Department of COMPUTER SCIENCE AND ENGINEERING

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

 
1 Semester - 2021 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MTAC121 ENGLISH FOR RESEARCH PAPER WRITING - 1 2 0
MTCS112 PROFESSIONAL PRACTICE - I - 2 1 50
MTCS133 ADVANCED ALGORITHMS - 3 3 100
MTCS135 ADVANCED DIGITAL IMAGE PROCESSING - 3 3 100
MTCS141E03 SOFTWARE PROJECT MANAGEMENT - 3 3 100
MTCS142E01 BIG DATA ANALYTICS - 3 3 100
MTCS151 ADVANCED ALGORITHMS LAB - 4 2 50
MTCS152 ADVANCED DIGITAL IMAGE PROCESSING LAB - 4 2 50
MTMC125 RESEARCH METHODOLOGY AND IPR - 3 3 100
2 Semester - 2021 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MTAC224 CONSTITUTION OF INDIA - 2 0 0
MTAC225 PEDAGOGY STUDIES - 2 0 0
MTAC226 STRESS MANAGEMENT BY YOGA - 2 0 0
MTAC227 PERSONALITY DEVELOPMENT THROUGH LIFE ENLIGHTENMENT SKILLS - 2 0 0
MTCS213 PROFESSIONAL PRACTICE - II - 2 1 50
MTCS231 COMPUTER COMMUNICATION NETWORKS - 3 3 100
MTCS232 DATA SCIENCE - 3 3 100
MTCS243E01 CLOUD COMPUTING - 3 3 100
MTCS243E02 ADVANCED MOBILE COMPUTING - 3 3 100
MTCS243E03 DISTRIBUTED COMPUTING - 3 3 100
MTCS243E04 ADVANCED PARALLEL COMPUTING - 3 3 100
MTCS244E01 INTERNET OF THINGS - 3 3 100
MTCS244E02 ADHOC NETWORKS - 3 3 100
MTCS244E03 ADVANCED EMBEDDED SYSTEM - 3 3 100
MTCS244E04 BIG DATA ANALYTICS FOR IOT - 3 3 100
MTCS244E05 NETWORK SECURITY - 3 3 100
MTCS251 NETWORKING LAB - 4 2 50
MTCS252 DATA SCIENCE LAB - 4 2 50
3 Semester - 2020 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MTCS345E06 MULTIMEDIA SYSTEMS - 3 3 100
MTCS381 INTERNSHIP - 4 2 50
MTCS382 DISSERTATION PHASE - I - 20 10 200
MTEC361 ADVANCED COMMUNICATION NETWORKS - 3 3 100
4 Semester - 2020 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MTCS483 DISSERTATION PHASE-II - 32 16 200
      

    

Department Overview:

Department of Computer Science & Engineering started of journey to produce qualified Engineers to society with variety of skills. The department offers the degrees Bachelor of Technology, Master of Technology, and Doctor of Philosophy in the areas of Computer Science Engineering and Information Technology. The department has rich knowledge pool of faculty resource who are well trained in various fields like Artificial Intelligence, Machine learning, Computer Vision, Algorithms design, Cryptography, Computer Networking, Data mining, Data science, BIG DATA, Digital Image Processing, text mining, knowledge representation, soft computing, Cloud computing, etc.. The department has wide variety of labs setup namely open source lab , Machine learning lab , CISCO Networking Lab etc.. specifically for students for their lab curriculum and for their research.

Mission Statement:

VISION: To fortify Ethical Computational Excellence MISSION: Imparts core and state-of-the-art knowledge in the areas of Computation and Information Technology. Promotes the culture of research and inspires innovation. Acquaints the students with the latest industrial practices, team building and entrepreneurship. Sensitizes the students to the environmental, social & ethical needs of society.

Introduction to Program:

The 2 year Post graduate program M.Tech in Computer Science and Engineering.started in 2011 . The course was started mainly to cater to the increasing demand for higher studies in the country. A growing intake with students from across the nation shows the popularity of the program. The Department strives to give skills essential to practicing engineering professionals; it is also an objective to provide experience in leadership, management, planning, and organization. The department understands its role in developing and evaluating methods that encourage students to continue to learn after leaving the university. We believe that the student opportunities and experiences should lead to an appreciation of the holistic development of individual. We also try to pass to our students our passion for what we do, and to have the students comprehend that we also desire to continue to learn.

Program Objective:

The Post graduate program aims to offer advanced knowledge in specific areas. Focus on research is a special feature of our program, in CHRIST(Deemed to be University) Faculty of Engineering where students are encouraged to undertake research level projects and have mandatory publications in national level conferences. Specific subjects for industry level skills are also offered for better employability. Program educational Objectives  PEO1: Ability to understand ,analyze and design solutions with professional competency for the real world problems ? PEO2: Ability to develop software/embedded solutions for the requirements, based on critical analysis and research. ? PEO3: Ability to function effectively in a team and as an individual in a multidisciplinary / multicultural environment. PEO4: To provide a learning environment that fosters computational excellence and promote life long learning with understanding of professional responsibilities and obligations to clients and public.

Assesment Pattern

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

            End semester practical examination                                      : 25 marks

            Records                                                                                   : 05 marks

            Mid semester examination                                                     : 10 marks

            Class work                                                                              : 10 marks

            Total                                                                                       : 50 marks

Examination And Assesments

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)

MTAC121 - ENGLISH FOR RESEARCH PAPER WRITING (2021 Batch)

Total Teaching Hours for Semester:15
No of Lecture Hours/Week:1
Max Marks:0
Credits:2

Course Objectives/Course Description

 

 

Students will be able to:

 

Understand that how to improve your writing skills and level of readability

 

·       Learn about what to write in each section

      Understand the skills needed when writing a Title and ensure the good quality of paper at very first-time submission

Learning Outcome

 

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

CO1; Write research paper which will have higher level of readability

CO2: Demonstrate what to write in each section

CO3: To write appropriate Title for the research paper

CO4: Write concise abstract

CO5: Write conclusions clearly explaining the outcome of the research work

Unit-1
Teaching Hours:3
Planning and Preparation
 

Word Order, Breaking up long sentences, Structuring Paragraphs and Sentences, Being Concise and Removing Redundancy, Avoiding Ambiguity and Vagueness

Unit-2
Teaching Hours:3
Clarifying Who Did What
 

Highlighting Your Findings, Hedging and Criticising, Paraphrasing and Plagiarism, Sections of a Paper, Abstracts. Introduction

Unit-3
Teaching Hours:3
Review of the Literature
 

Methods, Results, Discussion, Conclusions, The Final Check

Unit-4
Teaching Hours:3
Skills
 

Skills are needed when writing a Title, key skills are needed when writing an Abstract, key skills are needed when writing an Introduction, skills needed when writing a Review of the Literature,

Unit-5
Teaching Hours:3
Skills for Writing the Methods
 

Skills needed when writing the Results, skills are needed when writing the Discussion, skills are needed when writing the Conclusions useful phrases, how to ensure paper is as good as it could possibly be the first- time submission

Text Books And Reference Books:

Goldbort R (2006) Writing for Science, Yale University Press (available on Google Books)

Day R (2006) How to Write and Publish a Scientific Paper, Cambridge University Press

Essential Reading / Recommended Reading

Highman N (1998), Handbook of Writing for the Mathematical Sciences, SIAM. Highman’sbook .
Adrian Wallwork , English for Writing Research Papers, Springer New York Dordrecht Heidelberg London, 2011

Evaluation Pattern

It is Audit Course

MTCS112 - PROFESSIONAL PRACTICE - I (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:1

Course Objectives/Course Description

 

SUBJECT DESCRIPTION:

 During the seminar session each student is expected to prepare and present a topic on engineering / technology, it is designed to:

     Review and increase their understanding of the specific topics tested.

     Improve their ability to communicate that understanding to the grader.

     Increase the effectiveness with which they use the limited examination time.

 SUBJECT OBJECTIVE:

 Students are encouraged to use various teaching aids such as over head projectors, power point presentation and demonstrative models. This will enable them to gain confidence in facing the placement interviews and intended to increase the score they earn on the upcoming exam above what they would otherwise earn.

 

This course is specially designed for the students of higher degree. It aims to train and equip the students towards acquiring competence in teaching, laboratory skills, research methodologies and other professional activities including ethics in the respective academic disciplines.

 The course will broadly cover the following aspects:

    Teaching skills

    Laboratory skills and other professional activities

    Research methodology

For teaching suitable courses where strengthening in the training of the students is required will be identified and the student will be asked to prepare lectures on selected topics pertaining to the courses and present these lectures before a panel of faculty members. The student will also be required to prepare question papers which will test the concepts, analytical abilities and grasp in the subject. Wherever the laboratories are involved, students will also be asked to carry out laboratory experiments and learn about the use and applications of the instruments. The general guiding principle is that the students should be able to teach and participate in the undergraduate degree courses in his/her discipline in an effective manner. The students will also assist the faculty in teaching and research activities. The course will also contain the component of research methodology, in which a broad topic will be assigned to each student and he/ she is supposed to carry out intensive literature survey, data analysis and prepare a research proposal.

 

Each group will carry out many professional activities beside teaching and research. Such as, purchase of equipments, hardware, software and planning for new experiments and also laboratories etc. Along with these the students will also be assigned some well defined activities. The student is expected to acquire knowledge of professional ethics in the discipline.

Learning Outcome

-

Unit-1
Teaching Hours:30
na
 

na

Text Books And Reference Books:

-

Essential Reading / Recommended Reading

-

Evaluation Pattern

Head of the Department will assign a suitable instructor/faculty member to each student. Students and faculty members covering a broad area will be grouped in a panel consisting of 4-5 students and 4-5 faculty members.

 Within one week after registration, the student should plan the details of the topics of lectures, laboratory experiments, developmental activities and broad topic of research etc in consultation with the assigned instructor/faculty. The student has to submit two copies of the written outline of the total work to the instructor within one week.

 

In a particular discipline, Instructors belonging to the broad areas will form the panel and will nominate one of them as the panel coordinator. The coordinator together with the instructors will draw a complete plan of lectures to be delivered by all students in a semester. Each student will present 3- 4 lectures, which will be attended by all other students and Instructors. These lectures will be evenly distributed over the entire semester. The coordinator will announce the schedule for the entire semester and fix suitable meeting time in the week.

 

Each student will also prepare one presentation about his findings on the broad topic of research. The final report has to be submitted in the form of a complete research proposal. The References and the bibliography should be cited in a standard format. The research proposal should contain a) Topic of research b) Background and current status of the research work in the area as evident from the literature review c) Scope of the proposed work d) Methodology e) References and bibliography.

 

A report covering laboratory experiments, developmental activities and code of professional conduct and ethics in discipline has to be submitted by individual student.

 

The panel will jointly evaluate all the components of the course throughout the semester and the mid semester grade will be announced by the respective instructor to his student.

A comprehensive viva/test will be conducted at the end of the semester jointly, wherever feasible by all the panels in a particular academic discipline/department, in which integration of knowledge attained through various courses will be tested and evaluated.

Wherever  necessary  and  feasible,  the  panel  coordinator  in  consultation  with  the concerned  group may also seek participation of the faculty members from other groups in lectures and comprehensive viva.

Mid semester report and final evaluation report should be submitted in the 9th week and 15th week of the semester respectively. These should contain the following sections:

        Section (A): Lecture notes along with two question papers each of 180 min duration, one quiz paper (CIA-I) of 120 min duration on the topics of lectures. The question paper should test concepts, analytical abilities and grasp of the subject. Solutions of questions also should be provided. All these will constitute lecture material.

        Section (B): Laboratory experiments reports and professional work report.

 Section (C): Research proposal with detailed references and bibliography in a standard format.

Wherever necessary, respective Head of the Departments could be approached by Instructors/panel coordinators for smooth operation of the course. Special lectures dealing with professional ethics in the discipline may also be arranged by the group from time to time.

MTCS133 - ADVANCED ALGORITHMS (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

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

CO1: Summarize the properties of advanced data structures.

CO2: Design algorithms and employ appropriate advanced data structures for solving computing problems efficiently.

CO3: Analyze and compare the efficiency of algorithms.

CO4: Design and implement efficient algorithms for solving computing problems in a high-level object-oriented programming language.

CO5: Compare, contrast, and apply algorithmic trade-offs : time vs. space, deterministic vs. randomized, and exact vs. approximate

Unit-1
Teaching Hours:9
INTRODUCTION
 

Review of Analysis Techniques: Growth of Functions: Asymptotic notations; Standard notations and common functions; Recurrences and Solution of Recurrence equations- The substitution method, The recurrence – tree method, The master method; Amortized Analysis: Aggregate, Accounting and Potential Methods.

Unit-2
Teaching Hours:9
GRAPH ALGORITHMS AND POLYNOMIALS
 

Graph Algorithms: Bellman - Ford Algorithm; Single source shortest paths in a DAG; Johnson’s Algorithm for sparse graphs; Flow networks and Ford -Fulkerson method; Maximum bipartite matching.

Polynomials and the FFT: Representation of polynomials; The DFT and FFT; Efficient implementation of FFT.

Unit-3
Teaching Hours:9
NUMBER THEORETIC ALGORITHMS
 

Number -Theoretic Algorithms: Elementary notions; GCD; Modular Arithmetic; Solving modular linear equations; The Chinese remainder theorem; Powers of an element; RSA cryptosystem; Primality testing; Integer factorization

Unit-4
Teaching Hours:9
STRING MATCHING ALGORITHMS
 

String-Matching Algorithms: Naïve string Matching; Rabin - Karp algorithm; String matching with finite automata; Knuth-Morris-Pratt algorithm; Boyer – Moore algorithms.

Unit-5
Teaching Hours:9
PROBABILISTIC ALGORITHMS
 

Probabilistic and Randomized Algorithms: Probabilistic algorithms; Randomizing deterministic algorithms, Monte Carlo and Las Vegas algorithms; Probabilistic numeric algorithms.

Case Study: Comparison of Algorithm Design Strategies based on CPU, Memory, Disk and Network usages.

Text Books And Reference Books:
  1. T. H Cormen, C E Leiserson, R L Rivest and C Stein: “Introduction to Algorithms”, 3rd Edition, The MIT Press, 2014.
  2. Kenneth A. Berman, Jerome L. Paul: “Algorithms”, Cengage Learning, 2013.
Essential Reading / Recommended Reading
  1. Horowitz, Sahni, Rajasekaran, “Computer Algorithms”, University press 2008
  2. Tanenbaum A.S., Langram Y, Augestien M.J., ”Data Structures using Java”, Prentice Hall of India, 2009

3.   Mark Allen Weiss, “Data Structures and Algorithm Analysis in Java”, 3rd edition,Pearson Education, 2012.

4. Aho, Hopcroft, Ullman, “Data Structures and Algorithms”, Pearson Education, 2009.

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)

Components of the CIA

 

CIA I  :  Quizzes/Seminar/Case Studies/Project Work /Assignments     : 10 marks

 

CIA II  :   Mid Semester Examination (Theory)                                                : 25 marks

 

CIA III  : Quizzes/Seminar/Case Studies/Project Work /Assignments    : 10 marks

 

     Attendance                                                                            : 05 marks

 

     Total                                                                                       : 50 marks

 

MTCS135 - ADVANCED DIGITAL IMAGE PROCESSING (2021 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 learn the fundamental concepts of Image Processing.

The students will learn image enhancement techniques in spatial & frequency domain

The students will learn the restoration & compression models.

Help the students to segmentation and representation techniques for the region of interests.

The students will learn the how to recognize objects using pattern recognition techniques

 

Learning Outcome

Course Outcome 1: Apply the image fundamentals and mathematical transformations necessary for image processing

Course Outcome 2: Analyze image enhancement techniques in Spatial &frequency domain

Course Outcome 3: Apply restoration models and compression models for image processing

Course Outcome 4: Analyze and synthesis image using segmentation and representation techniques

Course Outcome 5:  Analyze and extract potential features of interest from the image

Course Outcome 6: Design object recognition systems using pattern recognition techniques

Unit-1
Teaching Hours:9
DIGITAL IMAGE FUNDAMENTALS
 

Image formation, Image transforms – Fourier transforms, Walsh, Hadamard, Discrete cosine, Hotelling transforms

Unit-2
Teaching Hours:9
IMAGE ENHANCEMENT & RESTORATION
 

Histogram modification techniques - Image smoothening - Image Sharpening - Image Restoration - Degradation Model – Noise models - Spatial filtering – Frequency domain filtering

Unit-3
Teaching Hours:9
IMAGE COMPRESSION & SEGMENTATION
 

Compression Models - Elements of information theory - Error free Compression -Image segmentation –Detection of discontinuities – Region based segmentation – Morphology

Unit-4
Teaching Hours:9
REPRESENTATION AND DESCRIPTION
 

Representation schemes- Boundary descriptors- Regional descriptors - Relational Descriptors

Unit-5
Teaching Hours:9
OBJECT RECOGNITION AND INTERPRETATION
 

Patterns and pattern classes - Decision-Theoretic methods - Structural methods-Case studies

Text Books And Reference Books:

1.      Gonzalez.R.C& Woods. R.E., “Digital Image Processing”, 3rd Edition, Pearson Education, Indian edition published by Dorling Kindersely India Pvt. Ltd. Copyright © 2009, Third impression 2011.

2.      Gonzalez.R.C& Woods. R.E., “Digital Image Processing using MATLAB”, 2nd Edition, McGraw Hill Education (India) Pvt Ltd  2011 (Asia)

     

 

Essential Reading / Recommended Reading
  1.  Madan, “ An Introduction to MATLAB for Behavioural Researchers”, Sage Publications, 2014
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)

Components of the CIA

CIA II   :   Mid Semester Examination (Theory)                    : 25 marks                  

CIA I  :  Assignments                                                            : 10 marks

CIA III            : Quizzes/Seminar/Case Studies/Project Work     : 10 marks

    Attendance                                                                             : 05 marks

               Total                                                                                       : 50 marks

MTCS141E03 - SOFTWARE PROJECT MANAGEMENT (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

The main goal of software development projects is to create a software system with a predetermined functionality and quality in a given time frame and with given costs. For achieving this goal. models are required for determining target values and for continuously controlling these values. This course focuses on principles, techniques, methods & tools for model-based management of software projects. Assurance of product quality and process adherence (quality assurance), as well as experience-based creation & improvement of models (process management). The goals of the course can be characterized as follows.

  • Understanding the specific roles within a software organization as related to project and process management
  • Understanding the basic infrastructure competences (e.g., process modeling and measurement)
  • Understanding the basic steps of project planning, project management. Quality assurance, and process management and their relationships.

Learning Outcome

 

CO1: Understanding the specific roles within a Conventional Software Management organization as related to the project.

L2

CO2: Describe and determine the purpose and importance of project management from the perspectives of planning, cost, tracking and completion of project.

L5

CO3: Evaluate a project to develop the scope of work, provide accurate cost estimates and to plan the various activities.

L5

CO4: Implement a project to manage project schedule, expenses and resources with the application of suitable protect management tools.

L4

CO5: Identify organization structures, project structures, resources required for a project and to produce a work plan and resource Schedule.

L4

 

Unit-1
Teaching Hours:9
Unit-1
 

Conventional Software Management: The waterfall model, conventional software Management performance. Evolution of Software Economics: Software Economics. Pragmatic software cost estimation.

Unit-2
Teaching Hours:9
unit-2
 

Improving Software Economics: Reducing Software product size, Improving software processes, improving team effectiveness. Improving automation, Achieving required quality, peer inspections. The old way and the new- The principles of conventional software engineering. Principles of modem software management, transitioning to an iterative process.

Unit-3
Teaching Hours:9
Unit-3
 

Life cycle phases: Engineering and production stages, inception. Elaboration, construction, transition phases. Artifacts of the process: The artifact sets. Management artifacts, Engineering artifacts, programmatic artifacts. Model based software architectures: A Management perspective and technical perspective.

Unit-4
Teaching Hours:9
unit-4
 

Work Flows of the process: Software process workflow, Inter trans workflows. Checkpoints of the Process: Major Mile Stones, Minor Milestones, Periodic status assessments. Iterative Process Planning Work breakdown structures, planning guidelines, cost and scheduled estimating, Interaction, planning process, Pragmatic planning.

Unit-5
Teaching Hours:9
Unit - 5
 

Project Control and Process instrumentation: The server care Metrics, Management indicators, and quality indicators. Life cycle expectations pragmatic Software Metrics, Metrics automation. Tailoring the Process: Process discriminates, Example. Future Software Project Management: Modem Project Profiles Next generation Software economics modem Process transitions.

Case Study: The Command Center Processing and Display System. Replacement (CCPDS. R).

Text Books And Reference Books:
  1. Software Project Management. Walker Royce, Pearson Education 2010.
  2. Software Project Management, Bob Hughes & Mike Cotterell, fourth edition, Tate McGraw HD 2012.
Essential Reading / Recommended Reading
  1. Applied Software Project Management, Andrew SteIbian 8 Jennifer Greene, O’Reilly. 2006
  2. Head First PMP, Jennifer Greene & Andrew Steliman, ORoiHy.2007
  3. Software Enneeñng Project Managent. Richard H. Thayer & Edward Yourdon, second edition, Wiley India, 2004.
  4. Ale Project Management, Jim Highsniith. Pearson education, 2004
  5. The art of Project management. Scott Berkun. O’Reilly, 2005.
  6. Software Project Management in Practice. PankajJalote. Pearson Educabon,2002.
  7. SEI.CMMI-Tutorial, ww.sei.cmu.edu/cmmi/publications/stc.presentations/tutorial.html 
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)

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

MTCS142E01 - BIG DATA ANALYTICS (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

To Understand big data for business intelligence

To Learn business case studies for big data analytics

To Understand Nosql big data management

To manage Big data without SQL

To understanding map-reduce analytics using Hadoop and related tools

Learning Outcome

CO1: Describe big data and use cases from selected business domains

CO2: Discuss open source technologies

CO3: Explain NoSQL big data management

CO4: Discuss basics of Hadoop and HDFS

CO5: Discuss map-reduce analytics using Hadoop and Use of Hadoop related tools such as HBase, Cassandra, Pig, and Hive for big data Analytics

 

Unit-1
Teaching Hours:9
UNDERSTANDING BIG DATA
 

What is big data – why big data –.Data!, Data Storage and Analysis, Comparison with Other Systems, Rational Database Management System , Grid Computing, Volunteer Computing, convergence of key trends – unstructured data – industry examples of big data – web analytics – big data and marketing – fraud and big data – risk and big data – credit risk management – big data and algorithmic trading – big data and healthcare – big data in medicine – advertising and big data– big data technologies – introduction to Hadoop – open source technologies – cloud and big data – mobile business intelligence – Crowd sourcing analytics – inter and trans firewall analytics.

Unit-2
Teaching Hours:9
NOSQL DATA MANAGEMENT
 

Introduction to NoSQL – aggregate data models – aggregates – key-value and document data models – relationships –graph databases – schema less databases – materialized views – distribution models – sharding –– version – Map reduce –partitioning and combining – composing map-reduce calculations

Unit-3
Teaching Hours:9
BASICS OF HADOOP
 

Data format – analyzing data with Hadoop – scaling out – Hadoop streaming – Hadoop pipes – design of Hadoop distributed file system (HDFS) – HDFS concepts – Java interface – data flow – Hadoop I/O – data integrity – compression – serialization – Avro – file-based data structures

Unit-4
Teaching Hours:9
MAPREDUCE APPLICATIONS
 

MapReduce workflows – unit tests with MRUnit – test data and local tests – anatomy of MapReduce job run – classic Map-reduce – YARN – failures in classic Map-reduce and YARN – job scheduling – shuffle and sort – task execution –MapReduce types – input formats – output formats

Unit-5
Teaching Hours:9
HADOOP RELATED TOOLS
 

Hbase – data model and implementations – Hbase clients – Hbase examples –praxis. Cassandra – Cassandra data model –cassandra examples – cassandra clients –Hadoop integration. Pig – Grunt – pig data model – Pig Latin – developing and testing Pig Latin scripts. Hive – data types and file formats – HiveQL data definition – HiveQL data manipulation –HiveQL queries-case study.

Text Books And Reference Books:
  1.  Tom White, "Hadoop: The Definitive Guide", 4th  Edition, O'Reilley, 2012.
  2. Eric Sammer, "Hadoop Operations",1st Edition, O'Reilley, 2012.
Essential Reading / Recommended Reading
  1. VigneshPrajapati, Big data analytics with R and Hadoop, SPD 2013.
  2. E. Capriolo, D. Wampler, and J. Rutherglen, "Programming Hive", O'Reilley, 2012.
  3.  Lars George, "HBase: The Definitive Guide", O'Reilley, 2011.
  4. Alan Gates, "Programming Pig", O'Reilley, 2011.
Evaluation Pattern

Assessment of each paper

·         Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks)

              Components of the CIA

CIA I  :  Quizzes/Seminar/Case Studies/Project Work /Assignments     : 10 marks

CIA II  :   Mid Semester Examination (Theory)                                                : 25 marks

CIA III  : Quizzes/Seminar/Case Studies/Project Work /Assignments    : 10 marks

     Attendance                                                                            : 05 marks         

     Total                                                                                       : 50 marks

·         End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

 

MTCS151 - ADVANCED ALGORITHMS LAB (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:50
Credits:2

Course Objectives/Course Description

 

● To increase the knowledge of advanced paradigms of algorithm design.
● To make the students learn an object oriented way of solving problems.
● To Enhance students’ capability of selecting the best and efficient way for encoding problems.

Learning Outcome

·         Effective use of mathematical techniques to construct robust algorithms.

 ·         Assess and to make critical judgment on the choices of algorithms for modern computer systems

Unit-1
Teaching Hours:6
List of Experiments
 
  1. Implementation of Dictionary using Binary Search trees.
  2. Implementation of Sorting Techniques like Bubble Sort, Selection Sort, Insertion Sort, Merge Sort, Quick Sort and Heap Sort and compare their performances.
  3. Implementation of Shortest Path Algorithm (Bellman Ford).
Unit-2
Teaching Hours:6
Programs on Data Structures and Algorithms
 
  1. Design, develop, and write a program to implement a Monte Carlo algorithm to test the primality of a given integer and determine its performance.
  2. Design, develop, and write a program to solve string matching problem using naïve approach, the KMP and Robin Karp algorithm. Compare their performances.
Unit-3
Teaching Hours:6
Programs on Cloud Computing
 
  1. Modeling and simulation Cloud computing environments, including Data Centers, Hosts and Cloudlets and perform VM provisioning using CloudSim: Design a host with two CPU cores, which receives request for hosting two VMs, such that each one requires two cores and plans to host four tasks units. More specifically, tasks t1, t2, t3 and t4 to be hosted in VM1, while t5, t6, t7, and t8 to be hosted in VM2. Implement spaceshared allocation policy and timeshared allocation policy. Compare the results.
Unit-4
Teaching Hours:6
Programs on Cloud Computing
 
  1. Implement MapReduce concept for
    A. Strassen’s Matrix Multiplication for a huge matrix.
    B. Computing the average number of citation index a researcher has according to age among some 1 billion journal articles.
    Consider a network of entities and relationships between them. It is required to calculate a state of each entity on the basis of properties of the other entities in its neighborhood. This state can represent a distance to other nodes, indication that there is a neighbor with the certain properties, characteristic of neighborhood density and so on. A network is stored as a set of nodes and each node contains a list of adjacent node IDs. Mapper emits messages for each node using ID of the adjacent node as a key. Reducer must recompute state and rewrite node with the new state. Implement this scenario.
Unit-5
Teaching Hours:6
Programs on Advanced Computer Architecture
 
  1. Implementation of a Parallel Search Algorithm.
  2. Case study of Load Balancing – Static & Dynamic.
  3. Case study of Job Sequencing & collision prevention.
Text Books And Reference Books:
  1.  T. H Cormen, C E Leiserson, R L Rivest and C Stein: “Introduction to Algorithms”, 3rd Edition, The MIT Press, 2014.
  2. Kenneth A. Berman, Jerome L. Paul: “Algorithms”, Cengage Learning, 2013.
Essential Reading / Recommended Reading
  1. Horowitz, Sahni, Rajasekaran, “Computer Algorithms”, University press 2008
  2. Tanenbaum A.S., Langram Y, Augestien M.J., ”Data Structures using Java”, Prentice Hall of India, 2009
  3. Mark Allen Weiss, “Data Structures and Algorithm Analysis in Java”, 3rd edition, Pearson Education, 2012.
  4. Aho, Hopcroft, Ullman, “Data Structures and Algorithms”, Pearson Education, 2009.
Evaluation Pattern

End semester practical examination                                               : 25 marks

            Records                                                                                   : 05 marks

            Mid semester examination                                                    : 10 marks

            Class work                                                                              : 10 marks

            Total                                                                                       : 50 marks

MTCS152 - ADVANCED DIGITAL IMAGE PROCESSING LAB (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:50
Credits:2

Course Objectives/Course Description

 

Students are expected to implement the image processing algorithms and techniques to solve the real life problems.

Learning Outcome

    

Apply principles and techniques of digital image processing in applications related to digital imaging system design and analysis

Analyze and implement image processing algorithms

Understand software tools for processing digital images

Experiment image processing problems and techniques

Examine image processing algorithms on computers

Demonstrate algorithms to solve image processing problems

Unit-1
Teaching Hours:12
unit 1
 

  1. Display of Grayscale Images,
Unit-2
Teaching Hours:12
unit 2
 

  1. Implementation of  various transforms and their use.
  2. Implementation of  Histogram Equalization,  Non-linear Filtering.

 

Unit-3
Teaching Hours:12
unit 3
 
  1. Implementation of   Edge detection using Operators,  2-D DFT and DCT.
  2. Implementation of   Filtering in frequency domain.
Unit-4
Teaching Hours:12
unit 4
 
  1. Implementation of   Segmentation using  various transform.
Unit-5
Teaching Hours:12
unit 5
 
  1. Implementation of  various Morphological algorithms.
  2. Implementation of IEEE/ACM paper in Digital image processing area.
Text Books And Reference Books:

1. Rafael C. Gonzalez, Richard E. Woods, Steven Eddins, “Digital Image Processing using MATLAB”, Pearson Education, Inc., 2004.

2. Gonzalez.R.C & Woods. R.E., “Digital Image Processing using MATLAB”, 2nd Edition, McGraw Hill Education (India) Pvt Ltd  2011 (Asia)

3. Madan, “ An Introduction to MATLAB for Behavioural Researchers”, Sage Publications, 2014

 


Essential Reading / Recommended Reading
  1. David A Forsyth & Jean ponce “Computer Vision: A Modern Approach” 2nd Edition, Pearson Education India 2015.
Evaluation Pattern

End semester practical examination                                     : 25 marks

            Records                                                                          : 05 marks

            Mid semester examination                                          : 10 marks

            Class work                                                                     : 10 marks

            Total                                                                                : 50 marks

MTMC125 - RESEARCH METHODOLOGY AND IPR (2021 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 the course is to introduce the research methodology, the understanding on the research, methods, designs, data collection methods, report writing styles and various dos and don’ts in research.

Learning Outcome

After going through this course the scholar will be able to

1. Explain the principles and concepts of research methodology.

2. understand the different methods of data collection

3. Apply appropriate method of data collection and analyze using statistical/software tools.

4. Present research output in a structured report as per the technical and ethical standards.

5. Create research design for a given engineering and management problem /situation.

Unit-1
Teaching Hours:9
Introduction to Research Methodology
 

 

Meaning, Objectives and Characteristics of research - Research methods Vs Methodology, Different Research Design: Types of research - Descriptive Vs. Analytical, Applied Vs. Fundamental, Quantitative Vs. Qualitative, Conceptual Vs. Empirical, Research process - Criteria of good research - Developing a research plan.

 

Unit-2
Teaching Hours:9
Literature Review and Research Problem Identification
 

 

Defining the research problem - Selecting the problem - Necessity of defining the problem - Techniques involved in defining the problem - Importance of literature review in defining a problem - Survey of literature - Primary and secondary sources - Reviews, treatise, monographs, thesis reports, patents - web as a source - searching the web - Identifying gap areas from literature review - Development of working hypothesis.

 

Unit-3
Teaching Hours:9
Data Collection & Analysis
 

Selection of Appropriate Data Collection Method: Collection of Primary Data, Observation Method, Interview Method, Email, Collection of Data through Questionnaires, Collection of Data through Schedules, Collection of Secondary Data – internal & external.

Sampling process: Direct & Indirect Methods, Non-probability sampling, Probability sampling: simple random sampling, systematic sampling, stratified sampling, cluster sampling, Determination of sample size; Analysis of data using different software tools.

Unit-4
Teaching Hours:9
Research Problem Solving
 

 

Processing Operations, Types of Analysis, Statistics in Research, Measures of: Central Tendency, Dispersion, Asymmetry and Relationship, correlation and regression, Testing of Hypotheses for single sampling: Parametric (t, z and F), Chi Square, Logistic regression, ANOVA, non-parametric tests. Numerical problems

 

Unit-5
Teaching Hours:9
IPR and Research Writing
 

IPR: Invention and Creativity- Intellectual Property-Importance and Protection of Intellectual Property Rights (IPRs)- A brief summary of: Patents, Copyrights, Trademarks, Industrial Designs; Publication ethics, Plagiarism check

Research Writing: Interpretation and report writing, Techniques of interpretation, Types of report – letters, articles, magazines, transactions, journals, conferences, technical reports, monographs and thesis; Structure and components of scientific writing: Paragraph writing, research proposal writing, reference writing, summarizing and paraphrasing, essay writing; Different steps in the preparation - Layout, structure and language of the report – Illustrations, figures, equations and tables.

Text Books And Reference Books:
  1.     Kothari C.R., “Research Methodology Methods and techniques”, New Age International, New Delhi, 2004.
  2.    Garg, B.L., Karadia, R., Agarwal, F. and Agarwal, “An introduction to Research Methodology”, RBSA Publishers, 2002.
  3.    Day, R.A., “How to Write and Publish a Scientific Paper”, Cambridge University Press, 1992.
Essential Reading / Recommended Reading

1.       Bjorn Gustavii, “How to Write and Illustrate Scientific Papers “ Cambridge University Press, 2/e.

2.        Sarah J Tracy, “Qualitative Research Methods” Wiley Balckwell- John wiley & sons, 1/e, 2013.

3.     .James Hartley, “Academic Writing and Publishing”, Routledge Pub., 2008.

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)

Components of the CIA

CIA I  :  Quizzes/Seminar/Case Studies/Project Work /Assignments     : 10 marks

CIA II  :   Mid Semester Examination (Theory)                                                : 25 marks

CIA III  : Quizzes/Seminar/Case Studies/Project Work /Assignments    : 10 marks

    Attendance                                                                            : 05 marks

            Total                                                                                       : 50 marks

MTAC224 - CONSTITUTION OF INDIA (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:0
Credits:0

Course Objectives/Course Description

 

Students will be able to:

 1. Understand the premises informing the twin themes of liberty and freedom from a civil rights perspective.

 2. To address the growth of Indian opinion regarding modern Indian intellectuals’ constitutional role and entitlement to civil and economic rights as well as the emergence of nationhood in the early years of Indian nationalism.

 3. To address the role of socialism in India after the commencement of the Bolshevik Revolution in 1917 and its impact on the initial drafting of the Indian Constitution.

Learning Outcome

Understand the basics of the Constitution of India. 

Unit-1
Teaching Hours:4
History of Making of the Indian Constitution
 

History of Making of the Indian Constitution: History Drafting Committee, ( Composition & Working)

Unit-2
Teaching Hours:4
Philosophy of the Indian Constitution
 

Philosophy of the Indian Constitution: Preamble Salient Features, Contours of Constitutional Rights & Duties: Fundamental Rights Right to Equality Right to Freedom Right against Exploitation Right to Freedom of Religion Cultural and Educational Rights Right to Constitutional Remedies Directive Principles of State Policy Fundamental Duties

Unit-3
Teaching Hours:4
Organs of Governance
 

Organs of Governance: Parliament Composition Qualifications and Disqualifications Powers and Functions Executive President Governor Council of Ministers Judiciary, Appointment and Transfer of Judges, Qualifications Powers and Functions

Unit-4
Teaching Hours:4
Local Administration
 

Local Administration: District’s Administration head: Role and Importance, Municipalities: Introduction, Mayor and role of Elected Representative, CEO of Municipal Corporation. Pachayati raj: Introduction, PRI: ZilaPachayat. Elected officials and their roles, CEO ZilaPachayat: Position and role. Block level: Organizational Hierarchy (Different departments), Village level: Role of Elected and Appointed officials, Importance of grass root democracy

Unit-5
Teaching Hours:4
Election Commission
 

Election Commission: Election Commission: Role and Functioning. Chief Election Commissioner and Election Commissioners. State Election Commission: Role and Functioning. Institute and Bodies for the welfare of SC/ST/OBC and women.

Text Books And Reference Books:

1.      The Constitution of India, 1950 (Bare Act), Government Publication.

2.      Dr. S. N. Busi, Dr. B. R. Ambedkar framing of Indian Constitution, 1st Edition, 2015.

       3.    M. P. Jain, Indian Constitution Law, 7th Edn., Lexis Nexis, 2014.

Essential Reading / Recommended Reading
  1. D.D. Basu, Introduction to the Constitution of India, Lexis Nexis, 2015
Evaluation Pattern

NA

MTAC225 - PEDAGOGY STUDIES (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:0
Credits:0

Course Objectives/Course Description

 

Students will be able to

 Review existing evidence on the review topic to inform programme design and policy making undertaken by the DfID, other agencies and researchers.

Identify critical evidence gaps to guide the development.

Learning Outcome

Explain the concepts of Pedagogy Studies 

Unit-1
Teaching Hours:4
Introduction and Methodology
 

Introduction and Methodology: Aims and rationale, Policy background, Conceptual framework and terminology Theories of learning, Curriculum, Teacher education. Conceptual framework, Research questions. Overview of methodology and Searching.

Unit-2
Teaching Hours:4
Thematic overview
 

Thematic overview: Pedagogical practices are being used by teachers in formal and informal classrooms in developing countries. Curriculum, Teacher education.

Unit-3
Teaching Hours:4
Evidence on the effectiveness of pedagogical practices
 

Evidence on the effectiveness of pedagogical practices Methodology for the in depth stage: quality assessment of included studies. How can teacher education (curriculum and practicum) and the school curriculum and guidance materials best support effective pedagogy? Theory of change. Strength and nature of the body of evidence for effective pedagogical practices. Pedagogic theory and pedagogical approaches. Teachers’ attitudes and beliefs and Pedagogic strategies.

Unit-4
Teaching Hours:4
Professional development
 

Professional development: alignment with classroom practices and follow-up support Peer support Support from the head teacher and the community. Curriculum and assessment Barriers to learning: limited resources and large class sizes

Unit-5
Teaching Hours:4
Research gaps and future directions
 

Research gaps and future directions Research design Contexts Pedagogy Teacher education Curriculum and assessment Dissemination and research impact.

Text Books And Reference Books:
  1. Ackers J, Hardman F (2001) Classroom interaction in Kenyan primary schools, Compare, 31 (2): 245-261.
Essential Reading / Recommended Reading

1.      Agrawal M (2004) Curricular reform in schools: The importance of evaluation, Journal of Curriculum Studies, 36 (3): 361-379.

2.      Akyeampong K (2003) Teacher training in Ghana - does it count? Multi-site teacher education research project (MUSTER) country report 1. London: DFID.

3.      Akyeampong K, Lussier K, Pryor J, Westbrook J (2013) Improving teaching and learning of basic maths and reading in Africa: Does teacher preparation count? International Journal Educational Development, 33 (3): 272–282.

4.      Alexander RJ (2001) Culture and pedagogy: International comparisons in primary education. Oxford and Boston: Blackwell.

5.      Chavan M (2003) Read India: A mass scale, rapid, ‘learning to read’ campaign.

 

Evaluation Pattern

NA

MTAC226 - STRESS MANAGEMENT BY YOGA (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:0
Credits:0

Course Objectives/Course Description

 

To achieve overall health of body and mind

To overcome stress

Learning Outcome

-

Unit-1
Teaching Hours:8
Unit-1
 

Definitions of Eight parts of yog. ( Ashtanga )

Unit-2
Teaching Hours:8
unit-2
 

Yam and Niyam. Do`s and Don’t’s in life. i) Ahinsa, satya, astheya, bramhacharya and aparigraha ii) Shaucha, santosh, tapa, swadhyay, ishwarpranidhan

Unit-3
Teaching Hours:8
Unit-3
 

Asan and Pranayam i) Various yog poses and their benefits for mind & body ii)Regularization of breathing techniques and its effects-Types of pranayam

Text Books And Reference Books:

Yogic Asanas for Group Tarining-Part-I” :Janardan Swami YogabhyasiMandal, Nagpur

Essential Reading / Recommended Reading

“Rajayoga or conquering the Internal Nature” by Swami Vivekananda, AdvaitaAshrama (Publication Department), Kolkata

Evaluation Pattern

NA

MTAC227 - PERSONALITY DEVELOPMENT THROUGH LIFE ENLIGHTENMENT SKILLS (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:0
Credits:0

Course Objectives/Course Description

 

  To learn to achieve the highest goal happily

To become a person with stable mind, pleasing personality and determination 

To awaken wisdom in students

Learning Outcome

Understand the concept of achieving  the highest goal happily.

Unit-1
Teaching Hours:8
Neetisatakam
 

Neetisatakam-Holistic development of personality Verses- 19,20,21,22 (wisdom) Verses- 29,31,32 (pride & heroism) Verses- 26,28,63,65 (virtue) Verses- 52,53,59 (dont’s) Verses- 71,73,75,78 (do’s)

Unit-2
Teaching Hours:8
Approach to day to day work and duties
 

Approach to day to day work and duties. ShrimadBhagwadGeeta : Chapter 2-Verses 41, 47,48, Chapter 3-Verses 13, 21, 27, 35, Chapter 6-Verses 5,13,17, 23, 35, Chapter 18-Verses 45, 46, 48.

Unit-3
Teaching Hours:8
Statements of basic knowledge
 

Statements of basic knowledge. ShrimadBhagwadGeeta: Chapter2-Verses 56, 62, 68 Chapter 12 -Verses 13, 14, 15, 16,17, 18 Personality of Role model. ShrimadBhagwadGeeta: Chapter2-Verses 17, Chapter 3-Verses 36,37,42, Chapter 4-Verses 18, 38,39 Chapter18 – Verses 37,38,63

Text Books And Reference Books:
  1. “Srimad Bhagavad Gita” by Swami SwarupanandaAdvaita Ashram (Publication Department), Kolkata 
Essential Reading / Recommended Reading
  1. Bhartrihari’s Three Satakam (Niti-sringar-vairagya) by P.Gopinath, 4. Rashtriya Sanskrit Sansthanam, New Delhi.
Evaluation Pattern

NA

MTCS213 - PROFESSIONAL PRACTICE - II (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:1

Course Objectives/Course Description

 

Duringtheseminarsessioneachstudentisexpectedtoprepare and presentatopicon engineering/ technology, itis designed to:

  • Review and increasetheir understandingof thespecific topics tested.
  • Improvetheir abilityto communicate that understandingto thegrader.
  • Increasetheeffectiveness with which theyusethelimited examinationtime.

 

Learning Outcome

 students towards acquiring competence in teaching, laboratoryskills, research methodologies and otherprofessional activities includingethics in the respective academicdisciplines.

The course will broadly cover the following aspects:

  • Teachingskills
  • Laboratoryskills andother professional activities
  • Research methodology

Unit-1
Teaching Hours:30
COURSE NOTICES
 

Notices pertaining to this course will be displayed on the respective departmental notice boards by the panel coordinator/instructor.Students may also check the exam notice board for notices issued by the exam division.

 

MAKEUPPOLICY:  All students are required to attend all the lectures and presentations in the panel. Participation and cooperation will also be taken into account in the final evaluation. Requests for makeup should normally be avoided. However,in genuine cases,panel will decide action on a case by case basis.

 

NOTE:Seminar shall be presented in the department in presence of a committee (Batch of Teachers)constituted by HOD.The seminar marks are to be awarded by the committee. Students shall submit the seminar report in the prescribed Standard format.

Text Books And Reference Books:

Selected domain related text book will be sugessted.

Essential Reading / Recommended Reading

Research papers for the selected domain

Evaluation Pattern

-

MTCS231 - COMPUTER COMMUNICATION NETWORKS (2021 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 concepts of internetwork.

·      To study the functions of different layers.

·      To introduce IEEE standards employed in computer networking.

              ·      To make the students to get familiarized with different protocols and network components.

Learning Outcome

Recognize the basic requirements of building of network and layering of protocols.

Distinguish the concept of internetworking and routing through internet protocol addressing.

 Discuss the role of different protocols in internetworking.

Examine the security issues and congestion control  in the networks

Determine the features and operations of various application layer protocols.

 

Unit-1
Teaching Hours:9
INTRODUCTION
 

Building a Network, Requirements, Perspectives, Scalable Connectivity, Cost-Effective Resource sharing, Support for Common Services, Manageability, Protocol layering, Performance, Bandwidth and Latency, Delay X Bandwidth Product, Perspectives on Connecting, Classes of Links, Reliable Transmission, Stop-and-Wait , Sliding Window, Concurrent Logical Channels.    

 

 

Unit-2
Teaching Hours:9
INTERNETWORKING- I
 

Switching and Bridging, Datagram’s, Virtual Circuit Switching, Source Routing, Bridges and LAN Switches, Basic Internetworking (IP), Service Model, Global Addresses, Datagram Forwarding in IP, subnetting and classless addressing, Address Translation(ARP), Host Configuration(DHCP), Error Reporting(ICMP), Virtual Networks and Tunnels.

Unit-3
Teaching Hours:9
INTERNETWORKING- II
 

Network as a Graph, Distance Vector (RIP), Link State (OSPF), Metrics, The Global Internet, Routing Areas, Routing among Autonomous systems (BGP), IP Version 6(IPv6), Mobility and Mobile IP.

Unit-4
Teaching Hours:9
NETWORK SECURITY
 

Simple Demultiplexer (UDP), Reliable Byte Stream(TCP), End-to-End Issues, Segment Format, Connecting Establishment and Termination, Sliding Window Revisited, Triggering Transmission, Adaptive Retransmission, Record Boundaries, TCP Extensions, Queuing Disciplines, FIFO, Fair Queuing, TCP  Congestion Control, Additive Increase/Multiplicative Decrease, Slow Start, Fast Retransmit and Fast Recovery.

Unit-5
Teaching Hours:9
APPLICATIONS
 

Congestion-Avoidance Mechanisms, DEC bit, Random Early Detection (RED), Source-Based Congestion Avoidance. The Domain Name System(DNS),Electronic Mail(SMTP,POP,IMAP,MIME),World Wide Web(HTTP),Network Management(SNMP).

Text Books And Reference Books:

TEXTBOOKS

1.  Larry Peterson and Bruce S Davis “Computer Networks: A System Approach” 5th Edition, Elsevier -2014

2.  Douglas E Comer, “Internetworking with TCP/IP, Principles, Protocols and Architecture” 6th Edition, PHI – 2014

 

REFERENCE BOOKS

1. Uyless Black “Computer Networks, Protocols, Standards and Interfaces” 2nd Edition – PHI

2. Behrouz A Forouzan “TCP /IP Protocol Suite” 4th Edition – Tata McGraw-Hill

3. Andrew S. Tanenbaum, “Computer Networks”, Pearson Education 4th edition, 2012.

4. Larry L.Peterson and Brule S.Davie, “Computer Networks – A System Approach” MarGankangmann – Harcourt Asia, Fifth Edition, 2011.

5. William Stallings, “SNMP, SNMP V2, SNMPV3, RMON 1 and 2”, Pearson 2006

6.J.F Kurose and K.W. Ross, “Computer Networking –A top –down approach featuring the internet”, Pearson, 2012.

7.William Stallings, “Data & Computer Communication”, 6th Edition, Pearson Education, 2007.

8.Mani Subramanian, “Network Management: Principles and Practice”, Addison Wesley, 2000.

 

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)

Components of the CIA

 

CIA I  :  Quizzes/Seminar/Case Studies/Project Work /Assignments     : 10 marks

 

CIA II  :   Mid Semester Examination (Theory)                                                : 25 marks

 

CIA III  : Quizzes/Seminar/Case Studies/Project Work /Assignments: 10 marks

 

                   Attendance                                                                            : 05 marks

 

              Total                                                                                       : 50 marks

 

MTCS232 - DATA SCIENCE (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

Objectives of this course are:

·        Able to apply fundamental algorithmic ideas to process data.

·        Learn to apply hypotheses and data into actionable predictions.

·        Document and transfer the results and effectively communicate the findings using visualization techniques.

Learning Outcome

1. Understand the foundations of data processing

2. Apply the clustering and Classification methods for modelling the data

3. Analysis of Statistical models and data distributions using Python Programming.

4. Analysis of distributed file system and Data Processing using Spark

5. Evaluating the results of data science experiment using Power BI.

Unit-1
Teaching Hours:9
INTRODUCTION AND THE DATA SCIENCE
 

Data science process – roles, stages in data science project – working with data from files –relational andNon-Relational databases – exploring data – managing data – cleaning and sampling for modeling and validation – Data preprocessing-Statistics for Data Science-Data Distributions

Unit-2
Teaching Hours:9
MODELING METHODS
 

Choosing and evaluating models – mapping problems to machine learning, evaluating clustering models, validating models – cluster analysis – K-means algorithm, Naïve Bayes – Memorization Methods – Linear and logistic regression – unsupervised methods.            

Unit-3
Teaching Hours:9
ANALYTICS WITH PYTHON
 

Data Analysis with Numpy  andPandas – Visualization with SeabornMatplotlib, Plotly and Cufflinks – Scikit-learn –Regression, KNN, PCA and SVM in Python– Recommender systems – NLP with NLTK – Neural Nets and Deep Learning with Tensor Flow

Unit-4
Teaching Hours:9
SPARK SYSTEMS
 

 

Introduction –Hadoop vs Spark - Spark Data Frame – Group by and Aggregate –RDD – Spark SQL – Spark Running on Cluster–Machine Learning with Mlib–Collaborative Filtering–NLP Applications–Spark Streaming.

 

Unit-5
Teaching Hours:9
DELIVERING RESULTS with POWER BI
 

 

Power BI Desktop – Connecting and Shaping Data – Creating Table Relationship – Database Normalization – Snow Flake Schema – Filter Flow - DAX Calculations – Implicit and Explicit DAX Measures – DAX Function Categories -  Visualization with Power BI Reports - Case studies.

 

Text Books And Reference Books:

1.      William McKinney- Python for Data Analysis: Data Wrangling with Pandas, NumPy, and Ipython, O'Reilly; Second edition, 2017

2.      Sandy Ryza, Uri Laserson.   Advanced Analytics with Spark: Patterns for Learning from Data at Scale  – O'Reilly 2017

3.      Brett Powell  Mastering Microsoft Power Bi, Packt Publishing, 2018

Essential Reading / Recommended Reading

1. Jake VanderPlas. Python Data Science Handbook: Essential Tools for Working with Data O'Reilly 2016.

2. Holden Karau, Andy Konwinski, Learning Spark: Lightning-Fast Big Data Analysis, O'Reilly 2015

3. Tony Ojeda, Sean Patrick Murphy, Benjamin Bengfort, AbhijitDasgupta, "Practical Data Science Cookbook", Packt Publishing Ltd., 2014.

4. AurÈlienGÈron  Hands-On Machine Learning with Scikit-Learn and Tensor Flow: Concepts, Tools, and Techniques to Build Intelligent Systems O'Reilly2017.

5. Devin Knight, Brian Knight. Microsoft Power BI Quick Start Guide: Build dashboards and visualizations to make your data come to life, Packt Publishing, 2018.

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)

Components of the CIA

CIA I   :     Quizzes/Seminar/Case Studies/Project Work                   : 10 marks                  

CIA II  :  Mid Semester Examination (Theory)                                        : 25 marks

CIA III            : Quizzes/Seminar/Case Studies/Project Work             : 10 marks

       Attendance                                                                                                     : 05 marks

                   Total                                                                                                                  : 50 marks

MTCS243E01 - CLOUD COMPUTING (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

Cloud  computing  is  a  model  for  enablingubiquitous,  convenient,  on-demand  access  to  a shared  pool  of  configurable  computing  resources. Cloud computing paradigm possesses tremendous momentum but its unique aspects exacerbate security and privacy challenges.  Cloud  computing enables  increasing  number  of  IT  services  to  be delivered  over  the  Internet.  The  cloud  platform enables  business  to  run  successfully  without dedicated  hardware,  software  and  services.  

Learning Outcome

Understand the fundamentals of Cloud Storage, Cloud Architecture and Cloud Computing

Explain Cloud Computing technologies with respect to platforms, services, network, security and applications

Analyze Virtualization techniques, Virtual machines provisioning and Migrating services.

Examine Work flow and Map-reduce programming models

Assess various Cloud applications, Security and Performance issues

Unit-1
Teaching Hours:9
UNDERSTANDING CLOUD COMPUTING
 

Cloud Computing – History of Cloud Computing – Cloud Architecture – Cloud Storage – Why Cloud Computing Matters – Advantages/Disadvantages of Cloud Computing – Types of Cloud – Architecture of Cloud– Cloud Services- Web-Based Application – Pros and Cons of Cloud Service Development. 

Unit-2
Teaching Hours:9
CLOUD COMPUTING ARCHITECTURE
 

Types of Cloud Service Development – Infrastructure / Hardware as a Service-Software as a Service – Platform as a Service – Web Services – On-Demand Computing – Migrating into a Cloud –Types of Clouds-Amazon Ec2 – Google App Engine – Microsoft Azure – IBM Clouds.

Unit-3
Teaching Hours:9
VIRTUALIZATION TECHNIQUES; VIRTUAL MACHINES PROVISIONING AND MIGRATION SERVICES
 

Characteristics of Virtualized Environment – Taxonomy of Virtualization Techniques–Virtualization and Cloud Computing – Pros and Cons of Virtualization – Technology Examples: Xen, VMware, Hyper-V- Virtual Machines Provisioning and Manageability–Virtual Machine Migration Services – Provisioning in the Cloud Context.

Unit-4
Teaching Hours:9
WORKFLOW AND MAP-REDUCE PROGRAMMING MODELS
 

Workflow Management Systems and Clouds- Architecture of Workflow Management Systems – Utilizing Clouds for Workflow Execution – Data-Intensive Computing– Technologies for Data-Intensive Computing – Storage Systems – Programming Platforms- Aneka MapReduce Programming – Major MapReduce Implementations for the Cloud.

Unit-5
Teaching Hours:9
CLOUD APPLICATIONS: SECURITY AND PERFORMANCE ISSUES
 

Case Study:  Business and Consumer Applications: CRM and ERP, Social Networking, Multiplayer Online Gaming – Technologies for Data Security in Cloud Computing – Cloud Computing and Data Security Risk- The Cloud, Digital Identity, and Data Security–Content Level Security-Data Privacy and Security Issues – HPC in the Cloud: Performance related Issues.

Text Books And Reference Books:

1.      RajkumarBuyya, Vecchiola, Selvi, “Mastering Cloud Computing”, McGraw Hill. 2013.

2.      Anthony Velte, Toby Velte, and Robert Elsenpeter. “Cloud Computing – A Practical Approach”, McGraw Hill. 2010.

3.    RajkumarBuyya, James Broberg, Andrzej M. Goscinski, “Cloud Computing: Principles and Paradigms”, Wiley 2013.

Essential Reading / Recommended Reading

1.      Massimo Cafaro and Giovanni Aloisio. “Grids, Clouds and Virtualization”. Springer 2012.

2.   Michael Miller, “Cloud Computing: Web-Based Applications That Change the Way You Work and Collaborate Online”, Que Publishing, August 2008.

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)

Components of the CIA

 

CIA I  :  Quizzes/Seminar/Case Studies/Project Work /Assignments     : 10 marks

CIA II  :   Mid Semester Examination (Theory)                                   : 25 marks

CIA III  : Quizzes/Seminar/Case Studies/Project Work /Assignments    : 10 marks

  Attendance                                                                            : 05 marks

  Total                                                                                       : 50 marks

 

MTCS243E02 - ADVANCED MOBILE COMPUTING (2021 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 student should understand

        The basics of wireless voice and data communications technologies.

        To build working knowledge on various telephone and satellite networks.

        To study the working principles of wireless LAN and  its standards.

        To build knowledge on various mobile computing algorithms.

        To build skills in working with wireless application protocols to develop mobile content applications.

Learning Outcome

Describe and compare different mobile application models/architectures and patterns.

To be able to Create, test and debug third party application by setting up development 3rd party frameworks like Phonegap, Native Script etc..

Develop an windows based universal applications which uses visual studio environment, universal windows platform as framework and user specific development languages like java, HTML, JSP etc..

Develop android applications which uses android studio, development language  java and demonstrates knowledge UI framework, persistent storage, Google maps, GPS etc..

Develop android applications which uses Animations and other graphical features.

Unit-1
Teaching Hours:9
Introduction to Mobile Communications and Computing
 

Introduction to Mobile Communications and Computing : Mobile Computing (MC) : Introduction to MC, novel applications, limitations, and architecture. Wireless Transmission Fundamentals: Introduction to wireless transmission, signal propagation, Multiplexing, Modulation, Spread Spectrum

Unit-2
Teaching Hours:9
GSM
 

GSM : Mobile services, System architecture, Radio interface, Protocols, Localization and calling, Handover, Security, and New data services. (Wireless) Medium Access Control :Motivation for a specialized MAC (Hidden and exposed terminals, Near and far terminals), SDMA, FDMA, TDMA, CDMA.

Unit-3
Teaching Hours:9
Mobile Network Layer
 

Mobile Network Layer : Mobile IP (Goals, assumptions, entities and terminology, IP packet delivery, agent advertisement and discovery, registration, tunneling and encapsulation, optimizations), Dynamic Host Configuration Protocol (DHCP).Mobile Transport Layer : Traditional TCP, Indirect TCP, Snooping TCP, Mobile TCP, Fast retransmit/ fast recovery, Transmission /time-out freezing, Selective retransmission, Transaction oriented TCP.

Unit-4
Teaching Hours:9
Wireless LAN Technology
 

Wireless LAN Technology-IEEE 802.11 (System Architecture, protocol architecture, physical layer, medium access control layer, mac management, 802.11b, 802.11a) Mobile Ad hoc Networks (MANETs): Overview, Properties of a MANET, spectrum of MANET applications, routing and various routing algorithms, security in MANETs.

Unit-5
Teaching Hours:9
Bluetooth
 

Bluetooth: User scenarios, Architecture, Radio layer, Baseband layer, Link manager protocol, L2CAP, Security , SDP , profiles , IEEE 802.15 . Wireless Application Protocol : Architecture, Wireless datagram protocol, Wireless transport layer security, Wireless truncation protocol, Wireless session protocol, Wireless application environment, Wireless markup language, WML Script, Wireless telephony application, Push architecture, Push/pull services, Examples stacks with WAP 1.X.

Text Books And Reference Books:

1.      J.Schiller, “Mobile Communication”,2nd Edition, Pearson Education,2012.

2.      Cory Beard,William Stallings, “Wireless Communication and Networks”,5/e, Pearson Education,2015.

3.      Raj Pandya,” Mobile and Personal Communication Systems and Services”, Prentice Hall of India. 2010

      4.    UweHansmann, LotherMerk, Martin Nicklous, Thomas Stober, “ Principles of Mobile Computing” , Springer International Edition. 2011

Essential Reading / Recommended Reading

1.      Singhal, “WAP-Wireless Application Protocol”, Pearson Education 2002.

2.      LotherMerk, Martin. S. Nicklaus and Thomas Stober, “Principles of Mobile Computing”,Second Edition, Dreamtech press 2006.

3.      William C.Y.Lee, “Mobile Communication Design Fundamentals”, John Wiley. 2011.

4.      Reza Behravanfar, “Mobile Computing Principles: Designing and Developing Mobile Applications with   UML and XML”, ISBN: 0521817331, Cambridge University Press, 2004.

5.      Adelstein, Frank, Gupta, Sandeep KS, Richard III, Golden ,Schwiebert, Loren, “Fundamentals of Mobile and Pervasive Computing”, ISBN: 0071412379, McGraw-Hill Professional, 2005.

              6. MartynMallick, “Mobile and Wireless Design Essentials”, Wiley DreamTech. 2003.

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)

Components of the CIA

 

CIA I  :  Quizzes/Seminar/Case Studies/Project Work /Assignments     : 10 mark

 

CIA II  :   Mid Semester Examination (Theory)                                                : 25 marks

 

CIA III  : Quizzes/Seminar/Case Studies/Project Work /Assignments    : 10 marks

 

     Attendance                                                                            : 05 marks

 

     Total                                                                                       : 50 marks

 

MTCS243E03 - DISTRIBUTED COMPUTING (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

        To get a comprehensive knowledge of the architecture of distributed systems.

        To understand the deadlock and shared memory issues and their solutions in distributed environments.

        To know the security issues and protection mechanisms for distributed environments.

    •               To get a knowledge of distributed transaction processing.

Learning Outcome

Experiment the concepts and principles of distributed computing.

Examine the methods of communication that is happening in a distributed environment.

Outline the architecture and the support of operating systems in a distributed environment and also

Analyze any architecture for the current issues prevailing.

Examine the concepts and issues of transaction that happens in a distributed Environment.

Unit-1
Teaching Hours:9
INTRODUCTION
 

Characterization of Distributed Systems - Examples - Trends-Resource Sharing- System Models – Physical, Architectural and Fundamental Models - Networking and Internetworking - Types of Networks - Network Principles - Internet Protocols - Case Studies

Unit-2
Teaching Hours:9
PROCESSES AND DISTRIBUTED OBJECTS
 

Interprocess Communication - The API for the Internet Protocols - External Data Representation and Marshalling - Client-Server Communication - Group Communication - Case Study Distributed Objects and Remote Invocation - -Request Reply protocols-Remote Procedure Call – Remote method Invocation - Java RMI Case Study.

Unit-3
Teaching Hours:9
OPERATING SYSTEM ISSUES ?I
 

The OS Layer - Protection - Processes and Threads - Communication and Invocation – OS Architecture - Security - Overview - Cryptographic Algorithms - Digital Signatures -Cryptography Pragmatics - Case Studies - Distributed File Systems - File Service Architecture Sun Network File System – CASE STUDY The Andrew File System

Unit-4
Teaching Hours:9
OPERATING SYSTEM ISSUES ? II
 

Directory Service - Clocks, Events and Process States - Synchronizing Physical Clocks - Logical Time And Logical Clocks - Global States - Distributed Debugging - Distributed Mutual Exclusion – Elections –Coordination and Agreement in group Communication- Consensus and Related Problems.

Unit-5
Teaching Hours:9
DISTRIBUTED TRANSACTION PROCESSING
 

Transactions - Nested Transactions - Locks - Optimistic Concurrency Control - Timestamp Ordering - Comparison - Flat and Nested Distributed Transactions - Atomic Commit Protocols- Concurrency Control in Distributed Transactions - Distributed Deadlocks - Transaction Recovery.

Text Books And Reference Books:
  1. Coulouris G, Dollimore J. &Kindberg T, G Blair , Distributed Systems Concepts And Design,    5th Edition, Addison-Wesley 2012.
Essential Reading / Recommended Reading

Andrew S Tanenbaum , Maartenvan Steen, “Distributed Systems –Principles and paradigms”Second edition , Pearson Education, 2015 (Reprint)

2. MugeshSinghal,Niranjan G Shivaratri, “Advanced Concepts in Operating Systems”, Tata McGraw Hill Edition, 2011

3. M.L.Liu, “Distributed Computing Principles and Applications”, Pearson Education, 2004.

4. SapeMullender, “Distributed Systems”, Addison Wesley, 2nd Edition, 1993.

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)

Components of the CIA

 

CIA I  :  Quizzes/Seminar/Case Studies/Project Work /Assignments     : 10 marks

 

CIA II  :   Mid Semester Examination (Theory)                                                : 25 marks

 

CIA III  : Quizzes/Seminar/Case Studies/Project Work /Assignments    : 10 marks

 

     Attendance                                                                            : 05 marks

 

     Total                                                                                       : 50 marks

 

MTCS243E04 - ADVANCED PARALLEL COMPUTING (2021 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 scalability, clustering issues, parallel programming models, shared memory programming and enabling technologies for parallel computing.

Learning Outcome

Justify the need for parallel computing from a performance point of view.

Explain massive parallelism in modern parallel computers with shared memory and distributed memory from an architectural perspective.

Categorize parallel computing models based on shared address space platforms, distributed memory systems and heterogeneous platforms.

Compare  the runtime performance of parallel programs with their serial implementations to propose improvements.

Design and Propose parallel algorithms using programming models – OpenMP, MPI and CUDA with improved performance.

Unit-1
Teaching Hours:9
SCALABILITY AND CLUSTERING
 

Evolution of Computer Architecture – Dimensions of Scalability – Parallel Computer Models – Basic Concepts Of Clustering – Scalable Design Principles – Parallel Programming Overview – Processes, Tasks and Threads – Parallelism Issues – Interaction / Communication Issues – Semantic Issues In Parallel Programs.

Unit-2
Teaching Hours:9
ENABLING TECHNOLOGIES
 

System Development Trends – Principles of Processor Design – Microprocessor Architecture Families – Hierarchical Memory Technology – Cache Coherence Protocols – Shared Memory Consistency – Distributed Cache Memory Architecture – Latency Tolerance Techniques – Multithreaded Latency Hiding.

Unit-3
Teaching Hours:9
SYSTEM INTERCONNECTS PARALLEL PROGRAMMING
 

Basics of Interconnection Networks – Network Topologies and Properties – Buses, Crossbar and Multistage Switches   Software Multithreading – Synchronization Mechanisms. 

Paradigms And Programmability – Parallel Programming Models – Shared Memory Programming using  OpenMP

Unit-4
Teaching Hours:9
PARALLEL PROGRAMMINGMESSAGE PASSING PROGRAMMING
 

Paradigms And Programmability – Parallel Programming Models – Shared Memory Programming.

Message Passing Paradigm – Message Passing Interface – MPI programming.

Unit-5
Teaching Hours:9
MESSAGE PASSING PROGRAMMING GPU AND CUDA PROGRAMMING
 

Message Passing Paradigm – Message Passing Interface. Parallel Virtual Machine. Case Study.

GPU Architecture – Basics of CUDA – CUDA Threads – CUDA Memories – Synchronization Handling – Performance Issues – Application Development

Text Books And Reference Books:
  1. Kai Hwang and Zhi.WeiXu, “Scalable Parallel Computing”, Tata McGraw-Hill,  2003
Essential Reading / Recommended Reading

1.      Michael J. Quinn, “Parallel Programming in C with MPI &OpenMP”, Tata McGraw-Hill, New Delhi, 2003.

2.      Kai Hwang, “Advanced Computer Architecture” Tata McGraw-Hill, New Delhi, 2003

3.      David E. Culler &Jaswinder Pal Singh, “Parallel Computing Architecture: A Hardware/Software Approach”, Morgan Kaufman Publishers, 1999

4.      Peter S. Pacheco, “An Introduction to Parallel Programming”, Morgan Kaufmann, 2011.

5.      John L. Hennessey and David A. Patterson, “Computer Architecture – A quantitative

Approach”, Morgan Kaufmann / Elsevier Publishers, 5th. Edition, 2012.

6.      Shane Cook, “CUDA Programming: ―A Developer's Guide to Parallel Computing with GPUs(Applications of GPU Computing)”, First Edition, Morgan Kaufmann, 2012.

7.      David B. Kirk, Wen-mei W. Hwu, “Programming Massively Parallel Processors - A Hands-onApproach”, Second Edition, Morgan Kaufmann, 2012

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)

Components of the CIA

 

CIA I  :  Quizzes/Seminar/Case Studies/Project Work /Assignments     : 10 marks

 

CIA II  :   Mid Semester Examination (Theory)                                                : 25 marks

 

CIA III  : Quizzes/Seminar/Case Studies/Project Work /Assignments    : 10 marks

 

     Attendance                                                                            : 05 marks

 

     Total                                                                                       : 50 marks

 

MTCS244E01 - INTERNET OF THINGS (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

This course introduces the basic concepts of IoT, the functionalities of different types of sensors, actuators and micro controllers. It covers the protocols used in different layers and gives insight on programming IoT for different domains.

Learning Outcome

Explain the fundamental building blocks of an IoT environment from a logical and physical perspective.

Experiment with Arduino and Raspberry Pi to choose the appropriate hardware for different IoT projects.

Summarize various IoT protocols in Application and Network layers by outlining their advantages and disadvantages.

Develop IoT solutions using Arduino and Raspberry Pi to solve real life problems.

Analyze the IoT design and cloud incorporation.

Unit-1
Teaching Hours:9
INTRODUCTION AND BACKGROUND
 

Definition and Characteristics of IoT, Physical Design of IoT: Things in IoT, Logical Design of IoT: IoT functional Blocks, IoT Communication Blocks, IoT communication APIs, IoT Enabling Technologies: WSN, Cloud Computing, Big Data Analysis, Communication Protocols, Embedded Systems.

Unit-2
Teaching Hours:9
IOT HARDWARE, DEVICES AND PLATFORMS
 

Basics of Arduino: The Arduino Hardware, The Arduino IDE, Basic Arduino Programming, Basics of Raspberry pi: Introduction to Raspberry Pi, Programming with Raspberry Pi, CDAC IoT devices: Ubimote, Wi-Fi mote, BLE mote, WINGZ gateway, Introduction to IoT Platforms, IoT Sensors and actuators.

Unit-3
Teaching Hours:9
IOT PROTOCOLS
 

IoT Data Link Protocols, Network Layer Routing Protocols, Network Layer Encapsulation Protocols, Session Layer Protocols, IoT Security Protocols, Service Discovery Protocols, Infrastructure Protocols

Unit-4
Teaching Hours:9
IOT PROGRAMMING
 

Arduino Programming: Serial Communications, Getting input from sensors, Visual, Physical and Audio Outputs, Remotely Controlling External Devices, Wireless Communication. Programming with Raspberry Pi: Basics of Python Programming, Python packages of IoT, IoT Programming with CDAC IoT devices.

Unit-5
Teaching Hours:9
IOT DESIGN AND CLOUD INCORPORATION
 

Case Studies- IoT Design and Cloud incorporation: Introduction to IOT Design, Home Automation, Smart Lighting , Home Intrusion Detection, Cities , Smart Parking , Environment , Weather Monitoring System , Weather Reporting Bot , Air Pollution Monitoring , Forest Fire Detection, Agriculture, Smart Irrigation, Productivity Applications , IoT Printer..

Text Books And Reference Books:

1. Vijay Madisetti and ArshdeepBahga, “Internet of Things (A Hands-on-Approach)”, 1st Edition, VPT, 2014.

2. Margolis, Michael. “Arduino Cookbook: Recipes to Begin, Expand, and Enhance Your Projects. " O'Reilly Media, Inc.", 2011.

3. Monk, Simon. Raspberry Pi cookbook: Software and hardware problems and solutions. " O'Reilly Media, Inc.", 2016.

Essential Reading / Recommended Reading

1.      The Internet of Things: Applications to the Smart Grid and Building Automation by – Olivier Hersent, Omar Elloumi and David Boswarthick – Wiley Publications -2012.

2.      Honbo Zhou, “The Internet of Things in the Cloud: A Middleware Perspective”, CRC Press, 2012.

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

4.      Al-Fuqaha, Ala, et al. "Internet of things: A survey on enabling technologies, protocols, and applications." IEEE Communications Surveys & Tutorials 17.4 (2015): 2347-2376.

5.      Tsitsigkos, Alkiviadis, et al. "A case study of internet of things using wireless sensor networks and smartphones." Proceedings of the Wireless World Research Forum (WWRF) Meeting: Technologies and Visions for a Sustainable Wireless Internet, Athens, Greece. Vol. 2325. 2012.

        Ye, Mengmei, et al. "Security Analysis of Internet-of-Things: A Case Study of August Smart Lock."

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)

Components of the CIA

 

CIA I  :  Quizzes/Seminar/Case Studies/Project Work /Assignments     : 10 marks

 

CIA II  :   Mid Semester Examination (Theory)                                                : 25 marks

 

CIA III  : Quizzes/Seminar/Case Studies/Project Work /Assignments    : 10 marks

 

     Attendance                                                                            : 05 marks

 

     Total                                                                                       : 50 marks

 

MTCS244E02 - ADHOC NETWORKS (2021 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 Software quality models, to understand the fundamentals of ad hoc technology, to know about various ad hoc routing protocols, to understand various security issues in ad hoc networks, to know about the QoS and energy management approaches.

Learning Outcome

Course Outcome 1 - To understand the fundamentals of ad hoc wireless communication technology

Course Outcome 2 - To understand various ad hoc routing protocols and apply for different problems

Course Outcome 3 - To understand the different multicast routing protocols and compare them with respect to different parameters

Course Outcome 4 - To know about the transport layer protocols and secure routing approaches in ad hoc wireless networks

Course Outcome 5 - To know about the quality of services and energy management approaches in ad hoc wireless networks

Unit-1
Teaching Hours:9
Introduction
 

Introduction-Fundamentals of Wireless Communication Technology - The Electromagnetic Spectrum - Radio Propagation Mechanisms - Characteristics of the Wireless Channel - IEEE 802.11a,b Standard – Origin Of Ad hoc: Packet Radio Networks - Technical Challenges - Architecture of PRNETs - Components of Packet Radios – Ad hoc Wireless Networks -What Is an Ad Hoc Network? Heterogeneity in Mobile Devices - Wireless Sensor Networks - Traffic Profiles - Types of Ad hoc Mobile Communications - Types of Mobile Host Movements - Challenges Facing Ad Hoc Mobile Networks-Ad hoc wireless Internet

Unit-2
Teaching Hours:9
Ad Hoc Routing Protocols
 

Introduction - Issues in Designing a Routing Protocol for Ad Hoc Wireless Networks - Classifications of Routing Protocols -Table-Driven Routing Protocols - Destination Sequenced Distance Vector (DSDV) - Wireless Routing Protocol (WRP) - Cluster Switch Gateway Routing (CSGR) - Source-Initiated On-Demand Approaches - Ad Hoc On-Demand Distance Vector Routing (AODV) - Dynamic Source Routing (DSR) -Temporally Ordered Routing Algorithm (TORA) - Signal Stability Routing (SSR) -Location-Aided Routing (LAR) - Power-Aware Routing (PAR) - Zone Routing Protocol (ZRP) .

Unit-3
Teaching Hours:9
Multicast Routing in Ad Hoc Networks
 

Introduction - Issues in Designing a Multicast Routing Protocol - Operation of Multicast Routing Protocols - An Architecture Reference Model for Multicast Routing Protocols -Classifications of Multicast Routing Protocols - Tree-Based Multicast Routing Protocols- Mesh-Based Multicast Routing Protocols - Summary of Tree-and Mesh-Based Protocols - Energy-Efficient Multicasting - Multicasting with Quality of Service Guarantees - Application-Dependent Multicast Routing - Comparisons of Multicast Routing Protocols

Unit-4
Teaching Hours:9
Transport Layer, Security Protocols
 

Introduction - Issues in Designing a Transport Layer Protocol for Ad Hoc Wireless Networks - Design Goals of a Transport Layer Protocol for Ad Hoc Wireless Networks -Classification of Transport Layer Solutions - TCP Over Ad Hoc Wireless Networks -Other Transport Layer Protocols for Ad Hoc Wireless Networks - Security in Ad Hoc Wireless Networks - Network Security Requirements - Issues and Challenges in Security Provisioning - Network Security Attacks - Key Management - Secure Routing in Ad Hoc Wireless Networks

Unit-5
Teaching Hours:9
QOS and Energy Management
 

Introduction - Issues and Challenges in Providing QoS in Ad Hoc Wireless Networks -Classifications of QoS Solutions - MAC Layer Solutions - Network Layer Solutions - QoS Frameworks for Ad Hoc Wireless Networks Energy Management in Ad Hoc Wireless Networks –Introduction - Need for Energy Management in Ad Hoc Wireless Networks - Classification of Energy Management Schemes - Battery Management Schemes - Transmission Power Management Schemes - System Power Management Schemes

Text Books And Reference Books:
  1.  C. Siva Ram Murthy and B.S. Manoj “Ad Hoc Wireless Networks: Architectures and Protocols”, Prentice Hall PTR 2011.
  2. C.K. Toh, “Ad Hoc Mobile Wireless Networks: Protocols and Systems”, Pearson Education 2009 
Essential Reading / Recommended Reading
  1. C. Siva Ram Murthy and B.S. Manoj “Ad Hoc Wireless Networks: Architectures and Protocols”, Pearson, 2006.
  2. C.K. Toh, “Ad Hoc Mobile Wireless Networks: Protocols and Systems”, Pearson, 2007.
  3. Charles E. Perkins, “Ad Hoc Networking”, Pearson, 2008
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)

Components of the CIA

 

CIA I  :  Quizzes/Seminar/Case Studies/Project Work /Assignments     : 10 marks 

 

CIA II  :   Mid Semester Examination (Theory)                                                : 25 marks

 

CIA III  : Quizzes/Seminar/Case Studies/Project Work /Assignments    : 10 marks

 

     Attendance                                                                            : 05 marks

 

Total                                                                                       : 50 marks

 

MTCS244E03 - ADVANCED EMBEDDED SYSTEM (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

To introduce students to the embedded systems, its hardware, software, embedded networking with programming concepts, real time operating systems, inter-task communication and an exemplary case of MUCOS – IIRTOS.

Learning Outcome

Understand concepts of embedded hardware and software

Determine the types of buses and devices used for various types of embedded system

Apply basic programing concepts to develop an application for embedded systems

Use basic concepts of RTOS in embedded systems

Compare various functions of Real Time Operating system

Unit-1
Teaching Hours:9
Introduction to Embedded Systems
 

Definition and Classification – Overview of Processors and hardware units in an embedded system – Software embedded into the system – Exemplary Embedded Systems – Embedded Systems on a Chip (SoC) and the use of VLSI designed circuits

Unit-2
Teaching Hours:9
Devices and Buses for Devices Network
 

I/O Devices - Device I/O Types and Examples – Synchronous - Iso-synchronous and Asynchronous Communications from Serial Devices - Examples of Internal Serial-Communication Devices - UART and HDLC - Parallel Port Devices - Sophisticated interfacing features in Devices/Ports- Timer and Counting Devices - ‘12C’, ‘USB’, ‘CAN’ and advanced I/O Serial high speed buses- ISA, PCI, PCI-X, cPCI and advanced buses.

Unit-3
Teaching Hours:9
Programming Concepts and Embedded Programming in C and C++
 

Programming in assembly language (ALP) vs. High Level Language - C Program Elements, Macros and functions -Use of Pointers - NULL Pointers - Use of Function Calls – Multiple function calls in a Cyclic Order in the Main Function Pointers – Function Queues and Interrupt Service Routines Queues Pointers – Concepts of EMBEDDED PROGRAMMING in C++ - Objected Oriented Programming – Embedded Programming in C++, ‘C’ Program compilers – Cross compiler – Optimization of memory codes.

Unit-4
Teaching Hours:9
Real-Time Operating Systems ? Part 1
 

Definitions of process, tasks and threads – Clear cut distinction between functions – ISRs and tasks by their characteristics – Operating System Services- Goals – Structures- Kernel - Process

Management – Memory Management – Device Management – File System Organization and Implementation – I/O Subsystems – Interrupt Routines Handling in RTOS, REAL TIME OPERATING SYSTEMS : RTOS Task scheduling models - Handling of task scheduling and latency and deadlines as performance metrics – Co-operative Round Robin Scheduling – Cyclic Scheduling with Time Slicing (Rate Monotonic Co-operative Scheduling) – Preemptive Scheduling Model strategy by a Scheduler – Critical Section Service by a Preemptive Scheduler – Fixed (Static) Real time scheduling of tasks

Unit-5
Teaching Hours:9
Real-Time Operating Systems ? Part 2
 

Study of Micro C/OS-II or Vx Works or Any other popular RTOS – RTOS System Level Functions – Task Service Functions – Time Delay Functions – Memory Allocation Related Functions – Semaphore Related Functions – Mailbox Related Functions – Queue Related Functions – Case Studies of Programming with RTOS

Text Books And Reference Books:
  1. Rajkamal, “Embedded Systems Architecture, Programming and Design”, TATA McGraw-Hill, Second Edition 2008.
Essential Reading / Recommended Reading
  1. Steve Heath, “Embedded Systems Design”, Second Edition-2003, Newnes,
  2. David E.Simon, “An Embedded Software Primer”, Pearson Education Asia, First Indian Reprint 2002.
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)

Components of the CIA

 

CIA I  :  Quizzes/Seminar/Case Studies/Project Work /Assignments     : 10 marks

 

CIA II  :   Mid Semester Examination (Theory)                                                : 25 marks

 

CIA III  : Quizzes/Seminar/Case Studies/Project Work /Assignments    : 10 marks

 

     Attendance                                                                            : 05 marks

 

     Total                                                                                       : 50 marks

 

MTCS244E04 - BIG DATA ANALYTICS FOR IOT (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

To learn the concepts of big data analytics

To learn the concepts about Internet of things

To understand and implement smart systems

Learning Outcome

Understand the application of Big data in IoT

Explain the RFID false authentications

Describe the concept of fog computation

Illustrate the web enhanced building

Examine the sustainability of data

Unit-1
Teaching Hours:9
BIG DATA PLATFORMS FOR THE INTERNET OF THINGS
 

Big  Data  Platforms  for  the  Internet  of  Things:  network  protocol-  data dissemination–current     state    of art- Improving Data andService Interoperability with Structure, Compliance, Conformance andContext Awareness: interoperability problem           in the IoT        context-           Big       Data Management  Systems for the Exploitation of Pervasive Environments – Big Data     challenges       and requirements       coming from different SmartCity  applications

Unit-2
Teaching Hours:9
RFID FALSE AUTHENTICATIONS
 

On RFID False Authentications: YA TRAP – Necessary and sufficient condition for false authentication prevention - Adaptive Pipelined Neural Network Structure in Self-aware Internet of Things: self-healing systems-Role of adaptive neural network- Spatial Dimensions of Big Data: Application of Geographical Concepts and Spatial Technology to the Internet of Things- Applying spatial relationships, functions, and models

Unit-3
Teaching Hours:9
FOG COMPUTING
 

Fog Computing: A Platform for Internet of Things and Analytics: a massively distributed number of sources - Big Data Metadata Management in Smart Grids: semantic inconsistencies – role of metadata

Unit-4
Teaching Hours:9
WEB ENHANCED BUILDING
 

Toward Web Enhanced Building Automation Systems: heterogeneity between existing installations and native IP devices - loosely-coupled Web protocol stack –energy saving in smart building- Intelligent Transportation Systems and Wireless Access in Vehicular Environment Technology for Developing Smart Cities: advantages and achievements- Emerging Technologies in Health Information Systems: Genomics Driven Wellness Tracking and Management System (GO-WELL) – predictive care – personalized medicine

Unit-5
Teaching Hours:9
SUSTAINABILITY DATA AND ANALYTICS
 

Sustainability Data and Analytics in Cloud-Based M2M Systems - potential stakeholders and their complex relationships to data and analytics applications - Social Networking Analysis - Building a useful understanding of a social network - Leveraging Social Media and IoT to Bootstrap Smart Environments : lightweight Cyber Physical Social Systems - citizen actuation

Text Books And Reference Books:

Stackowiak, R., Licht, A., Mantha, V., Nagode, L.,” Big Data and The Internet of Things Enterprise Information Architecture for A New Age”, Apress, 2015.

Essential Reading / Recommended Reading

Dr. John Bates , “Thingalytics - Smart Big Data Analytics for the Internet of Things”, john Bates, 2015

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)

Components of the CIA

 

CIA I  :  Quizzes/Seminar/Case Studies/Project Work /Assignments     : 10 marks 

 

CIA II  :   Mid Semester Examination (Theory)                                                : 25 marks 

 

CIA III  : Quizzes/Seminar/Case Studies/Project Work /Assignments    : 10 marks

 

     Attendance                                                                            : 05 marks

 

     Total                                                                                       : 50 marks

 

MTCS244E05 - NETWORK SECURITY (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

This course covers the major aspects of computer and network security. It starts with a general introduction to information security, and then proceeds to cover types of threats and attacks, hacking techniques, network vulnerabilities, security policies and standards, firewalls, cryptography, Authentication & digital signatures, the SSL protocol, Wireless security, intrusion detection and prevention. 

Learning Outcome

Evaluate the factors driving the need for network security.

Demonstrate the implications of implementing encryption at different levels of the OSI reference model.

Identify types of firewall implementation suitable for differing security requirements.

Experiment and explain simple filtering rules based on IP and TCP header information.

Distinguish between firewalls based on packet-filtering routers, application level gateways and circuit level gateways.

Unit-1
Teaching Hours:9
UNit-1
 

Security Attacks (Interruption, Interception, Modification and Fabrication), Security Services (Confidentiality,Authentication, Integrity, Non-repudiation, access Control and Availability) and Mechanisms, A model for Internetwork security, Internet Standards and RFCs Conventional Encryption Principles, Conventional encryption algorithms(DES, Triple DES,AES), cipher block modes of operation(CBC,CFB), location of encryption devices, key distribution Approaches of Message Authentication, Secure Hash Functions and HMAC.

Unit-2
Teaching Hours:9
Unit ? 2
 

Public key cryptography principles, public key cryptography algorithms, digital signatures, digital Certificates, Certificate Authority and key management, Kerberos, X.509 Directory Authentication Service.

Unit-3
Teaching Hours:9
Unit ? 3
 

Email privacy: Pretty Good Privacy (PGP) and S/MIME. IP Security Overview, IP Security Architecture, Authentication Header, Encapsulating Security Payload, Combining Security Associations and Key Management.

Unit-4
Teaching Hours:9
Unit ? 4
 

Web Security Requirements, Secure Socket Layer (SSL) and Transport Layer Security (TLS), Secure Electronic Transaction (SET). Intruders,

Unit-5
Teaching Hours:9
Unit ? 5
 

Viruses and related threats. Firewall Design principles, Trusted Systems. Intrusion Detection Systems

Text Books And Reference Books:

Network Security Essentials (Applications and Standards) by William Stallings Pearson Education, 5th  Edition 2013.

Essential Reading / Recommended Reading

1. Cryptography and network Security, Third edition, Stallings, PHI/ Pearson 2011

2. Principles of Information Security, Whitman, Thomson. 2010

3. Network Security:The complete reference,Robert Bragg,Mark Rhodes, TMH 2010

4. Introduction to Cryptography, Buchmann, Springer. 2012

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)

Components of the CIA

 

CIA I  :  Quizzes/Seminar/Case Studies/Project Work /Assignments     : 10 marks

 

CIA II  :   Mid Semester Examination (Theory)                                                : 25 marks

 

CIA III  : Quizzes/Seminar/Case Studies/Project Work /Assignments    : 10 marks

 

     Attendance                                                                            : 05 marks

 

     Total                                                                                       : 50 marks

 

MTCS251 - NETWORKING LAB (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:50
Credits:2

Course Objectives/Course Description

 
  • Developing a project to implement some of the areas in networking using different protocols and various techniques over wireless Ad-hoc networks with varying traffic loads.

Learning Outcome

  • Examine the performances of Routing protocol

    Experiment with different application layer protocols

    Experiment with different security techniques over peer to peer medium.

Unit-1
Teaching Hours:60
Design, develop the project to implement following areas in networks
 
  • TCP/IP suite like ICMP Protocol, TFTP, NNTP, Proxy Server, Application Firewall, Web browsers, ARP, DHCP, ICMP, DNS and SNMP.
  • Performance Evaluation of TCP and UDP over Wireless Ad-hoc Networks with varying traffic loads.
  • Prevention of ARP spoofing: A probe packet based technique.
  • Security techniques over media streaming over peer-to-peer networks.
  • Various techniques in optimization of bandwidth consumption, request for unauthorized access, signal-to-noise ratio, download channel capacity, packet delivery ratio and inter-packet delay.
Text Books And Reference Books:

TEXTBOOKS

1.  Larry Peterson and Bruce S Davis “Computer Networks: A System Approach” 5th Edition, Elsevier -2014

2.  Douglas E Comer, “Internetworking with TCP/IP, Principles, Protocols and Architecture” 6th Edition, PHI – 2014

 

REFERENCE BOOKS

1. Uyless Black “Computer Networks, Protocols, Standards and Interfaces” 2nd Edition – PHI

2. Behrouz A Forouzan “TCP /IP Protocol Suite” 4th Edition – Tata McGraw-Hill

3. Andrew S. Tanenbaum, “Computer Networks”, Pearson Education 4th edition, 2012.

4. Larry L.Peterson and Brule S.Davie, “Computer Networks – A System Approach” MarGankangmann – Harcourt Asia, Fifth Edition, 2011.

5. William Stallings, “SNMP, SNMP V2, SNMPV3, RMON 1 and 2”, Pearson 2006

6.J.F Kurose and K.W. Ross, “Computer Networking –A top –down approach featuring the internet”, Pearson, 2012.

7.William Stallings, “Data & Computer Communication”, 6th Edition, Pearson Education, 2007.

8.Mani Subramanian, “Network Management: Principles and Practice”, Addison Wesley, 2000.

 

 
   
Evaluation Pattern

End semester practical examination                                     : 25 marks

            Records                                                                                   : 05 marks

            Mid semester examination                                                    : 10 marks

            Class work                                                                              : 10 marks

            Total                                                                                       : 50 marks

Mid semester practical examination will be conducted during regular practical hour with prior intimation to all candidates. End semester practical examination will have two examiners an internal and external examiner.