III & IV Semester M.Tech. (Digital Electronics) ACADEMIC ... · It is certified that the scheme and syllabus for III &IV semester M.Tech. in ... 16PDEE356 Speech Processing 4 -0

SHRI DHARMASTHALA MANJUNATHESHWARA COLLEGE OF

ENGINEERING & TECHNOLOGY,

DHARWAD – 580 002 (An Autonomous Institution recognized by AICTE & Affiliated to VTU, Belagavi)

Ph: 0836-2447465 Fax: 0838-2464638 Web: www.sdmcet.ac.in

Academic Program: PG

Academic Year 2017-18

Syllabus

III & IV Semester M.Tech.

(Digital Electronics)

ACADEMIC AUTONOMY

http://www.sdmcet.ac.in/

It is certified that the scheme and syllabus for III &IV semester M.Tech. in Digital Electronics

Engineering is recommended by Board of Studies of E&C Engineering and approved by the

Academic Council, SDM College of Engineering &Technology, Dharwad. This scheme and

syllabus will be in force from the academic year 2017-18 till further revision.

Principal Dean (Academic Program) Chairman BOS & HOD

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SDM College of Engineering & Technology, Dharwad

Department of Electronics and Communication Engineering

(Our motto:)

College Vision and Mission

VISION:

To be a school of Dynamic Mindset focusing on Research, Innovation & Development and emerge as Central hub of Engineering Talents.

MISSION:

Committed towards continuous improvement in teaching & learning. Research in engineering and technology.

Encouraging intellectual, quality, ethical and creative pursuits amongst teaching

and students fraternity.

QUALITY POLICY:

CORE VALUES:

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DEPARTMENT VISION AND MISSION

Vision:

Fostering excellence in the field of Electronics & Communication Engineering

showcasing Innovation, Research and Performance with Continuous Industry – Institute

Interaction with the blend of Human Values.

Mission:

To provide quality education in the domain of Electronics & Communication

Engineering through state-of-the art curriculum, effective teaching learning process

and best of breed laboratory facilities.

To encourage innovation, research attitude and team work among students.

Interact and work closely with industries and research organization to accomplish

knowledge at par.

To train the students for attaining leadership in developing and applying technology

for the betterment of society and sustaining the global environment.

PROGRAMME EDUCATIONAL OBJECTIVES (PEOs):

PROGRAMME OUTCOMES (POS):

(font 12 for all the above)

1. Scholarship of Knowledge

Acquire in-depth knowledge of specific discipline or professional area, includingwider and

global perspective, with an ability to discriminate, evaluate, analyse andsynthesise existing and

new knowledge, and integration of the same for enhancementof knowledge.

2. Critical Thinking

Analyse complex engineering problems critically, apply independent judgement forsynthesising

information to make intellectual and/or creative advances for conductingresearch in a wider

theoretical, practical and policy context.

3. Problem Solving

Think laterally and originally, conceptualise and solve engineering problems, evaluate a wide

range of potential solutions for those problems and arrive at feasible, optimalsolutions after

considering public health and safety, cultural, societal andenvironmental factors in the core areas

of expertise.

4. Research Skill

Extract information pertinent to unfamiliar problems through literature survey andexperiments,

apply appropriate research methodologies, techniques and tools, design,conduct experiments,

analyse and interpret data, demonstrate higher order skill andview things in a broader

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perspective, contribute individually/in group(s) to the development of scientific/technological

knowledge in one or more domains of engineering.

5. Usage of modern tools

Create, select, learn and apply appropriate techniques, resources, and modernengineering and IT

tools, including prediction and modelling, to complex engineeringactivities with an

understanding of the limitations.

6. Collaborative and Multidisciplinary work

Possess knowledge and understanding of group dynamics, recognise opportunities andcontribute

positively to collaborative-multidisciplinary scientific research,demonstrate a capacity for self-

management and teamwork, decision-making based onopen-mindedness, objectivity and rational

analysis in order to achieve common goalsand further the learning of themselves as well as

others.

7. Project Management and Finance

Demonstrate knowledge and understanding of engineering and management principlesand apply

the same to one’s own work, as a member and leader in a team, manageprojects efficiently in

respective disciplines and multidisciplinary environments after consideration of economical and

financial factors.

8. Communication

Communicate with the engineering community, and with society at large, regarding complex

engineering activities confidently and effectively, such as, being able to comprehend and write

effective reports and design documentation by adhering to appropriate standards, make effective

presentations, and give and receive clear instructions.

9. Life-long Learning

Recognize the need for, and have the preparation and ability to engage in life-long learning

independently, with a high level of enthusiasm and commitment to improve knowledge and

competence continuously.

10. Ethical Practices and Social Responsibility

Acquire professional and intellectual integrity, professional code of conduct, ethics of research

and scholarship, consideration of the impact of research outcomes on professional practices and

an understanding of responsibility to contribute to the community for sustainable development

of society.

11. Independent and Reflective Learning

Observe and examine critically the outcomes of one’s actions and make corrective measures

subsequently, and learn from mistakes without depending on external feedback.

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Scheme for III Semester

Course

Code Course Title

Teaching Examination

L-T-P-S

(Hrs/Week) Credits

CIE Theory (SEE) Practical (SEE)

Max.

Marks

*Max.

Marks

Duration

in hours

Max.

Marks

Duration

In hours

16PDEC35X Internship***/Elective-VI 4-0-0-0 4 50 100 3

16PDEE35X Internship***/Elective VII 4-0-0-0 4 50 100 3.

16PDEE35X Internship***/Elective VIII

4-0-0-0 4 50 100 3

16PDEP300 Project Phase-I** 0-0-10-0 8 50 . 50 3

Total 12-0-10-0 20 200 300 50

Elective VI to VIII

16PDEE350 Advances in VLSI Design 4-0-0-0 4 50 100 3

16PDEE351 Advanced Computer Architecture

4-0-0-0 4

50 100 3

16PDEE352 Artificial Neural Networks 4-0-0-0 4 50 100 3

16PDEE353 Cryptographic System 4-0-0-0 4 50 100 3

16PDEE354 VLSI & DSP Systems 4-0-0-0 4 50 100 3

16PDEE355 IC Fabrication Technology 4-0-0-0 4 50 100 3

16PDEE356 Speech Processing 4-0-0-0 4 50 100 3

16PDEE357 Wireless Sensor Network 4-0-0-0 4 50 100 3

CIE: Continuous Internal Evaluation SEE: Semester End Examination L: Lecture T: Tutorials P: Practical

S: Self-study *SEE for theory courses is conducted for 100 marks and reduced to 50 marks.

** Project phase-I: The students are expected to formulate the problem and carry out the intensive literature survey along with preliminary investigations supporting the project phase-II in IV semester. Internship***: Internship should be from the reputed industries. Duration of internship is about 4 weeks during 2

nd to 3

rd semester break period. Students who undergo Internship are to be exempted for one

elective course in III semester.

Scheme for IV Semester

Course Code Course Title

Teaching Examination

L-T-P-S

(Hrs/Week) Credits

CIE Theory (SEE) Practical (SEE)

Max.

Marks

*Max.

Marks

Duration

in hours

Max.

Marks

Duration

In hours

16PDEP400 Project

phase-II 0-0-40-0 32 100 100 3

Total 00-0-40-0 50 100

CIE: Continuous Internal Evaluation SEE: Semester End Examination L: Lecture T: Tutorials P: Practical

S: Self-study

*SEE for theory courses is conducted for 100 marks and reduced to 50 marks.

** Project phase-II: The students are expected to work on a project for the full semester in an industry or in reputed organization with recognized R&D center

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Total Credits offered for the Second year: 52

Credits distribution:

Proposed As per guidelines

Program core course 20 20

Program Electives 28/32 30

Laboratory course 04 00

Project 40 40

Seminar 04 05

Internship/training 04/00 05

Total 100 100

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16PDEE351 Advanced Computer Architecture (4-0-0) 3 : 52 Hrs.

Course Learning Objectives: Advanced Computer Architecture is an elective theory course at Postgraduate III semester

level. The course deals with the understanding of principles guiding the computer system

design. It focuses on enhancing the performance by addressing parallelism at different levels

such as Instruction, thread, task, job. Evaluates memory hierarchy, speculations, ISA, ALU

architectures, choice of I/O is major motivation.

Course outcomes:

CO

Description of the Course Outcome:

At the end of the course the student will

be able to:

Mapping to POs (1,11)

Level 3 Substantial

Level 2 Moderate

Level 1 Slight

CO-1 Understand and analyze Performance; Quantitative Principles of computer design(understand and evaluate)

1,2,9 7,11

CO-2 Identify and address concepts and challenges of ILP

1, 2 3 4

CO-3 Investigate Hardware and Software for VLIW and EPIC

1,2,3 5

CO-4 Design and evaluating an I/O system 1,3 5

CO-5 Comprehend Critical Performance Issue and deduce Characteristics of Scientific Applications

2 4,6 9,11

CO-6 Analyze and Choose/utilise Computer Arithmetic units.

3,4

1- Introductory (Slight) 2 - Reinforce (Moderate) 3- Mastering (Substantial)

PO

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

Mapping levels

2.75 2.75 1.75 1.67 1 2 1 - 2 - 1

Pre-requisites:

Knowledge of Processor/Controllers, Languages-Compilers is appreciated. Course Contents:

Chapter No.

Chapter contents No of Hrs.

1 Introduction and Review of Fundamentals of Computer Design: Introduction; Classes computers; Defining computer architecture; Trends in Technology; Trends in power in Integrated Circuits; Trends in cost; Dependability, Measuring, reporting and summarizing Performance; Quantitative Principles of computer design; Performance and Price-Performance; Fallacies and pitfalls; Case studies.

4

2 Some topics in Pipelining: Instruction –Level Parallelism, Its 12

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Exploitation and Limits on ILP: Introduction to pipelining, ILP; Crosscutting issues, fallacies, and pitfalls with respect to pipelining; Basic concepts and challenges of ILP; Case study of Pentium 4, Fallacies and pitfalls.

3 Introduction to limits in ILP: Performance and efficiency in advanced multiple-issue processors.

4

4 Memory Hierarchy Design, Storage Systems: Review of basic concepts; Crosscutting issues in the design of memory hierarchies; Case study of AMD Opteron memory hierarchy; Fallacies and pitfalls in the design of memory hierarchies. Introduction to Storage Systems; Advanced topics in disk storage. Definition and examples of real faults and failures.

8

5 I/O performance, reliability measures, and benchmarks; Queuing theory; Crosscutting issues; Designing and evaluating an I/O system – The Internet archive cluster; Case study of NetAA FAS6000 filer; Fallacies and pitfalls

6

6 Hardware and Software for VLIW and EPIC Introduction: Exploiting Instruction-Level Parallelism Statically, Detecting and Enhancing Loop- Level Parallelism, Scheduling and Structuring Code for Parallelism, Hardware Support for Exposing Parallelism: Predicated Instructions, Hardware Support for Compiler Speculation, The Intel IA-64 Architecture and Itanium Processor, Concluding Remarks.

6

7 Large-Scale Multiprocessors and Scientific Applications Introduction, Inter-processor Communication: The Critical Performance Issue, Characteristics of Scientific Applications, Synchronization: Scaling Up, Performance of Scientific Applications on Shared-Memory Multiprocessors, Performance Measurement of Parallel Processors with Scientific Applications, Implementing Cache Coherence, The Custom Cluster Approach: Blue Gene/L, Concluding Remarks.

6

8 Computer Arithmetic: Introduction, Basic Techniques of Integer Arithmetic, Floating Point, Floating-Point Multiplication, Floating-Point Addition, Division and Remainder, More on Floating-Point Arithmetic, Speeding Up Integer Addition, Speeding Up Integer Multiplication and Division, Fallacies and Pitfalls.

6

Reference Books:

1. Hennessey and Patterson, “Computer Architecture A Quantitative Approach”, 4th Edition, Elsevier, 2007. 2. Kai Hwang, “Advanced Computer Architecture - Parallelism, Scalability,

Programmability”, 2nd Edition

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16PDEE352 Artificial Neural Networks (4-0-0-0) 4 : 52 Hrs.

Course Learning Objectives:

Artificial Neural Network is an elective theory course at postgraduate III semester level. The course focuses on both the classical and the new techniques of neural networks in supervised, unsupervised and reinforcement learning schemes. Particularly, a single perceptron and neurons, feed-forward neural networks, Kohonen's maps, associative memories, Hopfield's and many other recurrent networks will be considered. Course outcomes:

CO


At the end of the course the student

will be able to:

Mapping to Pos: 1-11

Level 3 Substantial

Level 2 Moderate

Level 1 Slight

CO-

1

Understand the role of neural networks in engineering, artificial intelligence and Learn basic neural network architecture.

1

CO-2

Understand the differences between networks for supervised and unsupervised learning.

1 2

CO-3

Design single and multi-layer feed-forward neural networks.

3

CO-4

Develop and train radial-basis function networks.

2

CO-5

Design support vector machines.

3

CO-6

Analyze principal component analysis and Apply Self Organizing Maps to image coding.

2


Pre-requisite: 1. Linear Algebra 2. Probability

Chapter

No. Chapter contents No of

Hrs.

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

Mapping Level 3 2.33 2.5 - - - - - - - -

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1 Introduction: Neural Network, The human brain, Models of a neuron, Neural networks viewed as directed graphs, Feedback, Network architectures, Knowledge representation, Learning processes, Learning tasks.

03

2 Rosenblatt’s Perceptron: Perceptron, The perceptron convergence theorem, Relation between the perceptron and Bayes classifier for a Gaussian environment, Computer experiment: pattern classification, The batch perceptron algorithm.

04

3 The Least-Mean-Square Algorithm: Filtering structure of the LMS algorithm, Unconstrained optimization: a review, The wiener filter, The Least-Mean-Square algorithm, Markov model portraying the deviation of the LMS algorithm from the Wiener filter, The Langevin equation: characterization of Brownian motion, Kushner’s direct-averaging method, Statistical LMS learning theory for small learning-rate parameter, Computer experiment I: Linear prediction, Computer experiment II: Pattern classification, Virtues and limitations of the LMS algorithm, Learning-rate annealing schedules.

06

4 Multilayer Perceptrons: Preliminaries, Batch learning and on-line learning, The back-propagation algorithm, XOR problem, Heuristics for making the back-propagation algorithm perform better, Computer experiment: Pattern classification, Back propagation and differentiation, The Hessian and its role in on-line learning, Optimal annealing and adaptive control of the learning rate, Generalization, Approximations of functions, Cross-validation, Complexity regularization and network pruning, Virtues and Limitations of Back-propagation learning, Supervised learning viewed as an optimization problem, Convolutional networks, Nonlinear filtering, Small-scale versus large-scale learning problems.

12

5 Kernel Methods and Radial-Basis Function Networks: Cover’s theorem on the separability of patterns, The interpolation problem, Radial-basis-function networks, K-means clustering, Recursive least-squares estimation of the weight vector, Hybrid learning procedure for RBF networks, Computer experiment: pattern classification, Interpretations of the Gaussian hidden units, Kernel regression and its relation to RBF networks.

10

6 Support Vector Machines: Optimal hyperplane for linearly separable patterns, Optimal hyperplane for nonseparable patterns, The support vector machine viewed as a Kernel machine, Design of support vector machines, XOR problem, Computer experiment: pattern classification, Regression: robustness considerations, Optimal solution of the linear regression problem, The representer theorem and related issues.

05

7 Principal-Components Analysis: Principles of self-organization, Self-organized feature analysis, Principal-Components Analysis: perturbation theory, Hebbian-based maximum Eigenfilter, Hebbian-based Principal-Components Analysis, Case study: Image coding,

06.

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Kernel Principal-Components Analysis, Basic issues involved in the coding of natural images, Kernel Hebbian algorithm.

8 Self-Organizing Maps: Two basic feature-mapping models, Self-organizing map, Properties of the feature map, Computer experiment I: Disentangling lattice dynamics using SOM, Contextual maps, Hierarchical vector quantization, Kernel self-organizing map, Computer experiment II: Disentangling lattice dynamics using Kernel SOM, Relationship between Kernel SOM and Kullback-Leibler divergence

06

Reference Books: 1. Simon Haykin, “Neural Networks and Learning Machines” (3rd Edition), Pearson Education, 2009. 2. R. P. Lippmann, “An Introduction to Computing with Neural Nets”, IEEE ASSP Magazine, PP: 4-22, 1987. 3. Robert J. Schalkoff, “Artificial Neural Networks", McGraw-Hill, 1997. 4. Laurene Fausett, “Fundamentals of Neural Networks-Architectures, Algorithms and Applications”, Pearson Education, 2004. 5. B. Yegnanarayana, “Artificial Neural Networks”, Prentice Hall, 2006. 6. S. N. Sivanandam, S. Sumathi, S. N. Deepa “Introduction to Neural

Networks using MATLAB 6.0", McGraw-Hill, 2007.

16PDEE353 Cryptographic System (4-0-0) 4 : 52 Hrs.

Course Learning Objectives: Cryptographic Systems is an elective theory course at III semester level. Knowledge of Finite

Fields and communication networks are required as a prerequisite. The course focuses on

security principles, architecture, services and encryption / decryption techniques.

Course outcomes:

CO



be able to:


Level 3 Substantial

Level 2 Moderate

Level 1 Slight

CO-1 Identify and Illustrate security threats,

security services and mechanisms to

counter them.

2 1

CO-2 Analyze and interpret detailed knowledge

and role of encryption and decryption to

protect the data.

3,4,11 2 1

CO-3 Apply and estimate different security

algorithms.

3,4,5,11 1,2

CO-4 Explain authentication functions, security

of Hash functions and MAC.

3,4,11 2 1

CO-5 Select the appropriate procedures for key 3,4,5,11 1,2

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management, analyze key exchange

algorithms.

CO-6 Discuss the authentication protocols and

algorithms for digital signature.

3,4,5,11 1,2


PO

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

Mapping Level 1.5 2 3 3 3 - - - - - 3

Pre Requisites: Communication networks and finite fields.

Course Contents: Chapter No


1 Overview: Introduction to ISO-OSI Model, Services, Mechanisms

and attacks, OSI security architecture, Model for network security.

Services, Mechanisms and attack models, Model for network

security, Symmetric cipher model, Substitution techniques,

Transposition techniques, Rotor machine, Steganography.

8

2 Block Ciphers and DES: Block cipher design principles, DES,

Block cipher modes of operation. Differential and Linear

cryptanalysis 3DES, Rijndael system, AES, IDEA, Fermat’s and

Euler’s theorem, Big-O notation, Chinese Remainder Theorem,

Fields, Group- isomorphism, Discrete Logarithm, Pohlig-Hellman

algorithm, Pollard’s p-1 factorization, pollard rho factorization

algorithm.

12

3 Public Key Cryptography and RSA: Principles of public key

cryptosystems, RSA algorithm, Integer Factorization and RSA,

Miller-Rabin Test, Massey-Omura, ElGamal crypto systems,

Knapsack problem.

6

4 Other Public Key Crypto Systems and Key Management: Key

management, Diffie-Hellman key exchange, DH with multiple

participants, Elliptic curve arithmetic, Elliptic curve cryptography,

Analog of Massey –Omura, Analog of ElGamal crypto systems.

Elliptic curve factorization - pollard’s p-1 method, Lenstra’s elliptic

curve factorization algorithm, Hyper elliptic curve cryptography.

12

5 Message Authentication and Hash Functions: Authentication 6

- 13 -

requirements, Authentication functions, Message authentication

codes, Hash functions, Security of hash functions and MAC.

6 Digital Signature and Authentication Protocol: Digital signature,

Authentication protocols, Digital signature standard, RSA digital

signatures, ElGamal digital signatures and DSA, ECDSA Zero-

knowledge proofs, Secret sharing schemes, Identification schemes.

8

Activity beyond Syllabus:

1. Simulation of cryptographic techniques.

References:

1) Neal Koblitz, “A Course in Number Theory and Cryptography”, -2nd Edition, Springer Verlag 2) Jeffrey Hoffstein,JillPipher,Joseph H. Silverman ,”An Introduction to Mathematical

Cryptography” -Springer 2008 3) Behrouz A .Forouzan, DebdeeepMukhopadhyay, “Cryptography and Network Security”, 2nd

Edition, McGraw Hill 4) William Stallings, “Cryptography and Network Security,” 4/e, Pearson Education (Asia) Pte.

Ltd. / Prentice Hall of India.

16PDEE354 VLSI and DSP Systems (4 - 0 - 0) 4 : 52 Hrs.

Course Learning Objectives: VLSI & DSP Systems is an Elective course at III semester which focuses on DSP Architecture, parallel processing issues in analyzing DSP Computation systems. The next part covers further the Systolic Architecture Design and pipe lined and parallel recursive and Adaptive filters. Course outcomes:

CO

Description of the Course

Outcome:

At the end of the course the

student will be able to:

Mapping to Pos: 1-11

Level 3 Substantial

Level 2 Moderate

Level 1 Slight

CO-1 Identify the typical signal processing tasks

1

CO-2 Gain knowledge possibility of reducing the computational complexity

1 2

CO-3 Acquire knowledge various architectures

1

CO-4 Acquire knowledge about optimization in view of power, area and speed.

1, 2

CO-5 Acquire knowledge about algorithms available for the purpose of optimization

2 3

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CO-6 Compare the techniques / architectures

3


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

Mapping Level

2.75 2.67 2.5 - - - - - - - -

Pre-requisites: Knowledge of 1. Digital Signal Processing 2. Analog and digital electronics 3. CMOS VLSI design. Chapter No


1 Introduction to DSP Systems: Introduction to DSP Systems, Iteration bound, Data Flow graphs (DFGs) representation, Loop Bound, Iteration rate, Critical loop, Critical path, Area-Speed-Power trade-offs, Algorithms for computing iteration bound, Pipelining of FIR Digital Filters, Parallel Processing, Pipelining and Parallel Processing for low power.

10

2 Algorithmic Transformations: Retiming Definitions and properties, Retiming Techniques, Clock period minimization, Unfolding, An algorithm for unfolding, Critical path, Applications of unfolding, Sample period reduction, Folding, Folding order, Folding Factor, register minimization techniques, register minimization in folded architecture, Forward Backward Register Allocation technique, folding of multi-rate systems, Folding Bi-quad filters, Retiming for folding.

12

3 Systolic Architecture Design and Fast Convolution: Introduction, system array design methodology, FIR systolic arrays, , Systolic Design for space representations containing delays Systolic architecture design methodology, Design examples of systolic architectures, selection of scheduling vector, matrix-matrix multiplication and 2-D systolic array design, Hardware Utilization efficiency, Cook-Toom Algorithm, Winograd Algorithm, Iterated Convolution, Cyclic Convolution, Design of fast convolution algorithm by inspection.

12

4 Algorithm Strength Reduction in filter: Introduction, Parallel FIR filters, Polyphase decomposition, Discrete Cosine Transform and Inverse Discrete Cosine Transform, parallel architectures for Rank Order filters.

08

5 Pipelined and Parallel Recursive and Adaptive Filters: Introduction, pipelining in 1st order IIR digital filters, pipelining in higher order IIR digital filters, parallel processing for IIR filters, combined pipelining and parallel processing for IIR filters, low power IIR Filter Design using pipelining and parallel processing, pipelined adaptive digital filters.

10

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References:

1. Parhi, K.K., “VLSI Digital Signal Processing Systems: Design and Implementation”, John Wiley 2007.

2. Oppenheim, A.V. and Schafer, R.W., “Discrete-Time Signal Processing”, Prentice Hall, 2009, 2nd edition.

3. Mitra, S.K., Digital Signal Processing. A Computer Based Approach, McGraw Hill, 2007, 3rd edition.

4. Wanhammar, L., DSP Integrated Circuits, Academic Press, 1999, 2005, ISBN: 978-0131543188

16PDEE355 IC Fabrication Technology (4 - 0 - 0) 4 : 52 Hrs.

Course Learning Objectives: IC Fabrication Technology is a core theory course at postgraduate III semester level. The

course focuses on understanding of crystal growth, wafer preparation, different VLSI

techniques like epitaxy, lithography, ion implementation and their comparison of

performances.

Course outcomes:

CO



be able to:


Level 3 Substantial

Level 2 Moderate

Level 1 Slight

CO-1 Discuss about crystal growth and wafer

preparation.

1,2

CO-2 Explain epitaxy and lithography techniques. 1,2,4,5

CO-3 Discuss reactive Plasma Etching 1,2,4

CO-4 Compare dielectric and Polysilicon Film Deposition methods.

1,2,4,5

CO-5 Analyze ion implantation methods. 1,2,3

CO-6 Discuss and Analyze VLSI Process Integration.

1,2,3


PO

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

Mapping levels

2.5

2.5

3.0

3.0

2.0

-----

-----

-----

-----

- ----

------

Pre Requisites:

- 16 -

Solid state devices, analog and digital VLSI. Course Contents: Chapter No


1 Crystal Growth and Wafer Preparation: Introduction, Electronic-Grade Silicon, Czochralski Crystal Growing, Silicon Shaping, Process Considerations.

7

2 Epitaxy: Introduction, Vapour-Phase Epitaxy, Molecular Beam Epitaxy, Silicon on Insulators, Epitaxial Evaluation.

7

3 Lithography: Introduction, Optical Lithography, Electron Lithography, X-ray Lithography, Ion Lithography.

7

4 Reactive Plasma Etching: Introduction, Plasma Properties, Feature-Size Control and Anisotropic Etch Mechanisms, Other Properties of Etch Processes, Reactive Plasma-Etching Techniques and Equipment, Specific Etch Processes.

8

5 Dielectric and Polysilicon Film Deposition: Introduction, Deposition Processes, Polysilicon, Silicon Dioxide, Silicon Nitride, Plasma- Assisted Depositions, Other Materials.

7

6 Ion Implantation: Introduction, Range Theory, Implantation Equipment, Annealing, Shallow Junctions, High-Energy Implantation. Metallization: Introduction, Metallization Applications, Metallization Choices, Physical Vapor Deposition, Patterning, Metallization Problems, New Role of Metallization.

8

7 VLSI Process Integration: Introduction, Fundamental Considerations for IC Processing, NMOS IC technology, CMOS IC Technology,MOS Memory IC Technology, Bipolar IC Technology, IC Fabrication. Packaging of VLSI Devices: Introduction, Package Types, Packaging Design Considerations.

7

Reference Books: 1. S. M. Sze, “VLSI Technology”, McGraw-Hill, Second Edition. 2. S.K. Ghandhi, "VLSI Fabrication Principles", John Wiley Inc., New York, 1994, Second

Edition.

16PDEE356 Speech Processing (4 - 0 - 0) 4 : 52 Hrs.

Course Learning Objectives: Speech Processing course focuses on classification of speech sounds, mathematical models for speech production mechanism, various speech processing techniques in time and frequency domains. It also deals with various speech processing applications.

Course outcomes:

CO



be able to:


Level 3

Substantial

Level 2

Moderate

Level 1

Slight

- 17 -

CO-1 Understand the characteristics of speech signal and classify speech sounds

4

CO-2 Develop mathematical models for speech production mechanism

1

CO-3 Analyze speech signal in time and

frequency domain 1 2 3

CO-4 Explain various feature extraction methods

of speech signal 1 2

CO-5 Differentiate between various techniques

of feature extraction and make a

comparative study

1

CO-6 Discuss applications of speech signal

processing 1,2,3


PO

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

Mapping

levels 2 1.7 1.5 2 ---- ----- ----- ----- ----- ---- -----

Pre-requisites:

1. Digital Signal Processing

Chapter No


1 Production and Classification of Speech Sounds: Introduction, mechanism of speech production, Acoustic phonetics: vowels, diphthongs, semivowels, nasals, fricatives, stop and affricates, Digital Models for Speech Sounds.

7

2 Time-domain Methods for Speech Processing: Time dependent processing of speech, short-time energy and average magnitude, short-time average zero crossing rate. Speech vs. silence detection, pitch period estimation using parallel processing approach, short-time autocorrelation function, Pitch period estimation using autocorrelation.

7

3 Frequency Domain Methods for Speech Processing: Introduction, definitions and properties, Fourier transforms interpretation and linear filter interpretation, sampling rates in time and frequency, Filter Bank Summation and Overlap Add methods for short-time synthesis of speech, Spectrographic displays, Pitch detection.

8

4 Linear Predictive Coding of Speech: Basic principles of linear

predictive analysis, computation of the gain of the model, Solution

of LPC equations, Prediction error signal, Frequency domain

interpretation of Linear Predictive Analysis, Relationship between

various speech parameters, Synthesis of speech from linear

predictive parameters, Applications of LPC parameters.

8

5 Homomorphic Speech Processing: Introduction, homomorphic 7

- 18 -

systems for convolution, the complex cepstrum of speech, Pitch

detection, Formant estimation, homomorphic vocoder.

6 Speech Synthesis: Principle, Synthesis Based on Waveform

Coding, Synthesis Based on Analysis-synthesis Method,

Synthesis Based on Speech Production Mechanism, Synthesis

by Rule, Text-to-speech Conversion.

7

7 Speech Recognition: Principles of Speech Recognition, Speech Period Detection, Spectral Distance Measures, Structure of Word Recognition System, Dynamic time Warping, Hidden Markov Model.

8

Beyond the Syllabus Coverage(Suggestive):

MATLAB simulation of theoretical concepts.

References:

1) L. R. Rabiner and R. W. Schafer, “Digital Processing of Speech Signals", Pearson Education (Asia), 2004.

2) SadaokiFurui, “Digital Speech Processing, Synthesis and Recognition”, Marcel Dekker,

INC

3) Lawrence Rabinar and B. Juang, “Fundamentals of Speech Recognition”, Pearson

Education, 2003.

4) T. F. Quatieri, “Discrete Time Speech Signal Processing”, Pearson Education Asia, 2004.

16PDEE357 Wireless Sensor Networks (4-0-0) 4 : 52 Hrs.

Course Learning Objectives:

Wireless Sensor Networks is an Elective course at post graduate III semester level. The

course focuses on basic Wireless Sensor Network technology and supporting protocols

with emphasis placed on standardization of basic sensor systems. This course also

provides a survey of sensor technology, medium access control protocols and address

physical layer issues. The other part of the course discusses key routing protocols for

sensor networks, design issues, sensor management, sensor network middleware, and

operating systems.

Course outcomes:

CO

Description of the Course

Outcome:

At the end of the course the

student will be able to:

Mapping to POs (1-11)

Level 3

Substantial

Level 2

Moderate

Level 1

Slight

- 19 -

CO-1 Explain the background, overview and architectural elements of wireless sensor networks.

1,2 8

CO-2 Apply knowledge of wireless

sensor networks(WSN) to various

application areas.

3,4 1,2,5

CO-3 Outline the basics of wireless

sensor technology 5 1,2

CO-4 Discuss & Analyse the MAC,

Transport, Routing protocols for

wireless sensor networks.

6,11

CO-5 Estimate all points related to the

Middleware for Wireless Sensor

Networks,

1,2,3,4

CO-6 Identify and observe issues

related to Network management,

Network operating systems for

Wireless Sensor Networks

1,2,3,4

CO-7 Illustrate Network Operating

Systems for Wireless Sensor

Networks.

4,5

CO-8 Conduct performance analysis of

WSN and manage WSN. 5,6


Pre requisite:

1. Basic of Computer communication networks.

2. Basics of wireless networks.

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

Mapping Level

2 2 1.5 2 2.5 3 - 1 - - 1

- 20 -

Chapter No


1 Introduction and Overview of Wireless Sensor Networks: Introduction, Background of Sensor Network Technology, Applications of Sensor Networks, Basic Overview of the Technology, Basic Sensor Network Architectural Elements, Brief Historical Survey of Sensor Networks, Challenges and Hurdles.

7

2 Applications of Wireless Sensor Networks : Introduction, Background, Range of Applications, Examples of

Category 2 WSN Applications, Home Control, Building

Automation, Industrial Automation, Medical Applications.

Examples of Category 1 WSN Applications, Sensor and Robots,

Reconfigurable Sensor Networks, Highway Monitoring, Military

Applications, Civil and Environmental Engineering Applications,

Wildfire Instrumentation, Habitat Monitoring, Nanoscopic Sensor

Applications,Taxonomy of WSN Technology.

7

3 Basic Wireless Sensor Technology : Introduction, Sensor Node Technology, Overview, Hardware and

Software, Sensor Taxonomy, WN Operating Environment, WN

Trends.

4

4 Medium Access Control Protocols for Wireless Sensor

Networks : Introduction, Background, Fundamentals of MAC

Protocols, Performance Requirements, Common Protocols, MAC

Protocols for WSNs, Schedule-Based Protocols, Random Access-

Based Protocols.

8

5 Routing Protocols for Wireless Sensor Networks : Introduction, Background, Data Dissemination and Gathering,

Routing Challenges and Design Issues in Wireless Sensor

Networks, Network Scale and Time-Varying Characteristics,

Resource Constraints, Sensor Applications Data Models,

Routing Strategies in Wireless Sensor Networks, WSN Routing

Techniques, Flooding and Its Variants, Sensor Protocols for

Information via Negotiation, Low-Energy Adaptive Clustering

Hierarchy, Power-Efficient Gathering in Sensor Information

Systems.

7

6 Transport Control Protocols for Wireless Sensor Networks

:Traditional Transport Control Protocols, TCP (RFC 793), UDP

7

- 21 -

(RFC 768), Mobile IP, Feasibility of Using TCP or UDP for

WSNs, Transport Protocol Design Issues, Performance of

Transport Control Protocols, Congestion, Packet Loss Recovery,

7 Middleware, Network Management Operating Systems for

Wireless Sensor Networks :Introduction, WSN Middleware

Principles, Middleware Architecture, Data-Related Functions,

Architectures, Examples Existing Middleware, Network

Management for Wireless Sensor Networks : Introduction,

Network Management Requirements, Traditional Network

Management Models.

Operating Systems for Wireless Sensor Networks :

Introduction, Operating System Design Issues, Examples of

Operating Systems

8

8 Performance and Traffic Management : Introduction, Background, WSN Design Issues: MAC, Routing and

Transport protocols, Performance Metrics of WSNs.

4

Activities beyond syllabus: Implementation of WSN scenario in Qualnet simulator. Reference Books: 1. Kazem Sohraby, Daniel Minoli, Taieb Znati : Wireless Sensor Networks Technology,

Protocols, and Applications -John Wiley & Sons, 2007

2. William C Y Lee: Mobile Communications Engineering Theory and Applications, 2nd

Edition, McGraw Hill Telecommunications 1998.

3. William Stallings: Wireless Communications and Networks, Pearson Education Asia,

2002.

- 22 -

16PDEP300 Project Phase I (0-0-10-0) 8

Course Objectives:

Project Phase I is a core course at III semester level with 8 credits (LTP: 0-0-10-0). The course concentrates on selection of project work based on student’s interest and their knowledge in the technical domain.

Course outcomes:

ID



be able to:


Level 3

Substantial

Level 2

Moderate

Level 1

Slight

CO-1 Identify a socially and technically

relevant problem

1,6 9 11

CO-2 Understand the content of technical

references and summarize the same.

2,9 5

CO-3 Analyze the content matter of the

technical papers and formulate better

solution.

1,3, 10 4 5

CO-4 Train oneself in the field relevant to the

project topic chosen.

5 2,9 11

CO-5 Choose techniques to carry out the

project work

5 6

CO-6 Organize the study carried out in a

systematic form

4,6 8,10 9


PO

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

Mapping

levels

3 2.5 3 2.5 2.3 2.2 ---- 2 2 2.5 1

- 23 -

16PDEP400 Project Phase II (0-0-40-0) 32

Course Objectives:

Project Phase II is a core course at IV semester level with 32 credits (LTP: 0-0-40-0). The course concentrates on carrying out of project work based on student’s interest and their knowledge in the technical domain.

Course outcomes:

ID



be able to:


Level 3

Substantial

Level 2

Moderate

Level 1

Slight

CO-1 Apply the technical knowledge gained

to develop the project

2,3 4,5

CO-2 Design and Implement the algorithm 5 7 9

CO-3 Experiment on the algorithm developed 5,6,7 4

CO-4 Compare the algorithm or methodology 1 8 9

CO-5 Integrate the project work carried out in

the form of producing a technical paper

publication

4,6,7, 10 8

CO-6 Organize the study carried out in a

systematic form

4,8 10


PO

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

Mapping

levels

3 3 3 2.5 2.3 3 2,3 1.6 1 2.5 ----

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III & IV Semester M.Tech. (Digital Electronics) ACADEMIC ... · It is certified that the scheme and syllabus for III &IV semester M.Tech. in ... 16PDEE356 Speech Processing 4 -0