2018 Batch VI Semester CSE

0

2018 Batch VI Semester CSE

S.No Course Code Course Name Dept. Instructor No of

Credits

1 CS 302 Artificial Intelligence CSE Prof. Kedar Khandeparkar 6

2 CS 304 Operating Systems CSE Prof. Gayathri

Ananthanarayanan 6

3 CS 406 Compilers CSE Prof. Nikhil Hegde 6

4 CS 312 Artificial Intelligence Lab CSE Prof. Kedar Khandeparkar 3

5 CS 314 Operating Systems Lab CSE Prof. Gayathri

Ananthanarayanan 3

6 CS 316 Compilers Lab CSE Prof. Nikhil Hegde 3

7 Elective III 6

Total credits 33

1

Syllabus

Name of Academic Unit: Computer Science and Engineering

Level: UG

Programme: B.Tech.

i Title of the course CS 302 Artificial Intelligence

ii Credit Structure (L-T-P-C) (3-0-0- 6)

iii Type of Course Core

iv Semester in which normally to be

offered

Spring

v Whether Full or Half Semester

Course

Full

vi Pre-requisite(s), if any (For the

students) – specify course

number(s)

vii Course Content Search: Problem representation; State Space Search; A*

Algorithm and its Properties; AO* search, Minimax and

alpha-beta pruning, AI in games. Logic: Formal Systems;

Notion of Proof, Decidability, Soundness, Consistency and

Completeness; Predicate Calculus (PC), Resolution

Refutation, Herbrand Interpretation, Prolog. Knowledge

Representation: PC based Knowledge Representation,

Intelligent Question Answering, Semantic Net, Frames,

Script, Conceptual Dependency, Ontologies, Basics of

Semantic Web. Leaning: Learning from Examples, Decision

Trees, Neural Nets, Hidden Markov Models, Reinforcement

Learning, Learnability Theory. Uncertainty: Formal and

Empirical approaches including Bayesian Theory, Fuzzy

Logic, Non-monotonic Logic, Default Reasoning. Planning:

Blocks World, STRIPS, Constraint Satisfaction, Basics of

Probabilistic Planning. Advanced Topics: Introduction to

topics like Computer Vision, Expert Systems, Natural

Language Processing, Big data, Neuro Computing, Robotics,

Web Search.

viii Texts/References Main Text:

1. Stuart J. Russel, Peter Norvig, Artificial Intelligence: A

Modern Approach (3rd ed.). Upper Saddle River: Prentice

Hall, 2010.

Other references:

1. N.J. Nilsson, Principles of Artificial Intelligence, Morgan

Kaufmann, 1985.

2. Malik Ghallab, Dana Nau, Paolo Traverso, Automated

Planning: Theory & Practice, The Morgan Kaufmann Series

in Artificial Intelligence, 2004.

3. Christopher Bishop, Pattern Recognition and Machine

Learning, Springer, 2006.

4. Mark Stefik, Introduction to Knowledge Systems, Morgan

Kaufmann, 1995. E. Rich and K. Knight, Artificial

Intelligence, Tata McGraw Hill, 1992.

ix Name(s) of Instructor(s) -

x Name(s) of other Departments/

Academic Units to whom the

course is relevant

No

2

xi Is/Are there any course(s) in the

same/ other academic unit(s)

which is/ are equivalent to this

course? If so, please give details.

No

x Justification AI is taught traditionally as it is driving force behind many

concepts in computer science and it is also precursor to

advanced courses like machine learning.

3


Level: UG

Programme: B.Tech.

i Title of the course CS 304 Operating Systems

ii Credit Structure (L-T-P-C) (3-0-0-6)


iv Semester in which normally to

be offered

Spring


Course

Full



number(s)

Exposure to Computer Architecture

vii Course Content Process Management, Memory Management, Storage

Management, Protection and Security, Virtual Machines,

Distributed Systems

viii Texts/References 1. Avi Silberschatz, Peter Baer Galvin, Greg Gagne,

“Operating Systems Concepts" 9th edition. Wiley.

2. Andrew S. Tanenbaum, Herbert Bos, ``Modern Operating

Systems”, 4th edition. Pearson.




course is relevant

Electrical Engineering





No

xii Justification/ Need for

introducing the course

Fundamental course in Computer Science and Engineering.

4


Level: B. Tech./MS

Programme: B.Tech./MS

i Title of the course Compilers

ii Credit Structure (L-T-P-C) 3-0-2-8

iii Type of Course Elective

iv Semester in which normally to be offered Autumn

v Whether Full or Half Semester Course Full

vi Pre-requisite(s), if any (For the students)

– specify course number(s)

Exposure to Data Structures and Algorithms,

Computer Architecture, Automata Theory

vii Course Content The compiled and interpreted execution models.

Lexical analysis and parsing using lex and yacc.

Scope and visibility analysis. The role of types.

Type analysis of a language with basic types,

derived types, parametric polymorphism and

subtypes. Binding times. Data layout and lifetime

management of data. Stack and heap as storage

structures. Implementation of function calls.

Activation records structures. Dynamic memory

allocation and Garbage collection.

Implementation of higher order functions -

closures. Implementation of control structures,

exception handling. Implementation of object

oriented concepts -- objects, inheritance and

dynamic dispatch. Implementation of a naive

code generator for a virtual machine. Security

checking of virtual machine code.

viii Texts/References 1. Alfred V. Aho, Monica S. Lam, Ravi Sethi and

Jeffrey D.Ullman: Compilers: Principles,

Techniques, and Tools, 2/E, AddisonWesley

2007.

2. Andrew Appel: Modern Compiler

Implementation in C/ML/Java, Cambridge

University Press, 2004

3. Dick Grune, Henri E. Bal, Cerial J.H. Jacobs

and Koen G. Langendoen: Modern Compiler

Design, John Wiley & Sons, Inc. 2000.

4. Michael L. Scott: Programming Language

Pragmatics, Morgan Kaufman Publishers, 2006.

ix Name(s) of Instructor(s)

x Name(s) of other Departments/ Academic

Units to whom the course is relevant

xi Is/Are there any course(s) in the same/

other academic unit(s) which is/ are

equivalent to this course? If so, please

give details.

No

xii Justification/ Need for introducing the

course

The knowledge on compilers helps to understand

how programs written in a high-level language is

converted to machine codes. This helps

programmers to write better programs.

5


Level: UG

Programme: B.Tech.

i Title of the course CS 312 Artificial Intelligence Laboratory




offered

Spring


Course

Full



number(s)

vii Course Content* The lab will closely follow and aim to elucidate the lessons

covered in the theory course CS344. Implementation and

study of A*, Usage of Prolog Inferencing, Expert System

Shells, Neural Net Platforms, Prediction and Sequence

Labeling using HMMs, Simulation of Robot Navigation and

such exercises are strongly recommended.

viii Texts/References Main Text:

1. Stuart J. Russel, Peter Norvig, Artificial Intelligence: A

Modern Approach (3rd ed.). Upper Saddle River: Prentice

Hall, 2010.

Other references:

1. N.J. Nilsson, Principles of Artificial Intelligence, Morgan

Kaufmann, 1985.

2. Malik Ghallab, Dana Nau, Paolo Traverso, Automated

Planning: Theory & Practice, The Morgan Kaufmann Series

in Artificial Intelligence, 2004.

3. Christopher Bishop, Pattern Recognition and Machine

Learning, Springer, 2006.

4. Mark Stefik, Introduction to Knowledge Systems, Morgan

Kaufmann, 1995. E. Rich and K. Knight, Artificial

Intelligence, Tata McGraw Hill, 1992.




course is relevant

No





No

x Justification AI is taught traditionally as it is driving force behind many

concepts in computer science and it is also precursor to

advanced courses like machine learning.

6


Level: UG

Programme: B.Tech.

i Title of the course CS 314 Operating Systems Laboratory




offered

Spring


Course

Full



number(s)

Exposure to Computer Architecture

vii Course Content Laboratory Assignments related to the topics covered in

the theory course: Process Management, Memory

Management, Storage Management, Protection and

Security, Virtual Machines, Distributed Systems

viii Texts/References 1. Avi Silberschatz, Peter Baer Galvin, Greg Gagne,

“Operating Systems Concepts" 9th edition. Wiley.

2. Andrew S. Tanenbaum, Herbert Bos, “Modern

Operating Systems”, 4th edition. Pearson.




course is relevant






No



Fundamental course in Computer Science and

Engineering.

7


Level: B.Tech/MS

Programme: BTech/MS.

i Title of the course Introduction to Compilers Lab




offered

Spring

v Whether full or half semester course Full

vi Pre-requisite(s), if any (for the students)

– specify course number(s)

Exposure to Data Structures and Algorithms,

Computer Architecture, Automata Theory,

and a programming

language such as C/C++/Java.

vi i Course content Design and implementation of a scanner using

scanner generator. Design and implementation

of a parser using parser generator. Symbol

table generation, Semantic actions for

expressions, control structures, and functions.

Implementing liveness analysis and applying

it to register

allocation.

vi ii Texts/References Alfred V. Aho, Monica S. Lam, Ravi Sethi

and Jeffrey D.Ullman: Compilers: Principles,

Techniques, and Tools, 2/E, AddisonWesley

2007.

Andrew Appel: Modern Compiler

Implementation in C/ML/Java, Cambridge

University Press, 2004

Dick Grune, Henri E. Bal, Cerial

J.H. Jacobs and Koen G. Langendoen:

Modern Compiler Design, John Wiley &

Sons, Inc. 2000.

Michael L. Scott: Programming Language

Pragmatics, Morgan Kaufman Publishers,

2006.

Fisher and LeBlanc: Crafting a

Compiler in C.

ix Name (s) of the instructor (s) Nikhil Hegde

x Name (s) of other departments /

Academic Units to whom the course is

relevant

EE



equivalent to this course? If so, please give

details.

No

xii Justification/ Need for introducing

the course

The knowledge on compilers helps to understand

how programs written in a high- level language is

converted to machine codes. This helps

programmers to write

better programs.

Electives For VI Semesters

S.N

o

Course

Code Course Name Instructor

No of

Credits

1 CH 302

Sustainable energy and

energy materials

Prof. Rajeshwara Rao

Prof. Sudheer Siddapureddy

Prof. Pratyasa Bhui 6

2 PH 403 Classical Mechanics Prof. D. Narasimha 6

3 HS 404 Applied Ethics Prof. Jolly Thomas 6

4 HS 406

Introduction to Game

Theory Prof. Gopal Parashari 6

5 MA 402

Discrete mathematics:

Combinatorics and

Codes Prof.N. S. N. Sastry 6

6 CS 408

Statisitcal Pattern

Recognition

Prof. Prabhuchandran

KJ 6

7 CS 412

Statisitcal Pattern

Recognition Lab

Prof. Prabhuchandran

KJ 3

8 EE 408

Neural Networks and

Deep Learning Prof. S R M Prasanna 6

9 EE 409 Speech Processing Prof. S R M Prasanna 6

10 EE 428

Neural Networks and

Deep Learning Lab Prof. S R M Prasanna 3

11 EE 414 Speech Processing lab Prof. S R M Prasanna 3

12 EE 404

Wireless

Communications Prof. Naveen M. B. 6

13 EE 406 VLSI Technology Prof. Ruma Ghosh 6

14 EE 202 Analog circuits Prof. Naveen K 6

15 EE 426

Optimization Theory

and Algorith Prof. Rajshekhar Bhat 6

16 EE 304 Robotics

Prof. Sangamesh

Deepak 6

17 EE 432 Information Theory Prof.Bharath B N 6

18 EE 434

Modeling And control

of Renewable energy

Resources Prof.Abhijit K 6

19 ME 409 Composite Materials Prof.A N Tiwari 6

20 ME 426

Introduction to

Computational Fluid

Dynamics Prof. Dhiraj V Patil 6

21 ME 428

Refrigerator - Air

Conditioning

Prof. S L Bapat 6

22 ME 430

Heat Exchangers

Prof. S V Prabhu 6

23 ME 306 Theory of Elasticity

Prof. Tejas P

Gothkhindi,

Prof. Amar Gaonkar 6

24 ME 407 IC Engines Prof. Surya Prakash 6

25 ME 406

Advanced Finite

Element Methods Prof. Amar Gaonkar 6

Name of Academic Unit: All

Level: UG

Programme: B.Tech.

i Title of the course CH 302 Sustainable energy and energy materials



iv Semester in which normally to be offered Spring



students) – specify course number(s)

First year undergraduate chemistry course (CH101)

vii Course Content Fuel cells, catalysis for fuel cells and sustainable

chemical processes • Batteries • Solar photovoltaics

Wind power: practical aspects • Tidal power •

Inorganic, Organic and functional biomaterials as

energy materials

viii Texts/References

ix Name(s) of Instructor(s) RRM/SSR



relevant

Course is relevant for students across all the

departments




give details.

No


course

Developing sustainable/renewable energy methods

are critical to meet the ever increasing global energy

demands. This course will shed light on various

methods which are currently under practice towards

generating sustainable energy and their detailed

mechanisms.

Classical Mechanics

Name of Academic Unit : PHYSICS

Level : B. Tech

Programme : B. Tech

i Title of the course Classical Mechanics


iii Type of Course Theory

iv Semester in which normally

to be offered

Spring

v Whether Full or Half

Semester Course

Full

vi Pre-requisite(s), if any (For

the students) – specify

course number(s)

None

vii Course Content Mechanics of Particles – Dynamical systems, Phase space dynamics,

stability analysis; Variational Principle, Lagrange's Equations; The

Central Force Motions, Scattering; Rigid Body Dynamics – moment

of inertia tensor; Conservation laws and cyclic coordinates;

Hamilton's Equation of Motion; Canonical Transformations;

Hamilton Jacobi Theory; Classical Perturbation Theory – periodic

motion, small oscillation, normal modes; Special theory of relativity-

Lorentz transformations, relativistic kinematics and mass–energy

equivalence; Optional: Chaos, Hamilton Jacobi Bellman Equation,

Lyapunov function

viii Texts/References 1. Classical Mechanics: H. Goldstein, C. P. Poole, and J. Safko,

Pearson 2011

2. Classical Mechanics: P. S. Jog and N. C. Rana, McGraw Hill,

2017

3. Introduction to Classical Mechanics: David Morin, Cambridge

University Press, 2008.

4. Mechanics: L.D. Landau and E. M. Lifshitz, Butterworth-

Heinemann, 3rd edition, 1982.

ix Name(s) of Instructor(s) Professor D. Narasimha, Department of Physics

x Name(s) of other

Departments/ Academic

Units to whom the course is

relevant

No.





No



Classical Mechanics is a mature field in Science describing the

motion of macroscopic objects. Consequently, most of the proposed

topics will be useful for Mechanical Engineers. The course

introduces topics like Lagrangian, Hamiltonian Formulation, Hamilton Jacobi Bellman equation, Lyapunov function which would

provide powerful techniques very useful in Control theory and other

topics relevant to Electrical & Computer Engineers.

Applied Ethics

Title of the course Applied Ethics

Credit Structure (L-T-P-C) (3-0-0-6)

Type of Course Elective Course

Semester in which normally to be

offered

Spring

Whether Full or Half Semester

Course

Full

Pre-requisite(s), if any (For the


--

Course Content Normative Ethics consists of fundamental theories of morality. The central question in Normative Ethics is the following. What is the standard/norm to

decide the rightness or wrongness of an action? Or what gives an act a moral worth? The following are the main approaches to such questions.

a. Consequentialist Theories

b. Immanuel Kant’s Deontological Ethics

c. Virtue Ethical Theories

Using the theoretical frameworks in Normative Ethics, some actual ethical

issues are studied. Thus, we have some issues or problems in Applied Ethics.

Under Applied Ethics, the following topics will be covered.

Business ethics, institutional ethics, ethics of the media, issues of medical

ethics and environmental ethics.

Texts/References 1.MacKinnon, Barbara, and Andrew Fiala. 2015. Ethics Theory and

Contemporary Issues. CT: Cengage Learning, Stamford, USA 2.Sher, George (ed.) 2012. Ethics: Essential Readings in Moral

Theory.Routledge.New York.

3. Cohen, Andrew I, and Christopher Heath Wellman (eds.) 2005.

Contemporary Debates in Applied Ethics. Blackwell Publishing, Oxford,

UK.

4. Frey R. G, and Christopher Heath Wellman (eds) 2005. A Companion to

Applied Ethics. Wiley-Blackwell, Oxford, UK.

5. Peter, Singer (Ed.).1986. Applied Ethics, OUP, UK.

Name(s) of Instructor(s) Prof. Jolly Thomas

Name(s) of other Departments/

Academic Units to whom the course

is relevant

NA

Is/Are there any course(s) in the

same/ other academic unit(s) which

is/ are equivalent to this course? If

so, please give details.

No

Justification/ Need for introducing

the course

The main objective is to look at some of the actual ethical issues and see how

one can make philosophical arguments regarding such issues. Such philosophical arguments would be stronger or would have more clarity if one

can distinguish between normative ethical concerns from applied ethical

concerns. In other words, to be able to critically think and examine any actual

problem mentioned in the applied ethics, primarily one should be able to

distinguish the normative ethical concerns from applied ethical concerns.

Thus, the objective is to see various approaches in normative ethics. After

that, analyze the problems in applied ethics.

4

Introduction to Game Theory

i Title of the course Introduction to Game Theory


iii Type of Course Elective course

iv Semester in which normally

to be offered Spring/Autumn

v Whether Full or Half

Semester Course Full

vi Pre-requisite(s), if any (For

the students) – specify

course number(s)

Nil

vii Course Content* Definition of games, normal form and strategies, Best response, dominance, Nash equilibrium, Iterated elimination of dominated strategies, Mixed strategies.

Applications: oligopoly, tariffs, crime, conflict, voting and auctions.

Bayesian games and applications. Extensive form games, backward

induction and sub game perfect equilibrium and applications. Perfect

Bayesian equilibrium. Repeated games. Bargaining games and applications.

Viii Texts/References 1. An Introduction to Game Theory by M. O. Osborne, Indian

ed. (2012), Oxford UniversityPress.

2. Game Theory by Drew Fudenberg& Jean Tirole, MIT

Press(1991) 3.Strategy: An Introduction to Game Theory by Joel

Watson, 2nded.(2013), VivaBooks.


***

Gopal Sharan Parashari

x Name(s) of other

Departments/ Academic

Units to whom the course is

relevant

NA

xi Is/Are there any course(s)

in the same/ other academic

unit(s) which is/ are

equivalent to this course? If


NA



This course provides basic to intermediate level of essential concepts in

applied game theory. Game theory issued to model strategic interactions

and finds its use in computer science, economics, politics,

electrical and electronics engineering, biology etc.

Name of Academic Unit: Mathematics

Level: Undergraduate

Programme: B.Tech.

1 Title of the course Discrete Mathematics: Combinatorics and codes

2 Credit Structure (L-T-P-C) L: 3 T: 0 P: 0 C: 6

3 Mention academic programme(s)

for which this course will be a core

course

(Write “elective” if not core for any)

Elective

4 Semester in which normally it is

offered

Tick mark (or underline) appropriate

option(s)

☐ Autumn (August-Nov)

☐ Spring (Jan-Apr)

☐ Summer ( May-July)

5 Whether full or half semester

course


option

☐ Full Semester ☐ Half Semester

6 Course content Designs: t-designs, incidence matrices, Fischer

inequality, symmetric designs, examples, Bruck-Ryser

Chowla theorem, projective spaces and projective planes

Strongly regular graphs: Bose-Mesner algebra, Krein

condition, integrality conditions

Inclusion-exclusion principle, Mobius function, Mobius

inversion formula, applications

Permanents: Bounds on permanents, permanents of

doubly stochastic matrices

Partitions: Partition functions, Ferrers diagrams, Euler

identity, Jacobi triple product product identity, young

tableaux and hook formula

Algebraic codes: Basic bounds, weight enumerator

polynomial; Hamming codes, Macwilliams identity,

codes and symmetric designs

7 Texts/References 1) Van Lint and Wilson: A course in combinatorics,

Cambridge University Press, UK, 2001

2) P.J. Cameron and Van Lint, Graphs, Codes and

Designs, LMS lecture notes, Cambridge University Press,

UK, 2001

8 Name (s) of the instructor (s) N. S. N. Sastry

9 Name (s) of other departments /


is relevant

10 Is/Are there any course(s) in the




No

11 Mandatory Pre-requisite(s) - specify

course number(s)

Linear Algebra, MA 106

12 Recommended Pre-requisite(s) -

specify course number(s)

None

13 Mention 8 to 12 keywords/phrases

about this course that would

facilitate automated course

recommendation and course

interdependency

(These may or may not be from the

syllabus content)

Designs, Strongly regular graphs, projective spaces,

projective planes, Mobius inversion formula, permanents,

stochastic matrices, partition functions, young tableaux,

algebraic codes Mac Williams identity, Jacobi triple

product identity

14 Justification/ Need for introducing

the course

Discrete mathematics is a fundamental intellectual tool in

science and technology. The emphasis on its teaching and

research is rather recent (say since 1950's), and

increasingly becoming important due to the

developments in computer science, information theory

and increasing sophistication in computer algorithms. An

introduction to some basic aspects of discrete

mathematics, particularly finite mathematics,

emphasizing the algebra and geometry over finite fields,

basic counting techniques, finite combinatorial structures,

will be useful for student particularly in computer science

and Information technology. Given the profusion of basic

elementary topics in discrete mathematics, several

introductory courses may be suggested. Here is one

which includes some of its major topics.


Level: B.Tech

Programme: B.Tech/M..S

i Title of the course Statistical Pattern Recognition





vi Prerequisite(s), if any (For the students) – specify course number(s)

Multivariate Calculus and Linear Algebra, Probability, Programming

vii Course Content Bayesian Decision Making and Bayes

Classifier, Parametric and Non Parametric

Estimation of Densities, General Linear

Models, Discriminative Learning based

Models, Dimensionality Reduction

Techniques, Empirical and Structural risk

minimization, Ensemble Methods, Pattern

Clustering

vii

i

Texts/References 1.R.O.Duda, P.E.Hart and D.G.Stork,

Pattern Classification, John Wiley, 2001.

2.C.M.Bishop, Pattern Recognition and

Machine Learning, Springer, 2006.

ix Name(s) of Instructor(s) Prabuchandran K.J.



relevant

EE




give details.

No


course

This course provides theoretical/statistical

underpinnings of pattern recognition and machine learning methods.


Level: B. Tech./MS

Programme: B.Tech./MS

i. Title of the Course Statistical Pattern Recognition Laboratory

ii. Credit Structure L T P C

0 0 3 3

iii. Prerequisite, if any Currently taking statistical pattern recognition theory course

iv. Course Content

(separate sheet may be

used, if necessary)

The lab will closely follow the theory course. The idea is to have the students

implement the basic algorithms on different topics studied in the statistical pattern

recognition theory course.

v. Texts/References


used, if necessary)

1. R.O.Duda, P.E.Hart and D.G.Stork, Pattern Classification, John

Wiley, 2001.

2. C.M.Bishop, Pattern Recognition and Machine Learning, Springer,

2006.

vi. Instructor (s) Prabuchandran K J

vii. Name of departments to

whom the course is

relevant

Computer Science and Engineering, Electrical Engineering and Mechanical

Engineering

viii Justification SPR Laboratory is important to reinforce different concepts that will be studied as part

of the theory course.

Name of Academic Unit: Electrical Engineering

Level: PG/UG

Programme: B. Tech/MS/PhD

i. Title of the Course Neural Networks And Deep Learning (NNDL)


3 0 0 6

iii. Prerequisite, if any Exposure to basic concepts in calculus and probability

iv. Course Content


used, if necessary)

Introduction to Artificial Neural Networks (ANN) and Deep Learning (DL):

Motivation, basics of ANN, overview of PRML, evolution deep learning and

different architectures. Applications of ANN vs DL.

Feedforward Neural Networks (FFNN): Working principle, basic architecture,

analysis of FFNN for different PRML tasks.

Feedback Neural Networks (FBNN): Working principle, basic architecture,

Boltzmann machine, analysis of FFNN for different PRML tasks.

Competitive learning Neural Networks (CLNN): Working principle, basic

architecture, analysis of CLNN for different PRML tasks.

Deep Learning (DL) Architectures: Deep FFNN, Convolutional neural networks

(CNN), Recurrent neural network (RNN), Longterm shortterm memory (LSTM),

Generative adversarial network (GAN), DL architectures with attention mechanism.

Some recent DL architectures.

Applications of DL: speech processing, image processing and other tasks.

v. Texts/References


used, if necessary)

1. B. Yegnanarayana, Artificial Neural Networks, PHI, 1999.

2. Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, MIT

Press, 2016.

vi. Instructor (s) S. R. Mahadeva Prasanna


whom the course is

relevant


Engineering

viii Justification This course aims at providing an overview to the neural networks and deep learning

areas. NNDL being an application area of probability, pattern recognition and machine

learning, the same will be suitable for both electrical engineering and computer science

and engineering students. The course contents include introduction to review of key

neural networks concepts, limitations of them, detailed study of mostly deep

architectures. Comparison of NN and DL architectures on different applications like

speech processing, image processing and NLP.

49


Level: PG/UG


i Title of the course Speech Processing

ii Credit Structure (L-T-P-C) (3 0 0 6)



be offered

Autumn or Spring


Course

Full



number(s)

Exposure to probability concepts.

vii Course Content* Introduction: Speech production and perception, nature of speech;

short-term processing: need, approach, time, frequency and time-

frequency analysis.

Short-term Fourier transform (STFT): overview of Fourier

representation, non-stationary signals, development of STFT,

transform and filter-bank views of STFT.

Cepstrum analysis: Basis and development, delta, delta-delta and

mel-cepstrum, homomorphic signal processing, real and complex

cepstrum.

Linear Prediction (LP) analysis: Basis and development, Levinson-

Durbin’s method, normalized error, LP spectrum, LP cepstrum, LP

residual.

Sinusoidal analysis: Basis and development, phase unwrapping,

sinusoidal analysis and synthesis of speech.

Applications: Speech recognition, speaker recognition, speech

synthesis, language and dialect identification and speech coding.

Viii Texts/References 1. L.R. Rabiner and R.W. Schafer, Digital Processing of Speech

Signals Pearson Education, Delhi, India, 2004

2. J. R. Deller, Jr., J. H. L. Hansen and J. G. Proakis, Discrete-Time

Processing of Speech Signals, Wiley-IEEE Press, NY, USA, 1999.

3. D. O’Shaughnessy, Speech Communications: Human and

Machine, Second Edition, University Press, 2005.

4. T. F. Quatieri, “Discrete time processing of speech signals”,

Pearson Education, 2005.

5. L. R. Rabiner, B. H. Jhuang and B. Yegnanarayana,

“Fundamentals of speech recognition”, Pearson Education, 2009.

ix Name(s) of Instructor(s) *** S R Mahadeva Prasanna



course is relevant

CS

50





No



This course aims at providing an overview to the speech processing

area. Speech processing being an application area of probability, signal

processing and pattern recognition, the same will be suitable for both

electrical engineering and computer science and engineering students.

The course contents include introduction to speech processing, speech

signal processing methods like short term Fourier transform, Cepstral

analysis, linear prediction analysis, sinusoidal analysis. Some of the

applications like speech recognition and speech synthesis will also be

taught.

51


Level: PG/UG


i. Title of the Course Neural Networks And Deep Learning (NNDL) Laboratory


0 0 3 3

iii. Prerequisite, if any Currently taking or already taken NNDL theory course

iv. Course Content


used, if necessary)

The lab will closely follow the theory course. The idea is to have the

students implement the basic algorithms on different topics studied in

the NNDL theory course.

v. Texts/References (separate

sheet may be used, if

necessary)

1. B. Yegnanarayana, Artificial Neural Networks, PHI, 1999.

2. Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep

Learning, MIT Press, 2016.



whom the course is

relevant

Computer Science and Engineering, Electrical Engineering and

Mechanical Engineering

viii Justification NNDL Laboratory is important to reinforce different concepts that will

be studied as part of the theory course.

52


Level: PG/UG


i. Title of the Course Speech Processing Laboratory


0 0 3 3

iii. Prerequisite, if any Currently taking or already taken Speech Processing theory course

iv. Course Content

(separate sheet may

be used, if necessary)

The lab will closely follow the theory course. The idea is to have the students

implement the basic algorithms on different topics studied in the speech

processing theory course.

v. Texts/References

(separate sheet may

be used, if necessary)

1. L.R. Rabiner and R.W. Schafer, Digital Processing of Speech

Signals Pearson Education, Delhi, India, 2004

2. J. R. Deller, Jr., J. H. L. Hansen and J. G. Proakis, Discrete-Time

Processing of Speech Signals, Wiley-IEEE Press, NY, USA, 1999.

3. D. O’Shaughnessy, Speech Communications: Human and

Machine, Second Edition, University Press, 2005.

4. T. F. Quatieri, “Discrete time processing of speech signals”,

Pearson Education, 2005.

5. L. R. Rabiner, B. H. Jhuang and B. Yegnanarayana,

“Fundamentals of speech recognition”, Pearson Education, 2009.


vii. Name of departments

to whom the course is

relevant


Engineering

viii Justification Speech Processing Laboratory is important to reinforce different concepts that

will be studied as part of the theory course.

53


Level: B. Tech. / MS(R) / PhD

Programme: B.Tech. / MS(R) / PhD

i Title of the course Wireless Communication







Signals and Systems, Probability (UG level),

Principles/Fundamentals of Communications

vii Course Content Review of fundamentals in probability theory,

random processes, spectral analysis of deterministic

and random signals; review of digital modulation

schemes, optimal receiver design under additive

white Gaussian noise (AWGN) and error rate

performance; orthogonal frequency division

multiplexing (OFDM); channel modeling, capacity

and diversity techniques in wireless communication;

multi-input multi-output (MIMO) systems and space

time block codes (STBC); cellular communication

systems, multiple-access and interference

management.

viii Texts/References 1) David Tse and Pramod Viswanath,

“Fundamentals Of Wireless Communication,”

Cambridge University Press, 2005.

2) Andrea Goldsmith, “Wireless Communications,”


ix Name(s) of Instructor(s) Naveen M B



relevant

Engineering Physics




give details.

None


course

This is an elective course for Communications spine.

54


Level: PG/UG


1 Title of the Course VLSI Technology

2 Credit Structure L T P C

3 0 0 6

3 Type of Course Elective

4 Semester in which

normally to be offered Even

5 Whether Full or Half

Semester Course Full semester

6 Prerequisite, if any Exposure to Electronic Devices

7 Course Content


used, if necessary)

Introduction on VLSI Design, Bipolar Junction

Transistor Fabrication, MOSFET Fabrication for IC,

Crystal Structure of Si, Defects in Crystal

Crystal growth techniques – Bridgeman, Czochralski

method, Floating-zone method

Epitaxy – Vapour phase Epitaxy, Doping during Epitaxy,

Molecular beam Epitaxy

Oxidation – Kinetics of Oxidation, Oxidation rate

constants, Dopant Redistribution, Oxide Charges, Oxide

Layer Characterization

Doping – Theory of Diffusion, Infinite Source, Actual

Doping Profiles, Diffusion Systems, Ion-Implantation

Process, Annealing of Damages, Masking during

Implantation

Lithography

Etching – Wet Chemical Etching, Dry Etching, Plasma

Etching Systems, Etching of Si, Sio2, SiN and other

materials,

Plasma Deposition Process

Metallization – Problems in Aluminum Metal contacts,

IC BJT – From junction isolation to LOCOS, Problems

in LOCOS, Trench isolation, Transistors in ECL Circuits,

MOSFET Metal gate vs. Self-aligned Poly-gate,

MOSFET II Tailoring of Device Parameters, CMOS

Technology, Latch – up in CMOS, BICMOS

Technology.

55

8 Texts/References


used, if necessary)

1. VLSI Technology by S. M. Sze

2. Silicon VLSI Technology by J.D. Plummer, M. Deal

and P.D. Griffin

3. VLSI Fabrication Principles by S. K. Gandhi

9 Instructor (s) Ruma Ghosh

10 Name of departments

to whom the course is

relevant


11 Justification VLSI is the process of integrating millions of

components (transistors, resistors etc.) in a single small

chip. This course introduces different concepts related to

the processes and steps involved in fabrication of

electronic devices and integrated circuits. This course

develops an understanding of the limitations and strength

of different fabrication techniques which in turn affect

the device performances

56


Level: B. Tech

Programme: B. Tech.

i Title of the course

Analog Circuits

ii Credit Structure (L-T-P-C)

(2 0 2 6)


iv Semester in

which normally to be offered

Spring


Course

Full

vi Pre-requisite(s),

if any (For the

students) –

specify course

number(s)

Analog Circuits

vii Course Content* Review of Single stage amplifiers and differential

amplifier

Cascode amplifiers

2 stage amplifiers (opamp) and its stability and

compensation

Non-idealities of opamps

NMOS output and PMOS output voltage regulators

Current and voltage references

Opamp based circuits

Howland Current source

Instrumentation amplifiers

Logarithmic amplifiers

Non-linear circuits

Multivibrators

A/D and D/A converters, sample and hold circuits

Lab component will contain experiments on Simulation of

amplifier and regulator circuits using NGSpice and

breadboard based experiments on current sources, log

amplifiers and voltage regulators using opamps and

discrete transistors.

Viii Texts/References 1) J.V.Wait, L.P.Huelsman and GA Korn, Introduction to

Operational Amplifier theory and applications, 2nd edition,

McGraw Hill, New York, 1992.

2) J. Millman and A. Grabel, Microelectronics, 2nd edition,

McGraw Hill, 1988.

3) Ramakant Gayakwad, Op-amps and Linear Integrated

Circuit, 4th edition, Pearson, 2000.

4) P. Horowitz and W. Hill, The Art of Electronics, 2nd edition,


57

5) Microelectronics, Behzad Razavi

ix Name(s) of Instructor(s) ***

Naveen K

x Name(s) of other

Departments/

Academic Units

to whom the

course is relevant

None

xi Is/Are there any

course(s) in the

same/ other

academic unit(s)

which is/ are

equivalent to this

course? If so,

please give

details.

No

xii Justification/

Need for

introducing the

course

This is a elective course which introduces advanced topics in

analog circuits, amplifiers and their applications. This course

will give the basis for advanced courses in VLSI, and

microelectronics specializations.

58

Name of Academic Unit: Electrical Engineering Department

Level: Tick mark (or underline) only one of the these: ☐ UG ☐ Masters ☐ PhD

1 Title of the course Optimization Theory & Algorithm




course


EE (Elective)


offered


option(s)





course


option


6 Course content Introduction · Mathematical optimization · Least-squares and linear programming · Convex optimization · Nonlinear optimization

Convex Sets

· Affine and convex sets · Operations that preserve convexity · Generalized inequalities · Separating and supporting hyperplanes · Dual cones and generalized inequalities

Convex functions

· Basic properties and examples · Operations that preserve convexity · Quasiconvex functions · Log-concave and log-convex functions

Convex Optimization problems

· Standard form · Convex and quasiconvex optimization problems · Linear and quadratic optimization · Geometric programming · Generalized inequality constraints · Semidefinite programming

Duality and KKT Conditions

· Lagrange dual problem · Weak and strong duality and geometric interpretation · Optimality and KKT conditions

59

· Perturbation and sensitivity analysis

Algorithms Gradient descent and Newton’s method for unconstrained problems, Equality constrained minimization, Inequality constrained minimization

7 Texts/References 1. Convex Optimization by Stephen Boyd and Lieven Vandenberghe, Cambridge University Press.

2. Convex Analysis by Rockafellar

8 Name (s) of the instructor (s) Rajshekhar V Bhat



course is relevant

CSE





No

11 Mandatory Pre-requisite(s) -


Calculus and Linear Algebra







interdependency


syllabus content)

Convex sets, Convex functions, Lagrangian Dual,

KKT Conditions, Algorithms


the course

This course is one the most important ones for conducting research on wireless communications, machine learning and allied fields. The concepts taught in the course are very generic and they will be useful to a wide set of audience.

60


Level: UG

Programme: B.Tech.

i Title of the course EE 304 Robotics




offered

Spring


Course

Full


students) specify course

number(s)

Undergraduate Control Systems or Engineering

Mechanics

vii Course Content • Introduction

• Actuators and Drives: DC motors, dynamics of

single axis drive systems, Power Electronics basics

etc.

• Sensors and control components: Robot control

using PWM amplifiers, microcontrollers etc.

• Robot Mechanisms: Robot linkages and joints

• Planar Kinematics: Planar kinematics of serial link

mechanisms, Kinematics of Parallel Link

Mechanisms etc.

• Differential motion: Properties of Jacobians

• Mechanics of Robots: Statics, Duality of differential

kinematics and statics, robot dynamics, non-

holonomic systems

• Inverse kinematics and trajectory generation

• Concepts of Control: PID control, Hybrid position-

force control, compliance control, torque control

etc.

• Advanced topics and case studies

• Demonstrations and assignments using MATLAB

and ARM based experimental set-ups

61

viii Texts/References 1. Asada, H., and J. J. Slotine. Robot Analysis and

Control. New York, NY: Wiley, 1986.

2. John J. Craig Introduction to Robotics: Mechanics

andControl, Addison-Wesley Publishing Company,

3rd Edition, 2003.

3. M. Spong, M. Vidyasagar, S. Hutchinson, Robot

Modeling and Control, Wiley & Sons, 2005.

4. R. M. Murray, Z. Li, S. Sastry, A Mathematical

Introduction to Robotic Manipulation, CRC press,

1994.

ix Name(s) of Instructor(s) AM



course is relevant

Mechanical Engineering





No



Robotics are being used in the industries for more than

two decades now. With decreasing cost of Electronics,

computational resources, now a day's robots are being

used, now a day, by not only in industries, but also in

the fields of medicine, prosthesis, home assistance,

agriculture and so on. Even after the wide-spread use,

the challenges in the field of Robotics are far from over

and a wide range of problems demanding research in

this field are still open. Due to the blend of immediate

applications as well as scope of research, a course on

Robotics is useful for students who will join the

industries as well as those who wish to pursue research

in this field.

62

Name of Academic Unit: Mechanical Engineering

Level: B. Tech.

Programme: B. Tech.

i Title of the course ‘Composite Materials: Manufacturing, Properties &

Applications’







Nil

vii Course Content • Introduction: Definition and classification,

Importance of composites over other materials.

Revision of some mechanical properties.

• Reinforcements: Functions of reinforcements and

their forms,

Glass fibers: Production, composition and properties,

Production and properties of carbon and aramid

fibers, Ceramic particulate and whisker

reinforcements.

• Micromechanics: Estimation of modulus and tensile

strength. Prediction of thermal and electrical

properties

• Role of matrix and characteristics of different matrix

materials.

• Reinforcement-matrix Interfaces: wettability,

interactions at the interfaces. Mechanical, physical

and chemical bonding.

• Polymer matrix composites (PMC): Important

polymeric matrices,

Manufacturing methods: Unit operations, hand lay-

up, spray-up, pressure bag molding, vacuum bagging,

prepags, compression molding, autoclaving, RTM,

filament winding and pultrusion.

• Metal matrix composites (MMC): Property

advantages, comparison between MMCs & PMCs.

Manufacturing of MMCs: Solid state processes:

Diffusion bonding and P/M routes, Liquid state

63

processes: Melt-infiltration, stir casting, in-situ

processing, spray deposition and electrodeposition.

• Properties and applications of selected PMCs and

MMCs in industry.

• Ceramic matrix composites (CMC): Types of

CMCs, main processing methods, and important

applications.

• Introduction to Nanocomposites.

viii Texts/References Text Books:

(1) K.K. Chawla, ‘Composite Materials: Science and

Engineering’, 3rd Ed. Springer-Verlag, N.Y. (2012).

(2) F.L. Matthews and R.D. Rawlings, ’Composite

Materials: Engineering and Science’, CRC,

Woodhead Pub. Ltd., Cambridge, England (2008).

References:

(1) N. Chawla and K. K. Chawla, ’Metal Metrix

Composites’ 2nd Ed, Springer, N.Y. (2013).

(2) ASM Handbook Vol.21: Composites, Eds. D.B.

Miracle and S. L. Donaldson ,

ASM International, Ohio (USA) (2001).

ix Name(s) of Instructor(s) ANT



relevant

Nil




give details.

Nil


course

The objectives of the course are to provide the

students with -

• An understanding of basics of reinforcements,

matrices and composite materials.

• Structure, processing and properties of

reinforcements and matrix materials.

• Basic understanding of composite micromechanics

and interfacial bonding.

• Manufacturing methods and engineering

applications of Polymer-, metal- and ceramic- matrix

composites (PMC, MMC, &CMC).

• Introduction to nanocomposites and their

application.

64


Level: B. Tech.

Programme: B.Tech.

i Title of the course Introduction to Computational Fluid Dynamics





vi Pre-requisite(s), if any –


ME 203 Fluid Mechanics; Numerical Analysis; Computer

Programming

vii Course Content 1. Review of Governing Equations: General

conservation equation; specific mass, momentum,

energy conservation equations.

2. Fundamentals of Numerical Methods: Direct and

iterative solvers for linear equations; PDE,

Classification, Basics of finite-difference, finite-

volume finite-volume methods; Notion of accuracy,

consistency, stability, convergence; Verification and

validation.

3. Diffusion Equation: 1-D steady conduction; Source

terms and non-linearity; 2-D steady conduction;

Unsteady conduction; Non-trivial boundary

conditions.

4. Advection-Diffusion Equation: Steady 1-D advection-

diffusion equation; Upwinding, numerical diffusion,

higher-order schemes; 2-D advection-diffusion

equation

5. Incompressible Navier-Stokes equations,

Incompressibility and pressure-velocity coupling;

Staggered vs collocated grids; SIMPLE and PISO

algorithms.

6. Special Topics: Non-Cartesian coordinate systems;

Curvilinear grids; Unstructured grids; Advanced

linear solution methods such as multigrid methods,

preconditioning; Use of numerical libraries;

Introduction to parallel programming for CFD.

7. Mesoscopic approaches to discrete simulation of fluid

dynamics

65

8. Tutorial on a commercial CFD code & an open-source

code (e.g. OpenFOAM).

viii Texts/References 1. “An Introduction to Computational Fluid Dynamics”,

by H. W. Versteeg and W. Malalasekera; 2nd edition,

Pearson Education Ltd., 2007. (ISBN:

9780131274983)

2. “Introduction to Computational Fluid Dynamics:

Development, Application and Analysis”, by Atul

Sharma; Wiley, 2016. (ISBN: 9781119002994)

ix Name(s) of Instructor(s) Dhiraj V Patil

x Name(s) of other Departments/ Academic Units to

whom the course is relevant

Departments of Mathematics,

Chemical, Civil, Physics

xi Is/Are there any course(s) in the same/ other

academic unit(s) which is/ are equivalent to this


NA



CFD is an integral part of the design process in

mechanical, aerospace, and chemical industries, as well as

a topic of active research. Training at the undergraduate

and early-postgraduate level will enable students to take

advantage of opportunities in these areas.

The course aims to provide an introduction to

discretization and solution of the equations of fluid

dynamics and heat transfer. Students will gain an

appreciation of the principles of the finite-volume method,

experience in writing and debugging scientific codes, and

solving and analysing a problem using a commercial/open-

source package. Students should expect to devote

significant time to learning via coding assignments and

project.

66


Level: UG

Programme: B. Tech.

i Title of the course Refrigeration and Air-conditioning




be offered

Odd/Even


Course

Full

vi Pre-requisite(s), if any –


vii Course Content Introduction: Review of the laws and concepts of

thermodynamics, coefficient of performance, heat transfer, history

of refrigeration, evolution of various refrigeration systems and

working fluids, broad classification of refrigeration systems and

motivation for high efficiency cooling systems (2 hr)

Refrigeration cycles and techniques: Reversed-Carnot cycle,

reversed-Brayton cycle, simple and actual vapour compression

cycles, aircraft refrigeration cycle, effect of design and operating

parameters, multi-pressure systems, vapour absorption cycles and

other methods such as evaporative and thermoelectric cooling,

vortex tube. (5 hr)

Refrigeration subsystems: Refrigerants, environmental impact of

refrigerants, brines, sorbents and dessicants, Compressors,

condensors, evaporators, expansion devices, capillary tubes,

component selection and balancing, lubrication, solubity of

refrigerants, operating and safety controls, sensing and actuating

elements (7 hr)

Refrigeration systems: Vapour compression and vapour

absorption systems (6 hr)

Prelude for air-conditioning systems: Properties of moist air and

psychrometric processes, comfort conditions, factors affecting

comfort, humidifiers and dehumidifiers, duct and air-handling

systems (6 hr)

Air-conditioning principles and systems: Basic equipments in

air-conditioning and classification of air-conditioning systems,

67

winter and summer air conditioning systems, domestic split and

window air-conditioners, central air-conditioning systems, room

sensible heat factor

Estimation of cooling load: sensible and latent heat gains, heat gains

from various sources (10 hr)

Applications of refrigeration and air-conditioning:

Description of thermodynamic principles and components of

specific systems such as domestic refrigerator, industrial

refrigerator, ice manufacturing plant (4 hr)

Enviromental impact and future of cooling systems:

Environmental impact of refrigeration, renewable energy-based

refrigeration, solar cooling (2 hr)

viii Texts/ References Textbook: C.P. Arora, Refrigeration and Air Conditioning,

McGraw Hill Edu.; 3rd Ed., 2017.

References: 1. G.F. Hundy, A.R. Trott, T.C. Welch, Refrigeration,

Air conditioning and Heat pumps, 5th ed., Elsevier, 2016, 2. RJ.

Dossat, Principles of Refrigeration, John Wiley & Sons, Inc., 5th

ed., 2001, 3. P.N. Ananthanarayana, Basic Refrigeration and

Airconditioning, McGraw-Hill Edu, 3rd ed., 2005. 4. ASHRAE

Handbook - Fundamentals (SI), 2017, 5. ASHRAE Handbook -

Heating, Ventilating, and Air-Conditioning APPLICATIONS (SI),

2015, 6. A.A.M. Sayigh J.C. McVeigh (eds.), Solar Air

Conditioning and Refrigeration, Pergamon, 1992. 7. R.S. Khurmi,

J.K. Gupta, A Textbook of Refrigeration and Air-conditioning, S

Chand, 5th Ed., 2018.

ix Name(s) of Instructor(s) ME faculty

x

Name(s) of other Departments/


course is relevant


xi Is/Are there any course(s) in

the same/ other academic

unit(s) which is/ are equivalent

to this course? If so, please give

details.

No



Refrigeration and air-conditioning systems take up a significant

portion of the energy demands in present-day society. The situation

68

will be aggravated in the future due to the increasing demand of

cooling requirements with the declining of conventional energy

sources. This demans design of high-efficiency cooling devices

with improved or novel thermodynamic cycles and devices. The

course primarily focuses on the methods employed in conventional

refrigeration and air-conditioning sytems. The course provides the

necessary domain knowledge and analytical skills for a student to

work in areas of design and analysis of cooling systems. In terms

of the academic pedagogy, being an applied course, its contents

provide a context for the concepts and principles encountered in

basic courses such as thermodynamics, fluid mechanics and heat

transfer.

69

Name of Academic Unit: Mechanical Engineering Department

Level: Tick mark (or underline) only one of the these: ☐ UG ☐ Masters ☐ PhD

1 Title of the course Design of Heat Exchangers




course


Mechanical Engineering (Elective)


offered


option(s)





course


option


6 Course content Classification of heat exchangers, Basic design methods of

heat exchangers

Single phase heat exchangers: Forced Convection

Correlations for the Single-Phase Side of Heat

Exchangers, Design of double pipe heat exchangers, shell

and tube heat exchangers, compact heat exchangers

Fundamentals of two phase flow, Essentials for the design

of two phase heat exchangers, Design Correlations for

Condensers and Evaporators, Design of evaporators and

condensers

7 Texts/References 1. Ramesh K. Shah, Dusan P. Sekulic, Fundamentals of

Heat Exchanger Design, John Wiley and Sons, USA,

2003, ISBN:9780471321712, First Edition 2. Sadik Kakac, Hongtan Liu, Anchasa

Pramuanjaroenkij, Heat Exchangers: Selection,

Rating, and Thermal Design, CRC Press, 2020, ISBN 9781138601864, Fourth Edition

3. W.M. Kays and A.L. London, Compact heat

exchangers, McGrawhill Book Company, 1984,

ISBN: 9780070334182, Third Edition

70

4. Arthur P Fraas, Heat Exchanger Design, John Wiley

and Sons, 1989, ISBN: 978-0-471-62868-2. Second Edition

8 Name (s) of the instructor (s) S.V.Prabhu, Sudheer S, Dhiraj S. Patil



is relevant

Nil





No

11 Mandatory Pre-requisite(s) - specify

course number(s)

Fluid Mechanics and Heat Transfer



ME 203 and ME 301





interdependency


syllabus content)

Design, heat exchangers, condensers, evaporators, single

phase, two phase, correlations, two phase


the course

Thermal design of the heat exchangers is essential as heat

exchangers are extensively used in several practical

applications.

71


Level: PhD

Programme: PhD

i Title of the course Theory of Elasticity




offered

Autumn




Exposure to Mechanics of Materials.

vii Course Content Module-1: Analysis of Stress: Stress tensors.

Cauchy's stress principle, direction cosines, stress

components on an arbitrary plane with stress

transformation. Principal stresses in three

dimensions, stress invariants, Equilibrium equations,

Octahedral stresses, Mohr's stress circle, construction

of Mohr Circle for two and three dimensional stress

systems, equilibrium equations in polar coordinates

for two-dimensional state of stresses. General state

of stress in 3D in cylindrical coordinate System.,

Module-2: Analysis of Strain: types of strain, strain

tensors, strain transformation. Principal strains,

strain invariants, octahedral strains, Mohr's Circle for

Strain, equations of Compatibility for Strain

Module-3: Stress-strain relations: Stress-strain

relations, Generalized Hooke's law, transformation of

compatibility Condition from Strain components to

stress components. Strain energy in an elastic body,

St. Venant's principle, Uniqueness theorem.

Module-4: Two dimensional problems in Cartesian

coordinate system: plane stress and plane strain

problems. Stress function, stress function for plane

stress and plane strain cases. Solution of two-

dimensional problems with different loading

conditions by the use of polynomials.

Module-5: Two dimensional problems in polar

coordinate system strain-displacement relations,

compatibility equation, stress- strain relations, stress

function and Biharmonic equation. Axisymmetric

problems, thick-walled cylinders, rotating disks of

uniform thickness, stress concentration, effect of

circular holes on stress distribution in plates

Module-6: Torsion of prismatic bars, general solution

of the torsion problem, stress function,

72

torsion of circular and elliptic cross sections. Prandtl's

membrane analogy, torsion of thin walled and

multiple cell closed sections.

Module-7: Thermal Stresses: Thermoelastic Stress–

Strain Relations, Equations of Equilibrium,Strain–

Displacement Relations, Some General Results:Thin

Circular Disk: Temperature Symmetrical about Centr,

Long Circular Cylinder.

viii Texts/References Texts

1.L. S. Srinath, Advanced Mechanics of Solids, 2nd Edition, TMH Publishing Co. Ltd., New Delhi, 2003

2.C.T. Wang, "Applied Elasticity", McGraw-Hill Book

Company, 1953.

References

1. Theory of Elasticity, S. P. Timoshenko, J. N. Goodier,

3rd Edition, McGraw Hill Publishing Co., 1970.

2. Elasticity: Theory, Applications, And Numerics, Martin H. Sadd, 3rd Edition, Academic Press, 2014.

3.Elasticity, J. R. Barber, 3rd edition, Springer, 2009.

4. Elasticity in Engineering Mechanics, Arthur P. Boresi, Ken Chong, James D. Lee, 2010, Wiley.

5. Applied Mechanics of Solids ,Allan F. Bower, 1st

Edition, 2009, CRC Press.

ix Name(s) of Instructor(s) TPG



relevant

NA




give details.

No


course

Theory of elasticity (TOE) is a course which

investigates effect of external loads on deformable

bodies. Unlike mechanics of materials, TOE is more

rigorous as it relaxes many assumptions of mechanics

of materials. Thus, it paves way to analyze solids

beyond structural elements like beams, trusses and

shafts. This approach for generalization invokes more

rigor mathematically. In this course, we linearize

strains and stress-strain relation to attempt problems

from mechanics of materials in the new perspective

i.e. from TOE approach but not limited to it. Thus, it

aims to appreciate the need for experimental

mechanics techniques like Photoelasticity,

Thermoelastic stress analysis, DIC and the need for

computational tools like FEM.

73


Level: B. Tech.

Programme: B.Tech.

i Title of the course I.C. Engines



iv Semester in which normally to be offered Even


vi Pre-requisite(s), if any – specify course number(s)

vii Course

Content

General concepts: Fundamental Operating Procedures - Open circuit, Closed circuit, Internal combustion, External combustion, Spark ignition, Compression ignition (2 hr)

Reciprocating engine technology: 2-stroke, 4-stroke, Pistons, connecting rods and crankshaft, Valve

train, camshaft and timing gear, Engine block, cylinder and head geometry, Manifold, surface finish,

track length, Fuel systems, carburettors, fuel injection, Turbo- and super-charger, Ignition, timing and

spark advance (4 hr)

Recall of thermodynamics - Definition and comparison of common internal combustion cycles, Otto

cycle, Diesel cycle, Dual cycle, Atkinson cycle (6 hr)

Fuel-air systems: Fuel Delivery Systems - Fuel delivery, The problem of part throttle operation, Air

intake systems, Intake manifold design and tuning, Turbo-charging, Super-charging, Fuel management

and control theory, Fuel injection, ECU operation, Sensors and instrumentation (6 hr)

Valve train and timing: Operation, Arrangement -- Push-rod; Single overhead cam shaft (SOHC)

design; Dual-overhead cam shaft (DOHC) design, Camshaft function and design considerations, Valve

timing, Valve-train design considerations; Component and Event Timing - Valve actuation timing,

Valve timing diagram, Spark ignition event and timing, Compression ignition injection event and timing

(6 hr)

Fuels & Combustion - Definition of hydrocarbon based fuels, Stoichiometric Burn Efficiency, Air

/ Fuel Ratio, Gasoline, Diesel, Octane rating, Cetane rating, Hydrocarbon emission, Flame types,

Thermodynamic efficiencies, Ignition requirements, Combustion chamber and head design (6 hr)

Ignition - Common ignition sources, Combustion abnormalities, Spark plug design considerations,

Ignition timing; (6 hr)

Emissions & Controls - Introduction to emissions, Chemistry of emissions, Emission controls,

Catalytic converter operation, Exhaust gas recirculation (EGR), Valve overlap control, Introduction to

variable camshaft timing (VCT) (4 hr)

viii Texts/

Referen

ces

1. Internal Combustion Engines – V Ganesan

2. Fundamentals of Internal Combustion Engines -- Gill P W., J H. Smith, E J. Ziury

3. Internal Combustion Engine Fundamentals – John B Heywood

4. IC Engines: Combustion and Emissions – B. P. Pundir

ix Name(s) of Instructor(s) Surya Prakash R.

x Name(s) of other Departments/ Academic Units to whom the course

is relevant --

xi Is/Are there any course(s) in the same/ other academic unit(s)

which is/ are equivalent to this course? If so, please give details. NA

xii Justification/ Need

for introducing the

course

Transportation is the basic need for humanity – IC Engines are the prime movers in today’s world. A mechanical engineer has to have the knowledge of this subject to be relevant to the

industry, especially the automobile sector.

74


Level: B. Tech/MTech.

Programme:

B.Tech/MTech.

i Title of the course Advanced Finite Element Methods

ii Credit Structure (L-T-

P-C)

(3-0-0-6)

iii Type of Course Elective (PG)



vi Pre-requisite(s), if any – specify course

number(s)

Finite Element Methods

vii Cours

e

Conte

nt

FEM formulation for time dependent problems (16 hours) - Transient heat transfer problems - Structural dynamics problem

- Explicit and Implicit methods of solutions

- stability, accuracy and convergence study of solution methods

Introduction to reduced order modelling technique: (6 hours) - Introduction to reduced order modeling - Methods of reduced order modeling

o Static condensation, o mode superposition, o component mode synthesis, o Krylov subspace technique.

Nonlinear Finite Element Method (18 hours) - Introduction to Nonlinear FEM - FEM for geometric nonlinearity and forcing nonlinearity, - FEM for elastic-plastic analysis

o Strain hardening model o Kinematic hardening model

- Methods to solve nonlinear problems o Newton Raphson method o Secant method o Continuation method

- Convergence of nonlinear solutions o Force convergence o Displacement convergence

viii Texts/

Refere

n ces

1. J.N. Reddy, Introduction to Finite Element Method, Tata McGraw-Hill, 2006 2. J. N. Reddy, An Introduction to Nonlinear Finite Element Analysis, Oxford

University Press, 2004. 3. K. J. Bathe, Finite Element Procedures, PHI Learning Pvt. Ltd., 1996

4. T. J. R. Hughes, The Finite Element Method: Linear Static and Dynamic Finite

Element Analysis, Dover Publications, 2000

5. Zu-Qing Qu, Model Order Reduction Techniques with Applications in Finite

Element Analysis, Springer, 2004

ix Name(s) of

Instructor(s)

Amar Keshav Gaonkar and Amlan Barua

x Name(s) of other Departments/ Academic Units to

whom the course is relevant

Mechanical Engineering, Electrical

Engineering

xi Is/Are there any course(s) in the same/ other

academic unit(s) which is/ are equivalent to this


No

75

xii Justification/

Need for

introducing the

course

This course is an extension to the introduction to finite element course. A

student will get exposure to the advance topics in FEM such as nonlinear

FEM, plate theory, dynamic problems, etc which will be helpful for finite

element problems in industry and research.

76

Name of Academic Unit: Electrical engineering

Level : B.Tech

Programme : B.Tech

i Title of the course Information theory

ii Credit Structure (L-

T-P-C)

(3 0 0 6)

iii Type of Course Institute elective

iv Semester in which

normally to be

offered

Fall

v Whether Full or

Half Semester

Course

Full

vi Pre-requisite(s), if

any (For the

students) – specify

course number(s)

Basic calculus, Introduction to Probability Theory

vii Course Content* ● Introduction: Revision of probability theory, revision

of basic digital communications, motivation to

information theory through examples from basic

statistics and communications.

● Introduction to basic tools and concepts in

information theory: Entropy and mutual information,

Chain rules and inequalities, Data processing, Fano's

inequality, Asymptotic equipartition property.

● Source coding: Guessing game, and its connection to

Source coding problem, Kraft’s inequality, Optimal

code length and Huffman code, Shannon-Fano-Elias

and arithmetic codes.

● Statistics and information theory: Hypothesis

testing, estimation theory, and its connection to

information theory.

● Channel capacity: Channel coding theorem, joint

typicality, Proof of channel coding theorem,

Hamming codes and its properties.

● Continuous channel case: Differential entropy,

Gaussian channel, and its capacity, sphere packing

argument, High-level introduction to Quantization

theory.

77

● Introduction to Kolmogorov Complexity: Models of

Computation, Kolmogorov Complexity and entropy,

Universal Gambling, MDLP.

viii Texts/References 1. T. Cover, and J. Thomas, “Elements of Information

Theory,” Second Edition. Wiley-Interscience, 2006.

2. David J. C. Mckay, “Information theory, Inference,

and Learning Algorithms,” Cambridge university

press, 2003.

ix Name(s) of

Instructor(s) ***

B. N. Bharath

x Name(s) of other

Departments/

Academic Units to

whom the course is

relevant

Computer science, physics, mathematics.

xi Is/Are there any

course(s) in the

same/ other

academic unit(s)

which is/ are

equivalent to this

course? If so, please

give details.

No

xii Justification/ Need

for introducing the

course

Information theory is a fundamental tool in communications

and computer science fields in particular, and statistics in

general. In the recent times, it has been used as tools in

machine learning theory. The course aims to develop these

tools in a general context with historical motivation to the

subject.

78


Level: B. Tech./MS

Programme: MS/Ph.D.

i Title of the course Modeling and Control of Renewable Energy Resources





vi Pre-requisite(s), if any (For the students) – specify course number(s)

Exposure to Power System Analysis, Electrical Machines, Power Electronics

vii Course Content Microgrids and distributed generation;

Introduction to renewable energy

technologies; electrical systems and

generators used in wind energy conversion

systems, diesel generators, combined heat

cycle plants, inverter based generation, solar

PV based systems, fuel cell and aqua-

electrolyzer, battery and flywheel based

storage system; Voltage and frequency

control in a microgrid; Grid connection

interface issues.

viii Texts/References 1) Anaya-Lara, Jenkins, Ekanayake,

Cartwright and Hughes, WIND ENERGY

GENERATION Modelling and Control”

Wiley, 1st Edison, 2009.

2) Bevrani, Francois and Ise, Microgrid

Dynamics and Control, Wiley; First edition,

2017.

3) Gilbert M. Masters, Renewable and

Efficient Electric Power Systems, Wiley

Interscience, 1st Edison, 2004.




relevant

None



equivalent to this course?

None


course

This a core course for MS with specialization in Power and Energy Systems.

Documents

2018 Batch VI Semester CSE