M.Tech. Programme in Computer Science and Engineering · PDF fileP.G. Programme in Computer Science and Engineering ... Engineering Students”, 2nd Edition, ... Strang “Introduction

1

Walchand College of Engineering, Sangli. (An Autonomous Institute)

Curriculum (Structures and Syllabus)

for

M.Tech. Programme in

Computer Science and Engineering

Academic Year 2016-17

2

Walchand College of Engineering, Sangli

(An Autonomous Institute) Teaching and Evaluation Scheme Effective from 2016-17

P.G. Programme in Computer Science and Engineering Semester I

Course Code Course

Teaching Scheme Evaluation Scheme

Component Marks

L T P Credits Max Min for

Passing

2IC501 (2IC601)

Research Methodology 2 - - 2

ISE 1 10 40 MSE 30

ISE 2 10 ESE 50 20

2IE5** (2CS6**) Institute Elective I 3 - - 3

ISE 1 10 40 MSE 30

ISE 2 10 ESE 50 20

2CO5** (2CS6**) Professional Elective I 3 1 - 4

ISE 1 10 40 MSE 30

ISE 2 10 ESE 50 20

2CO501 (2CS601)

Computational Mathematics 3 1 - 4

ISE1 10 40 MSE 30

ISE 2 10 ESE 50 20

2CO502 (2CS602)

Design of Database System 3 - - 3

ISE 1 10 40 MSE 30

ISE 2 10 ESE 50 20

2CO503 (2CS603)

Advanced Computer Networks 3 - - 3

ISE 1 10 40 MSE 30

ISE 2 10 ESE 50 20

2CO551 (2CS652)

Design of Database System Lab - - 2 1 ISE 50 20

ESE (POE) 50 20 2CO552

(2CS653) Advanced Computer Networks Lab - - 2 1 ISE 50 20

ESE (POE) 50 20 2CO541

(2CS654) Seminar - 2 1 ISE 100 40

Total 17 2 6 22 Total Credits: 22 Total Contact Hours: 25 hrs

2CO5**: Professional Elective I Course Code Course Name 2CO511 (2CS607)

Advanced Software Engineering

2CO512 (2CS608)

Advanced Data Structures and Algorithms

Note: Course code in the bracket indicates pre-revised code

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Walchand College of Engineering, Sangli

(An Autonomous Institute) Teaching and Evaluation Scheme Effective from 2015-16

P.G. Programme in Computer Science and Engineering Semester II

Course Code Course


Component Marks

L T P Credits Max Min for Passing

2IC502 (2IC602) Project Management 2 - - 2

ISE 1 10

40 MSE 30 ISE 2 10

ESE 50 20

2IE5** (2CS6**) Institute Elective II 3 - - 3

ISE 1 10 40 MSE 30

ISE 2 10 ESE 50 20

2CO5** (2CS6**) Professional Elective II 3 1 - 4

ISE 1 10 40 MSE 30

ISE 2 10 ESE 50 20

2CO521 (2CS611)

Modern Operating System 3 - - 3

ISE 1 10 40 MSE 30

ISE 2 10 ESE 50 20

2CO522 (2CS612) Parallel Computing 3 - - 3

ISE 1 10 40 MSE 30

ISE 2 10 ESE 50 20

2CO571 (2CS661)

Modern Operating System Lab - - 2 1 ISE 50 20

ESE (POE) 50 20 2CO572

(2CS662) Parallel Computing Lab - - 2 1 ISE 50 20 ESE (POE) 50 20

2CO573 (2CS663) Special Topics in CSE 2 - 2 3 ISE 50 20

ESE (POE) 50 20 2CO542

(2CS664) Pre dissertation Seminar - - 2 1 ISE 100 40

Total 16 1 8 21 Total Credits: 21 Total Contact Hours: 25 hrs

2CO5** : Professional Elective II Course Code Course Name 2CO531 (2CS616)

Mobile & Pervasive Computing

2CO532 (2CS617)

Data Mining


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Walchand College of Engineering, Sangli (An Autonomous Institute)

Teaching and Evaluation Scheme for Year 2016-17 P.G. Program in Computer Science and Engineering

Semester III

Course Code Course


Component Credits

Practical (Marks )

L T P Max Min for Passing

2CO691 (2CS691)

Dissertation Phase 1 ISE

--

--

5

ISE 4 100 40

2CO692 (2CS692)

Dissertation Phase 2 ISE -

- --

ISE 2 100 40

2CO693 (2CS692)

Dissertation Phase 2 ESE ESE 4 100 40

Total -- -- 5

Total Credit: 10 Average Contact hours/week/student: 5 Hrs

Semester IV

Course Code Course


Scheme Credits Practical (Marks)

L T P Max Min for Passing

2CO694 (2CS693)

Dissertation Phase 3 ISE

--

--

5

ISE 5 100 40

2CO695 (2CS694)

Dissertation Phase 4 ISE -

- --

ISE 5 100 40

2CO696 (2CS694)

Dissertation Phase 4 ESE ESE 10 100 40

Total -- -- 5

Total Credit: 20 Average Contact hours/week/student: 5 Hrs


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Title of the Course: Research Methodology 2IC501 (2IC601) L T P Cr 3 1 0 4

Pre-Requisite Courses: None Textbooks:

1. C. R. Kothari, “Research Methodology”, New Age international, 2004. 2. Deepak Chopra and Neena Sondhi, “Research Methodology : Concepts and cases”, Vikas Publishing

House, New Delhi, 2008. 3. Ranjit Kumar, “Research Methodology: A Step by Step Guide for Beginners”, 2nd Edition, Sage

Publisher, 2011. References:

1. E. Philip and Derek Pugh, “How to get a Ph. D. – A handbook for students and their supervisors”, open university press, 2005.

2. Stuart Melville and Wayne Goddard, “Research Methodology: An Introduction for Science & Engineering Students”, 2nd Edition, Juta Publisher, 2001

3. G. Ramamurthy, “Research Methodology”, 2nd Editon, Oxford University Press, 2005. Course Objectives :

1. Understand some basic concepts of research and its methodologies 2. Identify and formulate the research problems, state the hypothesis, 3. Organize and conduct and present research in a more appropriate manner 4. Prepare research artifacts to the college and papers to Conferences and Journals

Course Learning Outcomes: CO After completion of the course student should be able to Bloom’s Cognitive

level DescriptorCO1 identify/formulate the research problem for M. Tech. dissertation 2 UnderstandingCO2 write a review paper in the format of standard Journal/transactions

by reviewing at least 10 papers (from standard Journals/transactions/Reference Books/Handbooks etc.) related to a particular research area.

3 Applying

CO3 deliver a seminar on the same, prepare a presentation giving critical analysis of the subject and possible outcomes. And writing research summary/synopsis/technical notes after completion of the work for the degrees of M. Tech.

6 Creating

CO-PO Mapping :

1 2 3 4 5 6 7 8 9 10 11 12 CO1 2 1 3 CO2 2 2 1 CO3 2 1 3

Assessment: Two components of In Semester Evaluation (ISE), One Mid Semester Examination (MSE) and one End Semester Examination (ESE) having 20%, 30% and 50% weightage respectively.

Assessment Marks ISE 1 10 MSE 30 ISE 2 10 ESE 50

ISE 1 and ISE 2 are based on assignment, oral, seminar, test (surprise/declared/quiz), and group discussion.[One assessment tool per ISE. The assessment tool used for ISE 1 shall not be used for ISE 2] MSE: Assessment is based on 50% of course content (Normally first three modules) ESE: Assessment is based on 100% course content with70-80% weightage for course content (normally last three

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modules) covered after MSE.

Course Contents: Module 1: Introduction to Research Hrs. What is research? Literature survey and review, types of research, the process of research, 4Module 2: Research Procedures Hrs. Formulation of a research problem, Experimental design, Classification. Theoretical research, Formulating a problem, verification methods, modeling and simulations, ethical aspects, IPR issues, Copyrights and Patenting etc.

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Module 3: Research Methods Hrs. Steps in conducting research, Research Problem identification, Probable solutions, verification of the proposed methodology, conclusions. Meaning, Need and Types of research design, Research Design Process, Measurement and scaling techniques, Data Collection – concept, types and methods, Processing and analysis of data, Design of Experiment

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Module 4: Analysis Techniques Hrs. Quantitative Techniques Sampling fundamentals, Testing of hypothesis using various tests like Multivariate analysis, Use of standard statistical software, Data processing, Preliminary data analysis and interpretation, Uni-variate and bi-variate analysis of data, testing of hypotheses, techniques such as ANOVA, Chi square test etc., Nonparametric tests. Correlation and regression analysis

5

Module 5: Research Communications Hrs. Writing a conference paper, Journal Paper, Technical report, dissertation/thesis writing. Presentation techniques, Patents and other IPRs, software used for report writing such as WORD, Latex etc.

4

Module 6: Case Studies Hrs. Case studies related to the respective disciplines of Engineering. 4

Module wise Measurable Students Learning Outcomes : Module 1: Understand the process of research. Module 2: Formulation of a research problem in respective study domains Module 3: Learn the important steps in conducting research Module 4: Applying data analytics for research validation. Module 5: Learn methods for presenting the research results Module 6: Applying RM in respective disciplines of Engineering.

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Title of the Course: Computational Mathematics 2CO501 (2CS601) L T P Cr 3 1 0 4

Pre-Requisite Courses: Familiar with high-school level Mathematics

Textbooks: 1. Kenneth H. Rosen “Mathematics and Its Applications” Seventh Edition, MGH 2. J. Gilbert and L. Gilbert “Linear algebra and matrix theory “ Second Edition Brooks Cole. 3. G. Strang “Introduction to linear algebra “ Wellesley Cambridge 4. J.P. Tremblay &R. Manohar , “Discrete Mathematical structure with applications to computer”, MGH

References: 1. Kishor S.Trivedi, “Probability and Statistics with Reliability, Queuing and Computer Science Applications”

Second Edition Wiley. 2. Meyer Oxford , “ Introductory Probability and statistical applications “ IBH publications. 3. Edward R. Scheinerman “Mathematics A Discrete Introduction” Third Edition, Cengage learning.

Course Objectives : 1. To explore fundamentals of Computer Mathematics 2. To describe relation, functions and algebraic system. 3. To learn algebra of vector and matrices. 4. To understand Probability, random variables in computer Mathematics.

Course Learning Outcomes:

After the completion of the course the student should be able to Bloom’s Cognitive level Descriptor

CO1 describe the relation, functions and algebraic system. 2 understanding CO2 apply understanding of Vector, matrices, probability and random

variable in problem solving. 2,3 Understanding

,applying CO3 classify queuing system and its types. 3 Applying

CO-PO Mapping :

1 2 3 4 5 6 7 8 9 10 11 12 CO1 1 CO2 1 2 2 CO3 1 2



ISE 1 and ISE 2 are based on assignment, oral, seminar, test (surprise/declared/quiz), and group discussion.[One assessment tool per ISE. The assessment tool used for ISE 1 shall not be used for ISE 2] MSE: Assessment is based on 50% of course content (Normally first three modules) ESE: Assessment is based on 100% course content with70-80% weightage for course content (normally last three modules) covered after MSE.

Course Contents:

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Module 1 Relations and Functions: Relations, Pictorial representation of Relations, Properties of binary relation, Equivalence Relations, partition and covering of set, POSET and Hasse diagram Functions- types, Inverse and composition of functions.

6 Hrs.

Module 2. Algebraic systems Introduction, Operations, semigroups, Groups, subgroups, Rings, monoid, lattice.

5 Hrs

Module 3 Vector Algebra: Vector Algebra, Complex numbers, Definitions, Vector products , Properties, Amplitude & Modules of a complex number, De Moivere’s theorem and examples Vector spaces, subspaces, linear dependence basis, dimension, algebra of linear transformations

7 Hrs.

Module 4 Matrices algebra: Algebra of matrices, rank and determinant of matrices, linear equations. Eigenvalues and eigenvectors, Cayley-Hamilton’s theorem. Matrix representation of linear transformations. Change of basis, canonical forms, diagonal forms, triangular forms, Jordan forms. Inner product spaces, orthonormal basis.

7 Hrs.

Module 5 Introduction to Probability and Random Variable Sample spaces, Conditional probability and Bayes’ theorem, Independence of events Bernoulli trails. Random Variables: Cumulative distribution function, Probability Density function, Expected value and variance and Moments, Moment Generating function, Function of Random variable, Standard Random Variables: Binomial, Poisson, Geometric, Uniform, Exponential etc. Inequalities, Transformation of Random variable.

7 Hrs.

Module 6- Queuing Theory Introduction Cost equation, steady state probabilities, Models of single server exponential queuing system with no limit and with finite buffer capacity (M/M/I, M/M/N). Queuing system with bulk service,. The M/G/I system and application of work to M/G/I

8 Hrs.

Module wise Measurable Students Learning Outcomes : Module 1

Understand the relation, functions used in discrete structures. Module 2

Learn the algebraic system for discrete structure. Module 3

Study and Solve problems related to vector and matrices algebra Module 4

Study and Solve problems related to Probability and Random Variables

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Module 5 Mathematical problem solving using the concepts of probability and random variable

Module 6

Understand queuing system and its types

Title of the Course: Design of Database System 2CO502 (2CS602) L T P Cr 3 0 0 3

Pre-Requisite Courses: Database Engineering

Textbooks: 1. Thomas Connolly, Carolyn Begg, “Database Systems: A Practical Approach to Design, Implementation and

Management”, Pearson, 4th Edition. 2012 2. Silberschatz, Korth & Sudarshan, “Database System Concepts.”, MGH. 6th Edition 2011 1. Ramakrishnan & Gehrke, “Database Management System.”, MGH. 3rd Edition 2003 2. Ian Robinson, Jim Webber & Emil Eifrem, “Graph Databases”, O’REILY Publications 2nd Edition

References: 1. Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden, “Modern Database Management.”, Pearson, 6th

Edition 2002 2. Rob & Coronel, “Database Systems – Design, Implementation & Management.”, Thomson, 5th Edition 2003

Course Objectives : 1. To make students aware of phases of database design, database system development life cycle and design

methodology. 2. To expose to the students the design issues in specialized databases. 3. To address designing of graph and cloud databases for scalable performance


After the completion of the course the student should be able to

Bloom’s Cognitive

level Descriptor CO1 identify ways of constructing various types of database objects 1 Remembering CO2 define and understand issues involved in designing a database system

using different design methodologies 2 Understanding

CO3 differentiate the different databases systems based on their features and justify the use of that database for a particular application

4, 5 Analyzing, evaluating

CO-PO Mapping :

1 2 3 4 5 6 7 8 9 10 11 12 CO1 2 3 CO2 2 3 2 CO3 1 3


Assessment Marks ISE 1 10 MSE 30

10

ISE 2 10 ESE 50


Course Contents: Module 1 : Database Planning, Design and Administration The information system lifecycle, the database system development lifecycle, database planning, system definitions, requirement collection and analysis, database design, DBMS selection, application design, prototyping, implementation, data conversion and loading, testing, operational maintenance, CASE tools, data and database administration.

Hrs. 6

Module 2 : Database Design Methodology Introduction, Conceptual database design, Logical Database Design, Comparison of logical and physical database design, overview of physical database design methodology, physical database design methodology for relational databases.

Hrs. 7

Module 3 : Replication Databases Introduction, benefits of database replication, applications of replication, basic components of database replication, database replication environments, replication servers Mobile Databases : Introduction, architecture, design of mobile databases system, study of open source/commercial mobile databases.

Hrs.

3

4

Module 4 : Spatial, Temporal & Multimedia Databases Motivation, Time in databases, Spatial and Geographic data, Multimedia databases. Design issues of spatial, temporal and multimedia databases.

Hrs. 6

Module 5 : Cloud Databases Introduction, Architecture, Data Models, NoSQL databases : Apache Cassandra, CouchDB and MongoDB, Comparison of Relational databases and Cloud databases, Challenges to develop Cloud Databases.

Hrs. 7

Module 6 : Graph Databases Introduction, options for storing connected data, data modeling with graphs, building graph database application, graphs in the real world, graph database internals.

Hrs. 6

Module wise Measurable Students Learning Outcomes : After completion of course, the student will be able Module 1:

1. To apply the DBMS development life cycle and design strategy. 2. Plan application design and prototyping

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Module 2: 1. To apply different design methodologies – conceptual, logical and physical database design for a Relational

database Module 3:

1. To learn database replication and its application. 2. To identify the features that need to be supported by a mobile database.

Module 4: 1. To identify the ways of representing geographic data and indexing of spatial data in a spatial database. 2. To state the ways of addressing the issues involved in storing multimedia data in a database.

Module 5: 1. To visualize the architecture of cloud databases and identify the challenges involved. 2. To differentiate the features and nature of different types of cloud databases

Module 6: 1. To recognize the need of building graph databases 2. To analyze and design the graph database applications.

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Title of the Course: Advanced Computer Network 2CO503 (2CS603)

L T P Cr 3 0 0 3

Pre-Requisite Courses: Undergraduate Computer Networks course, Good Programming Background. Textbooks:

1. James Kurose and Keith Ross, "Computer Networking, A Top-Down Approach", Pearson, 5th Edition

References: 1. Larry Peterson and Bruce Davie, "Computer Networks, A Systems Approach", Morgan Kauffman, 2011. 2. W. Richard Stevens, "Unix Network Programming", Eastern Economy Edition, PHI, 1992.

Course Objectives : 1. To review established key abstractions, concepts and technologies 2. To learn and compare the various methods of routing. 3. To introduce students to Internet security and a set of advanced topics in networking and lead them to the

understanding of the networking research. Course Learning Outcomes:

CO After the completion of the course the student should be able to

Bloom’s Cognitive

level Descriptor

CO1 demonstrate the knowledge of networking abstractions and concepts and also identify the network performance issues.

3 Applying

CO2 differentiate and analyze networking protocols. 4 Analyzing

CO3 design and implement advanced routing algorithms. 6 Creating

CO-PO Mapping :

1 2 3 4 5 6 7 8 9 10 11 CO1 1 2 CO2 1 2 3 CO3 2 2




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Course Contents: Module 1 Internet Architecture and performance modeling Building a network, Applications, Requirements, The network Edge, The network core, Performance of networks, Delay, loss, and throughput in packet switched networks, Network architecture and protocols, Networks under attack, Implementing Network software, High-Speed Networks, Application Performance Needs.

6 Hrs.

Module 2 Transport layer: Transport protocol design, congestion control mechanisms and analysis of TCP. TCP variants, Advanced concepts in TCP: multipath TCP, Resource allocation and fairness, Packet scheduling algorithms.

6 Hrs.

Module 3 Advanced Routing The Global Internet: Routing Areas, Interdomain Routing (BGP), IP Version 6 (IPv6), Multicast: Multicast Addresses, Multicast Routing (DVMRP, PIM, MSDP), Multiprotocol Label Switching (MPLS): Destination-Based Forwarding, Explicit Routing, Virtual Private Networks and Tunnels, Routing among Mobile Devices: Challenges for Mobile Networking, Routing to Mobile Hosts (Mobile IP)

7 Hrs.

Module 4 Multimedia Networking and Internet security: Streaming audio and video, RTSP, jitter removal and recovery from lost packets; Protocols for real-time interactive applications: RTP, RTCP, SIP, H.323; Content distribution networks; Integrated and differentiated services, RSVP, Internet security: Network Layer security, Transport Layer Security, Application Layer Security, Firewalls.

8 Hrs.

Module 5 Network Programming TCP sockets, UDP sockets (datagram sockets), Server programs that can handle one connection at a time and multiple connections (using multithreaded server), Remote Method Invocation (Java RMI) -Basic RMI Process, Implementation details - Client-Server Application.

6 Hrs.

Module 6 Advanced topics: Software Defined Networking. Data center networking. Network Virtualization. Network Function Virtualization

6 Hrs.

Module wise Measurable Students Learning Outcomes : Module 1: Demonstrate the basic knowledge of computer networking and identify the issues in computer networking. Module 2: Familiar with transport layer design issues and congestion control algorithms. Module 3: Compare various advanced routing algorithms. Module 4: Understand protocols for real time interactive application Module 5: Realize communication between the applications using socket programming. Module 6: Understand new developments in computer networking. Identify the research areas in computer networking.

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Title of the Course: Institute Elective I - Image processing 2IE 581 (2CS 604) L T P Cr 3 0 0 3

Pre-Requisite Courses: Mathematics – Linear algebra , Probability

Textbooks: 1. R. C. Gonzalez, R. E. Woods, Digital Image Processing, 2nd Edition. 2002, PHI 2. A. K. Jain, Fundamentals of Digital Image Processing, PHI

References: 1. Milan Sonka, Vaclav Hlavac, Boyle, Digital Image Processing and Computer Vision, Cengage Learning 2. S. Jayaraman, S. Esakkirajan, T. Veerkumar, Digital Image Processing, Tata McGrawHill

Course Objectives : 1. To provide knowledge about fundamentals of digital image processing. 2. To make the students understand the concepts of image transforms, image enhancement, image

segmentation, morphological operations, color image processing, compression etc. 3. To gain experience in applying the algorithms to real problems. 4. To build the skills necessary to further explore advanced topics of Digital Image Processing.

Course Learning Outcomes: CO After the completion of the course the student should be able to Bloom’s Cognitive

Level Descriptor CO1 Explain fundamental concepts of digital image processing,

mathematical transforms, image enhancement, segmentation, morphology, compression etc.

2 Understanding

CO2 Write algorithms and apply the concepts mathematically to interpret the results with justification

3 Applying

CO3 Compare different algorithms of image processing and apply them to solve real life problems.

4 Analyzing

CO-PO Mapping : 1 2 3 4 5 6 7 8 9 10 11 12 CO1 1 CO2 1 2 3 CO3 2 1 2



ISE 1 and ISE 2 are based on assignment, oral, seminar, test (surprise/declared/quiz), and group discussion.[One assessment tool per ISE. The assessment tool used for ISE 1 shall not be used for ISE 2] MSE: Assessment is based on 50% of course content (Normally first three modules) ESE: Assessment is based on 100% course content with70-80% weightage for course content (normally last three

15

modules) covered after MSE.

Course Contents: Module 1: Digital Image Fundamentals 5 Hrs. Introduction: Concept, Fundamental Steps and Components of Image Processing System Digital Image Fundamentals: Image Acquisition, A simple image model, Sampling and Quantization, Imaging Geometry, Different types of digital images

Module 2: Image Transforms 8 Hrs. 2D systems and Necessary Mathematical preliminaries, 2D Orthogonal and Unitary Transforms, 1-D DFT, KL-Transforms, Cosine, Hadamard Transforms, Introduction to Wavelet transforms

Module 3: Image Enhancement 5 Hrs. Point Processing, Basic Gray Level Transformations, Histogram Processing, Spatial domain Filtering, Frequency domain filtering

Module 4: Image Segmentation and Analysis 8 Hrs. Edge Detection – using first and second order derivatives, LoG, Canny edge detector, Boundary Extraction – Connectivity, Heuristic Graph Search, Hough Transform, Active Contour, Watershed Transform, Region-based Segmentation – region growing, region splitting and merging, Feature Extraction

Module 5: Image Compression 7 Hrs. Fundamentals, Compression model, Lossless Vs Lossy Compression, Fundamentals of Information Theory, Run-length coding, Huffman coding, Dictionary-based compression, Predictive coding, Transform-based coding, Image Compression Standards

Module 6: Special Topics in Image Processing 6 Hrs. Study of any relevant topics or research paper(s) based on the current trends in related areas or any case study

Module wise Measurable Students Learning Outcomes : Students will be able to Module 1: • explain the fundamental concepts of Image Processing and its applications. Module 2: • describe, explain and use image processing transforms which play an significant role in image enhancement,

filtering, analysis and compression. Module 3: • explain and demonstrate various techniques to improve the quality of an image. Module 4: • describe and use segmentation which is one of the most important steps leading to image analysis. • explain and implement various methods to divide an image into parts or groups of pixels which are

homogeneous with respect to some criterion. Module 5: • explain the need of image compression i.e. the technique of reducing the amount of data required to represent a

digital image and various techniques for compression. Module 6: • carry out case study and/or study any research paper based on current trends in the related areas.

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Title of the Course: Institute Elective I - Artificial Intelligence 2IE 582 (2CS 605)

L T P Cr 3 0 0 3

Pre-Requisite Courses: Exposure to concepts in discrete structures, probability/statistics, and algorithmic analysis. Textbook:

1. Elaine Rich and Kelvin Knight ,Nair,“ Artificial Intelligence,” McGraw Hills 3rd edition 2. Janakiraman et al., “Foundations of Artificial Intelligence and Expert Systems”, Macmilan India Ltd. 3. Russell and Norvig,” Artificial Intelligence – A Modern Approach”, Prentice-Hall, 2010 (3rd edition).

References: 1. Saroj Kaushik, “Artificial Intelligence” 2. Townsend, “Introduction to Turbo prolog”

Course Objectives : 1. To learn theory developed in Artificial Intelligence. 2. To learn techniques used in major application areas of Artificial Intelligence. 3. To learn about the state of the art in Artificial Intelligence

Course Learning Outcomes: CO After the completion of the course the student should be able to

Bloom’s Cognitive Level Descriptor

CO1 apply schemes of knowledge representation. 3 applying CO2 demonstrate expert system. 3 applying CO3 evaluate performance of AI systems. 5 Evaluate

CO-PO Mapping :

1 2 3 4 5 6 7 8 9 10

11 12

CO1 2 3 CO2 1 3 CO3 2 3 2




Course Contents: Module 1:Introduction and searching in AI Problem, Problem Spaces and Search, Application, Characteristics of AI, Heuristic, A*,AO*.

Hrs. 6

Module 2:Knowledge Representation & Logic Logic & Deduction, Frames, Representing instance and ISA relationships

Hrs. 7

Module 3: Reasoning Non monotonic Reasoning, Reasoning with uncertainty, Fuzzy reasoning, Bayes’ n/w.

Hrs. 6

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Module 4:Game Playing and Planning Minimax Search procedure, Alpha Beta Cutoffs, The Block Worlds, Goal Stack planning, Components of planning.

Hrs. 7

Module 5: Understanding Problem, Understanding as constraint satisfaction.

Hrs. 6

Module 6: Natural Language Processing Syntactic Processing, Semantic Analysis.

Hrs. 7


1. Understanding AI by examining the nature of the difficult problems that AI seeks to solve.

Module 2 1. Exploring variety of methods for encoding knowledge in computer systems.

Module 3

1. Handling reasoning with uncertainty. Module 4

1. Providing intelligent problem solution. Module 5

1. Knowing difficulties in understanding and providing solution using constraint satisfaction.

Module 6 1. Understanding and evaluating processes for natural language processing.

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Title of the Course: Professional Elective-I Advanced Software Engineering 2CO511 (2CS607)

L

T

P

Cr

3 1 0 4 Pre-Requisite Courses: Software Engineering

Textbook: 1. Roger S. Pressman, “Software Engineering: Practitioner’s Approach”, McGraw Hill 2. Ian Sommerville, “Software Engineering”, Addison-Wesley, seventh edition. 3. Grady Booch, James Rambauch, Ivar Jacobson, “Unified Modeling Language: Users Guide”, 2nd edition,

Addison-Wesley.

References: 1. Pankaj Jalote, “An integrated approach to Software engineering”, Narosa Publishers, 2nd Edition. 2. Pankaj Jalote, “Software Project Management in practice”, Pearson education

Course Objectives : 1. To explore the knowledge of various models and practices used at IT industries for effective software

development. 2. To Focus on architectural design aspects for applications. 3. To Emphasize on Design aspect with latest UML technology. 4. To Realize the notion of software quality assurance through software testing methodologies and learn reverse

engineering concepts. 5. To Nurture the techniques of Project Management.


CO After the completion of the course the student should be able to,

Bloom’s Cognitive

level Descriptor

CO1 acquaint with industry processes & models for systematic software development.

1,2 Remembering, Understanding

CO2 realize articulation of latest design principles and Architectural aspects using UML.

3 Applying

CO3 analyses and evaluates importance of various testing methodology for quality assurance.

4,5 Analyzing, Evaluating

CO4 build proficiency to undertake real life software projects to meet the challenges of IT industry.

6 Creating

CO-PO Mapping :

1 2 3 4 5 6 7 8 9 10 11 12 CO1 2 3 CO2 2 2 3 CO3 1 2 3 CO4 1 2 1 3



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Course Contents: Module 1: Software Processes & Methodology Process paradigm, Process Models: Incremental and Evolutionary models, Team Software Process, Agile Process: Model and methodology, Process and Project Metrics, Empirical Models.

7 Hrs.

Module 2: Software Design & Architecture Design Concepts, Design Models, Importance of Architectural design, Architectures design Process, Architectural Styles, Design Principles, and Architectures design guidelines, Design Patterns, Applying Agile Principles.

7 Hrs.

Module 3: Modeling with UML & Coding OOAD benefits, Visual Modeling, UML support for OOAD, UML Diagrams, Case studies, Tools assisted Coding.

6 Hrs.

Module 4: Software Testing & Quality Assurance Software quality Assurance: Quality metrics, Software Reliability, Software testing: Path Testing, Control Structures, Testing, Black Box Testing, Integration, Validation and system testing Software, Maintenance.

7 Hrs.

Module 5: Project planning and Estimation Managing projects, structures and frameworks, developing realistic estimates, integrating schedule and critical pat, complex projects, accessing project viability, managing stake holders, function point based estimation, empirical estimation, COCOMO II model, Tools

6 Hrs.

Module 6: Web Engineering Web based applications, attributes, analysis, design and testing. Security, Service-oriented Software Engineering, Aspect–Oriented Software Development and Test Driven Development.

6 Hrs.

Module wise Measurable Students Learning Outcomes : The learner studies and realizes following, Module 1: Software Processes & Methodology

Awareness of notion of Software processes & methodologies, latest Models used at IT.

Module 2: Software Design & Architecture

Understand the notion of software architecture for variety of real life applications, Learn design aspect of procedural design, UI design and reusable design concepts.

Module 3: Modeling with UML & Coding

Hands on exposure with UML 2.0 concepts in practice, learn new features, specification techniques of UML, aware of Coding tools.

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Module 4: Software Testing & Quality Assurance

Appreciate the importance of quality assurance through various techniques of software testing, Also understand the maintenance aspects & Reverse Engineering. Module 5: Project planning and Estimation To realize that for successful rollout of projects project management Techniques are inevitable. Insight on developing realistic estimates using various estimation methods, Also aware scheduling methods, managing stake holders. Module 6: Web Engineering To aware about developing real life web based applications as a case study using latest techniques to visualize usability context of software engineering.

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Title of the Course: Professional Elective I- Advanced Data Structures and Algorithm 2CO512 (2CS608)

L T P Cr 3 1 0 4

Pre-Requisite Courses: Data Structures, Design and Analysis of Algorithms

Textbooks: 1. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein, “Introduction to Algorithms,”

3rd Edition, PHI, 2009 2. Mark de Berg, Otfried Cheong, Marc van Kreveld, Mark Overmars , Computational Geometry - Algorithms

and Applications”, 3rd Edition, Springer, 2008 3. MIT Courseware by Erik Demaine

References: 1. Joseph O’Rourke, “Computational Geometry in C”, Cambridge University Press 2. Reinhard Diestel, “Graph Theory”, Spinger-Verlag, 2000 3. Peter Brass, “ Advanced Data Structures”, Cambridge University Press

Course Objectives : a. To impart knowledge of advanced data structures such as temporal data structures and geometric data

structures. b. To make students familiar with advanced concepts related to trees, graphs, hashing and string matching. c. To acquaint students with the knowledge of advanced data structures in order for it to be applicable in real

world applications. Course Learning Outcomes:


Bloom’s Cognitive

level Descriptor CO1 Interpret and summarize the purpose and operation of advanced data

structures 2 Understanding

CO2 Apply and demonstrate knowledge of advanced data structures for solving real world problems.

3 Applying

CO3 Analyze algorithms, compare data structures and appropriately evaluate the performance of the advanced data structures

4, 5 Analyzing, Evaluating

CO-PO Mapping :

1 2 3 4 5 6 7 8 9 10 11 12 CO1 2 3 CO2 1 3 CO3 2 3




22

Course Contents: Module 1 6 Hrs. Temporal Data Structures: Persistent data structures - Model and definitions, Partial persistence, Full persistence, Retroactive data structures – Retroactivity, Full retroactivity, Nonoblivious Retroactivity Geometric data structures - Planar Point Location, Orthogonal range searching, Fractional Cascading

Module 2 7 Hrs. Advanced Trees Binary Search Trees, AVL trees, red-black trees (Book- Cormen – chapter 13), Splay Trees, Tango Trees

Module 3 8 Hrs. Selected Graph Problems – Vertex coloring, edge coloring, Network flows: Max flow – mincut theorem (Cormen Chapter 26), Probabilistic methods – Markov’s inequality.

Module 4 7 Hrs. Hashing – Hash Function, Basic Chaining, FKS Perfect Hashing, Linear Probing, Cuckoo Hashing Module 5 5 Hrs. String matching - Predecessor Problem, Suffix Trees, Suffix Arrays, DC3 Algorithm for Building Suffix Arrays, Tries

Module 6 6 Hrs. Miscellaneous - Dynamic trees - Link-cut Trees, Operations on link-cut trees, Dynamic Connectivity, Euler-Tour Trees, Other Dynamic Graph Problems, Augmenting Data Structures

Module wise Measurable Students Learning Outcomes : Students will be able to Module 1:Describe, explain and use temporal data structures such as persistent data structures and retroactive data structures, geometric data structures (MIT Courseware, de Berge) Module 2: Demonstrate and use advanced trees for various applications including efficient searching. (Cormen) Module 3: Explain and implement various advanced graph algorithms. (Cormen, NPTEL graph theory, Reinhard Diestel) Module 4: Explain and apply static as well as dynamic hashing techniques. (MIT courseware) Module 5: Demonstrate and use various text processing techniques required in real world applications. (Peter Brass) Module 6: Demonstrate and use dynamic trees and graphs, create augmented data structures as per requirement. (Cormen, MIT courseware)

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Title of the Course: Design of Database System Lab 2CO551 (2CS652)

L T P Cr 0 0 2 1

Pre-Requisite Courses: Database Engineering, Programming knowledge in C#.Net, PL/SQL-Oracle

Textbook: 1. Thomas Connolly, Carolyn Begg “Database Systems: A Practical Approach to Design, Implementation and

Management”, Pearson, 4th Edition. 2012 2. Silberschatz, Korth & Sudarshan, “Database System Concepts”, MGH. 6th Edition 2011 3. Ramakrishnan & Gehrke, “Database Management System.”, MGH. 3rd Edition 2003

References: 1. Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden, “Modern Database Management.”, Pearson, 6th

Edition 2002 2. Rob & Coronel, “Database Systems – Design, Implementation & Management.”, Thomson, 5th Edition 2003 3. Oracle 11g / IBM DB2 9.7 manuals.

Course Objectives: 1. To develop conceptual understanding of database management system 2. To make them understand how a real world problem can be ported onto a database 3. To present steps in designing the applications using various databases and compare their performance.


Bloom’s Cognitive

level Descriptor

CO1 Construct various types of database objects and query it 4,5 Analyzing, evaluating

CO2 Plan, design and create a database system using different design methodologies conforming to the database system development lifecycle

6 Creating

CO-PO Mapping :

1 2 3 4 5 6 7 8 9 10 11 12 CO1 2 3 2 CO2 3 1 2

Assessment: In Semester Evaluation (ISE), and End Semester Examination (ESE) having 50% weightage each.

Assessment Marks ISE 50 ESE 50

ISE is based on performance of student in laboratory, experimental write-up, presentation, oral, and test (surprise/declared/quiz). The course teacher shall use at least two assessment tools as mentioned above for ISE. ESE: Assessment is based on performance and oral.

Course Contents: • It should consist of 10-12 design experiments based on syllabus / research papers. • The thrust should be given to design, modeling and implementation using standard CASE tools. • The detail list of assignments will be display by subject teacher by making 60 % variations in the previous

year list. • Use C# as Programming Language. For database programming / scripting use PL/SQL in Oracle / IBM

DB2.

24

Oracle 11g or IBM DB2 9.7 as backend database server.

Module wise Measurable Students Learning Outcomes : Nil

25

Title of the Course: Advanced Computer Networks Lab 2CO552 (2CS653)

L T P Cr 0 0 2 1

Pre-Requisite Courses: Hands on on Linux/Unix/Windows system programming, Any network programmable Languages. Preferably C/C++, Java, python etc. Textbook:

1. James Kurose and Keith Ross, "Computer Networking, A Top-Down Approach", Pearson, 5th Edition

References: 1. Larry Peterson and Bruce Davie, "Computer Networks, A Systems Approach", Morgan Kauffman. 2. W. Richard Stevens, "Unix Network Programming", Eastern Economy Edition, PHI.

Course Objectives : 1. To learn how protocols and layering are represented in packets. 2. To distinguish and understand how to design and analyze different types of communication protocols 3. To understand and interpret functioning of socket programming.



Bloom’s Cognitive

level Descriptor

CO1 Demonstrate the function of Socket programming. 3 Applying CO2 Compare and analyze different types of communication protocols. 4 Analyzing CO3 Design and configure routing and application layer protocols. 6 Creating

CO-PO Mapping :

1 2 3 4 5 6 7 8 9 10 11 12 CO1 1 CO2 1 3 2 CO3 1 2 3




Course Contents:

1. Analyzing Protocol Layers using packet sniffer.

2. Analyzing TCP using Wireshark.

3. Design TCP Client and Server application to transfer file (using TCP/IP socket programming).

4. Program for providing security for transfer of data in the network.

5. Design a RPC application to add and subtract a given pair of integers.

26

6. Analyzing application layer protocols using packet sniffing tool.

7. Configuration of application layer protocols using packet tracer.

8. VLAN Configuration using packet tracer.

9. Network performance Analysis.

10. Simulation Programs using OPNET /NS2 or any other equivalent software.

Module wise Measurable Students Learning Outcomes :Nil

27

Title of the Course: Seminar 2CO541 (2CS654)

L T P Cr - 2 1

Pre-Requisite Courses: -- Textbooks: NA References:

1. College Digital Library 2. Journals and transactions from IEEE, ACM, Elsevier, Springer, Science Direct etc.

Course Objectives : 1. To be able to understand recent advancements in computer science and engineering. 2. To be able to develop self-learning ability through rigorous study of literature available in selected area of

interest. 3. To be able to communicate through delivery of a seminar, present the idea in effective way and prepare

report. Course Learning Outcomes:


Bloom’s Cognitive

Level Descriptor

CO1 outline an independent learning in the various areas of computer science and engineering.

2 Understanding

CO2 communicate effectively, deliver a talk, convince the audience with respect to the topic under consideration, write technical report

2 Understanding

CO3 demonstrate and present knowledge about emerging areas and highlight the scope for research and development

3, 5 Applying Evaluating

CO-PO Mapping :

1 2 3 4 5 6 7 8 9 10 11 12 CO1 2 3 CO2 2 3 1 CO3 1 3 2




Course Contents: A supervisor / guide will be assigned to each student at the beginning of the semester. The student has to work throughout the semester to come up with a seminar on emerging areas of Computer Science and Engineering. Students are required to refer to the reputed journals, transactions in computer science to have awareness of the recent developments and research in the area. It includes selection of a topic, literature survey, identifying methodology, innovations, reported results and future trends.

Module wise Measurable Students Learning Outcomes :

28

SEM-II

Title of the Course: Project Management 2IC502 (2IC602) L T P Cr 2 0 0 2

Pre-Requisite Courses: Textbooks:

1. Jack Gido, James P Clements, Project Management, Cengage Learning India Pvt. Ltd., 2nd Reprint 2011, ©2007

References: 1. John Adair, Strategic Leadership, Kogan Page Ltd., 1st ed. 2010. 2. B.C. Punmia and Khandelwal, Project Planning and Control with PERT and CPM, Lakshmi Publications Pvt.

Ltd., 4th Edition, 2008 3. K. Nagarajan, Project Management, New Age Int., 2nd ed. 2004. 4. B.M.Naik, Project Management-Scheduling and Monitoring by PERT/CPM, 1984.

Course Objectives : 1. To develop a holistic, integrated approach to manage projects, exploring both technical and managerial

challenges. 2. To inculcate leadership and ethical qualities in dealing with real life project environment. 3. To develop positive attitude towards individual responsibility in individual project execution. 4. To provide a strategic perspective, demonstrate means to manage projects at Program and Portfolio level. 5. To induce qualities for supporting industry’s life-long learning programs, working in interdisciplinary and

cross functional teams with effective communication skills and managerial challenges. Course Learning Outcomes:


Bloom’s Cognitive level Descriptor

CO1 Recognize the needs of hard information and real skills of management to work successfully in a project environment to accomplish project objectives.

1 Remembering

CO2 Comprehend the project management principles and pertain them within the context of business critically.

2 3

Understanding Applying

CO3 Devise techniques especially for scheduling, estimation and project control through PMS for effective articulation of imparted knowledge.

5,6 Evaluating Creating

CO-PO Mapping : 1 2 3 4 5 6 7 8 9 10 11 12 CO1 3 2 CO2 1 1 1 CO3 2 2 2



ISE 1 and ISE 2 are based on assignment, oral, seminar, test (surprise/declared/quiz), and group discussion.[One

29

assessment tool per ISE. The assessment tool used for ISE 1 shall not be used for ISE 2] MSE: Assessment is based on 50% of course content (Normally first three modules) ESE: Assessment is based on 100% course content with70-80% weightage for course content (normally last three modules) covered after MSE.

Course Contents: Module 1 Hrs. Project Management Concepts Attributes of Project, Project Life Cycle, Considerations for RFP, Project Process and Global Project Management.

4

Module 2 Hrs. Project Planning and Schedule WBS, Responsibility matrix, Devp. of non-network and network schedules, Activity duration estimates, Schedule calculations, Probability considerations, PMS.

5

Module 3 Hrs. Schedule control Project control process Updating schedule, Approaches to schedule control, Resource considerations.

4

Module 4 Hrs. Cost Planning and Performance Project cost estimates, Budget, Actual cost, Cost Forecasting, Managing cash flows. 4

Module 5 Hrs. Project Manager and Project Team Responsibilities and skills, Delegation, Managing Change, Devp. And effectiveness of project team, Ethics, Conflicts on Projects, Time Management.

5

Module 6 Hrs. Project communication and Documentation Personal communication, Effective listening, Meeting, Presentations and Report preparation, Types of Project organizations, their merits and demerits, SWOT.

4

Module wise Measurable Students Learning Outcomes : Students should be able to

Module 1: Recognize basic properties of projects; differentiate between project management practices and traditional business functions, project life cycle and concepts of project success. Module 2: Understand the key scheduling terminologies, apply logic for developing network schedules, perform duration calculations and indentify critical paths and floats. Module 3: Interpret the various steps involved in project control process, apply the changes in updating networks leading to new schedules in consideration to various resources. Module 4: Familiarize regarding baseline budget, analyzing cost performance index, Cost forecasting, Managing cash Flow. Module 5: Recognize the responsibilities of project manager and develop skills and techniques to ethically manage and control projects with effective delegation. Module 6: Understand the characteristics of organizational structures, develop the art of enhancing personal communication, handle effective project presentations, meetings and prepare project reports.

30

Title of the Course: Modern Operating System 2CO521 (2CS611) L T P Cr

3 0 0 3 Pre-Requisite Courses: Operating System

Textbooks: 1. P. K. Sinha, “Distributed Operating Systems Concepts and Design”, PHI. 2. Silberschatz, Galvin, Gagne “Operating System Concepts”, John Wiley, 8th Edition.2011 3. Rajkumar Buyya, Christian Vecchieola, S. Thamarai Selvi, “Mastering Cloud Computing”, (McGrawHill)

References: 1. S. Tanenbaum ,“Modern Operating Systems”, Pearson/PH 3rd Edition 2009. 2. S. Tanenbaum ,“Distributed Operating Systems”, Pearson, 5th Impression 2008. 3. “Cloud Computing for Dummies”, J. Hurwitz, R. Bloor, M Kaufman, F. Halper, (Wiley)

Course Objectives : 1. To deliver different components of advanced and distributed computing system. 2. To provide knowledge of issues involved in synchronization, resource and process management. 3. To induce steps involved in designing, simulating and implementing various operating systems.



CO1 Explain the advances in operating systems and characteristics of environment in which they are used

2 Understanding

CO2 Apply the communication techniques in distributed operating systems and implement and analyze the distributed file systems.

3,4 Applying, Analyzing

CO3 Design and implement the different algorithms in synchronization, resource and process management and build real time operating system kernel for different applications.

5,6 Evaluating, Designing

CO-PO Mapping :

1 2 3 4 5 6 7 8 9 10 11 12 CO1 2 CO2 1 3 CO3 1 3 2




Course Contents: Module 1 : Real Time Operating Systems Hrs.

31

Overview, System characteristics, Features of real time kernels, implementing real time operating systems, real time CPU scheduling. Case study of different RTOS

6

Module 2 : Mobile Operating System Android, Windows Phone

Hrs. 6

Module 3 : Distributed Operating Systems Introduction to distributed computing systems, models design issues of distributed operating system, distributed computing environment. Detail study of any one distributed operating system.

Hrs. 8

Module 4 : Cloud Operating System-I Introduction to cloud computing : Cloud computing at glance, historical developments, building cloud computing environments, computing platforms and technologies. Cloud Computing Architecture : Introduction, cloud reference model, types of clouds, economics of the cloud, open challenges.

Hrs. 7

Module 5 : Cloud Operating System-II Virtualization : Introduction, characteristics of virtualized environments, Taxonomy of virtualization Techniques, Virtualization and cloud computing, Pros and Cons of virtualization, technology examples.

Hrs. 6

Module 6 : Case Study of any two Cloud OS ClickOS, Drawbridge,GUK11, MiniOS,OSv or any latest cloud OS

Hrs. 6


1. Understand the components of RTOS. 2. Able to build the kernel for real time applications. 3. Apply the principles of real time CPU scheduling for optimal performance of RTOS.

Module 2 1. Understand the complete architecture of Android and Windows Phone OS. 2. Able to port any applications on Android / Windows Phone. 3. Able to apply core API of these OS for any optimization, interfacing or writing device driver.

Module 3 1. Able to analyze the distributed computing environments 2. Understand the different design issues in distributed OS. 3. Apply the design principles for building any distributed operating systems.

Module 4 1. Understand the fundamentals of cloud computing 2. Able to evaluate different cloud architectures.

Module 5 1. Able to design and implement virtualization 2. Able to analyze different issues in virtualization

Module 6 1. Understand the different open source / commercial cloud OS Implement any one cloud OS.

32

Title of the Course: Parallel Computing 2CO522 (2CS612)

L T P Cr 3 0 0 3

Pre-Requisite Courses: Data structures, Basic Programming knowledge. Textbooks:

1. Introduction to Parallel Computing (2nd ed.), by Ananth Grama, Anshul Gupta, George Karypis, and Vipin Kumar.

2. High Performance Cluster Computing : Programming and Applications, Volume 2 ByBuyya Raijkumar 3. CUDA Programming: A Developer's Guide to Parallel Computing with GPUs by shane cook

References: 1. Introduction to High-Performance Scientific Computing, Victor Eijkhout, 2011.

http://tacc web.austin.utexas.edu/staff/home/veijkhout/public_html/Articles/EijkhoutIntroToHPC.pdf 2. High Performance Computing, Charles Severance, 1998. http://cnx.org/content/col11136/latest/ 3. MPI: The Complete Reference, Marc Snir, Steve Otto, Steven Huss-Lederman, David Walker, and Jack

Dongarra, 1996. http://www.netlib.org/utk/papers/mpi-book/mpi-book.html 4. MPI: The Complete Reference, Marc Snir, Steve Otto, Steven Huss-Lederman, David Walker, and Jack

Dongarra, 1996. http://www.netlib.org/utk/papers/mpi-book/mpi-book.html 5. Designing and Building Parallel Programs, Ian Foster, 1995. http://www.mcs.anl.gov/~itf/dbpp/ 6. Parallel Programming in C with MPI and OpenMP, Michael J. Quinn, McGraw-Hill.

Course Objectives : 1. To provide an introduction to the arithmetic and software tools and techniques needed to implement

effective, high performance programs on modern parallel computing systems.

2. To be introduced with current trends in parallel computer architectures and programming models( i.e. languages and libraries) for shared memory, manycore/multicore architecture.

Course Learning Outcomes: CO After the completion of the course the student should be able to Bloom’s Cognitive

level Descriptor

CO1 describe principles of parallel algorithm design, analytical modeling of parallel programs, programming models for shared- and distributed-memory systems, parallel computer architectures, along with numerical and non-numerical algorithms for parallel systems

1,2 Remembering, Understanding

CO2 demonstrate understanding of learned concepts of parallel algorithmdesign, performance evaluation, communication operators by writingalgorithms and programs exploiting parallel architecture

3 Applying

CO3 analyze the efficiency of parallel algorithms designed for matrix,graph and sorting operations

4 Analyze

CO-PO Mapping : 1 2 3 4 5 6 7 8 9 10 11 12 CO1 1 2 CO2 1 1 CO3 1 3

Assessment: Two components of In Semester Evaluation (ISE), One Mid Semester Examination (MSE) and one End

33

Semester Examination (ESE) having 20%, 30% and 50% weightage respectively. Assessment Marks

ISE 1 10 MSE 30 ISE 2 10 ESE 50


Course Contents: Module 1 Hrs. Introduction to Parallel Computing: Implicit Parallelism, Limitations of Memory, Dichotomy of Parallel Computing Platforms, Physical Organization of Parallel Platforms, Communication Costs in Parallel Machines, Routing Mechanisms for Interconnection Networks, Impact of Process-Processor Mapping and Mapping Techniques.

06

Module 2 Hrs. Design Decomposition Techniques: Characteristics of Tasks and Interactions, Mapping Techniques for Load Balancing, Methods for Containing Interaction Overheads, Parallel Algorithm Models Basic Communication Operations One-to-All Broadcast and All-to-One Reduction, All-to-All Broadcast and Reduction, All-Reduce and Prefix-Sum Operations, Scatter and Gather

06

Module 3 Hrs. Performance Metrics for parallel systems. The effect of Granularity and Data Mapping on Performance. The Scalability of parallel systems, Isoefficiency metric of scalability, sources of parallel overhead, Minimum execution time and minimum cost-optimal execution time.

06

Module 4 Hrs. OpenMP, MPI, CUDA/OpenCL, Chapel, etc. Thread basics ,Work Sharing constructs, Scheduling, Reduction, Mutual Exclusion Synchronization & Barriers, The MPI Programming Model, MPI Basics, Global Operations , Asynchronous Communication, Modularity, Other MPI Features Basic of GPGPU, CUDA Programming model, CUDA memory type Performance Issues

08

Module 5 Hrs. Dense Matrix Algorithms: Matrix-Vector Multiplication, Matrix-Matrix Multiplication, Solving a System of Linear Equations Sorting: Issues, Sorting Networks, Bubble Sort and its Variants, Quicksort, Bucket and Sample Sort

07

Module 6 Hrs. Graph Algorithms Definitions and Representation, Minimum Spanning Tree: Prim's Algorithm, Single-Source Shortest Paths: Dijkstra's Algorithm, All-Pairs Shortest Paths 06

Module wise Measurable Students Learning Outcomes : Module 1: Understand the need of parallel algorithm Module 2: Decomposition strategies of problem Module 3: Knowledge about the measure the performance of parallel algorithm.

Module 4: Understanding the programming with MPI, OpenMP.

Module 5: Study applications of parallel computing Module 6: Ability to apply many core models for solving standard algorithms

34

Title of the Course: Institute Elective –II Computer Vision 2IE5** (2CS613)

L T P Cr 3 0 0 3

Pre-Requisite Courses: Fundamentals of Digital Image Processing

Textbooks: 1. R. C. Gonzalez, R. E. Woods, Digital Image Processing, 2nd Edition. 2002, PHI 2. Milan Sonka, Vaclav Hlavac, Boyle, Digital Image Processing and Computer Vision, Cengage Learning

References: 1. S. Jayaraman, S. Esakkirajan, T. Veerkumar, Digital Image Processing, Tata McGrawHill 2. D. A. Forsyth, J. Ponce, Computer Vision – A Modern approach, Pearson Education, Prentice Hall, 2005 3. Linda Shapiro, George C. Stockman, Computer Vision, Prentice Hall, 2000

Course Objectives : 1. To be able to learn advanced techniques in digital image processing and computer vision. 2. To be able to learn the concepts of color image processing, morphological operations, texture analysis,

object recognition, video processing, 3D imaging etc. and apply the algorithms to build applications. 3. To be able to compare various algorithms and select the appropriate for a particular application. 4. To be able to develop strong theoretical platform in the area of Computer Vision to excel in this stream for

further research. Course Learning Outcomes:


Bloom’s Cognitive Level Descriptor

CO1 explain the concepts of color image processing, morphological operations, fundamentals of texture analysis, object recognition methods, video processing concepts, 3D imaging

2 Understanding

CO2 write algorithms and apply the concepts mathematically to interpret the results with justification

3 Applying

CO3 apply the concepts to analyze the problem, use appropriate algorithms to build solutions to the real world computer vision problems.

3, 4 Applying , Analyzing

CO-PO Mapping : 1 2 3 4 5 6 7 8 9 10 11 12 CO1 1 CO2 2 3 CO3 2 3 2




35

Course Contents: Module 1: Color Image Processing 6 Hrs. Color Fundamentals, Color models, Gray level to color transformations, Basics of Color Image Processing, Color Transformations, Smoothing and Sharpening, Color Segmentation

Module 2: Morphological Image Processing 6 Hrs. Introduction, Dilation and Erosion, Opening and Closing, The Hit-or-miss transformation, Basic Morphological Algorithms, Boundary Extraction, Region Filling, Extraction of connected components, Thinning, Thickening

Module 3: Texture Analysis 7 Hrs. Definition, Types of texture, Texels, Texture analysis – concept and categories, Approaches to texture analysis, Statistics, Texture descriptors - statistical - Auto-correlation, co-occurrence matrices and features, edge density and direction, local binary partition, Law’s texture energy measures, Wavelets and texture analysis

Module 4: Object Recognition 7 Hrs. Object Detection Vs recognition, Patterns and Pattern Classes, Knowledge Representation, Statistical Pattern Recognition, Neural Nets, Syntactic Pattern Recognition, Optimization Techniques in Recognition

Module 5: Moving Object Detection and Tracking 7 Hrs. Introduction, Background Modeling, Connected Component Labeling, Shadow Detection, Object Tracking, Object representation, Discrete Kalman Filtering, Particle Filtering

Module 6: 3D Vision 6 Hrs. Introduction to 3D imaging and its applications. Study of any Research Paper(s) based on the current trends in 3D imaging or any case study.

Module wise Measurable Students Learning Outcomes : Students will be able to Module 1: • explain and use various color models, transformations and techniques of Color Image Processing. Module 2: • use Morphology operations for image pre-processing, enhancing, segmenting objects and describing objects. Module 3: • describe fundamentals of texture and its importance in analyzing images. • compute various texture descriptors and use it further for texture classification / retrieval. Module 4: • demonstrate and apply patterns recognition techniques to recognize objects in images for further understanding

the scene. Module 5: • explain concepts of video processing and practically work with detecting moving objects and techniques for

tracking. Module 6: • explain fundamentals of 3D imaging. • carry out case study and/or study any research paper based on current trends in 3D imaging.

36

Title of the Course: Institute Elective II - Machine Learning 2IE5** (2CS614)

L T P Cr 3 0 0 3

Pre-Requisite Courses: Textbooks:

1. Machine Learning Hands-On for Developers and Technical Professionals, Jason Bell. Wiley 2015 2. Machine Learning – Tom M. Mitchell, - MGH 3. Machine Learning: An Algorithmic Perspective, Stephen Marsland, Taylor & Francis (CRC)

References: 1. Machine Learning Methods in the Environmental Sciences, Neural Networks. William WHsieh, Cambridge

Univ Press. 2. Pattern classification, Richard o. Duda, Peter E. Hart and David G. Stork, John Wiley &Sons Inc., 2001 3. Neural Networks for Pattern Recognition, Chris Bishop, Oxford University Press, 1995

Course Objectives :

1. To formulate machine learning problems corresponding to different applications. 2. To understand a range of machine learning algorithms along with their strengths and weaknesses. 3. To apply machine learning algorithms to solve problems of moderate complexity.


After the completion of the course the student should be able to Bloom’s Cognitive

level Descriptor CO1 comprehend a range of machine learning algorithms along with their

strengths and weaknesses. 2 understanding

CO2 apply machine learning algorithms to solve typical problems in Machine Learning.

3 applying

CO3 analyze various machine learning tools 4 analyzing

CO-PO Mapping :

1 2 3 4 5 6 7 8 9 10 11 12 CO1 3 2 3 CO2 3 2 CO3 3 2




Course Contents:

37

Module 1 : What Is Machine Learning? History of Machine Learning, Algorithm Types for Machine Learning, The Human Touch Uses for Machine Learning, Languages for Machine Learning

6Hrs.

Module 2 :Planning for Machine Learning The Machine Learning Cycle Defining the Process, Building a Data Team, Data Processing, Data Storage, Data Privacy, Data Quality and Cleaning

6Hrs.

Module 3 : Working with Decision Trees The Basics of Decision Trees, Decision Trees in Weka, Bayesian Networks: Bayes’ Theorem, How Bayesian Networks Work, A Bayesian Network Walkthrough.

7Hrs.

Module 4 :Artificial Neural Networks (ANN) What Is a Neural Network?, ANN uses, Breaking Down the Artificial Neural Network, Data Preparation for Artificial Neural Networks, Artificial Neural Networks with Weka

6Hrs.

Module 5 : Association Rules Learning Where Is Association Rules Learning Used?, How Association Rules Learning Works ?,Algorithms, Mining the Baskets—A Walkthrough.

6Hrs.

Module 6 :Support Vector Machines What Is a Support Vector Machine?, Where Are Support Vector Machines Used? The Basic Classification Principles, How Support Vector Machines Approach Classification, Using Support Vector Machines in Weka Clustering : What Is Clustering?, Where Is Clustering Used?, Clustering Models k-Means, Clustering with Weka.

8Hrs.

Module wise Measurable Students Learning Outcomes : Module 1: Understand Machine Learning and it’s Languages Module 2: define Machine learning Process and build data team Module 3: demonstrate the working of Decision Trees Module 4: Apply ANN techniques Module 5: Apply association Rules Learning Module 6: 1.Classification using SVM . 2. Evaluating clustering Models.

38

Title of the Course: Professional Elective-II Mobile and Pervasive Computing 2CO531 (2CS616)

L

T

P

Cr

3 1 0 4 Pre-Requisite Courses: Pervasive Computing, Computer Networks, Distributed Systems

Textbook: 1. Seng Loke, “Context-Aware Computing Pervasive Systems”, Auerbach Pub., New York, 2007 2. Frank Adelstein, Sandeep KS Gupta, “Fundamentals of Mobile and Pervasive Computing”, Golden Richard,

McGraw-Hill 2005 3. Jochen Burkhardt, “Pervasive Computing: Technology and Architecture of Mobile Internet Applications”,

Addison-Wesley Professional; 3rd edition, 2007 4. John Krumm, “Ubiquitous Computing Fundamentals”, CRC Press

References: 1. Stefan Poslad, “Ubiquitous Computing: Smart Devices, Environments and Interactions”, Wiley, 2009.

2. James Keogh, “J2ME: The Complete Reference”, Tata McGraw Hill

Course Objectives : 1. To explore concepts related to Pervasive Computing. 2. To distinguish various protocols, architectures, communication technologies, context awareness & devices of

Pervasive Computing. 3. To practice pervasive concepts through designing and experimenting case studies.

Course Learning Outcomes: CO After the completion of the course the student should be able to,

Bloom’s Cognitive

level Descriptor

CO1 comprehend the concepts of Mobility and Pervasive Computing enablers.

2 Understanding

CO2 demonstrate the architectural aspects of various protocols used in Pervasive Computing.

3 Applying,

CO3 articulate hands on experiments of Context Aware and sensor based integrations and applying it for case studies.

5,6 Evaluating, Creating

CO-PO Mapping :

1 2 3 4 5 6 7 8 9 10 11 12 CO1 1 3 CO2 1 2 2 CO3 1 1 2 3


Assessment Marks ISE 1 10 MSE 30 ISE 2 10

39

ESE 50 ISE 1 and ISE 2 are based on assignment, oral, seminar, test (surprise/declared/quiz), and group discussion.[One assessment tool per ISE. The assessment tool used for ISE 1 shall not be used for ISE 2] MSE: Assessment is based on 50% of course content (Normally first three modules) ESE: Assessment is based on 100% course content with70-80% weightage for course content (normally last three modules) covered after MSE.

Course Contents: Module 1: Wireless Communications & Mobility Concepts Wireless Communications: Infrared vs radio transmission, Infrastructure and ad-hoc network, NFC, PAN, IEEE802.11 Standard, HiperLAN, Bluetooth, Zigbee. Mobile devices computability, benefits.

7 Hrs.

Module 2 . Pervasive Computing Enablers Ubiquitous computing, Context Awareness, Ambient Intelligence, Wearable Computing.

7 Hrs.

Module 3 Protocols & Supporting Architectures Open protocols- Service discovery technologies- SDP, Jini, SLP, UpnP protocols–data synchronization- SyncML framework. Web Application Design Concepts- Frameworks, WAP and Beyond-Voice Technologies, Personal Digital Assistants Server side programming-Pervasive Web application Architecture-scenarios

6 Hrs.

Module 4: Smart Devices and Localization Systems Smart Sensors devices, RFID, Embedded controllers: Arduno, Raspberry Pi, ARM controllers, Proximity Sensors and Actuators, Localizations Systems, GPS, Google, Communication and access services, Issues.

7 Hrs.

Module 5 Context Aware Computing Principles, Instrumenting Context, Context setting, Instrumenting Persons, Personalization, Context communication and Context Server administration.

6 Hrs.

Module 6 Potential Applications & Case Scenarios Smart Cities, Healthcare systems, Smart Home, Set top boxes, Wearable smart applications, Automotive computing, OnBoard Computing Systems, InVehicle networks, Entertainment Systems.

6 Hrs.

Module wise Measurable Students Learning Outcomes : Module 1 Wireless Communications & Mobility Concepts

1. Understand the notion of Wireless Communications. 2. Learn the Mobile Computing Concepts 3. Know the enablers of Pervasive computing. 4. Aware the challenges of Pervasive Computing

Module 2 Pervasive Computing Enablers

1. Drill down concepts of Ubiquitous computing 2. Context Awareness 3. Ambient Intelligence 4. Wearable Computing.

40

Module 3 Protocols & Supporting Architectures

1. Understand the Protocols used to enable Pervasive Technology 2. Learn various Controllers used in pervasive computing 3. Get acquainted with Web application framework concepts.

Module 4 Smart Devices and Localization Systems

1. Groom with smart devices and its integration. 2. Explore application access techniques via WAP, PDA and Voice. 3. Expedite various issues of Pervasive computing.

Module 5 Context Aware Computing

1. Learn how to instrument, configure Context. 2. Learn instrumenting persons and profile creations. 3. Learn Server side administration for contexts.

Module 6 Potential Applications & Case Scenarios

1. Get exposure to Pervasive Potential applications 2. Obtain confidence in enabling ubiquity to real life engineering applications

41

Title of the Course: Professional Elective II- Data Mining 2CO532 (2CS617)

L T P Cr 3 1 0 4

Pre-Requisite Courses: Data base management system, Advance database system, Some concepts of Mathematics and Statistics. Textbooks:

1. Margaret H. Dunham, Data Mining: Introductory and AdvancedTopics, Pearson Education. 2. S N Sivanandam, S Sumathi, Data Mining: Concepts, Tasks and Techniques, Thomson 3. RajanChattamvelli, Data Mining Methods : Concepts & Applications, Narosa Publishing House

References: 1. Sushmita Mitra, Tinku Acharya, Data Mining Multimedia, Soft Computing and Biometrics WILEY

Publication 2. S.Prabhu, N. Venkatesan, Data Mining & Warehousing, New Age International Publisher.

Course Objectives : 1. To provide students with an understanding of the theories and algorithms that forms the basis of Data

Mining and modeling. 2. To provide students with an overview of the key concepts of Data Mining with reference to applications in

real world technologies. 3. To address ways to summarize and communicate results of data mining effectively. 4. To inspire students to actively participate in analyzing diverse data types, using computer algorithms and

tools. Course Learning Outcomes:


CO1 identify and interpret the data mining algorithms. 1,2 Remembering Understanding

CO2 recognize the appropriate data mining algorithm and ways to check validity of the model and employ it for estimation & prediction.

4 analyzing

CO3 design and demonstrate computer programs for different types of algorithms used in data mining on real life data.

3,6 Applying, creating

CO-PO Mapping :

1 2 3 4 5 6 7 8 9 10 11 12 CO1 1 2 CO2 3 2 2 CO3 2 1 2



ISE 1 and ISE 2 are based on assignment, oral, seminar, test (surprise/declared/quiz), and group discussion.[One assessment tool per ISE. The assessment tool used for ISE 1 shall not be used for ISE 2] MSE: Assessment is based on 50% of course content (Normally first three modules)

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ESE: Assessment is based on 100% course content with70-80% weightage for course content (normally last three modules) covered after MSE.

Course Contents:

Module 1 : Introduction and related Concepts Basic Data Mining Tasks, Data Mining Issues, metrics, social implication of Data Mining, Data Mining and DBMS, Data Warehouse. OLTP, DSS, Dimension modeling, OLAP, Machine learning, Pattern matching, Statistics, Box-plot, Regression, Correlation.

Hrs. 6

Module 2 : Classification Introduction, Issues in classification, Statistical based algorithms, distance based algorithms, decision tree based algorithms, Neural network based algorithms and Rule based algorithms, Combining techniques.

Hrs. 6

Module 3 : Clustering Introduction, Requirement of clustering, Similarity measures. (Distance Functions),types of clustering algorithms, Hierarchical algorithms, Partitional algorithms, Clustering large databases, clustering with categorical attributes, comparison of clustering methods.

Hrs. 7

Module 4 : Association Rule

Introduction, itemset- Market basket analysis, Frequent itemset, Basic algorithms (Priori, Sampling, Partitioning), Parallel and Distributed Algorithms, Incremental rules, Advanced association rule techniques(Generalized, multiple level, quantitative, multiple minimum support, correlation rule), Measuring the quality of rules.

Hrs. 7

Module 5 : Spatial Mining Introduction, spatial data, spatial data mining, generalization and specialization, spatial rules, spatial classification algorithms, spatial clustering algorithms.

Hrs. 6

Module 6 : Temporal Mining Introduction, modeling temporal events, time series data mining, pattern detection, sequence analysis, temporal association rule. Introduction to Web mining.

Hrs. 7

Module wise Measurable Students Learning Outcomes : Module1: Students are aware about basics of DM process and able to build Data model from raw real world data. Module2: Students are able to write and apply different types of algorithm for classification of raw data in to useful classified data. Module3: Students are able to write and apply different types of algorithm for clustering of raw data in to clustered data. Module4: Students are able to write and apply different types of algorithm to form meaningful association rules from raw data. Module5: The theories and concepts of Data mining are applied on Spatial Data. Students are able to apply different types of algorithm on spatial data.

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Module6: The theories and concepts of Data mining are applied on Temporal Data. Students are able to apply different types of algorithm on Temporal data.

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Title of the Course: Modern Operating System Lab 2CO571 (2CS661)

L

T

P

Cr

0 0 2 1

Pre-Requisite Courses: Operating System, Programming knowledge on C#.Net, Java, C++ Textbook:

1. P. K. Sinha, “Distributed Operating Systems Concepts and Design”, PHI. 2. Silberschatz, Galvin, Gagne “Operating System Concepts”, John Wiley, 8th Edition.2011

References: 1. A. S. Tanenbaum ,“Modern Operating Systems”, Pearson/PH 3rd Edition 2009. 2. A. S. Tanenbaum ,“Distributed Operating Systems”, Pearson, 5th Impression 2008.

Course Objectives : 1. To inculcate the best practices in demonstrating and implementing the different components of distributed

computing system. 2. To familiarize the ways of developing and analyzing synchronization, resource and process management

algorithms for various operating systems. 3. To illustrate steps involved in designing modern operating systems like Android mobile OS, Windows

Phone. 4. To inspire students to implement new operating system features and/or upgrade existing one with

competitive performance. Course Learning Outcomes:


Bloom’s Cognitive

Level Descriptor

CO1 Apply the communication techniques in distributed operating systems and implement and analyze the distributed file systems.

3,4 Applying, Analyzing

CO2 Compare and evaluate modern operating system and apply the principles in modern operating system to design real time applications.

4,5 Analyzing, Evaluating

CO3 Design and implement the different algorithms in synchronization, resource and process management and build real time operating system kernel for different applications.

5,6 Evaluating, Designing

CO-PO Mapping :

1 2 3 4 5 6 7 8 9 10 11 12 CO1 3 3 CO2 2 1 CO3 1 3

Assessment: In Semester Evaluation (ISE), and End Semester Examination (ESE) having 50% weightageeach.


ISE is based on performance of student in laboratory, experimental write-up, presentation, oral, and test

45

(surprise/declared/quiz). The course teacher shall use at least two assessment tools as mentioned above for ISE. ESE: Assessment is based on performance and oral.

Course Contents: Laboratory Assignments : Two hour per week per batch is to be utilized for problem solving/designing/implementation, to ensure that students have properly learnt the topics covered in the theory course. The partial list is as follows (the list may be updated during actual implementation) :

1. Case study of commercial / freeware distributed computing systems. 2. Linux Clustering using MPI package. 3. Implement an On Line Distributed Resource Management System to manage and display different resources

available in distributed environment. [ Assume campus intranet ] 4. Extend the assignment number 3 to upload and monitor the computing task to any available resources. 5. Cluster computing using Microsoft Windows Compute Cluster Server 2003. 6. Hands on with Android OS – basic working. 7. API programming on Android OS 8. Application development on Android OS 9. Hands on with Windows Phone – basic working.

10. API programming on Windows Phone 11. Application development on Windows Phone

Module wise Measurable Students Learning Outcomes :

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Title of the Course: Parallel Computing Lab 2CO572 (2CS662)

L

T

P

Cr

0 0 2 1

Pre-Requisite Courses: Data structures, Basic Programming knowledge. Textbooks:

1. Introduction to Parallel Computing (2nd ed.), by Ananth Grama, Anshul Gupta, George Karypis, and Vipin Kumar.

2. High Performance Cluster Computing : Programming and Applications, Volume 2 ByBuyya Raijkumar 3. CUDA Programming: A Developer's Guide to Parallel Computing with GPUs by shane cook

References: Available online:

1. Introduction to High-Performance Scientific Computing, Victor Eijkhout, http://tacc web.austin.utexas.edu/staff/home/veijkhout/public_html/Articles/EijkhoutIntroToHPC.pdf

2. High Performance Computing, Charles Severance, 1998. http://cnx.org/content/col11136/latest/ 3. MPI: The Complete Reference, Marc Snir, Steve Otto, Steven Huss-Lederman, David Walker, and Jack

Dongarra, 1996. http://www.netlib.org/utk/papers/mpi-book/mpi-book.html 4. MPI: The Complete Reference, Marc Snir, Steve Otto, Steven Huss-Lederman, David Walker, and Jack

Dongarra, 1996. http://www.netlib.org/utk/papers/mpi-book/mpi-book.html 5. Designing and Building Parallel Programs, Ian Foster, 1995. http://www.mcs.anl.gov/~itf/dbpp/

Reference Books:- 1. Parallel Programming in C with MPI and OpenMP, Michael J. Quinn, McGraw-Hill.

Course Objectives : 1. To provide an introduction to the arithmetic and software tools and techniques needed to implement

effective, high performance programs on modern parallel computing systems.

2. To be introduced with current trends in parallel computer architectures and programming models( i.e languages and libraries) for shared memory, manycore/multicore architecture

Course Learning Outcomes: CO After the completion of the course the student should be Bloom’s Cognitive

level Descriptor

CO1 apply shared memory, Distributed memory parallel programming concepts while designing parallel algorithm .

3 Applying

CO2 implement parallel programs for large‐scale parallel sy�tems, sharedaddress space platforms, and heterogeneous platforms

3 Applying

CO3 analyze the efficiency of parallel algorithms designed for matrix,graph an� sorting operations

4 Analyze

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CO-PO Mapping :

1 2 3 4 5 6 7 8 9 10 11 12 CO1 2 1 CO2 2 3 CO3 1 3




List of Experiments:

1. To design and implement quick sort algorithm using openMP 2. To study different profilers like GPROF, GCOV, VTUNE Amplifier 3. To analyze the performance of developed algorithms using above profilers 4. Implementation of dense matrix using MPI 5. To design and implement algorithm for different communication operators 6. Study of Pthread library 7. To design and implement parallel program using CUDA architecture 8. To calculate isoefficiency for algorithm given serial and parallel run time 9. Design and implement different parallel graph algorithms 10. Implementation of parallel sorting techniques like bitonic sort, merge sort

Module wise Measurable Students Learning Outcomes : Module 1: Understand the need of parallel algorithm Module 2: Decomposition strategies of problem Module 3: Knowledge about the measure the performance of parallel algorithm.

Module 4: Understanding the programming with MPI, OpenMP.

Module 5: Study applications of parallel computing

Module 6: Ability to apply many core models for solving standard algorithms.

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Title of the Course: Special Topics in CSE 2CO573 (2CS663)

L T P Cr 2 0 2 3

Pre-Requisite Courses: -- Textbook: Prescribed at the time of offering

References: Prescribed at the time of offering

Course Objectives : 1. To expose students to state-of-the-art research topics in Computing, discover some of the currently active

areas of computer science research. 2. To learn how to make contributions to those areas. 3. To help the students to develop essential research and independent learning. 4. To broaden students' understanding of Computer Science by introducing additional special topics into the

curriculum. 5. To practically implement the concepts to get into the depth of topic.


Bloom’s Cognitive

Level Descriptor CO1 explain specialized knowledge from thrust areas in computer science. 2 Understanding CO2 demonstrate the abilities to learn independently to excel further in the

specialized areas. 3 Applying

CO3 apply knowledge of state-of-the-art in computer science research on a set of topics and demonstrate few of the concepts practically to have the insight.

3 Applying

CO-PO Mapping : 1 2 3 4 5 6 7 8 9 10 11 12 CO1 1 3 CO2 3 2 CO3 2 1 2




Course Contents: Course contents will vary depending on the thrust areas in computing. The advanced / special topics will be selected in the identified areas of computer science. The recent research papers or the emerging topics will be selected for study and discussions. Few areas (but not restricted) that have been identified are as under: Soft Computing, Computer Vision, Pattern Recognition, Cognitive Science, Human Computer Interaction, Embedded Systems, Artificial Intelligence, Computer Networks, Information Security, BIG data etc. Module wise Measurable Students Learning Outcomes : --

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Title of the Course: Pre Dissertation Seminar 2CO542 (2CS664)

L T P Cr - 2 1

Pre-Requisite Courses: -- Textbooks: NA References:

3. College Digital Library 4. Journals and transactions from IEEE, ACM, Elsevier, Springer, Science Direct etc.

Course Objectives : 1. To be able to understand recent advancements in computer science and engineering. 2. To be able to develop self-learning ability through rigorous study of literature available in selected area of

interest. 3. To be able to communicate through delivery of a seminar, present the idea in effective way, prepare report

and publish a paper. Course Learning Outcomes:


Bloom’s Cognitive

Level Descriptor

CO1 outline an independent learning in the identified area of computer science and engineering.

2 Understanding

CO2 communicate effectively, deliver a talk, convince the audience with respect to the topic under consideration, write technical report

2 Understanding

CO3 demonstrate and present knowledge about emerging trends and the scope for research and development by publishing the review or survey paper in the identified area

3, 5 Applying Evaluating

CO-PO Mapping :

1 2 3 4 5 6 7 8 9 10 11 12 CO1 2 3 CO2 2 3 1 CO3 1 3 1 2

Assessment: Assessment Marks

ISE 100 ISE is based on performance of student in laboratory, experimental write-up, presentation, oral, and test (surprise/declared/quiz). The course teacher shall use at least two assessment tools as mentioned above for ISE.

Course Contents:

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This seminar should be in the area of the proposed dissertation work to be carried out in second year of this programme leading to the problem statement. Students are required to refer to the reputed journals, transactions in computer science focusing on novel problems in identified area of interest. It is necessary that the student should carry out extensive literature review towards the proposed work and present the same. Also it is highly desirable to have a publication based on the study carried out in the identified area.

Module wise Measurable Students Learning Outcomes : --

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Title of the Course: Dissertation (Phases I to IV) (2CO691 to 2CO696) L T P Cr - - 10 30

Pre-Requisite Courses:- Pre-Research Seminar Textbooks: - References: National and International conference papers in Computer Science and Engineering from IEEE, ACM, Springer, Elsevier etc. National and International journals in Computer Science and Engineering from IEEE, ACM, Springer, Elsevier etc.

Course Objectives :

1. Inspire students to tackle real world problems by applying knowledge in Computer Science and Engineering.

2. Impart flexibility to the student to have increased control over his/ her learning. 3. Enhance student’s learning through increased interaction with peers and colleagues.


CO After the completion of the course the student should be able to Bloom’s Cognitive level Descriptor

CO1 Defend the objectives of the dissertation by grasping and analyzing through an extensive literature review in the significant area of study.

2 4

Understand Analyze

CO2 Formulate the methodology and execute the study through conduct

of analytical/experimental work to achieve the objectives.

3 6

Apply Create

CO3 Defend the outcomes of the dissertation through self-learning, analyzing and justifying the project work as per appropriate standards of documentation and presentation.

4 5

Analyze Evaluate

CO-PO Mapping :

1 2 3 4 5 6 7 8 9 10 11 12 CO1 1 2 2 CO2 2 1 1 2 2 CO3 2 3 3 1 3

Assessments : Teacher Assessment: In Semester Evaluation (ISE) and End Semester Evaluation (ESE)

Assessment Credits Marks Dissertation Phase I ISE 4 100 Dissertation Phase II ISE 2 100 Dissertation Phase II ESE 4 100 Dissertation Phase III ISE 5 100 Dissertation Phase IV ISE 5 100 Dissertation Phase IV ESE 10 100

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ISE for dissertation phase I is based on the efforts by the student for synopsis preparation. It shall be evaluated using the parameters extent of literature review, scope defined, objectives, and fundamental concepts, quality of presentation, and interaction during presentation, effort/work done, quality of report and interaction with guide. ISE for dissertation phase II is based on the progress made during the semester for the objectives defined in the synopsis and the report submitted by the students. It shall be evaluated through progress seminar(s) at the end of the semester. The parameters for evaluation include extent of work done, results and discussion/publication efforts, quality of presentation, quality of report, interaction during presentation and interaction with guide. ISE shall be conducted by Departmental Dissertation Evaluation Committee (DEC). ESE for dissertation phase II shall be conducted at the end of semester by a duly constituted examination panel composed of Chairman, internal examiner (guide) and external examiner. ISE for dissertation phase III is based on the work done by the student during fourth semester. It shall be evaluated using the parameters extent of work done after phase II, quality of presentation, interaction during presentation, and interaction with guide. ISE for dissertation phase IV is based on the work done during the semester and the report submitted by the students. It shall be evaluated through progress seminar(s) at the end of the semester. The parameters for evaluation include extent of work done, results and discussion/publication efforts, quality of presentation, quality of report, interaction during presentation and interaction with guide. ISE shall be conducted by Departmental Dissertation Evaluation Committee (DEC). ESE for dissertation phase IV shall be conducted at the end of semester by a duly constituted examination panel composed of Chairman, internal examiner (guide) and external examiner.

Course Contents: The third semester is completely devoted to dissertation work which is defined based on the interest of the students to specialize in a particular area. Students are expected to carry out an independent research work on the chosen topic. In this semester it is expected that the student has carried out substantial research work including exhaustive literature survey, formulation of the research problem, development/fabrication of experimental set-up (if any/required) and testing, and analysis of initial results thus obtained. In fourth semester, the students continue their dissertation work. It is expected that the student has completed most of the experimental/computation works and analyzed the results so obtained as proposed in the synopsis. The work should be completed in all respects in this semester. The students are required to submit the dissertation work in the form of report as per the institute rule. They are also encouraged to submit and present their work in reputed conference/journal. Module wise Measurable Students Learning Outcomes :-

Documents

M.Tech. Programme in Computer Science and Engineering · PDF fileP.G. Programme in Computer Science and Engineering ... Engineering Students”, 2nd Edition, ... Strang “Introduction