KEC


    Available courses

    IN THIS COURSE WE WILL STUDY ABOUT VARIOUS TYPES OF ATTACKS THAT CAN OCCOUR IN OUR SYSTEM AND SECURITY MEASURES FOR THOSE ATTACKS

    Design and Analysis of Algorithm syllabus

    UNIT-I

    Introduction: Algorithms, Analyzing Algorithms, Complexity of Algorithms, Growth of Functions, Performance Measurements, Sorting and Order Statistics - Shell Sort, Quick Sort, Merge Sort, Heap Sort, Comparison of Sorting Algorithms, Sorting in Linear Time.

    UNIT-II

    Advanced Data Structures: Red-Black Trees, B – Trees, Binomial Heaps, Fibonacci Heaps, Tries, Skip List

    UNIT III

    Divide and Conquer with Examples Such as Sorting, Matrix Multiplication, Convex Hull and Searching. Greedy Methods with Examples Such as Optimal Reliability Allocation, Knapsack, Minimum Spanning Trees – Prim’s and Kruskal’s Algorithms, Single Source Shortest Paths - Dijkstra’s and Bellman Ford Algorithms

    UNIT IV

    Dynamic Programming with Examples Such as Knapsack. All Pair Shortest Paths – Warshal’s and Floyd’s Algorithms, Resource Allocation Problem. Backtracking, Branch and Bound with Examples Such as Travelling Salesman Problem, Graph Coloring, n-Queen Problem, Hamiltonian Cycles and Sum of Subsets.

    UNIT V

    Selected Topics: Algebraic Computation, Fast Fourier Transform, String Matching, Theory of NPCompleteness, Approximation Algorithms and Randomized Algorithms

    Text books: 

    1. Thomas H. Coreman, Charles E. Leiserson and Ronald L. Rivest, “Introduction to Algorithms”, Printice Hall of India.

    2. E. Horowitz & S Sahni, "Fundamentals of Computer Algorithms",

     3. Aho, Hopcraft, Ullman, “The Design and Analysis of Computer Algorithms” Pearson Education, 2008. 

    4. LEE "Design & Analysis of Algorithms (POD)",McGraw Hill 

    5. Richard E.Neapolitan "Foundations of Algorithms" Jones & Bartlett Learning

     6. Jon Kleinberg and Éva Tardos, Algorithm Design, Pearson, 2005. 

    7. Michael T Goodrich and Roberto Tamassia, Algorithm Design: Foundations, Analysis, and Internet Examples, Second Edition, Wiley, 2006. 

    8. Harry R. Lewis and Larry Denenberg, Data Structures and Their Algorithms, Harper Collins, 1997

     9. Robert Sedgewick and Kevin Wayne, Algorithms, fourth edition, Addison Wesley, 2011. 

    10. Harsh Bhasin,”Algorithm Design and Analysis”,First Edition,Oxford University Press. 

    11. Gilles Brassard and Paul Bratley,Algorithmics:Theory and Practice,Prentice Hall,1995.

    The main objective of the course is to expose the students to soft computing, various types of soft computing techniques, and applications of soft computing. .Upon completion of this course, the student should be able to get an idea about Neural Networks, architecture, functions, and various algorithms involved. Also, they will be able to understand Fuzzy Logic, Various fuzzy systems, functions, and Genetic algorithm concepts.

    Distributed System 7 th sem CSE.

    DISTRIBUTED SYSTEM

    DETAILED SYLLABUS 3-1-0

    Unit Topic Proposed

    Lecture

    Characterization of Distributed Systems: Introduction, Examples of distributed Systems, Resource

    sharing and the Web Challenges. Architectural models, Fundamental Models. Theoretical

    Foundation for Distributed System: Limitation of Distributed system, absence of global clock,

    shared memory, Logical clocks ,Lamport’s & vectors logical clocks. Concepts in Message Passing

    Systems: causal order, total order, total causal order, Techniques for Message Ordering, Causal

    ordering of messages, global state, termination detection.

    Distributed Mutual Exclusion: Classification of distributed mutual exclusion, requirement of

    mutual exclusion theorem, Token based and non token based algorithms, performance metric for

    distributed mutual exclusion algorithms. Distributed Deadlock Detection: system model, resource

    Vs communication deadlocks, deadlock prevention, avoidance, detection & resolution, centralized

    dead lock detection, distributed dead lock detection, path pushing algorithms, edge chasing

    algorithms.


    Agreement Protocols: Introduction, System models, classification of Agreement Problem,

    Byzantine agreement problem, Consensus problem, Interactive consistency Problem, Solution to

    Byzantine Agreement problem, Application of Agreement problem, Atomic Commit in Distributed

    Database system. Distributed Resource Management: Issues in distributed File Systems,

    Mechanism for building distributed file systems, Design issues in Distributed Shared Memory,

    Algorithm for Implementation of Distributed Shared Memory.


    Failure Recovery in Distributed Systems: Concepts in Backward and Forward recovery, Recovery in

    Concurrent systems, Obtaining consistent Checkpoints, Recovery in Distributed Database Systems.

    Fault Tolerance: Issues in Fault Tolerance, Commit Protocols, Voting protocols, Dynamic voting

    protocols


    Transactions and Concurrency Control: Transactions, Nested transactions, Locks, Optimistic

    Concurrency control, Timestamp ordering, Comparison of methods for concurrency control.

    Distributed Transactions: Flat and nested distributed transactions, Atomic Commit protocols,

    Concurrency control in distributed transactions, Distributed deadlocks, Transaction recovery.

    Replication: System model and group communication, Fault - tolerant services, highly available

    services, Transactions with replicated data.


    Text books:

    1. Singhal&Shivaratri, "Advanced Concept in Operating Systems", McGraw Hill

    2. Ramakrishna,Gehrke,” Database Management Systems”, McGraw Hill

    3. Vijay K.Garg Elements of Distributed Compuitng , Wiley

    4. Coulouris, Dollimore, Kindberg, "Distributed System: Concepts and Design”, Pearson Education 5. Tenanuanbaum,

    Steen,” Distributed Systems”, PHI

    The course aims at imparting basic principles of thought process, reasoning and inference to identify the roots and details of some of the contemporary issues faced by our nation and try to locate possible solutions to these challenges by digging deep into our past. It  enables the students to understand the importance of our surroundings and encourage the students to contribute towards sustainable development. 

    Machine learning is a sub field of artificial intelligence (AI). The goal of machine learning generally is to understand the structure of data and fit that data into models that can be understood and utilised by people.

    Although machine learning is a field within computer science, it differs from traditional computational approaches. In traditional computing, algorithms are sets of explicitly programmed instructions used by computers to calculate or problem solve. Machine learning algorithms instead allow for computers to train on data inputs and use statistical analysis in order to output values that fall within a specific range. Because of this, machine learning facilitates computers in building models from sample data in order to automate decision-making processes based on data inputs.

    In this course we would learn about the machine Learning techniques and algorithms. 


    Unit 1-Introduction: Overview, Database System vs File System, Database System Concept and Architecture, Data Model Schema and Instances, Data Independence and Database Language and Interfaces, Data Definitions Language, DML, Overall Database Structure. Data Modeling Using the Entity Relationship Model: ER Model Concepts, Notation for ER Diagram, Mapping Constraints, Keys, Concepts of Super Key, Candidate Key, Primary Key, Generalization, Aggregation, Reduction of an ER Diagrams to Tables, Extended ER Model, Relationship of Higher Degree.

    Unit 2-Relational data Model and Language: Relational Data Model Concepts, Integrity Constraints, Entity Integrity, Referential Integrity, Keys Constraints, Domain Constraints, Relational Algebra, Relational Calculus, Tuple and Domain Calculus. Introduction on SQL: Characteristics of SQL, Advantage of SQL. SQl Data Type and Literals. Types of SQL Commands. SQL Operators and Their Procedure. Tables, Views and Indexes. Queries and Sub Queries. Aggregate Functions. Insert, Update and Delete Operations, Joins, Unions, Intersection, Minus, Cursors, Triggers, Procedures in SQL/PL SQL

    Unit 3-Data Base Design & Normalization: Functional dependencies, normal forms, first, second, 8 third normal forms, BCNF, inclusion dependence, loss less join decompositions, normalization using FD, MVD, and JDs, alternative approaches to database design

    Unit 4-Transaction Processing Concept: Transaction System, Testing of Serializability, Serializability of Schedules, Conflict & View Serializable Schedule, Recoverability, Recovery from Transaction Failures, Log Based Recovery, Checkpoints, Deadlock Handling. Distributed Database: Distributed Data Storage, Concurrency Control, Directory System

    Unit 5-Concurrency Control Techniques: Concurrency Control, Locking Techniques for Concurrency Control, Time Stamping Protocols for Concurrency Control, Validation Based Protocol, Multiple Granularity, Multi Version Schemes, Recovery with Concurrent Transaction, Case Study of Oracle

    Text books:

    1. Korth, Silbertz, Sudarshan,” Database Concepts”, McGraw Hill 

    2. Date C J, “An Introduction to Database Systems”, Addision Wesley 

    3. Elmasri, Navathe, “ Fundamentals of Database Systems”, Addision Wesley 

    4. O’Neil, Databases, Elsevier Pub. 

    5. RAMAKRISHNAN"Database Management Systems",McGraw Hill 

    6. Leon & Leon,”Database Management Systems”, Vikas Publishing House 

    7. Bipin C. Desai, “ An Introduction to Database Systems”, Gagotia Publications 

    8. Majumdar & Bhattacharya, “Database Management System”, TMH

    This Course contains concepts of Object Oriented Programming like Information hiding ,Abstraction ,Encapsulation, etc. After completing this course Students must be able to perform coding in C++ Programming Language.

    Hard Computing:  In 1996, LA Zadeh (LAZ) introduced the term hard computing. According to LAZ: We term a computing as ”Hard” computing, if

    • Precise result is guaranteed
    • Control action is unambiguous
    • Control action is formally defined (i.e. with  mathematical model
    Example:

    • Solving numerical problems (e.g. Roots of polynomials,  Integration etc.)
    • Searching and sorting techniques
    • Solving ”Computational Geometry” problems (e.g. Shortest  tour in Graph theory, Finding closest pair of points etc.)

    Soft computing: Soft computing was proposed by Lotfi A. Zadeh. First he gave the idea about Fuzzy Logic.

    Soft Computing is a collection of methodologies that aim to exploit the tolerance for imprecision and uncertainty to achieve tractability, robustness, and low solution cost.

     • Tolerance of imprecision means result is not precise

    • Uncertainty means different result at different time
    • Tractability means problem solved with in polynomial time
    • Robustness means it can tackle any sort of input including noise

    Soft Computing is a term applied to a field within computer science which is characterized by the use of inexact solutions to computationally hard task such as solution of NP- Complete problems for which an exact solution can not be derived with in polynomial time.

    Characteristics of Soft Computing:

    • It does not require any mathematical modeling of problem  solving
    • It may not yield the precise solution
    • Algorithms are adaptive (i.e. it can adjust to the change of  dynamic environment)
    • Use some biological inspired methodologies such as genetics,  evolution, Ant’s behaviors, human nervous  systems etc.

    Applications using Soft Computing:

    • Handwriting Recognition
    • Automotive systems and manufacturing
    • Image processing and data recognition
    • Decision support System
    • To Power System
    • Neuro Fuzzy System
    • Fuzzy Logic Control
    • Genetic Algorithms
    • Bio Informatics and Biomedicine


    Components of Soft Computing includes:

    • Neural Networks (NN)
    • Fuzzy System or Fuzzy Logic
    • Genetic Algorithm (Evolutionary Computing)

    Books Reference:

    1. S. Rajsekaran & G.A. Vijayalakshmi Pai, “Neural Networks,Fuzzy Logic and Genetic Algorithm:Synthesis and Applications” Prentice Hall of India.

    2. Dr. S.N Sivanandam & Dr. S.N Deepa “Principles of Soft Computing” Wiley



    Compiler Design (KCS-502)

     At the end of course , the student will be able to: 

    CO 1 Acquire knowledge of different phases and passes of the compiler and also able to use the compiler tools like LEX, YACC, etc. Students will also be able to design different types of compiler tools to meet the requirements of the realistic constraints of compilers. 

    CO 2 Understand the parser and its types i.e. Top-Down and Bottom-up parsers and construction of LL, SLR, CLR, and LALR parsing table.  

    CO 3 Implement the compiler using syntax-directed translation method and get knowledge about the synthesized and inherited attributes. 

    CO 4 Acquire knowledge about run time data structure like symbol table organization and different techniques used in that. 

    CO 5 Understand the target machine’s run time environment, its instruction set for code generation and techniques used for code optimization. 

    Unit 1: Introduction to Compiler: Phases and passes, Bootstrapping, Finite state machines and regular expressions and their applications to lexical analysis, Optimization of DFA-Based Pattern Matchers implementation of lexical analyzers, lexical-analyzer generator, LEX compiler, Formal grammars and their application to syntax analysis, BNF notation, ambiguity, YACC. The syntactic specification of programming languages: Context free grammars, derivation and parse trees, capabilities of CFG. 

    UNIT II: Basic Parsing Techniques: Parsers, Shift reduce parsing, operator precedence parsing, top down parsing, predictive parsers Automatic Construction of efficient Parsers: LR parsers, the canonical Collection of LR(0) items, constructing SLR parsing tables, constructing Canonical LR parsing tables, Constructing LALR parsing tables, using ambiguous grammars, an automatic parser generator, implementation of LR parsing tables.

    UNIT III : Syntax-directed Translation: Syntax-directed Translation schemes, Implementation of Syntaxdirected Translators, Intermediate code, postfix notation, Parse trees & syntax trees, three address code, quadruple & triples, translation of assignment statements, Boolean expressions, statements that alter the flow of control, postfix translation, translation with a top down parser. More about translation: Array references in arithmetic expressions, procedures call, declarations and case statements. 

    UNIT IV : Symbol Tables: Data structure for symbols tables, representing scope information. Run-Time Administration: Implementation of simple stack allocation scheme, storage allocation in block structured language. Error Detection & Recovery: Lexical Phase errors, syntactic phase errors semantic errors. 

    UNIT V : Code Generation: Design Issues, the Target Language. Addresses in the Target Code, Basic Blocks and Flow Graphs, Optimization of Basic Blocks, Code Generator. Code optimization: Machine-Independent Optimizations, Loop optimization, DAG representation of basic blocks, value numbers and algebraic laws, Global Data-Flow analysis.

     Text books: 1. K. Muneeswaran,Compiler Design,First Edition,Oxford University Press. 

    2. J.P. Bennet, “Introduction to Compiler Techniques”, Second Edition, Tata McGraw-Hill,2003.

     3. Henk Alblas and Albert Nymeyer, “Practice and Principles of Compiler Building with C”, PHI, 2001.

     4. Aho, Sethi & Ullman, "Compilers: Principles, Techniques and Tools”, Pearson Education 

    5. V Raghvan, “ Principles of Compiler Design”, TMH 

    6. Kenneth Louden,” Compiler Construction”, Cengage Learning. 

    7. Charles Fischer and Ricard LeBlanc,” Crafting a Compiler with C”, Pearson Education

    KCS-503: Design and Analysis of Algorithm

     

     

    UNIT

    TOPIC

    1

    Introduction: Algorithms, Analyzing Algorithms, Complexity of Algorithms, Growth of Functions, Performance Measurements, Sorting and Order Statistics - Shell Sort, Quick Sort, Merge Sort, Heap Sort, Comparison of Sorting Algorithms, Sorting in Linear Time.

    2

    Advanced Data Structures: Red-Black Trees, B – Trees, Binomial Heaps, Fibonacci Heaps, Tries, Skip List

     

    3

    Divide and Conquer with Examples Such as Sorting, Matrix Multiplication, Convex Hull and Searching. Greedy Methods with Examples Such as Optimal Reliability Allocation, Knapsack, Minimum Spanning Trees – Prim’s and Kruskal’s Algorithms, Single Source Shortest Paths - Dijkstra’s and Bellman Ford Algorithms

     

    4

    Dynamic Programming with Examples Such as Knapsack. All Pair Shortest Paths – Warshal’s and Floyd’s Algorithms, Resource Allocation Problem. Backtracking, Branch and Bound with Examples Such as Travelling Salesman Problem, Graph Coloring, n-Queen Problem, Hamiltonian Cycles and Sum of Subsets.

     

    5

    Selected Topics: Algebraic Computation, Fast Fourier Transform, String Matching, Theory of NP-Completeness, Approximation Algorithms and Randomized Algorithms

     

    References:

    1. Thomas H. Coreman, Charles E. Leiserson and Ronald L. Rivest, “Introduction to Algorithms”, Printice Hall of India.

    2. E. Horowitz & S Sahni, "Fundamentals of Computer Algorithms",

    3. Aho, Hopcraft, Ullman, “The Design and Analysis of Computer Algorithms” Pearson Education, 2008.

    4. LEE "Design & Analysis of Algorithms (POD)",McGraw Hill

    5. Gajendra Sharma, Design & Analysis of Algorithms, Khanna Publishing House

    6. Richard E.Neapolitan "Foundations of Algorithms" Jones & Bartlett Learning

    7. Jon Kleinberg and Éva Tardos, Algorithm Design, Pearson, 2005.

    8. Michael T Goodrich and Roberto Tamassia, Algorithm Design: Foundations, Analysis, and Internet Examples, Second Edition, Wiley, 2006.

    9. Harry R. Lewis and Larry Denenberg, Data Structures and Their Algorithms, Harper Collins, 1997

    10. Robert Sedgewick and Kevin Wayne, Algorithms, fourth edition, Addison Wesley, 2011.

    11. Harsh Bhasin,”Algorithm Design and Analysis”,First Edition,Oxford University Press.

    12. Gilles Brassard and Paul Bratley,Algorithmics:Theory and Practice,Prentice Hall,1995.

     

     

     

     


    1. Introduction: Overview, Database System vs File System, Database System Concept and Architecture, Data Model Schema and Instances, Data Independence and Database Language and Interfaces, Data Definitions Language, DML, Overall Database Structure. Data Modeling Using the Entity Relationship Model: ER Model Concepts, Notation for ER Diagram, Mapping Constraints, Keys, Concepts of Super Key, Candidate Key, Primary Key, Generalization, Aggregation, Reduction of an ER Diagrams to Tables, Extended ER Model, Relationship of Higher Degree. 

     2.  Relational data Model and Language: Relational Data Model Concepts, Integrity Constraints, Entity Integrity, Referential Integrity, Keys Constraints, Domain Constraints, Relational Algebra, Relational Calculus, Tuple and Domain Calculus. Introduction on SQL: Characteristics of SQL, Advantage of SQL. SQl Data Type and Literals. Types of SQL Commands. SQL Operators and Their Procedure. Tables, Views and Indexes. Queries and Sub Queries. Aggregate Functions. Insert, Update and Delete Operations, Joins, Unions, Intersection, Minus, Cursors, Triggers, Procedures in SQL/PL SQL 

    3.  Data Base Design & Normalization: Functional dependencies, normal forms, first, second, 8 third normal forms, BCNF, inclusion dependence, loss less join decompositions, normalization using FD, MVD, and JDs, alternative approaches to database design.

     4.  Transaction Processing Concept: Transaction System, Testing of Serializability, Serializability of Schedules, Conflict & View Serializable Schedule, Recoverability, Recovery from Transaction Failures, Log Based Recovery, Checkpoints, Deadlock Handling. Distributed Database: Distributed Data Storage, Concurrency Control, Directory System. 

     5.  Concurrency Control Techniques: Concurrency Control, Locking Techniques for Concurrency Control, Time Stamping Protocols for Concurrency Control, Validation Based Protocol, Multiple Granularity, Multi Version Schemes, Recovery with Concurrent Transaction, Case Study of Oracle.

    Hard Computing:  In 1996, LA Zadeh (LAZ) introduced the term hard computing. According to LAZ: We term a computing as ”Hard” computing, if

    • Precise result is guaranteed
    • Control action is unambiguous
    • Control action is formally defined (i.e. with  mathematical model
    Example:

    • Solving numerical problems (e.g. Roots of polynomials,  Integration etc.)
    • Searching and sorting techniques
    • Solving ”Computational Geometry” problems (e.g. Shortest  tour in Graph theory, Finding closest pair of points etc.)

    Soft computing: Soft computing was proposed by Lotfi A. Zadeh. First he gave the idea about Fuzzy Logic.

    Soft Computing is a collection of methodologies that aim to exploit the tolerance for imprecision and uncertainty to achieve tractability, robustness, and low solution cost.

     • Tolerance of imprecision means result is not precise

    • Uncertainty means different result at different time
    • Tractability means problem solved with in polynomial time
    • Robustness means it can tackle any sort of input including noise

    Soft Computing is a term applied to a field within computer science which is characterized by the use of inexact solutions to computationally hard task such as solution of NP- Complete problems for which an exact solution can not be derived with in polynomial time.

    Characteristics of Soft Computing:

    • It does not require any mathematical modeling of problem  solving
    • It may not yield the precise solution
    • Algorithms are adaptive (i.e. it can adjust to the change of  dynamic environment)
    • Use some biological inspired methodologies such as genetics,  evolution, Ant’s behaviors, human nervous  systems etc.

    Applications using Soft Computing:

    • Handwriting Recognition
    • Automotive systems and manufacturing
    • Image processing and data recognition
    • Decision support System
    • To Power System
    • Neuro Fuzzy System
    • Fuzzy Logic Control
    • Genetic Algorithms
    • Bio Informatics and Biomedicine

    Components of Soft Computing includes:

    • Neural Networks (NN)
    • Fuzzy System or Fuzzy Logic
    • Genetic Algorithm (Evolutionary Computing)

    Reference books:

    1. S. Rajsekaran & G.A. Vijayalakshmi Pai, “Neural Networks,Fuzzy Logic and Genetic Algorithm:Synthesis and Applications” Prentice Hall of India.

    2. Dr. S.N Sivanandam & Dr. S.N Deepa “Principles of Soft Computing” Wiley



    Object Oriented System Design (KCS-054)

    Course Outcome ( CO)

    Bloom’s Knowledge Level (KL)

    At the end of course , the student will be able to understand

    CO 1

    To Understand the application development and analyze the insights of object oriented programming to implement application

    K2, K4

    CO 2

    To Understand, analyze and apply the role of overall modeling concepts (i.e. System,

    structural)

    K2, K3

    CO 3

    To Understand, analyze and apply oops concepts (i.e. abstraction, inheritance)

    K2, K3, K4

    CO 4

    To learn concepts of C++ for understanding the implementation of object oriented concepts

    K2, K3

    CO 5

    To learn the programming concepts to implement object oriented modeling.

    K2, K3

    DETAILED SYLLABUS

    3-0-0

    Unit

    Topic

    Proposed

    Lecture

     

    I

    Introduction:  The  meaning  of  Object  Orientation,  object  identity,    Encapsulation, information hiding, polymorphism, generosity, importance of modelling, principles of modelling, object oriented

    modelling, Introduction to UML, conceptual model of the UML, Architecture.

     

    08

     

     

     

    II

    Basic Structural Modeling: Classes, Relationships, common Mechanisms, and diagrams. Class &Object Diagrams: Terms, concepts, modelling techniques for Class & Object Diagrams.

    Collaboration Diagrams: Terms, Concepts, depicting a message, polymorphism in collaboration Diagrams, iterated messages, use of self in messages. Sequence Diagrams: Terms, concepts, depicting asynchronous messages with/without priority, call-back mechanism, broadcast messages.

    Basic Behavioural Modeling: Use cases, Use case Diagrams, Activity Diagrams, State Machine , Process and thread, Event and signals, Time diagram, interaction diagram, Package diagram.

    Architectural Modeling: Component, Deployment, Component diagrams and Deployment diagrams.

     

     

     

    08

    III

    Object Oriented Analysis: Object oriented design, Object design, Combining three models, Designing

    08


     

    algorithms, design optimization, Implementation of control, Adjustment of inheritance, Object representation, Physical packaging, Documenting design considerations.

    Structured analysis and structured design (SA/SD), Jackson Structured Development (JSD).Mapping object oriented concepts using non-object oriented language, Translating classes into data structures, Passing arguments to methods, Implementing inheritance, associations encapsulation. Object oriented programming style: reusability, extensibility, robustness, programming in the

    large. Procedural v/s OOP, Object oriented language features. Abstraction and Encapsulation.

     

     

    IV

    C++ Basics : Overview, Program structure, namespace, identifiers, variables, constants, enum, operators, typecasting, control structures

    C++ Functions : Simple functions, Call and Return by reference, Inline functions, Macro Vs. Inline

    functions, Overloading of functions, default arguments, friend functions, virtual functions

     

    08

     

     

    V

    Objects and Classes : Basics of object and class in C++, Private and public members, static data and function members, constructors and their types, destructors, operator overloading, type conversion. Inheritance : Concept of Inheritance, types of inheritance: single, multiple, multilevel, hierarchical, hybrid, protected members, overriding, virtual base class

    Polymorphism : Pointers in C++, Pointes and Objects, this pointer, virtual and pure virtual

    functions, Implementing polymorphism

     

     

    08

    Text Books

    1.     James Rumbaugh et. al, “Object Oriented Modeling and Design”, PHI

    2.     Grady Booch, James Rumbaugh, Ivar Jacobson, “The Unified Modeling Language User Guide”, Pearson Education

    3.     Object Oriented Programming With C++, E Balagurusamy, TMH

    4.     C++ Programming, Black Book, Steven Holzner, dreamtech

    5.     Object Oriented Programming in Turbo C++, Robert Lafore, Galgotia

    6.     Object Oriented Programming with ANSI and Turbo C++, Ashok Kamthane, Pearson

    7.     The Compete Reference C++, Herbert Schlitz, TMH

     


    KCS-503: Design and
    Analysis of Algorithm    

    UNIT

    TOPIC

    1

    Introduction: Algorithms, Analyzing Algorithms, Complexity of Algorithms, Growth of Functions, Performance Measurements, Sorting and Order Statistics - Shell Sort, Quick Sort, Merge Sort, Heap Sort, Comparison of Sorting Algorithms, Sorting in Linear Time.

    2

    Advanced Data Structures: Red-Black Trees, B – Trees, Binomial Heaps, Fibonacci Heaps, Tries, Skip List

     

    3

    Divide and Conquer with Examples Such as Sorting, Matrix Multiplication, Convex Hull and Searching. Greedy Methods with Examples Such as Optimal Reliability Allocation, Knapsack, Minimum Spanning Trees – Prim’s and Kruskal’s Algorithms, Single Source Shortest Paths - Dijkstra’s and Bellman Ford Algorithms

     

    4

    Dynamic Programming with Examples Such as Knapsack. All Pair Shortest Paths – Warshal’s and Floyd’s Algorithms, Resource Allocation Problem. Backtracking, Branch and Bound with Examples Such as Travelling Salesman Problem, Graph Coloring, n-Queen Problem, Hamiltonian Cycles and Sum of Subsets.

     

    5

    Selected Topics: Algebraic Computation, Fast Fourier Transform, String Matching, Theory of NP-Completeness, Approximation Algorithms and Randomized Algorithms

     

    References:

    1. Thomas H. Coreman, Charles E. Leiserson and Ronald L. Rivest, “Introduction to Algorithms”, Printice Hall of India.

    2. E. Horowitz & S Sahni, "Fundamentals of Computer Algorithms",

    3. Aho, Hopcraft, Ullman, “The Design and Analysis of Computer Algorithms” Pearson Education, 2008.

    4. LEE "Design & Analysis of Algorithms (POD)",McGraw Hill

    5. Gajendra Sharma, Design & Analysis of Algorithms, Khanna Publishing House

    6. Richard E.Neapolitan "Foundations of Algorithms" Jones & Bartlett Learning

    7. Jon Kleinberg and Éva Tardos, Algorithm Design, Pearson, 2005.

    8. Michael T Goodrich and Roberto Tamassia, Algorithm Design: Foundations, Analysis, and Internet Examples, Second Edition, Wiley, 2006.

    9. Harry R. Lewis and Larry Denenberg, Data Structures and Their Algorithms, Harper Collins, 1997

    10. Robert Sedgewick and Kevin Wayne, Algorithms, fourth edition, Addison Wesley, 2011.

    11. Harsh Bhasin,”Algorithm Design and Analysis”,First Edition,Oxford University Press.

    12. Gilles Brassard and Paul Bratley,Algorithmics:Theory and Practice,Prentice Hall,1995.

           
     
     
     

    • Recognize the feasibility of applying a soft computing methodology for a particular problem
    • Know the concepts and techniques of soft computing and foster their abilities in designing and implementing soft computing based solutions for real-world and engineering problems.
    • Apply  neural  networks  to  pattern  classification  and  regression  problems         and  compare solutions by various soft computing approaches for a given problem.



    CO 4         Apply fuzzy logic and reasoning to handle uncertainty and solve engineering problems                     K3, K4

    CO 5        Apply genetic algorithms to combinatorial optimization problems                                                          

    In this subject we will be discussing about various phases of compiler

    Syllabus

    UNIT I

    Introduction: Algorithms, Analyzing Algorithms, Complexity of Algorithms, Growth of Functions, Performance Measurements, Sorting and Order Statistics - Shell Sort, Quick Sort, Merge Sort, Heap Sort, Comparison of Sorting Algorithms, Sorting in Linear Time.

    UNIT-II

    Advanced Data Structures: Red-Black Trees, B – Trees, Binomial Heaps, Fibonacci Heaps, Tries, Skip List

    UNIT-III

    Divide and Conquer with Examples Such as Sorting, Matrix Multiplication, Convex Hull and Searching. Greedy Methods with Examples Such as Optimal Reliability Allocation, Knapsack, Minimum Spanning Trees – Prim’s and Kruskal’s Algorithms, Single Source Shortest Paths - Dijkstra’s and Bellman Ford Algorithms.

    UNIT IV

    Dynamic Programming with Examples Such as Knapsack. All Pair Shortest Paths – Warshal’s and Floyd’s Algorithms, Resource Allocation Problem. Backtracking, Branch and Bound with Examples Such as Travelling Salesman Problem, Graph Coloring, n-Queen Problem, Hamiltonian Cycles and Sum of Subsets.

    UNIT V

    Selected Topics: Algebraic Computation, Fast Fourier Transform, String Matching, Theory of NPCompleteness, Approximation Algorithms and Randomized Algorithms

    Text Books:

    1. Thomas H. Coreman, Charles E. Leiserson and Ronald L. Rivest, “Introduction to Algorithms”, Printice Hall of India. 

    2. E. Horowitz & S Sahni, "Fundamentals of Computer Algorithms",

     3. Aho, Hopcraft, Ullman, “The Design and Analysis of Computer Algorithms” Pearson Education, 2008.

     4. LEE "Design & Analysis of Algorithms (POD)",McGraw Hill

     5. Richard E.Neapolitan "Foundations of Algorithms" Jones & Bartlett Learning

     6. Jon Kleinberg and Éva Tardos, Algorithm Design, Pearson, 2005. 

    7. Michael T Goodrich and Roberto Tamassia, Algorithm Design: Foundations, Analysis, and Internet Examples, Second Edition, Wiley, 2006. 

    8. Harry R. Lewis and Larry Denenberg, Data Structures and Their Algorithms, Harper Collins, 1997 

    9. Robert Sedgewick and Kevin Wayne, Algorithms, fourth edition, Addison Wesley, 2011.

     10. Harsh Bhasin,”Algorithm Design and Analysis”,First Edition,Oxford University Press. 

    11. Gilles Brassard and Paul Bratley,Algorithmics:Theory and Practice,Prentice Hall,1995

    Database Management System (KCS501)

    Course Outcome ( CO)

    Bloom’s Knowledge Level (KL)

    At the end of course , the student will be able to understand

    CO 1

    Apply knowledge of database for real life applications.

    K3

    CO 2

    Apply query processing techniques to automate the real time problems of databases.

    K3, K4

    CO 3

    Identify and solve the redundancy problem in database tables using normalization.

    K2, K3

    CO 4

    Understand the concepts of transactions, their processing so they will familiar with broad range

    of database management issues including data integrity, security and recovery.

    K2, K4

    CO 5

    Design, develop and implement a small database project using database tools.

    K3, K6

    DETAILED SYLLABUS

    3-1-0

    Unit

    Topic

    Proposed

    Lecture

     

     

    I

    Introduction: Overview, Database System vs File System, Database System Concept and Architecture, Data Model Schema and Instances, Data Independence and Database Language and Interfaces, Data Definitions Language, DML, Overall Database Structure. Data Modeling Using the Entity Relationship Model: ER Model Concepts, Notation for ER Diagram, Mapping Constraints, Keys, Concepts of Super Key, Candidate Key, Primary Key, Generalization, Aggregation,

    Reduction of an ER Diagrams to Tables, Extended ER Model, Relationship of Higher Degree.

     

     

    08

     

     

     

    II

    Relational data Model and Language: Relational Data Model Concepts, Integrity Constraints, Entity Integrity, Referential Integrity, Keys Constraints, Domain Constraints, Relational Algebra, Relational Calculus, Tuple and Domain Calculus. Introduction on SQL: Characteristics of SQL, Advantage of SQL. SQl Data Type and Literals. Types of SQL Commands. SQL Operators and Their Procedure. Tables, Views and Indexes. Queries and Sub Queries. Aggregate Functions. Insert, Update and Delete Operations, Joins, Unions, Intersection, Minus, Cursors, Triggers,

    Procedures in SQL/PL SQL

     

     

     

    08

     

    III

    Data Base Design & Normalization: Functional dependencies, normal forms, first, second, 8 third

    normal forms, BCNF, inclusion dependence, loss less join decompositions, normalization using FD, MVD, and JDs, alternative approaches to database design

     

    08

     

    IV

    Transaction Processing Concept: Transaction System, Testing of Serializability, Serializability of Schedules, Conflict & View Serializable Schedule, Recoverability, Recovery from Transaction Failures, Log Based Recovery, Checkpoints, Deadlock Handling. Distributed Database: Distributed

    Data Storage, Concurrency Control, Directory System.

     

    08

     

    V

    Concurrency Control  Techniques:  Concurrency Control, Locking Techniques for  Concurrency

    Control, Time Stamping Protocols for Concurrency Control, Validation Based Protocol, Multiple Granularity, Multi Version Schemes, Recovery with Concurrent Transaction, Case Study of Oracle.

     

    08

    Text books:

    1.      Korth, Silbertz, Sudarshan,” Database Concepts”, McGraw Hill

    2.     Date C J, “An Introduction to Database Systems”, Addision Wesley

    3.     Elmasri, Navathe, “ Fundamentals of Database Systems”, Addision Wesley

    4.     O’Neil, Databases, Elsevier Pub.

    5.     RAMAKRISHNAN"Database Management Systems",McGraw Hill

    6.     Leon & Leon,”Database Management Systems”, Vikas Publishing House

    7.     Bipin C. Desai, “ An Introduction to Database Systems”, Gagotia Publications

    8.     Majumdar & Bhattacharya, “Database Management System”, TMH


    CO 1 Recognize the feasibility of applying a soft computing methodology for a particular problem 

    CO 2 Understand the concepts and techniques of soft computing and foster their abilities in designing and implementing soft computing based solutions for real-world and engineering problems. 

    CO 3 Apply neural networks to pattern classification and regression problems and compare solutions by various soft computing approaches for a given problem. 

    CO 4 Apply fuzzy logic and reasoning to handle uncertainty and solve engineering problems .

    CO 5 Apply genetic algorithms to combinatorial optimization problems 

    IN THIS COURSE WE WILL DISCUSS ABOUT VARIOUS SECURITY METHODS HOW THE SECURITY HAS BEEN EVOLVED AND VARIOUS TYPES OF ATTACKS.

    Topics include elementary data structures, (including arrays, stacks, queues, and lists), advanced data structures (including trees and graphs), the algorithms used to manipulate these structures, and their application to solving practical engineering problems.

    In this course we will study about all of us. It presents a set of proposals so that we may start exploring our own self and our expanse of living in order to better understand our life - so we can understand what is valuable for us, what is our 'value' in the larger scheme of things we live in. We live in human society, in nature and we want to find out our role in this expanse. 

    SYLLABUS

    Module I: Partial Differential Equations

    Origin of Partial Differential Equations, Linear and Non Linear Partial Equations of first order,

    Lagrange’s Equations, Charpit’s method, Cauchy’s method of Characteristics, Solution of Linear

    Partial Differential Equation of Higher order with constant coefficients, Equations reducible to

    linear partial differential equations with constant coefficients.

    Module II: Applications of Partial Differential Equations:

    Classification of linear partial differential equation of second order, Method of separation of

    variables, Solution of wave and heat conduction equation up to two dimension, Laplace equation

    in two dimensions, Equations of Transmission lines.

    Module III: Statistical Techniques I:

    Introduction: Measures of central tendency, Moments, Moment generating function (MGF) ,

    Skewness, Kurtosis, Curve Fitting , Method of least squares, Fitting of straight lines, Fitting of

    second degree parabola, Exponential curves ,Correlation and Rank correlation, Regression

    Analysis: Regression lines of y on x and x on y, regression coefficients, properties of regressions

    coefficients and non linear regression.

    Module IV: Statistical Techniques II:

    Probability and Distribution: Introduction, Addition and multiplication law of probability,

    Conditional probability, Baye’s theorem, Random variables (Discrete and Continuous Random

    variable) Probability mass function and Probability density function, Expectation and variance,

    Discrete and Continuous Probability distribution: Binomial, Poission and Normal distributions.

    Module V: Statistical Techniques III:

    Sampling, Testing of Hypothesis and Statistical Quality Control: Introduction , Sampling

    Theory (Small and Large) , Hypothesis, Null hypothesis, Alternative hypothesis, Testing a

    Hypothesis, Level of significance, Confidence limits, Test of significance of difference of means,

    T-test, F-test and Chi-square test, One way Analysis of Variance (ANOVA).Statistical Quality

    Control (SQC) , Control Charts , Control Charts for variables ( X and R Charts), Control Charts

    for Variables ( p, np and C charts).

    Text Books

    1. Erwin Kreyszig, Advanced Engineering Mathematics, 9thEdition, John Wiley &

    Sons, 2006.

    2. P. G. Hoel, S. C. Port and C. J. Stone, Introduction to Probability Theory,

    Universal Book Stall, 2003(Reprint).

    3. S. Ross: A First Course in Probability, 6th Ed., Pearson Education India, 2002.

    4. W. Feller, An Introduction to Probability Theory and its Applications, Vol. 1, 3rd

    Ed., Wiley, 1968.

    Reference Books

    1. B.S. Grewal, Higher Engineering Mathematics, Khanna Publishers, 35th Edition, 2000.

    2. T.Veerarajan : Engineering Mathematics (for semester III), Tata McGraw-Hill, New

    Delhi.

    3. R.K. Jain and S.R.K. Iyenger: Advance Engineering Mathematics; Narosa Publishing

    House, New Delhi.

    4. J.N. Kapur: Mathematical Statistics; S. Chand & Sons Company Limited, New Delhi.

    5. D.N.Elhance,V. Elhance & B.M. Aggarwal: Fundamentals of Statistics; Kitab Mahal

    Distributers, New Delhi.

    In this course we will study about all of us. It presents a set of proposals so that we may start exploring our own self and our expanse of living in order to better understand our life - so we can understand what is valuable for us, what is our 'value' in the larger scheme of things we live in. We live in human society, in nature and we want to find out our role in this expanse. 

    IN THIS COURSE WE WILL DISCUSS ABOUT VARIOUS TYPE OF SECURITY METHODS AND HOW THEY EVOLVED

    In this course we will study about all of us. It presents a set of proposals so that we may start exploring our own self and our expanse of living in order to better understand our life - so we can understand what is valuable for us, what is our 'value' in the larger scheme of things we live in. We live in human society, in nature and we want to find out our role in this expanse. 

    Subject:- Mathematics-IV ( KAS-302)

    SYLLABUS

    Unit-I( Partial differential equations)

    Origin of Partial Differential Equations, Linear and Non Linear Partial Equations of first order, Lagrange’s Equations, Charpit’s method, Cauchy’s method of Characteristics, Solution of Linear Partial Differential Equation of Higher order with constant coefficients, Equations reducible to linear partial differential equations with constant coefficients.

    Unit-II (Applications of Partial differential equations)

    Classification of linear partial differential equation of second order, Method of separation of variables, Solution of wave and heat conduction equation up to two dimension, Laplace equation in two dimensions, Equations of Transmission lines.

     

    Unit-III (Statistical Techniques-I)

     Introduction: Measures of central tendency, Moments, Moment generating function (MGF) , Skewness, Kurtosis, Curve Fitting , Method of least squares, Fitting of straight lines, Fitting of second degree parabola, Exponential curves ,Correlation and Rank correlation, Regression Analysis: Regression lines of y on x and x on y, regression coefficients, properties of regressions coefficients and non linear regression

    Unit-IV(Statistical Techniques-II)

    Probability and Distribution: Introduction, Addition and multiplication law of probability, Conditional probability, Baye’s theorem, Random variables (Discrete and Continuous Random variable) Probability mass function and Probability density function, Expectation and variance, Discrete and Continuous Probability distribution: Binomial, Poission and Normal distributions.

    Unit-V (Statistical Techniques-III)

    Introduction , Sampling Theory (Small and Large) , Hypothesis, Null hypothesis, Alternative hypothesis, Testing a Hypothesis, Level of significance, Confidence limits, Test of significance of difference of means, T-test, F-test and Chi-square test, One way Analysis of Variance (ANOVA).Statistical Quality

    Control (SQC) , Control Charts , Control Charts for variables ( X and R Charts), Control Charts for Variables ( p, np and C charts).

    Text Books

    1. Erwin Kreyszig, Advanced Engineering Mathematics, 9thEdition, John Wiley & Sons, 2006.

    2. P. G. Hoel, S. C. Port and C. J. Stone, Introduction to Probability Theory, Universal Book Stall, 2003(Reprint).

    3. S. Ross: A First Course in Probability, 6th Ed., Pearson Education India, 2002.

    4. W. Feller, An Introduction to Probability Theory and its Applications, Vol. 1, 3rd Ed., Wiley, 1968.

     

    Reference Books

    1. B.S. Grewal, Higher Engineering Mathematics, Khanna Publishers, 35th Edition, 2000. 2.T.Veerarajan : Engineering Mathematics (for semester III), Tata McGraw-Hill, New Delhi.

    3. R.K. Jain and S.R.K. Iyenger: Advance Engineering Mathematics; Narosa Publishing House, New Delhi.

    4. J.N. Kapur: Mathematical Statistics; S. Chand & Sons Company Limited, New Delhi.

    5. D.N.Elhance, V. Elhance & B.M. Aggarwal: Fundamentals of Statistics; Kitab Mahal Distributers, New Delhi.

     

     

     

     


    OBJECTIVE                                                                                                                              The objective of the course is four-fold:                                                                              Development of a holistic perspective based on self-exploration about themselves (human being), family, society, and nature/existence.                                                             Understanding (or developing clarity) of the harmony in the human being, family, society, and nature/existence                                                                                                Strengthening of self-reflection.                                                                                          Development of commitment and courage to act

    IN THIS COURSE WE WILL DISCUSS ABOUT VARIOUS SECURITY METHODS HOW THEY EVOLVED AND VARIOUS TYPES OF ATTACKS

    In this course we will study about all of us. It presents a set of proposals so that we may start exploring our own self and our expanse of living in order to better understand our life - so we can understand what is valuable for us, what is our 'value' in the larger scheme of things we live in. We live in human society, in nature and we want to find out our role in this expanse. 

    Subject:- Mathematics-IV ( KAS-302)

    SYLLABUS

    Unit-I( Partial differential equations)

    Origin of Partial Differential Equations, Linear and Non Linear Partial Equations of first order, Lagrange’s Equations, Charpit’s method, Cauchy’s method of Characteristics, Solution of Linear Partial Differential Equation of Higher order with constant coefficients, Equations reducible to linear partial differential equations with constant coefficients.

    Unit-II (Applications of Partial differential equations)

    Classification of linear partial differential equation of second order, Method of separation of variables, Solution of wave and heat conduction equation up to two dimension, Laplace equation in two dimensions, Equations of Transmission lines.

     

    Unit-III (Statistical Techniques-I)

     Introduction: Measures of central tendency, Moments, Moment generating function (MGF) , Skewness, Kurtosis, Curve Fitting , Method of least squares, Fitting of straight lines, Fitting of second degree parabola, Exponential curves ,Correlation and Rank correlation, Regression Analysis: Regression lines of y on x and x on y, regression coefficients, properties of regressions coefficients and non linear regression

    Unit-IV(Statistical Techniques-II)

    Probability and Distribution: Introduction, Addition and multiplication law of probability, Conditional probability, Baye’s theorem, Random variables (Discrete and Continuous Random variable) Probability mass function and Probability density function, Expectation and variance, Discrete and Continuous Probability distribution: Binomial, Poission and Normal distributions.

    Unit-V (Statistical Techniques-III)

    Introduction , Sampling Theory (Small and Large) , Hypothesis, Null hypothesis, Alternative hypothesis, Testing a Hypothesis, Level of significance, Confidence limits, Test of significance of difference of means, T-test, F-test and Chi-square test, One way Analysis of Variance (ANOVA).Statistical Quality

    Control (SQC) , Control Charts , Control Charts for variables ( X and R Charts), Control Charts for Variables ( p, np and C charts).

    Text Books

    1. Erwin Kreyszig, Advanced Engineering Mathematics, 9thEdition, John Wiley & Sons, 2006.

    2. P. G. Hoel, S. C. Port and C. J. Stone, Introduction to Probability Theory, Universal Book Stall, 2003(Reprint).

    3. S. Ross: A First Course in Probability, 6th Ed., Pearson Education India, 2002.

    4. W. Feller, An Introduction to Probability Theory and its Applications, Vol. 1, 3rd Ed., Wiley, 1968.

     

    Reference Books

    1. B.S. Grewal, Higher Engineering Mathematics, Khanna Publishers, 35th Edition, 2000. 2.T.Veerarajan : Engineering Mathematics (for semester III), Tata McGraw-Hill, New Delhi.

    3. R.K. Jain and S.R.K. Iyenger: Advance Engineering Mathematics; Narosa Publishing House, New Delhi.

    4. J.N. Kapur: Mathematical Statistics; S. Chand & Sons Company Limited, New Delhi.

    5. D.N.Elhance, V. Elhance & B.M. Aggarwal: Fundamentals of Statistics; Kitab Mahal Distributers, New Delhi.


    Topics include elementary data structures, (including arrays, stacks, queues, and lists), advanced data structures (including trees and graphs), the algorithms used to manipulate these structures, and their application to solving practical engineering problems.

    OBJECTIVE: The objective of the course is four-fold:                                                      Development of a holistic perspective based on self-exploration about themselves (human being), family, society, and nature/existence.                                                              Understanding (or developing clarity) of the harmony in the human being, family, society, and nature/existence                                                                                                    Strengthening of self-reflection.                                                                                      Development of commitment and courage to act.

    UNIT I: Introduction [08]

    Introduction: Functional units of digital system and their interconnections, buses, bus architectureTypes of buses and bus arbitration, Register, bus and memory transfer, Processor organization, General registers organization, stack organization and addressing modes.

    UNIT II: Arithmetic and Logic Unit [08]

    Arithmetic and logic unit: Look ahead carries adders. Multiplication: Signed operand multiplication, Booths algorithm and array multiplier, Division and logic operations, floating point arithmetic operation, Arithmetic & logic unit design, IEEE Standard for Floating Point Numbers.

    UNIT III: Control Unit [08]

    Control Unit: Instruction types, formats, instruction cycles and sub cycles (Fetch and execute etc.), micro-operations, execution of a complete instruction, Program Control, Reduced Instruction Set Computer, Pipelining, Hardwired and micro programmed control: microprogrammed sequencing, concept of horizontal and vertical microprogramming.

    UNIT IV: Memory [08]

    Memory: Basic concept and hierarchy, semiconductor RAM memories, 2D & 2 1/2D memory organization. ROM memories, Cache memories: concept and design issues performance, address mapping and replacement, Auxiliary memories: magnetic disk, magnetic tape and optical disks, Virtual memory: concept implementation.

    UNIT V: Input/Output [08]

    Input / Output: Peripheral devices, I/O interface, I/O ports, Interrupts: interrupt hardware, types of interrupts and exceptions, Modes of Data Transfer: Programmed I/O, interrupt initiated I/O and Direct Memory Access, I/O channels and processors, Serial Communication: Synchronous & asynchronous communication, standard communication interfaces.


    TEXT BOOK:

    1. Computer System Architecture - M. Mano

    2. Carl Hamacher, Zvonko Vranesic, Safwat Zaky Computer Organization, McGraw-Hill, Fifth Edition, Reprint 2012

    3. John P. Hayes, Computer Architecture and Organization, Tata McGraw Hill, Third Edition, 1998. Reference books

    4. William Stallings, Computer Organization and Architecture-Designing for Performance, Pearson Education, Seventh edition, 2006.

    5. Behrooz Parahami, “Computer Architecture”, Oxford University Press, Eighth Impression, 2011.

    6. David A. Patterson and John L. Hennessy, “Computer Architecture-A Quantitative Approach”, Elsevier, a division of reed India Private Limited, Fifth edition, 2012

    7. Structured Computer Organization, Tannenbaum(PHI)

    UNIT – 1:

    Introduction to security attacks, services and mechanism, Classical encryption techniques- substitution ciphers and transposition ciphers, cryptanalysis, steganography, Stream and block ciphers. Modern Block Ciphers: Block ciphers principles, Shannon’s theory of confusion and diffusion, fiestal structure, Data encryption standard (DES), Strength of DES, Idea of differential cryptanalysis, block cipher modes of operations, Triple DES

     

    UNIT – 2:

    Introduction to group, field, finite field of the form GF(p), modular arithmetic, prime and relative prime numbers, Extended Euclidean Algorithm, Advanced Encryption Standard (AES) encryption and decryption, Fermat’s and Euler’s theorem, Primarily testing, Chinese Remainder theorem, Discrete Logarithmic Problem, Principals of public key crypto systems, RSA algorithm, security of RSA.

     

    UNIT – 3:

    Message Authentication Codes: Authentication requirements, authentication functions, message authentication code, hash functions, birthday attacks, security of hash functions, Secure hash algorithm (SHA) Digital Signatures: Digital Signatures, Elgamal Digital Signature Techniques, Digital signature standards (DSS), proof of digital signature algorithm,

    UNIT – 4:

    Key Management and distribution: Symmetric key distribution, Diffie-Hellman Key Exchange, Public key distribution, X.509 Certificates, Public key Infrastructure. Authentication Applications: Kerberos, Electronic mail security: pretty good privacy (PGP), S/MIME.

    UNIT – 5:

    IP Security: Architecture, Authentication header, Encapsulating security payloads, combining security associations, key management. Introduction to Secure Socket Layer, Secure electronic, transaction (SET) System Security: Introductory idea of Intrusion, Intrusion detection, Viruses and related threats, firewalls.


    Unit 1 Introduction: Introduction to Artificial Intelligence, Foundations and History of Artificial Intelligence, Applications of Artificial Intelligence, Intelligent Agents, Structure of Intelligent Agents. Computer vision, Natural Language Possessing. 

    Unit 2 Introduction to Search : Searching for solutions, Uniformed search strategies, Informed search strategies, Local search algorithms and optimistic problems, Adversarial Search, Search for games, Alpha - Beta pruning 

    Unit 3 Knowledge Representation & Reasoning: Propositional logic, Theory of first order logic, Inference in First order logic, Forward & Backward chaining, Resolution, Probabilistic reasoning, Utility theory, Hidden Markov Models (HMM), Bayesian Networks. 

    Unit 4 Machine Learning : Supervised and unsupervised learning, Decision trees, Statistical learning models, Learning with complete data - Naive Bayes models, Learning with hidden data - EM algorithm, Reinforcement learning, 

    Unit 5 Pattern Recognition : Introduction, Design principles of pattern recognition system, Statistical Pattern recognition, Parameter estimation methods - Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA), Classification Techniques – Nearest Neighbor (NN) Rule, Bayes Classifier, Support Vector Machine (SVM), K – means clustering. 

    Text books: 1. Stuart Russell, Peter Norvig, “Artificial Intelligence – A Modern Approach”, Pearson Education 2. Elaine Rich and Kevin Knight, “Artificial Intelligence”, McGraw-Hill 3. E Charniak and D McDermott, “Introduction to Artificial Intelligence”, Pearson Education 4. Dan W. Patterson, “Artificial Intelligence and Expert Systems”, Prentice Hall of India,

    In this Course we would be studying the the features of Blockchain and the algorithms associated to it. 

    Application of Soft Computing is an elective subject for IT 7th Sem students.

    Unit 1-Introduction: Overview, Database System vs File System, Database System Concept and Architecture, Data Model Schema and Instances, Data Independence and Database Language and Interfaces, Data Definitions Language, DML, Overall Database Structure. Data Modeling Using the Entity Relationship Model: ER Model Concepts, Notation for ER Diagram, Mapping Constraints, Keys, Concepts of Super Key, Candidate Key, Primary Key, Generalization, Aggregation, Reduction of an ER Diagrams to Tables, Extended ER Model, Relationship of Higher Degree.

    Unit 2-Relational data Model and Language: Relational Data Model Concepts, Integrity Constraints, Entity Integrity, Referential Integrity, Keys Constraints, Domain Constraints, Relational Algebra, Relational Calculus, Tuple and Domain Calculus. Introduction on SQL: Characteristics of SQL, Advantage of SQL. SQl Data Type and Literals. Types of SQL Commands. SQL Operators and Their Procedure. Tables, Views and Indexes. Queries and Sub Queries. Aggregate Functions. Insert, Update and Delete Operations, Joins, Unions, Intersection, Minus, Cursors, Triggers, Procedures in SQL/PL SQL

    Unit 3-Data Base Design & Normalization: Functional dependencies, normal forms, first, second, 8 third normal forms, BCNF, inclusion dependence, loss less join decompositions, normalization using FD, MVD, and JDs, alternative approaches to database design

    Unit 4-Transaction Processing Concept: Transaction System, Testing of Serializability, Serializability of Schedules, Conflict & View Serializable Schedule, Recoverability, Recovery from Transaction Failures, Log Based Recovery, Checkpoints, Deadlock Handling. Distributed Database: Distributed Data Storage, Concurrency Control, Directory System

    Unit 5-Concurrency Control Techniques: Concurrency Control, Locking Techniques for Concurrency Control, Time Stamping Protocols for Concurrency Control, Validation Based Protocol, Multiple Granularity, Multi Version Schemes, Recovery with Concurrent Transaction, Case Study of Oracle

    Text books:

    1. Korth, Silbertz, Sudarshan,” Database Concepts”, McGraw Hill 

    2. Date C J, “An Introduction to Database Systems”, Addision Wesley 

    3. Elmasri, Navathe, “ Fundamentals of Database Systems”, Addision Wesley 

    4. O’Neil, Databases, Elsevier Pub. 

    5. RAMAKRISHNAN"Database Management Systems",McGraw Hill 

    6. Leon & Leon,”Database Management Systems”, Vikas Publishing House 

    7. Bipin C. Desai, “ An Introduction to Database Systems”, Gagotia Publications 

    8. Majumdar & Bhattacharya, “Database Management System”, TMH


    Syllabus Description

    KCS-303: Discrete Structures & Theory of Logic

    UNIT – 1:

    Set Theory: Introduction, Combination of sets, Multisets, Ordered pairs. Proofs of some general identities on sets. Relations: Definition, Operations on relations, Properties of relations, Composite Relations, Equality of relations, Recursive definition of relation, Order of relations. Functions: Definition, Classification of functions, Operations on functions, Recursively defined functions. Growth of Functions.

    Natural Numbers: Introduction, Mathematical Induction, Variants of Induction, Induction with Nonzero Base cases. Proof Methods, Proof by counter – example, Proof by contradiction.

    UNIT – 2:

    Algebraic Structures: Definition, Groups, Subgroups and order, Cyclic Groups, Cosets, Lagrange's theorem, Normal Subgroups, Permutation and Symmetric groups, Group Homomorphisms, Definition and elementary properties of Rings and Fields.

    UNIT – 3:

    Lattices: Definition, Properties of lattices – Bounded, Complemented, Modular and Complete lattice. Boolean Algebra: Introduction, Axioms and Theorems of Boolean algebra, Algebraic manipulation of Boolean expressions. Simplification of Boolean Functions, Karnaugh maps, Logic gates, Digital circuits and Boolean algebra.

     

    UNIT – 4:

    Propositional Logic: Proposition, well formed formula, Truth tables, Tautology, Satisfiability, Contradiction, Algebra of proposition, Theory of Inference.

    Predicate Logic: First order predicate, well formed formula of predicate, quantifiers, Inference theory of predicate logic.

    UNIT – 5:

    Trees: Definition, Binary tree, Binary tree traversal, Binary search tree.

    Graphs: Definition and terminology, Representation of graphs, Multigraphs, Bipartite graphs, Planar graphs, Isomorphism and Homeomorphism of graphs, Euler and Hamiltonian paths, Graph coloring, Recurrence Relation & Generating function: Recursive definition of functions, Recursive algorithms, Method of solving recurrences.

    Combinatorics: Introduction, Counting Techniques, Pigeonhole Principle


    1. Students will be enabled to understand the nature and objective of Technical Communication relevant for the work place as Engineers.

    2. Students will utilize the technical writing for the purposes of Technical Communication and its exposure in various dimensions.

    3. Students would imbibe inputs by presentation skills to enhance confidence in face of diverse audience.

    4. Technical communication skills will create a vast know-how of the application of the learning to promote their technical competence.

    5. It would enable them to evaluate their efficacy as fluent & efficient communicators by learning the voice-dynamics.


    CO-1

    The student will be able to fabricate and design MOS transistors using Layout design rules, types & characteristics of MOS Inverters.

    CO-2

    The student will be able to understand the different models of delay estimation and power dissipation

    CO-3

    The student will understand the different sources of power dissipation in CMOS circuit.

    CO-4

    The student will learn to design Lower power CMOS Logic circuits through various scheme and able to design logic circuits layouts for both static and dynamic clocked CMOS circuits.

    Co-5

    The student will learn to design low power CMOS logic circuits through various scheme.


    1. Students will be enabled to understand the nature and objective of Technical Communication relevant for the work place as Engineers.

    2. Students will utilize the technical writing for the purposes of Technical Communication and its exposure in various dimensions.

    3. Students would imbibe inputs by presentation skills to enhance confidence in face of diverse audience.

    4. Technical communication skills will create a vast know-how of the application of the learning to promote their technical competence.

    5. It would enable them to evaluate their efficacy as fluent & efficient communicators by learning the voice-dynamics.

    SENSOR & INSTRUMENTATION (KOE-034)

    ELECTRONIC DEVICES (KEC 301)

    UNIT-I:

    Principles of Computer Graphics:

    Point plotting, drawing of lines, Bresenham’s circle algorithm.

    Transformation in Graphics:

    Co-ordinate system used in Graphics and windowing, view port, views.

    2D transformations – rotation, scaling, translation, mirror, reflection, shear - homogeneous transformations – concatenation.

    3D Transformation – Perspective Projection – Technique (Description of techniques only).

    Geometric Modelling:

    Classification of Geometric Modelling – Wire frame, Surface and Solid Modelling, applications –

    representation of curves and surfaces – Parametric form.

    Design of curved shapes- Cubic spline – Bezier curve – B-spline – Design of Surfaces - features of

    Surface Modelling Package – Solid Primitives, CSG.

    B-rep and description of other modelling techniques like Pure primitive instancing, cell decomposition,

    spatial occupancy enumeration, Boolean Operations (join, cut, intersection), Creating 3D objects from

    2D profiles (extrusion, revolving etc).

    UNIT-II:

    Graphics standard & Data storage:

    Standards for computer graphics GKS, PHIGS. Data exchange standards – IGES, STEP - Manipulation

    of the model - Model storage.

    Finite Element Modelling:

    Introduction, Mesh Generation – mesh requirements.

    Semi-Automatic Methods- Node-based approach, Region based approach, Solid-modelling-based methods.

    Fully Automatic Methods- Element-based approach, Application, Mesh Refinements using Isoperimetric Finite Elements, Meshing in high gradient areas, Transition Regions. Sub modelling Concept.

    An overview of modelling software’s like PRO-E, CATIA, IDEAS, SOLID EDGE etc.


    UNIT-III:

    CAM:

    Scope and applications – NC in CAM – Principal types of CNC machine tools and their construction features – tooling for CNC – ISO designation for tooling – CNC operating system – FANUC, SINUMERIK – LINUMERIK.

    Programming for CNC machining – coordinate systems – manual part programming – computer assisted part programming – CNC part programming with CAD system.

    Material handling in CAM environment:

    Types – AGVS – AS/RS – Swarf handling and disposal of wastes – single and mixed mode assembly lines – quantitative analysis of assembly systems.

    UNIT-IV:

    Robotics:

    Classification and specification – drive and controls – sensors - end effectors - grippers- tool handling and work handling – machine vision – robot programming concepts – case studies in assembly.

    Quality Function Deployment:

    Process Planning – CAPP – Variant and Generative systems- Concurrent Engineering and Design for Manufacturing.

    Advanced manufacturing Planning Computer Aided Production Planning and Control – Aggregate production planning and master production schedule – MRP – MRP II – ERP - Capacity planning.

    UNIT-V:

    Rapid prototyping:

    Need for rapid prototyping, Basic principles and advantages of RP, General features and classifications of different RP techniques with examples.

    Introduction to three representative RP techniques: Fusion Deposition Modelling, Laminated Object Manufacturing and Stereo-lithography.

    Flexible manufacturing cells:

    Systems – characteristics – economics and technological justification – planning, installation, operation and evaluation issues – role of group technology and JIT in FMS – typical case studies future prospects.

    Books and References:

    1. Chris Mcmahon and - CAD/CAM – Principle Practice and Manufacturing Management,Jimmie Browne Addision Wesley England, Second Edition,2000.

    2. Dr.Sadhu Singh - Computer Aided Design and Manufacturing, khanna Publishers, NewDelhi, Second Edition,2000.

    3. P.Radhakrishnan, - CAD/CAM/CIM, New Age International (P) Ltd., New Delhi.S.Subramanayanand V.Raju.

    4. Groover M.P. and - CAD/CAM; Computer Aided Design and Manufacturing, Prentice HallZimmers EW. International, New Delhi, 1992.

    5. Ibrahim Zeid - CAD/CAM theory and Practice, Tata McGraw Hill Publishing Co. Ltd.,Company Ltd., New Delhi, 1992.

    6. Mikell P.Groover - Automation , Production Systems and Computer IntegratedManufacturing, Second edition, Prentice Hall of India, 2002.

    7. S.Kant Vajpayee - Principles of Computer Integrated Manufacturing, Prentice Hall ofIndia, 1999.

    8. David Bed worth - Computer Integrated Design and Manufacturing, TMH, 1998.

    The strength of materials deals with strength,stability & rigidity of various structural or machine members such as beams,columns,shafts,springs,couplings,cylinders e.t.c.

    The study of strength of materials often refers to  various methods of calculating the stresses and strains  in structural members, such as beams, columns, and  shafts.
    •This subject is useful for a detailed study of forces and their effects . This knowledge is very essential for an engineer, to enable him, in designing all type of structure and machine.
    •To Provide the basic concepts and principles of strength of materials and to give an ability to analyze a given problem in a simple manner.
    •To give an ability to calculate stresses and deformations of objects under external forces.
    •To give an ability to apply the knowledge of strength of materials on engineering applications and design problems.
    •The main objective of the study of Mechanics / Strength of materials is to provide the future Engineer with the means of analyzing and designing various machines and load bearing structures.


    Unit‐I:

    Overview of Industrial Engineering: Types of production systems, concept of productivity, productivity measurement in manufacturing and service organizations, operations strategies, liability and process design.

    Facility location and layout: Factors affecting facility location; principle of plant layout design, types of plant layout; computer aided layout design techniques; assembly line balancing; materials handling principles, types of material handling systems, methods of process planning, steps in process selection, production equipment and tooling selection, group technology, and flexible manufacturing.

    Unit II:

    Production Planning and control: Forecasting techniques – causal and time series models, moving average, exponential smoothing, trend and seasonality; aggregate production planning; master production scheduling; materials requirement planning (MRP) and MRP‐II; routing, scheduling and priority dispatching, concept of JIT manufacturing system

    Project Management: Project network analysis, CPM, PERT and Project crashing.

    Unit III:

    Engineering economy and Inventory control: Methods of depreciation; break‐even analysis, techniques for evaluation of capital investments, financial statements, time‐cost trade‐off, resource levelling; Inventory functions, costs, classifications, deterministic inventory models, perpetual and periodic inventory control systems, ABC analysis, and VED analysis.

    Queuing Theory: Basis of Queuing theory, elements of queuing theory, Operating characteristics of a queuing system, Classification of Queuing models.

    Unit IV

    Work System Design: Taylor’s scientific management, Gilbreths’s contributions; work study: method study, micro‐motion study, principles of motion economy; work measurement –time study, work sampling, standard data, Predetermined motion time system (PMTS); ergonomics; job evaluation, merit rating, incentive schemes, and wage administration.

    Product Design and Development: Principles of product design, tolerance design; quality and cost considerations; product life cycle; standardization, simplification, diversification, value engineering and analysis, and concurrent engineering.

    Unit V:

    Operational Analysis: Formulation of LPP, Graphical solution of LPP, Simplex Method, Sensitivity Analysis, degeneracy and unbound solutions. transportation and assignment models; Optimality test: the stepping stone method and MODI method, simulation.

    Books and References:

    1. Industrial Engineering and Production Management by Martand T Telsang S. Chand Publishing 

    2. Industrial Engineering and Production Management by M. Mahajan Dhanpat Rai & Co. (P) Limited

    3. Industrial Engineering and Management by Ravi Shankar, Galgotia Publications Pvt Ltd

    4. Production and Operations Management by Adam, B.E. & Ebert, R.J., PHI

    5. Product Design and Manufacturing by Chitale A.V. and Gupta R.C., PHI

    6. Operations Research Theory & Applications by J K Sharma, Macmillan India Ltd,

    7. Production Systems Analysis and Control by J.L.Riggs, John Wiley & Sons

    8. Automation, Production Systems & Computer Integrated Manufacturing by Groover, M.P. PHI

    9. Operations Research, by A.M. Natarajan, P. Balasubramani, A. Tamilarasi, Pearson Education

    10. Operations Research by P. K. Gupta and D. S. Hira, S. Chand & Co.

    The internal combustion engines which are the subject of spark-ignition

    engines (sometimes called Otto engines, or gasoline or petrol engines, though

    other fuels can be used) and compression-ignition or diesel engines.

    The strength of materials deals with strength,stability & rigidity of various structural or machine members such as beams,columns,shafts,springs,couplings,cylinders e.t.c.

    The study of strength of materials often refers to  various methods of calculating the stresses and strains  in structural members, such as beams, columns, and  shafts.
    •This subject is useful for a detailed study of forces and their effects . This knowledge is very essential for an engineer, to enable him, in designing all type of structure and machine.
    •To Provide the basic concepts and principles of strength of materials and to give an ability to analyze a given problem in a simple manner.
    •To give an ability to calculate stresses and deformations of objects under external forces.
    •To give an ability to apply the knowledge of strength of materials on engineering applications and design problems.
    •The main objective of the study of Mechanics / Strength of materials is to provide the future Engineer with the means of analyzing and designing various machines and load bearing structures.

    1. Students will be enabled to understand the nature and objective of Technical Communication relevant for the work place as Engineers.

    2. Students will utilize the technical writing for the purposes of Technical Communication and its exposure in various dimensions.

    3. Students would imbibe inputs by presentation skills to enhance confidence in face of diverse audience.

    4. Technical communication skills will create a vast know-how of the application of the learning to promote their technical competence.

    5. It would enable them to evaluate their efficacy as fluent & efficient communicators by learning the voice-dynamics.


                                

    CIVIL 4TH YEAR DESIGN OF STEEL STRUCUTRES

    CIVIL 3RD YEAR CONCRETE TECHNOLOGY