ME_CSEIT_2011

FIRST YEAR

ME-CSE(Information Technology)

Pattern – A

SEMESTER I

(Theory)

ES0298: MATHEMATICAL FOUNDATIONS FOR COMPUTER ENGINEERING

PREREQUISITES:

Infinite Series, Ratio Test, Differential and integral calculus, elementary differential

equations, matrix analysis, some signal and system theory including Laplace and Fourier

transforms and a course on introduction to computer programming

OBJECTIVES:

The aim of this module is to provide an introduction to series solution, special functions,

probability, random variables, stochastic processes and mathematical statistics for

students of computer engineering, computer science, information technology and

reliability engineering.

Unit-1. Series Solutions of Differential Equations .Special Functions: ( 7 Hrs)

Power Series Method, Theory of Power Series Method, Legendre’s, Equations.

Legendre’s Polynomials P )(xn , Frobenius Method, Bessel’s Equation. Bessel Functions

J )(xν , Bessel Functions of the Second Kind Y )(xν , Sturm-Liouville Problems.

Orthogonal Functions, Orthogonal Eigenfunction Expansions

Unit-2. Discrete Random Variables (7 Hrs)

Probability Models , Sample Space, Events , Algebra of Events , Graphical Methods of

Representing Events , Probability Axioms , Combinatorial Problems ,Conditional

Probability , Independence of Events, Bayes' Rule , Bernoulli Trials. Discrete Random

Variables, Random Variables and Their Event Spaces ,The Probability Mass Function ,

Distribution Functions , Special Discrete Distributions ,The Probability Generating

Function , Discrete Random Vectors , Independent Random Variables .

Unit-3. Continuous Random Variables (7 Hrs)

The Exponential Distribution, The Reliability and Failure Rate, Some Important

Distributions, Functions of a Random Variable, Jointly Distributed Random Variables,

Order Statistics , Distribution of Sums , Functions of Normal Random Variables .

Unit-4. Expectation (7 Hrs)

Moments , Expectation Based on Multiple Random Variables , Transform Methods

Moments and Transforms of Some Distributions , Computation of Mean Time to Failure.

Inequalities and Limit Theorems.

Unit-5. Conditional Distribution and Expectation ` (7 Hrs)

Mixture Distributions, Conditional Expectation, Imperfect Fault Coverage and

Reliability, Random Sums.

Unit-6. Stochastic Processes (7 Hrs)

Classification of Stochastic Processes, The Bernoulli Process, The Poisson Process,

Renewal Processes , Availability Analysis, Random Incidence, Renewal Model of

Program Behavior

OUTCOMES:

By the end of the module students will be expected to demonstrate knowledge of

1. Series method of solving differential equations of second order especially by the

method of Frobenius series.

2. Solution of Bessel , Legendre’s differential equations and Sturm-Liouville

problems by orthogonal eigen function expansions ( generalized Fourier series.)

3. Mastering both “probability model building and the subsequent analysis of these

(certain games of chance) models.

4. The notion of random variables witch provides the power of abstraction , the

distribution and the density functions with standard important probability

distributions of discrete and continuous type.

5. Collection of random variables known as random processes and the Markov

process, the Poisson process and the knowledge of other processes.

TEXT BOOKS:

1. Erwin Kreyszig, ‘Advanced Engineering Mathematics’, John Wiley and sons (8th

Edition), Inc., 2003.

2. Kishor S.Trivedi, ‘Probability and Statistics with reliability, Queuing, and Computer

Science Applications’, Prentice Hall India New Delhi, India reprints, (2005).

REFERENCE BOOKS:

1. Dennis G. Zill and Michael R. Cullen, ‘Advanced Engineering Mathematics’, CBS

New Delhi, (2nd Edition), 2000.

2. Michael D.Greenberg, ‘Advanced Engineering Mathematics’, Prentice Hall

International, (Second Edition), 1998.

3. C. Ray Wylie, Louis C Barrett R, ‘Advanced Engineering Mathematics’, McGraw-

Hill Book Company, 6th Ed, 2003.

4. Pipes and Harvill, ‘Applied Mathematics for Engineers and Physicists’, McGraw-Hill

Book Company, 3rd Ed. 1984.

5. Alan Jeffrey ‘Advanced Engineering Mathematics’, Academic Press, 1st Ed., 2002.

6. Glyn James, Burley David Clements Dick, Dyke Phil, ‘Advanced Modern

Engineering Mathematics’, Prentice Hall, (3rd Edition), 2004.

7. Larry C., Andrews & Ronald L. Philips, ‘Mathematical Techniques for Engineers &

scientists’, PHI Pvt. Ltd., New Delhi, Indian reprint by SPIE, 2005.

8. Murray R. Spiegel, ‘Theory and Problems of Statistics’, Schaum’s out line series in

Mathematics, McGraw- Hill Book Company New York, 1961.

9. Albert Leon-Gracia, ‘Probability and Random Processes for electrical engineering’,

University of Toronto Pearson Education, 2nd Edition, 2004.

10. S. M. Ross, ‘Introduction to probability & statistics for engineers & scientists’,

Academic Press, 3rd edition 2005.

11. Allen, A.O., ‘Probability, Statistics and Queuing theory’, Academic Press, 2nd Ed,

2005.

12. Sheldon M. Ross, ‘Introduction to probability models’, Academic Press, seventh

Edition, 2001.

13. William Feller, ‘An introduction to probability theory and its application’, Vol.-I,

John Wiley & Sons, 3rd edition, 1950.

CS3018:ADVANCED OPERATING SYSTEMS Prerequisites: Data Structures Preliminary knowledge of LINUX C Programming Aim: This course covers computation representation by processes, system calls, interrupt processing and interrupt concurrency, process execution scheduling, and concurrency control methods. Advanced topics, such as system performance analysis, time keeping, clock synchronization, virtualization, real-time implications for system design and scheduling, and device driver implementation are introduced. Objectives:

1. Exposing students to current operating systems research and modern OS technology.

2. Providing insight in the design principles of kernels. 3. Providing experience in low-level systems programming in a realistic

development environment. 4. Providing experience in reading and evaluating research papers. 5. To study the implementation of Process Management, Memory Management and

File Management in Windows and Linux 6. To study the different design issues of Distributed Systems

Unit 1: Introduction to Operating Systems Internals (8 Hrs) OS concepts: Process Management, Memory Management, File Management, I/O Management. Study and comparison of different operating system architectures: Windows, Linux

Unit 2: Process Management (8 Hrs) Windows: System Mechanisms, Management Mechanisms, Startup and Shutdown, Process, Threads and Jobs Linux: Process Descriptor and Task Structure, Process Creation, Implementation of threads, Process Termination, Process Scheduling Unit 3: Memory Management (6 Hrs) Windows: Memory manager & its services, System memory pools, Virtual address space layout, address translation, page fault handling. Linux: Pages, Zones, kmalloc, vmalloc, slab layer, slab layer allocator, statically allocating on the stack, High memory mapping.

Unit 4: File Management (6 Hrs) Windows: Windows file system formats, FS driver architecture, troubleshooting FS problems, NTFS design goal and features, NTFS drivers, NTFS on disk structure.

Linux: Common File system Interface, File Abstraction Layer, Unix File System, VFS, Dentry Object, Super block Object, Inode Object, File Object, Data structure associated with File systems.

Unit 5: Communication in Distributed Systems (6 Hrs) Distributed Computer Systems, Issues in designing distributed operating systems, Group Communication, RPC Model, Implementing RPC, stub generation, RPC Messages, Communication Protocol for RPC, Client-Server binding, RPC in heterogeneous environment, Lightweight RPC.

Unit 6: Synchronization in Distributed Systems (6 Hrs) Clock synchronization, Event Ordering, Mutual Exclusion, Deadlock prevention, avoidance, detection algorithms in distributed systems, Election algorithms.

Self Study: I/O Management – Linux, Windows

Outcomes: At the end of the course, the students will be able to:

1. Work in an environment and on engineering problems related to OS 2. Understanding lowest-level OS code and its interaction with hardware and design

issues

Text Books: 1. Mark Russinovich, David Solomon, “Windows Internals”, Microsoft Press, 4th edition, 2005. 2. Robert Love, “Linux Kernel Development”, Pearson Education, 2nd edition, 2005.

Reference Books: 1. Pradeep K. Sinha “Distributed Operating Systems Concepts and Design” Publication: PHI, 1st edition, 1997 2. Andrew S. Tanenbaum & Maarten van Steen, Distributed Systems “Principles and

Paradigms”, PHI, 1st Indian Edition, 2002.

3. Daniel Bovet: “Understanding the Linux kernel”, O'Reilly Publications, 2nd edition, 2003.

CS3028:DATA MANAGEMENT TECHNIQUES

Prerequisite : Data Structures, Database Management System Aim : The course provides an overview of the relational database concepts. It focuses on advanced topics in database implementations such as data storage structures and query optimization. It further introduces advanced recent developments in database technology: Data Warehousing and Data Mining. Lastly, current trends in databases are discussed in terms of topics such as parallel, distributed, temporal, spatial, multimedia and deductive databases. Objectives:

1. To have an overview of the topics covered in data management system course. 2. To have an overview of indexing and hashing. 3. To study in detail the data structures used for data storage. 4. To study in detail the query processing and optimization techniques. 5. To understand the basics of data warehousing and how it facilitates data mining. 6. To understand the process of data mining and OLAP technique. 7. To study in detail the implementation of distributed and parallel databases. 8. To study current trends in application oriented databases.

Unit 1 : Overview of Relational Databases (6 Hrs)

Need of databases, Introduction to relational databases, Steps in database design.

Overview of Data Model: ER Diagram, Extended E-R features, Relational Model: SQL as DDL and DML, Keys, Referential Integrity, Overview of ER to Relational Mapping

Normalization and schema refinement: 4NF, 5NF, DKNF

Unit 2 : Data Storage and Querying (9 Hrs)

Overview of Storage and File Structure: File organization, Organization of records in files, Overview of indexing and hashing, Indexing types, Hash Indices, Static and dynamic hashing

Database Data Structures: B-Trees, B+Trees, k-d Trees, R-Trees

Query processing: Translation of SQL queries into Relational Algebra, Basic algorithms for executing query operations

Query Optimization: Query trees, Query execution plans, Selectivity and Cost estimates in query optimization, Cost functions

Unit 3 : Distributed and Parallel Databases (8 Hrs)

Distributed Vs Parallel Technology

Distributed Databases: Advantages of distributed databases, Data fragmentation, Data replication and allocation, Types of distributed database systems, Distributed transactions, 2 Phase Commit and 3 Phase Commit protocols, Concurrency control

and recovery in distributed databases, Deadlock handling, Query processing in distributed databases, Client-server architecture in distributed databases

Parallel Databases: I/O parallelism, Interquery and Intra query parallelism, Intra operation and Interoperation parallelism

Unit 4 : Data Warehousing (6 Hrs)

Introduction of Decision Support System (DSS), OLTP Vs Data Warehouse, Three tier architecture

Dimensional modeling: Star schema, Snowflakes schema, Fact constellations

OLAP: Data cube, OLAP Operations, OLAP Types

Unit 5 : Data Mining (7 Hrs)

Introduction, Data mining steps

Classification: Decision tree classifier, Back propagation algorithm, Fuzzy set

Clustering: K-means, K- medoids

Association rule mining, Prediction, Outlier analysis

Unit 6 : Advance Topics in Databases (8 Hrs)

Temporal databases: Time Representation, Calendars and Time Dimensions, Incorporating time in relational databases using tuple versioning Spatial databases: Range query, Nearest neighbor query, Special joins, Overlays Multimedia databases: CBIR, Image databases, Video/ audio source, Nature of multimedia data and applications, Data management issues, Open research problems, Multimedia database applications Active and Deductive databases: Introduction, Prolog/Datalog notation, Interpretation of rules, Basic inference mechanisms for logic programs, Datalog programs, Deductive database systems

Outcomes:

Upon completion of the course, the students will be able to: 1. Have an overview of relational databases 2. Write database queries in the most efficient manner 3. Define the concept, structure and major issues of data warehousing 4. Understand the OLAP technique 5. Define and recognize key areas and issues in knowledge discovery in data 6. Develop an in-depth understanding of several data mining techniques 7. Understand how transaction processing, concurrency control, query

processing works on distributed and parallel systems 8. Have introduction to application oriented database approaches

Text Books:

1. Silberschatz ,Korth and Sudarshan, “Database system Concepts”, Fifth edition,

McGraw Hill, International Edition 2006, ISBN – 007-124476-X. 2. Jiawei Han and Micheline Kamber “Data mining: concepts and techniques”, the

Morgan Kaufman,2001.

Reference Books:

1. Elmasri, Navathe, “Fundamentals of database system”, Third edition, Published by Pearson Education Pte. Ltd, ISBN – 81-7808-137-7.

2. Kimball, “Data warehouse life cycle toolkit”, John Wiley,1998 Additional Reading:

Raghu Ramkrishan and Johannes Gerkhe, ‘Database Management Systems’, 3rd edition, McGraw Hill, 2003. Rob, Coronel, “Database system, design, implementation and management”, Thomson Learning, Fourth Edition, 2001, ISBN – 0-07-123151-X.

Hand, Smyth, Mannila,”Principles of Data mining “, MIT press. Gagendra Sharma ,”Data mining, Data warehousing and OLAP”, S.K. Kataria and

sons, First edition, 2007-08. “R-Trees. A Dynamic Index Structure For Spatial Searching”, by Antomn Guttman,

University of Cahforma Berkeley, ACM Transactions, 1984 “A simple guide to five normal forms in relational database theory”, by William

Kent, International Business Machines Corporation, ACM Transactions, 1983. “A Normal Form for Relational Databases That Is Based on Domains and Keys”,

Ronald Fagin, IBM Research Laboratory, ACM Transactions on Database Systems, Vol. 6, No. 3, September 1981, Pages 387-415.

COMPUTER ENGINEERING M.E.-CSE(IT) Academic Year - 2010-11 Pattern - A

Elective –I

Theory & Practical


CS3058: DIGITAL SIGNAL PROCESSING

Prerequisites : Knowledge of basic Engineering Mathematics

Aim :

1 To learn methodology to analyze signals and systems 2 To get acquainted with the DSP Processors

Objectives: 1. To understand how to analyze and manipulate digital signals in various domains and design digital filters.

2. To get familiar to Adaptive Algorithms

Unit 1 : Classification of Signals and The Generic DSP System Introduction (8 Hrs )

Analog, Discrete-time and Digital, Basic sequences and sequence operations, Periodic Sampling, Sampling Theorem, Frequency Domain representation of sampling, reconstruction of a band limited Signal, A to D conversion Process: Sampling, quantization, quantization Error and encoding. Aliasing effect, Anti-alias and Reconstructions Filters Discrete-time systems, Properties of D. T. Systems and Classification, Linear Time Invariant Systems, impulse response, linear convolution and its properties, properties of LTI systems: stability, causality, parallel and cascade connection, Linear constant coefficient difference equations, Eigen functions for LTI systems and frequency response

Unit 2 Frequency Domain Analysis ( 7 Hrs )

Representation of Sequences by Fourier Transform, Symmetry properties of F. T., F. T. theorems: Linearity, time shifting, frequency shifting, time reversal, differentiation, convolution theorem, windowing theorem, Discrete FourierTransform, Properties of DFT: Linearity, circular shift, duality, symmetry, Circular Convolution, Linear Convolution using DFT, Effective computation of DFT and FFT, DIT FFT, DIF FFT, Inverse DFT using FFT,

Unit 3: Frequency Response of LTI Systems: ( 6 Hrs ) Ideal frequency selective filters, magnitude and phase response, group delay, System Functions for LTI Systems: Stability and causality, inverse systems, significance of poles/zeros, Frequency Response for Rational System Functions: Frequency Response of a single zero or pole, Frequency response from pole-zero plot using simple geometric construction


Unit 4 : Filters ( 7 Hrs ) Concept of filtering, Ideal filters and approximations, specifications, IIR filter, design from continuous time filters: Characteristics of Butterworth, Cheybyshev and elliptical approximations, impulse invariant and bilinear transformation Techniques. Design examples, FIR filter design using windows: properties of commonly used windows, Design Examples, Design using Kaiser window. Unit 5 : Realization of Filter ( 8 Hrs ) Comparison of IIR and FIR Filters, Block diagrams and Signal flow graph representation of LCCDE, Basic structures for IIR Systems: direct form, cascade form, parallel form, feedback in IIR systems, Transposed Forms, Basic Structures for FIR Systems: direct form, cascade form DSP Processors Architecture and Applications of DSP: Case Study

Unit 6 : Adaptive DSP Algorithms ( 6 Hrs )

Least squares (LS) and Least mean squares (LMS) Channel equalisation / Inverse system identification Echo Control for feedback suppression Acoustic echo control /noise control RLS, Kalman and QR algorithms

Outcomes:

At the conclusion of the course students will:

1. Describe the Sampling Theorem and how this relates to Aliasing 2. Determine the frequency response of FIR and IIR filters. 3. Understand the relationship between poles, zeros, and stability. 4. Determine the spectrum of a signal using the DFT, FFT, and spectrogram. 5. Be able to design, and implement digital filters in Matlab.

Text Books :

1. Oppenheim A., Schafer R., Buck J., "Discrete time signal processing", 2nd Edition, Prentice Hall, 2003, ISBN-81-7808-244-6

2. Proakis J., Manolakis D., "Digital signal processing", 3rd Edition, Prentice Hall, ISBN 81- 203-0720-8

Reference Books :

1. Babu R., “Digital Signal Processing”, 2nd Edition, Scitech Publications, ISBN 81- 873-

2852-5 2. Mitra S., "Digital Signal Processing: A Computer Based Approach", Tata

McGraw-


Hill, 1998, ISBN 0-07-044705-5 3. Vallavraj A., “Digital Signal Processing”, ISBN 0-07-463996-X 4. Manual - ADSP 21 XX family DSP 5. White S., "Digital Signal Processing”, Thomson Learning, ISBN –981-240-620-


CS8058 : DIGITAL SIGNAL PROCESSING

Objectives :

1 To learn methodology to analyze signals and systems 2 To get acquainted with the DSP Processors

Assignments to be framed on the following topics: 1) Verification of sampling Theorem: 2) Linear Convolution 3) Pole Zero Implementation 4) Magnitude and Phase response of a system 5) Circular convolution 6) FFT Algorithms 7) Design IIR filter using approximations 8) Designing FIR filters using windowing techniques 9) Kalman Filter application All assignments to be implemented using C, Matlab 3 of the above using DSP Processor

Outcomes:

At the conclusion of the course students will:

1. Describe the Sampling Theorem and how this relates to Aliasing 2. Determine the frequency response of FIR and IIR filters. 3. Understand the relationship between poles, zeros, and stability. 4. Determine the spectrum of a signal using the DFT, FFT, and spectrogram. 5. Be able to design, analyze, and implement digital filters in Matlab.

Be able to implement filters on a digital signal processor

Text Books :


1. Oppenheim A., Schafer R., Buck J., "Discrete time signal processing", 2nd Edition, Prentice Hall, 2003, ISBN-81-7808-244-6 2. Proakis J., Manolakis D., "Digital signal processing", 3rd Edition, Prentice Hall, ISBN 81-203-0720-8

Reference Books :

1. Babu R., “Digital Signal Processing”, 2nd Edition, Scitech Publications, ISBN 81- 873- 2852-5

2. Mitra S., "Digital Signal Processing: A Computer Based Approach", Tata McGraw-Hill, 1998, ISBN 0-07-044705-5

3. Vallavraj A., “Digital Signal Processing”, ISBN 0-07-463996-X 4. Manual - ADSP 21 XX family DSP 5. White S., "Digital Signal Processing”, Thomson Learning, ISBN –981-240-620-


CS3088 : INTELLIGENT SYSTEMS

Aim : This course covers the concepts of Intelligent Systems and Methodologies used to develop Intelligent System Applications. This course also covers diverse intelligent applications; this class focuses on principles, algorithms, and techniques common to intelligent systems and applications of these algorithms and techniques to important problems. Objectives:

1. To introduce, representations, techniques, and architectures used to build applied systems and to account for intelligence from a computational point of view.

2. To explore applications of rule chaining, heuristic search, logic, constraint propagation, constrained search, and other problem-solving paradigms.

Unit 1 : Introduction to Intelligent Agents ( 6 Hrs )

Introduction to Intelligent Agents, Definitions, Motivations, Task Domain, Problem Characteristics, Structure and Types of agents: Table driven, Simple Reflex, Goal Based, Utility Based, Task Environments.

Unit 2 : Problem Solving ( 8 Hrs )

Solving problems by Searching, Informed, Uninformed search and Exploration Constraint satisfaction Problems, Adversarial Search

Unit 3 : Knowledge and Reasoning ( 8 Hrs )

Logical Agents, First order Logic, Inference in First order Logic, Knowledge Representation, Rule-based expert systems, Characteristics, Chaining inference, Resolution, Conflict resolution, Expert systems: Basics of Prolog

Unit 4 : Uncertainty management ( 7 Hrs )

Basic probability, Bayesian reasoning, Certainty factors, Definition of hybrid, distributed, and uncertain systems, Definition of stable, robust, and fault-tolerant performance, Adaptation and learning

Unit 5 : Planning ( 7 Hrs )

Planning and acting in the real world, Conditional Planning, HTN planning, Partial order Planning, Goal stack Planning.

Unit 6 : Learning ( 6 Hrs )


Types of Learning, Rote Learning, Learning by taking advice, Learning in problem solving, Learning from Examples: Induction, Explanation based Learning

Outcomes:

A student completing this course will be able to: 1. Explain the basic knowledge representation, problem solving, and learning

methods of Artificial Intelligence. 2. Assess the applicability, strengths, and weaknesses of the basic knowledge

representation, problem solving, and learning methods in solving particular engineering problems.

3. Develop intelligent systems by assembling solutions to concrete computational problems.

4. Understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering.

5. Develop an interest in the field sufficient to take more advanced subjects.

Text Books : 1. Michael Negnetvitsky, Artificial Intelligence: a guide to intelligent systems,

Addison-Wesley, 2nd Edition, ISBN: 0-321-20466-2, 2005.

2. Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 2nd edition, Prentice Hall, 2003.

Reference Books

1. Elaine Rich, Kevin Knight, “ Artificial Intelligence “, Tata McGraw-Hill Edition

2. N.D. Padhy, “Artificial Intelligence and Intelligent Systems”, OXFORD University Press, 2005


CS8088 : INTELLIGENT SYSTEMS

Objectives:

1. To introduce, representations, techniques, and architectures used to build applied systems and to account for intelligence from a computational point of view.

2. To explore applications of rule chaining, heuristic search, logic, constraint propagation, constrained search, and other problem-solving paradigms.

Any 12 assignments from following topics

1. Minimax algorithm

2. A* heuristic Search

3. AO* heuristic Search

4. Unification

5. Resolution

Outcomes:

A student completing this course will be able to: 6. Explain the basic knowledge representation, problem solving, and learning

methods of Artificial Intelligence. 7. Assess the applicability, strengths, and weaknesses of the basic knowledge

representation, problem solving, and learning methods in solving particular engineering problems.

8. Develop intelligent systems by assembling solutions to concrete computational problems.

9. Understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering.

10. Develop an interest in the field sufficient to take more advanced subjects.

Text Books : 1. Michael Negnetvitsky, Artificial Intelligence: a guide to intelligent systems,

Addison-Wesley, 2nd Edition, ISBN: 0-321-20466-2, 2005.

2. Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 2nd edition, Prentice Hall, 2003.

Reference Books

3. Elaine Rich, Kevin Knight, “ Artificial Intelligence “, Tata McGraw-Hill Edition


4. N.D. Padhy, “Artificial Intelligence and Intelligent Systems”, OXFORD University Press, 2005


CS3118 : INTERNET ROUTING ALGORITHM Aim : To understand core part of the INTERNET which is routing. Objectives:

1. To explain routing algorithms in the Internet; link-state routing and distance-vector routing; broadcast and multicast routing algorithms.

2. To explain path determination, strengths of link state and distance vector protocols in differing configurations

3. To describe the goals of routing protocols and convergence as impacted by different protocols

4. To explain the similarities and differences between several types of routing protocols

Unit 1 : Network Basics (7 Hrs )

OSI Model, Network Hardware, Transmission media, Bridge, Router , Gateways, Network Software Components, MAC, Data Link Protocols, Switching Techniques TCP/IP Protocol suite.

Unit 2 : Networking and Network Routing (7 Hrs )

Addressing and Internet Service: An Overview, Network Routing, IP Addressing, Service Architecture, Protocol Stack Architecture, Router Architecture, Network Topology, Architecture, Network Management Architecture, Public Switched Telephone Network

Unit 3 : Routing Algorithms (7Hrs )

Shortest Path and Widest Path: Bellman–Ford Algorithm and the Distance Vector Approach, Dijkstra’s Algorithm, Widest Path Algorithm, Dijkstra-Based Approach, Bellman–Ford-Based Approach, k-Shortest Paths Algorithm. OSPF and Integrated IS-IS : OSPF: Protocol Features, OSPF Packet Format, Integrated IS-IS, Key Features, comparison BGP : Features ,Operations, Configuration Initialization, phases, Message Format. IP Routing and Distance Vector Protocol Family :RIPv1 and RIPv2

Unit 4 : Routing Protocols: Framework and Principles ( 7 Hrs )

Routing Protocol, Routing Algorithm, and Routing Table, Routing Information Representation and Protocol Messages, Distance Vector Routing Protocol, Link State Routing Protocol, Path Vector Routing, Protocol, Link Cost.


Unit 5 : . Internet Routing and Router Architectures (7 Hrs )

Architectural View of the Internet, Allocation of IP Prefixes and AS Number, Policy-Based Routing, Point of Presence, Traffic Engineering Implications, Internet Routing Instability. Router Architectures: Functions, Types, Elements of a Router, Packet Flow, Packet Processing: Fast Path versus Slow Path, Router Architectures

Unit 6 : Analysis of Network Algorithms (9 Hrs ) Network Bottleneck, Network Algorithmics, Strawman solutions, Thinking Algorithmically, Refining the Algorithm, Cleaning up, Characteristics of Network Algorithms.

IP Address Lookup Algorithms : Impact, Address Aggregation, Longest Prefix Matching, Naïve Algorithms, Binary , Multibit and Compressing Multibit Tries, Search by Length

Algorithms, Search by Value Approaches, Hardware Algorithms, Comparing Different Approaches IP Packet Filtering and Classification : Classification, Classification Algorithms, Naïve Solutions, Two-Dimensional Solutions, Approaches for d Dimensions,

Outcomes: After completing the course, the students will be able to:

1. Identify the router startup components and modes 2. Understand Router Elements, Router Components, Router Status, Managing

Configuration Files, The Running and Startup Configuration Files, Router Identification and Banner

3. Understand in depth the major network, routing, and transport protocols and mechanisms used in the Internet

4. Become familiar with the networking research tools of simulation, traffic analysis, and active measurements

5. Appreciate some open research issues related to Internet routers, routing, congestion control, traffic characteristics, naming & addressing

Text Books :

1. Network Routing: Algorithms, Protocols, and Architectures Deepankar Medhi and Karthikeyan Ramasamy (Morgan Kaufmann Series in Networking)


2. Network Algorithmics: An Interdisciplinary Approach to Designing Fast Networked Devices George Varghese (Morgan Kaufmann Series in Networking)


CS8118: INTERNET ROUTING ALGORITHM

Aim: To understand and implement some of the important Internet routing related concepts. Objectives:

1. To learn to develop a basic router 2. To understand the implementation details of IP Addresses and IP Packet

filtering. 3. To understand and implement different routing algorithms and/or protocols. 4. To understand the Internet and more specifically the Router architecture.

Assignments should be based on the following topics:

1. Network Routing – An Introduction through Implementation a. To setup Intranet : Installation and Configuration of Peer to Peer and

Client Server models, Web server, E-mail, Proxy, Firewall and DNS Configurations

b. Conversion of a simple machine into a router.

2. Routing Algorithms a. Bellman–Ford Algorithm and the Distance Vector Approach b. Comparison of the Bellman–Ford Algorithm and Dijkstra’s Algorithm c. Shortest and Widest Path Computations

3. Routing Protocols a. Distance Vector Routing b. Link State Routing

4. Routing in IP networks a. RIP b. OSPF c. BGP

5. Internet Architecture a. Address Assignment b. Traffic Engineering c. Policy-Based Routing

6. Router Architectures a. Routing Functions b. Packet Processing

7. IP Address Lookup Algorithms


8. IP Packet Filtering and Classification 9. Quality of Service Routing

Outcomes: After completing the course, the students will be able to:

1. Understand router elements, router components, router status, managing configuration files, the running and startup configuration files, router identification and banner

2. Understand in depth the major routing protocols and mechanisms used in the Internet

3. Appreciate some open research issues related to Internet routers, routing, traffic characteristics, naming & addressing

Text Books:

1. Network Routing: Algorithms, Protocols, and Architectures Deepankar Medhi and Karthikeyan Ramasamy (Morgan Kaufmann Series in Networking)

2. Network Algorithmics: An Interdisciplinary Approach to Designing Fast Networked Devices George Varghese (Morgan Kaufmann Series in Networking)


CS3148: OOSE AND DESIGN PATTERNS Prerequisites : 1. Software Engineering 2. Object Oriented Programming 3. Object Oriented Modeling and Design Aim : 1. This course is based around the notion of a design pattern: an abstraction of a proven

solution to a recurring problem in a particular context in system structure and behavior.

2. Design patterns are standard solutions to common software design problems. Instead of focusing on how individual components work, design patterns are a systematic approach that focus and describe abstract systems of interaction between classes, objects, and communication flow.

3. Techniques for designing both the structural and behavioral aspects of software systems are emphasized. This course will cover object-oriented approaches to system analysis, data modeling and design that combine both process and data views of systems.

Objectives: 1. To understand the practices of requirements elicitation, capture and analysis 2. To thoroughly understand the practices of analysis and design (OOA and OOD) 3. To understand the practical connections between the theory of object-oriented design

and the object-oriented programming languages 4. To understand the relative merits of the different UML diagrams, distinguishing those

diagrams most likely always to be useful to typical projects from those diagrams more likely to be of interest to more specialized projects

5. To trace the relationship between patterns and traditional methods 6. To learn the relationship among patterns, frameworks, object-oriented languages, and

software architecture and to identify fundamental and advanced concepts of design and architectural patterns

7. To focus on Archetypes and Architecture Documentation

Unit 1 :

Domain Model Engineering ( 6 Hrs )

Crunching Knowledge, Communication and the Use of Language, Binding Model and Implementation, Isolating the Domain, Model Expressed in Software, Life Cycle of a Domain Object, Making Implicit Concepts Explicit, Maintaining Model Integrity. , Distillation, Large-Scale Structure.


Unit 2 :Introduction to Primitive Workflows ( 7 Hrs )

Unified Process: What is UML?, What is the Unified Process?, The requirements workflow, Use case modeling., Advanced use case modeling., The analysis workflow., Objects and classes, Finding analysis classes, Relationships, Inheritance and polymorphism, Analysis packages, Use case realization. Advanced use case realization, Activity diagrams, Advanced activity diagrams. Design-driven Workflows: The design workflow. Design classes, Refining analysis relationships. Interfaces and components, Use case realization-design. State machines, Advanced state machines, The implementation workflow, Deployment, Introduction to OC, Model Driven Development.

Unit 3 : Introduction to Software Architecture ( 6 Hrs )

Software Architecture, Relationships to Other Disciplines, Multi-Disciplinary Overview, Foundations of Software Architecture, Software architecture in the context of the overall software life cycle, Architectural Styles, CASE study of Architectures

Unit 4 : Software Architecture Design ( 8 Hrs )

Designing, Describing, and Using Software Architecture, IS2000: The Advanced Imaging Solution, Global Analysis, Conceptual Architecture View, Module Architecture View, Styles of the Module Viewtype, Execution Architecture View, Code Architecture View. Component-and-Connector Viewtype, Styles of Component-and-Connector Viewtype, Allocation Viewtype and Styles, Documenting Software Interfaces, Documenting Behavior, Choosing the Views, Building the Documentation Package

Unit 5 : Archetype Patterns ( 8 Hrs )

Archetypes and Archetype Patterns, Model Driven Architecture with Archetype Patterns. Literate Modeling, Archetype Pattern. , Customer Relationship Management (CRM) Archetype Pattern, Product Archetype Pattern, Quantity Archetype Pattern, Rule Archetype Pattern

Unit 6 : Design Patterns and Patterns Types ( 7 Hrs )

Design Patterns: Creational Patterns, Patterns for Organization of Work, Access Control Patterns, Service Variation Patterns, Service Extension Patterns Pattern Types: Object Management Patterns Adaptation Patterns, Communication Patterns, Architectural Patterns, Structural Patterns, Patterns for Distribution, Patterns for Interactive Systems , Adaptable Systems, Frameworks and Patterns, Analysis Patterns


Outcomes:

Upon completion this course, students will be able to: 1. Understand the basic principles of object-orientation and acquire a working

knowledge of system modeling techniques 2. Create commonly expected "deliverables" of systems design including models of

structure, behavior and dynamics 3. Recognize and understand the various aspects of architectures 4. Be able to recognize, understand and use the most common architectural patterns

Text Books

1. Christine Hofmeister, Robert Nord, Deli Soni, Addison-Wesley Professional; 1st edition Applied Software Architecture , (November 4, 1999) ,ISBN-10: 0201325713 , ISBN-13: 978-0201325713

2. Ian Gorton Springer; 1 edition (2006) Essential Software Architecture, ISBN-10: 3540287132 ISBN-13: 978-3540287131

Reference Books

1. Frank Buschmann, Hans Rohnert, Kevin Henney, Douglas C. Schmidt,Pattern-Oriented Software Architecture Volume 1, 2, 3, 4, 5 by Publisher: Wiley; 1 edition (August 8, 1996-2004) ISBN-10: 0471958697 ISBN-13: 978-0471958697

2. Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides Publisher: Addison-Wesley Professional; 1st edition Design Patterns: Elements of Reusable Object-Oriented Software (Addison-Wesley Professional Computing Series) (January 15, 1995) ISBN-10: 0201633612 ISBN-13: 978-0201633610

3. Thomas Stahl, Markus Voelter, Krzysztof Czarnecki, Wiley ,Model-Driven Software Development: Technology, Engineering, Management, ISBN-10: 0470025700, ISBN-13: 978-0470025703 2006,

4. Eric Evans, Addison-Wesley Professional, Domain-Driven Design: Tackling Complexity in the Heart of Software, 2004, ISBN-10: 0321125215, ISBN-13: 9780321125217

5. Jim Arlow, Ila Neustadt, Addison-Wesley Professional, UML 2 and the Unified Process: Practical Object-Oriented Analysis and Design, 2/E, , ISBN-10: 0321321278, ISBN-13: 9780321321275, 2005


CS8148:OOSE AND DESIGN PATTERNS Objectives: 1. To understand the practices of requirements elicitation, capture and analysis 2. To thoroughly understand the practices of analysis and design (OOA and OOD) 3. To understand the practical connections between the theory of object-oriented design

and the object-oriented programming languages 4. To understand the relative merits of the different UML diagrams, distinguishing those


5. To trace the relationship between patterns and traditional methods 6. To learn the relationship among patterns, frameworks, object-oriented languages, and

software architecture and to identify fundamental and advanced concepts of design and architectural patterns

7. To focus on Archetypes and Architecture Documentation

List of Practicals 1. A. To narrate Requirement Definition Document for the target system with following three areas:

- Problem Identification - Problem Definition - Problem Statement

B. To narrate System Requirements Specification Document for target system with reference to the IEEE 610.12.1990 std guidelines. 2. To decompose and organize the problem domain area into broad subject areas and identify the boundaries of problem/system. Specify the behavior of the target system and map requirements to Use cases. The System Context Diagram depicts the overall System behavioral trace and Requirement Capture diagram depicts the hierarchical Use case Organization. The Use Case diagram should encompass

- Actors (External Users) - Transactions (Use Cases) - Event responses related to transactions with external agents. - Detection of System boundaries indicating scope of system.

3. To depict the dynamic behavior of the target system using sequence diagram. The Sequence diagram should be based on the Scenarios generated by the inter-object communication. The model should depict:

- Discrete, distinguishable entities (class). - Events (Individual stimulus from one object to another). - Conditional events and relationship representation.

4. To depict the state transition with the life history of objects of a given class model. The model should depict:

- Possible ways the object can respond to events from other objects. - Determine of start, end, and transition states.


5. To prepare Class Collaboration-Responsibility (CRC) cards for the Conceptual classes traced from System analysis phase. To develop logical static structure of target system with Class diagram. The model should depict

- Relationship between classes: inheritance, Assertion, Aggregation, Instantiation

- Identification of objects and their purpose. - Roles / responsibilities entities that determine system behavior.

6. To represent physical module that provides occurrence of classes or other logical elements identified during analysis and design of system using Component diagram. The model should depict allocation of classes to modules. To narrate the Program Design Language Constructs for the target system and implement the system according to specification. 7. Select a moderately complex system and narrate concise specification for the same. Implement the system features using Abstract Factory, Composite, Facade and Proxy design patterns. State the complete pattern specification and note the difference between the patterns. 8. Select a complex system and narrate concise specification for the same. Develop architecture specification and use archetypes to recognize the architectural elements.

Outcomes:

Upon completion this course, students will be able to: 1. Understand the basic principles of object-orientation and acquire a working

knowledge of system modeling techniques 2. Create commonly expected "deliverables" of systems design including models of

structure, behavior and dynamics 3. Recognize and understand the various aspects of architectures 4. Be able to recognize, understand and use the most common architectural patterns

Text Books

1. Christine Hofmeister, Robert Nord, Deli Soni, Addison-Wesley Professional; 1st edition Applied Software Architecture , (November 4, 1999) ,ISBN-10: 0201325713 , ISBN-13: 978-0201325713

2. Ian Gorton Springer; 1 edition (2006) Essential Software Architecture, ISBN-10: 3540287132 ISBN-13: 978-3540287131

Reference Books

1. Frank Buschmann, Hans Rohnert, Kevin Henney, Douglas C. Schmidt,Pattern-Oriented Software Architecture Volume 1, 2, 3, 4, 5 by Publisher: Wiley; 1 edition (August 8, 1996-2004) ISBN-10: 0471958697 ISBN-13: 978-0471958697

2. Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides Publisher: Addison-Wesley Professional; 1st edition Design Patterns: Elements of Reusable Object-Oriented Software (Addison-Wesley Professional Computing Series) (January 15, 1995) ISBN-10: 0201633612 ISBN-13: 978-0201633610


3. Thomas Stahl, Markus Voelter, Krzysztof Czarnecki, Wiley ,Model-Driven Software Development: Technology, Engineering, Management, ISBN-10: 0470025700, ISBN-13: 978-0470025703 2006,

4. Eric Evans, Addison-Wesley Professional, Domain-Driven Design: Tackling Complexity in the Heart of Software, 2004, ISBN-10: 0321125215, ISBN-13: 9780321125217

5. Jim Arlow, Ila Neustadt, Addison-Wesley Professional, UML 2 and the Unified Process: Practical Object-Oriented Analysis and Design, 2/E, , ISBN-10: 0321321278, ISBN-13: 9780321321275, 2005


Elective –II

Theory & Practical


CS3068: IMAGE PROCESSING Aim : To study fundamentals of Image processing and its applications. Objectives: Explore several different image processing techniques, and learn to improve images with them. Also get familiarized to image enhancement and compression.

Unit 1 :

Introduction to image processing ( 8 Hrs )

Components of image processing system, Human Visual System, color vision color model: RGB, HVS, YUV, CMYK, image sampling and quantization, some basic relationships between pixels, linear and non linear operations.

Unit 2 :

Image enhancement in spatial domain & frequency domain ( 8 Hrs )

Basic gray level transformations, histogram processing, enhancement using logical and arithmetic operations, basic spatial filtering, smoothing and sharpening spatial filters. Combining spatial enhancement methods. Smoothing frequency domain filters, sharpening frequency domain filters homomorphic filtering

Unit 3 :

Image transform ( 6 Hrs )

Introduction to two dimensional orthogonal and unitary transforms, properties of unitary transforms, one-two dimensional discrete Fourier Transform (DFT), Cosine, sine, slant, KL, affine transforms.

Unit 4 :

Compression fundamental ( 7 Hrs )

File format (bmp, tiff, pcx ,gif , jpeg.) Compression fundamentals, image compression models, elements of information theory, error free compression: VLC, LZW coding, Bit plane Coding, Lossless Predictive Coding, lossy compression: lossy Predictive Coding, Transform, vector Quantization, Factral, image compression standard : Binary Image, Continuous Tone Still Image, Video, JPEG compression standards.


Unit 5 :

Image segmentation ( 7Hrs )

Detection of Discontinuities, Edge Linking and Boundary Detection, Thresholding, Region –Based Segmentation, Segmentation by Morphological Watersheds, Use of motion in Detection.

Unit 6:

Applications in Image Processing ( 6Hrs )

Case Study: Pattern Recognition, Iris Recognition, and Biomedical Applications

Outcomes:

1. Gain knowledge and practical experience in digital image processing. 2. Learn practical skills and analytic background for building digital Image/multimedia applications

Text Books :

1 .Rafael Gonzalez & Richard Woods Digital Image Processing (Pearson publications). 2 Anil K. Jain Fundamental of Digital Image Processing . (PHI publication)

Reference Books :

1 Digital Image Processing by Pratt,Wiley Publication 2.K,R, Castleman, Digital Image Processing, Prentice Hall: Upper Saddle River, NJ, 3. 3. 3.Image Processing: Analysis and Machine Vision by Milan Sonka


CS8068 : IMAGE PROCESSING

Objectives: 1. Learn to use matlab to manipulate image files . 2. Explore several different image processing techniques, and learn to improve images with them. 3. Extract quantitative data from images.

List of Practicals

Implementation of the following using MATLAB / C: 1. To read and Display Image using ‘C’ programming. 2.To explore statistical properties of image and displaying histogram and profile 3.Histogram modification. 4.Image smoothing operations. 5.Pseudo-coloring of gray-level images. 6.Edge detection.-2 assignments 7.Image transforms-1 assignments 8.Image Compression 9.Image segmentation

Outcomes:

1. Gain knowledge and practical experience in digital image processing. 2. Learn practical skills and analytic background for building digital image/multimedia applications

Text Books :


1 Rafael Gonzalez & Richard Woods Digital Image Processing (Pearson publications). 2 Anil K. Jain Fundamental of Digital Image Processing . (PHI publication)

Reference Books :

1 Digital Image Processing by Pratt,Wiley Publication 2.K,R, Castleman, Digital Image Processing, Prentice Hall: Upper Saddle River, NJ, 3. 3. 3.Image Processing: Analysis and Machine Vision by Milan Sonka


CS3098: ARTIFICIAL NEURAL NETWORKS

Aim: To make students aware of theories and practices of Artificial Neural Networks.

Objectives:

1. To focus on the foundations of neural network theory and the application of neural network models in real world problem solving.

2. To make students capable to select an ANN model, design the topology and initialize the network parameters.

3. To make students aware about advantages and shortcomings of ANN. 4. To make them aware of modern neural network architecture. 5. To make them aware about hybrid neural systems like Fuzzy neural networks.

Unit 1: Introduction. ( 7 Hrs) Artificial intelligence and ANN :-Intelligent machines, motivation for ANN, Cognitive tasks, comparison of human brain and digital computers. Biological Neuron and models:-architecture of biological neuron, working, McCulloch-Pitts neuron model, general neuron model, activation functions and their types. Unit 2: Models and learning ( 7 Hrs)

Models of ANN:- feed forward feed back neural network. Neural processing:- association, classification Learning: learning and adaptation, Supervised and unsupervised learning. Learning rules:- Generalized learning rule, hebbian, perceptron, delta, Widrow-Hoff, Correlation winner-take-all, Outstar learning rule. Introduction to fuzzy sets and Hybrid neural system: Fuzzy Neural Network

Unit 3 : Single layer perceptron classifiers ( 7 Hrs)

Classification model, features and decision regions, discriminant functions(linear and non-linear), a linear minimum distance classifier, single discrete perceptron training algorithm, single continuous perceptron training algorithm, R-category discrete perceptron training algorithm.

Unit 4 : Multilayer feed forward networks. ( 7 Hrs)

Linearly nonseperable patterns, delta learning rule for multi-perceptron layer, generalized delta learning rule, feedforward recall and error back propogation training, learning factors. Introduction to Fuzzy sets, advantages of hybridization of fuzzy and neural networks, A modern architecture of FNN for classification suggested by staff.

Unit 5 : Self organization ( 7 Hrs)


Single layer feed-back networks:- dynamic system, Hopfield networks. Matching and self-organizing networks:- hamming net and MAXNET, unsupervised learning of clusters, feature mapping, self-organizing feature maps, cluster discovery network (ART1), A modern architecture of FNN for clustering suggested by staff.

Unit 6 : Introduction to Statistical Pattern Recognition ( 7 Hrs)

Feature extraction, Example character recognition, Preprocessing and feature extraction, A complete demonstration of a neural system for real world problem solving including preprocessing, feature extraction, feature normalization, neural learning and recall phase.

Outcomes: Upon completion of the course, the students will:

1. have an in-depth understanding of artificial neural networks fundamentals. 2. have a knowledge about various learning paradigms and models of ANN. 3. be able to classify real problems into neural OR non-neural problem. 4. have a good knowledge of feature extraction and selection. 5. Know about modern ANN architecture and hybrid neural systems (FNN).

Text Books

1. Jacek M. Zurada, “Introduction to Artificial neural System”, JAICO publishing house,2002,.

2. Haykin, S., Neural Networks - A Comprehensive Foundation (2nd Edition). Macmillan, 1999.

Reference Books

1. Christopher M. Bishop, “Neural Networks for Pattern Recognition”, Indian Edition, oxford,1996

2. B. Yegnarayana, Artificial Neural Networks, PHI, 2001. 3. Timothy J. Rosss, “Fuzzy logic with engineering applications”, Wiley, 2004. 4. Bart Kosko, “Neural networks and fuzzy systems”, prentice hall of India, 1994. 5. John Yen and Reza Langari, “Fuzzy logic, intelligence, control and information”, Pearson education. 6. Some basic and modern research papers suggested by staff in charge.


CS8098 :ARTIFICIAL NEURAL NETWORKS

Objectives:

6. To focus on the foundations of neural network theory and the application of neural network models in real world problem solving.

7. To make students capable to select an ANN model, design the topology and initialize the network parameters.

8. To make students aware about advantages and shortcomings of ANN. 9. To make them aware of modern neural network architecture.

List of Practical : Minimum eight experiments based on the following topics. 1. Supervised Leaning rules for a single neuron 2. Unsupervised Learning rules. 3. Simple Perceptron classifier 4. Feedforward neural network. 5. Backpropagation algorithm 6. MAXNET 7. Hamming Distance Classifier 8. Hopfield network 9. SOM 10. Feature extraction for a given real world problem of importance. 11. Design of Fuzzy sets for a given problem statement. 12. Two short assignments based on research papers suggested by staff.

Outcomes: Upon completion of the course, the students will:

1. Have an in-depth understanding of artificial neural networks fundamentals. 2. Have a knowledge about various learning paradigms and models of ANN. 3. Be able to classify real problems into neural OR non-neural problem. 4. Have a good knowledge of feature extraction and selection. 5. Know about modern ANN architecture and hybrid neural systems (FNN).

Text Books

1. Jacek M. Zurada, “Introduction to Artificial neural System”, JAICO publishing house,2002,.

2. Haykin, S., Neural Networks - A Comprehensive Foundation (2nd Edition).

Macmillan, 1999. Reference Books


1. Christopher M. Bishop, “Neural Networks for Pattern Recognition”, Indian Edition, oxford,1996

2. B. Yegnarayana, Artificial Neural Networks, PHI, 2001. 3. Timothy J. Rosss, “Fuzzy logic with engineering applications”, Wiley, 2004. 4. Bart Kosko, “Neural networks and fuzzy systems”, prentice hall of India, 1994. 5. John Yen and Reza Langari, “Fuzzy logic, intelligence, control and information”, 6. Pearson education. 7. Some basic and modern research papers suggested by staff in charge.


CS3128 : CONVERGENCE TECHNOLOGIES Aim : This course highlights fundamental concepts, standards and practices that combine telephony and data networks into convergence networks. The topics include industry standards and protocols, Voice over Internet Protocol (VoIP), and network convergence. This course will teach the vendor-independent networking skills and concepts required for professionals in the information technology or telecommunications industry. Objectives:

1. To Get an understanding of the architecture of voice communications 2. To make students more competent with knowledge of signaling, call quality work

within the data networks. Unit 1. Introduction to Convergence (6 Hrs) Why Convergence, Defining Convergence, benefits of converged networks. Voice and data network characteristics, Voice and data network growth factor, benefits of IP centric network, challenges of converged network. Unit 2. Switching Networks (6 Hrs) ISDN: conceptual view of ISDN, transmission structure, user-network interface configuration, ISDN Protocol Architecture, ISDN connection, Addressing, Interworking, LAPD, B-ISDN standards, Broadband services, B-ISDN architecture, B-ISDN protocol reference model, SONET/SDH, Overview of Frame Relay Networks UNIT 3. ATM Technology : (8 Hrs) ATM VPI& VCI, Creation of virtual channel, Definitions of Virtual circuit and permanent virtual circuit, step-by-step PVC example of how ATM network processes cells, Connection admission control (CAC). Cell Loss Priority (CLP), SVC Signaling Q.2931, Adaption layer from voice over ATM perspective AAL1,AAL2, AAL3 Unit 4: Access Signaling Types (6 Hrs) ISDN Q.931 signaling protocol, How PRI, BRI use signaling, Q.931 signaling, Q.931 call setup process. Comparison of signaling protocols based on ISDN Q.931 UNIT 5 : VoIP Convergence (8 Hrs) Investigating VoIP, Investigating Gatekeepers and Gateways, Troubleshooting VoIP Large Data Frames and Delay, Quality of Service (QoS) Issues, Comparing and Contrasting Transmission Media, Bandwidth Limitations for Voice Traffic, VoIP Software UNIT 6. Network Convergence (7 Hrs) Converged network environment, Selecting Codecs, Convergence Signaling Protocols, H-Series Protocols, Session Initiation Protocol (SIP), Media Gateway Control Protocol (MGCP), Network, Bandwidth Concerns, VoIP Service Providers, VoIP and Firewalls Planning a Convergent Network


Study 4 to 5 current international journal research papers related to this area. Outcomes: Upon completion of the course, the students will be able to:

6. Understand various standards agencies and major industry standards in convergence technologies and telecommunications industry

7. Compare and contrast in-band and out-of-band signaling 8. Understand the signaling functions of ISDN and SS7

Text Books: 1. ISDN and Broadband ISDN with frame relay and ATM by William Stallings, Prentice Hall Publication 2. Voice over IP Technologies by Mark A. Miller, P.E. Wiley Publications Reference Books: 1. VOIP by Ulyess Black 2. ATM network concepts and protocols by Sumeet Kasera and Pankaj Sethi, Tata McGraw Hill 3. Multimedia communication system techniques standards and networks by K.R. Rao , Zoran Bojkovic and Dragorad Milovanovic , Pearson Education . 4. Multimedia communication directions and innovations by Jerry Gibson academic Press


CS8128 : CONVERGENCE TECHNOLOGIES

Prerequisites : Computer Networks. Aim : This course highlights fundamental concepts, standards and practices that combine telephony and data networks into convergence networks. The topics include industry standards and protocols, Voice over Internet Protocol (VoIP), and network convergence. This course will teach the vendor-independent networking skills and concepts required for professionals in the information technology or telecommunications industry. Objectives:

1. To Get an understanding of the architecture of voice communications 2. To make students more competent with knowledge of signaling, call quality work

within the data networks.

List of Practicals

1. Study and implement a simulation of ATM technology. 2. Study and implement a simulation of ISDN technology. 3. Write a program to implement of SIP protocol. 4. Write a program to implement H.232. 5. Comparison of SIP and H.232 protocols.

Note: These assignments should be implemented by group of 4 -5 students. Outcomes: Upon completion of the course, the students will be able to:

1. Understand various standards agencies and major industry standards in convergence technologies and telecommunications industry

2. Compare and contrast in-band and out-of-band signaling 3. Understand the signaling functions of ISDN and SS7

Text Books

1. K.R. Rao ,Zoran Bojkovic and Dragorad Milovanovic,’Multimedia communication system techniques standards and networks’ Pearson Education .

2. Jerry Gibson ,’Multimedia communication directions and innovations’

academic Press.


Reference Books:

1 Ulyess Black ,’VOIP’. 2 Sumeet Kasera and Pankaj Sethi ,’ATM network concepts and protocols’ Tata McGraw Hill . 3. William Stallings,’ISDN and Broadband ISDN with frame relay and ATM’

Prentice Hall Publication.


CS3158: COMPONENT BASED DEVELOPMENT

Prerequisites :

1 Software Engineering 2 Object Oriented Programming 3 Object Oriented Modeling and Design

Aim : Software development is performed through the application of planned integration of software components. Components evolves over a Component Life Cycle and amenable for their selection and adaptation, deployment and integration. The course focuses on building Component Systems with the reuse perspective. Objectives: 1. To learn Component Development Cycle 2. To compare component based development with other methods 3. To develop Software Artifacts using the Reuse principles

Unit 1 : Product Development Paradigm ( 7 Hrs )

Critical aspects of Product Development Socio-technical Systems, Dependability, Software Processes, RE Processes, Systems Models, Formal Specification, Architectural Design, Object-oriented Design, User Interface Design, Software Reuse, Software Evolution, Configuration Management Introduction to Component Based Development Definition of Software Component and its Elements, The Component Industry Metaphor, Component Models and Component Services: Concepts and Principles, An Example Specification for Implementing a Temperature Regulator Software Component.

Unit 2 : Case for Components ( 7 Hrs )

The Business Case for Software Components, COTS Myths and Other Lessons Learned in Component-Based Software Development, Roles for Component-Based Development, Common High Risk Mistakes in Component-Based Software Engineering, CBSE Success Factors: Integrating Architecture, Process, and Organization

Unit 3 : Software Component Infrastructure ( 7 Hrs )


Software Components and the UML, Component Infrastructures: Placing Software Components in Context, Business Components, Components and Connectors: Catalysis Techniques for Defining Component Infrastructures, an Open Process for Component-Based Development, Designing Models of Modularity and Integration.

Unit 4 : Management of CBD ( 7 Hrs )

Measurement and Metrics for Software Components, The Practical Reuse of Software Components, Selecting the Right COTS Software: Why Requirements are Important, Build vs. Buy, Software Component Project Management Processes, The Trouble with Testing Software Components, Configuration Management and Component Libraries, The Evolution, Maintenance and Management of Component-Based Systems

Unit 5 : Component Technologies ( 7 Hrs )

Overview of the CORBA Component Model, Transactional COM+: Designing Scalable Applications, The Enterprise JavaBeans Component Model, Choosing Between COM+, EJB, and CCM, Software Agents as Next Generation Software Components

Unit 6 : Component Development Issues ( 7 Hrs )

Component frameworks, Architecture for component based design , Component management - assembly, configuration, distribution, Component databases, component information systems, Safety-critical issues ,Components and real-time systems, Evolution and Maintenance of Component Based Systems

Outcomes:

Upon completion this course, the students will be able: 1. Create Component Profile Creation and Usage 2. Create and understand Component Specification 3. Adopt Component Architectural Style 4. Develop Component Based System addressing a real world problem 5. Maintain and monitor Component Based System

Text Books

1. Clemens Szyperski Component Software , , Addison-Wesley Professional; 2 edition , 2002, ISBN-10: 0201745720, ISBN-13: 978-0201745726

2. Heineman, William T. Councill, Addison Component-Based Software Engineering: Putting the Pieces Together George T. -Wesley Professional, 2001 ISBN-10: 0201704854,ISBN-13:9780201704853

Reference Books


1. Ian Sommerville Software Engineering, 8th Edition, Addison-Wesley, 2006,ISBN-10: 0321313798, ISBN-13: 9780321313799

2. Roger S Pressman, McGraw Hill, Software Engineering: A Practitioner's Approach,

6/e2005, ISBN: 0072853182


CS8158:COMPONENT BASED DEVELOPMENT

Objectives: 1 To learn Component Development Cycle 2 To compare component based development with other methods 3. To develop Software Artifacts using the Reuse principles

List of Practicals

1. Select a small application and document its specification. Document the component infrastructure needed to implement it. Compare the available Component technologies for the same. Perform feasibility study of the selected Component Technology.

2. Select a Web application to demonstrate the Web service design, development,

deployment, and Web service client application. Create a reusable Component Library for the same.

3. For a custom application narrate the specification. Using appropriate architectural

Style, implement the system using J2EE and EJB components. 4. Create .NET components and using Client Server Architectural style, implement a

moderately complex system. Maintain Component Configuration catalogue. 5. Create a CORBA component Online and reuse it for target system.

Outcomes:

Upon completion this course, the students will be able: 1. Create Component Profile Creation and Usage 2. Create and understand Component Specification 3. Adopt Component Architectural Style 4. Develop Component Based System addressing a real world problem 5. Maintain and monitor Component Based System

Text Books

1. Clemens Szyperski Component Software , , Addison-Wesley Professional; 2 edition , 2002, ISBN-10: 0201745720, ISBN-13: 978-0201745726


2. Heineman, William T. Councill, Addison Component-Based Software Engineering: Putting the Pieces Together George T. -Wesley Professional, 2001 ISBN-10: 0201704854,ISBN-13:9780201704853

Reference Books

1. Ian Sommerville Software Engineering, 8th Edition, Addison-Wesley, 2006,ISBN-10: 0321313798, ISBN-13: 9780321313799

2. Roger S Pressman, McGraw Hill, Software Engineering: A Practitioner's Approach,

6/e2005, ISBN: 0072853182


ME – CSE(Information Technology)

Semester I

LABORATORY


CS8018: ADVANCED OPERATING SYSTEMS Objectives: 1. To get hand-on experience of Operating System Implementation 2. To study the Windows and Linux system architectures 3. To study the implementation of Process Management, Memory Management and File

Management in Windows and Linux 4. To study the different design issues of Distributed Systems

List of Practicals 1. Implementation of a multiprogramming operating system:

a. Stage I:

i. CPU/ Machine Simulation

ii. Supervisor Call through interrupt

b. Stage II:

i. Paging

ii. Error Handling

iii. Interrupt Generation and Servicing

iv. Process Data Structure

c. Stage III:

i. Multiprogramming

ii. Virtual Memory

iii. Process Scheduling and Synchronization

iv. Inter-Process Communication

v. I/O Handling, Spooling and Buffering

2. Linux Project

a. Introduction to Linux Kernel programming

i. To write a hello world module

ii. To display pid and other parameters of current process

iii. To display process tree of system with various parameters of process.

b. Display the contents of an inode using kernel programming.


c. Implement a linked list in kernel space using:

i. kmalloc

ii. lab allocator

Outcomes: Students will be able to learn the intricacies of the implementation of a multiprogramming operating system. Student will be able to implement systems based projects in industry on the LINUX and Windows platform. Since industry has specialized system oriented projects, this course will equip the student in handling those projects. Text Books: 1. Shaw, Alan C, “The Logical Design of Operating Systems”, Prentice Hall, 1974

2. Robert Love, “Linux Kernel Development”, Pearson Education, 2nd edition, 2005.

Reference Books: 3. Mark Russinovich, David Solomon, “Windows Internals”, Microsoft Press, 4th

edition, 2005.

4. Andrew S. Tanenbaum & Maarten van Steen, Distributed Systems “Principles and

Paradigms”, PHI, 1st Indian Edition, 2002.

5. Daniel Bovet: “Understanding the Linux kernel”, O'Reilly Publications, 2nd edition,

2003.


CS8028: DATA MANAGEMENT TECHNIQUES. Prerequisite: Data structures, Database Management Systems Objectives:

1. To interpret an entity relationship diagram (ERD) to express requirements, create data models into normalized designs and use SQL to create database objects, populate tables, and retrieve data

2. To implement the data structures used for databases to be physically stored on storage media

3. To write database queries in the most efficient manner and develop query execution plan.

4. To understand multi-dimensional data modeling techniques and ETL tools to design and build a data warehouse

5. To understand the implementation of various data mining algorithms. 6. To understand current approaches and trade-offs in the design and development

of database systems.

List of Practicals

1. Real world problem Design exercise using ER model. Convert ER to a set of tables and normalize it up to a given normal form. Implementation of the problem using an existing DBMS Oracle/My SQL/SQL-server

2. B+ Tree Implementation

3. Query optimization problem. Displaying query execution plan.

4. Simulation of 2 Phase Commit Protocol

5. Design a multi dimensional data model using star/ snowflake schema technique for any business fact corresponding to a department of an organization. ex. Star schema for sales for sales department

6. DWH – Use of ETL tool for extracting data from various data sources, transform it into unique schema, and load it to destination tables in DWH

7. Implementation of classification/ clustering algorithm using MATLAB

8. Implementation of distance based outlier analysis algorithm using MATLAB

9. Presentations on current trends/special issues/important topics from research papers in DBMS.

ME CSE (IT) First Year Academic Year - 2008-09 Pattern - A

Outcomes:

Upon completion of this course, the students will be able to: 1. Understand user requirements, develop and refine the conceptual data model, apply

normalization techniques with identification of data integrity and security requirements

2. Understand and implement the data structure to physically store a database 3. Write database queries in the most optimized and cost effective manner. 4. Understand multi-dimensional data modeling techniques and use of ETL tools 5. Understand and implement data mining algorithms. 6. Define and recognize key areas and issues in knowledge discovery in data.

Text Books:

1. Silberschatz ,Korth and Sudarshan, “Database system Concepts”, Fifth edition, McGraw

Hill, International Edition 2006, ISBN – 007-124476-X. 2. Jiawei Han and Micheline Kamber “Data mining: concepts and techniques”, the Morgan

Kaufman,2001.

Reference Books:

1. Elmasri, Navathe, “Fundamentals of database system”, Third edition, Published by Pearson Education Pte. Ltd, ISBN – 81-7808-137-7.

2. Kimball, “Data warehouse life cycle toolkit”, John Wiley,1998 Additional Reading:

1. Raghu Ramkrishan and Johannes Gerkhe, ‘Database Management Systems’, 3rd edition, McGraw Hill, 2003. Rob, Coronel, “Database system, design, implementation and management”, Thomson Learning, Fourth Edition, 2001, ISBN – 0-07-123151-X.

2. Hand, Smyth, Mannila,”Principles of Data mining “, MIT press. 3. Gagendra Sharma ,”Data mining, Data warehousing and OLAP”, S.K. Kataria and sons,

First edition, 2007-08. 4. “R-Trees. A Dynamic Index Structure For Spatial Searching”, by Antomn Guttman,

University of Cahforma Berkeley, ACM Transactions, 1984 5. “A simple guide to five normal forms in relational database theory”, by William Kent,

International Business Machines Corporation, ACM Transactions, 1983. 6. “A Normal Form for Relational Databases That Is Based on Domains and Keys”, Ronald

Fagin, IBM Research Laboratory, ACM Transactions on Database Systems, Vol. 6, No. 3, September 1981, Pages 387-415.


ME- CSE(Information Technology)

Semester II

(Theory)


CS3038: PROJECT MANAGEMENT

Prerequisites :

1. Software Engineering 2. Advanced Data Structures

Aim : 1. This course addresses the issues associated with the successful management of a software

development project. This includes planning, scheduling, tracking, cost and size estimating, risk management.

2. The major emphasis of this course is on using object-oriented modeling to define a system specification. A study of object-oriented techniques using Unified Modeling Language (UML) for the analysis and design of software systems will be performed.

3. Techniques for designing both the structural and behavioral aspects of software systems are emphasized. This course will cover object-oriented approaches to system analysis, data modeling and design that combine both process and data views of systems.

Objectives:

1. To provide an introduction to the theory and practice of project management 2. To provide an understanding of the project planning process 3. To enable the students to use project planning tools and techniques 4. To provide an understanding of project budgeting and risk analysis 5. To experience the insights necessary to obtain maximum benefit from object technology 6. To understand the need for, the place of, and aims of, requirements, analysis and design 7. To thoroughly understand the practices of analysis and design (OOA and OOD) 8. To understand the practical connections between the theory of object-oriented design and

the object-oriented programming languages 9. To become familiar with the unified modelling language (UML 1.x or UML 2.0) 10. To understand the relative merits of the different UML diagrams, distinguishing those


Unit 1 : Software Process Models ( 7 Hrs )

Overview of Software Engineering, Software Process Framework, Software Development Phases, Process Patterns, Personal and Team Process Models, Process Models: Code-and-Fix, Waterfall Model, Incremental Models, Evolutionary Models, Iterative Development, The Unified Process, Agile process, Extreme Programming, Cleanroom Methodology, CMMI, Impact of Processes and Outcomes, Software Engineering Principles and Practices

Unit 2 :Introduction to Project Management ( 7 Hrs )

Project Activities, Structures and Frameworks, Strategy and Project Management, Project Definition, Balancing Development Needs with Organizational Expectations, Driving the Implementation: Recognizing and Overcoming Challenges, Role of Teams, Types of Teams and


team Life Cycles, Teamwork, Management and Leadership in Projects, Best Practices for Project Planning, Time Planning, Developing Realistic Estimates Integrating the Schedule and Critical Path, Activity Planning

Unit 3 : Project Cost Estimation ( 7 Hrs )

Project Cost and Quality Planning, Risk analysis and Planning, Risk Estimation and Control, Complex Projects: Internal and External Risks, Empirical Cost Estimation, COCOMO-II, Modern techniques and impact of Cost Estimation, Software Sizing, Software Scope Management, Introduction to Function Point Analysis ,Sizing Data Functions, Sizing Transactional Functions ,General System Characteristics, Calculating And Applying Function Points

Unit 4 : Introduction to UML ( 7 Hrs )

Elements of UML: The importance of modeling, Building blocks: things, relationships and diagrams, Architectural views: use case, design, implementation, process and deployment, Levels of detail: visualization, specification and construction, Object oriented concepts UML 2.0 Programming In Small Versus Programming In Large, UML 2.0 History, New Features and introduction to MDA/ MOF/ XMI/ CORBA, Introduction to UML Metamodel, Extensibility Mechanisms and its usage, Introduction to OCL ,Behavioral Diagrams in UML ,Structural Diagrams in UML, Specification techniques of diagrams in UML

Unit 5 : Behavioral Model Development ( 7 Hrs )

Use Cases: Use Cases, Use Case Diagram Components, Use Case Diagram, Actor Generalization, Include and Extend, Template for Use Case Narrative Domain Analysis: The Domain Perspective, Data Dictionary: Finding the Objects, Responsibilities, Collaborators, and Attributes, CRC Cards, Class Models and Use Case Models, Judging the Domain Model Use case realization: Sequence diagrams, object lifelines and message types, Modeling collections multiobjects, Refining sequence diagrams, Operators Implementing memory in objects using state machines: States, events and actions Nested machines and concurrency, Converting sequence diagrams into communicating state machines Analyzing object behavior: Modeling methods with activity diagrams, Activity Diagrams: Decisions and Merges, Synchronization, Iteration, Partitions, Parameters and Pins, Expansion Regions, Swimlanes, concurrency and synchronization Communication Diagram, Interaction Overview Diagrams, Timing Diagrams

Unit 6 : Object Design and Implementation ( 7 Hrs )

Design of Objects: Design and Factoring, Design of Software Objects, Features and Methods, Cohesion of Objects, Coupling between Objects Coupling and Visibility, Inheritance Advanced Objects: Constructors & Destructors, Instance Creation, Abstract Classes


Polymorphism, Multiple Inheritance and associated Problems, Interfaces, Interfaces with Ball and Socket Notation, Templates Establishing The Object Model: Refining classes and associations, Analysis model vs. design model classes, Categorizing classes: entity, boundary and control , Modeling associations and collections, Preserving referential integrity , Achieving reusability Isolating reusable base classes, Reuse through delegation, Identifying and using service packages Packages and interfaces: Distinguishing between classes/interfaces, Exposing class and package interfaces, Subscribing to interfaces Component and deployment diagrams: Describing dependencies, Deploying components across threads, processes and processors

Outcomes:

By the end of the course, it is expected that the students will be able to : 1. Understand basic project management terminology, methodology and the role of project

manager 2. Understand how to produce a project plan with effective budgeting 3. Understand different perspectives about the systems development process 4. Understand the role and importance of requirements analysis and specification 5. Understand the basic principles of object-orientation 6. Acquire a working knowledge of system modeling techniques 7. Become aware of the emerging ideas relevant to object-oriented systems development. 8. Create commonly expected "deliverables" of systems design including models of

structure, behavior and dynamics

Text Books :

1. Quality Software Project Management, Robert T. Futrell, Donald F. Shafer, Linda I. Shafer Publisher: Prentice Hall PTR; 1st edition (January 24, 2002ISBN-10: 0130912972 ISBN-13: 978-0130912978

2. Jim Arlow, Ila Neustadt, “UML 2 and Unified Process: Practical Object Oriented Analysis and Design. ”, 2nd Edition, Addison- Wesley, ISBN – 0321321278.

Reference Books :

1. Tom Pender, “UML Bible”, John Wiley & sons, ISBN – 0764526049. 2. Martin Flower, “UML Distilled: A Brief Guide to The Standard Object Modeling Language

”, 3rd Edition, Addison- Wesley, ISBN – 0321193687 3. Desikan, Ramesh, ‘ Software Testing: principles and Practices”, Pearson Education, ISBN

81-7758-121-X 4. Burnstein, “Practical Software Testing”, Springer International Edition, ISBN 81-8128-089-

X 5. Software Project Management , Bob Hughes, Mike Cotterell Publisher: McGraw-Hill

Publishing Co.; 4Rev Ed edition (November 1, 2005) ISBN-10: 0077109899 ISBN-13: 978-0077109899

6. Software Engineering: A Practitioner's Approach, 6/e, Roger S Pressman, McGraw Hill, 2005, ISBN: 0072853182


CS3048: COMPUTER NETWORK DESIGN, MODELING AND

ANALYSIS

Aim : This course focuses on practical aspects of network protocols design and implementation. This course aims at the analysis and performance evaluation of computer communication networks as well as modeling of networks and traffic flows, and the delay and loss models as experienced in computer networks and networks of queues. Objectives:

1. To understand analysis of a problem, design of its solution, implementation of the solution, testing of the solution.

2. To describe the methods used in modeling, analysis and design communications systems 3. To describe the organization of computer networks and evaluate alternative organizations 4. To evaluate the protocols used in computer networks

Unit 1 :

Introduction to Queuing Theory ( 8 Hrs )

Multiplexing of Traffic on a Communication Link, Queuing Models- Little’s Theorem, Little’s Theorem, Probabilistic Form of Little’s Theorem, Application of Little’s Theorem, The M/M/1 Queuing System, Arrival Statistics, Service Statistics, Markov Chain Formulation, Deviation of the Stationary Distribution, Occupancy Distribution upon Arrival, Occupancy Distribution upon Departure, The M/M/m, M/M/∝, M/M/m/m, AND Other Markov Systems, The M/M/m: The m-Server Case, M/M/∝: The Infinite-Server Case, M/M/m/m: The m-Server Loss System, ltidimensional Markov Chains- Applications in Circuit Switching, The M/G/1 System, M/G/1 Queues with Vacations, Reservations and Polling, Priority Queuing, The D/D/1 Queue.

Unit 2 :

Delay Models in Data Networks (6 Hrs )

M/M/1 queue - Time-dependent behaviour , Limiting behavior , Direct approach Recursion , Generating function approach , Global balance principle, Mean performance measures , Distribution of the sojourn time and the waiting time , Priorities, Preemptive-resume priority , Non-preemptive priority, Busy period ,Mean busy period, Distribution of the busy period.

Unit 3 :

Network Design (7 Hrs )


Problem definition : Multipoint line layout heuristics, CMST algorithm, ESAU-William’s algorithm, Sharma’s algorithm, Unified algorithm, Bin packing, Terminal assignments, Concentrator location

Unit 4 :

Network Calculus ( 7 Hrs)

Models for data flows, Arrival Curves, Service Curves, Network Calculus Basics, Handling variable length packets, Effective Bandwidth and Equivalent Capacity, GPS and guaranteed rate nodes

Unit 5 :

Aggregate Scheduling (7 Hrs ) Transformation of arrival curve through aggregate scheduling, stability and bounds for a network with aggregate scheduling, stability results and explicit bounds.

Unit 6 :

Packet Smoothing (7 Hrs )

Limitations of service curves and GR node abstraction, Packet scale rate guarantee, adaptive guarantee, concatenation of PSRG nodes, Comparison of GR and PSRG.

Outcomes:

At the end of the course, the student will be able to: 1. Model and analyze computer communication networks and protocols, evaluate network

technologies and solutions, and understand and investigate tradeoffs arising in the design process of such systems

2. Apply knowledge of mathematics, probability, and statistics to model and analyze some network design problems.

3. Analyze and design an enterprise network that meets desired requirements. 4. Use techniques, skills, and modern networking tools necessary for network analysis,

design, and management.

Text Books

1. Kershenbaum A., “Telecommunication Network Design Algorithms”, Tata McGraw Hill 2. Bertsekas D. and Gallager R., “Data Networks,” 2nd Ed., Prentice-Hall, Englewood Cliffs, N.J., 1992. 3. Jean-Yves Le Boudec, Patrick Thiran, “Network Calculus- Theory of Deterministic Queuing Systems for the Internet”, Springer Verlag 2004

Reference Books


1. Vijay Ahuja, “Design and Analysis of Computer Communication Networks”, McGraw Hill 2. Stallings W., “High Speed Networks and Internet : Performance and Quality of Service”, Prentice- Hall

3 .Zacker, “Networking – The Complete Reference”, Tata McGraw Hill 4.Keshav S., “An Engineering Approach to Computer Networking,” Addison-Wesley,1997.


ME CSE-(Information Technology)

Semester II

LABORATORY


CS8038: PROJECT MANAGEMENT Objectives:

1. To provide an introduction to the theory and practice of project management 2. To provide an understanding of the project planning process 3. To enable the students to use project planning tools and techniques 4. To provide an understanding of project budgeting 5. To appreciate risk analysis and management 6. To establish clear project objectives and milestones 7. To create effective and deliverable project plans 8. To deal with changes and deviations from the plan List of Practicals 1. A. To narrate Requirement Definition Document for the target system with following three areas:




3. To perform Function Point and Use Case based estimation for the system under construction. 4. To perform Risk Analysis and Effort estimation for system under construction. 5. To devise Project Plan and schedule for system under construction. 6. To represent physical module that provides occurrence of classes or other logical elements identified during analysis and design of system using Component diagram. The model should depict allocation of classes to modules. To narrate the Program Design Language Constructs for the target system and implement the system according to specification. 7. To assess Product and Process performance using Verification and Validation.

Outcomes:

By the end of the course, it is expected that the students will be able to : 1. Understand basic project management terminology, methodology and the role of project

manager 2. Understand how to produce a project plan with effective budgeting 3. Appreciate why projects fail and how failure can be avoided


4. Be able to identify major issues in complex situations; and know how to prepare alternative solutions and make decisions.

5. Becoming aware of the need to think and act in an entrepreneurial manner by developing the ability to: critically and creatively understand innovations and development, work independently and collaboratively.

Text Books :

1. Essentials of Software Project Management, second edition, Richard Bechtold (Author) Publisher: Management Concepts; second edition (April 12, 2007) ISBN-10: 1567261868 ISBN-13: 978-1567261868

2. Software Project Management , Bob Hughes, Mike Cotterell Publisher: McGraw-Hill Publishing Co.; 4Rev Ed edition (November 1, 2005) ISBN-10: 0077109899 ISBN-13: 978-0077109899

Reference Books :

1. Quality Software Project Management, Robert T. Futrell, Donald F. Shafer, Linda I. Shafer Publisher: Prentice Hall PTR; 1st edition (January 24, 2002ISBN-10: 0130912972 ISBN-13: 978-0130912978

2. Software Engineering Project Management, 2nd Edition, Edward Yourdon, Richard H. Thayer Publisher: Wiley-IEEE Computer Society Pr; 2 Sub edition (May 10, 2000) ISBN-10: 0818680008 ISBN-13: 978-0818680007

3. Function Point Analysis: Measurement Practices for Successful Software Projects David Garmus, David Herron, Addison-Wesley Professional, 2001, ISBN-10: 0201699443 ISBN-13: 9780201699449

4. Software Engineering, Ian Sommerville, 8th Edition, Addison-Wesley, 2006,ISBN-10: 0321313798, ISBN-13: 9780321313799

5. Software Engineering: A Practitioner's Approach, 6/e, Roger S Pressman, McGraw Hill, 2005, ISBN: 0072853182

6. Project Management by Harvey Maylor, Pearson Education, Third Edition, ISBN:81-7758-036-1


CS8048: COMPUTER NETWORK DESIGN, MODELING AND ANALYSIS

Objectives:

1. To understand analysis of a problem, design of its solution, implementation of the solution, testing of the solution.

2. To describe the methods used in modeling, analysis and design communications systems 3. To describe the organization of computer networks and evaluate alternative organizations 4. To evaluate the protocols used in computer networks

List of Practicals 1. Implement and compare different PDF. 2. Implement a simulation of markov systems 3. Implement a CMST algorithm, 4. Implement a ESAU-William’s algorithm, 5. Implement a Sharma’s algorithm, 6. Implement a Unified algorithm. 7. Implement a performance analysis of DLL,NL 8. Study of Network simulator in detail.

Outcomes:

At the end of the course, the student will be able to: 1. Model and analyze computer communication networks and protocols, evaluate network

technologies and solutions, and understand and investigate tradeoffs arising in the design process of such systems

2. Apply knowledge of mathematics, probability, and statistics to model and analyze some network design problems.

3. Analyze and design an enterprise network that meets desired requirements. 4. Use techniques, skills, and modern networking tools necessary for network analysis,

design, and management.

Text Books

1. Kershenbaum A., “Telecommunication Network Design Algorithms”, Tata McGraw Hill 2. Keshav S., “An Engineering Approach to Computer Networking,” Addison-Wesley,1997.


Reference Books 1. Bertsekas D. and Gallager R., “Data Networks,” 2nd Ed., Prentice-Hall, Englewood Cliffs, N.J., 1992. 2. Vijay Ahuja, “Design and Analysis of Computer Communication Networks”, McGraw Hill 3. Stallings W., “High Speed Networks and Internet : Performance and Quality of Service”, Prentice- Hall 4. Zacker, “Networking – The Complete Reference”, Tata McGraw Hill


Elective –III

Theory & Practical


CS3078: ADVANCED DIGITAL SIGNAL PROCESSING

Prerequisites : Digital signal Processing, Engineering Mathematics Aim : To analyze signals using adaptive filters and applications using the same Objectives: 1.To enhance the awareness of different Multirate signals, design and implement DSP filters. 2. To introduce least means square filters, adaptive filter design, Applications of DSP

Unit 1 : Overview of DSP ( 4 Hrs )

Overview of Digital signal processing, Introduction to implementation of FIR and IIR filters, basic structure for IIR and FIR filters

Unit 2 : Multi rate signal Analysis ( 7 Hrs )

Introduction, decimation, interpolation, sampling rate conversion, filter design and implementation for sampling rate conversion, multistage implementation of sampling rate conversion, sampling rate conversion of band pass signals and by an arbitrary factor

Unit 3 : Prediction Filters ( 8 Hrs )

Representation of a stationary random process, forward and backward linear prediction, solution of the normal equations, properties of the linear prediction-error filters, MA, AR, ARMA models and their comparison, homomorphic deconvolution, Inverse systems, Weiner filters for filtering and prediction

Unit 4 : Adaptive Filters (9 Hrs )

Adaptive implementation of Weiner filters, correlation canceller loop, Widrow-Hoff LMS adaptation algorithm, adaptive linear combiner, adaptive FIR Weiner filter, speed convergence, adaptive channel equalizers, adaptive echo cancellers, adaptive noise canceling, adaptive line enhancer, adaptive linear prediction, gradient adaptive lattice filters, adaptive gram-Schmidt preprocessors, rank-one modification of covariance matrices, RLS adaptive filters, Fast RLS filters, RLS lattice filters

Unit 5 : Power spectrum Estimation ( 7 Hrs )

Estimation of Spectra From Finite duration observation of signals, Estimation of autocorrelation and power spectrum of random signal, Non parametric methods for power spectrum estimation- Bartlett window and Welch method, parametric methods for power spectrum estimation.


Unit 6: Application of ADSP ( 7 Hrs )

Applications of multirate signal processing, Applications of adaptive filters, Case studies: Voice processing, application to Radar, music and speech processing, biomedical applications.

Outcomes: At the conclusion of the course the student will

1. Demonstrate understanding of AR, MA, and ARMA models and how the parameters are estimated.

2. Demonstrate understanding of linear Minimum-Mean-Square-Error (MMSE) optimal filter design.

3. Demonstrate understanding of optimal lattice and lattice-ladder structures for prediction and filtering.

4. Demonstrate understanding of optimal linear Least-Squares (LS) filter design. 5. Demonstrate understanding of LS computational techniques . 6. Demonstrate basic understanding of Least-Mean-Square (LMS) and Recursive-Least-

Square (RLS) adaptive filtering. 7. Demonstrate basic understanding of parametric and nonparametric methods for

spectral estimation

Text Books

1. Proakis “ Advanced Digital Signal processing” , Pearson education.

2.Shalivahan, ”Digital signal processing”.,TMH.

Reference Books

1. E. C. Ifleachor and B. W. Jervis, “Digital Signal Processing- A Practical Approach”, 2nd Edition, Pearson education.


CS8078: ADVANCED DIGITAL SIGNAL PROCESSING LAB

Objectives:

1.To enhance the awareness of different Multirate signals, design and implement DSP filters. 2. To introduce Least means square filters, adaptive filter design, Applications of DSP

List of Practicals

1. Software implementation of Decimation and interpolation

2. Implementation of Least Mean Square (LMS) Algorithm.

3. Determination of FIR prediction filters using Forward and Backward prediction.

4. To implement Levinson Durbin Algorithm for Solution of Normal equations.

5. Realization of cascade Lattice of FIR Filter.

6. Power Spectrum Estimation using any one non-parametric method.

7. Implementation of any 2 assignments from 1 to 6 on DSP kits (Texas, ADSP or Motorola).

8. Implementation of Adaptive filters

Note : Implementation of the following using MATLAB and C:

Outcomes: Students would understand

1. Implement concepts of multirate signals and its applications 2. Prediction filter design and its applications 3. Adaptive filter design and its applications 4. Power spectrum Estimation


Text Books

1.Proakis “ Advanced Digital Signal processing” , Pearson education.

2.Shalivahan, ”Digital signal processing”.,

Reference Books

1. E. C. Ifleachor and B. W. Jervis, “Digital Signal Processing- A Practical Approach”, 2nd Edition, Pearson education.

2. Fundamentals of signals and systems using Matlab by Edward Kamen and Bonnie Heck, Prentice Hall.

3. Getting started with Matlab, Rudrapratap


CS3108: MACHINE LEARNING. Aim : This course emphasizes on construction of computer programs that learn in a broad sense in order to enable AI programs to improve their performance automatically over time. This course covers several important learning paradigms including learning from examples, decision tree learning, neural networks, support vector machines, Bayesian learning, learning rules, analytical learning, and reinforcement learning. Objectives:

1. To introduce students to several prominent areas of machine learning, including feature extraction, decision trees, neural networks, genetic algorithms, Bayesian learning, clustering, ensemble learning, support vector machines, and reinforcement learning, and illustrate what types of problems the different methods are suited for.

2. To give students hands-on experience with these methods and tools for implementing and using them on real-world problems.

3. To give students experience with performing simulations and doing statistical data analysis of the results.

Unit 1 : Fundamentals ( 7 Hrs )

Introduction, Concept Learning and the General-to-Specific Ordering

Unit 2 : Learning by experience ( 7 Hrs)

Decision Tree Learning. Artificial Neural Networks

Unit 3 : Using Hypothesis and Probability ( 6 Hrs)

Evaluating Hypotheses, Bayesian Learning

Unit 4 : Computational Learning and use of Instances ( 7 Hrs)

Computational Learning Theory, Instance-Based Learning

Unit 5 : Evolutionary Computation ( 7 Hrs )

Introduction to Genetic Algorithms, Learning Sets of Rules and

Unit 6 : Analytical and Inductive Learning ( 7 Hrs )

Introduction to analytical learning. Combining Inductive and Analytical Learning, Reinforcement Learning


Outcomes:

At the end of the course, the students will be able to: 1. Use the basic concepts and techniques of Machine Learning. 2. Develop skills of using recent machine learning software for solving practical problems. 3. Gain experience of doing independent study and research.

Text Books

a. T. Mitchell, “ Machine Learning”, McGraw-Hill, 1997.

Reference Books

1. Ethem Alpaydin, "Introduction to Machine Learning", MIT press, 2004. 2 . Jacek M. Zurada, “Introduction to Artificial neural System”, JAICO publishing house,2002,. 3. Research papers suggested by staff in charge.


CS8108 : MACHINE LEARNING . Objectives:

1. To introduce students to several prominent areas of machine learning, including feature extraction, decision trees, neural networks, genetic algorithms, Bayesian learning, clustering, ensemble learning, support vector machines, and reinforcement learning, and illustrate what types of problems the different methods are suited for.

2. To give students hands-on experience with these methods and tools for implementing and using them on real-world problems.

3. To give students experience with performing simulations and doing statistical data analysis of the results.

List of Practicals

1. Implementation of learning algorithms for simple real world problems. 2. Presentations on selected research topics. 3. A mini project for a real problem.

Outcomes:

At the end of the course, the students will be able to: 1. Use the basic concepts and techniques of Machine Learning. 2. Develop skills of using recent machine learning software for solving practical problems. 3. Gain experience of doing independent study and research.

Text Books

1) T. Mitchell, “ Machine Learning”, McGraw-Hill, 1997.

Reference Books

1). Ethem Alpaydin, "Introduction to Machine Learning", MIT press, 2004. 2). Jacek M. Zurada, “Introduction to Artificial neural System”, JAICO publishing house,2002,. 3). Research papers suggested by staff in charge.


CS3138:INFRASTRUCTURE MANAGEMENT

Aim: To study Storage & security implementation for IT infrastructure

Objectives : 1. To make student confident in storage implementation for IT infrastructure 2. To make student confident in security implementation for IT infrastructure

UNIT 1: Infrastructure Management Overview (6 Hrs.) Definitions, Infrastructure management activities, Evolutions of Systems since 1960s(Mainframes-to-Midrange-to-Client-server computing-to-New age systems) and their management, growth of internet, current business demands and IT systems issues, Complexity of today’s computing environment, Total cost of complexity issues, Value of Systems management for business.

UNIT 2: Preparing for Infrastructure Management (7 Hrs.) Factors of consider in designing IT organizations and IT infrastructure, Determining customer’s Requirements, Identifying System Components to manage, Exist Processes, Data, applications, Tools and their integration, Patterns for IT systems management, Introduction to the design process for information systems, Models, Information Technology Infrastructure Library (ITIL). UNIT 3: Service Delivery Processes (7 Hrs.) Service level management, financial management and costing, IT services continuity management, Capacity management, Availability management. UNIT 4: Service Support Processes (7 Hrs.) Configuration Management, Service desk, Incident management, Problem management, Change management, Release management. UNIT 5: Storage Management (6 Hrs.) Introduction Security, Identity management, Single sign-on, Access Management, Basics of network security, LDAP fundamentals Intrusion detection, firewall, security information management. UNIT 6: Security Management (7 Hrs.)


Introduction to storage, Backup & Restore, Archive & Retrieve, Space management, SAN & NAS, Disaster Recovery, Hierarchical space management, Database & Application protection, Bare machine recovery, Data retention. Outcomes:

1. Student will be confident in storage implementation for IT infrastructure 2. Student will be confident in security implementation for IT infrastructure

Text Books : 1. Foundation of IT Service Management: base on ITIL, by Jan Van Bon, Van Haren publishing, 2nd edition 2005. 2. High Availability: Design, Techniques, and processe, by Floyd Piedad, MachaelHawkins, Prentice Hall, 2000 Reference Books : 1. IT Oraganization: Building a Worldclass Infrastracture, by Harris Kern, Stuart Galup, Guy Nemiro, Publisher: Prentice Hall, 2000 2. IT Systems Management: Designing, Implimenting, and managing World – class Infrastractures Rich Schiesser, Prentice Hall PTR; 2001


CS8138: INFRASTRUCTURE MANAGEMENT

Aim: To study Storage & security implementation for IT infrastructure

Objectives : 1. To make student confident in storage implementation for IT infrastructure 2. To make student confident in security implementation for IT infrastructure

List of Practicals

1. Design of IT infrastructure for non-IT/IT organizations 2. Study and utilization of Information Technology Infrastructure Library (ITIL) 3. Design of help desk and problem-solving 4. Design of LDAP / Intrusion Detection System (IDS) / Firewalls for Security Mgt 5. Design and Implementation of Backup & Restore Policy for IT Infrastructure

Outcomes:

1. Student will be confident in storage implementation for IT infrastructure 2. Student will be confident in security implementation for IT infrastructure

Text Books : 1. Foundation of IT Service Management: base on ITIL, by Jan Van Bon, Van Haren publishing, 2nd edition 2005. 2. High Availability: Design, Techniques, and processe, by Floyd Piedad, MachaelHawkins, Prentice Hall, 2000 Reference Books : 1. IT Oraganization: Building a Worldclass Infrastracture, by Harris Kern, Stuart Galup, Guy Nemiro, Publisher: Prentice Hall, 2000 2. IT Systems Management: Designing, Implimenting, and managing World – class Infrastractures Rich Schiesser, Prentice Hall PTR; 2001


CS3168: ENTERPRISE SOFTWARE DEVELOPMENT

Aim : As enterprises spread their boundaries across different business areas, they acquire disparate technology solutions, technologies and applications. The objective of this course is to offer Enterprise Application Integration (EAI) techniques. The focus is towards Workflow Modeling and managing Workflow artifacts. The Business Process Modeling and Intelligence can also be one of the leading areas in this domain Objectives: 1. To study interoperability principles and standards 2. To learn essence of Business Processes and Modeling 3. To apply Enterprise Architecture and Integration 4. To learn development of Software Factories

Unit 1 : Business Process and Enterprise Architecture ( 6 Hrs )

Introduction to Business Processes, Business Process Design, Business Process Design Notation, Business Process Execution, Introduction to Enterprise Architecture

Unit 2 : Enterprise Architecture Integration ( 8 Hrs )

Defining EAI, Data-Level EAI, Application Interface-Level EAI. ,Method-Level EAI. , User Interface-Level EAI, The EAI Process—Methodology or Madness, An Introduction to EAI and Middleware, Transactional Middleware and EAI, RPCs, Messaging, and EAI, Distributed Objects and EAI, Database-Oriented Middleware and EAI, Java Middleware and EAI, Implementing and Integrating Packaged Applications—The General Idea, XML and EAI, Message Brokers—The Preferred EAI Engine, Process Automation and EAI. Unit 3 : Enterprise Architecture Patterns ( 7 Hrs ) Layering, Organizing Domain Logic, Mapping to Relational Databases, Web Presentation, Domain Logic Patterns, Data Source Architectural Patterns, Object-Relational Behavioral Patterns, Object-Relational Structural Patterns, Object-Relational Metadata Mapping Patterns, Web Presentation Patterns, Distribution Patterns, Offline Concurrency Patterns.

Unit 4 : Software Factories ( 7 Hrs )

Context: Monolithic Construction, Gratuitous Generality, One-Off Development, Process Immaturity, Critical Innovations, Systematic Reuse, Development by Assembly, Model-Driven Development, Process Frameworks Reuse Paradigm: Industrializing Software Development, Economics of Reuse, Economies of Scale and Scope, Systematic Software Reuse, Integrating the Critical Innovations, Software Factory, Software Factory Schema, Software Factory Template, Building a Software Factory, Implications of Software Factories, Development by Assembly, Software Supply Chains, Relationship Management ,Domain Specific Assets , Organizational Changes, Mass Customization of Software


Unit 5 : Reuse and Product Lines ( 7 Hrs ) Systematic Reuse : Software Product Families, Software as a Product, How Families Are Formed, Working Within a Family, Solving Problems in Advance, The Role of Architecture ,Commonality and Variability ,Where Families Are Found ,Software Product Lines, Creating Economies of Scope Software Product Lines: Product Line Development, Product Line Analysis, Product Line Design, Product Line Implementation, Product Development, Problem Analysis, Product Specification, Collateral Development, Product Implementation, Product Line Evolution, Product Line Deployment

Unit 6 : Transformation and Implementations ( 7 Hrs ) Mappings and Transformations: Transformations Types of Transformation, Vertical Transformations, Horizontal Transformations, Transformation Problems, Model-to-Model Transformations, Model-to-Code Transformations, Solving the Composition Problem, Solving the Traceability Problem Generating Implementations: Describing Transformations, Mapping Rules Have IF-THEN Parts, Implementing Mapping Rules, Specifying Horizontal Transformations,

Describing AspectWeaving, Describing Refactoring Rules, Patterns as Sets of Mapping Rules, Transformation Systems, Black-Box and White-Box Transformations, Grey-Box Transformation Systems, Applying Black-Box Transformations

Outcomes: Upon completion this course, the students will be able: 1. To create Enterprise Model of Target System 2. To understand and adapt to Business Processes 3. To use Enterprise Integration Strategies 4. To develop Workflow artifacts 5. To maintain Enterprise Catalogue

Text Books :

1. Patterns of Enterprise Application Architecture, Martin Fowler, Addison-Wesley Professional, 2003, ISBN-10: 0321127420 ISBN-13: 9780321127426

2. Enterprise Integration: An Architecture for Enterprise Application and Systems Integration, Fred A. Cummins, Wiley; 2002 ISBN-10: 0471400106 ISBN-13: 978-0471400103

Reference Books :


1. Enterprise Integration Patterns: Designing, Building, and Deploying Messaging Solutions, Gregor Hohpe, Bobby Woolf, Addison-Wesley Professional,2003, ISBN-10: 0321200683, ISBN-13: 978-0321200686

2. Essential Business Process Modeling, Michael Havey, O'Reilly Media, Inc., 2005, ISBN-10: 0596008430, ISBN-13: 978-0596008437

3. Business Modeling With UML: Business Patterns at Work, Magnus Penker, Hans-Erik Eriksson, Wiley; 2000, ISBN-10: 0471295515, ISBN-13: 978-0471295518

4. Enterprise Modeling with UML: Designing Successful Software through Business Analysis Chris Marshall, Addison-Wesley Professional, 2000, ISBN-10:0201433133 ISBN-13: 9780201433135

5. Enterprise Integration Patterns: Designing, Building, and Deploying Messaging Solutions, Gregor Hohpe, Bobby Woolf, Addison-Wesley Professional, 2004, ISBN-10: 0321200683 ISBN-13: 9780321200686

6. Jack Greenfield, Keith Short, Steve Cook, Stuart Kent, Software Factories: Assembling Applications with Patterns, Models, Frameworks, and Tools,Wiley Publication, ISBN: 978-0-471-20284-4, September 2004


CS8168:ENTERPRISE SOFTWARE DEVELOPMENT Aim : As enterprises spread their boundaries across different business areas, they acquire disparate technology solutions, technologies and applications. The objective of this course is to offer Enterprise Application Integration (EAI) techniques. The focus is towards Workflow Modeling and managing Workflow artifacts. The Business Process Modeling and Intelligence can also be one of the leading areas in this domain Objectives: 1. To study interoperability principles and standards 2. To learn essence of Business Processes and Modeling 3. To apply Enterprise Architecture and Integration 4. To learn development of Software Factories List of Practicals 1. A. To narrate Requirement Definition Document for the target system with following three areas:




3. To depict the dynamic behavior of the target system using sequence diagram. The Sequence diagram should be based on the Scenarios generated by the inter-object communication. The model should depict:

- Discrete, distinguishable entities (class). - Events (Individual stimulus from one object to another). - Conditional events and relationship representation.

4. To depict the state transition with the life history of objects of a given class model. The model should depict:

- Possible ways the object can respond to events from other objects. - Determine of start, end, and transition states.

5. To prepare Class Collaboration-Responsibility (CRC) cards for the Conceptual classes traced from System analysis phase. To develop logical static structure of target system with Class diagram. The model should depict


- Relationship between classes: inheritance, Assertion, Aggregation, Instantiation - Identification of objects and their purpose. - Roles / responsibilities entities that determine system behavior.

6. To represent physical module that provides occurrence of classes or other logical elements identified during analysis and design of system using Component diagram. The model should depict allocation of classes to modules. To narrate the Program Design Language Constructs for the target system and implement the system according to specification. 7. Select a moderately complex system and narrate concise specification for the same. Implement the system features using Abstract Factory, Composite, Facade and Proxy design patterns. State the complete pattern specification and note the difference between the patterns. 8. Select a complex system and narrate concise specification for the same. Develop architecture specification and use archetypes to recognize the architectural elements. Outcomes: Upon completion this course, the students will be able: 1. To create Enterprise Model of Target System 2. To understand and adapt to Business Processes 3. To use Enterprise Integration Strategies 4. To develop Workflow artifacts 5. To maintain Enterprise Catalogue

Text Books :

1. Patterns of Enterprise Application Architecture, Martin Fowler, Addison-Wesley Professional, 2003, ISBN-10: 0321127420 ISBN-13: 9780321127426

2. Enterprise Integration: An Architecture for Enterprise Application and Systems Integration, Fred A. Cummins, Wiley; 2002 ISBN-10: 0471400106 ISBN-13: 978-0471400103

Reference Books :

1. Enterprise Integration Patterns: Designing, Building, and Deploying Messaging Solutions, Gregor Hohpe, Bobby Woolf, Addison-Wesley Professional,2003, ISBN-10: 0321200683, ISBN-13: 978-0321200686

2. Essential Business Process Modeling, Michael Havey, O'Reilly Media, Inc., 2005, ISBN-10: 0596008430, ISBN-13: 978-0596008437

3. Business Modeling With UML: Business Patterns at Work, Magnus Penker, Hans-Erik Eriksson, Wiley; 2000, ISBN-10: 0471295515, ISBN-13: 978-0471295518

4. Enterprise Modeling with UML: Designing Successful Software through Business Analysis Chris Marshall, Addison-Wesley Professional, 2000, ISBN-10:0201433133 ISBN-13: 9780201433135

5. Enterprise Integration Patterns: Designing, Building, and Deploying Messaging Solutions, Gregor Hohpe, Bobby Woolf, Addison-Wesley Professional, 2004, ISBN-10: 0321200683 ISBN-13: 9780321200686

6. Jack Greenfield, Keith Short, Steve Cook, Stuart Kent, Software Factories: Assembling Applications with Patterns, Models, Frameworks, and Tools,Wiley Publication, ISBN: 978-0-471-20284-4, September 2004


Elective –IV

Theory & Practical


CS3178: COMPILER DESIGN

Prerequisites: Automata Theory: DFAs, NFAs, Regular Expressions Aim: This course is aimed at elaborating the implementation issues and strategies behind converting a program written in a high-level language into a form that will execute correctly on some target machine architecture. This course will emphasize the implementation of a compiler through the development of its various phases such as the scanner, parser, and code generator. Objectives:

1. To understand the theory and practice of compiler implementation; 2. To learn the application of grammars and formal languages in compiler writing; the

process involved in the design of a high-level programming language; 3. To learn lexical analysis, a variety of parsing techniques and semantic analysis of a

programming language, along with error detection and recovery; 4. To learn various storage allocation, code generation and code optimization techniques.

Unit 1: Introduction to Compilers & Lexical Analysi s ( 5 Hrs )

Introduction to Compilers, Assemblers, Pre-processors, Linkers and Loaders, Role of a Lexical Analyzer, Specification and Recognition of Tokens, Implementing Scanners, LEX

Unit 2: Syntax and Semantic Analysis ( 9 Hrs )

Expressing Syntax, Top-Down Parsing, Bottom-Up Parsing, LR Parsers, YACC, Type Checking, Type Conversion.

Unit 3: Syntax-Directed Translation & Intermediate Code Generation ( 7 Hrs )

Syntax-Directed Definitions, Bottom-Up Evaluation, Top-Down Translation, Intermediate Representations, Intermediate Code Generation

Unit 4: Runtime Storage Organization ( 7 Hrs )

Storage Organization, Storage Allocation Strategies, Symbol-Table Structure, Run-Time Support

Unit 5: Code Generation ( 7 Hrs )

Issues in Code Generation, Basic Blocks and Flow Graphs, Instruction Selection, Instruction Scheduling, Register Allocation.


Unit 6: Code Optimization ( 7 Hrs )

Early Optimizations, Redundancy Elimination, Loop Optimizations, Procedure Optimizations, Control and Data Flow Analysis

Outcomes:

Upon completion of the course, the students will be able to: 1. Describe the design of a compiler including its phases and components 2. Develop the various phases of a compiler such as the scanner, parser, code generator, and

optimizer 3. Identify the similarities and differences among various parsing techniques

Text Books

1. K. Cooper, L. Torczon, ‘Engineering a Compiler’, Morgan Kaufmann, 1st Edition, 2003

2. A. V. Aho, M. S. Lam, R. Sethi, J. D. Ullman, ‘Compilers: Principles, Techniques, and Tools’, Addison Wesley, 2nd Edition, 2007

Reference Books

1. K. Louden, ‘Compiler Construction: Principles and Practice’, Course Technology, 1st Edition, 1997

2. S. S. Muchnik, ‘Advanced Compiler Design and Implementation’, Morgan Kaufmann, 1st Edition, 1997

3. J. R. Levine, T. Mason, D. Brown, ‘Lex & Yacc’, O’Reilly, 2nd Edition, 1992


CS8178: COMPILER DESIGN LABORATORY

Prerequisites: Knowledge of C Objectives: To design and implement the various phases of a compiler for a concise programming language.

List of Practicals Implement the following for a subset of C

1. Lexical Analyzer using LEX 2. Parser using LEX and YACC

3. Generate and populate appropriate Symbol Table (Optional)

4. Semantic Analysis Operations (like type checking, verification of function parameters,

variable declarations and coercions), possibly using an Attributed Translation Grammar. (Optional)

5. Generation of appropriate Intermediate Code, possibly using an Attributed Translation

Grammar.

Implement the following assuming suitable processor details.

6. Generate appropriate Target Code from the intermediate code given 7. A Register Allocation algorithm that translates the given code into one with a fixed

number of registers. (Optional)

8. An Instruction Scheduling Algorithm (Optional) Implement the following using appropriate Intermediate Code

9. Local and Global Code Optimizations, such as Common Sub-expression Elimination, Copy Propagation, Dead-Code Elimination, Loop and Basic-Block Optimizations (Optional)


Outcomes:

The students will be able to develop a complete compiler for any concise programming language.

Text Books

1 K. Cooper, L. Torczon, ‘Engineering a Compiler’, Morgan Kaufmann, 1st Edition, 2003

2 A. V. Aho, M. S. Lam, R. Sethi, J. D. Ullman, ‘Compilers: Principles, Techniques, and

Tools’, Addison Wesley, 2nd Edition, 2007

Reference Books

1. J. R. Levine, T. Mason, D. Brown, ‘Lex & Yacc’, O’Reilly, 2nd Edition, 1992

2. K. Louden, ‘Compiler Construction: Principles and Practice’, Course Technology, 1st Edition, 1997

3. S. S. Muchnik, ‘Advanced Compiler Design and Implementation’, Morgan Kaufmann, 1st Edition, 1997


CS3188 :MOBILE COMPUTING

Aim : This course provides an in depth understanding of wireless access and core networks and mobility in cellular and wireless networks using the important standards of the industry like GSM, CDMA, GPRS and IEEE 802.11 WiFi technologies. The course will focus on understanding the quantitative techniques to evaluate the different, protocols, network architecture options and the application performances using the different types of wireless access technologies and mobility protocols. Objectives:

1. To understand network and transport protocols for wireless networks, including mobile IP and variants of TCP.

2. distributed systems platforms for mobile computing, including proxy based architectures and service discovery and interaction platforms.

3. To understand characteristics of local and wide area technologies such as Bluetooth, 802.11 and GSM.

4. To learn the file systems support for mobile computing.

Unit 1 : Introduction to wireless communication ( 6 Hrs )

Need and Applications of wireless communication, Wireless Data Technologies, Market for mobile communication, Mobile and wireless devices.

Unit 2 : Wireless transmission ( 6 Hrs )

Frequencies for radio transmission, signals, antennas, signal propagation, Multiplexing, Modulation, Spread spectrum and Cellular systems.

Unit 3 : Medium Access Control ( 7 Hrs )

Specialized MAC, SDMA, FDMA, TDMA and CDMA

Unit 4 : Telecommunication Systems ( 6 Hrs )

GSM, DECT systems – Architecture and protocols, Tetra frame structure, UMTS basic architecture and UTRA modes

Unit 5 : Wireless LAN ( 7 Hrs )

Introduction, Infrared v/s Radio transmission, Infrastructure and ad-hoc networks, IEEE 802.11, HIPERLAN, Blue Tooth.


Unit 6 : Mobile Network Layer and Mobile Transport Layer ( 7 Hrs )

Mobile Transport Layer : Traditional TCP, Indirect TCP, Snooping TCP, Mobile TCP, Fast and selective retransmission and recovery, Transaction oriented TCP.

Outcomes: 1. Use principles of Mobile Computing and its enabling technologies, and explore exciting

ideas, solutions, and paradigm shifts. 2. Understand wireless access and core networks and mobility in cellular and wireless

networks using the important standards like GSM, CDMA, GPRS and IEEE 802.11. 3. Evaluate the different, protocols, network architecture options and the application

performances.

Text Books

1. Jachen Schiller, ‘Mobile Communications’,Addison-Wesley 2. Peter T. Davis, Craig R. Mc Guffin, ‘Wireless LAN’ MGH International Edn

Reference Books:

1. Sandeep Singhal, Jari Alvinen and group, ‘The Wireless Application Protocol’,Addison-WesleyCharles Arehart and group, ‘Professional WAP’,SPD


CS8188 :MOBILE COMPUTING

Aim : This course provides an in depth understanding of wireless access and core networks and mobility in cellular and wireless networks using the important standards of the industry like GSM, CDMA, GPRS and IEEE 802.11 WiFi technologies. The course will focus on understanding the quantitative techniques to evaluate the different, protocols, network architecture options and the application performances using the different types of wireless access technologies and mobility protocols. Objectives:

1. To understand network and transport protocols for wireless networks, including mobile IP and variants of TCP.

2. distributed systems platforms for mobile computing, including proxy based architectures and service discovery and interaction platforms.

3. To understand characteristics of local and wide area technologies such as Bluetooth, 802.11 and GSM.

4. To learn the file systems support for mobile computing.

Assignment 1. : Write a simple program for displaying “Hello, World” on your mobile screen using J2ME Wireless Toolkit. Test output in different Emulators provided by J2ME Wireless Toolkit and Transfer it to the mobile. Assignment 2. : Write a program for sending SMS to your friend by using your mobile phone. Use J2ME Wireless Toolkit to develop your application. Test it in the Emulator provided by Toolkit and Transfer it to your mobile. Assignment 3. : Develop a simple calculator for your mobile by using J2ME Wireless Toolkit. Assignment 4. : Write a simple program to take a snapshot by using the Camera in your mobile. Save the snapshot in the image or video format. Use Camera Media API provided J2ME. Test it in Emulator and Transfer it to your mobile. Outcomes:

1. Use principles of Mobile Computing and its enabling technologies, and explore exciting ideas, solutions, and paradigm shifts.

2. Understand wireless access and core networks and mobility in cellular and wireless networks using the important standards like GSM, CDMA, GPRS and IEEE 802.11.

3. Evaluate the different, protocols, network architecture options and the application performances.


Text Books

1. Jachen Schiller, ‘Mobile Communications’,Addison-Wesley 2. Peter T. Davis, Craig R. Mc Guffin, ‘Wireless LAN’ MGH International Edn

Reference Books:

1. Sandeep Singhal, Jari Alvinen and group, ‘The Wireless Application Protocol’,Addison-WesleyCharles Arehart and group, ‘Professional WAP’,SPD


CS3198: ANALYSIS OF ALGORITHMS

Prerequisites: Data Structures and Algorithms. Aim: This course introduces basic algorithmic techniques, for time requirements of an algorithm and mathematical techniques used in analysis of algorithms. The emphasis will be learning analysis of algorithms for a wide variety of foundational problems occurring in computer science applications with discussions on complexity and NP-completeness. Objectives:

1. Fundamental understanding of the mathematics used to analyze, evaluate, and design algorithms

2. Develop the ability to assess the advantages and disadvantages of different types of algorithms.

3. Understand methods for designing time and space efficient algorithms. 4. Increased ability to design and implement efficient solutions to problems.

Unit 1 : Introduction ( 6 Hrs )

‘O’,’ Ω’ and ‘Ө’ asymptotic notations, Average, Best and Worst case analysis of algorithms for Time and Space complexity, Amortized Analysis, Solving Recurrence Equations, Proof Techniques: by Contradiction, by Mathematical Induction

Priority Queues : Heaps & Heap sort.

Unit 2 : Divide And Conquer And Greedy Strategy ( 9 Hrs )

Divide and Conquer: General Strategy, Exponentiation. Binary Search, Quick Sort and Merge Sort. Greedy Method ,General Strategy, Knapsack problem, Job sequencing with Deadlines, Optimal merge patterns, Minimal Spanning Trees and Dijkstra’s algorithm. (Proof of Correctness of algorithms to be taught)

Unit 3 : Dynamic Programming ( 7 Hrs )

General Strategy, Multistage graphs, OBST, 0/1 Knapsack, Traveling Salesperson Problem, Flow Shop Scheduling.

Unit 4 : Backtracking & Branch And Bound ( 7 Hrs )

Backtracking: General Strategy, 8 Queen’s problem, Graph Coloring, Hamiltonian Cycles, 0/1 Knapsack.

Branch and Bound: General Strategy, 0/1 Knapsack, Traveling Salesperson Problem


Unit 5 : Parallel Algorithms ( 7 Hrs )

Computational Model, Basic Techniques and Algorithms (Complete Binary Tree, Pointer Doubling, Prefix Computation), Selection, Merging, Sorting Networks, Parallel Sorting, Graph Problems (Alternate Algorithm for Transitive Closure, All pairs shortest path)

Unit 6 : NP-Hard And NP-Complete Problems ( 6 Hrs )

NP-Hard And NP-Complete Problems: Algorithms, Complexity-intractability, Non-Deterministic Polynomial time (NP) Decision problems, Cooks Theorem. NP-Complete problems- Satisfiability problem, vertex cover problem.

NP-Hard problems-graph, scheduling, code generation problems, Simplified NP Hard Problems

Approximate algorithms.

Outcomes:

On successful completion of this course, the student will be able to 1. analyze the average- and worst-case performance of algorithms, 2. use dynamic programming and the greedy paradigm effectively, 3. apply the concept of NP-completeness and be familiar with approximation algorithms 4. implement the families of algorithms in the appropriate high-level language.

Text Books

1. Horowitz, Sahani, “Fundamentals of computer Algorithms”, Galgotia. 2nd Edition, 1998.

2. Bressard, Bratley “Fundamentals of Algorithmics.” ,PHI, 2nd Edition,1996.

Reference Books

1. Thomas H Cormen and Charles E.L Leiserson, “Introduction to Algorithm” ,PHI 2nd edition, 2001.

2. A. V. Aho and J.D. Ullman, “Design and Analysis of Algorithms”, Addison Wesley. 2nd edition.


CS8198: ANALYSIS OF ALGORITHMS

Objectives:

1. Fundamental understanding of the mathematics used to analyze, evaluate, and design algorithms

2. Develop the ability to assess the advantages and disadvantages of different types of algorithms.

3. Understand methods for designing time and space efficient algorithms. 4. Increased ability to design and implement efficient solutions to problems.

List of Practicals

1. Recursive and iterative( non recursive) algorithm for specific problem and there

complexity measures(comparison expected).

2. Quick Sort/ Merge Sort implementations using divide and conquer approach. Time

complexity measure is to be obtained.

3. Minimal spanning Trees/ Job scheduling as an example of Greedy approach

4. Finding shortest path for multistage graph problem. (single source shortest path and all

pairs shortest path.)

5. OBST/Flow Shop Scheduling as an example of dynamic programming.

6. 0/1 knapsack's problem using Dynamic Programming, Backtracking and Branch &

Bound Strategies.

7. 8-Queen problem/ Graph coloring problem : general backtracking method and recursive

back tracking method and their comparison for space and time complexity.

8. A complete LC branch and bound algorithm for job sequencing with dead lines problem.

Use fixed tuple size formulation.

9. Algorithm implementation for `Traveling salesman' problem using -

(a) Dynamic programming approach.

(b) Branch & Bound approach.

10. Simulation/ Implementation of any Parallel Algorithms.


Outcomes:

On successful completion of this course, the student will be able to 1. analyze the average- and worst-case performance of algorithms, 2. use dynamic programming and the greedy paradigm effectively, 3. apply the concept of NP-completeness and be familiar with approximation algorithms 4. implement the families of algorithms in the appropriate high-level language.

Text Books

1. Horowitz/Sahani, “Fundamentals of computer Algorithms”, Galgotia.

2. Bressard, “Fundamental of Algorithm.” ,PHI

Reference Books

1. Thomas H Cormen and Charles E.L Leiserson, “Introduction to Algorithm” ,PHI

2. A. V. Aho and J.D. Ullman, “Design and Analysis of Algorithms”, Addison Wesley


CS3208:DATA WAREHOUSING & DATA MINING

Prerequisites: Data base management system Aim: This course introduces advanced recent developments in database technology: Data Warehousing and mining which addresses the problem of extracting information from the overwhelmingly large amounts of data which modern societies are capable of amassing. The course makes the students aware of advanced techniques for discovering hidden patterns in the rapidly growing data generated by businesses, science, web, and other sources with a focus on the key tasks of data mining, including data preparation, classification, clustering, association rule mining, and evaluation. . Objectives: -

1. To understand the process of data mining and the key steps involved well enough to lead/manage a real-life data mining project

2. Know the basics of data warehousing and how it facilitates data mining 3. To understand fundamental issues in statistical data analysis that cut across all

procedures, such as generalization to other data, basic tradeoffs, and validity of models. 4. To deliver an overview of web data mining and other significant mining techniques

Unit 1 : Data warehousing fundamentals. (7 Hrs )

Definition and problems in DSS, definition of Data warehouse (by Bill Inmon), Use of Data warehouse. DSS vss Data wareouse. Diffrence between OLTP and Data Warehouse, Data cube and OLAP, Concept hierarchies: total and partial, Set-grouping hierarchies, OLAP operations: drill-down, Roll-up and extreme Roll-up, slice-dice and pivot. Difference between OLAP and OLTP operations, star-net model, models of Data warehouse: Enterprise Warehouse, Data Mart, Virtual Warehouse (2).

Unit 2 : Data warehouse general architecture ( 7 Hrs )

Issues in building data warehouse (1), Data warehouse design views, 3-tier data warehouse architecture, ROLAP, MOLAP and HOLAP, data warehouse schemas: - Fact table, dimension table, star, snowflake, fact constellation, their comparison (2), warehousing internal storage: dimensional modeling and normalized approach, Meta data. Data staging.

Unit 3 : Performance optimization ( 6 Hrs )


Aggregation (materialized views). Data warehouse indexing: factors used to select an indexing technique, properties of a good indexing technique for data warehouse, indexing techniques: B-tree Index, B+-tree index, Projection Index, Bitmap Index (pure and encoded), Join Index and their comparison. Data reduction.

Unit 4 : Introduction to Data mining (6 Hrs )

Data mining primitives, Techniques:- Clustering, classification, association rules, linear and multiple regression, Feature selection, Mining text databases, multimedia databases.

Unit 5 : Classification ( 7 Hrs )

Decision tree classifier, neural network classification, back propagation, KNN classifier, Fuzzy set approach, Fuzzy-neuro classifier, classifier accuracy, other classifiers recommended by concerned staff.

Unit 6 : Clustering (7 Hrs )

K-means clustering, moment based clustering, neural network approach, outlier analysis, performance evaluation, fuzzy neuro-clustering. Interesting item set mining:- Item set association, a priori algorithm

Outcomes:

Upon completion of the course, the students will be able to: 1. Define and recognize key areas and issues in knowledge discovery and data 2. Develop an in-depth understanding of several data mining techniques 3. Define the concept, structure and major issues of data warehousing 4. Develop general awareness of data warehousing project management

Text Books 1. Jiawei Han and Micheline Kamber “Data mining: concepts and techniques”, the Morghan Kaufman, 2001. 2. Abraham Silberschatz, Henry F. Korth, S. Sudarshan, “Database Systems Concepts”, 5th edition, 2005.

References

1. T. Mitchell. “Machine Learning”, McGraw-Hill, 1997. 2. Hand, Smyth, Mannila “Principles of Data mining”, MIT press,2001. 3 Ralf Kimball, “Data warehouse life cycle toolkit”-John Wiley,1998. 4 Gagendra Sharma, “Data mining, Data warehousing and OLAP”, S.K. Kataria and sons, First edition,2007-08. 5 M. Dash and H. Liu. “Feature selection for classification” Intelligent Data Analysis, 1(3):131-156, 1997. 6 P. S. Dhabe, et. Al, “A clustering method based on geometrical moments ” in proc. of IEEE international conference on fuzzy systems FUZZ_IEEE 03, 2003. 7 Other research papers recommended by the staff in charge.


CS8208 : DATA WAREHOUSING & DATA MINING Objectives: -

1. To understand the process of data mining and the key steps involved well enough to lead/manage a real-life data mining project

2. Know the basics of data warehousing and how it facilitates data mining 3. To understand fundamental issues in statistical data analysis that cut across all

procedures, such as generalization to other data, basic tradeoffs, and validity of models.

4. To deliver an overview of web data mining and other significant mining techniques

List of Practicals

1. Design a star schema for a given data ware house. 2. Build a 3-D data cube for a realistic data and perform roll-up, slice and dice, drill-down

operations. 3. Implementing K-NN classifier for a small real data set. 4. Implementing K-means clustering algorithm using a data set. 5. Implementing a priori algorithm. 6. Generating association rules from a given large item set. 7. Implementing a linear regression model for a given problem. 8. Two class classification by perceptron learning. 9. Implementing Data cleansing method for a given problem. 10. Implementing data transformation for a given problem domain. 11. Design and implementing a fuzzy set for a given problem. 12. Implementing a bit map index for a given problem.

Outcomes:

Upon completion of the course, the students will be able to: 1. Define and recognize key areas and issues in knowledge discovery and data 2. Develop an in-depth understanding of several data mining techniques 3. Define the concept, structure and major issues of data warehousing 4. Develop general awareness of data warehousing project management


Text Books 1. Jiawei Han and Micheline Kamber “Data mining: concepts and techniques”, the Morghan Kaufman, 2001. 2. Abraham Silberschatz, Henry F. Korth, S. Sudarshan, “Database Systems Concepts”, 5th edition, 2005.

Reference Books:

1. T. Mitchell. “Machine Learning”, McGraw-Hill, 1997. 2. Hand, Smyth, Mannila “Principles of Data mining”, MIT press,2001. 3 Ralf Kimball, “Data warehouse life cycle toolkit”-John Wiley,1998. 4 Gagendra Sharma, “Data mining, Data warehousing and OLAP”, S.K. Kataria and sons, First edition,2007-08. 5 M. Dash and H. Liu. “Feature selection for classification” Intelligent Data Analysis, 1(3):131-156, 1997. 6 P. S. Dhabe, et. Al, “A clustering method based on geometrical moments ” in proc. of IEEE international conference on fuzzy systems FUZZ_IEEE 03, 2003. 7 Other research papers recommended by the staff in charge.


CS3218: INFORMATION THEORY AND CODING

Aim : This course introduces the principles of information theory and the practical aspects of data compression and error-control coding.. The course focuses on how information is measured in terms of probability and entropy, and the relationships among conditional and joint entropies; coding schemes, including error correcting codes; how discrete channels and measures of information generalize to their continuous forms; the Fourier perspective; and extensions to complexity and compression. Objectives:

1. To understand the difference between “data” and “information” in a message. 2. To learn how to analyze and measure the information per symbol emitted from a source. 3. To learn how to analyze the information-carrying capacity of the communication channel. 4. To learn how to design source compression codes to improve the efficiency of

information transmission. 5. To learn how to design data translation codes to tailor the transmitted data sequence to

the constraints of the channel.

Unit 1 : Introductory Topics ( 7 Hrs )

Information theory – Entropy and Uncertainty information content, Rate of a language, redundancy. Complexity theory – computational complexity P, NP, NP complete. Number theory -Modula arithmetic, Fermat’s little theorem, Chinese remainder theorem., Factoring, Prime number generation, Random number generation on discrete probability

Unit 2 : Cryptographic Techniques ( 7 Hrs )

Cryptographic protocols – introduction to protocols one-way function one-way hash function, Communication using public key cryptography. Algorithms block cipher modes.

Unit 3 : DES ( 6 Hrs )

Multiple encrypting, stream ciphers, encrypting data for storage /communication Data Encryption Standard (DES) Block algorithms. Public key algorithms

Unit 4 : Compression algorithms ( 6 Hrs )

Optimal compression, entropy, Compression algorithms – Huffman coding, Adaptive Huffman Compression, Statistical modelling, Dictionary based compression, Sliding window compression, and speech compression, LZW, RLE. Lossy compression schemes – images compression using DCT.


Unit 5 : Compression Application: ( 7 Hrs )

Introduction to data Compression Application, speech Compression, Telemetry Compression software implementation of speech Compression, techniques, Telemetry characteristics, Database compression, file Compression.

Unit 6 : Error control coding ( 7 Hrs )

Coding for rollable digital transmission and storage. Type of codes – error-checking codes, error Correcting codes, Coding Schemes – linear block codes. Coding schemes: - Cyclic codes, and error trapping decoding for cyclic codes. ( and topics suggested by staff incharge)

Outcomes:

At the end of the course students should be able to 1. Demonstrate ability to evaluate the information rate of various information sources. 2. Demonstrate ability to design lossless data compression codes for discrete memoryless

sources. 3. Demonstrate ability to evaluate the information capacity of discrete memoryless channels

and determine possible code rates to achievable on such channels. 4. Demonstrate an ability to compensate for channel memory through the design of

appropriate data translation codes. 5. Demonstrate an understanding of the mathematical theory of linear channel codes for

error detection and correction. 6. Demonstrate the ability to select and design simple linear block error correcting codes.

Text Books

1). Alfred Menezes, Paul Van Oorschot, Vanstone, “Handbook of Applied Cryptography”, CRC press,1997.

2) Mark Nelson, “The Data Compression Handbook”, M&T book, 1995.

References

1. Bruce Schneider, “Applied Cryptography; Protocols, Algorithms and Source Code in C”, John Wiley and Sons, Second Edition, 1995.

2. Vera Pleas, “Introduction to the Theory of the Error Correcting Codes”, John Wiley and Co, Third Edition, 1998.

3. Thomas J. Lynch, “Data Compression Techniques and Applications”, Van No strand Reinhold Company, 1985.

4. Darrel Hankerson, Grey A. Harris, Peter D. Johnson Jr., “Introduction to Information Theory and Data Compression”, Second Edition,CRC press, 2003.

5. Research papers selected by staff in charge based on above units.


CS8218 : INFORMATION THEORY AND CODING

Objectives:

1. To understand the difference between “data” and “information” in a message. 2. To learn how to analyze and measure the information per symbol emitted from a source. 3. To learn how to analyze the information-carrying capacity of the communication channel. 4. To learn how to design source compression codes to improve the efficiency of

information transmission. 5. To learn how to design data translation codes to tailor the transmitted data sequence to

the constraints of the channel. List of Practicals

1. Presentations on selected topics mostly from research papers. 2. Writing programs for Coding, Decoding and detection of errors. 3. Writing programs for compression and cryptography.

Outcomes:

At the end of the course students should be able to 1. Demonstrate ability to evaluate the information rate of various information sources. 2. Demonstrate ability to design lossless data compression codes for discrete memoryless

sources. 3. Demonstrate ability to evaluate the information capacity of discrete memoryless channels

and determine possible code rates to achievable on such channels. 4. Demonstrate an ability to compensate for channel memory through the design of

appropriate data translation codes. 5. Demonstrate an understanding of the mathematical theory of linear channel codes for

error detection and correction. 6. Demonstrate the ability to select and design simple linear block error correcting codes.

Text Books

1). Alfred Menezes, Paul Van Oorschot, Vanstone, “Handbook of Applied Cryptography”, CRC press,1997.

2) Mark Nelson, “The Data Compression Handbook”, M&T book, 1995.


References

1. Bruce Schneider, “Applied Cryptography; Protocols, Algorithms and Source Code in C”, John Wiley and Sons, Second Edition, 1995.

2. Vera Pleas, “Introduction to the Theory of the Error Correcting Codes”, John Wiley and Co, Third Edition, 1998.

3. Thomas J. Lynch, “Data Compression Techniques and Applications”, Van No strand Reinhold Company, 1985.

4. Darrel Hankerson, Grey A. Harris, Peter D. Johnson Jr., “Introduction to Information Theory and Data Compression”, Second Edition,CRC press, 2003.

5. Research papers selected by staff in charge based on above units.


CS3228 : VLSI DESIGN Prerequisites: Understanding of Digital Logic and sequential circuits. Aim : This course will introduce the design of very large scale integrated circuits. The material develops an understanding of the whole spectrum from semiconductor physics through transistor-level design and system design to architecture, and promotes the associated tools for computer aided design Objectives: 1. To understand Sequential analysis and design. 2. To understand VHDL analysis and design. 3. To understand the fabrication process and analyze its implications;

Unit 1: Introduction to Digital Integrated Circuit Design and Devices: (7Hrs) Overview of Bipolar and MOS Transistors , Design Abstraction Levels , Quality Metrics of a Digital Design -Voltage Transfer Characteristics, Noise Margins, Fan-In and Fan-Out, Propagation Delays, Power Consumption Diode , MOS Transistors - Threshold Voltage, VI Characteristics and Equations , Channel Length Modulation , MOS Transistor Capacitances , SPICE Models ,MOS Transistor Switches, CMOS Inverter - DC Characteristics , NOISE Margins ,Capacitance and Propagation Delay ,Transistor Sizing ,Pseudo-nMOS Inverter ,Transmission Gate Unit 2: CMOS Processing Technology (7 Hrs) Silicon wafer, photolithography ,Diffusion and Ion Implantation ,n-well, p-well and twin-tub process, CMOS Process flow , Metal and Polysilicon Interconnect ,Wires and Vias ,Tub ties and Latch up, Stick Diagram , Design Rules ,Packaging

Unit 3: CMOS Logic Design (7 Hrs) Static Complementary CMOS Design , NAND and NOR Gates , Pseudo n-MOS Logic , Pass Transistor Logic , Dynamic CMOS Design , Domino Logic , Complex Logic Circuits and CMOS Standard Cell Design , Logic Synthesis and Optimization Algorithms

Unit 4: Introduction to VHDL (7Hrs) Architectures, Behavioral Modeling Sequential Processing-Process Statements, Sequential Statements, Data Types, Functions, Procedures, Overloading Operators, VHDL Synthesis


Unit 5: Sequential Circuits Design (6 Hrs) Latches and Flop-Flops, Edge Triggered and Master Slave Logic , Clocking Strategies – Single phase and Two Phase Clocking , Sequential Systems Design - State Transition Diagram/Table, State Assignment and Synthesis., Sequential Machine Synthesis and Optimization Algorithms , Shift registers and Counters

Unit 6: Subsystem Design (8 Hrs)m Data path Operators - Adder , Parity Generator , Comparator , ALU , Multiplexer , Multiplier , Shifter , Memory Units - Read-Only Memories – ROM Cells ,Read/Write Memory - SRAM and DRAM Cells , Address Decoder , Sense Amplifier , Programmable Logic Arrays

Outcomes: The students should get well acquaint to VLSI designing and simulate them in simulator and can enhance scope to work in VLSI domain industry. Text books: 1. N. H. E. Weste and K. Eshraghian : Principles of CMOS VLSI Design : A Systems Perspective, Pearson Education, 2004.

2. Douglas Perry, ‘VHDL Programming by examples’ Tata McGraw-Hill 4th Edition 2002

Reference Books: 1. J. Rabaey, A. Chandrakasan and B. Nikolic: Digital Integrated Circuits: A Design Perspective, Prentice Hall of India, 2003.

2. M. Sarafzadeh and C. K. Wong: An Introduction to VLSI Physical Design, McGraw-Hill International Editions, 1996

3. D. D. Gajaski, N. D. Dutt , A C-H Wu and S Y-L Lin : High-Level Synthesis: Introduction to Chip and System Design, Kluwer Academic Publishers, 1992. 4 W. Wolf: Modern VLSI Design : Modern VLSI Design: Systems on Silicon, Pearson Education, 2000.


CS8228: VLSI DESIGN Objectives: 1. To understand Sequential analysis and design. 2. To understand VHDL analysis and design. 3. To understand the fabrication process and analyze its implications; List of Practicals To design :-

1. Adder , 2. Parity Generator , 3. Comparator , 4. Multiplexer , 5. Multiplier , 6. Shifter , 7. Memory units, 8. address Decoder , 9. counters, using design Description Languages – VHDL/VERILOG & simulate results

in simulator (multisim) 10. A mini project based on at least 4 above components.

Outcomes: The students should get well acquaint to simulator and can enhance his/her scope to work in VLSI domain industry. Text books :

1. N. H. E. Weste and K. Eshraghian : Principles of CMOS VLSI Design : A Systems Perspective, Pearson Education, 2004.

2. Douglas Perry, ‘VHDL Programming by examples’ Tata McGraw-Hill 4th Edition 2002

Reference Books:

1. J. Rabaey, A. Chandrakasan and B. Nikolic: Digital Integrated Circuits: A Design Perspective, Prentice Hall of India, 2003.

2. M. Sarafzadeh and C. K. Wong: An Introduction to VLSI Physical Design, McGraw-Hill International Editions, 1996

3. D. D. Gajaski, N. D. Dutt , A C-H Wu and S Y-L Lin : High-Level Synthesis: Introduction to Chip and System Design, Kluwer Academic Publishers, 1992. 4 W. Wolf: Modern VLSI Design : Modern VLSI Design: Systems on Silicon, Pearson Education, 2000.


CS3238: SOFTWARE QUALITY AND RELIABILITY Prerequisites :

1. Software Engineering Principles 2. Object Oriented Modeling and Design 3. Principles of Programming Language

Aim : 1. This course addresses processes, methods, and techniques for developing quality software,

for assessing software quality, and for maintaining the software quality. 2. This course explores tradeoffs between software cost and quality as well approaches for

integrating quality into the software development process. Quality methods including Formal Review and Inspection; Test planning; Software safety is covered.

3. Students will learn how to specify, manage, achieve, evaluate and demonstrate quality. Student will also learn quality systems such as the Capability Maturity Model (CMM) and ISO9000.

Objectives: 1. Understand and be able to apply software metrics and measurements for process

improvement, project management and software product quality improvement. 2. Understand the fundamental concepts, approaches, and methodologies in software quality

management and assurance. 3. Be able to develop and write a software quality management or assurance plan. 4. Understand the relationships between development processes, project management, and

quality management. 5. Understand the method for determining software process maturity. 6. Understand the framework and general approach of several Quality System Standards and

Total Quality Management (TQM). 7. Acquire a working knowledge of quality assurance techniques such as inspections, testing

and configuration management.

Unit 1 : Testing Techniques Overview ( 7 Hrs ) Defects, Defect Classification, Defect Density, Issues vs. Defects, Defect Distribution, White-Box Testing: Types of Structural Measures, Control Flow Structures Test Adequacy Criteria, Static Testing, Structural Testing, Code Complexity Testing, Mutation Testing Black-Box Testing: Test Case Design Criteria, Requirement Based Testing, Positive and Negative Testing, Boundary Value Analysis, Equivalence Partitioning, State Based Testing, Compatibility Testing, User Documentation Testing, Domain Testing

Unit 2 : System Level Testing ( 7 Hrs )

Test Plan, Test Management, Test Execution and Reporting, GUI Testing, Validation Testing, Integration Testing, System and Acceptance Testing, Regression Testing, Specification Based Testing, Performance Testing, Ad-hoc Testing, Usability and Accessibility Testing, Software Test Automation


Unit 3 : Software Quality ( 7 Hrs ) Quality Concepts, Software Quality Assurance, Six Sigma Principles, Malcolm Baldridge Assessment ISO 9000,Edward Deming’s Principles, Total Quality Management, Product Quality Metrics, In-Process Quality Metrics, Software Maintenance, Ishikawa’s Seven Basic Tools CMMI: Overview of CMM and CMMI, Staged and Continuous representation of CMMI version 1.1, IDEALSM Model, Characteristics of Capability Level 1-5 Process Areas, Detailed view of CMMI Level 2, Level 3, Level 4 and Level 5 Process Areas, SCAMPISM Method

Unit 4 : Software Inspections and Reliability ( 7 Hrs )

Inspections: Background, Purpose of Inspection, Cost of Inspections, Defect Detection and Prevention, Feedback, Feed-forwards and Control, Flow of Inspection Process, Activities of Inspection, Economics of Inspections Reliability: Problem and Solution, SRE process, Reliability Concepts: Hardware and Software, Operational Profiles implementation, Preparing for Test, Executing Test, Guiding Test, Deploying SRE

Unit 5 : Software Quality Metrics and Models ( 7 Hrs )

Product Quality Metrics, In-Process Quality Metrics, The Rayleigh Model, Exponential Distribution and Reliability Growth Models, In-Process Metrics for Software Testing, FP metrics for Software Process Improvement

Unit 6 : Critical Systems ( 7 Hrs ) Critical Systems, Availability, Safety, Security, Software Reliability Specification, Safety Specification, Security Specification, Fault Minimization, Fault Tolerance, Fault Tolerant Architectures, Safe System Design

Outcomes:

Upon completion of this course the student should be able to:

1. Demonstrate a critical understanding of the quality aspects of software systems and factors affecting such quality

2. Develop and apply a working knowledge of good management and engineering practices for the production of high quality software

3. Develop an IEEE standard Software Quality Assurance Plan

4. Understand the role of the various quality systems related to software development, such as Capability Maturity Model (CMM) and ISO9000.


Text Books:

1. Fenton, Pfleeger, “Software Metrics: A Rigourous and practical Approach”, Thomson Brooks/Cole, ISBN 981-240-385-X.

2. Desikan, Ramesh, ‘ Software Testing: principles and Practices”, Pearson Education, ISBN 81-7758-121-X.

Reference Books :

1. Stephen H. Kan, “Metrics and Models in Software Quality Engineering”, Pearson Education, ISBN 81-297-0175-8

2. Ramesh, Bhattiprolu, “Software Maintenance”, Tata McGraw Hill, ISBN 0-07-048345-0 3. Burnstein, “Practical Software Testing” , Springer International Edition, ISBN 81-8128-089-

X 4. John D. Musa, “ Software Reliability Engineering”, Tata McGraw Hill, ISBN 0-07-060319-7 5. Ronald Radice, “Software Inspections”, Tata McGraw Hill, ISBN 0-07-048340-X 6. Raghav S. Nandyal, “CMMI: A framework for Building World-Class Software and Systems

Enterprises”, ISBN 0-07-052801-2 7. Ian Sommerville, “Software Engineering”, Sixth Edition, Pearson Education, ISBN 81-

7808-497-X


CS8238 : SOFTWARE QUALITY AND RELIABILITY

Objectives: 1. Understand and be able to apply software metrics and measurements for process

improvement, project management and software product quality improvement. 2. Understand the fundamental concepts, approaches, and methodologies in software quality

management and assurance. 3. Be able to develop and write a software quality management or assurance plan. 4. Understand the relationships between development processes, project management, and

quality management. 5. Understand the method for determining software process maturity. 6. Understand the framework and general approach of several Quality System Standards

and Total Quality Management (TQM). 7. Acquire a working knowledge of quality assurance techniques such as inspections, testing

and configuration management. List of Practicals 1. To implement the System under construction and narrate Test Plan for the same. The Test plan consists of following issues

- Purpose of the test - Location and schedule of the test - Test descriptions - Test procedures

2. To identify Test cases, Test Procedures, Test log, Test Oracle for the System under test. The Test case Scenarios should correspond to the relevant features of the System. 3. To perform Unit testing especially indicating the traced Independent data paths, Control paths and Error handling paths. Prepare control flow graphs for the unit under test. Compute the Cyclomatic complexity of the unit. Record the Test criteria and Test conditions along with Test results. 4. To perform Black box Testing for all the units contained in the architectural segments. To perform Regression Testing / GUI Testing of the System under construction. 5. To perform mutation testing for the units contained in the System. To identify the baselines for maintaining System Configuration Logs and perform Installation tests for the Target System. 6. To use Function Points to estimate the size, effort, risk, economics for the system. To narrate SQA plan for target system evaluation.

Outcomes:

Upon completion of this course the student should be able to:

1. Demonstrate a critical understanding of the quality aspects of software systems and factors affecting such quality

2. Develop and apply a working knowledge of good management and engineering practices for the production of high quality software

3. Develop an IEEE standard Software Quality Assurance Plan


4. Understand the role of the various quality systems related to software development, such as Capability Maturity Model (CMM) and ISO9000.

Text Books:

1. Fenton, Pfleeger, “Software Metrics: A Rigourous and practical Approach”, Thomson Brooks/Cole, ISBN 981-240-385-X.

2. Desikan, Ramesh, ‘ Software Testing: principles and Practices”, Pearson Education, ISBN 81-7758-121-X.

Reference Books :

1. Stephen H. Kan, “Metrics and Models in Software Quality Engineering”, Pearson Education, ISBN 81-297-0175-8

2. Ramesh, Bhattiprolu, “Software Maintenance”, Tata McGraw Hill, ISBN 0-07-048345-0 3. Burnstein, “Practical Software Testing” , Springer International Edition, ISBN 81-8128-

089-X 4. John D. Musa, “ Software Reliability Engineering”, Tata McGraw Hill, ISBN 0-07-

060319-7 5. Ronald Radice, “Software Inspections”, Tata McGraw Hill, ISBN 0-07-048340-X 6. Raghav S. Nandyal, “CMMI: A framework for Building World-Class Software and

Systems Enterprises”, ISBN 0-07-052801-2 7. Ian Sommerville, “Software Engineering”, Sixth Edition, Pearson Education, ISBN 81-

7808-497-X


CS3248: USER INTERFACE DESIGN

Aim: To understand the importance and effectiveness of user interface design and usability issues with IT applications.

Objectives:

1. To make students understand importance of good user interface design and cost of bad interfaces.

2. To develop the skills necessary to choose media, mode and device for input and output interaction appropriate for the user population, application requirements and processing capability of the system.

Unit 1: Introduction (7 Hrs) Importance of good design, defining user interface, graphical user interface, web user interface, graphical business systems, principles of user interface design, human computer interaction (HCI), usability, design team

Unit 2: User, Client and Business (7 Hrs)

Understanding people’s interaction with computers, users and clients, human characteristics in design, human considerations in design, human interaction speeds, human performance and preference, understanding users, business and requirement analysis, basic business functions, design standards, style guide, system training, documentation.

Unit 3: User Interaction Design (7 Hrs) Human consideration in screen design, test for good design, bad designs, organization and structural guidelines, statistical graphics, technological considerations, cognitive models - GOMS, LUCID, task analysis, structures, formatting and navigation of menus, graphical menus, window characteristics, components of window, presentation styles, types of windows, organizing window functions, window management, multi-modal interaction,

Unit 4: User Interface and Controls (7 Hrs) Input and output devices, characteristics of device-based controls, selecting proper device-based control, operational controls, buttons, text entry / read-only controls, selection controls, check boxes, combo boxes, combination entry, other controls, sliders, tabs, scroll bars, custom controls, presentation controls, selecting proper controls.

Unit 5: Visual Interface Design (7 Hrs) Words, sentences, text, text design in web pages, error messages, instructions, providing proper feedback, blinking for attention, guidance and assistance, help, wizards, tips, international considerations, localization, accessibility, disabilities, icons, images, animation, multimedia, color – RGB, HSV, color uses, problems with color, cross-disciplinary and cross-cultural differences, color and human vision, choice of colors,


discrimination and harmony, accessibility considerations, choosing colors for textual or statistical graphics screens, choosing colors for web pages, uses of color to avoid

Unit 6: Others (7 Hrs) Groupware, shared applications and artifacts, computer–supported cooperative work, social issues, social acceptability and organizational change, ubiquitous computing, information kiosk, digital library, virtual and augmented realities, hypertext and hypermedia, interfaces that give and take advice, natural interfaces, context-aware computing, roomware, tangible user interfaces.

Outcomes:

At the end of the course, the students will be able to design effective, flexible and robust user interfaces.

Text Books: 1. Wilbert Galitz, ‘The Essential Guide to User Interface Design’, Wiley Dreamtech,

2005. 2. Ben Shneiderman, ‘Designing the User Interface’, Pearson Education, Third edition,

2002.

Reference Books: 1. Alan Dix, Janet Finlay, Gregory Abowd, Russell Beale, ‘Human Computer

Interaction’, Third Edition, 2004. 2. Donald Norman, ‘The Design of Everyday Things’, Basic Books, USA, 2002.


CS8248: USER INTERFACE DESIGN

Objectives: 1. To make students understand importance of good user interface design and cost of

bad interfaces. 2. To develop the skills necessary to choose media, mode and device for input and

output interaction appropriate for the user population, application requirements and processing capability of the system.

Students should complete following assignments in a group of four students (Max). Each assignment should be completed in 4 hrs. [Contribution of each student of a group should be 4 hrs for completion of each assignment.]

1. Design user persona for different user(s) of a computer product. 2. Study an innovative I/O device. 3. Perform GOMS analysis for a computer product. 4. Design a Web Interface for any application. 5. Conduct a usability survey and analyze the result. 6. Evaluate an interface using any usability testing and evaluation technique.

Outcomes: At the end of the course, the students will be able to design effective, flexible and robust user interfaces.

Text Books:

1. Wilbert Galitz, ‘The Essential Guide to User Interface Design’, Wiley Dreamtech, 2005.

2. Ben Shneiderman, ‘Designing the User Interface’, Pearson Education, Third edition, 2002.

Reference Books:

1. Alan Dix, Janet Finlay, Gregory Abowd, Russell Beale, ‘Human Computer Interaction’, Third Edition, 2004.

2. Donald Norman, ‘The Design of Everyday Things’, Basic Books, USA, 2002.


CS3258:EMBEDDED SYSTEMS Prerequisites: Understanding of Microprocessors, Peripheral Chips, Analogue Sensors , Conversion , Interfacing Techniques Aim : This course covers the principles of embedded and real-time systems inherent in many hardware platforms and applications being developed for engineering and science. This course introduces the concepts shared by most embedded systems and their software. It also introduces the techniques used in the development of embedded multitasking application software. Objectives:

1. To understand engineering issues in the development of embedded software, such as the importance of addressing the user’s concerns, working with limited resources, maintainability, dependability.

2. To design and implement module structure to solve a problem, and evaluate alternatives and check its correctness

3. To understand fundamentals of Embedded Systems Power Management I/o Interfacing. RTOS and two Real Time OS (RTLinux, VxWorks, Watcom)

Unit 1: Embedded Systems (7Hrs) Components, Classification , Characteristics of ES. Embedded Processor Technology, IC Technology., Design Metrics, Memories Unit 2: Embedded Hardware: (7Hrs) ARM Processor, PIC Microchip 12,14,16 Bit Processor, Detailed study of 16F877A. Unit 3: Interfacing Microcontrollers. : (7 Hrs) Bus , Bus Arbitration , Bus Protocols, ISA,EISA,PCI,I2C, CAN ,LIN. Wireless Protocol 802.11 , BlueTooth,IRDA, Smart Card, Unit 4: Chip Design and Programming (7 Hrs) Target Devices Different type of ASICS , FPGA , CPLD Architectures and programming Unit 5: RTOS (7 Hrs) Features Characteristics of RTOS,. Task Scheduling , Semaphores, Signals, Events, Queues, Mail Boxes. Creation of Threads and Inter Thread Communication. Unit 6: Case Study (7 Hrs) Study of any Two RTOS, eg. VxWorks, RTLinux, Watcom.


Outcomes:

Upon completion of the course, the students will be able to: 1. Use high-level system function units for designing embedded computer systems and be able to verify their performance and use it to modify the design

2. Design an embedded system with functional requirements for hardware and software components including processor, networking components, and sensors, along with applications, subsystem interfaces, networking, and middleware

3. Measure embedded system operating characteristics (for example, latency and reliability) and to determine system performance relative to functional requirements.

Text Books: 1. Raj Kamal ‘Embedded Systems ‘, Tata McGraw-Hill 2. Dr. K. V. K. K. Prasad "Embedded / real time System : Concepts, Design, & Programming -Black Book" Dreamtech Press Publication

Reference Books: 1. Dr. K. V. K. K. Prasad, Gupta Dass, Verma "Programming for Embedded system " Wiley -Dreamtech India Pvt. Ltd. 2. Sriram lyer , Pankaj Gupta,"Embedded Real time Systems Programming", Tata Me Graw Hill. . . 3. Tammy Nergaard "Embedded Systems Architecture - A Comprehensive Guide For Engineering & Programming", Elesevier Publication


CS8258: EMBEDDED SYSTEMS Objectives: 1. To understand engineering issues in the development of embedded software, such as the

importance of addressing the user’s concerns, working with limited resources, maintainability, dependability.

2. To design and implement module structure to solve a problem, and evaluate alternatives and check its correctness

3. To understand fundamentals of Embedded Systems Power Management I/o Interfacing. RTOS and two Real Time OS (RTLinux, VxWorks, Watcom)

List of Practicals

1. Study of PIC Microcontroller Development Kit 2. Generate a frequency == <YOUR ROLL NO.: * 1000> Hz. Using all 3 Timers on PIC

Kit. 3. Establish Serial Communication between PIC KIT and PC. 4. Read Analog Data , Convert to BCD / ASCII and display it on PC. 5. Read PIC Kit to Read input from a switch count number key presses and display on LED

Display. 6. Stepper Motor Controller using PIC KIT. Use switch press to change Direction and

Increase / Decrease Speed. 7. Function timing using the timer. 8. Demonstrate Non Pre-emptive scheduling. 9. Demonstrate Pre-emptive scheduling. 10. Create a Function delay using the Timer. 11. Write a linux Shell Script to display no of readable Writable Files in a Specific Directory. 12. Write a C program use 2 Threads One Thread reads from Keyboard . Second Thread

Displays the Data. Use Message Queue. 13. Write a C program use 2 Threads One Thread reads from Keyboard . Second Thread

Displays the Data. Use Shared Memory.. 14. Write a Producer Consumer Program using Semaphore. 15. Testing the Serial Port using the Watcom RTOS. 16. Semaphores. 17. Message Queues. 18. Priority Inversion. 19. Signals.

Note:1) Assignments 1 -10 to be carried out on PIC Microcontroller Kit 2) Assignment 11onwards to be performed on Linux/RTLinux /VxWorks platform


Outcomes:

Upon completion of the course, the students will be able to: 1. Use high-level system function units for designing embedded computer systems and be able to verify their performance and use it to modify the design

2. Design an embedded system with functional requirements for hardware and software components including processor, networking components, and sensors, along with applications, subsystem interfaces, networking, and middleware

3. Measure embedded system operating characteristics (for example, latency and reliability) and to determine system performance relative to functional requirements.

Text Books:

1. Raj Kamal ‘Embedded Systems ‘, Tata McGraw-Hill 2. Dr. K. V. K. K. Prasad "Embedded / real time System : Concepts, Design, & 3. Programming -Black Book" Dreamtech Press Publication

Reference Books: 1. Dr. K. V. K. K. Prasad, Gupta Dass, Verma "Programming for Embedded system " Wiley -Dreamtech India Pvt. Ltd. 2. Sriram lyer , Pankaj Gupta,"Embedded Real time Systems Programming", Tata Me Graw Hill. . . 3. Tammy Nergaard "Embedded Systems Architecture - A Comprehensive Guide For Engineering & Programming", Elesevier Publication


Elective –V

Open Elective

Theory


CS3278 Research Methodologies in Computer Science Aim: To imbibe research culture in terms of detailed understanding the research process and studying, publishing and presenting scientific papers Objectives:

1. To understand the concept and distinct types of engineering and science research

2. To understand the research process in detail in terms of ideation, concept, design and implementation of research.

3. To develop expertise in studigng, writing and presenting technical documentation such as papers, thesis, etc.

4. To understand and get exposed to different tools and techniques essential for research

Unit1: Basic Concepts of Research (8 Hrs) Overview: RE-Search, Definition, Research need, Research characteristics, Difference between methods and methodology, Research categories: Pure and applied, Conceptual and Empirical research, Descriptive and Analytical, Applied research: Qualitative Vs Quantitative Research, Research methods intro Overview of research process: Formulating the Research Problem, Extensive Literature Review, Developing the objectives, preparing the Research Design including Sample Design, Collecting the Data, Analysis of Data, Generalization and Interpretation, Preparation of the Report, Presentation of Results, Formal write ups of conclusions reached.

Unit 2: Formulating Research Problem and Hypothesis (8 Hrs) How to get new research ideas: Creating thinking, Intelligence Vs Creativity, Gift or Skill, Requirements for creativity, Preparations for improving thinking: Mental exercises, Analogies, Note Keeping, Levels of thinking, Strategies of problem soling Problem statement: Need, What is a research problem, Sources of research problem, Containing research problem components, Problem definition techniques, Research problem demarcation, What can not be a research problem, Problem finding case studies Defining Hypothesis: What is hypothesis, Characteristics of hypothesis, Hypothesis Vs Theory, Hypothesis Vs Problem statement

Unit 3: Literature Review (8 Hrs) Overview: What is literature survey, Need as the main sources of research problem, Functions of literature survey, Types of literature survey, Sources of information Maintaining a notebook: Forming a theoretical framework of the paper by analyzing how the references are slotted in the framework, Contextualized the findings, Developing a Bibliography


Summarizing a Technical Paper: Summary template Searching for Publications: Publication databases, Search Engines and patent databases, Find some/all of the references for a given paper Online tools: Google, CiteSeer, ACM Digital Library, IEEE, The on-line Computer Science bibliography, Searching patents

Unit 4: Research Design, Data Collection, Analysis and Hypothesis testing (9 Hrs) Research Design: What is research design, Research Design Parts, Research Design for exploratory and Descriptive Research, Principals of Research design. Methods of data collection: Data types, Data Collection Types: Observation, Interview, Questionnaire, Schedules Analysis and Processing of Data: Processing operations, Types of Analysis Hypothesis Testing: Null hypothesis, Alternative Hypothesis, Level of significance, Type I and Type II Errors, One tailed and two tailed hypothesis, Power of hypothesis tests

Unit 5: Reading and Publish a Scientific Paper (9Hrs)

Types of technical papers: Journal papers, Conference papers, Survey papers, Poster papers, Review papers Comparison, Structure of a survey, conference and journal paper, when to go for what type of technical paper in the research process How to read a scientific paper: The three pass approach, Comparing the approaches to find the holes How to write scientific paper: Paper Design Process, Readers, Concept Sheet, Embodiment, General advice about writing technical papers in English – Grammar, Punctuation, Tips for writing correct English, How to organize thesis/ Project report Research ethics and Legal issues: Intellectual Property rights, Patents, Copyrights, Plagiarism

Unit 6: Present Scientific Paper and Proposal Writing (6 Hrs) How to write a research proposal, How research is funded, Budgeting How to give a good research talk, Talk structure, Basic presentations skills Documentation and presentation tools: LATEX, Microsoft office, PowerPoint and SLITHY, Adobe Flash, Slide Rocket, Zoho Show


Outcomes: Upon completion of this course, the students will be able to:

1. Perform research in a more organized and efficient manner

2. Choose ideal method to select problem statement, design the research, collect and analyze data and test the hypothesis

3. Efficiently read and summarize technical documentation

4. Systematically write, publish and present technical documentation

5. Understand legal issue regarding research

6. Be aware of and use searching, documenting and presenting tools and technologies

Text Books: 1. ‘Research Methodology: Methods and Trends’, , by Dr. C. R. Kothari, 2nd revised edition,

New Age International Limited Publishers, ISBN: 978-81-224-1522-3

2. ‘Research Methodology: An Introduction’ by Wayne Goddard and Stuart Melville, 2nd Edition, Juta and Co. Ltd, ISBN: 0-70215660-4

Reference Books 1. ‘Research Methodology: A Step by Step Guide for Beginners’, by Ranjit Kumar, 2nd

Edition, APH Publishing Corporation

2. ‘Research methodology: an introduction for science & engineering students’, by Stuart Melville and Wayne Goddard

Additional Reading: 1. ‘Research Methodology’ by Dr. Jayant Tatke, 2009, Symbiosis Centre for Distance

Learning

2. ‘Operational Research’ by Dr. S.D. Sharma, Kedar Nath Ram Nath & Co

3. Online material provided by the faculty


ME ELECTIVE 1 ,2 and 3 (STREAMS) ELECTIVE 1 Digital Signal Processing (CS3058, CS8058) Intelligent Systems (CS3088, CS8088) Internet Routing Algorithms (CS3118, CS8118) OOSE & Design Patterns (CS3148, CS8148) ELECTIVE 2 Image Processing (CS3068, CS8068) Artificial Neural Networks (CS3098, CS8098) Convergence Technologies (CS3128, CS8128) Component Based Development(CS3158, CS8158) ELECTIVE 3 ADSP (CS3078, CS8078) Machine Learning (CS3108, CS8108) Infrastructure Management (CS3138, CS8138) .Enterprise Software Development(CS3268, CS8168) ELECTIVE 4 Compiler Design (CS3178, CS8178) Mobile Computing (CS3188, CS8188) Analysis of Algorithms (CS3198, CS8198) Data Mining and Warehousing (CS3208, CS8208) Information Theory and Coding (CS3218, CS8218) VLSI Design. (CS3228, CS8228) Software Quality and Reliability (CS3238, CS8238) User Interface Design (CS3248, CS8248) Embedded System (CS3258, CS8258) ELECTIVE 5 (OPEN ELECTIVE) Communication Engineering (CS3168) Research Methodologies in Computer Science (CS3278)

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ME_CSEIT_2011