MAHATMA GANDHI UNIVERSITYSCHEME 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 with at least one external examiner. 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.SEMESTER . Course No. 4 . constituted by the University. No.IV Hrs / Week Sl. constituted by the Department ** Thesis evaluation and Viva-voce will be conducted at the end of the fourth semester by a panel of examiners. service pattern. 3. “Fuzzy logic with Engineering applications”. Arrival pattern. Fuzzy Cartesian product.M. 2002. Crisp relations and Fuzzy relations. P.MCS* 101 MATHEMATICAL FOUNDATIONS FOR COMPUTER SCIENCE L T P C 3 1 0 4 Module 1: Fuzzy Mathematics Crisp sets and Fuzzy sets-.Wiley-India. Functions of random variables. Addison Wesley. Yager and Sugeno classes. The Markovian model M/M/1-steady state solutions-Little’s formula References: 1. Single and Multi-server models. Robertazzi T. 4. Module 2: Fuzzy Logic Fuzzy logic. “Probability Models for Computer Science”-Academic Press. Convex fuzzy sets.”Computer Networks and systems-Queuing Theory and Performance Evaluation”-Springer third edition. Fuzzification and Defuzzification techniques.G. Ross S. Module 3: Stochastic Process Random variables. Composition operators. ”Fuzzy sets and Fuzzy logic” Prentice-Hall of India.1995. Timothy J. Kleene-Dienes implication and Lukasiewicz implication). Sequence of random variables. 5. George J Clir and Tina A Foldger “Fuzzy sets –Uncertainty and Information” Prentice Hall of India. Equivalence and implication operators (Classical implication. Markov processes and queuing theory. 2. R. Markov chains. 6. Ross. approximate reasoning. Algebra of fuzzy sets. 5 . Fuzzy cardinality. union and intersection).1988. stochastic processes. George J Klir and Bo Yuan. α-cuts. 1994. Module 4: Queuing Models Queuing Theory-General concepts. Fuzzy tautologies and contradictions. Queue Disciplines – Markovian Queues. Standard fuzzy set operations-(complement. Mamdani implication. "Discrete and Combinatorial Mathematics: An Applied Introduction".. Fuzzy quantifiers and predicates. Operations on Fuzzy relations. Fuzzy Equivalence relations and similarity relations. Grimaldi. Process Addressing. 4. Mutual Exclusion. Communication Protocols for RPCs. Encoding and Decoding Message data. Structure of Shared Memory Space. PHI Learning Private Ltd. Group Communication. Process Migration. Distributed Computing system. Niranjan G Shivarathri. File Accessing models. Parameter Passing semantics. Coulouris. Pearson Education.RPC Model. 3rd edition. Mukesh Singhal. Multi-datagram Messages. Models. Marshaling Arguments and Results. Distributed File Systems: Features of good DFS. Module 2: Message Passing Features of a good Message Passing System. Consistency models. “Advanced Concepts in Operating Systems”.. Pradeep Sinha K. Replacement strategy. Popularity. Issues in IPC by Message Passing Synchronization. “Distributed Systems concepts and design”. Module 3: Distributed Shared Memory General architecture of DSM systems. 3. Exception handling. References: 1. RPC messages. call semantics. Synchronization: Clock Synchronization. “ Modern Operating System”. Load-Balancing and Load approach.MCS* 102 DISTRIBUTED OPERATING SYSTEMS L T P C 3 1 0 4 Module 1: Distributed computing systems fundamentals Introduction to Distributed computing systems.RPC in Heterogeneous Environments. Tanenbaum A S. Design issues of Distributed operating system. Event Ordering. Client Server Building. Security . Failure Handling. Task assignment approach. Granularity. “Distributed Operating Systems Concepts and Design”. Process Management: Introduction. PHI learning private limited. 6 .G. Design and implementation Issues of DSM. 4th edition. Deadlock. 2. Dollimore J & Kindberg T. Buffering. Transparency of RPC. Thrashing. Tata Mc-Graw Hill Ltd. Election Algorithms Module 4: Resource Management Features of global scheduling algorithm. Threads. File models. Lightweight RPC. Distributed computing environment. Server Management. Pearson Education. S. 5. BFS. Sahni. T. References: 1. S. 2. Module 2 Searches in Graphs: DFS. 7 . Multidimensional Search Trees: k-d Trees. Sahni. “Principles of Multimedia Database Systems” 1998. Dynamic Programming: 0/1 Knapsack. Maximum Flows. and Master Method. Second Edition. Set-Covering Problem. Gelder. R. S. V. Baase. Morgan Kaufman. C. 4. 2007. Rivest. Subset-Sum Problem. Splay Trees. Bi-connected Components. and A. Convex Hull. and C Stein. Prentice Hall of India. “Fundamentals of Data Structures in C++”. E. and S. Traveling Salesperson Problem. University Press.” Introduction to Algorithms”. Bipartite Matching. 2007. 2000. Divide and Conquer: Selection. Greedy Methods: Container Loading. 2009. 3. Advanced Structures for Dictionary ADT: Red-Black Trees. and D. Activity on Vertex and Activity on Edge Networks. Symmetric Min-Max Heaps. Recursion Tree. H. Flow Shop Scheduling. Mehta. Horowitz. Continuous Knapsack Problem. Advanced Structures for Priority Queues: Leftist Trees. “Computer Algorithms – Introduction to Design and Analysis”. Module 3 Solution of recurrence equations: Substitution Method. E. Binomial Heaps.MCS* 103 ADVANCED DATA STRUCTURES AND ALGORITHMS L T P 3 1 0 C 4 Module 1 Amortized Complexity Analysis. Introduction to Probabilistic Analysis and Randomized Algorithms. Cormen. Horowitz. Point Quadtrees. Traveling-Salesman Problem. Connected Components. Rajasekharan. Third Edition. Maximum-subarray problem. Third Edition. Leiserson. E. V. Second Edition. “Fundamentals of Computer Alllgorithms”. University Press. S. Subrahmanian. Module 4 Approximation Algorithms: Vertex-Cover Problem. CPU Performance & its factors. Kai Hwang. Ravi Prakash “Introduction to Parallel Processing” PHI. Fauntain. Dependencies between instructions. Dependability. Shared Memory Organization. Shelving.Pentium Pro. Pipelining: Introduction to pipelining. Register Renaming. Limitations of ILP. Case Study-Intel Duo Core Architecture References: 1. Module 4: The Memory System Memory hierarchy. Quantitative Principles of Computer Design. Cache Coherence. Superscalar instruction issue. Case Study.Basic computational models. 3. Memory Consistency.Patterson. ” Parallel Computer Architecture”. Module 2: Instruction level Parallelisms and Pipelining Instruction level Parallelisms: ILP concepts. Architecture of PVM. Kscucle. Pipeline hazards. Palsingh.Patterson.Hennessy and David A. John L. “Advanced Computer Architecture a design space approach. Module 3: Superscalar Processors Introduction. 6.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.” Pearson Edition. “Advanced Computer Architecture”. ”Computer Architecture-Hardware & Software Approach”. Distributed Systems: Parallel Virtual Machine. Programming model of PVM. Instruction pipeline design. Manesh Parashar. von-Neumann Computation Model. “Tools & Environments for Parallel and Distributed 8 . Salim Hariri. David Culler and J. 5. Parallel decoding. 4. 7. Cache Performance Issues. M. Sima. P. Dinesh Shikhare. Preserving sequential consistency-ROB. 2. SPEC Benchmarks.Hennessy and David A. John L.Sasikumar. Morgan Kaufmann. Power PC 620. “Computer Architecture-A Quantitative Approach” 4th Edition. Computational model:. 8.intel. http://www. Publication. A John Wiley & Sons INC.Computing”..htm 9 .com/technology/itj/2006/volume10issue02/art01_Intro_to_Core_Duo/ p02_intro. Finite-State parsing methods.Problems with the basic Top-Down parser .Lexicon-Free FSTs: The porter stammer .Combining FST lexicon and rules . Morphology and Finite-State Transducers: Survey of English morphology Finite-State Morphological parsing .Unification of feature structures .Language. Context-Free Grammars for English: Constituency Context-Free rules and trees . Module 3: Advanced Features and Syntax Features and Unification: Feature structures .problems with PCFGs .Rule-based part-of-speech tagging .Implementing unification .FiniteState automata. Module 4: Semantic Representing Meaning Computational desiderata for representations .Auxiliaries .Human morphological processing Module 2: Syntax Word classes and part-of-speech tagging: English word classes .Coordination Agreement .Sentence-level constructions .Parsing with unification constraints Types and Inheritance.Human parsing.Features structures in the grammar .First order predicate calculus .Models and Algorithms .Spoken language syntax Grammars equivalence and normal form .Part-ofspeech tagging .Tagsets for English .Some linguistically relevant concepts .Finite-State and Context-Free grammars .A Basic TopDown parser . Regular Expressions and automata: Regular expressions .The verb phase and sub categorization .Meaning structure of language .Related representational approaches Alternative approaches to meaning. Lexicalized and Probabilistic Parsing: Probabilistic context-free grammar . Semantic Analysis: Syntax-Driven semantic analysis 10 .MCSIS 105-1 NATURAL LANGUAGE PROCESSING L T P C 3 0 0 3 Module 1: Introduction Knowledge in speech and language processing .Ambiguity .Stochastic part-of-speech tagging Transformation-based tagging .Probabilistic lexicalized CFGs . Parsing with Context-Free Grammars: Parsing as search .Other issues.Dependency Grammars .The early algorithm . Thought and Understanding.The noun phrase .Grammars and human processing. Christopher D.Creativity and the lexicon. Daniel Jurafsky & James H. Kluwer academic Publishers.T. Gerald J. 2000. References: 1. “Natural Language Understanding”. 2002. Application: Word sense Disambiguation. Manning and Hinrich Schutze. “Foundations of Statistical Natural Language Processing “.Martin.Attachments for a fragment of English . “Information Storage and Retrieval Systems”. Tomek Strzalkowski “Natural Language Information Retrieval“. Pearson Education (Singapore) Pte. “Speech and Language Processing”. James Allen. 3. 1999. Kluwer academic Publishers.Integrating semantic analysis into the early parser Idioms and compositionality . MIT Press.. 11 . 1999. Lexical semantics: relational among lexemes and their senses . 2. 5. 2003.Robust semantic analysis. Maybury. 4.The Internal structure of words . Ltd. Kowalski and Mark. Pearson Education.WordNet: A database of lexical relations . Module 2: Sequence Analysis Introduction to Sequence alignment.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. SVM. polypeptic composition. References: 1. Module 4: Proteomics Protein and RNA structure prediction. Substitution matrices. Cell organelles and their functions. Local and Global alignment concepts. Neural network. lipids and nucleic acids – Different structural forms and functional organizations.Proteinprotein docking algorithms. Gene/Protein function prediction using Machine learning tools: supervised / unsupervised learning. SAGE.DBEST. DNA replication. Molecules of life: Introduction to carbohydrates. Strategies for generating ESTs and full length inserts. computational methods for identification of polypeptides from mass spectrometry. Protein-protein quaternary structure modeling. 2001. proteins. transcription and translation. secondary and tertiary structure. Scoring matrices –PAM and BLOSUM. PPI server. DIP. Protein-Protein Interaction: Experimental identification of protein-protein interactions. UNIGENE. Gene regulation. Cold Spring Harbor laboratory Press. algorithms for modeling protein folding. Multiple sequence alignment –Progressive alignment. dynamic programming methodology. 12 . Homology modeling. protein classification. Monte Carlo docking simulation. EST databases. David W. Module 3: Genomics Functional Genomics: Gene expression analysis by cDNA micro arrays. PPI databases: STRINGS. EST clustering and assembly. Mount “Bioinformatics Sequence and Genome Analysis”. dot plot. Database searches for homologous sequences – FASTA and BLAST versions. . Skills. 3. Baxevanis.”Biological Sequence Analysis: Probabilistic models of protein and Nucleic acids” Cambridge University Press 2007. Creighton. Andreqas D. S. 6. 13 . Applications”. Leach. “Molecular modelling: Principles and applications”. Thomas E. Prentice Hall Publications. B. Richard Durbin. G. A. 4. Francis Ouellette. Eddy. Rastogi.2. "Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins “. F. Namita Mendiratta. R. Andrew. Krogh. John Wiley and Sons. Parag Rastogi. ”Bioinformatics-Concepts. 5. “Proteins: structures and molecular properties”. C. New York 1998. Mitchison. The Complexity of Approximation Definitions . Languages and Computation”. Computing .Approximation Schemes .Space Completeness .Polynomial Space – Poly-logarithmic Space .Model-Independent Complexity Classes Deterministic Complexity Classes . 2.Restrictions of Hard Problems . 2001.The Turing Machine .Multitape Turing Machines .Issues of Computability . “Introduction to Automata Theory. NPCompleteness: Cook's Theorem .MCSIS 105-3 AUTOMATA THEORY L T P C 3 0 0 3 Module 1: Finite automata and Regular Languages States and Automata .Rice's Theorem and the Recursion Theorem .Provably Intractable Problems Module 4: Complexity Theory in Practice Circumscribing Hard Problems .Strong NPCompleteness .Determinism and Nondeterminism .Degrees of Unsolvability Module 3: Complexity Theory Classes of Complexity . Bernard Moret.Primitive Recursive Functions -Defining Primitive Recursive Functions Partial Recursive Functions .Equivalence of Finite Automata Epsilon Transitions .Choosing a Model of Computation . 1998.Constant-Distance Approximations .Regular Expressions and Finite Automata .Reviewing the Construction of Regular Expressions from Finite Automata. Module 2: Universal Models of Computation Encoding Instances .The Register Machine .Hierarchy Theorems . AW. References: 1.Properties of Finite Automata .Checking vs. “The Theory of Computation”.Finite Automata as Language Acceptors .Translation Between Models Computability Theory . AW.Certificates and non-determinism .Complete Problems.Promise Problems . John E Hopcroft. 14 .Fixed-Ratio Approximations and the Class OptNP The Power of Randomization. 3. John Savage. Complexity”. “Models of Computation. Automata and 4. Exploring the power of Computing”. J. 15 . Glenn Brookshear. AW. “Theory of Computation. Formal Languages. state diagram. 2. Source coding theorem. 5. Information rate. Module 2: Definitions and Principles Hamming weight. Kraft McMillan inequality. 2002.Discrete memory-less channels – BSC. 16 . Coding and Cryptography”. “Digital Communication”. Shannon limit. “Introduction to Error Control Codes”.Linear block codes Module 3 : Cyclic codes Cyclic codes . “Introduction to Data Compression” . classification of codes. S Gravano. Amitabha Bhattacharya. Pearson Education Asia. Module 4: Encoding and Decoding Sequential search and Viterbi algorithm – Principle of Turbo coding .Single parity codes. Elsevier 2006. 4. K Sayood. Hamming distance. “Information Theory. Convolutional codes – code tree. TMH 2006. Networks. “Multimedia Communications: Applications. Encoder and decoder . BEC – Channel capacity.CRC. Protocols and Standards”. Huffman coding. Hamming codes. Fred Halsall. Repetition codes .Syndrome calculation.Adaptive Huffman Coding.MCSIS 105-4 INFORMATION THEORY AND CODING L T P 3 0 0 C 3 Module 1: Introduction Information – Entropy. Extended Huffman coding Joint and conditional entropies. trellis. R Bose. Arithmetic Coding References: 1. 3. Shannon-Fano coding. Mutual information . Oxford University Press 2007. TMH 2007. Minimum distance decoding . Query tools and Applications – Online Analytical Processing (OLAP) Module 2: Data Mining Introduction . Elsevier.Data Preprocessing – Data Cleaning – Data Integration and Transformation – Data Reduction – Data Discretization and Concept Hierarchy Generation. Reprinted 2008. 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.Data Mining Applications – Trends in Data Mining References: 1. Cleanup. 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 .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. 17 . Jiawei Han and Micheline Kamber “Data Mining Concepts and Techniques” Second Edition. and Transformation Tools – Metadata – Reporting. Pearson Education. 2006. K. Prentice Hall of India. Data Mining & OLAP”. Pang-Ning Tan. 2006. 3 K. Michael Steinbach and Vipin Kumar “Introduction to Data Mining”. Easter Economy Edition. Soman. G. Tata McGraw – Hill Edition. 18 . 2007. Ajay “Insight into Data mining Theory and Practice”. Alex Berson and Stephen J. Gupta “Introduction to Data Mining with Case Studies”. Smith “Data Warehousing. Easter Economy Edition.2. Prentice Hall of India. Shyam Diwakar and V. 5. 4.P. Tenth Reprint 2007. concepts and depicting a message in collaboration diagrams. WILEY-Dreamtech India Pvt. Introduction to UML: Conceptual model of the UML. Patterns and Frameworks. Hans-Erik Eriksson. Ltd. 4. “ Fundamentals of Object Oriented Design in UML”. interfaces types and roles. Difference between collaboration and sequence diagram. Module 4: Architectural Modeling Artifacts. packages. 2000.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. instances. deployment and deployment diagrams. relationships. ‘The Unified Modeling Language User Guide”. Ivar Jacobson. Magnus Penker. Second Edition. Brian Lyons. building blocks of UML. object diagrams and components. importance of modeling. Addison Wesley. WILEY-Dreamtech India Pvt. process and threads. Pascal Roques. time and space. James Rumbaugh. Use Case Diagrams and activity diagrams. common mechanisms. state diagrams. Grady Booch. Pearson Education 2005. Use cases. “UML 2 Toolkit”. principles of modeling. class diagrams. Meilir Page-Jones. Module 3: Basic Behavioral Modeling Interactions. Asynchronous messages and concurrent execution. advanced relationships. architecture. object oriented modeling. 19 .. David Fado. 2. Terms and concepts in sequence diagrams. common mechanisms in UML. References: 1. Advanced Behavioral Modeling: Events and signals. state machines. 3. artifact diagrams. Interaction Diagrams. Module 2: Structural Modeling Basic Structural Modeling: Classes. Software Development Life Cycle. Terms. Ltd. Advanced Structural Modeling: Advanced classes. ”Modeling Software Systems Using UML2”. Defect Density and Defect Removal Efficiency. SQA Planning. Pareto Diagram. SEI CMMI. Quality Function Deployment (QFD). Causes of Software Errors.Reviews. Galin Daniel. Boundary Value Analysis. Six Sigma. Overview of SDLC Process Models. SCAMPI Assessment Method. Failure Mode Effect Analysis (FMEA). Personal Software Process (PSP). Team Software Process (TSP). 2004. P-CMMI. Cause and Effect Diagram. Software Quality Metrics.MCSIS 106-3 SOFTWARE QUALITY ASSURANCE L T P C 3 0 0 3 Module 1: Basic Concepts Definitions of Quality. Quality Management System (QMS). Zero Defects. Importance of Quality. Types of Testing. CASE Tools & their effect on Software Quality. 20 . Scatter Diagram. Quality Control vs. Statistical Process Control Module 3: Quality Standards. Cost of Quality. Total Quality Management (TQM). Test Case Design. QMS vs. Stratification. P-CMMI. ISBN 978-81-317-2395-1. Equivalence Partitioning. Kanban in Software Development. “Software Quality Assurance: From theory to implementation”. National Quality Awards – MBNQA. Verification & Validation. Walkthrough. SQA Activities . Quality Assurance. Pearson Education Ltd. SPICE. Certification and Assessment QMS Standard ISO 9001:2008. Module 2: Software Quality Assurance SQA Function – Objectives and Responsibilities. Quality Control Tools – Check Sheet. Clean Room Software Engineering. Inspection. Faults and Failures. TQM. Risk Management References: 1. Control Charts. Software Testing: Levels of Testing. Histogram. Software Configuration Management & CM Audits. Software Errors. Rajiv Gandhi National Quality Award Module 4: Other Topics Agile Methodology – Scrum. Software Reliability Metrics and Models. Testing and Audits. Brain Storming. Software Quality Factors. . Pearson Education Ltd. 5. 2003. Second Edition. 21 . Godbole. 4. Pearson Education Inc. Pankaj Jalote.2. “Software Quality Assurance: Principles and Practices”.. ISBN 0-201-72915-6. ISBN 81-7808-664-6. Kan Stephen H. “An Integrated Approach to Software Engineering”. 3. “Metrics and Models in Software Quality Engineering”. “Software Project Management in Practice”. Nina S. Narosa Publishing. Pankaj Jalote. 2005. Scope Definition. Risk Management Plan. Software Projects vs. Software Configuration Management – Software Configuration Items (SCI). Resource Assignment. Activity Sequencing. Top 10 Software Project Risks. Use Case Points. Project Charter. WBS based Estimation. PM Competencies. Other Plans – SQA Plan. Identifying Critical Path. Test Plan. Project Monitoring and Control. Need for Software Project Management. Project Management Framework – Project Stakeholders. Problems with under and over Estimates. Module 2: Project Planning Basic Objectives. Project Scheduling and Tracking using MS Project. Project Organisation Types. Work Breakdown Structure (WBS). Configuration Management Plan. Software Project Failures & Major Reasons. Contents of a Typical Software Project Plan. Gantt Chart. What is Project Management?. Overview of Software Estimation Techniques – Analogous Estimation. Risk Resolution. Project Management Life Cycle. Risk Analysis and Prioritization. Software Estimation Process. Expert Judgment based Estimation. Agile Project Management using Scrum. Activity Planning. Module 3: Software Effort Estimation Need for Software Estimation. CPM. Other Projects. Key Planning Tasks. 22 . Business Case. Problems with Software Projects. Project Constraints. Risk Tracking and Control. Effort and Schedule Estimation using COCOMO. Resource Plan. Change Control. Cost Benefit Analysis. Version Control. Project Tracking using Earned Value Analysis. Project Environment.MCSIS 106-4 SOFTWARE PROJECT MANAGEMENT L T P C 3 0 0 3 Module 1: Introduction Projects and Project Characteristics. Tracking Gantt. Network Models – PDM. Project Plan Development. Communication Plan. Function Point Analysis. Activity Duration Estimation. Risk Response Planning. Module 4: Other Topics Project Risk Management – Risk Identification. Pankaj Jalote. “Software Project Management in Practice”. “Managing Global Software Projects”. 5. Pankaj Jalote. Roger S Pressman. 23 . “Software Engineering: A Practitioner’s Approach”. “Software Project Management”. “An Integrated Approach to Software Engineering”. New Delhi. Tata McGraw Hill. Pearson Education Ltd. 2. Tata McGraw Hill.References: 1. Bob Huges & Mike Cotterell. 2002. 4. New Delhi. Gopalaswamy Ramesh. 2006. 2005. New Delhi. 2001. Tata McGraw Hill. 3. 10. NFS. Kernel compilation. Communication commands-mail. 3. write. trap. 7. join. System call implementation. if. Apache. sort. runlevel. Producer Consumer problem. cron… 5. Samba. DNS. Php syntax-variables-data types-functions-if. switch. while. cut. Setting up servers-DHCP. Programming in php environment-Installing and configuring apache and mysql for php demo. Implement the following: Dining philosopher Problem. 4. while loop. Process related commands . shift. kill. paste.. echo. Shell Programming Commands -Shell variables. for.else. do while. uniq. Wild card characters-grep-pipe-tee-command substitution-shell variables-subshells-filtershead. Editors-Vi and Emacs. quick sort implementation using shell scripting. nice. Introduction to PERL programming. archiving.MCS* 107 OPERATING SYSTEMS LAB L T P C 0 0 3 2 List of Experiments: 1. nl. 8. !!.ps. for loop. tail. case. Introduction to Linux-booting-login-simple commands. getting displaying manipulating form values. 6. Binary Search Implementation using shell scripting. 9. 24 . nohup. command line arguments. talk. Message queue. 2. 11. time. arrays. &&. test. init. set. read. My sql basics-connecting mysql with php-inserting & retrieving table data using php. tar-gzip-rpm. until. System Administration-Booting. presentation. He / she shall select the topic based on the References: from international journals of repute. The students should undertake a detailed study on the topic and submit a report at the end of the semester. 25 . participation in the seminar and the report submitted. preferably IEEE journals. Marks will be awarded based on the topic. Every student shall participate in the seminar. Programme. Tech. 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.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. SMI. Reliable Transmission. Real Time Data Transfer. HTTP Transaction.3) and Token Ring (802. 26 . Pearson Education. Wireless Propagation. William Stallings. Signal Encoding Techniques. Packet format. Routing Protocols -Distance Vector Routing-RIP. “Data Communications & Networking”.RTP. Packet format.Distribution of Name Space.TCP services and features. TCP state transitions.Architecture. Transmission Impairments. ICMP. IP Address – Sub netting / Super netting. MIB. TCP module’s algorithm. “Computer Networks -A systems approach”. 3. SNMP PDUs. SCTP. Voice over IP-Session Initiation Protocol. Kurose and Ross. Private IP and NAT. McGraw-Hill.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. Wireless Transmission. Tata McGraw-Hill. ARP. Channel Capacity. UDP datagram. Transmission MediaWired Transmission. Framing. 4. RARP. “Computer Networks A systems approach”. Congestion control. Components in IP software.SCTP services and features. State Transitions. DHCP . Pearson Education. Ethernet (802.5). TCP segment. Link-State Routing-OSPF Module 3: Transport Layer UDP. References: 1. “TCP/IP Protocol Suite”. SNMP. 4th edition. Flow and Error control. RTCP. Datagram Fragmentation. Module 4: Application Layer DNS. TCP connection. 5. Peterson and Davie. “Data and Computer Communications”. UDP operation. Flow and Error control. Line-of Sight Transmission. Behrouz A Forouzan. Module 2: Network Layer Connecting Devices. Behurouz A Forouzan. HTTP. SCTP connection. TCP.Port Addressing. Packet Forwarding with Classful / Classless Addressing. DNS messages. 2. Elsevier.Address allocation.Analog and Digital Transmission. Name Resolution. Data link layer: TCP/IP Protocol Architecture. Distributed Systems – Parallel Databases: I/O Parallelism – Inter and Intra Query Parallelism – Inter and Intra operation Parallelism – Distributed Database Concepts .Mobile Transaction Models –Concurrency Control Mechanism.Mobile database Recovery : Log management in mobile database systems – Mobile database recovery schemes References: 1. 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. Pearson Education/Addison Wesley.Genome Data Management.B.Geographic Information Systems . 2007.Case Studies.Distributed Data Storage – Distributed Transactions – Commit Protocols – Concurrency Control – Three Tier Client Server Architecture. “Fundamentals of Database Systems”.Mobile Database Systems . R. Module 3: Enhanced Data models Active Database Concepts and Triggers – Temporal Databases – Spatial Databases – Multimedia Databases – Deductive Databases – XML Databases: XML Data Model – DTD .Transaction Execution in MDS. Languages and Design: ODMG Model – ODL – OQL – Object Relational and Extended – Relational Systems: Object Relational features in SQL / Oracle – Case Studies. S.Location Dependent Data Distribution . Fifth Edition.Effect of Mobility on Data Management . Module 4: Emerging Technologies Mobile Databases: Location and Handoff Management .XML Schema XML Querying . Elmasri. 27 . Navathe.Transaction Commit Protocols.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. McGraw Hill. 3.J. Pearson Education. Raghu Ramakrishnan. Date.Kannan and S. 2006.”An Introduction to Database Systems”.2. “Database Management Systems”. Pearson Education..” Mobile Database Systems”. C. Abraham Silberschatz. Vijay Kumar. A John Wiley & Sons. Implementation and Management”. A Practical Approach to Design. “Database Systems. Henry F Korth. McGraw Hill. Thomas Cannolly and Carolyn Begg.Swamynathan. 4. 6. S. A. Sudharshan. Third Edition. Third Edition 2004. 28 . “Database System Concepts”. Fifth Edition. Inc. 5. 2006. Publication. Eighth Edition. Johannes Gehrke. 2007. secure hash algorithms. Architecture. Secure Electronic Transaction. HMAC. Radia Perl man. Elliptic curve arithmetic. RSA algorithm. cryptographic techniques – substitution and transposition techniques. Secure Socket Layer. message authentication codes. Virus Counter measures. Firewalls-Design Principles. Hash functions and their security. model. approaches and principles of digital information security. types of attacks. Viruses and related threats. 2. Trusted Systems. Pearson Prentice Hall.Intrusion Detection. 3. Combining Security Associations. Asymmetric key cryptography: Principles of public key crypto systems. Transport Layer Security. steganography techniques. TMGH. Password Management. Differential and linear cryptanalysis. Module 3: Message Authentication and Hash functions Authentication functions. 3 rd Edition. Kerberos. “Cryptography and network security. “Network Security private communication in a practice”. Blowfish. 29 . ESP. Diffie-Hellman key exchange.Web Security consideration. References: 1. IP Security-Overview. MD5 .509 authentication service. X. key management. Web Security:. DES. elliptic curve cryptography. Charlie Kaufman.MCS* 203 COMPUTER SECURITY AND APPLIED CRYPTOGRAPHY L T P C 3 1 0 4 Module 1: Introduction to cryptography Concepts. Digital signatures and authentication protocols. Digital Signature standards.principles and practice”. RCS. William Stallings. “Cryptography and network security“. 2nd Edition Pearson Prentice Hall. Symmetric Key cryptography: Block cipher design principles and criteria. Atul Kahate. PGP and S/MIME. Key Management. IDEA. AES. AH. System Security. Module 2 Introduction to Number Theory. Mike Speciner. Module 4 Network Security: Introduction. 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 . HIR. Koffman. 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) . 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. Intermediate representation – Issues – High level. Elsevier Science. Muchnick.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. medium level. Steven S. “Advanced Compiler Design & Implementation”.Optimization – Early optimization – Constant folding – scalar replacement of aggregates Simplification – value numbering – constant propagation – redundancy elimination – loop optimization. 30 . low level intermediate languages – MIR. Indian Reprint 2003.Compiler testing tools – SPIM References: 1. ” Introduction to Assembly language programming: for Pentium and RISC processors”. Sivarama P. Charles N. Benjamin-Cummings Publishing Co. CA. V. 1990. Inc. Addison Wesley. 31 . Alfred Aho. Keith D Cooper and Linda Torczon. “Crafting a compiler with C”. 4. Richard J. 3. Jeffery Ullman. 5. Dandamudi.. Leblanc. Prentice Hall of India.2. USA. “Engineering a Compiler”. 6. “Compilers Principles Techniques and Tools”. D. Elsevier Science. Fischer. 1988. Allen Holub “Compiler Design in C”. Ravi Sethi. Redwood City. India. Simon Haykin. References: 1. James A .Applications-Boltzmann machine. Structure growing algorithm. Bayes’ theorem-Implementing classification decisions with Bayes theorem. Module 3: Recurrent neurodynamical systems Dynamical systems – Stability-Linear and nonlinear dynamical systems-Lyapunov stability. Hopfield networks. 2. Module 2: Backpropagation network BPN Learning algorithm-Examples. fast relatives of BPN. 3. “Neural Networks :A comprehensive foundation “.conductance based models.MCS* 205-1 NEURAL NETWORKS L T P C 3 0 0 3 Module 1: Introduction to Neural Networks Introduction to biological neuron.Adaptive BAM-Applications. Feedforward neural networks and supervised learning.Integrate and fire neurons.pulsed neuron model.Abstraction .“An introduction to neural Networks “. Artificial Neuron. ART-Outstar.Activation functions – mathematical preliminaries – Architecture – Properties and applications. The new generation.Instar-ART1.Applications.Continuous BAM. Associative Memory. Pearson Education. 32 . Anderson PHI. Considerations in implementing Back Propagation Algorithm. BAM. Module 4: ART Noise saturation dilemma – solution. Geometry of binary threshold neurons and their networks. Satish Kumar “Neural Networks A classroom Approach”.BAM stability analysis. Perceptrons and LMS.Linear associative memory. The McGraw-Hill Companies.Applications of feed forward neural networks. low level operator and knowledge based techniques in Genetic Algorithm. 33 . optimization and machine learning”.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 . Melanie Mitchell.Nilsson. Tutorial sessions: Latest research papers in GA. Nils. 2. Module 2: Genetic technology Steady state algorithm . Module 4: Application Applications of Genetic based machine learning-Genetic Algorithm and parallel processors.J.A Vijayalakshmi Pai. New Delhi. Addition-Wesley-1999. 5. real life problem.E. 3. ”Neural Networks.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.Golberg. New Delhi-2003. Original edition-1999. multilevel optimization. Fuzzy logic and Genetic Algorithms Synthesis and Applications”. reproduction-elements of genetic algorithm-genetic modeling-comparison of GA and traditional search methods.A new synthesis”.Rajasekaran G.inversion. Prentice-Hall of India.fitness scaling . Prentice Hall of India. Genetic programming .Genetic Algorithm in scientific models and theoretical foundations – computer implementation . David. constraint optimization. uses of GA in solving NP hard problems. ”Artificial Intelligence. 4. References: 1. S. “Genetic algorithms in search. “An introduction to Genetic Algorithm”. Edition: 2004. Intelligent Agents-Problem Solving-Searching – Uninformed Search strategies-BFS.History . Nilsson.Local searchConstraint Satisfaction Problems – Backtracking search for CSPs .planning Graphs-planning and Acting in Nondeterministic Domains-Conditional Planning-Continuous Planning-MultiAgent Planning. Narosa Publishing House. 1992.Foundations .Decision Network.” Principles of Artificial Intelligence”. 1999.first.A Modern Approach”. 2002. Module 3: Planning Agents Planning Problem-State Space Search-Partial Order Planning. References: 1.First Order Inference-Unification-forward Chaining. Alpha-Beta pruning. Agent based on propositional logicFirst order logic.Backward chainingResolution Strategies-Knowledge Representation-Objects-Actions-Events.Utility Theory . John Wiley. Local search for CSPAdversarial Search-Game playing. 4. 2. Michael Wooldridge.Heuristics – Greedy best. 2002. “An Introduction to Multi Agent System”. Stuart Russell and Peter Norvig. Prentice Hall. Minmax algorithm. 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. 3. III Edition.DFS. 2 nd Edition. Module 2: Knowledge Representation and Reasoning Logical Agents – Reasoning pattern in propositional logic. Nils J. “Artificial Intelligence . AW. ” Artificial Intelligence” .MCSIS 205-3 AGENT BASED INTELLIGENT SYSTEMS L T P C 3 0 0 3 Module 1: Introduction Definitions . A*. 34 . Patrick Henry Winston. 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 .Fuzzy Sets and Fuzzy Numbers. Fuzzy relation equation.running fuzzy expert systems. Fuzzy relation.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 . concepts. Application: Natural Engineering.MCSIS 205-4 Module 1: Fuzzy Logic FUZZY LOGIC L T P C 3 0 0 3 Crisp sets & fuzzy sets: introduction. non-monotonic.Types of fuzzy Inference – nature of inference in a fuzzy expert system monotonic. fuzzy operations general aggregation of operation. Binary relation. Management & decision making & computer science.runaway programs and recursion – Programs that learn from experience . Equivalence & similarity relation. 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 .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 35 . 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 . “Fuzzy Sets.References: 1. 4. 3. Timothy J Ross. PHI.Folger. William Siler and James J Buckley. Uncertainty and Information“.Klir. Prentice Hall. George J. 2. “Fuzzy Logic with Engineering Applications”. George Klir. 2004. “Fuzzy Sets Uncertainty & Information”. 2004. 36 . Wiley Inter.science. 2005 Edition. Wiley. Tina A. “Fuzzy Expert Systems and Fuzzy Reasoning”. Parametric estimation . New York.O. Wiley. 2. “Pattern Classification and Scene Analysis” .String generation as pattern description .Maximum likelihood estimation .Clustering concept . New York.Recognition of syntactic description . ”Pattern Recognition : Statistical. References: 1. Tou and Gonzales.Minimum distance pattern classifier.Discriminant functions . Module 2: Unsupervised Classification Clustering for unsupervised learning and classification .Binary feature selection.Pattern classification by distance functions . Duda R.Parsing .” Pattern Recognition Principles”. 1974.Graph based structural representation. Robert J.Perceptron algorithm . Module 3: Structural Pattern Recognition Elements of formal grammars .Feature selection through functions approximation .Bayesian parameter estimation . and Hart.Loeve transformation .Stochastic grammars and applications .Supervised learning . London.Karhunen . John Wiley&Sons Inc.P. Module 4: Feature Extraction and Selection Entropy minimization ..E.LMSE algorithm . 37 .Schalkoff.Validity of clustering solutions. Structural and Neural Approaches” . 1973..Problems with Bayes approach . 1992. 3.MCSIS 206-1 PATTERN RECOGNITION L T P C 3 0 0 3 Module 1: Pattern Classifier Overview of pattern recognition .Graph theoretic approach to pattern clustering .C-means algorithm – Hierarchical clustering procedures . Wesley Publication Company.. Interpretation in Software Development Environments. Module 2: Architectural Styles Pipes and Filters-Data Abstraction and Object Oriented Organization-Event based. Module 3: Shared Information Systems Shared Information Systems Database Integration. Where do Architectures Come from. Module 4: Architectural Design Guidance Guidance for User-Interface Architectures -Design Space and rules-Design Space for User Inter face Architectures-Design. Prentice Hall India. Architectural Structures for Shared Information Systems Shared Information Systems Database Integration. 2. “Software Architecture”. Rules for User Interface Architecture applying the Design Space – Example – A Validation Experiment – How the Design Space Was Prepared. Paul Clements. Len Bass. 2000. 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. Interpretation in Software Development Environments. Mary Shaw. 38 . 2003. References: 1.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. Rick Kazman. Features of Good Architecture. Architecture Business Cycle. Architectural Structures for Shared Information Systems. David Garlan. “Software architectures in practice”. Software Processes and the Architecture Business Cycle. AddisonWesley. 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.Gonzalez. “Digital Image Processing”. Wood. 39 . 2004. 1989. 2008. “Fundamentals of Digital Image Processing”. Object recognition – structural methods. Image transforms: Discrete Fourier Transform – 2-D FFT – Walsh Hadamard Transform – Discrete Cosine Transform – Haar Transform – Hotelling Transform – KL Transform. Pratt. 2. 2001. 3 rd edition. Frequency domain filtering: Basics of filtering in frequency domain. Pearson Education. John Wiley. Eddins.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. Anil K Jain. Wavelet Transforms in one dimension – Wavelet transforms in two dimensions Module 4: Image segmentation Region based segmentation – motion in segmentation. “Digital Image Processing”. 3. Image compression: compression methods – Huffman coding – arithmetic coding – LZW coding – Bit plane coding. References: 1. Gonzalez and Richard E. Introduction to Deterministic and stochastic spatiotemporal image models. 1st Edition. Colour image processing. 4. Prentice Hall. Richard E. “Digital Image Processing Using MATLAB”.Spatial Filtering – Smoothing spatial filters – Sharpening spatial filters. Prentice Hall.Woods and Steven L. Rafael C. 3rd Edition. Module 2: Intensity Transformations and Spatial Filtering Basic intensity transformation functions – Histogram Processing . William K. Rafael C. References: 1. Voting: Hierarchical Organization. analysis and Design”. Exception-Handling. Algorithm-Based Fault Tolerance. Checkpoint Level. FaultTolerant Routing. Basic Measures of Fault Tolerance. Elsevier Science & Technology. Israel Koren. Data Replication. Mani Krishna. Recovery Block Approach. Fault-Tolerant Remote Procedure Calls. N-Version Programming. The Rate of Hardware Failures. Failure Rate. Types of Redundancy. Other Reliability Evaluation Techniques. Lala “Fault Tolerant and Fault testable hardware design”. and Assertions. Software Reliability Models. Case Studies: NonStop Systems. Cache-Aided Rollback Error Recovery (CARER). 2007. 3. Canonical and Resilient Structures . Checkpointing in Shared-Memory Systems. Postconditions. Reliability. 2. Optimal Checkpointing-An Analytical Model. Resilient Disk Systems. Parag K. Prentice-Hall International. Itanium. Martin L Shooman. Common Network Topologies and Their Resilience. Single-Version Fault Tolerance. Checkpointing in Distributed Systems. Stratus Systems. Primary-Backup Approach. Module 2: Information Redundancy Information Redundancy. 40 . and Mean Time to Failure. Module 4: Checkpointing Introduction. “Reliablity of Computer systems and networks : Fault Tolerance. Cassini Command and Data Subsystem. Coding. IBM Sysplex. ”Fault Tolerant Systems”. Preconditions.MCSIS 206-4 FAULT TOLERANT SYSTEMS L T P 3 0 0 C 3 Module 1: Introduction Fault Classification. IBM G5. 1985. Willey. Hardware Fault Tolerance. Checkpointing in Real-Time Systems. Fault-Tolerant Networks: Measures of Resilience. Module 3: Software Fault Tolerance Acceptance Tests. Siewiorek. 41 .4. Ltd. ”Software Fault tolerance Techniques and implementation”. Charles “An Introduction to reliability and maintainability Engineering”.. Artech House Computer Security Series . John Wiley. Ebeling.“Reliable computer systems: Design and evaluation”. 2001. “Probability and statistics with reliability queuing and computer science applications”. 1996. PHI. AK Peters. 7. 8. 6. McGrawHill Science. 3rd edition. DK Pradhan “FaultTolerant computer system Design”. LL Pullam. 2 nd Edition. 1996. Daniel P. 1998. 5. 7. 2. 42 . AODV …) using NS2.) in wired network using NS2. Simulate a wired network consisting of TCP and UDP Traffic using NS2 and then calculate their respective throughput using AWK script. 5. Vegas…. 9. Simulation of wireless Ad hoc networks using NS2. Compare the behavior of different variants of TCP (Tahoe. Simulate a wireless network consisting of TCP and UDP Traffic using NS2 and then calculate their respective throughput using AWK script.MCS* 207 NETWORK SIMULATION LAB L T P C 0 0 3 2 Experiment list: 1. 6. 4. Comparison can be done on the congestion window behavior by plotting graph. Performance evaluation of different routing protocols in wired network environment using NS2. 3. Familiarizing Network Simulator – 2 (NS2) with suitable examples. Create different Wired-cum-Wireless networks and MobileIP Simulations using NS2. Performance evaluation of different queues and effect of queues and buffers in wired network environment using NS2. 8. A thorough study of packet capturing tool called WireShark. DSR. Reno. Performance evaluation of different ad-hoc wireless routing protocols (DSDV. 10. He / she shall select the topic based on the References: from international journals of repute.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. preferably IEEE journals. 43 . The students should undertake a detailed study on the topic and submit a report at the end of the semester. presentation. 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. participation in the seminar and the report submitted. Marks will be awarded based on the topic. Every student shall participate in the seminar. 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. Projects can be developed either from a student’s own idea or it can be assigned by the faculty. development and implementation work that the candidate has executed should be submitted.E/M. Presenting the work in a National Conference with the consent of the guide a will carry an added weightage to the review process. Evaluation of the Mini Project will be based on the talk delivered by the candidate (presentation).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. The students can do the mini project externally only if they are guided by a faculty with minimum M.TECH qualification. Students doing application projects should demonstrate a working design to meet the specifications of the assigned project. 44 . the report submitted and demonstration of the work done. A detailed report of project work consisting of the design. At the end of the training he / she has to submit a report on the work being carried out. MCSIS 302 MASTER’S THESIS PHASE . Emphasis should be given for introduction to the topic. by a panel of internal examiners in which one will be the internal guide. Students should follow standard practice of thesis writing. 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. The examiners should give their suggestions in writing to the students so that it should be incorporated in the Phase–II of the thesis. carried out by the students in a National/International Conference is encouraged. preferably IEEE/ACM. Presenting the work. literature survey. 45 . 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. and scope of the proposed work along with some preliminary work carried out on the thesis topic.I L T 0 0 P 10 C 5 In master’s thesis Phase-I. 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. the students are expected to select an emerging research area in Computer Science or related fields. 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. 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. 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. the student has to continue the thesis work and after successfully finishing the work. Tech.MCSIS 401 MASTER’S THESIS L T 0 0 P C 30 15 In the fourth semester. evaluation and specific weightage should be given to accepted papers in reputed conferences. 46 .