R2018 Final Year Electronics Scheme Syllabus 23april21
R2018 Final Year Electronics Scheme Syllabus 23april21
R2018 Final Year Electronics Scheme Syllabus 23april21
(VJTI)
MATUNGA, MUMBAI 400 019
Curriculum
(Scheme of Instruction & Evaluation and Course contents)
For
Fourth Year Undergraduate Programme Leading
toBachelor of Technology (B.Tech.) Degree
in
Electronics Engineering
Curriculum
(Scheme of Instruction & Evaluation and Course contents)
For
Fourth Year Undergraduate Programme Leading
to Bachelor of Technology (B. Tech.)
in
To establish global leadership in the field of Technology and develop competent human resources for
Institute Mission
To provide students with comprehensive knowledge of principles of engineering with a multi-
To foster relationship with other leading institutes of learning and research, alumni and
Engineering and to develop competent human resources for providing service to society.
Department Mission
To provide student with comprehensive knowledge for taking up challenges in the field of
To foster relationship with renowned institutes of learning and research, alumni and
• Apply analysis, design, optimization and implementation skills in order to formulate and
• Take up higher studies, innovation, research & development and other such creative
efforts in technology.
• Use their skills in professional manner to raise the satisfaction level of stake holders.
PROGRAM OUTCOMES (POs)
Engineering Graduate will be able to:
1. Engineering knowledge: Apply the knowledge of mathematics, science, engineering
fundamentals, and an engineering specialization to the solution of complex engineering
problems.
2. Problem analysis: Identify, formulate, review research literature, and analyse complex
engineering problems reaching substantiated conclusions using first principles of
mathematics, natural sciences, and engineering sciences.
3. Design/development of solutions: Design solutions for complex engineering problems
and design system components or processes that meet the specified needs with appropriate
consideration for the public health and safety, and the cultural, societal, and environmental
considerations.
4. Conduct investigations of complex problems: Use research-based knowledge and
research methods including design of experiments, analysis and interpretation of data, and
synthesis of the information to provide valid conclusions.
5. Modern tool usage: Create, select, and apply appropriate techniques, resources, and
modern engineering and IT tools including prediction and modelling to complex engineering
activities with an understanding of the limitations.
6. The engineer and society: Apply reasoning informed by the contextual knowledge to
assess societal, health, safety, legal and cultural issues and the consequent responsibilities
relevant to the professional engineering practice.
7. Environment and sustainability: Understand the impact of the professional engineering
solutions in societal and environmental contexts, and demonstrate the knowledge of, and
need for sustainable development.
8. Ethics: Apply ethical principles and commit to professional ethics and responsibilities
and norms of the engineering practice.
9. Individual and team work: Function effectively as an individual, and as a member or
leader in diverse teams, and in multidisciplinary settings.
10. Communication: Communicate effectively on complex engineering activities with the
engineering community and with society at large, such as, being able to comprehend and
write effective reports and design documentation, make effective presentations, and give and
receive clear instructions.
11. Project management and finance: Demonstrate knowledge and understanding of the
engineering and management principles and apply these to one‘s own work, as a member
and leader in a team, to manage projects and in multidisciplinary environments.
12. Life-long learning: Recognize the need for, and have the preparation and ability to
engage in independent and life-long learning in the broadest context of technological
change.
PROGRAM SPECIFIC OUTCOMES (PSOs)
1. Design, develop and test electronic systems in the areas related to analog and digital
2. Analyze, design and implement electronic systems to strive balance between increasing
3. Design electronic software and hardware systems, components or process to meet desired
Program Elective II
Program Elective IV
Open Elective II
COURSE OUTCOMES
COURSE CONTENTS
TEXT BOOKS:
1. Behrouz. A. Forouzan, ―Data Communication and Networking‖, Tata
McGraw Hill.2007
2. Wllliam Stallings, ―Wireless Communication and Networks‖, Prentice Hall,
2nd edition, 2005.
3. Leon Garcia, Widjaja, ―Communication Networks‖, Tata McGraw Hill.2004
Additional Reading:
1. Larry L. Peterson, Bruce S. Davie, ―Computer networks‖, 4th Edition,
Elsevier.2007
2. Jean Walrand & PravinVaraiya, ―High Performance Communication
Networks‖, Elsevier.2014
3. Curt M. White, ―Data Communication and Computer Network‖ 6-th Edition,
2008.
Programme Name B. Tech. (Electronics Engineering), SEMESTER - VII
Course Code R4EC4002S
Course Title PRINCIPLES OF VLSI
Prerequisite Electronic Circuit Analysis and Design I & II, Integrated Circuits
and Applications
COURSE OUTCOMES:
Describe basic VLSI design flow, hierarchy, styles and design quality parameters.
Explain semiconductor grade silicon production, CMOS fabrication process and
should be able to draw and describe layout.
Describe MOSFET structure, operation, characteristics, physical effects and scaling
and should be able to calculate vital parameters related to MOSFET.
Analyze and design various MOSFET circuits using different MOSFET based
topologies (especially CMOS topology) functionally as well as for different parameter
(delay, power, noise) constraints.
Analyze and design functional units such as adders, multipliers, RAMs etc.
COURSE CONTENTS
Module 1 Introduction
Moore‘s law; VLSI Design flow; design hierarchy; concepts of regularity,
modularity and locality; VLSI design styles; design quality.
Module 2 Fabrication and Layout of CMOS Integrated Circuits
Semiconductor grade silicon production; CMOS fabrication process –
photolithography, diffusion, ion-implantation, CMOS process flow, isolation
– LOCOS and STI; modern CMOS process trends such as lightly doped
drain, copper interconnects, low-k and high-k dielectrics, three dimensional
IC; layout, layout design rules, CMOS inverter layout design; latchup and
latchup prevention techniques.
Module 3 Physics and Modeling of MOSFETs
Energy band diagram view of MOS system under external bias; MOSFET
structure and operation; first order V-I characteristics of MOSFET; channel
length modulation; substrate bias effect; MOSFET modeling – drain-source
resistance, MOSFET capacitance, junction leakage currents; MOSFET
scaling; Short channel effects such as classical short channel effect, reverse
short channel effect, mobility degradation, velocity saturation, hot carrier
effect, DIBL, subthreshold leakage; Narrow channel effect; Current equations
for velocity saturated MOSFETs.
Module 4 CMOS inverter: Analysis and Design
VTC of ideal inverter; noise margin; CMOS digital logic inverter – different
regions of operation, calculation of critical voltage points on VTC; CMOS
inverter switching characteristics; design of CMOS inverter; power
dissipation in CMOS inverter; comparison of various MOSFET based
inverter topologies with CMOS inverter; ratioed and ratioless designs.
Module 5 Static Logic Circuits
CMOS based gates such as NAND, NOR, XOR, XNOR and complex logic
circuits; transistor sizing for gates; adder, SR latch and D latch circuits;
CMOS SRAM cell; Schmitt trigger and tri-state output circuits;
implementation of logic gates using other MOSFET based topologies such as
pseudo nMOS etc.
Module 6 Transmission Gate & Dynamic Logic Circuits
nMOS and pMOS pass transistors; CMOS transmission gate; clock
feedthrough, charge leakage, charge sharing; bootstrapping; dynamic CMOS
logic; high performance dynamic CMOS circuits such as domino CMOS
logic, NORA and TSPC CMOS logic; DRAM cell.
TEXT BOOKS:
1 Sung-Mo Kang & Yusuf Leblebici, CMOS Digital Integrated Circuits-
Analysis and Design, 3rd edition, McGraw Hill
2 Jan M. Rabaey, Anantha Chandrakasan & Borivoje NIkolic, Digital
Integrated Circuits-A Design Perspective, 2nd edition, PHI
3 David A Hodges, Horace G Jackson & Resve A Saleh, Analysis and Design
of Digital Integrated Circuits in deep submicron technology, 3rd edition,
McGraw Hill
ADDITIONAL READING:
1 Neil H E Weste & Kamran Eshragian, Principles of CMOS VLSI Deisgn- A
systems perspective, Addison- Wesley
2 John P. Uyemura, CMOS Logic Circuit Design, Springer International
Edition
3 Adel S. Sedra & Kenneth C. Smith, Microelectronic Circuits, 5th edition,
Oxford University Press
4 S. M. Sze, VLSI Technology, 2nd edition, Bell Laboratories.
Programme Name B.Tech. (Electronics Engineering), SEMESTER - VII
Course Code R4EC4003T
Course Title EMBEDDED SYSTEMS
Prerequisite Microprocessors And Controllers
COURSE OUTCOMES
COURSE CONTENTS
Module I Introduction to Embedded system
Introduction To Embedded Systems, Definition Of Embedded System,
Embedded Systems Vs General Computing Systems, History Of Embedded
Systems, Classification, Major Application Areas, Purpose Of Embedded
Systems, Characteristics And Quality Attributes Of Embedded Systems.
Embedded Processor Requirements, Features, Types, RISC Processors,
Harvard Architecture, Super Harvard Architecture, Selection Of Processors &
Microcontrollers.
Module II Architecture of Embedded System
Hardware Architecture: 8051, Arm, Memory, Clock Circuitry, Watchdog
Timer, Chip Select, I/O Devices, Debug Port, Communication Interfaces,
Power Supply Unit. Software Architecture: Services Provided By OS,
Architecture Of Embedded OS, Categories Of Embedded OS, Application
Software, Communication Software, Development And Testing Tools.
Module III Communication Interfaces
Need For Communication Interfaces, OSI Reference Model, Basic Of
Networks, Network Topology, RS232/UART,RS422/RS485, USB,
Infrared, Ethernet, IEEE 802.11, Bluetooth, SPI, I2C, CAN, Wifi, Flex
Ray, LIN Bus, Zigbee.
Module IV Embedded Software
Software Developments Tools, Cross Platform Development, Programming
Languages Like Embedded C, Embedded C++ And J2ME , Device Drivers,
Debuggers, Profilers, Code Optimization, Overview Of RTOS, Architecture
Of Kernel, Task & Task Scheduler, ISR, Semaphore, Mutex, Mailbox,
Message Queues, Event Registers, Pipes, Signals, Timers, Memory
Management, Priority Inversion Problem.
Module V Embedded System Development & Testing
Different Embedded System Development Models, Requirement
Engineering, Design Tradeoff, Co-Design, Hardware Design, Software
Design, Implementation, Integration & Testing, Packaging, Configuration
Management, Managing Embedded System Development Projects,
Embedded System Fiascos.
Module VI Design Examples & Case Studies of Embedded System
Digital Thermometer, Navigation Systems, Smart Card, RF Tag
Text Books:
1. Raj Kamal ―Embedded system‖ Tata McGraw Hill.2003
2. Prasad ―Embedded Real time systems‖ Dream tech Wiley Publication.2003
Additional Reading:
1. David Simon, ―An embedded Software Primer‖ Pearson Publication, 1999
2. Frank Vahid, ―Embedded system- A unified Hardware Software
Introduction‖ John Wiley and Sons.2002
Programme Name B.Tech. (Electronics Engineering), SEMESTER - VII
Course Code R4EC4003P
Course Title EMBEDDED SYSTEM LAB
Prerequisite Microprocessors And Controllers
COURSE OUTCOMES
COURSE CONTENTS
Text Books:
1. Raj Kamal ―Embedded system‖ Tata McGraw Hill,2003.
2. Prasad ―Embedded Real time systems‖ Dream tech Wiley Publication.2003
Additional Reading:
1. Mazidi ―8051 microcontroller and embedded system‖ Pearson,2005
2. Cornel Amariei, Arduino Development Cookbook, Packet publishing
3. Pete Cockerell ―ARM assembly language programming‖
4. Rahul Dubey, ―Introduction to embedded system design using FPGA‖
Springer, 2013
Programme Name B.Tech. (Electronics Engineering), SEMESTER - VII
Course Code R4EC4004A
Course Title Information Technology Act
Prerequisite
COURSE OUTCOMES
COURSE OBJECTIVE
The course is designed to introduce the field of artificial neural networks systems and machine
learning. The course will give the student the basic idea and intuition behind modern data
processing algorithms as well as a bit more formal understanding of how, why, and when they
work.
COURSE OUTCOME
This course is aimed at the introductory graduate level. It will provide a foundational
understanding of how artificial intelligence, machine learning and statistical algorithms work.
Students will have a toolbox of algorithms that they can use on their own datasets after they leave
the course.
Student will be able to implement basis supervised learning algorithms.
Student will be able to use algorithms for unsupervised methods.
Student will be able to describe how statistical models work.
COURSE CONTENTS
COURSE OBJECTIVE
The course is designed to introduce the field of artificial neural networks systems and machine
learning. The course will give the student the practical and implementation aspects of data
processing algorithms.
COURSE OUTCOME
Course Contents
TEXT BOOK
1 Simon Haykin, ―Neural Networks and Learning Machines‖, Pearson Publication,
New Delhi, 2012.
Programme Name B. Tech. Electronics Engineering, Semester-VII
Course Code R4EC4102T
Course Title Audio, Video and Image Compression
Pre-requisite Digital Signal processing
COURSE OBJECTIVES
1. To evaluate various Lossless and Lossy compression techniques for different types
of data.
2. To develop coding for text compression and audio compression techniques
3. To develop image and video compression techniques
4. To analyze various quantization techniques
COURSE OUTCOMES
After completing this course, students will be able to
1. Understand and evaluate various lossless and lossy compression methods.
2. Understand various types of redundancies in an image and methods to remove them
3. Analyze and compare audio and video compression standards
4. Understand and analyze various quantization techniques
COURSE CONTENTS
Module I Data Compression Techniques
1.1 Loss less compression, Lossy compression, Entropy Measures of
performance, Modeling and Coding
1.2 Minimum variance Huffman coding, Extended Huffman coding, Adaptive
Huffman coding, Shannon Fano Coding, Arithmetic coding, Dictionary
coding techniques, LZ 77, LZ 78, LZW
Module II Audio Compression
2.1 High quality digital audio, Frequency, Spectral and Temporal masking,
Lossy sound compression, Format of Compressed Data
2.2 M-law and A-law companding, MPEG audio standard
2.3 DPCM and ADPCM audio compression, Frequency Domain coding
Module III Image and Video Compression
3.1 Two D Image Transforms, Lossless Image compression techniques, PCM,
DPCM, JPEG, JPEG –LS and JPEG 2000 standards
3.2 Video Compression, Intra frame coding, motion estimation and
compensation,
3.3 Introduction to MPEG - 2 H-264 encoder and decoder, MPEG Industry
Standards
Module IV Quantization
Problems in quantization
4.1 Uniform, adaptive, forward adaptive, backward adaptive, nonuniform
quantization
4.3 Vector quantization and algorithms (Linde Buzo Gray algorithm, tree,
pyramid, polar, lattice spherical quantization
Text Books:
1 Data Compression: The Complete reference, 4th edition 2007 by David
Salomon. Springer Publication.
2 Introduction to Data Compression: 3rd Edition 2006 by Khalid Sayood.
Morgan Kaufmann Series,
Reference Books:
1 The Data Compression Book 2nd Edition: by Mark Nelson. BPB publication,
2 Handbook of data compression, 2010 Salomon, David, Motta, Glovanni.
Spriger Publications
Programme Name B. Tech. Electronics Engineering, Semester-VII
Course Code R4EC4102P
Course Title Audio, Video and Image Compression LAB
Digital Signal processing
COURSE OUTCOMES
After completion of this course, the students will be able to
1. Implement Lossless and Lossy compression algorithms
2. Implement various image compression techniques
3. Simulate audio compression algorithm and estimate parameters
4. Study and simulate video compression algorithms
5. Implement various quantization techniques and measure parameters for performance evaluation
COURSE CONTENTS
Module I Data Compression Techniques
1.1 Loss less compression and Lossy compression Implementation
1.2 Entropy Measures of Performance Simulation
1.3 Modeling and Coding Simulation
1.4 Image Compression Coding
COURSE OBJECTIVES
1. To introduce the characteristics of Speech signals and the related time and frequency domain
representations.
2. To introduce the different applications of speech like synthesis, coding and recognition.
COURSE OUTCOMES
After successful completion of this course, students will be able to
COURSE CONTENTS
Module 1 Speech production and perception
Speech production mechanism, Auditory System and Hearing Mechanism,
Classification of speech, sounds, nature of speech signal, models of speech
production. Speech signal processing: purpose of speech processing, digital
models for speech signal, Digital processing of speech signals, Significance,
short time analysis.
Module 2 Time domain methods for speech processing
Time domain parameters of speech, methods for extracting the parameters,
Zero crossings, Auto correlation function, pitch estimation.
Module3 Frequency domain methods for speech processing
Short time Fourier analysis, filter bank analysis, spectrographic analysis,
Format extraction, pitch extraction, Analysis - synthesis systems, Auditory
models.
Module 4 Linear predictive coding of speech
Formulation of linear prediction problem in time domain, solution of normal
equations, Interpretation of linear prediction in auto correlation and spectral
domains.
Module5 Speech signal analysis
Cepstral analysis of speech, Mel frequency cepstral coefficients (MFCC),
format and pitch estimation.
Module6 Applications of speech processing
Speech Synthesis, Speech Coding, Speech and Speaker recognition and
verification. Vector quantization, Hidden Markov modeling for isolated word
and continuous speech recognition.
Text Books:
1. Lawrence Rabiner and Ronals Schafer, ―Theory and Applications of Digital
Speech Processing‖, Prentice Hall, 2011
2. T.F. Quatieri, Discrete-Time Speech Signal Processing, Prentice Hall 2002.
3. L.T. Rabiner and R. Schafer, Digital Processing of Speech Signals, Prentice
Hall, 1978.
Recommended Reading:
1. Douglas O‖Shaughnessy, Speech Communciations: Human and Machine,
Universities Press, 2001.
2. J.L Flanagan: Speech Analysis Synthesis and Perception - SprengerVertag,
3. I.H.Witten: Principles of Computer Speech, Academic press.
Programme Name B. Tech. Electronics Engineering, Semester-VII
Course Code R4EC4103P
Course Title Speech Processing Lab
Pre-requisite Digital Signal Processing
COURSE OBJECTIVES
1. To introduce the characteristics of Speech signals and the related time and frequency domain
representations.
2. To introduce the different applications of speech like synthesis , coding and recognition
COURSE OUTCOMES
After successful completion of this course, students will be able to
1. Analyze the speech signal and to identify the different parameters of speech signal like
voiced/unvoiced, vowel/consonant, types of articulation etc.
2. Develop various speech models using various features vectors like LPC, Cepstrum, MFCC
3. Develop simple speech processing applications like Speech Recognition, Speaker Recognition,
Speech Coding
COURSE CONTENTS
Module 1 Three experiments in time domain operations like Zero crossing detector,
Energy Estimation, Autocorrelation etc by recording speech signal
Module 2 Study of spectrogram for various combination of speech syllables
Module3 Three experiments on Frequency domain methods for speech processing
pitch extraction formant extraction etc.
Module 4 Experiments on simple some applications of speech.
Text Books:
1. Lawrence Rabiner and Ronals Schafer, ―Theory and Applications of
Digital Speech Processing‖, Prentice Hall, 2011
2 T.F. Quatieri, Discrete-Time Speech Signal Processing, Prentice Hall
2002.
3 L.T. Rabiner and R. Schafer, Digital Processing of Speech Signals,
Prentice Hall, 1978.
Programme Name B. Tech. Electronics Engineering, Semester-VII
Course Code R4EC4104T
Course Title MEDICAL ELECTRONICS
Prerequisite Instrumentation Systems
COURSE OUTCOMES
COURSE CONTENTS
Module 1 Fundamentals of Medical Instrumentation
Anatomy and Physiology, Physiological Systems of the Body, Problems in
measuring the Physiological variables, Components of Medical Instrument.
Module 2 Bioelectric Signals and Electrodes, Transducers
Origin of Bioelectric signals, Resting and Action Potentials, Depolarization
and Repolarization, Propagation of Action Potentials. Electrode Theory,
Recording Electrodes, Silver-Silver Chloride Electrodes, Microelectrodes.
Transducer Principle, Classification of Transducers, various Transducers for
the measurement of Physiological Events, Amplifiers and Signal Processing.
Module 3 The Cardiovascular System and Measurements
The Heart and Cardiovascular System: Heart Sounds and their measurements
with Phonocardiograph, Stethoscope etc., Phonocardiogram. Blood Flow:
Characteristics of Blood Flow, Measurement of Blood flow and Cardiac
output with Magnetic Blood Flow meter, Ultrasonic Blood Flow Meter &
Radio Graphic Method. Blood Pressure: Measurement of Blood Pressure with
Indirect and Direct methods, Sphygmomanometry, Programmed
Electrosphygmomanometry, Digital Blood Pressure meter, Impedance
Plethysmography.
Module 4 Generation & Recording of Bio Electrical Activities
Electrocardiogram: ECG Electrode Placement- ―Bipolar Limb Lead
Configuration by Einthoven, Unipolar Limb Leads(Wilson leads),Augmented
Unipolar Limb Leads, Precordial and Marriott Leads‖, ECG Recorders.
Electromyogram: EMG System, Electrodes used and their placement,
Latency, Applications.
Electro Encephalogram: EEG Electrodes and their placement-‗Anterior-
Posterior‘ and ‗Lateral‘ measurements, Recording Modes of EEG,
Applications of EEG.
Electro Retinogram: Human Eye System, ERG Recording techniques,
Standards of ERG, Applications of ERG.
ElectroOculogram: EOG basics, Recording methods, patient preparation,
Arden Index, Diagnostic Utility of EOG
Module 5 Measurements in the Respiratory System
Introduction, Physiology of the Respiratory System, Lung
Volumes/Capacities, Instrumentation for measuring the Mechanics of
Breathing- Kymograph, Spiro meter etc.
Module 6 Prosthesis
Introduction, Types of Prosthetic Devices, Application and working principle
of various prosthetic devices eg.Myoelectric Control System for paralyzed
arm, Audiometry and Hearing Aids.
Dialysis: Introduction, Function of the Kidneys, Artificial Kidney, Dialyzers,
Membranes for Dialysis, Haemodialysis, Peritoneal Dialysis.
Module 7 Therapeutic Equipment
Introduction, High Frequency Heat Therapy, Short-wave Diathermy,
Microwave Diathermy, Ultrasonic Therapy Unit., Endoscopy, Gastroscope,
Bronchoscope, Sigmoidoscope, Laproscope, Pacemakers and Defibrillators.
Module 8 Medical Imaging Systems
Introduction, X-ray Machines and Digital Radiography, Computed
Tomography, CT Scanners, Ultrasonic Imaging Systems, MRI & PET Scan,
Thermal Imaging Systems
Module 9 Bio Telemetry and Telemedicine
Introduction to Biotelemetry, The Components of a Biotelemetry System,
Implantable Units, Single-Channel/Multi-Channel/Multi-Patient Telemetry
Systems, Application of Telemetry in Patient Care, Telemedicine.
Module 10 Patient Care and Monitoring
The elements of Intensive-Care Monitoring, Patient-Monitoring Equipment –
Different types, The Organization of Hospital for Patient-Care Monitoring.
Module 11 Patient Safety
Physiological effects of Electric Current, Shock Hazards and Leakage
Currents, precautions to minimize Electric Shock Hazards and Leakage
Current, Methods of Accident Prevention, Safety codes for electro medical
equipment
Text Books:
1. ―Handbook of Biomedical Instrumentation‖ by R.S.Khandpur, Third Edition
2014, Tata McGraw Hill Education Private Limited
2 ―Biomedical Instrumentation and Measurements‖ by Leslie Cromwell, Fred J.
Weibell & Erich A. Pfeiffer, Second Edition (2011), Prentice Hall of India
publication
Additional Reading:
1 ―Introduction to Biomedical Equipment Technology‖ by Joseph J. Carr and
John M. Brown, Fourth Edition(2011), Pearson Education
2 ―Biophysical measurements‖ by Strong P., Second Edition, Measurement
Concepts publication
3 ―Principles of applied biomedical instrumentation‖ by Leslie Alexander
Geddes, L. E. Baker, Third Edition, Wiley publication
4 ―Medical Instrumentation Application and Design‖ by John G. Webster, Third
Edition (2011), Wiley publication
5 ―Medical Electronics‖ by G. E. Donovan, published by Butterworth & Co.
6 ―Biomedical Instruments: Theory and Design‖ by Walter Welkowitz, Sid
Deutsch & Metin Akay, Second Edition, Academic Press
Programme Name B. Tech. Electronics Engineering, Semester-VII
Course Code R4EC4104P
Course Title MEDICAL ELECTRONICS LAB
Prerequisite Instrumentation Systems
COURSE OUTCOMES
COURSE CONTENTS
Module 1 Analyze the salient traits of the following medical instruments and
demonstrate the related experimentation :-
ECG System
Module 2 BP Monitor
Module 3 Heart Rate Monitor
Module 4 Respiration Rate Monitor
Module 5 EMG System
Module 6 EEG System
Module 7 Phonocardiograph System
Module 8 Design and demonstration of ECG amplifier system
Module 9 Design and demonstration of signal conditioning system for biopotentials
Module 10 Develop algorithms for biopotentials processing (using MATLAB/ LabVIEW,
etc)
Text Books:
1. ―Handbook of Biomedical Instrumentation‖ by R.S.Khandpur, Third Edition
2014, Tata McGraw Hill Education Private Limited
2 ―Biomedical Instrumentation and Measurements‖ by Leslie Cromwell, Fred J.
Weibell & Erich A. Pfeiffer, Second Edition (2011), Prentice Hall of India
publication
Additional Reading:
1 ―Introduction to Biomedical Equipment Technology‖ by Joseph J. Carr and
John M. Brown, Fourth Edition(2011), Pearson Education
2 ―Biophysical measurements‖ by Strong P., Second Edition, Measurement
Concepts publication
3 ―Principles of applied biomedical instrumentation‖ by Leslie Alexander
Geddes, L. E. Baker, Third Edition, Wiley publication
4 ―Medical Instrumentation Application and Design‖ by John G. Webster, Third
Edition (2011), Wiley publication
5 ―Medical Electronics‖ by G. E. Donovan, published by Butterworth & Co.
6 ―Biomedical Instruments: Theory and Design‖ by Walter Welkowitz, Sid
Deutsch & Metin Akay, Second Edition, Academic Press
Programme Name B Tech Electronics Engineering Sem VII
Course Code R4EC4105T
Course Title Deep Learning
COURSE OUTCOME
COURSE CONTENTS
Module 1 Introduction
Motivations for Studying deep learning, Introduction to deep learning, perceptron
models, activation functions, Deep vs shallow neural network, Binary cross entropy loss,
squared error loss, Loss optimization, gradient descent (SGD, Adam, Adadelta Adagrad,
RMSProp)
Module 2 Classical networks
Convolutional neural networks, various architectures, RESNET, FuseNets, U-Net,
modified U-Net, MRA based CNN, MRA based U Net, Self-driving cars, feature
extraction, learning, fully connected nets
Module 3 Deep sequence modeling and tensor flow
Sequence modeling, word example, recurrent neural networks, Vanilla NN,
computational graphs, back propagation through time, gradient flow, long term
dependencies, Long short term memory, introduction to tensor flow
Module 4 Generative models and reinforcement learning
Supervised vs unsupervised learning, generative models, Debiasing, Outliers, latent
variables, autoencoders, Variational autoencoders (VEA), VEA optimizations,
generative adversarial networks, case studies on GAN, reinforcement learning, learning
in different environments, Q-function, value learning, Deep Q networks
Module 4 Neural Rendering and Case studies
Neurosymbolic AI, evolution of AI, ObjectNet, ImageNet, neural rendering, models vs
pictures, value and policy networks, RenderNet, Case studies: Debiasing face
recognition, word modeling
TEXTBOOKS
1 Deep learning with Python, Francois Chollet, Manning Publications, 2017
2 Simon Haykin, ―Neural Networks - A Comprehensive Foundation‖, Macmillan
Publishing Co., New York, 1994.
3 A Cichocki and R. Unbehauen, ―Neural Networks for Optimization and Signal
Processing‖, John Wiley and Sons, 1993.
Programme Name B Tech Electronics Engineering Sem VII
Course Code R4EC4105P
Course Title Deep Learning Lab
COURSE OBJECTIVE
The course is designed to introduce the field of artificial neural networks systems and machine
learning. The course will give the student the practical and implementation aspects of data processing
algorithms.
COURSE OUTCOME
Students will be able to simulate CNN implement basic supervised learning algorithms.
Students will be able to use algorithms for supervised and unsupervised methods.
Students will be able to simulate tensor flow.
Students will be able to simulate U net and MRA Net
Course Contents
Sr Experiments
1 To simulate CNN for various examples
(3 experiments)
2 To simulate and analyse U net for classification and semantic segmentation
(3 experiments)
3 To extract MRA features and simulate MRA nets
(2 experiments)
OPEN ELECTIVE II
Programme Name B. Tech. Electronics Engineering, Semester-VII
Course Code R4EC4601S
Course Title Introduction to NANO ELECTRONICS
COURSE OBJECTIVE
To study limitations of scaling of CMOS technology and its remedies.
To study transition from single gate to multigate technology.
To study different nanoscale devices like RTD, QCA, CNT, nanowire, SET.
To introduce spin phenomena and its applications for nanoscale devices.
To study molecular electronic devices.
COURSE OUTCOME
The student should be familiar with certain nanoelectronic systems and building
blocks such as: low-dimensional semiconductors, heterostructures, carbon nanotubes,
quantum dots, nanowires etc.
Design of electronic nanosystems like memory elements & Logic devices.
Finally, a goal is to familiarize students with the present research front in
Nanoelectronics and to be able to critically assess future trends.
Overview
Module 1: Introduction
CMOS Scaling, Scaling Issues, Limit to Scaling, System Integration limit, Interconnect
Issues, Shrinkdown approach, Strained Silicon, High k dielectric, Advance MOSFET
concept, UTB – Ultra Thin Body, and Metal Gate.
Module 2: FINFET
Structure, working, power optimization, logic design using FINFET, modes of operation,
TCMS circuit, logic design using TCMS, FINFET SRAM Design
Single Electron BOX, Single Electron Transistor (SET), and Application of Single ElectronDevices
for logic circuit.
Electronics properties, structure, Quantum Cellular Automata (QCA) , and Circuit Designusing QCA.
Physical Properties, Band Structure, Band Modulation, Electrical properties of CNTs, CNT Transistor,
CNT based Electronics Devices, Field Emission Devices, MEMS, Electrical Sensor, and SRAM Cells.
Module 7. Spintronics
Physical properties of Spintronic Devices, Spin Relaxation Mechanisms, Spin Injection, Spin
Detection. Spintronic Devices, Spin Filter, Spin Valves, Spin Pumps, Spin Diodes, Spin Transistors,
Spin-Based Optoelectronic Devices, Spintronic Computation.
Recommended Reading
1. Introduction to nanotechnology, C.P.Poole JV, F.J.Owens, Wiley (2003).
2. Nanoelectronics and information technology (Advanced electronic materials andNovel
Devices Waster Ranior, Wiley VCH (2003)
3. Nanoelectronics: Principles and Devices, 2nd Edition, M. Dragoman, D. Dragoman,Artech
House - 2008
4. Nanoelectronic Circuit Design, Niraj K. Jha, Deming Chen, Springer - 2010.
SEM-VIII
Programme Name B. Tech. Electronics Engineering, Semester-VIII
Course Code R4EC4011S
Course Title MICROCOMPUTER SYSTEM DESIGN
Prerequisite Microprocessor Systems
COURSE OUTCOMES
After successful completion of this course, students should be able to
COURSE CONTENTS
TEXT BOOKS:
1 Don Anderson et al, Pentium Processor System Architecture, Addison-Wesley
Professional; second edition, 1995
2 Tom Shanley et al, PCI System Architecture,. Addison-Wesley Professional;
fourth edition, 1999
3 Don Anderson, SATA storage Technology, Serial ATA, Mindshare Press, 2007
Jan Axelson, USB complete, Penram Publication, fourth edition, 2011
Programme Name B. Tech. (Electronics Engineering), SEMESTER – VIII
Course Code R4EC4012S
Course Title MICROWAVE AND OPTICAL COMMUNICATION
Prerequisite Electromagnetics and Fields, Principles Of Communication
COURSE OUTCOMES
After completion of the course, students should be able to
COURSE CONTENTS
TEXT BOOKS:
1 R. K. Shevgaonkar, Electromagnetic Waves, Tata McGraw-Hill,2005
2 David Pozar, Microwave Engineering, Wiley,2011
References
1 S.Liao, Microwave Devices and Circuits, Pearson Publication,3rd Edition 2003
2 Gerd Keiser, Optical Fiber Communication, McGraw Hill Publication,2008
Programme Name B. Tech. Electronics Engineering, Semester-VIII
Course Code R4EC4013S
Course Title WIRELESS COMMUNICATION SYSTEMS
Prerequisite Basics of Communication Engineering
COURSE OUTCOMES:
COURSE CONTENTS
TEXT BOOKS:
1 Rappaport, T.S., ―Wireless communications‖, Second Edition, Pearson
Education, 2010
2 Andreas. F. Molisch, ―Wireless Communications‖, John Wiley – India, 2006
3 Vijay Garg , ―Wireless Communications and networking‖, First Edition,
Elsevier 2007.
ADDITIONAL READING:
1 Young Kyun Kim and Ramjee Prasad, ―4 G Roadmap and Emerging
Communication Technologies‖, Artech house.2006
2 Raj Pandya, ―Mobile And Personal Communications Systems And Services‖,
Prentice hall.2000
3 Upena Dalal, ― Wireless Communication‖, Oxford University Press, 2009
4 C.Y Lee , ―Mobile Communication‖, Wiley.
.
COURSE OUTCOMES
After successful completion of this course, students will be able to
1) Comprehend utility of learning algorithms in healthcare
2) Analyze and interpret nature of clinical data
3) Understand structure of healthcare information system and flow
4) Apply learning algorithms on medical data for knowledge representation and inference
COURSE CONTENTS
Module 1 Introduction
Nature of clinical data, standards, data in narrative texts, gene, expression and
protein data, architecture of healthcare information systems
Module 2 Probabilistic and graphical models
Information theoretic metrics, decision support via probabilities and utilities,
decision support via expert systems, modeling and Bayesian network, learning
Bayesian networks, classical machine learning algorithms, feature extraction
and mapping
Module3 Knowledge representation and inference
Propositional and first order logic, rule based systems, graph search, constraint
satisfaction, privacy and security issues, medical expert systems
Module 4 Miscellaneous Topics
Informatics in radiology, patient monitoring and intensive care, predictive
genomics, patient data privacy, public health informatics, challenges from
health care, Telemedicine,
Module5 Case studies
Quantitative trait mapping, molecular traits, missing heritability, complex
traits, diagnosis by pattern matching
Text Books
1. Jones, Neil C., and Pavel Pevzner. An Introduction to Bioinformatics
Algorithms. MIT Press, 2004. ISBN: 9780262101066.
2. Shortliffe, E. H., L. E. Perreault, G. Wiederhold, and L. M. Fagan. Medical
Informatics: Computer Applications in Health Care and Biomedicine . 2nd ed.
New York, NY: Springer, 2003
Recommended Reading:
1. Schwartz, W. B., R. S. Patil, and P. Szolovits. "Artificial intelligence in
medicine: where do we stand." New England Journal of Medicine 316 (1987):
685-688.
2. Lasko, T. A., J. G. Bhagwat, K. H. Zou, and L. Ohno-Machado. The Use of
Receiver Operating Characteristic Curves in Biomedical Informatics.
Programme Name B. Tech. Electronics Engineering, Semester-VIII
Course Code R4EC4112S
Course Title Computer and Network Security
Pre-requisite
COURSE OUTCOMES
After successful completion of this course, students will be able to
1) Comprehend utility of number theory in security
2) Understand and explain various encryption algorithms like IDEA and AES
3) Understand and simulate encryption algorithms like RSA and Diffie-Hellman
4) Explain the network security mechanism including real time communication applications
COURSE CONTENTS
Module 1 Introduction
OSI model, active and passive attacks, layers and cryptography, Viruses,
Trojans Horses and Worms, multi-level model of security,
Module 2 Encryption
Secret codes, encryption scheme, integrity check, Perfect secrecy , one-time
pad, public key cryptography, digital signatures, cryptographic hash functions,
password hashing, message integrity, message fingerprint, hashing
applications and constructions, Secret key cryptography, data encryption
standard, International Data Encryption Algorithm, Advanced Encryption
Standard, modes and messages
Module3 Public Key Algorithms
Introduction, modular arithmetic, RSA, Diffie-Hellman key exchange and
crypto groups, pedestrian commitment, PK encryption, DDH, Chinese
remainder theorem, authentication, security handshakes elliptic curves
Module 4 Network Security and Protocols
SSL, HTTPs, private browsing, anonymous communication, side channel
attacks, Web security model, securing web applications, symbolic execution,
Firewalls
Module5 Real-time communication security
Perfect forward secrecy, IPsec: IKE, email security, public and secret keys,
Pretty Good privacy
Text Books
1. Stamp, Mark. Information Security: Principles and Practice. John Wiley &
Sons, 2011. ISBN: 9780470626399
2. Katz, Jonathan, and Yehuda Lindell. Introduction to Modern Cryptography.
Chapman and Hall / CRC, 2007. ISBN: 9781584885511
Recommended Reading:
1. Menezes, Alfred, Paul van Oorschot, and Scott Vanstone. Handbook of
Applied Cryptography. CRC Press, 1996
2. Kaufman, Charlie, Radia Perlman, and Mike Speciner. Network Security:
Private Communication in a Public World. 2nd ed. Prentice Hall, 2002
Programme Name B. Tech. (Electronics Engineering), SEMESTER - VIII
Course Code R4EC4113S
Course Title WIRELESS SENSOR NETWORKS
Prerequisite Wireless Communication, Data Communication
COURSE OUTCOMES
COURSE CONTENTS
TEXT BOOKS:
1. Holger Karl & Andreas Willig, "Protocols And Architectures for Wireless
Sensor Networks", John Wiley, 2005.
2. Feng Zhao & Leonidas J. Guibas, ―Wireless Sensor Networks- An
Information Processing Approach", Elsevier, 2007.
ADDITIONAL READING::
1. Kazem Sohraby, Daniel Minoli, & Taieb Znati, ―Wireless Sensor
Networks- Technology, Protocols, And Applications‖, John Wiley, 2007.
2. Anna Hac, ―Wireless Sensor Network Designs‖, John Wiley, 2003.
3. 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.
4. Philip Levis, ―TinyOS Programming‖.
5. Anna Ha´c, ―Wireless Sensor Network Designs‖, John Wiley & Sons Ltd.
Programme Name B. Tech. Electronics Engineering, Semester-VIII
Course Code R4EC4114S
Course Title Parallel Computing
Pre-requisite Microprocessor Systems
COURSE OUTCOMES
After successful completion of this course, students will be able to
1) Understand and explain sequential versus parallel programming examples
2) Understand and explain various types of parallelism
3) Explain SIMD and MIMD architectures
4) Analyze parallel computing case study for signal and image processing application
COURSE CONTENTS
Module 1 Introduction
Types of parallel computers, memory systems, cache design, pipelines,
instruction scheduling, loop unrolling, multiple processors and processes,
networks, applications, linear algebra, LAPACK and BLAS, gradient methods
Module 2 Parallelism
Parallel computing hardware, uniprocessor architecture (CPU, memory,
interfaces), instruction level parallelism, data level parallelism, thread level
parallelism, shared memory and caches, cache coherency, parallel
architectures: memory parallelism, interconnects, CPU parallelism, I/O and
networking for parallel processors, shared and distributed memory, hybrid
systems, scalability and load balance, pipeline parallelism, memory hierarchy
systems, performance analysis and tuning
Module3 Single Instruction Multiple Data (SIMD)
Introduction, data dependencies, pipelining and segmentation, branching and
conditional execution, basic linear algebra examples, recurrence polynomial
example, shared memory parallelism
Module 4 Multiple Instructions Multiple Data (MIMD)
MPI commands and examples, matrix and vector operations, distribution of
vectors, basic operation with vectors, two dimensional block cyclic matrix
distribution, MPI FFT example
Module5 Applications
Case studies, high performance computing in signal and image processing,
Monte-Carlo simulations
Text Books
1. Ananth Grama, Anshul Gupta, George Karypis and Vipin Kumar,
Introduction to Parallel Computing, 2nd Ed. Pearson, 2004.
Recommended Reading:
1. Jack Dongarra, Ian Foster, Geoffrey C. Fox, William Gropp, Ken Kennedy,
Linda Torczon, Andy White, The Morgan Kaufmann Series in Computer
Architecture and Design, Morgan Kaufmann Publishers, Year: 2003
PROGRAM ELECTIVE
IV
Programme Name B. Tech. (Electronics Engineering), Semester-VIII
Course Code R4EC4121T
Course Title RF Circuit Techniques
Pre-requisite Electromagnetic Wave Engineering, Microwave Engineering
COURSE OUTCOMES
After completion of this course, students will be able to
1. Describe the various components of RF circuits.
2. Estimate the circuit parameters of RF circuits.
3. Implement various Impedance matching Techniques for RF applications.
4. Design filters needed for RF telecommunication applications.
5. Design RF Amplifiers.
COURSE CONTENTS
Module 1 Introduction
Importance of Radio Frequency Design, Dimensions and Units, Frequency
Spectrum, RF Behavior of Passive Components, Chip Components and
Circuit Board Considerations, RF Circuit Manufacturing Processes.
Module 2 RF Circuit Fundamentals
Introduction, The decibel scale, Complex number review, Normalization, R-
L-C voltage-current relationships, Complex impedance and admittance
system, Unloaded and loaded Q definitions, Complex series impedance of RF
components, Complex parallel admittance of RF components, Series and
parallel L-C resonant circuits, Series and parallel conversions of lumped R-L-
C networks, One port and multi-port networks, Importance of power transfer
when cascading system components, Importance of impedance matching, RF
components and related issues, Lumped elements versus transmission lines,
Circuits parameter using waves relations, Impedance transformation and
matching, Single ended versus differential circuits, Time domain versus
frequency domain.
COURSE OUTCOMES
After completion of this course, students will be able to
1. Select components for RF circuits as per the given specification.
2. Design RF circuits using various EDA tools.
3. .Design RF passive circuits, RF filters, RF amplifiers and RF oscillators.
4. Test RF passive circuits, RF filters, RF amplifiers and RF oscillators for
telecommunication
COURSE CONTENTS
Module 1 Introduction
1.1 Study of RF Passive Components.
1.2 Study of RF active components.
1.3 RF PCB design.
1.4 Study of RF circuit design tools like ADS and APLAC.
1.5 Study of VNA.
Module 2 RF Circuit Fundamentals
2.1 RF R-L-C series circuits design and testing.
2.2 RF RLC parallel circuits design and testing.
2.3 RF tuned circuit design with loaded Q.
2.4 Torids design and testing.
2.5 RF components and related issues.
2.6 ABCD parameters measurements.
2.7 S-parameters measurements.
COURSE OBJECTIVE
1. The course is designed to provide a brief idea of linear algebraic functions and differentiate vectors
according to their properties.
2. Evaluation of the performance of binary and non-binary error correcting codes.
3. Study of different error correcting codes for wireless environment.
COURSE OUTCOME
After successful completion of this course, students will be able to
COURSE CONTENTS
Module 1 Information Theory and Linear Algebra
Entropy, Source coding Theorem, Lossless data compression Algorithm
Discrete memory less channels, Mutual Information, Channel capacity
Channel coding Theorem, Differential entropy and mutual information for
continuous random ensembles, Information capacity of colored noisy channel
Rate distortion Theory, Groups, Fields, Rings, Vector spaces, subspaces,
Galois field, Extension fields, Primitive element, primitive polynomial
Module 2 Modulation and Demodulation Techniques
Baseband techniques, Synchronization, ISI, Optimum filter,
Passband model, Signal space diagram, Probability of Error,
Coherent and noncoherent detection, Power spectra of coherent
BPSK, BFSK and QPSK and Geometric Representation, Generation
and detection of - M-array PSK, M-array QAM and their error
probability, Generation and detection of -Minimum Shift Keying,
Gaussian MSK, Non-coherent BFSK, DPSK and DEPSK, Channel
coding, waveform coding, Types of error control
Module 3 Coding Theory
Cyclic Codes: Properties, Various methods of generation and detection of
cyclic codes, error detecting capability, cyclic Hamming code and Golay
code.
Convolutional Codes: Encoder and decoder, structural properties, optimum
decoding of Convolutional codes, Viterbi, soft output Viterbi BCGR
algorithm.
T Turbo coding and Low Density Parity Check Codes: Encoding and decoding.
Encoding and detection, sum-product algorithm, simplification of sum
product algorithm
Module 4 Spread Spectrum Techniques
Allocation of the communication resource, Multiple access communication
system and Architecture, Access Algorithm, Multiple access techniques
Pseudonoise sequences, Direct sequence spread spectrum systems, Frequency
Hopping systems
Text Books:
1. Digital Communications fundamentals and applications 2nd edition, 2009 by
Bernard Sklar, Pabitra Kumar Ray. Pearson
2. Digital Communication System, 2014 by Simon Haykin. Wiley
Recommended Reading:
1. Shu Lin, Daniel J.Costello, ―Error Control Coding‖, 2nd Edition, Pearson,
Reprint 2012.
Programme Name B. Tech. Electronics Engineering, Semester-VIII
Course Code R4EC4123T
Course Title Natural Language Processing
Pre-requisite Digital Signal Processing, Speech Processing
COURSE OUTCOMES
After successful completion of this course, students will be able to
COURSE CONTENTS
Module 1 Introduction
Introduction to natural language processing, estimation techniques and
language modelling, Expectation, Correlation, Covariance, Review of Linear
Algebra, Linear Transformations
Module 2 Word Modeling
Automata and linguistics, Phonology, morphology, Kimmo
Module 3 Stochastic Modeling
EM algorithm in NLP, Parsing and Syntax, stochastic tagging, log-linear
models, probabilistic similarity measures and clustering, precision, recall and
accuracy
Module 4 Parsing and Lexicon
Parsing, shift reduce parsers, Earley‘s algorithm, chart parsing, context free
parsing, efficiency issues, feature based parsing, NL system design
considerations Hidden Markovian models
Module 5 Semantics and Machine Translation
Compositionality, Quantifiers, lexical semantics, constraint based systems,
machine translation, language learning, computational methods of language
change
Text Books:
1. Jurafsky, and Martin. Speech and Language Processing: An Introduction to
Natural Language Processing, Computational Linguistics and Speech.
Prentice Hall, 2000. ISBN: 0130950696.
2. Manning, C. D. and H. Schütze: Foundations of Statistical Natural Language
Processing. The MIT Press. 1999. ISBN 0-262-13360-1.
Recommended Reading:
1. Allen, J. Natural Language Understanding. The Benajmins/Cummings
Publishing Company Inc. 1994. ISBN 0-8053-0334-0.
Programme Name B. Tech. Electronics Engineering, Semester-VIII
Course Code R4EC4123P
Course Title Natural Language Processing LAB
Pre-requisite Digital Signal Processing, Speech Processing
COURSE OUTCOMES
After successful completion of this course, students will be able to
COURSE CONTENTS
Sr. Experiments
1 To compute moments and moment generating functions
2 To implement syntax modelling for words
3 To implement EM algorithm in NLP
4 To implement HMM in NLP
5 To perform parsing stochastic tagging
6 To implement log-linear models in NLP
7 To design and implement clustering algorithms and computing precision,
recall and accuracy
Programme Name B. Tech. Electronics Engineering, Semester-VIII
Course Code R4EC4124T
Course Title Advanced Digital Signal Processing
Pre-requisite Digital Signal Processing
COURSE OBJECTIVES
1. To understand statistical signal processing system in various domains.
2. To introduce and learn applications of linear algebra concepts with emphasis on Eigen analysis.
3. To understand and develop various random processes.
4. To analyze and design MRA systems.
5. To design and implement wavelet based methods for various applications.
COURSE OUTCOMES
After successful completion of this course, students will be able to
COURSE CONTENTS
Module 1 Multirate Digital Signal Processing
Decimation Interpolation Filter design and implementation Sampling rate
conversion Application of multirate signal processing.
Module 2 Review of Linear Algebra
Linear Algebra-Research part, abstractness, real life examples, Introduction to
Random processes, numerical approach.
Module 3 Filtering Discrete Time random processes
Spectral Estimation, Levinson Durbin, evolution of Tukey, MUSIC.
Module 4 Spectral Factorization
Minimum phase signals & systems Partial energy & minimum delay Minimum
phase & minimum delay property Spectral factorization theorem.
Module 5 Spectral Estimation by Classical methods
The periodogram The modified periodogram Barlett, Welch & Blackman-
Tuckey, approach.
Module 6 Multiresolution Analysis using Wavelets
Introduction to time frequency analysis Short-time Fourier transform Wigner-
Ville transform Continuous time wavelet transform Discrete wavelet transform
Tiling of the time-frequency plane and wavepacket analysis, Construction of
wavelets, orthogonal, biorthogonal basis
Text Books:
1. S.M. Kay, Modern Spectral Estimation, Prentice hall, 1988.
2. J. G. Proakis, D.G. Manolakis, and D. Sharma, ―Digital Time Signal
Processing: principles, algorithms, and applications,” Pearson Education,
2006.
3. DaFatta, D. J., Lucas, J. G., and Hodgkiss, W. S. ―Digital Signal Processing: A
system design approach,‖ Wiley publications, 1988.
Recommended Reading:
1. R. M. Rao, and A.S. Bopardikar, ―Wavelet Transforms,” Pearson Education,
2001.
2. C. S. Burrus, R. A. Gopinath, and H. Guo,. ―Introduction to Wavelets and
Wavelets Transforms,‖ PrenticeHall, 1998.
3. P. P. Vaidyanathan, Multirate Systems and Filter Banks, Prentice Hall, 1993.
Programme Name B. Tech. Electronics Engineering, Semester-VIII
Course Code R4EC4124P
Course Title Advanced Digital Signal Processing Lab
Pre-requisite Digital Signal Processing
COURSE OBJECTIVES
1. To understand and implement statistical signal processing algorithms.
2. To introduce and learn applications of linear algebra concepts with emphasis on Eigen analysis from
practical perspective.
3. To understand and develop various random processes practically.
4. To analyze and design MRA systems.
5. To design and implement wavelet based methods for various applications.
COURSE OUTCOMES
Students will be able to:
1. Apply and implement concept of linear algebra and Eigen analysis.
2. Implement ARMA, AR and MA processes.
3. Interpret and design MRA concepts.
4. Implement wavelet based MRA for speech and image signals.
COURSE CONTENTS
Module 1 Multirate Digital Signal Processing
Multirate sampling,
Module 2 Spectral Filtering
Auto Regressive, Moving average and ARMA processes.
Module 3 Filtering Discrete Time random processes
Levinson Durbin, Schur, Tukey, MUSIC algorithms.
Module 4 Spectral Estimation
Design and implementation of periodogram method, modified periodogram,
Barlett, Welch & Blackman-Tuckey algorithms approach.
Module 5 Multiresolution Analysis using Wavelets
MRA using filter banks.
Text Books:
1. J. G. Proakis, D.G. Manolakis, and D. Sharma, ―Digital Time Signal
Processing: principles, algorithms, and applications,” Pearson Education,
2006.
2. DaFatta, D. J., Lucas, J. G., and Hodgkiss, W. S. ―Digital Signal Processing: A
system design approach,‖ Wiley publications, 1988.
Recommended Reading:
1. R. M. Rao, and A.S. Bopardikar, ―Wavelet Transforms,” Pearson Education,
2001.
2. C. S. Burrus, R. A. Gopinath, and H. Guo,. ―Introduction to Wavelets and
Wavelets Transforms,‖ PrenticeHall, 1998.
3. P. P. Vaidyanathan, Multirate Systems and Filter Banks, Prentice Hall, 1993.