Department of Computer Science and Engineering

M.E.COMPUTER SCIENCE AND ENGINEERING

Curriculum & Syllabi(For the students admitted during the year 2012 - 2013 and onwards)

COIMBATORE INSTITUTE OF TECHNOLOGY(Government Aided Autonomous Institution Affiliated to Anna University

Chennai and Accredited by NBA)

COIMBATORE - 641 014, INDIA

THEORY1 12MCS11 Graph Theory and Finite State

Automata 3 0 0 32 12MCS12 Design and Analysis of Algorithms

using Data Structures 3 0 2 43 12MCS13 Software Engineering Methodologies 3 0 2 44 12MCS14 Parallel Processing Architecture 3 0 0 35 12MCS15 Database Engineering 3 0 2 46 12MCS16 Advanced Concepts in Operating

System 3 0 0 3Total Credits 21

Semester ICourseCode Course Title L T P CS.

No.

THEORY1 12MCS21 SOA and Web Services 3 0 2 42 12MCS22 Mobile Computing 3 0 0 33 12MCS23 Elective I 3 0 0 34 12MCS24 Elective II 3 0 0 35 12MCS25 Elective III 3 0 0 3

PRACTICAL1 12MCS26 Mini Project - - - 3

Total Credits 19

Semester IICourseCode Course Title L T P CS.

No.

Name of the Degree : M.E. (FULL TIME)Specialization : COMPUTER SCIENCE AND ENGINEERING

Curriculum for FULL TIME mode (4 semesters)From Academic Year 2012 - 2013 onwards

Department of Computer Science and Engineering

COIMBATORE INSTITUTE OF TECHNOLOGY(Government Aided Autonomous Institution Affiliated to Anna University

Chennai and Accredited by NBA)COIMBATORE - 641 014, INDIA

1

THEORY1 12MCS31 Cloud Computing 3 0 0 32 12MCS32 Elective IV 3 0 0 33 12MCS33 Technical Seminar - - - 3

PRACTICAL1 12MCS41 Project - Phase I 0 0 12 0

Total Credits 9

Semester IIICourseCode Course Title L T P CS.

No.

1 12MCS41 Project- Phase II 0 0 24 18TOTAL 0 0 0 18

Semester IVCourseCode Course Title L T P CS.

No.

Total Credits to be earned for the Award of the Degree = 67

12MCSE01 High Performance Networks 3 0 0 312MCSE02 Ad-hoc and Sensor Networks 3 0 0 312MCSE03 Soft Computing Techniques 3 0 0 312MCSE04 Machine Learning Techniques 3 0 0 312MCSE05 Internet Technologies 3 0 0 312MCSE06 Data Mining and Data Warehousing 3 0 0 312MCSE07 Data Compression Techniques 3 0 0 312MCSE08 Information Security 3 0 0 312MCSE09 Multicore High Performance Systems 3 0 0 312MCSE10 Software Metrics and Measurement 3 0 0 312MCSE11 Software Quality Assurance 3 0 0 312MCSE12 Enterprise Information System 3 0 0 312MCSE13 Bioinformatics 3 0 0 312MCSE14 Pervasive Computing 3 0 0 312MCSE15 Information Retrieval Techniques 3 0 0 312MCSE16 Embedded and Real-time Systems 3 0 0 312MCSE17 Distributed Systems 3 0 0 312MCSE18 Image Analysis 3 0 0 3

ELECTIVES FOR M.E. CSE

Code No Subject L T P C

2

Note : L-Lecture, T-Tutorial, P-Practical, C-Credits

THEORY1 12MCS11 Graph Theory and Finite State

Automata 3 0 0 32 12MCS12 Design and Analysis of Algorithms

using Data Structures 3 0 2 43 12MCS13 Software Engineering Methodologies 3 0 2 4

Total Credits 11

Semester ICourseCode Course Title L T P CS.

No.

THEORY1 12MCS21 SOA and Web Services 3 0 2 42 12MCS22 Mobile Computing 3 0 0 33 12MCS23 Elective I 3 0 0 3

Total Credits 10

Semester IICourseCode Course Title L T P CS.

No.

Name of the Degree : M.E. (PART TIME)Specialization : COMPUTER SCIENCE AND ENGINEERING

Curriculum for PART TIME mode (6 semesters)From Academic Year 2012 - 2013 onwards

Department of Computer Science and Engineering

COIMBATORE INSTITUTE OF TECHNOLOGY(Government Aided Autonomous Institution Affiliated to Anna University

Chennai and Accredited by NBA)COIMBATORE - 641 014, INDIA

3

THEORY1 12MCS14 Parallel Processing Architecture 3 0 0 32 12MCS15 Database Engineering 3 0 2 43 12MCS16 Advanced Concepts in Operating

System 3 0 0 3Total Credits 10

Semester IIICourseCode Course Title L T P CS.

No.

1 12MCS41 Project- Phase II 0 0 24 18TOTAL 0 0 0 18

Semester IVCourseCode Course Title L T P CS.

No.

Total Credits to be earned for the Award of the Degree = 67

THEORY1 12MCS24 Elective II 3 0 0 32 12MCS25 Elective III 3 0 0 3

PRACTICAL1 12MCS26 Mini Project - - - 3

Total Credits 9

Semester IVCourseCode Course Title L T P CS.

No.

THEORY1 12MCS31 Cloud Computing 3 0 0 32 12MCS32 Elective IV 3 0 0 33 12MCS33 Technical Seminar - - - 3

PRACTICAL1 12MCS41 Project -Phase I 0 0 12 0

TOTAL CREDITS 9

Semester VCourseCode Course Title L T P CS.

No.

Note : L-Lecture, T-Tutorial, P-Practical, C-Credits

4

12MCS11 - GRAPH THEORY AND FINITE STATEAUTOMATA

L T P C3 0 0 3

ASSESSMENT : THEORY

OBJECTIVE :

To provide fundamental ideas on Graph Theory and Automata Theoryfor the study of Computer Science.

EXPECTED OUTCOME :

At the end of this course students

• Can understand basic notations of Graph Theory

• Can study the Algorithmic Graph Theory

• Can have an understanding of Finite State and Pushdown Automata

• Can have knowledge of Regular Languages and Context-FreeLanguages

• Know the relation between Regular Language and Context-FreeLanguage.

GRAPHS AND TRANSPORT NETWORK

Graphs – Sub Graphs - Weighted Graphs – Connectedness -Components – Euler Graph – Hamiltonian Graph – Shortest Path –Travelling Salesman Problem – Transport Network – Max-Flow Min-Cut Theorem. (9)

GRAPHS ON SURFACES AND ENUMERATION OF GRAPHS

Planar Graphs – Detection of Planarity – Kuratowski’s Graph – Trees– Spanning Trees – Finding Minimal Spanning Tree – Types ofEnumeration – Counting Labeled and unlabeled Trees – Polya’sCounting Theorem. (9)

5

COLORING, COVERING AND MATCHINGVertex Coloring – Edge Coloring – Chromatic Number – ChromaticPartitioning – Chromatic Polynomial – Four Color Problem – Matching– Covering in Bipartite Graphs – Perfect Matching. (9)

FORMAL LANGUAGES AND FINITE AUTOMATAThe Chomsky’s Hierarchy: Grammar and Languages – Ambiguity –Pumping Lemma for Regular Languages – Finite Automata: Definition,Designing of Finite Automata – NonDeterminism: Definition,Equivalence of NFAs and DFAs - Regular Expressions: Definition,Equivalence with Finite Automata. (9)

CONTEXT-FREE LANGUAGES AND TURING MACHINESContext-free Grammars: Chomsky Normal Form(CNF) of Context-FreeGrammars, – Pushdown Automata: Definition, Equivalence withContext-Free Grammars – Non Context-Free Languages: PumpingLemma for Context-Free Languages – Turing Machines – The HaltingProblem. (9)

TOTAL : 45

REFERENCES1. Narsingh Deo, “Graph Theory: With Application to Engineering

and Computer Science”, PHI, 2003.

2. Michael Sipser,”Introduction to the Theory of Computation – 2nd

Edition”, Thomson Learning, 2005.

3. R.J. Wilson, “Introduction to Graph Theory”, Fourth Edition,Pearson Education, 2003.

4. Reinhard Diestel, “Graph Theory”, II Edition, Springer Publications,2006.

5. John E. Hopcroft, Rajeev Motwani and Jeffery D. Ullman,”AutomataTheory, Languages, and Computation (3rd. Edition)”, PearsonEducation, 2008.

6

12MCS12 - DESIGN AND ANALYSIS OF ALGORITHMSUSING DATA STRUCTURES

L T P C3 0 2 4

ASSESSMENT : THEORY & PRACTICAL

OBJECTIVE :

To study analysis of algorithms, algorithm design techniques and theirapplications.

EXPECTED OUTCOME :

Student should have learnt algorithm concepts and design algorithmsfor applications.

ALGORITHM ANALYSISFundamentals of algorithmic Problem Solving - Asymptotic Notations ,Basics of NP-Hard and NP-Completeness, Mathematical Analysis ofNon-recursive Algorithm – Mathematical Analysis of RecursiveAlgorithm – Empirical Analysis of Algorithms. (7)

DIVIDE AND CONQUERGeneral Method- Binary Search – Finding the Maximum and Minimum-Merge Sort – Quick Sort – Strassen’s Matrix Multiplication. (7)

GREEDY METHODGeneral Method - Knapsack Problem – Tree Vertex Splitting – JobSequencing with Deadlines – Optimal Merge Patterns – Single SourceShortest Paths. (7)

DYNAMIC PROGRAMMINGGeneral Method - Multistage Graphs – All-Pairs Shortest Paths – SingleSource Shortest Path - Optimal Binary Search Trees - String Editing -0/1 Knapsack Problem - Traveling Salesperson Problem – Flow ShopScheduling. (7)

7

BACKTRACKINGGeneral Method – 8 Queens Problem – Graph Coloring –HamiltonianCycles– Knapsack Problem. (7)

BRANCH AND BOUNDGeneral Method – 0/1 Knapsack Problem – Traveling SalespersonProblem. (4)

HEURISTIC ALGORITHMSGenetic Algorithm- Ant Colony Optimization- Bee Colony Optimization– Particle Swarm Optimization – Simulated Annealing. (6)

THEORY : 45TUTORIAL : 30

TOTAL : 75

REFERENCES

1. Ellis Horowitz, Sartaj Sahani and Sanguthevar Rajasekaran,“Computer Algorithms/C++”, Second Edition, Universities Press,2007.

2. Anany Levitin, “Introduction to the Design and Analysis ofAlgorithm”, Pearson Education Asia, Third Edition, 2011.

3. T.H. Cormen, C.E. Leiserson, R.L. Rivest and C. Stein, “Introductionto Algorithms”, MIT Press, Third Edition, 2011.

4. A.V.Aho, J.E. Hopcroft and J.D.Ullman, “The Design and Analysisof Computer Algorithms”, Pearson Education Asia, Fourth Edition,2009.

5. Jason Brownlee, “Clever Algorithms: Nature-Inspired ProgrammingRecipes”, Lulu Enterprises, January 2011.

8

12MCS13 - SOFTWARE ENGINEERINGMETHODOLOGIES

L T P C3 0 2 4

ASSESSMENT : THEORY & PRACTICAL

OBJECTIVE :

To understand the Software Engineering concepts and practices forthe design, development and testing of quality software.

EXPECTED OUTCOME :

The students shall obtain the necessary skills to analyze the problem,perform estimations, carry out the design, development and testingactivities to produce the software as required by the users. The studentshall also obtain the skills needed to prepare the software documents.

INTRODUCTION

Introduction – Software Myth-Generic view of process-Software ProcessFramework-Process models: waterfall-Incremental-evolutionarymodels- Agile process models (7)

REQUIREMENTS ENGINEERING

System engineering Hierarchy-System Modeling- RequirementsEngineering: Tasks-Initiating the process-Eliciting Requirements-Developing Use Cases-Negotiating requirements-Requirementsvalidation. Building the analysis model-concepts only. (7)

SOFTWARE ARCHITECTURE AND DESIGN

Software Architectures: Role-Architectural views-Architectural Styles-Documentation. Software Design: Design Concepts –Abstraction,Modularity—Cohesion, Coupling-Open closed principle. FunctionOriented Design: Structured charts-Structured Design Methodology(Dataflow oriented Design) Object Oriented design: OO Concepts-

9

Design Concepts-UML-Design Methodology.User Interface Design:User Interface Analysis and Design. Detailed Design: Logic/AlgorithmDesign-State Modeling of classes-Verification-Metrics. (12)

SOFTWARE TESTING

Testing Strategies for conventional and OO software: Unit testing andintegration testing considerations. Validation testing: Criteria-Alpha andBeta testing-System testing: Recovery test– Security test– Stress&Performance testing, Regression testing.Testing Process: Test Plan-Test Case Design-Test Case Execution.Black Box Testing: Equivalence-Boundary Value- Cause effect and State based testing. White boxtesting techniques: Control Flow based Criteria-Path testing-ControlStructure testing. (9)

ESTIMATION AND SCHEDULING

Estimation Techniques – COCOMO & Function Points, Scheduling –Basic Concepts – Project Scheduling – WBS and Time line Charts –Tracking the project schedule. (5)

SCM AND QUALITY ASSURANCE

Software Configuration and Management Features – SCM Process –Software Quality Concepts – Quality Assurance – Software Review –Technical Reviews – Quality Standards (5)

THEORY : 45

PRACTICAL : 30

TOTAL : 75

REFERENCES

1. Roger S.Pressman, “Software Engineering – A Practitioner’sApproach”, McGraw Hill, USA, 2007.

10

2. Pankaj Jolote, “Software Engineering-A Precise Approach”, WileyIndia, 2010.

3. Sommerville I, “Software Engineering”, Pearson Education India,New Delhi, 2006

4. Grady Booch, Ivar Jacobson Etc, “ Object Oriented Analysis andDesign with applications”, Addition Wesley, 3rd Ed, 2007

11

12MCS14 - PARALLEL PROCESSINGARCHITECTURE

L T P C3 0 0 3

ASSESSMENT : THEORY

OBJECTIVE :To learn the architecture, functions and characteristics of computersystems and to understand the design of various functional units.

EXPECTED OUTCOME :The learners will understand the basic structure and operations ofcomputers and will be able to design the functional units of the parallelsystem.

REVIEW OF FUNDAMENTALSQuantitative Principles of Computer Design - Basics of Pipelining:Implementation-Pipeline Hazards-Performance Issues. (8)

INSTRUCTION-LEVEL PARALLELISM AND LIMITATIONSInstruction-Level Parallelism: Concepts and Challenges - BasicCompiler Techniques for Exposing ILP - Static Prediction – DynamicPrediction – Dynamic Scheduling – Hardware Based Speculation –Limitations of ILP. (9)

MULTIPROCESSOR AND THREAD LEVEL PARALLELISMSymmetric Shared Memory Architectures - Performance of SymmetricShared Memory Multiprocessors - Distributed Shared Memory andDirectory Based Coherence - Synchronization: The Basics - Models ofMemory Consistency. (9)

MEMORY DESIGN AND STORAGECache Performance – Memory Technology and Optimizations -Protection: Virtual Memory And Virtual Machines - Storage Systems:Disk Storage- I/O Performance, Reliability Measures, And Benchmarks.

(10)

12

VECTOR PROCESSOR AND RISC ARCHITECTUREVector Processor: Architecture - Issues – Effectiveness of CompilerVectorization - Vector performance. RISC Architecture: Survey of RISCArchitecture - SPARC, PA-RISC. (9)

TOTAL : 45

REFERENCES1. John L. Hennessy, David A. Patterson “Computer Architecture A

Quantitative Approach”, Morgan Kaufmann Publishers Fourth andSecond Edition, 2007.

2. Kai Hwang and Faye Briggs, “Computer Architecture And ParallelProcessing “, McGraw hill International Edition, Singapore, 2000.

3. Carl Hamachar, Zvonco Vranesic and Safwat Zakv, “ComputerOrganization” McGraw hill, 2002.

13

12MCS15 - DATABASE ENGINEERINGL T P C3 0 2 4

ASSESSMENT: THEORY & PRACTICAL

OBJECTIVE:

To learn the basic concepts and importance of databases, relationalmodeling, relational database design and querying, distributed databasearchitecture and query optimization.

EXPECTED OUTCOME :

At the end of the course students shall obtain the fundamentalknowledge on different data models, database design, structured querylanguage, storage and distributed database.

DATABASE SYSTEM CONCEPTS AND ARCHITECTURE

Data Models, Schemas and Instances - Three-Schema Architectureand Data Independence- Database Languages and Interfaces - TheDatabase System Environment - Centralized And Client/ServerArchitectures For DBMSs - Classification of Database ManagementSystems - Relational Model Concepts - Relational Model ConstraintsAnd Relational Database Schemas - Update Operations, Transactionsand Dealing with Constraint Violations. (9)

ADVANCED SQL

SQL Data Definition And Data Types - Specifying Constraints in SQL -Basic Retrieval Queries in SQL - Insert, Delete and Update Statementsin SQL - Additional Features of SQL. Complex SQL: More ComplexSQL Retrieval Queries - Specifying Constraints as Assertions andActions as Triggers - Views (Virtual Tables) in SQL - Schema ChangeStatements in SQL. (8)

14

ADVANCED DATABASE MODELS AND SECURITY

Active Database and Triggers - Temporal Database - Spatial Database- Multimedia Database - Deductive Databases. Security: DiscretionaryAccess Control Based on Granting and Revoking Privileges - MandatoryAccess Control and Role-Based Access Control for Multilevel Security- SQL Injection - Introduction to Statistical Database Security -Introduction to Flow Control - Encryption and Public Key Infrastructures- Privacy Issues and Preservation - Challenges of Database Security- Oracle Label-Based Security. (9)

DISTRIBUTED DBMS

Distributed DBMS Architecture: ANSI/SPARC Architecture – CentralizedDBMS Architecture – Architectural Models for Distributed DBMSs –Autonomy –Distribution- Heterogeneity. Distributed Database Design:Issues – Fragmentation – Allocation. (9)

QUERY PROCESSING

Query Processing: Characterization Of Query Processors – Layers ofQuery Processing. Query Decomposition and Data Localization: QueryDecomposition – Localization of Distributed Data. Query Optimization:Centralized Query Optimization – Distributed Query Optimization. (10)

THEORY : 45

PRACTICAL : 30

TOTAL : 75

REFERENCES

1. Ramez Elmasri and Shamkant B. Navathe, “Fundamentals ofDatabase Systems” sixth edition, Pearson Education Inc, 2011.

2. M. Tamer Ozsu and Valduriez, “Principles Of Distributed DatabaseSystems”, Third Edition, Springer 2011.

3. Abraham Silberschatz Hendry F. Korth, S.Sudharshan, “ DatabaseSystem Concepts”, Tata McGraw Hill, Sixth Edition, 2010.

15

12MCS16 - ADVANCED CONCEPTS IN OPERATINGSYSTEM

L T P C3 0 0 3

ASSESSMENT : THEORY

OBJECTIVE :

To understand the main concepts of advanced operating systems suchas distributed operating systems, multiprocessor operating systemsand real time operating systems.

EXPECTED OUTCOME :

At the end of the course, students will gain knowledge on the advancedconcepts of various operating systems.

DISTRIBUTED CLOCK AND PROCESS SYNCHRONIZATION

Introduction - Issues - Communication Primitives - Inherent Limitations- Lamport’s Logical Clock - Vector Clocks - Causal Ordering - GlobalState. Distributed Mutual Exclusion: Classification of Mutual ExclusionAlgorithms - Non-Token Based Algorithms - Lamport’s Algorithm -Ricart-Agrawala Algorithm - Token-Based Algorithms - Suzuki-Kasami’sBroadcast Algorithm - Singhal’s Heuristic Algorithm - Raymond’s Treebased Algorithm. (9)

DISTRIBUTED DEADLOCK DETECTION

Introduction - Handling Strategies - Issues - Centralized DeadlockDetection Algorithms- Distributed Deadlock Detection Algorithms -Hierarchical Deadlock Detection Algorithms - Agreement Protocols -Classification - Solutions - Applications. (9)

DISTRIBUTED RESOURCE MANAGEMENT

Distributed File systems: Architecture - Mechanisms - Design Issues -Distributed Shared Memory: Architecture - Algorithms - Memory

16

Coherence - Protocols - Design Issues - Distributed Scheduling: Issuesin Load Distributing - Components - Algorithms. (10)

MULTIPROCESSOR OPERATING SYSTEMS

Introduction - Structures - Design Issues - Threads - ProcessSynchronization - Processor Scheduling - Memory Management: MacOperating System. (8)

EMBEDDED AND REAL TIME OPERATING SYSTEMS

Characteristics of Real Time Systems - Safety and Reliability - Typesof Real Time Tasks - Timing Constraints - Modeling Timing Constraints- Real Time Task Scheduling: Characteristics - Classification - ClockDriven Scheduling - Event Driven Scheduling - Hybrid Schedulers -Earliest Deadline First Scheduling - Rate Monotonic Algorithm. (9)

TOTAL: 45

REFERENCES

1. Mukesh Singhal and N. G. Shivaratri, “Advanced Concepts inOperating Systems”, Tata McGraw- Hill Edition, 2001.

2. Rajib Mall, “Real Time Systems: Theory and Practice”, PearsonEducation, 2009.

3. Abraham Silberschatz, Peter B. Galvin, G. Gagne, “OperatingSystem Concepts”, 8th Edition, John Wiley & Sons Incorporated,2009.

4. Pradeep K.Sinha,”Distributed Operating System-Concepts andDesign”, PHI, 2003.

5. Andrew S.Tanenbaum, “Distributed Operating System”, Pearsoneducation, 2003.

17

12MCS21 - SOA AND WEB SERVICESL T P C3 0 2 4

ASSESSMENT : THEORY & PRACTICAL

OBJECTIVE :

To understand the basics of SOA, web services and to build a SOA.

EXPECTED OUTCOME :

The students would be able to understand the fundamentals of webservices, their internals, design and build SOA and reuse.

WEB SERVICES TECHNOLOGIES

Web services, Evolution and differences with Distributed computing,WSDL,SOAP, UDDI, Transactions, Business Process ExecutionLanguage for Web Services,

WS-Security and the Web services security specifications, WS-ReliableMessaging, WS-Policy, WS-Attachments. (9)

SOA FUNDAMENTALS

SOA- Fundamentals, Business Values, Evolution, characteristics,concept of a service , misperceptions , Basic architecture, infrastructureservices, Enterprise Service Bus (ESB), Enterprise Software models,IBM On Demand operating environment. (9)

SOA PLANNING AND ANALYSIS

Stages of the SOA lifecycle, Delivery Strategies, service-orientedanalysis, determining non-functional requirements, business centricSOA and its benefits, Service modeling, Basic modeling building blocks,service models for legacy application integration - enterprise integration,Enterprise SolutionAssets(ESA). (9)

18

SOA DESIGN AND IMPLEMENTATION

Service-oriented design process, design activities, determine services-tasks based on business process model- standards, articulatearchitecture, mapping business processes to technology, designingservice integration environment, Tools for SOA, security implementation,implementation of integration patterns, services enablement, qualityassurance. (9)

MANAGING SOA ENVIRONMENT

Distributing service management and monitoring concepts, operationalmanagement challenges, Service-level agreement considerations, SOAgovernance, QoS compliance, role of ESB, impact of changes toservices in the SOA lifecycle. (9)

THEORY : 45

PRACTICAL : 30

TOTAL : 75

REFERENCES

1. Thomas Erl, “Service-Oriented Architecture: Concepts, Technology,and Design”, Prentice Hall Publication, 2005.

2. Norbert Bieberstein, Sanjay Bose, Marc Fiammante, Keith Jones,Rawn Shah, “Service- Oriented Architecture Compass: BusinessValue, Planning, and Enterprise Roadmap”, IBM Press Publication,2005.

3. Sandy Carter, “The New Language of Business: SOA & Web 2.0”,IBM Press, 2007.

4. Thomas Erl, “Service-Oriented Architecture: A Field Guide toIntegrating XML and Web Services”, Prentice Hall Publication, 2004

19

12MCS22 - MOBILE COMPUTINGL T P C3 0 0 3

ASSESSMENT : THEORY

OBJECTIVE :

To study about mobile communication and related issues, mobile OSand mobile application development

EXPECTED OUTCOME :

Students should have learnt about various mobile communicationstandards, Mobility Issues and Services, Mobile OS architecture anddevelop simple applications using Android/Bada

MOBILE COMMUNICATION

GSM – GPRS - CDMA based communication – Wi-Fi – WAP -WAPGateway - Transcoding Gateway - residential Gateway - Wirelessapplication environment – Bluetooth – IrDA – ZigBee – WiMax - Rfid –4G Network (9)

MOBILITY MANAGEMENT

Mobile IP – Location Management – Tunneling and Encapsulation –Route Optimization – Split TCP, Snoop TCP, Mobile TCP – ServiceDiscovery, Service Location Protocol – Mobile file system. (9)

DATA DISSEMINATION AND SYNCHRONIZATION

Data dissemination mechanisms – Broadcasting models – Selectivetuning - Digital audio broadcasting - Digital video broadcasting – Datareplication and synchronization – Synchronization Engine – MobileAgents for Synchronization – SyncML framework and protocolArchitecture – SMIL (9)

20

MOBILE COMPUTING ARCHITECTURES

Mobile OS: Architecture Overview of Symbian OS, Android, Bada –Context aware computing – Power aware computing – MobileComputing Architecture: Client – Server, Peer- to-Peer, Mobile AgentArchitecture, Mobile Agent System Interoperability Facility. (9)

MOBILE APPLICATIONS

Mobile Application Development Process- Characteristics of MobileSoftware – Mobile Services – GUI – VUI – Multimodal User Interface– Multimodal Integrator and Dialogue manager Architecture – SimpleMobile Application Development using Android/Bada. (9)

TOTAL : 45

REFERENCES

1. Raj Kamal, “ Mobile Computing”, Oxford University Press, Indianreprint 2009

2. Reza B’Far, “Mobile Computing Principles”, Cambridge Universitypress, Indian reprint, 2009

3. Ben Morris, “Introduction to Bada, A developer’s Guide”, Wiley,2010

4. Zigurd Mednieks, Laird Dornin,”Programming Android”, O’reilly ,2011

21

12MCS31 - CLOUD COMPUTINGL T P C3 0 0 3

ASSESSMENT : THEORY

OBJECTIVE :

Introduce the fundamental concepts of cloud computing architecture,virtualization, services and application and mobile cloud.

EXPECTED OUTCOME :

Student shall attain knowledge in the area of cloud computing and itsapplication.

INTRODUCTION

Introduction to Cloud computing: cloud computing- types- clouddeployment models- service model- characteristics – disadvantagesof cloud computing – accessing the role of open standards. Accessingthe value proposition - cloud architecture - services and applications.

(8)

CLOUD PLATFORMS

Abstraction and virtualization –capacity planning- exploring platformas a service- Google web services - Amazon web services – Microsoftcloud services. (9)

CLOUD INFRASTRUCTURE

Cloud infrastructures - Cloud Management - Cloud security. (9)

SERVICES AND APPLICATIONS

Services and Applications: service oriented architecture - Movingapplications to the cloud- working with cloud based storage productivitysoftware- web mail services- communicating with the cloud – mediaand streaming. (10)

22

MOBILE CLOUD :

Mobile Cloud: mobile devices – Working with mobile web services.(9)

TOTAL : 45

REFRENCES

1. Barrie Sosinsky, “Cloud Computing Bible”, Wiley publishing Inc,2011.

2. Michael Miller, “Cloud Computing: Web-Based Applications ThatChange the Way You Work and Collaborate Online”, QuePublishing, August 2008.

3. Haley Beard, “Cloud Computing Best Practices for Managing andMeasuring Processes for On-demand Computing, Applicationsand Data Centers in the Cloud with SLAs”, Emereo Pty Limited,July 2008.

23

12MCSE01 - HIGH PERFORMANCE NETWORKSL T P C3 0 0 3

ASSESSMENT : THEORY

OBJECTIVE :

To introduce the various approaches used to achieve better Quality ofservice related performance over existing networks and learn thefeatures of multimedia communication and latest networkingtechnologies.

EXPECTED OUTCOME :

Students will have better understanding of the various Quality of servicerelated issues and techniques, features of multimedia networks andother latest networks.

QoS BASED ARCHITECTURES

Integrated Services Model – Differentiated Services Architecture – CodePoint – Traffic Classification and Conditioning – DiffServ Specifications– Traffic management mechanisms – Resource reservation protocolmessages and operation – protocols for real-time Interactiveapplications. (9)

MPLS AND VPN

MPLS architecture – Labels – Frame mode - Cell Mode - Constraintbased shortest path first – Bandwidth Constraints models – Fast reroute– link and node protection – VPN models – VPN network topologies –VPN routing and forwarding tables – MPLS based VPN. (9)

BACKBONE NETWORKS

Performance requirements – Segmentation of Performance Targets –Latency and link Utilization – Best effort Backbone – Backbone withMPLS TE – DiffServ Backbone (9)

24

MULTIMEDIA NETWORKS

Characteristics and requirements of Multimedia traffic – network andtransport layer issues for multimedia - Integrated packet networks –Layered video coding – Error - resilient video coding – scalable ratecontrol – streaming video across Internet – Traffic specification forMPEG video transmission – Bandwidth allocation mechanism – Fine-grained scalable video coding for Multimedia across IP (9)

METRO NETWORKS

Metro Ethernet – Ethernet over SONET, MPLS, VPLS – Metro Ethernetservices - Resilient packet rings – Optical networks – wavelength routing– optical switching – GMPLS architecture – IPTV (9)

TOTAL : 45

REFERENCES

1. Santiago Alvarez, “Qos for IP/ MPLS Networks”, PearsonEducation, 2007.

2. K.R.Rao, Bojkovic, Milovanovic, “Multimedia CommunicationSystems”, Prentice Hall of India, 2005.

3. Sam Halabi, “Metro Ethernet”, Cisco Press, 2008.

25

12MCSE02 - AD-HOC AND SENSOR NETWORKSL T P C3 0 0 3

ASSESSMENT : THEORY

OBJECTIVE

To understand the design and implementation issues, routing andsecurity mechanisms in ad-hoc and sensor networks.

EXPECTED OUTCOME

Students should have understood about the various MAC protocols,routing techniques, power-awareness, QoS and Security aspects inAd-Hoc and sensor networks.

AD-HOC NETWORKS

Introduction – Challenges - Applications of Mobile Ad-hoc networks,MAC: Issues in designing a MAC protocol for Ad Hoc Wireless networks– Classification – Contention - Based protocols – Contention - Basedprotocols with reservation mechanisms – Contention - Based protocolswith Scheduling mechanisms - Power-aware MAC (9)

ROUTING IN AD-HOC NETWORKS

Introduction – Issues – Classification – Topology-Based routing-Position- Based routing - Other routing protocols - Signal stability routing, Poweraware routing, Associativity based routing, QoS routing - Multicastrouting protocols - Geocast routing protocols. Transport layer:Introduction -TCP and MANETs - TCP over Ad Hoc networks. (9)

WIRELESS SENSOR NETWORKS

Introduction - Sensor network architecture - Sensing andcommunication range-Design issues - MAC protocols – Applicationsof WSN – Sensor MAC – WPAN. (9)

26

ROUTING IN SENSOR NETWORKS

Sensor routing Layer - Quality of a sensor network - High levelapplication layer support - other issues: Energy - efficient design,Synchronization, Transport layer issues, Security, real-timecommunication. (9)

QOS AND SECURITY

Introduction - Issues and challenges in providing QoS - Classificationof QoS - MAC layer solutions - Network layer solutions - QoS frameworkSecurity in Ad-Hoc Networks - Secure routing protocols – Operatingsystems and Middleware for wireless sensor networks. (9)

TOTAL: 45

REFERENCES

1. Sivaram murthy, B.S Manoj “Ad Hoc wireless networks”, PearsonEdn, 2008

2. Carlos de morais cordeiro, Dharma Prakash agarwal “Ad-Hoc andsensor networks”, World Scientific, 2nd Edn, 2011.

3. Kazem Sohraby, Daniel Minoli, Taieb Znati, “Wireless SensorNetworks”, Wiley, Indian print, 2010.

27

12MCSE03 - SOFT COMPUTING TECHNIQUESL T P C3 0 0 3

ASSESSMENT : THEORY

OBJECTIVE

To learn the basic concepts of fuzzy sets, neural networks, neuro fuzzysystems, genetic algorithms and their applications.

EXPECTED OUTCOME

Students shall acquire the basic knowledge of fuzzy sets and neuralnetworks and their application in neuro-fuzzy modeling. In addition thestudents will get exposed to the basics of genetic algorithms and itsvarious operators.

GENETIC ALGORITHMS

Introduction to genetic algorithms (GA) - goals of optimization -differences and similarities between genetic algorithm and traditionalmethods - schemata - terminology of GA - strings, structure, parameterset - coding - fitness function - data structures - GA operators - algorithm.

(8)

MODERN SEARCH TECHNIQUES

Simulated annealing - introduction- algorithm - applications. Tabu search- introduction - algorithm -applications. Particle swarm optimizationalgorithm.- AI search algorithm - Predicate calculus - Rules ofinterference – Semantic networks - Frames - Objects - Hybrid models– Applications. (9)

FUZZY LOGIC

The concept of uncertainty and associated solutions - fuzzy sets - basicproperties and characteristics of fuzzy sets - fuzzy set operations -

28

fuzzy reasoning - applications of fuzzy logic- Fuzzy matrices - Fuzzyfunctions - Decomposition - Fuzzy automata and languages - Fuzzycontrol methods - Fuzzy decision making. (9)

ARTIFICIAL NEURAL NETWORKS

Basics of artificial neural networks (ANN) – characteristics of ANN -models of neuron – topology - basic learning laws - types.Kohenonself organizing network - back propagation network – learning curves -applications of ANN to engineering problems - Hopfield network. (9)

NEURO – FUZZY MODELLING

Neuro - Fuzzy modelling - adaptive neuro - fuzzy inference systems,neuro-fuzzy controller -feedback control, expert control, backpropagation through time and real - time recurrent learning,reinforcement learning control, gradient - free optimization -Classification and Regression Trees - Data clustering- Evolutionarycomputation. (10)

TOTAL : 45

REFERENCES

1. Goldberg, D.E., “Genetic Algorithms in Search, Optimization, andMachine Learning”, Addison-Wesley, 1989.

2. Timothy J.Ross, “Fuzzy Logic with Engineering applications”, TataMcGraw Hill New York, 3rd edition, 2010.

3. Laurene Fausett, “Fundamentals of Neural Networks”, PrenticeHall, 1994.

4. Jang J.S.R., Sun C.T. and Mizutani E, “Neuro-Fuzzy and Softcomputing”, Prentice Hall 1998.

5. Fred Glover, Manuel Laguna, “Tabu Search”, Kluwer AcademicPublishers, 1997.

29

6. M.Tim Rose, “Artificial Intelligence Application Programming”, FirstEdition, Dream tech Press, 2003.

7. S.Rajasekaran and A.Vijayalakshmi Pai, “Neural networks, Fuzzylogic and Genetic algorithms”, Prentice Hall of India Ltd, New Delhi,2008.

8. Deb, K, “Optimization for Engineering Design”, Prentice Hall ofIndia (P) Ltd., New Delhi, 2005.

9. Nih J.Nelsson, “Artificial Intelligence - A New Synthesis”, HarcourtAsia Ltd., 1998.

10. Dr.S.N Sivanandam and Dr.S.N.Deepa, “Principles of SoftComputing”, 2nd edition, Wiley-India, 2011.

30

12MCSE04 - MACHINE LEARNING TECHNIQUESL T P C3 0 0 3

ASSESSMENT : THEORY

OBJECTIVE :To understand the basics of machine learning, various machine learningtechniques.

EXPECTED OUTCOME :The learners shall understand the machine learning techniques-Bayesian, Decision Tree, analytical and Instance based learning andto apply these techniques in computing.

INTRODUCTIONDesigning a learning system – Perspectives and issues in machinelearning – Concept learning task – Concept learning as search – Versionspaces – Candidate Elimination learning algorithm – Inductive bias.

(9)

DECISION TREE LEARNINGDecision Tree representation – Appropriate Problems for Decision TreeLearning – Basic Decision Tree Learning algorithm – Hypothesis spacesearch and Inductive Bias in Decision Tree Learning – Issues in DecisionTree Learning.ANN: Perceptrons – Back propagationAlgorithms.Evaluating Hypothesis: Deriving confidence intervals –Hypothesis testing – comparing learning algorithms. (12)

BAYESIAN LEARNINGBayes Theorem and Concept learning – Maximum Likelihood and LeastSquared error hypotheses - Maximum Likelihood hypotheses forpredicting probabilities – Minimum description Length principle – Bayesoptimal classifier – Gibbs algorithm – Naïve Bayes classifier – BayesianBelief networks – EM algorithm (9)

31

ANALYTICAL AND COMBINING ANALYTICAL AND INDUCTIVELEARNINGAnalytical learning – Explanation based learning – Inductive Analyticalapproaches to learning - Using Prior knowledge to, initialize thehypotheses, after the search objective and augment search operators.

(6)

INSTANCE BASED AND REINFORCEMENT LEARNINGK- nearest neighbor learning – Locally weighted regression- Radialbasis functions – Case based reasoning – Reinforcement learning:Learning task – Q learning – Q function –Algorithm for learning Q –convergence- updating sequence – Temporal difference learning – Nondeterministic rewards and actions (9)

TOTAL : 45

REFERENCES1. Mitchell, Tom. Machine Learning. New York, McGraw-Hill,First

Edition, 2003.

2. Ethem Alpaydin,” Introduction to Machine Learning”, MIT Press,Second Edition, 2010.

3. Stephen Marsland,” Machine Learning – An AlgorithmicPerspective”, Chapman and Hall,First Edition, 2009.

4. Nils Nilsson, “Introduction to Machine Learning”, MIT Press, 1997.

5. Jude Shavlik, Thomas G Dietterich, “Readings in MachineLearning”, Morgan Kauffman Publishers, 1990.

32

12MCSE05 INTERNET TECHNOLOGIESL T P C3 0 0 3

ASSESSMENT : THEORY

OBJECTIVE :To understand the basics of internet, web scripting and web servers,xml technology, server side technologies and web services

EXPECTED OUTCOME :At the end of the course in depth knowledge in web services and internettechnologies.

WEB SCRIPTING AND WEB SERVERSHTML – CSS – DHTML – Java Script – Functions – Events – DOM.Web Server Functions – Web Security – Fire Wall – Proxy Servers –Virtual Directories – MIME – HTTP Headers – Deployment using webservers. (9)

XML TECHNOLOGYXML – benefits – Advantages of XML over HTML, EDI, Databases –XML based standards – Structuring with schemas - DTD – XMLSchemas – XML processing – DOM – SAX – presentation technologies– XSL – XFORMS – XHTML – Transformation – XSLT – XLINK – XPATH– XQuery. (9)

SERVER SIDE TECHNOLOGIES :ASP – Handling Request, Response – Session Management – Serverside Includes – JSP – Scriptlets – Custom Tag Library – Include andForward – Struts (9)

INTRODUCTION TO WEB SERVICES:Web Services protocol and standards – WSDL Documents - Overviewof UDDI - Calling a Web Service from a Browser - Calling a Web Service

33

by Using a Proxy - Creating a simple web service - Creating and Callinga Web Service by Using Visual Studio .NET. (9)

WEB SERVICE DEVELOPMENT :Enterprise Java web services – JAX-RPC – JAXP – Publishing andDiscovery using JAXR – JAXM – .NET Web Service – Interoperability.WEB 2.0 TECHNOLOGIES: Introduction to Ajax, Ajax Design Basics,J Blogs, Wikis, RSS feeds. (9)

TOTAL : 45

REFERENCES1. Deitel & Deitel, “Internet & World Wide Web How to Program”,

Pearson Education India, Third Edition, 2004.

2. Deitel & Deitel, “XML How to Program”, Pearson Education, 2001.

3. Negrino and Smith, “Javascript for the World Wide Web”, 5thEdition, Peachpit Press,2003

4. Keith Ballinger, “.NET Web Services Architecture andImplementation”, Pearson Education, 2003.

34

12MCSE06 - DATA MINING AND DATAWAREHOUSING

L T P C3 0 0 3

ASSESSMENT : THEORY

OBJECTIVE :

To learn the basic concepts of data warehousing, data mining and thevarious data mining functionalities and related algorithms.

EXPECTED OUTCOME :

At the end of the course, the students should have learnt the differentdata mining tasks, data warehousing and application oriented datamining concepts.

DATA WAREHOUSE

Data Warehouse-Introduction-Multidimensional Data Model-DataWarehouse Architecture-Data Warehouse Implementation-From DataWarehousing to Data Mining. (7)

DATA MINING AND DATA PREPROCESSING

Data Mining-On What Kind of Data-Data Mining Functionalities-Classification of Data Mining Systems-Data Mining Task Primitives-Integration of a Data Mining System with a Database or DataWarehouse System-Major Issues in Data Mining. Data Preprocessing:Needs Preprocessing the Data - Data Cleaning- Data Integration andTransformation-Data Reduction- Discretization and Concept HierarchyGeneration. (8)

ASSOCIATION RULES

Definition-Apriori Algorithm-Partitioning Algorithm-Pincer Search -Dynamic Item Set Counting Algorithm-FP Tree Algorithm-Discussionon different Algorithms-Incremental Algorithm-Border Algorithm-

35

Generation of Association Rules-Introduction to Mining Multilevel andMultidimensional Association Rules. (10)

CLASSIFICATION AND CLUSTERING

Classification and Prediction: Issues Regarding Classification andPrediction- Classification by Decision Tree Induction-BayesianClassification-Rule Based Classification-Classification byBackpropagation - Other Classification Methods- Prediction- ClustersAnalysis: Types of Data in Cluster Analysis- Categorization of MajorClustering Methods-Partitioning Methods-Hierarchical Methods-DensityBased methods- Grid Based methods-Outlier Analysis. (10)

APPLICATIONS

Mining Complex Types of Data: Multidimensional Analysis andDescriptive Mining of Complex-Data Objects-Mining Spatial Databases-Mining Multimedia Databases-Mining Time-Series and Sequence DataMining Text Databases-Mining the World Wide Web. Applications ofData Mining : Social Impacts of Data Mining Tools-An Introduction toDB Miner-Case studies. (10)

TOTAL : 45

REFERENCES

1. Jiawei Han & Micheline kamber, “Data Mining-Concepts andTechniques” Second Edition, Morgan Kaufmann Publishers, 2006.

2. Arun K Pujari, “Data Mining Techniques” Universities Press IndiaLtd., Second Edition, 2010.

3. Paulraj Ponnaiah “Data Warehousing Fundamentals”, WileyStudent Edition, 2001.

4. Dunham “Data Mining Introductory and Advanced Topics” PearsonEducation, New Delhi,First Edition,2008.

5. George M.Marakas “Modern Data Warehousing, Mining andVisualization Core Concepts”, Pearson Education, Reprint 2003.

36

12MCSE07 - DATA COMPRESSION TECHNIQUESL T P C3 0 0 3

ASSESSMENT : THEORY

OBJECTIVE :

Introduce the fundamental concepts of data compression in text, imageand audio.

EXPECTED OUTCOME :

Student shall attain knowledge in the area of data compression in text,image and audio.

INTRODUCTION

Data Compression - Minimum Redundancy Coding - Shannon FanoAlgorithm - Huffman Algorithm, - Adaptive Huffman Coding - Updating,Swapping, Enhancement - Escape Code – Overflow - Rescaling-Arithmetic Coding - Dictionary Based Compression - Static Vs Adaptive- Speech Compression - Sampling Variables - Lossless Compression- Lossy Compression - Silence Compression. (9)

CODING TECHNIQUES

Arithmetic coding: Encoding –Decoding-Adaptation. DictionaryTechniques: Static Techniques-Adaptive Coding the LZ family. ContextModeling: PPM-Burrows-wheeler-move to front-DMC. (9)

IMAGE COMPRESSION

Fourier Transform and the frequency domain – Cosine Transform -Multi Resolution – CCITT Group 3 and 4 –JBIG, JBIG2 - Lossy Coding–Distortion-Rate distortion-Linear System Models. (9)

37

AUDIO COMPRESSION

Monitoring- Digital Audio, Metering, and Volume- Monitoring Functions,A/B Listening, and the Processing Chain –Equalization-DynamicsProcessing- Removal of Noise and Artifacts. (9)

ADVANCED AUDIO COMPRESSION TECHNIQUES

Advanced Editing Techniques-Bits, Dither, and SR Conversion - QC,PQ Coding, CD Replication, and Data Integrity. (9)

TOTAL : 45

REFERENCES

1. Rafael C. Gonzalez and Richard E Woods - Digital ImageProcessing - Second Edition, Pearson Education Pvt. Ltd, NewDelhi. 2008.

2. Sayood ,Khalid ,”Introduction to Data Compression”,3rd edition,Morgan Kaufmann,2006

3. Netravali .A.N, “Digital pictures: Representation and Compression”,plenum, 1989.

4. Williams .R.N., “Adaptive data Compression”,Kluwer,1991

5. Mark Nelson And Jean Loup Gailly - The Data Compression Book- Bpb Publications Second Edition, 1995

6. Mastering Audio: The Art and the Science by Bob Katz, 2nd Edition,2007.

38

12MCSE08 - INFORMATION SECURITYL T P C3 0 0 3

ASSESSMENT : THEORY

OBJECTIVE :To learn the basic principles of Information Security, the SecurityPolicies, Standards & Controls, Security Technologies and Practices.

EXPECTED OUTCOME :The students shall understand the importance of securing theinformation assets, the security threats, security laws & legalframeworks, policies and controls for ensuring security/businesscontinuity and the current security standards.

INTRODUCTION, NEED, LEGAL, ETHICAL AND PROFESSIONALISSUESHistory of Information Security – Security – Critical Characteristics ofinformation – NSTISSC Security Model – Components of an InformationSystem – The Systems Development Life cycle – The Security SystemsDevelopment Life Cycle – Need for Security – Business Needs – Threats– Attacks. Ethics and Information Security - Codes of Ethics andProfessional Organizations - Organizational Liability and the Need forCounsel. (6)

RISK MANAGEMENT AND INFORMATION SECURITYIntroduction – An Overview of Risk Management – Risk Identification– Risk Assessment – Risk Control Strategies - Selecting a Risk ControlStrategy - Risk Management Discussion Points - Documenting Results– Recommended Practices in Controlling Risk. (6)

POLICIES STANDARDS, PRACTICES AND BUSINESS CONTINUITYIntroduction – Information Security Policy, Standards and Practices –The Information Security Blueprint: ISO 17799/BS 7799, ISO 27001

39

and its controls, NIST Security Models, Design of Security Architecture– Security Education, Training and Awareness Program – ContinuityStrategies. (9)

SECURITY TECHNOLOGY : INTRUSION DETECTION, ACCESSCONTROL AND SECURITY TOOLSIntroduction – Intrusion Detection Systems: IDS Terminology - Strengthsand Limitations of IDSs - Honey Pots, Honey Nets, and Padded CellSystems – Scanning and Analysis Tools, Access Control Devices –Physical Security - Security and Personnel. (11)

BIOMETRIC CONTROLS FOR SECURITYBiometrics – Nature of Biometrics Identification/AuthenticationTechniques - Biometric Techniques – Matching and Enrollment Process– Biometrics Benefits over Traditional Authentication Methods. (4)

SECURITY OF WIRELESS NETWORKSecurity of Wireless Network – Other Risks in Wireless Networks,Management and Mitigations for Wireless Networks Attacks. (4)

LAWS AND LEGAL FRAMEWORK FOR INFORMATION SECURITYIntroduction – Information Security and the Law: The Rising Need –Understanding the Laws of Information Security – A ConceptualFramework - the Indian IT Act _ Laws for Intellectual Property Rights(IPR) –Patent Law, Copyright Law, Indian Copyright Act - HealthInsurance Portability and Accountability Act(HIPAA) – Gramm LeachBiley Act (GLBA) – Overview of Sarbances –Oxley (SOX) – Buildingsecurity into Software/System Development Life cycle – FederalInformation Security Management Act (FISMA). (5)

TOTAL : 45

40

REFERENCES1. Michael E.Whitman and Herbert J.Mattord,” Principles of

Information Security”, Course Technology, New Delhi, SecondEdition, 2009 Reprint.

2. Nina Godbole, “Information Systems Security – SecurityManagement, Metrics, Frameworks and Best Practices”, WileyIndia Pvt. Ltd., New Delhi, First Edition, 2009.

3. Eric Maiwald,”Fundamentals of Network Security”, Tata McGraw-Hill Edition 2010.

41

12MCSE09 - MULTICORE HIGH PERFORMANCESYSTEMS

L T P C3 0 0 3

ASSESSMENT : THEORY

OBJECTIVE :

Introduce the fundamental concepts of parallel processing, parallelprogramming, parallel computing and limitations in parallel computing.

EXPECTED OUTCOME :

Student shall attain knowledge in the area of parallel processing, parallelprogramming, parallel computing and limitations in parallel computing.

PARALLEL PROCESSING CONCEPTS

Levels of parallelism instruction – Transaction – Task – Thread – Memory- Function -Models: SIMD – MIMD – SIMT – SPMD- Dataflow Models-Demand-driven Computation Architectures: N-wide superscalararchitectures, Multi-Core Multi Threaded. (9)

PARALLEL PROGRAMMING WITH CUDA

Processor Architecture –Interconnect – Communication - MemoryOrganization and Programming Models in high performance computingarchitectures: IBM CELL BE, NVIDIA Tesla GPU, Intel Larrabee Microarchitecture and Intel Nehalem micro architecture -Memory hierarchyand transaction specific memory design-Thread Organization. (9)

FUNDAMENTAL DESIGN ISSUES IN PARALLEL COMPUTING

Synchronization - Scheduling -Job Allocation- Job Partitioning-Dependency Analysis Mapping Parallel Algorithms onto ParallelArchitectures-Performance Analysis of Parallel Algorithms. (9)

42

FUNDAMENTAL LIMITATIONS FACING PARALLEL COMPUTING

Bandwidth Limitations -Latency Limitations-Latency Hiding/ToleratingTechniques and their limitations- Power-Aware Computing andCommunication-Power-aware Processing Techniques -Power-awareMemory Design -Power-aware Interconnect Design-Software PowerManagement. (9)

ADVANCED TOPICS

Petascale Computing - Optics in Parallel Computing -QuantumComputers - Recent developments in Nanotechnology and its impacton HPC. (9)

TOTAL : 45

REFERENCES

1. George S. Almasi and Alan Gottlieb, “Highly Parallel Computing”,Benjamin/Cummings Publication. , 1994.

2. Kai Hwang, “Advanced Computer Architecture: Parallelism,Scalability, Programmability”, McGraw Hill, 1993, First Edition.

3. Kai Hwang, “Scalable Parallel Computing”, McGraw Hill, 1998, 2nd

Edition.

4. William James Dally and Brian Towels, “Principles and Practiceson Interconnection Networks”, Morgan Kauffman, 2004.

5. Ananth Grama, Anshul Gupta, George Karypis, and Vipin Kumar,“Introduction to Parallel Computing”, Second edition, Addison-Welsey, 2003.

6. David A. Bader (Ed.), Chapman & Hall,”PetascaleComputing: Algorithms and Applications”, CRC ComputationalScience Series, 2007.

43

12MCSE10 - SOFTWARE METRICS ANDMEASUREMENT

L T P C3 0 0 3

ASSESSMENT : THEORY

OBJECTIVE :To understand the basic software metrics, measurements, relatedterminology and the methods of data collection and analysis.

EXPECTED OUTCOME :The students shall obtain the fundamental knowledge on the varioussoftware metrics, the methods of collection of metrics and apply themin tracking the project status.

FUNDAMENTALS OF SOFTWARE MEASUREMENTMeasurement in software engineering - scope of software metrics -representational theory of measurement - measurement and models -measurement scales and scale types - classifying software measures- determining what to measure - software measurement validation.Software metrics data collection - Analyzing software measurementdata: Introduction, Analyzing the results of experiments, Simple analysisTechniques, Overview of statistical tests. (11)

SOFTWARE METRICSProduct quality metrics, In- Process quality metrics - Complexity metricsand models – Size metrics – Effort , cost and Time measurement –Object Oriented metrics - software maintenance metrics – In- processmetrics for software Testing. (12)

SOFTWARE RELIABILITY MEASUREMENTBasics of reliability theory- software reliability problem- parametricreliability growth models- the recalibration of software reliability growthpredictions. (9)

44

METRICS TO MANAGE PROJECTSTracking software progress - software project metrics - utilization andefficient project management. (6)MEASUREMENT AND MANAGEMENTPlanning a measurement program – Metrics plan - developing goals,questions and metrics - mapping measures to activities - measurementtools - measurers, analysts and audience – Measurement in practice.

(7)TOTAL : 45

REFERENCES1. Stephen H Kan, “Metrics and Models in Software Quality

Engineering”, Pearson Education, Second Indian Reprint, NewDelhi, 2007.

2. Norman Fenton and Shari Lawrence Pfleeger, “Software Metrics– A Rigorous & Practical Approach”, Second Edition, Revisedprinting, Thomson Asia Pte Ltd, Singapore, 2002.

3. International Function Point Users Group “IT Measurement – APractical Advice from the Experts “ , Pearson Education, Asia

45

12MCSE11 - SOFTWARE QUALITY ASSURANCEL T P C3 0 0 3

ASSESSMENT : THEORY

OBJECTIVE :

To understand the concept of software quality , to become familiarwith various quality assurance activities like inspections, reviews, defectprevention and configuration management and also to get exposureon the quality system standards and assessments.

EXPECTED OUTCOME :

The students shall obtain the needed skills to plan and carry out variousquality assurance activities. In addition the students shall be exposedto the quality frameworks, standards and assessment procedures.

SQA FUNDAMENTALS

Introduction - Software Quality Challenges-Definitions & Objectives ofS/W Quality and SQA—Quality Factors: McCall’s quality model-threecategories, Components of Software Quality Assurance system-overview (7)

SOFTWARE INSPECTIONS & TESTING

Reviews: Types of Reviews-Inspection Objectives-InspectionPrinciples-Conduct of Inspection-Inspection Reports and Tracking .

S/W testing-Principles-Types/levels of testing-Methods of testing-TestPlanning-Test Execution and Reporting (8)

DEFECT PREVENTION

Defect Prevention: Principles of software defect prevention – Processchanges for defect prevention – Defect prevention considerations –Managements role – Framework for software process change –Managing resistance to software process change (8)

46

CONFIGURATION MANAGEMENT

Need for configuration Management – Software product nomenclature– Configuration management functions – Baselines – Change ControlBoard – SCM Plan – Requirements & Design control -Implementationphase – Test phase – SCM Tools – Configuration accounting and audits.

(7)

SQA PLANS AND AUDITS

SEPG and its Roles-Development & Quality Plan-objectives-elementsof development & quality plan – Internal Quality Audit & audit reports-corrective and Preventive actions. (4)

SQA STANDARDS AND ASSESSMENTS

Establishing standards-Guidelines-Software process assessmentoverview-Assessment phases-Principals-Conduct and AssessmentReports-Overview of ISO and CMMI process standards (11)

TOTAL : 45

REFERENCES

1. Watts S. Humphrey,” Managing the Software Process”, Addison-Wesley, 1998.

2. Daniel Galin,”Software Quality Assurance-From theory toimplementation”, Pearson Education ,2011

3. Milind Limaye.”Software Qulaity Assurance”, TMH, 2011.

47

12MCSE12 - ENTERPRISE INFORMATION SYSTEML T P C3 0 0 3

ASSESSMENT : THEORY

OBJECTIVE :

To learn the basic concepts and features of E-Commerce, EnterpriseResource Planning, CRM, SCM and BI, the major functions, modules,tools and applications required for building a successful enterpriseinformation system.

EXPECTED OUTCOME :

At the end of the course students shall understand various technologiesin e-commerce, core functions in ERP modules, CRM architecturesand optimized process in CRM, planning and forecasting technologiesin SCM and various technologies in business intelligence.

E- COMMERCE

E-commerce and E-business – Features of E-commerce Technology– Types of E-commerce - E-commerce Business Models andConcepts:B2C – B2B – C2C- P2P- M-Commerce- E-Commerce sitetools – Technology Solutions – Payment Systems – Growth of Auctionsand Dynamic Pricing. (10)

ENTERPRISE RESOURCE PLANNING

Introduction – Business Modules of a ERP Package- Finance-Manufacturing – Human Resources – Plant Maintenance- MaterialsManagement – Quality Management –Marketing – Sales Distributionand Service – Marketplaces- SAP AG – Oracle -PeopleSoft. (12)

CUSTOMER RELATIONSHIP MANAGEMENT

Introduction – Closed-loop Marketing- CRM Architecture – Customerprofitability- Customer Acquisition – Cross selling – Customer Retention

48

– Customer Segmentation – Optimizing the CRM – Process – CRM –Tool Markets. (8)

SUPPLY CHAIN MANAGEMENT

Understanding the supply chain – Drivers and metrics – Network designin the supply chain – Demand Forecasting – Aggregate Planning –Information technology in supply chain. (10)

BUSINESS INTELLIGENCE

Business Intelligence – Information Exploitation – The Value of BI-Business Case Assessment – Project Planning – Data Analysis –Metadata Repository analysis – BI Maturity Models. (5)

TOTAL : 45

REFERENCES

1. Alexis Leon, “ERP Demystified”, Second Edition, Tata McGrawHill, Hill Education, 2008.

2. Larissa Terpeluk Moss, S. Atre “Business Intelligence Roadmap:The Complete Project Lifecycle for Decision SupportApplications”,Addison -Wesley Professional, 2003.

3. Alex Berson, Stephen Smith, Kurt Thearling “Building Data miningapplications for CRM”, Tata McGraw Hill, 2007.

4. Kenneth.C. Laudon, Carol Guercio Traver “E-commerce: business,technology, society”, Pearson Education, 2009.

5. Sunil Chopra, Peter Meindl,” Supply Chain Management: Stratergy,Planning and Operation”, Prentice Hall of India, 2008.

6. Min-Hooi Chuah1, Kee-Luen Wong, “Review of BusinessIntelligence and its Maturity Models”, African Journal of BusinessManagement Vol. 5(9), pp. 3424-3428, 4 May, 2011

49

12MCSE13 - BIOINFORMATICSL T P C3 0 0 3

ASSESSMENT : THEORY

OBJECTIVE :

To understand the basics of bioinformatics, processing and the variouscomputational techniques

EXPECTED OUTCOME :

At the end of the course, students shall learn the basic aspects of thebiological patterns, information retrieval strategies, sequencealignments and the issues in proteins and drug discovery.

BIOLOGY FOR BIOINFORMATICS

Basic concepts - cells- Archaebacteria, Bio membranes, Nucleus,Organelles, Mitochondria, Chloroplasts, Viruses, Bacteria Phage,Genetic contents of a cell - Viral Proteins - Amino acid, DNA and RNA- Forms of DNA. (9)

GENETIC CODE

Genome - Gene Expressions - Protein Synthesis - Transcription RNA- Processing- Capping- Splicing - Editing, Cell Signalling, DNA cloningGenomic library – cDNA library - Probes - Screening. Databases:Characteristics of Bioinformatics, Database - Categorizing, Navigating,Information Retrieval systems, Sequence Databases, StructureDatabases. (9)

SEQUENCE ALIGNMENT

Introduction to Sequence Alignment – dotplot - dotplots and sequencealignment – Measures of Sequence similarity – Alignment of twosequences using dynamic programming algorithm – Multiple SequenceAlignment – Applications – Phylogeny – Phylogenetic trees. (8)

50

PROTEIN STRUCTURE DISCOVERY

Protein stability and folding - Applications of Hydrophobicity - Superposition of structures – DALI – Evolution of protein Structures –Classification of Protein Structures - Protein structure prediction – andmodelling – Assignment of protein structures to genomes – predictionof protein function – Drug Discovery and development. (8)

MACHINE LEARNING IN BIOINFORMATICS

Gradient descent - EM/GEM algorithms –Markov chain Monte-Carlomethods - simulated annealing – Evolutionary & genetic algorithms:ACO, B-Colony and PSO. (11)

TOTAL : 45

REFERENCES

1. Orpita Bosu, Simminder Kaur Thukral, “Bioinformatics Databases,Tools and Algorithms”, Oxford University Press, 2007.

2. Arthur M Lesk, “Introduction to Bioinformatics”, Oxford UniversityPress, India, Thrid Edition.

3. Pierre Baldi and Soren Brunak, “Bioinformatics: the MachineLearning Approach”, MIT Press, 1998.

4. David W. Mount, “Bioinformatics: Sequence and GenomeAnalysis”, Cold Spring Harbor Laboratory Press, Second edition,2004.

51

12MCSE14 - PERVASIVE COMPUTINGL T P C3 0 0 3

ASSESSMENT : THEORY

OBJECTIVE :

To understand the concept of pervasive computing, devices technology,sensor networks and RFIDs, local area and wide area wirelesstechnologies and protocols and applications.

EXPECTED OUTCOME :

Student shall attain knowledge in the area of cloud computing and itsapplication.

ARCHITECTURE

Relationship of Wireless Computing, Ubiquitous Computing, InternetComputing. Related ideas: Ambient Computing. Elements of Pervasivearchitecture. Requirements of computational infrastructure. Failuremanagement. General issues: security, performance, dependability.Web architectures. Local networks. Store and forward. Multi-networkarchitectures (e.g. Wireless LAN to LAN to Internet, hand heldsynchronized to PC to LAN). (9)

DEVICES TECHNOLOGY

Device and network technologies. Devices categories. Devicescharacteristic Heterogeneity and Interoperability. Mobile Agents. devicemanagement. Compaq iPAQ. 3G devices. Palm Tungsten. WindowsCE devices. Symbian devices. J2ME-enabled devices. (9)

SENSOR NETWORKS AND RFID

Introduction to Sensor networks. Types of sensor networks. BerkeleyMotes. Sensor network organization. Sensor network routing

52

mechanisms. Platforms for Wireless sensor networks, Sensor NodeArchitecture, Sensor Network Architecture. RFID: Introduction,transponder and reader architecture. Types of tags and readers.Frequencies of operation. Selection criteria for RFID systems.Information processing in the transponder and reader. Fundamentaloperating principles. Antennas for RFID. (9)

LOCAL AREA AND WIDE AREA WIRELESS TECHNOLOGIES

Local area wireless networks: IEEE 802.11 technologies. Mobile IP.Infrared technologies. Bluetooth networks (OBEX Protocol). MessagingSystems. Personal Area Networks. Network Management. Quality ofService. Wireless protocols. Establishing Wide area wireless networks:Concept and structure of cell. Call establishment and maintenance.Channel management. Frequency Assignment techniques. Differencefrom a wired network. (9)

PROTOCOLS AND APPLICATIONS

Protocols: Networking protocols. Packet switched protocols. RoutingProtocols for Sensor Networks. Data Centric Protocols. HierarchicalProtocols. Location-based protocols. Multimedia Messaging Service(MMS) Protocols. Wireless Application Protocol (WAP). Applications:Mobile access to patient information in a hospital, sales support,retailing, services support, tracking applications, designing for smallscreen devices, Search interfaces, Context-awareness, Determining“locality”. (9)

TOTAL : 45

REFERENCES

1. Burkhardt, Henn, Hepper, Rintdorff, Schaeck. “PervasiveComputing”. Addison Wesley, 2002.

53

2. F. Adelstein, S.K.S. Gupta, “Fundamentals of Mobile and PervasiveComputing”. The McGraw-Hill, 2005.

3. Jochen Burkhardt, Horst Henn, Stefan Hepper, Klaus Rindtorff,Thomas Schack, “Pervasive Computing: Technology andArchitecture of Mobile Internet Applications”, 2002, Addison-Wesley.

4. Uwe Hansmann, L. Merk, M. Nicklous, T. Stober, U. Hansmann,“Pervasive Computing (Springer Professional Computing) “, 2003,Springer Verlag.

54

12MCSE15 - INFORMATION RETRIEVALTECHNIQUES

L T P C3 0 0 3

ASSESSMENT : THEORY

OBJECTIVE :

To provide fundamental ideas, design and implementation of text-basedinformation systems.

EXPECTED OUTCOME :

At the end of this course the students can

• Understand basic concepts of Information Retrieval.

• Understand the core components of IR include Text StatisticalCharacteristics, Information Needs and Documents Representationand several important Retrieval Models.

IR BASICS AND INDEX CONSTRUCTION

Boolean Retrieval: An example information retrieval problem – IndexConstruction – Index Compression. (9)

SCORING AND EVALUATION IN IR

Scoring, Term Weighting and the Vector Space Model – ComputingScores in Search Systems – Relevance Feedback and QueryExpansion. (9)

XML RETRIEVAL AND TEXT CLASSIFICATION

XML Retrieval – Probabilistic IR: The Binary Independence Model –Language Models for IR – Text Classification – Naïve Bayes. (9)

VECTOR SPACE CLASSIFICATIONS

Vector Space Classification – Support Vector Machines – MachineLearning on Documents. (4)

55

CLUSTERING MODELS

Flat Clustering: Clustering in Information Retrieval, Evaluation ofClustering, K-means – Hierarchical Clustering: Hierarchicalagglomerative clustering, Single-link and Complete-link clustering,Centroid clustering – Matrix Decomposition and Latent SemanticIndexing. (5)

WEB SEARCH BASICS AND CRAWLERS

Web Search Basics: Overview, Distributing Indexes, Connectivityservers– Web Crawling: Crawler architecture. (4)

LINK ANALYSIS

The Web as a Graph – PageRank: Markov chains, The PageRankComputation, Topic-sensitive PageRank – Hubs and Authorities. (5)

TOTAL : 45

REFERENCES

1. Christopher D. Manning, Prabhakar Raghavan and HinrichSchutze,” Introduction to Information Retrieval “, CambridgeUniversity Press, 2008.

2. David A.Grossman and Ophir Frieder,”Information Retrieval –Algorithms and Heuristics”, Second Edition, Springer InternationalEdition, 2009.

3. William B. Frakes and Ricardo Baeza-Yates,” Information Retrieval:Data Structures & Algorithms - 1st Edition”, Prentice Hall, 1992.

56

12MCSE16 - EMBEDDED AND REAL-TIME SYSTEMSL T P C3 0 0 3

ASSESSMENT : THEORY

OBJECTIVE :Introduce the fundamental concepts of embedded system andapplication in real time systems.

EXPECTED OUTCOME :Student shall attain knowledge in the area of embedded system andapplication in real time systems.

INTRODUCTION TO EMBEDDED SYSTEMSEmbedded Systems-Applications of Embedded Systems-Processorsin the System-Other Hardware Units-Software Embedded into System-Examples of Embedded Systems-Embedded System-on-Chip (SOC)and use of VLSI Design technology. (7)

DEVICES AND BUSES FOR DEVICES NETWORKI/O Devices-Timer and Counting Devices-Serial Communication usingI2C, CAN and USB. Parallel Communication using PCI, PCIX andAdvanced Parallel High Speed Buses. (8)

DEVICE DRIVERS AND INTERRUPTS SERVICING MECHANISM Device Drivers-Parallel Port Device Drivers in a System, Serial PortDevice Drivers in a System, Device Drivers for Internal ProgrammableTiming Devices – Interrupt Servicing Mechanism-Context and thePeriods for Context Switching, Deadline and Interrupt Latency. (9)

REAL-TIME OPERATING SYSTEMCharacteristics of Real-Time Systems-Safety and Reliability - Types ofReal-Time Tasks-Timing Constraints - Modeling Timing Constraints -Real-Time Task Scheduling: Characteristics - Classification - Clock

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Driven Scheduling - Event Driven Scheduling-Hybrid Schedulers -Earliest Deadline First Scheduling - Rate Monotonic Algorithm-DeadlineMonotonic Algorithm - Self Suspension with Context SwitchingOverhead. (10)

REAL-TIME DATABASES & NETWORKSCharacteristics of Temporal Data-Concurrency Control: Locking basedProtocols, Optimistic Concurrency Control Protocols - Real TimeCommunications: Soft Real-Time Communication in a LAN- Hard Real-Time Communication in a LAN: Global Priority Based Scheduling –Calendar Based Protocol- Bounded Access Protocols For LANs : IEEE802.4- RETHER- Switched Real –Time Ethernet- Real –TimeDatabases: characteristics of Temporal Data -Temporal Consistency–Concurrency Control in Real – Time Databases: Locking BasedConcurrency Control- Optimistic Concurrency Control -Protocols–Speculative Concurrency Control. (11)

TOTAL : 45

REFERENCES1. Rajib Mall, “Real-Time Systems: Theory and practice”, Pearson

Education, 2007.

2. Rajkamal, “Embedded Systems: Architecture, Programming andDesign”, Tata McGraw-Hill, 2008.

3. David E Simon, “An Embedded Software Primer” PearsonEducation Asia, 2006.

4. Phillip A. Laplante, “Real Time Systems Design and Analysis: AnEngineer’s Hand book” II Edition, Prentice Hall of India, New Delhi,2000.

5. Arnold Berger, “Embedded System Design: An Introduction toProcesses, Tools, and Techniques”, CMP Books, 2001.

6. Wayne Wolf, “Computers as Components” Morgan KaufmannPublishers, 2005.

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12MCSE17 - DISTRIBUTED SYSTEMSL T P C3 0 0 3

ASSESSMENT : THEORY

OBJECTIVE :

To learn the distributed system architecture and concepts likecommunication, consistency, replication, fault tolerance, transactionmanagement and file systems.

EXPECTED OUTCOME :

At the end of the course, students will gain knowledge on architectureof distributed system, processes, communication mechanism,synchronization, consistency, replication, fault tolerance and filesystems.

FUNDAMENTALS OF DISTRIBUETD SYSTEMS

Definition – Goals – Types of Distributed Systems. Architecture: SystemArchitectures – Architecture Vs Middleware – Self Management inDistributed Systems. (9)

CONSISTENCY AND REPLICATION

Data centric Consistency Model: Continuous Consistency – ConsistentOrdering of Operations. Client centric Consistency Models: EventualConsistency – Monotonic Reads- Monotonic Writes – Read Your Writes– Writes Follow Reads. Replication: Replica Management –Consistency Protocols. (9)

FAULT TOLERANCE

Failure Models – Process Resilience – Reliable Client ServerCommunication – Reliable Group Communication – Distributed Commit– Recovery. (8)

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DISTRIBUTED TRANSACTION

Transaction & Concurrency Control: Transactions – NestedTransactions – Locks – Optimistic Concurrency Control – TimestampOrdering. Distributed transaction: Flat and Nested DistributedTransactions – Atomic Commit Protocols – Concurrency Control inDistributed Transactions – Distributed Deadlocks – TransactionRecovery. (10)

DISTRIBUTED FILE SYSTEM

File Service Architecture – Sun Network File System – Virtual FileSystem - Client Integration – Mount Service – Pathname Translation –Server Caching – Client Caching – Securing NFS With Kerberos.Hadoop: Architecture & Design – Map Reduce. (9)

TOTAL : 45

REFERENCES

1. Andrew S.Tanenbaum, Maarten Van Steen, “DISTRIBUTEDSYSTEMS PRICIPLES AND PARADIGMS”, Second Edition,Eastern Economy, 2008.

2. George Coulouris, Jean Dollimore. Tim Kindberg, “DISTRIBUTEDSYSTEMS CONCEPTS AND DESIGN”, Fourth Edition, PearsonEducation 2009.

3. http://hadoop.apache.org

4. http://static.usenix.org/event/osdi04/tech/full_papers/dean/dean.pdf

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12MCSE18 - IMAGE ANALYSISL T P C3 0 0 3

ASSESSMENT : THEORY

OBJECTIVE :

Introduce the fundamental concepts of Biomedical Signals,Deterministic Signal and Image Processing and Image Segmentationand Registration.

EXPECTED OUTCOME :

Student shall attain knowledge in the area of Biomedical Signals,Deterministic Signal and Image Processing and Image SegmentationAnd Registration.

BIOMEDICAL SIGNALS AND IMAGES

ECG: Cardiac electrophysiology, relation of electrocardiogram (ECG)components to cardiac events, clinical applications - Speech Signals:The source-filter model of speech production, spectrographic analysisof speech - Speech Coding: Analysis-synthesis systems, channelvocoders, linear prediction of speech, linear prediction vocoders. (9)

IMAGING MODALITIES

Survey of major modalities for medical imaging: ultrasound, CT, MRI,PET, and SPECT - MRI: Physics and signal processing for magneticresonance imaging. (7)

FUNDAMENTALS OF DETERMINISTIC SIGNAL AND IMAGEPROCESSING

Data Acquisition: Sampling in time, aliasing, interpolation, andquantization. Digital Filtering: Difference equations, FIR and IIR filters,basic properties of discrete-time Systems convolution (9)

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DTFT: The discrete-time Fourier transform and its properties. FIR filterdesign using windows - DFT: The discrete Fourier transform and itsproperties, the fast Fourier transform (FFT), the overlap-save algorithm,digital filtering of continuous-time signals. (8)

IMAGE SEGMENTATION AND REGISTRATION

Image Segmentation: statistical classification, morphological operators,connected components - Image Registration I: Rigid and non-rigidtransformations, objective functions. (9)

CASE STUDY

Hand Written Signature, IRIS and Finger Print Analysis (3)

TOTAL : 45

REFERENCES

1. Oppenheim, A. V., and R. W. Schafer, with J. R. Buck. Discrete-Time Signal Processing. 2nd ed. Upper Saddle River, NJ: Prentice-Hall, 1999. ISBN: 9780137549207.

2. Papoulis, A., and S. U. Pillai. Probability, Random Variables, andStochastic Processes. New York, NY: McGraw Hill, 2001. ISBN:9780072817256.

3. Siebert, W. M. Circuits, Signals and Systems. Cambridge, MA:MIT Press, 1985. ISBN: 9780262192293.

4. Gonzalez, R., and R. E. Woods. Digital Image Processing. 2nd ed.Upper Saddle River, NJ: Prentice-Hall, 2002. ISBN:9780201180756.

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