PSG TECH | AM&CS

23XW11 Calculus and its applications

BASIC CONCEPTS: Introduction to databases – Characteristics of database approach- Conventional file processing –– Advantages of using DBMS – Database System Concept and Architecture: Data Models – Instances and Schema – Three Schema Architecture - Data Independence – Components of a DBMS. (5)

CONCEPTUAL DATA MODELING: ER DATA MODEL: Entities, Attributes, Relationships – Role and Structural constraints – Weak and Strong entity types – Entity Relationship diagrams – Generalization – Aggregation– Applications – Introduction to Network data model and Hierarchical data model . RELATIONAL MODEL: Basic concepts – Constraints – Mapping ER model into Relational model. (12)

REALTIONAL QUERIES: Relational Algebra - Tuple relational calculus - Structured Query Language (SQL): SQL Commands for CRUD operations – Functions in SQL – Aggregation – Categorization – Views in SQL –– PL/SQL Basics – Procedures – Functions – Triggers. (7)

RELATIONAL DATABASE DESIGN: Anomalies in a database – Functional dependencies – Axioms – Normal forms based on primary keys – Second Normal form, Third Normal form, Boyce – Codd Normal form – Examples – Multi-valued dependencies – Fourth Normal form – Join dependencies – Fifth Normal Form – Physical database design and tuning (8)

FILE ORGANIZATION: Storage device characteristics – Constituents of a file – Operations on file – Sequential files – Index sequential files – Direct files – Primary and Secondary Key Retrieval – Types of indexes - Indexing using Tree Structures – SQL Query Processing (6)

TRANSACTION PROCESSING AND CONCURRENCY CONTROL: Transactions, Locking techniques, Concurrent access, Deadlock handling. (4)

DATABASE SECURITY, INTEGRITY CONTROL: Security and Integrity threats – Defense mechanisms – Discretionary Access Control and Mandatory Access Control. (3)

Total L: 45

TEXT BOOKS:
1. Elmasri R and Navathe SB, “Fundamentals of Database Systems”, Pearson Education, 2017.
2. Silberschatz A, Korth H and Sudarshan S, “Database System Concepts”, McGraw Hill, 2019

REFERENCES:
1. Hector Garcia-Molina, Jeffrey D. Ullman, Jennifer Widom, ”Database Systems: The Complete Book,”, Pearson Education, 2011.
2. Raghu Ramakrishnan and Johannes Gehrke, “Database Management System”, McGraw Hill, 2018.

23XW33 TRANSFORM TECHNIQUES

3 2 0 4

Prerequisites:

Basics of Analytical Geometry

TRANSFORM METHODS: Basic waveforms and their properties, Operational calculus, concept of transformation, integral transforms, kernel of a transform, examples of transforms, linearity property. (2+1)

LAPLACE TRANSFORM: Definition – Transforms of standard functions – Transform of unit step function – Dirac -Delta function- Transforms of derivatives and integrals – Transforms of Periodic functions – Inverse Laplace transform – Convolution theorem – Solving ordinary linear differential equations with constant coefficient by Laplace transform technique. Transfer functions, applications to linear systems. (12+7)

FOURIER SERIES: Dirichlet’s conditions, statement of Fourier theorem, Fourier coefficients, change of scale, Even and odd functions, Half-range sine and cosine series, RMS value, Parseval’s theorem, Applications to signals and systems. (4+2)

FOURIER TRANSFORM: Fourier integrals – Fourier transform – Infinite Fourier sine and Cosine transform – Transforms of standard functions – properties, Convolution theorem (Concept &Statement) – relation with Laplace transform. (7+5)

DISCRETE FOURIER TRANSFORM: Discrete convolution – Periodic sequence and circular convolution –Decimation- in-time and decimation-in-frequency algorithms – Computation of inverse DFT. (6+4)

Z-TRANSFORM: Z - transform of standard functions, inverse Z-transform – properties of Z – transform – Difference equations – Modeling, Solution of difference equations. (7+5)

WAVELET TRANSFORM: Continuous wavelet transform, admissibility condition, Haar-wavelet, Mexican-hat-wavelet, Morlet-wavelet - Convolution - Inverse transform - Comparison with Fourier transform. Application – detection of signal changes. (7+6)

Total L: 45+T: 30=75

TEXT BOOKS:
1. Anthony Croft, Robert Davison, Martin Hargreaves, “Engineering Mathematics - A Foundation for Electronic, Electrical, Communications & Systems Engineers”, Pearson Education, 2013.
2. Hans-Georg Stark, Wavelets and Signal Processing, Springer, 2009.

REFERENCES:
1. Erwin Kreyszig, “Advanced Engineering Mathematics”, John Wiley, 2013.
2. Lokenath Debnath and Dambaru Bhatta, Integral Transforms and Their Applications, Chapman & Hall/CRC, 2010.
3. Michael D. Greenberg, “Advanced Engineering Mathematics”, Pearson Education, 2013.

23XW34 DESIGN AND ANALYSIS OF ALGORITHMS

3 0 0 3

Prerequisites:

20XW22 DISCRETE STRUCTURES

20XW14 ANALOG AND DIGITAL ELECTRONICS

INTRODUCTION: Review of analysis of algorithms – Average case time complexity - Analysis of recursive algorithms- Master’s Theorem. (5)

HEAP: Max heap-Min heap-operations-heapsort. (4)

AVL TREES:Height – Searching – Insertion and deletion of elements – AVL rotations - Analysis. (5)

MULTIWAY SEARCH TREES: Indexed Sequential Access – M-way search trees – B-Tree – searching, insertion and deletion – B+Tree – Insertion and deletion. (7)

DIVIDE AND CONQUER: Method – Merge sort, Quick sort, Binary Search – Large integer multiplication-Strassen’s matrix multiplication. (7)

GREEDY METHOD: Optimization problems – Method – examples – Minimum cost spanning tree (Kruskal’s and prim’s algorithms), Topological sorting and Huffman codes. (5)

DYNAMIC PROGRAMMING: Method – All pairs shortest path problem – longest common subsequence problem-Traveling salesman problem. (6)

NP AND COMPUTATIONAL INTRACTABILITY: Basic concepts – Polynomial time reductions, efficient certification and NP, NP hard and NP complete problems (8)

Total L:45

TEXT BOOKS:
1. Thomas H. Cormen, Charles E Leiserson and Ronald L Rivest, “Introduction to Algorithms”, MIT Press, 2022.
2. Alfred V Aho, John E Hopcraft, Jeffrey D Ullman, ”Data structures and Algorithms”, Pearson Education,2011.

REFERENCES:
1. SartajSahni, “Data Structures, Algorithms and Application in C++”, Silicon Press, 2013.
2. AnanyLevitin, “Introduction to the Design and Analysis of Algorithms”, Pearson, Education, 2014.
3. Parag H Dave, Himanshu B Dave, “Design and Analysis of Algorithms”, Pearson Education, 2014.
4. Mark Allen Weiss, “Data Structures and Algorithm Analysis in C++”, Addison-Wesley, 2014.

23XW35 MICROPROCESSOR AND EMBEDDED SYSTEMS

3 0 0 3

Prerequisites:

20XW25 Computer Organization

INTRODUCTION TO MICROPROCESSORS: Evolution of Microprocessors - Microprocessor based systems - Advantages and limitations. (4)

INTEL 8086/88 PROCESSOR: Block diagram of 8086 - Addressing modes – Instruction format - Instructions - assembler directives – Construction of Machine code. (5)

ASSEMBLY LANGUAGE PROGRAMMING: Programs for multi precision addition, subtraction-block moves-array processing-string processing-procedures and macros. (5)

8086 INTERRUPT SYSTEMS: Advantages and disadvantages of interrupts - Interrupt systems of 80x86 processors – Programmable Interrupt Controller, INT 21H. (4)

PENTIUM PROCESSOR: Special Pentium Registers – Super scalar Architecture – Pipelining – Branch Prediction. (5)

INTRODUCTION TO EMBEDDED SYSTEMS: Definition – Examples of Applications – Important characteristics of these applications – real-time system and definitions – real –time system – Common misconceptions – Overview of science of real-time systems and examples of research problems. (6)

HARDWARE FUNDAMENTALS: Microprocessors –Programmable Array Logic (PAL) – Application Specific Integrated Circuit (ASIC) – Watch dog Timer. (4)

EMBEDDED SYSTEM INTERRUPTS: Saving and Restoring the content - The Shared–data Problem – Shared–Data bug – Solving Atomic and Critical sections – Interrupt Latency. (6)

EMBEDDED SOFTWARE ARCHITECTURE: Round – Robin with interrupts, Example – characteristics – Function- Queue- Scheduling Architecture – Real Time Operating System Architecture. (6)

Total L: 45

TEXT BOOKS:
1. Barry B Brey, "The Intel Microprocessors - 8086/88, and 80186, 80286, 80386, and 80486", Prentice Hall,2009.
2. Douglus V Hall, "Microprocessors and Interfacing", Tata McGraw Hill, 2010.
3. David E Simon, “An Embedded Software Primer “, Pearson Education, 2002.
4. Peter C Dibble, “Real –Time Java Platform Programming”, Books Surge, 2008.

REFERENCES:
1. Walter A. Triebel, Avtar Sing, “8088 and 8086 Microprocessors Programming”, Pearson Education, 2008.
2. Jane W S Liu, “Real - time Systems”, Pearson Education, 2009.
3. Andrew Wellings, “Concurrent and Real Time Programming in Java”, John Wiley, 2004.
4. Albert M K Cheng, “Real-Time Systems Scheduling, Analysis and Verification”, John Wiley, 2003.

23XW36 DESIGN AND ANALYSIS OF ALGORITHMS LAB

0 0 4 2

Implementation of the following problems:
1. AVLtree including Rotations.
2. Splay trees including rotations.
3. Implementation of B Trees.
4. Divide and Conquer versions of Merge sort, Quick sort and binary search.
5. Greedy method implementation of Minimum cost spanning tree (Prim’s & Kruskal).
6. Dynamic Programming implementation of Traveling Salesperson problem.
7. Eight queen's problem, graph colouring application using backtracking.
8. 0/1 knapsack and traveling salesman problem using branch and bound.

Total: P:60

23XW37 EMBEDDED SYSTEMS LAB

0 0 4 2

Implement the following for assembly language programming:
1. Study of Assembler (Turbo) and Assembler Directives.
2.Study of INT 21H functions for input and output.
3.Multi-precision addition and subtraction.
4.Packing and unpacking of BCD digits.
5.Conversion of BCD numbers into ASCII characters and vice versa.
6.Delay loop implementation.
7.Arrangement of numbers in ascending and descending order.
8.Checking whether a given character string is a PALINDROME.
9.Usage of MACROS - Examples.
10.BCD to Binary conversion and vice versa.
11.To check whether a given string is a substring of another.
12.Implementation of LEFT(), RIGHT(), SUBSTR() functions.
13.To display the contents of the given memory locations.
14.Encryption and decryption of a message.
15.To find the Minimum and the Maximum number of a given array.

Implement the following for assembly language programming:
1.Study of ARM evaluation system
2.Interfacing ADC and DAC.
3.Interfacing LED and PWM.
4.Interfacing real time clock and serial port.
5.Interfacing keyboard and LCD.
6.Interfacing EPROM and interrupt.
7.Mailbox.
8.Interrupt performance characteristics of ARM and FPGA.
9.Flashing of LEDS.
10.Interfacing stepper motor and temperature sensor.
11.Implementing zigbee protocol with ARM.

Total: P:60

23XW38 RDBMS LAB

0 0 4 2

Implementation of the following problems:
1. Creating database structures such as tables, constraints and views using DDL
2.Practicing DML for manipulation of single, multiple tables and Report Generation.
3.Activating access rights and privileges using DCL.
4.Database programming using PL/SQL- Triggers and stored procedures.
5.Working on TCL commands to manage transactions in databases.
6.Establish Database Connectivity - Applications development

Total: P:60

23XW41 ACCOUNTING AND FINANCIAL MANAGEMENT

4 0 0 4

Prerequisites:

Familiarity with basic Mathematics

FINANCIAL ACCOUNTING: Accounting concepts & conventions - Double Entry Book keeping – Objectives - Books of Accounts – Accounting Rules for Journalising – Preparation of Journal entries, Ledger and subsidiary books – Simple problems on Profit & Loss Account & Balance Sheet. (14)

FINANCIAL STATEMENT ANALYSIS:Needs for financial analysis – Role of financial Ratios – Classification and types of Ratios - Problems on Ratios involving Profit & Loss Account and Balance Sheet - Interpretation of financial statements based on ratios (9)

COST ACCOUNTING: Definition – classification and types within – costing methods - Preparation of Cost sheet - Services Costing – Concept of cost volume profit analysis – simple problems; Concept of variance – Overview on Modern techniques / concepts of Cost Control / Cost Management like Value Analysis, Value Engineering, Activity Based Costing & Target Costing . (13)

PRINCIPLES OF CAPITAL BUDGETING: Different methods of evaluating investment proposals - using present values - Payback, Net present value, method – problems (6)

WORKING CAPITAL MANAGEMENT: Definition and importance of working capital – factors affecting working capital – Estimate of overall working capital requirements– Various sources of financing. Inventory management – simple problems – Receivables Management – cash Budget Preparation. (7)

OBJECTIVES OF FINANCIAL MANAGEMENT:Sources of investments - Organised and Unorganised sectors. (2)

BASICS OF TAXATION,INSURANCE AND BANKING:Basic provisions & applicability - Income Tax – Goods and Service Tax (GST) ; Various types of insurances ; Services offered by commercial banks – Fund based & Non Fund Based (5)

INTERNATIONAL FINANCIAL MANAGEMENT: Foreign Exchange - Exchange rate mechanism - Forward Contracts - OPTIONS – Futures – currency swaps - Arbitraging – Hedging. (2)

ARTIFICIAL INTELLIGENCE & ITS APPLICATIONS IN ACCOUNTING AND FINANCIAL MANAGEMENT : Chatbots - Machine Learning - Robotic Process Automation – Smart Analytics - focussing on statutory compliance, timeliness, decision support systems. (2)

Total L:60

TEXT BOOKS:
1. Khan M Y, Jain P K, "Cost Accounting and Financial Management", Tata McGraw Hill,2008.
2. Gupta R.L, Radhaswamy M, "Advanced Accountancy", Sultan Chand & Sons, 2009.
3. P.G.Apte, “International Financial Management”, Tata McGraw Hill, 2020

REFERENCES:
1. Sharma R K and Shashi K Gupta, "Management Accounting - Principles and Practice", Kalyani Publishers, 2011.
2. KuchalS C, "Financial Management",Chaitanya Publishing House,2006.

23XW42 COMPUTER NETWORKS

3 0 0 3

Prerequisites:

20XW25 COMPUTER ORGANIZATION

20XW35 MICROPROCESSOR SYSTEMS AND PROGRAMMING

Introduction: Network goals - OSI Reference Model – Network Types and Topologies- Applications (3)

DATA COMMUNICATION: Transmission medium-Impairments-Bandwidth and data rate-.Bit Rate, Baud Rate- Sampling Rate-Types of Multiplexing-Packet Switching (3)

Data Link Layer: Error Detection and Correction - Cyclic Redundancy Check Code -.Hamming Code-Flow Control - ARQ. (6)

Local Area Networks: Random Access protocols- CSMA CD/CA-Comparative Study of Ethernet , Fast Ethernet and Gigabit Ethernet – Internetworking- LAN -LAN Connections – Repeaters- Hubs - Bridge – Spanning tree-Switches – Routers

IP:TCP/IP Protocol Structure - Internet Protocol – IP addressing-Subnetting-NAT- ARP-DHCP (9)

ROUTING: Distance vector routing - Link state Routing – RIP – OSPF (6)

TRANSPORT LAYER:TCP concepts - Port number -Sockets– Connection control – Congestion Control -UDP (6)

APPLICATIONS:SMTP – HTTP- DNS. (6)

Total L: 45

TEXT BOOKS:
1. Behrouz A Forouzan, “Data Communications and Networking”, Tata McGraw Hill, 2021
2. Behrouz A Forouzan, “TCP/ IP Protocol Suite”, Tata McGraw Hill, 2017.
3. Peterson, Larry L., and Bruce S. Davie. Computer networks: a systems approach. Elsevier, 2012.

REFERENCES:
1. Kevin Fall R and Richard Stevens W, "TCP/IP Illustrated, Volume 1: The Protocols”, Addison-Wesley, 2011.
2. , Keith Ross, “Computer Networking: A Top-Down Approach”, Pearson Addison-Wesley, 2017.
3. Douglas Comer, “Internetworking with TCP/IP”, Prentice Hall, 2014.
4. William Stallings, "Data and Computer Communications", Prentice Hall, 2014.

23XW43 OPERATIONS RESEARCH

4 0 0 4

Prerequisites:

20XW11 CALCULUS AND ITS APPLICATIONS

20XW22 PROBABILITY AND STATISTICS

LINEAR PROGRAMMING: Introduction to Operations Research – Modeling with linear programming - Graphical method for two dimensional problems – Simplex Algorithm – Two Phase Simplex Method – Special cases of Simplex Method – Sensitivity analysis - Revised Simplex Method. (14)

SIMPLEX MULTIPLIERS : Dual and Primal – Dual Simplex Method – Post Optimal Analysis – Transportation problem and its solution – Assignment problem and its solution by Hungarian method. (12)

DECISION THEORY: Decision Analysis – Decision making under certainty, uncertainty and risk. (6)

NON LINEAR PROGRAMMING (UNCONSTRAINED OPTIMIZATION): Introduction – Random search method – Univariate method – Pattern search methods – Hooke and Jeeves method – Indirect Search Methods – steepest descent method – Conjugate gradient method. (14)

DYNAMIC PROGRAMMING: Introduction – multistage decision processes – Principles of optimality – Computation procedures. (8)

CPM AND PERT: Critical path network model – CPM computations – PERT calculations. (6)

Total L: 60

TEXT BOOKS:
1. Hamdy A Taha, “Operations Research – An introduction”, Pearson, 2022
2. Hillier and Lieberman, “Introduction to Operations Research”, McGraw Hill, 2021.

REFERENCES:
1. Richard W. Cottle and Mukund N. Thapa , “Linear and Non linear optimization”, Springer-Verlag, 2017.
2. Wayne L. Winston, “Operations Research: Applications and Algorithms”, Duxbery press, 2004.
3. D.BertSimas and Tsitsiklis, “Introduction to Linear Optimization”, Athena Scientific, 1997.

23XW44 OPERATING SYSTEMS

3 0 0 3

Prerequisites:

20XW25 COMPUTER ORGANIZATION

20XW35 MICROPROCESSOR SYSTEMS AND PROGRAMMING

INTRODUCTION: Abstract view of an operating system - Operating Systems Objectives and Functions – Evolution of Operating Systems - Dual-mode operation - System calls- Structure of Operating System. (3)

PROCESS DESCRIPTION AND CONTROL: Process concepts - Process Creation – Process Termination - Process states - Process Description – Process Control - shell, boot and init processes in UNIX. (5)

PROCESS AND THREADS: Relationship between process and threads – Thread States – Thread Synchronization – Types of Thread – Multithreading model (6)

PROCESS SCHEDULING: Scheduling basics - CPU-I/O interleaving- (non-)preemption - context switching – Saving the context of a process - CPU Scheduler – Types of Scheduling – Scheduling Criteria - Scheduling Algorithms – Algorithm evaluation – Real-time scheduling –Scheduling parameters in UNIX. (7)

PROCESS COMMUNICATION AND SYNCHRONIZATION: Inter-Process Communication (IPC) - Concurrent Process – Principles of Concurrency – Race Condition - Mutual Exclusion – Critical section problems – Software support – Hardware Support – Operating System Support: Semaphore, Monitor – Classical problems of synchronization – Synchronization examples. (8)

DEADLOCK:Principles- Characterization – Methods for handling deadlock - Deadlock prevention, Avoidance, Detection, and recovery. (4)

MEMORY MANAGEMENT: Memory hierarchy –Memory Management requirements - Memory partitioning: Fixed partitioning, dynamic partitioning, Buddy systems – Simple paging – Page table structures – Simple Segmentation – segmentation and paging (8)

VIRTUAL MEMORY MANAGEMENT: Need for Virtual Memory management – Demand Paging –Copy on write – Page Stealer – Page Fault Types - Page Fault handling – Page replacement - Frame allocation- Thrashing - working set model - Swapping (7)

I/O MANAGEMENT AND DISK SCULING:Organization of I/O function – Evolution of I/O function – Types of I/O devices – Logical Structure of I/O functions – I/O Buffering – Disk I/O – Disk Scheduling algorithms – RAID - Disk Cache -– Buffer Cache in UNIX

FILE SYSTEM MANAGEMENT: Files – Access methods - File system architecture – Inode Management in UNIX- Functions of file management –Directory and disk structure -Mounting - File sharing –File system implementation – Directory implementation - File Allocation – Free space management (6)

Total: L: 45

TEXT BOOKS:
1. Silberschatz A, Galvin, PB. and Gagne, G. “Operating System Concepts”, John Wiley & Sons, Inc.,2018.
2. William Stallings, “Operating Systems: Internals and Design Principles”, Pearson Education, 2021.
3. Maurice J Bach, "Design of the UNIX Operating System", Pearson, 2015.

REFERENCES:
1. Andrew S Tanenbaum, Herbert Bos, "Modern Operating System", Pearson, 2022.
2. McHoes, AM and Flynn, I.M. “Understanding Operating Systems”, Cengage Learning, 2016.
3. Dhamdhere D M, “Operating Systems: A Concept-based Approach”, McGraw-Hill, 2015.

23XW45 SOFTWARE ENGINEERING TECHNIQUES

3 2 0 4

Prerequisites:

20XW24 Object Oriented Programming

INTRODUCTION: Software application domains – Nature of software - Why Software engineering – problems and challenges. (3)

MODELING THE PROCESS AND LIFE CYCLE: Software methodologies – Software lifecycle – Project Management - Software Teams – Unified process – Agile development – Capability Maturity Model. (3)

PLANNING AND MANAGNG THE PROJECT: Software Estimation and Measurement. (3)

UNDERSTANDING REQUIREMENTS: Requirements gathering – Use Cases – Software Requirement Specification. (3)

REQUIREMENTS MODELING: Scenarios – Data modeling – Class-based modeling – Flow-oriented modeling – Behavioral model. (5)

DESIGNING THE SYSTEM: Design concepts – Architectural design – Pattern-based design - User Interface design – Component-level design. (6)

QUALITY MANAGEMENT: Quality concepts – Review techniques – Software quality assurance – Six Sigma. (5)

SYSTEM TESTING: Unit testing – Integration testing - Test Cases – Debugging – Test-driven Development. (5)

DELIVERING THE SYSTEM: Deployment - Software Reuse strategies – Maintenance – Software configuration management. (5)

UML MODELING: Business Modeling workflow – Requirements workflow - Use Case diagrams - Analysis workflow – Analysis classes – Sequence diagrams – Collaboration diagrams - Design workflow – Class diagrams - State diagrams – Object diagrams – Component diagrams - Deployment workflow – Deployment diagram – Static vs. Dynamic modeling. (7)

TUTORIAL PRACTICE:
1. Effort Estimation using Function Point Analysis
2. Process model Analysis
3. Requirements Capturing and Requirements Engineering - Prepare Software Requirements Specification
4. Analysis Modeling - Identify use cases, scenarios, and Analysis classes
5. Design Modeling - DFD, ERD, Structure chart, HIPO chart, Interface Design, Database Design
6.Unified Modeling Language Diagrams
7. Generate Test cases

Total L:45

TEXTBOOKS:
1. Roger S Pressman, Bruce R Maxim, “Software Engineering: A Practitioner’s Approach”, McGraw Hill, 2019.
2. Shari Lawrence Pfleeger, Joanne M Atlee, “Software Engineering: Theory and Practice”, Pearson, 2014.

REFERENCES:
1. Ian Sommerville, “Software Engineering”, Pearson Education, 2018.
2. Craig Larman, “Applying UML and Patterns: An Introduction to Object-Oriented Analysis and Design and Iterative Development”, PHI Learning, 2011.

23XW46 COMPUTER NETWORKS AND TCP/IP LAB

0 0 4 2

1. Familiarize with GNS3 simulator.
2. Implement Hamming code and CRC.
3. Implement a primitive email server.
4. Familiarize with packet capturing tools in Java and Wireshark.
5. Implement a simple firewall system.
6. Analyse the existing routing protocols and implement any one of them.
7. Write a program where a single entity can communicate with other entities by using IP-multicasting.
8. Assignments using the network simulator.

Total P: 60

23XW47 UNIX SYSTEM PROGRAMMING LAB

0 0 4 2

1. Writing shell scripts and awk scripts
2. Writing programs using process-related system calls – fork, exec, wait
3. Thread creation and execution using the pthread library
4. Process / Thread synchronization using semaphores and signals
5. Solving classical synchronization problems using Inter-process Communication (using shared memory, pipes, and message queues)
6. Creating OS Modules

Total P: 60

23XW48 WEB DEVELOPMENT LAB

0 0 4 2

INTRODUCTION: WWW – presentation / business logic layer-Browser architecture – HTTP architecture, Methods, Web Server Architecture.

HTML: Basic Structure – HTML tags – Tables – Forms – Links – Frames – DOM – Styling Tags.

CSS: Introduction – Types (Where to place CSS) – Rules – Selectors – Styling Fonts – Layouts – Positioning – Boot Strap.

JavaScript: Scripting Languages – Syntax – Variables – Data Types – Operators – Expressions – Conditional Statements – Loops – Arrays – Functions – Event Handling – Enhancing HTML Documents with JavaScript - AngularJS

PHP:Evaluation of PHP – Basic Syntax – Variables – Constants – Data Types – Operator – Expression – Form Processing – Looping – Functions – Arrays – Strings – PHP Global Array - Sessions – Cookies - NoeJS

WEB PUBLISHING / HOSTING: Host Registration – Domain Registering – Server FTP Upload – AJAX – JSON - MySQL

Total L: 60

EXERCISES:
1. Create a simple website using HTML.
2. Create a website using CSS and JavaScript.
3. Create a simple PHP page to get the name of the user.
4. Create a form and receive the data using PHP.
5. Connect PHP to database and create a functional website
6. Create and upload a website to the web using FTP.
7. Deploy a website to Netlify / Firebase
REFERENCES:
1. Elizabeth Castro and Bruce Hyslop, “Visual Quickstart Guide: HTML5 and CSS3“, Peachpit Press, 2013.
2. David Flanagan, ”JavaScript: The Definitive Guide”, O'Reilly Media, 2011.
3. Larry Ullman, ”PHP for the Web”, Peachpit Press, 2016.
4. Luke Welling, “PHP and Web Development”, Addison Wesley, 2008.

23XW51 BIG DATA AND MODERN DATABASE SYSTEMS.

3 0 0 3

Prerequisites:

20XW25 COMPUTER ORGANIZATION

20XW35 MICROPROCESSOR SYSTEMS AND PROGRAMMING

OBJECT AND SPATIAL DATABASES : Object Oriented Databases - Complex data types - Structured types and Inheritance - Query Processing in Object databases - Spatial Databases: Geometric Information System - Spatial Data Types – Spatial Queries - Spatial indexing techniques. (6)

PARALLEL AND DISTRIBUTED DATABASES: Architecture of parallel databases – Parallel query evaluation, Parallel query optimization – Distributed DBMS Architecture, Distributed Database Design, Distributed Query Processing. (5)

DATA MODELLING FOR BIG DATA:Big Data and Challenges, Big Data models, NoSQL data models, Principles of NoSQL models, BASE properties, CAP Theorem (5)

NOSQL DATABASES – Document and Graph Databases :Document Oriented Stores – MongoDB - Graph databases: Neo4J (8)

NOSQL DATABASES - Key Value and Columnar Databases: Key -Value Stores (in-memory) :Redis , Column Oriented Store: Cassandra - Hbase - BigTable (8)

BIG DATA PLATFORM: Hadoop and HDFS-Map-reduce - SPARK- Real time Streaming(8)

DATABASE INTEGRATION: Data warehousing, Virtual Data Integration - Schema directed data integration - Schema mapping and information preservation-Applications (5)

Total L: 45
TEXT BOOKS:
1. M.TamerOzsu, PatrickValduriez, “Principles of Distributed Database Systems”, Springer,2019.
2. Tomasz Wiktorski, “Data-intensive Systems: Principles and Fundamentals using Hadoop and Spark (Advanced Information and Knowledge Processing)”, Springer, 2019.
3. Pramod J. Sadalage and Martin Fowler, “NoSQL Distilled - Brief Guide to the Emerging World of Polyglot Persistence”, Pearson, 2013

REFERENCES:
1. ElmasriRamez and Navathe SB, “Fundamentals of Database Systems”, Pearson, 2017.
2. Anhai Doan, Alon Halevy, Zachary Ives, “Principles of data integration”, Morgan Kaufmann, 2012.

23XW52 JAVA PROGRAMMING

3 0 0 3

Prerequisites:

20XW15 C Programming

20XW24 Object Oriented Programming

INTRODUCTION: Data Types - Operators - Declarations - Control Structures - Arrays and Strings - Input/Output-Java Classes - Fundamentals - Methods - Constructors - Scope rules - this keyword - Object based vs Oriented programming – Inheritance-Reusability - Composing class - Method Overloading - Abstract classes –Dynamic Method Dispatch. (5)

PACKAGES AND INTERFACES: Packages - Access protection - Importing packages - Interface - Defining and Implementing Interface - Applying Interface – Interface Enhancements- default , Static - Functional Interfaces. (5)

EXCEPTION HANDLING: Fundamentals - Exception types - Uncaught Exception - Using Try and Catch - Multiple catch clauses - Nested Try statements - Throw - Throws - Java Built-in Exception –Chained Exceptions – Declaring New Exceptions. (6)

COLLECTIONS FRAMEWORK:Generics – Autobox – Auto-unboxing –Interface Collection and Class Collections-List – Set – Queue – Map. (8)

MULTI THREADED PROGRAMMING: Java thread model - Priorities - Synchronization - Messaging - Thread class and runnable Interface - Main thread - Creating the Thread - Synchronization - Interthread Communication – Deadlock. (8)

I/O AND GUI DEVELOPMENT : I/O basics - Stream - Stream Classes – Predefined stream - Reading/Writing console input -NIO Classes-ObjectSerialization –- Native methods.- GUI Components - JavaFX Basics – UI Controls – Event Driven Programming.(8)

DATABASE CONNECTION : Setting up a Java DB Database - Manipulating Databases – RowSet Interface –Prepared Statements-Stored Procedures-Transaction Processing (5)

Total L: 45

TEXT BOOKS:
1. Herbert Schildt, "JAVA - The Complete Reference", Tata McGraw Hill, 2021.
2. Paul J. Deitel and Harvey Deitel, "JAVA -How to Progam", Pearson Education, 2018

REFERENCES:
1. Y Daniel Liang, “Introduction to Java Programming”, Pearson Education, 2019.
2. Joyce Farrell, “Java Programming”, Cengage Learning, 2019.
3. Herbert Schildt, “Java: A beginners Guide”,McGraw Hill, 2022.
4. https://docs.oracle.com/javase/tutorial/

23XW53 MACHINE LEARNING

3 0 0 3

Prerequisites:

20XW21 APPLIED LINEAR ALGEBRA

20XW22 PROBABILITY AND STATISTICS

20XW43 OPERATIONS RESEARCH

INTRODUCTION: Basics of Machine learning – Types – Convex set - Convex functions – Convex optimization - Loss functions in machine learning - Gradient descent – variants. (5)

SUPERVISED LEARNING : Regression – Linear – Polynomial – Multiple regression – Evaluation measures – Bias–variance tradeoff – over-fitting – under fitting – Regularization (7)

Classification: Linear models – Logistic regression – Bayesian Classifier – Maximum A Posteriori estimation – Maximum Likelihood Estimation - Linear Discriminant Analysis - Support Vector Machines (SVM) – Linear, Soft margin (10)

NON-LINEAR MODELS:SVM for Linearly non-separable data, Kernel functions - Decision trees: Introduction – Purity measures – Entropy, information gain, gain ratio, Gini Index – ID3 – K nearest neighbor classifier - Model selection & Evaluation measures (8)

Neural networks: – Perceptron - Activation functions - Multilayer perceptron - Backpropagation. (5)

UNSUPERVISED LEARNING:–Clustering –Types - K-means clustering – Mixture of Gaussians –Spectral clustering - Cluster validity measures – dimensionality reduction- Principal components analysis (PCA) – Linear Discriminant Analysis (LDA) - Independent components analysis (ICA) - Applications : image segmentation – Image compression – Outlier Analysis. (10)

Total L: 45

TEXT BOOKS:
1. AlpaydinEthem, “Introduction to Machine Learning”, MIT Press, 2020.
2. Christopher M Bishop, “Pattern Recognition and Machine Learning”, Springer, 2016.
3. Richard O Duda, Peter E Hart and David G Stork, “Pattern Classification (Digitized)”, John Wiley, 2012.

REFERENCES:
1. Shai Shalev-Shwartz and Shai Ben-David, “Understanding Machine Learning”, Cambridge University Press. 2015
2. Trevor Hastie, Robert Tibshirani and Jerome Friedman, “The Elements of Statistical Learning”, Springer, 2013.
3. Kevin Patrick Murphy, Probabilistic Machine Learning, MIT Press, 2022.
4. Tom M. Mitchell, Machine Learning , McGraw Hill Education, 2017

23XW54 THEORY OF COMPUTING

3 2 0 4

Prerequisites:

20XW23 Data structures and algorithms

20XW24 Object oriented Programming

20XW31 Mathematical Foundations Of Computing

INTRODUCTION TO LANGUAGES AND GRAMMAR: Overview of Languages and grammars – Alphabets – Strings – Operations on languages – Types of grammars

FINITE STATE AUTOMATA AND REGULAR EXPRESSION:Finite state automata (FSA) – DFA – NFA – Equivalence of NFA and DFA -(with and without empty string) and DFA - Minimization of FA. (9)

REGULAR EXPRESSION AND GRAMMAR:Regular expressions – Conversion between RE and FSA - Regular grammars – Conversion between regular grammar and FSA. (5)

PROPERTIES OF REGULAR LANGUAGES:Decision and closure properties of regular languages – Pumping Lemma for Regular languages – Finite State Transducers – Mealy and Moore Machines. (6)

CONTEXT FREE GRAMMARS:Derivations and Parse Trees – Leftmost and rightmost derivation – Ambiguous and unambiguous grammars – Chomsky Normal Form – Greibach Normal Form – CYK algorithm. (10)

PUSHDOWN AUTOMATA:Pushdown Automata – Deterministic Pushdown Automata – Equivalence of CFG and PDA (5)

PROPERTIES OF CONTEXT FREE GRAMMARS:Decision and closure properties of context-free - Pumping Lemma for CFG. (4)

TURING MACHINES:Turing Machines – Recursive and recursively enumerable languages – Chomsky hierarchy – Variants of Turing Machines – Universal Turing Machine – Decidable and closure properties of recursive and recursively enumerable languages – Rice’s theorem. (8)

UNDECIDABLE PROBLEMS:Decidability and undecidability – Halting problem – Post Correspondence Problem. (6)

Total L: 45 + P:30 =75

TEXT BOOKS:
1. Peter Linz, “An Introduction to Formal languages and Automata”, Jones & Bartlett Learning, 2017.
2. John E Hopcroft, Rajeev Motwani and Jeffrey D Ullman, "Introduction to Automata Theory, Languages and Computation", Pearson Education, 2014.
3. Michael Sipser, “Theory of Computing”, Cengage Learning, 2012.

REFERENCES:
1. Kamala Krithivasan, R. Rama, “Introduction to Formal Languages, Automata Theory and Computation”, Pearson, 2009.
2. George Tourlakis, “Theory of Computation”, John Wiley, 2012.
3. Harry R. Lewis, Christos H. Papadimitriou, “Elements of the Theory of Computation”, Prentice-Hall, 1998.

23XW55 PROFESSIONAL ELECTIVE I

23XW56 BIG DATA AND MODERN DATABASES LAB

0 0 4 2

1. ORDB, Spatial databases.
2. Creating and querying of object databases and object relational databases
3. Implementing of spatial databases and spatial queries
4. Implementation of No-SQL databases: MongoDB, Redis, Cassandra, DynamoDB, HBASE, Neo4J.
5. Big Data Analytics using Spark
6. Distribution using Map-Reduce on Big Data (Hadoop, Spark).
7. Data Integration from heterogeneous Databases.
8. Use Cloud Storage with big data- Amazon DynamoDB, Google Cloud platform Fire base: Real time and
Firestore 9. Data integration tools –Talend, Logstash
10. Large scale big data analytics

Total P: 60

23XW57 JAVA PROGRAMMING LAB

0 0 4 2

1. To create runtime polymorphism using abstract class, interface
2. To create callback feature using interface.
3. To create a program for interface inheritance
4. To implement a user defined package
5. To implement a user defined checked exception and unchecked exception
6. To create threads, thread groups
7. To create inter-thread communication using shared memory, piper stream.
8. To implement socket connections (UDP, TCP).
9. GUI development using JAVA FX.

Total P: 60

23XW58 MACHINE LEARNING LAB

0 0 4 2

Download the datasets from UCI machine learning repository / www.kaggle.com for classification and clustering; Evaluate Performance measures for classification / clustering.
1. Implement linear, polynomial and multiple regression.
2. Implement the following Classification algorithms for the above datasets.
a. Naïve Bayes Algorithm
b. Decision tree
c. SVM
d. K nearest neighbour
e. Perceptron
f. Multi-Layer Perceptron
3. Do tenfold cross validation experiments and statistical validation using t-test and ANOVA.
4. Implement different clustering techniques : K means, AGNES
5. Develop a user interface (UI) and Deploy ML models in cloud
6. Evaluate your models and fine tune the hyper parameters using any of the open source tools using Weights and Biases or Auto ML.

Total P: 60

23XW61 CLOUD COMPUTING

3 0 0 3

Prerequisites:

23XW31 MATHEMATICAL FOUNDATIONS OF COMPUTING

23XW52 JAVA PROGRAMMING

INTRODUCTION: Various Computing Paradigms - Cloud Computing Overview: Characteristics – challenges – benefits – limitations - Evolution of Cloud Computing - cluster computing Grid computing, Grid computing versus Cloud Computing - Serverless computing – AWS Lambda – Firebase Cloud Functions.- Cloud computing architecture- Cloud Reference Model (NIST Architecture) - open group cloud ecosystem reference model -

VIRTUAL MACHINES: Process and System VMs - Taxonomy of VMs - Types of Virtualization: Hardware Emulation - Full Virtualization with binary translation - Hardware assisted - Operating System Virtualization - OS assisted /Para virtualization (5)

HYPERVISOR AND VM PORTABILITY: Hypervisor - Type 1, Type 2 Para-virtualization - Server Virtualization - Desktop Virtualization - VM portability Clones – Templates - Snapshots – OVF - Hot and Cold Cloning - Protecting & Increasing Availability - Light Weight Virtual machine: Container / Docker (7)

CLOUD SERVICE MODELS: Service Models –Characteristics – Benefits - Enabling Technologies (IaaS/PaaS/SaaS) - Case Study: SaaS: Salesforce.com, Online Collaboration Services IaaS : AWS, OpenStack PaaS : IBM Bluemix, GAE (5)

CLOUD DEPLOYMENT MODELS AND RESOURCE MANAGEMENT: Public/Private/Multi-cloud deployments - Hybrid Cloud - Community Cloud - Shared Private Cloud - Dedicated Private Cloud - Dynamic Private Cloud - Shared Resources – Resource Pool –Usage and Administration Portal – Benefits of Cloud models - Savings and cost impact - Resource Management – Elastic Environment – Resilient Environment–Security – Workload Distribution – Dynamic provisioning - AWS Spot Instances - Web services delivered from cloud (5)

KUBERNETES:Docker – Containers and images – Container Orchestration – Pods – Deployments – Services – Storage – Secrets. (3)

CLOUD ECO SYSTEMS:The concept of a cloud ecosystem - Actors and Roles in the Cloud Eco System - Cloud adoption vision - identifying your use cases - developing your plan - Understanding the implications of Cloud Service Layers - Utilizing cloud to gain strategic advantage (4)

CLOUD DATA CENTERS:Historical Perspective - Datacenter Components - Design Considerations - Power Calculations - Evolution of Data Centers (4)

APPLICATION DEVELOPMENT FRAMEWORK:Accessing the clouds: Web application vs Cloud Application - Frameworks: Model View Controller (MVC) - Struts – Spring - Cloud platforms in Industry – Google AppEngine- Microsoft Azure – Openshift – CloudFoundry – Cloud Orchestration – Cloud pricing models – finding right cloud provider (5)

Total L: 45

TEXT BOOKS:
1. Rajkumar Buyya, Christian Vecchiola, ThamaraiSelvi S, “Mastering Cloud Computing”, McGraw Hill, 2022.
2. Dan C. Marinescu, "Cloud Computing: Theory and Practice", Morgan Kaufmann (Elsevier) Publishers, 2017
3. Thomas Erl, Robert Cope, Amin Naserpour, "Cloud Computing Design Patterns", Pearson, 2017

REFERENCES:
1. Thomas Erl, Zaigham Mahmood, Ricardo Puttini, “Cloud Computing: Concepts, Technology and Architecture”, Pearson, 2019.
2. Kai Hwang, Min Chen, “Big Data Analytics for Cloud, IoT and Cognitive Computing”, Wiley, 2018.

23XW62 ARTIFICIAL INTELLIGENCE

3 0 0 3

PREREQUISITE

23XW22 PROBABILITY AND STATISTICS

23XW24 Object Oriented Programming

INTRODUCTION: The foundations of AI - The History of AI - Intelligent agents - Agent based system. (2)

PROBLEM SOLVING: State Space models - Searching for solution - Uninformed search – Heuristics: Functions--properties- Informed search – Greedy best first search -A* search – Local Search algorithms: Hill-climbing search - Genetic Algorithm - Adversary based search: Minimax – Alpha Beta pruning-Imperfect real time decisions: Cutting-off search-Stochastic games : Expectimax – Constraint satisfaction problem: Inference- Backtracking search (16)

KNOWLEDGE REPRESENTATION AND REASONING:Knowledge representation - Logics - bivalent logic - inference - Fuzzy logic: membership - Fuzzy rules and reasoning - Fuzzy inference

UNCERTAIN KNOWLEDGE AND PROBABILISTIC REASONING:Uncertainty - Probabilistic reasoning - Semantics of Bayesian network - Exact inference in Bayesian network- Approximate inference in Bayesian network - Probabilistic reasoning over time – Inference in temporal models - Hidden Markov Models – Dynamic Bayesian Networks (10)

DECISION-MAKING:Basics of utility theory –Decision Networks- Sequential decision problems - Markov decision process - Value iteration - Policy iteration - Decisions in Multi agent system: Single move games - Group decision making. (9)

Total: L: 45+T: 30 = 75

TEXT BOOKS:
1. Stuart Russell and Peter Norvig, “Artificial Intelligence: A Modern Approach”, Pearson Education, 2020.
2. David Pool and Alan Mackworth, “Artificial Intelligence: Foundations of Computational agents”, Cambridge University Press, 2017.
3. Timothy Ross, “ Fuzzy Logic with Engineering Applications”, John Wiley and sons, 2016.

REFERENCES:
1. Christopher M.Bishop, “Pattern Recognition and Machine Learning”, Springer, 2016.
2. Daphne Koller and N Friedman, “Probabilistic Graphical Models - Principles and Techniques”, MIT press, 2009.

23XW63 DISTRIBUTED COMPUTING

3 2 0 4

Prerequisites:

23XW44 Software Engineering Techniques

INTRODUCTION : Introduction – Basics of distributed computing - Centralized vs Distributed systems –Design Goals- Classification of Distributed systems – Architectural Styles – Middleware and distributed systems- Layered-system architectures- Symmetrically distributed system architectures- Hybrid system architectures (4)

COMMUNICATION IN DISTRIBUTED SYSTEMS: Communication Models-Remote Procedure Call (RPC)-Message Passing- Group Communication -Distributed Algorithms-Consensus-Leader Election-Distributed Mutual Exclusion -Distributed Clock Synchronization (6)

DISTRIBUTED DATA STRUCTURES : Distributed Hash Tables -Distributed Queues- Distributed Graphs. (4)

FAULT TOLERANCE: Introduction-Process Resilience-Reliable Client Server Communication-Group Communication-Distributed Commit-Recovery- Byzantine Fault Tolerance-PAXOS (6)

FRAME WORKS:Java Server Faces – Struts- Spring –MVC- Hibernate – Vert.x – Dropwizard (8)

EVENT DRIVEN ARCHITECTURE:Introduction – Benefits –Apache Kafka – Event Streams – gRPC (9)

CASE STUDIES:Google File System (GFS)-Distributed lookup services -Distributed Data Processing System-Distributed Messaging System-Distributed Data Stores. (8)

TUTORIAL PRACTICE:
1. Implementation of distributed algorithms
2. Create multi-tiered application using the latest front and back end technologies.
3. Application development using any one of the frameworks
4. API Development with any framework
5. Microservices Development with any framework
6. Kafka Streaming Exercises

Total L: 45+T: 30=75

TEXT BOOKS:
1. Maarten van Steen, Andrew S. Tanenbaum, “Distributed Systems”, Create Space Independent Publishing Platform, 2023.
2. Craig Walls, “Spring in Action”, Manning Publications, Fifth Edition, 2023.
3. Emil Koutanov, “Effective Kafka”, Leanpub Publications, 2019.
REFERENCES:
1. G Coulouris, J Dollimore and T Kindberg, “Distributed Systems Concepts and Design” Fifth Edition, Pearson Education, 2017.
2. Felipe Gutierrez, Joseph B. Ottinger, “Introducing Spring Framework 6: Learning and Building Java-based Applications With Spring”, Apress, 2022.
3. Rod Johnson, Juergen Hoeller, AlefArendsen, Thomas Risberg and Colin Sampaleanu, “Professional Java Development with the Spring Framework”, Wiley, 2008.
4. Kai Hwang, Geoffrey C. Fox, Jack G. Dongarra, “Distributed and Cloud Computing, From Parallel Processing to the Internet of Things”, Morgan Kaufmann Publishers, 2012.

23XW64 SOFTWARE PATTERNS

3 2 0 4

Prerequisites:

20XW33 OPERATING SYSTEMS

20XW42 Computer Networks and TCP/IP

INTRODUCTION TO PATTERNS: Reusable object oriented software, Motivation, Best design practices of object oriented software, Coupling and Cohesion, Types of Cohesion and Coupling, Benefits of patterns, Definition of a Pattern, Types, Pattern description, Pattern Language, IDIOMS, Framework, Architecture. (6)

DESIGN PATTERNS:Creational patterns – Abstract factory, Builder, Factory method, Prototype, Singleton, Structural patterns – Adapter, Bridge, Composite, Decorator, Façade, Flyweight, Proxy, Behavioral patterns – Command, Interpreter, Iterator, Mediator, Memento, Observer, State, Strategy, Template method, Visitor, Chain of Responsibility, Case Studies. (15)

ARCHITECTURAL PATTERNS:From Mud to Structure – Layers, Pipes and Filters, Blackboard, Distributed systems – Broker, Interactive Systems – Model View Controller (MVC), Presentation Abstraction Control, Adaptable Systems – Reflection, Microkernel. Anti-Patterns. (13)

REFACTORING :What is refactoring, Principles in refactoring, Bad smells in code, Refactoring Techniques - Composing methods, Moving features between objects, Organizing data, Simplifying conditional expressions, Making method calls simpler, Dealing with generalization. Design Refactoring – Technical Debt, Design Smells, Abstraction Smells, Encapsulation Smells, Modularization Smells, Hierarchy Smells, Architectural Refactoring. Refactoring Tools. (11)

TUTORIAL PRACTICE:
l. Developing object oriented systems using Design Patterns.
2. Designing and giving architectural solutions to real time systems using Architectural Patterns.
3. Refactoring open source projects using Refactoring tools.
4. Developing simple refactoring tools.
5. Adopt new refactoring techniques to make the implementation more reusable.

Total L: 45+T: 30=75

TEXT BOOKS:
1. Erich Gamma, Richard Helm, Ralph Johnsons and John Vlissides, “Design Patterns: Elements of Reusable Object-Oriented Software”, Pearson Education, 2009.
2. Frank Buschman, Regine Meunier, Hans Rohnert, Peter Sommerlad and Michael Stal, “Pattern-Oriented Software Architecture: A System of Patterns”, John Wiley, 2013
3. Martin Fowler “Refactoring: Improving the Design of Existing Code”, Addison-Wesley Longman, 2019.

REFERENCES:
1. Sherif Yacoub, Hany Ammar, “Pattern-Oriented Analysis and Design: Composing Patterns to Design Software Systems”, Pearson Addison-Wesley, 2003.
. 2. Girish Suryanarayana, Ganesh Samarthyam, Tushar Sharma, “Refactoring for Software Design Smells: Managing Technical Debt”, Morgan Kaufmann Publishers, 2014
3. Steve McConnell, “Code Complete”, Microsoft Press, 2016
4. Petri silén, “Clean Code Principles and Patterns: A Software Practitioner’s Handbook”, Leanpub, 2023.

20XW65 PROFESSIONAL ELECTIVE II

23XW66 CLOUD COMPUTING LAB

0 0 4 2

1. Working with AWS - ECS, EKS, Lamda, S3, Pipelines
2. OpenStack
3. Containers: Working with docker
4. PaaS: Working with Heroku
5. NoSQL: CouchDB
6. Load Balancing with Nginx
7. Designing MapReduce Applications
8. Openshift
9. Azure Cloud services

Total P: 60

23XW67 ARTIFICIAL INTELLIGENCE LAB

0 0 4 2

1. Search Techniques: A* algorithm for 8 – puzzle and Missionaries and Cannibals problem, Hill climbing, genetic algorithm and Constraint satisfaction techniques
2. Simple games – minimax and expectimax
3. Logic based exercises, Fuzzy Inference System.
4. Decision making: Implementing HMM models, sequential and multi agent decision making

Note:Separate Problem Sheets will be provided.

Total P: 60

23XW68 MOBILE APPLICATION DEVELOPMENT LAB

0 0 4 2

Android SDK installation and study 1. Defining Layouts.
2. Single Activity Application, Application with multiple activities, using intents to Launch Activities.
3. Application using GUI Widgets.
4. Application with Notifications.
5. Application using resources and media.
6. Application studying background services.
7. Application tracking mobile devices.
8. Creating and Saving Shared Preferences and Retrieving Shared Preferences.
9. Usage of SQLite Databases for storage.
10. Working with Retrofit library in Android Applications.
11. Android Automated Testing Frameworks.
12. Study of Android Jetpack components.
13. Case Study: Dagger Framework for Android.
14. Case Study: Cross Platform applications.

TEXT BOOKS:
1. Chris Stewart , Bryan Sills, Kristin Marsicano and Brian Gardner ,“Android Programming: The big Nerd Ranch guide”, Addison Wesley,2022.
2. Jeff McWherter and Scott Gowell , “Professional Mobile Application Development”, John Wiley, 2012..
3. Ronan Schwarz, Phil Dutson, James Steele and Nelson To, “The Android Developer's Cookbook -Building Applications with the Android SDK”, Addison Wesley,2014.
4. Mark Murphy, “The Busy Coder's Guide to Android Development”, Commons Ware,2019.

23XWP1 PROJECT WORK I

23XW81 PRINCIPLES OF COMPILER DESIGN

3 0 0 3

Prerequisites:

23XW32 DATABASE MANAGEMENT SYSTEM

23XW44 Software Engineering Techniques

INTRODUCTION TO COMPILERS:Compilers and interpreters - Phases of compilers - AST - Intermediate representation - Assembly - Examples of compilers (4)

SYNTAX ANALYSIS :Lexical Analysis - Top-down Parsing – LL(1) parsing - Bottom up parsing - LR(1) parsing (9)

SEMANTIC ANALYSIS:Abstract Syntax Tree - AST Construction - Types (4)

INTERMEDIATE REPRESENTATION:Types of IR - Three Address Code, Stack Code - Control Flow Graphs - SSA Form - Generation of IR from AST - IR Constructs (Basic Blocks, Functions, Modules, Arithmetic Operations, Arrays, Structs, Loops, Control Flow Statements, Procedure Calls) (8)

MACHINE INDEPENDENT OPTIMIZATIONS:Local Optimizations - Global Optimizations -Examples of optimizations - Dead Code Elimination, Common Subexpression Elimination, Constant Propagation - Data Flow Analysis - Live Variable Analysis - Constructing SSA form - Interaction of Optimizations - Phase Sequencing (8)

RUN-TIME ENVIRONMENTS:Runtime Stack - Procedure Calls-Storage Allocation – Activation Records - Activation Trees - ABI - Calling Conventions - Passing Parameters - Returning Values (4)

CODE GENERATION AND OPTIMIZATION:Computer Architectures - Register Allocation - Instruction Scheduling – Loop optimization – Peephole optimization (8)

Total L: 45

TEXT BOOKS:
1. Keith D. Cooper, Linda Torczon, “Engineering a Compiler”, Morgan Kaufmann Publishers, 2011

REFERENCES:
1. https://www.cs.cornell.edu/courses/cs6120/2020fa/self-guided/.
2. Alfred Aho, Monica Lam, Ravi Sethi, Jeffrey Ullman, “Compilers: Principles, Techniques and Tools”, Pearson, 2014.
3. Steven Muchnick, “Advanced Compiler Design Implementation”,Morgan Kaufmann, 2003.

23XW82 DATA MINING

3 0 0 3

Prerequisites:

20XW53 MACHINE LEARNING

INTRODUCTION: Motivation for Data Mining – Importance – Definition – Kinds of data for Data Mining – Data Mining functionalities – Patterns – Classification of Data Mining Systems – Major issues in Data Mining-Overview of Data Mining Techniques. PREPROCESSING: Types of data, Data cleaning-Smoothing, Handling missing values- Feature subset selection Sampling methods.(10)

DATA WAREHOUSE and OLAP TECHNOLOGY: Overview- Need for Data Warehouse- multidimensional data model-Data Warehouse architecture -Data warehousing Schemas - Data Warehousing to Data mining (4)

MINING FREQUENT PATTERNS, ASSOCIATIONS AND CORRELATIONS: Basic concepts – Efficient and Scalable Frequent Itemset Mining methods – Apriori, FP Tree. CLASSIFICATION AND PREDICTION: Overview of Classification techniques –Ensemble Learning-bagging, boosting, cascading, stacking. CLUSTERING: Hierarchical – Density based(10)

MINING DATA STREAMS:Challenges- Characteristics of Streaming Data, Issues and Challenges, Streaming Data Mining Algorithms, Any time stream Mining. (6)

SEQUENCE MINING: Characteristics of Sequence Data, Problem Modeling, Sequential Pattern Discovery, Timing Constraints, Applications in Bioinformatics Multivariate Time Series (MVTS) Mining: Importance of MVTS data - Sources of MVTS data - Mining MVTS data (8)

APPLICATIONS AND TRENDS IN DATA MINING: Spatial Data Mining –Graph Mining- Web Mining –Text Mining.(6)

Total L:45

TEXT BOOKS:
1. Jiawei Han and Micheline Kamber , “Data Mining – Concepts and Techniques”, Morgan Kaufmann Publishers, 2012.
2. Tan, Steinbach and Kumar, “Introduction to Data Mining”, Pearson Education, 2014.

REFERENCES:
1. Trevor Hastie, Robert Tibshirani and Jerome Freidman, “The Elements of Statistical Learning: Data Mining, Inference, and Prediction”, Springer Series in Statistics, 2011.
2. Ian Witten, Frank Eibe and Mark A Hall, "Data Mining: Practical Machine Learning Tools and Techniques”, Elsevier, 2011.

23XW83 SOFTWARE PROJECT MANAGEMENT

3 0 0 3

Prerequisites:

Introduction: Software Projects various other types of projects - Problems with software projects - an overview of project planning - Project evaluation - Project Analysis and technical planning - Project estimates - Preparation of Estimates - COCOMO model - Function Point Analysis - Putnam Model - Non-development overheads. (10)

Activity planning: Project schedules - Sequencing and scheduling projects - Network planning models - Shortening project duration - Identifying critical activities. (9)

Risk management: Resource allocation - Monitoring and Control - Managing people and organizing teams - Planning for small projects - Handling large projects - Divide and Conquer - Software Project survival. (9)

Software configuration management: Basic functions, responsibilities, standards, configuration Management, Prototyping - Models of prototyping. (9)

Case study using Project management tools.

Total L: 45

TEXT BOOKS:
1. Mike Cotterell and Bob Hughes, "Software Project Management - Inclination", Tata McGraw Hill, 2019.
2. Robert K Wysocki, Robert Beck Jr and David B Crane, "Effective Project Management", John Wiley, 2017.

REFERENCES:
1. Steve McConnell, "Software Project Survival Guide", Microsoft Press, 2014.
2. Gerald M Weinberg, "Quality Software Management: Systems Thinking“, Dorset House, 2014.
3. Gerald M. Weinberg," Quality Software Management: First Order Measurement”, Dorset House, 2019.
4. Pressman R S, "Software Engineering - A Practitioner's Approach", Tata McGraw Hill,2019.
5. Darrel Ince, "An Introduction to S/W Quality Assurance and its Implementation", Tata McGraw Hill, 2018.

23XW84 PROFESSIONAL ELECTIVE III

23XW85 OPEN ELECTIVE I

23XW86 COMPILER DESIGN LAB

0 0 4 2

EXERCISES

Implementation of the following 1. Implementation of Symbol Table.
2. Develop a lexical analyzer to validate the patterns underlying identifiers, constants, operators.
3. Implementation of Lexical Analyzer using Lex Tool.
4. Generate YACC specification for a few syntactic categories.
5. To recognize valid arithmetic expressions that uses operators like +, – , * , /.
6. Implementation of Calculator using LEX and YACC.
7. Implementation of Predictive Parser Table.
8. Generate Abstract Syntax Tree.

Total P: 60

23XW87 DATA MINING LAB

0 0 4 2

1. Implementation of data mining techniques using WEKA.
2. Implementation of Association rule mining using Apriori algorithm and FP Growth algorithm
3. Classification rules using Decision Tree classifier, Ensemble of Classifiers.
4. Implementation of clustering algorithms
5. Case studies using R programming
6. A Package using data mining techniques based on research papers.

Visualization:
Explore software like R, Python, Google Vision, Google Refine, and ManyEyes ; Data sets are available on Gap minder,
1. Visualization of static data.
2. Visualization of web data.
3. Visualization of sensor data.
4. Visualization of protein data.

Total P: 60

23XW88 CAPSTONE PROJECT LAB

0 0 4 2

A capstone project is a multi-faceted body of work that serves as a culminating academic and intellectual experience for students. The list of activities are
Introduction
Literature review
Methodology
Results
Discussion

Total P: 60

23XW91 DIGITAL IMAGE PROCESSING AND COMPUTER VISION

3 0 0 3

Prerequisites:

23XW21 APPLIED LINEAR ALGEBRA

23XW22 PROBABILITY AND STATISTICS

23XW24 Object Oriented Programming

OVERVIEW: Computer Imaging Systems: Image formation and Sensing, Color representation, Image Acquisition, Image digitization, Noise, Image Representation. (4)

DIGITAL IMAGE ANALYSIS: Pre-processing, Binary Image Analysis, Edge detection - First order derivative, Second order detection, Color edge detection, Pyramid edge detection, Edge linking and boundary detection. Segmentation - Region based segmentation, clustering techniques, thresholding. (8)

IMAGE ENHANCEMENT: Gray-Scale Modification, Histogram processing, Image Sharpening, Image Smoothing - Image Restoration - Noise Models, Noise removal using spatial filters, Geometric transforms, Image Reconstruction. (6)

IMAGE TRANSFORMS: Overview of discrete transforms, Fourier Transform, Discrete Cosine transform, Discrete Haar transform, Principal components transform, Discrete Wavelet Transform, Filtering in Frequency domain - Inverse filter, Weiner filter, Homomorphic filter, Least Squares filter. (6)

MORPHOLOGICAL OPERATIONS: Binary Dilation, Erosion, Opening and Closing, Hit-or-Miss Transform, Basic Morphological Algorithms, Extension to Gray-Scale Images. (5)

IMAGE FEATURE ANALYSIS:Overview, Feature Extraction - Shape, color, spectral, textural features, feature Analysis. (5)

VIDEO ANALYSIS :Video Acquisition, Detecting Changes, Background subtraction, Image Differencing, Tracking by Detection, Tracking Multiple Objects. (5)

APPLICATIONS:Classification with machine learning and deep learning algorithms, Performance Measures. Real Time Applications: Object Detection, Biometric Authentication, Content Based Image Retrieval, Document processing, Pattern recognition methods, Face detection, Face recognition, Surveillance – foreground-background separation – human gait analysis Application, In-vehicle vision system: locating roadway – road markings – identifying road signs – locating pedestrians, Image based security. (6)

Total L: 45

TEXT BOOKS:
1. Umbaugh, S. E., “Digital Image Processing And Analysis: Applications with Matlab and CVIPTOOLS”, CRC press, 2017.
2. Richard Szeliski, “Computer Vision: Algorithms and Applications”, Springer, 2022.
REFERENCES:
1. R.C. Gonzalez and R.E. Woods, “Digital Image Processing”, Pearson Education, 2018.
2. Richard Hartley and Andrew Zisserman, “Multiple View Geometry in Computer Vision”, Cambridge University Press, 2014.
3. Milan Sonka, VáclavHlavác; Roger Boyle, “Image Processing, Analysis, and Machine Vision”, Cengage Learning, 2015.
4. David A. Forsyth, Jean Ponce, “Computer Vision: A modern approach”, Prentice Hall, 2015.
5. Thomas B. Moeslund, “Introduction to Video and Image Processing”, Springer, 2012.

23XW92 INFORMATION RETRIEVAL

3 0 0 3

Prerequisites:

23XW15 C Programming

INTRODUCTION:Overview of IR systems- Goals of IR-The impact of IR on the Web (2)

TEXT REPRESENTATION & RETRIEVAL MODELS:Statistical Characteristics of Text: Zipf's law, Heap’s Law - Data Structures for IR: Indexing techniques - Boolean Matching, Vector Space Models: TF-IDF weighting – Notion of similarity - LSI - Binary independence model. (8)

LEARNING TO RANK IN IR: Learning to Rank framework - Neural Nets for IR - Co-occurrence matrix – Word Embedding: Glove, Fasttext, Doc2Vec; Cross-language information retrieval -Topic Modelling: LDA – Deep Learning for IR – RNN, BERT (12)

QUERY MODELING& EVALUATION: Query Expansion – Feed back in Vector Space Model-Evaluation metrics: Recall, Precision and F- measure- Precision @k, R-Precision, Interpolated Precision, PR graph, Mean Average Precision, NDCG. (3)

WEB SEARCH: Web IR - Search engines - Crawl architecture - Duplicate detection – Min-hashing - Link Analysis: Random walk model - PageRank – HITS. (7)

KNOWLEDGE GRAPH & INFORMATION EXTRACTION: Key Concepts– Entity Recognition, Relation Extraction- Extracting information from text - Co-reference resolution (5)

INFORMATION FILTERING: Recommender System – types - Neighbourhood based approaches – Latent factor models – NNMF – Handling incomplete data – Multi-objective recommender systems- Sequential recommendations (8)

TotalL: 45

TEXT BOOKS:
1. Christopher D. Manning, Prabhakar Raghavan and HinrichSchütze, “Introduction to Information Retrieval”, Cambridge University Press, 2012.
2. B.Croft, D. Metzler, T. Strohman, “Information Retrieval in Practice”, Pearson, 2015.
3. TommasoTeofili , “Deep Learning for Search”, Manning Publications, 2019.

REFERENCES:
1. Mayank Kejriwal, Craig A. Knoblock and Pedro Szekely, Knowledge Graphs, MIT Press, 2021.
2. Francesco Ricci, LiorRokach, Bracha Shapira, Paul B. Kantor, “Recommender Systems – Handbook”, Springer, 2015.

23XW93 DEEP LEARNING

3 2 0 4

Prerequisites:

20XW53 MACHINE LEARNING

INTRODUCTION: Basic concepts – Convex sets, convex functions – loss functions – Gradient descent – Variants - Perceptron – Activation functions - Geometric representation – Perceptron Convergence theorem (4)

FEED FORWARD NETWORKS :Multi layer Perceptron – back propagation - Learning XOR – Auto encoder - Deep neural networks (6)

TRAINING NEURAL NETWORKS: Optimization methods for neural networks - Adagrad, Adadelta, rmsprop, adam, NAG - second order methods for training, Saddle point problem in neural networks, Regularization methods - dropout, batch normalization,Ridge and Lasso (10)

COVOLUTIONAL NETWORKS: Structure – properties – Variants (5)

RECURRENT NETWORKS: Recurrent neural networks(RNN) – Gated Recurrent unit – Long Short Term Memory - Bidirectional RNNs - Deep recurrent network – Methodology – Encoder and Decoder- Attention and self attention models - Transformer architecture- Applications. (8)

DEEP LEARNING RESEARCH:Linear Factor Models, variants of Autoencoders, Representational Learning, Structured probabilistic models for deep learning, Monte Carlo Methods, Generative adversarial networks - Deep generative models. (9)

APPLICATIONS:Natural language processing, Big Data, Brain Computer Interface, Vision, IoT (3)

TEXT BOOKS:
1. Ian Goodfellow, Yoshua Bengio, and Aaron Courville ,, The MIT Press, 2016.
2. Yoshua Bengio, Learning Deep Architectures for AI, Foundations & Trends in Machine Learning, 2009.

23XW94 PROFESSIONAL ELECTIVE IV

23XW95 OPEN ELECTIVE II

23XW96 DIGITAL IMAGE PROCESSING AND COMPUTER VISION LAB

0 0 4 2

EXERCISES: 1. Implementation of Viewing digital images, bits and bytes, sampling and quantization.
2. Apply scaling, translation and rotation, sums and differences with the grayscale and color images.
3. Implementation of Histograms and stretches, convolutional filters.
4. Implement Fourier transforms and the frequency domain, non-linear filters.
5. Construct edge detection algorithms using Operators.
6. Implement the morphological operations.
7. Apply various image encoding methods with grayscale images.
8. Implement various image segmentation methods.
9. Extract various image features and implement clustering and classification methods.
10. Developing simple image analysis applications

Total P: 60

20XW97 INFORMATION RETRTEVAL LAB

0 0 4 2

EXERCISES:

1. Designing search engine – Desktop, Website.
2. Plagiarism Checker – Code Plagiarism & Document Plagiarism.
3. Developing Web crawler & Extracting data.
4. Index creation and compression.
5. Recommendation Engine – Health care, E-commerce, E-learning.
6. Creating Knowledge Graph.
7. DNA sequence matching.

Note:Separate Problem Sheets will be provided.

Total P: 60

23XW98 DEEP LEARNING LAB

0 0 4 2

Total L: 60

1. Collect data sets from the url :http://deeplearning.net/datasets/

2. Use TensorFlow library for visualization of data sets in different domains and analysis:

a. Given a set of images of handwritten digits from MNIST, classify the images into digits b. Do image captioning using RCNN c. Text classification using CNN d. Language modeling using RNN e. Speech processing f. Optical character recognition using CNN and RNN g. Sentiment analysis and classification using LSTM h. Document classification / Radiology reports classification i. Visualization of CNN and RNN parameters j. POS tagging using RNN k. Dimensionality reduction using Autoencoders

23XWP2 PROJECT WORK II

23XWA1 SOFTWARE METRICS

3 2 0 4

Prerequisites:

23XW24 Object Oriented Programming

FUNDAMENTALS OF MEASUREMENT:: Measurement in Software Engineering-Scope of Software Metrics - Measurement and Models-Measurement scales and scale types-Classifying software measures - Software Measurement validation - Software Metrics Data collection - Analyzing software measurement data. (10)

MEASURING INTERNAL PRODUCT ATTRIBUTES: Size and Structure - Measuring external product attributes. (5)

SOFTWARE RELIABILITY: Measurement and prediction - Parametric Reliability Growth models - The recalibration of software reliability growth predictions. (10)

RESOURCE MEASUREMENT: Productivity, teams and tools- Making process predictions - Good estimates - Models of effort and cost - Dealing with Problems of current estimation methods. (10)

MEASUREMENT AND MANAGEMENT: Planning - Measurement program - Measurement tools-Measurers - analysts - audience - Measurement in practice. (10)

TUTORIAL PRACTICE:
1. Complete the time recording log and Defect Recording log.
2. PSP Programming assignment.
3. Assess the Quality of the Student’s PSP Data and record your observations in the specified format.
4. Estimate the size of the program using PSP Techniques and record it in the specified format.
5. Design Review Exercise.
6. Code Review exercise.
7. Exercise for measuring process and product quality.
8. Development of Project Plan.
9. Measurement of the quality of Team’s process and Product.

Total L: 45+T: 30=75

TEXT BOOKS:
1. NormanE Fenton and Shari Lawrence Pfleeger, "Software Metrics", Thomson Brooks/Cole, 2018.
2. Stephen H Khan, "Metrics and Models in Software Quality Engineering", Pearson Education, 2015.

REFERENCES:
1.Dick B Simmons and Newton C Ellis, "Software Measurement", Prentice Hall, 2014.
Allain Abran, "Software Metrics and Software Metrology", Wiley, 2010.
Capers Jones, “Applied Software Measurement”, McGraw Hill, 2018.

23XWA2 PARALLEL PROGRAMMING

3 2 0 4

Prerequisites:

23XW32 DATABASE MANAGEMENT DESIGN

23XW34 DESIGN AND ANALYSIS OF ALGORITHMS

INTRODUCTION: Moore’s Law – Parallelism - Latency and Bandwidth - Pipelining - Branch Prediction - Parallel Computation Models - Parallel random access models and variants - programming abstractions - simulations - Synchronous and asynchronous models, network models, performance analysis. (4)

SHARED MEMORY PROGRAMMING MODEL:Vectorization - SIMD – Tools - Shared Memory Machines - Parallel loops - OpenMP - OpenMP parallel loop construct - Sharing of variables - Dependencies and restructuring - Loop schedules - Parallel construct (without "for") – Synchronization - Critical sections - Sequential consistency - Flush construct - Case studies - Cache related performance issue - False sharing - Nested parallelism - Explicit dependencies - Tasks. (12)

MESSAGE PASSING PROGRAMMING MODELS:Pthreads- C++11 Atomics - Parallel Queues - Distributed Memory Machines - Basic MPI Send/Recv Variants - Collective operations – subcommunicators - parallel Prefix - Cost Model - One-sided communication - Hybrid programming (MPI + OpenMP).(8)

DATA PARALLEL PROGRAMMING: SIMT models - programming abstractions - CUDA and related models. (4)

PERFORMANCE MODELING AND OPTIMIZATION OF PARALLEL PROGRAMS:roof line model - external memory and Logp models - Memory System Optimization (Data Reuse, Data Layout, Replication for Conflict Free Memory access) - Fine Grained Computation Models – use of HLS for application acceleration - Systolic Arrays - space time computation - Communication bounds- tradeoffs - parallel and distributed communication avoiding algorithms. (12)

STREAM AND HETEROGENEOUS PROGRAMMING MODELS:Cloud programming - MapReduce - high level models – Accelerators - interface mechanisms - performance modeling - Extensions to OpenMP for accelerated computing - OpenCL, OPAE, Vitis. (5)

TUTORIAL PRACTICE:
1. Multithreading using pthread library.
2. OpenMP – Basic programs such as Vector addition, Dot Product.
3. OpenMP – Loop work-sharing and sections work-sharing .
4. OpenMP – Combined parallel loop reduction and Orphaned parallel loop reduction.
5. OpenMP – Matrix multiply (specify run of a GPU card, large scale data … Complexity of the problem need to be specified).
6. MPI – Basics of MPI.
7. MPI – Communication between MPI process .
8. MPI – Advanced communication between MPI process .
9. MPI – Collective operation with ‘synchronization’.
10. MPI – Collective operation with ‘data movement’.
11. MPI – Collective operation with ‘collective computation’.
12. MPI – Non-blocking operation.

Total L:45+T:30=75

TEXT BOOKS:
1. Wen-meiHwu, David Kirk, Izzat El Hajj, “Programming Massively Parallel Processors: A Hands-on Approach”, Elsevier, 2022.
2. Ruud van der Pas, Eric Stotzer and Christian Terboven , “Using OpenMP – The Next Step“, MIT Press, 2017.

REFERENCES:
1. Subodh Kumar, “Introduction to Parallel Programming”, Cambridge University Press, 2022.
2. Robert Robey, Yuliana Zamora, “Parallel and High Performance Computing”, Manning, 2021.
3. Documentation: MPI, Pthread, OpenMP and CUDA.

23XWA3 DATA COMMPRESSION

3 2 0 4

Prerequisites:

23XW22 PROBABILITY & STATISTICS

23XW44 SOFTWARE ENGINEERING TECHNIQUES

DATA COMPRESSION LEXICON: Introduction to Data Compression-Lossless and Lossy compression - Modeling and coding. (2)

INTRODUCTION TO INFORMATION THEORY:Minimum Redundancy Coding - The Shannon - Fano Algorithm, The Huffman Algorithm - Counting the Symbols, Building the tree. (4)

ADAPTIVE HUFFMAN CODING:Adaptive Coding - Updating the Huffman Tree - Escape code. (5)

ARITHMETIC CODING:Arithmetic Coding with floating point data type and integral data type representation. (6)

STATISTICAL MODELING:Higher-order Modeling - Finite Context Modeling – Order one modeling – Order two Modeling. DICTIONARY-BASED COMPRESSION:b LZ77 Compression and Decompression - LZSS Compression and Decompression - LZ78 Compression and Decompression - LZW Compression and Decompression – LZMW Compression and Decompression - LZAP Compression and Decompression. (8)

IMAGE COMPRESSION:

Introduction-Types of Redundancies-Various approaches in image compression-JPEG Standard-JPEG 2000. (7)

VIDEO COMPRESSION:

Introduction - Motion compensation - H.261 standard - MPEG-1 Video Standard - MPEG-4 Advanced Video Coding. (7)

AUDIO COMPRESSION

Introduction – ADPCM - Psychoacoustic Model - Spectral masking - Temporal masking- MPEG Audio Coding. (6)

TUTORIAL PRACTICE:
1. Implement Shannon Fano algorithm and Huffman algorithm.
2. Design compression and decompression program using adaptive Huffman coding.
3. Implement arithmetic coding algorithms.
4. Design compression and decompression program using LZW algorithm.
5. Design compression and decompression program using LZ77 algorithm.

Total L: 45+T: 30=75

TEXT BOOKS:
1. Khalid Sayood, “Introduction to Data Compression”, Morgan Kaufmann, 2013.
2. David Salomon, “Data Compression: The Complete Reference”, Springer, 2014.

REFERENCES:
1.Charles K. Chui, Qingtang Jiang, "Applied Mathematics: Data Compression, Spectral Methods, Fourier Analysis, Wavelets and Applications", Atlantic Press, 2013.

23XWA4 COMPUTER GRAPHICS

3 2 0 4

Prerequisites:

23XW25 Computer Organization

23XW35 MICROPROCESSOR SYSTEMS AND PROGRAMMING

23XW33 OPERATING SYSTEMS

GRAPHICS INPUT - OUTPUT DEVICES: Raster scan Displays - Random scan displays - Flat panel displays , Touch panels - LCD – IO Devices FOR Graphics Environment -. GRAPHICAL USER INTERFACE AND INTERACTIVE INPUT METHODS: The user dialog - Input of graphical data - Input function - Interactive picture construction techniques - Virtual reality, Augmented Reality -Environments, Devices. (4)

COLOR MODELS :Properties of light, Color Models, Standar Primaries and the Chromaticity Diagram, RGB Color Model, YIQ and related Color models, CMY and CYMK color models, HSV Color model, HLS Color Model (3)

OPENGL: Architecture, The OpenGL API, Primitives and Attributes, Color, Viewing, Control Functions, Programming Event-Driven Input, OpenGL Extensions. (4)

TWO DIMENSIONAL GRAPHICS:Basic transformations - Matrix representation and homogeneous coordinates - Composite transformations, 3D transformation - Line drawing algorithms: DDA and Bresenham's algorithms - Circle generation algorithms: Mid point circle algorithm - Point clipping - Line clipping: Cohen Sutherland algorithm, Liang-Barsky algorithm - Polygon clipping: Sutherland Hodgeman algorithm, Weiler-Atherton clipping. (8)

RASTER GRAPHICS: Fundamentals: generating a raster image, representing a raster image, scan converting a line drawing, displaying characters, speed of scan conversion, natural images - Solid area scan conversion: Scan conversion of polygons, Y-X algorithm, properties of scan conversion algorithms - Interactive raster graphics: painting model, moving parts of an image, feed back images. (5)

CURVES AND SURFACES: Parametric representation of curves - Bezier curves – B-Spline curves - Parametric representation of surfaces - Bezier surfaces - Curved surfaces - Ruled surfaces - Quadric surfaces – Concatenation of two curve segments – Order of Continuity. (7)

LIGHTING AND SHADING:Lighting model, shading model, material properties, light properties, light sources, Specifying lighting parameters - implementing a lighting model - shading of the sphere model - perfragment lighting - global illumination (4)

THREE DIMENSIONAL GRAPHICS: Viewing 3D graphical data - Orthographic, oblique, perspective projections - Hidden lines and hidden surface removal – Z-Buffer, A-Buffer, BSP Tree, Area-Subdivision algorithm. (6)

FRACTAL-GEOMETRY METHODS:Tiling the plane - Recursively defined curves - Koch curves - C curves - Dragons - Space filling curves - Fractals - Grammar based models - Graftals - Turtle graphics - Ray tracing. (4)

TUTORIAL PRACTICE:
1. Implementation of Simple transformations.
2. Implementation of Line drawing algorithms.
3. Windowing and Line Clipping.
4. Polygon clipping.
5. Implementation of an Analog Clock.
6. Polygon filling algorithms.
7. Merging of a circle and square.
8. Fractal drawing.
9. Lighting a Scene.
10. Setting material colors.

Total L: 45+T: 30=75

TEXT BOOKS:
1. Donald Hearn and Pauline Baker M, “Computer Graphics with OpenGL", Pearson Education, 2014.
2. William M. Newmannand Robert F Sproull, “Principles of Interactive Computer Graphics”, Tata McGraw Hill, 2013.

REFERENCES:
1. Edward Angel, “Interactive Computer Graphics: A Top-Down Approach using WebGL”, 7thEdition, Addison-Wesley, 2014.
2. Francis S. Hill, Stephen M. Kelley , “Computer Graphics”, Pearson, 2015.
3. David F Rogers, “Procedural Elements for Computer Graphics”, 2ndEdition, McGraw Hill, 2017.
4. John F. Hughes, James D. Foley, “Computer Graphics: Principles and Practice”, Addison-Wesley, 2014.
5. Sumanta Guha, “Computer Graphics through OpenGL – From Theory to Experiments”, CRC Press, 2015.

23XWA5 PRINCIPLES OF PROGRAMMING LANGUAGES

3 2 0 4

INTRODUCTION: The Role of Programming Languages: Toward Higher-level Languages, Problems of Scale, Programming Paradigms, Language Implementation Bridging the Gap - Language Description:- Syntactic Structure: Expression Notations, Abstract Syntax Trees, Lexical Syntax, Context -Free Grammars, Grammars for Expressions, Variants of Grammars. (9)

IMPERATIVE PROGRAMMING: Statements: Structured Programming:- The Need for Structured Programming, Syntax-Directed Control Flow, Design Considerations: Syntax, Handling Special Cases in Loops, Programming with invariants, Proof Rules for Partial Correctness, Control flow in C - Types: Data Representation:- The Role of Types, Basic Types, Arrays Sequences of Elements, Records: Named Fields, Unions and variant Records, Sets, Pointers: Efficiency and Dynamic Allocation, Two String Tables, Types and Error Checking - Procedure Activations:- Introduction to Procedures, Parameter-passing Methods, Scope Rules for Names, Nested Scopes in the Source Text, Activation Records, Lexical Scope: Procedures as in C, Lexical Scope: Nested Procedures and Pascal. (12)

OBJECT ORIENTED PROGRAMMING: Groupings of Data and Operations:- Constructs fro Program Structuring, Information Hiding, Program Design with Modules, Modules and Defined Types, Class Declarations in C++, Dynamic Allocation I C++, Templates: Parameterized Types, Implementation of Objects in C++. - Object-Oriented Programming:- What is an Object?, Object-Oriented Thinking - Objects in Smalltalk. (6)

FUNCTIONAL PROGRAMMING: Elements of Functional Programming:- A little Language of expressions, Types : Values and Operations, Function declarations, Approaches to Expression Evaluation, Lexical Scope, Type Checking - Functional Programming in a Typed Languages:- Exploring a List, Function Declaration by Cases, Functions as First-Class Values, ML: Implicit Types, Data Types, Exception Handling in M, Little quit in Standard ML - Functional Programming with Lists:- Scheme, a Dialect of Lisp, The Structure of Lists, List Manipulation, A Motivating Example: Differentiation, Simplification of Expressions, Storage Allocation for Lists. (10)

OTHER PARADIGMS: Logic Programming:- Computing with Relations, Introduction to Prolog, Data Structures in Prolog, Programming techniques, Control in Prolog, Cuts - An Introduction to Concurrent Programming:- Parallelism in Hardware, Streams: Implicit Synchronization, Concurrency as interleaving, Liveness Properties, Safe Access to Shared Data, Concurrency in Ada, Synchronized Access to Shared variables. (8)

TUTORIAL PRACTICE:
1. Language tools like LEX, YACC.
2. Inter – Intra sequence control mechanism.
3. Parameter passing mechanism in C, C++.
4. Comparing Object oriented concepts in C++, Java.
5. List Operations in Prolog.
6. Fact finding & Theorem proving in Prolog.
7. Recursive functions in Functional programming language.
8. Expression evaluation in functional programming language.

Total L: 45+T: 30=75

TEXT BOOKS:
1. Terrence W Pratt, Marvin VSelkowitz and TVGopal, “Programming Languages Design and Implementation”, Pearson Education, 2006.
2. Robert Harber, “Programming in standard ML”, Carnegie Mellon University, 2005.

REFERENCES:
1. Ravi Sethi, “Programming Languages Concepts and Constructs “, Pearson Education, 2009.
2. Robert W Sebesta, “Concepts of Programming Languages”, Pearson Education, 2009.
3. Al Kelley and Ira Pohl, “A Book on C “, Pearson Education,2009.

23XWA6 AGILE SOFTWARE DEVELOPMENT

3 2 0 4

Prerequisites:

23XW44 Software Engineering Techniques

AGILE COMPUTING: - An Introduction– The Problem with parsing experience-Three levels of listening Cooperative game of Invention and Communication-Individuals-Overcoming Failure modes-Working Better in some ways than others - Drawing on Success modes. (9)

AGILE PROCESS MODELS: – Extreme programming, ASD, DSDM, Scrum, Crystal, FDD, Agile Modeling (9)

TEAM COMMUNICATION: -Communicating and Cooperating teams – Convection currents of information-Jumping communication gaps-Teams as communities-Teams as Ecosystems (10)

AGILE METHODOLOGIES: -Agile and self-adapting-The crystal methodologies-Crystal orange web-The agile software development manifesto-The agile alliance-Peter Naur, Programming as TheoryBuilding. (12)

Case Studies (5)

TUTORIAL PRACTICE:
1. Exercise for modular development.
2. Exercise for Incremental delivery approach.
3. Development of Metaphor.
4. Exercise for proving the productivity using pair programming approach.
5. Exercise for understanding the concept of “Simple Design”.
6. Exercise to understand “Test first “technique.
7. Writing user stories.
8. Creation of vision card.
9. Writing acceptance tests.
10. Exercise for refactoring the code.

Total L: 45+T: 30=75

TEXT BOOKS:
1. Alistair Cockburn, “Agile Software Development”, Pearson Education, 2019.
2. Craig Larman, “Agile and Iterative Development”, Pearson Education, 2017.

REFERENCES:
2. Mike Cohn, “Agile Estimating and Planning”, Pearson Education,2018.

23XWA7 SOCIAL NETWORK ANALYSIS

3 2 0 4

INTRODUCTION: Motivation - different sources of network data - types of networks - tools for visualizing network data - review of graph theory basics. (9)

GRAPH THEORETIC PROPERTIES OF SOCIAL NETWORKS: Notions of centrality - Strong and weak ties – Homophily - Structural Balance. (5)

DYNAMIC PROPERTIES OF NETWORKS:Information diffusion - networks effects on information diffusion - maximizing influence spread - power law and heavy tail - preferential attachment models - small world phenomenon - cascading behavior on networks - Epidemics. (11)

BEHAVIORAL PROPERTIES ON NETWORKS:Network economics - Bargaining and power in networks - Sponsored search markets. (10)

MINING GRAPHS: Community and cluster detection: random walks - spectral methods - link analysis for web mining – Overview of social tagging and applications. (10)

TUTORIAL PRACTICE:
1. Getting acquainted with UCINET and Netdraw.
2. Implementing graph-theoretic/social network metrics using UCINET.
3. Working with Visualization, Ego networks, Centrality, Community Detection etc.

Total L: 45+T: 30=75

TEXT BOOKS:
1. David Easley and Jon Kleinberg, “Networks, Crowds, and Markets: Reasoning About a Highly Connected World”, Cambridge University Press, Cambridge, 2010.

REFERENCES:
1. Stanley Wasserman, Katherine Faust, “Social network analysis: methods and applications”, Cambridge University Press, Cambridge,1998.
2. Peter R. Monge, Noshir S. Contractor, “Theories of communication networks”, Oxford University Press, 2003.
3. Duncan J Watts. “Six degrees: the science of a connected age”, Norton, 2004.
4. Narahari, Y., Garg, D., Ramasuri, N., and Prakash, H, “Game Theoretic Problems in Network Economics and Mechanism Design Solutions”, Springer-Verlag, 2008.

23XWA8 PREDICTIVE ANALYTICS

3 2 0 4

Prerequisites:

20XW44 Software Engineering Techniques

DATA WRANGLING:DataIngest,Data Cleaning - Exploratory data analysis - Univariate data – Bivariate data, Multivariate data (5)

LINEAR REGRESSION: Coefficient of determination, Significance test, Residual analysis, Confidence and Prediction intervals (5)

MULTIPLE LINEAR REGRESSIONS:Coefficient of determination, Interpretation of regression coefficients, Categorical variables, heteroscedasticity, Multi-co linearity outliers, Auto regression and Transformation of variables, Model Building. (10)

LOGISTIC AND MULTINOMIAL REGRESSION: Logistic function, Estimation of probability using Logistic regression, Variance, Wald Test, HosmerLemshow Test, Classification Table, Gini Co-efficient. (5)

DECISION TREES:introduction, CHI-Square Automatic Interaction Detectors (CHAID), Classification and Regression Tree (CART), Analysis of Unstructured data. (5)

FORECASTING:Moving average, Exponential Smoothing, Casual Models. (7)

TIME SERIES ANALYSIS:Moving Average Models, ARMA, ARIMA models, Multivariate Models. (8)

Case Studies (5)

TUTORIAL PRACTICE:
Implementation of the following problems using Statistical Packages:
1. Classification and tabulation of data and Graphical and diagrammatic presentation of data.
2. Perform calculations that measure the central tendency and dispersion of data and Implementation of measures of Skewness, moments and kurtosis.
3. Determination of point and interval estimates.
4. Solving linear regression, polynomial regression and non-linear regression based problems and solving multiple regression and correlation analysis based problems.
5. Solving the problems based on Time series analysis and forecasting and implementing statistical quality control charts.

Total L: 45+T: 30=75

TEXT BOOKS:
1. Max Kuhn Kjell Johnson, “Applied Predictive Modeling”, Springer, 2014.
Thomas W.Miller, “Modeling Techniques in Predictive Analytics with Python and R: A guide to Data Science”, Pearson Education, 2014.

REFERENCES:
1. Richard A. Johnson, Irwin Miller and John Freund, “Probability and Statistics for Engineers”, Pearson Education, 2014.
2. Ronald E. Walpole, Raymond H. Meyers, Sharon L. Meyers, “Probability and Statistics for Engineers and Scientists”, Pearson Education, 2014.

23XWA9 SECURITY IN COMPUTING

3 2 0 4

Prerequisites:

23XW44 SOFTWARE ENGINEERING TECHNIQUES

SECURITY BASICS:Overview of security principles –Threats - Attacks - vulnerabilities - Services and Mechanisms - Classical cryptosystem – Symmetric key and Asymmetric key cryptosystem – AES and RSA – Attacks on RSA- Data Integrity - Hashing - Properties- Digital signatures – DSS algorithm. (7)

AUTHENTICATION AND ACCESS CONTROL: Types of authentication – Challenge response protocol –Fiat Shamir protocol - Zero knowledge protocol – Access control - models (4)

SOFTWARE SECURITY AND TRUSTED SYSTEMS:Malicious and non-Malicious programs – Buffer overflows - Fast flux - Covert channels -Defense mechanisms – Operating system security -Security Policies - Types - Trusted Computing - Trusted OS design -Virtualization security -containers (6)

OPERATING SYSTEM SECURITY:Windows security - Understanding User Authentication- Securing Access with Permissions - Unix Security Overview -Achieving Unix Security - Protecting User Accounts and Strengthening Authentication - Limiting Super user Privileges - Securing Local and Network File Systems - Network Configuration - Hardening Linux and Unix. (8)

SECURITY AT NETWORK LAYER:Network layer threats and security controls –Security problems in TCP/IP protocol suite – DNS Cache poisoning - IPSec – modes – security protocols – SA – Internet key exchange (6)

ETHICAL HACKING AND PENETRATION TESTING: Principles of Intrusion detection – types– Architecture - Intrusion Detection and response - Network penetration testing- reconnaissance – scanning- Exploitation (8)

WEB APPLICATION SECURITY:Email security – PGP –– S/MIME – Web Security – Cross site scripting – SQL injection attacks – Defense methods- Session integrity for web applications- SSL Architecture – Secure session management-- Session hijacking - securing a web server (6)

TUTORIAL PRACTICE:
1. Design of a Client server application for a basic cryptosystem .
2. Performing a frequency analysis attack on a cipher text enciphered with Affine cipher.
3. Detection of a Buffer overflow attack .
4. Packet Sniffing using Wireshark Tool to perform the traffic analysis attack.
5. Generation of keys using pseudorandom generators.
6. Implementation of RSA cryptosystem .
7. Key distribution using RSA( KDC) – Key hacking .
8. Key exchange using Diffie- Hellman technique – MITM attack .
9. Authentication of File transfer using Hashing / Message digest.
10. Digital signature, generation and verification.
11. Password authentication .
12. Transaction security using SQL Injection attacks.
13. Security testing for applications.
14. Packages using the concepts of IPSec, SSL and Query control.

Total L: 45+T: 30=75

TEXT BOOKS:
1. Roberta Bragg, Mark Rhodes, Keith Strass Berg J, “Network Security - The complete reference”, Tata McGraw Hill, 2017.
2. John r. Vacca , “Network and system security”, Syngress Elsevier, 2014.
3. Patrick Engebretson, David Kennedy, “The Basics of Hackingand Penetration Testing”, Syngress Elsevier, 2013.
4. DarrilGibson , “Microsoft Windows Security Essentials”, Wiley, 2011.

REFERENCES:
1. William Stallings, “Cryptography and Network Security: Principles and Practice”, Pearson Education, 2014.
2. Charles P. Pfleeger and Lawrence Pfleeger,”Security in Computing”, Pearson Education, 2006 .
3. Jaegar A, “Operating Systems Security – Synthesis Lectures on Information Security, Privacy and Trust”, Morgan & Claypool Publishers, 2008.
4. Matt Bishop, “Introduction to Computer Security”, Pearson Education, 2009.

23XWAA CLOUD COMPUTING

3 2 0 4

Prerequisites:

23XW57 JAVA PROGRAMMING LAB

23XW68 DISTRIBUTED ENTERPRISE COMPUTING

LINUX SYSTEM: Design Principles – Kernel Modules – Process Management Scheduling – Memory Management – Input-Output Management – File System – Interprocess Communication. iOS and Android: Architecture and SDK Framework – Media Layer – Services Layer – Core OS Layer. (6)

OVERVIEW OF SYSTEM CALLS:anatomy of a system call and x86 mechanisms for system call implementation - MMU/memory translation, segmentation, and hardware traps interact - create kernel-user context separation – virtualization. (6)

THE KERNEL EXECUTION AND PROGRAMMING CONTEXT: Live debugging and tracing – Hardware and software support for debugging – Dtrace: programming, implementation/design, internals – Kprobes and SysTrace: Linux catching up. (7)

LINKING AND LOADING:Executable and Linkable Format (ELF) – Internals of linking and dynamic linking – Internals of effective spinlock implementations on x86. (4)

PROCESS AND THREAD KERNEL DATA STRUCTURES: process table traversal – lookup, allocation and management of new structures - /proc internals – optimizations. (4)

VIRTUAL FILE SYSTEM AND THE LAYERING OF A FILE SYSTEM CALL FROM API TO DRIVER: Object-orientation patterns in kernel code – a review of OO implementation generics. (8)

KMEM AND VMEM ALLOCATORS:OO approach to memory allocation – Challenges of multiple CPUs and memory hierarchy – Overview of the kernel network stack implementation – Path of a packet through a kernel – Berkeley Packet Filter architecture – Linux Netflter architecture.

TUTORIAL PRACTICE:
case studies

Total L: 45+T: 30=75

TEXT BOOKS:
1. Robert Love, “Linux System Programming”, O’Reilly, 2013.

REFERENCES:
1. Yang Lixiang, Liang Wenfeng, “The Art of Kernel Linux design”, CRC Press, 2016
2. Rami Rosen, “Linux Kernel Networking : Implementation and Theory”, Apress, 2014.

23XWAB STATISTICAL LEARNING

3 2 0 4

Prerequisites:

20XW53 MACHINE LEARNING

THEORETICAL FOUNDATIONS: Review of Statistical Inference, Review of Probability, Testing of Hypothesis – Introduction to Function Spaces – Vector Spaces - Metric Spaces – Cauchy Sequence – Complete Metric Space – Normed Space, Inner Product Space – Banach Space - Hilbert Space – Sobolev – Examples - Mercer Kernels - Reproducing Kernel Hilbert Space (RKHS), Concentration of Measure : Measures of Complexity - Rademacher Complexity. (10)

LINEAR REGRESSION: Simple, Multiple, Other Considerations in the Regression Model – Resampling Methods – Cross-Validation, Bootstrap – Linear Model Selection & Regularisation – Subset Selection, Shrinkage Methods – Ridge, Lasso, Dimension Reduction Methods. (8)

NON-LINEAR REGRESSION: Polynomial Estimators, Step Functions, Basis Functions, Regression Spline, Smoothing Splines, Local Regression, Generalised Additive Models. (4)

LINEAR CLASSIFICATION: Review of Classification Models, Logistic Regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Comparison of Classification Methods. (6)

TREE BASED METHODS: Regression Trees, Classification Trees,Bagging, Random Forests, Boosting. (9)

SUPPORT VECTOR MACHINES:Maximal Margin Classifier – Support Vector Classifiers - Support Vector Machines – Non-linear Decision Boundaries – SVMs with more than 2 classes. (4)

UNSUPERVISED LEARNING:Principal Components Analysis – Clustering Methods – K-Means Clustering, Hierarchical Clustering. (4)

TUTORIAL PRACTICE:
Solve the following problems using R 1. Simple Regression, Multiple Regression, Ridge Regression and Lasso Regression.
2. Non-linear Regression, Splines and Additive Models
3. Linear Classification,
4. Tree based methods
5. Support Vector machines
6. Clustering Methods

Total L: 45+T: 30=75

TEXTBOOKS:
1. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, “An introduction to Statistical learning”, Springer, 2013.
2. Trevor Hastie, Robert Tibshirani, Jerome Friedman, “Elements of Statistical Learning: Data Mining, Inference and Prediction”, Springer, 2013.

REFERENCES:
1. Vladimir N Vapnik, “Statistical learning theory”, Wiley, 1998.
2. Robert Schapire, Yoav Freund, “Boosting : Foundations and Algorithms”, The MIT Press, 2012.
3. Yutaka Yamamoto, ‘From Vector Spaces to Function Spaces : Introduction to Functional Analysis with Applications’, SIAM, 2012.

23XWAC APPLIED GRAPH THEORY

3 2 0 4

INTRODUCTION: Graphs and digraphs, graph models, matrix representations, Hand-shaking lemma, degree sequence, Havel-Hakimi theorem, subgraphs, walk, trail, path, connectedness, distance, radius, diameter, Common families of graphs, isomorphic graphs. Trees - spanning trees, characterizations, Matrix tree theorem, counting labelled trees. (9)

CONNECTIVITY: Vertex and edge cuts, blocks, Vertex and edge connectivity, relationship between vertex and edge connectivity. Whitney’s theorem, Characterizations of 2-connected graphs, Menger’s theorem - Harary’s construction of optimal k-connected graphs. Connectivity in digraphs. (7)

MATCHING, VERTEX COLORING AND DOMINATION: Maximum Matching - Perfect matching - augmenting path - Bipartite matching - Hall’s theorem - Job assignments - Edmonds’ Blossom Algorithm. Proper vertex-coloring, chromatic number, upper and lower bounds, Brook’s and Welsh-Powell theorems. Sequential and Largest degree first vertex coloring algorithms. Dominating set - total, independent, bipartite, connected, distance dominations, domination number and bounds. (11)

EULERIAN AND HAMILTONIAN GRAPHS: Eulerian trails, characterizations, Hierholzer’s algorithm, Route inspection problem. Hamiltonian cycle, Gray codes, Dirac’s and Ore’s conditions, Travelling salesperson problem. (8

PLANAR GRAPHS: Properties, Kuratowski’s theorem, (Statement) Hopcroft Tarjan Planarity testing algorithm. (5)

RANDOM GRAPHS:Random graph – Definitions of G(n, p) and G(n, M) models, power law degree distribution, Web graph models, applications to real world networks. (5)

Total L: 45+T: 30=75

TEXT BOOKS:
1. Bondy J A, Murty U S R, “Graph Theory”, Springer, 2013.
2. Alan Frieze, Michal Karonski, “Introduction to Random Graphs”, Cambridge University Press, 2016.
3. Douglas B West, “Introduction to Graph Theory”, Pearson, 2018.

REFERENCES:
1. Balakrishnan R, Ranganathan K, “A Textbook of Graph Theory”, Springer, 2019.
2. Jonathan Gross, Jay Yellen, Mark Anderson, “Graph Theory and its Applications”, Chapman and Hall/CRC Press, 2019.
3. Thulasiraman K, Swamy M N S, “Graphs: Theory and Algorithms”, Wiley, 2014.

23XWAD WIRELESS NETWORKS

3 2 0 4

Prerequisites:

20XW24 OBJECT ORIENTED PROGRAMMING

WIRELESS NETWORK OVERVIEW:Wired and wireless Networks- Effect of mobility on systems- Introduction to wireless technologies- RF Overview - Wireless Signal Propagation-Signal-to-Noise Ratio – Modulation - ISM Spectrum - Frequency Hopping Spread Spectrum (FHSS) - Direct Sequence Spread Spectrum (DSSS)-Orthogonal Frequency Division Multiplexing (OFDM) - Coordination mechanisms and MAC protocols for multi-user network access. (8)

MOBILE NETWORK LAYER : IEEE 802.11 System architecture, protocol architecture, 802.11b, 802.11a - WPAN WPA2- WEP - Bluetooth – IEEE 802.15.4Zigbee Wireless data networks- 6LoWPAN -GPRS architecture. (9)

WIRELESS THREATS AND RISKS: Introduction - Mobile IP: IP packet delivery, Agent discovery, tunneling and encapsulation, IPV6-Network layer in the internet- Mobile IP session initiation protocol - mobile ad-hoc network: Routing: Destination Sequence distance vector, IoT: CoAP, MQTT – DHCP – Mobile TCP (12)

FUTURE TRENDS: merging WLAN Related Technologies – 802.16 – 802.20 – 802.22 – UWB, Cognitive Radios, RFID – 4G and Data Communications Convergence. 4G features and challenges - Applications of 4G – 4G Technologies: Multicarrier Modulation, Smart antenna techniques, IMS Architecture, LTE, Advanced Broadband Wireless Access and Services -

TUTORIAL PRACTICE :
1. Simulation of a IEEE 802.11 LAN under various conditions using chosen simulator.
2. Simulation of a priority MAC protocol using chosen simulator.
3. Simulation of different routing protocols using simulators.
4. Simulation of TCP over error-prone wireless network using simulator.
5. Development of Mobile application using blue tooth

Total L: 45+T: 30=75

TEXT BOOK:
1. Siva Ram Murthy C, Manoj B S , "Ad Hoc Wireless Networks: Architectures and Protocols", Prentice Hall, 2017
2. William Stallings, “Wireless Communication and Networks”, Pearson Education, 2016.
3. Gary. S. Rogers and John Edwards, “An Introduction to Wireless Technology”, Pearson Education, 2012.
4. KavehPahlavan, Prashant K. Krishnamurthy, “Principles of Wireless Networks : A Unified Approach”, John Wiley, 2011.
5. Vijay.K. Garg , "Wireless Communication and Networking", Morgan Kaufmann Publishers, 2017.

REFERENCE:
1. Dharma Prakash Agrawal and Qing-An Zeng, “Introduction to Wireless and Mobile Systems”, Thomson Press, 2007.
2. Feng Zhao and LeonidasGuibas, “Wireless Sensor Networks-An Information Processing Approach”, Elsevier, 2004.
3. Ivan Stojmenovic, “Handbook of Wireless Networks and Mobile Computing”, John wiley, 2006.
4. SavoGlisic, “Advanced Wireless Communications 4G Technologies”, Wiley Publications, 2006.

23XWAE NETWORK FORENSICS

3 2 0 4

Prerequisites:

23XW32 DATABASE MANAGEMENT SYSTEM

INTRODUCTION:Footprints - Concepts in Digital Evidence - Network Forensics Investigative Methodology (OSCAR) - Sources of Network-Based Evidence - Evidence Acquisition (6)

TRAFFIC ANALYSIS:Protocol Analysis - Packet Analysis - Flow Analysis – Higher Layer Traffic Analysis (8)

STATISTICAL FLOW ANALYSIS: Process Overview – Sensors - Flow Record Export protocols - Collection and Aggregation – Analysis. (7)

NETWORK INTRUSION DETECTION AND ANALYSIS:Why Investigate NIDS/NIPS? -Typical NIDS/NIPS Functionality - Modes of Detection - Types of NIDS/NIPSs - NIDS/NIPS Evidence Acquisition - Comprehensive Packet Logging - Snort (9)

EVENT LOG AGGREGATION, CORRELATION, AND ANALYSIS: Sources of Logs - Network Log Architecture - Collecting and Analyzing Evidence – Switch Evidence – Router Evidence – Firewall Evidence (6)

WEB PROXIES:Why Investigate Web Proxies? - Web Proxy Functionality - Evidence - Squid - Web Proxy Analysis - Encrypted Web Traffic (9)

TUTORIAL PRACTICE :
1. Analysis of the packets and flow analysis using Wireshark and tshark.
2. Analysis of higher level protocols like DHCP, DNS, SMTP
3. Familiarize with various tools like netflow, silk for flow analysis
4. Familiarize with Network Intrusion detection tools like Snort
5. Log analysis and event correlation
6. Web proxy analysis.

Total L: 45+T: 30=75

TEXT BOOKS:
1. Davidoff, Sherri, and Jonathan Ham, “Network forensics: tracking hackers through cyberspace”, Prentice hall, 2012.

REFERENCES:
1. “Investigating Network Intrusions and Cybercrime”, EC Council, 2016.
2. Jessy Bullock, Jeff Parker, “Wireshark for Security Professionals: Using Wireshark and the Metasploit Framework” , Wiley, 2017.
3. Bejtlich, Richard, “The practice of network security monitoring: understanding incident detection and response”, No Starch Press, 2013.

23XWAF REINFORCEMENT LEARNING PROBLEM

3 2 0 4

Prerequisites:

23XW53MACHINE LEARNING

23XW62 ARTIFICIAL INTELLIGENCE

REINFORCEMENT LEARNING PROBLEM :Interactive ML - Sequential decision making –- RL Framework - Exploration Exploitation Dilemma - Goals and Rewards. (3)

MULTI ARM BANDITS:K Armed Bandit problem – Definition – Uses – MAB Algorithms: Greedy, UCB, PAC – Thompson Sampling - Contextual Bandits. (6)

MARKOV DECISION PROCESS (MDP):Markov Reward Process – Definition of MDP - POMDP – Returns - Policies - Value functions – Bellman Equations - Optimality in MDP. (5)

EXACT SOLUTION METHODS: Dynamic Programming:Policy Evaluation – Policy Improvement - Value Iteration, Asynchronous DP- Efficiency of DP - Stochastic DP ;Monte Carlo: Model based Vs Model free approaches - Policy Evaluation - Policy Improvement- On-policy and Off- policy Monte Carlo controls - Incremental implementation – Temporal Difference Learning: TD-prediction- Optimality of TD – SARSA - Q-Learning – Expected SARSA – n-step TD- Eligibility traces. (13)

FUNCTION APPROXIMATION:Tabular methods Vs Parameterized functions - Value function approximation – Stochastic gradient methods - Linear methods – Artificial Neural Network – Deep Q Networks, Experience Replay – Lazy learning. (10)

POLICY GRADIENT METHODS: Policy Approximation - REINFORCE algorithm, Estimating gradients, Actor-Critic methods. (5)

PLANNING AND LEARNING:Model and planning – Dyna Q - Prioritized sweeping - Heuristic search: Monte Carlo Tree search (3)

TUTORIAL PRACTICE:
1. N-armed Bandit problem – Recommender systems.
2. Frozen lake problem – Monte Carlo methods
3. Focused Crawler – TD approaches
4. Solving GridWorld problems.
5. Games – Atari, AlphaGo.

Total: L:45

TEXT BOOKS:
1. Sutton R. S. and Barto A. G, "Reinforcement Learning: An Introduction", MIT Press, 2018.
2. Lattimore, T. and Szepesvári C,” Bandit Algorithms”, Cambridge University Press, 2018.
3. Dimitri P. Bertsekas, "Reinforcement Learning and Optimal Control”, Athena Scientific, 2019.
4. CsabaSzepesvári, “Algorithms for Reinforcement Learning”, Morgan & Claypool, 2010.

REFERENCES:
1. Masashi Sugiyama, “Statistical Reinforcement Learning: Modern Machine Learning Approaches”, CRC Press, Taylor & Francis Group, 2015
2. Stuart Russell and Peter Norvig, “Artificial Intelligence: A Modern Approach”, Pearson, 2020

23XWAH ADVANCED ALGORITHM DESIGN AND ANALYSIS

3 2 0 4

Prerequisites:

23XW15 C PROGRAMMING

23XW45 OPERATING SYSTEMS

INTRODUCTION:Fundamentals of Algorithmic Problem solving- Logic and Proving techniques- Mathematical induction (3)

STRING ALGORITHMS:Naïve algorithm, Rabin Karp algorithm, Knuth-Morris-Pratt algorithms, complexity analysis (4)

NETWORK ALGORITHMS AND MATCHING:Flow networks and flow-Network with multiple sources and working with flows, The Ford-Fulkerson’s Method, Max flow min cut-reducing bipartitie matching to network flow (4)

COPING WITH NP:NP completeness and intractrability: Polynomial time reductions- satsifiability-reduction from satisfiability to indepenedent set, satisfiability to Hamiltonian cycle-Co NP and Assymetry of NP (4)

RANDOMIZED ALGORITHMS:Introduction to random numbers, Random guess and performance- quick select, linearity of expectation, Indicator random variables - Las Vegas and Monte-Carlo algorithms - Karger’s min cut- randomized quick sort- randomized data structures-Treaps, skiplist, hashing (11)

ONLINE ALGORITHMS :Introduction – Competitive analysis- randomized Paging – k server problem (4)

APPROXIMATION ALGORITHMS:Introduction- approximation factor-2 approximation for vertex cover- set cover log(n) approximation – 2 approximation for travelling salesman problem – Strong NP hardness and Psuedo polynomial time algoirhtms –knapsack (10)

PARAMETERIZED ALGOWITHMS:Introduction – vertex cover problem (4)

TUTORIAL PRACTICE:
1. Problems on string algorithms.
2. Problems related to Topological sorting and connectedness.
3. Problems related to network flow and their real time applications.
4. Randomized quick sort implementation.
5. Problems on randomized hash tables.
6. Problems on online algorithms.
7. Solving problems using parallel algorithms

Total L: 45+P: 30=75

TEXT BOOK:
1. Thomas H.Cormen, Charles E Leiserson, and Ronald L Rivest, “Introduction to Algorithms” , MIT Press, Cambridge, 2009.
2. Motwani R and Raghavan P, “Randomized Algorithms”, Cambridge University Press, Cambridge, 2013.
3. Vijay Vazirani, Approximation Algrorithms” Springer, 2010.

REFERENCES:
1. Anany Levitin, “ Introduction to design and analysis of algorithm”, Pearson Education, New Delhi, 2016.
2. Sanjoy Dasgupta, Christos Papadimitriou, Umesh Vazirani ,“Algorithms”,Tata McGraw Hill, New Delhi, 2008.
3. David P Williamson, David B Shmoys, “The Design of Approximation Algorithms”, Cambridge University Press, 2011.
4. Rolf Niedermeier, “Invitation to Fixed-Parameter Algorithms”, Oxford Univ. Press, 2006.

23XWO1 PRINCIPLES OF MANAGEMENT AND BEHAVIOURAL SCIENCES

3 2 0 4

PRINCIPLES OF MANAGEMENT: Meaning, Definition and Significance of Management, Basic Functions of Management – Planning, Organizing, Staffing, Directing and Controlling. Organizational Environment – Social, Economic, Technological and Political. Corporate Social Responsibility - Case discussion (8)

INDUSTRIAL AND BUSINESS ORGANIZATION: Growth of Industries (Small Scale, Medium Scale and Large Scale Industries). Forms of Business Organizations. Resource Management – Internal and External Sources. (7)

ORGANIZATIONAL BEHAVIOUR: Significance of OB, Impact of culture on organization. Role of leadership and leadership styles. Personality and Motivational Theories. Attitudes, Values and Perceptions at work - Case discussion (7)

GROUP BEHAVIOUR: Group dynamics, Group formation and development, group structure and group cohesiveness. Informal organization – Sociometry – Interaction analysis - Exercises (8)

HUMAN RESOURCE MANAGEMENT: Objectives and Functions, Selection and Placement, Training and Development – Conflict management – Stress management - Human resource management in global environment - Human resource information system(HRIS) - Case discussion. (10)

Total L:45

TEXT BOOKS:
1. Harold Koontz, Heinz Weihrich and Ramachandra Aryasri, “Principles of Management”, Tata McGraw Hill, 2014.
2. Mamoria CB, “Personnel Management”, Sultan Chand & Sons, 2005.

REFERENCES:
1. John W Newstrom and Keith Davis, “Organizational Behavior”, Tata McGraw Hill, 2010.
2. Stephen P Robbins, ”Organisational behavior”, Prentice Hall, 2010.
3. Errene Leela Rout and Nelson Omiko, “Corporate Conflict Management”, PHI Learning Pvt. Ltd., Delhi, 2014.

23XWO2 ENTERPRENEURSHIP

3 2 0 4

INTRODUCTION TO ENTREPRENEURSHIP:Definition – characteristics and functions of an entrepreneur – common myths about entrepreneurs, importance or entrepreneurship. Creativity and innovation - role of creativity, innovation process, sources of new ideas, methods of generating ideas, creative problem solving, entrepreneurial process. (10)

FORMS OF BUSINESS ORGANIZATION:Sole proprietorship, partnership, limited liability partnership, joint stock companies and cooperatives, starting a small-scale industry.Developing an Effective Business Model -importance and component of an effective business model. (11)

APPRAISAL OF PROJECTS: Importance of evaluating various options and future investments- entrepreneurship incentives and subsidies, appraisal techniques. Financing the new venture - determining financial needs, sources of financing, equity, and debt funding - Evaluating financial performance. (12)

THE MARKETING FUNCTION:Industry analysis, competitor analysis, marketing research for the new venture – defining the purpose or objectives, gathering data from primary and secondary sources, analyzing and interpreting the results, the marketing process. (7)

INTELLECTUAL PROPERTY PROTECTION AND ETH ICS:Patents, copyright, trademark, geographical indications – ethical and social responsibility - challenges. Case Studies. (5)

TUTORIAL PRACTICE:
1. Case studies

Total L: 45+T: 30=75

TEXT BOOKS:
1. Donald F.Kuratko and Richard M.Hodgetts, “Entrepreneurship”, South-Western, 2003.
2. The Dynamics of Entrepreneurial Development and Management, Vasant Desai, Himalaya Publishing House, 2010.

REFERENCES:
1. S.L.Gupta, Arun Mittal, “Entrepreneurship Development”, International Book House, 2012.
2. G. S. Sudha, “Management and Entrepreneurship Development”, Indus Valley Publication, 2009.
3. V. Badi, N. V. Badi , Business Ethics, R, Vrinda Publication, 2012.
4. Prasanna Chandra Projects- Planning, Analysis, Financing, Implementation andreview, TATA McGraw Hill, 2012.

23XWO3 ENVIRONMENTAL SCIENCE AND GREEN COMPUTING

3 2 0 4

NATURAL RESOURCES, ECOSYSTEMS AND BIODIVERSITY: Environment, Definition, Scope and importance, Forest resources, Use and overexploitation, Water resources: Use and over utilization. Eco system; Structure and functions of an eco system, energy flow in the eco system. Bio Diversity; values of biodiversity, biodiversity at global, national and local levels – threats to bio diversity. Conservation of bio diversity – In-situ & Ex-situ conservation. (9)

ENERGY SOURCES: Growing energy needs, Renewable and non renewable energy sources, Hydro power, Solar Power: Photovoltaic Energy – Motivation for going Solar – Solar Electricity – PV cells. Wind Power: – Using the Wind: Generating Power at Remote Sites,– Measuring the Wind – Estimating the output. Use of alternate energy sources. (9)

SOCIAL ISSUES AND THE ENVIRONMENT: From unsustainable to sustainable development, Urban problems related to energy, Water conservation, Rain water harvesting, Watershed management, Environment and human health, Role of information technology in environment and human health. Environment Protection Act: Air (Prevention and Control of Pollution) Act – Water Act, Forest Conservation Act, Wildlife Protection Act, Introduction to EIA and ISO 14000. (9)

ENVIRONMENTAL POLLUTION AND DISASTER MANAGEMENT: Definition – causes, effects and control measures of air pollution, water pollution, soil pollution, noise pollution, thermal pollution and nuclear hazards. Disaster management - floods, earthquake, cyclone and landslides. Solid waste management - causes, effects and control measures of municipal solid wastes (Biomedical wastes, hazardous wastes). Role of an individual in prevention of pollution. (9)

GLOBAL ATMOSPHERIC CHANGE& GREEN FUNDAMENTALS: The Atmosphere of Earth – Global Temperature – Global Energy Balance , The Greenhouse Effect - Environmental Issues and Green Computing, Electronic waste management: Introduction;- Environment and society, producer responsibility legislation – the Waste Electrical and Electronic Equipment (WEEE) directive, Materials Composition of WEEE: Mobile Phones – Television – Washing Machines, - Current and new electronic waste recycling technology- Future perspectives of electronic scrap. (9)

TUTORIAL PRACTICE:
1. Case Studies

Total L: 45+T: 30=75

TEXT BOOKS:
1. Mackenzie L. Davis, and David A. Cornwell, “Introduction to Environmental Engineering”, Tata McGraw Hill, 2010.
2. Chetan Singh Solanki, “Solar Photovoltaics”, PHI, 2011.
3. Siraj Ahmed, “Wind Energy : Theory and Practice”, PHI, 2011.
4. Mahajan S. P. Pollution Control in Process Industries, Tata McGraw Hill, 1985.
5. R. E. Hester and R. M. Harrison, “Electronic Waste Management”, Royal Society of Chemistry, 2009.

REFERENCES:
1. William W. Nazarodd and Lisa Alvarez-Cohen, “Environmental Engineering Science”, Wiley-India, 2010
2. AnubhaKaushik and Kaushik C P, “Environmental Science and Engineering”, New Age International, 2005.
3. Martha Maeda, “How to Solar Power your Home”, Atlantic Publishing Group, 2011.
4. Paul Gipe, “Wind Power – Renewable Energy for Home, Farm and Business”, Sterling Hill Publications, 2008.
5. Klaus Hieronymi, RamzyKahhat, Eric Williams, “E-Waste Management : From Waste to resource”, Routledge – Taylor and Fransis, New York, 2012.
6. Diane GowMcdilda, “The Everything Green Living Book”, Adams Media, 2007.

23XWO4 MATHEMATICAL MODELLING

3 2 0 4

INTRODUCTION TO MODELING: Modeling process, Overview of different kinds of model. (6)

EMPIRICAL MODELING WITH DATA FITTING: Error function, least squares method; fitting data with polynomials and splines.(4)

CAUSAL MODELING AND FORECASTING: Introduction, Modeling the causal time series, forecasting by regression analysis, predictions by regression. Planning, development and maintenance of linear models, trend analysis, modeling seasonality and trend, trend removal and cyclical analysis, decomposition analysis. Modeling financial time series. Econometrics and time series models. Non seasonal models: ARIMA process for univariate and multivariate. (8)

PORTFOLIO MANAGEMENT Simple market models, risk-free assets, risky assets, discrete time market model. Portfolio - Introduction, risk and return of a portfolio. Portfolio with two-securities- Risk and expected return, risk-reward analysis, asset pricing models, portfolio optimization, Markowitz model and efficient frontier method, Capital Asset Pricing Models (CAPM). (11)

MODELING WITH BIOINFORMATICS: Introduction, Biological data- types, mode of collection, documentation and submission. Sequence alignment- Definition, significance, dot matrix method, dynamic programming- Global and local alignment tools, scoring matrices and gap penalties. Multiple sequence alignment: Iterative methods. Hidden Markovian models, statistical methods, position specific scoring matrices. (15)

TUTORIAL PRACTICE:
1. Implementing curve fitting techniques for data and error analysis
2. Causal models for financial time series data- trend analysis, seasonality indices identification, cyclical analysis
3. Implementing Portfolio optimization models- Risk award analysis,
4. Markowitz model and efficient frontier method for determining optimal portfolios.
5. Implementation of capital asset pricing model
6. Multiple sequence alignment using Hidden Markovian models.

Total L: 45+P: 30=75

TEXT BOOKS:
1. Giordano F R, Weir M D and Fox W P,“A First Course in Mathematical Modeling”, Brooks/Cole, 2014.
2. Richard I.Levin, David S. Rubin ,Sanjay Rastogi and Massod Husain Siddiqui, “Statistics for Management”,Pearson,2014
3. David W. Mount, “Bioinformatics Sequence and Genome Analysis”, Cold Spring Harbor Laboratory, 2005
4. Capinski M. and ZastawniakT,“Mathematics for Finance: An Introduction to Financial Engineering”, Springer, 2011

REFERENCES:
1. Hamdy A Taha, “Operation Research- An Introduction”, Pearson, 2022
2. Christoffersen P, “Elements of Financial Risk Management”, Academic Press, 2012.
3. Alexander Isaev, Introduction to Mathematical Methods in Bioinformatics, Springer, 2006

23XWO5 COMPUTATIONAL FINANCE

3 2 0 4

SIMPLE MARKET MODEL: Basic notions and Assumptions, No-Arbitrage Principles, one step Binomial model,risk and return, Forward contracts, Call and Put options. (7)

RISK FREE ASSETS: Time Value of Money- Simple interest , periodic compounding, streams of payments ,continuous compounding, Comparison of compounding methods. Money market- Zero- Coupon bonds, coupon bonds. (7)

PORTFOLIO THEORY: Introduction - Portfolio theory with matrix algebra - Review of constrained optimization methods, Markowitz algorithm, Markowitz Algorithm using the solver and matrix algebra – Portfolio choice and linear pricing – Statistical analysis of efficient portfolios. Sharpe’s single index model. (11)

BASIC OPTIONS THEORY: Definitions – Pay off diagrams – Single period binomial options theory – Multi period binomial options theory – Real options – American options, Simulation methods for options pricing – Random variable generation – simulation of stochastic processes. Black Schole’s formula. (11)

CONTINUOUS TIME MODEL: Limitations of discrete models, Continuous time limit- choice of N-step Binomial model. Black Scholes model. (8)

TUTORIAL PRACTICE:
1. Problems using Capital Asset Pricing model. 2. Problems using Auto correlation. 3. Plot time series data and find outliers 4. Monte Carlo Simulation of options pricing 5. Finding minimum variance portfolio 6. Finding optimal portfolio 7. Implementation Cox-Ross Rubinstein Formula

Total L: 45+T:30 = 75

TEXTBOOKS: 1, Capinski M. and Zastawniak T, “Mathematics for Finance: An Introduction to Financial Engineering”, Springer, 2011.
2. Sheldon M. Ross “ An elementary introduction to Mathematical Finance”, Cambridge university,2011
3. David Ruppert, and David S. Matteson “Statistics and Data Analysis for Financial Engineering”, Springer,2015.
REFERENCES:
1. Simon Benninga, “Financial Modeling”, MIT Press, 2014.
2. Edwin J. Elton, Martin J. Gruber, Stephen J. Brown and William N. Goetzmann “Modern Portfolio Theory and Investment Analysis”, John Wiley, 2014.

23XWO6 ETHICAL HACKING

3 2 0 4

INTRODUCTION TO HACKING: Introduction to Hacking – Importance of Security – Elements of Security – Phases of an Attack –Types of Hacker Attacks – Hacktivism – Vulnerability Research – Introduction to Footprinting – Information Gathering Methodology –Footprinting Tools – WHOIS Tools – DNS Information Tools – Locating the Network Range – Meta Search Engines. (10)

SCANNING AND ENNUMERATION: Introduction to Scanning – Objectives – Scanning Methodology – Tools – Introduction to Enumeration – Enumeration Techniques – Enumeration Procedure – Tools. (7)

SYSTEM HACKING: Introduction – Cracking Passwords – Password Cracking Websites – Password Guessing – Password Cracking Tools – Password Cracking Counter measures – Escalating Privileges –Executing Applications – Keyloggers and Spyware. (9)

VULNERABILITY ANALYSIS: Vulnerability Assessment Concept - Life-Cycle - Vulnerability Assessment Solutions – Vulnerability Scoring Systems - Vulnerability Scanning - Nessus Tool - Windows OS Vulnerabilities – Tools for Identifying Vulnerabilities – Countermeasures – Linux OS Vulnerabilities – Tools for Identifying Vulnerabilities – Countermeasures. (10)

SOCIAL ENGINEERING AND DENIAL-OF-SERVICES:Social Engineering – Concepts - Phases of a Social Engineering Attack - Types of Social Engineering- Insider Attack- The process of Identity theft- Social Engineering Countermeasures – DoS/DDoS – Attack Techniques - Basic Categories - Botnets - Other DDoS Attack tools - countermeasure Strategies (9) (9)

TUTORIAL PRACTICE:
1. Demonstration of ARP, IP and DNS Spoofing attacks using open-source tools like Arpspoof, Synner, and dns-spoof.
2. Exploration of vulnerabilities in the TCP / IP protocols using open-source testbed:
ARP cache poisoning,
SYN flooding attack,
TCP RST attack and
TCP session hijacking attack.
3. Information Gathering about target system using Nmap, DNSRecon, recon-ng, Nikto, and Malgeto.
4. Scanning the target host to find the vulnerabilities using Nessus, OpenVAS, Burp suite and Retina.
5. Demonstration of signature based IDS with Snort and Suricata.
6. Vulnerability Exploitation and gaining access into the target host using Metasploit, Armitage, SQLmap, and Social Engineering Toolkit.
7. Maintaining the access with target system with Netcat, Meterpreter, backdoor Factory.

Total L: 45 + T: 30 = 75

TEXT BOOKS:
1. Ec-Council, “Ethical Hacking and Countermeasures: Attack Phases”, Delmar Cengage Learning, 2009
2. Michael T. Simpson, Kent Backman, James E. Corley, “Hands-On Ethical Hacking and Network Defense”, Cengage Learning, 2012

REFERENCES:
1. Patrick Engebretson, “The Basics of Hacking and Penetration Testing – Ethical Hacking and Penetration Testing Made Easy”, Syngress Media, 2013.
2. Jon Erickson, “Hacking: The Art of Exploitation”, No Starch Press, 2008.
3. RafayBaloch, “Ethical Hacking And Penetration Testing Guide”, CRC Press, Taylor & Francis Group,2015

23XWO7 NATURAL LANGUAGE PROCESSING

3 2 0 4

INTRODUCTION:Analysis in NLP: morphological – syntactic, semantic - pragmatic – Applications – Morphological parsing - Regular expressions - Finite state automata - Finite state transducer - Syntax Analyser - context free grammar, CYK Parser, Earley Parser - Probabilistic CFG - Dependancy Parsing - Semantic Analysis –Lexical semantics - Syntax directed semantic analysis – semantic similarities – word net based similarity - Word sense disambiguation, Machine learning based approaches (15)

PART OF SPEECH TAGGING (POS):Rule based, HMM based POS tagger, Brill Tagger, Maximum entropy model, Maximum Entropy Markov model (10)

LANGUAGE MODELS : N gram models - Smoothing Techniques, Laplacian, Add-One, Good Turing, Interpolation, Backoff - Evaluating language models – Perplexity (6)

MACHINE TRANSLATION: Rule base translation - Statistical machine translation – Parameter learning in IBM models using EM - Neural Machine translation (4)

INFORMATION EXTRACTION:Named Entity Recognition (NER) – Relation Extraction - Natural Language generation – Topic modelling using Latent Dirichlet Allocation (LDA), Non-Negative Matrix Factorization (NMF) (5)

DEEP LEARNING ARCHITECTURES: Attention models - Transformers - BERT - Applications - Question answering , Classification, Summarization, Chatbot . (5)

TUTORIAL PRACTICE:
1. Sentiment analysis and classification using n gram models, RNN andLSTM
2. Document classification / Radiology reports classification using RNN and LSTM
3. Visualization of text data
4. POS tagging on text data using HMM
5. Language modeling using n gram models
6. Machine translation using Deep learning and HMM
7. Optical character recognition using
8. Word sense disambiguation

Total L: 45+P: 30=75

TEXT BOOKS:
1. Daniel Jurafsky and James H. Martin, “Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition”, Prentice Hall, ,2023.
2. Philipp Koehn , “Statistical Machine Translation”, Cambridge University Press, 2010.
3. Jacob Eisenstein, Introduction to Natural language processing, The MIT Press, 2019.

REFERENCES:
1. Christopher Manning and HinrichSchütze, “Foundations of Statistical Natural Language Processing”, MIT Press,2008.
2. James Allen, “Natural Language Understanding”, Addison Wesley,1995.

23XWO8 INTERNET OF THINGS

3 2 0 4

Prerequisites:

23XW35 MICROPROCESSOR AND EMBEDDED SYSTEMS

23XW35 MICROPROCESSOR AND EMBEDDED SYSTEMS

INTRODUCTION TO IoT AND ARCHITECTURE: Introduction to Internet of Things (IoT) – Machine to Machine (M2M) –- Features and Definition of IoT– Recent Trends in the Adoption of IoT- IoT ARCHITECTURE: Functional Requirements - IoT Enabling Technologies –Basic Architecture Components of IoT: Embedded Computation Units, Microcontrollers, System on Chip (SoCs) - Sensors – Actuators – Communication Interfaces (8)

IOT DATA COMMUNICATION: IPv6 - Sensor Networks- RFID – NFC - Low Power Personal Area networks (Low PAN): Overview, 6LowPAN - IEEE 802.15.4, BLE, Zigbee, -LPWAN: Concepts and features, SigFox, LoRaWAN, LPWAN-3GPP, - SDN for IoT - Comparing different LPWAN technologies (10)

APPLICATION LAYER PROTOCOLS IN IoT: CoAP: Architecture- Features, Applications - MQTT: Architecture, Feature, Applications - AMQP – Rabbit MQ -Comparing different IoT Application Layer Protocols- design of web application using RESTful web API (6)

IOT DATA MANAGEMENT: Cloud platforms for IoT - Sensor cloud – Fog /Edge Computing - Data Storage and retrieval – database connectivity with IoT devices- MySQL, NoSQL, NewSQL- Data Analysis using IPython – Visualization and interpretation of data (10)

PROTOTYPING AND APPLICATIONS: Prototyping embedded devices - Open source vs Closed source – Arduino and Raspberry Pi Implementation - Smart Homes - Energy - Health care - Smart transportation - Smart grid - Smart cities - Smart Agriculture (4))

SECURITY IN IoT ENABLED DEVICES: Security attacks in IoT- malware propagation and control in IoT- privacy preservation – Authentication in IoT – Authorization in IoT- OAuth 2.0 in IoT - Blockchain in IoT security (7)

TUTORIAL PRACTICE:
1. Smart Home automation using Raspberry PI and Arduino
2. IoT cloud platforms
3. ThingSpeak API and MQTT
4. Interfacing ESP 8266 with Web services
5. Connected Vehicle applications
6. IoT Based Fall Detection system
7. Smart Energy Meter Monitoring
8. IoT Based safety alarm System
9. IoT security applications

Total L: 45+P: 30=75

TEXT BOOKS:
1. Milan Milenkovic, ”Internet of Things: Concepts and system design”, Springer , 2021
2. Mayur Ramgir, “Internet of Things: Architecture, Implementation and Security”, Pearson, 2019
3. Vijay Madisetti ans Arshdeep Bagha,” Internet of Things( A hands on Approach)”, VPT, 2014
4. Honbo Zhou,” The internet of tings in the Cloud: A middleware perspective”, CRC Press, 2012

REFERENCES:
1. Simone Cirani, Gianluigi Ferrari,Internet of Things,Architectures, Protocols and Standards,John Wiley , 2019
2. ohammad Ilyas,The Handbook of Ad Hoc Wireless Networks, CRC Press,2017
3. Dieter Uckelmann, Mark Harrison, Florian Michahelles, “Architecting the Internet of Things”, Springer, 2011.
4. Adrian McEwen, Hakim Cassimally, “Designing the Internet of Things”, John Wiley, 2014

23XWO9 VIRTUAL AND AGUMENTED REALITY

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Prerequisites: