-processing of lecture and exercise
Editing exercises and programming assignments
Credit points: 6 credit points for 180 hours of work
Requirements under the examination regulations:
None
Recommended prerequisites: Knowledge of a programming language
Algorithms and Data Structures
Machine Learning, Data Mining
Algebra, optimization
Targeted learning outcomes: Application of adequate modeling techniques for the design of fuzzy systems
Using the methods of fuzzy data analysis and fuzzy rule learning
Ability to develop fuzzy systems
Content: Introduction to fuzzy set theory, the fuzzy logic and fuzzy
79 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
arithmetic
Applications of control engineering, the approximate closing and the data analysis
Study / exam achievements: Examination in oral form, length: 30 minutes required inputs: o Processing of at least two-thirds of all exercises during the
semester o Successful presentation of two exercises
Note: o Processing of at least two-thirds of all exercises during the
semester o Successful presentation of two exercises o Timely submission of two programming tasks o Successful completion of the oral colloquium
Regardless of the type of the examination a regular and active participation in lectures and exercises are required.
Forms of media:
Literature: Michael R. Berthold and David J. Hand. Intelligent Data Analysis: An Introduction (2nd edition).Springer-Verlag, Berlin, 2002. Christian Borgelt, Frank Klawonn, Rudolf Kruse, and Detlef Nauck. Neuro-Fuzzy Systems (3rd edition).Vieweg, brewing nschweig / Wiesbaden, 2003. George J. Klir and Bo Yuan. Fuzzy Sets and Fuzzy Logic - Theory and Applications.Prentice Hall, Upper Saddle River, NJ, 1995. Rudolf Kruse, Jörg Gebhardt, und Frank Klawonn. Fuzzy-Systeme (2nd edition). Teubner, Stuttgart, 1994. Rudolf Kruse, Jörg Gebhardt and Frank Klawonn. Foundations of Fuzzy Systems.Wiley, Chichester, United Kingdom, 1994. Kai Michels, Frank Klawonn, Rudolf Kruse, und Andreas Nürnberger. Fuzzy-Regelung. Springer-Verlag, Heidelberg, 2002.
80 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Information and Coding Theory
Engl. Module name:
Module level, if applicable:
Abbrevation, if applicable:
Subheading, if applicable:
Classes, if applicable:
Semester: 3rd-6th
Module coordinator: Professor of High Frequency and Communication Technology
Lecturer:
Language: German
Classification within the curriculum:
CV-B, application-image Information Technology (Elective)
Teaching format / class hours per week during the semester:
Lecture and optional exercise
Workload: Attendance time
2SWS (lecture) + 1 SWS (optional exercise) Independent work
Lecture follow up
Credit points: 3 Credit Points = 90h (28h Attendance time +62 h independent Work) Grading scale according to examination regulations
Requirements for Examination regulations:
nobe
Recommended prerequisites: University basic knowledge in mathematics
Targeted learning outcomes: Learning outcomes and competences to be acquired:
Exchange of information theoretical concepts information content, entropy, redundancy, source coding, channel capacity, channel coding, Hamming space and Hamming distance
Creation of mathematical model for the above Concepts
A method for the treatment of selected resources and channel coding
Treatment of selected error-correcting decoding method
Content: Information content and entropy of discrete information
Redundancy, memory and source coding (Shannon-Fano and Huffman method)
Continuous sources
Discrete and continuous channels, channel entropies and channel capacity
Channel coding and Hamming space
Linear block codes
Cyclic codes
Syndrome decoding
Study / exam achievements: Oral examination or participation form
Forms of media:
Literature:
81 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Data Mining for Changing Environments
Module level, if applicable: Master
Abbrevation, if applicable: DMCE
Subheading, if applicable:
Classes, if applicable:
Semester: 1-2 (for 4-semester courses: 1-3)
Module coordinator: Professor of Applied Computer science / business computer science II - KMD
Lecturer: Dr. Myra Spiliopoulou
Language: English, German by arrangement
Classification within the curriculum:
Elective: Master CV, DKE, INF, INGINF, WIF
Master CV: WPF Focus:
Methods of Data and Knowledge Engineering (MDKE)
Master DKE: WPF in focus
Methods I
Methods II
Master INF: WPF in the priority areas:
Applied computer science
Computational Intelligence
Data-intensive scenarios
Economic computer science
Master INGINF as WPF INF in the priority areas
Applied computer science
Data-intensive scenarios
Methods of Data and Knowledge Engineering (MDKE)
Master WIF:
WIF WPF WPF INF or in the priority areas Business Intelligence
Very Large Business Applications
Information Systems in Management Exchange focus INF under
Applied computer science
Computational Intelligence Data-intensive scenarios
Teaching format / class hours per week during the semester:
Lectures (2 SWS), exercise (2 hours)
Workload: Workload: attendences: 2 hrs Lecture + 2 hrs exercise Independent work:
Pre-and post-preparation of the lecture
Development of solutions to the exercises
Preparation for the final exam
Credit points: 6 Credit Points = 180 h = 4 h =
Attendance 56h + 124h independent work
Grading scale according to examination regulations
Requirements under the None
82 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
examination regulations:
Recommended prerequisites: Basics: Data Mining
Targeted learning outcomes: Learning objectives and competences acquired:
Understanding of the side effects of obsolete models and profiling for prediction and decision making in business
Acquisition of knowledge about learning methods for adapting and comparison of models
Acquisition of knowledge about learning methods for data streams
Feeling comfortable with English literature related to the topic
Content: Incremental learning methods
Learning methods for data streams
Applications, including: analytical CRM, analysis of social networks, , analysis of blogs
Study / exam achievements: Examination: oral
Forms of media:
Literature: Mainly scientific articles, see
http:omen.cs.uni-magdeburg.de/itikmd
83 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Cognitive Systems *
(Prof. Wendemuth)
Course Module description will be given later
84 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Mesh Processing
Module level, if applicable:
Abbrevation, if applicable:
Subheading, if applicable:
Classes, if applicable:
Semester: 5, 6
Module coordinator: Professor of Visual Computing
Lecturer: Dr. Christian Horse Inn
Language: German / English on demand
Classification within the curriculum:
CV-B: Elective area Computational IngINF-B: Major: computer science techniques INF-B: Major: Computer Graphics / Image Processing
WIF-B: elective computer science / computer science economy
Teaching format / class hours per week during the semester:
Seminar, internship
Workload: Attendance time: 3 hours lecture / 1 hour exercise
Independent work: Exercises
Credit points: 5 Credit Points = 150 h = 4 h = 56h Attendance time + 94h independent work, grading scale according to examination regulations
Requirements for Examination regulations:
none
Recommended prerequisites: Mathematics I, Mathematics II, Computer Graphics 1
Targeted learning outcomes: Learning objectives and competences to be acquired:
Knowledge and skills in handling Triangle meshes
Implementation and evaluation of some basic Algorithms
Content: Foundations, discrete differential geometry
Data structures for triangle meshes
Quality measures for networks
Smoothing networks
Parameterization of networks
Decimation and remeshing
Editing and deformation of networks
Numerical Aspects
Study / exam achievements: Preliminary examinations will be announced in the lecture
Oral examination 30 min.
Forms of media:
Literature: s Course
85 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Modeling with population balances
Module level, if applicable
Abbrevation, if applicable: PBM
Subheading, if applicable:
Classes, if applicable:
Semester:
Module coordinator: Professor for Thermal Process Engineering
Lecturer: Jun.-Prof. Dr.-Ing. M. Peglow
Language: English
Curriculum
Teaching format / class hours per week during the semester:
Lectures and Exercises
Workload: Presence: Weekly Lecture 2 SWS Weekly exercises with hands-on 1 SWS Autonomous work: Complementary reading and self-learning
Credit points: 3 Credit Points = 90 h (42 h attendance time+ 48 h autonomous work) Grades following official instructions
Requirements under the examination regulations:
Recommended prerequisites:
Targeted learning outcomes: Aims and competences: The participants will learn to
characterize systems with coupled properties involving density functions
model processes like nucleation, growth and agglomeration
solve population balances (analytical solutions, momentum approaches, sectional models)
apply population balances to real problems, in particular for process engineering
Content: Content
Concept of population balances, properties of disperse systems
Interaction between particles and continuous phase
Relevant properties (internal coordinates)
Temporal solution
Heat, mass and momentum transfer between the disperse and the continuous phases
Interactions between individual particles of the disperse phase
Detailed consideration of key processes: nucleation, growth, breakage, agglomeration
Study / exam achievements: Exam: oral
Forms of media:
Literature: Ramkrishna, “Population balances: theory and applications to
86 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
particulate systems in engineering”, Academic Press (2000) Further literature given during first lecture
87 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Multimedia Retrieval
Module level, if applicable:
Abbrevation, if applicable: ME
Subheading, if applicable:
Classes, if applicable:
Semester: 1 (Master)
Module coordinator: Professor of Data and Knowledge Engineering
Lecturer: Prof. Dr.-Ing. Andreas Nürnberger
Language: German
Classification within the curriculum:
CV, DKE, INGIF, WIF
Teaching format / class hours per week during the semester:
Lectures, frontal exercises, independent work (solving exercises, literature studies, ...)
Workload: Attendance time: weekly lectures, 2 SWS
weekly exercises 2 SWS
Independent work: Exercises & Test Preparation
Credit points: 6 Credit Points = 180h (56h Attendance time in lectures and exercises + 124h independent work) Grading scale according to examination regulations
Requirements under the examination regulations:
none
Recommended prerequisites: Basic knowledge of databases
Targeted learning outcomes: Learning objectives and competences acquired: Basic understanding of search in collections of multimedia data
Knowledge of concepts of information retrieval
Knowledge for similarity calculation between media objects Knowledge of algorithms and data structures for efficient
similarity computation
Knowledge of the production and use of descriptive characteristics (features) from multimedia objects (text, image, sound, video)
Ability to select and assess alternative approaches to similarity search for specific scenarios of the (interactive) search
Content: Introduction and concepts Principles of information retrieval Feature extraction and transformation process Distance functions Algorithms and data structures for efficient search
Query languages User interface for multimedia retrieval systems
Study / exam achievements: Regular attendance of lectures Solving the exercises and successful presentation in the exercises Written or oral exam at the end of the module
88 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Forms of media: PowerPoint, Blackboard
Literature: Ähnlichkeitssuche in Multimedia-Datenbanken (Ingo Schmitt), Oldenbourg Wissenschaftsverlag GmbH, München, 2005.
Modern Information Retrieval (Ricardo Baeza-Yates and Berthier Ribiero-Neto), Addison Wesley, 1999.
Foundations of Statistical Natural Language Processing
(Chris Manning and Hinrich Schütze), MIT Press, Cambridge, MA, 1999.
Information Retrieval: Data Structures and Algorithms (William B. Frakes and Ricardo Baeza-Yates), Prentice-Hall, 1992.
Soft Computing in Information Retrieval (Fabio Crestani and Gabriella Pasi), Physica Verlag, 2000.
89 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Name of the module Numerical Methods in Biomechanics *
Contents and objectives of the module
Learning outcomes and competences acquired: In this course, students will acquire knowledge in the application of numerical methods for computer-oriented mechanics with a particular focus on biomechanical and medical applications.The course provides an introduction to the mathematical model creation the basics of approximate calculation of technical problembs. The students will be introduced to today's popular sof tware tools used for solving technical problems known and acquire skills to solve problems of biomechanics independently.
Contents: Overview of modern numerical methods Introduction to the modeling of problems in biomechanics Fundamentals of discretization and learning about important discretization: o Finite Difference Method o Finite-volume method o Finite Element Method Introduction to multi-body dynamics Numerical solution of selected problems of biomechanics: o Strength of bone, problems of stability o Notch stress problems o Biological optimization principle o Forces in motion processes (running, jumping)
Methods of Teaching Lecture, exercise, small project work
Prerequisites for participation
Engineering mechanics in the amount of 6-8 SWS; biomechanics internship (1 SWS)
Applicability of the module There are no interactions with other modules Creditable for all master programs of other faculties, whose study regulations allow it.
Requirements for awarding credit points
Oral examination
Credits and grades 4 hours / 8 credit points = 150 h (42 h Attendance time + 108 hours + 90 hours of project work independently) Grading scale according to examination regulations
Workload Attendance time: 2 hours lecture, 1 SWS exercise Independent work: reworking the lecture, independent editing a project, exam preparation
Frequency of occurrence Every year in WS
Duration of module A semester
Module Coordinator Prof. U. Gabbert, Prof. Strackeljan FMB, IFME
90 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Security of embedded systems *
(Prof. Dittmann) Lecture (German) Module description will be given later
91 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Speech processing *
(Prof. Wendemuth)
Course Module description will be given later
92 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Name of the module Electromagnetic Theory
Contents and objectives of the module
Learning outcomes and competences acquired: Mediation of the system of Maxwell's equations as a basis for the physical understanding and the mathematical description of electric, magnetic and electromagnetic phenomena
Systematic treatment of the electromagnetic fields and adequate computational methods as well as making reference to the real problems in the fields of electrical engineering, electronics, communications technology
Development of skills for solving specific tasks
Contents:
Maxwell's equations in differential and integral form and the derivation of general conclusions as well as a classification of electromagnetic fields.
On this basis, the treatment of the different field types follows.
An electrostatic field, flow stationary electric field, a stationary magnetic field flows, quasi-stationary electromagnetic field wavefields
Methods of Teaching Lecture, excercies
Prerequisites for participation GET 1 and 2 and Get 3
Applicability of the module Bachelor ETIT
Requirements for awarding credit points
Exam 180 min
Credits and grades 6 hours / 8 credit points = 240 h (84 h Attendance time + 156 hours independent work) Grading scale according to examination regulations
Workload attendance timein SS: 2 hours lecture, 1 SWS exercise attendance timein WS: 2 hours lecture, 1 SWS exercise Independent work: solving exercises and exam preparation
Frequency of occurrence Every year starting in SS
Duration of module Two semesters
Responsible Prof. Dr.-Ing. Marco Leone (FEIT-IGET)
93 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Name of the module Theory of electrical cables
Content and Aims module
Learning outcomes and competences acquired:
Deeper physical insight into compensation and propagation processes on line connections for fast temporal changes or high frequencies, when their expansion with respect to the delay time and wave length can not be neglected.
Knowledge of the basic solutions and approximate models in special cases in the fields of energy, electronics / circuit technology and communication technology
Mathematical description and analysis of the dynamic processes on lines in the time and frequency domain at any line suppressor circuit: Transmission line equations in complex form, reflection coefficient, SWR, resistance transformation, Smith chart, quadrupole compensation ciruits, chain ladder
Multiple lines: Line differential equation systems, parameter matrices, modal transformation.
Contents:
Introduction:Conducted electromagnetic waves and wave types.
TEM waves on lines: Derivation of the differential equations and differential equivalent circuit of the double line solution in the time and frequency domain, lossless and lossy case Phase and Group velocity.
Non-stationary time-domain analysis: Simple transients, reflection and refraction, wave equivalent circuits, multiple reflection (wave schedule, Bergeronverfahren, Network (SPICE) model of the double line, impulse behavior in dispersive lines
Stationary analysis in the frequency domain: Electricity and voltage along the lossy line, quadrupole presentation, impedance transformation.
Multiple lines: Definition and differential equivalent circuit transmission line equations and Wave equation, Modal (eigenmodes) solution, line crosstalk
Methods of Teaching Lecture, excercises
Requirements for participation
Fundamentals of Electrical Engineering I-III, Theoretical Electrical Engineering
Availability of Module
Compulsory subject in the Electrical Engineering option Elective in all other options
Prerequisite for the award of Credit points
Oral examination
94 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Power points Notes
SWS 3/4 Credit Points = 120 h (42 h attendance time + 78 h independent work)
Workload Attendance time: 2 hours lecture, 1 SWS exercise Independent work: exercises, exam preparation
Frequency of occurrence Every year in the summer term
Duration of module A semester
Responsible Prof. Dr.-Ing. M. Leone, FEIT-IGET
95 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: THREE-DIMENSIONAL & ADVANCED INTERACTION
Module level, if applicable: Master
Abbrevation, if applicable: TAI
Subheading, if applicable:
Classes, if applicable:
Semester: Winter semester
Module coordinator: ISG: User Interface & Software Engineering AG, AG visualization
Lecturer: Jun.-Prof. Dr.-Ing. Raimund Dachselt, Prof. Dr.-Ing. hat. Bernhard Preim
Language: English
Classification within the curriculum:
Master CV: Applications of Computational Master CSE / IF / WIF: Applied computer science Master CSE / CV: software and algorithm engineering
Master DKE: Applications FIN-diploma courses, advanced study
Teaching format / class hours per week during the semester:
Lecture and exercise / 4 SWS
Workload: Attendance time:
2 SWS lecture
2 SWS exercise
Independent work:
Reworking the lecture
Edit the seminar-exercises
Exam Preparation
Credit points: 6 Credit Points = 180 h (2 * 28h Attendance time + 124h independent work)
Requirements for Examination regulations:
nobe
Recommended prerequisites: Lecture Interactive Systems, Lecture User Interface Engineering, further conditions will be announced in the lecture
Targeted learning outcomes: Learning objectives and competences to be acquired:
Understanding the nature and importance of future user interfaces as well as related challenges and problems
Acquaintance, analysis and evaluation of technologies, interaction techniques and methods for the development of advanced user interfaces
Ability to select appropriate technologies and interaction techniques in the field of three-dimensional modern and post-WIMP user interfaces
Ability to critically analyze scientific literature and knowledge of scientific publishing
Ability to work on own research postgraduate level in the area of advanced user interfaces
96 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Content: Introduction to Post-WIMP and Reality-based User Interfaces
3D-Interaction: Tasks, Devices, 3D-Widgets, 3D UIs
Augmented Reality Interaction
Pen-based Interaction Techniques and Sketching
Multitouch: Technologies, Gestures, Applications
Gestural Interaction: Tracking, Freehand Gestures
Tangible Interaction
Advanced Topics: Gaze-based Interaction, Organic Interfaces, Everywhere Interfaces
Study / exam achievements: Oral examination
Grading scale according to examination regulations
Forms of media: PowerPoint, blackboard, video, software demonstrations
Literature: Bowman, Kruijff, Laviola, Jr., Poupyrev: "3D User Interfaces: Theory and Practice", Addison-Wesley, 2004
Müller-Tomfelde (Ed.): "Tabletops - Horizontal Interactive Displays", Springer, 2010
Saffer: "Designing Gestural Interfaces", O'Reilly Media, 2008
Shaer, Hornecker: "Tangible User Interfaces: Past, Present and Future Directions". In Foundations and Trends in Human-Computer Interaction, 3 (1), 2010
Further references during the lecture and on the current web page for the module ( http://isgwww.cs.uni-magdeburg.de/uise/Studium/WS2010/VorlesungTAI/ )
97 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Introduction to concurrency control
Module level, if applicable:
Abbrevation, if applicable: 103202
Subheading, if applicable: TV
Classes, if applicable:
Semester:
Module coordinator: Professor of Practical computer science / information systems and databases
Lecturer: Dr. Thomas Leich
Language:
Classification within the curriculum:
Teaching format / class hours per week during the semester:
Lectures, frontal exercises, independent work (solving exercises, literature studies, ...)
Workload: Attendance time:
weekly lectures, 2 SWS
weekly exercises 2 SWS
Independent work:
Exercises & Test Preparation
Credit points: 6 Credit Points = 180h (56h Attendance time in lectures and exercises + 124h independent work) Grading scale according to examination regulations
Requirements under the examination regulations:
none
Recommended prerequisites: Event "Databases"
Targeted learning outcomes: Learning objectives and competences acquired:
Basic understanding of the problem of transaction management Knowledge of theoretical foundations Knowledge of algorithms and methods for synchronizing
Knowledge of algorithms and procedures to maintain the ACID properties
Content: Transaction concept
Serialisierbarkeitstheorie
Synchronization method
Recovery and data backup
Transaction management in distributed database systems (Distributed synchronization, Distributed Commit, etc.)
Advanced transaction models
Study / exam achievements: Regular attendance of lectures Solving the exercises and successful presentation in the exercises Written or oral exam at the end of the module
Forms of media:
Literature: See http://wwwiti.cs.uni-magdeburg.de/iti_db/lehre/tv/index.html
98 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module:
Transport phenomena in granular, particulate and porous media
Objectives of the Module (Competencies):
Dispersed solids find broad industrial application as raw materials (e.g. coal), products (e.g. plastic granulates) or auxiliaries (e.g. catalyst pellets). Solids are in this way involved in numerous important processes, e.g. regenerative heat transfer, adsorption, chromatography, drying, heterogeneous catalysis. To the most frequent forms of the dispersed solids belong fixed, agitated and fluidized beds. In the lecture the transport phenomena, i.e. momentum, heat and mass transfer, in such systems are discussed. It is shown, how physical fundamentals in combination with mathematical models and with intelligent laboratory experiments can be used for the design of processes and products, and for the dimensioning of the appropriate apparatuses. ● Master transport phenomena in granular, particulate and porous media ● Learn to design respective processes and products ● Learn to combine mathematical modelling with lab experiments
-
Content
● Transport phenomena between single particles and a fluid ● Fixed beds: Porosity, distribution of velocity, fluid-solid transport phenomena Influence of flow maldistribution and axial dispersion on heat and mass transfer Fluidized beds: Structure, expansion, fluid-solid transport phenomena ● Mechanisms of heat transfer through gas-filled gaps ● Thermal conductivity of fixed beds without flow Axial and lateral heat and mass transfer in fixed beds with fluid flow ● Heat transfer from heating surfaces to static or agitated bulk materials ● Contact drying in vacuum and in presence of inert gas ● Heat transfer between fluidized beds and immersed heating elements
Teaching methods: Lectures / Exercises
Prerequisite for participation:
Workload: 3 SWS
Attendance: 42 hours
Self-study: 48 hours
Assessment / examination / Credits:
- M 3 CP
Responsible for Module: Prof. Tsotsas
99 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Name of the module Uncertain knowledge
Contents and objectives of the module
Learning outcomes and competences acquired:
Understanding of the concepts for dealing with uncertain knowledge in modeling, estimation, classification and decision making
Ability of development and parameterization of a Bayesian network
Understanding the concepts of estimation theory and their use
Ability to use stochastic filtering
Contents:
Foundations of uncertain knowledge processing
Bayesian networks, topology, parameterization, inference
Stochastic estimation
Wiener filter
Kalman filter
Methods of Teaching Lecture and exercises
Prerequisites for participation Fundamentals of Probability and Statistics
Applicability of the module There is no interaction with other modules. Eligibility:
Elective in Master Electrical and Computer Engineering Faculty
Elective in Master in other faculties
Requirements for awarding credit points
Exam or oral exam
Credits and grades 3 Credit Points = 90 h (28 h Attendance time + 62 hours independent work) Grading scale according to examination regulations
Workload Attendance time: Weekly Lectures: 2 SWS
Independent work: Preparation of the lectures, preparation for the exam
Frequency of occurrence each year in WS
Duration of module A semester
Responsible Prof. G. Rose, FEIT, IESK
100 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Module name: Distributed Real-Time Systems
Module level, if applicable:
Abbrevation, if applicable: VES
Subheading, if applicable:
Classes, if applicable:
Semester:
Module coordinator: Chair of Technical computer science / systems and real-time communication
Lecturer: Edgar Nett
Language:
Classification within the curriculum:
Master IngINF / IF / WIF: Applied computer science
Master IngINF / CV: Technical computer science (TI) Master IF / WIF: Network Computing
Teaching format / class hours per week during the semester:
Lecture, practical and theoretical exercises, independent work
Workload: Presence time = 56 h
2 SWS lecture
2 SWS excercises Independent Work = 124 h
Processing of exercise and programming assignments & exam preparation
Credit points: 6 credit points
Requirements under the examination regulations:
none
Recommended prerequisites: Participation in introductory courses on distributed and embedded systems is recommended
Targeted learning outcomes: Learning objectives and competences acquired:
Comprehensive overview of the equirements of Real-time systems and their applications
Ability to control and analyze the basic design principles and their inherent trade-offs
Competence in the practical application of a real-time operating system and its rogramming
Content: Algorithms for CPU Schedulung
Design of real-time communication protocols (wired / wireless)
Routing - protocols
Memory access protocols (Priority Version)
Clock synchronization
Models of real-time and embedded systems
Study / exam achievements: Services:
Regular attendance at lectures and excercises,
Successful completion of exercises Examination: written or oral
Forms of media:
Literature: Literature data on the current web page for the module
101 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
(http://euk.cs.ovgu.de/de/lehrveranstaltungen)
102 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
8.Digital Engineering Project
103 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.
Digital Engineering Project *
Module description will be given later In parallel with the professional specialization in the 3rd Semester, students are involved in a project Digital engineering. Here students will be directly integrated into ongoing research projects, which will be offered by cooperating faculties and in cooperation with and under utilization of resources by partners of industry-related research, such as the Virtual Development and Training Centre (VDTC). In addition to their specialization an introduction to scientific work follows, for example through participation in scientific publications or participation in scientific events.