Department of COMPUTER SCIENCE AND ENGINEERING

Syllabus for
Bachelor of Technology (Computer Science and Engineering)
Academic Year  (2021)

 
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 Entity-Relationship (E-R) model.

CO 2 : Apply E-R 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

Unit-1
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.

Unit-2
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. High-level language extension with Cursors.

4.High level language extension with Triggers

Unit-3
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- B-Tree - B+ Tree – Query Processing.

Lab Programs 

5. Procedures and Functions.

6. Embedded SQL.

Unit-4
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 E-R model and Normalization.

8. Design and implementation of Payroll Processing System.

Unit-5
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, McGraw-Hill, 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 McGraw-Hill 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 array-based 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

Unit-1
Teaching Hours:11
INTRODUCTION
 

 

Definition- Classification of data structures: primitive and non-primitive- Operations on data structures- Algorithm Analysis.

Lab Progrsm:

1.To determine the time complexity of a given logic.

Unit-2
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 (de-queue) priority queue, operations on all types of Queues.

 

  1. Implement the applications Stack ADT 
  2. Implement the applications for Queue ADT
  3. Operations on stack [e.g.: infix to postfix, evaluation of postfix]

 

Unit-3
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.

 

Unit-4
Teaching Hours:14
SORTING
 

Preliminaries – Insertion Sort – Shell sort – Heap sort – Merge sort – Quicksort – External Sorting.

Lab Program: 

  1. Heap Sort
  2. Quick Sort
Unit-5
Teaching Hours:12
GRAPHS
 

Definitions – Topological Sort – Shortest-Path Algorithms – Unweighted Shortest Paths – Dijkstra’s Algorithm – Minimum Spanning Tree – Prim’s Algorithm – Applications of Depth- First Search – Undirected Graphs – Bi-connectivity – Introduction to NP-Completeness-case study.

Lab Program: 

  1. Implementing any of the shortest path algorithms
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.

 

Unit-1
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.

Unit-2
Teaching Hours:9
SOFTWARE REQUIREMENTS
 

Functional and non-functional - 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

Unit-3
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.

Unit-4
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.

Unit-5
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, McGraw-Hill 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 cyber-attacks 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 cyber-attacks using technologies like cryptography and Intrusion prevention systems.

L3

Unit-1
Teaching Hours:6
UNIT 1
 

Security Fundamentals-4 As Architecture Authentication Authorization Accountability, Social Media, Social Networking and Cyber Security.Cyber Laws, IT Act 2000-IT Act 2008-Laws for Cyber-Security, Comprehensive National Cyber-Security Initiative CNCI – Legalities

Unit-2
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 Hardening-TCP/IP attack-SYN Flood

Unit-3
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

Unit-4
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 Cyber-Security.

Unit-5
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, 6th impression, ISBN: 978-81-7758-425-7.

R2. Thomas R, Justin Peltier, John, “Information Security Fundamentals”, Auerbach Publications.

R3. AtulKahate, “Cryptography and Network Security”,  2nd Edition, Tata McGrawHill.2003

R4. Nina Godbole, SunitBelapure, “Cyber Security”, Wiley India 1st 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

 

Unit-1
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.

Unit-2
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 Quine-McClusky method.

Unit-3
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, Bit-pair 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.

Unit-4
Teaching Hours:9
SEQUENTIAL CIRCUITS
 

Sequential Circuit Elements: Latches -RS latch and JK latch, Flip-flops-RS, JK, T     and D flip flops, Master-slave flip flops, Edge-triggered flip-flops. Analysis and     Design of Synchronous Sequential Circuits: Models of sequential circuits - Moore     machine and Mealy machine, Flip-flops - 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 flip-flop 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, Floating-pint adder/subtractor - Design of control circuit, Floating - point multiplier.

Unit-5
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 7th 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. Sanguine-Pearson”, 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, ” 10th  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}

Unit-1
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. 

Unit-2
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

Unit-3
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.

Unit-4
Teaching Hours:6
Communication and Writing
 

Writing a speech, Public speaking, Formal presentations, Presentation aids, Group communication, Discussions, Organizational GD, Meetings & Conferences.

Unit-5
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: 07828357-4)

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

Sl NO

DESCRIPTION

REVISED BLOOM’S TAXONOMY (RBT)LEVEL

1

Checking  the  consistency of system of linear equations and solving by Gauss Jordan and Gauss Elimination methods, finding the spectral matrix with the aid of eigen values and eigen vector

L3

2

Finding the differentiation of multivariable functions using the concept of total derivatives, Jacobian,  evaluating definite integrals by Leibnitz rule of differentiation under integral sign

L4

3

Evaluating the definite integrals as surface area and volume of solid of revolution by   tracing the curves and using reduction formulae

L4

4

Solving first order nonlinear differential equations by reducing into homogenous, linear and exact forms

L3

5

Finding the velocity and acceleration of a moving particle, vector potential, scalar potential

L3

Unit-1
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.

Unit-2
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

Unit-3
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.

Unit-4
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.

Unit-5
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, 5th Edition, Prism Engineering Education Series2018.

5.      R5. S Santha, Discrete Mathematics with Combinatorics and Graph Theory  Cengage, 1st 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) 

 

 

S.No

Assessment

Marks

Weightagemarks

1

CIA I

20

10

2

CIA II

     (MSE: Mid Semester Examination)

50

25

3

CIA III

20

10

4

Attendance

10

05

5

ESE

(End Semester Examination)

100

50

Total

100

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:

  • The MSE is conducted for 50 marks of 2 hours duration.
  • Question paper pattern; Five out of Six questions have to be answered. Each  question carries 10 marks

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 process-based 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. 

  1. Introduction to different media and rendering techniques.
  2. Introduction to principles of composition, developing keen sensitivity to space, scale, proportion, light, wind, sound, texture.
  3. To understand basic principles of freehand drawing and color.
  4. Introduction to the representation of the human body and anthropometrics /ergonomics.
  5. To translate abstract principles of design into architectural processes, forms, and solutions.
  6. To introduce the Architectural Design Language – technical drafting and presentation and to impart the appropriate manual skills for visualization and technical representation.

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 

Unit-1
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)

Unit-2
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.

Unit-3
Teaching Hours:20
Elements of Design & theory of visual perception
 
  1. Elements of design, Developing skills of analysis, synthesis, interpretation, and communication through elements and composition.
  2. Introduction to the theory of visual perception through color, form, space, light and shadow, texture, and tones.
Unit-4
Teaching Hours:30
Pictorial Projections, Sciography & Rendering
 
  1. Developing pictorial representations -Isometric Projection, Axonometric projection, and Perspective projections 
  2. Introduction to Sciography and principles of shades and shadows.
  3. Rendering the pictorial projections.
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, VIVA-VOCE): 50 Marks

TOTAL:100 Marks

Note: For this course, a minimum of 50% marks in CIA is required to be eligible for VIVA-VOCE 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

 
  • To learn the systematic way of solving problems.
  • To understand the different methods of organizing large amounts of data.
  • To efficiently implement the different data structures.
  • To efficiently implement solutions for specific problems.

Learning Outcome

Sl NO

DESCRIPTION

REVISED BLOOM’S TAXONOMY (RBT)LEVEL

1.

Explain the basic concepts of data structures and solve the time complexity of the algorithm

L3

2.

Experiment with various operations on Linear Data structures

L3

3.

Examine the Structures and Operations of Trees and Heaps Data Structures

L4

4

Compare various given sorting techniques with respect to time complexity

L4

5

 Choose various shortest path algorithms to determine the minimum spanning path for the given graphs

L5

Unit-1
Teaching Hours:14
INTRODUCTION
 

Definition- Classification of data structures: primitive and non-primitive- Operations on data structures- Algorithm Analysis.

LAB Programs:

1a. Sample C Programs 1b. To determine the time complexity of a given logic. 

Unit-2
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 (de-queue) 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]

Unit-3
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

Unit-4
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. 

Unit-5
Teaching Hours:14
GRAPHS
 

Definitions – Topological Sort – Shortest-Path Algorithms – Unweighted Shortest Paths – Dijkstra‘s Algorithm – Minimum Spanning Tree – Prim‘s Algorithm – Applications of Depth- First Search – Undirected Graphs – Bi-connectivity – Introduction to NP-Completeness-case 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, Addison-Wesley, 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

Unit-1
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, Self-learning mode)

 

Unit-2
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

Unit-3
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

Unit-4
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

Unit-5
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 (13th Edition). Tata McGraw Hill Publications.

 

Essential Reading / Recommended Reading

Recommended Reading

1.     Daft, R. L. (2013). The new era of management (10th 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 (6th Edition). Pearson Publications.

4.      Joseph L Massie, Essentials of Management. Prentice-Hall India, New York.

Evaluation Pattern

Test & Exam

 Exam conducted for

Marks conversion

Weightage

Total

CIA-I

20

10

20%

10

CIA-II

50

25

25%

25

CIA-III

20

10

10%

10

Attendance

 

5

5%

5

CIA – I, II, and III

50

50%

50

End-term

100

50

50%

50

Total

100

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:  

  • To know about the types of transducers available.
  • To understand the function of signal generators and analyzers.
  • To gain information about data acquisition, data logging, and application of sensors incondition-based monitoring.

Learning Outcome

Course outcomes:

CO1. Summarize the working and construction of sensors measuring various physical
parameters.

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

Unit-1
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
sensor, and Nuclear radiation sensor – Stress- Strain measurements Strain gauges (resistiveand Optical) types Uniaxial and Multiaxial strain gauges with signal conditioning circuits(half, quarter, and full bridges)

Unit-2
Teaching Hours:9
SMART SENSORS
 

Introduction - primary sensors, characteristic, Information coding / processing, Datacommunication - Recent trends in sensors and Technology - Film sensor, MEMS and NanoSensors.

Unit-3
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 Amplifier-V/F and F/V converter.

Unit-4
Teaching Hours:9
DATA ACQUISITION
 

Data Acquisition conversion-General configuration-single channel and multichanneldata acquisition – Digital filtering – Data Logging – Data conversion – Introduction to DigitalTransmission system.

Unit-5
Teaching Hours:9
SENSORS FOR CONDITION MONITORING
 

Introduction to condition monitoring - Non destructive testing (vs) condition
monitoring- Intelligent fault detection- Accelerometers- Acoustic Emission sensors- Thermalimaging cameras- Vibration Signature based monitoring techniques - Acoustic emissionholography - oil Analysis- Ultrasound based Non Destructive Evaluation techniques.

Text Books And Reference Books:

T1. Patranabis. D, “Sensors and Transducers”, PHI, New Delhi, 2ndEdition, 2003.

T2. Ernest O. Doebelin, “Measurement Systems – Applications and Design”, TataMcGraw-Hill, 2009.

T3. David G. Alciatore and Michael B. Histand, “Introduction to Mechatronics andMeasurement systems”, Tata McGraw-Hill, 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 Gad-el-Hak, “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 sub-fields on various basic processes underlying human behavior.

  1. To understand the fundamental processes underlying human behavior
  2. To become aware of one’s idiosyncrasies and predispositions
  3. To apply the understanding of concepts in day-to-day activities

Learning Outcome

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

  1. Explain human behaviors using theoretical underpinnings
  2. Understand oneself and others, respecting the differences
  3. Demonstrate their understanding of psychological processes in daily activities

Unit-1
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

Unit-2
Teaching Hours:12
Perception
 

Definition, Understanding perception, Gestalt laws of organization, Illusions and Perceptual constancy; Various sensory modalities; Extrasensory perception.

Practicum:  Muller-Lyer Illusion

Unit-3
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

Unit-4
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

Unit-5
Teaching Hours:12
Personality
 

Definition, Type and trait theories of personality, Type A, B & C. Psychoanalytic -  Freudian perspective; Types of personality assessment.

Practicum: NEO-FFI 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.

Nolen-Hoeksema, 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

Group Assignment

Individual Assignment

Mid semester

20

20

25

 

Mid Semester Examination

Section A

(Definition)

Section B

(Short note)

Section C

(Essay)

Section D

(Case Question)

Total

5×2=10

4×5=20

1×10=10

1×10=10

50

 

End Semester Examination

Section A

(Definition)

Section B

(Short note)

Section C

(Essay)

Section D

(Case Question)

Total

5×2=10

4×5=20

1×10=10

1×10=10

50

 

 

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

 

Unit-1
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 home-based 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.

Unit-1
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.

Unit-2
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.

Unit-3
Teaching Hours:9
TWO DIMENSIONAL RANDOM VARIABLES
 

Joint distributions - Marginal and conditional distributions – Covariance Correlation and regression - Transformation of random variables - Central limit theorem.

Unit-4
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.

Unit-5
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 Research-An 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
  • Continuous Internal Assessment (CIA) : 50% (50 marks out of 100 marks)

●   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.

Unit-1
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

Unit-2
Teaching Hours:9
PROCESS MANAGEMENT
 

Process Management: Process Concept, Process Scheduling, Operations on Processes, Inter-process Communication; Threads: Overview, Multithreading Models, Thread Libraries; CPU Scheduling: Basic Concepts, Scheduling Criteria, Scheduling Algorithms, Multiple- Processor Scheduling

Unit-3
Teaching Hours:9
PROCESS SYNCHRONIZATION AND DEADLOCKS
 

Process Synchronization: Background, The Critical Section Problem, Petersons Solution, Synchronization Hardware, Semaphores, Classical Problems of Synchronization, Monitors, Synchronization Examples, Deadlocks 

Unit-4
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

 

Unit-5
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, Swap-Space 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

COURSES WITH THEORY AND PRACTICAL

 

Component

Assessed for

Minimum marks

 to pass

Maximum

marks

1

Theory CIA

30

-

30

2

Theory ESE

30

12

30

3

Practical CIA

35

14

35

4

Attendance

05

-

05

4

Aggregate

100

40

100

 

DETAIL OF MARK FOR COURSES WITH THOERY AND PRACTICAL

THEORY

PRACTICAL

 

Component

Assessed for

Scaled down to

Minimum marks to pass

Maximum marks

Component

Assessed for

Scaled down to

Minimum marks to pass

Maximum marks

1

CIA-1

20

10

-

10

Overall CIA

50

35

14

35

2

CIA-2

50

10

-

10

 

 

 

3

CIA-3

20

10

-

10

 

 

 

4

Attendance

05

05

-

05

Attendance

NA

NA

-

-

5

ESE

100

30

12

30

ESE

NA

NA

-

-

 

 

TOTAL

65

-

65

TOTAL

 

35

14

35

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 object-oriented 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.

Unit-1
Teaching Hours:9
OBJECT-ORIENTED 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. 

Unit-2
Teaching Hours:9
OBJECT-ORIENTED PROGRAMMING - INHERITANCE
 

Inheritance – class hierarchy – polymorphism – dynamic binding – final keyword – abstract classes – the Object class – Reflection – interfaces – object cloning – inner classes.

Unit-3
Teaching Hours:9
EVENT-DRIVEN 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 – Model-View- Controller design pattern – buttons – layout management – Swing Components

Unit-4
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.

Unit-5
Teaching Hours:9
CONCURRENT PROGRAMMING
 

Multi-threaded programming – interrupting threads – thread states – thread properties – thread synchronization – thread-safe Collections – Executors – synchronizers – threads and event-driven 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

 

  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)

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.

 

Unit-1
Teaching Hours:8
Automaton
 

Introduction to formal proof – Additional forms of proof – Inductive proofs –Finite Automata (FA) – Deterministic Finite Automata (DFA) – Non-deterministic Finite Automata (NFA) – Finite Automata with Epsilon transitions.

Unit-2
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.

Unit-3
Teaching Hours:10
Context-Free Grammar and Languages
 

Context-Free 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.

Unit-4
Teaching Hours:9
Properties of Context-Free Languages
 

Normal forms for CFG – Pumping Lemma for CFL - Closure Properties of CFL – Turing Machines – Programming Techniques for TM.

Unit-5
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 

  • 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)

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

Unit-1
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.

 

Unit-2
Teaching Hours:9
COMPUTER ARTHIMETIC
 

Addition and Subtraction – Multiplication – Division – Floating Point Representation – Floating Point Operations – Subword Parallelism

 

Unit-3
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.

 

Unit-4
Teaching Hours:9
PARALLELISM
 

Parallel processing challenges – Flynn‘s classification – SISD, MIMD, SIMD, SPMD, and Vector Architectures - Hardware multithreading – Multi-core processors and other Shared Memory Multiprocessors - Introduction to Graphics Processing Units, Clusters, Warehouse Scale Computers and other Message-Passing Multiprocessors.

 

Unit-5
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)

 

Unit-1
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.   

Unit-2
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

Unit-3
Teaching Hours:6
Environmental Pollution
 

Causes and Impacts – Air pollution, Water pollution, Soil Pollution, Noise Pollution, Marine Pollution, Municipal Solid Wastes, Bio Medical and E-Waste. Solid Waste Management

Unit-4
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

Unit-5
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]

Unit-1
Teaching Hours:6
THE INDIVIDUAL
 

Foundations of individual behaviour, individual differences. Ability. Attitude, Aptitude, interests. Values.

Unit-1
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).

Unit-2
Teaching Hours:6
LEARNING
 

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

Unit-2
Teaching Hours:6
PERCEPTION
 

Definition, Factors influencing perception, attribution theory, selective perception, projection, stereotyping, Halo effect.

Unit-3
Teaching Hours:6
MOTIVATION
 

Maslow's Hierarchy of Needs theory, Mc-Gregor's theory X and Y, Hertzberg's motivation Hygiene theory, David Mc-Clelland’s three needs theory, Victor Vroom's expectancy theory of motivation.

Unit-3
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.

Unit-4
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.

Unit-5
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, ISBN-81-7808-561-5 2002

T2: Organizational Behaviour, Fred Luthans, 9th Edition, Mc Graw Hill International Edition, ISBN-0-07-120412-12002

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

ASSESSMENT PATTERN FOR PROFESSIONAL ETHICS COURSE

 

 

Component

Assessed for

Scaled down to

1

CIA

50

25

2

ESE

50

25

 

 

TOTAL

50

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

Unit-1
Teaching Hours:90
Environmental Design & Socio-cultural Context
 

The understanding of habitat in a cultural setting where architecture is explored in the context of craft-making – 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 cross-cultural history. Oxford: Oxford University Press.

T2. Rapoport, A (1969). House Form and Culture. Prentice-Hall, Inc. Englewood Cliffs, NJ USA Pearson

T3. Bary, D. & Ilay, C. (1998) Traditional Buildings of India, Thames & Hudson, ISBN-10 : 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 Anti-Aesthetic: 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, VIVA-VOCE): 50 Marks

TOTAL:100 Marks

Note: For this course, a minimum of 50% marks in CIA is required to be eligible for VIVA-VOCE 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

Unit-1
Teaching Hours:90
Digital Architecture
 
  1. Imparting the knowledge of creative planning and execution of visual communication and the latest technological advancements in architecture.
  2. Concepts of geometries and surface, media and architecture.
  3. Spatial and regional designs with help of diagrams, geometry, and surface parameters.
  4. Contemporary Design approach with the help of theories.
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, ISBN-10 : 1616894067.

T3: Linn C. D. & Fortmeyer, R. (2014) Kinetic Architecture: Designs for Active Envelopes, Images Publishing Group Pty Ltd., ISBN-10 : 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_Algorithms-Aided 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, ISBN-100789302640.

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, VIVA-VOCE): 50 Marks

TOTAL:100 Marks

Note: For this course, a minimum of 50% marks in CIA is required to be eligible for VIVA-VOCE 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

Unit-1
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 climate-environmental parameters and social-cultural 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. Prentice-Hall, 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, VIVA-VOCE): 50 Marks

TOTAL:100 Marks

Note: For this course, a minimum of 50% marks in CIA is required to be eligible for VIVA-VOCE 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 job-related attitudes. 

CLO4: Design motivational techniques for job design, employee involvement, incentives, rewards & recognitions. 

CLO5: Manage effective groups and teams in organizations.

 

Unit-1
Teaching Hours:12
Unit-1: 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.

Cross-cultural management, managing multicultural teams, communicating across cultures, OB in the digital age.

Unit-2
Teaching Hours:12
Unit-2: 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.

Unit-3
Teaching Hours:12
Unit-3: 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.

Unit-4
Teaching Hours:12
Unit-4: 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, Self-Efficacy theory, Equity theory/Organizational justice, Expectancy theories, Motivation theories applied in organizations: Job design, employee involvement, rewards and global implications

Unit-5
Teaching Hours:12
Unit-5: 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

Test & Exam

Exam conducted for

Marks conversion

Weightage

Total

CIA – I

20

10

20%

10

CIA – II

50

25

25%

25

CIA – III

20

10

10%

10

Attendance

5

5%

5

CIA – I, II, and III

 

50

50%

50

End – term

100

50

50%

50

Total

100

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 to

CO1. 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 end-effectors

CO4. Explain the basics of machine vision and its operation.

CO5. Program robots using different programming methods.

Unit-1
Teaching Hours:9
INTRODUCTION
 

Basic Structure, Classification of robot and Robotic systems, laws of robotics,
workspace and precision of movement. Drives and control systems - Robot drive
mechanisms- hydraulic – electric – servomotor- stepper motor - pneumatic drives.
Control systems for robots.

Unit-2
Teaching Hours:9
KINEMATICS OF ROBOT MANIPULATOR:
 

Introduction to manipulator kinematics, homogeneous transformations and robot
kinematics, Denavit-Hartenberg (D-H) representation, concept of forward and inverse
kinematics.

Unit-3
Teaching Hours:9
SENSORS AND ROBOT END EFFECTORS
 

Sensors in robotics -Position sensors, Velocity sensors, Acceleration Sensors,
Force/Torque sensor, Touch and Tactile sensors, Proximity, Range and sniff sensors,
RCC and IRCC systems, VOICE recognition and synthesizers. Robot End Effectors -
Types of end effectors, Mechanical grippers – Types of Gripper mechanisms – Grippers
force analysis, other types of Grippers – Vacuum cups – Magnetic Grippers – Adhesive
Grippers, Active and passive grippers, Robot end effector interface.

Unit-4
Teaching Hours:9
MACHINE VISION
 

Image Sensing and Digitizing - Image definition, Image acquisition devices –
videcon camera and digital camera, specialized lighting techniques. Digital Images -
Sampling, Quantization and Encoding. Image storage. Image Processing and Analysis -Data reduction – digital conversion and windowing. Segmentation – Thresholding, Edgedetection and Region growing. Binary Morphology and grey morphology operations.
Feature Extraction, Object recognition, Depth measurement. Application of Vision
systems.

Unit-5
Teaching Hours:9
Robot programming:
 

Introduction; On-line programming: Manual input, lead
through programming, teach pendant programming; Off-line programming languages,Simulation.

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.
R2. Fu K S, Gonzalez R C, Lee C.S.G, "Robotics: Control, Sensing, Vision andIntelligence", McGraw Hill, 1987.

R3. Ramesh Jam, Rangachari Kasturi, Brain G. Schunck, Machine Vision, Tata McGrawHill, 1991.

R4. Yoremkoren, Robotics for Engineers, McGraw-Hill, USA, 1987.

R5. P.A. Janaki Raman, Robotics and Image Processing, Tata McGraw-Hill, 1991

Evaluation Pattern

CIA Marks

50

ESE Marks

50

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.

 

  1. To understand different ways of thinking about people and the perception of self in social situations
  2. To comprehend factors of affect related to cognition in a social context
  3. To develop knowledge about the dynamics of person in different situation in a social living

Learning Outcome

At the end of the course students will be able:

  1. To understand the thinking patterns of people and the perception of self in various cultural contexts
  2. To comprehend factors of affect related to cognition in a social context
  3. To inculcate dynamics of person in different situation
  4. To evaluate the person and situation by using psychometric tests

 

 

 

Unit-1
Teaching Hours:12
Introduction to Self
 

Definition, Person perception; Self-concept; Self-presentation; Self-esteem.

Unit-2
Teaching Hours:12
Affect and Cognition
 

Emotions - Positive and negative affect; Thoughts and expressions; Selective attention; Information processing; Memory; Cognitive appraisal; Judgment and Decision Making; Problem Solving. 

Practicum: Decision making & Problem Solving scale

Unit-3
Teaching Hours:12
The Person in the Situation - I
 

 

Justifying our actions, Social Relations: Stereotypes; Prejudice: Definition and Types, Sources of Prejudice, Consequences of Prejudice; Strategies to reduce prejudice; Attribution, Attitude and Attitude Change.

 

Unit-4
Teaching Hours:12
The Person in the Situation - II
 

Aggression: Perspectives, Causes; Prevention and Control of Aggression; Pro-social Behavior.

Practicum: Pro-social behavior scale

Unit-5
Teaching Hours:12
Group Dynamics
 

Nature of Groups; Basic Processes, Group Performance, Group Decision Making; Group Interaction (Facilitation, Loafing)

Practicum: Sociometry

Text Books And Reference Books:

Myers, D.G (2002) Social Psychology,.New York: McGraw Hill Companies.

Baron, Robert A. and Byrne, D. (2001) .Social Psychology 8 th Edition (Reprint).New Delhi:Prentice-Hall of India Pvt Ltd.

Baumeister.R.F. and Bushman,B.J. (2008).Social Psychology and Human nature. Belmont,CA:Thomson Wadsworth

 

Essential Reading / Recommended Reading

Tuffin, K. (2005). Understanding critical social psychology. London: Sage Publications.

Brehm, S.S. and Kassin, SN. (1996) Social Psychology. Boston : Houghton Mifflin Company.

Crisp, R.J. and Turner, R.N. (2007), Essential Social Psychology. New Delhi: Sage Publications India Pvt., Ltd.

Taylor ,S .E, Peplau, L.A and Sears, D.O. (2006) Social Psychology. New Delhi: Pearson Prentice-Hall of India.

Misra, G., & Dalal, A. K. (2001). Social Psychology in India: Evolution and Emerging Trends. In K. A. Dala, & G. Misra, New Directions in Indian Psychology. New Delhi: Sage.

Evaluation Pattern

CIA Evaluation pattern

Group Assignment

Individual Assignment

Mid semester

20

20

25

 

Mid Semester Examination

Section A

(Definition)

Section B

(Short note)

Section C

(Essay)

Section D

(Case Question)

Total

5×2=10

4×5=20

1×10=10

1×10=10

50

 

End Semester Examination

Section A

(Definition)

Section B

(Short note)

Section C

(Essay)

Section D

(Case Question)

Total

5×2=10

4×5=20

1×10=10

1×10=10

50

 

CEOE561E01 - SOLID WASTE MANAGEMENT (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 give  introduction to solid waste management, collection and transportation, treatment/processing techniques, incineration , composting, sanitary land filling, disposal methods, recycle and reuse.

 Objective of this course is to provide insight to manage  solid waste. It is designed as a source of information on solid waste management , includiing the principles of solid waste management , processing and treatment, final disposal, recycle and reuse

 

 

Learning Outcome

CO1Define and explain important concepts in the field of solid waste management, such as waste hierarchy, waste prevention, recirculation, municipal solid waste etc.

CO2 Suggest and describe suitable technical solutions for biological and thermal treatment.

CO3Suggest, motivate and describe a way to tackle the problem from a system analysis approach.

CO4 Describe the construction and operation of a modern landfill according to the demands

CO5 Discuss social aspects connected to handling and recirculation of solid waste from a local as well as global perspective.

Unit-1
Teaching Hours:9
Sources
 

Classification and characteristics – municipal, commercial & industrial. Methods of quantification

Unit-1
Teaching Hours:9
Introduction
 

Definition, Land Pollution – scope and importance of solid waste management, functional elements of solid waste management. 

Unit-2
Teaching Hours:9
Collection and Transportation
 

Systems of collection, collection equipment, garbage chutes, transfer stations – bailing and compacting, route optimization techniques and problems.                               

Unit-3
Teaching Hours:9
Treatment/Processing Techniques
 

Components separation, volume reduction, size reduction, chemical reduction and biological processing problems.                     

Unit-3
Teaching Hours:9
Incineration
 

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

Unit-4
Teaching Hours:9
Composting
 

Aerobic and anaerobic composting, factors affecting composting, Indore and Bangalore processes, mechanical and semi mechanical composting processes. Vermi composting.

Unit-4
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, geo-synthetic fabricsin sanitary landfills.   

Unit-5
Teaching Hours:9
Recycle and Reuse
 

Material and energy recovery operations, reuse in other industries, plastic wastes, environmental significance and reuse.     

Unit-5
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

Sl No.

Evaluation Component

Module

Duration

(min)

Nature of Component

Validation

1

CIA I

Quiz, assignment, & test

------

Closed Book/ Open book

Written test

2

CIA II

MSE

120

Closed Book

MSE

3

CIA  III

Seminar/assignment, Test

-----

Closed/Open Book

Seminar and test

4

Semester Exam

ESE

180

Closed Book

ESE

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)

 

Unit-1
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

 

Unit-2
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.

 

Unit-3
Teaching Hours:8
Natural resources and Energy sources
 

 

Renewable and non-renewable 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.

Unit-4
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, CFCs and Alternatives, Causes of Climate Change Energy Use: past, present and future, Role of Engineers.

 

Unit-5
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”, Wiley-Blackwell, 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

 

Ser No

Evaluation Component

Module

Duration (Mins)

Nature Of Component

Weightage Of Module

Validation

1

CIA I

Assignment

Quizes

 

Open Book

Assignment 50%  Quiz 30% Class participation 20% 100%

 

2

CIA II

MSE

120

CLOSED BOOK

 

 

4

SEMESTER EXAM

ESE

180

CLOSED BOOK

 

Written Test

 

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

Unit-1
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.

Unit-2
Teaching Hours:15
DATA LINK LAYER
 

Error – detection and correction – Parity – LRC – CRC – Hamming code – low Control and Error control - stop and wait – go back-N ARQ – selective repeat ARQ- sliding window – HDLC. - LAN - Ethernet IEEE 802.3 - IEEE 802.4 - IEEE 802.5 - IEEE 802.11 – FDDI - SONET – Bridges.

Unit-3
Teaching Hours:15
NETWORK LAYER
 

Internetworks – Packet Switching and Datagram approach – IP addressing methods – Subnetting – Routing – Distance Vector Routing – Link State Routing – Routers.

 

Unit-4
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.

 

Unit-5
Teaching Hours:15
APPLICATION LAYER
 

Domain Name Space (DNS) – SMTP – FTP – HTTP - WWW – Security – Cryptography-Case study.

Text Books And Reference Books:

T1: Behrouz A. Forouzan, “Data communication and Networking”, Tata McGraw-Hill, 2013.

Essential Reading / Recommended Reading

R1: James F. Kurose and Keith W. Ross, “Computer Networking: A Top-Down 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”, 5th 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

 

Unit-1
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.

Unit-2
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 real-time decision – games that include an element of chance.

Unit-3
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.

Unit-4
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.

Unit-5
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.
T2. Elaine Rich and Kevin Knight, “Artificial Intelligence”, 3rd Edition, Tata McGraw-Hill, 2012.
T3. Francois Chollet “Deep Learning with Python”, 1st Edition Manning Publication, 2018

Essential Reading / Recommended Reading


R1. Nils J. Nilsson, “Artificial Intelligence: A New Synthesis”, 1st Edition, Harcourt Asia Pvt.Ltd., 2012.
R2. George F. Luger, “Artificial Intelligence-Structures and Strategies for Complex Problem Solving”, 6th Edition, Pearson Education / PHI, 2009.

Evaluation Pattern

CIA ASSESSMENT DETAILS - THEORY

CIA for Theory: Continuous Internal Assessment 50 Marks (50 Marks out of 100 Marks)

CIA 1: 10 Marks

CIA 2: 25 Marks

CIA 3: 10 Marks

Attendance : 5 Marks

End Semester Exam for Theory: 50 Marks ( 50 Marks out of 100 Marks)

Total: 100 Marks

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 

Unit-1
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 Non-recursive Algorithm, Mathematical Analysis of Recursive Algorithm and examples, Empirical Analysis of Algorithms and Algorithm Visualization.

Unit-2
Teaching Hours:9
ALGORITHM DESIGN TECHNIQUES
 

Brute Force and Exhaustive Search: Selection Sort, Bubble Sort, Sequential Search and Brute-force 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

Unit-3
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.

Unit-4
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.

Unit-5
Teaching Hours:9
ALGORITHM DESIGN TECHNIQUES
 

Limitations of Algorithm Power:  Decision Trees, P, NP and NP Complete Problems, Challenges in Numerical Algorithms.

Backtracking: n-Queen’s Problem, Hamiltonian Circuit problem and Subset-Sum problem.

Branch and Bound: Assignment problem, Knapsack problem and Traveling salesman problem.

Text Books And Reference Books:
  1. AnanyLevitin, “Introduction to the Design and Analysis of Algorithm”, 3/e, Pearson Education Asia, Reprint 2012.
  2. Michael T. Goodrich, Roberto Tamassia, Michael H. Goldwasser, “Data Structures and Algorithms in Java”, 6/e, Wiley, 2014.
Essential Reading / Recommended Reading
  1. T. H Cormen, C E Leiserson, R L Rivest and C Stein: “Introduction to Algorithms”, 3rd Edition, The MIT Press, 2014.
  2. Ellis Horowitz, SartajSahni and SanguthevarRajasekaran, Computer Algorithms, Second Edition, Universities Press, 2007.
  3. Richard Neapolitan, “Foundations of Algorithms”, 5/e, Jones & Bartlett Learning, 2014.
  4. Richard Johnsonbaugh, Marcus Schaefer, “Algorithms”, Pearson Education, 2009.
  5. Clifford A Shaffer, “Data Structures and Algorithm Analysis in Java”, 3rd Edition, Courier Corporation, 2014.
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)

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

 
  • The students will gain a strong foundation on concept of Computer Graphics;
  • The students will learn the various Input and Output graphics devices;
  • The students will understand the techniques of 2D and 3D transformations;
  • The students will study OpenGL in Java and JOGL and how to create graphics object with JOGL

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 Cross-platform API for writing applications.

CO4: Evaluate various properties of geometry

CO5: Support transformation principles ,various types of light and material properties.

 

Unit-1
Teaching Hours:9
I
 

A survey of Computer Graphics, Video Display Devices, Raster-Scan Systems, Graphics Workstation and Viewing Systems, Input Devices, Hard-Copy Devices, Graphics Networks, Graphics on the Internet.

 

Unit-2
Teaching Hours:9
II
 

Line Drawing Algorithms, DDA Algorithms, Bresenham's Line Algorithm, Circle-Generating 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.

Unit-3
Teaching Hours:9
III
 

Java Graphics in 2D, Two-Dimensional 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

Unit-4
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

Unit-5
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:
  1. David J. Eck, “Fundamentals of Computer Graphics with Java, OpenGL and JOGL”, Hobart and Williams Smith colleges, 2010.
  2.  Donald Hearn, Pauline Baker and Warren Carithers, “Computer Graphics with OpenGL”, 4th Edition Pearson, 2010.
Essential Reading / Recommended Reading
  1.  Donald Hearn and M.Pauline Baker, “Computer Graphics C Version”, Pearson Education, 2003.
  2.  Foley, Vandam, Feiner and Huges, “Computer Graphics: Principles & Practice”, second edition, Pearson Education, 2003.
Evaluation Pattern

 

  •  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)

 

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.

 

Unit-1
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

Unit-2
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.

Unit-3
Teaching Hours:9
JAVASCRIPT
 

Basic JavaScript Instructions, Functions, Methods & Objects, Decisions & Loops, Document Object Model, Events

Unit-4
Teaching Hours:6
NOSQL
 

Installing MariaDB, Configuring MariaDB, MariaDB Security, MariaDB User Account Management,  MariaDBDatatypes, Date and String functions in MaraiaDB,  Using MariaDB,  

Unit-5
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 Front-End 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.

 

Unit-1
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.

Unit-2
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.

Unit-3
Teaching Hours:9
Authentication & Hash Functions
 

Authentication Requirements, Authentication Functions, Message Authentication Codes, Hash Functions, MD5, SHA, RIPEMD and HMAC Standards

Unit-4
Teaching Hours:9
Network Security
 

Authentication Applications: Kerberos – X.509 Authentication Service – Electronic Mail Security – PGP – S/MIME - IP Security – Web Security.

Unit-5
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 CASE-Study

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 McGraw-Hill, 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 short-term 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.

 

Unit-1
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.

 

Total No. of Internship Hours
(5)

Learning Objectives
(10)

Performance
Contribution
(10)

Personal and
Professional
Development (10)

Quality of Study/work/paper (10)

Submission of Report (5)

Total
(50)

 

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 High-Dimensional 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 High-Dimensional Regression. L5

 

Unit-1
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.

Unit-2
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.

Unit-3
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.

Unit-4
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.

Unit-5
Teaching Hours:9
High-Dimensional 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, High-Dimensional Regression, Feature Assessment and the Multiple-Testing 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 sub-system/system

CO5: To analyze about best possible patterns to cluster the possible solutions for different vulnerabilities

Unit-1
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

Unit-2
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 

Unit-3
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 

Unit-4
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.

Unit-5
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 McGraw-Hill 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

Sl NO

DESCRIPTION

REVISED BLOOM’S TAXONOMY (RBT)LEVEL

1.

Understand and explain concepts associated to Statistical Analytics and Data manipulation

L2

2.

Infer details of supervised and unsupervised learning mechanisms.

L2

3.

Analyse concepts of Convolutional Neural Networks.

L4

4.

Appraise concepts of Support Vector Machine.

L5

5.

Solve problems using Random Forests and Ensemble Learning.

L3

Unit-1
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)

 

Unit-2
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

Unit-3
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)

 

Unit-4
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

Unit-5
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:
  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
  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

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

 

Sl No

CIA Component

Unit(s) Covered

CO

RBT Level

1

CIA – 1

1.Closed Book Test

Unit 1.

CO1

L2

2

CIA -1

2.Quiz

Unit 1 and 2

CO1 and CO2

L2

3

CIA-3

1. Closed Book Test

Unit 4 and 5

CO4 and CO5

L3,L5

4

CIA-3

2.Quiz

Unit 4 and 5.

CO4 and CO5

L3,L5

 

2)      LAB ASSESSMENT DETAILS

 

Sl No

Lab Component

CO

RBT Level

1

Mid Semester Examination for Lab

CO1, CO2 and CO3

L2,L4

2.

End Semester Examination for Lab

CO1,CO2,CO3,CO4 and CO5

L2,L3,L4,L5

 

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, add-on 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

Unit-1
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

Unit-2
Teaching Hours:9
AUTOMOTIVE INSTRUMENTATION CONTROL
 

Sampling, Measurement and signal conversion of various parameters.  Sensors and Actuators, Applications of sensors and actuators

Unit-3
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

Unit-4
Teaching Hours:9
VEHICLE MOTION CONTROL AND AUTOMOTIVE DIAGNOSTICS
 

Cruise control system, Digital cruise control, Timing light, Engine analyzer, On-board and off-board diagnostics, Expert systems. Stepper motor based actuator, Cruise control electronics, Vacuum – antilock braking system, Electronic suspension system Electronic steering control, Computer-based instrumentation system, Sampling and Input\output signal conversion, Fuel quantity measurement, Coolant temperature measurement, Oil pressure measurement, Vehicle speed measurement, Display devices, Trip-Information- Computer, Occupant protection systems

Unit-5
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. Alternative-Fuel 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 Electronics-Systems and Components, Networking and Hybrid Drive", 5th 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

Unit-1
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

Unit-2
Teaching Hours:9
MATLAB USING IP TOOL BOX
 

Introduction to