Department of COMPUTER SCIENCE AND ENGINEERING 

Syllabus for

3 Semester  2020  Batch  
Course Code 
Course 
Type 
Hours Per Week 
Credits 
Marks 
CS331P  DATABASE MANAGEMENT SYSTEMS    5  4  100 
CS332P  DATA STRUCTURES AND ALGORITHMS    5  4  100 
CS333  SOFTWARE ENGINEERING    3  3  100 
CY321  CYBER SECURITY    2  2  50 
EC337  DIGITAL SYSTEMS    3  3  100 
HS311  TECHNICAL WRITING    2  2  50 
MA334  DISCRETE MATHEMATICS    3  3  100 
MIA351  FUNDAMENTALS OF DESIGN    6  04  100 
MICS331P  INTRODUCTION TO DATA STRUCTURES AND ALGORITHMS    5  4  100 
MIMBA331  PRINCIPLES OF MANAGEMENT    4  3  100 
MIME331  SENSORS AND DATA ACQUISITION    45  4  100 
MIPSY331  UNDERSTANDING HUMAN BEHAVIOR    4  4  100 
4 Semester  2020  Batch  
Course Code 
Course 
Type 
Hours Per Week 
Credits 
Marks 
BS451  ENGINEERING BIOLOGY LABORATORY    2  2  50 
CS431  PROBABILITY AND QUEUING THEORY    3  3  100 
CS432P  OPERATING SYSTEMS    5  4  100 
CS433P  PROGRAMMING PARADIGM    5  4  100 
CS434  FORMAL LANGUAGE AND AUTOMATA THEORY    3  3  100 
CS435P  COMPUTER ORGANIZATION AND ARCHITECTURE    5  4  100 
EVS421  ENVIRONMENTAL SCIENCE    2  0  0 
HS422  PROFESSIONAL ETHICS    2  2  50 
MIA451A  ENVIRONMENTAL DESING AND SOCIO CULTURAL CONTEXT    6  04  100 
MIA451B  DIGITAL ARCHITECTURE    6  04  100 
MIA451C  COLLABORATIVE DESIGN WORKSHOP    6  04  100 
MIMBA431  ORGANISATIONAL BEHAVIOUR    4  3  100 
MIME432  ROBOTICS AND MACHINE VISION    45  4  100 
MIPSY432  PEOPLE THOUGHTS AND SITUATIONS    4  4  100 
5 Semester  2019  Batch  
Course Code 
Course 
Type 
Hours Per Week 
Credits 
Marks 
CEOE561E01  SOLID WASTE MANAGEMENT    3  3  100 
CEOE561E03  DISASTER MANAGEMENT    4  3  100 
CS531P  COMPUTER NETWORKS    5  4  100 
CS532  INTRODUCTION TO ARTIFICAL INTELLIGENCE    3  3  100 
CS533P  DESIGN AND ANALYSIS OF ALGORITHMS    5  4  100 
CS541E01  COMPUTER GRAPHICS WITH OPEN GL    3  3  100 
CS541E02  INTERNET AND WEB PROGRAMMING    3  3  100 
CS541E04  CRYPTOGRAPHY AND NETWORK SECURITY    3  3  100 
CS581  INTERNSHIP  I    2  1  50 
CSHO531AIP  STATISTICAL FOUNDATION FOR ARTIFICIAL INTELLIGENCE    5  4  100 
CSHO531CSP  PROBABILITY AND RANDOM PROCESS    5  4  100 
CSHO531DAP  STATISTICAL FOUNDATION FOR DATA ANALYTICS    5  4  50 
ECOE5603  AUTOMOTIVE ELECTRONICS    3  3  100 
ECOE5608  FUNDAMENTALS OF IMAGE PROCESSING    3  3  100 
ECOE5610  EMBEDDED BOARDS FOR IOT APPLICATIONS    3  3  100 
EE536OE03  INTRODUCTION TO HYBRID ELECTRIC VEHICLES    4  3  100 
EE536OE06  ROBOTICS AND AUTOMATION    4  3  100 
HS521  PROJECT MANAGEMENT AND FINANCE    3  3  100 
MIMBA531  ANALYSIS OF FINANCIAL STATEMENTS    4  4  100 
MIPSY533  HUMAN ENGINEERING    4  4  100 
PH536OE1  NANO MATERIAL AND NANO TECHNOLOGY    4  3  100 
6 Semester  2019  Batch  
Course Code 
Course 
Type 
Hours Per Week 
Credits 
Marks 
CS631P  INTERNET OF THINGS    5  4  100 
CS632P  COMPILER DESIGN    5  4  100 
CS633P  DESIGN PATTERNS    5  4  100 
CS642E01  MOBILE APPLICATION DEVELOPMENT    3  3  100 
CS642E02  REAL TIME SYSTEMS    3  3  100 
CS642E03  ADVANCED DATABASES    3  3  100 
CS642E04  COMPUTER ORIENTED NUMERICAL ANALYSIS    3  3  100 
CS642E05  OBJECT ORIENTED ANALYSIS AND DESIGN    3  3  100 
CS642E06  SYSTEM SOFTWARE    3  3  100 
CS642E07  DATA WAREHOUSING AND DATA MINING    3  3  100 
CSHO631AIP  ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING    5  4  100 
CSHO631CSP  MOBILE AND NETWORK BASED ETHICAL HACKING    5  4  100 
CSHO631DAP  BIG DATA ANALYTICS    5  4  100 
CSHO632AIP  ROBOTICS AND PROCESS AUTOMATION    5  4  100 
CSHO632CSP  CYBER FORENSICS AND MALWARE DETECTION    5  4  100 
CSHO632DAP  BIG DATA SECURITY ANALYTICS    5  4  100 
IT642E02  FOUNDATIONS TO BLOCKCHAIN TECHNOLOGY    3  3  100 
MIMBA631  DATA ANALYSIS FOR MANAGERS    4  4  100 
MIPSY634  SCIENCE OF WELL BEING    4  4  100 
7 Semester  2018  Batch  
Course Code 
Course 
Type 
Hours Per Week 
Credits 
Marks 
BTGE 732  ACTING COURSE    2  2  100 
BTGE 734  DIGITAL WRITING    2  2  100 
BTGE 737  PROFESSIONAL PSYCHOLOGY    4  2  100 
BTGE 744  DIGITAL MARKETING    2  2  100 
BTGE 745  DATA ANALYTICS THROUGH SPSS    2  2  100 
BTGE735  DIGITAL MEDIA    2  2  100 
BTGE736  INTELLECTUAL PROPERTY RIGHTS    4  2  100 
BTGE738  CORPORATE SOCIAL RESPONSIBILITY    2  2  100 
BTGE739  CREATIVITY AND INNOVATION    2  2  100 
BTGE741  GERMAN    2  2  100 
BTGE749  PAINTING AND SKETCHING    2  2  100 
BTGE750  PHOTOGRAPHY    2  2  100 
BTGE754  FUNCTIONAL ENGLISH    2  2  50 
CS731  ARTIFICIAL INTELLIGENCE    4  4  100 
CS732  CLOUD COMPUTING    3  3  100 
CS733P  MOBILE APPLICATION DEVELOPMENT    5  4  100 
CS735E01  NATURAL LANGUAGE PROCESSING    3  3  100 
CS736E01  GRAPH THEORY    3  3  100 
CS736E03  WIRELESS NETWORKS    3  3  100 
CS771  INTERNSHIP    2  2  50 
CS772  SERVICE LEARNING    2  2  50 
CSHO731AIP  COMPUTER VISION    5  4  100 
CSHO731CSP  INTRUSION DETECTION AND INCIDENT RESPONSE    5  4  100 
CSHO731DAP  WEB ANALYTICS    5  4  50 
CSHO781AIP  AI PROJECT/CERTIFICATE COURSES    5  4  100 
CSHO781CSP  CS PROJECT/CERTIFICATE COURSES    5  4  100 
CSHO781DAP  DA PROJECT/CERTIFICATE COURSES    5  4  100 
IT735E01  INFORMATION SECURITY    3  3  100 
IT736E02  DATA BASE ADMINISTRATION    3  3  100 
IT736E04  NETWORK ADMINISTRATION    3  3  100 
8 Semester  2018  Batch  
Course Code 
Course 
Type 
Hours Per Week 
Credits 
Marks 
CS831E01  QUANTUM COMPUTING    3  3  100 
CS831E02  GRID COMPUTING    3  3  100 
CS831E03  MOBILE COMPUTING    3  3  100 
CS832E01  SOFTWARE TESTING    3  3  100 
CS832E02  SOFTWARE PROCESS AND PROJECT MANAGEMENT    3  3  100 
CS832E03  SOFTWARE QUALITY MANAGEMENT    3  3  100 
CS833E01  COMPUTER AIDED DECISION SUPPORT SYSTEMS    3  3  100 
CS833E02  INTRODUCTION TO DATA SCIENCE    3  3  100 
CS833E03  SOFT COMPUTING    3  3  100 
CS833E04  DIGITAL IMAGE PROCESSING    3  3  100 
CS833E05  INFORMATION STORAGE AND MANAGEMENT    3  3  100 
CS871  PROJECT WORK    12  6  200 
CS872  COMPREHENSION    4  2  50 
CY821  CYBER SECURITY    2  2  50 
IC821  CONSTITUTION OF INDIA    2  0  50 
IT831E01  PARALLEL COMPUTING    3  3  100 
IT831E02  HIGH SPEED NETWORKS    45  3  100 
IT832E01  SOFTWARE ARCHITECTURE    3  3  100 
IT832E02  WEB SERVICES AND SERVICE ORIENTED ARCHITECTURE    3  3  100 
IT832E03  SOFTWARE REQUIREMENT ESTIMATION    3  3  100 
IT833E01  ROBOTICS    3  3  100 
IT833E02  HIGH PERFORMANCE MICROPROCESSORS    3  3  100 
IT833E03  NETWORK STORAGE TECHNOLOGIES    3  3  100 
IT833E04  PROFESSIONAL ETHICS AND HUMAN VALUES    3  3  100 
CS331P  DATABASE MANAGEMENT SYSTEMS (2020 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:5 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

· To learn the fundamentals of data models and to conceptualize and depict a database system using ER diagram. · To make a study of SQL and relational database design. · To understand the internal storage structures using different file and indexing techniques which will help in physical DB design. · To know the fundamental concepts of transaction processing concurrency control techniques and recovery procedure. · To have an introductory knowledge about the emerging trends in the area of distributed DB OO DB Data mining and Data Warehousing and XML. · To implement the design of the tables in DBMS. · To write queries to get optimized outputs. · To store, retrieve and view the contents. To generate report based on customized need.


Learning Outcome 

CO 1: Summarize the fundamental concepts of databases and EntityRelationship (ER) model. CO 2 : Apply ER Model and Normalization principles to create relational databases for the given problems. CO 3 :Compare and contrast different file organization concepts for data storage in Relational databases CO 4: Apply the transaction management principles on relational databases CO 5: Demonstrate the current trends such as object oriented databases, distributed data storage in database technology 
Unit1 
Teaching Hours:15 
INTRODUCTION AND CONCEPTUAL MODELING


Introduction to File and Database systems Database system structure – Data Models – Introduction to Network and Hierarchical Models – ER model – Relational Model – Relational Algebra and Calculus. Lab Programs 1. Data Definition Language (DDL) commands in RDBMS 2. Data Manipulation Language (DML) and Data Control Language (DCL) commands in RDBMS.  
Unit2 
Teaching Hours:15 
RELATIONAL MODEL


SQL – Data definition Queries in SQL Updates Views – Integrity and Security – Relational Database design – Functional dependences and Normalization for Relational Databases (up to BCNF). Lab programs 3. Highlevel language extension with Cursors. 4.High level language extension with Triggers  
Unit3 
Teaching Hours:15 
DATA STORAGE AND QUERY PROCESSING


Record storage and Primary file organization Secondary storage Devices Operations on Files Heap File Sorted Files Hashing Techniques – Index Structure for files –Different types of Indexes BTree  B+ Tree – Query Processing. Lab Programs 5. Procedures and Functions. 6. Embedded SQL.  
Unit4 
Teaching Hours:15 
TRANSACTION MANAGEMENT


Transaction Processing – Introduction Need for Concurrency control Desirable properties of Transaction Schedule and Recoverability Serializability and Schedules – Concurrency Control – Types of Locks Two Phases locking Deadlock Time stamp based concurrency control – Recovery Techniques – Concepts Immediate Update Deferred Update  Shadow Paging. Lab Programs 7. Database design using ER model and Normalization. 8. Design and implementation of Payroll Processing System.  
Unit5 
Teaching Hours:15 
CURRENT TRENDS


Object Oriented Databases – Need for Complex Data types OO data Model Nested relations Complex Types Inheritance Reference Types  Distributed databases Homogenous and Heterogenous Distributed data Storage – XML – Structure of XML Data XML Document Schema Querying and Transformation. – Data Mining and Data Warehousing. Lab Programs: 9. Design and implementation of Banking System 10.Design and implementation of Library Information System
 
Text Books And Reference Books:
1. Abraham Silberschatz, Henry F. Korth and S. Sudarshan “Database System Concepts”, Sixth Edition, McGrawHill, 2010.
 
Essential Reading / Recommended Reading REFERENCE BOOKS 1. RamezElmasri and Shamkant B. Navathe, “Fundamental Database Systems”, Third Edition, Pearson Education, 2008. 2. Raghu Ramakrishnan, “Database Management System”, Tata McGrawHill Publishing Company, 2003
 
Evaluation Pattern Continuous Internal Assessment (CIA) for Theory papers: 70% (70 marks out of 100 marks) · End Semester Examination(ESE) : 30% (30 marks out of 100 marks)  
CS332P  DATA STRUCTURES AND ALGORITHMS (2020 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:5 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

Course Description: The course focuses on basic and essential topics in data structures, including arraybased lists, linked lists, trees, sorting algorithms, and graphs. Course Objectives: To understand the basic concept of data structures for storage and retrieval of ordered or unordered data. Data structures include: arrays, linked lists, binary trees, heaps, and hash tables. 

Learning Outcome 

Explain the basic concepts of data structures and solve the time complexity of the algorithm Experiment with various operations on Linear Data structures Examine the Structures and Operations of Trees and Heaps Data Structures Compare various given sorting techniques with respect to time complexity Choose various shortest path algorithms to determine the minimum spanning path for the given graphs 
Unit1 
Teaching Hours:11 
INTRODUCTION


Definition Classification of data structures: primitive and nonprimitive Operations on data structures Algorithm Analysis. Lab Progrsm: 1.To determine the time complexity of a given logic.  
Unit2 
Teaching Hours:20 
LISTS, STACKS AND QUEUES


Abstract Data Type (ADT) – The List ADT – The Stack ADT: Definition, Array representation of stack, Operations on stack: Infix, prefix and postfix notations Conversion of an arithmetic Expression from Infix to postfix. Applications of stacks. The Queue ADT: Definition, Array representation of queue, Types of queue: Simple queue, circular queue, double ended queue (dequeue) priority queue, operations on all types of Queues.
 
Unit3 
Teaching Hours:18 
TREES


Preliminaries – Binary Trees – The Search Tree ADT – Binary Search Trees – AVL Trees – Tree Traversals – Hashing – General Idea – Hash Function – Separate Chaining – Open Addressing –Linear Probing – Priority Queues (Heaps) – Model – Simple implementations – Binary Heap. Lab Program Search Tree ADT  Binary Search Tree Implementing a Hash function/Hashing Mechanism.
 
Unit4 
Teaching Hours:14 
SORTING


Preliminaries – Insertion Sort – Shell sort – Heap sort – Merge sort – Quicksort – External Sorting. Lab Program:
 
Unit5 
Teaching Hours:12 
GRAPHS


Definitions – Topological Sort – ShortestPath Algorithms – Unweighted Shortest Paths – Dijkstra’s Algorithm – Minimum Spanning Tree – Prim’s Algorithm – Applications of Depth First Search – Undirected Graphs – Biconnectivity – Introduction to NPCompletenesscase study. Lab Program:
 
Text Books And Reference Books:
Mark Allen Weiss, “Data Structures and Algorithm Analysis in Java”, 3rd Edition, Pearson Education 2013.  
Essential Reading / Recommended Reading R1. Fundamentals of data structure in C by Ellis Horowitz, Sarataj Shani 3rd edition, Galgotia book source PVT,2010. R2.Classic Data Structures , Debasis Samanta ,2nd Edition, PHI Learning PVT,2011  
Evaluation Pattern Continuous Internal Assessment (CIA) for Theory+Practical papers: 70% (70 marks out of 100 marks) · End Semester Examination (ESE) : 30% (30 marks out of 100 marks)  
CS333  SOFTWARE ENGINEERING (2020 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 

Course Description: Software engineering course provides: Different life cycle models, Requirement dictation process, Analysis modelling and specification, Architectural and detailed design methods, Implementation and testing strategies, Verification and validation techniques, Project planning and management and Use of CASE tools. Course objectives: To be aware of Different life cycle models; Requirement dictation process; Analysis modeling and specification; Architectural and detailed design methods; Implementation and testing strategies; Verification and validation techniques; Project planning and management and Use of CASE tools. 

Learning Outcome 

CO1: Explain the fundamental of Software development Life cycle and different software development process models. CO2: Apply various requirement elicitation methods in software development process. CO3: Develop the software processes and concepts using various design technique CO4: Analyze different testing techniques and maintenance principles in software development process. CO5: Formulate the cost estimation techniques and project scheduling methods in software development process.

Unit1 
Teaching Hours:9 

SOFTWARE PROCESS


Introduction –S/W Engineering Paradigm – life cycle models (water fall, incremental, spiral, WINWIN spiral, evolutionary, prototyping, object oriented)  system engineering – computer based system – verification – validation – life cycle process – development process –system engineering hierarchy.  
Unit2 
Teaching Hours:9 

SOFTWARE REQUIREMENTS


Functional and nonfunctional  user – system –requirement engineering process – feasibility studies – requirements – elicitation – validation and management – software prototyping – prototyping in the software process – rapid prototyping techniques – user interface prototyping S/W document. Agile methods, Extreme Programming, SCRUM  
Unit3 
Teaching Hours:9 

DESIGN CONCEPTS AND PRINCIPLES


Design process and concepts – modular design – design heuristic – design model and document. Architectural design – software architecture – data design – architectural design – transform and transaction mapping – user interface design – user interface design principles. Real time systems  Real time software design – system design – real time executives – data acquisition system  monitoring and control system. SCM – Need for SCM – Version control – Introduction to SCM process – Software configuration items.  
Unit4 
Teaching Hours:9 

TESTING


Taxonomy of software testing – levels – test activities – types of s/w test – black box testing – testing boundary conditions – structural testing – test coverage criteria based on data flow mechanisms – regression testing – testing in the large. S/W testing strategies – strategic approach and issues  unit testing – integration testing – validation testing – system testing and debugging.  
Unit5 
Teaching Hours:9 

SOFTWARE PROJECT MANAGEMENT


Measures and measurements – S/W complexity and science measure – size measure – data and logic structure measure – information flow measure. Software cost estimation – function point models – COCOMO model Delphi method. Defining a Task Network – Scheduling – Earned Value Analysis – Error Tracking  Software changes – program evolution dynamics – software maintenance – Architectural evolution. Taxonomy of CASE tools – Case Study.  
Text Books And Reference Books: T1. Roger S. Pressman, Software engineering A Practitioner’s Approach, McGrawHill International Edition, 8th Edition 2019.  
Essential Reading / Recommended Reading R1. AnirbanBasu, “Software Quality Assurance, Testing and Metrics”, First Edition, PHI Learning, 2015. R2. Ian Sommerville, “Software engineering,” Pearson education Asia, 9th Edition 2013. R3. PankajJalote “An Integrated Approach to Software Engineering,” Narosa publishing house 2011. R4. James F Peters and WitoldPedryez, “Software Engineering – An Engineering Approach”, John Wiley and Sons, New Delhi, 2010. R5. Ali Behforooz and Frederick J Hudson, “Software Engineering Fundamentals”, OUP India 2012.
 
Evaluation Pattern Continuous Internal Assessment CIA Marks 50 End Semester Exams ESE 50 Total 100  
CY321  CYBER SECURITY (2020 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 

Max Marks:50 
Credits:2 

Course Objectives/Course Description 

This mandatory course is aimed at providing a comprehensive overview of the different facets of Cyber Security. In addition, the course will detail into specifics of Cyber Security with Cyber Laws both in Global and Indian Legal environments 

Learning Outcome 

CO 1 Describe the basic security fundamentals and cyber laws and legalities. L2 CO 2 Describe various cyber security vulnerabilities and threats such as virus, worms, online attacks, Dos and others. L2 CO 3 Explain the regulations and acts to prevent cyberattacks such as Risk assessment and security policy management. L3 CO 4 Explain various vulnerability assessment and penetration testing tools. L3 CO 5 Explain various protection methods to safeguard from cyberattacks using technologies like cryptography and Intrusion prevention systems. L3 
Unit1 
Teaching Hours:6 
UNIT 1


Security Fundamentals4 As Architecture Authentication Authorization Accountability, Social Media, Social Networking and Cyber Security.Cyber Laws, IT Act 2000IT Act 2008Laws for CyberSecurity, Comprehensive National CyberSecurity Initiative CNCI – Legalities  
Unit2 
Teaching Hours:6 
UNIT 2


Cyber Attack and Cyber Services Computer Virus – Computer Worms – Trojan horse.Vulnerabilities  Phishing  Online Attacks – Pharming  Phoarging – Cyber Attacks  Cyber Threats  Zombie stuxnet  Denial of Service Vulnerabilities  Server HardeningTCP/IP attackSYN Flood  
Unit3 
Teaching Hours:6 
UNIT 3


Cyber Security Management Risk Management and Assessment  Risk Management Process  Threat Determination Process Risk Assessment  Risk Management Lifecycle.Security Policy Management  Security Policies  Coverage Matrix Business Continuity Planning  DisasterTypes  Disaster Recovery Plan  Business Continuity Planning Process  
Unit4 
Teaching Hours:6 
UNIT 4


Vulnerability  Assessment and Tools: Vulnerability Testing  Penetration Testing Black box white box.Architectural Integration: Security Zones  Devicesviz Routers, Firewalls, DMZ. Configuration Management  Certification and Accreditation for CyberSecurity.  
Unit5 
Teaching Hours:6 
UNIT 5


Authentication and Cryptography: Authentication  Cryptosystems  Certificate Services, Securing Communications: Securing Services  Transport – Wireless  Steganography and NTFS Data Streams. Intrusion Detection and Prevention Systems: Intrusion  Defense in Depth  IDS/IPS IDS/IPS Weakness and Forensic AnalysisCyber Evolution: Cyber Organization – Cyber Future  
Text Books And Reference Books: R1. Matt Bishop, “Introduction to Computer Security”, Pearson, 6^{th} impression, ISBN: 9788177584257. R2. Thomas R, Justin Peltier, John, “Information Security Fundamentals”, Auerbach Publications. R3. AtulKahate, “Cryptography and Network Security”, 2^{nd} Edition, Tata McGrawHill.2003 R4. Nina Godbole, SunitBelapure, “Cyber Security”, Wiley India 1^{st} Edition 2011 R5. Jennifer L. Bayuk and Jason Healey and Paul Rohmeyer and Marcus Sachs, “Cyber Security Policy Guidebook”, Wiley; 1 edition , 2012 R6. Dan Shoemaker and Wm. Arthur Conklin, “Cyber security: The Essential Body Of Knowledge”, Delmar Cengage Learning; 1 edition, 2011 R7. Stallings, “Cryptography & Network Security  Principles & Practice”, Prentice Hall, 6th Edition 2014  
Essential Reading / Recommended Reading NIL  
Evaluation Pattern Only CIA will be conducted as per the University norms. No ESE Maximum Marks : 50  
EC337  DIGITAL SYSTEMS (2020 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 

· To study the fundamentals of digital circuits and concepts used in the analysis and design of various digital systems. 

Learning Outcome 

At the end of the course, students will be able to 1.Describe the characteristics of various digital integrated circuit families, logic gates and classify digital circuits based on their construction. L2:Understand 2.Demonstrate the methods of minimization of complex circuits using Boolean Algebra.L3: Apply 3.Interpret the methods of Designing combinational circuit.L3: Apply 4Illustrate the methods of Designing sequential circuits.L3: Apply 5Analyze the digital circuits design using VHDL.L4:Analyze

Unit1 
Teaching Hours:9 
INTRODUCTION


Switching Theory: Laws of Boolean algebra, Theorems of Boolean algebra, Switching functions, Methods for specification of switching functions  Truth tables and Algebraic forms, Realization of functions using logic gates. Digital Logic Elements: Electronic logic gates, Positive and negative logic, Logic families TTL, ECL and CMOS, Realization of logic gates.  
Unit2 
Teaching Hours:9 
BOOLEAN ALGEBRA


Simplification of Boolean Expressions and Functions: Algebraic methods, Canonical forms of Boolean functions, Minimization of functions using Karnaugh maps, Minimization of functions using QuineMcClusky method.  
Unit3 
Teaching Hours:9 
COMBINATIONAL CIRCUITS


Design of Combinational Logic Circuits: Gate level design of Small Scale Integration (SSI) circuits, Modular combinational logic elements  Decoders, Encoders, Priority encoders, Multiplexers and Demultiplexers. Design of Integer Arithmetic Circuits using Combinational Logic: Integer adders  Ripple carry adder and Carry look ahead adder, Integer subtractors using adders, Unsigned integer multipliers  Combinational array circuits, Signed integer multipliers  Booth's coding, Bitpair recoding, Carry save addition and Wallace tree multiplier, Signed integer division circuits  Combinational array circuits, Complexity and propagation delay analysis of circuits. Design of Combinational Circuits using Programmable Logic Devices (PLDs): Programmable Read Only Memories (PROMs), Programmable Logic Arrays (PLAs), Programmable Array Logic (PAL) devices, Design of multiple output circuits using PLDs.  
Unit4 
Teaching Hours:9 
SEQUENTIAL CIRCUITS


Sequential Circuit Elements: Latches RS latch and JK latch, FlipflopsRS, JK, T and D flip flops, Masterslave flip flops, Edgetriggered flipflops. Analysis and Design of Synchronous Sequential Circuits: Models of sequential circuits  Moore machine and Mealy machine, Flipflops  Characteristic table, Characteristic equation and Excitation table, Analysis of sequential circuits Flipflop input expressions, Next state equations, Next state maps, State table and State transition diagram, Design of sequential circuits  State transition diagram, State table, Next state maps, Output maps, Expressions for flipflop inputs and Expressions for circuit outputs, Modular sequential logic circuits Shift registers, Registers, Counters and Random access memories, Design using programmable logic sequencers (PLSs). Design of Arithmetic Circuits using Sequential Logic : Serial adder for integers, Unsigned integer multiplier, Unsigned integer division circuits, Signed integer division, Floatingpint adder/subtractor  Design of control circuit, Floating  point multiplier.  
Unit5 
Teaching Hours:9 
CASE STUDY AND INFORMAL LABORATORY


Case study: Learn the Fundamentals of Digital Logic Design with VHDL Informal Laboratory: · Design and implementation of binary adder / subtractor using basic gates · Design and implementation of applications using multiplexers · Design and implementation of Synchronous & Asynchronous Counters · Design and implementation of Shift Registers Coding Combinational Circuits using Hardware Description Language (HDL)  
Text Books And Reference Books:
T1  Donald P Leach, Albert Paul Malvino&GoutamSaha, “Digital Principles and Applications” , Tata McGraw Hill 7^{th} Edition, 2010.
 
Essential Reading / Recommended Reading R1 Stephen Brown. ZvonkoVranesic, “Fundamentals of Digital Logic Design with VHDL”, Tata McGraw Hill, 2nd Edition 2005. R2 R D Sudhaker Samuel, “Illustrative Approach to Logic Design. SanguinePearson”, 2010. R3 Charles H. Roth, “Fundamentals of Logic Design”, Cengage Learning, 5th Edition, 2004. R4 Ronald J. Tocci, Neal S. Widmer. Gregory L. Moss, “Digital Systems Principles and Applications, ” 10^{th} Edition. Pearson Education, 2007 R5 M Morris Mano, “Digital Logic and Computer Design”, Pearson Education, 10th Edition, 2008.
 
Evaluation Pattern Assessment is based on the performance of the student throughout the semester. Assessment of each paper · Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks) · End Semester Examination(ESE) : 50% (50 marks out of 100 marks) Components of the CIA CIA I : Mid Semester Examination (Theory) : 25 marks CIA II : Assignments : 10 marks CIA III : Quizzes/Seminar/Case Studies/Project Work : 10 marks Attendance : 05 marks Total : 50 marks For subjects having practical as part of the subject
Assessment of Practical paper Conduct of experiments : 25 marks Observations/Lab Record : 15 marks Viva voce : 10 marks Total : 50 marks (All the above assessments are carried for each experiment during regular lab classes and averaged to max 50 marks at the end of the semester)  
HS311  TECHNICAL WRITING (2020 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

The goal of this course is to prepare engineering students with individual and collaborative technical writing and presentation skills that are necessary to be effective technical communicators in academic and professional environments. 

Learning Outcome 

CO1: Understand the basics of technical communication and the use of formal elements of specific genres of documentation. {L1}{PO 10} CO2: Demonstrate the nuances of technical writing, with reference to english grammar and vocabulary. {L2}{PO5, PO10} CO3: Recognize the importance of soft skills and personality development for effective communication. {L2}{PO6,PO9} CO4: Understand the various techniques involved in oral communication and its application. {L3}{PO9,PO10,PO12} CO5: Realize the importance of having ethical work habits and professional etiquettes. {L2}{PO6,PO8,PO12} 
Unit1 
Teaching Hours:6 

Design and Development


Communication – Process, Flow , Barriers. Analysing different kinds of technical documents, Reports – types, Writing Engineering reports – Types, Importance, Structure of formal reports, Factors information and document design.  
Unit2 
Teaching Hours:6 

Grammar and Editing


Vocabulary for professional writing. Idioms and collocations, Writing drafts and revising, writing style and language. ,advanced grammar, Writing Emails, resumes  
Unit3 
Teaching Hours:6 

Self Development and Assessment


Self development process, Personality development, Types of personality, Perception and attitudes, Emotional intelligence, Time Management, Values and belief, Personal goal setting, Creativity, Conflict management, Career planning.  
Unit4 
Teaching Hours:6 

Communication and Writing


Writing a speech, Public speaking, Formal presentations, Presentation aids, Group communication, Discussions, Organizational GD, Meetings & Conferences.  
Unit5 
Teaching Hours:6 

Business Etiquettes


Email etiquettes, Telephone Etiquettes, Time Management, Role and responsibility of engineer, Work culture in jobs, Engineering ethics  
Text Books And Reference Books: T1 : David F. Beer and David McMurrey, Guide to writing as an Engineer, John Willey. New York, 2004 T2: Diane Hacker, Pocket Style Manual, Bedford Publication, New York, 2003. (ISBN 0312406843) T3: Raman Sharma, Technical Communications, Oxford Publication, London, 2004  
Essential Reading / Recommended Reading R1.Dale Jungk, Applied Writing for Technicians, McGraw Hill, New York, 2004. (ISBN: 078283574) R2. Sharma, R. and Mohan, K. Business Correspondence and Report Writing, TMH New Delhi 2002. R3. Xebec, Presentation Book, TMH New Delhi, 2000. (ISBN 0402213)  
Evaluation Pattern CIA 1  10 Marks Mid Semester Exams  25 Marks CIA 2  10 Marks End Semester Exams  50 Marks Attendance  5 marks  
MA334  DISCRETE MATHEMATICS (2020 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 

Max Marks:100 
Credits:3 

Course Objectives/Course Description 

To extend student’s mathematical maturity and ability to deal with abstraction and to introduce most of the basic terminologies used in computer science courses and application of ideas to solve practical problems. The objective of the paper is to develop: · The knowledge of the concepts needed to test the logic of a program.· Knowledge which has application in expert system, in data base and a basic for the programing language.· An understanding in identifying patterns on many levels.· Awareness about a class of functions which transform a finite set into another finite set that relates to input output functions in computer science. 

Learning Outcome 


Unit1 
Teaching Hours:9 

Propositional Calculus:


Propositions Logical connectives Compound propositions Conditional and bi conditional propositions Truth tables Tautologies and contradictions Contrapositive Logical equivalences and implications De Morgans Laws  Normal forms Principal conjunctive and disjunctive normal forms Rules of inference Arguments  Validity of arguments.  
Unit2 
Teaching Hours:9 

Predicate Calculus:


Predicates Statement Function Variables Free and bound variables Quantifiers Universe of discourse Logical equivalences and implications for quantified statements Theory of inference The rules of universal specification and generalization Validity of arguments  
Unit3 
Teaching Hours:9 

Set Theory


Basic concepts Notations Subset Algebra of sets The power set Ordered pairs and Cartesian product Relations on sets Types of relations and their properties Matrix and Graph representation of a relation Partial ordering Poset Hasse diagram Lattices and their properties Sublattices Boolean algebra.  
Unit4 
Teaching Hours:9 

Functions:


Definitions of functions Classification of functions Types of functions  Examples Composition of functions Inverse functions Characteristic function of a set Hashing functions Permutation functions.  
Unit5 
Teaching Hours:9 

Groups:


Groups  Properties Subgroups  Cosets and Lagranges theorem Normal subgroups Algebraic system with two binary operations Preliminaries of Coding  Hamming Metric  group codes: Basic notions of error correction  Error recovery in group codes.  
Text Books And Reference Books: Text Books T1. Trembly J.P and Manohar R, Discrete Mathematical Structures with Applications to Computer Science, Tata McGrawHill Pub.Co. Ltd, New Delhi, 2003. T2. Ralph. P. Grimaldi, Discrete and Combinatorial Mathematics: An Applied Introduction, Fifth Edition, Pearson Education Asia,Delhi, 2009.  
Essential Reading / Recommended Reading Reference Books 1. R1. Bernard Kolman, Robert C. Busby, Sharan Cutler Ross, Discrete Mathematical Structures, Fourth Indian reprint, Pearson Education Pvt Ltd., New Delhi, 2003. 2. R2. Kenneth H. Rosen, Discrete Mathematics and its Applications, Fifth Edition, Tata McGraw Hill Pub. Co. Ltd., New Delhi, 2003. 3. R3. .Richard Johnsonbaugh, Discrete Mathematics, Fifth Edition,Pearson Education Asia, New Delhi, 2002. 4. R4. Dr K.S.C , Discrete Mathematical Structures, 5^{th} Edition, Prism Engineering Education Series2018. 5. R5. S Santha, Discrete Mathematics with Combinatorics and Graph Theory Cengage, 1^{st} Edition, 2019  
Evaluation Pattern Continuous Internal Assessment (CIA): 50% (50 marks out of 100 marks) End Semester Examination(ESE) : 50% (50 marks out of 100 marks)
Components of the CIA CIA I : Subject Assignments / Online Tests : 10 marks CIA II : Mid Semester Examination (Theory) : 25 marks CIAIII:Quiz/Seminar/Case Studies/Project/Innovative Assignments/presentations/publications : 10 marks Attendance : 05 marks Total : 50 marks Mid Semester Examination (MSE) : Theory Papers:
End Semester Examination (ESE): The ESE is conducted for 100 marks of 3 hours duration. The syllabus for the theory papers are divided into FIVE units and each unit carries equal Weightage in terms of marks distribution. Question paper pattern is as follows. Two full questions with either or choice will be drawn from each unit. Each question carries 20 marks. There could be a maximum of three sub divisions in a question. The emphasis on the questions is to test the objectiveness, analytical skill and application skill of the concept, from a question bank which reviewed and updated every year The criteria for drawing the questions from the Question Bank are as follows 50 %  Medium Level questions 25 %  Simple level questions 25 %  Complex level questions  
MIA351  FUNDAMENTALS OF DESIGN (2020 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 

Max Marks:100 
Credits:04 

Course Objectives/Course Description 

The studio intends to contextualize the student towards aesthetical approach and sensitize them towards local and heterogeneous culture of ours. Today, the biggest challenge is lying in the areas of aesthetical thinking and processbased techniques, where we try to enhance aesthetic sense, creativity, responsive and reflective ecology in which they live and connect. They connect their creativity and aesthetical sensibility to local knowledge and culture of their own environment. Also, there are things to learn and adapt from the diversity of craftsmanship and knowledge system.


Learning Outcome 

CO1: To have a comprehensive understanding of architectural drawing techniques and pictorial presentation. Level: Basic CO2: Ability to sensitively observe and record various aspects of the immediate environment including human relationships, visual language, aesthetic characteristics and space, elements of nature, etc. Level: Basic CO3: Ability to achieve skills of visualization and communication, through different mediums and processes. Level: Basic 
Unit1 
Teaching Hours:20 

Familiarizing surrounding


Observing, experiencing, analyzing the manmade environment and organic environment. To create awareness of human abilities like perception, intuition, Identification, and observation, enjoying our senses through a nature walk, (by seeing, hearing, touching, smelling, and tasting)  
Unit2 
Teaching Hours:20 

Principles of art & drawing


To understand basic principles of art and drawing as an extension of seeing and a tool to create awareness of different visualization techniques.  
Unit3 
Teaching Hours:20 

Elements of Design & theory of visual perception


 
Unit4 
Teaching Hours:30 

Pictorial Projections, Sciography & Rendering


 
Text Books And Reference Books: T1. Cari LaraSvensan and William Ezara Street, Engineering Graphics. T2. Bhatt, N. D., Engineering Drawing, Charotar Publishing House Pvt. Ltd T3. Venugopal, K., Engineering Drawing and Graphics, New Age International Publishers. T4. S. Rajaraman, Practical Solid Geometry.  
Essential Reading / Recommended Reading R1. Francis D. K. Ching, ‘Drawing, Space, Form, Expression’. R2. Alexander W. White, ‘The Elements of Graphic Design, Allworth Press R3. Alexander W. White, ‘The Elements of Graphic Design, Allworth Press; 1 edition (Nov 1, 2002)  
Evaluation Pattern The Evaluation pattern comprises of two components; the Continuous Internal Assessment (CIA) and the End Semester Examination (ESE). CONTINUOUS INTERNAL ASSESSMENT (CIA): 50 Marks END SEMESTER EXAMINATION (ESE, VIVAVOCE): 50 Marks TOTAL:100 Marks Note: For this course, a minimum of 50% marks in CIA is required to be eligible for VIVAVOCE which is conducted as ESE.  
MICS331P  INTRODUCTION TO DATA STRUCTURES AND ALGORITHMS (2020 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:5 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 



Learning Outcome 


Unit1 
Teaching Hours:14 
INTRODUCTION


Definition Classification of data structures: primitive and nonprimitive Operations on data structures Algorithm Analysis. LAB Programs: 1a. Sample C Programs 1b. To determine the time complexity of a given logic.  
Unit2 
Teaching Hours:17 
LISTS, STACKS AND QUEUES


Abstract Data Type (ADT) – The List ADT – The Stack ADT: Definition,Array representation of stack, Operations on stack: Infix, prefix and postfix notations Conversion of an arithmetic Expression from Infix to postfix. Applications of stacks. The Queue ADT: Definition, Array representation of queue, Types of queue: Simple queue, circular queue, double ended queue (dequeue) priority queue, operations on all types of Queues LAB Programs: 2. Implement the applications Stack ADT. 3. Implement the applications for Queue ADT. 4.Operations on stack[e.g.: infix to postfix, evaluation of postfix]  
Unit3 
Teaching Hours:16 
TREES


Preliminaries – Binary Trees – The Search Tree ADT – Binary Search Trees – AVL Trees – Tree Traversals – Hashing – General Idea – Hash Function – Separate Chaining – Open Addressing –Linear Probing – Priority Queues (Heaps) – Model – Simple implementations – Binary Heap. LAB PROGRAMS: 5. Search Tree ADT  Binary Search Tree  
Unit4 
Teaching Hours:14 
SORTING


Preliminaries – Insertion Sort – Shell sort – Heap sort – Merge sort – Quicksort – External Sorting. LAB PROGRAMS 6. Heap Sort. 7. Quick Sort. 8.Applications of Probability and Queuing Theory Problems to be implemented using data structures.  
Unit5 
Teaching Hours:14 
GRAPHS


Definitions – Topological Sort – ShortestPath Algorithms – Unweighted Shortest Paths – Dijkstra‘s Algorithm – Minimum Spanning Tree – Prim‘s Algorithm – Applications of Depth First Search – Undirected Graphs – Biconnectivity – Introduction to NPCompletenesscase study LAB PROGRAMS 9. Implementing a Hash function/Hashing Mechanism. 10. Implementing any of the shortest path algorithms.
 
Text Books And Reference Books: TEXT BOOK 1.Mark Allen Weiss , “Data Structures and Algorithm Analysis in C”, 2nd Edition, AddisonWesley, 1997  
Essential Reading / Recommended Reading 1. Michael T. Goodrich, Roberto Tamassia and Michael H. Goldwasser , ―Data Structures and Algorithms in Python ‖, First Edition, John Wiley & Sons, Incorporated, 2013.ISBN1118476735, 9781118476734  
Evaluation Pattern Components of the CIA CIA I : Assignment/MCQ and Continuous Assessment : 10 marks CIA II : Mid Semester Examination (Theory) : 10 marks CIA III : Closed Book Test/Mini Project and Continuous Assessment: 10 marks Lab marks :35 marks Attendance : 05 marks End Semester Examination(ESE) : 30% (30 marks out of 100 marks) Total: 100 marks  
MIMBA331  PRINCIPLES OF MANAGEMENT (2020 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 

Course Description: This is offered as a core course in first trimester. This course will provide a general introduction to management principles and theories, and a brief outline on history and development of management thought. Course Objectives: This course describes the steps necessary to understand an organisation that are aligned with business objectives and provides an insight to address a range of challenges that every manager encounters. It aims to prepare students for an exciting challenging and rewarding managerial career through case studies on ‘Global Perspective’. 

Learning Outcome 

Course Learning Outcomes: On having completed this course students should be able to: CLO1 Understand different management approaches CLO2 Demonstrate planning techniques CLO3 Able to work in dynamic teams within organizations CLO4 Analyze different processes in staffing and controlling 
Unit1 
Teaching Hours:12 

Nature, Purpose and Evolution of Management Thought


Meaning; Scope; Managerial levels and skills; Managerial Roles; Management: Science, Art or Profession; Universality of Management. Ancient roots of management theory; Classical schools of management thought; Behavioral School, Quantitative School; Systems Approach, Contingency Approach; Contemporary Management thinkers & their contribution. Ancient Indian Management systems & practices. Comparative study of global management systems & practices. Social responsibility of managers, Managerial Ethics. Evolution of Management: Teaching management through Indian Mythology (Videos of Devdutt Pattanaik, Selflearning mode)
 
Unit2 
Teaching Hours:12 

Planning


Types of Plans; Steps in Planning Process; Strategies, level of Strategies, Policies and Planning; Decision making, Process of Decision Making, Techniques in Decision Making, Forecasting & Management by Objectives (MBO). Planning: Emerald Case and Projects of Events  
Unit3 
Teaching Hours:12 

Organizing


Organizational structure and design; types of organizational structures; Span of control, authority, delegation, decentralization and reengineering. Social responsibility of managers, Managerial Ethics. Organizing: Holacracy form of organization structure  
Unit4 
Teaching Hours:12 

Staffing


Human resource planning, Recruitment, selection, training & development, performance appraisal, managing change, compensation and employee welfare. Motivation: Concept, Forms of employee motivation, Need for motivation, Theories of motivation, Stress Management Staffing: Stress Management & Career path, Emerald Case  
Unit5 
Teaching Hours:12 

Leading and Controlling


Leadership concept, leadership Styles, leadership theories, leadership communication. Nature of organizational control; control process; Methods and techniques of control; Designing control systems, Quality Management Leading: Article on Styles of leadership by Daniel Goleman Controlling: Projects of Events
 
Text Books And Reference Books: Koontz, H. & Heinz, W. (2013). Management (13^{th} Edition). Tata McGraw Hill Publications.
 
Essential Reading / Recommended Reading Recommended Reading 1. Daft, R. L. (2013). The new era of management (10^{th }Edition). Cengage Publications. 2. Prasad, L.M., Principles and practices of management. New Delhi: Sultan Chand & Sons. 3. Stoner, J.F., Freeman, E. R., & Gilbert, D.R. (2013). Management (6^{th }Edition). Pearson Publications. 4. Joseph L Massie, Essentials of Management. PrenticeHall India, New York.  
Evaluation Pattern
 
MIME331  SENSORS AND DATA ACQUISITION (2020 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:45 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

Course objectives:


Learning Outcome 

Course outcomes: CO1. Summarize the working and construction of sensors measuring various physical CO2. Design suitable signal conditioning and filter circuits for sensors. CO3. Outline operations of various data acquisition and transmission systems. CO4. Distinguish smart sensors from normal sensors by their operation and construction. C05. Classify various sensing methods used in condition monitoring 
Unit1 
Teaching Hours:9 
SENSORS AND TRANSDUCERS


Sensors and classifications – Characteristics environmental parameters – Selectionand specification of sensors – Introduction to Acoustics and acoustic sensors Ultrasonicsensor Types and working of Microphones and Hydrophones – Sound level meter, Humidity  
Unit2 
Teaching Hours:9 
SMART SENSORS


Introduction  primary sensors, characteristic, Information coding / processing, Datacommunication  Recent trends in sensors and Technology  Film sensor, MEMS and NanoSensors.  
Unit3 
Teaching Hours:9 
SIGNAL CONDITIONING


Amplification, Filtering – Level conversion – Linearization  Buffering – Sample andHold circuit – Quantization – Multiplexer / Demultiplexer – Analog to Digital converter –Digital to Analog converter I/P and P/I converter  Instrumentation AmplifierV/F and F/V converter.  
Unit4 
Teaching Hours:9 
DATA ACQUISITION


Data Acquisition conversionGeneral configurationsingle channel and multichanneldata acquisition – Digital filtering – Data Logging – Data conversion – Introduction to DigitalTransmission system.  
Unit5 
Teaching Hours:9 
SENSORS FOR CONDITION MONITORING


Introduction to condition monitoring  Non destructive testing (vs) condition  
Text Books And Reference Books: T1. Patranabis. D, “Sensors and Transducers”, PHI, New Delhi, 2^{nd}Edition, 2003. T2. Ernest O. Doebelin, “Measurement Systems – Applications and Design”, TataMcGrawHill, 2009. T3. David G. Alciatore and Michael B. Histand, “Introduction to Mechatronics andMeasurement systems”, Tata McGrawHill, 2nd Edition, 2008. T4. John Turner and Martyn Hill, Instrumentation for Engineers and Scientists, OxfordScience Publications, 1999.  
Essential Reading / Recommended Reading R1. Cornelius Scheffer and PareshGirdhar “Practical Machinery Vibration Analysis andPredictive Maintenance” Elsevier, 2004. R2. A.K. Sawney and PuneetSawney, “A Course in Mechanical Measurements andInstrumentation and Control”, 12th edition, DhanpatRai& Co, New Delhi, 2001. R3.Mohamed GadelHak, “The MEMS handbook”, Interpharm/CRC. 2001 R4. Dr.Ing.B.V.A. RAO, “Monograph on Acoustics & Noise control”, NDRF, TheInstitution of Engineers (India), 2013.  
Evaluation Pattern CIA Marks: 50 ESE Marks: 50
 
MIPSY331  UNDERSTANDING HUMAN BEHAVIOR (2020 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

This course focuses on the fundamentals of psychology. It is an introductory paper that gives an overall understanding about the human behavior. It will provide students with an introduction to the key concepts, perspectives, theories, and subfields on various basic processes underlying human behavior.


Learning Outcome 

After the completion of this course students will be able to:

Unit1 
Teaching Hours:12 

Sensation


Definition, Characteristics of Sensory modalities: Absolute and difference threshold; Signal detection theory; sensory coding; Vision, Audition, Other Senses. Assessment of Perception and Sensation Practicum: Aesthesiometer  
Unit2 
Teaching Hours:12 

Perception


Definition, Understanding perception, Gestalt laws of organization, Illusions and Perceptual constancy; Various sensory modalities; Extrasensory perception. Practicum: MullerLyer Illusion  
Unit3 
Teaching Hours:12 

Learning and Memory


Learning:Definition, Classical conditioning, Instrumental conditioning, learning and cognition; Memory: Types of Memory; Sensory memory, working memory, Long term memory, implicit memory, Constructive memory, improving memory; Assessment of memory. Practicum: Memory drum  
Unit4 
Teaching Hours:12 

Individual Differences


Concepts and nature of Individual differences; Nature vs. nurture; Gender difference in cognitive processes and social behavior; Intelligence: Definition, Contemporary theories of intelligence; Tests of intelligence; Emotional, Social and Spiritual intelligence. Practicum: Bhatia’s Battery of Performance  
Unit5 
Teaching Hours:12 

Personality


Definition, Type and trait theories of personality, Type A, B & C. Psychoanalytic  Freudian perspective; Types of personality assessment. Practicum: NEOFFI 3  
Text Books And Reference Books: Baron, R. A. (2001). Psychology. New Delhi: Pearson Education India. Rathus, S. A. (2017). Introductory Psychology, 5thEd. Belmont, CA: Wadsworth. NolenHoeksema, S., Fredrickson, B.L. & Loftus, G.R. (2014). Atkinson & Hilgard'sIntroduction to Psychology.16th Ed. United Kingdom: Cengage Learning.
 
Essential Reading / Recommended Reading Feldman, R. S. (2011). Understanding Psychology. New Delhi: Tata McGraw Hill. Morgan, C. T., King, R. A., & Schopler, J. (2004). Introduction to Psychology. New Delhi: Tata McGraw Hill. Kalat, J. W. (2016). Understanding Psychology. New York: Cengage Learning  
Evaluation Pattern CIA Evaluation pattern
Mid Semester Examination
End Semester Examination
 
BS451  ENGINEERING BIOLOGY LABORATORY (2020 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 

Max Marks:50 
Credits:2 

Course Objectives/Course Description 

Understanding and application of MATLAB and TINKERCAD for biological analysis which would results in better healthcare and any engineer, irrespective of the parent discipline (mechanical, electrical, civil, computer, electronics, etc.,) can use the disciplinary skills toward designing/improving biological systems. This course is designed to convey the essentials of human physiology.
The course will introduce to the students the various fundamental concepts in MATLAB and TINKERCAD for numerical analysis and circuit design using arduino.


Learning Outcome 

CO1Perform basic mathematical operation and analysis on biological parameters as BMI, ECG using MATLAB.L4 CO2Perform basic image processing on RGB images pertaining to medical data using MATLABL4 CO3Perform analysis on biological parameters using TinkerCad and design mini projects applicable for healthcare and biosensing.L4

Unit1 
Teaching Hours:30 
LIST OF EXPERIMENTS


1. To familiarize with Matlab Online and getting used to basic functionalities used in Matlab (arrays, matrices, tables, functions) 2. To calculate the Body Mass Index (BMI) of a person and determine under what category the person falls under – underweight, normal, overweight 3. To determine the R peaks in given ECG and to find HRV using Matlab. 4. To determine the R peaks in given ECG and to find HRV using Matlab. 5. To determine the R peaks in given ECG and to find HRV using Matlab. 6. Introduction to Tinkercad and using the various tools available for running a simple program of lighting a LED bulb using Arduino (digital). 7. To design a driver motor in Tinkercad using Arduino and driver motor 8. To design a temperature sensor in Tinkercad using Arduino and TMP36 9. To design and simulate gas sensors using potentiometers, Arduino and servo motors 10. To design and simulate measuring pulse sensors using photodiodes, IR LED and Arduino 11. Preparation of biopolymers (polylactic acid) at home using homebased ingredients.  
Text Books And Reference Books:
 
Essential Reading / Recommended Reading
 
Evaluation Pattern As per university norms  
CS431  PROBABILITY AND QUEUING THEORY (2020 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 

At the end of the course, the students would have a fundamental knowledge of the basic probability concepts. Have a well – founded knowledge of standard distributions which can describe real life phenomena. Acquire skills in handling situations involving more than one random variable and functions of random variables. Understand and characterize phenomena which evolve with respect to time in a probabilistic manner. Be exposed to basic characteristic features of a queuing system and acquire skills in analyzing queuing models. 

Learning Outcome 

CO1: Explain the basic perceptions of probability of an event and associated random variables. CO2: Compare and contrast various standard distributions with suitable statistical analysis. CO3: Apply and solve two dimensional random variable problems through joint distributions and central limit theorem. CO4: Analyze probabilistic environment using random process and markov chain techniques. CO5: Build and implement queuing model associated to stochastic process. 
Unit1 
Teaching Hours:9 
PROBABILITY AND RANDOM VARIABLE


Axioms of probability  Conditional probability  Total probability – Baye’s theorem Random variable  Probability mass function  Probability density function  Properties – Moments  Moment generating functions and their properties.  
Unit2 
Teaching Hours:9 
STANDARD DISTRIBUTIONS


Binomial, Poisson, Geometric, Negative Binomial, Uniform, Exponential,Gamma, Weibull and Normal distributions and their properties  Functions of a random variable.  
Unit3 
Teaching Hours:9 
TWO DIMENSIONAL RANDOM VARIABLES


Joint distributions  Marginal and conditional distributions – Covariance Correlation and regression  Transformation of random variables  Central limit theorem.  
Unit4 
Teaching Hours:9 
RANDOM PROCESSES AND MARKOV CHAINS


Classification  Stationary process  Markov process  Poisson process  Birth and death process  Markov chains  Transition probabilities  Limiting distributions. Transition Diagram.  
Unit5 
Teaching Hours:9 
QUEUING THEORY


Markovian models – M/M/1, M/M/C, finite and infinite capacity  M/M/∞ queues  Finite source model  M/G/1 queue (steady state solutions only) – Pollaczek – Khintchine formula – Tools for statistical analysis  
Text Books And Reference Books: T1. Ross, S., “A first course in probability”, 9th Edition, Pearson Education, Delhi, 2019. T2. Medhi J., “Stochastic Processes”, New Age Publishers, New Delhi, 2017. (Chapters 2, 3,4) T3. T. Veerarajan, “Probability, Statistics and Random process”, Second Edition, Tata McGraw Hill, New Delhi, 2017.
 
Essential Reading / Recommended Reading R1. Allen A.O., “Probability, Statistics and Queuing Theory”, Academic press, New Delhi, 2010. R2. Taha H. A., “Operations ResearchAn Introduction”, Seventh Edition, Pearson Education Edition Asia, Delhi, 2014. R3. John F. Shortle , James M. Thompson, Donald Gross, Carl M. Harris Fundamentals of Queueing Theory; Wiley Series 2018
 
Evaluation Pattern
● End Semester Examination(ESE) : 50% (50 marks out of 100 marks)
 
CS432P  OPERATING SYSTEMS (2020 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:5 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

Objectives of this course is to have an overview of different types of operating systems. They also include an understanding of the components of an operating system; To develop knowledge of process management and have a thorough knowledge of storage management; To know the concepts of I/O and file systems.


Learning Outcome 

CO1: Demonstrate the Structure, Components and its basic functionalities of Operating System CO2: Distinguish various process management principles for given problem using appropriate tool CO3: Elucidate the process synchronization mechanisms, deadlock environment and its solutions in the given processes CO4: Inspect various memory management strategies for the given problems in memory systems CO5: Build file structure to distribute the same across the memory. 
Unit1 
Teaching Hours:9 

INTRODUCTION


Introduction : What operating systems do, Computer System Architecture, Operating System Structure, Operating System Operations, Process Management, Memory Management, Storage Management, Protection and Security; System Structures: Operating System Services, User Operating System Interface, System Calls, Types of System Calls  
Unit2 
Teaching Hours:9 

PROCESS MANAGEMENT


Process Management: Process Concept, Process Scheduling, Operations on Processes, Interprocess Communication; Threads: Overview, Multithreading Models, Thread Libraries; CPU Scheduling: Basic Concepts, Scheduling Criteria, Scheduling Algorithms, Multiple Processor Scheduling  
Unit3 
Teaching Hours:9 

PROCESS SYNCHRONIZATION AND DEADLOCKS


Process Synchronization: Background, The Critical Section Problem, Peterson’s Solution, Synchronization Hardware, Semaphores, Classical Problems of Synchronization, Monitors, Synchronization Examples, Deadlocks  
Unit4 
Teaching Hours:9 

MEMORY MANAGEMENT AND VIRTUAL MEMORY


Memory Management: Background, Swapping, Contiguous Memory Allocation, Paging, Virtual Memory: Background, Demand Paging, Copy on Write, Page Replacement, Allocation of frames, Thrashing, Allocating Kernel Memory
 
Unit5 
Teaching Hours:9 

FILE SYSTEM INTERFACE AND FILE SYSTEM IMPLEMENTATION & MASS STORAGE STRUCTURE


File System Interface: File System: File Concept, Access Methods, Directory Structure, File System Mounting, File Sharing, Protection; File System Implementation & Mass Storage Structure: Implementing File Systems: File System Structure, File System Implementation, Directory Implementation, Allocation Methods, Free Space Management. Disk structure, Disk Attachment, Disk Scheduling Methods, Disk Management, SwapSpace Management  
Text Books And Reference Books: 1. Abraham Silberschatz, Peter Baer Galvin and Greg Gagne, “Operating System Concepts”, Ninth Edition, John Wiley & Sons (ASIA) Pvt. Ltd, 2013.  
Essential Reading / Recommended Reading 1. Harvey M. Deitel, “Operating Systems”, Third Edition, Pearson Education Pvt. Ltd, 2007. 2. Andrew S. Tanenbaum, “Modern Operating Systems”, Prentice Hall of India Pvt. Ltd, 2009. 3. William Stallings, “Operating System”, Pearson Education 2009 4. Pramod Chandra P. Bhatt – “An Introduction to Operating Systems, Concepts and Practice”, PHI, 2010.  
Evaluation Pattern
 
CS433P  PROGRAMMING PARADIGM (2020 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:5 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

Software development in business environment has become more sophisticated, the software implementation is becoming increasingly complex and requires the best programming paradigm which helps to eliminate complexity of large projects. Object Oriented Programming (OOP) has become the predominant technique for writing software at present. Many other important software development techniques are based upon the fundamental ideas captured by objectoriented programming. The course also caters to the understanding of event driven programming, generic programming and concurrent programming. By the end of this COURSE, the student should acquire the basic knowledge and skills necessary to implement the concepts of various programming paradigms. 

Learning Outcome 

CO1: Demonstrate the fundamental concepts of Object Oriented Programming. CO2: Make use of the inheritance and interface concepts for effective code reuse. CO3: Inspect dynamic and interactive graphical applications using AWT and SWING. CO4: Build an application using generic programming and exception handling concepts. CO5: Assess and design concurrent and parallel applications using multithreaded concepts. 
Unit1 
Teaching Hours:9 

OBJECTORIENTED PROGRAMMING  FUNDAMENTALS


Review of OOP  Objects and classes in Java – defining classes – methods  access specifiers – static members – constructors – finalize method – Arrays – Strings  Packages – JavaDoc comments.  
Unit2 
Teaching Hours:9 

OBJECTORIENTED PROGRAMMING  INHERITANCE


Inheritance – class hierarchy – polymorphism – dynamic binding – final keyword – abstract classes – the Object class – Reflection – interfaces – object cloning – inner classes.  
Unit3 
Teaching Hours:9 

EVENTDRIVEN PROGRAMMING


Graphics programming – Frame – Components – working with 2D shapes – Using color, fonts, and images  Basics of event handling – event handlers – adapter classes – actions – mouse events – AWT event hierarchy – introduction to Swing – ModelView Controller design pattern – buttons – layout management – Swing Components  
Unit4 
Teaching Hours:9 

GENERIC PROGRAMMING


Motivation for generic programming – generic classes – generic methods – generic code and virtual machine – inheritance and generics – reflection and generics – exceptions – exception hierarchy – throwing and catching exceptions.  
Unit5 
Teaching Hours:9 

CONCURRENT PROGRAMMING


Multithreaded programming – interrupting threads – thread states – thread properties – thread synchronization – threadsafe Collections – Executors – synchronizers – threads and eventdriven programming, Parallel programming –fork, join framework.  
Text Books And Reference Books: 1. Herbert Schildt, “Java The Complete Reference” , Ninth Edition, McGraw Hill Publishers 2014. 2. Cay S. Horstmann and Gary Cornell, “Core Java: Volume I – undamentals”, Eighth Edition, Sun Microsystems Press, 2008.  
Essential Reading / Recommended Reading 1. Paul Deitel and Harvey Deitel , “Java How to program”, Tenth Edition, Deitel, 2016. 2. Ivan BratikoPROLOG: Programming for Artificial Intelligence, Third Edition, Pearson Publisher, 2002. 3. Bruce Eckel, “Thinking in Java”, 4th Edition, February 20, 2006. 4. Doug Rosenberg, Matt Stephens, “Use Case Driven Object Modeling with UML: Theory and Practice (Expert's Voice in UML Modeling)”, January 16, 2013.  
Evaluation Pattern
 
CS434  FORMAL LANGUAGE AND AUTOMATA THEORY (2020 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 

Max Marks:100 
Credits:3 

Course Objectives/Course Description 

To have an understanding of finite state and pushdown automata. To have a knowledge of regular languages and context free languages. To know the relation between regular language, context free language and corresponding recognizers. To study the Turing machine and classes of problems. 

Learning Outcome 

CO1 Design finite automata with conversion between types of finite automata. CO2 Develop regular expression and minimize the given finite automata for any regular language. CO3 Develop context free grammar, parse trees and pushdown automata for a given context free language. CO4 Experiment with CFLs and design of Turing machine for a given language. CO5 Explain decidable and undecidable problems, solvable and unsolvable problems with their complexity analysis.

Unit1 
Teaching Hours:8 
Automaton


Introduction to formal proof – Additional forms of proof – Inductive proofs –Finite Automata (FA) – Deterministic Finite Automata (DFA) – Nondeterministic Finite Automata (NFA) – Finite Automata with Epsilon transitions.  
Unit2 
Teaching Hours:10 
Regular Expressions and Languages


Regular Expression – FA and Regular Expressions – Proving languages not to be regular – Closure properties of regular languages – Equivalence and minimization of Automata.  
Unit3 
Teaching Hours:10 
ContextFree Grammar and Languages


ContextFree Grammar (CFG) – Parse Trees – Ambiguity in grammars and languages – Definition of the Pushdown automata – Languages of a Pushdown Automata – Equivalence of Pushdown automata and CFG, Deterministic Pushdown Automata.  
Unit4 
Teaching Hours:9 
Properties of ContextFree Languages


Normal forms for CFG – Pumping Lemma for CFL  Closure Properties of CFL – Turing Machines – Programming Techniques for TM.  
Unit5 
Teaching Hours:8 
Undecidability


A language that is not Recursively Enumerable (RE) – An undecidable problem that is RE – Undecidable problems about Turing Machine – Post’s Correspondence Problem  The classes P and NP.  
Text Books And Reference Books: 1. J.E.Hopcroft, R.Motwani and J.D Ullman, “Introduction to Automata Theory, Languages and Computations”, Pearson Education, 200  
Essential Reading / Recommended Reading R1. H.R. Lewis and C.H. Papadimitrou, “Elements of the Theory of Computation”, Second Edition, Pearson Education/PHI, 2003 R2. J.Martin, “Introduction to Languages and the Theory of Computation”, Third Edition, TMH, 2003. R3. Michael Sipser, “Introduction of the Theory and Computation”, Thomson Brokecole, 1997.  
Evaluation Pattern Assessment of each paper
 
CS435P  COMPUTER ORGANIZATION AND ARCHITECTURE (2020 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:5 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

This course will help the students to learn about basic structure of computer system, design of arithmetic and logic unit with the implementation of fixed and floating point numbers. Further, it will give knowledge about design of control unit and pipelined processing concepts. It discusses about various parallel processing architectures, different memory systems and I/O Communication systems 

Learning Outcome 

CO1: Demonstrate the functions of basic components of computer system and Instruction set Architecture CO2: Identify suitable control unit design and pipelining principles in computer architecture design CO3: Utilize appropriate instruction level parallelism concepts in multiprocessing environment CO4: Select suitable arithmetic algorithm to solve given arithmetic and logical problems CO5: Choose suitable memory and I/O system design 
Unit1 
Teaching Hours:9 
FUNDAMENTALS OF COMPUTER SYSTEM


Functional Units – Basic Operational Concepts – Performance – Instructions: Language of the Computer – Operations, Operands – Instruction representation – Logical operations – decision making – MIPS Addressing.
 
Unit2 
Teaching Hours:9 
COMPUTER ARTHIMETIC


Addition and Subtraction – Multiplication – Division – Floating Point Representation – Floating Point Operations – Subword Parallelism
 
Unit3 
Teaching Hours:9 
BASIC PROCESSING AND CONTROL UNIT


A Basic MIPS implementation – Building a Datapath – Control Implementation Scheme – Pipelining – Pipelined datapath and control – Handling Data Hazards & Control Hazards – Exceptions.
 
Unit4 
Teaching Hours:9 
PARALLELISM


Parallel processing challenges – Flynn‘s classification – SISD, MIMD, SIMD, SPMD, and Vector Architectures  Hardware multithreading – Multicore processors and other Shared Memory Multiprocessors  Introduction to Graphics Processing Units, Clusters, Warehouse Scale Computers and other MessagePassing Multiprocessors.
 
Unit5 
Teaching Hours:9 
MEMORY AND I/O


Memory Hierarchy  memory technologies – cache memory – measuring and improving cache performance – virtual memory, TLB‘s – Accessing I/O Devices – Interrupts – Direct Memory Access – Bus structure – Bus operation – Arbitration – Interface circuits  USB.
 
Text Books And Reference Books: T1. David A. Patterson and John L. Hennessy, “Computer Organization and Design: The Hardware/Software Interface”, Fifth Edition, Morgan Kaufmann / Elsevier, 2014. T2. Carl Hamacher, ZvonkoVranesic, SafwatZaky and NaraigManjikian, “Computer Organization and Embedded Systems”, Sixth Edition, Tata McGraw Hill, 2012.  
Essential Reading / Recommended Reading R1. William Stallings, “Computer Organization and Architecture – Designing for Performance”, Eighth Edition, Pearson Education, 2010. R2. John L. Hennessey and David A. Patterson, “Computer Architecture – A Quantitative Approach”, Fifth Edition, Morgan Kaufmann / Elsevier Publishers, 2012.  
Evaluation Pattern Continuous Internal Assessment (CIA) for Theory+Practical papers: 70% (70 marks out of 100 marks) · End Semester Examination (ESE) : 30% (30 marks out of 100 marks)  
EVS421  ENVIRONMENTAL SCIENCE (2020 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:0 
Credits:0 
Course Objectives/Course Description 

To understand the scope and importance of environmental science towards developing a conscious community for environmental issues, both at global and local scale. 

Learning Outcome 

CO1. Explain the components and concept of various ecosystems in the environment (L2, PO7) CO2. Explain the necessity of natural resources management (L2, PO1, PO2 and PO7) CO3.Relate the causes and impacts of environmental pollution (L4, PO1, PO2, and PO3, PO4) CO4.Relate climate change/global atmospheric changes and adaptation (L4,PO7) CO5. Appraise the role of technology and institutional mechanisms for environmental protection (L5, PO8)

Unit1 
Teaching Hours:6 
Introduction


Environment and Eco systems – Definition, Scope and importance. Components of environment. Concept and Structure of eco systems. Material Cycles – Nitrogen, Carbon, Sulphur, Phosphorous, Oxygen. Energy Flow and classification of Eco systems.  
Unit2 
Teaching Hours:6 
Natural Resources


Classification and importance Forest, Water, Mineral, Food, Energy. Management of natural resources – challenges and methods. Sustainable development – Goals, Agriculture, Industries  
Unit3 
Teaching Hours:6 
Environmental Pollution


Causes and Impacts – Air pollution, Water pollution, Soil Pollution, Noise Pollution, Marine Pollution, Municipal Solid Wastes, Bio Medical and EWaste. Solid Waste Management  
Unit4 
Teaching Hours:6 
Climate change/Global Atmospheric Change


Global Temperature, Greenhouse effect, global energy balance, Global warming potential, International Panel for Climate Change (IPCC) Emission scenarios, Oceans and climate change. Adaptation methods. Green Climate fund. Climate change related planning small islands and coastal region. Impact on women, children, youths and marginalized communities  
Unit5 
Teaching Hours:6 
Environmental Protection


Technology, Modern Tools – GIS and Remote Sensing,. Institutional Mechanisms  Environmental Acts and Regulations, Role of government, Legal aspects. Role of Nongovernmental Organizations (NGOs) , Environmental Education and Entrepreneurship  
Text Books And Reference Books: T1Kaushik A and Kaushik. C. P, “Perspectives in Environmental Studies”New Age International Publishers, New Delhi, 2018 [Unit: I, II, III and IV] T2Asthana and Asthana, “A text Book of Environmental Studies”, S. Chand, New Delhi, Revised Edition, 2010 [Unit: I, II, III and V] T3Nandini. N, Sunitha. N and Tandon. S, “environmental Studies” , Sapana, Bangalore, June 2019 [Unit: I, II, III and IV] T4R Rajagopalan, “Environmental Studies – From Crisis to Cure”, Oxford, Seventh University Press, 2017, [Unit: I, II, III and IV]
 
Essential Reading / Recommended Reading R1.Miller. G. T and Spoolman. S. E, “Environmental Science”, CENAGE Learning, New Delhi, 2015 R2.Masters, G andEla, W.P (2015), Introduction to environmental Engineering and Science, 3rd Edition. Pearson., New Delhi, 2013. R3.Raman Sivakumar, “Principals of Environmental Science and Engineering”, Second Edition, Cengage learning Singapore, 2005. R4.P. Meenakshi, “Elements of Environmental Science and Engineering”, Prentice Hall of India Private Limited, New Delhi, 2006. R5.S.M. Prakash, “Environmental Studies”, Elite Publishers Mangalore, 2007 R6.ErachBharucha, “Textbook of Environmental Studies”, for UGC, University press, 2005. R7. Dr. Pratiba Sing, Dr. AnoopSingh and Dr. PiyushMalaviya, “Textbook of Environmental and Ecology”, Acme Learning Pvt. Ltd. New Delhi.  
Evaluation Pattern No Evaluation  
HS422  PROFESSIONAL ETHICS (2020 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

This paper deals with the various organizational behaviours like learning, perception, motivation and method of managing stress and conflicts and the basic principles of communication. 

Learning Outcome 

CO1: To communicate in an effective manner in an organization. [L1] [PO1] CO2: To motivate the team members in an organization. [L3] [PO2] CO3: To Study the various motivational theories. [L2] [PO3] CO4: To study the various methods of learning. [L1] [PO2] CO5: To effectively manage the stress and conflicts in an organization.[L1] [P1] 
Unit1 
Teaching Hours:6 

THE INDIVIDUAL


Foundations of individual behaviour, individual differences. Ability. Attitude, Aptitude, interests. Values.  
Unit1 
Teaching Hours:6 

Introduction


Definition of Organization Behaviour and Historical development, Environmental context (Information Technology and Globalization, Diversity and Ethics, Design and Cultural, Reward Systems).  
Unit2 
Teaching Hours:6 

LEARNING


Learning: Definition, Theories of Learning, Individual Decision Making, classical conditioning, operant conditioning, social learning theory, continuous and intermittent reinforcement.  
Unit2 
Teaching Hours:6 

PERCEPTION


Definition, Factors influencing perception, attribution theory, selective perception, projection, stereotyping, Halo effect.  
Unit3 
Teaching Hours:6 

MOTIVATION


Maslow's Hierarchy of Needs theory, McGregor's theory X and Y, Hertzberg's motivation Hygiene theory, David McClelland’s three needs theory, Victor Vroom's expectancy theory of motivation.  
Unit3 
Teaching Hours:6 

THE GROUPS


Definition and classification of groups, Factors affecting group formation, stages of group development, Norms, Hawthorne studies, group processes, group tasks, group decision making.  
Unit4 
Teaching Hours:6 

CONFLICT AND STRESS MANAGEMENT


Definition of conflict, functional and dysfunctional conflict, stages of conflict process. Sources of stress, fatigue and its impact on productivity. Job satisfaction, job rotation, enrichment, job enlargement and reengineering work process.  
Unit5 
Teaching Hours:6 

PRINCIPLE OF COMMUNICATION


Useful definitions, communication principles, communication system, role of communication in management, barriers in communication, how to overcome the barriers, rule of effective communication.  
Text Books And Reference Books: T1. Organizational Behaviour, Stephen P Robbins, 9th Edition, Pearson Education Publications, ISBN8178085615 2002 T2: Organizational Behaviour, Fred Luthans, 9th Edition, Mc Graw Hill International Edition, ISBN00712041212002  
Essential Reading / Recommended Reading R1.Organizational Behaviour, Hellriegel, Srocum and Woodman, Thompson Learning, 9th Edition, Prentice Hall India, 2001 R2.Organizational Behaviour, Aswathappa  Himalaya Publishers. 2001 R3.Organizational Behaviour, VSP Rao and others, Konark Publishers.2002 R4.Organizational Behaviour, {Human behaviour at work} 9th Edition, John Newstron/ Keith Davis. 2002  
Evaluation Pattern
 
MIA451A  ENVIRONMENTAL DESING AND SOCIO CULTURAL CONTEXT (2020 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 

Max Marks:100 
Credits:04 

Course Objectives/Course Description 

Elective subjects have been suggested which are related to specialized areas in Architecture. The student may choose any one subject of interest. The detailed syllabus of the electives chosen and the modus operandi of teaching will be taken up by the faculty in charge. Course Objective: To expose the students to specialized areas of architecture. 

Learning Outcome 

To acquire the knowledge of the chosen area of specialization; to apply or innovate the fundamentals and details learnt, in design. Level: Basic 
Unit1 
Teaching Hours:90 
Environmental Design & Sociocultural Context


The understanding of habitat in a cultural setting where architecture is explored in the context of craftmaking – ecology, people, and architecture. Reading of the context and site intuitively and technically and initiate the design exercise of a Pavilion. Exploration of local material resources that inform architecture. Design development of a Pavilion comprising of a simple function for “Me and my environment”.  
Text Books And Reference Books: T1.Ingersoll, R. And Kostof, S. (2013). World architecture: a crosscultural history. Oxford: Oxford University Press. T2. Rapoport, A (1969). House Form and Culture. PrenticeHall, Inc. Englewood Cliffs, NJ USA Pearson T3. Bary, D. & Ilay, C. (1998) Traditional Buildings of India, Thames & Hudson, ISBN10 : 0500341613 T4. McHarg I. (1978), Design with Nature. NY: John Wiley & Co.  
Essential Reading / Recommended Reading R1. Tillotsum G.H.R. (1989) The tradition of Indian Architecture Continuity, Controversy – Change since 1850, Delhi: Oxford University Press. R2. René Kolkman and Stuart H. Blackburn (2014). Tribal Architecture in Northeast India. R3. Richardson, V. (2001) New Vernacular Architecture; Laurance King Publishing. R4. Kenneth, F. (1983). Towards a Critical Regionalism: Six points for an architecture of resistance, In the AntiAesthetic: Essays on Postmodern Culture. (Ed.) Hal, F. Seattle: Bay Press. R5. Brunskill, R. W. (1987). Illustrated Handbook of Vernacular Architecture. Castle Rock: Faber & Faber. R6. Frampton, K., & Cava, J. (1995). Studies in tectonic culture: The poetics of construction in nineteenth and twentieth century architecture. Cambridge, Mass.: MIT Press.  
Evaluation Pattern The Evaluation pattern comprises of two components; the Continuous Internal Assessment (CIA) and the End Semester Examination (ESE). CONTINUOUS INTERNAL ASSESSMENT (CIA): 50 Marks END SEMESTER EXAMINATION (ESE, VIVAVOCE): 50 Marks TOTAL:100 Marks Note: For this course, a minimum of 50% marks in CIA is required to be eligible for VIVAVOCE which is conducted as ESE.  
MIA451B  DIGITAL ARCHITECTURE (2020 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:100 
Credits:04 
Course Objectives/Course Description 

Course Description: Elective subjects have been suggested which are related to specialized areas in Architecture. The student may choose any one subject of interest. The detailed syllabus of the electives chosen and the modus operandi of teaching will be taken up by the faculty in charge. Course objectives: To expose the students to specialized areas of architecture.


Learning Outcome 

To acquire the knowledge of the chosen area of specialization; to apply or innovate the fundamentals and details learned, in design.
Level: Basic 
Unit1 
Teaching Hours:90 
Digital Architecture


 
Text Books And Reference Books: T1. Achim Menges, Sean Ahlquist . (2011) Computational Design thinking T2: Fox, M. (2009) Interactive Architecture: Adaptive World, Princeton Architectural Press, ISBN10 : 1616894067. T3: Linn C. D. & Fortmeyer, R. (2014) Kinetic Architecture: Designs for Active Envelopes, Images Publishing Group Pty Ltd., ISBN10 : 1864704950 T4: Ali Rahim, 'Contemporary Process in Architecture', John Wiley & Sons, 2000. T5. Ali Rahim (Ed), 'Contemporary Techniques in Architecture, Halsted Press, 2002.  
Essential Reading / Recommended Reading R1. Arturo Tedeschi.(2014) AAD_AlgorithmsAided Design. R2. Kostas Terzidis.(2006) Algorithmic Architecture R4. Lisa Iwamoto.(2009) Digital Fabrications: Architectural and Material Techniques, Architecture Briefs R5.Eisenmann, P. (1999) Diagram Diaries, Universe Publishing, ISBN100789302640.  
Evaluation Pattern The Evaluation pattern comprises of two components; the Continuous Internal Assessment (CIA) and the End Semester Examination (ESE). CONTINUOUS INTERNAL ASSESSMENT (CIA): 50 Marks END SEMESTER EXAMINATION (ESE, VIVAVOCE): 50 Marks TOTAL:100 Marks Note: For this course, a minimum of 50% marks in CIA is required to be eligible for VIVAVOCE which is conducted as ESE.  
MIA451C  COLLABORATIVE DESIGN WORKSHOP (2020 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:100 
Credits:04 
Course Objectives/Course Description 

Elective subjects have been suggested which are related to specialized areas in Architecture. The student may choose any one subject of interest. The detailed syllabus of the electives chosen and the modus operandi of teaching will be taken up by the faculty in charge. Course objective: To expose the students to specialized areas of architecture. 

Learning Outcome 

To acquire the knowledge of the chosen area of specialization; to apply or innovate the fundamentals and details learned, in design. Level: Basic 
Unit1 
Teaching Hours:90 
Collaborative Design Workshop


Engage in a rural outreach program through an architecture design project by adopting appropriate technology that seeks solutions to environmental, social concerns and addresses the sustainability paradigm. Design and execution of an architectural project of a dwelling environment of a small community, with a focus on ideas of type and typology through site studies and analysis. Study of correlation between climateenvironmental parameters and socialcultural patterns as generators of an architectural space. Construction and commissioning of the approved architectural design that is externally funded.  
Text Books And Reference Books: T1. Dean, A., & Hursley, T. (2002). Rural Studio: Samuel Mockbee and an Architecture of Decency. Princeton Architectural Press. T2. Ching, F. D. K. (2015). Architecture: Form, Space, & Order (Fourth edition.). New Jersy: John Wiley. T3. Givoni, B. (1969). Man, climate and architecture. Elsevier.  
Essential Reading / Recommended Reading R1. Minke. G (2012). Building with Bamboo, Design and Technology of a Sustainable Architecture. Birkhauser, Basel Switzerland. R2. Rapoport, A (1969). House Form and Culture. PrenticeHall, Inc. Englewood Cliffs, NJ USA Pearson R3. Clark, R. H., & Pause, M. (2012). Precedents in architecture: Analytic diagrams, formative ideas, and partis (4th ed.). Hoboken, N.J.: John Wiley & Sons R4. Carter, R. (2012). On and By Frank Lloyd Wright: A Primer of Architectural Principles. Phaidon Press. R5. Curtis, W. (1994). Le Corbusier: Ideas and Forms. Phaidon Press; Revised edition. R6. Mertins, D., & Lambert, P. (2014). Mies. New York: Phaidon.  
Evaluation Pattern The Evaluation pattern comprises of two components; the Continuous Internal Assessment (CIA) and the End Semester Examination (ESE). CONTINUOUS INTERNAL ASSESSMENT (CIA): 50 Marks END SEMESTER EXAMINATION (ESE, VIVAVOCE): 50 Marks TOTAL:100 Marks Note: For this course, a minimum of 50% marks in CIA is required to be eligible for VIVAVOCE which is conducted as ESE.  
MIMBA431  ORGANISATIONAL BEHAVIOUR (2020 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 

Course Description: The course is offered as a mandatory core course for all students in Trimester II. The course introduces students to a comprehensive set of concepts and theories, facts about human behaviour and organizations that have been acquired over the years. The subject focuses on ways and means to improve productivity, minimize absenteeism, increase employee engagement and so on thus, contributing to the overall effectiveness. The basic discipline of the course is behavioral science, sociology, social psychology, anthropology and political science. Course Objectives: To make sense of human behaviour, use of common sense and intuition is largely inadequate because human behaviour is seldom random. Every human action has an underlying purpose which was aimed at personal or societal interest. Moreover, the uniqueness of each individual provides enough challenges for the managers to predict their best behaviour at any point of time. A systematic study of human behaviour looks at the consistencies, patterns and cause effect relationships which will facilitate understanding it in a reasonable extent. Systematic study replaces the possible biases of intuition that can sabotage the employee morale in organizations. 

Learning Outcome 

Course Learning Outcomes: On having completed this course student should be able to: At the end of the course the student will be able to: CLO1: Determine the individual and group behavior in the workplace. CLO2: Assess the concepts of personality, perception and learning in Organizations. CLO3: Analyze various jobrelated attitudes. CLO4: Design motivational techniques for job design, employee involvement, incentives, rewards & recognitions. CLO5: Manage effective groups and teams in organizations.

Unit1 
Teaching Hours:12 

Unit1: Introduction to Organizational Behaviour


Historical Development, Behavioural sciences and Organizational behaviour, Meaning, Importance, Basic concepts, methods and tools for understanding behaviour, Challenges and Opportunities, OB model, ethical issues in organizational Behaviour. Crosscultural management, managing multicultural teams, communicating across cultures, OB in the digital age.  
Unit2 
Teaching Hours:12 

Unit2: Individual Behaviour ? Personality, Perception and Learning


Personality: Foundations of individual behaviour, Personality, Meaning and Importance, Development of personality, Determinants of personality, Theories of personality, Relevance of personality to managers. Perception: Nature, Importance and Definition of Perception, Factors involved in perception, The Perceptual Process, Perceptual Selectivity and Organization, Applications in Organizations. Learning: Definition and Importance, Theories of learning, Principles of learning, Shaping as managerial tool.  
Unit3 
Teaching Hours:12 

Unit3: Attitudes, Values & Job Satisfaction


Attitudes: Sources and types of attitudes, Attitude formation and change, Cognitive Dissonance Theory. Effects of employee attitude, Job related attitudes Values: meaning, importance, source and types, and applications in organizations. Job satisfaction: Measuring Job Satisfaction, Causes of Job Satisfaction, impact of satisfied and dissatisfied employees on the workplace.  
Unit4 
Teaching Hours:12 

Unit4: Motivation


Meaning, process and significance of motivation, Early Theories of motivation: Hierarchy of Needs, Theory X Theory Y, Two Factor theory, McClelland Theory of Needs, Contemporary Theories of Motivation: Goal Setting theory, SelfEfficacy theory, Equity theory/Organizational justice, Expectancy theories, Motivation theories applied in organizations: Job design, employee involvement, rewards and global implications  
Unit5 
Teaching Hours:12 

Unit5: Groups & Teams


Groups: Meaning, classification and nature of groups, Stages of group development, an alternative model for Temporary Groups with punctuated equilibrium model, Group properties: Roles, Norms, Status, Size and Cohesiveness, Group decision making. Teams: Meaning of teams, Types of teams, Creating Effective teams, what makes individuals into effective team players, Team development, Team decision making.  
Text Books And Reference Books: Core Text Books: T1. Robbins, S P., Judge, T A and Vohra, N (2018). Organizational Behavior. 18th Edition, Prentice Hall of India.  
Essential Reading / Recommended Reading Rao V S P & V Sudeep 2018, Managing Organisational Behavior, Trinity Press, 3rd edition, New Delhi.  
Evaluation Pattern
 
MIME432  ROBOTICS AND MACHINE VISION (2020 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:45 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

Course objectives: 1. To understand the basics of drives and power transmission system. 2. To learn about the kinematics of robot 3. To understand the basics of sensors and the different types of robotic End Effectors 4. To learn about the machine vision systems and its application To gain information about the different types of robot programming methods. 

Learning Outcome 

Course outcomes:After successful completion of this course, the students should be able toCO1. Explain the basics of robots, drives and power transmission system.CO2. Solve and analyze the kinematics of robotic manipulator.CO3. Illustrate different sensors and robotic endeffectorsCO4. Explain the basics of machine vision and its operation.CO5. Program robots using different programming methods. 
Unit1 
Teaching Hours:9 

INTRODUCTION


Basic Structure, Classification of robot and Robotic systems, laws of robotics,  
Unit2 
Teaching Hours:9 

KINEMATICS OF ROBOT MANIPULATOR:


Introduction to manipulator kinematics, homogeneous transformations and robot  
Unit3 
Teaching Hours:9 

SENSORS AND ROBOT END EFFECTORS


Sensors in robotics Position sensors, Velocity sensors, Acceleration Sensors,  
Unit4 
Teaching Hours:9 

MACHINE VISION


Image Sensing and Digitizing  Image definition, Image acquisition devices –  
Unit5 
Teaching Hours:9 

Robot programming:


Introduction; Online programming: Manual input, lead  
Text Books And Reference Books: T1. S. R. Deb and S. Deb, „Robotics Technology and Flexible Automation‟, TataMcGraw Hill Education Pvt. Ltd, 2010. T2. Saeed B. Niku, „Introduction to Robotics‟,Prentice Hall of India, 2nd Edtion 2001. T3. Mikell P. Groover, "Industrial Robots  Technology, Programming andApplications", McGraw Hill, New York, 2008  
Essential Reading / Recommended Reading R1. Richard D Klafter, Thomas A Chmielewski, Michael Negin, "Robotics Engineering –An Integrated Approach", Eastern Economy Edition, Prentice Hall of India P Ltd.,2006.
 
Evaluation Pattern
 
MIPSY432  PEOPLE THOUGHTS AND SITUATIONS (2020 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

The course is an exploration of the prevailing theories and empirical methods that explain about people’s thoughts, feelings and behaviors in a social context. This throws light on cognitive and social factors that influence human behavior, especially in situations populated by others.


Learning Outcome 

At the end of the course students will be able:

Unit1 
Teaching Hours:9 

Sources


Classification and characteristics – municipal, commercial & industrial. Methods of quantification  
Unit1 
Teaching Hours:9 

Introduction


Definition, Land Pollution – scope and importance of solid waste management, functional elements of solid waste management.  
Unit2 
Teaching Hours:9 

Collection and Transportation


Systems of collection, collection equipment, garbage chutes, transfer stations – bailing and compacting, route optimization techniques and problems.  
Unit3 
Teaching Hours:9 

Treatment/Processing Techniques


Components separation, volume reduction, size reduction, chemical reduction and biological processing problems.  
Unit3 
Teaching Hours:9 

Incineration


Process – 3 T’s, factors affecting incineration process, incinerators – types, prevention of air pollution, pyrolsis, design criteria for incineration.  
Unit4 
Teaching Hours:9 

Composting


Aerobic and anaerobic composting, factors affecting composting, Indore and Bangalore processes, mechanical and semi mechanical composting processes. Vermi composting.  
Unit4 
Teaching Hours:9 

Sanitary land filling


Different types, trench area, Ramp and pit method, site selection, basic steps involved, cell design, prevention of site pollution, leachate & gas collection and control methods, geosynthetic fabricsin sanitary landfills.  
Unit5 
Teaching Hours:9 

Recycle and Reuse


Material and energy recovery operations, reuse in other industries, plastic wastes, environmental significance and reuse.  
Unit5 
Teaching Hours:9 

Disposal Methods


Open dumping – selection of site, ocean disposal, feeding to hogs, incineration, pyrolsis, composting, sanitary land filling, merits and demerits, biomedical wastes and disposal.  
Text Books And Reference Books: T1 Bhide and Sunderashan “Solid Waste Management in developing countries”, T2 Tchobanoglous “Integrated Solid Waste Management”, Mc Graw Hill.  
Essential Reading / Recommended Reading R1. Peavy and Tchobanoglous “Environmental Engineering”, R2. Garg S K “Environmental Engineering”, Vol II R3. “Biomedical waste handling rules – 2000”. R4. Pavoni J.L. “Hand book on Solid Waste Disposal”  
Evaluation Pattern
 
CEOE561E03  DISASTER MANAGEMENT (2019 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:3 

Course Objectives/Course Description 

Course would help to understand the scope and relevance of Multi Disciplinary approach in Disaster Management in a dynamic world and to realize the responsibilities of individuals and institutions for effective disaster response and disaster risk reduction 

Learning Outcome 

CO1 : Explain Hazards and Disasters (L2, PO 4) CO2 :Assess managerial aspects of Disaster Management, plan and explain risk analysis (L3, PO5) CO3 : Relate Disasters and Development (L4, PO7) CO4 : Compare climate change impacts and develop scenarios (L5, PO6) CO5: Categorize policies and institutional mechanisms in Disaster Management and the impacts on society (L5, PO7) 
Unit1 
Teaching Hours:8 

Introduction to Hazard and Disasters


Principles of Disaster Management, Hazards, Risks and Vulnerabilities; Natural Disasters (Indicative list: Earthquake, Floods, Fire, Landslides, Tornado, Cyclones, Tsunamis, Human Induced Disasters (e.g Nuclear, Chemical, Terrorism. Assessment of Disaster Vulnerability of a location and vulnerable groups; Pandemics  
Unit2 
Teaching Hours:8 

Disaster Management Cycle and Humanitarian Logistics


Prevention, Preparedness and Mitigation measures for various Disasters, Post Disaster Relief & Logistics Management, Emergency Support Functions and their coordination mechanism, Resource & Material Management, Management of Relief Camp, Information systems & decision making tools, Voluntary Agencies & Community Participation at various stages of disaster, management.  
Unit3 
Teaching Hours:8 

Natural resources and Energy sources


Renewable and nonrenewable resources, Role of individual in conservation of natural resources for sustainable life styles. Use and over exploitation of Forest resources. Use and over exploitation of surface and ground water resources, Conflicts over water, Dams benefits and problems.  
Unit4 
Teaching Hours:10 

Global Environmental Issues


Global Environmental crisis, Current global environment issues, Global Warming, Greenhouse Effect, role of Carbon Dioxide and Methane, Ozone Problem, CFC‟s and Alternatives, Causes of Climate Change Energy Use: past, present and future, Role of Engineers.  
Unit5 
Teaching Hours:11 

Disaster Risk Reduction and Development


Disaster Risk Reduction and Institutional Mechanisms Meteorological observatory – Seismological observatory  Volcanology institution  Hydrology Laboratory; National Disaster Management Authority (India); Disaster Policies of Foreign countries. Integration of public policy: Incident Command System; National Disaster Management Plans and Policies; Planning and design of infrastructure for disaster management, Community based approach in disaster management, methods for effective dissemination of information, ecological and sustainable development models for disaster management. Technical Tolls for Disaster Management: Monitoring, Management program for disaster mitigation ; Geographical Information System(GIS) ; Role of Social Media in Disaster Management  
Text Books And Reference Books:
T1. Paul, B.K, “Environmental Hazards and Disasters: Contexts, Perspectives and Management”, WileyBlackwell, 2011. (Unit 1 – Chapter 1; Unit 2 – Chapter 1, 3; Unit 3 – Chapter 4; Unit 4 – Chapter 5 & 6) T2. Keller, Edward, and Duane DeVecchio. “Natural hazards: earth's processes as hazards, disasters, and catastrophe”s. Pearson Higher Education AU, 2015. (Unit 5 – Chapter 6 & 7)  
Essential Reading / Recommended Reading R1. Coppola, D, “Introduction to International Disaster Management “Elsevier, 2015.
R2. Fookes, Peter G., E. Mark Lee, and James S. Griffiths. "Engineering geomorphology: theory and practice." Whittles Publications, 2007.
R3. Tomasini, R. And Wassanhove, L.V (2009). Humanitarian Logistics. Pangrave Macmillan.  
Evaluation Pattern
 
CS531P  COMPUTER NETWORKS (2019 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:5 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

1. To understand the concepts of data communications.
2. To study the functions of different layers. To introduce IEEE standards employed in computer networking.
3. To make the students to get familiarized with different protocols and network components.
4. To build foundation of Networks in Algorithms and its analysis, Software Engineering Models and Theory of Automata.


Learning Outcome 

CO1: Outline the basic concepts of reference models and identify the functionality of physical layer in computer communications CO2: Illustrate the data link layer protocols for error detection and corrections mechanism CO3: Demonstrate the IP addressing schemes and routing protocols in network layer CO4: Distinguish the functionality and features used in UDP and TCP protocols CO5: Examine the Application layer protocols and cryptographic algorithms used in networking environment 
Unit1 
Teaching Hours:15 
DATA COMMUNICATIONS


Components – Direction of Data flow – networks – Components and Categories – types of Connections – Topologies –Protocols and Standards – ISO / OSI model – Transmission Media – Coaxial Cable – Fiber Optics – Line Coding – Modems – RS232 Interfacing sequences.  
Unit2 
Teaching Hours:15 
DATA LINK LAYER


Error – detection and correction – Parity – LRC – CRC – Hamming code – low Control and Error control  stop and wait – go backN ARQ – selective repeat ARQ sliding window – HDLC.  LAN  Ethernet IEEE 802.3  IEEE 802.4  IEEE 802.5  IEEE 802.11 – FDDI  SONET – Bridges.  
Unit3 
Teaching Hours:15 
NETWORK LAYER


Internetworks – Packet Switching and Datagram approach – IP addressing methods – Subnetting – Routing – Distance Vector Routing – Link State Routing – Routers.
 
Unit4 
Teaching Hours:15 
TRANSPORT LAYER


Duties of transport layer – Multiplexing – Demultiplexing – Sockets – User Datagram Protocol (UDP) – Transmission Control Protocol (TCP) – Congestion Control – Quality of services (QOS) – Integrated Services.
 
Unit5 
Teaching Hours:15 
APPLICATION LAYER


Domain Name Space (DNS) – SMTP – FTP – HTTP  WWW – Security – CryptographyCase study.  
Text Books And Reference Books: T1: Behrouz A. Forouzan, “Data communication and Networking”, Tata McGrawHill, 2013.  
Essential Reading / Recommended Reading R1: James F. Kurose and Keith W. Ross, “Computer Networking: A TopDown Approach Featuring the Internet”, Pearson Education, 2012. R2: Larry L.Peterson and Peter S. Davie, “Computer Networks”, Fifth Edition, Harcourt Asia Pvt. Ltd., Second Edition, Publishers, 2012. R3: Andrew S. Tanenbaum, “Computer Networks”, 5^{th} Edition, Pearson 2012. R4: William Stallings, “Data and Computer Communication”, Sixth Edition, Pearson Education, 2007.  
Evaluation Pattern 1. Continuous Internal Assessment (CIA) for Theory + Practical papers: 70% (70 marks out of 100 marks) 2. End Semester Examination (ESE): 30% (30 marks out of 100 marks)  
CS532  INTRODUCTION TO ARTIFICAL INTELLIGENCE (2019 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 

This course provides a strong foundation of fundamental concepts in Artificial Intelligence. To provide a basic exposition to the goals and methods and to enable the student to apply these techniques in applications which involve perception, reasoning and learning. 

Learning Outcome 

CO1:Identify the fundamental knowledge of Intelligent agents, searching strategies and syntax and semantics of first order logic. CO2: Discover the complex problem solving agents, constraint satisfaction problems and optimal decisions in game. CO3: Inspect the knowledge engineering in first order logic, knowledge representation and chaining mechanisms, knowledge in learning and different forms of learning CO4: Determine and build planning strategies, Communication and analysis of grammar and its interpretation CO5: Asses a system that utilize artificial intelligence to a complicated task with limited resources in the form of time and computations

Unit1 
Teaching Hours:9 
INTRODUCTION


Intelligent Agents – Agents and environments  Good behavior – The nature of environments – structure of agents  Problem Solving  problem solving agents – example problems – searching for solutions – uniformed search strategies  avoiding repeated states – searching with partial information.  
Unit2 
Teaching Hours:9 
SEARCHING TECHNIQUES


Informed search and exploration – Informed search strategies – heuristic function – local search algorithms and optimistic problems – local search in continuous spaces – online search agents and unknown environments  Constraint satisfaction problems (CSP) – Backtracking search and Local search for CSP – Structure of problems  Adversarial Search – Games – Optimal decisions in games – Alpha – Beta Pruning – imperfect realtime decision – games that include an element of chance.  
Unit3 
Teaching Hours:9 
KNOWLEDGE REPRESENTATION


First order logic – representation revisited – Syntax and semantics for first order logic – Using first order logic – Knowledge engineering in first order logic  Inference in First order logic – prepositional versus first order logic – unification and lifting – forward chaining – backward chaining  Resolution  Knowledge representation  Ontological Engineering  Categories and objects – Actions  Simulation and events  Mental events and mental objects.  
Unit4 
Teaching Hours:9 
LEARNING


Learning from observations  forms of learning  Inductive learning  Learning decision trees  Ensemble learning  Knowledge in learning – Logical formulation of learning – Explanation based learning – Learning using relevant information – Inductive logic programming  Statistical learning methods  Learning with complete data  Learning with hidden variable  EM algorithm  Instance based learning  Neural networks  Reinforcement learning – Passive reinforcement learning  Active reinforcement learning  Generalization in reinforcement learning.  
Unit5 
Teaching Hours:9 
DEEP LEARNING


Convolutional Neural Networks, Motivation, Convolution operations, Pooling, Image classification, Modern CNN architectures, Recurrent Neural Network, Motivation, Vanishing/Exploding gradient problem, Applications to sequences, Modern RNN architectures.  
Text Books And Reference Books: T1. Stuart Russell and Peter Norvig, “Artificial Intelligence – A Modern Approach”, 3rd Edition, Pearson Education, 2014.  
Essential Reading / Recommended Reading
 
Evaluation Pattern CIA ASSESSMENT DETAILS  THEORY  
CS533P  DESIGN AND ANALYSIS OF ALGORITHMS (2019 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:5 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

To introduce basic concepts of algorithms; To introduce mathematical aspects and analysis of algorithms; To introduce sorting and searching algorithms; To introduce various algorithmic techniques; To introduce algorithm design methods. 

Learning Outcome 

CO1: Demonstrate the process of algorithmic problem solving with time and space complexity CO2: Identify algorithm design techniques for searching and sorting CO3: Inspect algorithms under divide and conquer technique CO4: Solve problems by applying dynamic programming technique and determine the efficiency of algorithms CO5: Interpret the limitations of Algorithm power and demonstrate backtracking technique 
Unit1 
Teaching Hours:9 
INTRODUCTION AND FUNDAMENTALS OF THE ANALYSIS OF ALGORITHM EFFICIENCY


Introduction, Notion of Algorithm, Fundamentals of Algorithmic Solving, Important Problem Types, Fundamentals of the Analysis Framework, Mathematical Analysis of Nonrecursive Algorithm, Mathematical Analysis of Recursive Algorithm and examples, Empirical Analysis of Algorithms and Algorithm Visualization.  
Unit2 
Teaching Hours:9 
ALGORITHM DESIGN TECHNIQUES


Brute Force and Exhaustive Search: Selection Sort, Bubble Sort, Sequential Search and Bruteforce string matching, Travelling Salesman Problem, Knapsack Problem, Assignment Problem. Decrease and Conquer: Insertion Sort and Topological Sorting and Fake Coin Problem, Russian Peasant Multiplication, Josephus Problem  
Unit3 
Teaching Hours:9 
ALGORITHM DESIGN TECHNIQUES


Divide and conquer: Merge sort, Quick Sort, Binary Tree Traversals and Related Properties and Multiplication of Large Integers and Strassen’s Matrix Multiplication. Transform and Conquer: Presorting, Notion of Heap and Heapsort, Horner’s Rule and Binary Exponentiation.  
Unit4 
Teaching Hours:9 
ALGORITHM DESIGN TECHNIQUES


Space and Time Trade  Offs: Sorting by Counting, Horspool’s and Boyer  Moore Algorithm for String Matching, Hashing. Dynamic Programming: Knapsack Problem, Warshall’s and Floyd’s Algorithm. Greedy Techniques: Prim’s Algorithm, Kruskal’s Algorithm, Dijkstra’s Algorithm.  
Unit5 
Teaching Hours:9 
ALGORITHM DESIGN TECHNIQUES


Limitations of Algorithm Power: Decision Trees, P, NP and NP Complete Problems, Challenges in Numerical Algorithms. Backtracking: nQueen’s Problem, Hamiltonian Circuit problem and SubsetSum problem. Branch and Bound: Assignment problem, Knapsack problem and Traveling salesman problem.  
Text Books And Reference Books:
 
Essential Reading / Recommended Reading
 
Evaluation Pattern
 
CS541E01  COMPUTER GRAPHICS WITH OPEN GL (2019 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 



Learning Outcome 

CO1: Demonstrate the fundamentals of applications and techniques involved in computer graphics. CO2: Build 2D and 3D transformations using matrices representations in homogeneous coordinates. CO3: Examine OpenGL functions and relate to Crossplatform API for writing applications. CO4: Evaluate various properties of geometry CO5: Support transformation principles ,various types of light and material properties.

Unit1 
Teaching Hours:9 
I


A survey of Computer Graphics, Video Display Devices, RasterScan Systems, Graphics Workstation and Viewing Systems, Input Devices, HardCopy Devices, Graphics Networks, Graphics on the Internet.
 
Unit2 
Teaching Hours:9 
II


Line Drawing Algorithms, DDA Algorithms, Bresenham's Line Algorithm, CircleGenerating Algorithms, Midpoint Circle Algorithms, Ellipse Algorithms, Basic Two Dimensional Transformations, Matrix Representation, Three Dimensional Translation, Three Dimensional Rotation, Three Dimensional Scaling, Other Three Dimensional Transformations  Reflection and Shears.  
Unit3 
Teaching Hours:9 
III


Java Graphics in 2D, TwoDimensional Graphics in Java, Transformations and Modeling, Basics of OpenGL and JOGL, Basic OpenGL 2D Programs, Into the Third Dimension, Drawing in 3D, Normal and Textures  
Unit4 
Teaching Hours:9 
IV


Geometry, Vectors, Matrices and Homogeneous Coordinates, Primitives, Polygonal Meshes, Drawing Primitives, Viewing and Projections, Perspective Projection, Orthographic Projection, The Viewing Transform, A Simple Avatar, Viewer Nodes in Scene Graphics  
Unit5 
Teaching Hours:9 
V


Light and Material, Vision and Color, OpenGL Materials, OpenGL Lighting, Lights and Materials in Scenes, Case Study: Textures, Texture targets, Mipmaps and Filtering, Texture Transformations, Creating Texture with OpenGL, Loading Data into Texture, Texture Coordinate Generation, Texture Objects  
Text Books And Reference Books:
 
Essential Reading / Recommended Reading
 
Evaluation Pattern
 
CS541E02  INTERNET AND WEB PROGRAMMING (2019 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 

Explain tools for developing applications in Web programming; Describe scripting languages –Java Script; Under case study: Exposure to a web platform. 

Learning Outcome 

CO1: Build the basic web page using HTML concepts. CO2: Experiment with the concepts of CSS to build the web pages. CO3: Determine the usage of Javascript scripts for making the effective web pages. CO4: Develop backend connection using MariaDB CO5: Design web applications using platforms like Node.js.

Unit1 
Teaching Hours:9 
HTML5


Why HTML5 exists? Structuring a Web Page, Forms, Multimedia (video, audio) markup and APIs, Canvas, Data Storage, Drag & Drop, Messaging & Workers  
Unit2 
Teaching Hours:9 
CSS3


Understanding CSS and the Modern Web, Learning CSS Syntax and Adding Presentational Styles, Creating Styles Using Property Values, Adding Presentational Styles, Creating A Basic Page Structure, Understanding Display, Position, and Document Flow, Changing and styling fonts, Adding transitions and animations.  
Unit3 
Teaching Hours:9 
JAVASCRIPT


Basic JavaScript Instructions, Functions, Methods & Objects, Decisions & Loops, Document Object Model, Events  
Unit4 
Teaching Hours:6 
NOSQL


Installing MariaDB, Configuring MariaDB, MariaDB Security, MariaDB User Account Management, MariaDBDatatypes, Date and String functions in MaraiaDB, Using MariaDB,  
Unit5 
Teaching Hours:12 
CASE STUDY  Node.js


The Node Module System, The Node Programming Model, Events and Timers, The Command Line Interface, The File System, Streams, Binary Data, Executing Code, Network Programming, HTTP, Express Framework  
Text Books And Reference Books: TEXT BOOKS: 1. Bruce Lawson, Remy Sharp, “Introducing HTML 5”, Pearson Education, 2011. 2. Ian Lunn, “CSS3 Foundations”, Wiley Publishers, 2012. 3. Jon Duckett, “JavaScript and JQuery: Interactive FrontEnd Web Development”, Wiley Publishers: 2014. 4. Daniel Bartholomew, “Getting started with MariaDB”, 2013. 5. Colin J. Ihrig, “Pro Node.js for Developers”, APRESS, 2013.  
Essential Reading / Recommended Reading REFERENCE BOOKS: 1. Matt west, “HTML5 Foundations”, Wiley Publishers: 2012. 2. Training Guide Programming in HTML5 with JavaScript and CSS3 (MCSD) (Microsoft Press Training Guide), 2013. 3. Elizabeth Castro, Bruce Hyslop, “HTML and CSS: Visual QuickStart Guide” 8th edition, 2013.  
Evaluation Pattern Assessment of each paper Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks) End Semester Examination (ESE): 50% (50 marks out of 100 marks)
 
CS541E04  CRYPTOGRAPHY AND NETWORK SECURITY (2019 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 

To understand the principles of encryption algorithms; conventional and public key cryptography. To have a detailed knowledge about authentication, hash functions and Network & application level security mechanisms. 

Learning Outcome 

CO1: Explain various features of Security mechanisms and services to study Standard Block Ciphers along with their design principles CO2: Utilize the basic concepts and algorithms of Public key encryption mechanism for secure data transmission. CO3: Compare various Cryptographic authentications protocols, Hash Functions, Algorithms and Standards. CO4: Identify Various Protocols and Standards in Network Security. CO5: Make use of various research directions at system level security.

Unit1 
Teaching Hours:9 
Introduction


OSI Security Architecture, Classical Encryption techniques, Cipher Principles, DES, Crypto analysis of DES, AES, Block Cipher Design Principles and Modes of Operation, Triple DES, Placement of Encryption Function, Traffic Confidentiality.  
Unit2 
Teaching Hours:9 
Public Key Cryptography


Introduction to Number theory, Deffie Hellman Key Exchange, Key Management, Elliptic curve Cryptography, Confidentiality using Symmetric Encryption, Public Key Cryptography and RSA.  
Unit3 
Teaching Hours:9 
Authentication & Hash Functions


Authentication Requirements, Authentication Functions, Message Authentication Codes, Hash Functions, MD5, SHA, RIPEMD and HMAC Standards  
Unit4 
Teaching Hours:9 
Network Security


Authentication Applications: Kerberos – X.509 Authentication Service – Electronic Mail Security – PGP – S/MIME  IP Security – Web Security.  
Unit5 
Teaching Hours:9 
Application Security


Intrusion detection – password management – Viruses and related Threats – Virus Counter measures – Firewall Design Principles – Trusted Systems, Secret sharing schemes, Probabilistic encryption, Quantum Encryption, Attribute and Identity Encryption CASEStudy  
Text Books And Reference Books: T1.William Stallings, “Cryptography and Network Security – Principles and Practices”, 6th Edition, 2016.  
Essential Reading / Recommended Reading R1. AtulKahate, “Cryptography and Network Security”, Tata McGrawHill, 2013. R2.Bruce Schneier, “Applied Cryptography”, John Wiley & Sons Inc, 2015. R3.Charles B. Pfleeger, Shari Lawrence Pfleeger, “Security in Computing”, Fifth Edition, Pearson Education, 2015.
 
Evaluation Pattern ASSESSMENT  ONLY FOR THEORY COURSE (without practical component) ● Continuous Internal Assessment (CIA) : 50% (50 marks out of 100 marks) ● End Semester Examination(ESE) : 50% (50 marks out of 100 marks)
 
CS581  INTERNSHIP  I (2019 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:1 
Course Objectives/Course Description 

Internships are shortterm work experiences that will allow a student to observe and participate in professional work environments and explore how his interests relate to possible careers. They are important learning opportunities through industry exposure and practices. Course Objectives: •Identify how the internship relates to their academic courses and preferred career path •Integrate existing and new technical knowledge for industrial application •Understand lifelong learning processes through critical reflection of internship experiences. •Articulate their experience and skills to potential employers 

Learning Outcome 

CO1: Design solutions to real time complex engineering problems using the concepts of Computer Science and Information Technology through independent study. CO2: Demonstrate teamwork and leadership skills with professional ethics. CO3: Prepare an internship report in the prescribed format and demonstrate oral communication through presentation of the internship work.

Unit1 
Teaching Hours:30 

Regulations


1.The student shall undergo an Internship for30 days starting from the end of 4th semester examination and completing it during the initial period of 5th semester. 2.The department shall nominate a faculty as a mentor for a group of students to prepare and monitor the progress of the students. 3. The students shall report the progress of the internship to the mentor/guide at regular intervals and may seek his/her advise. 4. The Internship evaluation will be completed by the end of 5th semesters. 5. The students are permitted to carry out the internship outside India with the following conditions, the entire expenses are to be borne by the student and the University will not give any financial assistance. 6. Students can also undergo internships arranged by the department during vacation. 7. After completion of Internship, students shall submit a report to the department with the approval of both internal and external guides/mentors. 8. There will be an assessment for the internship for 1 credit, in the form of report assessment by the guide/mentor and a presentation on the internship given to department constituted panel.  
Text Books And Reference Books: Nil  
Essential Reading / Recommended Reading Nil  
Evaluation Pattern Maximum Marks = 50(Only credit will be displayed in the score card) Passing marks 40% min Internship assessment will be carried out based on the following parameters, during the 5th semester as a single Presentation evaluation.
 
CSHO531AIP  STATISTICAL FOUNDATION FOR ARTIFICIAL INTELLIGENCE (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:5 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

Course objectives: •Discuss the core concepts Statistical Analytics and Data manipulation •Apply the basic principles, models, and algorithms supervised and unsupervised learning mechanisms. •Analyse the structures and algorithms of regression methods •Explain notions and theories associated to Convolutional Neural Networks •Solve problems in HighDimensional Regression


Learning Outcome 

•Understand and explain concepts associated to Statistical Analytics and Data manipulation L2 •Infer details of supervised and unsupervised learning mechanisms. L2 •Solve problems connected to regression methods. L3 •Analyse concepts of Convolutional Neural Networks. L4 • Appraise concepts of HighDimensional Regression. L5

Unit1 
Teaching Hours:9 
Statistical Analytics and Data manipulation


Knowledge discovery: finding structure in data, Data quality versus data quantity, Statistical modeling versus statistical description. Data types, Data summarization, Means, medians, and central tendency, Summarizing variation, Summarizing (bivariate) correlation, Data diagnostics and data transformation, Outlier analysis, Entropy, Data transformation Simple smoothing techniques, Binning, Moving averages, Exponential smoothing. Introduction to SPSS (IBM’s) statistical tool.  
Unit2 
Teaching Hours:9 
Techniques for supervised and unsupervised learning


The simple linear model, Multiple inferences and simultaneous confidence bands, Regression diagnostics, Weighted least squares (WLS) regression, Correlation analysis. Unsupervised versus supervised learning, Principal component analysis, Principal components, Implementing a PCA, Exploratory factor analysis.  
Unit3 
Teaching Hours:9 
Neural Networks


Projection Pursuit Regression, Neural Networks, Fitting Neural Network, Some Issues in Training Neural Networks, Bayesian Neural Nets, 0 Computational Considerations.  
Unit4 
Teaching Hours:9 
Random Forests and Ensemble Learning


Definition of Random Forests, Details of Random Forests Out of Bag Samples, Variable Importance, Proximity Plots; Analysis of Random Forests; Ensemble Learning, Boosting and Regularization Paths, Learning a Good Ensemble, Rule Ensembles.  
Unit5 
Teaching Hours:9 
HighDimensional Problems: p ≫ N


Diagonal Linear Discriminant Analysis and Nearest Shrunken Centroids, Linear Classifiers with Quadratic Regularization, Linear Classifiers with L1 Regularization, Classification When Features are Unavailable, HighDimensional Regression, Feature Assessment and the MultipleTesting Problem  
Text Books And Reference Books: Text Books: 1.Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media, 2017. 2.Russell, Stuart J., and Peter Norvig. Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited,, 2016.
 
Essential Reading / Recommended Reading Reference Books: 1.Ghahramani, Zoubin. "Probabilistic machine learning and artificial intelligence." Nature 521.7553 (2015): 452. 2.Ian Goodfellow and Yoshua Bengio and Aaron Courville,” Deep Learning ”, MIT Press, March 2018. 3.Wu, James, and Stephen Coggeshall. Foundations of predictive analytics. Chapman and Hall/CRC, 2012. 4.Marcoulides, George A., and Scott L. Hershberger. Multivariate statistical methods: A first course. Psychology Press, 2014. 5.Morgan, George A., et al. IBM SPSS for introductory statistics: Use and interpretation. Routledge, 2012
 
Evaluation Pattern Assessment of each paper Continuous Internal Assessment (CIA) for Theory papers: 70% (70 marks out of 100 marks) End Semester Examination (ESE): 30% (30 marks out of 100 marks) Marks CIA I: 10 Marks CIA II: 10 Marks CIA III: 10 Marks Lab: 35 Marks Total Marks: 70 Marks End Sem: 30 Marks  
CSHO531CSP  PROBABILITY AND RANDOM PROCESS (2019 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:5 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

After learning the course for a semester, the student will be aware of the important statistical information for addressing cryptography, error correction and coding, information theory and cryptanalysis. The student would also get a clear idea on some of the cases with their analytical studies in information coding and its related fields. 

Learning Outcome 

CO 1: To define pattern searching algorithms for different applications CO 2: To classify vulnerability of subsystem based on the information gathered from different resources CO 3: To estimate different optimized process and models CO4: To provide means to find the similarities between the applications and vulnerabilities of the subsystem/system CO5: To analyze about best possible patterns to cluster the possible solutions for different vulnerabilities 
Unit1 
Teaching Hours:9 

Probability Fundamentals


Probability Fundamentals, Bayes’ rule, Markov chains and application to pattern search algorithms, Classical statistical inference, Bayesian statistical inference, Regression techniques  
Unit2 
Teaching Hours:9 

Information Coding


Information coding, Pseudorandom number generators, discrete random variables, special distributions and mixed random variables, link and rank analysis , probability bounds, limiting theorem and convergence  
Unit3 
Teaching Hours:9 

Statistical Learning


Risk M Basics of statistical learning: models, regression, curse of dimensionality, overfitting, etc. Optimization and convexity, Gradient descent, Newton’s method  
Unit4 
Teaching Hours:9 

Classification


Classification and similarity analysis, linear discriminative analysis, regression analysis, iterative permutation analysis, Support vector machines, nearest neighbor and application of entropy.  
Unit5 
Teaching Hours:9 

Clustering Algorithms


Clustering algorithms, graph analysis, pattern detection, Knowledge driven system design, learning with errors, Basics of neural networks  
Text Books And Reference Books: 1.Gnedenko, Boris V. Theory of probability. Routledge, 2018. 2.Beichelt, Frank. Applied Probability and Stochastic Processes. Chapman and Hall/CRC, 2016. 3.Li, X. Rong. Probability, random signals, and statistics. CRC press, 2017  
Essential Reading / Recommended Reading 1.Grimmett, Geoffrey, Geoffrey R. Grimmett, and David Stirzaker. Probability and random processes. Oxford university press, 2001. 2.Papoulis, Athanasios, and S. Unnikrishna Pillai. Probability, random variables, and stochastic processes. Tata McGrawHill Education, 2002. 3.Rozanov, Yu. Probability theory, random processes and mathematical statistics. Vol. 344. Springer Science & Business Media, 2012.  
Evaluation Pattern Continuous Internal Assessment (CIA) for Theory papers: 70% (70 marks out of 100 marks) End Semester Examination(ESE) : 30% (30 marks out of 100 marks)
 
CSHO531DAP  STATISTICAL FOUNDATION FOR DATA ANALYTICS (2019 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:5 

Max Marks:50 
Credits:4 

Course Objectives/Course Description 

•Discuss the core concepts Statistical Analytics and Data manipulation •Apply the basic principles, models and algorithms supervised and unsupervised learning mechanisms. •Analyse the structures and algorithms of regression methods •Analyse the use of SVM in Data Science •Explain notions and theories associated to Convolutional Neural Networks


Learning Outcome 


Unit1 
Teaching Hours:9 

Statistical Analytics and Data manipulation


Knowledge discovery: finding structure in data, Data quality versus data quantity, Statistical modeling versus statistical description. Data types, Data summarization, Means, medians, and central tendency, Summarizing variation, Summarizing (bivariate) correlation, Data diagnostics and data transformation, Outlier analysis, Entropy, Data transformation Simple smoothing techniques, Binning, Moving averages, Exponential smoothing. Introduction to SPSS (IBM’s) statistical tool. Statistical parameters (eg: Correlation analysis)
 
Unit2 
Teaching Hours:9 

Techniques for supervised and unsupervised learning


The simple linear model, Multiple inferences and simultaneous confidence bands, Regression diagnostics, Weighted least squares (WLS) regression, Correlation analysis. Unsupervised versus supervised learning, Principal component analysis, Principal components, Implementing a PCA, Exploratory factor analysis Linear and polynomial Regression  
Unit3 
Teaching Hours:9 

Neural Networks


Projection Pursuit Regression, Neural Networks, Fitting Neural Network, Some Issues in Training Neural Networks, Bayesian Neural Nets, Computational Considerations. Prediction analysis (eg: Stocks)
 
Unit4 
Teaching Hours:9 

Support Vector Machines and Flexible Discriminants


Introduction, The Support Vector Classifier, Support Vector Machines and Kernels, Generalizing Linear Discriminant Analysis, Flexible Discriminant Analysis, Penalized Discriminant Analysis, Mixture Discriminant Analysis Time Series: predict web traffic  
Unit5 
Teaching Hours:9 

Random Forests and Ensemble Learning


Definition of Random Forests, Details of Random Forests Out of Bag Samples, Variable Importance, Proximity Plots; Analysis of Random Forests; Ensemble Learning, Boosting and Regularization Paths, Learning a Good Ensemble, Rule Ensembles.
Convolutional Neural Network  Step by Step  
Text Books And Reference Books:
 
Essential Reading / Recommended Reading
 
Evaluation Pattern Continuous Internal Assessment (CIA) for Theory papers: 70% (70 marks out of 100 marks) End Semester Examination(ESE) : 30% (30 marks out of 100 marks) Components of the CIA CIA I :Closed Book Test and Quiz: 10 marks CIA II :Mid Semester Examination (Theory): 10 marks CIA III :Closed Book Test and Quiz:10 marks Lab marks :35 marks Attendance: 05 marks
1) CIA ASSESSMENT DETAILS  THEORY
2) LAB ASSESSMENT DETAILS
 
ECOE5603  AUTOMOTIVE ELECTRONICS (2019 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 

Max Marks:100 
Credits:3 

Course Objectives/Course Description 

The aim of this course is to enable student to understand the complete dynamics of automotive electronics, design and implementation of the electronics that contributes to the safety of the automobiles, addon features, and comforts. 

Learning Outcome 

At the end of the course, the student will be able to : CO1:Implement various control requirements in the automotive system CO2: Comprehend dashboard electronics and engine system electronics CO3:Identify various physical parameters that are to be sensed and monitored for maintaining the stability of the vehicle under dynamic conditions CO4:Understand and implement the controls and actuator system pertaining to the comfort and safety of commuters CO5: Design sensor network for mechanical fault diagnostics in an automotive vehicle 
Unit1 
Teaching Hours:9 
AUTOMOTIVE FUNDAMENTALS


Use of Electronics In The Automobile, Antilock Brake Systems, (ABS), Electronic steering control, Power steering, Traction control, Electronically controlled suspension  
Unit2 
Teaching Hours:9 
AUTOMOTIVE INSTRUMENTATION CONTROL


Sampling, Measurement and signal conversion of various parameters. Sensors and Actuators, Applications of sensors and actuators  
Unit3 
Teaching Hours:9 
BASICS OF ELECTRONIC ENGINE CONTROL


Integrated body Climate controls, Motivation for Electronic Engine Control, Concept of An Electronic Engine Control System, Definition of General Terms, Definition of Engine Performance Terms, Electronic fuel control system, Engine control sequence, Electronic Ignition, air flow rate sensor, Indirect measurement of mass air flow, Engine crankshaft angular position sensor, Automotive engine control actuators, Digital engine control, Engine speed sensor ,Timing sensor for ignition and fuel delivery, Electronic ignition control systems, Safety systems, Interior safety, Lighting, Entertainment systems  
Unit4 
Teaching Hours:9 
VEHICLE MOTION CONTROL AND AUTOMOTIVE DIAGNOSTICS


Cruise control system, Digital cruise control, Timing light, Engine analyzer, Onboard and offboard diagnostics, Expert systems. Stepper motor based actuator, Cruise control electronics, Vacuum – antilock braking system, Electronic suspension system Electronic steering control, Computerbased instrumentation system, Sampling and Input\output signal conversion, Fuel quantity measurement, Coolant temperature measurement, Oil pressure measurement, Vehicle speed measurement, Display devices, TripInformation Computer, Occupant protection systems  
Unit5 
Teaching Hours:9 
FUTURE AUTOMOTIVE ELECTRONIC SYSTEMS


Alternative Fuel Engines, Collision Wide Range Air/Fuel Sensor, Alternative Engine, Low Tire Pressure Warning System, Collision avoidance Radar Warning Systems, Low Tire Pressure Warning System, Radio Navigation, Advance Driver information System. AlternativeFuel Engines, Transmission Control , Collision Avoidance Radar Warning System, Low Tire Pressure Warning System, Speech Synthesis Multiplexing in Automobiles, Control Signal Multiplexing, Navigation Sensors, Radio Navigation, Sign post Navigation , Dead Reckoning Navigation Future Technology, Voice Recognition Cell Phone Dialing Advanced Driver information System, Automatic Driving Control  
Text Books And Reference Books: T1.A William B. Ribbens, "Understanding Automotive Electronics",6th Edition SAMS/Elsevier publishing, 2007  
Essential Reading / Recommended Reading R1. Robert Bosch Gmbh,"Automotive Electrics and Automotive ElectronicsSystems and Components, Networking and Hybrid Drive", 5^{th} Edition, Springer, Vieweg, 2007  
Evaluation Pattern As per university norm  
ECOE5608  FUNDAMENTALS OF IMAGE PROCESSING (2019 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 

The aim of this course is to introduce image processing fundamentals making the students to understand the different methods available to process an image and also give them an insight about the toolbox in MATLAB which can be used to do simulations in image processing. 

Learning Outcome 

At the end of the course, the student will be able to : CO1: Understand the basic principles of image processing CO2: Understand the tools used for image processing applications CO3: Analyze the methods used for image preprocessing CO4: Apply the compression techniques and analyze the results CO5: Develop an image processing system for a given application 
Unit1 
Teaching Hours:9 
DIGITAL IMAGE FUNDAMENTALS


Concept of Digital Image, conversion of analog image to digital, General Applications of image processing, Fundamental Steps in Digital Image Processing. Components of an Image Processing System. Elements of Visual Perception. Light and the Electromagnetic Spectrum. Image Sensing and Acquisition. Image Sampling and Quantization  
Unit2 
Teaching Hours:9 
MATLAB USING IP TOOL BOX


Introduction to 