Computer science and information systems

MAHATMA GANDHI UNIVERSITY

SCHEME AND SYLLABI FOR M. Tech. DEGREE PROGRAMME IN COMPUTER SCIENCE AND ENGINEERING WITH SPECIALIZATION IN INFORMATION SYSTEMS
(2011 ADMISSION ONWARDS)

SCHEME AND SYLLABI FOR M. Tech. DEGREE PROGRAMME IN COMPUTER SCIENCE AND ENGINEERING WITH SPECIALIZATION IN INFORMATION SYSTEMS SEMESTER - I
Hrs / Week Sl. No. Course No. Subject L T P TA 1 2 3 MCS* 101 MCS* 102 MCS* 103 Mathematical Foundations For Computer Science Distributed Operating Systems Advanced Data Structures and Algorithms Parallel Computer Architecture Elective - I Elective-II Operating Systems Lab Seminar-I Total 3 3 3 1 0 1 1 0 3 3 3 18 1 0 5 6 7 8 MCSIS 105 MCSIS 106 MCS* 107 MCSIS 108 0 0 0 0 4 0 0 3 2 5 25 25 25 25 50 25 25 25 25 50 50 50 50 50 400 100 100 100 100 0 700 150 150 150 150 50 1100 4 3 3 2 1 25 25 25 50 100 150 4 0 25 25 25 25 50 50 100 100 150 150 4 4 CT Evaluation Scheme (Marks) Sessional Sub Total ESE Total Credits (C)

4

MCS* 104

Elective – I (MCSIS 105) MCSIS 105-1 MCSIS 105-2 MCSIS 105-3 MCSIS 105-4 Natural Language Processing Bio Informatics Automata Theory Information Theory And Coding MCS* 106-1 MCSIS 106-2 MCSIS 106-3 MCSIS 106-4

Elective – II (MCSIS 106) Data Warehousing and Data Mining Software Structure and UML Software Quality Assurance Software Project Management

L – Lecture, T – Tutorial, P – Practical TA – Teacher’s Assessment (Assignments, attendance, group discussion, Quiz, tutorials, seminars, etc.) CT – Class Test (Minimum of two tests to be conducted by the Institute) ESE – End Semester Examination to be conducted by the University MCS* – Subjects common for Computer Science specializations, Computer Science and Engineering / Computer Science and Information Systems Electives: New Electives may be added by the department according to the needs of emerging fields of technology. The name of the elective and its syllabus should be submitted to the University before the course is offered. Seminar: Students may select a topic for their seminar preferably in the same area as that of their project

1

SEMESTER - II
Hrs / Week Sl. No. Course No. Subject L T P TA 1 MCS* 201 Modern Computer Networks 3 1 0 2 MCS* 202 Advanced Database Systems Computer Security and Applied Cryptography Compiler Design Elective-III Elective-IV Network Simulation Lab Seminar-II Total 3 1 0 3 4 5 6 7 8 MCS* 203 MCS* 204 MCSIS 205 MCSIS 206 MCS* 207 MCSIS 208 3 3 3 3 18 1 0 1 0 0 4 0 0 0 3 2 5 25 25 25 25 25 50 25 25 25 25 25 50 50 50 50 50 50 400 100 100 100 100 100 0 700 150 150 150 150 150 50 1100 4 4 3 3 2 1 25 25 25 50 100 150 4 25 25 50 100 150 4 CT Evaluation Scheme (Marks) Sessional Sub Total ESE Total Credits (C)

Elective – III (MCSIS 205) MCS* 205-1 Neural Networks MCSIS 206-1 MCSIS 206-2 MCSIS 206-3 MCSIS 206-4

Elective – IV (MCSIS 206) Pattern Recognition Software Architecture Image Processing Fault Tolerant Systems

MCSIS 205-2 Genetic Algorithms and Applications MCSIS 205-3 Agent based Intelligent Systems MCSIS 205-4 Fuzzy Logic

L – Lecture, T – Tutorial, P – Practical TA – Teacher’s Assessment (Assignments, attendance, group discussion, Quiz, tutorials, seminars, etc.) CT ESE – – Class Test (Minimum of two tests to be conducted by the Institute) End Semester Examination to be conducted by the University New Electives may be added by the department according to the needs of emerging fields of technology. The name of the elective and its syllabus should be submitted to the University before the course is offered.

Electives:

2

SEMESTER - III
Hrs / Week Sl. No. Course No. Subject L Industrial Training and Mini project Thesis – Phase I Total T P TA* 1 2 MCSIS 301 MCSIS 302 0 0 0 0 20 10 30 50 100*** CT 0 0 Evaluation Scheme (Marks) Sessional Sub Total 50 100 150 ESE** Total (Oral) 100 0 100 150 100 250 Credits (C)

10 5 15

*

50% of the marks to be awarded by the Industrial Training and mini project guide and the remaining 50% to be awarded by a panel of examiners, including project guide, constituted by the department.

**

Industrial Training and Mini project evaluation will be conducted at end of the third semester by a panel of examiners, with at least one external examiner, constituted by the university.

***

The marks will be awarded by a panel of examiners constituted by the concerned institute.

Note:   Students may choose their mini project preferably in the same area as that of their project. Students are encouraged to publish their thesis work in National and International journals and conferences. This may have an additional weightage in the evaluation process.



Any paper ready for publication, the students should discuss with the guide and take necessary action to publish the paper along with the guide in due course of the semester.

3

SEMESTER - IV
Hrs / Week Sl. No. Course No. Subject L T P TA* 1 2 MCSIS 401 MCSIS 402 Thesis Evaluation Master’s Comprehensive Viva Total Grand Total of all Semesters 0 0 30 100 CT 0 Evaluation Scheme (Marks) Sessional Credits ESE** (C) (Oral Total & Sub Total Viva) 100 100 100 200 100 300 2750 15 80 15

*

50% of the marks to be awarded by the Project Guide and the remaining 50% to be awarded by a panel of examiners, including the Project Guide, constituted by the Department

** Thesis evaluation and Viva-voce will be conducted at the end of the fourth semester by a panel of examiners, with at least one external examiner, constituted by the University.

4

MCS* 101

MATHEMATICAL FOUNDATIONS FOR COMPUTER SCIENCE

L T P C 3 1 0 4

Module 1: Fuzzy Mathematics Crisp sets and Fuzzy sets-, α-cuts, Convex fuzzy sets, Fuzzy cardinality, Algebra of fuzzy sets, Standard fuzzy set operations-(complement, union and intersection), Yager and Sugeno classes. Crisp relations and Fuzzy relations, Operations on Fuzzy relations. Fuzzy Cartesian product. Fuzzy Equivalence relations and similarity relations.

Module 2: Fuzzy Logic Fuzzy logic, Fuzzy tautologies and contradictions, Equivalence and implication operators (Classical implication, Mamdani implication, Kleene-Dienes implication and Lukasiewicz implication). Composition operators, Fuzzy quantifiers and predicates, approximate reasoning. Fuzzification and Defuzzification techniques.

Module 3: Stochastic Process Random variables, Functions of random variables, Sequence of random variables, stochastic processes, Markov chains, Markov processes and queuing theory.

Module 4: Queuing Models Queuing Theory-General concepts, Arrival pattern, service pattern, Queue Disciplines – Markovian Queues, Single and Multi-server models. The Markovian model M/M/1-steady state solutions-Little’s formula

References: 1. R. P. Grimaldi, "Discrete and Combinatorial Mathematics: An Applied Introduction",
Addison Wesley, 1994. 2. George J Clir and Tina A Foldger “Fuzzy sets –Uncertainty and Information” Prentice Hall of India,1988. 3. George J Klir and Bo Yuan, ”Fuzzy sets and Fuzzy logic” Prentice-Hall of India,1995. 4. Timothy J. Ross, “Fuzzy logic with Engineering applications”- Wiley-India. 5. Robertazzi T.G,”Computer Networks and systems-Queuing Theory and Performance Evaluation”-Springer third edition. 6. Ross S.M., “Probability Models for Computer Science”-Academic Press, 2002.

5

MCS* 102

DISTRIBUTED OPERATING SYSTEMS

L T P C 3 1 0 4

Module 1: Distributed computing systems fundamentals Introduction to Distributed computing systems, Models, Popularity. Distributed Computing system. Design issues of Distributed operating system. Distributed computing environment.

Module 2: Message Passing Features of a good Message Passing System. Issues in IPC by Message Passing Synchronization, Buffering, Multi-datagram Messages, Encoding and Decoding Message data, Process Addressing, Failure Handling, Group Communication.RPC Model, Transparency of RPC, RPC messages, Marshaling Arguments and Results. Server Management, Parameter Passing semantics, call semantics, Communication Protocols for RPCs, Client Server Building, Exception handling, Security ,RPC in Heterogeneous Environments, Lightweight RPC.

Module 3: Distributed Shared Memory General architecture of DSM systems. Design and implementation Issues of DSM, Granularity, Structure of Shared Memory Space. Consistency models, Replacement strategy, Thrashing. Synchronization: Clock Synchronization. Event Ordering, Mutual Exclusion, Deadlock, Election Algorithms

Module 4: Resource Management Features of global scheduling algorithm. Task assignment approach, Load-Balancing and Load approach. Process Management: Introduction, Process Migration, Threads. Distributed File Systems: Features of good DFS, File models, File Accessing models.

References: 1. Pradeep Sinha K., “Distributed Operating Systems Concepts and Design”, PHI Learning Private Ltd. 2. Mukesh Singhal, Niranjan G Shivarathri, “Advanced Concepts in Operating Systems”, Tata Mc-Graw Hill Ltd. 3. Coulouris.G, Dollimore J & Kindberg T, “Distributed Systems concepts and design”, 4th edition, Pearson Education. 4. Tanenbaum A S, “ Modern Operating System”, PHI learning private limited, 3rd edition. 6

MCS* 103

ADVANCED DATA STRUCTURES AND ALGORITHMS

L T P 3 1 0

C 4

Module 1 Amortized Complexity Analysis. Advanced Structures for Dictionary ADT: Red-Black Trees, Splay Trees. Multidimensional Search Trees: k-d Trees, Point Quadtrees. Advanced Structures for Priority Queues: Leftist Trees, Binomial Heaps, Symmetric Min-Max Heaps.

Module 2 Searches in Graphs: DFS, BFS, Connected Components, Bi-connected Components. Activity on Vertex and Activity on Edge Networks. Maximum Flows, Bipartite Matching.

Module 3 Solution of recurrence equations: Substitution Method, Recursion Tree, and Master Method. Divide and Conquer: Selection, Convex Hull, Maximum-subarray problem. Greedy Methods: Container Loading, Continuous Knapsack Problem. Dynamic Programming: 0/1 Knapsack, Traveling Salesperson Problem, Flow Shop Scheduling.

Module 4 Approximation Algorithms: Vertex-Cover Problem, Traveling-Salesman Problem, Set-Covering Problem, Subset-Sum Problem. Introduction to Probabilistic Analysis and Randomized Algorithms.

References: 1. E. Horowitz, S. Sahni, and D. Mehta, “Fundamentals of Data Structures in C++”, Second Edition, University Press, 2007. 2. E. Horowitz, S. Sahni, and S. Rajasekharan, “Fundamentals of Computer Alllgorithms”, Second Edition, University Press, 2007. 3. T. H. Cormen, C. E. Leiserson, R. Rivest, and C Stein,” Introduction to Algorithms”, Third Edition, Prentice Hall of India, 2009. 4. V. S. Subrahmanian, Morgan Kaufman, “Principles of Multimedia Database Systems” 1998. 5. S. Baase, and A. V. Gelder, “Computer Algorithms – Introduction to Design and Analysis”, Third Edition, Pearson Education, 2000. 7

MCS* 104

PARALLEL COMPUTER ARCHITECTURE

L T P C 3 1 0 4

Module 1: Introduction Basics of Computer Design & Performance Evaluation:-Defining Computer Architecture, Dependability, Quantitative Principles of Computer Design, CPU Performance & its factors, SPEC Benchmarks. Computational model:- Basic computational models, von-Neumann Computation Model.

Module 2: Instruction level Parallelisms and Pipelining Instruction level Parallelisms: ILP concepts, Dependencies between instructions, Preserving sequential consistency-ROB, Limitations of ILP. Pipelining: Introduction to pipelining, Instruction pipeline design, Pipeline hazards.

Module 3: Superscalar Processors Introduction, Parallel decoding, Superscalar instruction issue, Shelving, Register Renaming, Case Study- Pentium Pro, Power PC 620.

Module 4: The Memory System Memory hierarchy, Cache Coherence, Memory Consistency, Cache Performance Issues, Shared Memory Organization. Distributed Systems: Parallel Virtual Machine, Architecture of PVM, Programming model of PVM. Case Study-Intel Duo Core Architecture

References: 1. John L.Hennessy and David A.Patterson, “Computer Architecture-A Quantitative Approach” 4th Edition. 2. John L.Hennessy and David A.Patterson, ”Computer Architecture-Hardware & Software Approach”. 3. Sima, Fauntain, Kscucle, “Advanced Computer Architecture a design space approach.” Pearson Edition. 4. Kai Hwang, “Advanced Computer Architecture”. 5. David Culler and J. Palsingh, Morgan Kaufmann, ” Parallel Computer Architecture”. 6. M.Sasikumar, Dinesh Shikhare, P. Ravi Prakash “Introduction to Parallel Processing” PHI. 7. Salim Hariri, Manesh Parashar, “Tools & Environments for Parallel and Distributed 8

Computing”, A John Wiley & Sons INC., Publication. 8. http://www.intel.com/technology/itj/2006/volume10issue02/art01_Intro_to_Core_Duo/ p02_intro.htm

9

MCSIS 105-1

NATURAL LANGUAGE PROCESSING

L T P C 3 0 0 3

Module 1: Introduction Knowledge in speech and language processing - Ambiguity - Models and Algorithms - Language, Thought and Understanding. Regular Expressions and automata: Regular expressions - FiniteState automata. Morphology and Finite-State Transducers: Survey of English morphology Finite-State Morphological parsing - Combining FST lexicon and rules - Lexicon-Free FSTs: The porter stammer - Human morphological processing

Module 2: Syntax Word classes and part-of-speech tagging: English word classes - Tagsets for English - Part-ofspeech tagging - Rule-based part-of-speech tagging - Stochastic part-of-speech tagging Transformation-based tagging - Other issues. Context-Free Grammars for English: Constituency Context-Free rules and trees - Sentence-level constructions - The noun phrase - Coordination Agreement - The verb phase and sub categorization - Auxiliaries - Spoken language syntax Grammars equivalence and normal form - Finite-State and Context-Free grammars - Grammars and human processing. Parsing with Context-Free Grammars: Parsing as search - A Basic TopDown parser - Problems with the basic Top-Down parser - The early algorithm - Finite-State parsing methods.

Module 3: Advanced Features and Syntax Features and Unification: Feature structures - Unification of feature structures - Features structures in the grammar - Implementing unification - Parsing with unification constraints Types and Inheritance. Lexicalized and Probabilistic Parsing: Probabilistic context-free grammar - problems with PCFGs - Probabilistic lexicalized CFGs - Dependency Grammars - Human parsing.

Module 4: Semantic Representing Meaning Computational desiderata for representations - Meaning structure of language - First order predicate calculus - Some linguistically relevant concepts - Related representational approaches Alternative approaches to meaning. Semantic Analysis: Syntax-Driven semantic analysis 10

Attachments for a fragment of English - Integrating semantic analysis into the early parser Idioms and compositionality - Robust semantic analysis. Lexical semantics: relational among lexemes and their senses - WordNet: A database of lexical relations - The Internal structure of words - Creativity and the lexicon. Application: Word sense Disambiguation.

References: 1. Daniel Jurafsky & James H.Martin, “Speech and Language Processing”, Pearson Education (Singapore) Pte. Ltd., 2002. 2. James Allen, “Natural Language Understanding”, Pearson Education, 2003. 3. Gerald J. Kowalski and Mark.T. Maybury, “Information Storage and Retrieval Systems”, Kluwer academic Publishers, 2000. 4. Tomek Strzalkowski “Natural Language Information Retrieval“, Kluwer academic Publishers, 1999. 5. Christopher D. Manning and Hinrich Schutze, “Foundations of Statistical Natural Language Processing “, MIT Press, 1999.

11

MCSIS 105-2

BIOINFORMATICS

L 3

T P C 0 0 3

Module 1: Fundamentals of Biological Systems Introduction to cells: Structure of prokaryotic and eukaryotic cells. Cell organelles and their functions. Molecules of life: Introduction to carbohydrates, proteins, lipids and nucleic acids – Different structural forms and functional organizations. DNA replication, transcription and translation. Gene regulation.

Module 2: Sequence Analysis Introduction to Sequence alignment, Substitution matrices, Scoring matrices –PAM and BLOSUM. Local and Global alignment concepts, dot plot, dynamic programming methodology, Multiple sequence alignment –Progressive alignment. Database searches for homologous sequences – FASTA and BLAST versions.

Module 3: Genomics Functional Genomics: Gene expression analysis by cDNA micro arrays, SAGE, Strategies for generating ESTs and full length inserts; EST clustering and assembly; EST databases- DBEST, UNIGENE. Gene/Protein function prediction using Machine learning tools: supervised / unsupervised learning, Neural network, SVM.

Module 4: Proteomics Protein and RNA structure prediction, secondary and tertiary structure, polypeptic composition, computational methods for identification of polypeptides from mass spectrometry, algorithms for modeling protein folding, protein classification. Protein-Protein Interaction: Experimental identification of protein-protein interactions, PPI databases: STRINGS, DIP, PPI server. Protein-protein quaternary structure modeling- Proteinprotein docking algorithms, Homology modeling, Monte Carlo docking simulation.

References: 1. David W. Mount “Bioinformatics Sequence and Genome Analysis”, Cold Spring Harbor laboratory Press, 2001. 12

2. C.

Rastogi,

Namita

Mendiratta,

Parag Rastogi.

”Bioinformatics-Concepts,

Skills,

Applications”. 3. Andreqas D. Baxevanis, B. F. Francis Ouellette., "Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins “, John Wiley and Sons, New York 1998. 4. Andrew, R. Leach, “Molecular modelling: Principles and applications”, Prentice Hall Publications. 5. Richard Durbin, S. Eddy, A. Krogh, G. Mitchison,”Biological Sequence Analysis: Probabilistic models of protein and Nucleic acids” Cambridge University Press 2007. 6. Thomas E. Creighton, “Proteins: structures and molecular properties”.

13

MCSIS 105-3

AUTOMATA THEORY

L T P C 3 0 0 3

Module 1: Finite automata and Regular Languages States and Automata - Finite Automata as Language Acceptors - Determinism and Nondeterminism - Checking vs. Computing - Properties of Finite Automata - Equivalence of Finite Automata Epsilon Transitions - Regular Expressions and Finite Automata - Reviewing the Construction of Regular Expressions from Finite Automata.

Module 2: Universal Models of Computation Encoding Instances - Choosing a Model of Computation - Issues of Computability - The Turing Machine - Multitape Turing Machines - The Register Machine - Translation Between Models Computability Theory - Primitive Recursive Functions -Defining Primitive Recursive Functions Partial Recursive Functions - Rice's Theorem and the Recursion Theorem - Degrees of Unsolvability

Module 3: Complexity Theory Classes of Complexity - Hierarchy Theorems - Model-Independent Complexity Classes Deterministic Complexity Classes - Certificates and non-determinism - Complete Problems. NPCompleteness: Cook's Theorem - Space Completeness - Polynomial Space – Poly-logarithmic Space - Provably Intractable Problems

Module 4: Complexity Theory in Practice Circumscribing Hard Problems - Restrictions of Hard Problems - Promise Problems - Strong NPCompleteness - The Complexity of Approximation Definitions - Constant-Distance

Approximations - Approximation Schemes - Fixed-Ratio Approximations and the Class OptNP The Power of Randomization.

References: 1. Bernard Moret, “The Theory of Computation”, AW, 1998. 2. John E Hopcroft, “Introduction to Automata Theory, Languages and Computation”, AW, 2001. 14

3. J. Glenn Brookshear, “Theory of Computation, Complexity”, AW.

Formal Languages, Automata and

4. John Savage, “Models of Computation, Exploring the power of Computing”.

15

MCSIS 105-4

INFORMATION THEORY AND CODING

L T P 3 0 0

C 3

Module 1: Introduction Information – Entropy, Information rate, classification of codes, Kraft McMillan inequality, Source coding theorem, Shannon-Fano coding, Huffman coding, Extended Huffman coding Joint and conditional entropies, Mutual information - Discrete memory-less channels – BSC, BEC – Channel capacity, Shannon limit.

Module 2: Definitions and Principles Hamming weight, Hamming distance, Minimum distance decoding - Single parity codes, Hamming codes, Repetition codes - Linear block codes

Module 3 : Cyclic codes Cyclic codes - Syndrome calculation, Encoder and decoder - CRC, Convolutional codes – code tree, trellis, state diagram.

Module 4: Encoding and Decoding Sequential search and Viterbi algorithm – Principle of Turbo coding - Adaptive Huffman Coding, Arithmetic Coding

References: 1. K Sayood, “Introduction to Data Compression” , Elsevier 2006. 2. S Gravano, “Introduction to Error Control Codes”, Oxford University Press 2007. 3. Amitabha Bhattacharya, “Digital Communication”, TMH 2006. 4. R Bose, “Information Theory, Coding and Cryptography”, TMH 2007. 5. Fred Halsall, “Multimedia Communications: Applications, Networks, Protocols and Standards”, Pearson Education Asia, 2002.

16

MCS* 106-1

DATA WAREHOUSING AND DATA MINING

L T P C 3 0 0 3

Module 1: Data Warehousing and Business Analysis Data warehousing Components –Building a Data warehouse – Mapping the Data Warehouse to a Multiprocessor Architecture – DBMS Schemas for Decision Support – Data Extraction, Cleanup, and Transformation Tools – Metadata – Reporting, Query tools and Applications – Online Analytical Processing (OLAP)

Module 2: Data Mining Introduction - Data Preprocessing – Data Cleaning – Data Integration and Transformation – Data Reduction – Data Discretization and Concept Hierarchy Generation. Association Rule Mining: Efficient and Scalable Frequent Item set Mining Methods – Mining Various Kinds of Association Rules – Association Mining to Correlation Analysis – Constraint-Based Association Mining.

Module 3: Classification and Prediction Issues Regarding Classification and Prediction –Classification by Decision Tree Introduction – Bayesian Classification – Rule Based Classification – Classification by Back propagation – Support Vector Machines – Associative Classification – Lazy Learners – Other Classification Methods – Prediction – Accuracy and Error Measures – Evaluating the Accuracy of a Classifier or Predictor – Ensemble Methods – Model Section

Module 4: Cluster Analysis and Applications and Trends in Data Mining Types of Data in Cluster Analysis – A Categorization of Major Clustering Methods – Partitioning Methods – Hierarchical methods – Density-Based Methods – Grid-Based Methods – ModelBased Clustering Methods – Clustering High-Dimensional Data – Constraint-Based Cluster Analysis - Data Mining Applications – Trends in Data Mining

References: 1. Jiawei Han and Micheline Kamber “Data Mining Concepts and Techniques” Second Edition, Elsevier, Reprinted 2008.

17

2. Alex Berson and Stephen J. Smith “Data Warehousing, Data Mining & OLAP”, Tata McGraw – Hill Edition, Tenth Reprint 2007. 3 K.P. Soman, Shyam Diwakar and V. Ajay “Insight into Data mining Theory and Practice”, Easter Economy Edition, Prentice Hall of India, 2006. 4. G. K. Gupta “Introduction to Data Mining with Case Studies”, Easter Economy Edition, Prentice Hall of India, 2006. 5. Pang-Ning Tan, Michael Steinbach and Vipin Kumar “Introduction to Data Mining”, Pearson Education, 2007.

18

MCSIS 106-2

SOFTWARE STRUCTURES AND UML

L 3

T P C 0 0 3

Module 1: Object Oriented Design and Modeling Object Oriented Fundamentals, importance of modeling, principles of modeling, object oriented modeling. Introduction to UML: Conceptual model of the UML, building blocks of UML, common mechanisms in UML, architecture, Software Development Life Cycle.

Module 2: Structural Modeling Basic Structural Modeling: Classes, relationships, common mechanisms, class diagrams. Advanced Structural Modeling: Advanced classes, advanced relationships, interfaces types and roles, packages, instances, object diagrams and components.

Module 3: Basic Behavioral Modeling Interactions, Interaction Diagrams. Terms, concepts and depicting a message in collaboration diagrams. Terms and concepts in sequence diagrams. Difference between collaboration and sequence diagram. Asynchronous messages and concurrent execution. Use cases, Use Case Diagrams and activity diagrams. Advanced Behavioral Modeling: Events and signals, state machines, process and threads, time and space, state diagrams.

Module 4: Architectural Modeling Artifacts, artifact diagrams, deployment and deployment diagrams, Patterns and Frameworks.

References: 1. Grady Booch, James Rumbaugh, Ivar Jacobson., ‘The Unified Modeling Language User Guide”, Second Edition, Pearson Education 2005. 2. Hans-Erik Eriksson, Magnus Penker, Brian Lyons, David Fado, “UML 2 Toolkit”, WILEY-Dreamtech India Pvt. Ltd. 3. Meilir Page-Jones, “ Fundamentals of Object Oriented Design in UML”, Addison Wesley, 2000. 4. Pascal Roques, ”Modeling Software Systems Using UML2”, WILEY-Dreamtech India Pvt. Ltd. 19

MCSIS 106-3

SOFTWARE QUALITY ASSURANCE

L T P C 3 0 0 3

Module 1: Basic Concepts Definitions of Quality, Software Quality Factors, Software Quality Metrics, Importance of Quality, Quality Control vs. Quality Assurance, Cost of Quality, Software Errors, Faults and Failures, Causes of Software Errors, Defect Density and Defect Removal Efficiency, Overview of SDLC Process Models, Quality Management System (QMS), Total Quality Management (TQM), QMS vs. TQM.

Module 2: Software Quality Assurance SQA Function – Objectives and Responsibilities, Verification & Validation, SQA Planning, SQA Activities - Reviews, Walkthrough, Inspection, Testing and Audits, Software Testing: Levels of Testing, Types of Testing, Test Case Design, Equivalence Partitioning, Boundary Value Analysis. Quality Control Tools – Check Sheet, Stratification, Pareto Diagram, Brain Storming, Cause and Effect Diagram, Histogram, Scatter Diagram, Control Charts, Failure Mode Effect Analysis (FMEA), Quality Function Deployment (QFD), Statistical Process Control

Module 3: Quality Standards, Certification and Assessment QMS Standard ISO 9001:2008, SEI CMMI, P-CMMI, SCAMPI Assessment Method, P-CMMI, SPICE, Six Sigma, Zero Defects, National Quality Awards – MBNQA, Rajiv Gandhi National Quality Award

Module 4: Other Topics Agile Methodology – Scrum, Kanban in Software Development, Personal Software Process (PSP), Team Software Process (TSP), Clean Room Software Engineering, CASE Tools & their effect on Software Quality, Software Reliability Metrics and Models, Software Configuration Management & CM Audits, Risk Management

References: 1. Galin Daniel, “Software Quality Assurance: From theory to implementation”, Pearson Education Ltd, 2004. ISBN 978-81-317-2395-1. 20

2. Kan Stephen H, “Metrics and Models in Software Quality Engineering”, Second Edition. Pearson Education Inc., 2003. ISBN 0-201-72915-6. 3. Pankaj Jalote, “An Integrated Approach to Software Engineering”. 4. Pankaj Jalote, “Software Project Management in Practice”, Pearson Education Ltd., 2005, ISBN 81-7808-664-6. 5. Nina S. Godbole, “Software Quality Assurance: Principles and Practices”, Narosa Publishing.

21

MCSIS 106-4

SOFTWARE PROJECT MANAGEMENT

L T P C 3 0 0 3

Module 1: Introduction Projects and Project Characteristics, Project Constraints, Software Projects vs. Other Projects, Problems with Software Projects, Software Project Failures & Major Reasons, What is Project Management?, Need for Software Project Management, Project Management Framework – Project Stakeholders, PM Competencies, Project Environment, Project Organisation Types, Project Management Life Cycle, Business Case, Cost Benefit Analysis, Project Charter.

Module 2: Project Planning Basic Objectives, Key Planning Tasks, Scope Definition, Work Breakdown Structure (WBS), Activity Planning, Activity Sequencing, Activity Duration Estimation, Network Models – PDM, CPM, Identifying Critical Path, Resource Assignment, Gantt Chart, Project Plan Development, Other Plans – SQA Plan, Test Plan, Risk Management Plan, Configuration Management Plan, Resource Plan, Communication Plan, Contents of a Typical Software Project Plan, Project Monitoring and Control, Project Tracking using Earned Value Analysis, Tracking Gantt, Project Scheduling and Tracking using MS Project.

Module 3: Software Effort Estimation Need for Software Estimation, Problems with under and over Estimates, Software Estimation Process, Overview of Software Estimation Techniques – Analogous Estimation, Expert Judgment based Estimation, WBS based Estimation, Function Point Analysis, Use Case Points, Effort and Schedule Estimation using COCOMO.

Module 4: Other Topics Project Risk Management – Risk Identification, Top 10 Software Project Risks, Risk Analysis and Prioritization, Risk Response Planning, Risk Resolution, Risk Tracking and Control, Software Configuration Management – Software Configuration Items (SCI), Change Control, Version Control, Agile Project Management using Scrum.

22

References: 1. Bob Huges & Mike Cotterell, “Software Project Management”, Tata McGraw Hill, New Delhi, 2002. 2. Pankaj Jalote, “Software Project Management in Practice”, Pearson Education Ltd, 2005. 3. Gopalaswamy Ramesh, “Managing Global Software Projects”, Tata McGraw Hill, New Delhi, 2006. 4. Roger S Pressman, “Software Engineering: A Practitioner’s Approach”, Tata McGraw Hill, New Delhi, 2001. 5. Pankaj Jalote, “An Integrated Approach to Software Engineering”.

23

MCS* 107

OPERATING SYSTEMS LAB

L T P C 0 0 3 2

List of Experiments: 1. Introduction to Linux-booting-login-simple commands. 2. Wild card characters-grep-pipe-tee-command substitution-shell variables-subshells-filtershead, tail, cut, paste, sort, uniq, nl, join. 3. Editors-Vi and Emacs. 4. Communication commands-mail, talk, write, cron… 5. Process related commands - ps, kill, nohup, nice, time, archiving, tar-gzip-rpm. 6. Shell Programming Commands -Shell variables, read, echo, command line arguments, &&, !!, if, while, case, for, until, test, set, shift, trap. 7. Implement the following: Dining philosopher Problem, Producer Consumer problem, Binary Search Implementation using shell scripting, quick sort implementation using shell scripting, Message queue, Kernel compilation, System call implementation. 8. System Administration-Booting, init, runlevel. 9. Setting up servers-DHCP, DNS, NFS, Apache, Samba. 10. Programming in php environment-Installing and configuring apache and mysql for php demo. Php syntax-variables-data types-functions-if..else, switch, for loop, while loop, do while, arrays, getting displaying manipulating form values. My sql basics-connecting mysql with php-inserting & retrieving table data using php. 11. Introduction to PERL programming.

24

MCSIS 108

SEMINAR – I

L T P C 0 0 2 1

Each student shall present a seminar on any topic of interest related to the core / elective courses offered in the first semester of the M. Tech. Programme. He / she shall select the topic based on the References: from international journals of repute, preferably IEEE journals. They should get the paper approved by the Programme Co-ordinator / Faculty member in charge of the seminar and shall present it in the class. Every student shall participate in the seminar. The students should undertake a detailed study on the topic and submit a report at the end of the semester. Marks will be awarded based on the topic, presentation, participation in the seminar and the report submitted.

25

MCS* 201

MODERN COMPUTER NETWORKS

L T P C 3 1 0 4

Module 1: Physical Layer and Data link layer Physical Layer: Data Transmission- Analog and Digital Transmission, Transmission Impairments, Channel Capacity. Transmission MediaWired Transmission, Wireless

Transmission, Wireless Propagation, Line-of Sight Transmission, Signal Encoding Techniques. Data link layer: TCP/IP Protocol Architecture, Framing, Reliable Transmission, Ethernet (802.3) and Token Ring (802.5).

Module 2: Network Layer Connecting Devices. ARP, RARP. IP Address – Sub netting / Super netting, Packet Forwarding with Classful / Classless Addressing, Datagram Fragmentation, Components in IP software, Private IP and NAT. ICMP. Routing Protocols -Distance Vector Routing-RIP, Link-State Routing-OSPF

Module 3: Transport Layer UDP- Port Addressing, UDP datagram, UDP operation. TCP- TCP services and features, TCP segment, TCP connection, TCP state transitions, TCP module’s algorithm, Flow and Error control, Congestion control. SCTP- SCTP services and features, Packet format, SCTP connection, State Transitions, Flow and Error control.

Module 4: Application Layer DNS- Distribution of Name Space, Name Resolution, DNS messages, HTTP- Architecture, HTTP Transaction, DHCP - Address allocation, Packet format. SNMP- SMI, MIB, SNMP PDUs, Real Time Data Transfer- RTP, RTCP, Voice over IP-Session Initiation Protocol.

References: 1. William Stallings, “Data and Computer Communications”, Pearson Education. 2. Behrouz A Forouzan, “TCP/IP Protocol Suite”, Tata McGraw-Hill. 3. Peterson and Davie, “Computer Networks -A systems approach”, Elsevier. 4. Kurose and Ross, “Computer Networks A systems approach”, Pearson Education. 5. Behurouz A Forouzan, “Data Communications & Networking”, 4th edition, McGraw-Hill. 26

MCS* 202

ADVANCED DATABASE SYSTEMS

L 3

T P 1 0

C 4

Module1: Parallel and Distributed Databases Database System Architectures: Centralized and Client-Server Architectures – Server System Architectures – Parallel Systems- Distributed Systems – Parallel Databases: I/O Parallelism – Inter and Intra Query Parallelism – Inter and Intra operation Parallelism – Distributed Database Concepts - Distributed Data Storage – Distributed Transactions – Commit Protocols – Concurrency Control – Three Tier Client Server Architecture- Case Studies.

Module 2: Object and Object relational databases Concepts for Object Databases: Object Identity – Object structure – Type Constructors – Encapsulation of Operations – Methods – Persistence – Type and Class Hierarchies – Inheritance – Complex Objects – Object Database Standards, Languages and Design: ODMG Model – ODL – OQL – Object Relational and Extended – Relational Systems: Object Relational features in SQL / Oracle – Case Studies.

Module 3: Enhanced Data models Active Database Concepts and Triggers – Temporal Databases – Spatial Databases – Multimedia Databases – Deductive Databases – XML Databases: XML Data Model – DTD - XML Schema XML Querying - Geographic Information Systems - Genome Data Management.

Module 4: Emerging Technologies Mobile Databases: Location and Handoff Management - Effect of Mobility on Data Management - Location Dependent Data Distribution - Mobile Database Systems - Transaction Execution in MDS- Mobile Transaction Models –Concurrency Control Mechanism- Transaction Commit Protocols- Mobile database Recovery : Log management in mobile database systems – Mobile database recovery schemes

References: 1. R. Elmasri, S.B. Navathe, “Fundamentals of Database Systems”, Fifth Edition, Pearson Education/Addison Wesley, 2007.

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2. Thomas Cannolly and Carolyn Begg, “Database Systems, A Practical Approach to Design, Implementation and Management”, Third Edition, Pearson Education, 2007. 3. Vijay Kumar,” Mobile Database Systems”, A John Wiley & Sons, Inc., Publication. 4. Henry F Korth, Abraham Silberschatz, S. Sudharshan, “Database System Concepts”, Fifth Edition, McGraw Hill, 2006. 5. C.J. Date, A.Kannan and S.Swamynathan,”An Introduction to Database Systems”, Eighth Edition, Pearson Education, 2006. 6. Raghu Ramakrishnan, Johannes Gehrke, “Database Management Systems”, McGraw Hill, Third Edition 2004.

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MCS* 203

COMPUTER SECURITY AND APPLIED CRYPTOGRAPHY

L T P C 3 1 0 4

Module 1: Introduction to cryptography Concepts, approaches and principles of digital information security, types of attacks, model, cryptographic techniques – substitution and transposition techniques, steganography techniques.

Module 2 Introduction to Number Theory, Elliptic curve arithmetic. Symmetric Key cryptography: Block cipher design principles and criteria, DES, IDEA, AES, RCS, Blowfish, Differential and linear cryptanalysis. Asymmetric key cryptography: Principles of public key crypto systems, RSA algorithm, key management, Diffie-Hellman key exchange, elliptic curve cryptography.

Module 3: Message Authentication and Hash functions Authentication functions, message authentication codes, Hash functions and their security, MD5 , secure hash algorithms, HMAC. Digital signatures and authentication protocols, Digital Signature standards, Kerberos, X.509 authentication service, PGP and S/MIME.

Module 4 Network Security: Introduction, IP Security-Overview, Architecture, AH, ESP, Combining Security Associations, Key Management. System Security- Intrusion Detection, Password Management, Viruses and related threats, Virus Counter measures, Firewalls-Design Principles, Trusted Systems, Web Security:- Web Security consideration, Secure Socket Layer, Transport Layer Security, Secure Electronic Transaction.

References: 1. William Stallings, “Cryptography and network security- principles and practice”, 3 rd Edition, Pearson Prentice Hall. 2. Charlie Kaufman, Radia Perl man, Mike Speciner, “Network Security private communication in a practice”, 2nd Edition Pearson Prentice Hall. 3. Atul Kahate, “Cryptography and network security“, TMGH.

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MCS* 204

COMPILER DESIGN

L 3

T P C 1 0 4

Module 1 Principles Of Compiler – Compiler Structure – Properties of a Compiler – Optimization – Importance of Code optimization – Structure of Optimizing compilers – placement of optimizations in optimizing compilers – ICAN – Introduction and Overview – Symbol table structure – Local and Global Symbol table management. Intermediate representation – Issues – High level, medium level, low level intermediate languages – MIR, HIR, LIR – ICAN for Intermediate code

Module 2 Run-time support – Register usage – local stack frame – run-time stack – Code sharing – position–independent code – Symbolic and polymorphic language support - Optimization – Early optimization – Constant folding – scalar replacement of aggregates Simplification – value numbering – constant propagation – redundancy elimination – loop optimization. Procedure optimization – in-line expansion – leaf routine optimization and shrink wrapping

Module 3 Register allocation and assignment – graph coloring – control flow and low level optimizations Inter-procedural analysis and optimization – call graph – data flow analysis – constant propagation – alias analysis – register allocation – global References: – Optimization for memory hierarchy. Code Scheduling – Instruction scheduling – Speculative scheduling – Software pipelining – trace scheduling – percolation scheduling

Module 4 Case Studies – Sun Compilers for SPARC – IBM XL Compilers – Alpha compilers – PA –RISC assembly language – COOL – ( Classroom Object oriented language) - Compiler testing tools – SPIM

References:
1. Steven S. Muchnick, Koffman, “Advanced Compiler Design & Implementation”, Elsevier Science, Indian Reprint 2003.

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2. Keith D Cooper and Linda Torczon, “Engineering a Compiler”, Elsevier Science, India. 3. Sivarama P. Dandamudi,” Introduction to Assembly language programming: for Pentium and RISC processors”. 4. Allen Holub “Compiler Design in C”, Prentice Hall of India, 1990. 5. Alfred Aho, V. Ravi Sethi, D. Jeffery Ullman, “Compilers Principles Techniques and Tools”, Addison Wesley, 1988. 6. Charles N. Fischer, Richard J. Leblanc, “Crafting a compiler with C”, Benjamin-Cummings Publishing Co., Inc. Redwood City, CA, USA.

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MCS* 205-1

NEURAL NETWORKS

L T P C 3 0 0 3

Module 1: Introduction to Neural Networks Introduction to biological neuron, Artificial Neuron, Feedforward neural networks and supervised learning- Abstraction - Activation functions – mathematical preliminaries – Architecture – Properties and applications. Geometry of binary threshold neurons and their networks, Perceptrons and LMS.

Module 2: Backpropagation network BPN Learning algorithm-Examples. Considerations in implementing Back Propagation Algorithm. Structure growing algorithm, fast relatives of BPN- Applications of feed forward neural networks. Bayes’ theorem-Implementing classification decisions with Bayes theorem.

Module 3: Recurrent neurodynamical systems Dynamical systems – Stability-Linear and nonlinear dynamical systems-Lyapunov stability. Associative Memory- Linear associative memory, Hopfield networks- Applications-Boltzmann machine. BAM- BAM stability analysis- Continuous BAM- Adaptive BAM-Applications.

Module 4: ART Noise saturation dilemma – solution. ART-Outstar- Instar-ART1- Applications. The new generation- pulsed neuron model- Integrate and fire neurons- conductance based models.

References: 1. Satish Kumar “Neural Networks A classroom Approach”, The McGraw-Hill Companies. 2. James A ,“An introduction to neural Networks “, Anderson PHI. 3. Simon Haykin, “Neural Networks :A comprehensive foundation “, Pearson Education.

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MCSIS 205-2

GENETIC ALGORITHMS AND APPLICATIONS

L T P C 3 0 0 3

Module 1: Fundamentals of genetic algorithm A brief history of evolutionary computation-biological terminology-search space -encoding, reproduction-elements of genetic algorithm-genetic modeling-comparison of GA and traditional search methods.

Module 2: Genetic technology Steady state algorithm - fitness scaling - inversion. Genetic programming - Genetic Algorithm in problem solving

Module 3: Genetic Algorithm in engineering and optimization Genetic Algorithm in engineering and optimization-natural evolution –simulated annealing and Tabu search .Genetic Algorithm in scientific models and theoretical foundations – computer implementation - low level operator and knowledge based techniques in Genetic Algorithm.

Module 4: Application Applications of Genetic based machine learning-Genetic Algorithm and parallel processors, constraint optimization, uses of GA in solving NP hard problems, multilevel optimization, real life problem.

References: 1. Melanie Mitchell, “An introduction to Genetic Algorithm”, Prentice-Hall of India, New Delhi, Edition: 2004. 2. David.E.Golberg, “Genetic algorithms in search, optimization and machine learning”, Addition-Wesley-1999. 3. S.Rajasekaran G.A Vijayalakshmi Pai, ”Neural Networks, Fuzzy logic and Genetic Algorithms Synthesis and Applications”, Prentice Hall of India, New Delhi-2003. 4. Nils.J.Nilsson, ”Artificial Intelligence- A new synthesis”, Original edition-1999. 5. Tutorial sessions: Latest research papers in GA.

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MCSIS 205-3

AGENT BASED INTELLIGENT SYSTEMS

L T P C 3 0 0 3

Module 1: Introduction Definitions - Foundations - History - Intelligent Agents-Problem Solving-Searching – Uninformed Search strategies-BFS,DFS- Heuristics – Greedy best- first, A*- Local searchConstraint Satisfaction Problems – Backtracking search for CSPs , Local search for CSPAdversarial Search-Game playing, Minmax algorithm, Alpha-Beta pruning.

Module 2: Knowledge Representation and Reasoning Logical Agents – Reasoning pattern in propositional logic, Agent based on propositional logicFirst order logic- First Order Inference-Unification-forward Chaining- Backward chainingResolution Strategies-Knowledge Representation-Objects-Actions-Events.

Module 3: Planning Agents Planning Problem-State Space Search-Partial Order Planning- planning Graphs-planning and Acting in Nondeterministic Domains-Conditional Planning-Continuous Planning-MultiAgent Planning.

Module 4: Agents and Uncertainty Acting under uncertainty – Probability Notation-Bayes Rule and use –Semantics of Bayesian Networks- Inference in Bayesian networks-Other Approaches-Time and Uncertainty-Temporal Models- Utility Theory - Decision Network.

References: 1. Stuart Russell and Peter Norvig, “Artificial Intelligence - A Modern Approach”, 2 nd Edition, Prentice Hall, 2002. 2. Michael Wooldridge, “An Introduction to Multi Agent System”, John Wiley, 2002. 3. Patrick Henry Winston, ” Artificial Intelligence” , III Edition, AW, 1999. 4. Nils J. Nilsson,” Principles of Artificial Intelligence”, Narosa Publishing House, 1992.

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MCSIS 205-4 Module 1: Fuzzy Logic

FUZZY LOGIC

L T P C 3 0 0 3

Crisp sets & fuzzy sets: introduction, concepts, fuzzy operations general aggregation of operation. Fuzzy relation, Binary relation, Equivalence & similarity relation. Fuzzy relation equation. Application: Natural Engineering, Management & decision making & computer science.

Module 2: Fuzzy Preliminaries Expert Knowledge- Rules Antecedent and Consequents – Forward and Backward Chaining – Program Modularization and Blackboard systems – Handling uncertainties in an expert system Fuzzification and defuzzification - Fuzzy Sets and Fuzzy Numbers- Algebra of Fuzzy Sets – T norms and T conorms– Approximate Reasoning – Hedges – Fuzzy Arithmetic – extension principle – alpha cut and interval arithmetic – comparing between fuzzy numbers

Module 3: Fuzzy Expert System Inference in Fuzzy Expert System - Types of fuzzy Inference – nature of inference in a fuzzy expert system monotonic, non-monotonic, downward monotonic inference – test of procedures – modification of existing data by rule consequent instructions – selection of reasoning type and grades of membership – discrete fuzzy sets -invalidation of data : non-monotonic reasoning – modeling the entire rule space – conventional method – data mining and combs method – reducing number of required rules - running fuzzy expert systems.

Module 4: Running and Debugging Expert System Debugging tools – Isolating Bugs – data Acquisition from User Vs Automatic data Acquisition – ways of solving one tree search problem – Expert knowledge in Rules – expert knowledge in database – other applications of sequential rule firing – rules that are referable - runaway programs and recursion – Programs that learn from experience - Learning by adding rules – Learning by adding facts – general way of creating new rules and data descriptors – detection of artifacts in input data stream – data smoothing – types of rules suitable for real time work – memory management

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References: 1. William Siler and James J Buckley, “Fuzzy Expert Systems and Fuzzy Reasoning”, Wiley Inter- science, 2004. 2. Timothy J Ross, “Fuzzy Logic with Engineering Applications”, Wiley, 2004. 3. George Klir, “Fuzzy Sets Uncertainty & Information”, Prentice Hall. 4. George J.Klir, Tina A.Folger, “Fuzzy Sets, Uncertainty and Information“, PHI, 2005 Edition.

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MCSIS 206-1

PATTERN RECOGNITION

L T P C 3 0 0 3

Module 1: Pattern Classifier Overview of pattern recognition - Discriminant functions - Supervised learning - Parametric estimation - Maximum likelihood estimation - Bayesian parameter estimation - Perceptron algorithm - LMSE algorithm - Problems with Bayes approach - Pattern classification by distance functions - Minimum distance pattern classifier.

Module 2: Unsupervised Classification Clustering for unsupervised learning and classification - Clustering concept - C-means algorithm – Hierarchical clustering procedures - Graph theoretic approach to pattern clustering - Validity of clustering solutions.

Module 3: Structural Pattern Recognition Elements of formal grammars - String generation as pattern description - Recognition of syntactic description - Parsing - Stochastic grammars and applications - Graph based structural representation.

Module 4: Feature Extraction and Selection Entropy minimization - Karhunen - Loeve transformation - Feature selection through functions approximation - Binary feature selection.

References: 1. Robert J.Schalkoff, ”Pattern Recognition : Statistical, Structural and Neural Approaches” , John Wiley&Sons Inc., New York, 1992. 2. Tou and Gonzales,” Pattern Recognition Principles”, Wesley Publication Company, London, 1974. 3. Duda R.O., and Hart.P.E., “Pattern Classification and Scene Analysis” , Wiley, New York, 1973.

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MCSIS 206-2

SOFTWARE ARCHITECTURE

L T P C 3 0 0 3

Module 1: Introduction Introduction To Software Architecture An Engineering Discipline for Software, Architecture Business Cycle, Where do Architectures Come from, Software Processes and the Architecture Business Cycle, Features of Good Architecture.

Module 2: Architectural Styles Pipes and Filters-Data Abstraction and Object Oriented Organization-Event based, Implicit Invocation-Layered Systems-Repositories-Interpreters-Process Control-Process control

Paradigms-Software Paradigm for Process Control-Distributed processes-Main program / subroutine organizations – Domain – specific software architecture – heterogeneous architectures.

Module 3: Shared Information Systems Shared Information Systems Database Integration, Interpretation in Software Development Environments, Architectural Structures for Shared Information Systems Shared Information Systems Database Integration, Interpretation in Software Development Environments, Architectural Structures for Shared Information Systems.

Module 4: Architectural Design Guidance Guidance for User-Interface Architectures -Design Space and rules-Design Space for User Inter face Architectures-Design. Rules for User Interface Architecture applying the Design Space – Example – A Validation Experiment – How the Design Space Was Prepared.

References: 1. Mary Shaw, David Garlan, “Software Architecture”, Prentice Hall India, 2000. 2. Len Bass, Paul Clements, Rick Kazman, “Software architectures in practice”, AddisonWesley, 2003.

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MCSIS 206-3

IMAGE PROCESSING

L T P C 3 0 0 3

Module 1: Digital image fundamentals Image representation – color space – image sampling and quantization – relationship between pixels – mathematical tools used in digital image processing, Image transforms: Discrete Fourier Transform – 2-D FFT – Walsh Hadamard Transform – Discrete Cosine Transform – Haar Transform – Hotelling Transform – KL Transform.

Module 2: Intensity Transformations and Spatial Filtering Basic intensity transformation functions – Histogram Processing - Spatial Filtering – Smoothing spatial filters – Sharpening spatial filters, Frequency domain filtering: Basics of filtering in frequency domain- Image smoothing – Image sharpening – Selective filtering

Module 3: Image restoration Image degradation/restoration model – Noise models – Periodic noise reduction by frequency domain filtering – Inverse filtering – Minimum mean square error filtering – Image reconstruction from projections, Wavelet Transforms in one dimension – Wavelet transforms in two dimensions

Module 4: Image segmentation Region based segmentation – motion in segmentation, Image compression: compression methods – Huffman coding – arithmetic coding – LZW coding – Bit plane coding, Object recognition – structural methods, Colour image processing, Introduction to Deterministic and stochastic spatiotemporal image models.

References: 1. Rafael C. Gonzalez and Richard E. Wood, “Digital Image Processing”, 3rd Edition, Prentice Hall, 2008. 2. Anil K Jain, “Fundamentals of Digital Image Processing”, Prentice Hall, 1989. 3. William K. Pratt, “Digital Image Processing”, 3 rd edition, John Wiley, 2001. 4. Rafael C.Gonzalez, Richard E.Woods and Steven L. Eddins, “Digital Image Processing Using MATLAB”, 1st Edition, Pearson Education, 2004. 39

MCSIS 206-4

FAULT TOLERANT SYSTEMS

L T P 3 0 0

C 3

Module 1: Introduction Fault Classification, Types of Redundancy, Basic Measures of Fault Tolerance, Hardware Fault Tolerance, The Rate of Hardware Failures, Failure Rate, Reliability, and Mean Time to Failure, Canonical and Resilient Structures , Other Reliability Evaluation Techniques.

Module 2: Information Redundancy Information Redundancy, Coding, Resilient Disk Systems, Data Replication, Voting: Hierarchical Organization, Primary-Backup Approach, Algorithm-Based Fault Tolerance, Fault-Tolerant Networks: Measures of Resilience, Common Network Topologies and Their Resilience, FaultTolerant Routing.

Module 3: Software Fault Tolerance Acceptance Tests, Single-Version Fault Tolerance, N-Version Programming, Recovery Block Approach, Preconditions, Postconditions, and Assertions, Exception-Handling, Software Reliability Models, Fault-Tolerant Remote Procedure Calls.

Module 4: Checkpointing Introduction, Checkpoint Level, Optimal Checkpointing-An Analytical Model, Cache-Aided Rollback Error Recovery (CARER), Checkpointing in Distributed Systems, Checkpointing in Shared-Memory Systems, Checkpointing in Real-Time Systems, Case Studies: NonStop Systems, Stratus Systems, Cassini Command and Data Subsystem, IBM G5, IBM Sysplex, Itanium.

References: 1. Israel Koren, Mani Krishna, ”Fault Tolerant Systems”, Elsevier Science & Technology, 2007. 2. Parag K. Lala “Fault Tolerant and Fault testable hardware design”, Prentice-Hall International, 1985. 3. Martin L Shooman, Willey, “Reliablity of Computer systems and networks : Fault Tolerance, analysis and Design”. 40

4. DK Pradhan “FaultTolerant computer system Design”, PHI, 1996. 5. LL Pullam, ”Software Fault tolerance Techniques and implementation”, Artech House Computer Security Series , 2001. 6. Siewiorek, Daniel P,“Reliable computer systems: Design and evaluation”, AK Peters, Ltd., 3rd edition, 1998. 7. John Wiley, “Probability and statistics with reliability queuing and computer science applications”, 2 nd Edition. 8. Ebeling, Charles “An Introduction to reliability and maintainability Engineering”, McGrawHill Science. 1996.

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MCS* 207

NETWORK SIMULATION LAB

L T P C 0 0 3 2

Experiment list: 1. A thorough study of packet capturing tool called WireShark. 2. Familiarizing Network Simulator – 2 (NS2) with suitable examples. 3. Simulate a wired network consisting of TCP and UDP Traffic using NS2 and then calculate their respective throughput using AWK script. 4. Performance evaluation of different routing protocols in wired network environment using NS2. 5. Performance evaluation of different queues and effect of queues and buffers in wired network environment using NS2. 6. Compare the behavior of different variants of TCP (Tahoe, Reno, Vegas….) in wired network using NS2. Comparison can be done on the congestion window behavior by plotting graph. 7. Simulation of wireless Ad hoc networks using NS2. 8. Simulate a wireless network consisting of TCP and UDP Traffic using NS2 and then calculate their respective throughput using AWK script. 9. Performance evaluation of different ad-hoc wireless routing protocols (DSDV, DSR, AODV …) using NS2. 10. Create different Wired-cum-Wireless networks and MobileIP Simulations using NS2.

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MCSIS 208

SEMINAR – II

L T P C 0 0 2 1

Each student shall present a seminar on any topic of interest related to the core / elective courses offered in the second semester of the M. Tech. Programme. He / she shall select the topic based on the References: from international journals of repute, preferably IEEE journals. They should get the paper approved by the Programme Co-ordinator / Faculty member in charge of the seminar and shall present it in the class. Every student shall participate in the seminar. The students should undertake a detailed study on the topic and submit a report at the end of the semester. Marks will be awarded based on the topic, presentation, participation in the seminar and the report submitted.

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MCSIS 301

INDUSTRIAL TRAINING AND MINIPROJECT

L 0

T 0

P 20

C 10

The student shall undergo Industrial training of one month duration and a Mini Project of two month duration. Industrial training should be carried out in an industry / company approved by the institution and under the guidance of a staff member in the concerned field. At the end of the training he / she has to submit a report on the work being carried out. Projects can be developed either from a student’s own idea or it can be assigned by the faculty. Students doing application projects should demonstrate a working design to meet the specifications of the assigned project. The students can do the mini project externally only if they are guided by a faculty with minimum M.E/M.TECH qualification. A detailed report of project work consisting of the design, development and implementation work that the candidate has executed should be submitted. Evaluation of the Mini Project will be based on the talk delivered by the candidate (presentation), the report submitted and demonstration of the work done. Presenting the work in a National Conference with the consent of the guide a will carry an added weightage to the review process.

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MCSIS 302

MASTER’S THESIS PHASE - I

L T 0 0

P 10

C 5

In master’s thesis Phase-I, the students are expected to select an emerging research area in Computer Science or related fields, After conducting a detailed literature survey, they should compare and analyze research work done and review recent developments in the area and prepare an initial design of the work to be carried out as Master’s Thesis. It is expected that the students should refer National and International Journals and proceedings of National and International conferences while selecting a topic for their thesis. He/She should select a recent topic from a reputed International Journal, preferably IEEE/ACM. Emphasis should be given for introduction to the topic, literature survey, and scope of the proposed work along with some preliminary work carried out on the thesis topic. Students should submit a copy of Phase-I thesis report covering the content discussed above and highlighting the features of work to be carried out in Phase-II of the thesis. Students should follow standard practice of thesis writing. Presenting the work, carried out by the students in a National/International Conference is encouraged. The candidate should present the current status of the thesis work and the assessment will be made on the basis of the work and the presentation, by a panel of internal examiners in which one will be the internal guide. The examiners should give their suggestions in writing to the students so that it should be incorporated in the Phase–II of the thesis.

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MCSIS 401

MASTER’S THESIS

L T 0 0

P

C

30 15

In the fourth semester, the student has to continue the thesis work and after successfully finishing the work, he / she have to submit a detailed thesis report. The work carried out should lead to a publication in a National / International Conference. They should have submitted the paper before M. Tech. evaluation and specific weightage should be given to accepted papers in reputed conferences.

MCSIS 402

MASTER’S COMPREHENSIVE VIVA

A comprehensive viva-voce examination will be conducted at the end of the fourth semester by an internal examiner and external examiners appointed by the university to assess the candidate’s overall knowledge in the respective field of specialization.

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