CHRIST (Deemed to University), Bangalore

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

School of Engineering and Technology

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
MTAC226 STRESS MANAGEMENT BY YOGA Skill Enhancement Course 2 0 0
MTCS213 PROFESSIONAL PRACTICE - II Skill Enhancement Course 2 1 50
MTCS231 COMPUTER COMMUNICATION NETWORKS Core Courses 3 3 100
MTCS232 DATA SCIENCE Core Courses 3 3 100
MTCS243E01 CLOUD COMPUTING Discipline Specific Elective 3 3 100
MTCS244E01 INTERNET OF THINGS Discipline Specific Elective 3 3 100
MTCS251 NETWORKING LAB Core Courses 4 2 50
MTCS252 DATA SCIENCE LAB Core Courses 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
    

    

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.

Programme Outcome/Programme Learning Goals/Programme Learning Outcome:

PO1: An ability to apply Engineering knowledge of computing, mathematics, science, and computer science & engineering fundamentals for Problem solving.

PO2: An ability to think critically to identify, formulate, and solve complex computer science & engineering problems by developing models, evaluating validity and accuracy of solutions in terms of computer science and engineering validity measures.

PO3: An ability to analyse, design of complex problems, implement, and evaluate a computer-based system, to meet expected needs with appropriate considerations such as economic / environmental/societal.

PO4: An ability to conduct experiments to investigate problems based on changing requirements, analyze and interpret results.

PO5: An ability to create, select, adapt appropriate techniques and use of the modern computational tools, techniques and skills, and best of engineering practices.

PO6: To understand the impact of contextual knowledge on social aspects and cultural issues.

PO7: An ability to understand contemporary issues related to social & environmental context for sustainable development of engineering solutions.

PO8: An ability to understand professional & ethical responsibility to contribute for societal and national needs.

PO9: An ability to function and coordinate effectively as an individual, as a member or leader in diverse, multicultural& multidisciplinary teams

PO10: An ability to communicate effectively.

PO11: An understanding of computer science and engineering & management principles to manage software projects.

PO12: A recognition and realization of the need for, and an ability to engage in lifelong learning

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

Course Outcome

C01: Write research paper which will have higher level of readability

C02: Demonstrate what to write in each section

C03: To write appropriate Title for the research paper

CO4: Write concise abstract

C05: 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.

Course Outcome

CO 1: Demonstrate the concepts of Teaching through Black board and ICT techniques

CO 2: To apply and analyze the Newer Research directions in areas of Computer science and Engineering

Unit-1
Teaching Hours:30
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Text Books And Reference Books:

-https://www.scholarify.in/teaching-support-system

Essential Reading / Recommended Reading

-https://testbook.com/learn/ict-based-teaching/

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

Course 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

 

Course Outcome

CO1: Apply the image fundamentals and mathematical transformations necessary for image processing

CO2: Analyze image enhancement techniques in Spatial &frequency domain

CO3: Apply restoration models and compression models for image processing

CO4: Analyze and synthesis image using segmentation and representation techniques

CO5: Analyze and extract potential features of interest from the image

CO6: 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.

Course Outcome

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

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

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

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

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

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

Course 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: 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.

Course Outcome

CO1: Effective use of mathematical techniques to construct robust algorithms.

CO2: ยท 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.

Course Outcome

CO1: Apply principles and techniques of digital image processing in applications related to digital imaging system design and analysis

CO2: Analyze and implement image processing algorithms

CO3: Understand software tools for processing digital images

CO4: Experiment image processing problems and techniques

CO5: Examine image processing algorithms on computers

CO6: 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.

Course Outcome

CO1: Explain the principles and concepts of research methodology.

CO2: Understand the different methods of data collection

CO3: Apply appropriate method of data collection and analyze using statistical/software tools.

CO4: Present research output in a structured report as per the technical and ethical standards.

CO5: 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

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

Course Outcome

CO1: Explain the effectiveness of stress management techniques through Yoga.

CO2: Apply various Yoga Techniques.

CO3: Assess and analyze the symptoms, causes and effects of personal and academic stressors in order to implement appropriate stress management techniques.

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

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.

 

Course Outcome

CO 1: To implement a case study in the area of study

CO 2: To analyze the results obtained by implementation of case study in the area of study.

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.

Course Outcome

CO1: Recognize the basic requirements of building of network and layering of protocols.

CO2: Distinguish the concept of internetworking and routing through internet protocol addressing.

CO3: Discuss the role of different protocols in internetworking.

CO4: Examine the security issues and congestion control in the networks

CO5: 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.

Course Outcome

CO1: Understand the foundations of data processing

CO2: Apply the clustering and Classification methods for modelling the data

CO3: Analysis of Statistical models and data distributions using Python Programming.

CO4: Analysis of distributed file system and Data Processing using Spark

CO5: 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.  

Course Outcome

CO1: Understand the fundamentals of Cloud Storage, Cloud Architecture and Cloud Computing

CO2: Explain Cloud Computing technologies with respect to platforms, services, network, security and applications

CO3: Analyze Virtualization techniques, Virtual machines provisioning and Migrating services.

CO4: Examine Work flow and Map-reduce programming models

CO5: 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

 

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.

Course Outcome

CO1: Explain the fundamental building blocks of an IoT environment from a logical and physical perspective.

CO2: Experiment with Arduino and Raspberry Pi to choose the appropriate hardware for different IoT projects.

CO3: Summarize various IoT protocols in Application and Network layers by outlining their advantages and disadvantages.

CO4: Develop IoT solutions using Arduino and Raspberry Pi to solve real life problems.

CO5: 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

 

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.

Course Outcome

CO1: Examine the performances of Routing protocol

CO2: Experiment with different application layer protocols

CO3: 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.

MTCS252 - DATA SCIENCE LAB (2021 Batch)

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

Course Objectives/Course Description

 

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.

Course Outcome

CO1: Apply and evaluate the clustering and Classification methods for modeling the data

CO2: Apply the Statistical models and data distributions using Python Programming.

Unit-1
Teaching Hours:60
List of Experiments
 
  • Introduction to the Weka machine learning toolkit
  • To learn to perform exploratory data analysis using the R language
  • Introduction to linear regression using R
  • Classification using the Weka toolkit – Part 1
Text Books And Reference Books:

-

Essential Reading / Recommended Reading

-

Evaluation Pattern

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

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.

MTCS345E06 - MULTIMEDIA SYSTEMS (2020 Batch)

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

Course Objectives/Course Description

 
  1. To study the supporting operating systems.
  2. To study the multimedia concepts and various I/O technologies.
  3. To study multimedia systems and synchronization.

 

 

Course Outcome

CO1: Demonstrate the different QoS

CO2: Illustrate the various RTS

CO3: Discuss different methods in File systems and networks

CO4: Establish different communication methods

CO5: Examine the synchronization mechanisms

Unit-1
Teaching Hours:9
INTRODUCTION AND QOS
 

Introduction-QOS Requirements and Constraints-Concepts-Resources- Establishment Phase-Run-Time Phase-Management Architectures.

Unit-2
Teaching Hours:9
OPERATING SYSTEMS
 

Real-Time Processing-Scheduling-Interprocess Communication-Memory and Management-Server Architecture-Disk Management. 

Unit-3
Teaching Hours:9
FILE SYSTEMS AND NETWORKS
 

Traditional and Multimedia File Systems-Caching Policy-Batching-Piggy backing-Ethernet-Gigabit Ethernet-Token Ring-100VG AnyLAN-Fiber Distributed Data Interface (FDDI)- ATM Networks-MAN-WAN. 

Unit-4
Teaching Hours:9
COMMUNICATION
 

Transport Subsystem-Protocol Support for QOS-Transport of Multimedia-Computer Supported Cooperative Work-Architecture-Session Management-MBone Applications. 

Unit-5
Teaching Hours:9
SYNCHRONIZATION
 

Synchronization in Multimedia Systems-Presentation-Synchronization Types-Multimedia Synchronization Methods-Case Studies-MHEG-MODE-ACME. 

Text Books And Reference Books:

1.  Ralf Steinmetz and Klara Nahrstedt, “Multimedia Systems”, Springer, I Edition 2004. (Latest edition/ reprint available in market).

2.  K. R. Rao, Zoran S. Bojkovic, Dragorad A. Milovacovic, D. A. Milovacovic , “Multimedia Communication Systems: Techniques, Standards, and Networks”, Prentice Hall, 1st Edition, 2002 (Latest edition/ reprint available in market)

3.  Ze-Nian Li and Mark S. Drew, “Fundamentals of Multimedia”, Pearson, 2004. (Latest edition/ reprint available in market)

Essential Reading / Recommended Reading

1.  Ralf Steinmetz and Klara Nahrstedt , “Media Coding and Content Processing”, Prentice hall, 2002.

2.  Vaughan T, “Multimedia”, 9th Edition, Tata McGraw Hill, 1999.

3.  Mark J.B., Sandra K.M., “Multimedia Applications Development using DVI technology”, McGraw Hill, 1992.

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 CIA

CIA 1     Assignment and MCQ                       -   10 marks

CIA 2     Mid Semester Examination               -   25 marks

CIA 3     MOOC Course and Closed book test  -   10 marks

                               Attendance                    - 5  marks

Sl No

CIA Component

Unit(s) Covered

CO

RBT Level

1

CIA-I: Component1 -Assignment

Unit 1 and 2

CO1,CO2

L2

2

CIA-I: Component-2 Quiz - MCQ

 Unit 1 and 2

CO1, CO2

L3

3

MSE

Unit 1,2 and 3

CO1,CO2,CO3

L2

4

CIA-III: Component-1 Assignment

Unit 4 and 5

CO4,CO5

L3

5

CIA-III: Component-2 Closed Book Test

Unit 4 and 5

CO4,CO5

L4

 

 

MTCS381 - INTERNSHIP (2020 Batch)

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

Course Objectives/Course Description

 

Internships are short-term work experiences that will allow  a student to observe and participate in professional work environments and explore how his interests relate to possible careers. They are important learning opportunities trough industry exposure and practices.   More specifically, doing internships is beneficial because they provide the opportunity to: 

Course Outcome

CO1: Explain inside view of an industry and organization/company.

CO2: Make use of professional connections and enhance student's network.

CO3: Illustrate how to get experience in a field to allow the student to make a career transition.

Unit-1
Teaching Hours:60
Regulations
 

1.The student shall undergo an Internship for30 days  starting from the end of 2nd semester examination and completing it during the initial period of 3rd semester.

2.The department shall nominate a faculty as a mentor for a group of students to prepare and monitor the progress of  the students

3. The students shall report the progress of the internship to the mentor/guide at regular intervals and may seek his/her advise.

4. The Internship shall be completed by the end of  2nd semesters.

5. The students are permitted to carry out the internship outside India with the following conditions, the entire expenses are to be borne by the student and the University will not give any financial assistance.

6. Students can also undergo internships arranged by the department during vacation.

7. After completion of Internship, students shall submit a report to the department with the approval of both internal and external guides/mentors.

 

8. There will be an assessment for the internship for 2 credits, in the form of report assessment by the guide/mentor  and a presentation on the internship given to department constituted panel.

Text Books And Reference Books:

Related to the Internship domain text books are sugessted.

Essential Reading / Recommended Reading

Readings Related to the Internship domain

Evaluation Pattern

Internal 50 Marks

MTCS382 - DISSERTATION PHASE - I (2020 Batch)

Total Teaching Hours for Semester:200
No of Lecture Hours/Week:20
Max Marks:200
Credits:10

Course Objectives/Course Description

 

During this project phase I session, each student is expected to prepare and present a topic on engineering/ technology on their domain interest to persue the project work, it is designed to: 

  • Review and increase their understanding of the specific topics identified.
  • Improve their ability to communicate that understanding to the grader.
  • Increase the effectiveness with which they use the limited examination time.

Course Outcome

CO1: Analyze literature review, domain identification

CO2: Demonstrate the concept of framing the research problem

CO3: Apply the project design and analysis concepts.

Unit-1
Teaching Hours:200
DISSERTATION PHASE -1
 

Project Work 

Text Books And Reference Books:

Journal article, industry white papers text books basedon the domain on which the student will be doing his/her work.

Essential Reading / Recommended Reading

Recommendation will be given Based on the domian in which student will be interested and planning to do the dissertation work

Evaluation Pattern

      Assessment of Project Work(Phase I)

▪          Continuous Internal Assessment:100 Marks

♦        Presentation assessed by Panel Members

♦        Guide

♦        Mid semester Project Report

End semester Examination :100 Marks

Presentation assessed by Panel Members

♦        Guide

♦        End semester Project Report

MTEC361 - ADVANCED COMMUNICATION NETWORKS (2020 Batch)

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

Course Objectives/Course Description

 

This course aims at making the students understand the different communication protocols, understand advanced concepts in communication networking and also the concept of QoS in communication networks

Course Outcome

CO1: Understand advanced concepts in Congestion Control and Flow control including algorithms

CO2: Describe the quality of service requirements in the internet with the integrated services model and protocols

CO3: Understand the scheduling requirement in Internet including scheduler, classifier and IP addressing

CO4: Explain the admission control in internet protocol including the algorithms and differentiated services model for quality of service requirement

CO5: Understand the network interconnection models and protocols including MPLS

Unit-1
Teaching Hours:9
Unit - 1
 

Overview of -ATM. TCP/IP Congestion and Flow Control in Internet-Throughput analysis of TCP congestion control. TCP for high bandwidth delay networks. Fairness issues in TCP

Unit-2
Teaching Hours:9
Unit -2
 

Real Time Communications over Internet. Adaptive applications. Latency and throughput issues. Integrated Services Model (intServ). Resource reservation in Internet. RSVP, Characterization of Traffic by Linearly Bounded Arrival Processes (LBAP). Leaky bucket algorithm and its properties

Unit-3
Teaching Hours:9
Unit 3
 

Packet Scheduling Algorithms-requirements and choices. Scheduling guaranteed service Connections, IP address lookup-challenges. Packet classification algorithms, IPV4 and IPv6 address

Unit-4
Teaching Hours:9
Unit -4
 

Admission control in Internet. Concept of Effective bandwidth. Measurement based admission control. Differentiated Services in Internet (DiffServ). DiffServ architecture and framework

Unit-5
Teaching Hours:9
Unit 5
 

IP switching and MPLS, Overview of IP over ATM and its evolution to IP switching. MPLS architecture and framework. MPLS Protocols. Traffic engineering issues in MPLS

Text Books And Reference Books:
  1. Jean Wairand and PravinVaraiya, “High Performance Communications Networks”, 2nd edition, 2000
  2. Jean Le Boudec and Patrick Thiran, “Network Calculus A Theory of Deterministic Queueing Systems for the Internet”, Springer Veriag, 2001

 

Essential Reading / Recommended Reading
  1. Zhang Wang, “Internet QoS”, Morgan Kaufman, 2000
  2. Anurag Kumar, D. Manjunath and Joy Kuri, “Communication Networking: An Analytical Approach”, Morgan Kaufman Publishers, 2004.
  3. George Kesidis, “ATM Network Performance”, Kluwer Academic, Research Papers, 2005

 

 

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

 

MTCS483 - DISSERTATION PHASE-II (2020 Batch)

Total Teaching Hours for Semester:480
No of Lecture Hours/Week:32
Max Marks:200
Credits:16

Course Objectives/Course Description

 

During this project phase I session, each student is expected to prepare and present a topic on engineering/ technology on their domain interest to persue the project work, it is designed to: 

  • Review and increase their understanding of the specific topics identified.
  • Improve their ability to communicate that understanding to the grader.
  • Increase the effectiveness with which they use the limited examination time.

Course Outcome

CO1: Interpret Teaching, Laboratory & Research outcome by Academic participation

CO2: Impart research Focus based on Study of Journal publications.

CO3: Analyze domain identification, framing the research problem and Project design analysis

Unit-1
Teaching Hours:480
DISSERTATION PHASE -II
 

Project Work 

Text Books And Reference Books:

Journal article, industry white papers text books basedon the domain on which the student will be doing his/her work.

Essential Reading / Recommended Reading

Recommendation will be given Based on the domian in which student will be interested and planning to do the dissertation work

Evaluation Pattern

Assessment of Project Work(Phase II) and Dissertation

▪ Continuous Internal Assessment:100 Marks

♦ Presentation assessed by Panel Members

♦ Assessed by Guide

♦ Mid Semester Project Report

▪ End Semester Examination:100 Marks

♦ Viva Voce

♦ Demonstration

♦ Project Report

▪ Dissertation (Exclusive assessment of Project Report): 100 Marks

♦ Internal Review : 50 Marks

♦ External review : 50 Marks