msrit 7-8 sem syllabus book

8/10/2019 msrit 7-8 sem syllabus book

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M.S. RAMAIAH INSTITUTE OF TECHNOLOGY

BANGALORE

(Autonomous Institute, Affiliated to VTU)

Computer Science and Engineering

SYLLABUS


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History of the Institute

M. S. Ramaiah Institute of Technology was started in 1962 by the late Dr. M.S. Ramaiah, ourFounder Chairman who was a renowned visionary, philanthropist, and a pioneer in creating

several landmark infrastructure projects in India. Noticing the shortage of talented engineering

professionals required to build a modern India, Dr. M.S. Ramaiah envisioned MSRIT as aninstitute of excellence imparting quality and affordable education. Part of Gokula Education

Foundation, MSRIT has grown over the years with significant contributions from various

professionals in different capacities, ably led by Dr. M.S. Ramaiah himself, whose personalcommitment has seen the institution through its formative years. Today, MSRIT stands tall as

one of Indias finest names in Engineering Education and has produced around 35,000engineering professionals who occupy responsible positions across the globe.

History of the Department of Computer Science

Year of Establishment 1984

Names of the Programmes offered 1.UG: B.E. in Computer science and Engineering

2.

PG: M.Tech. in Computer Science and Engineering3.Ph.D

4.M.Sc(Engg.) by research


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Faculty

Sl. No. Name Qualification Designation

1.

Dr. K G Srinivasa M.E, Ph.D Professor

2. Dr. Ramamurthy Badrinath Ph.DAICTE-INAE distinguished

Visiting Professor

3. Dr. R. Srinivasan D.Sc. Professor(Emeritus)

4. Dr. S. Ramani Ph.D Professor(Emeritus)

5. Dr. Anita Kanavalli M.E., Ph.D Professor

6. Dr. Seema S M.S., Ph.D Associate Professor

7.

Dr. Annapurna P. Patil M. Tech, Ph.D Associate Professor8. Jagadish S Kallimani M.Tech, (Ph.D) Associate Professor

9. D.S. Jayalakshmi M.Sc(Engg), (Ph.D) Associate Professor

10. Dr. Monica R Mundada M.Tech, Ph.D Associate Professor

11. Sanjeetha R M.Tech Assistant Professor

12. A Parkavi M.E. (Ph.D) Assistant Professor

13. Veena GS M.Tech (Ph.D) Assistant Professor

14.

J Geetha M.Tech, (Ph.D) Assistant Professor15. T.N.R. Kumar M. Tech (Ph.D) Assistant Professor

16. Mamatha V. M.Tech Assistant Professor

17. Chethan C T B.E. Assistant Professor

18. Sini Anna Alex M.E, (Ph.D) Assistant Professor

19. Vandana Sardar M.E. Assistant Professor

20. Meera Devi M.Tech Assistant Professor

21.

Mallegowda M M.Tech Assistant Professor

22. Divakar Harekal M.E. Assistant Professor

23. Chandrika Prasad M.Tech Assistant Professor


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Vision and Mission of the Institute

Vision

To evolve into an autonomous institution of International standards for imparting quality

Technical Education

Mission

MSRIT shall deliver global quality technical education by nurturing a conducive learning

environment for a better tomorrow through continuous improvement and customization.

Quality PolicyWe at M. S. Ramaiah Institute of Technology, Bangalore strive to deliver comprehensive,

continually enhanced, global quality technical and management education through an established

Quality Management system complemented by the synergistic interaction of the stake holdersconcerned.

Vision and Mission of the Department

Vision

To build a strong learning and research environment in the field of Computer Science andEngineering that responds to the challenges of 21

stcentury.

Mission

To produce computer science graduates who, trained in design and implementation of

computational systems through competitive curriculum and research in collaboration withindustry and other organizations.

To educate students in technology competencies by providing professionally committed faculty


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Process for Defining the Vision and the Mission of the Department

Programme Educational Objectives (PEOs)

A B.E. (Computer Science & Engineering) graduate of M. S. Ramaiah Institute of Technology

should, within three to five years of graduation

1. Pursue a successful career in the field of Computer Science & Engineering or a related field

utilizing his/her education and contribute to the profession as an excellent employee, or asan entrepreneur

2. Be aware of the developments in the field of Computer Science & Engineering,

continuously enhance their knowledge informally or by pursuing graduate studies


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PEO Derivation Process


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5. An ability to identify, formulate, study, analyze and solve problems using the first

principles of mathematics and natural sciences as well as computer science & engineering

techniques.

6. An understanding of professional and ethical responsibilities in professional engineeringpractice.

7. An ability to communicate effectively.

8. The broad education necessary to understand the impact of engineering solutions in an

environmental and societal context.

9.

Recognition of the need for, and an ability to engage in life-long learning.10.An ability to create and use the techniques, algorithms, models and processes, and modern

software/hardware tools necessary for computer engineering practice.

11.An ability to apply knowledge of contemporary issues to assess the societal, legal andcultural issues related to the practice of computer science and engineering.

12.An understanding of the engineering and management principles required for project and

finance management.

PO Derivation Process


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Mapping of PEOs and POs

Sl.

No.

ProgrammeEducational

Objectives

Programme Outcomes

PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12

1Excel in

careerX X X X X X X X X X X X

2Life-long

learningX X X X X X X X X X

3Research and

InnovationsX X X X X X X X X X X

4Work indiverse

teams

X X X X X X X X

5

Leadership

and

contribution

to society

X X X X X X X X

Curriculum Breakdown Distribution

Sl. No. Courses Weightage

1 Basic Science Core Courses 13%

2 Basic Engineering Science Core Courses 13%

3 Humanities and Social Science Core Courses 3%

4 Professional Courses and Electives 62%

5 Major Project 9%6 Mandatory Learning Courses 0%


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Board of Studies for the Term 2014-2015

1.Head of the Department concerned:

2.At least five faculty members at different

levels covering different specializations

constituting nominated by the Academic

Council

3.Special invitees

4.Two experts in the subject from outside

the college

5. One expert from outside the college,

nominated by the Vice Chancellor

6. One representative from

industry/corporate sector allied area

relating to placement nominated by the

Academic Council

7.One postgraduate meritorious alumnus

to be nominated by the Principal

Dr. K G Srinivasa

Dr. Anita KanavalliProf. Seema SDr. Annapurna Patil

Prof. Jayalakshmi D SProf. Sanjeetha R

Dr. R. Srinivasan

Dr. T. S. B. Sudarshan, Head, Amrita School ofEngg, Bangalore

Dr. Kavi Mahesh, Professor, PESITDr. N.K. Srinath, Professor, RVCE

Dr. A Srinivas, Professor, Dept of CSE, PESITDr. K G. Mohan, Prinicipal, KGIT, Kolar

Dr. Udaya Kumar K, Former Principal, BNMIT,Bangalore

Dr. Shyam Vasudev, Director, Philips Healthcare

Dr. R Badrinath, HP Labs, IndiaMr. Lawrence Mohanraj, IBM

Mr. Sachin Kumar R.S., IBM

Krishnaprasad C, Qikwell Technologies,Bangalore

Chairperson

MemberMemberMember

MemberMember

Member

Member

MemberMember

MemberMember

Member

MemberMember

Member

Member


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Department Advisory Board for the term 2014-2015

1. Head of the Department concerned

2.

Experts from other organizations for

Department Advisory Board

Dr. K G Srinivasa

Dr. L M Patnaik, Honorary Professor, IISc

Prof. Rajkumar Buyya, Director, CLOUDS Lab,

Dept of Computing and Information Systems,

University of Melbourne

Dr. T S B SudarshanProfessor and Chair, Dept of CSE, Amrita

School of Engg, Bangalore

Member

Member

Member

Member

Industry Advisory Board for the Term 2014-2015

1. Head of the Department concerned

2.

Experts from industry constituting

the Industry Advisory Board

Dr. K G Srinivasa

Dr. Badrinath Ramamurthy, HP Labs, IndiaDr. N.C. Narendra, CTS

Mr. Raghu Hudli, Object orbMr. Sreekanth Iyer, IBM

Mr. Nishant Kulkarni, IBM

Mr. Rohith Athanikar, YahooMr. Pramod N., Thoughtworks Inc

Member

Member

MemberMemberMember

Member

MemberMember


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Scheme of Studies for Fourth Year B.E. (CSE) for the batch 2011-2015

VII Semester Total Credits: 25Code Subject L T P Credit

CS721 Advanced Computer Architecture 3 0 0 3

CS725 Computer Graphics & Visualization 3 0 0 3

CS723 Project Management & Engineering

Economics

3 0 0 3

CS724 Cryptography and Network Security 3 1 0 4

Elective4 * * * 3

Elective5 * * * 4

Open Elective * * * 3CSL716 High Performance Computing Laboratory 0 0 1 1

CSL712 Computer Graphics Laboratory 0 0 1 1

VIII Semester Total Credits: 24

Code Subject L T P Credit

Elective - 6 * * * 4

CS812 Project - - 18 18

CS813 Seminar (for Regular Students) - - 2 2

CS8T1 Technical Seminar (for Lateral Entrystudents)

- - 1 1

VII semester / VIII Semester

Elective 4 / Elective 5 / Elective 6

1 CSPE710 Bio Informatics (3:0:0) 8 CSPE731 Cloud Computing ( 3:0:0)

2 CSPE712 Distributed Systems (3:0:1) 9 CSPE719 Wireless Networks and MobileComputing (4:0:0)

3 CSPE715 Data Mining (3:0:0) 10 CSPE720 Business Intelligence & Applications

(3:0:1)

4 CSPE717 S i O i t d A hit t 11 CSPE724 M lti di C ti (3 0 1)


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Course Title: Advanced Computer Architecture Course Code: CS721

Credits (L:T:P) : 3:0:0 Core/ El ective: Core

Type of course: Lecture Total Contact Hours: 56

Prerequisites: The student should have undergone the course on CS412: Computer Organization,

CS414- Introduction to Microprocessor

Course Objectives:

The objectives of this course are to:

1.

Provide the study of different processor architecture, performances, cost, technology and understand the architectural

modifications by applying Amdahls law.2.

Analyze and understand the different compiler techniques used for exposing the ILP and techniques to overcome the

hazards.

3.

Provide the study of different memory architectures.

4.

Identify and understand the different optimization techniques of cache performance and study on virtual machines.

5.

Provide the study of warehouse scale computers and SIMD instruction set.

Course Contents:

Unit 1

Fundamentals of Quantitative Design and Analysis:Classes of Computers, Defining Computer Architecture, Trends inTechnology, Trends in Cost, Dependability, Measuring Reporting and Summarizing Performance, Quantitative Principles

of Computer Design, Introduction to Pipelining and Pipeline Hazards.

Unit 2

InstructionLevel Parallelism: Concepts and Challenges, Basic Compiler Techniques for Exposing ILP, Reducing

Branch cost with Advanced branch Prediction, Overcoming Data Hazards with Dynamic Scheduling examples and the

Algorithm, Exploiting ILP Using Multiple Issue and Static Scheduling and Dynamic Scheduling, Case study-The Intel

Core i7.

Unit 3

ThreadLevel Parallelism: Introduction, Centralized Shared-Memory Architectures, Performance of symmetric shared

memory Multiprocessors, Distributed Shared Memory and Directory-Based Coherence, Synchronization: The Basics,

Models of Memory Consistency.

Unit 4


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What To Whom

When/ Where

(Frequency in

the course)

Max

Marks

Evidence

Collected

Contribution to

Course Outcomes

DirectAssessment

Methods

CIE

Internal

Assessment

Tests

Students

Thrice(Average of

the best two will

be computed)

25 Blue Books 1,2,3,4,5

Laboratory Test Once 25Test Data

Sheets1,2,3,4,5

SEE Standard

Examination

End of Course

(Answering

5 of 10 questions)

100Answer

scripts1,2,3,4,5,

Indirect

Assessment

Methods

Middle of the course

survey

Students

Middle of the

course- Questionnaire

1, 2, 3

Delivery of the course

End of Course

SurveyEnd of the course - Questionnaire

1, 2,3,4,5

Effectiveness of

Delivery of

instructions &

Assessment Methods

Course Outcomes:At the end of the course students should be able to:

1.

Demonstrate the growth in processor performance, development of IC for higher reliability and availability, and

architectural modifications.

2.

Understand and explain the concept of parallelism and describe the challenges associated with instruction level

parallelism.

3. Recognize the complexity of different types of memory architectures.

4. Identify the techniques to optimize the cache, and design virtual machines.

5. Understand the different architectures under data level parallelism and warehouse scale computers.

Mapping Course Outcomes with pr ogram Outcomes:

Course Outcomes Program Outcomes

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


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Course Ti tle: Graphics and Visualization Course Code: CS725

Credits (L:T :P): 3:0:0

Core/Elective:Core

Type of Course: Lecture Total Contact Hours: 42

Prerequisites:Nil

Course Objectives:

At the end of the course the students should be able to:1. Identify the software and hardware components of a computer graphics system,

2. Understand basics of OpenGL APIs and write graphics programs with input interaction using mouse and keyboard.3. Understand the concept of geometrical transformations, coordinate systems and frames used in graphics systems, and

Understand rasterization, clipping and viewing of graphics primitives in three-dimensions.4. Understand the rendering and shading techniques.

5. Design and create graphics application using OpenGL.

Course Contents:

Unit 1Introduction: Applications of computer graphics, A graphics system, Images: Physical and synthetic, Imaging Systems, The syntheticcamera model, The programmers interface, Graphics architectures, Programmable Pipelines, Performance Characteristics, GraphicsProgramming: The OpenGL: The OpenGL API, Primitives and attributes, Color, Viewing, Control functions

Unit 2

Input and Interaction:Interaction, Input devices, Clients and Servers, Display Lists, Display Lists and Modeling, Programming EventDriven Input, Menus, Picking, A simple CAD program, Building Interactive Models, Animating Interactive Programs, Design ofInteractive Programs, Logic Operations.Geometric Objects and Transformations:Scalars, Points, and Vectors, Three-dimensional Primitives, Coordinate Systems and Frames,

Modeling a Colored Cube, Affine Transformations, Rotation, Translation and Scaling.

Unit 3Transformations: Geometric Objects and Transformations, Transformation in Homogeneous Coordinates, Concatenation ofTransformations, OpenGL Transformation Matrices, Interfaces to three-dimensional applications, Quaternions.Implementation: Basic Implementation Strategies, Four major tasks, Clipping, Line-segment clipping, Polygon clipping, Clipping ofother primitives. Clipping in three dimensions, Rasterization, Bresenhams algorithm, Polygon Rasterization, Hidden -surface removal,Antialiasing, Display considerations.

Unit 4Viewing : Classical and computer viewing, Viewing with a Computer, Positioning of the camera, Simple projections, Projections inOpenGL, Hidden-surface removal, Interactive Mesh Displays, Parallel-projection matrices, Perspective-projection matrices, Projectionsand Shadows.

Unit 5


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QUIZ Once 20Test Data

Sheets1,2,3,4,5

SEE Standard

Examination

End of Course(Answering

5 of 10 questions)100 Answer scripts 1,2,3,4,5,

Indirect

Assessment

Methods

Middle of the course

survey

Students

Middle of the course - Questionnaire1, 2, 3


End of Course


1, 2,3,4,5 Effectivenessof Delivery of

instructions &Assessment Methods

Course Outcomes:

At the end of the course the students will be able to:1. Describe the software and hardware components of a computer graphics system, Graphics Architecture and basics of

OpenGL APIs.2. Identify the input and output devices of graphics system and design interactive graphics programs using OpenGL.3. Explain the geometrical transformations in different coordinate systems and clipping, rasterization and hidden surface

algorithms, and implement using OpenGL. Identify different types of viewing and projections in OpenGL and derive theirmatrix formulations.

4. Identify different types of viewing and projections in OpenGL and derive their matrix formulations.

5.

Apply the rendering and shading techniques to 3D graphics using OpenGL.

Mapping Cour se Outcomes with Progr amme Outcomes:


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

Describe the software and hardware components of a computer graphicssystem, Graphics Architecture and basics of OpenGL APIs.

X X X

Identify the input and output devices of graphics system and designinteractive graphics programs using OpenGL.

X X X

Explain the geometrical transformations in different coordinate systems

and clipping, rasterization and hidden surface algorithms, and implementusing OpenGL.Identify different types of viewing and projections inOpenGL and derive their matrix formulations.

X X X X X

Identify different types of viewing and projections in OpenGL and derivetheir matrix formulations

X X X X


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Course Ti tle: Project Management & Engineering Economics Cour se Code: CS723

Credits (L :T :P) : 3:0:0 Core/ Elective: CoreType of Course: L ectur e, Seminar Total Contact Hours: 42

Prerequisites:NIL

Course Objectives:

This course will help students to achieve the following objectives:

1.

Understand the basic concepts of engineering economics and time value of money

2.

Compare investment alternatives based on present worth, future worth and equivalent annual worth.

3.

Distinguish the different rates of returns.

4.

Understand the basics of project management, project phases and project cycles.

5. Discuss the techniques for scope, cost, time, quality, communication and risk management of software projects.

Course Contents:Unit 1

Introduction to Engineering Economics:Engineering Decision Makers, Engineering and Economics, Economics: A Capsule View,Problem Solving and Decision Making.Time Value of Money:Interest and the Time Value of Money, Reasons for Interest, Simple Interest, Compound Interest, Time Value

Equivalence, Compound Interest Factors, Cash Flow Diagrams, Calculation of Time Value Equivalences.

Present Worth Comparisons: Conditions for Present Worth Comparisons, Basic Present Worth Comparison Patterns, Comparison ofAssets that have unequal lives, Comparison of Assets assumed to have infinite lives.

Unit 2

Present Worth Comparisons: Comparison of deferred investments, Future worth comparisons, Valuation, Payback ComparisonMethod. Equivalent Annual Worth Comparisons: Utilization of Equivalent Annual Worth Comparisons, Consideration of Asset Life,Use of a sinking fund, Equivalent uniform payments when interest rates vary, Annuity contract for a guaranteed income.

Unit 3

Rate of Return Calculations: Rate of Return, Minimum Acceptable rate of return, internal rate of return, Consistency of IRR withother economic comparison methods, IRR Misconceptions, Final comments on theory and practice behind interest rates.Introduction to Project Management: What is project and project management? Role of project manager, A system view of projectmanagement, project phases and project cycle, Context of IT projects.

Strategic Planning and Project Selection: Preliminary scope statements, project management plans, project execution, monitoringand control of project work,

Unit 4

Project scope management: what is project scope management? Scope planning and scope management plan, scope definition and

j t t t t ti k b kd t t ifi ti


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Course Assessment and Evaluation Scheme:

WhatTo

Whom

When/ Where

(Frequency inthe course)

Max

Marks

Evidence

Collected

Contribution to

Course Outcomes

DirectAssessment

Methods CIE

Internal

Assessment

Tests

Students

Thrice(Average

of the best two

will be

computed)

30 Blue Books 1,2,3,4 &5

Quiz/

Case studyOnce 20

Quiz Answers/

Reports1-5

SEE

Standard

Examination

End of Course

(Answering5 of 10 questions) 100 Answer scripts 1,2,3,4 &5

Indirect

Assessment

Methods

Midsem survey

Students

Middle of the

course- Feedback forms

1, 2 & 3


End of Course


1, 2, 3, 4, 5 & 6

Effectiveness of

Delivery of instructions

& Assessment Methods

Course Outcomes:

At the end of the course students should be able to:

1.

Explain the basic concepts of engineering economics, derive the compound interest factors, calculate time value

equivalence of money, explain the basic conditions for present worth comparisons and compare assets based on

their asset lives.

2.

Calculate present worth, future worth and equivalent annual worth of investments and compare investment

alternatives.

3. Recognize different rates of returns, analyze the scope of a software project and prepare a project plan.

4. Estimate the time and cost of a software project.

5.

Identify Discuss the quality issues, communication issues and risks in a software project.Mapping Course Outcomes with Programme Outcomes:

Course Outcomes

Programme Outcomes

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


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Course Ti tle: Cryptography & Network Security Cour se Code: CS724

Credits (L :T :P) : 3:1:0 Core/ Elective: CoreType of Course: Lecture Total Contact Hours: 56

Prerequisites: Knowledge of Computer Networks.

Course Objectives:

1.

Provide deeper understanding of security goals , type of possible attacks and how security mechanisms provide

services and meet the goals at various levels

2. Present Private Key Cryptosystems DES, AES structure.

3.

Identify the need of cryptographic hash function and Digital Signature and Public Key Cryptosystems

4.

Identify the need of Key Management and Identification Management5.

Identify the need for application level security, transport layer, and network layer

Course Contents:

Unit 1

Introduction: Security Goals, Cryptographic Attacks, Services and Mechanism, Techniques.

Mathematics of Cryptography: Integer Arithmetic, Modular Arithmetic, Matrices, Linear Congruence.

Unit IIPrivate Key Cryptosystems: Classical Ciphers, DES Family, Modern Private-Key Cryptographic Algorithms( FEAL),

IDEA, RC6

Advanced Encryption Standard: Introduction, Transformations, Key Expansion, Examples, Analysis of AES.Unit III

Public Key Cryptosystems: Concept of public key cryptosystem, RSA Cryptosystem

Hashing: Properties of Hashing, Birthday Paradox, MD Family

Digital Signature: Properties of Digital Signature, Generic Signature Scheme, RSA Signature

Unit IV

Identification: Basic Identification, User Identification, Passwords, Challenge-Response Identification

Key Management: Symmetric-Key Distribution, Kerberos, Symmetric-Key Agreement, Public-Key Distribution,

Hijacking. Unit VSecurity at the Application Layer: PGP and S/MIME: Email, PGP, S/MIME.

Internet Protocol Security(IPsec): Security Associations, Authentication Header Protocol, Encapsulating Security

Payload protocol Internet Key Exchange Virtual Private Network


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Course Ti tle: Graphics and Visualization Lab Course Code: CSL712

Credits (L:T :P): 0:0:1 Core/Elective: Core

Type of Course: Practical Total Contact Hours: 28

Prerequisites:Nil

Course Objectives:

At the end of the course the students should be able to:1. Demonstrate proficiency with 3D interactive OpenGL programming, including a user interface.

2. Evaluate ethical situations in the use of visualization.3. understand the interactive computer graphics architecture; possess in-depth knowledge of display systems, image synthesis,

shape modeling, and interactive control of 3D computer graphics applications;4. Enhance their perspective of modern computer system with modeling, analysis and interpretation of 2D and 3D visual

information.

5. Understand, appreciate and follow the development and advancement of computer graphics technologies, including advancedtechnologies for 3D modelling, high performance rendering.

Course Contents:

Part A: Using C++ and OpenGL APIs,students are required write programs on the following topics:

1. Input Interactions2. Menu driven programs, programs showing the use of display lists and picking.3. Programs on animation effect.4.

Programs on scan converting line, circle and polygon.

5. Programs on clipping lines.6. Modeling 3d objects.7. Applying transformation and viewing to 3D graphics.8. Applying rendering and Shading to objects.

Part B:

1. Students in groups are required to develop a graphics application demonstrating the concept of transformation, viewing,rendering and shading.

Text Book:1. Edward Angel: Interactive Computer Graphics - A Top-Down Approach with OpenGL, 5thEdition, Pearson Education, 2011.

Reference Books:1. Donald Hearn and Pauline Baker: Computer Graphics with OpenGL, 3rdEdition, Pearson Education, 2011.

2 F S Hill Jr : Computer Graphics Using OpenGL 3rd Edition Pearson Education 2009


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Course Outcomes:

At the end of the course the students will be able to:1. Learn basic and fundamental computer graphics techniques;

2.

Gain greater insight into important OpenGL capabilities.3. Use OpenGL write code to implement basic scan converting algorithms, clipping.4. Use OpenGL to model 3D graphics. Apply transformation and viewing to 3D graphics.5. Should be able to use OpenGL to solve challenging rendering problems, learn how to identify and evaluate multiple

approaches to solving rendering and shading problems.

Mapping Cour se Outcomes with Progr amme Outcomes:


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

Describe the software and hardware components of a computer graphics

system, Graphics Architecture and basics of OpenGL APIs.

X X X

Identify the input and output devices of graphics system and designinteractive graphics programs using OpenGL.

X X X

Explain the geometrical transformations in different coordinate systemsand clipping, rasterization and hidden surface algorithms, and implement

using OpenGL.Identify different types of viewing and projections inOpenGL and derive their matrix formulations.

X X X X X

Identify different types of viewing and projections in OpenGL and derivetheir matrix formulations.

X X X X

Apply the rendering and shading techniques to 3D graphics usingOpenGL.

X X X X


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Course Ti tle: Project Cour se Code: CS812

Credits (L :T :P) : 0:0:18 Core/ Elective: CoreType of Course: Pr actical Total Contact Hours: 32 Hours/Week

As a part of term end project, all the eligible final year students must carry out the following activities:

1.

Students should form a group to carry out their project. The minimum group size is 2 and maximum group size is 4.

2.

The groups will be attached to one Internal Guide (and Co-guide if necessary) by the Department.

3. Students can carry out their project in-house or in a reputed organization (to be approved by Internal Guide and

HOD).

4. The project synopsis must be finalized within 2 weeks from the beginning of the semester.

5.

The CIE Component is based on two mid-term evaluations. The evaluation will be done by the internal guide and aco-examiner.

I Evaluation: At 7 weeks from the beginning of the semester

Students must do a group presentation and produce documents of problem definition, literature

survey, system requirements, and system design

II Evaluation: at the end of 12 weeks of the semester.

Students should complete the implementation and testing of the project work in this phase. The

presentation should include implementation details, testing, and results. All projects must be

demonstrated in the Departments labs. A draft version of the complete project report must be

submitted.

6.

The End Semester Viva will be conducted in presence of one Internal Examiner and One External Examiner.

Semester: VIII Year: 2013-14

Course Title: Seminar Course Code: CS813

Credits (L:T:P) : 0:0:2 Core/ Elective: Core

Type of Course: Practical Total Contact Hours: 4 Hours/Week

This is offered for regular students.

An individual seminar should be given by every eligible student of the final year as per the schedule decided by theDepartment.

Students will be guided by their project guides in the selection of topic and preparation for the seminar.

Students can choose any current topic in Computer Science and must obtain the approval of the Guide.

D i h i d d k i h i h i d b i h


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Course Ti tle: Service Oriented Architecture Cour se Code: CSPE717

Credits (L: T:P) : 4:0:0 Core/ Elective: ElectiveType of Course: Lecture Total Contact Hours: 56

Prerequisites: Basic knowledge of internet technologies

Course Objectives:

This course will help students to achieve the following objectives:

1. Understand SOA, Service Orientation, and web service.

2.

Build SOA with Web service.

3. Analyze Service orientation principles.

4. Feature provided by key WS-*Specification.

5.

Understand how SOA support in J2EE and .NET platform.

Course Contents:

Unit 1

Introduction to SOA, Evolution of SOA: Fundamental SOA; Common Characteristics of contemporary SOA; Common

tangible benefits of SOA; An SOA timeline (from XML to Web services to SOA); The continuing evolution of SOA

(Standards organizations and Contributing vendors); The roots of SOA (comparing SOA to Past architectures). Web

Services and Primitive SOA: The Web services framework; services (as Web services); Service descriptions (with

WSDL); Messaging (with SOAP).

Unit 2Web Services and Contemporary SOA: Message exchange patterns; Service activity; Coordination; Atomic Transactions;

Business activities; Orchestration; Choreography. Addressing; Reliable messaging; Correlation; Polices; Metadata

exchange; Security; Notification and eventing

Unit 3Principles of Service Orientation: Services-orientation and the enterprise; Anatomy of a service-oriented architecture;

Common Principles of Service-orientation; How service orientation principles inter relate; Service-orientation and object-

orientation; Native Web service support for service- orientation principles

Unit 4

Service Layers: Service-orientation and contemporary SOA; Service layer abstraction; Application service layer, Businessservice layer, Orchestration service layer; Agnostic services; Service layer configuration scenarios. Business Process

Design: WS-BPEL language basics; WS-Coordination overview; Service-oriented business process design; WS-

addressing language basics; WS-Reliable Messaging language basics

U it 5


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Surprise Quiz Once 10 Quiz AnswersRecollection

Skills

SEE StandardExamination

End of Course

(Answering5 of 10 questions)

100 Answer scripts 1,2,3 & 4

Assessment

Students

Feedback

Students

Middle of the course -Feedback

forms

1, 2, 3


End of Course


1, 2 ,3, 4, 5 Effectiveness

of Delivery of instructions

& Assessment Methods

Course outcomes:

At the end of the course, a student should be able to

1.

Distinguish between Web Service and Service oriented Architecture.

2.

Identify the principles of contemporary SOA.

3.

Recognize the layers of Service Oriented Architecture.

4.

Evaluate how the service oriented principles are inter related with each other.

5.

Categorize SOA support in J2EE and SOA support in .NET focusing on platform overview.

Mapping Course Outcomes with Program Outcomes:

Course OutcomesProgram Outcomes

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

Distinguish between Web Service and Service

oriented ArchitectureX X X X X X X

Identify the principles of contemporary SOA X X X X X

Recognize the layers of Service Oriented

ArchitectureX X X X X X

Evaluate how the service oriented principles are

inter related with each other.

X X X X X X

Categorize SOA support in J2EE and SOA

support in .NET focusing on platform overviewX X X X X X


25/55

Course Ti tle: Information Storage and management Cour se Code: CSPE718

Credits (L: T:P): 4:0:0 Core/ El ective: Elective

Type of Course: Lecture/Seminar Total Contact Hours: 56

Prerequisites:

The student should have undergone the course on COMPUTER NETWORKS/DATA COMMUNICATION

Course Objectives :

Objectives of this course is to:

1)

Provide understanding storage architecture its evolution, data access and storage problem

2)

Present an understanding of Raid, hotspare. Impact on disk performance

3)

Analyze fiber channel protocol stack. Zoning , network attached storage,4)

Provide an understanding of object storage ,backup replication and archive

5)

Analyze business continuity planning.

Cour se Contents:

Unit I

Introduction: Information Storage, Evolution of Storage Architecture, Data Centre Infrastructure, Virtualization andCloud

Computing.Data Centre Environment: Application, DBMS, Host, Connectivity, Storage, Disk Drive Components, Disk

Drive Performance, Host Access to Data, Direct-Attached Storage, Storage Design Based on Application, Disk Native

Command Queuing, Introduction to Flash Drives. Unit IIData Protection: RAID Implementation Methods, Array Components, Techniques, Levels, Impact on Disk Performance,

Comparison, Hot Spares.Intelligent Storage System: Components, Storage Provisioning, Types.

Unit IIIFibre Channel Storage Area Networks: FC Overview, Evolution, Components, FC Connectivity, Ports, FC Architecture,

Fabric Services, Login Types, Zoning, FC Topologies, Virtualization in SAN.IP SAN and FCoE: iSCSI, FCIP, FCoE.

Network-Attached Storage: Benefits, Components, NAS I/O Operation, Implementations, File Sharing Protocols, I/O

Operations, Factors Affecting NAS Performance, File-Level Virtualization

Unit IV

Object Based and Unified Storage: Object Based Storage Devices, Content Addressed Storage, CAS Use Cases, UnifiedStorage. Backup Archive and Replication

Unit V

Business Continuity: Information Availability, Terminology, Planning Lifecycle, Failure Analysis, Impact Analysis,


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Course Outcomes:


1.

Understanding of storage design based on application

2.

Understanding of diferent variants of Raid and their impact on performance

3.

Recognize fiber channel protocol stack, layers, services and isci

4.

Analyze different backup methods and replication, Advantages of object storage device, their key features5.

Recognize steps for business continuity planning for storage in an enterprise.

Mapping Course Outcomes with Program Outcomes:

Course OutcomesProgram Outcomes

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

Understanding of storage design based on

application

X X X X X

Understanding of diferent variants of Raid and

their impact on performance

X X X X X X

R i fib h l l k l X X X X X X X

Class-room

Surprise Quiz

Twice(Summation

of the two will be

computed)

20 Class-room

Surprise Quiz2 & 3

SEEStandard

Examination

End of Course

(Answering

5 of 10 questions)

100 Answer scripts 1,2, 3,4 & 5

IndirectAssessment

M

ethods

Students

Feedback

Students

Middle of the

course-

Feedback

forms

1, 2 & 3, Delivery of

the course

End of Course

Survey End of the course - Questionnaire

1, 2 & 3,

Effectiveness of

Delivery of

instructions &

Assessment

Methods


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Course Ti tle: Parallel Programming using CUDA Cour se Code: CSPE730

Credits (L: T:P) : 3:0:1Core/ Elective: Elective

Type of Course: Lecture/Seminar Total Contact Hours: 56

Prerequisites:NIL

Course Objectives

The objectives of this course are to

1. Provide an understanding Graphical Processing Units and their architecture.

2. Analyze the features GPUs and their functionalities

3.

Provide understanding of using GPUs as accelerators

4.

Design parallel applications using CUDA-C

5. Analyze parallel algorithms implemented on heterogeneous computing environments with sequential versions

Course Contents:

Unit 1

Introduction: GPUs as Parallel Computers, Architecture of a Model GPU, Why More Speed or Parallelism? Parallel

Programming Languages and Models, Overarching Goals.

History of GPU Computing:Evolution of Graphics Pipelines, GPU Computing.

Introduction to CUDA:Data Parallelism, CUDA Program Structure, A Matrix-Matrix Multiplication Example, Device

Memories and Data Transfer, Kernel Functions and Threading.

Unit 2

CUDA Threads: CUDA Thread Organization, Using blockIdx and threadIdx, Synchronization and Transparent

Scalability, Thread Assignment, Thread Scheduling and Latency Tolerance.

CUDA Memories: Importance of Memory Access Efficiency, CUDA Device Memory Types, A Strategy for Reducing

Global Memory Traffic, Memory as a limiting Factor to Parallelism.

Performance Considerations: More on Thread Execution, Global Memory Bandwidth, Dynamic Partitioning of SM

Resources, Data Perfecting, Instruction Mix, Thread Granularity, Measured Performance and Summary.

Unit 3Floating Point Considerations: Floating Point Format, Representable Numbers, Special Bit Patterns and Precision,

Arithmetic Accuracy and Rounding, Algorithm Considerations.

Parallel Programming and Computational Thinking: Goals of Parallel Programming, Problem Decomposition,


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Course Delivery

The course will be delivered through lectures, presentations, classroom discussions, practice exercises and practical

sessions. The course is basically learnt using Project based Learning Method.

Course Assessment and evaluati on:

WhatTo

Whom

When/ Where

(Frequency in the

course)

Max

Marks

Evidence

Collected

Contribution to

Course

Outcomes

Direct

AssessmentMethods

CIE

Internal

Assessment

Tests

Students

Thrice (Average of the

best two will be

computed)

15 Blue Books 1-5

Mini Projects

Will be carried out by

a batch of two

students. Evaluation is

at the end of the

Semester

35

Project

Report, Code

Repository

1,2,4-5

SEESemester End

Examination

End of Course

(Answering

5 of 10 questions)

100Answer

scripts1-5

Indirect

Assessment

Methods

StudentsFeedback

Students

Middle of the course - Feedbackforms

1-3, Delivery ofthe course

End of Course


1-5, Relevance of

the course

Course Outcomes

At the end of the course students should be able to:1. Identify the advantages and need of GPUs as an emerging technology

2. Design the programs using CUDA-C/OPENCL

3.

Demonstrate Heterogeneous Computing on CPUs and GPUs


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Course Ti tle: Cloud Computing Cour se Code: CSPE731

Credits (L: T:P) : 3:0:1 Core/ Elective: Elective

Type of Course: Lectur e, Practical Total Contact Hours: 70

Prerequisites:NILCourse Objectives

The objectives of this course are to

1.

Provide an understanding cloud computing delivery models.

2.

Analyze the features cloud applications and Paradigms

3.

Provide understanding of Virtualization

4.

Identify policies and mechanisms for resource management5.

Analyze scheduling algorithms for cloud computing systems and cloud security

Course Contents:

Unit 1Introduction:Network centric computing and network centric content, Peer-to-peer systems, Cloud Computing: an old

idea whose time has come, Cloud Computing delivery models & Services, Ethical issues, Cloud vulnerabilities,

Challenges, Cloud Infrastructure: Amazon, Google, Azure & online services, open source private clouds. Storage

diversity and vendor lock-in, intercloud, Energy use & ecological impact of data centers, service level and compliance

level agreement, Responsibility sharing, user experience, Software licensing.

Unit 2Cloud Computing Applications & Paradigms: Challenges, existing and new application opportunities, Architectural

styles of cloud applications, Workflows coordination of multiple activities, Coordination based on a state machine model -

the Zoo Keeper, The Map Reduce programming model, Apache Hadoop, A case study: the GrepTheWeb application,

Clouds for science and engineering, High performance computing on a cloud, Social computing, digital content, and cloud

computing.

Unit 3

Cloud Resource Virtualization:Layering and virtualization, Virtual machine monitors, Virtual machines Performance

and security isolation, Full virtualization and paravirtualization, Hardware support for virtualization Case study: Xen -a

VMM based on paravirtualization, Optimization of network virtualization inXen2.0, vBlades-paravirtualization targeting

ax86-64Itanium processor, A performance comparison of virtual machines, Virtual machine security, The darker side ofvirtualization, Software fault isolation.

Unit 4

Cloud Resource Management and Scheduling: Policies and mechanisms for resource management, Applications of


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11.

Study of Future Grid

Text Book:

1.

Cloud Computing: Theory and Practice, Dan Marinescu, 1stedition, MK Publishers, 2013.

Reference Books:1.

Cloud Computing: Theory and Practice, Dan Marinescu, 1stedition, MK Publishers, 2013.

2.

Distributed and Cloud Computing, From Parallel Processing to the Internet of Things, Kai Hwang, Jack Dongarra,

Geoffrey Fox. MK Publishers.

3.

Cloud Computing: A Practical Approach, Anthony T. Velte, Toby J. Velte, Robert Elsenpeter, McGraw Fill, 2010.

Course DeliveryThe course will be delivered through lectures, presentations, classroom discussions, practice exercises and practical

sessions.

Course Assessment and evaluation:

WhatTo

Whom

When/ Where(Frequency in the

course)

Max

Marks

Evidence

Collected

Contribution toCourse

Outcomes

Direct

Asse

ssmentMethods

CIE

Internal

Assessment

Tests

Students

Thrice (Average of

the best two will be

computed)

25 Blue Books 1-5

Lab test

Twice (Average of

the two will be

computed)

25 Data Sheets 1,2,4-5

SEE Semester EndExamination

End of Course(Answering

5 of 10 questions)

50 Answer scripts 1-5

Indirect

A

ssessment

Methods

Students

Feedback

Students

Middle of the course -Feedback

forms

1-3, Delivery of

the course

Mid Sem Survey Middle of the course -Feedback

forms

1-3, Relevance of

the course

End of Course


1-5, Relevance of

the course


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Course OutcomesProgramme Outcomes

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

Create combinatorial auctions for cloud schedulingalgorithms for computing clouds.

X X X X

Assess the Cloud security, the risks involved, its impact

and cloud service providers.X X X X


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Course Ti tle: Big Data and Data Science Cour se Code: CSPE733


Type of Course: Lectur e, Practical Total Contact Hours: 56

Prerequisites:Nil

Course ObjectivesThe objectives of this course are to

1.

Understand how organizations these days use their data a decision supporting tool and to build data - intensive

products and services.

2.

Understand the collection of skills required by organizations to support these functions has been grouped under theterm Data Sciences

3.

Understand the basic concepts of big data, methodologies for analyzing structured and unstructured data

4.

Understand the relationship between the Data Scientist and the business needs

Course Contents:

Unit 1

Introduction

Introduction: Data Processing Architectures, components and processes; Data Stores and Data kind, Challenges " Big

Data" and otherwise Special Considerations in Big Data Analysis: Background, Theory in Search of Data, Data in

Search of Theory, Overfitting, Bigness Bias, Too Much Data, Fixing Data, Data Subsets in Big Data: Neither Additive

nor Transitive, Additional Big Data Pitfalls. Providing Structure to Unstructured Data: Background, MachineTanslation, Autocoding, Indexing and Term Extraction

Unit 2

Data and Features

Component Parts of Data Science: Data Types, Classes of Analytic Techniques, Learning Models, Execution Models;

Fractal Analytic Model, Analytic Selection Process: Implementation Constraints Feature Engineering: Feature

Selection, Data Veracity, Application of Domain Knowledge, Curse of Dimensionality

Unit 3

Data and Analysis

Simple Analytic Techniques: Background, Look at the Data, Data Range, Denominator, Frequency Distributions, Meanand Standard Deviation, EstimationOnly Analyses Deep Dive into Analysis: Background, Analytic Tasks, Clustering,

Cassifying, Recommending, and Modelling, Data Reduction, Normalising and Adjusting Data, Find RelationshipsNot

Similarities


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Course Assessment and evaluation:

WhatTo

Whom

When/ Where

(Frequency in the

course)

Max

Marks

Evidence

Collected

Contribution to

Course

Outcomes

Direct

Assessment

Methods CIE

Internal

Assessment

Tests

Students

Thrice (Average of

the best two will be

computed)

25 Blue Books 1-5

Lab test Once 25 Data Sheets 1,2,4-5

SEESemester End

Examination

End of Course

(Answering

5 of 10 questions)

50 Answer scripts 1-5

Indirect

Assessmen

t

Methods

StudentsFeedback

Students

Middle of the course - Feedbackforms

1-3, Delivery ofthe course

End of Course


1-5, Relevance of

the course

Course Outcomes


1. Identify the differences between Big Data and Small Data

2. Design the programs to analyze big data

3.

Demonstrate the analysis of big data4. Analyze the Ontologies, Semantics, Introspection, Data Integration and Measurement techniques of big data

5. Illustrate the stepwise approach to big data analysis and understand the legalities and societal issues involved

Mapping Course Outcomes with Programme Outcomes:

Course OutcomesProgramme Outcomes

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

Identify the differences between Big Data and Small

DataX X X

Design the programs to analyze big data X X X X X

Demonstrate the analysis of big data X X X X X

Analyze the Ontologies, Semantics, Introspection, DataX X X X


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Course Ti tle: Bio Informatics Cour se Code: CSPE710

Credits (L :T :P) : 4:0:0 Core/ Elective: Elective

Type of course: Lecture Total Contact Hours: 56 Hrs

Unit 1

Introduction to Bioinformatics:branches ofBioinformatics, aim, scope / research areas of bioinformatics.

Biological Databases: Sequence file Formats, sequence conversion tools, Molecular File formats, molecular file format

conversion, Databases in Bioinformatics -An Introduction, classification schema of biological databases, biological

database retrieval systems.

Unit 2Biological Sequence Databases: NCBI, EMBL Nucleotide Sequence database, PIR, Protein 3D Structure and

Classification Databases: Protein data bank, Molecular modeling database, E_MSD, 3-d Genomics, Gene3D, Protein

Structural classification Database.

Unit 3

Bio-algorithms and Tools:Sequence Alignments, Scoring matrices, PAM, BLOSUM, alignment of pairs of sequences,

algorithms, heuristics, MSA.

Gene Prediction Methods: Principles and Challenges, Computational methods of gene prediction, Molecular Phylogeny,

Molecular Viewers.

Unit 4

Protein Modeling and Drug Design: levels of protein structure, secondary structure of a protein: - conformational

parameters, types, prediction, prediction software, methods of protein modeling, model refinement, threading or fold

recognition, Microarray data Analysis, Primer Design.

Unit 5

Bioinformatics in Computer-aided Drug Design: the drug discovery process, structural bioinformatics, SAR, QSAR,

Graph theory, molecular docking.

Modeling of Biomolecular Systems: Monte Carlo Methods, molecular dynamics, energy minimization, Leading MDsimulation packages, Markov Chain and Hidden Markov models, Application of Vitterbi Algorithm, Application and

Advantages of HMMs.

Commercial Bio-software:GCG Wisconsin Package, Insight II, Discovery Studio 2.0 .


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Course Ti tle: Pattern Recognition Cour se Code: CSPE711


Type of course: Lecture Total Contact Hours: 56 Hrs

Unit 1

Introduction: Machine perception, an example, Pattern Recognition System, The Design Cycle, Learning and

Adaptation. Bayesian Decision Theory: Introduction, Bayesian Decision Theory, Continuous Features, Minimum error

rate, classification, classifiers, discriminant functions, and decision surfaces, the normal density, Discriminant functions

for the normal density.

Unit 2Maximum-likelihood and Bayesian Parameter Estimation: Introduction, Maximum-likelihood estimation, Bayesian

Estimation, Bayesian parameter estimation: Gaussian Case, general theory, Hidden Markov Models. Non-parametric

Techniques: Introduction, Density Estimation, Parzen windows, KN Nearest- Neighbor Estimation, The Nearest-

Neighbor Rule, Metrics and Nearest-Neighbor Classification.

Unit 3

Linear Discriminant Functions:Introduction, Linear Discriminant Functions and Decision Surfaces, Generalized Linear

Discriminant Functions, The Two-Category Linearly Separable case, Minimizing the Perception Criterion Functions,

Relaxation Procedures, Non-separable Behavior, Minimum Squared-Error procedures, The Ho-Kashyap procedures.Stochastic Methods: Introduction, Stochastic Search, Boltzmann Learning, Boltzmann Networks and Graphical Models,

Evolutionary Methods.

Unit 4

Non-Metric Methods:Introduction, Decision Trees, CART, Other Tree Methods, Recognition with Strings, Grammatical

Methods.

Unit 5

Unsupervised Learning and Clustering: Introduction, Mixture Densities and Identifiability, Maximum-Likelihood

Estimates, Application to Normal Mixtures, Unsupervised Bayesian Learning, Data Description and Clustering, Criterion

Functions for Clustering.

Text Book:


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Course Ti tle: Business Intelligence & Applications Cour se Code: CSPE720


Type of Course: Lecture, Practicals Total Contact Hours: 56

Unit 1

Introduction to Business Intelligence: Types of digital data, Introduction to OLTP, OLAP and Data Mining, BI

definitions and Concepts, Business Applications of BI, BI Framework, Role of Data Warehousing in BI, BI Infrastructure

ComponentsBI Process, BI Technology, BI Roles and Responsibilities.

Unit 2

Basics of Data Integration: Basics of Data Integration(ETL), Concepts of Data Integration, Need and advantages ofusing data integration, introduction to common data integration approaches, introduction to data quality, data profiling

concepts and applications.

Unit 3

Introduction to Data Integration: Introduction to SSIS Architecture, Introduction to ETL using SSIS, Integration

Services objects, Data flow components Sources, Transformations and Destinations, Working with transformations,

containers, tasks, precedence constraints and event handlers.

Unit 4Introduction to Multi-Dimensional Data Modeling: Introduction to data and dimension modeling, multidimensional

data model, ER Modeling vs. multi dimensional modeling, Concepts of dimensions, facts, cubes, attribute, hierarchies,

star and snowflake schema, introduction to business metrics and KPIs, Creating cubes using SSAS.

Unit 5

Basics of Enterprise Reporting: Introduction to enterprise reporting, Concepts of dashboards, balanced scorecards,

Project: Data warehouse creation and designing reports, Introduction to SSRS Architecture, Enterprise reporting using

SSRS, and Use of Business Intelligence Development Studio (BIDS).

Text Books:

1. Prasad Rn, Seema Acharya: Fundamentals of Business Analytics, First Edition, Wiley India Pvt. Ltd, 2012.

2 William H Inmon: Building the Data Warehouse 4th Edition Wiley India Ed Reprint 2012


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Course Ti tle: Software Testing Cour se Code: CSPE721



Course contents:

Unit 1

A Perspective on Testing, Examples: Basic definitions, Test cases, Insights from a Venn diagram, Identifying test cases,

Error and fault taxonomies, Levels of testing. Examples: Generalized pseudo code, The triangle problem, The NextDate

function, The commission problem, The SATM (Simple Automatic Teller Machine) problem, The currency converter,

Saturn windshield wiper.

Unit 2Boundary Value Testing, Equivalence Class Testing, Decision Table-Based Testing: Boundary value analysis,

Robustness testing, Worst-case testing, Special value testing, Examples, Random testing, Equivalence classes,

Equivalence test cases for the triangle problem, NextDate function, and the commission problem, Guidelines and

observations. Decision tables, Test cases for the triangle problem, NextDate function, and the commission problem,

Guidelines and observations. Path Testing, Data Flow Testing: DD paths, Test coverage metrics, Basis path testing,

guidelines and observations, Definition-Use testing, Slice-based testing, Guidelines and observations.

Unit 3

Levels of Testing, Integration Testing: Traditional view of testing levels, Alternative life-cycle models, The SATM

system, Separating integration and system testing. A closer look at the SATM system, Decomposition-based, call graph-

based, Path-based integrations, System Testing, Interaction Testing: Threads, Basic concepts for requirements

specification, Finding threads, Structural strategies and functional strategies for thread testing, SATM test threads, System

testing guidelines, ASF (Atomic System Functions) testing example. Context of interaction, A taxonomy of interactions,

Interaction, composition, and determinism, Client/Server Testing.

Unit 4

Process Framework: Validation and verification, Degrees of freedom, Varieties of software. Basic principles:

Sensitivity, redundancy, restriction, partition, visibility, Feedback. The quality process, Planning and monitoring, Quality

goals, Dependability properties, Analysis, Testing, Improving the process, Organizational factors, Fault-Based Testing,

Test Execution: Overview, Assumptions in fault-based testing, Mutation analysis, Fault-based adequacy criteria,

Variations on mutation analysis. Test Execution: Overview, from test case specifications to test cases, Scaffolding,Generic versus specific scaffolding, Test oracles, Self-checks as oracles, Capture and replay.

Unit 5Planning and Monitoring the Process, Documenting Analysis and Test: Quality and process, Test and analysis


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Course Ti tle: Multiprocessor Programming Cour se Code: CSPE723


Type of Course: Lecture, Tutori als Total Contact Hours: 56

Unit 1

Introduction: Shared Objects and Synchronization, A Fable, The Producer-Consumer Problem, The Readers-Writers

Problem, The Harsh Realities of Parallelization, Parallel Programming. Principles: Mutual Exclusion, Time, Critical

Sections, Thread Solutions, The Filter Lock, Fairness, Lamports Bakery Algorithm, Bounded Timestamps, Lower

Bounds on the Number of Locations. Concurrent Objects: Concurrency and Correctness, Sequential Objects, Quiescent

Consistency, Sequential Consistency, Linearizability, Formal Definitions, Progress Conditions, The Java Memory Model.Unit 2

Foundations of Shared Memory: The Space of Registers, Register Constructions, Atomic Snapshots. The Relative

Power of Primitive Synchronization Operations: Consensus Numbers, Atomic Registers, Consensus Protocols, FIFO

Queues, Multiple Assignment Objects, Read-Modify-Write Operations, Common2 RMW Operations. Universality of

Consensus: Introduction, A Lock-Free Universal Construction, A Wait-Free Universal Construction.

Unit 3

Spin Locks and Contention:Test and Set Locks, TAS Based Spin Locks, Exponential Backoff, Queue Locks, A Queue

of Lock with Timeouts, A Composite Lock, Hierarchical Locks, One Lock to Rule Them All.

Monitors and Blocking Synchronization: Introduction, Monitor Locks and Conditions, Readers-Writers Locks, Our ownReentrant Lock, Semaphores.

Unit 4

Linked Lists: The Role of Locking: Introduction, List-Based Sets, concurrent Reasoning, Coarse-Grained

Synchronization, Fine-Grained Synchronization, Optimistic Synchronization, Lazy Synchronization, Non-blocking

Synchronization.

Concurrent Queues and the ABA Problem: Introduction, Queues, A Bounded Partial Queue, An Unbounded Total Queue,

An Unbounded Lock-Free Queue, Memory Reclamation and the ABA Problem, Dual Data Structures.

Unit 5

Concurrent Stacks and Elimination: An Unbounded Lock-Free Stack, Elimination, The Elimination Backoff Stack.

Counting, Sorting and Distributed Coordination: Introduction, Shared Counting, Software Combining, QuiescentlyConsistent Pools and Counters, Counting Networks, Diffracting Trees, Parallel Sorting, Sorting Networks, Sample

Sorting, Distributed Coordination.


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Course Ti tle: Multimedia Computing Cour se Code: CSPE724



Unit 1

Introduction, Media and Data Streams, Audio Technology: Multimedia Elements, Multimedia Applications,

Multimedia Systems Architecture, Evolving Technologies for Multimedia Systems, Defining Objects for Multimedia

Systems, Multimedia Data Interface Standards, The need for Data Compression, Multimedia Databases. Media:

Perception Media, Representation Media, Presentation Media, Storage Media, Characterizing Continuous Media Data

Streams. Sound: Frequency, Amplitude, Sound Perception and Psychoacoustics, Audio Representation on Computers,Three Dimensional Sound Projection, Music and MIDI Standards, Speech Signals, Speech Output, Speech Input, Speech

Transmission. Graphics and Images, Video Technology, Computer-Based Animation: Capturing Graphics and Images

Computer Assisted Graphics and Image Processing, Reconstructing Images, Graphics and Image Output Options. Basics,

Television Systems, Digitalization of Video Signals, Digital Television, Basic Concepts, Specification of Animations,

Methods of Controlling Animation, Display of Animation, Transmission of Animation, Virtual Reality Modeling

Language.

Unit 2

Data Compression: Storage Space, Coding Requirements, Source, Entropy, and Hybrid Coding, Basic Compression

Techniques, JPEG: Image Preparation, Lossy Sequential DCT-based Mode, Expanded Lossy DCT-based Mode, Lossless

Mode, Hierarchical Mode. H.261 (Px64) and H.263: Image Preparation, Coding Algorithms, Data Stream, H.263+ and

H.263L, MPEG: Video Encoding, Audio Coding, Data Stream, MPEG-2, MPEG-4, MPEG-7, Fractal Compression.

Unit 3

Optical Storage Media: History of Optical Storage, Basic Technology, Video Discs and Other WORMs, Compact Disc

Digital Audio, Compact Disc Read Only Memory, CD-ROM Extended Architecture, Further CD-ROM-Based

Developments, Compact Disc Recordable, Compact Disc Magneto-Optical, Compact Disc Read/Write, Digital Versatile

Disc. Content Analysis : Simple Vs. Complex Features, Analysis of Individual Images, Analysis of Image Sequences,

Audio Analysis, Applications.

Unit 4

Data and File Format Standards: Rich-Text Format, TIFF File Format, Resource Interchange File Format (RIFF),MIDI File Format, JPEG DIB File Format for Still and Motion Images, AVI Indeo File Format, MPEG Standards,

TWAIN.

Unit 5


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Course Ti tle: Machine Learning Technique Cour se Code: CSPE727



Unit 1

Introduction: Probability theory (Bishop ch-1 & Appendix B,C); What is machine learning, example machine learning

applications (Alpaydin ch-1) Supervised Learning:Learning a Class from Examples, VC-dimension, PAC learning,

Noise, Learning multiple classes, Regression, Model selection and generalisation. (Alpaydin ch-2)

Unit 2

Bayesian Learning: Classification, losses and risks, utility theory (Alpaydin ch3 (3.1, 3.2, 3.3, 3.5)) MLE, Evaluating anestimator, bayes estimator, parametric classificaion (Alpaydin ch4 - 4.1-4.5) (Bishop 4.2); Discriminant functions

Introduction, Discriminant functions, Least squares classification, Fishers linear discriminant, fixed basis functions,

logistic regression (Bishop 4.1,4.3.1,4.3.2)

Unit 3

Multivariate methods: Multivariate Data,Parameter Estimation,Estimation of Missing Values,Multivariate Normal

Distribution,Multivariate Classification,Tuning Complexity,Discrete Features,Multivariate Regression (Alpaydin ch-5)

Nonparametric methods: Nearest Neighbor Classifier, Nonparametric Density Estimation (Alpaydin ch-8 selected

topics)

Unit 4

Maximum margin classifiers: SVM, Introduction to kernel methods, Overlapping class distributions, Relation to logistic

regression,Multiclass SVMs, SVMs for regression (Bishop ch 6 and 7 only covered topics). Mixture models and EM

K-means clustering, Mixture of gaussians, Hierarchical Clustering, Choosing the Number of Clusters (Bishop 9.1,9.2,

Alpaydin 7.7,7.8)

Unit 5

Dimensionality reduction - (Alpaydin ch6) 10. Combining Models (Bishop ch-14)

Course Outcomes:

At the end of the course, a student should be able to

1.

Explain the concepts and issues of learning systems.

2.

Analyze and evaluate decision tree based learning algorithm.

3 A l d l t B i l i l ith


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Course Ti tle: Distributed Systems Cour se Code: CSPE712



Unit 1

Characterization of Distributed Systems: Introduction, Examples of distributed systems, Resource sharing and the

Web, Challenges. System Models: Architectural models, Fundamental models. Interprocess Communication:

Introduction, The API for the Internet protocols,

Unit 2

External data representation and marshalling: Client-Server communication, Group communication, Case study:Interprocess communication in Unix. Distributed Objects and Remote Invocation: Communication between distributed

objects, Remote procedure call, events and notifications.

Unit 3

Overview of security techniques:cryptographic algorithms, digital signatures, cryptographic pragmatics, case studies:

Needham-Schroeder, Kerberos, TLS. Distributed File Systems: File service architecture, Sun network file system,

Andrew file system, Recent advances.

Unit 4Time and Global States:Introduction, clocks, event and process states, synchronizing physical clocks, logical time and

logical clocks, global states, distributed debugging. Coordination and agreement: Introduction, Distributed mutual

exclusion, elections, multicast communication, consensus and related problems.

Unit 5

Distributed Transactions: Flat and nested distributed transactions, atomic commit protocols, concurrency control in

distributed transactions, distributed deadlocks, transaction recovery. Distributed Shared Memory: Design and

Implementation issues, sequential consistency and Ivy, Release consistency and Munin, other consistency models.

Text Book:

1.

George Coulouris, Jean Dollimore, Tim Kindlberg: Distributed Systems, Concept and Design, 4th edition,

Pearson Education, 2011.


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Course Ti tle: Data Mining Cour se Code: CSPE715



Unit 1

Introduction:What is Data Mining? Motivating Challenges, The origins of data mining, Data Mining, Tasks. Types of

Data, Data Quality. Data Data Preprocessing, Measures of Similarity and Dissimilarity.

Unit 2

Classification :Preliminaries, General approach to solving a classification problem, Decision tree induction, Rule-basedclassifier, Nearest-neighbor classifier. Association Analysis - Problem Definition, Frequent Itemset generation, Rule

Generation.

Unit 3

Compact representation of frequent itemsets: Alternative methods for generating frequent itemsets. Association

Analysis FP-Growth algorithm, Evaluation of association patterns, Effect of skewed support distribution, Sequential

patterns.

Unit 4

Cluster Analysis Overview: K-means, Agglomerative hierarchical clustering, DBSCAN, Overview of Cluster

Evaluation. Further Topics in Data Mining Multidimensional analysis and descriptive mining of complex data objects,

Spatial data mining, Multimedia data mining.

Unit 5

Applications Text mining: Mining the WWW,w Outlier analysis, Data mining applications, Data mining system

products and research prototypes, Additional themes on Data mining, Social impact of Data mining, Trends in Data

mining.

Text Books:1.

Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data Mining, First edition, Pearson Education,

2012.

2.

Jiawei Han and Micheline Kamber: Data MiningConcepts and Techniques, 3rdEdition, Morgan Kaufmann,


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Course Ti tle: Web 2.0 and Web Services Cour se Code: CSPE716



Unit 1

The basics of Web Services:An Example, Next generation of the Web, Interacting with Web Services, The technology

of Web Services, XML for business collaboration: ebXML, Web Services versus other technologies, Additional

technologies.

Unit 2XML:An example, Instance and schema, processing XML documents, Namespaces, Transformation, XML specification

and information.

WSDL: Basics, WSDL elements, The extensible WSDL framework, Importing WSDL elements, WSDL related

namespaces, Extensions for binding to SOAP.

Unit 3

SOAP:Example, The SOAP specifications, SOAP message processing, SOAP use of Namespaces.

Unit 4

UDDI Registry:The UDDI organization, The concepts underlying UDDI, how UDDI works? UDDI SOAP APIs, Usage

scenarios, Using WSDL with UDDI with UDDI, UDDI for private use, UDDI support for SOAP, Complex business

relationships and Unicode and EBXML.

Unit 5

Introduction, Web Services:What is Web 2.0?, Folksonomies and Web 2.0, Software As a Service (SaaS), Data and

Web 2.0, Convergence, Iterative development, Rich User experience, Multiple Delivery Channels, Social Networking.

Web Services: SOAP, RPC Style SOAP, Document style SOAP, WSDL, REST services, JSON format, What is JSON?,

Array literals, Object literals, Mixing literals, JSON Syntax, JSON Encoding and Decoding, JSON versus XML.

Text Books:

1.

Eric Newcomer: Understanding Web Services XML, WSDL, SOAP and UDDI, 1 stEdition, Pearson Education,

2002.


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Course Ti tle: Wireless Networks and Mobile Computing Cour se Code: CSPE719



Unit 1

Mobile Computing Architecture: Types of Networks, Architecture for Mobile Computing, 3-tier Architecture, Design

Considerations for Mobile Computing, Wireless Networks 1: GSM and SMS: Global Systems for Mobile

Communication GSM and Short Service Messages ( SMS): GSM Architecture, Entities, Call routing in GSM, PLMN

Interface, GSM Addresses and Identities, Network Aspects in GSM, Mobility Management, GSM Frequency allocation,

Introduction to SMS, SMS Architecture, SM MT, SM MO, SMS as Information bearer, applicationsUnit 2Wireless Networks 2: GPRS : GPRS and Packet Data Network, GPRS Network Architecture, GPRS Network

Operations, Data Services in GPRS, Applications for GPRS, Billing and Charging in GPRS,Wireless Networks 3:

CDMA, 3G and WiMAX:Spread Spectrum technology, IS-95, CDMA versus GSM, Wireless Data, Third Generation

Networks, Applications on 3G, Introduction to WiMAX.

Unit 3

Mobile Client: Moving beyond desktop, Mobile handset overview, Mobile phones and their features, PDA, Design

Constraints in applications for handheld devices. Mobile IP: Introduction, discovery, Registration, Tunneling, Cellular

IP, Mobile IP with IPv6

Unit 4Mobile OS and Computing Environment: Smart Client Architecture, The Client: User Interface, Data Storage,

Performance, Data Synchronization, Messaging. The Server: Data Synchronization, Enterprise Data Source, Messaging.

Mobile Operating Systems: WinCE, Palm OS, Symbian OS, Linux, Proprietary OS Client Development: The

development process, Need analysis phase, Design phase, Implementation and Testing phase, Deployment phase,

Development Tools, Device Emulators.

Unit 5

Building, Mobile Internet Applications: Thin client: Architecture, the client, Middleware, messaging Servers,

Processing a Wireless request, Wireless Applications Protocol (WAP) Overview, Wireless Languages: Markup

Languages, HDML, WML, HTML, cHTML, XHTML, VoiceXML. J2ME: Introduction, CDC, CLDC, MIDP,Programming for CLDC, MIDlet model, Provisioning, MIDlet life-cycle, Creating new application, MIDlet event

handling, GUI in MIDP, Low level GUI Components, Multimedia APIs, Communication in MIDP, Security

Considerations in MIDP.


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Course Ti tle: Software Architecture Cour se Code: CSPE725



Unit 1

Introduction: The Architecture Business Cycle: Where do architectures come from? Software processes and the

architecture business cycle, What makes a good architecture? What software architecture is and what it is not, Other

points of view, Architectural patterns, reference models and reference architectures, Importance of software architecture,

Architectural structures and views.

Unit 2Architectural Styles and Case Studies: Architectural styles, Pipes and filters, Data abstraction and object-oriented

organization, Event-based, implicit invocation, Layered systems, Repositories, Interpreters, Process control, Other

familiar architectures, Heterogeneous architectures. Case Studies: Keyword in Context, Instrumentation software, Mobile

robotics, Cruise control, Three vignettes in mixed style.

Unit 3

Quality: Functionality and architecture, Architecture and quality attributes, System quality attributes, Quality attribute

scenarios in practice, Other system quality attributes, Business qualities, Architecture qualities. Achieving Quality:

Introducing tactics, Availability tactics, Modifiability tactics, Performance tactics, Security tactics, Testability tactics,

Usability tactics, Relationship of tactics to architectural patterns, Architectural patterns and styles.

Unit 4

Architectural Patterns:Introduction, From mud to structure: Layers, Pipes and Filters, Blackboard. Distributed Systems:

Broker, Interactive Systems: MVC, Presentation-Abstraction- Control. Adaptable Systems: Microkernel, Reflection.

Unit 5

Some Design Patterns:Structural decomposition: Whole Part, Organization of work: Master Slave, Access Control:

Proxy. Designing and Documenting Software Architecture: Architecture in the life cycle, Designing the architecture,

Forming the team structure, Creating a skeletal system, Uses of architectural documentation, Views, Choosing therelevant views, Documenting a view, Documentation across views.

Text Books:


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Course Ti tle: Network Management Cour se Code: CSPE728


Type of Course: L ectur e, Practi cal Total Contact Hours: 56

Unit 1

Introduction : Some Common Network Problems, Network Management: Goals, Organization, and Functions, Current

Status and Future of Network Management.Network Management Standards, Network Management Model, Organization

Model, Information Model Management Information Trees, Managed Object Perspectives, Communication Model,

ASN.1- Terminology, Symbols, and Conventions, Objects and Data Types, Object Names, An Example of ASN.1 from

ISO 8824, Encoding Structure, Macros, Functional Model.Unit 2SNMPv1 Network Management - Managed Network: The History of SNMP Management, Internet Organizations and

standards, Internet Documents, The SNMP Model, The Organization Model, System Overview. The Information Model

Introduction, The Structure of Management Information, Managed Objects, Management Information Base. The SNMP

Communication Model The SNMP Architecture, Administrative Model, SNMP Specifications, SNMP Operations,

SNMP MIB Group, Functional Model. SNMP Management RMON : Remote Monitoring, RMON SMI and MIB,

RMONI1- RMON1 Textual Conventions, RMON1 Groups and Functions, Relationship Between Control and Data

Tables, RMON1 Common and Ethernet Groups, RMON Token Ring Extension Groups, RMON2 The RMON2

Management Information Base, RMON2 Conformance Specifications, ATM Remote Monitoring.

Unit 3Broadband Network Management: ATM Networks Broadband Networks and Services, ATM Technology Virtual

Path-Virtual Circuit, TM Packet Size, Integrated Service, SONET, ATM LAN Emulation, Virtual LAN, ATM Network

Management The ATM Network Reference Model, The Integrated Local Management Interface, The ATM

Management Information Base, The Role of SNMP and ILMI in ATM Management, M1 Interface: Management of ATM

Network Element, M2 Interface: Management of Private Networks, M3 Interface: Customer Network Management of

Public Networks, M4 Interface: Public Network Management, Management of LAN Emulation, ATM Digital Exchange

Interface Management.

Unit 4

Broadband Network Management : Broadband Access Networks and Technologies Broadband Access Networks,Broadband Access Technology, HFCT Technology The Broadband LAN, The Cable Modem, The Cable Modem

Termination System, The HFC Plant, The RF Spectrum for Cable Modem, Data Over Cable Reference Architecture, HFC

Management Cable Modem and CMTS Management, HFC Link Management, RF Spectrum Management, DSL


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1.

Identify the common problems in various networks and study of the introductory aspects of network

management

2.

Analyze the history of SNMP management and study of different communication models of SNMP and RMON

3.

Analyze the ATM networks, broadband networks, broadband network services and their management.

4.

Understand the broadband networks and their technologies

5. Design and build Configuration Management, Network Provisioning, Inventory Management, Network

Topology and Fault Management applications


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Course Ti tle: VLSI Design and Algorithms Cour se Code: CSPE729


Type of Course: L ectur e, Practi cal Total Contact Hours: 56

Unit 1

Digital Systems and VLSI: Why design Integrated Circuits? Integrated Circuits manufacturing, CMOS Technology,

Integrated Circuit Design Techniques, IP-based Design, Fabrication and Devices: Fabrication Processes, Transistors,

Wires and vias, SCMOS Design Rules, Layout design and tools.

Unit 2

Logic Gates1: Combinatorial logic functions, Static Complementary gates, Switch Logic, Logic Gates 2:Alternativegate Circuits, Low Power gates, Delay through resistive interconnect, Delay through inductive interconnect, Design for

yield, Gates as IP.

Unit 3Combinational Logic Networks: Standard cell-based layout, Combinatorial network delay, Logic and interconnect

design, Power Optimization, Switch logic networks, Combinational logic testing.

Unit 4Sequential Machines: Latches and Flip-flops, Sequential systems and clocking disciplines, Clock generators, Sequential

systems design, Power optimization, Design validation, Sequential testing, Architecture Design:Register Transfer design,High Level Synthesis, Architecture for Low Power, Architecture testing.

Unit 5

Design Problems and Algorithms: Placement and Partitioning: Circuit Representation, Wire-length Estimation, Types

of Placement Problems, Placement Algorithms, Constructive Placement, Iterative Improvement, Partitioning, The

Kernighan-Lin Partitioning Algorithm. Floor Planning: Concepts, Shape functions and floor plan sizing,Routing: Types of

Local Routing Problems, Area Routing, Channel Routing, Introduction to Global Routing, Algorithms for Global Routing

Text Books:1.

Wayne Wolf: Modern VLSI Design - IP-Based Design, 4 thEdition, PHI Learning, 2009

2.

Sabih H. Gerez: Algorithms for VLSI Design Automation, 2ndEdition, Wiley India, 2011.


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Course Exit Survey FormDept of CSE, MSRIT, Bangalore

Name & USN of the student: Course code:

Contact details: Course name:

Sl

No.Question

Responses

Excellent Very Good Good Satisfactory Poor

1. Quality of the course content

2.For the number of credits, the courseworkload was

3. Relevance of the textbook to this course

4.Ideas/Concepts that you have found difficultto grasp

List

5.Concepts/topics that should be removed fromthe syllabus

List

6. New inclusions in the syllabus List

7.Were the lectures clear/well organized and

presented at a reasonable pace?Yes/No

8. Did the lectures stimulate you intellectually? Yes/No

9.What approaches/aids would facilitate yourlearning? You can check multiple options.

Lectures/ Programming Assignments/ Presentations/ Tutorials/ Demonstrations/ PracticalExercises/ Mini projects/ Group discussions/ Student seminars/ Expert guest lectures

10.

Did the problems worked out in theclassroom help you to understand how to

solve questions on your own?

Yes/No

11.Is the grading scheme clearly outlined andreasonable/fair?

Yes/No

12.Are the assignment/lab experiment

procedures clearly explained?Yes/No

13. Attainment level of CO1

14.

Attainment level of CO215. Attainment level of CO3



Signature of the student with date


52/55

Midsem Survey FormDept of CSE, MSRIT, Bangalore

Name & USN of the student: Course code:

Contact details: Course name:

Sl

No.Question

Responses

Excellent Very Good Good Satisfactory Poor

18. Quality of the course content

19.For the number of credits, the courseworkload was

20. Relevance of the textbook to this course

21.Ideas/Concepts that you have found difficultto grasp

List

22.Concepts/topics that should be removed fromthe syllabus

List

23. New inclusions in the syllabus List

24.Were the lectures clear/well organized and

presented at a reasonable pace?Yes/No

25. Did the lectures stimulate you intellectually? Yes/No

26.What approaches/aids would facilitate yourlearning? You can check multiple options.

Lectures/ Programming Assignments/ Presentations/ Tutorials/ Demonstrations/ PracticalExercises/ Mini projects/ Group discussions/ Student seminars/ Expert guest lectures

27.

Did the problems worked out in theclassroom help you to understand how to

solve questions on your own?

Yes/No

28.Is the grading scheme clearly outlined andreasonable/fair?

Yes/No

29.Are the assignment/lab experiment

procedures clearly explained?Yes/No


31.

Attainment level of CO232. Attainment level of CO3

Signature of the student with date


53/55

Employer Survey FormDept of CSE, MSRIT, Bangalore

Name of the Company:

Name & Designation of the assessor:

Assessors contact details:

Name & Designation of the employee:

Experience (in yrs) of the employee under the current assessor:

Sl.

No.Questions

Responses

Strongly

agreeAgree Neutral Disagree

Strongly

Disagree

NA/

Cant

say

1.He/She is sufficiently capable of applying mathematics and science

to solve engineering problems in your field

2.He/She is capable of identifying and formulating problems in

engineering field

3.He/She is quite innovative and can design engineering products,

processes or service

4.He/She is capable of comprehending and analyzing the real lifeengineering problems

5.He/She is capable of designing and conducting engineeringexperiments on their own and satisfactorily interpret the results

6.He/She possesses skills to handle modern machines and software toanalyze engineering problems

7. He/She is well aware of professional and ethical responsibilities

8. He/She is well inclined to life-long learning

9.He/She gels well with coworkers/colleagues when they are a part of

teams problem solving effort and can take leadership role too.

10.

He/She is able to see engineering problems in the backdrop of

contemporary issues, and able to explain the impact of theirengineering solution o