24X101 CALCULUS AND ITS APPLICATIONS

4 0 0 4

FUNCTIONS AND CONTINUITY: Functions - Graphs of standard functions — Limits and continuity - Piecewise continuous functions - Periodic functions — Fundamental theorem of Calculus - Application problems. (14)

FUNCTIONS OF TWO VARIABLES: Partial derivatives - Geometrical interpretation - Saddle points - Taylor's series - Maxima and minima - Constrained maxima and minima - Lagrange multiplier method — Applications. (10)

INTEGRAL CALCULUS: Multiple integrals — Change of order of integration - Application of multiple integrals in finding area and Volume. (12)

ORDINARY DIFFERENTIAL EQUATIONS: Linear Differential Equations of first order - Exact differential equations - Integrating factors - Linear Differential Equations of second order with constant coefficients - Method of variation of parameters — Modeling:Mass-spring systems, electrical circuits- (14)

VECTOR CALCULUS: Vector differentiation - Gradient, Divergence, Curl and directional derivatives — Vector Integration - Line and surface integrals - Green's, Gauss divergence and Stoke's theorems (Statement only). (10)

Total: 60

TEXT BOOKS:
1. Joel R. Hass, Christopher E Heil and Maurice D- Weir "Thomas Calculus", Pearson Education, 2022
2. Erwin Kreyszig, "Advanced Engineering Mathematics", John Wiley and Sons, 2017.

REFERENCES:
1. Riley K. F., Hobson M.P. and Bence S.J., "Mathematical Methods for Physics and Engineering", Cambridge University Press, 2022.
2. Ray Wylie C and Louis C_ Barrett, "Advanced Engineering Mathematics", McGraw Hill Education, 2017.
3. Lial, Hungerford, Holcomb and Mullins, "Mathematics with Applications", Pearson Education, 2023.

24X102 DISCRETE MATHEMATICS

4 0 0 4

MATHEMATICAL LOGIC: Proposition, Logical operators, System specifications, Propositional Equivalences, Predicates and Quantifiers, Rules of inference, Validity of arguments. (13)

RELATIONS: Definition and properties of binary relations, Representing Relations, Closures of Relations, Composition of Relations, Equivalence Relations, Partitions and Covering of Sets, Partial Orderings. (12)

RECURRENCE RELATIONS: Some Recurrence Relation Models- Solutions of linear homogeneous recurrence relations with constant coefficients- solution of linear non-homogeneous recurrence relations by the method of characteristic roots. (10)

GRAPHS AND TREES: Graphs and digraphs, graph models, matrix representations, Hand-shaking lemma, degree sequence, subgraphs, Common families of graphs, isomorphic graphs. Trees - spanning trees, characterizations, Matrix tree theorem. (13)

GROUP THEORY: Groups, Subgroups, generators, Cosets and Lagrange's theorem, permutation groups, Homomorphism & Isomorphism — Application problems. (12)

Total: 60

TEXT BOOKS:
1. Kenneth H Rosen, "Discrete Mathematics and its Application", McGraw Hill, 2021.
2. Tremblay J P and Manohar R, "Discrete Mathematical Structures with application to Computer Science", McGraw Hill,2017.

REFERENCES:
1. Bernard Kolman, Robert C Busby and Sharon Ross, "Discrete Mathematical Structures", Pearson, 2015.
2. Bondy J A and Muny U S R, "Graph Theory", Springer, 2021
3. Ralph P Grimaldi, "Discrete and Combinatorial Mathematics — An Applied Introduction", Pearson, 2019.

24X103 C PROGRAMMING

4 0 0 4

PROBLEM SOLVING: Introduction to Problem Solving- Program development- Analyzing and Defining the Problem- Modular Design — Algorithm - Flow Chart - What is a programming language- Types of programming language- Program Development Environment. (5)

C LANGUAGE: Introduction to C Language - C character set - Identifiers and Keywords - Data Types - Constants - Variables - Arrays - Declarations - Expressions - Statements - Symbolic constants - Operators and Expressions - Library Functions - Data Input and Output Functions. (7)

CONTROL STATEMENTS: While Statement - Do While Statement- For Loop - Nested Loop - If Else - Switch - Break - Continue- Comma Operator — Goto Statement - (6)

FUNCTIONS: Defining Function - Accessing a Function - Passing Arguments to Functions - Specifying Arguments Data Types - Function Prototypes - Storage Classes - Auto - Static - Extern and Register Variables. (8)

ARRAYS: Defining Array — Processing array - Passing array to a function - Multi-dimensional array - Array and strings. (6)

POINTERS: Declarations - Pointers to a function - Pointers and one-dimensional arrays - Operating a pointer - Pointer and multi-dimensional arrays - arrays of pointers - passing functions to other functions. (10)

STRUCTURES AND UNIONS: Definition of Structure and Union - Processing a structure — Bit field representations - Structures and pointers - Passing structure to functions - Self-referential structures — Nested structure. (7)

FILES: File Structure concepts introduction - Definitions, concept of record, file operations: Storing, creating, retrieving, updating Sequential, relative, indexed and random access mode, Files with binary mode(Low level), performance of Sequential Files — Operations on Files — Types of Files, Various input and output functions on Files. (7)

Enumerated Data Type — Typedef - Preprocessor Directives - Command Line Arguments. (4)

Total L: 60

TEXT BOOKS:
1. Kemighan B. W. and Ritchie D. M., "C Programming Language (ANSI C)", Prentice Hall, 2023.
2. Deitel H. M. and Deitel P. J., "C How to Program", Prentice Hall, 2023.

REFERENCES:
1. Herbert Schildt, "C The Complete Reference", Tata McGraw Hill, 2017.
2. Dey, Pradip, and Manas Ghosh. "Programming in C." Oxford University Press, 2018.
3. Gottfried Byron, "Programming with C", Tata McGraw Hill, 2018.

24X104 DIGITAL ELECTRONICS

3 0 0 3

NUMBER SYSTEMS AND CODES: Introduction to decimal, binary, octal, hexadecimal number systems - Interconversions of Number Systems - Binary Codes: BCD, Excess-3, Gray, ASCII Codes - 2’s complement addition, subtraction - BCD addition, Error detection and correction - Hamming code generation - Single bit error detection. (9)

LOGIC GATES AND FAMILIES: AND, OR, NOT, NAND, NOR, Exclusive-OR and Exclusive-NOR - Implementations of Logic Functions using Universal gates - Programmable logic – Basics concepts of the AND array. (9)

BOOLEAN ARITHMETIC AND THEOREM: Boolean laws and theorems - Boolean expressions – Minimization - Sum of Products (SOP) - Product of Sums (POS) - Minterm - Maxterms - Canonical forms - Karnaugh map minimization - two, three and four variable Karnaugh maps - Quine-McCluskey-NAND-NAND realizations. (9)

COMBINATIONAL LOGIC CIRCUITS: Half Adder and Full Adders – Half Subtractor and Full Subtractors - Encoders – Decoders – Multiplexers – Demultiplexers – PAL, PLA Logics. (9)

SEQUENTIAL LOGIC CIRCUITS AND REGISTERS: Flip-flops - SR, D, T, JK Flip Flops, Counters - Asynchronous counters - Synchronous counters - Up and Down counters, Ring counters – Shift Registers - Parallel/serial in/out shift registers, applications. (9)

Total L: 45

TEXT BOOKS:
1. V.K Mehta & Rohit Mehta “Principles of Electronics” S. Chand & Company Ltd.,2020.
2. Thomas L Floyd. “Electronic devices”, Prentice-Hall”, 9th Edition., 2012

REFERENCES:
1. Morris Mano M., “Digital Design”, Prentice Hall of India Private Limited, 2012.
2. D.P. Leach, A. P. Malvino, Goutam Guha, “Digital Principles and Applications”, Tata McGraw Hill, New Delhi, 2018.
3. Charles H. Roth, Jr, Larry L. Kinney “Fundamentals of Logic Design”, Cengage Learning,2014.

24X105 ENGLISH

3 0 0 3

VOCABULARY BUILDING: The concept of Word Formation: Compounding, Backformation, Clipping, Blending - Root words from foreign languages and their use in English - Acquaintance with prefixes and suffixes from foreign languages in English to form derivatives - Synonyms, antonyms, and standard abbreviations: Acronyms. (5)

GRAMMAR: Identifying Common Errors in English – Tenses – Modal auxiliary verbs - Subject-verb agreement - Noun-pronoun agreement - Articles – Prepositions – Word order - Different types of sentences: simple, compound, complex, Idioms and Phrases, Transformation of sentences: Active and Passive voice. (6)

READING COMPREHENSION: Developing Reading Skills like Skimming and Scanning for information, Critical Reading, Inferential, Cognition, and analytical Skills - appropriate reading texts to be used from general, scientific, and literary genres. (9)

BASIC WRITING SKILLS: Importance of proper punctuation - Creating coherence: Arranging paragraphs & Sentences in logical order - Creating Cohesion: Organizing principles of paragraphs - Techniques for writing precisely - Nature and Style of sensible Writing: Describing, Defining, Classifying - Writing introduction and conclusion - Precis Writing - Essay Writing - Writing Letters: Formal, Informal - Writing E-mail. (9)

LISTENING SKILLS: Understanding listening - Listening Techniques - Listening short comprehension passages - Conversational practice in both social and professional contexts. (6)

PRACTICALS: Oral presentation - Short speeches and conversation Practice - Listening integrated tasks. (10)

Total L: 45

TEXT BOOK:
1. Shoba, K N and Lourdes Joavani Rayen., “Communicative English: a workbook”. New Delhi: Cambridge University Press, 2021.

REFERENCES:
1. Sanjay Kumar and Pushp Lata., “Communication Skills”, New Delhi: Oxford University Press, 2018.
2. Means, L. Thomas., “English & Communication for Colleges”, Delhi: Cengage Learning India Private Limited, 2017.
3. Shoba, K N and Praveen Sam, D., “Technical English: a workbook”, New Delhi: Cambridge University Press, 2019.
4. Utthamkumar, N “Communicative English: Text Book”, Sahana Publishers, Coimbatore, 2022.

24X106 C PROGRAMMING LABORATORY

0 0 4 2

1. Simple programs to understand the concepts of data types.
2. Familiarizing conditional, control and repetition statements.
3. Usage of single and double dimensional arrays including storage operations.
4. Implementation of functions, recursive functions.
5. Defining and handling structures, array of structures and union.
6. Implementation of pointers, operation on pointers dynamic storage allocation.
7. Creating and processing data files.

Total P:60

24X107 WEB DESIGN LABORATORY

0 0 4 2

Exercises pertaining to the following concepts are to be implemented by following good design considering principles and Search Engine Optimization (SEO).

1. HTML formatting for images, text including list and link.
2. Practice on HTML table element.
3. Practice on Menu, iframe and page layout.
4. Create a User interface using forms with audio and video.
5. Create an impressive web page using internal/external CSS.
6. Bootstrap commands.
7. Responsive web page creation using java Script.
8. Practice Java script control structure, functions, and event handlers.
9. A complete web site development using Angular JS framework .

Total P: 60

24X108 DIGITAL ELECTRONICS LABORATORY

0 0 4 2

1. Verification of logic gates.
2. Implementation of Binary to Gray and Gray to Binary code converter.
3. Realization of Boolean expression using logic gates.
4. Construction and verification of Adders and Subtractors using logic gates.
5. Implementation of Encoder and Decoder.
6. Construction of Multiplexers and Demultiplexers.
7. Construction of different types of Flip-flops.
8. Implementation of Synchronous counter and Asynchronous counter.

Total P: 60

24X201 LINEAR ALGEBRA

3 2 0 4

Prerequisites:

LINEAR SYSTEMS: System of linear equations - Consistent and inconsistent systems - Geometric interpretation of linear system in 2 and 3 unknowns - Row reduction and Echelon forms – Gauss Elimination method - Gauss Seidel method - Gauss Jacobi method - Applications of linear systems. (7+4)

VECTOR SPACES: Vector spaces and subspaces - Linear Combination of vectors - Linear Span of vectors - Linear independent and dependent vectors - Basis and dimension of a vector space - Change of basis. (9+5)

LINEAR TRANSFORMATIONS: Introduction, Kernel and range, - Rank and nullity theorem - Linear Transformation from Rn to Rm, Matrices of linear transformations - Row space, Column space, Null space and Range Space - Similarity - Isomorphism. (10+7)

INNER PRODUCT SPACES: Inner products - Length and Angle in inner product spaces - Orthonormal bases - Gram Schmidt process - Orthogonal matrices - QR decomposition - Best Approximation and Least-squares. (10+7)

EIGEN VALUES AND EIGEN VECTORS: Eigen values and Eigen vectors - Diagonalization - Symmetric Matrices - Orthogonal Diagonalization – Singular Value Decomposition – Applications. (9+7)

Total L: 45 + T: 30 = 75

TEXT BOOKS:
1. Howard Anton., “Elementary Linear Algebra”, Academic Press Inc, 2022.
2. David C. Lay., “Linear Algebra and Its Applications”, Pearson Education, 2021.

REFERENCES:
1. Gilbert Strang., “Linear Algebra and its Applications”, Thomson Learning, 2017.
2. Steven J. Leon and Lisette de. Pillis., “Linear Algebra with Applications”, Pearson, 2020.

24X202 PROBABILITY THEORY

3 2 0 4

Prerequisites:

COMBINATORICS: The pigeonhole Principle - The inclusion and exclusion principle - The basic principle of counting - Permutations - Combinations - The binomial theorem - Multinomial coefficients. (7+4)

PROBABILITY BASIC CONCEPTS: Sample space and events - Axiomatic approach to probability - Conditional probability - Law of multiplication - Law of total probability and Bayes’ theorem - Independent events. (8+5)

RANDOM VARIABLES: Discrete and continuous random variables - Probability mass and density function - Distribution function - Expectation and variance – Theoretical distributions Discrete : Bernoulli, Binomial, Poisson and Geometric distributions – Continuous: Uniform, exponential, normal and Weibull. (13+9)

BIVARIATE DISTRIBUTIONS: Joint probability mass function - Joint probability density function - Marginal and conditional distributions - Independent random variables - Conditional expectation - Moments and moment generating functions - Sums of independent random variables. (10+6)

LIMIT THEOREMS: Limit theorems: Markov and Chebyshev inequalities, Law of large numbers, Central Limit Theorem. (7+6)

Total L: 45 + T: 30 = 75

TEXT BOOKS:
1. Saeed Ghahramani., “Fundamentals of Probability with Stochastic Processes”, Pearson Education, 2018.
2. Sheldon M. Ross., “A First Course in Probability”, Pearson Education, 2022.
3. Robert B. Ash., “Basic Probability Theory” Dover Publication, 2022.

REFERENCES:
1. Joseph K. Blitzstein and Jessica Hwang., “Introduction to Probability”, CRC Press, 2019.
2. Jay L. Devore., “Probability and Statistics for Engineering and the Sciences”, Cengage Learning, 2020.
3. Y.A.Rozanov., “Probability Theory - A Concise Course” Dover Publications, 2013.

24X203 COMPUTER ARCHITECTURE

3 0 0 3

Prerequisites:

INTRODUCTION: Elements of a Computer system – Jon Von Newmann Architecture, Harvard Architecture - RTL. (3)

ALU DESIGN: Arithmetic micro operations – Logic micro operations – Shift micro operations – Arithmetic, logic, shift units (6)

CONTROL UNIT: Computer Instructions - Instruction codes- Instruction Cycle –Timing & Control – Types of Instructions – Memory Reference, Register Reference , Input & Output, Interrupt Instructions. (10)

CPU DESIGN: General Register organization–Stack Organization–Instruction formats–Addressing modes. (7)

INPUT OUTPUT ORGANIZATION: I/O devices and interface – Asynchronous data transfer- Modes of data transfer- Direct Memory Access (DMA) – I/O processor. (9)

MEMORY ORGANIZATION: Memory hierarchy – Main memory – Auxiliary memory – Associative memory – Cache memory - Virtual memory. (10)

Total L: 45

TEXT BOOKS:
1. Morris Mano., “Computer Systems Architecture”, Prentice Hall, 2022.
2. William Stallings., “Computer Organization & Architecture Designing for Performance”, Pearson Education, 2021.

REFERENCES:
1. Hamachar V.C, Vranesic Z.G and Zaky S.G., “Computer Organization”, Tata McGraw Hill, 2023.
2. David A. Patterson and John L. Hennessy., “Computer Organization and Design: The Hardware/Software Interface”, Elsevier, 2020.

24X204 DATA STRUCTURES

3 0 0 3

Prerequisites:

INTRODUCTION: Primitive Data structures - Abstract data Types - Analysis of Algorithms. (4)

ARRAYS: Operations –Implementation - Sparse matrices – Operations. (5)

STACKS: Primitive operations - sequential implementation - Applications: Functions - Recursion – Postfix Evaluation, Parentheses matching – Infix to postfix conversion. (4)

QUEUES: Primitive operations - Sequential implementation –Circular Queue - Priority Queues - Dequeue – Applications. (5)

LISTS: Primitive Operations - Singly linked lists, doubly linked lists, Circular lists, Multiply linked lists - Applications: Addition of Polynomials; Sparse Matrix representation – Linked stacks - Linked queues. (7)

TREES: Terminologies - implementation - Binary Tree: Properties - sequential and linked representation - binary tree operations - traversals - Expression trees - Infix, Postfix and Prefix expressions. (7)

GRAPHS: Representations - Depth First Search – Breadth First Search – Dijikstra’s algorithm. (5)

HASH TABLE: Introduction – Operations – Implementation – Hash Function – Successful and Unsuccessful search - Collision Resolution handling. (4)

SORTING AND SEARCHING: Insertion sort, Bubble sort, Selection sort, Radix sort, Linear search and Binary search. (4)

Total L: 45

TEXT BOOKS:
1. Michael T. Goodrich, Roberto Tamassia, and David M. Mount., “Data Structures and Algorithms in C++”, John Wiley, 2017.
2. Sahni Sartaj., "Data Structures, Algorithms and Applications in C++", Silicon Press, 2011.

REFERENCES:
1. Nell Dale., “C++ Plus Data Structures”, Jones & Bartlett, 2011.
2. Mark Allen Weiss., “Data Structures and Algorithm Analysis in C”, Pearson Education, 2017.
3. Robert L. Kruse, Bruce P. Leung and Clovin L. Tondo.,“Data Structures and Program Design in C”, Pearson Education, 2013.
4. Aaron M. Tanenbaum, Moshe J. Augenstein and YedidyahLangsam., "Data structures using C and C++", Pearson Education, 2010.

24X205 OBJECT ORIENTED PROGRAMMING WITH C++

3 0 0 3

Prerequisites:

PRINCIPLES OF OBJECT-ORIENTED PROGRAMMING: Software crisis, Software Evolution - Procedure Oriented Programming - Object Oriented Programming Paradigm - Basic Concepts and Benefits of OOP - Object Oriented Programming Language - Application of OOP - Structure of C++ - Tokens, Expressions and Control Structures -Operators in C++ - Manipulators. (6)

FUNCTIONS IN C++: Function Prototyping - Call by Reference - Return by reference - Inline functions - Default, Const Arguments - Function Overloading - Classes and Objects - Member functions - Nesting of Member functions – Private member functions - Memory allocation for Objects - Static data members - Static Member Functions - Arrays of Objects -Objects as Function Arguments - Friend Functions - Returning Objects - Const Member functions - Pointers to Members and Function pointers. (10)

CONSTRUCTORS: Parameterized Constructors - Multiple Constructors in a Class - Constructors with Default Arguments -Dynamic Initialization of Objects - Copy and Dynamic Constructors – Destructor overloading. (5)

OPERATOR OVERLOADING: Overloading Unary and Binary Operators - Overloading Binary Operators using Friend functions – Operator Type conversion (4)

INHERITANCE: Defining Derived Classes - Single Inheritance - Making a Private Member Inheritable - Multiple Inheritance -Hierarchical Inheritance - Hybrid Inheritance - Virtual Base Classes - Abstract Classes - Constructors in Derived Classes -Member Classes - Nesting of Classes – Composition – Aggregation (9)

POLYMORPHISM: Basics of polymorphism – Types of polymorphism - Compile and Run Time Polymorphism – Virtual functions – Object Slicing – Virtual Destructor – Dynamic binding (4)

TEMPLATES & EXCEPTION HANDLING: Introduction to Templates, Generic Functions and Generic Classes – Exception Handling – Examples. (3)

STREAMS: String I/O -Character I/O - Object I/O - I/O with multiple Objects - File pointers - Disk I/O with member function. (4)

Total L: 45

TEXT BOOKS:
1. Bjarne Stroustrup., “The C++ Programming Language”, Pearson Education, 2022.
2. Stanley B Lippman, Josee Lajoie and Barbara E Moo., “The C++ Primer”, Pearson Education, 2022.

REFERENCES:
1. Harvey M Deitel and Paul J Deitel., “C++ How to Program”, Prentice Hall, 2017.
2. Herbert Schildt., “C++ - The Complete Reference”, Tata McGraw Hill, 2017.

24X206 PYTHON PROGRAMMING LABORATORY

0 0 4 2

INTRODUCTION: Python interpreter – Program execution – Interactive prompt – IDLE User Interface.

TYPES AND OPERATIONS: Python object types – Numeric types – Dynamic typing – String fundamentals – Lists – Dictionaries – Tuples – Type objects.

CONTROL STATEMENTS: Python statements – Assignments – Expressions – if Tests – while Loops – for Loops – Iterations – Comprehensions.

FUNCTIONS AND GENERATORS: Function basics – Scopes – Arguments – Recursive functions – Anonymous functions – lambda – Generator functions.

OBJECT-ORIENTED DESIGN: Inheritance – Polymorphism

STANDARD PACKAGES: NumPy – Pandas – Matplotlib.

FILES: Opening files – Reading and writing files – Text files – Binary files.

Implementation of the following problems using Python:
1. Testing basic coding skills in Python using data types, control statements and iteration.
2. Implementing Python data structures like lists, tuples, dictionaries, and sets.
3. General programming concepts such as functions, strings, regular expressions, reading / writing files and exceptions.
4. Implement object-oriented concepts.
5. Packaging programs into reusable libraries.
6. Usage of mathematical Libraries like Matplotlib.
7. Creating and processing data files using Pandas.
8. Use libraries for numerical programming and data visualization – Numpy and Matplotlib etc.

REFERENCES:
1. Tony Gaddis, “Starting out with Python”, Pearson, 2021.
2. Christian Hill, “Learning Scientific Programming with Python”, Cambridge University Press, 2021.
3. John M. Stewart, “Python for Scientists”, Cambridge University Press, 2019.
4. Kent D. Lee, “Python Programming Fundamentals”, Springer, 2017.
5. Allen Downey, “Python for Software Design”, Cambridge University Press, 2023.

Total P: 60

24X207 DATA STRUCTURES LABORATORY

0 0 4 2

Implementation of the following
1. Sparse Matrix operations using arrays.
2. Arrays operations.
3. Implementation of Stacks using Arrays.
4. Parenthesis matching using stack.
5. Conversion of infix expression to postfix expression and evaluation.
6. Queues using array.
7. Linked Lists: Singly linked, Doubly linked and Circular lists and applications.
8. Linked Stacks and Queues.
9. Tree traversals.
10. Breadth First Search and Depth First Search.
11. Hash Table with collision handling.
12. Sorting and Searching algorithms.

Total P: 60

24X208 OBJECT ORIENTED PROGRAMMING WITH C++ LABORATORY

0 0 4 2

Exercises pertaining to the following outlines are to be experimented using C++:
1. Creating and processing array of objects of a class.
2. Usage of static member to count the number of instances of a class.
3. Illustration of the need of default arguments and function overloading.
4. Creation of a class having read-only member function and processing the objects of that class.
5. Initializing the object of a class using constructor and destroying the same using destructor.
6. Illustration of a data structure using dynamic objects.
7. Usage of a function to perform the same operation on more than one data type.
8. Creation of a class with generic data member.
9. Overloading the operators to do arithmetic operations on objects.
10. Overloading stream operators and creation of user-defined manipulators.
11. Acquisition of the features of an existing class and creation of a new class with added features in it.
12. Implementation of run time polymorphism.
13. Implementation of derived class which has direct access to both its own and public members of the base class.
14. Implementation of streams to store and maintain Library system, with the features of Book Issue and Book Return.
15. Usage of C++ Standard Template Library (STL) for effective implementation.

Total P: 60

24X301 STATISTICAL METHODS

3 2 0 4

Prerequisites:

DESCRIPTIVE STATISTICS: Frequency Distributions - Bar chart, Pie chart, Histogram, Ogive, Pareto chart, Stem-and-Leaf plot, Scatter plot - Central tendencies - Mean, median and mode – Measures of dispersion - Range - Quartile deviation - Mean deviation - Standard Deviation – Moments, Skewness and Kurtosis. (8+5)

ESTIMATION: Sampling distribution - Type of Estimates: Point estimation, interval estimation - Criteria of a good estimator – Interval estimation of mean, proportion, and variance (single sample and two samples) - Maximum likelihood estimator - Incorporating priors with Bayesian inference, Bayesian inference for Normal distributions. (11+7)

TESTING HYPOTHESES: Errors in hypothesis testing - One-and two-tailed tests - Tests concerning mean, proportion, and variance - Tests for goodness of fit and independence of attributes. (10+7)

CORRELATION AND REGRESSION: Introduction - Estimation using the regression line - Correlation analysis -Limitations, errors, and caveats of using regression and correlation analysis - Multiple regression analysis. (9+6)

ANALYSIS OF VARIANCE: Introduction to design of experiments, Analysis of variance - Completely Randomized Design and Randomized Block Design. (7+5)

Total L: 45 + T: 30 = 75

TEXT BOOKS:
1. Douglas C. Montgomery and George C. Runger., “Applied Statistics and Probability for Engineers”, John Wiley & Sons, Inc, 2018.
2. Ronald E. Walpole, Raymond H. Meyers and Sharon L. Meyers., “Probability and Statistics for Engineers and Scientists”, Pearson Education, 2016.
3. Jay L. Devore., “Probability and Statistics for Engineering and Sciences”, Cengage Learning, 2020.

REFERENCES:
1. Kandethody M. Ramachandran and Chris P. Tsokos., “Mathematical Statistics with Applications”, Elsevier Academic Press, 2018.
2. David R. Anderson, Dennis Sweeney, Thomas A. Williams, Jeffrey D. Camm and James J. Cochran., “Statistics for Business and Economics”, Cengage Learning, 2017.
3. Richard I. Levin and David S. Rubin., “Statistics for Management”, Pearson Education, 2017.

24X302 DATABASE MANAGEMENT SYSTEMS

3 0 0 3

Prerequisites:

BASIC CONCEPTS: Introduction to databases – Conventional file processing – Purpose of database system – Characteristics of database approach – Advantages of using DBMS – Database concept and schema architecture – Data Abstraction – Data Models – Instances and Schema – Data Independence – Components of a DBMS – Database Languages-Database users (6)

DATA MODELING: Introduction – Entities, attributes, relationships – structural constraints – Weak and Strong entity types - Design of Entity Relationship data models (ERD) – Generalization – Aggregation – Conversion of ERD into Relational database-Introduction to Network data model and Hierarchical data model. (6)

RELATIONAL MODEL: Introduction to Relational Data Model – Basic concepts – Enforcing data Integrity constraints – Relational Algebra Operations – Extended Relational Algebra Operations. (5)

RELATIONAL DATABASE MANIPULATION: Introduction to Structured Query Language (SQL) – SQL Commands for defining Database, Constructing database, Manipulations on database – Basic data retrieval operations – Advanced Queries in SQL – Functions in SQL – Aggregation – Categorization – Updates in SQL – Views in SQL – PL/SQL Basics – Procedures – Functions – Triggers. (8)

FILE ORGANIZATION: Storage device characteristics – Constituents of a file – Operations on file – Serial files – Sequential files – Index sequential files – Indexing using Tree Structures. (5)

DATA BASE DESIGN THEORY: Relational Data base design process – Anomalies in a database – Functional dependencies – Axioms – Normal forms based on primary keys – Second Normal form, Third Normal form, Boyce – Codd Normal form. (8)

TRANSACTION PROCESSING: The Concept of a Transaction – ACID properties – Transactions and schedules – Concurrent execution of transactions – Security and Integrity threats. (7)

Total L: 45

TEXT BOOKS:
1. Silberschatz A., Korth H. and Sudarshan S., “Database System Concepts”, Tata McGraw Hill, 2021.
2. Elmasri R. and Navathe S.B., “Fundamentals of Database Systems”, Pearson Education, 2021.

REFERENCES:
1. Date C. J., Kannan A., and Swamynathan S., “An Introduction to Database Systems”, Pearson Education, 2009.
2. Raghu Ramakrishnan and Johannes Gehrke., “Database Management System (Digitized)”, Tata McGraw Hill, 2014.
3. Hector Garcia-Molina, Jeffrey D. Ullman and Jennifer Widom., “Database Systems: The Complete Book”, Pearson Education, 2013.

24X303 MICROPROCESSORS AND MICROCONTROLLERS

3 2 0 4

Prerequisites:

INTRODUCTION TO MICROPROCESSORS: Introduction to Microprocessors – Architecture of 8086 – Memory Segmentation- Addressing modes. (6)

ASSEMBLY LANGUAGE PROGRAMMING: Instruction Set Architecture (ISA) -Instruction format - 8086 Arithmetic and Logic Instructions – Control flow instructions and program structures - Array processing- String processing - Assembler directives - Procedures and Macros. (12)

INTERFACING CONCEPTS: I/O Interfacing concepts - Interfacing I/O devices– 8255 Programmable Peripheral Interface - 8237 DMA Controller. (6)

INTERRUPT SYSTEMS: Introduction - Types of interrupts – Priorities of interrupt – Interrupt Instructions in 8086 - Implementing Interrupt schemes in 8086 processors – 8259 Programmable Interrupt Controller. (7)

MICROCONTROLLER ARCHITECTURE: Introduction- Architecture of 8051 – Special Function Registers (SFRs) - I/O pins ports and circuits – Instruction Set-Addressing modes – Assembly Language Programming. (7)

INTERFACING MICROCONTROLLER: Programming 8051 timers - Serial port programming –Interrupts programming – LCD and keyboard interfacing – ADC, DAC & Sensor interfacing – External Memory interface. (7)

TUTORIALS PRACTICE:
1. Study of 8086 Emulator Tool
2. Familiarizing with Instruction Set
3. Number System Conversions
4. Exercises on Arithmetic, Logical and Branching Operations
5. Implementation of Control Structures
6. Programs using Arrays
7. Implementations of String Functions
8. Programs using Special Instructions
9. Programs Using INT 10h, 21h Functions
10. Package Implementation

Total L: 45 + T: 30 = 75

TEXT BOOKS:
1. Walter A. Triebel and Avtar Sing., “8088 and 8086 Microprocessors Programming”, Pearson Education, 2017.
2. Walter A. Triebel and Kenneth J Ayala., “The 8088 and 8086 Microprocessors Programming, Interfacing, Software, Hardware and Applications”, Pearson Education, 2018.
3. Mohamed Ali Mazidi, Janice Gillispie Mazidi and Rolin McKinlay., “The 8051 Microcontroller and Embedded Systems: Using Assembly and C”, Second Edition, Pearson education, 2018.

REFERENCES:
1. Yu-Cheng Liu and Glenn A.Gibson., “Microcomputer Systems: The 8086 / 8088 Family - Architecture, Programming and Design”, Prentice Hall of India, 2018.
2. Douglus V. Hall., "Microprocessors and Interfacing", Tata McGraw Hill, 2017.
3. Barry B. Brey., "The Intel Microprocessors - 8086/88, and 80186, 80286, 80386, and 80486", Prentice Hall, 2018.

24X304 OPERATING SYSTEMS

3 0 0 3

Prerequisites:

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. (4)

PROCESS AND THREADS: Relationship between process and threads – Thread States – Thread Synchronization – Types of Thread – Multithreading model. (3)

PROCESS SCHEDULING: Scheduling basics - CPU-I/O interleaving- (non-)preemption - context switching- Types of Scheduling – Scheduling Criteria - Scheduling Algorithms – Algorithm evaluation – Real-time scheduling. (5)

PROCESS SYNCHRONIZATION: 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. (7)

DEADLOCK: Principles - Characterization – Methods for handling deadlock - Deadlock prevention, Avoidance, Detection, and recovery. (2)

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. (7)

VIRTUAL MEMORY MANAGEMENT: Need for Virtual Memory management – Demand Paging –Copy on write -Page Fault handling - Page replacement - Frame allocation- Thrashing - working set model. (3)

I/O MANAGEMENT AND DISK SCHEDULING: 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. (5)

FILE SYSTEM MANAGEMENT: Files – Access methods - File system architecture – 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 Essentials”, John Wiley,2021.
2. William Stallings., “Operating Systems: Internals and Design Principles”, Pearson Education, 2021.
3. Andrew S Tanenbaum., "Modern Operating System", Prentice Hall, 2022.

REFERENCES:
1. Elmasri, E., Carrick A.G. and Levine, D.. “Operating Systems: A Spiral Approach”, McGraw Hill, 2022.
2. McHoes, AM and Flynn, I.M.. “Understanding Operating Systems”, Cengage Learning, 2017.
3. Dhamdhere D M., “Operating Systems: A Concept-based Approach”, Tata McGraw-Hill, 2017.

24X305 DESIGN AND ANALYSIS OF ALGORITHMS

3 0 0 3

Prerequisites:

INTRODUCTION: Algorithm – analysis of algorithms – best case and worst case complexities, asymptotic notations (4)

HEAPS: Max heap - Min heap – Build heap - Insertion and deletion of elements – Application: Heap sort. (6)

BINARY SEARCH TREES: Searching – Insertion and deletion of elements – Analysis. (5)

AVL TREES: Definition – Height – searching – insertion and deletion of elements, AVL rotations. (6)

MULTIWAY SEARCH TREES: Indexed Sequential Access – m-way search tree - B-Tree – searching, insertion and deletion. (4)

DIVIDE AND CONQUER: Master theorem – Merge sort – Quick sort - Strassen’s matrix multiplication. (7)

GREEDY METHOD: Minimum Cost Spanning Tree - Kruskal’s and Prim’s algorithms - Topological sorting – Huffman codes. (6)

DYNAMIC PROGRAMMING: Principles of dynamic programming – All pairs shortest paths - 0/1 knapsack problem - Longest common subsequence problem. (7)

Total L: 45

TEXT BOOKS:
1. Thomas H. Cormen, Charles E. Leiserson and Ronald L. Rivest., “Introduction to Algorithms”, Prentice Hall, 2022.
2. Mark Allen Weiss., “Data Structures and Algorithm Analysis in C++”, Addison-Wesley, 2017.

REFERENCES:
1. Alfred V. Aho, John E. Hopcraft and Jeffrey D. Ullman., “Data structures and Algorithms”, Pearson Education, 2011.
2. Sahni Sartaj., “Data Structures, Algorithms and Application in C++”, Silicon Press, 2013.
3. Ellis Horowitz and Sahni Sartaj and Dinesh Mehta., “Fundamental of Computer Algorithms”, University Press, 2008.
4. Robert L. Kruse, Clovis L. Tondo, Bruce P. Leung and Shashi Mogalla., “Data Structures and Program design in C”,Pearson Education, 2013.

24X306 DATABASE MANAGEMENT SYSTEMS LABORATORY

0 0 4 2

1. Practicing DDL and DML for creation and manipulation of single, multiple tables and Report Generation.
2. Activating access rights and privileges using DCL.
3. Working on TCL commands to manage transactions in databases.
4. Database programming using PL/SQL- stored procedures, Functions and Triggers.
5. Establish Database Connectivity - Application development.

Total P: 60

24X307 OPERATING SYSTEMS LABORATORY (Linux)

0 0 4 2

1. Overview of an Operating System, Boots and Shutdown.
2. UNIX Commands.
3. SHELL Programming.
4. UNIX System Calls.
5. Process Creation and Execution.
6. Thread Creation and Execution.
7. Process / Thread Synchronization using semaphore.
8. Developing Application using Inter Process communication (using shared memory, pipes or message queues).
9. Implementation of Memory Management Schemes.
10. Implementation of file allocation technique (Linked, Indexed or Contiguous).

Total: P: 60

24X308 DESIGN AND ANALYSIS OF ALGORITHMS LABORATORY

0 0 4 2

Implementation of the following problems:
1. Heap – Insertion and deletion.
2. Heap sort.
3. Binary Search Tree – Insertion and Deletion.
4. AVL tree insertion and deletion.
5. B trees.
6. Merge sort and quick sort.
7. Strassen’s Martix Multiplication algorithm.
8. Minimum cost spanning trees.
9. Topological sorting.
10. All pairs shortest path.
11. 0/1 Knapsack problem.
12. Longest common subsequence problem.

Total P: 60

24X401 COMPUTER NETWORKS

3 0 0 3

Prerequisites:

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-Circuit Switching-Packet Switching. (4)

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 – Switches – Routers. (7)

IP: TCP/IP Protocol Structure - Internet Protocol – IP addressing-Subnetting-ARP-DHCP (8)

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

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

APPLICATIONS: SMTP – HTTP- DNS. (5)

Total L: 45

TEXT BOOKS:
1. Behrouz A Forouzan, "Data Communication and Networking", Tata McGraw Hill, 2022.
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, 2020.

REFERENCES:
1. Kevin Fall R and Richard Stevens W, "TCP/IP Illustrated, Volume 1: The Protocols", Addison-Wesley, Ann Arbor, 2011.
2. James F. Kurose and Keith Ross, "Computer Networking: A Top-Down Approach", Addison-Wesley, 2017.
3. Douglas Comer, "Internetworking with TCP/IP", Prentice Hall, 2013.
4. William Stallings, "Data and Computer Communications", Pearson, 2017.

24X402 JAVA PROGRAMMING

3 0 0 3

Prerequisites:

JAVA PROGRAMMING: Introduction - Data Types - Operators - Declarations - Control Structures - Enhanced for Loop - Arrays and Strings - Fundamentals - Methods - Constructors - Scope rules - this keyword- Composing Classes–Inheritance - Reusability - Polymorphism - Abstract classes – Dynamic Method Dispatch. (8)

INTERFACES AND PACKAGES: Interface - Defining and Implementing Interface - Applying Interface - Packages - Access protection - Importing packages. (8)

EXCEPTION HANDLING: Exception types - Uncaught Exception - Using Try and Catch - Multiple catch clauses - Nested try statements - Throw - Throws - Java Built-in Exception – Chained Exceptions - Custom exception. (5)

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

GUI PROGRAMMING: Swing – JavaFX Basics – UI Controls – Event Driven Programming. (6)

I/O AND JAVA COLLECTION API: I/O Basics – Stream - Stream Classes – Object Serialization – NIO Classes - Collection Framework – Generics – Autobox – Auto unboxing - List – Set – Queue – Map. (6)

JDBC: Establishing a Connection - Manipulating Data - Error Handling - Closing a Connection. (4)

Total L: 45

TEXT BOOKS:
1. Herbert Schildt, "JAVA: The Complete Reference", Tata McGraw Hill, 2021.
2. Cay S Horstmann, “Core Java Volume1 - Fundamentals”, Pearson Education, 2021.

REFERENCES:
1. Harvey M Deitel and Paul J Deitel, "Java How to Program, Early Objects ", Pearson Education, 2018.
2. Joyce Farrell, "Java Programming", Cengage Learning, 2022.
3. Y Daniel Liang, "Introduction to Java Programming", Pearson Education, 2023.

24X403 SOFTWARE ENGINEERING

3 0 0 3

Prerequisites:

INTRODUCTION: Nature of a software –Software Engineering -Objectives & Benefits of Software Engineering – Management Spectrum-People involved in the systems development -Quality attributes of a software product. (4)

SOFTWARE PROCESS: Software Process Structure– Generic Process Model – Prescriptive Process Model – Specialized Process Model – Unified Process Model – Agile Development – Agile Process – Extreme programming-Agile process models. (6)

SOFTWARE PLANNING: Software Project Estimation - Decomposition Techniques –Empirical Estimation model - COCOMO & PUTNAM models. (5)

REQUIREMENTS ENGINEERING: Requirements Engineering –Establishing the Ground Work– Eliciting Requirements – Building the Analysis Model-Negotiating Requirements. (5)

REQUIREMENT MODELING: Scenario Based Modeling-UML model that supplements the use case - Class Based Modeling –Creating a Behavioral Modeling – State Chart Diagrams – Package Diagrams – Component Diagrams – Deployment Diagrams. (8)

DESIGN ENGINEERING: Design Process & Design Quality – Design Concepts – The Design Model: Architectural Design– User Interface Design – Component level Design – Pattern Based Design. (6)

SOFTWARE TESTING & IMPLEMENTATION: Testing Strategies – Testing Tactics – Testing Methodologies and Debugging Methods – Quality Concepts: Software quality- The Cost of Quality-Software Quality Factors - Quality Assurance versus Quality Control Reviews Techniques. (7)

CASE STUDIES: Project Scheduling and Tracking – Agile Framework-User story Scrum board, Sprint planning, Tools for Agile project management- JIRA ,Kanban. (4)

Total L: 45

TEXT BOOK:
1. Pressman R S, “Software Engineering – A Practitioner’s Approach”, Tata McGraw Hill, 2019.

REFERENCES:
1. Ian Sommerville, “Software Engineering”, Pearson Education, 2017.
2. Shari Lawrence Pfleeger and Joanne M. Atlee, “Software Engineering Theory and Practice”, Pearson Education, 2021.
3. James Rumbaugh, Ivar Jacobson and Grady Booch, “The Unified Modeling Language Reference Manual”, Pearson Education, 2011.
4. Martin Fowler, “UML Distilled”, Pearson Education, 2015.

24X404 PRINCIPLES OF COMPILER DESIGN

3 2 0 4

Prerequisites:

SYSTEMS PROGRAMMING: Need and working of Assemblers, Macro processors, Linkers, Loaders, Interpreters and Compilers. (6)

LEXICAL ANALYSIS: Role of a Lexical Analyzer – Deterministic Finite Automata and Nondeterministic Finite Automata - Regular Expressions to Nondeterministic Finite Automata – Regular expressions to Deterministic Finite Automata – Nondeterministic Finite Automata to Deterministic Finite Automata - Minimizing the number of states of Deterministic Finite Automata – Implementation of a lexical analyzer. (10)

SYNTAX ANALYSIS: Context free grammars – Derivations and Reductions - Parse trees – Ambiguity – Capabilities of context free grammars. Top down and bottom up parsing – Shift reduce parsing – Operator precedence parsing – Recursive descent parsing -Predictive parsing – Construction of Predictive parsing table - LR parsing – Construction of Simple LR parsing tables – Construction of Canonical LR parsing tables. (14)

SYNTAX DIRECTED TRANSLATION AND INTERMEDIATE CODE GENERATION: Semantic actions – Implementations of syntax directed translators – Intermediate code formats: Postfix notation, Quadruples, Triples, Indirect triples –Methods of translation of assignment statements, Boolean expressions and control statements - Representing information in a symbol table. (10)

CODE OPTIMIZATION AND CODE GENERATION: Introduction to code optimization – Basic blocks – Loop optimization techniques - DAG representation – Error detection and recovery – A simple code generator. (5)

TUTORIAL PRACTICE:
1. Implementing the transition diagram to strip off comment statements from a given source program.
2. Implementing the task of recognizing tokens from a given input program using LEX.
3. Using YACC to check the syntax of the statements in a given input program.
4. Using YACC to generate intermediate codes.
5. Designing a symbol table.

Total L: 45 + T: 30 = 75

TEXT BOOKS:
1. John J. Donovan, "Systems Programming", Tata McGraw Hill, 2012.
2. Alfred V. Aho, Monica S. Lam, Ravi Sethi, and Jeffrey D. Ullman, "Compilers: Principles, Techniques and Tools", Pearson Education, 2013.

REFERENCES:
1. Dhamdhere D. M., "Systems Programming", Tata McGraw Hill, 2012.
2. Alles I. Holub, "Compiler Design in C (Digitized)", Prentice Hall, 2015.

24X405 OPTIMIZATION TECHNIQUES

3 2 0 4

Prerequisites:

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. (13+9)

SIMPLEX MULTIPLIERS: Dual and Primal – Dual Simplex Method– Transportation problem and its solution – Assignment problem and its solution by Hungarian method. (10+8)

DECISION THEORY: Decision Analysis – Decision making under certainty, uncertainty and risk. (8+5)

NON LINEAR PROGRAMMING (UNCONSTRAINED OPTIMIZATION): Introduction – Random search method – Univariate method Gradient of a function – steepest descent method – Conjugate gradient method. (6+4)

CPM AND PERT: Critical path network model – CPM computations – PERT calculations. (8+4)

TUTORIALS PRACTICE:
1. Solving inequalities using Simplex, Two-Phase, Dual Simplex, Revised Simplex method.
2. Finding initial basic feasible solution using North-West corner rule, Matrix minimum and Vogel’s approximation method and optimal test using MODI method.
3. Solving Assignment problem using Hungarian method.
4. Solving Decision theory problems
5. To find the critical path for the given PERT and CPM networks
6. Solving problems under Random Search and Steepest descent method

Total L: 45 + T: 30 = 75

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 Nonlinear optimization", Springer-Verlag, 2017.
2. Wayne L. Winston, "Operations Research: Applications and Algorithms", Duxbery Press, 2008.

24X406 COMMUNICATION SKILLS

0 0 4 2

Prerequisites:

SOFT SKILLS: Process of Communication - Types: Intra & Interpersonal Communication, Cross – Cultural Communication - Barriers (6)

VERBAL AND NON-VERBAL COMMUNICATION: Body language, Etiquette -Telephone conversation (6)

PRESENTATION SKILLS: Professional Presentation - Public Speaking - Group Communication -Case Study based Presentation- - Meetings - Interview Techniques (12)

READING SKILLS: Comprehension and Techniques for Good Comprehension – Focus on Syntax, Vocabulary use, Discourse Markers, and Variety of expression (12)

WRITING SKILLS: Professional Reports: Characteristics - Categories - Format and style and writing techniques, The 7Cs of Writing letters - Official and Business letters - Effective Email writing - Resume Writing practices (12)

PRACTICALS: Professional Presentations - Group Discussions and Meetings - Mock Interviews (12)

Total H: 60

TEXT BOOK:
1. Sabina Pillai & Agna Fernandez, "Soft Skills & Employability Skills", Cambridge University Press, New Delhi, 2018.

REFERENCES:
1. Abirami K, "Professional English", R K Publishers, Coimbatore, 2021.
2. Deepa Mary Francis, et al., "English for Science and Technology – II", Cambridge University Press, New Delhi, 2023.
3. Kumar, Satendra, "Professional Communication Skills", Yking Books, Jaipur, 2018.
4. Kul Bhushan Kumar and Salaria, RS, "Effective Communication Skills", Khanna Publishers, New Delhi, 2022.

24X407 COMPUTER NETWORKS LABORATORY

0 0 4 2

1. Chat server implementation using TCP and UDP protocols
2. Explore the functionalities of various layers of the network stack using wire shark.
3. Simulate LAN technologies and IP subnetting using packet tracer
4. Configure the various routing protocol like distance vector routing and link state routing.
5. Implement an e-mail server
6. Implement the following:
(i) DNS server
(ii) DHCP Server
(iii) HTTP web Server

Total P: 60

24X408 JAVA PROGRAMMING LABORATORY

0 0 4 2

1. Runtime polymorphism using abstract class and interface.
2. Callback feature using interface.
3. Illustrate a program for interface inheritance.
4. User defined packages.
5. Illustrate a program for user defined exception, checked exception and unchecked exception.
6. Threads, thread groups, inter-thread communication using shared memory, piped stream.
7. Files and I/O handling.
8. Collections Framework API’s.
9. GUI Development using JAVA FX.
10. JDBC API.

Total P: 60

24X501 DISTRIBUTED ENTERPRISE COMPUTING

3 2 0 4

Prerequisites:

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. (7)

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. (8)

EVENT DRIVEN ARCHITECTURE: Introduction - GFS Architecture - HDFS Architecture – Hbase - Google Big Table – Hive - Map Reduce – Apache Kafka – Event Streams – gRPC. (8)

ENTERPRISE WEB COMMUNICATION: Java Servlets – JSP – JSF - AJAX – JSON – SOAP API – REST API – SOA – Web 2.0 - Microservices. (8)

FRAMEWORKS: Struts - Spring – MVC- Hibernate – Vert.x – Dropwizard. (6)

TUTORIALS PRACTICE:
1. Implementation of Distributed Algorithms.
2. Create multi-tiered application using the latest front and back end technologies.
3. Developing full stack distributed environment applications.
4. Application programs using Servlet and JSP.
5. Application development using any one of the frameworks.
6. API Development using framework.
7. Micro Services Development using framework.
8. Map Reduce Programs.

Total L: 45 + T: 30 = 75

TEXT BOOKS:
1. Maarten van Steen, Andrew S. Tanenbaum., “Distributed Systems”, 2023.
2. Craig Walls., “Spring in Action”, Manning Publications, 2023.
3. Emil Koutanov., “Effective Kafka”, Leanpub Publications, 2019.

REFERENCES:
1. G Coulouris, J Dollimore and T Kindberg., “Distributed Systems Concepts and Design”, Pearson Education , 2017.
2. Felipe Gutierrez, Joseph B. Ottinger., “Introducing Spring Framework 6: Learning and Building Java-based Applications With Spring”, Apress, 2022.
3. Liu M. L., “Distributed Computing: Principles and Applications”, Pearson Education, 2013.

24X502 COMPUTER GRAPHICS

3 0 0 3

Prerequisites:

INTRODUCTION AND OVERVIEW OF GRAPHICS SYSTEMS: Use of Computer graphics, Video Display Devices, Refresh Cathode-Ray Tubes, Raster and Random Scan Displays, Colour CRT Monitors, Direct View Storage Tubes, Flat Panel Displays, Three-Dimensional Viewing Devices, Stereoscopic & Virtual Reality Systems, Raster and Random Scan Systems, Different Input and Hard Copy Devices, Graphics Softwares. (6)

OUTPUT PRIMITIVES: Points and Lines, Line Drawing Algorithms (DDA & Bresenham’s), Circle and Ellipse Generating Algorithms. (6)

TWO-DIMENSIONAL GEOMETRIC TRANSFORMATIONS: Different types of transformations and their matrix representations, Homogeneous Coordinates, Composite Transformations, transformations between Coordinate Systems, Affine transformations, Window-to-Viewport Coordinate transformation, Clipping-Point, Line, Polygon, Curve and Text Clipping. (9)

THREE-DIMENSIONAL CONCEPTS AND OBJECT REPRESENTATION: Three Dimensional Display Methods, Polygon Surfaces, Curved Lines & Surfaces, Quadric Surfaces, Spline Representations, Cubic Spline interpolation methods, Bezier Curves and Surfaces. (8)

THREE DIMENSIONAL TRANSFORMATIONS AND VIEWING: Translation, Rotation, Scaling, Reflection, Shears, Composite Transformations, Projections- Parallel and Perspective, Projection Transformations, Clipping. (8)

VISIBLE SURFACE DETECTION METHODS: Classification of Visible Surface Detection Algorithms, Back Face Detection, Depth Buffer Method, A-Buffer Method, Scan-Line Method, Depth Sorting Method, BSP-Tree Method & Area Subdivision Method. Polygon- Rendering Methods. (8)

Total L: 45

TEXT BOOKS:
1. Hearn D. & M.P. Baker., “Computer Graphics with open GL”, Pearson Education, 2022.
2. Prabat K. Andleigh and Kiran Thakrar., “Multimedia Systems and Design”, Pearson Education, 2015.

REFERENCES:
1. Newman W.M., “Principle of Interactive Computer Graphics”, Tata McGraw Hill, 2013.
2. Foley James D, Vandam Andries and Hughes John F., “Computer Graphics: Principles and Practice”, Addison Wesley, 2013.
3. Angel, “Interactive Computer Graphics: A top down approach with open GL”, Addison Wesley, 2011.
4. David F Rogers., “Procedural Elements for Computer Graphics”, Tata McGraw Hill, 2012.

24X503 MACHINE LEARNING

3 0 0 3

Prerequisites:

INTRODUCTION: Machine learning – Basics - Convex set - Convex functions – Unconstrained Convex Optimization - Gradient Ascent/Descent - Loss functions in ML – Types – Supervised learning, Unsupervised, Reinforcement learning. (5)

SUPERVISED LEARNING: Regression – Linear – Polynomial – Multiple regression – Evaluation measures – Bias –Variance – Overfitting – Underfitting – Regularization. (8)

CLASSIFICATION: Linear classifier - Logistic Regression – Support Vector Machines – Linear, Soft margin, Linearly non separable data - Kernel functions - Naive Bayes Classifier - Maximum Likelihood Estimation – Maximum a Posteriori Estimate – Multivariate classification – K nearest neighbor classifier. (9)

NEURAL NETWORKS: Perceptron - Multilayer perceptron - Back propagation. (7)

DECISION TREES: Introduction – Purity measures – Entropy, Cross Entropy, Information Gain, Gain Ratio, Gini Index – Regression trees – ID3 – Pruning – Model selection – Performance Measures – Receiver operating characteristic curve (ROC) – AUC. (9)

UNSUPERVISED LEARNING: Partitioning methods – Hierarchical methods – Cluster Validity Measures. (7)

Total L: 45

TEXT BOOKS:
1. Alpaydin Ethem., “Introduction to Machine Learning”, Massachusetts Institute of Technology Press, 2020.
2. Christopher M Bishop., “Pattern Recognition and Machine Learning”, Springer, 2016.

REFERENCES:
1. David Barber., “Machine Learning: A Probabilistic Approach”, http://www.idiap.ch/~barber, 2006.
2. Trevor Hastie, Robert Tibshirani and Jerome Friedman., “The Elements of Statistical Learning”, Springer, 2017.
3. Richard O Duda, Peter E Hart and David G Stork., “Pattern Classification (Digitized)”, John Wiley, 2012.

24X504 MOBILE APPLICATION DEVELOPMENT LABORATORY

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

Total P: 60

24X505 COMPUTER GRAPHICS LABORATORY

0 0 4 2

1. OpenGL IDE and MINGW setup, Implementation of A sample program in OpenGL
2. Designing primitive objects in OpenGL
3. Applications for keyboard and mouse interactions
4. Line drawing algorithms – basic line equation method, DDA Algorithm
5. Bresenham Line drawing algorithms and simple primitives using Bresenham algorithm.
6. Circle and Ellipse Drawing algorithm.
7. Basic 2D transformations and applications
8. Window – Viewport simulation
9. Line Clipping Algorithm Implementation
10. Polygon Clipping Algorithm Implementation
11. Drawing 2D curves using Bezier, B-Spline
12. Applications for 3D Transformation
13. Implementation of 3D Projections
14. Implementation of Visible Surface Detection - Z-Buffer Method

Total P: 60

24X506 MACHINE LEARNING LABORATORY

0 0 4 2

Download datasets from UCI machine learning repository / www.kaggle.com for regression, classification, and clustering tasks.
1. Implement linear, polynomial, and multiple regression and choose the best model for the given data.
2. Implement the following Classification algorithms for the above datasets:
  a. Naive Bayes Algorithm
  b. Decision tree
  c. SVM
  d. K nearest neighbor
  e. Logistic regression
  f. Simple perceptron
3. Perform tenfold cross-validation experiments and statistical validation using t-test and ANOVA.
4. Implement the Backpropagation algorithm for training neural networks.
5. Implement different clustering techniques such as K-means clustering, hierarchical clustering, DBSCAN, etc.
6. Evaluate Performance measures for classification/clustering tasks.

Total P: 60

24X601 CLOUD COMPUTING

3 2 0 4

Prerequisites:

OVERVIEW OF COMPUTING PARADIGM: Evolution of Cloud Computing - Business driver for adopting Cloud Computing – Need for DevOps - DevOps Perspective - DevOps and Agile - Team Structure – Barriers (8)

CLOUD COMPUTING ARCHTECTURE: Cloud Computing Stack - Comparison with traditional computing architecture (client/server) - Services at Various Levels - Role of Networks in Cloud computing - Role of Web Services - Service Models (XaaS) - Infrastructure as a Service(IaaS), Platform as a Service(PaaS), Software as a Service(SaaS) - Deployment Models - Private Cloud, Public Cloud, Hybrid cloud, Community cloud. (6)

DEVOPS IMPLEMENTATION: Introduction – Operations Services – Operations and DevOps – Overall Architecture Structure - DevOps Consequences of the unique cloud Features – CI/CD Pipeline - Migrating to Microservices - Operators as a process - The Future of DevOps. (8)

VIRUTUALIZATION: Basics of Virtualization - Types of virtualization - Implementation Levels of virtualization: Application level, Server level, Storage level and Networking - Tools for Virtualization – KVM, VMware - Virtualization for Cloud. (7)

CLOUD SECURITY: Threats and Vulnerabilities - Infrastructure, Data, and Access Control - Risk Management and Risk Assessment Cloud Service Provider Risks – Virtualization Security Management in the Cloud - Trusted Cloud Computing - Identity Management and Access Control. (7)

CASE STUDIES: EC2 - S3 - Azure – Jenkins – Docker – Gitlab - OpenStack. (9)

TUTORIALS PRACTICE:
1. Hands on virtualization using VMware
2. Hands on containerisation using Docker
3. Deployment and Configuration options in Amazon (AWS)
4. Deployment and Configuration options in Google Cloud
5. Deployment and Configuration options in Microsoft Azure
6. Building and Deploying an application for the cloud
7. Continuous Deployment - using VSTS Release Management
8. Infrastructure as Code - using PowerShell Desired State Configuration
9. Configuration Management using Azure Automation and PowerShell
10. Deployment Pipelines using Jenkins and Visual Studio Release Management

Total L: 45 + T: 30 = 75

TEXT BOOKS:
1. Anthony T. Velte, Toby J. Velte and Robert Elsenpeter., “Cloud Computing: A Practical Approach”, Tata McGraw Hill, 2017.
2. Gene Kim, John Willis, Patrick Debois, Jez Humble and John Allspaw., “DevOps Handbook”, IT Revolution Press, 2021.

REFERENCES:
1. Mathew Portnoy., “Virtualization Essentials”, John Wiley and Sons, 2023.
2. Thomar Erl., “Cloud computing: Concepts, Technology and Architecture”, Pearson, 2019.
3. Gene Kim, George Spafford, Kevin Behr., “The Phoenix Project: A Novel about IT, DevOps and Helping your Business Win”, IT Revolution Press, 2018.
4. Joakim Verona., “Practical DevOps”, Packt Pub Ltd, 2018.

24X602 SOFTWARE TESTING

3 2 0 4

Prerequisites:

INTRODUCTION: Need for testing – Psychology of testing – Testing economies – Software Testing Life Cycle: Requirements Analysis/Design– Traceability Matrix– Test Planning– Objective, Scope of Testing, Schedule, Approach, Roles & Responsibilities, Assumptions, Risks & Mitigations, Entry & Exit Criteria, Test Automation, and Deliverables - SDLC vs STLC. (6)

TYPES OF TESTING: Unit Testing, Integration Testing, System Testing, Smoke, Regression Testing, Acceptance Testing, Acceptance testing - Installation testing, Functional/Non-Functional Testing, Manual Testing, Automation Testing. (6)

SOFTWARE TESTING METHODOLOGIES: Validation & Verification– White/Glass Box Testing - Black Box Testing - Grey Box Testing - Statement Coverage Testing - Branch Coverage Testing- Path Coverage Testing- Conditional Coverage Testing- Loop Coverage Testing- Boundary Value Analysis- Equivalence Class Partition- State Based Testing- Decision Table - Testing GUI. (15)

TEST PLAN & TEST EXECUTION: Types of Test cases– Preparation of test plan – Test script – Execute test cases – Error/Defect Detecting and Reporting – DRE (Defect Removal Efficiency) – Object – Types of Bugs – Debugging Approaches – Reporting the Bugs –Test Closure – Criteria for test closure – Test summary report. (8)

TEST METRICS: Test Measurements, Test Metrics, Metric Life Cycle, Types of Manual Test Metrics, Static metrics: Halsted Metrics & Cyclomatic Complexity. (3)

TECHNIQUES FOR AUTOMATING TEST EXECUTION: Testing and test automation – the V model – common problems of test automation – limitations of automating software testing. (7)

TUTORIALS PRACTICE:
1. Exercise for code review process.
2. Implementing Testing Techniques: White box testing, Basis Path, Looping, Black box methods.
3. Test the package for functional regression testing.
4. Preparation of test plan, test cases for developed package.
5. Design test cases using Rational test manager.
6. Use rational robot for functional testing for developed package.
7. Use Configuration management tool for recording test artifacts.
8. Testing the package for load test using load runner.
9. Test the package for coverage analysis using tools.
10. Test the package for reliability testing using tools.
11. Test the package for memory management using Open source tools.

Total L: 45 + T: 30 = 75

TEXT BOOK:
1. William Perry., "Effective Methods for Software Testing", Wiley, 2015.

REFERENCES:
1. John Watkins., “Testing IT: An off the shelf software testing process”, Cambridge Press, 2011.
2. Boriz Beizer., “Software Testing Techniques”, Dream Tech, 2014.

24X0A1 WEB SERVICES

3 2 0 4

Prerequisites:

INTRODUCTION: Distributed Computing Infrastructure - Middleware: Synchronous, Asynchronous, Message-Oriented, Transaction-Oriented - Enterprise Application and e-business Integration. (7)

WEB SERVICES: Definition - Concept of SAAS - Typical scenarios - Characteristics - Web Service Development Life Cycle - Architecture Types: SOA, REST - B2B Integration before and after Web Services. (12)

SERVICE ORIENTED ARCHITECTURE: Service Roles: Provider, Consumer, Broker - Service Operations: Publish, Find, Bind - Technology Stack. (10)

REST ARCHITECTURE: Web APIs - Principles of REST APIs - Architecture - Microservice APIs - SOA vs REST. (7)

SERVICE COMPOSITION: Workflow - Service Orchestration and Choreography - WS-Coordination - WS-Security. (7)

APPLICATIONS: Real-world examples and implementation. (2)

TUTORIALS PRACTICE:
1. Validating XML using DTD and Schema
2. Formatting XML documents using CSS/XSLT
3. Implementation of Web Services Architecture
4. Creating Web Services communication in Windows Platform
5. Implementation of Web Services using Java Technology
6. Open source contributions

Total L: 45 + T: 30 = 75

TEXT BOOKS:
1. Gustavo Alonso, Fabio Casati, Harumi Kuno, and Vijay Machiraju, "Web Services: Concepts, Architectures and Applications", Springer, 2013.
2. Michael P. Papazoglou, "Web Services: Principles and Technology", Pearson Education, 2012.

REFERENCES:
1. Jose Haro Peralta, "Microservice APIs: Using Python, Flask, FastAPI, OpenAPI and more", Manning, 2023.
2. Thomas Erl, "Service Oriented Architecture: Concepts, Technology and Design", Prentice Hall, 2016.

24X0A2 OPEN SOURCE SOFTWARE

3 2 0 4

INTRODUCTION: Proprietary Software, Free Software, Open Software, Licenses, Version Control, Explore GitHub - GitHub Workflows, Git Basics, Git Branching, Git on the Server, Distributed Git, GitHub, Git Tools, Customizing Git. (6)

PHP PROGRAMMING LANGUAGE: Basics - Data types - Operators and flow control - String - Arrays - Functions - PHP with HTML - Client-side validation - Working with Databases. (9)

RUBY PROGRAMMING LANGUAGE: Foundations and Scaffolding - Ruby Building Blocks, Ruby Ecosystem, The Core of Ruby - Classes, Objects, and Modules, Projects and Libraries, Error Handling, Files and Databases, Deploying Ruby Applications, Ruby Online. (8)

RUBY ON RAILS: Scaling Rails, rails server, Deploying - Heroku Setup, User Resource, Microposts Resource, Static and Slightly Dynamic Pages, Rails Flavored Ruby, Filling in the Layout, Modeling Users, Sign Up, Sign In, Sign Out, Updating, Showing, Deleting Users, User Microposts, Following the users. (8)

WEB SERVER: Application Server Vs Web server - Characteristics of Web server - Exploring Apache Tomcat Web Server. (7)

CASE STUDY: Explore Open Source Tools for Data visualization, DevOps, Testing. (7)

TUTORIALS PRACTICE:
1. Explore and contribute to GitHub.
2. Working with PHP and MySQL.
3. Exercises in Ruby.
4. Application Development and Deployment using Rails Framework.
5. Installation of Apache Tomcat Web Server.
6. Exploring open-source data visualization and testing tools.

Total L: 45 + T: 30 = 75

TEXT BOOKS:
1. Sufyan Bin Uzayr, "PHP: The Ultimate Guide", CRC Press, 2022.
2. Michael Hartl, "Ruby on Rails Tutorial", Addison Wesley, 2022.
3. Rich Bowen and Ken Coar, "Apache Cookbook:", O’Reilly, 2020.

REFERENCES:
1. Brent Laster, "Professional Git", Wiley, 2016.
2. John Duckett, "PHP & MYSQL", Wiley, 2022.
3. Jack Dougherty and Ilya Ilyankou, "Hands on Data Visualization", O’Reilly, 2021.

24X0A3 ARTIFICIAL INTELLIGENCE

3 2 0 4

Prerequisites:

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

PROBLEM SOLVING: State Space models - Searching for solution - Uninformed/Blind search - Informed/ Heuristic search - A*, Hill-climbing - Meta Heuristic: Genetic Algorithm - Adversary based search: Minimax, Expectimax – Alpha Beta pruning – Constraint satisfaction problem - Backtracking search (11)

KNOWLEDGE REPRESENTATION AND REASONING: Knowledge representation - Logic - inference - Fuzzy logic: membership - Fuzzy rules and reasoning - Fuzzy inference. (10)

UNCERTAIN KNOWLEDGE AND PROBABILISTIC REASONING: Uncertainty - Probabilistic reasoning - Semantics of Bayesian network - Exact inference in Bayesian network- Approximate inference in Bayesian network (12)

DECISION-MAKING: Basics of utility theory, Utility functions - Sequential decision problems - Markov decision process

TUTORIALS PRACTICE:
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

Total L: 45 + T: 30 = 75

TEXT BOOKS:
1. Stuart Russell and Peter Norvig., “Artificial Intelligence: A Modern Approach”, Pearson Education, 2021.
2. David Pool and Alan Mackworth., “Artificial Intelligence: Foundations of Computational agents”, Cambridge University Press, 2023.

REFERENCES:
1. Elaine Rich., “Artificial Intelligence 3E”, Mc Graw Hill Education, 2019
2. Parag Kulkarni and Prachi Joshi., “Artificial Intelligence: Building Intelligent Systems”, PHI Learning, 2015.

24X0A4 DATA MINING

3 2 0 4

Prerequisites:

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. (5)

DATA PREPROCESSING: Types of data - Data cleaning – Data Aggregation – Data Discretization - Sampling – Data Reduction – LDA and PCA - Feature subset selection – Correlation analysis – Numerical attributes and Categorical attributes. (6)

MINING FREQUENT PATTERNS, ASSOCIATION AND CORRELATIONS: Basic concepts – Efficient and scalable frequent item set mining methods – Apriori, FP tree, ECLAT. (8)

ENSEMBLE OF CLASSIFIERS: Classification – Ensemble Learning – Bagging, Boosting, Cascading – Ensemble Pruning. (9)

OUTLIER ANALYSIS: Statistical Methods – Proximity based methods – Clustering based methods. (9)

MINING DATA STREAMS: Challenges - Mining time series databases and sequence data –Stationary data stream learning- Hoeffding trees- Evolving data stream mining. (4)

APPLICATIONS AND TRENDS IN DATA MINING: Spatial Data Mining – Graph Mining- Web Mining –Text Mining. (4)

TUTORIALS PRACTICE:
Download the datasets from UCI machine learning repository / www.kaggle.com and do the following:
1. Data Cleaning.
2. Data Aggregation.
3. Correlation analysis.
4. Association rule mining using Apriori and FP-tree algorithms.
5. Outlier detection and removal from the dataset.
6. Ensemble learning techniques for Classification and their evaluation.

Total L: 45 + T: 30 = 75

TEXT BOOKS:
1. Jiwei Han and Micheline Kamber, “Data Mining – Concepts and Techniques”, Morgan Kaufmann, 2022.
2. Tan, Steinbach, Kumar., “Introduction to Data Mining”, Pearson Education, 2016.

REFERENCES:
1. AnandRajaraman, and Jeffrey Ullman., “Mining Massive Data sets”, Cambridge University Press, 2016.
2. Trevor Hastie, Robert Tibshirani, Jerome Freidman., ”The Elements of Statistical Learning: Data Mining, Inference and Prediction”, Springer, 2013.
3. Ian Witten, Frank Eibe, Mark A Hall and Geffery Holmes., "Data Mining: Practical Machine Learning Tools”, Elsevier, 2011.

21X0A5 NATURAL LANGUAGE PROCESSING

3 2 0 4

Prerequisites:

INTRODUCTION: Natural language processing techniques - analysis in NLP: morphological – syntactic, semantic – pragmatic – Applications (2)

WORDS: Regular expressions – Automata – Morphology – Finite state Transducers – Finite state morphological parsing – Combining FST lexicon and rules – Porter Stemmer Algorithm – Minimum edit distance – Language models - N-Grams – Smoothing – Evaluating language models: Entropy, Perplexity - Part of Speech Tagging (PoS) – Context Free Grammars - Top down parser – Earley Algorithm – Bottom-up parsing – CYK parser – Probabilistic parsing (14)

SEMANTICS & PRAGMATICS: First order predicate calculus – Syntax-driven semantic analysis – Attachments for a fragment of English – Word Sense Disambiguation – Machine learning approaches – Dictionary-based approaches – Pragmatics: Discourse –Text coherence (10)

DEEP LEARNING MODELS FOR NLP: Text representation – Term Frequency (TF)- Inverse document Frequency (IDF) – TF-IDF – One Hot Vector - Word2Vec models – Recurrent neural network (RNN) – Long short term memory (LSTM) (7)

NLP APPLICATIONS: Mail spam, web spam detection, Fake news detection - Sentiment Analysis - Information extraction – Topic models - Automatic summarization - Question answering - Named entity recognition and relation extraction - IE using sequence labeling – Machine translation (12)

TUTORIALS PRACTICE:

1. Language modeling using N-gram models and LSTM.
2. POS Tagging using HMM and LSTM.
3. Sentiment analysis and classification using n-gram models, RNN and LSTM.
4. Text classification using RNN and LSTM.
5. Visualization of text data.
6. Machine translation using Deep learning and HMM.
7. Word sense disambiguation.

Total L: 45 + T: 30 = 75

TEXTBOOKS:
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. Jacob Eisenstein, "Introduction to Natural Language Processing", The MIT Press, 2019.

REFERENCES:
1. Christopher Manning and Hinrich Schutze, “Foundations of Statistical Natural Language Processing”, MIT Press, 2016.

24X0A6 DEEP LEARNING

3 2 0 4

Prerequisites:

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

FEED FORWARD NETWORKS: Multilayer Perceptron – back propagation - Learning XOR – Autoencoder - 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)

CONVOLUTIONAL NETWORKS: Structure – properties – Region-based CNN - LeNet – Alex net (5)

RECURRENT NETWORKS: Recurrent Neural Networks (RNN) – Gated Recurrent unit – Long Short Term Memory - Bidirectional RNNs - Deep recurrent network – Methodology – 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)

TUTORIALS PRACTICE:

1. Collect datasets from the URL: http://deeplearning.net/datasets/.
2. Use TensorFlow library for visualization of datasets 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.

Total L: 45 + T: 30 = 75

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

REFERENCES:
1. Li Deng, Dong Yu, "Deep Learning: Methods and Applications", Now Publishers, 2014.
2. Jon Krohn, "Deep Learning for Natural Language Processing: Applications of Deep Neural Networks to Machine Learning Tasks", Addison-Wesley Professional, 2017.

24X0A7 GRAPH THEORY

3 2 0 4

Prerequisites:

BASIC CONCEPTS: Graphs, digraphs, subgraphs, graph models, matrix representations, Hand-shaking lemma, degree sequence, Havel-Hakimi theorem, walk, trail, path, connectedness, distance, radius, diameter, Common families of graphs, isomorphic graphs. Trees - spanning trees, characterizations, Matrix tree theorem. (10+7)

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 theorems. Harary’s construction of optimal k-connected graphs. Connectedness in digraphs. (8+5)

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, Nearest neighbor algorithm. (9+6)

MATCHING AND VERTEX COVER: Maximum Matching, Perfect matching, Augmenting path algorithm, Bipartite matching, Hall’s theorem, job assignments problem. Vertex cover, minimum vertex cover, Independent set, Konig’s theorem. (10+7)

VERTEX-COLORING AND PLANARITY: Proper vertex-coloring, chromatic number, upper and lower bounds, Brooks and Welsh – Powell theorems. Sequential and Largest degree first vertex coloring algorithms. Planarity and non-planarity, Euler’s formula, Kuratowski’s theorem, Vertex coloring in planar graphs. (8+5)

Total L: 45 + T: 30 = 75

TEXT BOOKS:
1. Bondy J.A. and Murty U.S.R., “Graph Theory”, Springer, 2013.
2. Douglas B. West., “Graph Theory”, Prentice Hall, 2018.

REFERENCES:
1. Balakrishnan R and Ranganathan K., “A Textbook of Graph Theory”, Springer, 2019.
2. Jonathan Gross and Jay Yellen, Mark Anderson., “Graph Theory and its Applications”, Chapman and Hall / CRC Press, 2018.
3. Thulasiraman K and Swamy M N S., “Graphs: Theory and Algorithms”, Wiley, 2014.

24X0A8 SOFTWARE PATTERNS

3 2 0 4

Prerequisites:

INTRODUCTION: Reusable Software, Reusable object oriented software, Patterns, Definition, Overview & motivation, Categories, Relationship between patterns, Pattern description. (5)

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

ARCHITECTURE 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. (5)

REFACTORING AND CODE SMELLS: Refactoring, Principles in Refactoring, Bad smells in Code, A Catalog of Refactoring with examples. (10)

IDIOMS: Antipatterns in Software development, Pattern mining, Pattern Language. (10)

TUTORIALS PRACTICE:

1. Identifying any of the 23 GOF design patterns in the given design problem.
2. Design and Implementation of the patterns using Java with appropriate case studies.
3. Creating reusable solution to a design problem using a case study.
4. Use architecture styles like MVC, Pipes and Filters, and Layers to develop computational system.

Total L: 45 + T: 30 = 75

TEXT BOOKS:
1. Erich Gamma, Richard Helm, Ralph Johnson and John Vlissides., “Design Patterns: Elements of Reusable Object-Oriented Software”, Pearson Education, 2005.
2. Frank Buschman, Regine Meunier, Hans Rohnert, Peter Sommerlad and Michael Stal., “Pattern-Oriented Software Architecture: A System of Patterns”, John Wiley and Sons, 2001.

REFERENCES:
1. Martin Fowler, Kent Back, John Brant and William Opdyke., “Refactoring: Improving the Design of Existing Code”, Addison Wesley Professional, 1999.
2. Steven John Metsker and William C. Wake., “Design Patterns in Java”, Addison Wesley, 2006.
3. Eric Freeman, Bert Bates, Kathy Sierra and Elisabeth Robson., “Head First Design Patterns”, O’Reilly, 2004.

24X0A9 MODERN DATABASE MANAGEMENT SYSTEMS

3 2 0 4

Prerequisites:

QUERY PROCESSING: Database Catalog - Query Processing Methodology - Query Evaluation - Query Interpretation - Equivalence of Expressions – Selection, Projection and Natural Join Operations - Estimation of Query Processing Cost - Estimation of access costs using Indices - Algorithm for executing query operations – Query Optimization - Heuristic Query optimization– Cost based query optimization. (8)

OBJECT DATABASES: Introduction to Object Relational Data Model - Complex data types- Structured types and Inheritance-Nesting -un nesting of Relations – Query Processing in ORDBMS- Object oriented data model - Object Identity - Persistent Programming Languages - Type and Class Hierarchies and Inheritance - Complex Objects - Object Oriented Database Design - Query Processing in object oriented database-Comparison of Object Oriented and Object Relational databases. (5)

PARALLEL AND DISTRIBUTED DATABASES: Architecture of parallel databases – Parallel query evaluation, Paralyzing individual operations, Parallel query optimization - Homogeneous and Heterogeneous databases - Architecture of distributed data bases - Storing data in distributed data bases, Distributed Transactions - Concurrency control in Distributed databases - Distributed query processing. (10)

MODERN NOSQL DATABASES: Key - Value Stores – Amazon’s DynamoDB, Key -Value Stores (in-memory): Redis, Wide Column Store: Cassandra, Google BigTable - Document Oriented Stores – MongoDB - Graph databases: Neo4J. (10)

SPATIAL DATABASES: Fundamentals of GIS - Spatial Data Types- Spatial relations – Spatial Queries -Spatial indexing techniques - R-trees, KD trees - Quad trees-Applications of spatial databases (4)

DATABASE INTEGRATION: Data integration: schema directed data integration - Data exchange: Schema mapping and information preservation - automatic schema matching - Information Preserving XML Schema Embedding. (8)

TUTORIALS PRACTICE:

1. Object Relational Databases - including object orientation features in relational databases and creation of nested relations. Projects using OR databases.
2. Document store: Learning to understand document data model with MongoDB
3. Graph database – Handling highly connected data and querying using Cypher QL with Neo4j.
4. Spatial databases – Creation and querying of spatial databases
5. Exploring Hadoop – HDFS and map reduce frameworks

Total L: 45 + T: 30 = 75

TEXT BOOKS:
1. Elmasri R, Navathe SB., “Fundamentals of Database Systems”, Pearson Education, 2016.
2. M.Tamer Ozsu and Patrick Valduriez., “Principles of Distributed Database Systems”, Springer, 2020.
3. Anhai Doan, Alon Halevy and Zachary Ives., “Principles of data integration”, Morgan Kaufmann, 2011.

REFERENCES:
1. Pramod J. Sadalage and Martin Fowler., “NoSQL Distilled - Brief Guide to the Emerging World of Polyglot Persistence”, Pearson Education, 2013.
2. Guy Harrison., “Next generation Databases: NoSQL and BigData”, Apress, 2015.
3. Kristina Chodorow, Shannon Bradshaw and Eoin Brazil., “MongoDB: The Definitive Guide”, O’Reilly Media, 2019.
4. Holden Karau, Andy Konwinski, Patrick Wendell and Matei Zaharia., “Learning Spark: Lightning-Fast Big Data Analysis”, O'Reilly Media, 2015.

24X0AA EMBEDDED SYSTEM AND DESIGN

3 2 0 4

Prerequisites:

Introduction to Embedded Systems: Definition of Embedded System, Embedded Systems Vs General Computing Systems, History of Embedded Systems, Classification, Major Application Areas, Purpose of Embedded Systems, Characteristics and Quality Attributes of Embedded Systems. (9)

Typical Embedded System: Core of the Embedded System: General Purpose and Domain Specific Processors, ASICs, PLDs, Commercial Off-The-Shelf Components (COTS), Memory: ROM, RAM, Memory according to the type of Interface, Memory Shadowing, Memory selection for Embedded Systems, Sensors and Actuators, Communication Interface: Onboard and External Communication Interfaces. (10)

Embedded Firmware: Reset Circuit, Brown-out Protection Circuit, Oscillator Unit, Real Time Clock, Watchdog Timer, Embedded Firmware Design Approaches and Development Languages. (8)

RTOS Based Embedded System Design: Operating System Basics, Types of Operating Systems, Tasks, Process and Threads, Multiprocessing and Multitasking, Task Scheduling. (9)

Task Communication: Shared Memory, Message Passing, Remote Procedure Call and Sockets, Task Synchronization: Task Communication/Synchronization Issues, Task Synchronization Techniques, Device Drivers. An example of RTOS. (9)

TUTORIALS PRACTICE:

1. Functional testing of devices: Flashing the OS onto the device into a stable functional state by porting desktop environment with necessary packages
2. GPIO programming: programming of the GPIO pins of the corresponding devices using native programming language.
3. Interfacing of I/O devices like LED,/Switch and testing the functionalities
4. Design of Real time System program using Round Robin method.
5. Design of RTS program using semaphore.
6. Design of RTS program which uses message queue, mail box, pipe.

Total L: 45 + T: 30 = 75

TEXT BOOKS:
1. David E Simon, “An Embedded Software Primer”, Pearson Education, 2013.
2. Shibu K.V , “Introduction to Embedded Systems”, Mc Graw Hill, 2016
3. Marilyn Wolf, “Computers as components: principles of embedded computing system design”, Elsevier, 2014

REFERENCES:
1. Jane W S Liu, “Real - time Systems”, Pearson Education, 2012.
2. Arnold Berger, “Embedded System Design: introduction to process, tools and techniques”, Elsevier, 2010

24X0AB INFORMATION RETRIEVAL

3 2 0 4

Prerequisites:

INTRODUCTION: Overview of IR Systems - Historical Perspectives - Goals of IR - The impact of the web on IR - The role of artificial intelligence (AI) in IR. (3)

TEXT REPRESENTATION: Statistical Characteristics of Text: Zipf's law; Porter stemmer; morphology; index term selection; using thesauri. Basic Tokenizing, Indexing: Simple tokenizing, stop-word removal, and stemming; inverted indices; Data Structure and File Organization for IR - efficient processing with sparse vectors. (6)

RETRIEVAL MODELS: Similarity Measures and Ranking - Boolean Matching – Extended Boolean models - Ranked retrieval - Vector Space Models -, text-similarity metrics - TF-IDF (term frequency/inverse document frequency) weighting - cosine similarity, Probabilistic Models, Evaluations on benchmark text collections. (8)

QUERY PROCESSING: Query Operations and Languages- Query expansion; Experimental Evaluation of IR: Performance metrics: recall, precision, and F-measure. (5)

TEXT CATEGORIZATION AND CLUSTERING: Categorization: Rocchio; Naive Bayes, kNN; Clustering: Agglomerative clustering; k-means; Expectation Maximization (EM); Dimension Reduction: LSI, PCA. (6)

WEB SEARCH: IR Systems and the WWW - Search Engines: Spidering, Meta Crawlers and near duplicate pages, Question answering, Link analysis: Hubs and Authorities, Google PageRank, Duplicate Detection. (5)

INFORMATION FILTERING TECHNIQUES: Introduction to Information Filtering, Relevance Feedback - Applications of Information Filtering: RECOMMENDER SYSTEMS: Collaborative filtering and Content-Based recommendation of documents and products. (6)

INFORMATION EXTRACTION AND INTEGRATION: Extracting data from text; Basic Techniques: Named Entity Recognition, Co-reference Resolution, Relation Extraction, Event Extraction; Extracting and Integrating specialized information on the Web, Web Mining and Its Applications. (6)

TUTORIALS PRACTICE:

1. Different retrieval models - Boolean, Vector space and Probability based retrieval.
2. Query refinement techniques.
3. Evaluation of the set based and ranked retrieval algorithms.
4. Dimension Reduction techniques.
5. Classification and Clustering techniques.
6. Web based retrieval - Link based retrieval, combining content and link information.
7. Recommender systems- Collaborative and Content Based Filtering.
8. Information Extraction techniques.

Total L: 45 + T: 30 = 75

TEXT BOOKS:
1. Christopher D. Manning, PrabhakarRaghavan and HinrichSchutze, “Introduction to Information Retrieval”, Cambridge University Press, 2012.
2. Stefan Buttcher, Charles L. A. Clarke and Gordon V. Cormack, “Information Retrieval – Implementing and Evaluating Search Engines“, The MIT Press, 2016.
3. B.Croft, D. Metzler and T. Strohman, “Search Engines: Information Retrieval in Practice”, Pearson Education, 2015.

REFERENCES:
1. Ricardo Baeza-Yates and Berthier Ribeiro-Neto, “Modern Information Retrieval”, Pearson Education, 2010.
2. Francesco Ricci, LiorRokach, Bracha Shapira and Paul B. Kantor, “Recommender Systems – Handbook”, Springer, 2015.

24X0AC VIRTUAL AND AUGMENTED REALITY

3 2 0 4

Prerequisites:

Introduction to AR & VR: Categorizing the realities – Virtual Reality, Augmented Reality & Mixed Reality, Introduction, features and application areas of Virtual Reality, Augmented Reality & Mixed Reality. (6)

VR SDK’s: VR SDK’S and Frameworks , VR Concept Integration- Motion Tracking, Controllers, Camera , Hardware and Software requirements, Mobile VR Controller Tracking, Object Manipulation, Text optimizing and UI for VR. (8)

AR Foundation: Detection of surfaces, identifying feature points, track virtual objects in real world, face and object tracking. AR Algorithms – Briefing on SLAM Algorithm (Simultaneous Localization and Mapping), understanding uncertain spatial relationship, Anatomy of SLAM, Loop detection and Loop closing Unity AR concepts- Pose tracking, Environmental detection, Raycasting and physics for AR, Light estimation, Occlusion, working with ARCore and ARKit. (10)

VR Devices: Structure and working of VR Devices. AR Components – Scene Generator, Tracking system, monitoring system, display, Game scene AR Devices – Optical See- Through HMD, Virtual retinal systems, Monitor based systems, Projection displays, Video see-through systems. INERTIAL MEASUREMENTS UNITS: gyros, accelerometers, magnetometers, sensor fusion, complementary filter, Arduino -Advantages and Disadvantages of AR and VR technologies. (8)

Trending Application Areas: Gaming and Entertainment, Architecture and Construction, Science and Engineering, Health and Medicine, Aerospace and Defence, Education, Telerobotics and Telepresence. (9)

Human Factors, Legal and Social Considerations: Human Factors Considerations, Legal and Social Considerations, The Future. (4)

TUTORIALS PRACTICE:

1. Develop a scene that includes a cube, plane and sphere, apply transformations on the objects.
2. Add video and audio source.
3. Create new material and texture separately. Change color, material and texture of each object separately.
4. Create a scene that includes a sphere and plane. Apply rigid body component, material and Box collider to the objects.
5. Develop a simple UI menu with images, canvas, sprites and button. Interact with the UI menu through VI trigger button such that on each successful trigger interaction display score on a scene.
6. Create an immersive environment (living room / battle field / tennis court) with only static game objects. 3D games objects could be created using 3D design tools.
7. Include animation and interaction in the immersive environment.
8. Create VR environment for any use case. This application can include at least 4 scenes which could be changed dynamically. (VR application to visit a Zoo).
9. Create AR environment for online furniture sales.
10. Create a multiplayer game using VR / AR.

Total L: 45 + T: 30 = 75

TEXT BOOKS:
1. Dieter Schmalstieg and Tobias Hollerer., “Augmented Reality: Principles and Practice”, Pearson Education, 2016.
2. William R. Sherman and Alan B. Craig., “Understanding Virtual Reality: Interface, Application, and Design”, Morgan Kaufmann Publishers, 2018.

REFERENCES:
1. Steve Aukstakalnis., “Practical Augmented Reality: A Guide to the Technologies, Applications, and Human Factors for AR and VR”, Addison-Wesley Professional, 2016.
2. Gerard Kim., “Designing Virtual Reality Systems: The Structured Approach”, Springer, 2009.
3. Alan B. Craig, William R. Sherman and Jeffrey D. Will., “Developing Virtual Reality Applications”, Morgan Kaufmann, 2009.

24X0AD DIGITAL IMAGE PROCESSING AND COMPUTER VISION

3 2 0 4

Prerequisites:

DIGITAL IMAGE FUNDAMENTALS: Image Sampling and Quantization, Digital Image Representation, Image Types, Pixel neighborhood. (3)

IMAGE ENHANCEMENT: Gray-Scale Modification, Histogram processing, Image Sharpening, Image Smoothing - Image Restoration - Noise Models, Noise removal using spatial filters, Color Image Enhancement. Image Transforms - Fourier Transform, Discrete Cosine Transform, Discrete Wavelet Transform, Filtering in Frequency domain. (8)

EDGE DETECTION: First order derivative, Second order detection, Color edge detection, Pyramid edge detection, Edge linking and boundary detection. (6)

DIGITAL MORPHOLOGY: Binary Dilation, Erosion, Opening and Closing, Hit-or-Miss Transform, Basic Morphological Algorithms. (5)

GREY-LEVEL SEGMENTATION: Basics of Grey-Level Segmentation, The Use of Regional Thresholds, Moving Averages, Cluster-Based Thresholds, Multiple Thresholds, Region-based segmentation, Watershed Transform. (7)

IMAGE RESTORATION: Image Degradations, The Inverse Filter, The Wiener Filter, Structured Noise, Motion Blur, The Homomorphic Filter, Least Square Filters, Generalized Inverse & Iterative Methods, Recursive filtering, Bayesian Methods. (8)

IMAGE ANALYSIS: Feature Extraction - color, texture and shape features, Dimensionality Reduction, Classification with machine learning and deep learning algorithms, Performance Measures. (8)

TUTORIALS PRACTICE:

1. Basic image processing techniques like sampling and quantization.
2. Image segmentation and edge detection.
3. Histogram equalization.
4. 2-D DFT and DCT.
5. Feature extraction.
6. Image filtering methods in spatial and frequency domain.
7. Image restoration.
8. Image classification and clustering.
9. Developing simple image analysis applications.

Total L: 45 + T: 30 = 75

TEXT BOOKS:
1. Umbaugh, S. E., “Digital Image Processing And Analysis: Applications with Matlab and CVIPTOOLS”, CRC press, 2017.
2. Milan Sonka, Václav Hlavác and Roger Boyle., “Image Processing, Analysis, and Machine Vision”, Cengage Learning, 2015.

REFERENCES:
1. Richard Szeliski., “Computer Vision: Algorithms and Applications”, Springer-Verlag, 2022.
2. Richard Hartley and Andrew Zisserman., “Multiple View Geometry in Computer Vision”, Cambridge University Press, 2014.
3. R.C. Gonzalez and R.E. Woods., “Digital Image Processing”, Addison- Wesley, 2017.
4. David A. Forsyth and Jean Ponce., “Computer Vision: A modern approach”, Prentice Hall, 2015.

24X0AE CRYPTOGRAPHY

3 2 0 4

Prerequisites:

BASICS OF NUMBER THEORY: Divisibility- primes - GCD - Euclidean Algorithm- Modular arithmetic- Congruence – Basic properties- Solving linear congruences - Chinese remainder Theorem- Quadratic Congruence- Euler totient function- Euler’s theorem – Fermat’s little theorem. (9)

BASIC CRYPTOGRAPHIC TECHNIQUES: Encryption and Decryption, Classical ciphers- Substitution ciphers - Monoalphabetic ciphers -Polyalphabetic ciphers – one time pad – transposition ciphers – Cryptanalysis. (8)

SYMMETRIC KEY CRYPTOGRAPHY: Stream ciphers – Block ciphers – DES – Modes of operation. (6)

PUBLIC KEY CRYPTOGRAPHY: Concept of public key cryptography – Hard problem - Factorization Problem - Discrete Log Problem - RSA cryptosystem - ElGamal cryptosystem – Cryptanalysis. (6)

DATA INTEGRITY TECHNIQUES: Cryptographic hash functions – Security of hash functions- Iterated hash function -MAC,Digital signatures – RSA signature - ElGamal signature. (8)

AUTHENTICATION AND KEY DISTRIBUTION PROTOCOLS: Data origin authentication and entity authentication – password based authentication– Challenge response protocols – Fiat Shamir protocol - Symmetric key distribution – Kerberos – Symmetric Key Agreement Protocol – Diffie-Hellman key Agreement –-man in the middle attack- station to station protocol - Public key Infrastructure. (8)

TUTORIALS PRACTICE:

1. Extended Euclidean algorithms
2. Modular inverse – modular exponentiation
3. Polyalphabetic ciphers
4. One time pad
5. RSA cryptosystem
6. ElGamal cryptosystem
7. ElGamal signature scheme
8. RSA signature scheme
9. Diffie- Hellman key pre-distribution

Total L: 45 + T: 30 = 75

TEXT BOOKS:
1. Douglas R Stinson., “Cryptography Theory and Practice”, CRC Press, 2018.
2. Behrouz A Forouzan and Debdeep Mukhopadhyay., “Cryptography and network security”, Tata McGraw Hill, 2017.

REFERENCES:
1. Jonathan Katz, Yehuda Lindell., “Introduction to Modern Cryptography”, CRC press, 2015.
2. Neal Koblitz., “A course in Number Theory and Cryptography”, Springer, 2012.
3. Alfred J, Menezes, Paul C, Van Oorschot and Scott A Vanstone., “Hand Book of Applied Cryptography”, CRC press, 2010.

24X0AF MULTIMEDIA SYSTEMS

3 2 0 4

Prerequisites:

INTRODUCTION TO MULTIMEDIA SYSTEMS DESIGN: Multimedia definition – Interactive media-Hypermedia -Classification of media types –Characteristics of multimedia-Benefits of multimedia-uses of multimedia- Multimedia applications - Defining objects for Multimedia systems- Multimedia I/O technologies- -open source multimedia Authoring Tools. (6)

MULTIMEDIA AUDIO AND VIDEO: Digital medium - Digital audio technology - sound cards - recording - editing -- MIDI fundamentals - Working with MIDI - audio file formats - adding sound to Multimedia project- How video works - broadcast video standards - digital video fundamentals – digital video production and editing techniques - file formats. (7)

MULTIMEDIA ANIMATION: Computer animation fundamentals - Kinematics - morphing - animation s/w tools and techniques.(3)

MULTIMEDIA OS: Multimedia Process scheduling-File system paradigm-File placement-caching-Disk scheduling. (6)

MULTIMEDIA DATABASES: characteristics of Multimedia database management- Temporal segmentation-Video Indexing -Similarity based retrieval-Semantic context based retrieval for videos. (6)

COMPRESSION: Introduction to image compression -Types of Redundancies- various approaches in image compression - JPEG standard - Introduction to video compression-motion compensation - H261 Standard-Introduction to audio compression-APDCM audio compression. (12)

MULTIMEDIA NETWORKING/COMMUNICATION: Multimedia architecture-quality of service-Applications-Networks-Protocols-Distributed multimedia systems. (5)

TUTORIALS PRACTICE:

1. Study of Adobe Photoshop
2. Working with Tools, Layers and Filters using Adobe Photoshop
3. Exercise on Color Picker and Color Balance using Adobe Photoshop
4. Image Editing and panorama using Adobe Photoshop
5. Study of Macromedia Flash
6. Exercise on Motion and Shape Tweening using Macromedia Flash
7. Working on Layers and Masking Effects using Macromedia Flash
8. Animation using Macromedia Flash
9. Video editing using Adobe - Premiere Pro.
10. Construction of Multimedia database
11. Data Compression and decompression on multimedia data.
12. Mini Project using above tools

Total L: 45 + T: 30 = 75

TEXT BOOKS:
1. Ralf steinmetz and klara Nahrstedt., “Multimedia computing,communication and Applications”, Pearson Education 2012.
2. Fred Halsall , James F. Kurose., “Multimedia Communications”, Pearson Education Limited, ISBN,2005.
3. John F. Koegel Buford., “Multimedia Systems”, Pearson Education Limited, 2013.

REFERENCES:
1. Ranjan Parekh,,”Principles of Multimedia”, Tata McGraw Hill Publishing company Limited, 2012.
2. Tay Vaughan, “Multimedia Making it Work", Tata McGraw Hill, New Delhi, 2008.
3. Khalid sayood,”Introduction to data compression”, Morgan Kautmann,2013.

24X0O1 NUMERICAL ANALYSIS

3 2 0 4

Prerequisites:

TYPES OF ERRORS: Different types of errors. (3)

SOLUTION OF ALGEBRAIC EQUATIONS: Bisection method, method of false position, Newton Raphson method, modified Newton Raphson method, Graeffe’s method, Bairstow’s method. (8)

SOLUTION OF ALGEBRAIC SIMULTANEOUS EQUATIONS: Gauss elimination, Gauss Jordan, Crout’s method - Cholesky method, Gauss Jacobi method, Gauss – Seidel method. (8)

EIGENVALUES AND EIGENVECTORS: Power method, inverse power method, Jacobi method. (4)

FINITE DIFFERENCES AND INTERPOLATION: Finite difference operators – Interpolation: Newton’s divided difference formula, Lagrange’s interpolation formula, Newton’s - Gregory forward and backward interpolation. (8)

DIFFERENTIATION AND INTEGRATION: Numerical differentiation using Newton’s - Gregory forward and backward polynomials. Numerical Integration: Gaussian Quadrature, Trapezoidal rule, Simpson’s one third rule. (6)

ORDINARY DIFFERENTIAL EQUATIONS: Taylor series method, Euler method and its Modifications, Runge - Kutta methods, Predictor-corrector methods: Milne’s method, Adam – Bashforth method. (8)

TUTORIALS PRACTICE:

1. Solution of Non-linear equations (Bisection method, Regula Falsi method, Graeffe’s method, Bairstow’s method)
2. Solution of system of linear equations (Gauss-Jordan elimination, Gauss Jacobi and Gauss Seidel methods)
3. Finding Eigenvalues and Eigenvectors(Power method and Jacobi method)
4. Interpolation (Newton forward, Newton backward, Newton divided difference, Lagrange’s interpolation)
5. Numerical integration (Trapezoidal rule, Simpson’s one-third rule, Gaussian quadrature)
6. Solution of ordinary differential equations(Euler and modified Euler methods, Runge-Kutta method and Milne’s method)

Total L: 45 + T: 30 = 75

TEXT BOOKS:
1. Steven C. Chapra and Raymond P. Canale., “Numerical Methods for Engineers with Software and Programming Applications”, McGraw Hill, 2011.
2. Curtis F. Gerald, and Patrick O. Wheatley., “Applied Numerical Analysis” Pearson, 2019.
3. Yousef Saad.. “Numerical methods for large eigen value problems”, University Press, 2011.

REFERENCES:
1. Richard L. Burden and Dougglas Faires J., “Numerical Analysis”, Cengage Learning India Pvt. Ltd., 2019.
2. Brian Bradie., “A Friendly introduction to Numerical Analysis”, Pearson, 2006.

24X0O2 CYBER SECURITY

3 2 0 4

Prerequisites:

INTRODUCTION: Need for Security-Security goals- Security Attacks - Security Services and Mechanisms - Threat- Types of Threats- Vulnerabilities, Controls and Counter measures. (7)

CRYPTOGRAPHY: Cryptographic attacks - Encryption and Decryption- Cryptosystem- Symmetric-key ciphers- Asymmetric-key ciphers - Cryptanalysis. (7)

NETWORK LAYER SECURITY: Introduction-IP spoofing-Teardrop attack-ICMP attacks-smurf attacks and their countermeasures-Need of network layer security-Internet Protocol Security- Defense Mechanisms. (8)

ETHICAL HACKING AND PENETRATION TESTING: Introduction to Hacking – Importance of Security – Elements of Security –Phases of Ethical Hacking - Types of Attacks –Tools used for ethical hacking – Network penetration testing -Defense Mechanisms. (8)

INTRUSION DETECTION: Principles of Intrusion detection – types– Architecture - Intrusion Detection and response - Denial of Service attack, Distributed Denial of Service- Honeypots – DNS attacks –Firewall – types & Architecture. (8)

WEB APPLICATION SECURITY: Email security –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. (7)

TUTORIALS PRACTICE:

1. Hacking web applications
2. Hacking web server
3. Network hacking
4. Database hacking
5. Password cracking
6. Mobile device tracking
7. IP tracking

Total L: 45 + T: 30 = 75

TEXT BOOKS:
1. William Stallings, “Cryptography and Network Security: Principles and Practice”, Pearson, 2023.
2. James Graham, Richard Haward and Ryan Olson, “Cyber Security Essentials” CRC Press, 2011.
3. Charles P. Pfleeger and Shari Lawrence Pfleeger, “Analyzing Computer Security – A threat/vulnerability / Counter measure approach”, Pearson Education, 2014.
4. Charles J. Brooks, Christopher Grow, Philip Craig and Donald Short, “Cyber Security Essentials” Wiley 2018.
5. Patrick Engebretson ,David Kennedy , “The Basics of Hacking and Penetration Testing”, Elsevier, 2013.

REFERENCES:
1. Michael T Simpson, Kent Backman and James E.Corley “Hands-On Ethical hacking and Network Defense” Cengage Learning, 2013.
2. Nina GodBole and Sunit Belapure, “Cyber Security-Understanding cyber-crimes, computer forencics, and legal perspective, Wiley, 2011.
3. Jennifer L. Bayuk, Jason Healey, Paul Rohmeyer, Marcus H.Sachs, Jeffrey Schmidt and Joseph Weiss, “Cyber Security Policy Guidebook”, John Wiley & Sons, 2012.
4. Himanshu Sharma, “Kali Linux- An Ethical Hacker’s CookBook” Packt Publishing, Second Edition 2019.

24X0O3 ENTERPRISESHIP

3 2 0 4

INTRODUCTION TO ENTREPRENEURSHIP: Definition – Characteristics and Functions of an Entrepreneur – Common myths about entrepreneurs – Importance or Entrepreneurship. (5)

CREATIVITY AND INNOVATION: The role of creativity – The innovation Process – Sources of New Ideas – Methods of Generating Ideas – Creative Problem Solving – Entrepreneurial Process. (6)

DEVELOPING AN EFFECTIVE BUSINESS MODEL: The Importance of a Business Model – Starting a small scale industry - Components of an Effective Business Model. (5)

APPRAISAL OF PROJECTS: Importance of Evaluating Various options and future investments- Entrepreneurship incentives and subsidies – Appraisal Techniques. (8)

FORMS OF BUSINESS ORGANIZATION: Sole Proprietorship – Partnership – Joint Stock Companies and Cooperative organizations. FINANCING THE NEW VENTURE: Determining Financial Needs – Sources of Financing – Equity and Debt Funding – Case studies in 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 Secondary Sources – Gathering Information from Primary Sources – Analyzing and Interpreting the Results – The Marketing Process. INTELLECTUAL PROPERTY PROTECTION AND ETHICS: Patents – Copyright - Trademark- Geographical indications – Ethical and social responsibility and challenges. (9)

TUTORIALS PRACTICE:
Case studies

Total L: 45 + T: 30 = 75

TEXT BOOKS:
1. Donald F.Kuratko and Richard M.Hodgetts, “Entrepreneurship : Theory, Process and Practice”, South-Western, 2007.
2. Vasant Desai, The Dynamics of Entrepreneurial Development and Management, Himalaya Publishing House, 2011.

REFERENCES:
1. S.L.Gupta and Arun Mittal, “Entrepreneurship Development”, International Book House, 2012.
2. G. S. Sudha, “Management and Entrepreneurship Development”, Indus Valley Publication, 2009.
3. V. Badi and N. V. Badi , “Business Ethics”, R, Vrinda Publication (P) Ltd., 2014.
4. Prasanna Chandra, “Projects- Planning, Analysis, Financing, Implementation and review”, TATA McGraw Hill, 2012.
5. S. S. Khanka and C. B. Gupta, “Entrepreneurship and Small Business Management”, Sultan Chand and Sons, 2022.

24X0O4 HUMAN COMPUTER INTERFACE DESIGN

3 2 0 4

Prerequisites:

INTRODUCTION: Human – Machine – Interaction – Paradigms (8)

INTERACTION DESIGN: Process – Navigation – Screen Design and Layout – HCI in the Software Process – Design Rules – Evaluation Techniques – Universal Design – User Support (8)

INTERACTION MODELS: Cognitive Models – Socio-organizational Issues and Stakeholder Requirements – Communication and Collaboration Models (8)

TASK ANALYSIS: Introduction - Task Decomposition – Knowledge Based Analysis – ER based Techniques – Uses (8)

USER INTERFACE DESIGN: Dialog Design – Diagrammatic Notations – Textual Dialog notations – Dialog Semantics – Dialog Analysis and Design - Modelling Rich Interaction (8)

UTILITIES: Groupware – Ubiquitous Computing and Augmented Realities – Hypertext, Multimedia and WWW (5)

TUTORIALS PRACTICE:
1. Analyzing a Usability Problem on Machines.
2. Information Visualization.
3. Time and Motion Study of GUI.
4. Widget Survey.
5. Sketch People and Task Decomposition.

Total L: 45 + T: 30 = 75

TEXT BOOKS:
1. Alan Dix, Janet Finlay, Gregory D. Abowd, Rusell Beale, “Human Computer Interaction”, Pearson Education, 2015.
2. Ben Shneiderman, Catherine Plaisant, Maxine S.Cohen, Steven M.Jacobs, Nicholas Diakopoulos and Niklas Elmqvist, “Designing the User Interface: Strategies for Effective Human-Computer Interaction”, Addison Wesley, 2017.

REFERENCES:
1. Preece, Rogers and Sharp, “Interaction Design”, Wiley, 2015.
2. Jenifer Tidwell, ”Designing Interfaces”, O’Reilly, 2011.

24X0O5 INTERNET OF THINGS

3 2 0 4

Prerequisites:

INTRODUCTION TO IoT: Introduction to Internet of Things (IoT) – Machine to Machine (M2M - Wireless Sensor Networks (WSN) - Features of IoT– Recent Trends in the Adoption of IoT – Societal Benefits. (3)

IoT ARCHITECTURE: Functional Requirements - IoT Enabling Technologies –Basic Architecture - Components of IoT: Embedded Computation Units, Microcontrollers, System on Chip (SoCs) - Sensors – Actuators – Communication Interfaces. (7)

PROTOCOLS IN IoT: Communication Protocols: RFID – NFC - Low Power Personal Area networks (LowPAN), BLE, Zigbee, Zwave, and Thread. (12)

APPLICATION PROTOCOLS: HTTP – CoAP – MQTT - Comparing different IoT Application Layer Protocols. (8)

PROTOTYPING: Prototyping embedded devices - Open Source versus Closed Source - Embedded Computing Basics - Arduino - Raspberry Pi - Implementation. (8)

APPLICATIONS IN IoT: Smart homes – Energy – Health Care – Smart Transportation – Smart Living – Smart Cities- Smart Grid – Smart Agriculture. (7)

TUTORIALS PRACTICE:
1. Study of Arduino Uno
2. Study of Raspberry Pi
3. Traffic Signal Monitoring & Control System
4. IoT Based Fire Department Alerting System
5. Smart home automation
6. IoT Based Person/Wheelchair Fall Detection
7. Gas Pipe Leakage Detector
8. Smart Energy Meter Monitoring
9. IoT Based Fire Department Alerting System
10. Simulating Wireless Sensor Networks
11. Connected Vehicle applications

Total L: 45+P: 30=75

TEXTBOOKS:
1. Dieter Uckelmann, Mark Harrison, Florian Michahelles, “Architecting the Internet of Things”, Springer, New York, 2014
2. Adrian McEwen and Hakim Cassimally, “Designing the Internet of Things”, John Wiley and Sons Ltd, UK, 2018.
3. Madisetti and ArshdeepBahga, “Internet of Things: (A Hands-on Approach)”, Universities Press (INDIA) Private Limited 2014.

REFERENCES:
1. Olivier Hersent, David Boswarthick and Omar Elloumi, “The Internet of Things: Key Applications and Protocols”, John Wiley and Sons Ltd., UK 2018.
2. William Stallings, “Foundations of Modern Networking: SDN, NFV, QoE, IoT, and Cloud” Addison-Wesley, 2015

24X0O6 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)

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)

SOCIAL ISSUES AND THE ENVIRONMENT: From unsustainable to sustainable development, Urban problems related to energy, Water conservation, Rainwater 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)

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)

TUTORIALS PRACTICE:
Case studies

Total L: 45 + P: 30 = 75

TEXT BOOKS:
1. Mackenzie L. Davis and David A. Cornwell, “Introduction to Environmental Engineering”, Tata McGraw Hill, 2017.
2. Chetan Singh Solanki, “Solar Photovoltaics”, PHI Learning Private Ltd., 2018.
3. Siraj Ahmed, “Wind Energy : Theory and Practice”, PHI Learning Private Ltd., 2016.
4. Mahajan S. P. Pollution Control in Process Industries, Tata McGraw Hill, 2017.
5. Hester R.E. and Harrison R.M. “Electronic Waste Management”, Royal Society of Chemistry, 2009.

REFERENCES:
1. Anubha Kaushik and Kaushik C P, “Environmental Science and Engineering”, New Age International Pvt Ltd, 2010.
2. Martha Maeda, “How to Solar Power your Home”, Atlantic Publishing Group, 2015.
3. Paul Gipe, “Wind Power – Renewable Energy for Home, Farm and Business”, Sterling Hill Publications, 2008.
4. Klaus Hieronymi, Ramzy Kahhat and Eric Williams, “E-Waste Management: From Waste to resource”, Routledge – Taylor and Francis, 2012.
5. Diane Gow Mcdilda, “The Everything Green Living Book”, Adams Media, 2007.

24X0O7 AGILE SOFTWARE DEVELOPMENT

3 2 0 4

Prerequisites:

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 Theory Building. (12)

Case Studies (5)

TUTORIALS PRACTICE:
1. Modular development.
2. Incremental delivery approach.
3. Development of Metaphor.
4. Proving the productivity using pair programming approach.
5. Exercise for understanding the concept of “Simple Design”.
6. “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 BOOK:
1. Alistair Cockburn, “Agile Software Development”, Pearson Education, 2014.

REFERENCES:
1. Craig Larman, “Agile and Iterative Development”, Pearson Education, 2012.
2. Mike Cohn, “Agile Estimating and Planning”, Pearson Education, 2012.