DETAILED SYLLABUS

REFERENCE BOOKS:
1. Chakravarthi, Veena, “A Practical Approach to VLSI System on Chip (SoC)
Design”, Springer, 2020.
2. Ricardo Reis, “Design of System on a Chip: Devices and Components”,
Springer, 1st Edition, 2004.
3. Prakash Rashinkar, Peter Paterson and Leena Singh L, “System on Chip
Verification – Methodologies and Techniques”, Kluwer Academic Publishers,
2001.

101

VLSI TESTING

HOURS/WEEK C MARKS Total
YEAR SEMESTER CIE SEE

L T P/D

3- 24 40 60 100

Pre-requisite Digital Electronics, VLSI Design

COURSE OUTCOMES:
At the end of the course, the students will develop ability to

1. Able to carry out research and development in the area of testing and
verification of VLSI circuits.

2. Apply techniques to improve testability of VLSI circuits.
3. Utilize logic simulation methods, ATPG, BIST and boundary scan

techniques in testing of VLSI circuits.
4. Apply functional, timing and formal verification methods at various design

abstractions of VLSI circuits.
5. Solve practical and state of the art testing and verification problems to

serve VLSI industries
UNIT –I
Introduction to VLSI testing: Importance of testing, Challenges in VLSI testing, Levels of
abstractions in VLSI testing, Functional vs. Structural approach to testing, Complexity of the
testing problem, Controllability and Observability, Generating test for a single stuck at fault
in combinational logic, D-algorithm, FAN and PODEM algorithms, Test optimization and
fault coverage.
UNIT –II
Design for testability (DFT): Testability analysis, Scan cell design, Scan architectures, Scan
design rules, Scan design flow, Special purpose scan designs Logic and fault simulation,
Fault detection, Adhoc and structured approaches to DFT, Various kinds of scan design, Fault
models for PLAs, Bridging and delay faults and their tests.
UNIT -III
Test generation: Random test generation, Boolean difference, ATPG algorithms for
combinational circuits, Sequential ATPG, Untestable faults, IDDQ testing The LFSRs and
their use in random test generation and response compression (including MISRs).
UNIT –IV
Built-in self-test (BIST): Design rules, Exhaustive testing, Pseudo-random testing, Pseudo-
exhaustive testing, Output response analysis, Logic BIST architectures Test compression:
Test stimulus compression, Test response compaction, Architectures for test compression.
UNIT –V
Boundary scan and core based testing: IEEE standards for digital boundary scan,
Embedded core test standards Analog and mixed signal testing, Delay testing, Physical
failures, Soft errors Reliability, FPGA testing, MEMS testing, RF testing, High speed I/O
testing.
TEXT BOOKS:

1. Parag K. Lala, An Introduction to Logic Circuit Testing, Morgan & Claypool Publishers
2. Thomas Kropf, Introduction to Formal Hardware Verification, Springer
REFERENCE BOOKS:
1. Michael L. Bushnell and Vishwani D. Agrawal, Essentials of Electronic Testing, Springer India
2. M. Abramovici, M. Breuer, and A. Friedman, Digital System Testing and Testable

Design, Jaico Publishing House

102

VLSI INTERCONNECTS

Year Semester Hours/Week P/D C Marks Total
Pre-requisite LT CIE SEE 100
40 60
3 - 24

Transmission Lines

COURSE OUTCOMES:
At the end of the course, the students will develop ability to

1. Understand the concepts of Technology scaling and Interconnect scaling
2. Review the basics of Transmission Lines.
3. Understand the procedure to extract the Interconnect Parasitics R, L, C.
4. Understand the basic models of Interconnect and effects due different
parameters.
5. Analyze the crosstalk noise effects in Interconnects.

UNIT –I
Introduction: Moores law, Technological trends, Interconnect scaling, Interconnect
Architecture in an IC.
Evolution of Interconnect Materials: Aluminium (Al), Copper (Cu) Interconnect, Graphene-
based nanomaterial’s (Carbon nanotubes (CNTs) and Graphene nanoribbons (GNRs)).

UNIT –II
The Interconnect Wire:
Interconnect Parameters: Capacitance, Resistance, and Inductance, Electrical Wire Models:
The Ideal Wire, The Lumped Model, The Lumped RC model, The
Distributed rc Line, Transmission Line, SPICE Wire Models: Distributed rc Lines in SPICE,
Transmission Line Models in SPICE.

UNIT -III
Interconnect Parasitic Extraction: Introduction, Electromagnetic Formulation, Resistance
Extraction, Capacitance Extraction, Inductance Extraction.

UNIT –IV
Basic Models and Effects: Lossless transmission line model: Resistive driver model,
CMOS driver with open circuit load, Lossy transmission line model: Effect of semiconductor
substrate, Optimum line model selection: Effect of input rise time, Effect of driver
impedance, Effect of line length. Elmore Delay model.

UNIT –V
Crosstalk noise in Interconnects: Modeling of coupled transmission lines, Calculation of
coupling constant, Capacitance and Inductance Matrices, Crosstalk in homogeneous and
Inhomogeneous mediums, Near-end and Far-end noise, Coupling between Inhomogeneous
lines, Reducing Crosstalk.

TEXT BOOKS:
1. Analysis and Design of Digital Integrated Circuits – A Design Perspective by Jan

M. Rabaey, Tata Mc-Graw Hill.

103

2. Interconnection Noise in VLSI Circuits by F. Moll and M. Roca, Kluwer Academic
Publishers.

3. Interconnect Technology and Design for Gigascale Integration by Jeffrey A., Meindl,
James D. Springer US 2003

REFERENCE BOOKS:
1. H. B. Bakoglu; Circuits, Interconnections, and Packaging for VLSI
2. Introduction to VLSI Circuits and Systems by J. P. Uymera, Wiley Student Edition.
3. CMOS Digital Integrated Circuits – Analysis and Design by S. M. Kang and L.
Yusuf, Tata Mc-Graw Hill

104

PROGRAM ELECTIVES

105

Program Elective - I to V

S. Course Code Course Hours/Week
No.

L T P/D/J C

1. Mobile Computing 2 -2 3

2. CMOS RF Circuit Design 2 -2 3

3. Wireless Sensor Networks 2 -2 3

4. Fundamentals of IoT 2 -2 3

5. IoT Applications 2 -2 3

6. Memory Architectures 2 -2 3

7. Sensor Technologies 2 -2 3

8. Machine Learning in VLSI 2 -2 3

9. Mixed Signal IC Design 2 -2 3

10. Parallel Processing 2 -2 3

11. High Speed VLSI System Design 2 -2 3

12. Introduction to IoT 2 -2 3

13. Nanoscience and Nano Technology 2 -2 3

14. Fundamentals -2 3
Of Aritifical Intelligence For Robotics 2

15. Computer Organisation 2 -2 3

16. Advanced Wireless Technologies 2 -2 3

17. Applied Machine Learning in Python 2 -2 3

Digital Signal Processors and
18. Architectures
2 -2 3

19. RISC Processor Design Using HDL 2 -2 3

20. Virtual Reality 2 -2 3

21. Computational Intelligence 2 -2 3

22. Deep Learning for Image Processing 2 -2 3

23. Mobile Robotics 2 -2 3

24. Biomedical Instrumentation 2 -2 3

25. Microwave Engineering 2 -2 3

26. Satellite Communication 2 -2 3

27. Radar Systems 2 -2 3

28. IC Fabrication Technology 2 -2 3

29. Genetic algorithm and Machine Learning 2 -2 3

30. AI in Speech Processing 2 -2 3

31. Distributed IoT Systems 2 -2 3

32. Security in IoT 2 -2 3

106

COMPUTER ORGANIZATION

Year Semester Hours/Week Marks Total
C CIE SEE

L T P/D

2 - 2 3 40 60 100

Pre-requisite

UNIT- I
Fundamentals of Computer Design: Fundamentals of Computer design, Changing faces of
computing and task of computer designer, Technology trends, Cost price and their trends,
measuring and reporting performance, quantitative principles of computer design, Amdahl’s
law.
Instruction set principles and examples- Introduction, classifying instruction set- memory
addressing- type and size of operands, operations in the instruction set.
UNIT – II
Pipelines: Introduction, basic RISC instruction set, Simple implementation of RISC
instruction set, Classic five stage pipe line for RISC processor, Basic performance issues in
pipelining, Pipeline hazards, Reducing pipeline branch penalties.
Memory Hierarchy Design: Introduction, review of ABC of cache, Cache performance,
Reducing cache miss penalty, Virtual memory.

UNIT - III
Instruction Level Parallelism the Hardware Approach: Instruction-Level parallelism,
Dynamic scheduling, Dynamic scheduling using Tomasulo’s approach, Branch prediction,
high performance instruction delivery- hardware based speculation.

UNIT – IV
Multi Processors and Thread Level Parallelism: Multi Processors and Thread level
Parallelism- Introduction, Characteristics of application domain, Systematic shared memory
architecture, Distributed shared – memory architecture, Synchronization.

UNIT – V
Inter Connection and Networks: Introduction, Interconnection network media, Practical
issues in interconnecting networks, Examples of inter connection, Cluster, Designing of
clusters.
TEXT BOOK:
1. John L. Hennessy, David A. Patterson, “Computer Architecture: A Quantitative

Approach”, 3rd Edition, Elsevier.
2. John P. Shen and Miikko H. Lipasti, “Modern Processor Design: Fundamentals

of Super Scalar Processors”, 2002, Beta Edition, McGraw-Hill

REFERENCE BOOKS
1. Kai Hwang, Faye A.Brigs., “Computer Architecture and Parallel Processing”, Mc Graw

Hill.
2. Dezso Sima, Terence Fountain, Peter Kacsuk, “Advanced Computer Architecture

- A Design Space Approach”, Pearson Education.

107

INTRODUCTION TO AI AND PROGRAMMING TOOLS

Year Semester Hours/Week Marks Total
C CIE SEE

L T P/D

2 - 2 3 40 60 100

Pre-requisite Having good computer programming knowledge

COURSE OUTCOMES
After completion of this course, the candidate will be able to:
1. Operate in Linux OS environment.
2. Design and write python applications.
3. Learn basics of database management systems and write python programs to interact with

DBMS.
4. Write python programs to do data analysis and visualization using various libraries
5. Write R programs and use its various data structures for data analysis, Do data

visualization using R.
6. Solve problems involving probability and do statistical data analysis using statistics and

probability distribution methods.

UNIT I
Linux basics, Python Basics Data Types, Conditional Statements, Looping, Control
Statements, String, List And Dictionary Manipulations, Python Functions, Modules And
Packages, Object Oriented Programming in Python, Regular Expressions, Exception
Handling, Popular python packages like pandas for data handling

UNIT II
Introduction to Database Management System & SQL, Database Interaction in Python,
Data Analysis & visualization – using numpy, matplotlib, scipy

UNIT III
R Programming:- Basics - Vectors, Factors, Lists, Matrices, Arrays, Data Frames, Reading
data.

UNIT IV
Data visualization - barplot, pie, scatterplot, histogram, scatter matrix

UNIT V
Probability and Statistics-Probability, Mean, Median, SD, Variance, Probability
distributions in R- Normal distribution, Poisson distribution, Binomial distribution.
Correlation and Regression.

108

TEXTBOOKS:
1. Machine Learning an algorithmic Perspective by Stephen Marshland
2. Programming in Python by Mark Summerfield
3. Learning Python By Mark Lutz, David Ascher
4. Introduction to Machine Learning with python by Andreas C Muller, Sarah Guido,
REFERENCE BOOKS:
1. Artificial Intelligence- Reshaping Life and Business by Prabhath Kumar
2. R for everyone by Jared P Lander
3. https://scikit-learn.org/
4. https://www.tensorflow.org/
5. https://keras.io/
Additional Resources:
➢ https://www.arduino.cc
➢ https://github.com/arduino
Recommended hardware/software tools:
1. High end Servers and client machines
2. GPU/TPU
3. Linux based Software infrastructure including python and packages like Scikit-learn,

Keras, Tensor Flow, etc.

109

OPTIMIZATION TECHNIQUES

Year Semester Hours/Week Marks Total
C CIE SEE

L T P/D

3 - 2 3 40 60 100

Pre-requisite Basic Mathematics and programming languages

COURSE OUTCOMES
By the end of the course, students should be able to:
1. Learn efficient computational procedures to solve optimization problems.
2. Be able to model engineering minima/maxima problems as optimization problems.
3. Be able to use Matlab to implement optimization algorithms.

UNIT-I
Mathematical preliminaries
Linear algebra and matrices, Vector space, eigen analysis, Elements of probability theory,
Elementary multivariable calculus

UNIT-II
Linear Programming
Introduction to linear programming model, Simplex method, Duality, Karmarkar's method

UNIT-III
Unconstrained optimization
One-dimensional search methods, Gradient-based methods, Conjugate direction and quasi-
Newton methods

UNIT-IV
Constrained Optimization
Lagrange theorem, FONC, SONC, and SOSC conditions

UNIT-V
Non-linear problems
Non-linear constrained optimization models, KKT conditions, Projection methods

TEXTBOOKS:
1. An introduction to Optimization by Edwin P K Chong, Stainslaw Zak
2. Nonlinear Programming by Dimitri Bertsekas
REFERENCE BOOKS:
6. Artificial Intelligence- Reshaping Life and Business by Prabhath Kumar
7. R for everyone by Jared P Lander
8. https://scikit-learn.org/
9. https://www.tensorflow.org/

110

10. https://keras.io/
Additional Resources:
➢ https://www.arduino.cc
➢ https://github.com/arduino
Recommended hardware/software tools:
1. High end Servers and client machines
2. GPU/TPU
3. Linux based Software infrastructure including python and packages like Scikit-learn,

Keras, Tensor Flow, etc.

111

ANTENNA AND WAVE PROPAGATION

Year Semester Hours/Week Marks Total
C CIE SEE 100

L T P/D

3 - 2 3 40 60

Pre-requisite Electromagnetic Waves and Transmission Lines

COURSE OUTCOMES
At the end of the course, the students will develop ability to
1. Explain the parameters and fundamental concepts of antennas.
2. Demonstrate electric and magnetic fields and radiation patterns for different types of

antennas.
3. Construct different arrays of antennas in order to improve their gain and directivity.
4. Classify the various types of antennas depending upon frequency and working.

UNIT I
Antenna Basics
Introduction, basic antenna parameters – patterns, Beam Area, Radiation Intensity, Beam
Efficiency, Directivity- Gain- Resolution, Antenna Apertures, Effective height Illustrative
problems. Fields from oscillating dipole, field zones, front-to-back ratio, antenna theorems,
and related problems.

UNIT II
Thin Linear Wire Antennas
Retarded potentials, Radiation from Small Electric Dipole, Quarter wave Monopole and Half
wave Dipole – Current Distributions, Radiated Power, Radiation Resistance, Illustrative
problems, Types of Loop antennas.

UNIT III
Antenna Arrays
Antenna Arrays: 2-element arrays – Array factor, N-elements Linear Arrays – Broadside,End
fire arrays , Principle of Multiplication of patterns, Binomial Arrays, Uniformly spaced arrays
with uniform and non uniform excitation amplitudes.
Non-Resonant Radiators: Introduction, Travelling wave radiators – basic concepts, V antennas,
Rhombic antennas.

UNIT IV
VHF, UHF and Microwave Antennas: Arrays with parasitic elements, folded dipoles, Yagi-
Uda antenna, Log periodic Array, corner reflectors, Paraboloidal Reflectors – Characteristics,
types of feeds, spill over, aperture blocking, offset feed, Cassegrain Feeds. Horn Antennas –
Types, characteristics, optimum horns. Lens Antennas – features,

UNIT V
Wave Propagation

112

Ground Wave Propagation – Characteristics, Ionosphere – formation of layers and
mechanism of propagation, Critical Frequency, MUF, Skip distance, space wave propagation,
M-Curves, Tropospheric Propagation.
TEXT BOOKS:
1. John D. Kraus and Ronald J. Mathefka, “Antennas”, TMH.
2. E.C. Jordan and K.G. Balman, “Electromagnetic Waves and Radiating Systems”, Prentice

Hall India Learning Private Limited, 2nd Edition, 1964.
REFERENCE BOOKS:
1. K.D. Prasad, “Antennas and Wave Propagation”, Sataya Prakashan Publication.
2. F.E. Terman, “Electronic and Radio Engineering”, McGraw Hill Publication.
3. Balanis, Constantine-A, “Antenna Theory”, John Wiley, New Delhi, 2nd Edition, 2008.
4. Raju GSN, “Antenna and Wave Propagation”, Pearson, New Delhi, 2006.

113

Year Semester RISC Processor Design using HDL Marks Total
Hours/Week SEE 100
60
L T P/D C CIE

2- 23 40

Pre-requisite Digital Electronics

UNIT I
CISC vs RISC Designs, simple implementation schemes, datapath design, control unit:
hardwired realization vs micro-programmed realization, multi-cycle implementation.
Instruction level parallelism, instruction pipelining, pipeline hazards.

UNIT II
Design of single cycle - Multicycle and pipelined architectures of MIPS. Introduction to
superscalar - Super pipelined architectures - Performance evaluation of super scalar
processors. Verilog design of a pipelined MIPS processor.

UNIT III
SIMULATION Different simulation modes, behavioral, functional, static timing, gate level,
switch level, transistor/circuit simulation, design of verification vectors, Low power FPGA,
Reconfigurable systems, SoC related modeling of data path design and control logic,
Minimization of interconnects impact, clock tree design issues.

UNIT IV
LOW POWER SOC DESIGN Design synergy, Low power system perspective- power
gating, clock gating, adaptive voltage scaling (AVS), Static voltage scaling, Dynamic clock
frequency and voltage scaling (DCFS), building block optimization, building block memory,
power down techniques, power consumption verification.

UNIT V
SYNTHESIS AND OPTIMIZATION Technology independent and technology dependent
approaches for synthesis, optimization constraints, Synthesis report analysis Single core and
Multi core systems, dark silicon issues, HDL coding techniques for minimization of power
consumption, Fault tolerant designs.

REFERENCE BOOK:
1. Hubert Kaeslin, “Digital Integrated Circuit Design: From VLSI Architectures to CMOS

Fabrication”, Cambridge University Press, 2008.
2. B. Al Hashimi, “System on chip-Next generation electronics”, The IET, 2006
3. RochitRajsuman, “System-on- a-chip: Design and test”, Advantest America R & D

center, 2000.
4. P Mishra and N Dutt, “Processor Description Languages”, Morgan Kaufmann, 2008
5. Michael J. Flynn and Wayne Luk, “Computer System Design: System-on-Chip”. Wiley,

2011.

114

Extensive Reading:
https://www.cerc.utexas.edu/~jaa/soc/lectures/1-2.pdf
https://www.cl.cam.ac.uk/teaching/1516/SysOnChip/materials.d/socdam-notes00.pdf
https://nptel.ac.in/courses/108102045/10

115

ADVANCED WIRELESS TECHNOLOGIES

Hours/Week Marks
SEE
Year Semester L T P/D C CIE 60 Total
100
2- 23 40

Pre-requisite Nil

COURSE OUTCOMES:
After the successful completion of this course, the student will be able to
1. Recognize the significance of cellular concept and the capacity of wireless

communication.
2. Explain the mobile radio propagation mechanism.
3. Describe the working and application of GSM, CDMA and 3G (UMTS, IMT 2000)

mobile systems.
4. Describe the techniques and technological advancement in LTE and 4G networks.
UNIT-I
Introduction to cellular system, Frequency reuse, handoff, interference, methods of improving
the capacity of cellular systems, Packet radio.
Mobile Radio Propagation
Large scale path loss, reflection, ground reflection model (2 ray model), diffraction, practical
link budget design using path loss models, small scale fading and multi-path, small-scale
multipath propagation, parameter of multi-path channels, types of small scale fading,
Rayleigh and Ricean distribution.
UNIT-II
2G Technologies:
Global System for Mobile Communication (GSM)
GSM-services, features, radio specifications, system architecture, channel types, frame
structure, security aspects, and network operations
GSM evolution: GPRS and EDGE; Architecture and services offered, Code Division
Multiple Access (CDMA) digital cellular standard: Soft hand off and power control, radio
specifications, forward and reverse CDMA channel.
UNIT-III
3G Technologies:
Universal Mobile Terrestrial System (UMTS):
System architecture, air interface specification, forward and reverse channels in Wideband
CDMA (WCDMA) and CDMA 2000.
UNIT-IV
3GPP LTE and 4G
Introduction and system overview, Frequency bands and spectrum, network structure, and
protocol structure, Frame slots and symbols,
Logical and Physical Channels: Mapping of data on to logical sub-channels physical layer
procedures, establishing a connection, retransmission and reliability, power control.
4G: Introduction, features and architecture Multi antenna Technologies: MIMO

116

UNIT-V
Emerging Technologies:
5G
Characteristics envisioned for 5G, Specifications and architecture
SDN (Software Defined Network)
Objective and architecture
REFERENCES
1. Ekram Hossain, Dong In Kim, Vijay K. Bhargava , “Cooperative Cellular Wireless

Networks”, Cambridge University Press, 2011.
2. Ekram Hossain, Vijay K. Bhargava(Editor), Gerhard P. Fettweis (Editor), “Green Radio

Communication Networks”, Cambridge University Press, 2012.
3. F. Richard Yu, Yu, Zhang and Victor C. M. Leung “Green Communications and

Networking”, CRC press, 2012.
4. Mazin Al Noor, “Green Radio Communication Networks Applying Radio-Over-Fibre

Technology for Wireless Access”, GRIN Verlag, 2012.
5. Mohammad S. Obaidat, Alagan Anpalagan and Isaac Woungang, “Handbook of Green

Information and Communication Systems”, Academic Press, 2012.
6. Ramjee Prasad and Shingo Ohmori, Dina Simunic, “Towards Green ICT”, River

Publishers, 2010.
7. Jinsong Wu, Sundeep Rangan and Honggang Zhang, “Green Communications:

Theoretical Fundamentals, Algorithms and Applications”, CRC Press, 2012.

117

ARTIFICIAL NEURAL NETWORKS

Year Semester Hours/Week Marks SEE Total
L T P/D J C 60 100

CIE

2- 2 - 3 40

Pre-requisite Introduction to AI and Programming Tools

UNIT-I
INTRODUCTION: The foundations of AI - The History of AI- Intelligent agents- Agent
based system. (2)
PROBLEM SOLVING: State Space models- Searching for solution- Uninformed/Blind
search - Informed/ Heuristic search - A* search - Hill-climbing search- Genetic Algorithm–
Markovian Decision Process (MDP) – Maximum value policies, Adversarial games–
value/policy iteration – Minimax – Alpha-beta pruning – Temporal difference (TD) -
Constraint satisfaction problem - factor graphs - Backtracking search. (8)

UNIT-II
KNOWLEDGE REPRESENTATION AND REASONING: Knowledge representation -
Logics – First order logic- Inference in first order logic – Higher order logic - Markov logic.
(5)
UNCERTAIN KNOWLEDGE AND PROBABILISTIC REASONING: Uncertainty-
Probabilistic reasoning - Semantics of Bayesian network -, Exact inference in Bayesian
network- Approximate inference in Bayesian network- Direct sampling methods, Inference
by Markov chain simulation - Probabilistic reasoning over time – Hidden Markov Models.
(5)

UNIT-III
DECISION-MAKING: basics of utility theory, sequential decision problems - decision
network– policy -Decision process in infinite horizon: Optimal policy, Value iteration -
policy iteration- Partially observable decision process – Decisions in Multi agent system:
elementary game theory, (6)

UNIT-IV
LEARNING: Learning from observation - Knowledge in learning – Supervised Learning -
Unsupervised and Reinforcement learning. (2)

TEXT BOOKS:
1. Stuart Russell and Peter Norvig, ―Artificial Intelligence: A Modern Approach‖, Pearson

Education, 2014.
2. David Pool and Alan Mackworth, ―Artificial Intelligence: Foundations of

Computational agents‖, Cambridge University, 2011.
3. Daphne Koller and N Friedman, ―Probabilistic Graphical Models - Principles and

Techniques‖, MIT, 2009.
6. Tsang and Edward, ―Foundations of Constraint Satisfaction: The Classic Text‖, BoD–

Books on Demand, 2014.

118

REFERENCES:
1. Christopher M.Bishop, ―Pattern Recognition and Machine Learning‖, Springer, 2013.
2. Nils J. Nilsson, ―The Quest for Artificial Intelligence: A History of Ideas and

achievements‖, Cambridge University Press, 2010.

119

DIGITAL SIGNAL PROCESSORS AND ARCHITECTURE

Hours/Week Marks

Year Semester L T P/D C CIE SEE Total

2- 23 40 60 100

Pre-requisite Microcontrollers for Embedded Systems, Signal and Systems

COURSE OUTCOMES:
At the end of the course, the students will develop ability to
1. List out the various computational errors in DSP.
2. Differentiate various programmable architectures.
3. Apply programming of TMS320C54XX processors.
4. Interpret various DSP interfacing techniques.
5. Design different applications of programmable DSP devices.

UNIT I
Computational Accuracy in DSP Implementations: Number formats for signals and
coefficients in DSP systems, Dynamic Range and Precision, Sources of error in DSP
implementations.

UNIT II
Architectures for Programmable DSP Devices: Basic Architectural features, DSP
Computational Building Blocks, Bus Architecture and Memory, Data Addressing
Capabilities, Address Generation UNIT, Programmability and Program Execution, Speed
Issues, Features for External interfacing.

UNIT III
Programmable Digital Signal Processors: Commercial Digital signal-processing
Devices, Data Addressing modes of TMS320C54XX DSPs, Data Addressing modes of
TMS320C54XX Processors, Memory space of TMS320C54XX Processors, Program
Control, TMS320C54XX Instructions and Programming, On-Chip Peripherals, Interrupts
of TMS320C54XX Processors, Pipeline Operation ofTMS320C54XX Processors.

UNIT IV
Interfacing Memory and I/O Peripherals to Programmable DSP Devices: Memory
interface, Parallel I/O interface, Programmed I/O, Interrupts and I/O, Direct memory access
(DMA),MCBSP, CODEC interface circuit.

UNIT V
Applications of Programmable DSP Devices:
DSP Based Biotelemetry Receiver- Pulse Position Modulation(PPM),Decoding Scheme
for the PPM Receiver, Biotelemetry Receiver Implementation, ECG Signal Processing for
heart rate Determination, A Speech Processing System

120

TEXT BOOKS:
1. Avtar Singh and S Srinivasan, “Digital Signal Processing”, Thomson Publications, 2004.
2. B Venkata Ramani and M Bhaskar, “Digital Signal Processors, Architecture,

Programming and Applications”, TMH, 2004.
REFERENCE BOOKS:
1. Jonatham Stein, “Digital Signal Processing”, JohnWiley, 2005.
2. V.Udayshankara, “Modern Digital Signal Processing”, PHI Publication, 2nd Edition.
3. Richard G Lyons, “Understanding Digital Signal Processing”, Pearson, New

Delhi, 2nd Edition, 2004.

121

GENETIC ALGORITHM AND ITS APPLICATIONS

Year Semester Hours/Week Marks Total
C CIE SEE

L T P/D

Pre-requisite 2 - 2 3 40 60 100

Artificial Intelligence, Basic Mathematics and programming
languages

COURSE OUTCOMES
By the end of the course, students should be able to:
1. Provides a introduction to genetic algorithm including fundamentals of genetic concepts.
2. To have a clear view of genetic operators.
3. To explore Genetic Algorithm optimization problems.
4. Discuss applications of Genetic Algorithms for various optimization problems.

COURSE CONTENTS:

UNIT-I

Historical Development of Evolutionary Computing, Genetic Algorithms and Genetic
Programming, Terminologies and operators of GA, Key elements, Individuals, Diploidy,
Dominance etc. Inversion and Reordering. Order Crossover and Cycle crossover, Genetic
programming (GP), Comparison of GP and other algorithms, Genetic operators. Tree based
GP, Representation of GP, Specific Applications of Genetic Algorithms, GA in network
synthesis, Control systems engineering and Fuzzy based speed, control of Brushless DC
motor.

UNIT-II
Features of Evolutionary Computation, Advantages of Evolutionary computation, Breeding,
Selection, Crossover, Mutation and Replacement, Micro operators: Segregation and
translocation, Duplications and Deletion, Sexual determination, Attributes in GP. Steps of
GP,
Characteristics of GP, Feature selection in machine learning using GA. Designing texture
filters with GA.

UNIT-III
Genetic algorithms-Biological background, Cell, Chromosomes, Genetics, Reproduction and
Natural selection, Search Termination or Convergence criteria, Non-binary representation,
Multi-objective optimization, combined optimization and Knowledge based techniques,
Applications of Genetic Programming, GA based knowledge acquisition in Image
Processing, Object localization in image using GA.

UNIT-IV
Search space, GA world, Evolution and optimization, Best individual, Worst individual, Sum
of
fitness and Medium fitness, Classification of GAs. Simple Genetic algorithms (SGA).
Parallel and distributed Gas, GA Optimization problems: Fuzzy optimization problems, Multi
objective

122

Reliability Design Problem, Network and bicriteria reliability problems, Data mining
applications such as feature selection in data mining, GA in intrusion detection, etc.
UNIT-V
Evolution and genetic algorithms, Conventional optimization and search techniques, Building
block hypothesis, Master-slave, Fine-grained parallel Gas, Multiple-Deme Parallel Gas,
Combinatorial Optimization problems, Linear integer model, Applications in wireless
networks for topology planning, GA application in ATM network.

TEXTBOOKS:
1. S.N. Sivanandam and S.N. Deepa , "Introduction to Genetic Algorithms”, Springer,
2nd edition (2008)
2. Mitsuo Gen and Runwei Cheng, “Genetic Algorithms and Engineering Optimization”,
John Wiley, Fourth edition (2010)
3. Michael Negnevitsky, "Artificial Intelligence, A Guide to Intelligent Systems",
Second edition ((2005))

ADDITIONAL RESOURCES:
➢ https://www.arduino.cc
➢ https://github.com/arduino
Recommended hardware/software tools:

1. High end Servers and client machines
2. GPU/TPU
3. Linux based Software infrastructure including python and packages like Scikit-learn,

Keras, Tensor Flow, etc.

123

SENSORS AND INSTRUMENTATION

Year Hours/Week P/D C Marks Total
Semester CIE SEE

LT

2- 23 40 60 100

Pre-requisite Fundamental knowledge of Measurements

UNIT-I
Introduction to Sensor and Instrumentation: Mathematical model of transducer – Zero,

first order transducers, Response to step, ramp, impulse input, Mathematical model of
transducer – second order transducers, Active and passive transducers and their classification

UNIT-II

Introduction to MEMS and Brief recap of Macro devices, Microelectronics and scaling

laws, Chemical, Biomedical, Piezoelectric type of Micro sensors, Thermal, SMA,

Piezoelectric and electro static type Micro Actuators, Chemical and mechanical properties of

Si compounds, Chemical and mechanical properties of GaAs and Quartz, Chemical and

mechanical properties of Polymers, Chemical and mechanical properties of Piezoelectric

devices

UNIT-III

Industrial Sensors: Proximity sensors Radiation sensors, Smart sensors, Introduction to

Fiber optic sensors, Film sensors, Nano sensors, Digital transducers.

UNIT-IV
Miscellaneous Sensors: Accelerometer and Vibrometer – Eddy current transducers. Hall
effect transducers – Photo electric detector, different types and characteristics – Optical

sensors,
UNIT – V
Smart Transducers: IC sensor for temperature –AD 590, LM335. fiber optic sensors –

Temperature, pressure, flow and level measurement using fiber optic sensors. Intelligent and

smart transducers- principle- design approach interface design, configuration support,

communication in smart transducer networks.

Text Book:
1. Dr.S.Renganathan, Transducer Engineering,

2. M.Madou, Tai Ran Tsu

3. Sawhney, A. K., A Course in Electrical and Electronic Measurement and

Instrumentation

124

CLOUD COMPUTING

Year Semester Hours/Week C Marks Total
L T P/D J CIE SEE 100
40 60
2- - 2 3

Pre-requisite Distributed IoT

COURSE OUTCOMES
At the end of the course, the students will be able to
1. Understand the concept of cloud computing.
2. Appreciate the evolution of cloud from the existing technologies.
3. Have knowledge on the various issues in cloud computing.
4. Familiar with the lead players in cloud.
5. Appreciate the emergence of cloud as the next generation computing paradigm

UNIT-I
INTRODUCTION:
Introduction to Cloud Computing-Definition, Evolution of Cloud Computing, Underlying
Principles of Parallel and Distributed Computing, Characteristics, Components
Unit-II
CLOUD COMPUTING SERVICES: Cloud provider, SAAS, PAAS, IAAS and other
Organizational scenarios of clouds.

UNIT-III
RESOURCE MANAGEMENT AND SECURITY IN CLOUD: Inter Cloud Resource
Management – Resource Provisioning and Resource Provisioning Methods – Global
Exchange of Cloud Resources – Security Overview – Cloud Security Challenges – Software-
as-a-Service Security – Security Governance – Virtual Machine Security – IAM – Security
Standards.

UNIT-IV
CLOUD DEPLOYMENT: Deploy application over cloud. Comparison among SAAS,
PAAS, IAAS

UNIT-V
Edge and Fog Computing: Introduction to Edge Computing Scenario's and Use cases -
Edge computing purpose and definition, Edge computing use cases, Edge computing
hardware architectures, Edge platforms. Introduction to Fog Computing, Characteristics,
Application Scenarios, Issues, and challenges.
Fog Computing Architecture: Communication and Network Model, Programming Models,
Edge vs Fog Computing, Communication Models - Edge, Fog and M2M.

TEXT BOOKS:
1. Barrie Sosinsky ,"Cloud Computing Bible", Wiley-India, 1st Edition, 2011.

125

2. Toby Velte , Anthony Velte , Robert C. Elsenpeter, "Cloud Computing: A Practical
Approach", Tata McGraw Hill, 1 st Edition, 2009.

3. Kumar Saurabh, ”Cloud Computing”, Wiley India, 1st Edition, 2016.
REFERENCES:
1. George Reese, “Cloud Application Architectures: Building Applications and

Infrastructure in the Cloud”, O'reilly, 1 st Edition, 2009.
2. John W. Rittinghouse, James F. Ransome, “Cloud Computing Implementation,

Management, and Security”, CRC Press, 1st Edition, 2009.

126

DATA COMMUNICATIONS AND NETWORKS

Year Hours/Week C Marks Total
Semester P/D CIE SEE 100

LT

2 - 2 3 40 60

Pre-requisite Nil

COURSE OUTCOMES
At the end of the course, the students will develop ability to
1. Describe the architecture of computer communication networks.
2. Analyze various multiplexing and switching techniques in physical layer
3. Apply error detection and correction methods in data link layer
4. Analyze various routing algorithms used in computer networks.
5. Apply different algorithms for congestion control and quality of service

UNIT I
Introduction to networks, internet, protocols and standards, the OSI model, layers in OSI
model, TCP/IP suite, Addressing.

UNIT II
Physical Layer
Digital transmission, multiplexing, transmission media, circuit switched networks, datagram
networks, virtual circuit networks.

UNIT III
Data Link Layer
Introduction, Block Coding, cyclic codes, checksum, framing, flow and error control,
Noiseless channels, noisy channels, HDLC, point to point protocols.
Medium Access Sub Layer: Random access, controlled access, channelization, IEEE
standards, Ethernet, Fast Ethernet. Giga-Bit Ethernet.

UNIT IV
Network Layer
Logical addressing, internetworking, uni-cast routing protocols, Multicast routing protocols.

UNIT V
Transport Layer
Process to process delivery, UDP and TCP protocol, congestion, congestion control
techniques, Quality Of Service(QOS), Quality Of Service techniques.

TEXT BOOKS
1. Andrew S Tanenbaum, “Computer Networks”, 4th Edition, Pearson Education.
2. Behrouz A. Forouzan, “Data Communications and Networking”, 4th Edition, TMH,

2006.

127

REFERENCE BOOKS
1. William Stallings, “Wireless Communications and Networks”, 2nd Edition, Pearson

Hall.
2. WA Shay, “Understanding Communications and Networks”, 3rd Edition, Cengage

Learning.
3. Nader F. Mir, “Computer and Communication Networks”, Pearson Education.

128

WIRELESS SENSOR NETWORKS

Year Semester Hours/Week Marks SEE Total
C CIE 60 100

L T P/D

2 - 2 3 40

Pre-requisite Advanced Wireless Technologies

COURSE OUTCOMES
1. Technical knowhow in building a WSN network.
2. Analysis of various critical parameters in deploying a WSN

UNIT-I
Characteristics Of WSN: Characteristic requirements for WSN - Challenges for WSNs –
WSN vs Adhoc Networks - Sensor node architecture – Commercially available sensor nodes
–Imote, IRIS, Mica Mote, EYES nodes, BTnodes, TelosB, Sunspot -Physical layer and
transceiver design considerations in WSNs, Energy usage profile, Choice of modulation
scheme, Dynamic modulation scaling, Antenna considerations.

UNIT-II
Medium Access Control Protocols: Fundamentals of MAC protocols - Low duty cycle
protocols and wakeup concepts – Contention based protocols - Schedule-based protocols -
SMAC - BMAC - Traffic-adaptive medium access protocol (TRAMA) - The IEEE 802.15.4
MAC protocol.

UNIT-III
Routing And Data Gathering Protocols: Routing Challenges and Design Issues in Wireless
Sensor Networks, Flooding and gossiping – Data centric Routing – SPIN – Directed
Diffusion – Energy aware routing - Gradient-based routing - Rumor Routing – COUGAR –
ACQUIRE – Hierarchical Routing - LEACH, PEGASIS – Location Based Routing – GF,
GAF, GEAR, GPSR – Real Time routing Protocols – TEEN, APTEEN, SPEED, RAP - Data
aggregation - data aggregation operations - Aggregate Queries in Sensor Networks -
Aggregation Techniques – TAG, Tiny DB.

UNIT-IV
Embedded Operating Systems: Operating Systems for Wireless Sensor Networks –
Introduction - Operating System Design Issues - Examples of Operating Systems – TinyOS –
Mate – MagnetOS – MANTIS - OSPM - EYES OS – SenOS – EMERALDS – PicOS –
Introduction to Tiny OS – NesC – Interfaces and Modules- Configurations and Wiring -
Generic Components -Programming in Tiny OS using NesC, Emulator TOSSIM.

UNIT-V
Applications Of WSN: WSN Applications - Home Control - Building Automation -
Industrial Automation - Medical Applications - Reconfigurable Sensor Networks - Highway
Monitoring - Military Applications - Civil and Environmental Engineering Applications -
Wildfire Instrumentation - Habitat Monitoring - Nanoscopic Sensor Applications – Case

129

Study: IEEE 802.15.4 LR-WPANs Standard - Target detection and tracking - Contour/edge
detection - Field sampling.
TEXT BOOKS
1. Kazem Sohraby, Daniel Minoli and Taieb Znati, “Wireless Sensor Networks

Technology, Protocols, and Applications“, John Wiley & Sons, 2007.
2. Holger Karl and Andreas Willig, “Protocols and Architectures for Wireless Sensor

Networks”, John Wiley & Sons, Ltd, 2005.
REFERENCE BOOKS:
1. K. Akkaya and M. Younis, “A survey of routing protocols in wireless sensor networks”,

Elsevier Ad Hoc Network Journal, Vol. 3, no. 3, pp. 325--349
2. Philip Levis, “ TinyOS Programming” 3.Anna Ha´c, “Wireless Sensor Network Designs”,

John Wiley & Sons Ltd,

130

FUZZY LOGIC AND ITS APPLICATIONS

Year Semester Hours/Week Marks Total
C CIE SEE 100

L T P/D

2 - 2 3 40 60

Pre-requisite Basic Mathematics and programming languages

COURSE OUTCOMES
By the end of the course, students should be able to:
1. Acquire the knowledge on Basics of Fuzzy Logic
2. Understand the basic concepts in Machine learning.
3. Apply the knowledge of Clustering in Fuzzy logics.
4. Apply the concept of Classification in Fuzzy Logics
5. Acquire the knowledge on Neuro-Fuzzy resoning
6. Acquire the insight of Neuro-Fuzzy Modeling

UNIT I
Fuzzy Logic Introduction: Comparison of traditional logic and fuzzy logic, Machine learning:
Importance of ML, Fuzzy Clustering Basics: Cluster analysis, Objective function-based,
cluster analysis, Fuzzy analysis of data, Fuzzy Integral Classification: Introduction and
Notation, Reduction vs. Ordering Neuro Fuzzy Modelling: ANFIS – Adaptive Neuro Fuzzy
Inference system

UNIT II
Basic History of Fuzzy Logic Types of Machine Learning: Supervised Learning-
Unsupervised, Learning, reinforcement Learning, Special objective functions, A principal
clustering algorithm The Borda Count ANFIS - architecture

UNIT III
The case of Imprecision, A Historical perspective, The Curse of dimensionality, Overfitting
and linear regression, Classical Fuzzy Clustering Algorithms: The fuzzy c-means algorithm,
The Average Rule , The Median Alternative Hybrid learning algorithm, The Utility of Fuzzy
systems,
Limitations of Fuzzy systems, Gustafson-Kessel algorithm, Product Rule, MaxMax and
MaxMin Rules, Coactive Neuro fuzzy modeling: Towards generalized ANFIS

UNIT IV
Fuzzy sets and membership, Bias and Variance Learning Curve, Intersection Method, Union
Rule Framework, Chance Vs Fuzziness, Gath-Geva algorithm, Logistic Regression: The
Logit Transform and Maximum Likelihood, Estimation Neuron functions for adaptive
netwoks

131

UNIT V
Classical relations : Cartesian product crisp relations, Measuring(dis)similarity-Evaluating
the output of clustering method, The adaptive fuzzy clustering algorithm, Maximizing the
Fuzzy Integral, Neuro-fuzzy spectrum, Fuzzy relations: cardinality of fuzzy relations,
operations on fuzzy relations, Hierarchical clustering.
TEXTBOOKS:
1. Vojislav Kecman, Learning and soft computing: Support vector Machines, Neural

networks and Fuzzy logic models, A Bradford Book, The MIT Press., 2001, ISBN : 0-
262-11255-8
2. Timothy J. Ross,University of New Mexico, USA., Fuzzy Logic with Engineering
Applications, 3rd Edition, Wiley, 2010. ISBN 978-0-470-74376-8
3. Frank Höppner, Frank Klawonn, Rudolf Kruse and Thomas Runkler: Fuzzy Cluster
Analysis, Wiley (1999)ISBN 0-471-98864-2
REFERENCE BOOKS:
1. Timothy Masters, Assessing andImproving Predictionand ClassificationTheory and
Algorithms in C++, ISBN-13 (pbk): 978-1-4842-3335-1 ISBN-13 (electronic): 978-1-
4842-3336-8 ,https://doi.org/10.1007/978-1-4842-3336-8,2018.
2. Jyh-Shing, Roger Jang, Chuen-Tsai sun, Eiji Mizutani., Neuro fuzzy and softcomputing –
A computational approach to learning and machine intelligence, Prentice Hall (1997) ,
ISBN : 0-13-2610663
3. Kevin P. Murphy,“MachineLearning: AProbabilistic Perspective”,MIT Press, 2012
4. EthemAlpaydin,“Introduction to MachineLearning”,Prentice Hall ofIndia, 2005
5. TomMitchell,"MachineLearning",McGraw-Hill, 1997.

132

BIOMEDICAL INSTRUMENTATION

Year Semester Hours/Week C Marks SEE Total
Pre-requisite LT P/D CIE 60 100

2 - 2 3 40

Electronics Measurement and Instrumentation

COURSE OUTCOMES
At the end of the course, the student will develop ability to
1. Discuss the basic concepts of Physiological system of the body, Biomedical Engineering

and Sources of Bioelectric Potentials.

2. Analyze the cardiovascular measurements using different methods

3. Explain the patient care and monitoring using different elements and equipments.

4. Compare various Diagnostic Techniques

5. Demonstrate the concepts of Bio Telemetry.

UNIT I
Introduction
The age of Biomedical Engineering, Development of Biomedical Instrumentation, Man–
Instrumentation system, Components, Physiological system of the body, Problem
encountered in measuring a living system.
Transducers
The Transducers and Transduction principles, Active transducers, Passive Transducers,
Transducer for Biomedical Applications.
UNIT II
Sources of Bioelectric Potentials
Resting and Action potentials, propagation of active potential, The Bioelectric potentials-
ECG, EEG, EMG, and Invoked responses
Electrodes
Electrode theory, Bio potential Electrodes–Microelectrodes Body surface electrodes, Needle
Electrodes, Reference electrodes, PH electrodes, and Blood Gas electrodes.
UNIT III
Cardiovascular Measurements
Electrocardiography – ECG amplifiers, Electrodes and leads, ECG recorders – Three channel,
Vector Cardiographs, Continuous ECG recording (Holter recording), Blood pressure
measurement, Blood flow measurement, Heart sound measurements.
Patient Care and Monitoring
Elements of Intensive Care monitoring, patient monitoring displays, Diagnosis, pacemakers
and Defibrillators.
UNIT IV
Measurements in Respiratory System
Measurement of breathing mechanics- Spiro meter, Respiratory Therapy equipments:
Inhalators ventilators and Respirators, Humidifiers, Nebulizers and Aspirators.
UNIT V
Diagnostic Techniques

133

Ultrasonic Diagnosis Echocardiography, Echo Encephalography, Ophthalmic scans, X-Ray
and Radio-isotope Instrumentation.
Bio Telemetry
The basic components of Biotelemetry system, Implantable units, Telemetry for ECG
measurements for Emergency patient monitoring.
TEXT BOOKS
1. Harry N Norton, “Biomedical Sensors- Fundamentals and Applications”, William

Andrew Publications, 1982.
2. Richard S C Cobbold, “Transducers for Biomedical Measurements”, Krieger Publishing

Company, 1974.
REFERENCE BOOKS
4. Khandpur R S, “Biomedical Instrumentation”, Tata McGraw Hill.
5. Tompkins, “Biomedical DSP: C Language Examples and Laboratory Experiments for the

IBM PC”, Prentice Hall of India.
6. Geddes L.A and Baker L.E, “Principles of Applied Biomedical Instrumentation”, Wiley-

Inter Science, 1989.

134

INTRODUCTION TO ROBOTICS SYSTEMS

Hours/Week Marks
Year Semester C
L T P/D J Total
CIE SEE 100

2- 2 3 40 60

Pre-requisite Microcontroller for embedded system

COURSE OUTCOMES
At the end of the course, the students will be able to
1. Describe the properties of robotic systems
2. Describe the Cortex-M7 processor
3. Explain about the controlling of DC motors
4. Outline the purpose of optical in robotics
5. Describe the implementation of closed loop control system

UNIT I
Introduction:
meaning of robotics, properties of robotic systems, interacting robotic system with its
environment using the sense, perceive, and act model

UNIT II
Arm Cortex-M7 Processor Architecture:
Features of ARM cortex-M7 processor, registers and their functions, processor components,
bus interconnect and debug system, processor memory map, instruction set. Interrupts and
their functions,

UNIT III
DC Motors and Motor controllers:
Internal components of DC motor, functions and operating principles of a Field Effect
Transistor (FET), use of single FET switch in design of motor controllers, Identify the motor
controller topologies; single FET, Half-bridge and H-bridge, servos motor direction control
using PWM.

UNIT IV
Optical Sensing in Robotics:
Distinguish between Op-Amp based inverting and non-inverting amplifier configurations,
application of optical rotary encoders in sensing velocity, optical line camera in line
following operation.

UNIT V
Control for Autonomous cars:
functions and properties of a feedback control system for an autonomous car, effect of
friction and drag, proportional and derivative steering control in autonomous vehicle line
following operation, implementation of a closed loop steering control system

135

TEXT BOOKS:
1. Robotics, Mechatronics, and Artificial Intelligence by Newton C. Braga
2. Advanced Mechatronics and MEMS Devices by Dan Zhang
3. Intelligent Mechatronic Systems: Modeling, Control and Diagnosis by Rochdi Merzouki

and Arun Kumar Samantaray
4. Robot Modeling and Control by Mark W. Spong, Seth Hutchinson, and M. Vidyasagar
REFERENCE BOOKS:
1. Introduction to Robotics: Mechanics and Control -John J. Craig
2. A Textbook of Robotics 1: Basic Concepts - Shoham, M.

136

APPLICATIONS OF IOT AND MULTIMEDIA TECHNOLOGY

Year Hours/Week Marks
Semester C
L T P/D J Total
CIE SEE 100

2- 2 3 40 60

Pre-requisite Cloud Computing

COURSE OUTCOMES
At the end of the course, the students will develop ability to
1. Able to apply the latest computing technologies like cloud computing technology.
2. Develop web services to access/control IoT devices.
3. Deploy an IoT application and connect to the cloud.
4. Become familiar with standard security and privacy preserving mechanisms, and

understand different cloud integration methods.
5. Analyze applications of IoT in real time scenario.

UNIT I
Fundamentals of IoT
Introduction to Internet of Things- The Internet of Things Today, Towards the IoT Universe,
Internet of Things Vision,IoT Concepts, IoT Standards, Components of IoT System, Domain
Specific IoTs -IoT Applications - Home, Cities, Environment, Energy Systems, Retail,
Logistics, Industry, Agriculture, Health and Life style.

UNIT II

Building IoT With Microcontroller
Various Real time applications of Iot – Connecting Iot to cloud – CLOUD STORAGE FOR
IOT – Data Analytics for IoT – Software & Management Tools for IoT, Multimedia

Technology and Industrial IoT Implementations.

UNIT III
Introduction to IoT Security – Vulnerabilities, Attacks and Counter measures .Information
Assurance. Attack types. New security threats and vulnerabilities. Fault Trees and CPS.
Counter measures to thwart attack. Threat Modelling.

UNIT IV

Security Management & Cryptology - Security Controls - Authentication, Confidentiality,
Integrity; Access Control, Key Management and Protocols, Cipher – Symmetric Key
Algorithms, Public Private Key Cryptography; Attacks – Dictionary and Brute Force, Lookup
Tables, Reverse Look Tables, Rainbow Tables, Hashing – MDS, SHA256. SHA 512, Ripe

MD, WI, Data Mining

UNIT V

Attack Surface and Threat Assessment – Embedded Devices – UART, SPI, I2C, JTAG,
Attacks – Software and cloud components, Firmware devices, Web and Mobile Applications.

137

TEXT BOOKS:
1. IoT Fundamentals: Networking Technologies, Protocols, and Use Cases for the Internet

of Things, by David Hanes, Gonzalo Salgueiro, Rob Barton, 2017,ISBN:
9780134307091.
2. Practical Internet of Things Security, Brian Russell & Drew Van Duren – 2016
REFERENCE BOOKS
1. Arshdeep Bahga, Vijay Madishetti, “Internet of Things – A hands – on
approach”,Universities Press, 2015.
2. Getting Started with Raspberry Pi, Matt Richardson & Shawn Wallace, O’Relly
3. (SPD), 2014, ISBN:9789350239759.
4. Manoel Carlos Ramon, “Intel Galileo Gen 2: API Features and Arduino Projects for
Linux Programmers”, Apress, 2014.

138

FUNDAMENTALS OF ARITIFICAL INTELLIGENCE FOR ROBOTICS

Year Semester Hours/Week Marks Total
C CIE SEE

L T P/D

2 - 2 3 40 60 100

Pre-requisite Artificial Intelligence

INSTRUCTIONAL OBJECTIVES
To familiarize the students with the
1. Fundamental concept of AI and expert system.
2. Concept of AI programming languages.
3. Applications of AI in the field of Robotics.

UNIT I
INTRODUCTION: Introduction – History, Definition of AI, Emulation of human cognitive
process, Intelligent agents – The concept of rationality, the nature of environments, the
structure of agents.

UNIT II
SEARCH METHODS: Problem – Solving Agents : Problem Definitions, Formulating
Problems, Searching for solutions – Measuring Problem – Solving Performance with
examples. Search Strategies : Uninformed search strategies – Breadth – first Search, Uniform
– Cost Search, depth –first search, depth – limited search, Iterative deepening depth – first
search, bidirectional search, comparing uniformed search strategies. Informed search
strategies – Heuristic information, Hill climbing methods, best – first search, branch – and –
bound search, optimal search and A* and Iterative deepening A*.

UNIT III
ROBOTICS: Introduction, Robotic perception – localization, mappings planning to move –
configuration space, cell decomposition methods, skeletonization methods, Planning
uncertain movements – Robust methods. Moring –dynamics and control, Potential Field
control, reactive control, Robotics software architecture, Applications. 27 SRM-M.Tech-
Robotics-2015-16

UNIT IV
PROGRAMMING AND LOGICS IN ARTIFICIAL INTELLIGENCE: LISP and other
programming languages – Introduction to LISP, Syntax and numerical function, LISP and
PROLOG distinction, input, output and local variables, interaction and recursion, property
list and arrays alternative languages, formalized symbolic logics – properties of WERS, non-
deductive inference methods.

UNIT V
EXPERT SYSTEM: Expert system – Introduction, difference between expert system and
conventional programs, basic activities of expert system – Interpretation, Prediction,

139

Diagnosis, Design, Planning, Monitoring, Debugging, Repair, Instruction, Control. Basic
aspects of expert system – Acquisition module, Knowledge base – Production rules, semantic
net, frames. Inference engine – Backward chaining and forward chaining. Explanatory
interface.
REFERENCES
1. Russell Stuart, Norvig Peter, “Artificial Intelligence Modern Approach”, Pearson

Education series in AI, 3rd Edition, 2010.
2. Dan.W. Patterson, “Introduction to Artificial Intelligence and Expert Systems”, PHI

Learning, 2009. 3. Donald A. Waterman, “A guide to Expert Systems”, Pearson, 2002.

140

INTRODUCTION TO MODERN COMPILER DESIGN

Year Semester Hours/Week Marks Total
C CIE SEE 100

L T P/D

2 - 2 3 40 60

Pre-requisite Introduction to Programming

COURSE OUTCOMES
At the end of course students will be able to:
1. Understand fundamentals of compiler and identify the relationships among different

phases of the compiler.
2. Understand the application of finite state machines, recursive descent, production rules,

parsing, and language semantics.
3. Analyze & implement required module, which may include front-end, back-end, and a

small set of middle-end optimizations.
4. Use modern tools and technologies for designing new compiler.

COURSE CONTENT

UNIT I
Introduction
Introduction to Compiler, Phases and passes, Bootstrapping, Finite state machines and regular
expressions and their applications to lexical analysis, Optimization of DFA-Based Pattern
Matchers implementation of lexical analyzers, lexical-analyzer generator, LEX-compiler,
Formal grammars and their application to syntax analysis, BNF notation, ambiguity,
YACC.The syntactic specification of programming languages: Context free grammars,
derivation andparse trees, capabilities of CFG.

UNIT II
Basic Parsing Techniques
Parsers, Shift reduce parsing, operator precedence parsing, top down parsing, predictive
parsers Automatic Construction of efficient Parsers: LR parsers, the canonical Collection of
LR (0) items, constructing SLR parsing tables, constructing Canonical LR parsing tables,
Constructing LALR parsing tables, using ambiguous grammars, an automatic parser
generator, and implementation of LR parsing tables.

UNIT III
Syntax-directed Translation
Syntax-directed Translation schemes, Implementation of Syntax directed Translators,
Intermediate code, postfix notation, Parse trees & syntax trees, three address code, quadruple
& triples, translation of assignment statements, Boolean expressions, statements that alter the
flow of control, postfix translation, translation with a top down parser. More about
translation: Array references in arithmetic expressions, procedures call, declaration sand case
statements.

141

UNIT IV
Symbol Tables
Data structure for symbols tables, representing scope information. Run-Time Administration:
Implementation of simple stack al-location scheme, storage allocation in block structured
language. Error Detection & Recovery: Lexical Phase errors, syntactic phase errors semantic
errors.
UNIT V
Code Generation
Selected Topics: Algebraic Computation, Fast Fourier Transform, String Matching, Theory
of NP-completeness, Approximation algorithms and Randomized algorithms..
TEXT BOOKS
1. ALFRED V AUTOR AHO, JEFFREY D AUTOR ULLMAN “Principles of Compiler

Design”.
2. V Raghvan, “ Principles of Compiler Design”, TMH
3. Kenneth Louden,” Compiler Construction”, Cengage Learning.
REFERENCE BOOKS
1. Aho, Sethi & Ullman, “Compilers: Principles, Techniques and Tools”, Pearson

Education2
2. Charles Fischer and Ricard LeBlanc,” Crafting a Compiler with C”, Pearson Education

142

AI IN SPEECH PROCESSING

Year Semester Hours/Week Marks SEE Total
C CIE 60 100

L T P/D

2- 24 40

Pre-requisite Artificial Intelligence

COURSE OUTCOMES:
Upon completion of the course, the learners will be able to:
1. Understand the basic concepts of speech and fundamental signal processing approaches to

speech spectral analysis.
2. Analyze various features of speech and understand the techniques of extracting the

features and pattern comparison techniques.
3. Apply statistical modeling techniques.
4. Understand the architecture and various models of continuous speech recognition system.
5. Apply methods of text to speech synthesis for different applications.
6. Investigate recent developments in speech recognition.

UNIT-I
Basic Concepts Speech Fundamentals: Articulatory Phonetics – Production and Classification
of Speech Sounds; Acoustic Phonetics, Acoustics of speech production; Review of Digital
Signal Processing concepts; ShortTime Fourier Transform, Filter-Bank and LPC Methods.

UNIT-II
Speech Analysis Features; Feature Extraction and Pattern Comparison Techniques; Speech
distortion measures– mathematical and perceptual, Log, Spectral Distance, Cepstral
Distances, Weighted Cepstral Distances and Filtering; Likelihood Distortions; Spectral
Distortion using a Warped Frequency Scale, LPC, PLP and MFCC Coefficients; Time
Alignment and Normalization; Dynamic Time Warping, Multiple Time, Alignment Paths.

UNIT-III
Speech Modelling Hidden Markov Models: Markov Processes, HMMs, Evaluation, Optimal
State Sequence, Viterbi Search, Baum-Welch Parameter Re-estimation; Implementation
issues.

UNIT-IV
Speech Recognition Large Vocabulary Continuous Speech Recognition: Architecture of a
large vocabulary continuous speech recognition system, acoustics and language models,
ngrams, context dependent sub-word units; Applications and present status.

UNIT-V
Speech Synthesis Text-to-Speech Synthesis: Concatenative and waveform synthesis methods,
sub-word units for TTS, intelligibility and naturalness; role of prosody; applications and
current status.

143

TEXT BOOKS:
1. L. Rabiner and B. Juang, Fundamentals of Speech Recognition, Pearson Education.
2. D. Jurafsky and J. Martin, Speech and Language Processing – An Introduction to Natural

Language Processing, Computational Linguistics, and Speech Recognition, Pearson
Education.
REFERENCE BOOKS:
1. S. Smith, The Scientist and Engineer’s Guide to Digital Signal Processing, California
Technical Publishing.
2. T. Quatieri, Discrete-Time Speech Signal Processing – Principles and Practice, Pearson
Education.
3. C. Becchetti and L. Ricotti, Speech Recognition, John Wiley and Sons.
4. B. Gold and N. Morgan, Speech and Audio Signal Processing, Processing and Perception
of Speech and Music, Wiley- India Edition.

144

ARTIFICIAL INTELLIGENCE IN BIOINFORMATICS

Year Semester Hours/Week Marks Total
C CIE SEE 100

L T P/D

2 - 2 3 40 60

Pre-requisite Artificial Intelligence

COURSE OUTCOMES:
Upon completion of the course, the learners will be able to:
1. Understand the concepts of molecular biology, DNA analysis with respect to data

processing.
2. Analyze biological sequences and score matrices with respect to data processing.
3. Implement data mining algorithms on microarray, gene expression, feature selection for

proteomic and genomic data.
4. Understand ethics in using bioinformatics.
5. Apply AI in medical field for development of contributive solutions.
6. Investigate state-of-the-art research and developments in bioinformatics. Prerequisites:

Fundamentals of Artificial Intelligence

UNIT-I
Introduction: Introduction to Bioinformatics and Data Mining; Molecular Biology
background: Analysing DNA; Bioinformatics perspective of how individuals of a species
differ and how different species differ; Bioinformatics challenges and opportunities.

UNIT-II
Biological Sequence Analysis DNA sequence analysis; DNA databases; Protein structure and
function; Protein sequence databases; Sequence alignment; Sequence comparison, Sequence
similarity search; Longest common subsequence problem; Scoring matrices for similarity
search PAM, BLOSUM, etc.

UNIT-III
Mining Biological Data Protein structural classification; Protein structural prediction;
Modeling text retrieval in biomedicine; Mining from microarray and gene expressions;
Feature selection for proteomic and genomic data mining.

UNIT-IV
Ethics in Bioinformatics Ethical and social challenges of electronic health information;
Public access to anatomic images; Evidence based medicine; Outcome measures and practice
guidelines for using data mining in medicine; Computer assisted medical and patient
education.

UNIT-V

145

AI in Medical Informatics Infectious disease informatics and outbreak detection;
Identification of biological Relationships from text documents; Medical expert systems;
Telemedicine and tele surgery; Internet grateful med (IGM).
TEXT BOOKS:
1. S. Rastogi, N. Mendiratta and P. Rastogi, Bioinformatics: Methods and Applications:

Genomics, Proteomics and Drug Discovery, PHI.
2. Z. Ghosh, B. Mallick, Bioinformatics: Principles and Applications, Oxford University

Press.
REFERENCE BOOKS:
1. J. Chen and S. Lonardi, Biological Data Mining, Chapman and Hall/CRC.
2. V. Buffalo, Bioinformatics Data Skills, O′Reilly Publishing.
3. H. Zengyou, Data Mining for Bioinformatics Applications, Woodhead Publishing.
4. L. Low, Bioinformatics: A Practical Handbook of Next Generation Sequencing and its

Applications, World Scientific Publishing.
5. M. Model, Bioinformatics Programming Using Python, O′Reilly Publishing.

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COMPUTER VISION

Year Semester Hours/Week Marks Total
C CIE SEE

L T P/D

2 - 2 3 40 60 100

Pre-requisite Continuous And Discrete Time Signals And Systems

COURSE OUTCOMES
At the end of the course, the students will develop ability to
1. Know and understand the basics and fundamentals of Digital image processing, such as

digitization, sampling and quantization
2. Acquire knowledge of 2D FFT and transforms
3. Operate on images using the techniques of smoothing, sharpening and enhancement.
4. Understand the restoration concepts and filtering techniques
5. Learn the basics of segmentation and features extraction.

UNIT I
Fundamentals of Image Processing
Image Acquisition, Image Model, Sampling, Quantization, Relationship between pixels,
distance measures, connectivity, Image Geometry, Photographic film. Histogram: Definition,
decision of contrast basing on histogram, operations basing on histograms like image
stretching, image sliding, Image classification, definition and Algorithm of Histogram
equalization.

UNIT II
Image Transforms
2-D FFT, Properties, walsh transform, Hadamard Transform, Discrete cosine Transform,
Haar transform, Slant transform, Hotelling transform

UNIT III
Image Enhancement
Arithmetic and logical operations, point operations, Smoothing filters-Mean, Median, Mode
filters. Edge enhancement filters – Directorial filters, Sobel, Laplacian, Robert, KIRSCH
Homogeneity and DIFF Filters, Prewitt filter, Contrast Based edge enhancement techniques,
low Pass filters, High Pass filters, sharpening filters. Colour image processing, Color
fundamentals, color models.

UNIT IV
Image Compression
Definition, A brief discussion on – Run length encoding, contour coding, Huffman Course
Code, compression due to change in domain, compression due to quantization Compression
at the time of image transmission. Brief discussion on image compression standards.

UNIT V
Image Segmentation

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Detection of discontinuities. Edge linking and boundary detection, Thresholding, Region
oriented segmentation.
UNIT VI
Image Restoration
Degradation model, Algebraic approach to restoration, Inverse filtering, least mean square
filters, Constrained Least Squares Restoration, Interactive Restoration
TEXT BOOKS
1. R.C. Gonzalez and R.E. Woods, “Digital Image Processing”, Addison Wesley / Pearson

Edition, 2nd Edition, 2002.
2. A K Jain, “Fundamentals of Digital Image Processing”, Prentice Hall of India.
REFERENCE BOOKS
1. Rafael C. Gonzalez, Richard E Woods and Steven L, “Digital Image Processing Using

MAT LAB”, PEA, 2004.
2. William K. Pratt, “Digital Image Processing”, John Wiley, 3rd Edition, 2004.
3. Anjireddy and M. Harishankar, “Text Book of Digital Image Processing”, BSP,

Hyderabad, 2013.

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PATTERN RECOGNITION TECHNIQUES IN MACHINE LEARNING

Year Semester Hours/Week C Marks
L T P/D CIE SEE Total

2 - 2 3 40 60 100

Pre-requisite Computer Vision, Artificial Intelligence, Machine Learning

OBJECTIVES

1. To learn the fundamentals of Pattern Recognition techniques

2. To learn the various Statistical Pattern recognition techniques

3. To learn the various Syntactical Pattern recognition techniques

4. To learn the Neural Pattern recognition techniques

UNIT I

PATTERN RECOGNITION OVERVIEW: Pattern recognition, Classification and
Description—Patterns and feature Extraction with Examples—Training and Learning in PR
systems—Pattern recognition Approaches

UNIT II

STATISTICAL PATTERN RECOGNITION: Introduction to statistical Pattern
Recognition—supervised Learning using Parametric and Non Parametric Approaches.

UNIT III

LINEAR DISCRIMINANT FUNCTIONS AND UNSUPERVISED LEARNING AND
CLUSTERING: Introduction—Discrete and binary Classification problems—Techniques to
directly Obtain linear Classifiers -- Formulation of Unsupervised Learning Problems—

Clustering for unsupervised learning and classification.

UNIT IV
SYNTACTIC PATTERN RECOGNITION: Overview of Syntactic Pattern Recognition—
Syntactic recognition via parsing and other grammars–Graphical Approaches to syntactic
pattern recognition—Learning via grammatical inference.

UNIT V
NEURAL PATTERN RECOGNITION: Introduction to Neural networks—Feed forward
Networks and training by Back Propagation—Content Addressable Memory Approaches and

Unsupervised Learning in Neural PR.

REFERENCES
1. Robert Schalkoff, “Pattern Recognition: Statistical Structural and Neural Approaches”,

John wiley & sons , Inc,1992.
2. Earl Gose, Richard johnsonbaugh, Steve Jost, “Pattern Recognition and Image Analysis”,

Prentice Hall of India,.Pvt Ltd, New Delhi, 1996.
3. Duda R.O., P.E.Hart & D.G Stork, “ Pattern Classification”, 2nd Edition, J.Wiley Inc

2001.
4. Duda R.O.& Hart P.E., “Pattern Classification and Scene Analysis”, J.wiley Inc, 1973. 5.

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

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DEEP LEARNING FOR COMPUTER VISION

Year Semester Hours/Week Marks Total
C CIE SEE 100

L T P/D

2 - 2 3 40 60

Pre-requisite Computer Vision

COURSE OUTCOMES
1. Thorough Understanding of the fundamentals of Deep Learning

2. Practical Engineering tricks for training and fine-tuning the networks by developing the

skill to use multiple packages required to build AI systems for signal and image analysis

3. Gaining Knowledge of standard deep convolutional architectures and use the pre-trained

model for transfer learning for signal and image analysis

UNIT-I
Image Formation Models, Monocular imaging system, Orthographic & Perspective
Projection, Camera model and Camera calibration, Binocular imaging systems, Image
Processing and Feature Extraction Image representations (continuous and discrete)
Edge detection Motion Estimation Regularization theory, Optical computation, Stereo
Vision Motion estimation Structure from motion Shape representation and Segmentation,
Deformable curves and surfaces Snakes and active contours, Level set representations Fourier
and wavelet descriptors, Medial representations
UNIT-II
Multi-resolution analysis, Object recognition, Hough transforms and other simple object
recognition methods, Shape correspondence and shape matching, Principal component
analysis, Shape priors for recognition.
UNIT-III
Machine learning:
Introduction to probability, Classification and K-NN, Decision Trees and Rule Learning, The
Naive Bayes algorithm, Linear Regression, Logistic Regression,
UNIT-IV
The Perceptron algorithm, Neural networks and Deep Belief Networks, SVMs and Margin
Classifiers, SVMs: Duality and kernels, Evaluating and Comparing Classifiers
Experimentally, PAC Learning, Clustering,
UNIT-V
Bias-Variance Decomposition, Ensemble Methods, Bayesian networks, HMMs - inference,
HMMs - learning.
TEXT BOOKS / REFERENCE BOOKS
1. ‘Deep Learning’, Ian Goodfellow, YoshuaBengio and Aaron Courville, Second edition,

MIT Press, 2016
2. ‘MATLAB/PYTHON Deep Learning with Machine Learning, Neural Networks and

Artificial Intelligence’, Phil Kim, Apress, 2017

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