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Vishwakarma Institute of Technology Issue 01 : Rev No. 1 : Dt. 24/03/17

Structure and syllabus of M. Tech. CSE (IT) Pattern A-16, A.Y. 2017-18

Bansilal Ramnath Agarwal Charitable Trust’s

Vishwakarma Institute of Technology (An Autonomous Institute affiliated to University of Pune)

Structure & Syllabus of

ME-CSE (Information Technology) Pattern ‘A-16’

Effective from Academic Year 2017-18

Prepared by: - Board of Studies in Computer Engineering

Approved by: - Academic Board, Vishwakarma Institute of Technology, Pune

Signed by

Chairman – BOS Chairman – Academic Board



Content

Sr. No. Title Page No.

1 Course Structure – Semester I 4

2 Course Syllabi for Courses - Semester I 5

2.1 ES501TH Linear Algebra and Statistics 5

2.2 CS501THP Database Mgmt Technique 7

2.3 CS502THP Embedded & real time OS 10

2.4 CS503THP High Performance Network 12

2.5 ES502TH Research Methodology 15

2.6 HS551TH Quantitative Aptitude-I 17

3 Course Structure – Semester II 18

4 Course Syllabi for Courses - Semester II 19

4.1 CS504THP Software Process Management 19

4.2 CS505THP Network Security 23

4.3 Dept. Electives 30

4.4 Institute/Open

4.5 HS552TH Quantitative Aptitude-II 26

4.6 HS553TH Engg. &managerial Economics 27

5 Course Structure – Semester III

6 Course Syllabi for Courses - Semester III

6.1 Technical Seminar-II

6.2 Project Dissertation-I

7 Course Structure - Semester IV



7.1 Project Dissertation-II

7.2 Guidelines for M.Tech CSE(IT) Dissertation



Sem-I Course

Code

Course Name Contact Hours / Week Credits

Theory Project Based Lab

Regular Lab

Maths course

ES501TH TH Linear Algebra and Statistics

4 4

Theory2 CS501THP THP Database Mgmt Technique

3 2 4

Theory3 CS502THP THP Embedded & real time OS

3 2 4

Theory4 CS503THP THP High Performance Network

3 2 4

Theory5 ES502TH TH Research Methodology

2 2

HSS HS551TH TH Quantitative Aptitude-I

2 2

TOTAL 20



FF No. : 654

ES501TH: Linear Algebra and Statistics (THP

Credits: 04 Teaching Scheme:- Theory 4 Hrs/Week

Unit-1.Vector Spaces: (7 Hrs)

Rank, Graphs and networks, Vector spaces, subspaces, fundamental subspaces, basis and dimension, Linear Transformation, Co-ordinate systems, Change of Basis.

Unit 2. Orthogonality: (7 Hrs)

Inner product, inner product spaces, length and orthogonality, Orthogonal Projections, Gram-Schmidt Process, least square problem, Applications to linear models.

Unit 3. Digonalization (7 Hrs)

Diagonalization, Eigen vectors and Linear Transformation, Discrete Dynamical Systems, Quadratic forms, Constrained optimization, and Singular value decomposition.

Unit-4. Discrete and Continuous Random Variables (7 Hrs)

Discrete and continuous Random Variables, Random Variables and Their Event Spaces, The Probability Mass and density Function, Distribution Functions, The Probability Generating Function, expectations, Discrete Random Vectors, Independent Random Variables .

Unit-5.Standard probability distributions (7 Hrs)

Some Important Distributions namely uniform, Poisson, geometric, Hypergeometric, The Exponential Distribution, The Reliability and Failure Rate, joint Distributed Random Variables, Order Statistics. Distribution of Sums , Functions of Normal Random Variables. Mixture Distributions, Conditional Expectation, Imperfect Fault Coverage and Reliability, Random Sums.

Unit-6. Stochastic Processes (7 Hrs)

Classification of Stochastic Processes, The Bernoulli Process, The Poisson Process, Renewal Processes, Availability Analysis, Random Incidence, Renewal Model of Program Behavior

Course outcomes:

By the end of the module students will be able to



1. Demonstrate Knowledge of vector spaces, inner product spaces, orthogonanlity, positive definite matrices, random variables and probability distributions

2. Apply the Knowledge of vector spaces, orthogonality, diagonaliztion, Random variables, and probability distributions.

3. Determine and analyze basis, dimensions, Networks, types of Matrices, Faulure and Hazard Rate, random sums, stochastic process.

4. Understand Collection of random variables known as random processes and the Markov process, the Poisson process and the knowledge of other processes.

TEXT BOOKS:

1. Gilbert Starng, ‘Linear Algebra and its Applications’, 4th Edition, Cengage learning 2006.. 2. Kishor S.Trivedi, ‘Probability and Statistics with reliability, Queuing, and Computer Science

Applications’, Prentice Hall India New Delhi, India reprints, (2005).

REFERENCE BOOKS:

1. Ron Larson and David C. Falvo, ‘ Linear Algebra An Introduction’,1st Edition, Cengage Learning ( Indian Edition).

2. H. Anton, Elementary linear algebra with applications (8th Edition), John Wiley Publications 3. S. Kumaresan,’ Linear algebra - A Geometric approach’, 1st Edition, Prentice Hall of

India(Indian Edition) 4. David C Lay, ‘Linear Algebra and its Applications’, 3rd Edition, Pearson. 5. Albert Leon-Gracia, ‘Probability and Random Processes for electrical engineering’, University

of Toronto Pearson Education, 2nd Edition, 2004. 6. S. M. Ross, ‘Introduction to probability & statistics for engineers & scientists’, Academic Press,

3rd edition 2005. 7. Allen, A.O., ‘Probability, Statistics and Queuing theory’, Academic Press, 2nd Ed, 2005

8. Sheldon M. Ross, ‘Introduction to probability models’, Academic Press, seventh Edition, 2001.

9. William Feller, ‘An introduction to probability theory and its application’, Vol.-I, John Wiley & Sons, 3rd edition, 1950.



FF No. : 654

CS501THP: Database Mgmt Technique (THP)


Project: 2 Hrs/Week

Unit 1 : Overview of Relational Databases (7 Hrs)

Need of databases, Introduction to relational databases, Steps in database design.

Overview of Data Model: ER Diagram, Extended E-R features, Relational Model: SQL as DDL and DML, Keys, Referential Integrity, Overview of ER to Relational Mapping

Normalization and schema refinement: 4NF, 5NF, DKNF

Unit 2 : Data Storage and Querying (7 Hrs)

Overview of Storage and File Structure: File organization, Organization of records in files, Overview of indexing and hashing, Indexing types, Hash Indices, Static and dynamic hashing

Database Data Structures: B-Trees, B+Trees, k-d Trees, R-Trees

Query processing: Translation of SQL queries into Relational Algebra, Basic algorithms for executing query operations

Query Optimization: Query trees, Query execution plans, Selectivity and Cost estimates in query optimization, Cost functions

Unit 3 : Distributed and Parallel Databases (7 Hrs)

Distributed Vs Parallel Technology

Distributed Databases: Advantages of distributed databases, Data fragmentation, Data replication and allocation, Types of distributed database systems, Distributed transactions, 2 Phase Commit and 3 Phase Commit protocols, Concurrency control and recovery in distributed databases, Deadlock handling, Query processing in distributed databases, Client-server architecture in distributed databases

Parallel Databases: I/O parallelism, Interquery and Intra query parallelism, Intra operation and Interoperation parallelism

Unit 4 : Fundamental of Data Warehousing (7 Hrs)



Introduction of Decision Support System (DSS), OLTP Vs Data Warehouse, Use of Data warehouse

Dimensional modeling: Star schema, Snowflakes schema, Fact constellations, Concept hierarchies: total and partial, Set-grouping hierarchies, Measures: Their Categorization and Computation

OLAP: Data cube, OLAP operations: drill-down, Roll-up and extreme Roll-up, slice-dice and pivot

Unit 5: Fundamentals Data Mining (7 Hrs)

Introduction, Data mining primitives, Data mining steps, Techniques:- Clustering: Partitioning Methods, Classification: Decision Tree Induction, Frequent Item set mining and Association rules: Apriori Algorithm, Outlier analysis: Statistical approaches, Data Mining on Different kind of Data

Unit 6 : Advance Topics in Databases (7 Hrs)

Temporal databases: Time Representation, Calendars and Time Dimensions, Incorporating time in relational databases using tuple versioning

Spatial databases: Range query, Nearest neighbor query, Special joins, Overlays

Multimedia databases: CBIR, Image databases, Video/ audio source, Nature of multimedia data and applications, Data management issues, Open research problems, Multimedia database applications

Active and Deductive databases: Introduction, Prolog/Datalog notation, Interpretation of rules, Basic inference mechanisms for logic programs, Datalog programs, Deductive database systems

Total Contact Hours: 42

Text Books:

1. Silberschatz ,Korth and Sudarshan, “Database system Concepts”, Fifth

edition, McGraw Hill, International Edition 2006, ISBN – 007-124476-X.

2. Jiawei Han and Micheline Kamber “Data mining: concepts and techniques”, the Morgan Kaufman,2001.

Reference Books :

1. Elmasri, Navathe, “Fundamentals of database system”, Third edition, Published by Pearson Education Pte. Ltd, ISBN – 81-7808-137-7.

2. Kimball, “Data warehouse life cycle toolkit”, John Wiley,1998



3. Raghu Ramkrishan and Johannes Gerkhe, ‘Database Management Systems’, 3rd edition, McGraw Hill, 2003. Rob, Coronel, “Database system, design, implementation and management”, Thomson Learning, Fourth Edition, 2001, ISBN – 0-07-123151-X.

4. Hand, Smyth, Mannila,”Principles of Data mining “, MIT press. 5. Gagendra Sharma ,”Data mining, Data warehousing and OLAP”, S.K. Kataria and sons, First

edition, 2007-08. 6. “R-Trees. A Dynamic Index Structure For Spatial Searching”, by Antomn Guttman, University

of Cahforma Berkeley, ACM Transactions, 1984 7. “A simple guide to five normal forms in relational database theory”, by William Kent,

International Business Machines Corporation, ACM Transactions, 1983. 8. “A Normal Form for Relational Databases That Is Based on Domains and Keys”, Ronald Fagin,

IBM Research Laboratory, ACM Transactions on Database Systems, Vol. 6, No. 3, September 1981, Pages 387-415.



FF No. : 654

CS502THP: Embedded & real time OS (THP)


Project: 2 Hrs/Week

Unit 1: INTRODUCTION TO EMBEDDED SYSTEMS (8 Hours)

Categories, Architecture, Hard ware Architecture, software architecture, Application software, Application areas, Recent trends

Unit 2: PROGRAMMING OF EMBEDDED SYSTEMS (8 Hours)

Memory management, timing, Device drivers, Productivity tools, code optimization. Process of embedded system development,

Unit 3: RTOS CONCEPTS (6 Hours)

Architecture of kernel, Task and task scheduler, Interrupt service routines, semaphores, Mutex, mailboxes, message queues, event registers, pipes, signals, timers. QNX Neutrino, VxWorks, RTLinux.

Unit 4: REAL TIME MODELS AND LANGUAGES (6 Hours)

Event Based, Process Based and Graph based Models, Petrinet Models, Real Time Languages, RTOS Tasks, RT scheduling, Interrupt processing, Synchronization, Control Blocks, Memory Requirements.

Unit 5: DEVICE DRIVERS (6 Hours)

Bootloaders, Device Driver Basics: Character Device, PCI Device Drivers, File Systems, Device Tree, MTD Subsystem, Embedded Development Environment

Unit 6: RTOS APPLICATION DOMAINS (6 Hours)

RTOS for Image Processing, Embedded RTOS for voice over IP, RTOS for fault Tolerant Applications, RTOS for Control Systems. Navigation system, mobile applications, RFID system, DSP based Embedded systems.



Text Books: (As per IEEE format)

1. C.M. Krishna, Kang, G.Shin, “Real Time Systems”, McGraw Hill, 1997. 2. Lyla B. Das, “Embedded Systems: An Integrated Approach” Pearson, ISBN:978-81-317- 8766-3

(Chapters 7,8,10,11) 3. Christopher Hallinan, “Embedded Linux Primer”,Prentice Hall, ISBN-10: 0-13-167984-8, ISBN-

13: 978-0-13-167984-9 Reference Books: (As per IEEE format)

1. Raj Kamal, “Embedded Systems – Architecture, Programming and Design" 2nd edition, McGraw Hill.

2. Frank Vahid and Tony Givargis, “ Embedded System Design – A Unified hardware/ Software introduction ” 3rd edition, Wiley.

2. Raymond J.A.Bhur, Donald L.Bailey, “An Introduction to Real Time Systems”, PHI 1999

3. Charles Crowley, “Operating Systems-A Design Oriented approach”, McGraw Hill 1997.

4. Peter Barry, Patric Crowley, “Modern Embedded Computing, Elsevier, ISBN: 978-93- 81269-77-0 (Chapter 4,5,6,7,8,10,14 Only, ) 2.

5. Karim Yaghmour, “Embedded Android”,O'Reilly, ISBN: 978-1-449-30829-2, Pdf

Course Outcomes: The student will be able to make the student learn fundamentals of Operating Systems, implementation aspects of real time concepts and few applications on RTOS.



FF No. : 654

CS503THP: High Performance Network (THP)


Project: 2 Hrs/Week

Unit 1: Physical Layer (08 Hours)

Network Architectures: Client-Server; Peer To Peer; Ad-hoc Network, Distributed and SDN, Design issues for Layers, IEEE Standards, OSI Model, TCP/IP Model, N-ISDN , ATM Reference Model, B-ISDN, Physical Layer in Ethernet, ISDN and ATM, Network Performance: Bandwidth Utilization, Throughput, Efficiency.

Unit 2: Logical Link Control (07 Hours)

Design Issues: Services to Network Layer, Framing, Error Control and Flow Control. Parity Bits, Hamming Codes and CRC. Stop and Wait, Sliding Window Protocol, WAN Connectivity : PPP and HDLC, Adaptation Layer in ATM, LAPD ISDN protocol at LLC Layer

Unit 3: Medium Access Control (07 Hours)

Pure and Slotted ALOHA, CSMA, WDMA, Ethernet, CSMA/CD, Binary Exponential Back -off algorithm, Fast Ethernet, Gigabit Ethernet, Radio Spectrum, IEEE 802.11a/b/g/n Standards and Frame Formats, MAC Layer in 802.11 Wireless Networks, CSMA/CA, ATM Layer

Unit 4: Network and Transport Layer (07 Hours)

Switching techniques, Circuit Switching, IP Protocol, IPv4 and IPv6 addressing schemes, Subnetting, NAT, CIDR, ICMP, TCP Protocol, TCP Connection Management, TCP Timer Management, TCP and UDP for Wireless, ATM Planes

Unit 5: Routing in Internet (07 Hours)

Routing Protocols: Distance Vector, Link State, Path Vector, Routing in Internet: RIP ,OSPF, BGP, MPLS, Routing in MANET : AODV, DSR

Unit 6: Discussion on Research Papers (04 Hours)

Discussion on Recent Research Papers in the area of Computer Networking



List of Projects: (For THP)

Operating System recommended :- 64-bit Open source Linux or its derivative

Programming tools recommended: - Open Source C,C++, JAVA, PYTHON, Programming tool like G++/GCC, Wireshark, Ethereal and Packet Tracer

Project 1. : To Setup a wired and wireless LAN using Layer 2 Switch and Wireless Access Point. Demonstrate the PING packets captured traces using Wireshark Packet Analyzer Tool. Design and develop any suitable web application using HTML, CSS and XML in consultation of course instructor. Perform validation of all fields using Java script/JQuery. Add dynamic web application essence in using Servlet, JSP and backend or using PHP, MySQL database connectivity and AJAX controls. Re-Design, develop and deploy using Angular JS. Prepare a report about all activities carried out.

Project 2. : Develop a client server using TCP socket for File transfer. Capture packet headers using Wireshark Packet Analyzer Tool for peer to peer mode. Analyze packets formats captured through Wireshark for wired and wireless network[Ethernet and IEEE 802.11 networks]. Prepare the report about all activities happening from application layer to physical layer.

Project 3. : Design a simulation model of 14 node network (using NS3/OMNet/OPNet simulation tool) for the use of WDM technology over OC-192/768 in m number of OC-n channels. Prepare a report about all activities carried out.

Project 4.: Use network simulator NS2 to implement: a. Monitoring traffic for the given topology b. Analysis of CSMA and Ethernet protocols c. Network Routing: Shortest path routing, AODV. d. Analysis of congestion control (TCP and UDP). Prepare a report about all activities carried out.

………….


1. Andrew S. Tenenbaum, “Computer Networks”,5th Edition, PHI, ISBN 81-203-2175-8. 2. A Network Routing: Algorithms, Protocols, and Architectures Deepankar Medhi and Karthikeyan Ramasamy

(Morgan Kaufmann Series in Networking) 3. C. Siva Ram Murthy and B. S. Manoj, “Ad Hoc Wireless Networks: Architectures and Protocols” Prentice Hall,

2005, 2nd Edition, ISBN: 978-81-8404-163-7



Reference Books: (As per IEEE format)

1. Fourauzan B., "Data Communications and Networking", 5th edition, Tata McGraw- Hill, Publications, 2006 2. Kurose, Ross “Computer Networking a Top Down Approach Featuring the Internet”, Pearson; 6th edition

(March 5, 2012), ISBN-10: 0132856204 3. Matthew S. Gast “802.11 Wireless Networks”, O’Reilly publications; 2nd Edition. 4. Holger Karl and Andreas Willig, “Protocols and Architectures for Wireless Sensor Networks”, Wiley, ISBN: 0-

470-09510-5



FF No. : 654

ES502TH: Research Methodology (TH)


Unit1: Formulating Research Problem and Literature Review (9 Hrs)

Overview: RE-Search, Definition, Research characteristics, Difference between methods and methodology, Research categories, Overview of research process.

How to get new research ideas: Creating thinking, Preparations for improving thinking Defining research problem statement: Need, What is a research problem, Sources of research problem, research problem components Literature Survey Overview: What is literature survey, Types of literature survey, Sources of information, Types of technical papers, Publication and patent databases, How to read a scientific paper, How to write scientific paper, writing technical papers in English – Grammar, Punctuation, Tips for writing correct English, How to write a research proposal, How research is funded, How to give a good research talk, Presentation tools Research Ethics and Legal Issues: Intellectual Property rights, Patents, Copyrights, Plagiarism

Unit 2: Research Design and Data Collection (6 Hrs)

Research Design: What is research design, Research Design Parts, Research Design for exploratory and Descriptive Research, Principals of Research design. Sampling Design: Steps in sampling Design, Different Types of Sample Design

Unit 3: Data Collection and Analysis (6 Hrs)

Methods of data collection: Data types, Data Collection Types: Observation, Interview, Questionnaire, Schedules, Collection of Secondary Data Analysis and Processing of Data: Processing operations, Types of Analysis, statistics in Research, Measures of central Tendency, Measures of Dispersion, Measures of Asymmetry, Measures of Relationship, simple regression Analysis, Multiple correlation and regression, association in case of attributes

Unit 4: Hypothesis testing (9 Hrs)



Defining Hypothesis: What is hypothesis, Characteristics of hypothesis, Hypothesis Vs Problem Statement Hypothesis Testing: Null hypothesis, Alternative Hypothesis, Level of significance, Type I and Type II Errors, One tailed and two tailed hypothesis, Power of hypothesis tests Parametric Tests: z-test, t-test, chi-square test, F-test, ANOVA

Total Contact Hours: 30

Text Books:

1. ‘Research Methodology: Methods and Trends’, , by Dr. C. R. Kothari, 2nd revised edition, New Age International Limited Publishers, ISBN: 978-81-224-1522-3

2. ‘Research Methodology: An Introduction’ by Wayne Goddard and Stuart Melville, 2nd Edition, Juta and Co. Ltd, ISBN: 0-70215660-4

Reference Books

1. ‘Research Methodology: A Step by Step Guide for Beginners’, by Ranjit Kumar, 2nd Edition, APH Publishing Corporation

2. ‘Research methodology: an introduction for science & engineering students’, by Stuart Melville and Wayne Goddard

3. ‘Research Methodology’ by Dr. Jayant Tatke, 2009, Symbiosis Centre for Distance Learning

4. ‘Operational Research’ by Dr. S.D. Sharma, Kedar Nath Ram Nath & Co

5. Online material provided by the faculty



FF No. : 654

HS551TH: Quantitative Aptitude-I (TH)


Unit 1: Numbers, Surds and Indices & Logarithms (7 Hours) Numbers, Average, Decimal fractions, Problem on ages, Simplification, Problems on numbers, Square roots & cube roots, Logarithms, Surds and Indices, HCF and LCM of Numbers. Unit 2: Time ,distance and work (7 Hours) Time and distance, Problems on trains, Boats and Streams, Time and Work , Pipes and Cisterns, Alligation or mixture Unit 3: Measures of Statistical Data (7 Hours) Percentage, Profit and loss, Ratio and Proportion, Simple interest, Compound interest, Partnership, Chain Rule. Unit 4: Logical Reasoning (7Hours) Race and Games , Odd Man Out and Series, Number Series, Analogies, Logical Problems, Letter and Symbol Series, Statement and Conclusion, Artificial Language

Text Books 1. Quantitative Aptitude For Competitive Examinations”, Dr. R. S. Aggarwal, S. Chand. 2. “How to Prepare for Quantitative Aptitude”, Arun Sharma, Tata Mcgraw-Hill. Reference Books 1. Quantitative Aptitude Quantum Cat Common Admission Test”, K. Sarvesh Verma., Arihant. 2. “Quantitative Aptitude for Competitive Examinations”, Abhijit Guha, Fourth Quarter.

Course Outcomes

The students will be able to:

1 improve their employability skills 2 improve aptitude, problem solving skills and reasoning ability 3 critically evaluate various real life situations by resorting to analysis of key issues and

factors. 4 demonstrate various principles involved in solving mathematical problems and thereby

reducing the time taken for performing job functions



Sem-II Course Code

Course Name Contact Hours / Week Credits


Regular Lab

Theory1 CS504THP

Software Process Management

3 2 4

Theory2 CS505THP

Network Security 3 2 4

Theory3 Elective

Dept. 3 2 4

Theory4 Elective

Institute/Open 3 2 4

Theory5 HS552TH

Quantitative Aptitude-II

2 2

HS553TH

Engg. Economics 2 2

TOTAL 20



FF No. : 654

CS504THP: Software Process Management (THP)


Project: 2 Hrs/Week

Unit 1: Project Management Principles (7 Hours )

Project Activities, Structures and Frameworks, Strategy and Project Management, Project Definition, PMBOK Areas, Program and Portfolio Management, Project Management Issues, Project Processes, Introduction to Complex Projects, Stakeholder Management, Introduction to Project Contract Management, Types of contracts, Stages in contract placement, Typical terms of a contract, Contract management, Estimation: Scope, Time and Cost, Project Internal and External Risks, Role of Project Teams, Types of Teams and team Life Cycles, Teamwork, Management and Leadership in Projects, CMMI

Unit 2: Introduction to Agile Methodology (7 Hours)

Introduction to Agile Project Management, Agile History and the Agile Manifesto, Agile Requirements: Team, Program, Portfolio Level, Scrum Overview, Scrum Framework, Agile Principles, Sprints Design, Time-Boxing, Kanban, and Theory of Constraints, Requirements and User Stories, Stakeholders, User Personas, and User Experiences, Product Backlog, Agile Planning, Estimation and Velocity, Product Backlog, Technical Debt, Agile Roles: Product Owner, Scrum Master, Development Team, Development Team, Managers

Unit 3: Agile: Utility and Expansion (7 Hours)

Agile Planning: Scrum Planning Principles, Multilevel Planning, Portfolio Planning, Envisioning (Product Planning), Release Planning (Longer-Term Planning) Sprinting: Sprint Planning, Sprint Execution, Sprint Review, Sprint Retrospective, Agile Architecture and Re-architecting with Flow, Scaling Agile to an Enterprise Level, Adapting an Agile Approach to Fit a Business, Enterprise-Level Agile Transformations, Enterprise-Level Agile Frameworks: Scaled Agile Framework, Managed Agile Development, Disciplined Agile Delivery Framework

Unit 4: Business and System Analysis (7 Hours)

Business Process Management: Introduction to Business modeling, Introduction to Business Processes, Business Process Modeling Foundation, Process Orchestrations, Process Choreographies, Properties of Business Processes, BPMN

System Analysis: Introduction to UML 2.X and UML 2.X Metamodel, Meta-Modeling principles and Model Transformation, Static Behavior Modeling with Use Cases, Dynamic



Behavior Modeling with Sequence, Communication, Interaction Overview, Activity and Timing diagrams

Unit 5: System Design-Implementation Practices (7 Hours)

Design Concepts, Design Qualities, Design Principles, Software Architecture Vs Software Design, Software Reuse, Software Design with UML Class, Component, Deployment, Package, Composite Structure and Profile Diagrams, Source Code Management, Build Engineering, Environment Configuration, Change Control, Release Management, Deployment, CM Practices, Agile Values and CM, CM Practices for Agile, CM Standards and Frameworks to Support Agile

Unit 6: Design Patterns (7 Hours )

Introduction to Design Pattern, Describing Design Patterns, Catalogue of Design Patterns Creational Patterns: Abstract Factory, Builder, Factory Method, Prototype, Singleton, Structural Patterns: Adapter, Bridge, Composite, Decorator, Facade, Flyweight, Proxy, Behavioral Patterns: Chain of Responsibility, Command, Interpreter, Iterator, Mediator, Memento, Observer, State, Strategy, Template Method, Visitor, Antipatterns, Applications of Design Patterns

List of Practical:

1. A real-world problem issue is required to be identified with manageable scope. The problem scenarios are required to be identified for target system to be developed. The scenarios are stated in the form of Statement-of-Work template. The SOW document shall address the vision, goals, and objectives of the project.

2. The initial requirements and feature set for the target system is required to be identified. The requirements are required to be synthesized with stakeholder participation. The project roles are assigned to the project team with clear indicator of responsibilities. The initial requirements summary document with adequate and minimal infrastructure is required to be developed using multiple iterations. A concise requirement specification document (SRS) is required to be prepared.

3. The product backlog for the project aimed at maintaining a prioritized queue of project requirements shall be created. - It should be dynamic and should be continuously groomed as the project progresses.

Agile projects generally use an iceberg strategy for grooming the product backlog. - The items that are near the top of the iceberg and are closest to going into

development should get the most attention. - There should typically be about two to three sprints worth of stories at the top of the

backlog that are well-groomed and ready to go into development in order to avoid a situation where the project team is waiting for work to do.

4. Sprint-level planning activity accommodating story points, planning poker shall be performed. The Sprint-plan and Sprint-design indicating detailed activity planner shall be developed.



5. To create Business Process Diagrams for all the scenarios identified using BPMN 2.0 and BPM practices. Process modeling captures the ordered sequence of activities within a process along with supporting information from end to end. In process modeling, the business process is framed in a BPD to reflect the activities, the roles that conduct those activities, conditional branching, and the sequence of the workflow between the activities.

6. To decompose and organize the problem domain area into broad subject areas and identify the boundaries of problem/system using UML Use Case diagrams along with depicting the dynamic behavior of the target system using sequence diagram.

7. To depict Dynamic behavior using State Chart and Activity diagrams. Activity is a parameterized behavior represented as coordinated flow of actions. The flow of execution is modeled as activity nodes connected by activity edges.

8. To develop logical static structure of target system with Software Class diagram. To prepare Class Collaboration-Responsibility (CRC) cards for the Conceptual classes traced from System analysis phase. To represent physical module that provides occurrence of classes or other logical elements identified during analysis and design of system using Component diagram along with representation of deployment view of the system through Deployment diagram.

9. To incorporate suitable Design Pattern for the target system and depict it through software class diagram.

Text Books:

1. Essentials of Software Project Management, second edition, Richard Bechtold (Author) Publisher: Management Concepts; second edition (April 12, 2007) ISBN-10: 1567261868 ISBN-13: 978-1567261868

2. Charles G. Cobb, The Project Manager’s Guide To Mastering Agile: Principles and Practices for an Adaptive Approach, Wiley Publications, ISBN: 978-1-118-99104-6 (paperback), ISBN 978-1-118-99177-0 (epdf), 2015

3. Tom Pender, “UML Bible”, John Wiley & sons, ISBN – 0764526049

Reference Books :

1. Jim Arlow, Ila Neustadt, “UML 2 and Unified Process: Practical Object Oriented Analysis and Design.”, 2nd Edition, Addison- Wesley, ISBN – 0321321278.

2. Erich Gamma, Richard Helm, Ralph Johnson,“Design Patterns: Elements of Reusable Object-Oriented Software” (Addison-Wesley Professional Computing Series) ,John Vlissides, Publisher: Addison-Wesley Professional, 1st edition (January 15, 1995) , ISBN-10: 0201633612 ISBN-13: 978-0201633610

3. Kenneth S. Rubin, Essential SCRUM: A Practical Guide To The Most Popular Agile Process, Addison-Wesley, ISBN-13: 978-0-13-704329-3, 2012

4. Bob Aiello and Leslie Sachs, Configuration Management Best Practices, Addison Wesley, ISBN-13: 978-0-321-68586-5, 2010

5. Mario E. Moreira, Adapting Configuration Management for Agile Teams, Wiley Publications, ISBN: 9780470746639, 2010 Essential Business Process Modeling, Michael Havey, First Edition August 2005 Oreilly, ISBN 10: 0-596-00843-0 | ISBN 13: 9780596008437



Course Outcomes:

Upon completion of the course, graduates will be able to –

1. Summarize capabilities and impact of Software Project Management Principles and justify recognize the project processes that best fit the technical and market demands of a modern software project.

2. Formulate system specifications by analyzing User-level tasks and compose software artifacts using agile principles, practices and Scrum framework

3. Propose and demonstrate realistic solutions supported by well-formed documentation with application of agile roles, sprint management, and agile architecture focusing project backlogs and velocity monitoring.

4. Compose system analysis and design specifications indicating logical, physical, deployment, and concurrency viewpoints using object-oriented analysis and design principles and Model Driven Engineering practices.

5. Construct and justify the evolutionary system description models expressing high-level architecture accommodating compatible to requirements and behavioral models using UML-supported modeling tools.

6. Comprehend the nature of design patterns by understanding a small number of examples from different pattern categories and apply these patterns in creating a correct design using design heuristics, published guidance, applicability, reasonableness, and relation to other design criteria.



FF No. : 654

CS505THP: Network Security (THP)


Project: 2 Hrs/Week

Prerequisites: Computer Networks.

Unit 1: (7 Hrs)

Introduction

Introduction to Security: Vulnerabilities, Threats, Measures of security, attack and attack types, Avoiding attacks, Security services.

Tradeoffs of balancing key security properties - Confidentiality, Integrity, Availability.

Protocol Vulnerabilities and attacks due to Protocol Vulnerabilities

Software vulnerabilities and attacks due to Software vulnerabilities

Unit 2: (8 Hrs)

Private key cryptography

Mathematical background for cryptography: modulo arithmetic, GCD (Euclids algorithm), algebraic structures (Groups, Rings, Fields, Polynomial Field).

Data Encryption Standard: Block cipher, Stream cipher, Feistel structure, round function, block cipher modes of operation, S-DES, Attacks on DES, S-AES, AES. RC4.

Chinese remainder theorem

Unit 3: (7 Hrs)

Public key cryptography

RSA: RSA algorithm, Key generation in RSA, attacks on RSA.

Diffie-Hellman key exchange: Algorithm, Key exchange protocol, Attack.

Elliptic Curve Cryptography (ECC): Elliptic Curve over real numbers, Elliptic Curve over Zp, Elliptic Curve arithmetic. Diffie-Hellman key exchange using ECC. Unit 4: (8 Hrs)

Authentication and Application Security



Message authentication and Hash Function. Authentication: One-Way Authentication, Mutual Authentication, SHA-512, Centralized Authentication, The Needham-Schroeder Protocol. Authentication Applications: Kerberos, X.509 authentication service, public key infrastructure.

Network layer security: IPSec for IPV4 and IPV6.

Transport layer security: SSL and TLS.

Application layer security: Security services, S/MIME, PGP, Https, Honey pots.

Unit 5: (6 Hrs)

Mobile Security

Introduction to Mobile Security, Security of GSM Networks, Security of UMTS Networks, LTE Security, WiFi and Bluetooth Security, SIM/UICC Security, Mobile Malware and App Security, Android Security Model, Emerging Trends in Mobile Security

Unit 6: (5 Hrs)

Cyber Security:

Cyber Attack, Cyber Reconnaissance, Crimes in Cyber Space-Global Trends & classification, e-commerce security, Computer forensics, facebook forensic, mobile forensic, cyber forensic, digital forensic, Introduction to MQTT and CoAP for IoT.

Text Books

1. “Cryptography and Network Security-Principles and Practices” by William Stallings, Pearson Education, 2006, ISBN 81-7758-774-9, 4th Edition.

2. “Network Security and Cryptography”, by Bernard Menezes, Cengage Learning, 2010, ISBN 81-315-1349-1, 1st Edition.

Reference Books

1. “Computer Security: Art and Science”, by Matt Bishop, Pearson Education, 2002, ISBN 0201440997, 1st Edition.

2. “Network security, private communication in a public world”, by Charlie Kaufman, Radia Perlman and Mike Spencer, Prentice Hall, 2002, ISBN 9780130460196, 2nd Edition.

3. “Cryptography and Information Security”, by V.K. Pachghare, PHI, 2015, ISBN-978-81-203-5082-3, Second Edition.



Additional Reading

1. “Security architecture, design deployment and operations”, by Christopher M. King, Curtis Patton and RSA press, McGraw-Hill, 2001, ISBN 0072133856, 1st Edition.

2. ‘Inside Network Perimeter Security” by Stephen Northcott, Leny Zeltser, et al, Pearson Education Asia, ISBN 8178087618, 1st Edition.

Course Outcomes

Upon completion of the course, the students will be able to:

1. Analyze cryptographic techniques using a mathematical approach by examining nature of attack.

2. Establish and identify type of attack on a given system. 3. Justify various methods of authentication for application of technologies to various

sections of industry and society. 4. Design a secure system for protection from the various attacks for 7 layer model by

determining the need of security from various departments of an organization. 5. Analyze various threats of Mobile and Cyber security 6. Estimate future needs of security for a system by researching current environment on a

continuous basis for the benefit of society.



FF No. : 654

HS552TH: Quantitative Aptitude-II (TH)


Unit 1: Area, Volume, Permutation and Combinations (7 Hours) Area, Volume and Surface Areas, Calendar, Clocks, Permutations and Combinations, Probability, Heights and Distances. Unit 2: Data Interpretation (7 Hours) Tabulations: Tabulations of Imports and Exports of Data, Analysis of Tabulated Data, Bar Graphs: Vertical or Horizontal Bars, Pie Charts: Pie Graphs, Central angle, Line Graphs. Unit 3: Probability

(7 Hours)

Introduction to probability, Structure of probability, Results of probability, Revision of probability: BAYES’ RULE, and examples; Random variable and probability distribution: Discrete and Continuous distribution, Expected value and variance of a distribution. Unit 4: Correlation & Regression Analysis

(7 Hours)

Regression analysis (Linear only), Correlation analysis, Karl Pearson’s correlation coefficient, Spearman’s Rank correlation coefficient

Text Books 1. Quantitative Aptitude For Competitive Examinations”, Dr. R. S. Aggarwal, S. Chand. 2. “How to Prepare for Quantitative Aptitude”, Arun Sharma, Tata Mcgraw-Hill. 3. Probability & Statistics for Engineers- Richard Johnson – Prentice Hall of India, 4. Statistics for Management- Richard Levin , Rubin - Prentice Hall of India, Reference Books 1 Quantitative Aptitude Quantum Cat Common Admission Test”, K. Sarvesh Verma., Arihant. 2 “Quantitative Aptitude for Competitive Examinations”, Abhijit Guha, Fourth Quarter. Course Outcomes

The students will be able to:

1 improve their employability skills 2 improve aptitude, problem solving skills and reasoning ability 3 critically evaluate various real life situations by resorting to analysis of key issues and

factors. 4 demonstrate various principles involved in solving mathematical problems and thereby

reducing the time taken for performing job functions



FF No. : 654

HS553TH: Engineering & Managerial Economics (TH)


Unit I (07 Hrs)

Engineering Economic Analysis

A. Introduction, Concept of Money – Its Functions & worth. Inflation – Concept, Causes, Remedies to control inflation, Value of Currency, Factors governing exchange rates, Currency Fluctuations. Concept of Taxes, Types of Taxes – Direct & Indirect, Depreciation. Effect of above concepts on decision making.

B. Significance of above concept in real life decision making

Unit II (07 Hrs)

Time Value of Money& Life Cycle Costing

A. Concept of Interest, Time Value of Money – Basis for comparison of alternatives, Discount Rate, Compound Rate, Present Worth, Future Worth, Annual Worth, Annuity, Perpetuity. Life Cycle Costing - Introduction, methodology, applications of LCC in industrial world, differentiation with traditional costing methods, Capital Budgeting: DCF & NDCF Techniques: Payback, Discounted Payback, ARR, IRR, NPV, Annual Worth, Cost Benefit Ratio

B. Numerical Applications on Time Value of Money

Unit III (07 Hrs)

Concept of Demand and Supply

A. Law of Demand & Supply: Meaning and Determinants of Demand. Demand Function. Law of Demand, Market Demand, Elasticity of demand. Types of elasticity. Measurement of elasticity. Significance and uses of the elasticity. Meaning and Determinants of Supply, Law of supply. Equilibrium of demand and supply i.e. price determination. B. Exceptions of Law of Demand & Supply



Unit IV (07 Hrs)

Concept of Utility, Competition

A. Law of Diminishing Marginal Utility – Concept, Law of Diminishing Marginal Utility Price Determination, Competition – Concept, Types (Monopoly, Oligopoly, etc.), Benefits to Buyer & Seller, Economies of Scales, Law of Variable Proportions

B. Cases related with above concepts

Text Books

1. Theusen H.G., Engineering Economic Analysis, Prentice Hall of India 2. Henry M. Steiner, Engineering Economic Principles, McGraw Hill 3. S.M. Mahajan, Engineering Economics, Everest Publishing House, Pune 4. Samuelson PA, Nordhaus WD, Economics, Tata McGraw Hill Reference Books

1. Colin Drury, “Management and Cost Accounting”, English Language Book Society, Chapman and Hall London.

2. Khan M. Y., Jain P. K., “Financial Management”, Tata McGraw Hill

Course Outcomes:

Our students will be able to: 1. Analyze the effect of inflation, currency fluctuations, and taxes on decision

making 2. Compare and select investment alternatives based on costs and time value of

money 3. Analyze the impact of demand and supply on pricing of product and competition 4. Understand the concept of utility and competition and its relevance in business

environment



Electives:

Subject code Subject Name

CS506THP Mobile Computing CS507THP PCGPU CS508THP Cloud Computing CS509THP Data Science CS510THP UID CS511THP IoT CS512THP Machine Learning CS513THP Wireless Sensor network CS514THP Image Processing



FF No. : 654

CS506THP: Mobile Computing (THP)


Lab: 2 Hrs/Week

Unit 1: Introduction to Cellular Networks (08 Hours)

Personal Communication System (PCS), PCS Architecture, Cell phone generation-1G to 5G, Low Tier, High Tier, Wideband Cellular Systems, Mobile Station, SIM, Base Station, Base Station Controller, Mobile Switching Center, MSC Gateways, HLR and VLR, AuC/EIR/OSS, Radio Spectrum, Free Space Path Loss, S/N Ratio, Line of sight transmission, Length of Antenna, Fading in Mobile Environment.

Unit 2: Cellular Network Design (07 Hours)

Performance Criterion, Frequency Reuse, Co-channel Interference and System Capacity, Channel Planning, Cell Splitting, Cell splitting. Mobility Management in GSM and CDMA

Unit 3: Medium Access Control (07 Hours)

Specialized MAC, SDMA, FDMA, TDMA, CDMA, Frequency Hopping Spread Spectrum (FHSS), Direct Sequence Spread Spectrum (DSSS), GMSK Modulation

Unit 4: GSM (07 Hours)

GSM – Architecture, GSM Identifiers, Spectrum allocation, Physical and Logical Traffic and Control channels, GSM Bursts, GSM Frame, GSM Speech Encoding and decoding, Location Update, Incoming and Outgoing Call setup, GPRS

Unit 5: 3G and 4G Technologies for GSM and CDMA (07 Hours) EDGE, W-CDMA: Wideband CDMA, CDMA2000, UMTS, HSPA (High Speed Packet Access), HSDPA, HSUPA, HSPA+, LTE (E-UTRA) 3GPP2 family CDMA2000 1x, 1xRTT, EV-DO (Evolution-Data Optimized), Long Term Evolution (LTE) in 4G

Unit 6: Discussion on Research Papers (04 Hours)

Discussion on Recent Research Papers in the area of Mobile Computing



List of Projects:

Operating System recommended :- 64-bit Open source Linux or its derivative

Hardware Requirements: Pentium i7 with minimum 8GB RAM, Mobile Device

Programming tools recommended: - Android Studio 2.x, JAVA, PYTHON

Project 1. : Design mobile app to perform the task of creating the splash screen for the application using timer, camera options and integrate google map api on the first page of the application. Make sure map has following features:

i) Zoom & View change ii) Navigation to specific locations iii) Marker & getting location with touch iv) Monitoring of location

Project 2. : Create an app to add of a product to SQLite database and make sure to add following features

i) SMS messaging and email provision ii) Bluetooth options iii) Accessing Web services iv) Asynchronous remote method call v) Use Alert box for user notification

Project 3. : Create an application for Bank using spinner, intent

a) Form 1: Create a new account for customer b) Form 2: Deposit money in customer account. c) Link both forms, after completing of first form the user should be directed to the second

form d) Provide different menu options

Project 4. : Create the module for payment of fees for College by demonstrating the following methods.

i) FeesMethod()- for calculation of fees



ii) Use customized Toast for successful payment of fees iii) Implement an alarm in case someone misses out on the fee submission deadline iv) Demonstrate the online payment gateway

Project 5. : Create the module for collecting cellular mobile network performance parameters using telephony API Manager

i) Nearest Base Station ii) Signal Strengths iii) SIM Module Details iv) Mobility Management Information


1.Wireless and Mobile network Architecture-Yi-Bing Lin, John Wiley & Sons; 2005, ISBN: 0-471-74922-2.Mobile Communications - Jochen Schiller, Second Edition, Pearson Education, 2004

3. https://developer.android.com

Reference Books: (As per IEEE format)

1. Wireless Communications – Principles and Practice by Theodore S Rappaport, Pearson Education. 2. Wireless Communication and Networks by William Stallings, Second Edition, Prentice Hall



FF No. : 654

CS507THP: Parallel Computing on GPU (THP)


Project: 2 Hrs/Week

Unit 1: Introduction to Parallel Computing (6)

Motivating Parallelism, Scope of Parallel Computing, Parallelism Vs. Concurrency, Types and levels of parallelism, Flynn’s classification, Parallel computer architectures : Distributed memory systems , Shared memory systems and cache coherence, Heterogeneous system architecture, Parallel programming models: Foster’s design methodology, Parallel Programming constructs: Synchronization, Deadlocks, Critical sections, Data sharing etc.

Unit 2: Introduction to GPU (6)

Basic description of GPU’s, Comparison of GPU and CPU architectures, Concept of thread and process, Modern GPU architecture, Overview of the graphics pipeline, Internals of an NVIDIA GPU : Parallel streaming processors ,Multiprocessors ,Shared instruction caches ,Memory hierarchy – Global, Constant, Shared, and Texture memory; Case studies: NVIDIA Kepler K20/K40

Unit 3: Programming on the GPU (8)

Introduction to CUDA programming and/ OpenCL

Compute Unified Device Architecture (CUDA): CUDA Architecture, CUDA programming model, Concept of grid, block and thread, thread index generation, warp, memory banks and bank conflicts. Programming for GPU’s in C/C++ using CUDA API:

Memory transfers, Writing and executing kernel functions, Writing device functions, Using shared and texture memories to speed execution, Thread synchronization, Data Dependences and Race Conditions, .

Unit 4: Optimizations and Tools (8)

Memory coalescing, Page locked host memory, Reduction operation using prefix sum example, CUDA code optimization: Memory, Execution configuration, instruction and control flow; Performance issues in algorithms, CUDA example programs (Vector dot product, Matrix multiplication (with the usage of tiling and shared memory) etc.). Need of profilers and analyzers, Introduction to CUDA Tools: MemCheck, Command line & Visual Profilers.

Unit 5: Problem solving using GPUs (6)



Single vs. double precision, Sparse matrix representations, Fast Fourier transforms , Binomial coefficients, light weight scientific computing exercises , Matrices etc .Instructor will choose the problems from several domains with which students are already aware.

Unit 6: GPU Programming using OpenCL (6)

Introduction to OpenCL, Programming model, objects, synchronization model, L memory model, Programming examples: Square matrix transpose, Square matrix multiplication

Text Books

1. CUDA: Programming Massively Parallel Processors: A Hands-On Approach by David Kirk, Wen-mei Hwu © ELSEVIER Inc.

2. CUDA by Example: An Introduction to General-Purpose GPU Programming by Jason Sanders and Edward Kandrot ISBN-13: 978-0131387683

3. Advanced computer architecture by Kai Hwong, Tata McGraw-Hill Edition 4. Heterogeneous Computing with OpenCL, David R. Kaeli, Perhaad Mistry, Dana Schaa, Dong

Ping Zhang

Reference Books

1. http://developer.nvidia.com/ 2. www tutorials on introduction to parallel computing 3. John Nickolls, Ian Buck, Michael Garland, Kevin Skadron, Scalable Parallel Programming with

CUDA, ACM Queue, Vol. 6, No. 2. (March 2008), pp. 40-53. 4. Other references suggested by instructor

Course Outcomes:

Student will be able to -

1. Understand the different models used for parallel computing 2. Explore the knowledge about GPU architectures 3. Design parallel programs for GPU using CUDA 4. Optimize CUDA programs for better performance 5. Solve problems using GPU computing 6. Design parallel programs for GPU using OpenCL



FF No. : 654

CS508THP: Cloud Computing (THP)


Project: 2 Hrs/Week

Unit 1 : Basics Cloud Computing ( 6 Hrs ) Introduction: Cloud Computing Definition, Characteristics, Components, Cloud provider Cloud Service Models: SAAS, PAAS, IAAS and Others, Organizational scenarios of clouds, Administering & Monitoring cloud services, benefits and limitations, Deploy application over cloud, Comparison among SAAS, PAAS, IAAS. Data center architecture: FatTree, DCell, BCube Unit 2 : Cloud Technologies

( 6 Hrs )

Virtualization Technology: virtual machine basics, process virtual machine, system virtual machine, virtual machine placement problem, virtualization applications in enterprises, virtual machine migration, pitfalls of virtualization. Multitenant System: Multi-entity support, Multi-schema approach, Multitenance using cloud data stores, Data access control for enterprise applications. Unit 3 : Data in the cloud

( 7 Hrs )

Cloud file-systems: GFS and HDFS, BigTable, Features and comparisons among GFS, HDFS etc. Databases on Cloud: NoSQL, MogoDB, HBase, Hive, Dynamo, Graph databases



Unit 4: Map Reduce (8 Hrs) Map-Reduce and extensions: Introduction to cloud development, Parallel computing, The map-Reduce model, Parallel efficiency of Map-Reduce, Relational operations using Map-Reduce, Applications of Map Reduce: Distributed Grep, Large-Scale PDF Generation, Geographical Data based Application, Enterprise batch processing

Unit 5 : Cloud Security

( 7 Hrs )

Cloud security fundamentals, Privacy and Security in cloud. Cloud computing security architecture: General Issues, Trusted Cloud computing, Secure Execution Environments and Communications, Micro-architectures; Identity Management and Access control; Autonomic Security Cloud computing security challenges; Virtualization security management, virtual threats, VM Security Recommendations, VMSpecific Security techniques, Secure Execution Environments and Communications in cloud. Unit 6 : Current trends and Challenges in Cloud Computing

( 6 Hrs )

Network Virtualization, Service Level Agreement, Virtual Machine placement, Virtual Machine migration, Resource allocation in Hybrid-Cloud Environment, etc. Total Contact Hours: 40 Text Books

1. Cloud Computing for Dummies by Judith Hurwitz, R.Bloor, M.Kanfman, F.Halper (Wiley India Edition)

2. Enterprise Cloud Computing by Gautam Shroff,Cambridge 3. Cloud Security by Ronald Krutz and Russell Dean Vines, Wiley-India

Reference Books:

1. Google Apps by Scott Granneman,Pearson 2. Cloud Security & Privacy by Tim Malhar, S.Kumaraswammy, S.Latif (SPD,O’REILLY) 3. Cloud Computing : A Practical Approach, Antohy T Velte, et.al McGraw Hill, 4. Cloud Computing Bible by Barrie Sosinsky, Wiley India 5. Cloud Computing, Michael Miller,Que Publishing.

Course Outcomes: i. Students will explain the core concepts of the cloud computing paradigm: how and why

this paradigm shift came about and the influence of several enabling technologies in cloud computing.

ii. Students will examine the process of working on a large research project under the mentorship of a teaching staff member. They will study how applications for clouds are written, deployed and analyzed. In the process, they will develop the needed skills to go through project planning, design, implementation, analysis and reporting.

iii. Students will identify some of the emerging cloud research challenges.



Note: The faculty members are requested to refer the relevant research papers as a reference for the topics mentioned in the syllabus.



FF No. : 654

CS509THP: Data Science (THP)


Project: 2 Hrs/Week

Unit 1: Data Pre-processing (7 Hours)

Mechanisms of data collection and challenges involved therein. Typical preprocessing operations: combining values into one, handling incomplete or incorrect data, handling missing values, recoding values, sub-setting, sorting, transforming scale, determining percentiles, data manipulation, removing noise, removing inconsistencies, transformations, standardizing, normalizing - min-max normalization, zscore standardization, and rules of standardizing data.

Descriptive Statistics: role of statistics in analytics, types of data (scales of measurement

- NOIR), data distributions, measures of variability (range, quartile, five number summary, variance, std dev, coeff of variation), analyzing distributions, Chebychev’sInequality, measures of shape (skewness, kurtosis), measures of association (covariance,correlation), outliers

Unit 2: Inferential Analytics (7 Hours)

Role of probability in analytics. Need for sampling, generating samples, sampling and non-sampling error. Sampling Distribution of Mean, Central Limit Theorem, Standard Error.

Estimation: Point and Interval Estimates, Confidence Intervals, level of confidence,sample size.

Hypothesis Testing: basic concepts, Errors in hypothesis testing, Power of test, Level of significance, p-value, general procedure for hypothesis testing. Parametric tests – z test, t

test, chi-square test. Hypothesis testing of means: two tailed and one-tailed tests. Chi square test for independence and goodness of fit. Hypothesis testing for comparing two related samples. Limitations of hypothesis testing. Picking up the right test for a given scenario.

Unit 3: Predictive Analytics :Regression (7 Hours)

Correlation and regression, Simple Linear Regression Model, Least Squares Method. Making Data Models more flexible, making data models more selective, dealing with Categorical variables, Interpretation of regression coefficients, fine tuning data models (assessing the fit,



model fitting), Coefficient of determination, Significance tests, Residual analysis, Prediction intervals. Model evaluation techniques. Assumptions of regression analysis.

Unit 4: Predictive Analytics: Supervised Method (7 Hours)

Bias-Variance Dichotomy,Model Validation Approaches,Logistic Regression Linear Discriminant Analysis,Quadratic Discriminant Analysis,Regression and Classification Trees,Support Vector Machines, Ensemble Methods: Random Forest Neural Networks,Deep learning.

Unit 5: Predictive Analytics: Unsupervised Method (6 Hours)

Similarity Measures, Design of recommender systems, user based and item based Collaborative filtering, Clustering, Associative Rule Mining

Unit 6: Advanced Analytics and Big Data (6 Hours)

Creating data for analytics through designed experiments, creating data for analytics through Active learning, creating data for analytics through Reinforcement learning. Introduction to Big Data Technologies.

List of Project areas: (For THP courses)

1. Implementing a simple Recommender System based on user buying pattern.

2. Data analysis case study using R for readily available data set using any one machine learning algorithm

Text Books:

1. “Business Analytics” by James R Evans, Pearson

2. Hastie, Trevor, et al.The elements of statistical learning.Vol. 2. No. 1. New York: springer, 2009.

Reference Books:



1.Montgomery, Douglas C., and George C. Runger.Applied statistics and probability for engineers. John Wiley & Sons, 2010

2.“Data Mining: Concepts and Techniques”, Jiawei Han and Micheline Kamber, Morgan Kaufman, ISBN 978-81-312-0535-8, 2nd Edition

3. “Fundamentals of Business Analytics”, by R. N. Prasad, Seema Acharya,ISBN: 978-81-256-3203-2,Wiley-India

4.“Business Intelligence for Dummies”

Course Outcomes:

The student will be able to –

1. Understand the process of converting data into a required format required for particular analysis.

2. Analyze data, test claims, and draw valid conclusions using appropriate statistical methodology.

3. Utilize statistical tools in deriving insights from data . 4. Apply analytic techniques and algorithms (including statistical and data mining

approaches) to large data sets to extract meaningful insights. 5. Use appropriate resources to research, develop and contribute to advances and trends

within the field of Data Science. 6. Interpret and present visually, orally and in written form, valid conclusions drawn from

data analysis.



FF No. : 654

CS510THP: USER INTERFACE DESIGN (THP)


Project: 2 Hrs/Week

Unit1: Introduction (7Hrs)

Importance of good design, defining user interface, graphical user interface, web user interface, graphical business systems, principles of user interface design, measurable human factors, human computer interaction (HCI), usability and design team

Unit 2: User, Client and Business (7 Hrs)

Understanding people’s interaction with computers, users and clients, human characteristics in design, human considerations in design, specialized users, human interaction speeds, human performance and preference, understanding users, business and requirement analysis, basic business functions, design standards, style guide, system training, user persona, user survey and documentation

Unit 3: Design and Evaluation (7 Hrs)

Human consideration in screen design, test for good design, bad designs, organization and structural guidelines, statistical graphics, technological considerations, contextual inquiry, cognitive models - GOMS, LUCID, task analysis, structures, usability evaluation methods – heuristic evaluation, cognitive walkthrough, semiotic analysis, window management and multi-modal interaction

Unit 4: User Interface and Controls (7 Hrs)

Input and output devices, characteristics of device-based controls, selecting proper device-based control, interaction styles, operational controls, buttons, text entry / read-only controls, selection controls, check boxes, combo boxes, combination entry, other controls, sliders, tabs, scroll bars, custom controls, presentation controls and selecting proper controls

Unit 5: Visual Interface Design (7 Hrs)



Words, sentences, text, text design in web pages, error messages, instructions, providing proper feedback, blinking for attention, guidance and assistance, help, wizards, tips, international considerations, localization, accessibility, disabilities, icons, images, animation, multimedia, color – RGB, HSV, color uses, problems with color, cross-disciplinary and cross-cultural differences, color and human vision, choice of colors, discrimination and harmony, accessibility considerations and selection of colors in design

Unit6: Types of Applications and Interfaces (7Hrs)

Groupware, shared applications and artifacts, computer–supported cooperative work, social networking sites, social issues, social acceptability and organizational change, life-critical systems, ubiquitous computing, information kiosk, digital library, virtual and augmented realities, mobile interfaces, interfaces that give and take advice, natural interfaces, eye-free interfaces, context-aware computing, roomware, tangible user interfaces.

Outcomes: At the end of the course, the students will be able to analyze, design and evaluate various types of user interfaces.

Text Books:

1. Wilbert Galitz, ‘The Essential Guide to User Interface Design’, Wiley Dreamtech, 2005. 2. Ben Shneiderman, ‘Designing the User Interface’, Pearson Education, Third edition, 2002.

Reference Books:

1. Alan Dix, Janet Finlay, Gregory Abowd, Russell Beale, ‘Human Computer Interaction’, Third Edition, 2004.

2. Donald Norman, ‘The Design of Everyday Things’, Basic Books, USA, 2002. 3. Donald Norman, ‘The Design of Future Things’, Basic Books, USA, 2007.



FF No. : 654

CS511THP: Internet of Things (THP)


Project: 2 Hrs/Week

UNIT 1: Introduction & Application to Internet of Things (6 Hrs)

The Internet of Things , Importance of IoT, Towards the IoT Universe IoT Strategic Research and Innovation Directions, IoT Applications, Future Internet Technologies, Infrastructure, Networks and Communication, Processes, Data Management, Iot Smart X Application: Smart Cities, Smart Energy & Smart Grid, Smart Mobility & transport, Smart Home, Smart Building & Infrastructure, Smart Factory & Manufacturing, Smart Health, Smart Logistics & Retails

UNIT 2: Embedded Suite for IoT (6 Hrs)

Introduction to ARM Processor, introduction to ARM Processor, ARM Processor Architecture, Programmer’s model, Modes of operation, Interrupt Structure and Applications. Management of Power Supply, Introduction to Raspberry Pi, Understanding the Raspberry Pi board and its components, recognizing the Input/output, GPIO connectivity

UNIT 3: WIRELESS TECHNOLOGIES supporting IoT (6 Hrs)

Protocol Standardization for IoT, Machine to machine (M2M) and WSN Protocols, Basics of RFID, RFID Protocols & NFC protocols, Issues with IoT Standardization, Unified Data Standards, Protocols – IEEE 802.15.4, ZigBee, IPv6 technologies for the IoT

UNIT 4: IoT Networking (6 Hrs) Networking Architectures: Star, Mesh, Tree, and Overview of networking Protocols: TCP/IP, 6LowPan, RPL: Routing protocol for lossy & low power network. IoT Devices Application Level Protocols: Introduction to MQTT, Quality of Service parameter in MQTT, CoAP, XMPP

UNIT 5: IoT:PRIVACY, SECURITY& GOVERNANCE (6 Hrs)



Overview of Governance, Privacy and Security Issues, Contribution from FP7 Projects, Security, Privacy and Trust in IoT-Data-Platforms for Smart Cities, First Steps towards a Secure Platform, Smart Approach. Data Aggregation for the IoT in Smart Cities, Security UNIT 6: Connecting IoT to Cloud (6 Hrs) Introduction to cloud computing, Difference between Cloud Computing and IoT, Fog Computing: The Next Evolution of Cloud Computing, Role of Cloud Computing in IoT. Living on the Edge, Connecting IoT to cloud, Cloud Storage for IoT. Cloud-to-Device Connectivity, Challenge in integration of IoT with Cloud

Textbook:

1. Jan Holler, Vlasios Tsiatsis, Catherine Mulligan, Stefan Avesand, Stamatis Karnouskos, David Boyle, “From Machine-to-Machine to the Internet of Things: Introduction to a New Age of Intelligence”, 1st Edition, Academic Press, 2014.

2. Francis daCosta, “Rethinking the Internet of Things: A Scalable Approach to Connecting Everything”, 1st Edition, Apress Publications, 2013



FF No. : 654

CS512THP: Machine Learning (THP)


Project: 2 Hrs/Week

Unit 1 : Fundamentals ( 7 Hrs ) Introduction: Soft computing and hard computing, types, applications. Well posed learning problem, Designing a learning system, Perspectives and issues in soft computing, Concept Learning and the General-to-Specific Ordering: Task, search, Find S algorithm, Version space and the candidate elimination algorithm, list then eliminate algorithm, inductive bias, Decision Tree Learning: representation, appropriate problems, Basic design tree learning algorithm, Hypothesis space, inductive bias, issues in decision tree learning. Unit 2 : Learning by experience ( 7 Hrs) Artificial Neural Networks: representation, perceptron, learning rules, activation functions, Backprapogation algorithm, Support Vector Machines and Kernel based methods, obtaining the optimal hyperplane, SVM formulation with slack variables; nonlinear SVM classifiers, Kernel Functions for nonlinear SVMs; Mercer and positive definite Kernels, Support Vector Regression and ε-insensitive Loss function, examples of SVM learning,Overview of SMO and other algorithms for SVM; ν-SVM and ν-SVR; SVM as a risk minimizer, Positive Definite Kernels; RKHS; Representer Theorem Feature Selection, Model assessment and cross-validation, Feature Selection and Dimensionality Reduction; ensemble classifiers, Principal Component Analysis, Assessing Learnt classifiers; Cross Validation, evaluation. Unit 3 : Hypothesis and Probability ( 6 Hrs) Evaluating Hypotheses, Evaluating hypothesis accuracy, Sampling theory, Central limit theorem, hypothesis testing, comparing learning algorithms. Bayesian Learning, Bayes theorem and concept learning, Maximum likelihood hypothesis, minimum description length primciple, Gibbs algorithm, k-means algorithm. Unit 4 : Computational Learning and use of Instances ( 7 Hrs) Computational Learning Theory, probably learning, sample complexity for finite hypothesis spaces, sample complexity for infinite hypothesis spaces, Mistake bound model of learning, weighted majority algorithm, Instance-Based Learning: k-nearest neighbor learning, locally weighted regression, Unit 5 : Evolutionary Computation ( 7 Hrs ) Introduction to Genetic Algorithms: hypothesis space search, genetic programming, models of evaluation and learning, Learning Sets of Rules: sequential covering algorithms, learning rule sets summary, learning first order rules, inverted deduction, resolution Genetic algorithm. Unit 6 : Analytical and Inductive Learning ( 7 Hrs )



Introduction to analytical learning. Combining Inductive and Analytical Learning, Reinforcement Learning. Hybrid systems, fuzzy logic. Classical set, Fuzzy sets, Fuzzy relations, Fuzzification, Membership functions, Defuzzification, Methods of de-fuzzification, Fuzzy rules. implementation of all these algorithms. Course Outcomes: At the end of the course, the students will be able to: 1. Use the basic concepts and techniques of Machine Learning. 2. Develop skills of using recent machine learning software for solving practical problems. 3. Gain experience of doing independent study and research. Text Books 1. T. Mitchell, “ Machine Learning”, McGraw-Hill, 1997. 2. Anup Kumar Srivastava, Soft Computing, Alpha Science International limited. 2009. Reference Books 1. Ethem Alpaydin, "Introduction to Machine Learning", MIT press, 2004. 2 . Jacek M. Zurada, “Introduction to Artificial neural System”, JAICO publishing house,2002,. 3. Research papers suggested by the faculty.



FF No. : 654

CS513THP: Wireless Sensor network (THP)


Project: 2 Hrs/Week

Unit 1: Introduction and Overview (4 Hrs)

Background of Sensor Network Technology, Basic Overview of the Technology, Basic Sensor Network Architectural Elements, Brief Historical Survey of Sensor Networks, Challenges and Hurdles, Networks stack, Hardware requirement, Use of WSN in IoT Unit 2: Medium Access Control Protocols (6 Hrs) Fundamentals of MAC Protocols , Performance Requirements, Common Protocols, MAC Protocols for WSNs: Schedule-Based Protocols, Random Access-Based Protocols, Protocol Overview: Periodic Listen and Sleep Operations, Schedule Selection and Coordination, Schedule Synchronization, Adaptive Listening, Access Control and Data Exchange, Message Passing, Unit 3: Routing Protocols (8 Hrs) Data Dissemination and Gathering, Routing Challenges and Design Issues: Network Scale and Time-Varying Characteristics, Resource Constraints, Sensor Applications Data Models, Routing Strategies in Wireless Sensor Networks: WSN Routing Techniques, Flooding and Its Variants, Sensor Protocols for Information via Negotiation, Low-Energy Adaptive Clustering Hierarchy, Power-Efficient Gathering in Sensor Information Systems, Directed Diffusion, Geographical Routing Unit 4: Transport Control Protocols (8 Hrs) TCP (RFC 793), UDP (RFC 768), Mobile IP, Feasibility of Using TCP or UDP for WSNs, Transport Protocol Design Issues, Existing Transport Control Protocols: CODA (Congestion Detection and Avoidance), ESRT (Event-to-Sink Reliable Transport), RMST (Reliable Multi segment Transport), PSFQ (Pump Slowly, Fetch Quickly), GARUDA, ATP (Ad Hoc Transport Protocol), Problems with Transport Control Protocols, Performance of Transport Control Protocols: Congestion, Packet Loss Recovery, Unit 5: Performance and Traffic Management (6 Hrs) WSN Design Issues: MAC Protocols, Routing Protocols, Transport Protocols, Performance Modeling of WSNs: Performance Metrics, Basic Models, Network Models, Quality of Service. Unit 6: Case Studies of WSN (4 Hrs)

Applications of Sensor Networks: Home Control, Building Automation, Industrial Automation, Medical Applications, Agriculture application, IoT implementation



Reference: 1. Wireless Sensor Networks: Technology, Protocols and Applications, Kazem Zohraby et al, Wiley Publication



FF No. : 654

CS514THP: Image Processing (THP)


Project: 2 Hrs/Week

Unit 1: Introduction (6 Hours)

Introduction Elements of digital image processing, Image model, Sampling and quantization, Relationships between pixels

Unit 2: Image Transforms (6 Hours)

Discrete Fourier Transform, Discrete Cosine Transform, Haar Transform, Hadamard Transform

Unit 3: Image Enhancement (6 Hours)

Enhancement by point processing, Spatial filtering, Enhancement in the frequency domain, Color Image Processing

Unit 4: Image Segmentation (8 Hours)

Discontinuity detection, Edge linking and boundary detection, Thresholding, Region oriented segmentation, Use of motion for segmentation

Unit 5: Image Representation (8 Hours)

Representation and Description, Boundary description, Regional description

Unit 6: Morphological Image Processing (6 Hours)

Dilation and Erosion, Opening and Closing, Some basic morphological algorithms, Extensions to gray level images

Text Books:

1. Digital Image Processing, Rafael Gonzalez, 2nd edition Addison-Wesley

2. Digital Image Processing, A.K. Jain

Reference Books:

1. Digital Image Processing, Sridhar, OXFORD



Sem-III Course Code

Course Name

Contact Hours / Week Credits


Regular Lab

Technical Seminar-II

4 4

Project Dissertatio

n-I

8 16

TOTAL 20

Sem-IV Course Code

Course Name

Contact Hours / Week Credits


Regular Lab

Project Dissertatio

n-II

10# 20

TOTAL 20



Guidelines for M.Tech CSE(IT) Dissertation

In the first semester students are expected to complete the following sub-components of Thesis and present it to panel of examiners. Hard copy of the semester I report should include the following.

1. Motivation behind the Research 2. Need of the Research 3. Information Gathering Survey Report 4. Scope of the Thesis 5. Problem specification with System Requirement Specification (SRS). 6. System Analysis and Feasibility study Report covering feasibility in terms of

implementation, usability.

In the second semester students are expected to complete the following sub-components of Thesis and present it to panel of examiners.

1. System Design 2. Implementation 3. Testing

It is mandatory to publish the at least one Journal/Conference paper before the submission of Thesis.

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