JSPM’s RAJARSHI SHAHU COLLEGE OF ENGINEERING … Computer... · 2020-03-03 · 503103B Soft...

JSPM’s RAJARSHI SHAHU COLLEGE OF ENGINEERING

TATHAWADE, PUNE-33 Autonomous Institute Affiliated to Savitribai Phule Pune University

M.Tech Computer Engineering

M Tech. Semester-I COMPUTER ENGINEERING

Course

Code Course

Teaching

Scheme

Semester Examination Scheme of Marks Credits

TH TU PR ISE

(15)

MSE

(25)

ESE

(60) TW PR OR TOTAL

TH

TU

PR

TOTAL

503101 Bio-Inspired

Optimization

Algorithms

4 -- -- 15 25 60 -- -- -- 100 4 -- -- 4

503102 Software

Development

Management

3 -- -- 15 25 60 -- -- -- 100 3 -- -- 3

503103 Elective-I 3 -- -- 15 25 60 -- -- -- 100 3 -- -- 3

503104 Elective-II 3 -- -- 15 25 60 -- -- -- 100 3 -- -- 3

503105 Research

Methodology and

Intellectual Property

Rights

3 -- -- 15 25 60 -- -- -- 100 3 -- -- 3

503106 Lab Practice I -- -- 4 -- -- -- 50 -- 50 100 -- -- 2 2

503107 Online

course/certification

-- -- -- -- -- -- 50 -- 50 100 -- -- 2 2

AUDIT Audit Course -- -- -- -- -- -- -- -- -- -- -- -- -- --

Total of Semester-I 16 -- 4 75 125 300 100 -- 100 700 16 -- 4 20

Elective-I:

Elective-II

Code No. Title Code No. Title

503103A Parallel Computing 503104A Multicore Architecture

503103B Soft Computing 503104B Machine Learning

503103C Data preparation and Analysis 503104C Information Retrieval and Web

Mining

503103D Network Design and Analysis 503104D Wireless Sensor Network




M Tech. Semester-II COMPUTER ENGINEERING

Course

Code Course

Teaching

Scheme


TH TU PR ISE

(15)

MSE

(25)

ESE

(60) TW PR OR TOTAL

TH

TU

PR

TOTAL

507201 Mathematical

Foundation of

Computer

Science

4 -- -- 15 25 60 -- -- -- 100 4 -- -- 4

503208 System

Simulation and

Modeling

3 -- -- 15 25 60 -- -- -- 100 3 -- -- 3

503209 Randomized

and

Approximation

Algorithms

3 -- -- 15 25 60 -- -- -- 100 3 -- -- 3

503210 Elective-III 3 -- -- 15 25 60 -- -- -- 100 3 -- -- 3

503211 Elective-IV 3 -- -- 15 25 60 -- -- -- 100 3 -- -- 3

503212 Lab Practice II -- -- 4 -- -- -- 50 -- 50 100 -- -- 2 2

Total of Semester-II 16 -- 4 75 125 300 50 -- 50 600 16 -- 2 18

Elective-III

Elective-IV

Code No. Title Code No. Title

503210A Fault Tolerant systems 503211A Fog Computing

503210B Deep Structured Learning 503211B Deep Neural Network

503210C Optimization Techniques 503211C Big Data Analytics

503210D Network Security 503211D Network Multimedia System




M Tech. Semester-III COMPUTER ENGINEERING

Course

Code Course

Teaching

Scheme


TH TU PR ISE

(15)

MSE

(25)

ESE

(60) TW PR OR TOTAL

TH

TU

PR

TOTAL

603101 Elective-V 3 -- -- 15 25 60 -- -- -- 100 3 -- -- 3

603102 Dissertation

Phase-I

-- -- 6 -- -- -- 75 -- 50 125 -- -- 3 3

603103 Dissertation

Phase-II

-- -- 12 -- -- -- 100 -- 75 175 -- -- 6 6

Total of Semester-III 3 -- 18 15 25 60 175 -- 125 400 3 -- 9 12




M Tech. Semester-IVCOMPUTER ENGINEERING

Course

Code Course

Teaching

Scheme


TH TU PR ISE

(15)

MSE

(25)

ESE

(60) TW PR OR TOTAL

TH

TU

PR

TOTAL

603204 Dissertation Phase-III

(Industry/Research)

-- -- 40 -- -- -- 150 -- 100 250 -- -- 20 20

Total of Semester-IV -- -- 40 -- -- -- 150 -- 100 250 -- -- 20 20



M.Tech. Computer Engineering

First Year of M. Tech (Computer Engineering)

[503101]: Bio-Inspired Optimization Algorithms

Teaching Scheme:

TH: 4 Hours/Week

Credit:04

Examination Scheme: In Sem. Evaluation : 15 Marks

Mid Sem. Exam : 25 Marks

End Sem. Exam : 60 Marks

Total :100 Marks

Prerequisites Courses : Design and Analysis of Algorithms.

Course Objective: To learn how natural and biological systems influence computational field. To understand the strengths and

weaknesses of nature-inspired algorithms. To learn the functionalities of various Bio-inspired optimization

algorithms.

Course Outcome:

After completion of the course, student will be able to –

CO1 : Describe the natural phenomena that motivate the algorithms.

CO2 : Apply nature-inspired algorithms to optimization.

CO3 : Select the appropriate strategy or optimal solution based on bio-inspired algorithms.

Course Contents

UNIT-I Natural computing 07 Hours

From nature to natural computing, Introduction, sample idea, Philosophy of natural computing, Natural

computing approaches, Conceptualization – introduction, general concept, Problem solving as a search track,

Hill climbing, Simulated annealing.

UNIT-II Evolutionary Computation 08 Hours

Foundation of Evolutionary theory, Evolutionary Strategies, Evolutionary programming, Evolutionary

Algorithms, Evolutionary Algorithm Case Study, Genetic Algorithm, Genetic Representations, Initial

Population, Fitness Function, Selection and Reproduction, Genetic Operators(Selection, Crossover, Mutation),

Artificial Immune Systems, Other Algorithms Harmony Search, Honey-Bee Optimization, Memetic

Algorithms, Co-evolution, Multi Objective Optimization, Artificial Life, Constraint Handling.

UNIT-III Collective Systems 08 Hours

Collective Behavior and Swarm Intelligence, Particle Swarm Optimization and Ant Colony Optimization,

Artificial evolution of Competing Systems, Artificial Evolution of cooperation and competition. Recent topics

from research papers. Swarm intelligence-biological motivation, from natural to artificial, standard algorithm of

Ant colony optimization, Ant clustering algorithm and Particle swarm optimization.




UNIT-IV Biological Motivation 07 Hours

Biological motivation, from natural to artificial, standard algorithm of cuckoo search, bat algorithm, flower

pollination, firefly algorithm, framework for self tuning algorithms - case study of firefly algorithm.

UNIT-V Immune Systems 08 Hours

Immune system, Artificial immune systems - biological motivation, Design principles, main types of

algorithms - Bone marrow, Negative selection, Clonal selection, Continuous immune network models, Discrete

immune network models, Scope of artificial immune systems.

UNIT-VI Artificial Life 07 Hours

The essence of life, Examples of ALife projects- flocks, herds and schools, computer viruses, synthesizing

emotional behavior, AIBO robot, Turtles, termites, and traffic jams, framsticks, Scope of artificial life, Current

trends and open problems.

BOOKS:

Reference books:

R1. L. N. de Castro, “Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications”,

2006, CRC Press, ISBN-13: 978-1584886433.

R2.D. Floreano and C. Mattiussi, “ Bio-Inspired Artificial Intelligence: Theories, Methods, and

Technologies”, 2008, MIT Press, ISBN-13: 978-0262062718.

R3. Sam Jones (Editor), “Bio Inspired Computing-Recent Innovations and Applications”, Clanrye

International; 2nd

edition (2 January 2015), ISBN-10: 1632400812.

R4. Yang Xiao (Editor), “Bio-Inspired Computing and Networking”, CRC Press, “Machine Nature: The

Coming Age of Bio-Inspired Computing”, New York: McGraw-Hill, 2002) .

R5. Adries Engelbrecht, “ Computational Intelligence”, Wiley, ISBN:978-0-470-03561-0 .

R6. D. Simon, “Evolutionary Optimization Algorithms”, 2013, Wiley, ISBN: 10: 0470937416;13: 978-

0470937419 .

R7. Russell C. Eberhart , Yuhui Shi , James Kennedy, “ Swarm Intelligence: The Morgan Kaufmann Series

in Evolutionary Computation”, 1st Edition, ISBN-13: 978-1558605954.

R8. M. Goodrich, Tamassia, “Algorithm Design & Applications”, Wiley, ISBN:978-1-118-33591-8 .





[503102]: Software Development Management

Teaching Scheme:

TH: 3 Hours/Week

Credit:03




Total :100 Marks

Prerequisites Courses: Software Engineering, Object Oriented Modeling and Design.

Course Objective: To enable students to understand software design issues, To understand software architectures and patterns , To

acquaint software solutions to engineering Problems, To learn the significance of Version Control, To know and

utilize version controls.

Course Outcome:


CO1 : Design in the software development process.

CO2 : Select and apply the design patterns to software development.

CO3 : Design software for real engineering Problems.

CO4 : Software Configuration Management to build the project.

CO5 : Demonstrate team work for development of software in collaborative environment.

CO6 : Use of open source version control tool.

Course Contents

UNIT-I Software Development 07 Hours

Design in the software development process, quality attributes of the design product, describing the design

solution, design representations, design processes and design strategies. Design practices- incremental, object

based and component based.

Case study – Software design of a Social Networking site like LinkedIn, Twitter, Facebook.

UNIT-II Software Architecture Design 07 Hours

Models of Software architecture design, Data centered architecture, Hierarchical architecture, Distributed

architecture, heterogeneous architecture, product line architecture, product line engineering, and software

technology for systematic reuse.

Case study – Software architecture of a Mobile Robot System (with specific focus on External sensors and

actuators, Real-time responsiveness, Acquire sensor Input, control motion and plan future paths).




UNIT-III Software Architecture Quality 07 Hours

Software Architecture - quality attributes, architecture in agile projects, documenting software architectures,

architecture implementation and testing, architecture reconstruction and conformance.

Case study – Architecting in cloud environment for multi-tenancy.

UNIT-IV Software Configuration Management 08 Hours

Software Configuration Management - Scope of SCM, source code management core concepts, Build

Engineering core concepts, Build tools evaluation and selection, Environment configuration control - goals,

principles and importance, release management, deployment, configuration management-driven development,

compliance, standards and frameworks for configuration management.

Case study – Case Study of Improving Quality of Processes by System Virtualization.

UNIT-V Software Version Control 08 Hours

Software Version Control -Introduction, Version control types, centralized & Distributed, Centralized Version

Control - Basics, Subversion Distributed Version Control - Basics, Advantages, Weaknesses .

Case Study : Version Control Best Practices on Git (for Management of Files).

UNIT-VI Software Version Control Tools 08 Hours

Software Version Control tools - Basic introduction to open source version control tools like GIT, GitHub,

CVS, Apache Subversion, SVN, Mercurial, Bazaar.

Case Study - Setup of a version control tool like Git with understanding Basic configuration, Commits,

Branching, Merging, Naming, History.

Case Study - Setup of a version control tool like Git with understanding Basic configuration, Commits,

Branching, Merging, Naming, History.

BOOKS:

Reference books:

R1. Ian Gorton, “Essential Software Architecture”, Springer, ISBN 13: 9783642191763.

R2. Jorge Luis Ortega-Arjona, “Patterns for Parallel Software Design”, Wiley Series, ISNB:978-0-470-

69734-4

R3. Kai Qian et al., “ Software Architecture and Design Illuminated”, Jones and Bartlett Publishers

International, ISBN 13: 9780763754204.

R4. Len Bass, Paul Clements, Rick Kazman, “Software architecture in practice”, 3rd edition, Addison

Wesley, ISBN 13: 9780321815736

R5. Ben Collins-Sussman, Brian William Fitzpatrick, C. Michael Pilato, “Version Control with

Subversion”, O'Reilly Media , ISBN 13: 9781440495878

R6. Scott Chacon and Ben Straub, “ Pro Git”, Apress, ISBN 13: 9781484200766

R7. Richard E. Silverman, “ Git Pocket Guide: A Working Introduction”, O'Reilly Media, ISBN13:

9781449325862

R8. 828-2012 - IEEE Standard for Configuration Management in Systems and Software Engineering.





[503103A]: Elective I – Parallel Computing Teaching Scheme:

TH: 3 Hours/Week

Credit:03




Total :100 Marks

Prerequisites Courses: Advance Algorithm and Programming.

Course Objective: To design parallel algorithms for different applications, To analyze the role of various programming platform in

designing high performance computing systems, To explain how massive parallelisms are implemented in

accelerated architecture, To design parallel algorithm for GPGPU.

Course Outcome:

After completion of the course, student will be able to–

CO1 : To understand parallel algorithms for different applications.

CO2 : To understand and implement shared memory Parallelism.

CO3 : To understand and apply parallelism model in CUDA .

CO4 : To understand the concepts of memory management including virtual memory.

CO5 : Implement Parallel Patterns Convolution and Prefix sum.

CO6 : To understand Heterogeneous Programming.

Course Contents

UNIT-I Parallel Algorithm Design 07 Hours

Principles of Parallel Algorithm Design: Preliminaries, Decomposition Techniques Characteristics of Tasks and

Interactions, Mapping Techniques for Load Balancing Methods for containing Interaction Overheads, Parallel

Algorithm Models.

UNIT-II Shared Memory Parallelism: Basic and Programming 07 Hours

Programming Shared Address Space Platforms: Thread Basics, Why Threads?, The POSIX Thread API

Thread Basics: Creation and Termination, Synchronization Primitives in Pthreads, Controlling Thread and

Synchronization Attributes Thread Cancellation, Composite Synchronization Constructs Tips for Designing

Asynchronous Programs, OpenMP: a Standard for Directive Based Parallel Programming.

UNIT-III

GPU computing and CUDA

07 Hours

CUDA data parallelism model, CUDA program structure, Device memories and data transfer, Kernel function

and threading, CUDA threads organization using blockIdx and ThreadIdx synchronization and transparent

scalability, Thread assignment Tthread scheduling and Latency Tolerance.

UNIT-IV

CUDA Memories

08 Hours




Importance of memory access efficiency, Memory types, Global memory traffic, Performance consideration:

Thread execution, Global memory bandwidth, Dynamic partitioning of SM resources, Data prefetching,

instruction mix, Thread Granularity, Measured Performance.

UNIT-V

Parallel Patterns Convolution and Prefix sum

08 Hours

1D Parallel convolution :A basic algorithm ,constant memory and caching ,Tiled 1D convolution with Halo

elements ,a Simpler Tiled 1D convolution, A simple parallel scan ,Work efficiency considerations ,a Work

efficiency parallel scan , parallel scan for arbitrarily scan Length inputs.

UNIT-VI

Heterogeneous Programming

08 Hours

Introduction ,overlapping computation and communication ,MPI CUDA-C programming, OpenMP CUDA-C

programming, Open ACC.

BOOKS:

Reference books:

R1. Ananth Grama, Anshul Gupta, George Karypis, and Vipin Kumar, "Introduction to Parallel

Computing", 2nd edition, Addison-Wesley, 2003, ISBN: 0-201-64865-2.

R2. Jason sanders, Edward Kandrot, “CUDA by Example”, Addison-Wesley, ISBN-13: 978-0- 13-138768-

3.

R3. Shane Cook, “CUDA Programming: A Developer's Guide to Parallel Computing with GPUs”, Morgan

Kaufmann Publishers Inc. San Francisco, CA, USA 2013 ISBN: 9780124159884.

R4. David Culler Jaswinder Pal Singh, ”Parallel Computer Architecture: A Hardware/Software Approach”,

Morgan Kaufmann,1999, ISBN 978-1-55860-343-1 .

R5. Rod Stephens, “ Essential Algorithms”, Wiley, ISBN: ISBN: 978-1-118-61210-1.





[503103B]: Elective I - Soft Computing

Teaching Scheme:

TH: 3 Hours/Week

Credit:03




Total :100 Marks

Prerequisites Courses: Mathematics, Algorithms, Critical Thinking and Problem Solving.

Course Objective: The objective of this course is to learn various soft computing frameworks, design of various neural networks,

fuzzy logic and genetic programming.

Course Outcome:

After completion of the course, student will be able to -

CO1 : Understanding the basic concept of soft computing.

CO2 : Describe Fuzzy Logic and its applications.

CO3 : Analyzing genetic algorithms.

CO4 : Apply multi-objective optimization problems using evolutionary algorithms.

CO5 : Understanding the basic concept of artificial neural network.

Course Contents

UNIT-I Introduction to Soft Computing 08 Hours

Concept of Computing Systems, Soft versus Hard Computing, Characteristics of Soft Computing, Applications

of Soft Computing Techniques.

UNIT-II Fuzzy Logic 08 Hours

Introduction to Fuzzy Logic, Fuzzy Sets and Membership Functions, Operations on Fuzzy Sets, Fuzzy

Relations, Rules, Propositions, Implications and Inferences, Defuzzification Techniques, Fuzzy Logic

controller Design, Applications.

UNIT-III Genetic Algorithms 08 Hours

Concept of Genetics and Evolution, Applications of Probabilistic Search Techniques, Basic GA Framework

and its Variance, GA Operators: Encoding, Crossover, Selection, Mutation, Solving Single-objective

Optimization Problems using GA’s.

UNIT-IV Multi-objective Optimization Problem Solving 07 Hours

Concept of Multi-objective Optimization Problems (MOOPs) and its Issues, Multi-Objective Evolutionary

Algorithm (MOEA), Non-Pareto approaches to Solve MOOPs, Pareto-based Approaches to solve MOOPs,

Some applications with MOEAs.




UNIT-V Artificial Neural Networks 07 Hours

Biological Neurons and its working, Simulation of Biological Neurons to Problem Solving, Different ANN

Architectures, Training Techniques for ANN, Applications of ANN to Solve Real Life Problems.

UNIT-VI GA and Fuzzy Based Backpropagation Networks 07 Hours

GA Based Weight Determination, K-factor Determination in Column, LR Type Fuzzy Numbers, Fuzzy

Neuron, Fuzzy BP Architecture, Learning in Fuzzy BP, Application of Fuzzy BP Networks.

BOOKS:

Reference books:

R1. F. Martin, Mc-neill, and Ellen Thro, “Fuzzy Logic: A Practical approach”, AP Professional, 2000.

Timothy J. Ross, “Fuzzy Logic with Engineering Applications”, 3rd

Edition, Willey, 2010.

R2.David E. Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning”, Pearson

Education, 2002.

R3.Nikola K. Kasabov, “Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering”,

MIT Press, 1998.

R4.Ahmed M. Ibrahim, “Fuzzy Logic for Embedded Systems Applications”, Elsevier Press, 2004.

R5.Melanie Mitchell, “An Introduction to Genetic Algorithms”, MIT Press, 2000.

R6. Randy L. Haupt and Sue Ellen Haup, “Practical Genetic Algorithms”, John Willey & Sons, 2002.

R7. S. Rajasekaran, G. A. Vijayalakshmi Pai, “Neural Networks, Fuzzy Logis and Genetic Algorithms:

Synthesis, and Applications”, Prentice Hall of India, 2007.

R8. D. K. Pratihar, “Soft Computing”, Narosa, 2008.

R9. S. R. Jang, T. Sun, and E. Mizutani, “Neuro-Fuzzy and soft Computing”, PHI Learning, 2009.

R10.Simon Haykin, “Neural Networks and Learning Machines”, 3rd

Edition, PHI Learning, 2011.





[503103C]: Elective I – Data preparation and Analysis

Teaching Scheme:

TH: 3 Hours/Week

Credit:03




Total :100 Marks

Prerequisites Courses : Research Methodology.

Course Objective: To understand the philosophy of research in general , problem analysis, statistical analysis, optimization

techniques, to learn effective modern tool usage, to become aware of data collection and analysis.

Course Outcome:


CO1 : Identify appropriate topics for research work in Computer Engineering.

CO2 : Select and define appropriate problem analysis.

CO3 : Design the use of major experimental methods statistical analysis.

CO4 : Use appropriate tools, techniques, and processes of doing optimization techniques.

CO5 : Become aware of the usage of modern tools.

CO6 : Select and define appropriate data collection and analysis.

Course Contents

UNIT-I Research Methodology 07 Hours

Science and Research, Verification Vs. Falsification, Objectivity: Facts, theory and concepts, Basic Steps for

doing Research, Formulation of Research Problem, Scientific method Vs Arbitrary Method, Deductive and

Inductive Reasoning, Error Analysis and Accuracy, Descriptive Statistics, Probability, Random Variables,

Sampling distribution and Probability Distribution, Hypothesis Testing, Regression Analysis, Multivariate

Analysis.

UNIT-II Problem analysis 07 Hours

Problem analysis: Identify, formulate, review research literature, and analyze complex engineering problems

reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering

sciences.

UNIT-III Statistical Analysis 07 Hours

Statistical Analysis: Introduction, Sources of error and uncertainty, One-Dimensional Statistics: combining

errors and uncertainties, t-test, ANOVA statistics, example, Two-Dimensional Statistics: example, Multi-

Dimensional Statistics: partial correlation coefficients, example, Null hypothesis testing.




UNIT-IV Optimization Techniques 08 Hours

Optimization Techniques: Introduction, Two-parameter optimization methods: sequential uniform sampling,

Monte Carlo optimization, Simplex Optimization method, Gradient Optimization method, Multi-parameter

optimization methods, The cost function.

UNIT-V Modern tool usage 08 Hours

Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT

tools including prediction and modeling to complex engineering activities with an understanding of the

limitations.

UNIT-VI Data Collection and analysis 08 Hours

Data Collection and analysis: Execution of the research - Observation and Collection of data, Methods of data

collection, Sampling Methods, Data Processing and Analysis strategies, Data Analysis with Statistical

Packages, Hypothesis-testing -Generalization and Interpretation.

BOOKS:

Reference books:

R1. Louis Cohen, Lawrence Manion and Keith Morrison,” Research Methods in Education”, 7th Edition,

Cambridge University Press, ISBN – 978-0415-58336-7.

R2. Anthony, M., Graziano, A.M. and Raulin, M.L., “Research Methods: A Process of Inquiry”, Allyn and

Bacon.

R3. Ranjit Kumar, “Research Methodology: A Step by Step Guide for Beginners”, 2nd Edition, APH

Publishing Corporation.

R4. Leedy, P.D. and Ormrod, J.E., “Practical Research: Planning and Design”, Prentice Hall.

R5. Fink, A., “Conducting Research Literature Reviews: From the Internet to Paper”. Sage Publications.

R6. Satarkar, S.V., “Intellectual Property Rights and Copy Right”, ESS Publications.





[503103D]: Elective I - Network Design and analysis Teaching Scheme:

TH: 3 Hours/Week

Credit:03




Total :100 Marks

Prerequisites Courses : Computer Network

Course Objective: Understand the theoretical issues in protocol design and apply it to Quality of service in networks, issues in the

design of network processors and apply them to design network systems

Course Outcome:


CO1 : Simulate working of wired and wireless networks to understand networking concept.

CO2 : Develop solutions by applying knowledge of mathematics, probability, and statistics to network design

problems.

CO3 : Understand the basics of software defined networking and explore research problems in that area.

Course Contents

UNIT-I Internetworking 08 Hours

Congestion control and Resource allocation: Issues of Resource Allocation, Queuing Disciplines: FIFO, Fair

Queuing, TCP Congestion Control: Additive Increase/Multiplicative Decrease, Slow Start, Fast Retransmit and

Fast Recovery. Congestion-Avoidance Mechanisms: DEC bit, Random Early Detection (RED), Source-Based

Congestion Avoidance, Quality of Service: Application Requirements, Integrated Services (RSVP),

Differentiated Services (EF, AF).

UNIT-II Routing 07 Hours

IPv4 Routing Principles, Routing Information Protocol (RIP),IGRP and EIGRP, OSPF for IPv4 and IPv6,

Border Gateway Protocol (BGP), EIGRP, High Availability Routing.

UNIT-III

IPv6

07 Hours

IPv4 deficiencies, patching work done with IPv4, IPv6 addressing, multicast, Anycast, ICMPv6, Neighbor

Discovery, Routing, Resource Reservation, IPv6 protocols.

UNIT-IV

Network Design

08 Hours

Designing the network topology and solutions-Top down Approach: PPDIOO – Network Design Layers -

Access Layer, Distribution Layer, Core/Backbone Layer, Access Layer Design, Backbone Network Design,

Enterprise LAN Design: Ethernet Design Rules and Campus Design best practices, Virtualization and Data

Center Design, Wireless LAN Design, WAN Design: Traditional WAN Technologies, VPN Design.




UNIT-V Ad Hoc Wireless Networks 08 Hours

MAC Protocols for Ad Hoc Wireless Networks: MACA/W, MACA-BI, DPRMA, MACA/PR. Routing

Protocols for Ad HocWireless Networks: DSDV, DSR, AODV, and ZRP. Transport Layer: ATCP.

UNIT-VI

Software Defined Networking and OpenFlow 07 Hours

Introduction to Software Defined Networking, Control and Data Planes, SDN Controllers, Introduction to

Openflow Protocol, Network Function Virtualization-Concepts.

BOOKS:

Reference books:

R1. Larry L. Peterson and Bruce S. Davie, “Computer Networks: A Systems Approach”, Elsevier, Fourth

Edition.

R2. Philip M. Miller, “TCP / IP: The Ultimate Protocol Guide Applications, Access and Data Security” Vol

2,Wiley.

R3. Pete Loshin, “IPv6: Theory, Protocols and Practice”, Morgan Kaufmann, 2nd Edition, 2004.

R4. C. Siva Ram Murthy, B.S. Manoj, “Ad Hoc Wireless Networks: Architectures “, Prentice Hall, 2004.

R5. Thomas D NAdeau and Ken Grey, “Software Defined Networking”, O'Reilly, 2013.

R6. William Stallings,”High-Speed Networks and Internets”, Pearson Education, 2nd

Edition, 2002.





[503104A]: Elective II – Multi core Architecture Teaching Scheme:

TH: 3 Hours/Week

Credit:03




Total :100 Marks

Prerequisites Courses : Computer Organization

Course Objective: To understand the recent trends in the field of Computer Architecture and identify performance related

parameters, appreciate the need for parallel processing, expose the students to the problems related to

multiprocessing, understand the different types of multi core architectures, expose the students to warehouse-

scale and embedded architectures.

Course Outcome:


CO1 : Identify the limitations of ILP and the need for multi core architectures.

CO2 : Discuss the issues related to multiprocessing and suggest solutions.

CO3 : Point out the salient features of different multi core architectures and how they exploit parallelism.

CO4 : Critically analyze the different types of inter connection networks.

CO5 : Discuss the architecture of GPUs, warehouse-scale computers and embedded processors.

Course Contents

UNIT-I Fundamentals of Quantitative Design and Analysis 07 Hours

Classes of Computers – Trends in Technology, Power, Energy and Cost– Dependability– Measuring, Reporting

and Summarizing Performance– Quantitative Principles of Computer Design– Classes of Parallelism - ILP,

DLP, TLP and RLP- Multithreading - SMT and CMP Architectures – Limitations of Single Core Processors-

The Multi core era– Case Studies of Multi core Architectures.

UNIT-II DLP in Vector, SIMD And GPU Architectures 07 Hours

Vector Architecture - SIMD Instruction Set Extensions for Multimedia – Graphics Processing Units - Detecting

and Enhancing Loop Level Parallelism - Case Studies.

UNIT-III TLP and Multiprocessors 07 Hours

Symmetric and Distributed Shared Memory Architectures – Cache Coherence Issues - Performance Issues –

Synchronization Issues – Models of Memory Consistency -Interconnection Networks – Buses, Crossbar and

Multi-stage Interconnection Networks.




UNIT-IV RLP and DLP in Warehouse-Scale Architectures 08 Hours

Programming Models and Workloads for Warehouse-Scale Computers – Architectures for Warehouse-Scale

Computing – Physical Infrastructure and Costs – Cloud Computing –Case Studies.

UNIT-V Architectures for Embedded Systems 08 Hours

Features and Requirements of Embedded Systems – Signal Processing and Embedded Applications – The

Digital Signal Processor – Embedded Multiprocessors - Case Studies.

UNIT-VI High Performance enabled Advanced Technologies 08 Hours

Search Algorithms for Discrete Optimization Problems: Search Overhead Factor, Parallel Depth-First Search,

Parallel Best-First Search, Introduction to (Block Diagrams only if any) Peta scale Computing, Optics in

Parallel Computing Quantum Computers, Recent developments in Nanotechnology and its impact on HPC

Power-aware Processing Techniques in HPC.

BOOKS:

Reference books:

R1. John L. Hennessey and David A. Patterson, “ Computer Architecture – A Quantitative Approach”,

Morgan Kaufmann / Elsevier, 5th edition, 2012.

R2. Kai Hwang, “Advanced Computer Architecture”, Tata McGraw-Hill Education, 2003.

R3. Richard Y. Kain, “Advanced Computer Architecture a Systems Design Approach”, Prentice Hall, 2011.

R4. David E. Culler, Jaswinder Pal Singh, “Parallel Computing Architecture : A Hardware/ Software

Approach” , Morgan Kaufmann / Elsevier, 1997.

R5. www.eng.auburn.edu/.../Single%20Chip%20multi%20processor.ppt‎.





[503104B]: Elective II - Machine Learning Teaching Scheme:

TH: 3 Hours/Week

Credit:03




Total :100 Marks

Prerequisites Courses : Database , Artificial Intelligence.

Course Objective: Formulate machine learning problems corresponding to different applications. Understand a range of machine

learning algorithms along with their strengths and weaknesses. Understand the basic theory underlying machine

learning. Apply machine learning algorithms to solve problems of moderate complexity. Read current research

papers and understands the issues raised by current research.

Course Outcome:


CO1 : Understand basic concepts of machine learning problems.

CO2 : Describe philosophy of artificial neural networks and evaluation hypothesis.

CO3 : Describe principles of learning sets of rules and analytical learning.

CO4 : Explain the concept of Bayesian learning & genetic algorithms.

CO5 : Describe principles of combining inductive and analytical learning & reinforcement learning.

CO6 : Explain advanced technologies in machine learning.

Course Contents

UNIT-I Introduction 07 Hours

Well-posed learning problems, Designing a learning system, Perspectives and issues in machine learning

Concept learning and the general to specific ordering – Introduction, A concept learning task, Concept learning

as search, Find-S: finding a maximally specific hypothesis, Version spaces and the candidate elimination

algorithm, Remarks on version spaces and candidate elimination, Inductive bias.

UNIT-II Artificial Neural Networks & Evaluation Hypotheses 07 Hours

Introduction, Neural network representation, Appropriate problems for neural network learning, Perceptions,

Multilayer networks and the back propagation algorithm, Remarks on the back propagation algorithm, An

illustrative example faces recognition. Advanced topics in artificial neural networks.

Evaluation Hypotheses – Motivation, Estimation hypothesis accuracy, Basics of sampling theory, A general

approach for deriving confidence intervals, Difference in error of two hypotheses, Comparing learning

algorithms.




UNIT-III Learning Sets of Rules & Analytical Learning 07 Hours

Introduction, Sequential Covering Algorithms, Learning Rule Sets: Summary, Learning First Order Rules,

Learning Sets of First Order Rules: FOIL, Induction as Inverted Deduction, Inverting Resolution.

Analytical Learning - Introduction, Learning with Perfect Domain Theories: Prolog-EBG Remarks on

Explanation-Based Learning, Explanation-Based Learning of Search Control Knowledge.

UNIT-IV Bayesian learning & Genetic Algorithms 08 Hours

Introduction, Bayes theorem, Bayes theorem and concept learning, Maximum likelihood and least squared error

hypotheses, Maximum likelihood hypotheses for predicting probabilities, Minimum description length

principle, Bayes optimal classifier, Gibs algorithm, NaïveBayes classifier, An example learning to classify text,

Bayesian belief networks The EM algorithm Motivation, Genetic Algorithms: An illustrative Example,

Hypothesis Space Search, Genetic Programming, Models of Evolution and Learning, Parallelizing Genetic

Algorithms.

UNIT-V Combining Inductive and Analytical Learning & Reinforcement

Learning

08 Hours

Motivation, Inductive-Analytical Approaches to Learning, Using Prior Knowledge to Initialize the Hypothesis,

Using Prior Knowledge to Alter the Search Objective, Using Prior Knowledge to Augment Search Operators,

Reinforcement Learning – Introduction, The Learning Task, Q Learning, Non-Deterministic, Rewards and

Actions, Temporal Difference Learning, Generalizing from Examples, Relationship to Dynamic Programming.

UNIT-VI Symmetric Weights and Deep Belief Networks 08 Hours

Energetic Learning: The Hopfield Network Associative Memory, Making an Associative Memory, Capacity of

the Hopfield Network, The Continuous Hopfield Network. Stochastic Neurons-The Boltzmann Machine:- The

Restricted Boltzmann Machine, Deriving the CD Algorithm, Supervised Learning, The RBM as a Directed

Belief Network, Deep Belief Networks (DBN).

BOOKS:

Reference books:

R1.Tom M. Mitchell,”Machine Learning An Artificial Intelligent Approach”, MGH

R2. Stephen Marsland, Taylor & Francis (CRC),”Machine Learning: An Algorithmic Perspective”,2nd

Edition

R3.Chris Bishop, “Neural Networks for Pattern Recognition”, Oxford University Press, 1995.

R4.Peter Flach,“Machine Learning: The Art and Science of Algorithms that Make Sense of

Data”,Cambridge, ISBN978-1-107-09639-4,1st Edition.

R5.William W. Hsieh,”Machine Learning Methods In The Environmental Sciences”,Cambridge University

Press, 978-0-521-79192-2 1st Edition..





[503104C]: Elective II - Information Retrieval & Web Mining

Teaching Scheme:

TH: 3 Hours/Week

Credit:03




Total :100 Marks

Prerequisites Courses : Database.

Course Objective: The objective of this course is to elaborate on the fundamentals of information retrieval (IR), study of indexing,

searching, retrieval models, relevance, classification and organization of information with focus on web data

Retrieval and web Information integration using web mining.

Course Outcome:


CO1 : Identify and design the various components of an Information Retrieval system.

CO2 : Apply machine learning techniques to text classification and clustering.

CO3 : Understand how statistical models of text can be used to solve problems in IR.

CO4 : Analyze the Web content structure.

CO5 : Understand techniques of opinion mining and sentiment analysis.

CO6 : Design an efficient search engine using mining methodologies.

Course Contents

UNIT-I Introduction to Information Retrieval 07 Hours

Basic Concepts, Retrieval Process,the nature of unstructured and semi-structured text. Inverted index and

Boolean queries, Retrieval Evaluation –Word Sense Disambiguation Querying: Languages, Key Word based

Querying, Pattern Matching, Structural Queries, Query Operations.

UNIT-II Text Indexing, Storage and Compression 07 Hours

Text encoding: tokenization, stemming, stop words, phrases, index optimization. Index compression: lexicon

compression and postings, lists compression. Gap encoding, gamma codes, Zipf's Law. Index construction.

Postings size estimation, merge sort, dynamic indexing, positional indexes, n-gram indexes, real-world issues.

UNIT-III Retrieval Models 07 Hours

Boolean, vector space, TFIDF, Okapi, probabilistic, language modeling, latent semantic indexing. Vector space

scoring. The cosine measure. Efficiency considerations. Document length normalization. Relevance feedback

and query expansion, Rocchio.




UNIT-IV Web Mining 08 Hours

Overview the concept of web mining. Study the challenges of knowledge extraction from web scale datasets,

Mining the web page layout structure, mining web link structure, mining multimedia data on the web,

Automatic classification of web documents. Hypertext, web crawling, search engines, ranking, link analysis,

Page Rank, HITS, XML and Semantic web.

UNIT-V Opinion Mining and Sentiment Analysis 08 Hours

Opinion Mining and Sentiment Analysis: The Problem of Opinion Mining, Document Sentiment Classification,

Sentence Subjectivity and Sentiment Classification, Opinion Lexicon Expansion, Aspect-Based Opinion

Mining, Opinion Search and Retrieval, Opinion Spam Detection.

UNIT-VI Advanced Topics 08 Hours

Summarization, Topic detection and tracking, Personalization, Question answering, Cross language information

Retrieval.

BOOKS:

Reference books:

R1. David A. Grossman, Ophir Frieder, “Information Retrieval – Algorithms and Heuristics”.

R2. Wilbert Liu, Bing, ” Web Data Mining”, 2nd

Edition, Elseiver.

R3. R. Baeza-Yates and B. Ribeiro Neto,”Modern Information Retrieval: The Concepts and

Technology behind Search”, Second Edition, Addison Wesley, 2011.

R4. Christopher D. Manning, Prabhakar Raghavan and Hinrich Schutze,” Introduction to Information

Retrieval”, Cambridge University Press, 2008.

R5. Soumen Chakrabarti, “ Mining the Web”, Morgan-Kaufmann Publishers, Elseiver.





[503104D]: Elective II - Wireless Sensor Networks

Teaching Scheme:

TH: 3 Hours/Week

Credit:03




Total :100 Marks

Prerequisites Courses: Computer Networks.

Course Objective: The Objective of this course is to make students learn and understand the concept of Architect sensor networks

for various application setups. Explore the design space and conduct trade-off analysis between performance

and resources. Devise appropriate data dissemination protocols and model links cost. Determine suitable

medium access protocols and radio hardware.

Course Outcome:


CO1 : Explain the basic concepts of wireless sensor network.

CO2 : Apply the transmission media concept in WSN.

CO3 : Understand the concept of MAC protocols in wireless sensor network.

CO4 : Explain Transport layer protocols & middleware applications.

CO5 : Apply network management system in WSNs.

CO6 : Analyze operating system used in WSNs.

Course Contents UNIT-I Introduction, Overview and Applications of Wireless Sensor

Networks

07 Hours

Introduction, Basic overview of the Technology, Applications of Wireless Sensor Networks: Introduction,

Background, Range of Applications, Examples of Category 2 WSN Applications, Examples of Category 1

WSN Applications, Another Taxonomy of WSN Technology.

UNIT-II Basic Wireless Sensor Technology and Systems 07 Hours

Introduction, Sensor Node Technology, Sensor Taxonomy, WN Operating Environment, WN Trends, Wireless

Transmission Technology and Systems: Introduction, RadioTechnology Primer, Available Wireless

Technologies. UNIT-III MAC and Routing Protocols for Wireless Sensor Networks 07 Hours

Introduction, Background, Fundamentals of MAC Protocols, MAC Protocols for WSNs, Sensor-MAC case

Study, IEEE 802.15.4 LR-WPANs Standard Case Study. Routing Protocols for Wireless Sensor Networks:

Introduction, Background, Data Dissemination and Gathering, Routing Challenges and Design Issues in WSNs,




Routing Strategies in WSNs.

UNIT-IV Transport Control and Middleware for Wireless Sensor Networks 08 Hours

Traditional Transport Control Protocols, Transport Protocol Design Issues, Examples of Existing Transport

Control Protocols, Performance of Transport Control Protocols. Middleware for Wireless Sensor Networks:

Introduction, WSN Middleware Principles, Middleware Architecture, Existing Middleware.

UNIT-V Network Management Wireless Sensor Networks 08 Hours

Introduction, Network Management Requirements, Traditional Network Management Models, Network

Management Design Issues.

UNIT-VI Operating System for Wireless Sensor Networks 08 Hours

Introduction, Operating System Design Issues, Examples of Operating Systems.

BOOKS:

Reference books:

R1. Ian F. Akyildiz, Mehmet Can Vuran “Wireless Sensor Networks”, Wiley 2010 2.

R2. Feng Zhao & Leonidas J. Guibas, “Wireless Sensor Networks- An Information Processing Approach”,

Elsevier, 2007.





[503105]: Research Methodology and Intellectual Property Rights Teaching Scheme:

TH: 3 Hours/Week

Credit:03




Total :100 Marks

Prerequisites Courses :

Course Objective:

To give an overview of the research methodology and explain the technique of defining a research Problem ,to

explain the functions of the literature review in research, to explain carrying out a literature search, its review,

developing theoretical and conceptual frameworks and writing a review, to explain various research designs

and their characteristics, to explain the details of sampling designs, and also different methods of data

collections, to explain the art of interpretation and the art of writing research reports, to explain various forms

of the intellectual property, its relevance and business impact in the changing global business environment ,to

discuss leading International Instruments concerning Intellectual Property Rights.

Course Outcome:


CO1 : Discuss research methodology and the technique of defining a research problem.

CO2 : Explain the functions of the literature review in research, carrying out a literature search, developing

theoretical and conceptual frameworks and writing a review.

CO3 : Explain various research designs and their characteristics.

CO4 : Explain the art of interpretation and the art of writing research reports.

CO5 : Intellectual Property.

CO6 : Intellectual Property related to trade.

Course Contents

UNIT-I Research Methodology 07 Hours

Introduction, Meaning of Research, Objectives of Research, Motivation in Research, Types of Research,

Research Approaches, Significance of Research, Research Methods versus Methodology, Research and

Scientific Method, Importance of Knowing How Research is Done, Research Process, Criteria of Good

Research, and Problems Encountered by Researchers in India.

UNIT-II Literature Review 07 Hours

Defining the Research Problem: Research Problem, Selecting the Problem, Necessity of Defining the Problem,

Technique Involved in Defining a Problem, An Illustration Reviewing the literature: Place of the literature

review in research, Bringing clarity and focus to your research problem, Improving research methodology,

Broadening knowledge base in research area, Enabling contextual findings, How to review the literature,

searching the existing literature, reviewing the selected literature, Developing a theoretical framework,




Developing a conceptual framework, Writing about the literature reviewed.

UNIT-III Research Design 07 Hours

Research Design: Meaning of Research Design, Need for Research Design, Features of a Good Design,

Important Concepts Relating to Research Design, Different Research Designs, Basic Principles of Experimental

Designs, Important Experimental Designs. Design of Sample Surveys: Introduction, Sample Design, Sampling

and Non-sampling Errors, Sample Survey versus Census Survey, Types of Sampling Designs.

UNIT-IV Data Collection 08 Hours

Data Collection: Experimental and Surveys, Collection of Primary Data, Collection of Secondary Data,

Selection of Appropriate Method for Data Collection, Case Study Method. Interpretation and Report Writing:

Meaning of Interpretation, Technique of Interpretation, Precaution in Interpretation, Significance of Report

Writing, Different Steps in Writing Report, Layout of the Research Report, Types of Reports, Oral

Presentation, Mechanics of Writing a Research Report, Precautions for Writing Research Reports.

UNIT-V Intellectual Property 08 Hours

The Concept, Intellectual Property System in India, Development of TRIPS Complied Regime in India, Patents

Act, 1970, Trade Mark Act, 1999,The Designs Act, 2000, The Geographical Indications of Goods (Registration

and Protection) Act1999, Copyright Act,1957,The Protection of Plant Varieties and Farmers’ Rights Act,

2001,The Semi-Conductor Integrated Circuits Layout Design Act, 2000, Trade Secrets, Utility Models, IPR and

Biodiversity, The Convention on Biological Diversity (CBD) 1992, Competing Rationales for Protection of

IPRs, Leading International Instruments Concerning IPR, World Intellectual Property Organisation

(WIPO),WIPO and WTO, Paris Convention for the Protection of Industrial Property, National Treatment, Right

of Priority, Common Rules, Patents, Marks, Industrial Designs, Trade Names, Indications of Source, Unfair

Competition, Patent Cooperation Treaty (PCT), Advantages of PCT Filing, Berne Convention for the

Protection of Literary and Artistic Works, Basic Principles, Duration of Protection.

UNIT-VI Intellectual Property 08 Hours

Trade Related Aspects of Intellectual Property Rights(TRIPS) Agreement, Covered under TRIPS Agreement,

Features of the Agreement, Protection of Intellectual Property under TRIPS, Copyright and Related Rights,

Trademarks, Geographical indications, Industrial Designs, Patents, Patentable Subject Matter, Rights

Conferred, Exceptions, Term of protection, Conditions on Patent Applicants, Process Patents, Other Use

without Authorization of the Right Holder, Layout-Designs of Integrated Circuits, Protection of Undisclosed

Information, Enforcement of Intellectual Property Rights, UNSECO.

Reference books:

R1 C.R. Kothari, Gaurav Garg , “Research Methodology: Methods and Techniques”, New Age International

4th

Edition,2018.

R2 Ranjit Kumar ,”ResearchMethodologyastep-by-stepguide for beginners”,SAGE Publications Ltd 3rd

Edition,2011.

R3 Study Material(For the topic Intellectual Property, The Institute of Company Secretaries of India, Statutory

Body Under an Act of Parliament, September 2013.

R4 Trochim , “Research Methods: the concise knowledge base” , Dog Publishing 2005

R5 Fink ,”Conducting Research Literature Reviews: From the Internet to Paper” ,A Sage Publications 2009





[503106]: LPI Lab. Practice-I

Teaching Scheme:

Practical: 04 Hours/Week

Credit:02 Examination Scheme: Oral: 50 Marks

TW : 50 Marks

Total:100Marks

Laboratory Practice I (LP I) is companion course of theory courses (core and elective) in Semester I. It is

recommended that set of assignments or at least one mini-project/study project per course is to be completed.

Set of problem statements are suggested. Course/ Laboratory instructor may frame suitable problem statements.

Student has to submit a report/Journal consisting of appropriate documents - prologue, Certificate, table of

contents, and other suitable write up like (Introduction, motivation, aim and objectives, outcomes, brief theory,

requirements analysis, design aspects, algorithms, mathematical model, complexity analysis, results, analysis

and conclusions).

Suitable platform/framework/language is to be used for completing mini-project/assignments.

Suggested List of Laboratory Assignments

A Bio-Inspired Algorithms

1 Ant Colony Algorithm:

The Traveling Salesman Problem is a problem of a salesman who, starting from his hometown, wants

to find the shortest tour that takes him through a given set of customer cities and then back home,

visiting each customer city exactly once." Each city is accessible from all other cities.. Use ant colony

algorithm for generating good solutions to both symmetric and asymmetric instances of the Traveling

Salesman Problem. Use appropriate representation for graph and an appropriate heuristic that defines

the distance between any two nodes of the graph. Use parallel approach to optimize solution

2 Job Scheduling using PSO, Optimization techniques for N-Queen’s problem, Management and

allocation of resources in a safety division of any pharmaceutical company, To automate the strategic

planning process in an industry., Optimize Staff allocation problem in an organization, Railway

Transportation/ Air Transportation : A case study of Transportation problem, Time table generation.

B Software Development and Version Control

1 Study of Software design of a Social Networking site like Linkedin, Twitter, Facebook.

2 Study of any open source system/application software like Version Control in Linux Kernel.

C Elective-I

Course instructor is authorized to frame suitable problem statement for Assignments/ mini project.

D Elective-II





E Research Methodology and Intellectual property rights

1. Use an academic web search to locate a journal paper which describes a design outcome in your

field of interest (i.e. your engineering discipline).You must enter several keywords which relate to

your topic. Read the paper and, using your own words, demonstrate your understanding of the

paper by:

Brief Contribution.

Performance metric, data set, comparative analysis and outcomes.

Writing out the major conclusions of the paper.

Outlining the verification method(s) used to support these conclusions.

Describing the author’s reflective comments on the quality of the design (positive and

negative).

The positive and negative environmental impacts.

After reading a published research paper, write down the research question you think the author

have addressed in undertaking this research. Do you think the paper adequately supports the

conclusions reached in addressing the question?

2.

Consider a journal article in your discipline that was published approximately five years ago. Note

the keywords and type them into one of the web-based academic search engines (e.g.

googlescholar.com). Does the original article appear in the search results? How many citations does

this article have? Have the same authors published further work in this field?

Compare the citations of this paper with those from the most highly cited paper in the search

results? How many citations does this highly cited article have? If this paper was published before

your original article, is it cited in your article? Do you think this high-cited paper should have been

listed as a reference in your original article? Give reasons for your decision.

Read a journal paper from your discipline. Following the format of patents, write out one or more

important outcomes from the paper in terms of one or more Patent Claims 1, 2….

.These claims must not only be new, they must be not-obvious from previous work

3. a) Literature Review Quality: Using a Journal paper selected in your engineering discipline of

interest, write a 400 word evaluation of the quality of Literature Review. In particular, review the

quality and relevance of cited papers, the comments made on those papers contribution to the

general field, and any omission of papers which are of major importance in the field.

b) Develop a new research proposal from a published paper: From selected published Journal paper,

read the paper. In particular read the discussion and conclusion section and find Suggestions for

further work. Apply one of the question words(How?, Why?, What?, When?) and write one or more

research questions arising from this paper. This can be used as guide to help you to develop your

own research project proposal




4. a) Download a set of weather data from the Internet covering the temperature and atmospheric

pressure over a four day period. Present the data using 2D and 3D plots, and so deduce if the

weather conditions are trending either higher or lower over this four day period. (Possible web sites

include http://www.bom. gov.au/climate/ data/ and http://www.silkeborg-vejret.dk/english/

regn.php).

b) Numerical modeling: Find a paper in which nunicricil modeling has been used to verify the

experimental results. Comment on the differences between the experimental and modeling results.

Have the authors commented on the accuracy of the experimental and modeling procedures? What

suggestions do you have to improve the quality of the modeling reported in the paper?

c) Statistical review: In your engineering discipline review a published paper which includes a

statistical analysis. Write a brief report on the statistical methods used. Can you suggest an

improved statistical analysis? Suggest some additional parameters that might have been measured

during the data acquisition stage and so explain how you would analyze the total data set to deduce

the influence (and statistical significance) of these additional measurements.





[503107]: Online course/certification

Teaching Scheme:

-

Credit

02 Examination Scheme: Oral: 50 Marks

TW : 50 Marks

Total : 100 Marks


Basics analysis or design concepts of the selected course.

Course Objective:

The objective of this course is, to prepare students to learn the courses using online teaching aids.

Contents:

The students should complete at least one NPTEL Certification course which will be offered by NPTEL

Courses during the same semester. The students should select the subjects relevant to M.Tech. (Computer

Engineering) and which should not included in the specified curriculum.





[Code AUDIT - 01]: Technical Paper Writing

Teaching Scheme:

TH: Hours/Week

Credit Examination Scheme:

In Sem. Evaluation :

Mid Sem. Exam :

End Sem. Exam :

Course Objectives:

1. Understand that how to improve your writing skills and level of readability

2. Learn about what to write in each section

3. Understand the skills needed when writing a Title

Course Outcome:

On completion of the course, student will be able to–

CO1: Write a paper and report.

CO2 : Properly present papers in conferences with neat flow

Course Contents

UNIT-I 06 Hours

Planning and Preparation, Word Order, Breaking up long sentences, Structuring Paragraphs and

Sentences, Being Concise and Removing Redundancy, Avoiding Ambiguity and Vagueness.

UNIT-II 06 Hours

Clarifying Who Did What, Highlighting Your Findings, Hedging and Criticizing, Paraphrasing and

Plagiarism, Sections of a Paper, Abstracts. Introduction.

UNIT-III 08 Hours

Review of the Literature, Methods, Results, Discussion, Conclusions, The Final Check.

UNIT-IV 08 Hours

Key skills are needed when writing a Title, key skills are needed when writing an Abstract, key skills are

needed when writing an Introduction, skills needed when writing a Review of the Literature.

UNIT-V 08 Hours

Skills are needed when writing the Methods, skills needed when writing the Results, skills are needed

when writing the Discussion, skills are needed when writing the Conclusions.

UNIT-VI 06 Hours




Useful phrases, how to ensure paper is as good as it could possibly be the first- time submission.

References:

R1. Writing for Science, Goldbort R, Yale University Press

R2. How to Write and Publish a Scientific Paper, Day R, Cambridge University Press

R3. Handbook of Writing for the Mathematical Sciences, Highman N, SIAM. Highman’s book

R4.English for Writing Research Papers, Adrian Wallwork, Springer New York Dordrecht Heidelberg

London





[Code AUDIT-02]: Disaster Management

Teaching Scheme:

TH: Hours/Week

Credit

Examination Scheme:

In Sem. Evaluation : Marks

Mid Sem. Exam : Marks

End Sem. Exam : Marks


Course Objectives:

1. Learn to demonstrate a critical understanding of key concepts in disaster risk reduction and

humanitarian response.

2. Critically evaluate disaster risk reduction and humanitarian response policy and practice from multiple

perspectives.

3. Develop an understanding of standards of humanitarian response and practical relevance in specific

types of disasters and conflict situations.

4. Critically understand the strengths and weaknesses of disaster management approaches, planning and

programming in different countries, particularly their home country or the countries they work in.

Course Outcome:


CO1: Respond in critical Disaster Situation

CO2: Help in affected areas.

Course Contents

UNIT-I 06 Hours

Introduction: Disaster: Definition, Factors and Significance; Difference between Hazard and Disaster;

Natural and Manmade Disasters: Difference, Nature, Types and Magnitude.

UNIT-II 06 Hours

Repercussions of Disasters and Hazards: Economic Damage, Loss of Human and Animal Life,

Destruction of Ecosystem. Natural Disasters: Earthquakes, Volcanisms, Cyclones, Tsunamis, Floods,

Droughts and Famines, Landslides and Avalanches, Man-made disaster: Nuclear Reactor Meltdown,

Industrial Accidents, Oil Slicks and Spills, Outbreaks of Disease and Epidemics, War and Conflicts.

UNIT-III 08 Hours

Disaster Prone Areas in India: Study of Seismic Zones; Areas Prone to Floods and Droughts, Landslides

and Avalanches; Areas Prone to Cyclonic and Coastal Hazards with Special Reference to Tsunami; Post-

Disaster Diseases and Epidemics.




UNIT-IV 08 Hours

Disaster Preparedness and Management: Preparedness: Monitoring of Phenomena Triggering a Disaster

or Hazard; Evaluation of Risk: Application of Remote Sensing, Data From Meteorological and other

Agencies, Media Reports: Governmental and Community Preparedness

UNIT-V 08 Hours

Risk Assessment: Disaster Risk: Concept and Elements, Disaster Risk Reduction, Global and National

Disaster Risk Situation. Techniques of Risk Assessment, Global Cooperation in Risk Assessment and

Warning, People’s Participation in Risk Assessment. Strategies for Survival

UNIT-VI 06 Hours

Disaster Mitigation: Meaning, Concept and Strategies of Disaster Mitigation, Emerging Trends in

Mitigation. Structural Mitigation and Non-Structural Mitigation, Programs of Disaster Mitigation in India

BOOKS:

References:

R1. Disaster Management in India: Perspectives, issues and strategies, R. Nishith, Singh AK, New Royal

book Company.

R2. Disaster Mitigation Experiences and Reflections, Sahni, Pardeep Et .Al. (Eds.), Prentice Hall Of

India, New Delhi.

R3. Disaster Administration And Management Text And Case Studies, Goel S. L., Deep &Deep

Publication Pvt. Ltd., New Delhi





[Code AUDIT-03]: Value Education in Engineering

Teaching Scheme:

TH: Hours/Week

Credit

Examination Scheme:





Course Objectives:

1. Understand value of education and self- development.

2. Imbibe good values in students.

3. Let the should know about the importance of character

Course Outcome:


CO1: Knowledge of self-development

CO2: Learn the importance of Human values

CO3: Developing the overall personal

Course Contents

UNIT-I 06 Hours

Values and self-development –Social values and individual attitudes. Work ethics, Indian vision of

humanism. Moral and non- moral valuation. Standards and principles. Value judgements.

UNIT-II 06 Hours

Importance of cultivation of values. Sense of duty. Devotion, Self-reliance. Confidence, Concentration.

Truthfulness, Cleanliness. Honesty, Humanity. Power of faith, National Unity. Patriotism. Love for

nature, Discipline.

UNIT-III 08 Hours

Personality and Behavior Development - Soul and Scientific attitude. Positive Thinking. Integrity and

discipline. Punctuality, Love and Kindness. Avoid fault Thinking. Free from anger, Dignity of labour.

Universal brotherhood and religious tolerance. True friendship. Happiness Vs suffering, love for truth.

Aware of self-destructive habits. Association and Cooperation. Doing best for saving nature.

UNIT-IV 08 Hours

Character and Competence –Holy books vs. Blind faith. Self-management and Good health. Science of

reincarnation. Equality, Nonviolence, Humility, Role of Women. All religions and same message. Mind




your Mind, Self-control. Honesty, Studying effectively.

References:

R1: Chakroborty, S.K. ,”Values and Ethics for organizations Theory and practice”, Oxford University

Press, New Delhi

R2: Value Education in Engineering, S Chand Publi.





[Code AUDIT-04]: Constitution of India

Teaching Scheme:

TH: Hours/Week

Credit

Examination Scheme:





Course Objectives:

1.Understand the premises informing the twin themes of liberty and freedom from a civil rights

perspective.

2. To address the growth of Indian opinion regarding modern Indian intellectuals’ constitutional role and

entitlement to civil and economic rights as well as the emergence of nationhood in the early years of

Indian nationalism.

3. To address the role of socialism in India after the commencement of the Bolshevik Revolution in 1917

and its impact on the initial drafting of the Indian Constitution.

Course Outcome:


CO1: Discuss the growth of the demand for civil rights in India for the bulk of Indians before the arrival

of Gandhi in Indian politics.

CO2: Discuss the intellectual origins of the framework of argument that informed the conceptualization

of social reforms leading to revolution in India.

CO3: Discuss the circumstances surrounding the foundation of the Congress Socialist Party [CSP] under

the leadership of Jawaharlal Nehru and the eventual failure of the proposal of direct elections

through adult suffrage in the Indian Constitution.

Course Contents

UNIT-I 06 Hours

History of Making of the Indian Constitution: History, Drafting Committee, (Composition & Working).

welfare of SC/ST/OBC and women.

UNIT-II 06 Hours

Philosophy of the Indian Constitution: Preamble, Salient Features.

08 Hours




UNIT-III

Contours of Constitutional Rights & Duties: Fundamental Rights, Right to Equality, Right to Freedom,

Right against Exploitation, Right to Freedom of Religion, Cultural and Educational Rights, Right to

Constitutional Remedies, Directive Principles of State Policy, Fundamental Duties

UNIT-IV 08 Hours

Organs of Governance: Parliament, Composition, Qualifications and Disqualifications, Powers and

Functions, Executive, President, Governor, Council of Ministers, Judiciary, Appointment and Transfer of

Judges, Qualifications, Powers and Functions.

UNIT-V 08 Hours

Local Administration: District’s Administration head: Role and Importance, Municipalities: Introduction,

Mayor and role of Elected Representative, CEO of Municipal Corporation. Pachayati raj: Introduction,

PRI: Zila Pachayat. Elected officials and their roles, CEO ZilaPachayat: Position and role. Block level:

Organizational Hierarchy (Different departments), Village level: Role of Elected and Appointed officials,

Importance of grass root democracy

UNIT-VI 06 Hours

Election Commission: Election Commission: Role and Functioning. Chief Election Commissioner and

Election Commissioners. State Election Commission: Role and Functioning. Institute and Bodies for

thewelfare of SC/ST/OBC and women

References:

R1. The Constitution of India, 1950 (Bare Act), Government Publication.

R2. Dr. S. N. Busi, “Dr. B. R. Ambedkar framing of Indian Constitution”, 1st Edition

R3. M. P. Jain, “Indian Constitution Law”, 7th

Edition, Lexis Nexis

R4. D.D. Basu, “Introduction to the Constitution of India”, Lexis Nexis





[507201]: Mathematical Foundation of Computer Science Teaching Scheme:

Lectures: 4 Hours/Week

Credit:04




Total :100 Marks


Elementary notions of Statistics, Probability and Matrices.

Course Objective:

The aim of the course is to make the students familiarize with mathematical concepts those are prerequisites for

a variety of courses like Data mining, Network protocols, analysis of Web traffic, Computer security, Software

engineering, Computer architecture, operating systems, distributed systems, Bioinformatics, Machine learning.

To develop the understanding of the mathematical and logical basis to many modern techniques in information

technology like machine learning, programming language design and concurrency. To study various sampling

and classification problems.

Course Outcome:

After successful completion of the course, students will able to -

CO1 : Apply the concepts of linear algebra in engineering field.

CO2 : Apply the concepts of graph theory to solve problems of connectivity.

CO3 : Explain the basic notions of discrete and continuous probability.

CO4 : Explain the methods of statistical inference and the role that sampling distributions play in those

methods.

Course Contents

UNIT-I Linear Algebra-Vector Space 08 Hours

Vector Space, Subspaces, Span of a set, Linear Dependence and Independence, Basis and Dimensions, Rank-

nullity theorem. Inner Product Space, Orthogonality, orthogonal projection, Gram – Schmidt Method,

Applications to Computer Engineering.

UNIT-II Linear Algebra-Linear Transformation 08 Hours

Linear Transformation, Matrix of Linear transformation, Nonsingular Linear Transformation, inverse of Linear

Transformation, Geometric properties of Linear operators on and

Eigen values and Eigen vectors, Diagonalization, Quadratic forms, Singular Value Decomposition,

Applications to Computer Engineering .

UNIT-III Graph Theory 08 Hours

Simple graphs: Isomorphism , connectivity, Euler cycles, Hamilton circuits, Graph coloring, shortest path

algorithm trees, forests, spanning trees, application to minimum spanning tree problem ,Directed graphs,




Planer graphs.

UNIT-IV Probability Theory 08 Hours

Probability mass function, density function, random variable(discrete and continuous),mathematical

expectation, Binomial and Poisson probability distribution, sampling theory, tests of hypothesis by t-test, chi-

square distribution.

UNIT-V Regression Analysis 08 Hours

Least-square curve fitting, the coefficient of determination, Linear regression, trend detection and slope

estimation, correlation analysis, Principal Component Analysis.

UNIT-VI Stochastic Processes and Discrete time Markov chains 08 Hours

Classification of Stochastic process, Bernoulli process, Poisson process, Renewal process, Markov modulated

Bernoulli process, Irreducible finite chains.

BOOKS:

Reference books:

R1. K. Trivedi, “Probability and Statistics with Reliability, Queuing, and Computer Science Applications”

Wiley.

R2. Kenneth H. Rosen, “Discrete Mathematics and its Applications”, 6th edition, McGraw-Hill, 2007.

ISBN 978-0-07-288008.

R3. Ron Larson, David C. Falvo,“Linear Algebra- An introduction”.

R4. Gilbert Strang, “Introduction to Linear Algebra” ,Wellesley-Cambridge Press,

R5. U Dinesh Kumar, “Business Analytics”, Wiley.

R6. J. L. Mott, A.Kandel, T.P. Baker,“Discrete Mathematics for Computer Scientists and Mathematicians”,

PHI.

R7. John Truss “Discrete Mathematics for Computer Science”, Pearson International, 2001.

R8. John Vince, “Foundation Mathematics for Computer Science”, Springer.

R9. M. Mitzenmacher and E. Upfal, “Probability and Computing: Randomized Algorithms and Probabilistic

Analysis”.

R10. Alan Tucker, “Applied Combinatorics”, Wiley.





[503208]: System Simulations and Modeling Teaching Scheme:

TH: 3 Hours/Week

Credit:03




Total :100 Marks

Prerequisites Courses : Basic knowledge of numerical mathematics, probability and statistics, and

Programming skills in one or more of the following programming languages: Java, C, or C++.

Course Objective:

The Objective of this course is to make students learn and understand basic Concepts of Systems behavior ,

various Modeling schemes and applications to simulate the systems. To acquaint with the theory of simulation.

Course Outcome:

After successful completion of the course, students will able to –

CO1 : Apply modeling to understand system behavior.

CO2 : Design the simulation scheme for particular system.

CO3 : Analyze the modeled and simulated systems.

CO4 : Compare the results of simulations confined to real world application.

Course Contents

UNIT-I: Introduction 07 Hours

The Nature of Systems, Event-Driven Model, Characterizing Systems, Simulation Diagrams, The Systems

Approach. Dynamical Systems: Initial-Value Problems, Higher-Order Systems, Autonomous Dynamic

Systems, Multiple-Time-Based Systems, Handling Empirical Data.

UNIT-II: System Models 07 Hours

Uniformly Distributed Random Numbers, Statistical Properties of U[0,1] Generators, Generation of Non-

Uniform Random Variates, Generation of Arbitrary Random Variates, Random Processes, Characterizing

Random Processes, Generating Random Processes, Random Walks, White Noise. Stochastic Data

Representation: Random Process Models, Moving-Average (MA) processes, Autoregressive (AR) processes,

Big-Z notation, Autoregressive Moving-Average (ARMA) models, additive noise.

UNIT-III: Spatial Distributions 07 Hours

Sampled Systems, Spatial Systems, Finite-Difference Formulae, Partial Differential Equations, Finite

Differences for Partial Derivatives, Constraint Propagation. Exhogenous Signals and Events: Disturbance

Signals, State Machines, Petri Nets, Analysis of Petri Nets, System Encapsulation.

UNIT-IV: Modelling Input Signals 08 Hours

Modeling Input Signals, Nomenclature, Discrete Delays, Distributed Delays, System Integration, Linear

Systems, Motion Control Models, Numerical Experimentation. Event Driven Models: Simulation Diagrams,

Queuing Theory, M/M/1 Queues, Simulating Queuing Systems, Finite-Capacity Queues, Multiple Servers,




M/M/c Queues.

UNIT-V: Output Data Analysis 08 Hours

Transient and Steady-State Behavior of a Stochastic Process, Types of Simulations with Regard to Output

Analysis, Statistical Analysis for Terminating Simulations, Statistical Analysis for Steady-State Parameters,

Statistical Analysis for Steady-State Cycle Parameters, Multiple Measures of Performance, Time Plots of

Important Variables.

UNIT-VI: Simulation of Manufacturing System 08 Hours

Simulation of Manufacturing System: Introduction, Objectives of Simulation in Manufacturing, Simulation

Software for Manufacturing, Modeling System Randomness with extended example, A simulation case study

of a Metal-Parts Manufacturing Facility.

BOOKS:

Reference books:

R1. Frank L. Severance, “System Modeling and Simulation a Introduction”, Severance, John Wiley & Sons

Ltd., ISBN 9812-53-175-0.

R2. Averill M Law, “Simulation Modeling and Analysis”, McGraw Hill Education, ISBN-13: 978-0-07-

066733-4.

R3. Daniele Gianni, Andrea D'Ambrogio, and Andreas Tolk (editors), “Modeling and Simulation-Based

Systems Engineering Handbook”, CRC Press, 2014., ISBN: 1466571462 .

R4. Gould, H. and Tobochnik, J., “Computer Simulation Methods part I and II”, Addison Wesley, 1987,

ISBN:0-691-13744-7.





[503209]: Randomized and Approximation Algorithms

Teaching Scheme:

TH: 3 Hours/Week

Credit:03




Total :100 Marks


Data Structures , Algorithms, Discrete Probability Theory, Combinatorics, Number Theory and Algebra.

Course Objective: To design randomized algorithms for solving complex problems and estimate their expected running time and error

probability, Design approximation algorithms for hard problems and compute their ratio-bounds.

Course Outcome:


CO1 : Construct Las Vegas algorithms for a given problem and compute the expected running time.

CO2 : Construct Monte-Carlo algorithms for a given problem and compute the probability of getting an

incorrect output.

CO3 : Design solutions for complex problems using randomization design paradigms like Foiling the

Adversary, Abundance of Witnesses, Fingerprinting, Random Sampling, Amplification and Random

Rounding.

CO4 : Analyze NP-hard problems from the view-point of approximability.

CO5 : Develop approximation algorithms for a given problem by evaluating various possibilities, techniques

and design trade-offs.

CO6 : Compute ratio-bounds while designing combinatorial approximation algorithms and approximation

algorithms based on Linear Programming techniques and Semi-definite Programming.

Course Contents

UNIT-I Introduction to Randomized Algorithms 07 Hours Review on Algebra, Number theory, Combinatorics and Probability theory, Randomness as a source of efficiency-

designing a communication protocol, Models of Randomized Algorithms.

UNIT-II Classification of Randomized Algorithms 08 Hours

Classification-Las Vegas, Monte-Carlo (one-sided error, bounded-error and unbounded-error), Classification of

Randomized Algorithms for Optimization problems.

UNIT-III Design Paradigms for Randomized Algorithms 08 Hours

Design Paradigms: Foiling the Adversary, Abundance of Witnesses, Fingerprinting, Random Sampling,

Amplification, Random Rounding.




UNIT-IV Representative Algorithms 07 Hours

Foiling the Adversary – Universal Hashing, Fingerprinting – Communication protocols, Verification of Matrix

Multiplication, Equivalence of Two polynomials, Success Amplification and Random Sampling – Min-Cut,

Satisfiability and repeated random sampling,Abundance of Witnesses and Optimization & random rounding –

Primality Testing, Max-SAT review, hybrid sampling & rounding, Derandomization Techniques.

UNIT-V Introduction to Approximation Algorithms 08 Hours

Review on Complexity theory, Performance Ratios for approximation algorithms, Cardinality vertex-cover problem,

Well-characterized problems and min-max relations, Travelling Salesperson problem. Combinatorial Algorithms:

Set Cover, Steiner Tree and TSP, Multi-way Cut and k-Cut, Bin Packing.

UNIT-VI LP-based Algorithms 07 Hours

LP-duality, Set cover via dual fitting, Set cover via the primal-dual schema, Rounding applied to Set Cover, Multi-

way Cut Semi-definite Programming: Strict quadratic programs and vector programs, The semi-definite

programming problem, Randomized rounding algorithm, Guarantee improvement for MAX-2SAT.

BOOKS:

Reference books:

R1. Vijay V. Vazirani – “Approximation Algorithms”, First edition, Springer, 2001.

R2. Juraj Hromkovic– “Design and Analysis of Randomized Algorithms”, First edition, Springer,2005.

R3. David P. Williamson, David B. Shmoys, “The Design of Approximation Algorithms “ ,Cambridge

University Press, 2011.

R4. Rajeev Motwani, PrabhakarRaghavan, “Randomized Algorithms”, Cambridge University Press, 1995.

R5. Charles E. Leiserson, Thomas H. Cormen, Ronald L.Rivest and Clifford Stein ,”Introduction to

Algorithms”, third edition, PHI, 2010.

R6. Gilles Brassard and Paul Bratley ,” Fundamentals of Algorithmics “, PHI, 2000.

R7. Sara Baase ,“Computer algorithms: Introduction to Design and Analysis”, AddisonWesley publication,

1998.v.





[503210A]: ELECTIVE III -Fault Tolerant Systems

Teaching Scheme:

TH: 3 Hours/Week

Credit:03




Total :100 Marks

Prerequisites Courses : Distributed Systems.

Course Objective: To identify and understand the need of redundancies in the systems, reliability and accountability in the systems,

the instances where fault tolerance is inevitable .

Course Outcome:


CO1 : To understand the redundancy techniques in fault tolerance.

CO2 : To understand the fault modeling and fault simulation algorithm.

CO3 : To identify different fault Tolerant routing algorithms.

CO4 : To apply Fault Tolerance and Reliability in Hierarchical Interconnection Networks.

CO5 : To implement Fault Tolerance in computer network.

CO6 : To apply Fault Tolerance in Distributed System and Mobile Networks.

Course Contents

UNIT-I Fault Tolerance and Reliability Analysis 07 Hours

Introduction, Redundancy Techniques- Hardware Redundancy, Software Redundancy, Information

Redundancy, Time Redundancy, Reliability Modeling and Evaluation - Empirical Models, Analytical

Techniques.

UNIT-II Fault Modeling, Simulation and Diagnosis 07 Hours

Fault Modeling, Fault Simulation, Fault Simulation Algorithms- Serial Fault Simulation Algorithm, Parallel

Fault Simulation, Deductive Fault Simulation, Concurrent Fault Simulation, Critical Path Tracing, Fault

Diagnosis- Combinational Fault Diagnosis, Sequential Fault Diagnosis Methods.

UNIT-III Fault-Tolerant Routing in Multi-Computer Networks 07 Hours

Fault-Tolerant Routing Algorithms in Hypercube- Depth-First Search Approach, Iterative-Based Heuristic

Routing Algorithm, Routing in Faulty Mesh Networks- Node Labeling Technique, A FT Routing Scheme for

Meshes with Non-convex Faults.




UNIT-IV Fault Tolerance and Reliability in Hierarchical Interconnection

Networks 08 Hours

Block-Shift Network (BSN)- BSN Edges Groups, BSN Construction, BSN Degree and Diameter, BSN

Connectivity, BSN Fault Diameter, BSN Reliability, Hierarchical Cubic Network (HCN)- HCN Degree and

Diameter, HINs versus HCNs, The Hyper-Torus Network (HTN).

UNIT-V Fault Tolerance and Reliability of Computer Networks 08 Hours

Fault Tolerance in Loop Networks - Reliability of Token-Ring Networks, Reliability of Bypass-Switch

Networks, Double Loop Architectures, Multi-Drop Architectures, Daisy-Chain

Architectures, Fault Tolerance in High Speed Switching Networks - Classification of Fault-Tolerant Switching

Architectures, Architecture-Dependent Fault Tolerance.

UNIT-VI Fault Tolerance in Distributed System and Mobile Networks 08 Hours

Faults, Errors and Failures, failure models, process resilience, reliable client-server communication, reliable

group communication, Check pointing Techniques in Mobile Networks- Minimal Snapshot Collection

Algorithm, Mutable Checkpoints, Adaptive Recovery, Message Logging Based Checkpoints, Hybrid

Checkpoints.

BOOKS:

Reference books:

R1. Mostafa Abd-El-Barr, “Design and Analysis of Reliable and Fault-Tolerant Computer Systems”, World

Scientific Publishing, ISBN 1281867497.

R2. Andrew Tanenbaum, “Distributed Systems Principles and Paradigms”, Pearson Prentice Hall, ISBN:

978-15-302817-5-6.

R3. Dhiraj K. Pradhan, “ Fault Tolerant Computer System Design”, Prentice Hall, ISBN-13: 978-

0130578877.

R4. Martin L. Shooman, “Reliability of Computer Systems and Networks: Fault Tolerance”, ISBN:

471464066 .

R5. Jan Vytopil, “Formal Techniques in Real-Time and Fault-Tolerant Systems“, ISBN: 1461532205.





[503210B]: Elective III - Deep Structured Learning Teaching Scheme:

TH: 3 Hours/Week

Credit:03




Total :100 Marks

Prerequisites Courses : Calculus, Linear Algebra, Probability & Statistics.

Course Objective: The objective of this course is to understand mathematical, statistical and computational challenges of building

stable representations for high-dimensional data, such as images, text, data and to Deep learning recent models

from both supervised and unsupervised learning.

Course Outcome:


CO1 : Understanding the basic concept of Deep learning.

CO2 : Explaining the concept of Convolution neural network.

CO3 : Describing the different types of deep supervised learning classification.

CO4 : Describing the different types of deep unsupervised learning classification.

CO5 : Explaining the concept of RNN and LSTM.

CO6 : Describing the different advances in deep learning.

Course Contents

UNIT-I Understanding of Deep Learning 07 Hours

Introduction To Deep Learning and Recent Developments, Ways to Improve Generalization, Limiting Size of

Weights, using Noise as a Regularizer, Ups and Downs of Back Propagation, Dropout.

UNIT-II Convolutional Neural Networks 08 Hours

CNN Architecture, Invariance, Stability, Variability Models (Deformation Model, Stochastic Model),

Scattering Networks.

UNIT-III Deep Supervised Learning: Classification 08 Hours

Properties of CNN Representations: Invertibility, Stability, Invariance, Covariance/Invariance: Capsules and

Related Models, Connections with other Models: Dictionary Learning, LISTA, Tasks: Localization,

Regression, Embeddings, Inverse Problems, Extensions to Non-Euclidean Domains.

UNIT-IV Deep Unsupervised Learning: Clustering 07 Hours

Autoencoders (Standard, Denoising, Contractive), Variational Autoencoders , Adversarial Generative

Networks, Maximum Entropy Distributions.

https://www.youtube.com/watch?v=vShMxxqtDDs

https://class.coursera.org/neuralnets-2012-001/lecture/97










UNIT-V RNN & LSTM 08 Hours

Introduction to RNN and LSTM, Implementation of RNN And LSTM, Language Modeling, Image

Captioning, Visual Question Answering, Soft Attention, Use of RNN in Dynamical Systems.

UNIT-VI Advances in Deep Learning 07 Hours

Non-Convex Optimization for Deep Networks, Memory Models Language Modeling, Image Captioning,

Visual Question Answering, Soft Attention, Deep Reinforcement Learning, Policy Gradients, Hard Attention,

Q-Learning, Actor-Critic.

BOOKS:

Reference books:

R1. Duda R.O., Hart P.E., Stork, D.G., “Pattern Classification”,. Wiley InterScience. 2nd

Edition, 2001.

R2. Theodoridis S., Koutroumbas K, “Pattern Recognition”, 4th

Edition, Academic Press, 2008.

R3. Russell S., Norvig N., “Artificial Intelligence: A Modern Approach”, Prentice Hall Series in Artificial

Intelligence, 2003.

R4. Bishop, C. M., “Neural Networks for Pattern Recognition”, Oxford University Press, 1995.

R5. Hastie T., Tibshirani R., Friedman J., “The Elements of Statistical Learning”, Springer, 2001.

R6. Koller, D., Friedman, N., “Probabilistic Graphical Models”, MIT Press, 2009.





[503210C]: Elective III - Optimization Techniques

Teaching Scheme:

TH: 3 Hours/Week

Credit:03




Total :100 Marks

Prerequisites Courses : Database Management system , Design and Analysis of Algorithms.

Course Objective: Introduction to optimization techniques using both linear and non-linear programming. The focus of the course

is on convex optimization though some techniques will be covered for non-convex function optimization too.

After an adequate introduction to linear algebra and probability theory, students will learn to frame engineering

minima maxima problems in the framework of optimization problems.

Course Outcome:


CO1 : Understand importance of optimization of industrial process management. CO2 : Apply basic concepts of mathematics to formulate an optimization problem using Linear programming.

CO3 : Learn efficient computational procedures to solve optimization problems using Non Linear

Programming.

CO4 : Formulation simplex methods variable with upper bounds.

CO5 : Understand the maximization and minimization of convex functions.

CO6 : Understand Neural network based optimization techniques.

Course Contents


Introduction: Engineering application of optimization, statement of an optimization problem with example for

minimum weight and optimum cost consideration, classification of optimization problems and techniques,

Single variable, multi-variable with equality and inequality constraints and without constraints.

UNIT-II Linear Programming 08 Hours

Introduction, basic terminology Techniques of linear programming: Simplex method, Revised simplex method:

Dual Simplex Method, decomposition principle, post-optimality analysis.

UNIT-III Non Linear Programming 08 Hours

Introduction, elimination methods: various search methods Fibonacci method and golden section method

Interpolation method-Quadratic and cubic interpolation methods, KKT conditions, Direct root method.




UNIT-IV Unconstrained optimization Techniques 07 Hours

Introduction; Standard form of the problem and basic terminology; Direct search method- Simplex method,

Random search method, Univariate and pattern search method Indirect search method-Steepest Descent

(Cauchy) method, Conjugate gradient method, Newton’s method, Application to engineering problems.

UNIT-V Constrained Optimization Introduction 08 Hours

Standard form of the problem and basic terminology; Direct method: Sequential Linear Programming;

Generalized Reduced gradient method, Methods of feasible direction Indirect method: Penalty function method

Interior and exterior penalty function method, Convex programming problem, Check for convergence

Application to engineering problems.

UNIT-VI Introduction to non-traditional methods 07 Hours

Genetic Algorithm: Introduction, Representation of design variables, objective function and constraints,

Genetic operators and numerical results. Introduction to Neural network based optimization.

BOOKS:

Reference books:

R1. S. S. Rao, Engineering Optimisation- “Theory and Practice”, New Age International.

R2. Deb K., “Optimisation for Engineering Design-Algorithms and Example”, Prentice Hall.

R3. U.Kirsch, “Optimum structural design”, McGrawHill, New York.

R4. Gallagher and O.C Zeinkiewicz, “Optimum Structural Design Theory & Applications”, John Wiley.

R5. D. Bertsekas “Nonlinear programming”, 2nd Edition, Athena Scientific, 1999, Nashua.

R6. R. K. Sundaram, “A first course in optimization theory”, 1996, Cambridge University Press,

Cambridge.





[503210D]: Elective III - Network Security

Teaching Scheme:

TH: 3 Hours/Week

Credit:03




Total :100 Marks

Prerequisites Courses : Computer Network, Programming experience in C/C++ or JAVA.

Course Objective:

To explain security principles and apply to solve problems, to evaluate risks faced by computer system, to

describe and generalize various software vulnerabilities and detect common vulnerabilities in software, to

analyze and evaluate software systems for its security properties, to explain how various security mechanisms

work, and correlate these security mechanisms with security principles, to compare various security

mechanisms, and articulate their advantages and limitations.

Course Outcome:


CO1 : Apply the knowledge of network security to resolve problems in sound engineering.

CO2 : Explain the Security principles and apply to solve problems.

CO3 : To describe and generalize various software vulnerabilities and detect common vulnerabilities in

software.

CO4 : To analyze and evaluate software systems for its security properties.

CO5 : To explain how various security mechanisms work, and correlate these security mechanisms with

security principles.

CO6 : To compare various security mechanisms, and articulate their advantages and limitations.

Course Contents


Overview of security in networking, Vulnerabilities in TCP/IP model, IP Attacks, ICMP Attacks, Routing

Attacks, TCP Attacks, Application Layer Attacks, Denial of Service attacks (DOS), Distributed DOS, Network

threats and protection: Malware, And Spam, Phishing attacks, Remote-Access Trojan, Identifying Network

Worms and Viruses, Botnets and Cyber Security.

UNIT-II Authentication Mechanisms 07 Hours

Authentication Basics, Passwords, Authentication Tokens, Certificate-based authentication, Biometric

Authentication, Kerberos, Key Distribution Centres (KDC), Security Handshake Pitfalls, Single Sign On (SSO)




UNIT-III Web Security Protocols 08 Hours

Basic concepts, Secure Socket Layer (SSL), Transport Layer Security (TLS), Secure Hyper Text Transfer

Protocol (SHTTP), Secure Electronic Transaction (SET), SSL verses SET, 3-D Secure Protocol, Email

Security, Pretty Good Privacy (PGP), S/MIME.

UNIT-IV Digital Certificates and PKI 07 Hours

Digital Certificates, Private-Key Management, The PKIX model, Public key Cryptography Standards(PKCS),

XML and PKI security, Cross-site Scripting vulnerability.

UNIT-V IPSec and VPN 08 Hours

IP security overview, Authentication Header, Encapsulating Security Payload, Virtual Private Network (VPN),

IPSec verses VPN, Network Address Translation (NAT), Secure Routing , Secure Multi casting.

UNIT-VI Firewalls and IDS 08 Hours

Firewall basics, Demilitarized zone, typical firewall configuration, Firewall types, Intrusion Detection systems,

Detection verses Prevention, types of IDS, Intrusion Prevention Systems (IPS), Honeypots.

BOOKS:

Reference books:

R1. Christopher M. King, “Security architecture, design deployment and operations”, Curtis patton and

RSA Press.

R2. Stephen Northcatt, Leny Zeltser, “INSIDE NETWORK Perimeter Security”, Pearson Education Asia.

R3. Robert Bragge, Mark Rhodes, Heith straggberg, “Network Security the Complete Reference”, Tata

McGraw Hill Publication.





[503211A]: Elective IV - Fog Computing

Teaching Scheme:

TH: 3 Hours/Week

Credit:03




Total :100 Marks

Prerequisites Courses : Cloud Computing.

Course Objective: The Objective of this course is to make students learn and understand the concept of the design space and

conduct trade-off analysis between performance and resources. Devise appropriate data dissemination protocols

and model links cost.

Course Outcome:


CO1 : Explain the basic concepts of fog computing.

CO2 : Design working model of fog computing.

CO3 : Understand the concept of protocols in fog computing.

CO4 : Apply Big Data tools in fog computing.

CO5 : Understand real time applications in fog computing.

CO6 : Analyze security issues in fog computing.

Course Contents

UNIT-I Introduction To Fog Computing 07 Hours

Fog Computing-Definition-Characteristics-Application Scenarios - Issues -Fog Computing and Internet of

Things-Pros and Cons-Myths of Fog Computing -Need and Reasons for Fog Computing Fog Computing and

Edge Computing-IoT , FOG, Cloud benefits.

UNIT-II Architecture 07 Hours

Working Procedure -Performance Evaluation Components- Software Systems – Architecture-Modeling and

Simulation –Challenges.

UNIT-III Fog Protocols 07 Hours

Fog Protocol-Fog Kit- Proximity Detection Protocols- DDS/RTPS computing protocols.

UNIT-IV Management of Data And Security Analysis 08 Hours

Smart Management of Big Data-Smart Data-Structure of Smart Data- Smart Data Life Cycle-System

Architecture-Multi-dimensional Payment Plan- -Security and Privacy Issues-Multimedia Fog Computing-

Architecture-Deduplication-Hybrid Secure Deduplication- Security Challenges-Security Requirements.




UNIT-V Case Study 08 Hours

Case Study: Wind Farm - Smart Traffic Light System, Wearable Sensing Devices, Wearable Event Device

,Wearable System, Demonstrations , Post Application Example . . Event Applications Example.

UNIT-VI Security 08 Hours

Fog computing security: a review of current applications, challenges and security solutions.

BOOKS:

Reference books:

R1. Amir Vahid Dastjerdi and ajkumarBuyya,” Fog Computing: Helping the Internet of Things Realize its

Potential”,University of Melbourne.

R2. Fog Computing: A Platform for Internet of Things and Analytics, FlavioBonomi, Rodolfo Milito,

PreethiNatarajan and Jiang Zhu, Big Data and Internet of Things: A Roadmap for Smart Environments,

Studies in Computational Intelligence 546, DOI: 10.1007/978-3-319-05029-4_7, © Springer

International Publishing Switzerland 2014.

R3. Spencer Lewson, “Fog Protocol and FogKit: A JSON-Based Protocol and Framework for

Communication Between Bluetooth-Enabled Wearable Internet of Things Devices “,Spencer Lewson

June 2015.

R4. FlavioBonomi, Rodolfo Milito, Jiang Zhu,SateeshAddepalli, “Fog Computing and Its Role in the

Internet of Things”, MCC’12, August 17, 2012, Helsinki, Finland. Copyright 2012 ACM 978-1-4503-

1519-7/12/08.





[503211B]: Elective IV -Deep Neural Networks Teaching Scheme:

TH: 3 Hours/Week

Credit:03




Total :100 Marks

Prerequisites Courses: Linear Algebra, Calculus, Probability and Stats.

Course Objective: Introduce deep learning algorithms and their applications to solve real world problems.

Course Outcome:


CO1 : Understand basic concepts of deep neural network.

CO2 : Describe the concepts of feed forward network, training neural network and conditional random fields.

CO3: Explain the concepts of CNN, RNN and deep Belief Network.

CO4: Describe the concepts of Probabilistic Neural Network.

CO5 : Explain the concepts of Deep Learning Research.

CO6 : Describe the different Deep Learning Tools.

Course Contents


Various paradigms of learning problems, Perspectives and Issues in Deep Learning Framework, Review of

fundamental learning techniques.

UNIT-II Feed forward neural network 09 Hours

Artificial Neural Network, Activation Function, Multi-layer Neural Network. Training Neural Network: Risk

minimization, loss function, back propagation, regularization, model selection and optimization. Conditional

Random Fields: Linear chain, partition function, Markov network, Belief propagation, Training CRFs, Hidden

Markov Model, Entropy.

UNIT-III Deep Learning 09Hours

Deep Feed Forward Network, Regularizations, Training Deep Models, Dropouts, Convolution Neural Network,

Convolution and Pooling ConvNets outside vision, Recurrent Neural Network, Deep Belief Network.

UNIT-IV Probabilistic Neural Network 07 Hours

Hopfield Net, Boltzman Machine, RBMs, Sigmoid Net, Autoencoders.




UNIT-V Deep Learning Research 07 Hours

Object recognition, Sparse coding, Computer vision, Un-supervised and Semi-Supervised Learning, Natural

Language Processing.

UNIT-VI Deep Learning Tools 06 Hours

Use of CPU Vs GPU, TensorFlow, Theano, Torch, Caffe.

BOOKS:

Reference books:

R1. Goodfellow, I., Bengio,Y., and Courville, “A., Deep Learning”, MIT Press, 2016.

R2. Bishop, C. ,M., “Pattern Recognition and Machine Learning”, Springer, 2006.

R3. Yegnanarayana, B., “Artificial Neural Networks”, PHI Learning Pvt. Ltd, 2009.

R4. Golub, G.,H., and Van Loan,C.,F., “Matrix Computations”, JHU Press,2013.

R5. Satish Kumar, “Neural Networks: A Classroom Approach”, Tata McGraw-Hill Education, 2004.





[503211C]: Elective IV - Big Data Analytics

Teaching Scheme:

TH: 3 Hours/Week

Credit:03




Total :100 Marks

Prerequisites Courses : Mathematical Foundation of Computer Science, Data preparation and Analysis.

Course Objective:

To be acquainted with the fundamental concepts of big data and analytics, to describe various tools and

practices for working with big data, to explore various big data visualization tools, to be aware of statistical and

data analytics methods.

Course Outcome:


CO1 : Explain the fundamental concepts of big data.

CO2 : Summarize the fundamental concepts of data analytics.

CO3 : Discuss the working of Hadoop and its ecosystem.

CO4 : Selecting appropriate data visualization tools for big data visualization.

CO5 : Apply Advanced Analytics and Statistical Modeling for Big Data.

Course Contents

UNIT-I Introduction To Big Data 07 Hours

Evolution of Big data – Best Practices for Big data Analytics – Big data characteristics – Validating – The

Promotion of the Value of Big Data – Big Data Use Cases- Characteristics of Big Data Applications –

Perception and Quantification of Value -Understanding Big Data Storage – A General Overview of High-

Performance Architecture – HDFS – MapReduce and YARN – Map Reduce Programming Model.

UNIT-II

Overview of Data Analytics Lifecycle 08 Hours

phases of a typical analytics lifecycle – discovery, data preparation, model planning, model building,

communicating results and findings, and operationalizing. Data Analytic Life Cycle: Overview, phase 1-

Discovery, Phase 2- Data preparation, Phase 3- Model Planning, Phase 4- Model Building, Phase 5-

Communicate Results, Phase 6- Opearationalize.

UNIT-III

Technologies for Handling Big Data

08 Hours

Big Data is primarily characterized by Hadoop. This module cover topics such as Introduction to Hadoop,

functioning of Hadoop, Cloud computing (features, advantages, applications) etc., Hadoop and its ecosystem

which includes HDFS, MapReduce, YARN.




UNIT-IV Hadoop Ecosystems 07 Hours

HBase, Hive, Pig, Sqoop, Zookeeper, Flume, Oozie etc., framework of MapReduce and uses of MapReduce.

UNIT-V Big Data Visualization 07 Hours

Why Visualize Data? Importance of data visualization, Examples of data visualization, Input for Visualization:

Data and Tasks, Common Visualization Idioms Bar Chart, Vertical & Horizontal Pie Chart and Coxcomb Plot,

Line Chart, Area Char, Encoding Data using Color, Encoding Data using Size, Stacked & Grouped Bar Chart,

Stacked Area Chart & Streamgraph, Line Chart with Multiple Lines, Data Reduction : Histograms,

Aggregating Data with Group-By, Hexbin MappingCross-filtering,

UNIT-VI Advanced Analytics and Statistical Modeling for Big Data 07 Hours

Naïve Bayesian Classifier, categorization using K-means clustering and association rules, predictive modeling

using decision trees, linear and logistic regression, and time-series analysis, and text analysis.

BOOKS:

Reference books: R1. David Dietrich, Barry Hiller, “Data Science and Big Data Analytics”, EMC education services, Wiley

publications, 2012, ISBN0-07-120413-X.

R2. Chris Eaton, Dirk deroos et al., “Understanding Big data”, McGraw Hill, 2012.

R3. Visualization Analysis & Design by Tamara Munzner (2014) (Links to an external site.) Links to an

External site. (ISBN 9781466508910). R4. Maheshwari Anil, Rakshit, Acharya, “Data Analytics”, McGraw Hill, ISBN: 789353160258.

R5. Luís Torgo, “Data Mining with R, Learning with Case Studies”, CRC Press, Talay and Francis Group,

ISBN9781482234893.

R6. Vignesh Prajapati, “Big Data Analytics with R and Haoop”, Packet Publishing 2013.





[503211D]: Elective IV - Network Multimedia System Teaching Scheme:

TH: 3 Hours/Week

Credit:03




Total :100 Marks

Prerequisites Courses: Computer Graphics, Multimedia.

Course Objective: Introduce Basic concepts, fundamentals of multimedia and design of network multimedia systems.

Course Outcome:


CO1 : Understand basic concepts to networked multimedia system design.

CO2 : Describe the concepts of Audio and Speech.

CO3 : Explain the concepts of Images and Video.

CO4 : Describe the concepts of Multimedia Communication.

CO5 : Explain the concepts and different techniques of Hypermedia Presentation.

CO6 : Describe the concepts of Multimedia Information Systems.

Course Contents

UNIT-I Introduction to Multimedia Systems 07 Hours An overview of multimedia system and media streams architecture and components, synchronization & quality of service

(QOS).

UNIT-II Audio and Speech 07 Hours

Data acquisition, sampling and quantization, human speech, digital model of speech production, analysis and

synthesis, psychoacoustics, low bit rate speech compression, MPEG audio compression.

UNIT-III Images and Video 07 Hours

Image acquisition and representation, bilevel image compression standards: ITU (formerly CCITT) Group III

and IV standards, JPEG image compression standards, MPEG, H.264/AVC video compression standards,

Transcoding.

UNIT-IV Multimedia Communication 08 Hours

Fundamentals of data communication and networking, Bandwidth requirements of different media, Real time

constraints: latency, video data rate, multimedia over LAN and WAN, Multimedia conferencing, video-on-

demand broadcasting issues.

UNIT-V Hypermedia Presentation 08 Hours

Authoring and publishing, Linear and non-linear presentation, Structuring Information, Different approaches of




authoring hypermedia documents, Hyper-media data models and standards.

UNIT-VI Multimedia Information Systems 08 Hours

Operating system support for continuous media applications: Media stream protocol, file system support for

continuous media, data models for multimedia and hypermedia information, multimedia servers, databases and

content management.

BOOKS:

References:

R1. Jerry D. Gibson, Toby Berger, Tom Lookabaugh, Dave Lindergh and Richard L. Baker “Digital

Compression for Multimedia: Principles and Standards”, Elsevier, 2006.

R2. Ralf Steinmetz and Klara Nahrstedt, “Multimedia: Computing, Communications, and Application”,

Prentice Hall, 1995.

R3. Khalid Sayood. “Introduction to Data Compression”, 3rd Edition, Elsevier, 2006.

R4. Asit Dan and Dinkar Sitaram ,“Multimedia Servers”, Elsevier, 2006.





[503212]: Laboratory Practice-II Teaching Scheme:

Practical: 04 Hours/Week

Credit:02 Examination Scheme: Oral: 50 Marks

TW: 50 Marks

Total: 100Marks

Laboratory Practice II (LP II) is companion course of theory courses (core and elective) in Semester II. It is

recommended that set of assignments or at least one mini-project/study project per course is to be completed.

Set of problem statements is suggested. Course/ Laboratory instructor may frame suitable problem statements.

Suggested List of Laboratory Assignments System Simulation and Modeling

1 Using suitable simulation Tool simulate any one of-

Automobile Manufacturing Model.

Simulation of Inventory Control System .

2 Using suitable simulation Tool simulate any one of-

Simulation of Single Server queuing system.

Customer Queuing System .

Transportation Model.

Randomized and Approximation Algorithms

1. Construct and Analyze any one of Randomized algorithms.

Las Vegas algorithms for a given problem and compute the expected running time.

Monte-Carlo algorithms for a given problem and compute the expected running time.

2. Construct and Analyze any one of Approximation Algorithms.

Vertex Cover.

Travelling Salesman Problem.

Elective-III


Elective-IV


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