Abhi PYQ

8th Semester Syllabus

Computer Science & Engineering

Credits Table

Course Title L T P Credits
Open Elective- III 3 0 0 3
Open Elective- IV 3 0 0 3
Program Elective- IV 3 0 0 3
Program Elective- V 3 0 0 3
Project- II 0 0 12 6
TOTAL 18

Theory Subjects

Lab Subjects & Projects

Project- II

Details for Project-II will be provided by the department. This component carries 6 credits.

Open Elective: VLSI System Design [OEC CS 801]

  • Module 1: (Lectures 6 hrs.)
    • Introduction To VLSI Design
    • Moore's Law
    • Scale Of Integration
    • Types Of VLSI Chips
    • Design Principles (Digital VLSI)
    • Design Domains (Y-Chart)
    • Challenges Of VLSI Design- Power
    • Timing Area
    • Noise
    • Testability Reliability
    • Yield
    • CAD Tools For VLSI Design
  • Module 2: (Lectures 7 hrs.)
    • Introduction To VLSI Technology
    • VLSI Technology - An Overview
    • Wafer Processing
    • Oxidation
    • Epitaxial Deposition
    • Ion-implantation And Diffusion
    • The Silicon Gate Process
    • Basic CMOS Technology
    • Basic N-well CMOS Process
    • P-well CMOS Process
    • Twin Tub Process
    • Silicon On Insulator
    • CMOS Process Enhancement-interconnect
    • Circuit Elements
    • 3-D CMOS
  • Module 3: (Lectures 7 hrs.)
    • Analysis Of CMOS Logic Circuits
    • MOSFET As Switch
    • Recapitulation Of MOS
    • CMOS Inverter
    • CMOS Logic Circuits
    • NAND Gate And NOR Gate
    • Complex Logic Circuits
    • Pass Transistor Logic
    • CMOS Transmission Gate
    • CMOS Full Adder
  • Module 4: (Lectures 4 hrs.)
    • Advanced Techniques In CMOS Logic Circuit
    • Pseudo NMOS
    • Tri-state
    • Clocked CMOS
    • Dynamic CMOS Logic
    • Domino
    • NORA
    • Zipper
    • Etc
    • Dual Rail Logic Networks
  • Module 5: (Lectures 2 hrs.)
    • Memories
    • Static RAM
    • SRAM Arrays
    • Dynamic RAMS
    • ROM Arrays
    • Logic Arrays
  • Module 6: (Lectures 8 hrs.)
    • Timing Issues In VLSI System Design
    • Timing Classification
    • Synchronous Timing Basics
    • Skew And Jitter
    • Latch Based Clocking
    • Self-timed Circuit Design
    • Self-timed Logic
    • Completion Signal Generation
    • Self-timed Signaling-synchronizers And Arbiters
  • Module 7: (Lectures 6 hrs.)
    • Verilog Hardware Description Language
    • Overview Of Digital Design With Verilog HDL
    • Hierarchical Modeling Concepts
    • Modules And Port Definitions
    • Gate Level Modeling
    • Data Flow Modeling
    • Behavioral Modeling
    • Task & Functions
    • Test Bench

Text Books:

  1. Neil H. E. Weste and Kamran Eshraghian, "Principles of CMOS VLSI Design", 2nd edition, Pearson Education Asia, 2000.
  2. John P. Uyemura, "Introduction to VLSI Circuits and Systems", John Wiley and Sons, Inc., 2002.
  3. Samir Palnitkar, "Verilog HDL", 2nd Edition, Pearson Education, 2004.

Reference Books:

  1. Eugene D. Fabricius, "Introduction to VLSI Design", TMH International Editions, 1990.
  2. Bhasker J., "A Verilog HDL Primer", 2nd Edition, B. S. Publications, 2001.
  3. Pucknell, "Basic VLSI Design", Prentice Hall of India Publication, 1995.
  4. Wayne Wolf, "Modern VLSI Design System on chip", Pearson Education, 2002

Open Elective: Embedded Systems [OEC CS 802]

  • Module 1: (Lecture 10 hrs.)
    • Embedded Computing
    • Introduction
    • Complex Systems And Microprocessors
    • The Embedded System Design Process
    • Formalization For System Design
  • Module 2: (Lecture 10 hrs.)
    • Instruction Sets CPUs
    • Instruction And Preliminaries ARM And SHARC Processors
    • Programming I/O CPU Performance And Power Consumption
  • Module 3: (Lecture 10 hrs.)
    • The Embedded Computing Platform And Program Design
    • Introduction
    • The CPU Bus
    • Component Interfacing
    • Designing With Microprocessors
    • Development And Debugging
  • Module 4: (Lecture 10 hrs.)
    • Program Design And Analysis
    • Introduction Program Design
    • Assembly
    • Linking
    • Basic Compilation Techniques
    • Analysis Optimization Of Executive Time

Text Book:

  1. Wayner Wolf., "Computers as components - Principle of Embedded Computing System Design", Morgan Kaufmann/ Hercourt India Pvt. Ltd.

Reference Books:

  1. Raj Kamal - Embedded Systems, TMH, New Delhi 2004.
  2. F. Vahid& T. givargis- Embedded system Design, John wiley, India Edition, 2005.

Open Elective: Digital Signal Processing [OEC CS 803]

  • Module 1: (Lecture 8 hrs.)
    • Introduction
    • Characterization And Classification Of Signals
    • Typical Signal Processing Operations
    • Review Of Discrete-time Signal And System Analysis
    • Advantages And Typical Applications Of DSP
  • Module 2: (Lecture 8 hrs.)
    • Sampling And Quantization
    • Sampling And Discrete-time Processing Of Continuous Time Signals
    • Sampling Of Low-pass And Band-pass Signals
    • Uniform And Non-uniform Quantization
    • Lloyd-Max Algorithm
    • Log-companding
    • A-law
    • μ-law
    • Adaptive Quantization And Prediction
  • Module 3: (Lecture 8 hrs.)
    • Orthogonal Transforms
    • Properties And Applications Of DFT
    • Implementing Linear Time Invariant Systems Using DFT
    • Circular Convolution
    • Linear Convolution Using DFT
    • Fast Fourier Transform
    • FFT Algorithms
    • Decimation In Time
    • Decimation In Frequency
    • Goertzel Algorithm
    • Application Of Transform In Speech
    • Audio
    • Image And Video Coding
    • Karhunen-Loeve Transform
    • DCT
    • JPEG And MPEG Coding Standards
  • Module 4: (Lecture 8 hrs.)
    • Digital Filter Design Techniques
    • IIR And FIR Filters
    • Filter Design Specifications
    • Design Of Digital IIR Filters
    • Impulse Invariant
    • Bilinear Transformation Techniques For Butterworth And Chebyshev Filters
    • Design Of FIR Filters
    • Windowing
    • Frequency Sampling Filter Design
    • Optimum Approximations Of FIR Filters
  • Module 5: (Lecture 6 hrs.)
    • Multi-rate Signal Processing
    • Fundamentals Of Multirate Systems
    • Decimation And Interpolation
    • Application Of Multirate DSP In Sampling Rate Conversion
    • Filter Banks
    • Polyphase Structures
    • Quadrature-mirror Filter Bank
    • Wavelet Transform And Its Relation To Multi-rate Filter Banks
    • Applications To Speech And Audio Coding
  • Module 6: (Lecture 4 hrs.)
    • Basic Concept Of Adaptive Digital Signal Processing
    • Adaptive Wiener Filter And LMS Algorithm
    • Applications Of Adaptive Filtering To Echo Cancellation And Equalization

Text Books:

  1. Digital Signal Processing-A Computer Based Approach, Mitra, S.K.,
  2. Discrete Time Signal Processing, Oppenheim, A.V. and Schafer, R.W. with Buck, J.R
  3. Digital Signal Processing: A Practical Approach, Ifeachor, E.C. and Jervis, B.W

Reference Books:

  1. Multirate Systems and Filter Banks, Vaidyanathan, P.P.

Open Elective: High Performance Computing [OEC CS 804]

  • Module 1: (Lectures 10 hrs.)
    • Introduction To Parallel Computing
    • Parallel Processing Terminology
    • Pipelining Vs Data Parallelism
    • Control Parallelism
    • Scalability
    • Control Parallel Approach
    • Data Parallel Approach
    • Data Parallel Approach With I/O Parallel Reduction
    • Prefix Sums
    • List Ranking
    • Preorder Tree Traversal
    • Merging Two Sorted Lists
    • Graph Coloring
    • Reducing The Number Of Processors
    • Problems Defying Fast Solutions On PRAMS
  • Module 2: (Lectures 10 hrs.)
    • Thread And Process Level Parallel Architectures
    • MIMD
    • Multi-threaded Architectures
    • Distributed And Shared Memory MIMD Architectures
  • Module 3: (Lectures 8 hrs.)
    • Dynamic Interconnection Networks
    • Mapping And Scheduling
    • Mapping Data To Processors On Processor Arrays And Multicomputers
    • Dynamic Load Balancing On Multicomputers
    • Static Scheduling On UMA Multiprocessors
    • Deadlock
  • Module 5: (Lectures 12 hrs.)
    • Parallel Programming And Parallel Algorithms
    • Programming Models
    • Parallel Programming On Multiprocessors And Multicomputers
    • Parallel Algorithm Structure
    • Analyzing Parallel Algorithm
    • Elementary Parallel Algorithms
    • Matrix Algorithms
    • Sorting
    • Graph Algorithms

Text Book:

  1. Quinn, Parallel computing - theory and practice, Tata McGraw Hill.

Reference Books:

  1. Selim G. Akl, The Design and Analysis of Parallel Algorithms, PH International
  2. Ghosh, Moona and Gupta, Foundations of parallel processing, Narosa
  3. Mehdi R. Zargham, Computer Architectures single and parallel systems, PHI. publishing.
  4. Ed. Afonso Ferreira and Jose' D. P. Rolin, Parallel Algorithms for irregular problems - State of the art, Kluwer Academic Publishers.

Open Elective: Introduction to Communication Systems [OEC CS 805]

  • Module 1: (Lectures 8 hrs.)
    • Introduction
    • Communication Model
    • Transmission Line
    • Data Communication Concepts
    • Data Transmission
    • Parallel Transmission
    • Serial Transmission
    • Asynchronous Transmission
    • Synchronous Transmission
    • Data Encoding
    • Non-Return To Zero (NRZ)
    • Return To Zero (RZ)
    • Modern Concept
    • Modern Operation
  • Module 2: (Lectures 6 hrs.)
    • Basic Signal Processing Operations In Digital Communication
    • Analog Pulse Modulation
    • Sampling Theorem For Base-band And Pass-band Signals
    • Quadrature Sampling Of Band Pass Signal Reconstruction Of Message From Its Samples
    • Signal Distortion In Sampling
  • Module 3: (Lectures 8 hrs.)
    • Pulse Amplitude Modulation
    • Modulation Generation And Demodulation
    • PAM/TDM System
    • Digital Pulse Modulation
    • Quantization
    • PCM
    • DPCM
    • Delta Modulation
    • Adaptive Delta Modulation-Design Of Typical Systems And Performance Analysis
    • Signal Space Concepts
    • Geometric Structure Of The Signal Space
    • Vector Representation
    • Distance
    • Norm And Inner Product
    • Orthogonality
    • Gram-Schmidt Orthogonalization Procedure
  • Module 4: (Lectures 8 hrs.)
    • Filtering And Receivers
    • Matched Filter Receiver
    • Inter Symbol Interference
    • Pulse Shaping
    • Nyquist Criterion For Zero ISI
    • Eye Diagram
    • Equalizer
    • Scrambling And Descrambling
    • Review Of Gaussian Random Process
    • Optimum Threshold Detection
    • Optimum Receiver For AWGN Channel
    • Matched Filter And Correlation Receivers
  • Module 5: (Lectures 10 hrs.)
    • Decision Procedure
    • Maximum Aposteriori Probability Detector
    • Maximum Likelihood Detector
    • Error Probability Performance Of Binary Signaling
    • Digital Band Pass Modulation Schemes
    • ASK
    • FSK
    • PSK
    • MSK
    • Digital M-ary Modulation Schemes
    • Signal Space Representation
    • Error In Communication
    • Detection Of Signals In Gaussian Noise
    • Coherent & Non-coherent Detection
    • Differential Modulation Schemes
    • Error Performance Of Binary And M-ary Modulation Schemes
    • Probability Of Error Of Binary DPSK
    • Performance Of M-ary Signaling Schemes In AWGN Channels
    • Power Spectra Of Digitally Modulated Signals
    • Performance Comparison Of Digital Modulation Schemes

Text Books:

  1. Digital Communications, Simon Haykin John Wiley & Sons, Indian edition
  2. Modern Digital and Analog Communication Systems, fourth edition by B.P. Lathi and Zhi Ding, Oxford University Press
  3. Introduction to data communications and networking, Behrouz Forouzan

Reference Books:

  1. Fundamentals of Communication Systems by J Proakis and M Salehi
  2. Signals and Systems, second edition, by A. Oppenheim and A. Willsky

Open Elective: Ad-Hoc and Sensor Networks [OEC CS 806]

Objectives:

→ Understand The Design Issues In Ad Hoc And Sensor Networks

→ Learn The Different Types Of MAC Protocols

→ Be Familiar With Different Types Of Ad-hoc Routing Protocols

→ Be Expose To The TCP Issues In Ad-hoc Networks

→ Learn The Architecture And Protocols Of Wireless Sensor Networks

  • Module 1: Introduction (Lectures 8 hrs.)
    • Fundamentals Of Wireless Communication Technology
    • The Electromagnetic Spectrum
    • Radio Propagation Mechanisms
    • Characteristics Of The Wireless Channel
    • Mobile Ad-hoc Networks (MANETS) And Wireless Sensor Networks (WSNs)
    • Concepts And Architectures
    • Applications Of Ad-hoc And Sensor Networks
    • Design Challenges In Ad-hoc And Sensor Networks
  • Module 2: Mac Protocols for Ad-hoc Wireless Networks (Lectures 8 hrs.)
    • Issues In Designing A MAC Protocol
    • Classification Of MAC Protocols
    • Contention Based Protocols
    • Contention Based Protocols With Reservation Mechanisms
    • Contention Based Protocols With Scheduling Mechanisms
    • Multi Channel MAC
    • IEEE 802.11
  • Module 3: Routing Protocols and Transport Layer in Ad-hoc Networks (Lectures 8 hrs.)
    • Issues In Designing A Routing And Transport Layer Protocol For Ad hoc Networks
    • Proactive Routing
    • Reactive Routing (on-demand)
    • Hybrid Routing
    • Classification Of Transport Layer Solutions
    • TCP Over Ad hoc Wireless Networks
  • Module 4: Wireless Sensor Networks (WSNs) And MAC Protocols (Lectures 8 hrs.)
    • Single Node Architecture
    • Hardware And Software Components Of A Sensor Node
    • WSN Network Architecture
    • Typical Network Architectures
    • Data Relaying And Aggregation Strategies
    • MAC Layer Protocols
    • Self-organizing
    • Hybrid TDMA/FDMA And CSMA Based MAC
    • IEEE 802.15.4
  • Module 5: Security in Ad Hoc and Sensor Networks (Lectures 8 hrs.)
    • Security Attacks
    • Key Distribution And Management
    • Intrusion Detection
    • Software Based Anti-tamper Techniques
    • Water Marking Techniques
    • Defense Against Routing Attacks
    • Secure Ad hoc Routing Protocols
    • Broadcast Authentication WSN Protocols
    • TESLA
    • Biba
    • Sensor Network Security Protocols
    • SPINS

Text Book:

  1. C. Siva Ram Murthy and B.S.Manoj, "Ad Hoc Wireless Networks - Architectures and Protocols", Pearson Education, 2006.
  2. Holger Karl and Andreas Willig, "Protocols and Architectures for Wireless Sensor Networks", Wiley, 2005

References Book:

  1. Carlos De Morais Cordeiro, Dharma Prakash Agrawal "Ad Hoc & Sensor Networks: Theory and Applications", World Scientific Publishing Company, 2006.
  2. Feng Zhao and Leonides Guibas, "Wireless Sensor Networks", Elsevier Publication - 2002.

Course Outcomes:

Upon completion of the course, the student should be able to:

→ Identify Different Issues In Wireless Ad Hoc And Sensor Networks

→ Analyze Protocols Developed For Ad Hoc And Sensor Networks

→ Identify And Understand Security Issues In Ad Hoc And Sensor Networks

Program Elective: Data Mining [PEC CS 801]

  • Module 1: (Lecture 8 hrs.)
    • Data Warehousing And Business Analysis
    • Data Warehousing Components
    • Building A Data warehouse
    • Data Warehouse Architecture
    • DBMS Schemas For Decision Support
    • Data Extraction
    • Cleanup
    • Transformation Tools
    • Metadata
    • Reporting
    • Query Tools And Applications
    • Online Analytical Processing (OLAP)
    • OLAP And Multidimensional Data Analysis
  • Module 2: (Lecture 8 hrs.)
    • Data Mining
    • Data Mining Functionalities
    • Data Preprocessing
    • Data Cleaning
    • Data Integration And Transformation
    • Data Reduction
    • Data Discretization And Concept Hierarchy Generation
    • Architecture Of A Typical Data Mining Systems
    • Classification Of Data Mining Systems
    • Association Rule Mining
    • Efficient And Scalable Frequent Item set Mining Methods
    • Mining Various Kinds Of Association Rules
    • Association Mining To Correlation Analysis
    • Constraint-Based Association Mining
  • Module 3: (Lecture 8 hrs.)
    • Classification And Prediction
    • Issues Regarding Classification And Prediction
    • Classification By Decision Tree Introduction
    • Bayesian Classification
    • Rule Based Classification
    • Classification By Back propagation
    • Support Vector Machines
    • Associative Classification
    • Lazy Learners
    • Other Classification Methods
    • Prediction
    • Accuracy And Error Measures
    • Evaluating The Accuracy Of A Classifier Or Predictor
    • Ensemble Methods
    • Model Section
  • Module 4: (Lecture 8 hrs.)
    • Cluster Analysis
    • Types Of Data In Cluster Analysis
    • A Categorization Of Major Clustering Methods
    • Partitioning Methods
    • Hierarchical Methods
    • Density-Based Methods
    • Grid-Based Methods
    • Model-Based Clustering Methods
    • Clustering High-Dimensional Data
    • Constraint-Based Cluster Analysis
    • Outlier Analysis
  • Module 5: (Lecture 8 hrs.)
    • Mining Object
    • Spatial
    • Multimedia
    • Text And Web Data
    • Multidimensional Analysis And Descriptive Mining Of Complex Data Objects
    • Spatial Data Mining
    • Multimedia Data Mining
    • Text Mining
    • Mining The World Wide Web

Text Book

  1. Jiawei Han, Micheline Kamber and Jian Pei, "Data Mining Concepts and Techniques", Third Edition, Elsevier, 2011.

Reference Books

  1. Alex Berson and Stephen J. Smith "Data Warehousing, Data Mining &OLAP", Tata McGraw-Hill Edition, Tenth Reprint 2007.
  2. P. Soman, ShyamDiwakar and V. Ajay "Insight into Data mining Theory and Practice", Easter Economy Edition, Prentice Hall of India, 2006.
  3. K. Gupta "Introduction to Data Mining with Case Studies", Easter Economy Edition, Prentice Hall of India, 2006.
  4. Pang-Ning Tan, Michael Steinbach and Vipin Kumar "Introduction to Data Mining", Pearson Education, 2007.

Program Elective: Computational Number Theory [PEC CS 802]

  • Module 1: (Lecture 8 hrs.)
    • Algorithms For Integer Arithmetic
    • Divisibility
    • GCD Computation
    • Euclid's Algorithm
    • Extended Euclid's Algorithm
    • Modular Arithmetic
    • Modular Exponentiation
    • Montgomery Arithmetic
    • Congruence
    • Chinese Remainder Theorem
    • Hensel Lifting
    • Orders And Primitive Roots
    • Quadratic Residues
    • Integer And Modular Square Roots
    • Prime Number Theorem
    • Continued Fractions And Rational Approximations
  • Module 2: (Lecture 8 hrs.)
    • Representation Of Finite Fields
    • Prime And Extension Fields
    • Representation Of Extension Fields
    • Polynomial Basis
    • Primitive Elements
    • Normal Basis
    • Optimal Normal Basis
    • Irreducible Polynomials
    • Algorithms For Polynomials
    • Root-finding And Factorization
    • Lenstra-Lenstra-Lovasz Algorithm
    • Polynomials Over Finite Fields
  • Module 3: (Lecture 6 hrs.)
    • Elliptic Curves
    • The Elliptic Curve Group
    • Elliptic Curves Over Finite Fields
    • Schoof's Point Counting Algorithm
  • Module 4: (Lecture 8 hrs.)
    • Primality Testing Algorithms
    • Fermat Test
    • Miller-Rabin Test
    • Solovay-Strassen Test
    • AKS Test
    • Integer Factoring Algorithms
    • Trial Division
    • Pollard Rho Method
    • P-1 Method
    • CFRAC Method
    • Quadratic Sieve Method
    • Elliptic Curve Method
  • Module 5: (Lecture 8 hrs.)
    • Computing Discrete Logarithms Over Finite Fields
    • Baby-step-giant-step Method
    • Pollard Rho Method
    • Pohlig-Hellman Method
    • Index Calculus Methods
    • Linear Sieve Method
    • Coppersmith's Algorithm
    • Applications
    • Algebraic Coding Theory
    • Cryptography

Reference Books:

  1. Shoup, A computational introduction to number theory and algebra, Cambridge University Press.
  2. Mignotte, Mathematics for computer algebra, Springer-Verlag.
  3. Niven, H. S. Zuckerman and H. L. Montgomery, An introduction to the theory of numbers, John Wiley.
  4. von zur Gathen and J. Gerhard, Modern computer algebra, Cambridge University Press.
  5. Lidl and H. Niederreiter, Introduction to finite fields and their applications, Cambridge University Press.
  6. J. Menezes, editor, Applications of finite fields, Kluwer Academic Publishers.
  7. H. Silverman and J. Tate, Rational points on elliptic curves, Springer International Edition.
  8. R. Hankerson, A. J. Menezes and S. A. Vanstone, Guide to elliptic curve cryptography, Springer-Verlag.
  9. Das and C. E. Veni Madhavan, Public-key cryptography: Theory and practice, Pearson Education Asia.
  10. Cohen, A course in computational algebraic number theory, Springer-Verlag.

Program Elective: Advanced Operating Systems [PEC CS 803]

Objectives of the course

The Objective Of This Course Is To Impart Necessary And Practical Knowledge To Identify And Solve Problems In Distributed, Multiprocessor And Database Operating Systems

  • Module 1: (Lecture 6 hrs.)
    • Introduction To Advance Operating System
    • Comparative Study Of OS
    • LINUX
    • Linux File System + Measurements
    • The Log Structured File System
    • Server Less Network File Systems
    • The Coda File System
    • AFS
    • Virtual Memory
    • Stack/ Buffer Overflow
    • Address Space Layout Randomization (ASLR) User-Level Virtual Memory
    • Global Network Scheduling
    • Network Optimization
    • Extensible Operating Systems
    • Issues Of Security In OS
    • Cryptographic File Systems
  • Module 2: (Lecture 6 hrs.)
    • Distributed Operating Systems
    • System Architecture Types
    • Issues In Distributed Operating Systems
    • Naming
    • Scalability
    • Security
    • Client-Server Model
    • Process Synchronization
    • Global Knowledge
    • Etc
    • RPC
    • Message Passing
    • Absence Of Global Lock
    • Absence Of Shared Memory
    • Lamport's Logical Clock
    • Chandy Lamport's Algorithm
    • Termination Detection
    • Distributed Mutual Exclusion
    • Non Token Based Algorithms
    • Ricart Agarwala Algorithm
    • Lamport's Algorithm
    • Generalized Non-Token Based Algorithm
    • Comparative Performance Analysis
  • Module 3: (Lecture 6 hrs.)
    • Synchronization
    • Clock Synchronization
    • Event Ordering
    • Mutual Exclusion
    • Deadlock
    • Election Algorithms
    • Desirable Features Of Good Global Scheduling Algorithms
    • Task Assignment Approach
    • Load Balancing Approach
    • Load Sharing Approach
    • Process Management
    • Process Migration
    • Threads Distributed Deadlock Detection
    • Centralized/Distributed/ Hierarchical Control
    • Path Pushing Algorithm
    • Edge-Chasing Algorithm
    • Ho-Ramamoorthy Algorithms
  • Module 4: (Lecture 6 hrs.)
    • Resource Management In Distributed Systems
    • Distributed File Systems
    • Mounting
    • Caching
    • Bulk Data Transfer
    • Design Issues
    • Cache Consistency
    • Scalability
    • Log Structured File systems
    • Distributed Shared Memory
    • Central-Server Algorithm
    • Full-Replication Algorithm
    • Etc
    • Coherence Protocols
    • Granularity
    • Page Replacement
    • Distributed Scheduling
    • Load
    • Classification
    • Load Balancing And Load Sharing
    • Policies For Transfer
    • Selection
    • Location
    • Information
    • Stability
    • Load Balancing Algorithms
    • Load Sharing Case Studies
  • Module 5: (Lecture 6 hrs.)
    • Fault Tolerance
    • Recovery
    • Protection And Security
    • Atomic Actions And Commit
    • Commit Protocols
    • Voting Protocols
    • Dynamic Voting
    • Classification Of Failures
    • Backward And Forward Error Recovery
    • Synchronous/Asynchronous Checkpoints And Recovery
    • Recovery In Concurrent Systems
    • Access Matrix Model
    • Advanced Models Of Protection
    • Cryptography
  • Module 6: (Lecture 6 hrs.)
    • Multiprocessor And Database Operating Systems
    • Tightly And Loosely Coupled Systems
    • Interconnect Networks
    • Caching
    • Hypercube Architectures
    • Threads
    • Process Synchronization In MP Systems
    • Process Scheduling In MP Systems
    • Requirements Of Database OS
    • Transactions
    • Conflicts
    • Serializability Theory
    • Distributed Database Systems
    • Concurrency Control Algorithms
    • Lock Based Algorithms
    • Timestamp Based Algorithms
    • 2PL
  • Module 7: (Lecture 6 hrs.)
    • Virtualization
    • Introduction
    • Simulation
    • Emulation
    • Para-Virtualization
    • Full virtualization
    • X86 Virtualization
    • Privileged Instructions
    • Control Sensitive Instructions
    • Trap And Emulate
    • Binary Translation
    • X86 Hardware Virtualization Vmxon/vmxoff
    • Vmentry
    • Vm Exit
    • Intel VTd
    • VMCS
    • Shadow Page Tables
    • EPT/NPT

Suggested books:

  1. Pradeep K. Sinha, "Distributed Operating Systems: Concepts and Design", Wiley.
  2. Andrew S. Tanenbaum, "Distributed Operating Systems", Pearson.
  3. Mukesh Singhal & Niranjan Shivaratri, "Advanced Concepts in Operating Systems", McGraw Hill Education.

Course Outcomes

Students should be able to:

→ Identify And Solve Problems In Distributed, Multiprocessor And Database Operating Systems

→ Explain The Architectural Features And Solutions For Implementing Various Virtualization Features In Operating Systems

→ Solve Synchronization Problems Involving Distributed And Virtualized Environments

Program Elective: Genetic Algorithm [PEC CS 804]

  • Module 1: (Lecture 8 hrs.)
    • Introduction To Genetic Algorithm
    • Genetic Algorithms
    • Traditional And Search Methods And Their Differences
    • A Simple Genetic Algorithm
  • Module 2: (Lecture 8 hrs.)
    • Genetic Algorithms Revisited
    • The Fundamental Theorem
    • Schema Processing
  • Module 3: (Lecture 8 hrs.)
    • Two & K-Armed Bandit Problem
    • Hypothesis
    • Schemata And Revisited
  • Module 4: (Lecture 8 hrs.)
    • Computer Implementation Of A Genetic Algorithm
    • Data Structures
    • Reproduction
    • Crossover And Mutation
    • A Time To Reproduce
    • A Time To Cross
    • How Well Does It Work
    • Mapping Objective Functions To Fitness Form
    • Fitness Scaling
    • Coding
    • A Multiparameter Mapped
    • Fixed-Point Coding
    • Discretization
    • Constraints
  • Module 5: (Lecture 8 hrs.)
    • Applications Of Genetic Algorithms
    • The Rise Of Genetic Algorithms
    • Genetic Algorithm Applications Of Historical Interest
    • De Jong And Function Optimization
    • Improvements In Basic Technique
    • Current Applications Of Genetic Algorithms
    • Genetics-Based Machine Learning
    • Whence It Came
    • What Is Classifier System
    • Rule And Message
    • Genetic Algorithm

Reference Books:

  1. D.E. Goldberg -Genetic Algorithms in Search Optimization and Machine Learning, Pearson Education, New Delhi, 2005.
  2. M. D. Vose - The Simple Genetic Algorithm, PHI, New Delhi, 2004

Program Elective: Cloud Computing [PEC CS 805]

Objective:

This Course Will Cover The Study Of Various Cloud Services, Deployment Model, Resource Provisioning And Scheduling Algorithms Involved In Better Implementing The Cloud-based Systems

  • Module 1: (Lecture 4 hrs.)
    • Introduction
    • Distributed Computing And Enabling Technologies
    • Cloud Fundamentals
    • Cloud Definition
    • Evolution
    • Architecture
    • Applications
    • Deployment Models
    • Service Models
  • Module 2: (Lecture 5 hrs.)
    • Virtualization
    • Issues With Virtualization
    • Virtualization Technologies And Architectures
    • Internals Of Virtual Machine Monitors/hypervisors
    • Virtualization Of Data Centers
    • Issues With Multitenancy
  • Module 3: (Lecture 6 hrs.)
    • Implementation
    • Study Of Cloud Computing Systems Like Amazon EC2 And S3
    • Google App Engine
    • Microsoft Azure
    • Build Private/Hybrid Cloud Using Open Source Tools
    • SLA Management
  • Module 4: (Lecture 12 hrs.)
    • Resource Management
    • Cloud Resource Provisioning Plan (advance Reservation
    • On Demand Plan
    • Spot Instances)
    • Various Scheduling And Load Balancing Techniques To Improve QoS Parameters
    • Resource Optimization Algorithms
    • Task Migration And VM Migration Technique
  • Module 5: (Lecture 7 hrs.)
    • Security
    • Vulnerability Issues And Security Threats
    • Application-level Security
    • Data Level Security
    • Virtual Machine Level Security
    • Infrastructure Security
    • Multi-tenancy Issues
  • Module 6: (Lecture 6 hrs.)
    • Advances
    • Green Cloud
    • Mobile Cloud Computing
    • Fog Computing
    • Internet Of Things

Suggested Books:

  1. Cloud Computing Principles and Paradigms, Rajkumar Buyya, James Broberg, Andrzej Goscinski, Wiley Publishers 2011
  2. Cloud Computing Bible, Barrie Sosinsky, Wiley Publishers 2010
  3. Mastering Cloud computing, Rajkumar Buyya, Christian Vacchiola, S Thamarai Selvi, McGraw Hill 2013
  4. Cloud Security and Privacy: An Enterprise Perspective on Risks and Compliance, Tim Mather, Subra Kumaraswamy, Shahed Latif, O'Reilly 2010
  5. Cloud Computing by Shailendra Singh 2018

Course outcomes:

→ Articulate The Main Concepts, Key Technologies, Strengths, And Limitations Of Cloud Computing And The Possible Applications For State-of-the-art Cloud Computing

→ Identify The Architecture And Infrastructure Of Cloud Computing, Including SaaS, Paas, laas, Public Cloud, Private Cloud, Hybrid Cloud, Etc

→ Identify Problems, And Explain, Analyze, And Evaluate Various Cloud Computing Solutions

→ Provide The Appropriate Cloud Computing Solutions And Recommendations According To The Applications Used

→ Attempt To Generate New Ideas And Innovations In Cloud Computing

Program Elective: Quantum Computing [PEC CS 806]

Objective of the Course:

The Objective Of This Course Is To Impart Necessary Knowledge To The Learner So That He/she Can Develop And Implement Algorithm And Write Programs Using These Algorithm

  • Module 1: (Lectures 6 hrs.)
    • Introduction To Quantum Computing
    • Motivation For Studying Quantum Computing
    • Major Players In The Industry (IBM, Microsoft, Rigetti, D-Wave Etc)
    • Origin Of Quantum Computing
    • Overview Of Major Concepts In Quantum Computing
    • Qubits And Multi-qubits States
    • Bra-ket Notation
    • Bloch Sphere Representation
    • Quantum Superposition
    • Quantum Entanglement
  • Module 2: (Lectures 9 hrs.)
    • Math Foundation For Quantum Computing
    • Matrix Algebra
    • Basis Vectors And Orthogonality
    • Inner Product And Hilbert Spaces
    • Matrices And Tensors
    • Unitary Operators And Projectors
    • Dirac Notation
    • Eigen Values And Eigen Vectors
  • Module 3: (Lectures 8 hrs.)
    • Building Blocks For Quantum Program
    • Architecture Of A Quantum Computing Platform
    • Details Of Q-bit System Of Information Representation
    • Block Sphere
    • Multi-qubits States
    • Quantum Superposition Of Qubits (valid And Invalid Superposition)
    • Quantum Entanglement
    • Useful States From Quantum Algorithmic Perceptive E.g. Bell State
    • Operation On Qubits
    • Measuring And Transforming Using Gates
    • Quantum Logic Gates And Circuit
    • Pauli
    • Hadamard
    • Phase Shift
    • Controlled Gates
    • Ising
    • Deutsch
    • Swap Etc
    • Programming Model For A Quantum Computing Program
    • Steps Performed On Classical Computer
    • Steps Performed On Quantum Computer
    • Moving Data Between Bits And Qubits
  • Module 4: (Lectures 18 hrs.)
    • Quantum Algorithms
    • Basic Techniques Exploited By Quantum Algorithms
    • Amplitude Amplification
    • Quantum Fourier Transform
    • Phase Kick-back
    • Quantum Phase Estimation
    • Quantum Walks
    • Major Algorithms
    • Shor's Algorithm
    • Grover's Algorithm
    • Deutsch's Algorithm
    • Deutsch - Jozsa Algorithm
    • OSS Toolkits For Implementing Quantum Program
    • IBM Quantum Experience
    • Microsoft Q
    • Rigetti PyQuil (QPU/QVM)

List of Suggested Books:

  1. Michael A. Nielsen, "Quantum Computation and Quantum Information", Cambridge University Press.
  2. David McMahon, "Quantum Computing Explained", Wiley
  3. IBM Experience: https://quantumexperience.ng, bluemix.net
  4. Microsoft Quantum Development Kit https://www.microsoft.com/en-us/quantum/development-kit
  5. Forest SDK PyQuil: https://pyquil.readthedocs.io/en/stable/

Learning Outcomes:

At the end of this course, the students will be able to:

→ Explain The Working Of A Quantum Computing Program, Its Architecture And Program Model

→ Develop Quantum Logic Gate Circuits

→ Develop Quantum Algorithm

→ Program Quantum Algorithm On Major Toolkits

Program Elective: Transaction Processing Systems [PEC CS 807]

  • Module 1: (Lecture 8 hrs.)
    • Consistency
    • Atomicity
    • Durability
    • Isolation
    • Flat Transactions
    • Providing Structure Within A Transaction
    • Structuring An Application As Multiple Transactions
  • Module 2: (Lecture 8 hrs.)
    • Schedules And Schedule Equivalence
    • Recoverability
    • Cascaded Aborts And Strictness
    • Models For Concurrency Control
    • A Strategy For Immediate-Update Pessimistic Concurrency Controls
    • Design Of An Immediate-Update Pessimistic Concurrency Control
    • Objects And Semantic Commutativity
    • Atomicity
    • Recoverability And Compensating Operations
    • Locking And SQL Isolation Levels
    • Granular Locking
    • Intention Locks And Index Locks
    • Tuning Transactions
    • Multi version Concurrency Controls
  • Module 3: (Lecture 8 hrs.)
    • Crash
    • Abort And Media Failure
    • Immediate-Update Systems And Write-Ahead Logs
    • Recovery In Deferred-Update Systems
    • Recovery From Media Failure
  • Module 4: (Lecture 8 hrs.)
    • Transaction Processing In A Centralized System
    • Transaction Processing In A Distributed System
    • Global Atomicity And The Transaction Manager
    • Remote Procedure Call
    • Pear-to-Pear Communication
    • Event Communication
    • Storage Architectures
    • Transaction Processing On The Internet
    • Implementing The ACID Properties
    • Distributed Deadlock
    • Global Serialization
  • Module 5: (Lecture 8 hrs.)
    • Authentication
    • Authorization And Encryption
    • Digital Signatures
    • Key Distribution And Authentication
    • Authorization
    • Authenticated RPC
    • Electronic Commerce
    • Certificates
    • Passport
    • SSO
    • SET Protocol
    • Dual Signatures
    • Goods Atomicity
    • Certified Delivery
    • Escrow

Text Books

  1. Michael Kifer, Arthur Bernstein and Philip M. Lewis, "Database Systems: An Application-Oriented Approach", Addison Wesley, 2006
  2. Philip A. Bernstein and Eric Newcomer, "Principles of Transaction Processing", 2nd Edition, Morgan Kaufmann Publishers, Elsevier, 2009

Program Elective: Pattern Recognition [PEC CS 808]

  • Module 1: (Lecture 6 hrs.)
    • Introduction
    • Importance Of Pattern Recognition
    • Features
    • Feature Vectors
    • Classifiers
    • Supervised Versus Unsupervised Pattern Recognition
  • Module 2: (Lecture 6 hrs.)
    • Bayes Decision Theory
    • Discriminant Functions And Services O The Normal Distribution
    • Bayesian Classification
    • Estimating Probability Density Functions
    • Nearest Neighbour Rules Bayesian Networks
  • Module 3: (Lecture 6 hrs.)
    • Linear And Nonlinear Classifiers
    • The Perceptron Algorithm
    • Least-Squares Methods
    • Nonlinear Classifiers
    • Multilayer Perceptron's
    • Back Propagation Algorithm
    • Decision Trees
    • Combinations Of Classifiers
    • Boosting
  • Module 4: (Lecture 6 hrs.)
    • Feature Selection
    • Data Pre-processing
    • ROC Curves
    • Class Separability Measures
    • Feature Subset Selection
    • Bayesian Information Criterion
  • Module 5: (Lecture 6 hrs.)
    • Dimensionality Reduction
    • Basis Vectors
    • Singular Value Decomposition
    • Independent Component Analysis
    • Kernel PCA
    • Wavelets
  • Module 6: (Lecture 6 hrs.)
    • Additional Features And Template Matching
    • Texture
    • Shape And Size Characterization
    • Fractals
    • Features For Audio
    • Template Matching Using Dynamic Time Warping And Edit Distance
    • Context Dependent Classification
  • Module 7: (Lecture 6 hrs.)
    • Clustering
    • Sequential Algorithms
    • Hierarchical Algorithms
    • Functional Optimization-Based Clustering Graph Clustering
    • Learning Clustering
    • Clustering High Dimensional Data
    • Subspace Clustering
    • Cluster Validity Measure

Text Books:

  1. Pattern recognition, Sergios Theodoridis
  2. Pattern classification, second edition, duda, hart and stork, wiley
  3. Pattern recognition, Sergios Theodoridis Konstanti Nos Koutrou M Bas

Reference Books

  1. Introduction to Statistical Pattern Recognition, Fukunaga Academic Press
  2. Pattern Recognition and Machine learning, C. M. Bishop, Springer
  3. Statistical Methods in Bioinformatics, Ewens & Grant, Springer
  4. The Elements of Statistical Learning, Hastie, Tibshirani, Friedman, Springer

Program Elective: Deep Learning [PEC CS 809]

Objectives of the course:

This Course Will Provide A Basic Understanding Of Deep Learning And Their Applications To Solve Real World Problems. Open Source Tools Will Be Used To Demonstrate Different Applications

  • Module 1: Introduction (Lecture 4 hrs.)
    • Brief Introduction Of Big Data Problem
    • Overview Of Linear Algebra
    • Probability
    • Numerical Computation
    • Basics Of Machine learning/Feature Engineering
  • Module 2: Basics of Neural networks (Lecture 4 hrs.)
    • Neural Networks
    • Tools For Deep Learning Network
    • Shallow Vs Deep Network
  • Module 3: Feed forward Networks (Lecture 6 hrs.)
    • Multilayer Perceptron
    • Gradient
    • Loss Function
    • Gradient Descent
    • Stochastic Gradient Descent (SDD)
    • Backpropagation Algorithm
    • Empirical Risk Minimization
    • Regularization
    • Gradient Based Learning
    • Cost Function
    • Learning Rate
    • Soft Max
    • Sigmoid Function
    • Hidden Unit
    • ReLU
    • Logistic Sigmoid
    • Hyperbolic Tangent Architecture Design
    • Heuristics For Faster Training
  • Module 4: Unsupervised learning (Lecture 10 hrs.)
    • Deep Belief Network
    • Deep Boltzmann Machine (DBM)
    • Factor Analysis
    • Auto-encoders (standard, Sparse, Denoising, Contractive, Etc)
    • Variational Auto-encoders
    • Adversarial Generative Networks
    • Auto-encoder
    • Regularization Optimization For Training Deep Model
  • Module 5: Advanced topics (Lecture 10 hrs.)
    • Convolutional Neural Network (CNN)
    • Architectures
    • Convolution/pooling Layers
    • Recurrent Neural Network (RNN)/ Sequence Modeling
    • Long Short Term Memory Networks (LSTM)
    • GRU
    • Encoder Decoder Architectures Reinforcement Learning
  • Module 6: Practical applications (Lecture 6 hrs.)
    • Application Of Deep Learning To Computer Vision
    • Speech Recognition
    • Natural Language Processing
    • Etc

Suggested books:

  1. Ian Goodfellow, Yoshua Bengio and Aaron Courville, "Deep Learning", Book in preparation for MIT Press, 2016.
  2. Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie, "The elements of statistical learning", Springer Series in Statistics, 2009.
  3. Charu C Aggarwal, "Neural Networks and Deep Learning", Springer.

Learning Outcomes

→ Identify The Deep Learning Algorithms Which Are More Appropriate For Various Types Of Learning Tasks In Various Domains

→ Implement Deep Learning Algorithms And Solve Real-world Problems

Program Elective: Computational Geometry [PEC CS 810]

Objective:

To Introduce Geometric Algorithms And To Give An Exposure To Algorithms And Data Structures For Geometric Problems

  • Module 1: (Lecture 6 hrs.)
    • Polygon Triangulation
    • Triangulation Theory
    • Area Of Polygon
    • Segment Intersection
    • Segment-triangle Intersection
    • Polygon Partitioning
    • Monotone Partitioning
    • Trapezoidalization
    • Partition Into Monotone Mountains
    • Linear-Time Triangulation
    • Convex Partitioning
  • Module 2: (Lecture 5 hrs.)
    • Convex Hulls In Two Dimensions
    • Definitions Of Convexity And Convex Hulls
    • Naive Algorithms For Extreme Points
    • Gift Wrapping
    • QuickHull
    • Graham's Algorithm
    • Lower Bound
    • Incremental Algorithm
    • Divide And Conquer
  • Module 3: (Lecture 6 hrs.)
    • Convex Hulls In Three Dimensions
    • Polyhedra And Data Structures
    • Gift Wrapping
    • Preparata-Hong Algorithm
    • Incremental Algorithm
    • Randomized Incremental Algorithm
  • Module 4: (Lecture 6 hrs.)
    • Voronoi Diagrams
    • Definitions And Basic Properties
    • Delaunay Triangulations
    • Algorithms
    • Applications In Detail
    • Medial Axis
    • Connection To Convex Hulls
    • Connection To Arrangements
  • Module 5: (Lecture 6 hrs.)
    • Arrangements
    • Combinatorics Of Arrangements
    • Incremental Algorithm
    • Three And Higher Dimensions
    • Duality
    • Higher-Order Voronoi
    • Diagrams
    • Applications
  • Module 6: (Lecture 8 hrs.)
    • Search And Intersection
    • Segment-Segment Intersection
    • Segment- Triangle Intersection
    • Point In Polygon
    • Point In Polyhedron
    • Intersection Of Convex Polygons
    • Intersection Of Segments
    • Intersection Of Nonconvex Polygons
    • Extreme Point Of Convex Polygon
    • Extremal Polytope
    • Queries
    • Planar Point Location
  • Module 7: (Lecture 5 hrs.)
    • Motion Planning
    • Shortest Paths
    • Moving A Disk
    • Translating A Convex Polygon
    • Moving A Ladder
    • Robot Arm Motion
    • Separability

Suggested Books:

  1. M. de Berg, M van Kreveld, M. Overmars, O. Schwarzkopf, Computational Geometry: Algorithms and Applications (2nd Edition), Springer - Verlag 2000
  2. O'Rourke, Computational Geometry in C, 2nd ed., Cambridge Univ. Press, 1998.
  3. Casselman, Mathematical Illustrations: A Manual of Geometry and PostScript, Springer-Verlag,. (http://www.math.ubc.ca/~cass/graphics/manual) 2005
  4. Mulmuley, Computational Geometry: An Introduction Through Randomized Algorithms, Prentice Hall. 1994

Course Outcome:

Upon successful completion of this course, students will be able to:

→ Analyze Randomized Algorithms For Small Domain Problems

→ Use Line-point Duality To Develop Efficient Algorithms

→ Apply Geometric Techniques To Real-world Problems In Graphics

→ Solve Linear Programs Geometrically

Program Elective: Big Data Analytics [PEC CS 811]

Course Objectives:

→ To Understand The Competitive Advantages Of Big Data Analytics

→ To Understand The Big Data Frameworks

→ To Learn Data Analysis Methods

→ To Learn Stream Computing

→ To Gain Knowledge On Hadoop Related Tools Such As HBase, Cassandra, Pig, And Hive For Big Data Analytics

  • Module 1: (Lecture 7 hrs.)
    • Introduction To Big Data
    • Definition
    • Characteristic Features
    • Big Data Applications
    • Big Data Vs Traditional Data
    • Risks Of Big Data
    • Structure Of Big Data
    • Challenges Of Conventional Systems
    • Web Data
    • Evolution Of Analytic Scalability
    • Evolution Of Analytic Processes
    • Tools And Methods
    • Analysis Vs Reporting
    • Modern Data Analytic Tools
  • Module 2: (Lecture 9 hrs.)
    • HADOOP Framework
    • Distributed File Systems
    • Large-Scale File System
    • Organization
    • HDFS Concepts
    • Map Reduce Execution
    • Algorithms Using Map Reduce
    • Matrix-Vector Multiplication
    • Hadoop YARN
  • Module 3: (Lecture 10 hrs.)
    • Data Analysis
    • Statistical Methods
    • Regression Modelling
    • Multivariate Analysis
    • Classification
    • SVM & Kernel Methods
    • Rule Mining
    • Cluster Analysis
    • Types Of Data In Cluster Analysis
    • Partitioning Methods
    • Hierarchical Methods
    • Density Based Methods
    • Grid Based Methods
    • Model Based Clustering Methods
    • Clustering High Dimensional Data
    • Predictive Analytics
    • Data Analysis Using R
  • Module 4: (Lecture 7 hrs.)
    • Mining Data Streams
    • Streams
    • Concepts
    • Stream Data Model And Architecture
    • Sampling Data In A Stream
    • Mining Data Streams And Mining Time-series Data
    • Real Time Analytics Platform (RTAP) Applications
    • Case Studies
    • Real Time Sentiment Analysis
    • Stock Market Predictions
  • Module 5: (Lecture 9 hrs.)
    • Big Data Frameworks
    • Introduction To NoSQL
    • Aggregate Data Models
    • Hbase
    • Data Model And Implementations
    • Hbase Clients
    • Examples
    • Cassandra
    • Data Model
    • Examples
    • Cassandra Clients
    • Hadoop Integration
    • Pig
    • Grunt
    • Pig Data Model
    • Pig Latin
    • Developing And Testing Pig Latin Scripts
    • Hive
    • Data Types And File Formats
    • HiveQL Data Definition
    • HiveQL Data Manipulation
    • HiveQL Queries

Course Outcomes:

At the end of this course, the students will be able to:

→ Understand How To Leverage The Insights From Big Data Analytics

→ Analyze Data By Utilizing Various Statistical And Data Mining Approaches

→ Perform Analytics On Real-time Streaming Data

→ Understand The Various NoSql Alternative Database Models

Suggested Reference Books:

  1. Bill Franks, -Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics | |, Wiley and SAS Business Series, 2012.
  2. David Loshin, "Big Data Analytics: From Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph", 2013.
  3. Michael Berthold, David J. Hand, -Intelligent Data Analysis | |, Springer, Second Edition, 2007.
  4. Michael Minelli, Michelle Chambers, and Ambiga Dhiraj, "Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Businesses", Wiley, 2013.
  5. J. Sadalage and M. Fowler, "NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence", Addison-Wesley Professional, 2012.
  6. Richard Cotton, "Learning R - A Step-by-step Function Guide to Data Analysis,, O'Reilly Media, 2013.

Program Elective: Real Time Systems [PEC CS 812]

  • Module 1: (Lecture 10 hrs.)
    • Introduction
    • Hard Vs. Soft Real Time Systems
    • A Reference Model Of Real Time System
    • Real-time Scheduling
    • Clock Driven Approach
    • Weighted Round-robin Approach
    • Priority Driven Approach
    • Dynamic Vs. Static System
    • Effective Release Times And Deadlines
    • EDF And LST Algorithm
    • Optimality And Non-Optimality Of The EDF And LST Algorithms
    • Off Line Vs. Online Scheduling
  • Module 2: (Lecture 6 hrs.)
    • Clock-Driven Scheduling
    • Static
    • Time-Driven Scheduler
    • General Structure Of Cyclic Schedules
    • Cyclic Executives
    • Improving The Average Response Time Of Aperiodic Jobs
    • Scheduling Sporadic Jobs
  • Module 3: (Lecture 7 hrs.)
    • Priority Driven Scheduling Of Periodic Tasks
    • Fixe-priority Vs. Dynamic Priority Algorithms
    • Maximum Schedulable Utilization
    • Optimality Of The RM And DM Algorithms
    • A Schedulability Test For Fixed-priority Tasks With Short Response Times
    • Sufficient Schedulability Conditions For The RM And DM Algorithms
  • Module 4: (Lecture 6 hrs.)
    • Scheduling Aperiodic And Sporadic Jobs In Priority-Driven Systems
    • Assumptions And Approaches
    • Deferrable Servers
    • Sporadic Servers
    • Constant Utilization
    • Total Bandwidth And Weighted Fair-Queueing Servers
  • Module 5: (Lecture 8 hrs.)
    • Resources And Resource Access Control
    • Resource Contention
    • Resource Access Control
    • Non-preemptive Critical Section
    • Basic Priority-Inheritance Protocol
    • Basic Priority Ceiling Protocol
    • Stack Based
    • Priority-ceiling Protocol
    • Preemption Ceiling Protocol
  • Module 6: (Lecture 5 hrs.)
    • Multiprocessor Scheduling
    • Resource Access Control
    • Synchronization
    • Model Of Multiprocessor & Distributed Systems
    • Task Assignment
    • Multiprocessor Priority-ceiling Protocol
    • Elements Of Scheduling Algorithms For End-to-End Periodic Tasks
    • IPS Protocols
    • PM Protocols
    • MPM Protocol

Suggested Books:

  1. Real-Time system by Jane W. S. Liu, Pearson Education
  2. Real-Time Systems by C. M. Krishna and K. G. Shin, McGraw Hill

Course Outcome:

After learning this subject, students will learn various types of Real Time Systems, Periodic and Aperiodic tasks, different types of scheduling algorithms in RTS(Clock Driven, Priority Driven), Priority Driven Scheduling Of Periodic Tasks, Priority Driven Scheduling of Aperiodic and Sporadic Jobs, Different protocols for resource access controls, Scheduling approach in multiprocessor Real Time Systems etc.

Program Elective: Block Chain [PEC CS 813]

Objective of the Course:

To Provide Conceptual Understanding Of How Block Chain Technology Can Be Used To Innovate And Improve Business Processes. The Course Covers The Technological Underpinning Of Block Chain Operations

  • Module 1: (Lectures 6 hrs.)
    • Introduction
    • Overview Of Block Chain
    • Public Ledgers
    • Bitcoin
    • Smart Contracts
    • Block In A Block Chain
    • Transactions
    • Distributed Consensus
    • Public Vs Private Block Chain
    • Understanding Crypto Currency To Block Chain
    • Permissioned Model Of Block Chain
    • Overview Of Security Aspects Of Block Chain Basic Crypto Primitives
    • Cryptographic Hash Function
    • Properties Of A Hash Function
    • Hash Pointer And Merkle Tree
    • Digital Signature
    • Public Key Cryptography
    • A Basic Cryptocurrency
  • Module 2: (Lectures 10 hrs.)
    • Understanding Block Chain With Crypto Currency
    • Bitcoin And Block Chain
    • Creation Of Coins
    • Payments And Double Spending
    • Bitcoin Scripts
    • Bitcoin P2P Network
    • Transaction In Bitcoin Network
    • Block Mining
    • Block Propagation And Block Relay
    • Working With Consensus In Bitcoin
    • Distributed Consensus In Open Environments
    • Consensus In A Bitcoin Network
    • Proof Of Work (PoW)
    • Basic Introduction
    • Hashcash PoW
    • Bitcoin PoW
    • Attacks On Pow And The Monopoly Problem
    • Proof Of Stake
    • Proof Of Burn And Proof Of Elapsed Time
    • The Life Of A Bitcoin Miner
    • Mining Difficulty
    • Mining Pool
  • Module 3: (Lectures 12 hrs.)
    • Understanding Block Chain For Enterprises
    • Permissioned Block Chain
    • Permissioned Model And Use Cases
    • Design Issues For Permissioned Block Chains
    • Execute Contracts
    • State Machine Replication
    • Overview Of Consensus Models For Permissioned Block Chain
    • Distributed Consensus In Closed Environment
    • Paxos
    • RAFT Consensus
    • Byzantine General Problem
    • Byzantine Fault Tolerant System
    • Lamport-Shostak-Pease BFT Algorithm
    • BFT Over Asynchronous Systems
    • Enterprise Application Of Block Chain
    • Cross Border Payments
    • Know Your Customer (KYC)
    • Food Security
    • Mortgage Over Block Chain
    • Block Chain Enabled Trade
    • We Trade Trade Finance Network
    • Supply Chain Financing
    • Identity On Block Chain
  • Module 4: (Lectures 12 hrs.)
    • Block Chain Application Development
    • Hyperledger Fabric
    • Architecture
    • Identities And Policies
    • Membership And Access Control
    • Channels
    • Transaction Validation
    • Writing Smart Contract Using Hyperledger Fabric
    • Writing Smart Contract Using Ethereum
    • Overview Of Ripple And Corda

List of Suggested Books:

  1. Melanie Swan, "Block Chain: Blueprint for a New Economy", O'Reilly, 2015
  2. Josh Thompsons, "Block Chain: The Block Chain for Beginners- Guide to Block Chain Technology and Leveraging Block Chain Programming"
  3. Daniel Drescher, "Block Chain Basics", Apress; 1st edition, 2017
  4. Anshul Kaushik, "Block Chain and Crypto Currencies", Khanna Publishing House, Delhi.
  5. Imran Bashir, "Mastering Block Chain: Distributed Ledger Technology, Decentralization and Smart Contracts Explained", Packt Publishing
  6. Ritesh Modi, "Solidity Programming Essentials: A Beginner's Guide to Build Smart Contracts for Ethereum and Block Chain", Packt Publishing
  7. Salman Baset, Luc Desrosiers, Nitin Gaur, Petr Novotny, Anthony O'Dowd, Venkatraman Ramakrishna, "Hands-On Block Chain with Hyperledger: Building Decentralized Applications with Hyperledger Fabric and Composer", Import, 2018

Learning Outcomes:

At the end of this course, the students will be able to:

→ Understand Block Chain Technology

→ Develop Block Chain Based Solutions And Write Smart Contract Using Hyperledger Fabric And Ethereum Frameworks

→ Build And Deploy Block Chain Application For On Premise And Cloud Based Architecture

→ Integrate Ideas From Various Domains And Implement Them Using Block Chain Technology In Different Perspectives

Program Elective: Optimization Techniques [PEC CS 814]

Objectives of the course

To Introduce Optimization Techniques Using Both Linear And Non-linear Programming

After An Adequate Introduction To Linear Algebra And Probability Theory And Optimization Techniques Students Will Learn To Frame Engineering Minima Maxima Problems In The Framework Of Optimization Problems

  • Module 1:(Lectures 10 hrs.)
    • Optimization Techniques
    • Model Formulation
    • Models
    • General L.R Formulation
    • Simplex Techniques
    • Sensitivity Analysis
    • Inventory Control Models
  • Module 2:(Lectures 8 hrs.)
    • Formulation Of A LPP
    • Graphical Solution Revised Simplex Method
    • Duality Theory
    • Dual Simplex Method
    • Sensitivity Analysis
    • Parametric Programming
  • Module 3:
    • T Flow Problem
    • Max Flow Problem
    • CPM/PERT
  • Module 4:(Lectures 7 hrs.)
    • Scheduling And Sequencing
    • Single Server And Multiple Server Models
    • Deterministic Inventory Models
    • Probabilistic Inventory Control Models
    • Geometric Programming
  • Module 5:(Lectures 8 hrs.)
    • Competitive Models
    • Single And Multi-channel Problems
    • Sequencing Models
    • Dynamic Programming
    • Flow In Networks
    • Elementary Graph Theory
    • Game Theory Simulation

Suggested Books:

  1. H.A. Taha, Operations Research, An Introduction, PHI, 2008
  2. H.M. Wagner, Principles of Operations Research, PHI, Delhi, 1982

Suggested Reference Books:

  1. J.C. Pant, Introduction to Optimisation: Operations Research, Jain Brothers, Delhi, 2008
  2. Hitler Libermann Operations Research: McGraw Hill Pub. 2009
  3. Pannerselvam, Operations Research: Prentice Hall of India 2010
  4. Harvey M Wagner, Principles of Operations Research: Prentice Hall of India 2010

Course Outcomes:

After completion of course:

→ Students Should Able To Apply The Dynamic Programming To Solve Problems Of Discreet And Continuous Variables

→ Students Should Able To Apply The Concept Of Non-linear Programming

→ Students Should Able To Carry Out Sensitivity Analysis

→ Student Should Able To Model The Real World Problem And Simulate It

Program Elective: Information Retrieval [PEC CS 815]

Objective of the Course:

To Introduce Students To Information Retrieval Systems And To Study Theoretical Aspects As Well As Implementation Issues Of Classical And Modern Retrieval Problems. Discuss Mechanisms Of Web Search Along With The Details Of Ranking Algorithms. Introduce Basic Concepts Of Text Categorization And Recommender Systems

  • Module 1: (Lectures 12 hrs.)
    • Introduction To Information Retrieval
    • Goals And History Of IR
    • The Impact Of The Web On IR
    • Unstructured And Semi-structured Text
    • Basic IR Models Inverted Index And Boolean Queries
    • Boolean And Vector-space Retrieval Models
    • Ranked Retrieval
    • Text-similarity Metrics
    • TF-IDF (term Frequency/inverse Document Frequency) Weighting
    • Cosine Similarity
    • Basic Tokenizing
    • Indexing
    • Implementation Of Vector-Space Retrieval
    • Simple Tokenizing
    • Stop-word Removal
    • Stemming
    • Inverted Indices
    • Efficient Processing With Sparse Vectors
  • Module 2: (Lectures 8 hrs.)
    • Experimental Evaluation Of IR
    • Performance Metrics
    • Recall
    • Precision
    • F-measure
    • Query Operations
    • Relevance Feedback
    • Query Expansion
    • Query Languages
    • Text Representation
    • Word Statistics
    • Porter Stemmer
    • Index Term Selection
    • Using Thesauri
    • Metadata And Markup Languages (SGML, HTML, XML)
  • Module 3: (Lectures 10 hrs.)
    • Web Search
    • Introduction
    • Spidering
    • Interfaces
    • Link Analysis Text Categorization
    • Categorization Algorithms
    • Language-Model Based Retrieval
    • Text Clustering
    • Applications To Web Search And Information Organization
  • Module 4: (Lectures 10 hrs.)
    • Recommender Systems
    • Collaborative Filtering And Content-based Recommendation Of Documents And Products
    • Information Extraction And Integration
    • Extracting Data From Text
    • Semantic Web
    • Collecting And Integrating Specialized Information On The Web

List of Suggested Books:

  1. Manning, Raghavan and Schutze, "Introduction to Information Retrieval", Cambridge University Press.
  2. Baeza-Yates and Ribeiro-Neto, "Modern Information Retrieval", Addison Wesley.

Program Elective: Bitcoin and Crypto Currencies [PEC CS 816]

  • Module 1: (Lecture 8 hrs.)
    • Introduction To Cryptography
    • Cryptographic Hash Functions
    • SHA-256
    • Hash Pointers And Data Structures
    • Merkle Tree
  • Module 2: (Lecture 8 hrs.)
    • Digital Signatures
    • Elliptic Curve Group
    • Elliptic Curve Digital Signature Algorithm (ECDSA)
    • Public Keys As Identities
    • A Simple Crypto Currency
  • Module 3: (Lecture 8 hrs.)
    • Centralization Vs. Decentralization
    • Distributed Consensus
    • Consensus Without Identity Using A Block Chain
    • Incentives And Proof Of Work
    • Bitcoin Transactions
    • Bitcoin Scripts
    • Applications Of Bitcoin Scripts
    • Bitcoin Blocks
    • The Bitcoin Network
  • Module 4: (Lecture 8 hrs.)
    • Simple Local Storage
    • Hot And Cold Storage
    • Splitting And Sharing Keys
    • Online Wallets And Exchanges
    • Payment Services
    • Transaction Fees
    • Currency Exchange Markets
  • Module 5: (Lecture 8 hrs.)
    • Bitcoin Mining
    • Mining Pools
    • Mining Incentives And Strategies
    • Bitcoin And Anonymity
    • Anonymity Basics
    • Mixing
    • Zerocoin And Zerocash

Reference Book:

  1. Arvind Narayanan, Joseph Bonneau, Edward Felten, Andrew Miller and Steven Goldfeder, Bitcoin and Cryptocurrency Technologies, 2016.
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