msrit 7-8 sem syllabus book

M.S. RAMAIAH INSTITUTE OF TECHNOLOGY BANGALORE (Autonomous Institute, Affiliated to VTU) Computer Science and Engineering

History of the Institute M. S. Ramaiah Institute of Technology was started in 1962 by the late Dr. M.S. Ramaiah, our Founder 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 an institute 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 personal commitment has seen the institution through its formative years. Today, MSRIT stands tall as one of India’s finest names in Engineering Education and has produced around 35,000 engineering professionals who occupy responsible positions across the globe.

History of the Department Department of Computer Science Year of Establishment Names of the Programmes Programm es offered

1984 1.UG: 1. UG: B.E. in Computer science and Engineering 2. PG: M.Tech. in Computer Science and Engineering 3. Ph.D 4. M.Sc(Engg.) by research

History of the Institute M. S. Ramaiah Institute of Technology was started in 1962 by the late Dr. M.S. Ramaiah, our Founder 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 an institute 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 personal commitment has seen the institution through its formative years. Today, MSRIT stands tall as one of India’s finest names in Engineering Education and has produced around 35,000 engineering professionals who occupy responsible positions across the globe.

History of the Department Department of Computer Science Year of Establishment Names of the Programmes Programm es offered

1984 1.UG: 1. UG: B.E. in Computer science and Engineering 2. PG: M.Tech. in Computer Science and Engineering 3. Ph.D 4. M.Sc(Engg.) by research

Faculty Sl. No. 1.

Name Dr. K G Srinivasa

Qualification M.E, Ph.D

2.

Dr. Ramamurthy Badrinath

Ph.D

3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22.

Dr. R. Srinivasan Dr. S. Ramani Dr. Anita Kanavalli Dr. Seema S Dr. Annapurna P. Patil Jagadish S Kallimani D.S. Jayalakshmi Dr. Monica R Mundada Sanjeetha R A Parkavi Veena GS J Geetha T.N.R. Kumar Mamatha V. Chethan C T Sini Anna Alex Vandana Sardar Meera Devi Mallegowda M Divakar Harekal

D.Sc. Ph.D M.E., Ph.D M.S., Ph.D M. Tech, Ph.D M.Tech, (Ph.D) M.Sc(Engg), (Ph.D) M.Tech, Ph.D M.Tech M.E. (Ph.D) M.Tech (Ph.D) M.Tech, (Ph.D) M. Tech (Ph.D) M.Tech B.E. M.E, (Ph.D) M.E. M.Tech M.Tech M.E.

Designation Professor AICTE-INAE distinguished Visiting Professor Professor(Emeritus) Professor(Emeritus) Professor Associate Professor Associate Professor Associate Professor Associate Professor Associate Professor Assistant Professor

Assistant Professor Assistant Professor Assistant Professor Assistant Professor Assistant Professor Assistant Professor Assistant Professor Assistant Professor Assistant Professor Assistant Professor Assistant Professor

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 Policy “We 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 holders concerned”.

Vision and Mission of the Department Vision

To build a strong learning and research environment in the field of Computer Science and st Engineering that responds to the challenges of 2 1 century. Mission

To produce computer science graduates who, trained in design and implementation of computational systems through competitive curriculum and research in collaboration with industry and other organizations.

Process for Defining the Vision and the Mission of the Department

Programme 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 Co mputer Science & Engineering or a related field utilizing his/her education and contribute to the profession as an excellent employee, or as an entrepreneur

PEO Derivation Process

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 engineering practice. 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 and cultural 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

Mapping of PEOs and POs Sl. No.

1 2 3 4

5

Programme Educational Objectives Excel in career Life-long learning Research and Innovations Work in diverse teams Leadership and contribution to society

Programme Outcomes PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

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Curriculum Breakdown Distribution Sl. No. 1 2 3 4 5 6

Courses Basic Science Core Courses Basic Engineering Science Core Courses Humanities and Social Science Core Courses Professional Courses and Electives Major Project Mandatory Learning Courses

Weightage 13% 13% 3% 62% 9% 0%

X

Board of Studies for the Term 2014-2015 Dr. K G Srinivasa Dr. Anita Kanavalli Prof. Seema S Dr. Annapurna Patil Prof. Jayalakshmi D S Prof. Sanjeetha R

Chairperson Member Member Member Member Member

Dr. R. Srinivasan Dr. T. S. B. Sudarshan, Head, Amrita School of Engg, Bangalore Dr. Kavi Mahesh, Professor, PESIT Dr. N.K. Srinath, Professor, RVCE

Member Member

4. Two experts in the subject from outside the college

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

Member Member

5. One expert from outside the college, nominated by the Vice Chancellor

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

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

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. Shyam Vasudev, Director, Philips Healthcare Dr. R Badrinath, HP Labs, India Mr. Lawrence Mohanraj, IBM Mr. Sachin Kumar R.S., IBM

Member Member

Member Member Member Member

Member Krishnaprasad Bangalore

C,

Qikwell

Technologies,

Department Advisory Board for the term 2014-2015

1. Head of the Department concerned

Dr. K G Srinivasa

Member

2. Experts from other organizations for Department Advisory Board

Dr. L M Patnaik, Honorary Professor, IISc

Member

Prof. Rajkumar Buyya, Director, CLOUDS Lab, Dept of Computing and Information Systems, University of Melbourne

Member

Dr. T S B Sudarshan Professor and Chair, Dept of CSE, Amrita School of Engg, Bangalore

Member

Industry Advisory Board for the Term 2014-2015 1. Head of the Department concerned

Dr. K G Srinivasa

Member

2. Experts from industry constituting the Industry Advisory Board

Dr. Badrinath Ramamurthy, HP Labs, India Dr. N.C. Narendra, CTS Mr. Raghu Hudli, Object orb Mr. Sreekanth Iyer, IBM Mr. Nishant Kulkarni, IBM Mr. Rohith Athanikar, Yahoo Mr. Pramod N., Thoughtworks Inc

Member Member Member Member Member Member Member

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

1 2

CSPE710 CSPE712

3

CSPE715

VII Semester Code Subject CS721 Advanced Computer Architecture CS725 Computer Graphics & Visualization CS723 Project Management & Engineering Economics CS724 Cryptography and Network Security Elective – 4 Elective – 5 Open Elective CSL716 High Performance Computing Laboratory CSL712 Computer Graphics Laboratory

Total Credits: 25 L T P Credit 3 0 0 3 3 0 0 3 3 0 0 3

VIII Semester Code Subject Elective - 6 CS812 Project CS813 Seminar (for Regular Students) CS8T1 Technical Seminar (for Lateral Entry students)

Total Credits: 24 L T P Credit * * * 4 - 18 18 2 2 1 1

3 * * * 0 0

1 * * * 0 0

0 * * * 1 1

4 3 4 3 1 1

VII semester / VIII Semester Elective 4 / Elective 5 / Elective 6 Bio Informatics (3:0:0) 8 CSPE731 Cloud Computing ( 3:0:0) Distributed Systems (3:0:1) 9 CSPE719 Wireless Networks and Mobile Computing (4:0:0) Mining (3:0:0) 10 CSPE720 Business Intelligence & Applications

Course Title: Advanced Computer Architecture

Course Code: CS721

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

Core/ El ective: Core

Type of course: L ecture

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 Amdahl’s 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 in Technology, Trends in Cost, Dependability, Measuring Reporting and Summarizing Performance, Quantitative Principles of Computer Design, Introduction to Pipelining and Pipeline Hazards. Unit 2 Instruction – Level 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 Thread – Level Parallelism: Introduction, Centralized Shared-Memory Architectures, Performance of symmetric shared memory Multiprocessors, Distributed Shared Memory and Directory-Based Coherence, Synchronization: The Basics,

Internal Assessment Tests

When/ Where (Frequency in the course) Thrice(Average of the best two will be computed)

Laboratory Test

Once

25

Test Data Sheets

1,2,3,4,5

End of Course (Answering 5 of 10 questions)

100

Answer scripts

1,2,3,4,5,

Middle of the course

-

Questionnaire

End of the course

-

Questionnaire

What

t n e m s s s d e o s s h t A e t M c e r i D

t n s t e d c e o r m s h i t d s e n e s I s M A

E I C

To Whom

Max Marks

Evidence Collected

Contribution to Course Outcomes

25

Blue Books

1,2,3,4,5

Students E E S

Standard Examination

Middle of the course survey End of Course Survey

Students

1, 2, 3 Delivery of the course 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. M apping Cour se Outcomes with pr ogram Out comes:

Course Ti tle : Graphics and Visualization Credits (L:T :P) : 3:0:0 Type of Cour se: Lecture

Course Code: CS725 Core/Elective: Core Total Contact Hours : 42

Prerequisites: Nil Cour se Objectives:

At the end of the course th e students should be able to: 1. Identify the software and hardware components of a computer graphics system, 2. Understand basics of OpenGL API’s 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 1 Introduction: Applications of computer graphics, A graphics system, Images: Physical and synthetic, Imaging Systems, The synthetic camera model, The programmer’s interface, Graphics architectures, Programmable Pipelines, Performance Characteristics, Graphics Programming: 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 Event Driven Input, Menus, Picking, A simple CAD program, Building Interactive Models, Animating Interactive Programs, Design of Interactive 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 3 Transformations: Geometric Objects and Transformations, Transformation in Homogeneous Coordinates, Concatenation of Transformations, OpenGL Transformation Matrices, Interfaces to three-dimensional applications, Quaternion’s. Implementation: Basic Implementation Strategies, Four major tasks, Clipping, Line-segment clipping, Polygon clipping, Clipping of other primitives. Clipping in three dimensions, Rasterization, Bresenham’s algorithm, Polygon Rasterization, Hidden -surface removal, Antialiasing, Display considerations. Unit 4 Viewing : Classical and computer viewing, Viewing with a Computer, Positioning of the camera, Simple projections, Projections in

E E S

t n s t e d c e o m r s h i t d s e e n s I s M A

QUIZ

Once

20

Test Data Sheets

1,2,3,4,5



100

Answer scripts

1,2,3,4,5,


-

Questionnaire

End of the course

-

Questionnaire

Middle of the course survey End of Course Survey

Students

1, 2, 3 Delivery of the course 1, 2,3,4,5 Effectiveness of Delivery of instructions & Assessment Methods

Cour se Outcomes:

At the end of the course th e students will be able to: 1. Describe the software and hardware components of a computer graphics system, Graphics Architecture and basics of OpenGL API’s. 2. Identify the input and output d evices 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 their matrix 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. M apping Cour se Outcomes with Progr amme Outcomes:

Course Outcomes

Describe the software and hardware components of a computer graphics system, Graphics Architecture and basics of OpenGL API’s. Identify the input and output devices of graphics system and design interactive graphics programs using OpenGL. 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

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Course Ti tle : Project Management & Engineering Economics

Cour se Code : CS723

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


Type of Course: L ectur e, Semi nar


Prerequisites: NIL Course Objectives:

This course will help students to achieve the following objectives: 1. 2. 3. 4. 5.

Understand the basic concepts of engineering economics and time value of money Compare investment alternatives based on present worth, future worth and equivalent annual worth. Distinguish the different rates of returns. Understand the basics of project management, project phases and project cycles. 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 of Assets 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 Comparison Method. 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 with other 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 project management, project phases and project cycle, Context of I T projects. Strategic Planning and Project Selection: Preliminary scope statements, project management plans, project execution, monitoring and control of project work,

Course Assessment and Evaluation Scheme: What

t n e m s CIE s s d e o s s h t A e t M c e r i SEE D t n s t e d c e o r m s h i t d s e n e s I s M A

To Whom

Internal Assessment Tests Quiz/ Case study

Students


Midsem survey End of Course Survey

When/ Where (Frequency in the course) Thrice(Average of the best two will be computed)

Max Marks

Evidence Collected


30

Blue Books

1,2,3,4 &5

Once

20

Quiz Answers/ Reports

1-5


100

Answer scripts

1,2,3,4 &5


-

Feedback forms

1, 2 & 3 Delivery of the course

Questionnaire

1, 2, 3, 4, 5 & 6 Effectiveness of Delivery of instructions & Assessment Methods

Students End of the course

-

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 Ti tle : Cryptography & Network Security


Credits (L :T :P) : 3:1: 0


Type of Course: L ecture


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 Management 5. 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 II Private 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 V Security at the Application Layer : PGP and S/MIME: Email, PGP, S/MIME.

t n e m s s s d e o s s h t A e t M c e r i D t n e m s s s d e s o s h A t e t c M e r i d n I

CIE

Internal Assessment Tests Project

SEE

Students


Students Feedback

Thrice(Average of the best two will be computed)

30

Blue Books

1,2,3,4 &5

Once

20

Project and Report

2,3 & 4


100

Answer scripts

1,2,3,4 &5


-

Feedback forms

1, 2 & 3, Delivery of the course

End of the course

-

Questionnaire

4, 5 & Effectiveness of Delivery of instructions & Assessment Methods

Students End of Course Survey

. Course Outcomes: At the end of the course students should be able to: 1. Understand the security goals and the threats to security 2. Understand Private Key Cryptosystems and Identify and formulate the type of encryption method DES or AES depending on the need and security threat perception 3. Demonstrate the implementation of hash function and Digital Signatures and its utility 4. Describe the fundamentals of Key Management and Identity Management 5. Understand different ways in which security goal is achieved at application layer, transport layer and network layer. Mapping Course Outcomes with Program Outcomes:

Course Outcomes

Program Outcomes

Course Ti tle : Graphics and Visualization Lab Credits (L:T :P) : 0:0:1 Type of Cour se: Practical

Course Code: CSL712 Core/Elective: Core Total Contact Hours : 28

Prerequisites: Nil Cour se Objectives:

At the end of the course th e 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 advanced technologies for 3D modelling, high performance rendering. Cour se Contents:

Part A: Using C++ and OpenGL API’s, students are required write programs on the f ollowing topics: 1. Input Interactions 2. 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, 5th Edition, Pearson Education, 2011.

Cour se Outcomes:

At the end of the course th e students will be able to: 1. Learn basic and fundamental computer graphics techniques; 2. Gain greater insight into important Op enGL 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. M apping Cour se Outcomes with Progr amme Outcomes:

Course Outcomes

Describe the software and hardware components of a computer graphics system, Graphics Architecture and basics of OpenGL API’s. Identify the input and output devices of graphics system and design interactive graphics programs using OpenGL. 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 their matrix formulations. Identify different types of viewing and projections in OpenGL and derive their matrix formulations. Apply the rendering and shading techniques to 3D graphics using OpenGL.

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Course Ti tle : Project


Credits (L :T :P) : 0:0:18


Type of Cour se: 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 a co-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 Department’s 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 Seminar Course Title: Credits (L:T:P) : 0:0:2 Type of Course: Practical  

Year: 2013-14

Course Code: CS813 Core/ El ective: Core 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 the Department. Students will be guided by their project guides in the selection of topic and preparation for the seminar.

Course Ti tle : Service Oriented Architecture

Cour se Code : CSPE717

Credits (L: T: P) : 4:0:0

Core/ El ective: Elective



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 2 Web 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 3 Principles 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 objectorientation; Native Web service support for service- orientation principles Unit 4 Service Layers: Service-orientation and contemporary SOA; Service layer abstraction; Application service layer, Business service layer, Orchestration service layer; Agnostic services; Service layer configuration scenarios. Business Process

SEE

t n e m s s e s s A

Surprise Quiz

Once

10

Quiz Answers

Recollection Skills



100

Answer scripts

1,2,3 & 4


-

Feedback forms

1, 2, 3 Delivery of the course

End of the course

-

Questionnaire

1, 2 ,3, 4, 5 Effectiveness of Delivery of instructions & Assessment Methods

Students Feedback End of Course Survey

Students

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 Outcomes Distinguish between Web Service and Service oriented Architecture Identify the principles of contemporary SOA Recognize the layers of Service Oriented Architecture Evaluate how the service oriented principles are inter related with each other. Categorize SOA support in J2EE and SOA

Program Outcomes 1

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Course Ti tle : Information Storage and management




Type of Course : Lecture/Seminar


Prerequisites :

The student should have undergone the course on COMPUTER NETWORKS/DATA COMMUNICATION Cour se 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 Cont ents:

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 II Data Protection: RAID Implementation Methods, Array Components, Techniques, Levels, Impact on Disk Performance, Comparison, Hot Spares.Intelligent Storage System: Components, Storage Provisioning, Types. Unit III Fibre 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, Unified Storage. Backup Archive and Replication

SEE

t n e m s s s d e s o s h t A e t c M e r i d n I

Class-room Surprise Quiz

Twice(Summation of the two will be computed)


20

Class-room Surprise Quiz

2&3


100

Answer scripts

1,2, 3,4 & 5


-

Feedback forms

1, 2 & 3, Delivery of the course

Questionnaire

1, 2 & 3, Effectiveness of Delivery of instructions & Assessment Methods

Students Feedback Students End of Course Survey

End of the course

-

Course Outcomes: At the end of the course students should be able to: 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 features 5. Recognize steps for business continuity planning for storage in an enterprise. Mapping Course Outcomes with Program Outcomes:

Course Outcomes

Program Outcomes

1

2

3

Understanding of storage design based on application

X

X

Understanding of diferent variants of Raid and

X

4

X

5

X

6

7

8

X

X

X

9

10

11 X

X

X

12

Course Ti tle : Parallel Programming using CUDA Credits (L: T: P) : 3:0:1

Cour se Code : CSPE730 Core/ Elective: Elective

Type of Course: Lecture/Seminar


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

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. Cour se Assessment and evaluati on:

What

s d o h t e t M c t e r n i D e m s s e s s A

t n s t e d c e o r m s h i t s d e e n s I s M A

To Whom

Internal Assessment Tests CIE Mini Projects

SEE

Students

Semester End Examination Students Feedback

When/ Where (Frequency in the course) Thrice (Average of the best two will be computed) Will be carried out by a batch of two students. Evaluation is at the end of the Semester End of Course (Answering 5 of 10 questions)

Max Marks

Evidence Collected


15

Blue Books

1-5

35

Project Report, Code Repository

1,2,4-5

100

Answer scripts

1-5


-

Feedback forms

1-3, Delivery of the course

End of the course

-

Questionnaire

1-5, Relevance of the course

Students End of Course Survey

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

Course Ti tle : Cloud Computing



Core/ Elective: Elective

Type of Course: L ectur e, Practi cal


Prerequisites: NIL Course 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 management 5. Analyze scheduling algorithms for cloud computing systems and cloud security Course Contents: Unit 1 Introduction: 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 2 Cloud Computing Applications & Paradigms: Challenges, existing and new application opportunities, Architectural styles of cloud applications, Workflows coordination of multiple activiti es, 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 in Xen 2.0, vBlades -paravirtualization targeting a x86-64 Itanium processor, A performance comparison of virtual machines, Virtual machine security, The darker side of virtualization, Software fault isolation.

11. Study of Future Grid Text Book: 1. Cloud Computing: Theory and Practice, Dan Marinescu, 1 st edition, MK Publishers, 2013. Reference Books: st 1. Cloud Computing: Theory and Practice, Dan Marinescu, 1 edition, 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 Delivery The course will be delivered through lectures, presentations, classroom discussions, practice exercises and practical sessions. Cour se Assessment an d evaluati on:

What

s d o h t e t M c e r t n i D e m s s e s s A

t n s t e d c e o r m s h i t s d e e n s I s M A

CIE

Internal Assessment Tests Lab test

SEE

To Whom

Students

Semester End Examination

When/ Where (Frequency in the course) Thrice (Average of the best two will be computed) Twice (Average of the two will be computed) End of Course (Answering 5 of 10 questions)

Max Marks

Evidence Collected


25

Blue Books

1-5

25

Data Sheets

1,2,4-5

50

Answer scripts

1-5

Students Feedback


-

Feedback forms


Mid Sem Survey


-

Feedback forms


End of Course

Students

End of the

Questionnaire

1-5, Relevance of

Course Outcomes

Create combinatorial auctions for cloud scheduling algorithms for computing clouds. Assess the Cloud security , the risks involved, its impact and cloud service providers.

Programme Outcomes 1

2

X

X

X

X

3

4

5

6

7

8

X X

X

9

10

X

11

12

Course Ti tle : Big Data and Data Science



Core/ Elective: Elective

Type of Course: L ectur e, Practi cal


Prerequisites: Nil Course Objectives The 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 the term “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, Machine Tanslation, 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, Mean and Standard Deviation, EstimationOnly Analyses Deep Dive into Analysis: Background, Analytic Tasks, Clustering,

Cour se Assessment an d evaluati on:

What

t n s CIE e d t c o m e s h r t s i e e D s s M A SEE n s t e d c e m o r s t h i s t d e e n s s M I A

To Whom

Internal Assessment Tests Lab test

End of Course Survey

Max Marks

Evidence Collected


25

Blue Books

1-5

Once

25

Data Sheets

1,2,4-5


50

Answer scripts

1-5


-

Feedback forms


End of the course

-

Questionnaire


Students

Semester End Examination Students Feedback

When/ Where (Frequency in the course) Thrice (Average of the best two will be computed)

Students

Course Outcomes At the end of the course students should be able to: 1. Identify the differences between Big Data and Small Data 2. Design the programs to analyze big data 3. Demonstrate the analysis of big data 4. 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 Outcomes

Identify the differences between Big Data and Small Data Design the programs to analyze big data

Programme Outcomes 1

2

3

4

5

X X

6

7

8

9

10

X X

X

X

11

X X

12

Course Ti tle : Bio Informatics



Core/ El ective: El ective


Total Contact H ours : 56 Hrs

Unit 1 Introduction to Bioinformatics: branches of Bioinformatics, 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 2 Biological 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 MD simulation packages, Markov Chain and Hidden Markov models, Application of Vitterbi Algorithm, Application and

Course Ti tle : Pattern Recognition



Core/ El ective: El ective


Total Contact H ours : 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 2 Maximum-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.

Course Ti tle : Business Intelligence & Applications




Type of Course: L ecture, Practicals


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 Components – BI 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 of using 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 4 Introduction 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:

Course Ti tle : Software Testing






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 2 Boundary 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.

4. 5.

Recognize approaches for Test Execution : from test case specifications to test cases, Scaffolding, Generic versus specific scaffolding Identify analysis strategies and plans, to Test design specifications documents, to Test and analysis reports.

Course Ti tle : Digital Image Processing






Unit 1 Digitized Image and its properties: Basic concepts, Image digitization, Digital image properties. Image Preprocessing: Image pre-processing: Brightness and geometric transformations, local preprocessing. Unit 2 Segmentation: Thresholding, Edge-based segmentation. Region based segmentation, Matching. Unit 3 Image Enhancement: Image enhancement in the spatial domain: Background, Some basic gray level transformations, Histogram processing, Enhancement using arithmetic / logic operations, Basics of spatial filtering, Smoothing spatial filters, Sharpening spatial filters. Image enhancement in the frequency domain: Background, Introduction to the Fourier transform and the frequency domain, Smoothing Frequency-Domain filters, Sharpening Frequency Domain filters, Homomorphic filtering. Unit 4 Image Compression: Image compression: Fundamentals, Image compression models, Elements of information theory, Error-Free Compression, Lossy compression. Unit 5 Shape representation: Region identification, Contour-based shape representation and description, Region based shape representation and description, Shape classes. Morphology: Basic morphological concepts, Morphology principles, Binary dilation and erosion, Gray-scale dilation and erosion, Morphological segmentation and watersheds. Text Books: 1. Milan Sonka, Vaclav Hlavac and Roger Boyle: Image Processing, Analysis and Machine Vision, 2nd Edition,

Course Ti tle : Multiprocessor Programming




Type of Cour se: L ecture, Tutori als


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, Lamport’s 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 own Reentrant 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, Quiescently Consistent Pools and Counters, Counting Networks, Diffracting Trees, Parallel Sorting, Sorting Networks, Sample

Course Ti tle : Multimedia Computing






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,

Course Ti tle : Machine Learning Technique






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 an estimator, bayes estimator, parametric classificaion (Alpaydin ch4 - 4.1-4.5) (Bishop 4.2); Discriminant functions Introduction, Discriminant functions, Least squares classification, Fisher’s 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

Course Ti tle : Distributed Systems






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 a dvances. Unit 4 Time 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:

Course Ti tle : Data Mining






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-based classifier, 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,

Course Ti tle : Web 2.0 and Web Services






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 2 XML: 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:

Course Ti tle : Wireless Networks and Mobile Computing






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, applications Unit 2 Wireless 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 4 Mobile 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

Course Ti tle : Software Architecture






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 2 Architectural 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 the relevant views, Documenting a view, Documentation across views.

Course Ti tle : Network Management




Type of Cour se: L ectur e, Practi cal


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, Organizatio n 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 2 SNMPv1 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 3 Broadband 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

1. 2. 3. 4. 5.

Identify the common problems in various networks and study of the introductory aspects of network management Analyze the history of SNMP management and study of different communication models of SNMP and RMON Analyze the ATM networks, broadband networks, broadband network services and their management. Understand the broadband networks and their technologies Design and build Configuration Management, Network Provisioning, Inventory Management, Network Topology and Fault Management applications

Course Ti tle : VLSI Design and Algorithms




Type of Cour se: L ectur e, Practi cal


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 Gates – 1: Combinatorial logic functions, Static Complementary gates, Switch Logic, Logic Gates – 2: Alternative gate Circuits, Low Power gates, Delay through resistive interconnect, Delay through inductive interconnect, Design for yield, Gates as IP. Unit 3 Combinational Logic Networks: Standard cell-based layout, Combinatorial network delay, Logic and interconnect design, Power Optimization, Switch logic networks, Combinational logic testing. Unit 4 Sequential 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: th 1. Wayne Wolf: Modern VLSI Design - IP-Based Design, 4 Edition, PHI Learning, 2009

Course Exit Survey Form Dept of CSE, MSRIT, Bangalore Name & USN of the student: Contact details:

Sl No. 1.

2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17.

Question

Quality of the course c ontent For the number of credits, the course workload was Relevance of the textbook to this course Ideas/Concepts that you have found difficult to grasp Concepts/topics that should be removed from the syllabus New inclusions in the syllabus Were the lectures clear/well organized and presented at a reasonable pace? Did the lectures stimulate you intellectually? What approaches/aids would facilitate your learning? You can check multiple options. Did the problems worked out in the classroom help you to understand how to solve questions on your own? Is the grading scheme clearly outlined and reasonable/fair? Are the assignment/lab experiment procedures clearly explained? Attainment level of CO1 Attainment level of CO2 Attainment level of CO3 Attainment level of CO4 Attainment level of CO5

Course code: Course name:

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Very Good

Responses Good

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Midsem Survey Form Dept of CSE, MSRIT, Bangalore Name & USN of the student: Contact details:

Sl Question No. 18. Quality of the course c ontent For the number of credits, the course 19. workload was 20. Relevance of the textbook to this course Ideas/Concepts that you have found difficult 21. to grasp Concepts/topics that should be removed from 22. the syllabus 23. New inclusions in the syllabus Were the lectures clear/well organized and 24. presented at a reasonable pace? 25. Did the lectures stimulate you intellectually? What approaches/aids would facilitate your 26. learning? You can check multiple options. Did the problems worked out in the 27. classroom help you to understand how to solve questions on your own? Is the grading scheme clearly outlined and 28. reasonable/fair? Are the assignment/lab experiment 29. procedures clearly explained? 30. Attainment level of CO1 31. Attainment level of CO2 32. Attainment level of CO3

Course code: Course name:

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Very Good

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List List List Yes/No Yes/No Lectures/ Programming Assignments/ Presentations/ Tutorials/ Demonstrations/ Practical Exercises/ Mini projects/ Group discussions/ Student seminars/ Expert guest lectures Yes/No Yes/No Yes/No

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Employer Survey Form Dept of CSE, MSRIT, Bangalore Name of the Company: Name & Designation of the assessor: Assessor’s contact details: Name & Designation of the employee: Experience (in yrs) of the employee under the current assessor:

Responses Sl. No.

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

Questions

Strongly agree

Agree

Neutral

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Strongly Disagree

NA/ Can’t say

He/She is sufficiently capable of applying mathematics and science to solve engineering problems in your field He/She is capable of identifying and formulating problems in engineering field He/She is quite innovative and can design engineering products, processes or service He/She is capable of comprehending and analyzing the real life engineering problems He/She is capable of designing and conducting engineering experiments on their own and satisfactorily interpret the results He/She possesses skills to handle modern machines and software to analyze engineering problems He/She is well aware of professional and ethical responsibilities He/She is well inclined to life-long learning He/She gels well with coworkers/colleagues when they are a part of team’s problem solving effort and can take leadership role too. He/She is able to see engineering problems in the backdrop of contemporary issues, and able to explain the impact of their engineering solution on those issues He/She is able to easily communicate even complex technical ideas/thoughts to their colleagues He/She has appreciated the need for multi-disciplinary approach to solve modern engineering problems

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Alumni Survey Form Dept of CSE, MSRIT, Bangalore Name: Year of graduation: Name of the degree: Sl No. 1. 2. 3. 4. 5. 6. 7. 8. 9.

10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27.

Organization: Contact details:

Questions

Responses

Do you work for a tier 1, tier 2 or a tier 3 company? How many promotions have you received so far? (In figures) What position do you hold currently? Have you made significant technical contributions to your employer or research group? (Y/N). Indicate its nature if yes. Have you served as a leader of a computer engineering project or design team? Have you authored or co-authored any technical white papers/proposals? Have you mentored any junior employee/intern/new hire? Have you taken any significant decisions requiring you to analyze engineering/business tradeoffs? How do you rate your contribution towards delivering a product/process? You're Reading a Preview Have you enrolled/completed higher studies? (Y/N) If yes, indicate the degree obtained / e nrolled and the corresponding University/ Institute. Unlock full access with a free trial. Have you learnt a new skill, tool, or system independently during your career? How many certification courses do you have in your credit? (In figures) How many technical conferences/ symposiums/ workshops/ tutorials have you attended during your employment? Download Free Trial Are you a member of any professional body (IEEE, ACM etc)? (Y/N)With If yes, which? How many papers have you published in journal/ conference? How many patents have you under your credit or have you applied for? Have you encountered situations in your workplace that required you to make an ethical decision? How often have you utilized the existing knowledge in varied applications? How often have you worked across teams consisting of people from diverse disciplines, cultures and nationalities? Have you made effective utilization of tools for collaboration such as teleconferencing, video conferencing, etc? Have you been able to communicate effectively with your clients/teammates? Have you taken appropriate decisions regarding delegation of work, allocation of resources (time, man power, and hardware and software assets) and responsibilities? Do you have the ability to foresee a problem and take appropriate team decisions to resolve it? Have you been elected or appointed a to leadership position in a professional society? Have you participated in/lead any competitive activities like team sports, quiz, debates, e tc.? Have you participated in/lead any community outreach activities as in cultural events, civic actions, health initiatives? Do you have any suggestions for improving the BE program curriculum, courses, assessments, skills?

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msrit 7-8 sem syllabus book

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