Department of
COMPUTER-SCIENCE-AND-ENGINEERING






Syllabus for
Master of Technology (Computer Science and Engineering)
Academic Year  (2019)

 
1 Semester - 2019 - Batch
Paper Code
Paper
Hours Per
Week
Credits
Marks
MTCS111E01 ENGLISH FOR RESEARCH PAPER WRITING 2 0 0
MTCS112 PROFESSIONAL PRACTICE - I 2 1 0
MTCS131 RESEARCH METHODOLOGY AND IPR 4 3 100
MTCS133 ADVANCED ALGORITHMS 4 3 100
MTCS135 ADVANCED DIGITAL IMAGE PROCESSING 4 3 100
MTCS141E03 SOFTWARE PROJECT MANAGEMENT 4 3 100
MTCS142E01 BIG DATA ANALYTICS 4 3 100
MTCS151 ADVANCED ALGORITHMS LABORATORY 4 2 50
MTCS152 DIGITAL IMAGE PROCESSING LABORATORY 4 2 50
2 Semester - 2019 - Batch
Paper Code
Paper
Hours Per
Week
Credits
Marks
MTCS212E01 CONSTITUTION OF INDIA 2 0 0
MTCS212E02 PEDAGOGY STUDIES 2 0 0
MTCS212E03 STRESS MANAGEMENT BY YOGA 2 0 0
MTCS212E04 PERSONALITY DEVELOPMENT THROUGH LIFE ENLIGHTENMENT SKILLS 2 0 0
MTCS213 PROFESSIONAL PRACTICE - II 2 1 0
MTCS231 COMPUTER COMMUNICATION NETWORKS 4 3 100
MTCS232 DATA SCIENCE 4 3 100
MTCS243E01 CLOUD COMPUTING 4 3 100
MTCS243E02 ADVANCED MOBILE COMPUTING 4 3 100
MTCS243E03 DISTRIBUTED COMPUTING 4 3 100
MTCS243E04 ADVANCED PARALLEL COMPUTING 4 3 100
MTCS244E01 INTERNET OF THINGS 4 3 100
MTCS244E02 ADHOC NETWORKS 4 3 100
MTCS244E03 ADVANCED EMBEDDED SYSTEM 4 3 100
MTCS244E04 BIG DATA ANALYTICS FOR IOT 4 3 100
MTCS244E05 NETWORK SECURITY 4 3 100
MTCS251 NETWORKING LABORATORY 4 2 50
MTCS252 DATASCIENCE LABORATORY 4 2 50
3 Semester - 2018 - Batch
Paper Code
Paper
Hours Per
Week
Credits
Marks
CY01 CYBER SECURITY 2 2 50
MTCS331E03 WEB TECHNOLOGY 4 3 100
MTCS332E01 MACHINE LEARNING 4 3 100
MTCS333E01 SOFTWARE PROJECT MANAGEMENT 4 3 100
MTCS371 PROJECT WORK (PHASE I) 12 3 100
MTCS373 INTERNSHIP 2 2 50
4 Semester - 2018 - Batch
Paper Code
Paper
Hours Per
Week
Credits
Marks
MTCS471 PROJECT WORK (PHASE-II) AND DISSERTATION 20 9 300
        

  

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)

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: 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 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 Objective:
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.

MTCS111E01 - ENGLISH FOR RESEARCH PAPER WRITING (2019 Batch)

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

Course Objectives/Course Description

 

-

Learning Outcome

-

Unit-1
Teaching Hours:4
Unit-1
 

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:4
Unit-2
 

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

Unit-3
Teaching Hours:4
Unit-3
 

Review of the Literature, Methods, Results, Discussion, Conclusions, The Final Check.

Unit-4
Teaching Hours:4
unit-4
 

key 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:4
Unit-5
 

skills are needed when 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)

Essential Reading / Recommended Reading

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

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

-

MTCS112 - PROFESSIONAL PRACTICE - I (2019 Batch)

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

MTCS131 - RESEARCH METHODOLOGY AND IPR (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
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   :   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

MTCS133 - ADVANCED ALGORITHMS (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
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

Summarize the properties of advanced data structures.

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

Analyze and compare the efficiency of algorithms.

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

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   :   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

MTCS135 - ADVANCED DIGITAL IMAGE PROCESSING (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
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: Ability to apply the image fundamentals and mathematical transformations necessary for image processing

Course Outcome 2: Ability to analyze image enhancement techniques in Spatial &frequency domain

Course Outcome 3: Ability to apply restoration models and compression models for image processing

Course Outcome 4: Ability to synthesis image using segmentation and representation techniques

Course Outcome 5: Ability to analyze and extract potential features of interest from the image

Course Outcome 6: Ability to 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)

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

 

Essential Reading / Recommended Reading

Author Name(s), “Book title”, Edition, Publisher Name, Year (if it is old edition, reprint details should be given)

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

MTCS141E03 - SOFTWARE PROJECT MANAGEMENT (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
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

 

 

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

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

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

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

Identify the resources required for a project and to produce a work plan and resource Schedule.

Compare and differentiate organization structures and project structures.

 

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 (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
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

Describe big data and use cases from selected business domains

Discuss open source technologies

Explain NoSQL big data management

Discuss basics of Hadoop and HDFS

Discuss map-reduce analytics using Hadoop

Use 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.

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.

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)

·         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

MTCS151 - ADVANCED ALGORITHMS LABORATORY (2019 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 - DIGITAL IMAGE PROCESSING LABORATORY (2019 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

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

MTCS212E01 - CONSTITUTION OF INDIA (2019 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

-

Unit-1
Teaching Hours:4
Unit-1
 

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

Unit-2
Teaching Hours:4
Unit-2
 

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
Unit -3
 

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
Unit ? 4
 

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
Unit ? 5
 

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

D.D. Basu, Introduction to the Constitution of India, Lexis Nexis, 2015.

Evaluation Pattern

-

MTCS212E02 - PEDAGOGY STUDIES (2019 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. Review existing evidence on the review topic to inform programme design and policy making undertaken by the DfID, other agencies and researchers.

2. Identify critical evidence gaps to guide the development.

Learning Outcome

-

Unit-1
Teaching Hours:4
Unit ? I
 

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
Unit ? 2
 

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
Unit-3
 

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
unit 4
 

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
Unit ? 5
 

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

Text Books And Reference Books:

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.

www.pratham.org/images/resource%20working%20paper%202.pdf.

Evaluation Pattern

-

MTCS212E03 - STRESS MANAGEMENT BY YOGA (2019 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

-

MTCS212E04 - PERSONALITY DEVELOPMENT THROUGH LIFE ENLIGHTENMENT SKILLS (2019 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

-

Unit-1
Teaching Hours:8
Unit-1
 

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
unit-2
 

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
Unit-3
 

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:

“Srimad Bhagavad Gita” by Swami SwarupanandaAdvaita Ashram (Publication Department), Kolkata 

Essential Reading / Recommended Reading

Bhartrihari’s Three Satakam (Niti-sringar-vairagya) by P.Gopinath, 4. Rashtriya Sanskrit Sansthanam, New Delhi.

Evaluation Pattern

 -

MTCS213 - PROFESSIONAL PRACTICE - II (2019 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:0
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 (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
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:12
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:12
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:12
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:12
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:12
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   :   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

MTCS232 - DATA SCIENCE (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:3

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.

Learning Outcome

  • Understand the foundations of data processing

    Apply the clustering methods for modelling the data

    Analysis of Statistical models and data distributions using R Programming

    Analysis of distributed file system and Map reducing technique using Hadoop

    Evaluating the results of data science experiment using R Programming.

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

 

Data science process – roles, stages in data science project – working with data from files – working with relational databases – exploring data – managing data – cleaning and sampling for modeling and validation – introduction to NoSQL.

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
INTRODUCTION TO R
 

Reading and getting data into R – ordered and unordered factors – arrays and matrices – lists and data frames – reading data from files – probability distributions – statistical models in R - manipulating objects – data distribution.

Unit-4
Teaching Hours:9
MAP REDUCE
 

Introduction – distributed file system – algorithms using map reduce, Matrix-Vector Multiplication by Map Reduce – Hadoop - Understanding the Map Reduce architecture - Writing HadoopMapReduce Programs - Loading data into HDFS - Executing the Map phase - Shuffling and sorting - Reducing phase execution.

Unit-5
Teaching Hours:9
DELIVERING RESULTS
 

Documentation and deployment – producing effective presentations – Introduction to graphical analysis – plot() function – displaying multivariate data – matrix plots – multiple plots in one window - exporting graph - using graphics parameters. Case studies.

Text Books And Reference Books:

1.NinaZumel, John Mount, “Practical Data Science with R”, Manning Publications, 2014.

2. Jure Leskovec, AnandRajaraman, Jeffrey D. Ullman, “Mining of Massive Datasets”, Cambridge University Press, 2014. 

 

Essential Reading / Recommended Reading

1. Mark Gardener, “Beginning R - The Statistical Programming Language”, John Wiley & Sons, Inc., 2012.

2. W. N. Venables, D. M. Smith and the R Core Team, “An Introduction to R”, 2013.

5. Tony Ojeda, Sean Patrick Murphy, Benjamin Bengfort, AbhijitDasgupta, “Practical Data Science Cookbook”, Packt Publishing Ltd., 2014.

6. Nathan Yau, “Visualize This: The FlowingData Guide to Design, Visualization, and Statistics”, Wiley, 2011.

7.“Professional Hadoop Solutions”, Wiley, ISBN: 9788126551071, 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   :   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

MTCS243E01 - CLOUD COMPUTING (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
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   :   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

MTCS243E02 - ADVANCED MOBILE COMPUTING (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
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

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.

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   :   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

MTCS243E03 - DISTRIBUTED COMPUTING (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
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 implement 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:

Coulouris G, Dollimore J. &Kindberg T, G Blair , Distributed Systems Concepts And Design,    5th Edition, Addison-Wesley 2012.

Essential Reading / Recommended Reading

1. 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   :   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

MTCS243E04 - ADVANCED PARALLEL COMPUTING (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
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:

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.

             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   :   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

MTCS244E01 - INTERNET OF THINGS (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
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.

Survey successful IoT products and solutions to analyze their architecture and technologies.

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
DOMAIN SPECIFIC IOT
 

Home automation, Smart cities, Smart Environment, IoT in Energy, Logistics, Agriculture, Industry and Health & Life style secors. Case Studies: A Case study of Internet of Things Using Wireless Sensor Networks and Smartphones, Security Analysis of Internet-of-Things: A Case Study of August Smart Lock, OpenIoT platform.

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   :   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

  

MTCS244E02 - ADHOC NETWORKS (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
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.

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.

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   :   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

MTCS244E03 - ADVANCED EMBEDDED SYSTEM (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
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:

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,

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   :   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

MTCS244E04 - BIG DATA ANALYTICS FOR IOT (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
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   :   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

MTCS244E05 - NETWORK SECURITY (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
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   :   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

MTCS251 - NETWORKING LABORATORY (2019 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.

MTCS252 - DATASCIENCE LABORATORY (2019 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.

Learning Outcome

-

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.

CY01 - CYBER SECURITY (2018 Batch)

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

Course Objectives/Course Description

 

Cyber Security is defined as the body of technologies, processes and practices designed to protect networks, computers, programs and data from attack, damage or unauthorized access. Similar to other forms of security, Cyber Security requires coordinated effort throughout an information system.  This course will provide a comprehensive overview of the different facets of Cyber Security.  In addition, the course will detail into specifics of Cyber Security for all parties who may be involved keeping view of Global and Indian Legal environment. 

Learning Outcome

After learning the course for a semester, the student will be aware of the important cyber laws in the Information Technology Act (ITA) 2000 and ITA 2008 with knowledge in the areas of Cyber-attacks and Cyber-crimes happening in and around the world. The student would also get a clear idea on some of the cases with their analytical studies in Hacking and its related fields.

Unit-1
Teaching Hours:6
Unit-I
 

Security Fundamentals, Social Media and Cyber Security Security Fundamentals - Social Media –IT Act- CNCI – Legalities

Unit-2
Teaching Hours:6
Unit-II
 

Cyber Attack and Cyber Services Vulnerabilities - Phishing - Online Attacks. – Cyber Attacks - Cyber Threats - Denial of Service Vulnerabilities  - Server Hardening  

Unit-3
Teaching Hours:6
Unit-III
 

Risk Management and Assessment - Risk Management Process - Threat Determination Process - Risk Assessment - Risk Management Lifecycle – Vulnerabilities, Security Policy Management - Security Policies  - Coverage Matrix, Business Continuity Planning - Disaster Types - Disaster Recovery Plan - Business Continuity Planning - Business Continuity Planning Process.

Unit-4
Teaching Hours:6
Unit-IV
 

Vulnerability - Assessment and Tools: Vulnerability Testing - Penetration Testing Architectural Integration: Security Zones - Devices viz Routers, Firewalls, DMZ Host, Extenuating Circumstances viz. Business-to-Business, Exceptions to Policy, Special Services and Protocols, Configuration Management - Certification and Accreditation

Unit-5
Teaching Hours:6
Unit-V
 

Authentication and Cryptography: Authentication - Cryptosystems - Certificate Services Securing Communications: Securing Services - Transport – Wireless - Steganography and NTFS Data Streams, Intrusion Detection and Prevention Systems: Intrusion - Defense in Depth - IDS/IPS  -  IDS/IPS Weakness and Forensic Analysis, Cyber Evolution: Cyber Organization - Cyber Future

Text Books And Reference Books:

TEXT BOOKS:   

  1. Jennifer L. Bayuk and Jason Healey and Paul Rohmeyer and Marcus Sachs, Cyber Security Policy Guidebook, Wiley; 1 edition , 2012,  ISBN-10: 1118027809 
  2. Dan Shoemaker and Wm. Arthur Conklin, Cybersecurity: The Essential Body Of Knowledge,   Delmar Cengage Learning; 1 edition (May 17, 2011) ,ISBN-10: 1435481690
  3. Jason Andress, The Basics of Information Security: Understanding the Fundamentals of InfoSec in Theory and Practice, Syngress; 1 edition (June 24, 2011) ,  ISBN-10: 1597496537
  1. Stallings, “Cryptography & Network Security - Principles & Practice”, Prentice Hall, 3rd Edition 2002. 
  2. Bruce, Schneier, “Applied Cryptography”, 2nd Edition, Toha Wiley & Sons, 2007. 
  3. Man Young Rhee, “Internet Security”, Wiley, 2003. 
  4. Pfleeger & Pfleeger, “Security in Computing”, Pearson Education, 3rd Edition, 2003.  

 

REFERENCES:

  1. Information Technology Act 2008 Online 2. IT Act 2000.

 

 

Essential Reading / Recommended Reading

Research papers from reputed journals.

Evaluation Pattern

Internal 50 Marks.

MTCS331E03 - WEB TECHNOLOGY (2018 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:3

Course Objectives/Course Description

 

This course is designed to introduce programming experience to techniques associated with the World Wide Web. The course will introduce web-based media-rich programming tools for creating interactive web pages. Basic animation programming is also introduced with an emphasis on media-rich content creation, distribution and tracking capabilities.

Learning Outcome

 

·         Analyze Build web applications using PHP, JSP and Servlets and client side script technologies like HTML, CSS and JavaScript with Apache web server.

 

·         Design and Integrate database environment to web applications being developed. Describe sessions conceptually and implement using cookies and URL. 

 

·         Analyze the XML applications with DTD and style sheets that span multiple domains and across various platforms. 

 

·     Analyze the reasons and effects of nonstandard client-side scripting language characteristics, such as limited data types, dynamic variable types and properties, and extensive use of automatic type conversion.

 

 

Unit-1
Teaching Hours:9
INTRODUCTION
 

Introduction – Network concepts – Web concepts – Internet addresses - Retrieving Data with URL – HTML – DHTML: Cascading Style Sheets - Scripting Languages: JavaScript.

 

Unit-2
Teaching Hours:9
COMMON GATEWAY INTERFACE
 

Common Gateway Interface: Programming CGI Scripts – HTML Forms – Custom Database Query Scripts – Server Side Includes – Server security issues 

Unit-3
Teaching Hours:9
XML AND RICH INTERNET APPLICATIONS
 

XML- XSL, XSLT, DOM ,RSS, Client Technologies- Adobe Flash, Flex, Microsoft Silverlight.

Unit-4
Teaching Hours:9
SERVER SIDE PROGRAMMING-I
 

Server side Programming – PHP- Passing variables between pages, Using tables, Form elements. Active server pages – Java server pages

Unit-5
Teaching Hours:9
SERVER SIDE PROGRAMMING-II & APPLICATIONS
 

Java Servlets: Servlet container – Exceptions – Sessions and Session Tracking – Using Servlet context – Dynamic Content Generation – Servlet Chaining and Communications.

Simple applications – Internet Commerce – Database connectivity.

Text Books And Reference Books:

1.      Deitel, Deitel and Neito, “INTERNET and WORLD WIDE WEB – How to program”, Pearson education asia, 4th Edition , 2011

2.      Beginning PHP, Apache, MySql Web Development , Timothy, Elizabath, Jason, Wrox ,2012

Essential Reading / Recommended Reading

1.      Eric Ladd and Jim O’Donnell, et al, “USING HTML 4, XML, and JAVA1.2”, PHI publications, 2003.

2.      Jeffy Dwight, Michael Erwin and Robert Nikes “USING CGI”, PHI Publications, 1999

Evaluation Pattern

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

of 100 marks)

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

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

MTCS332E01 - MACHINE LEARNING (2018 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:3

Course Objectives/Course Description

 
  • To understand the concepts of machine learning
  • To appreciate supervised and unsupervised learning and their applications
  • To understand the theoretical and practical aspects of Probabilistic Graphical Models
  • To appreciate the concepts and algorithms of reinforcement learning
  • To learn aspects of computational learning theory

Learning Outcome

Upon Completion of the course, the students will be able to

  • Implement a neural network for an application of your choice using an available tool
  • Implement probabilistic discriminative and generative algorithms for an application of your choice and analyze the results
  • Implement typical clustering algorithms for different types of applications
  • Design and implement an HMM for a sequence model type of application
  • Identify applications suitable for different types of machine learning with suitable justification

Unit-1
Teaching Hours:9
INTRODUCTION
 

Machine Learning - Machine Learning Foundations –Overview – applications - Types of machine learning - basic concepts in machine learning Examples of Machine Learning -Applications – Linear Models for Regression - Linear Basis Function Models - The Bias-Variance Decomposition - Bayesian Linear Regression - Bayesian Model Comparison

Unit-2
Teaching Hours:9
SUPERVISED LEARNING
 

Linear Models for Classification - Discriminant Functions -Probabilistic Generative Models -

Probabilistic Discriminative Models - Bayesian Logistic Regression. Decision Trees – Classification Trees- Regression Trees - Pruning. Neural Networks -Feed-forward Network Functions - Error Backpropagation - Regularization - Mixture Density and Bayesian Neural Networks - Kernel Methods - Dual Representations - Radial Basis Function Networks. Ensemble methods- Bagging- Boosting.                            

Unit-3
Teaching Hours:9
UNSUPERVISED LEARNING
 

Clustering- K-means - EM - Mixtures of Gaussians - The EM Algorithm in General -Model selection for latent variable models - high-dimensional spaces -- The Curse of Dimensionality –Dimensionality Reduction - Factor analysis - Principal Component Analysis - Probabilistic PCA- Independent components analysis

Unit-4
Teaching Hours:9
PROBABILISTIC GRAPHICAL MODELS
 

Directed Graphical Models - Bayesian Networks - Exploiting Independence Properties – From Distributions to Graphs -Examples -Markov Random Fields - Inference in Graphical Models – Learning –Naive Bayes classifiers-Markov Models – Hidden Markov Models – Inference – Learning- Generalization – Undirected graphical models- Markov random fields- Conditional independence properties - Parameterization of MRFs - Examples - Learning - Conditional random fields (CRFs) - Structural SVMs

Unit-5
Teaching Hours:9
ADVANCED LEARNING
 

Sampling – Basic sampling methods – Monte Carlo. Reinforcement Learning - K-Armed Bandit - Elements - Model-Based Learning - Value Iteration - Policy Iteration. Temporal Difference Learning- Exploration Strategies- Deterministic and Non-deterministic Rewards and Actions- Eligibility Traces- Generalization- Partially Observable States- The Setting- Example. Semi - Supervised Learning. Computational Learning Theory - Mistake bound analysis, sample complexity analysis, VC dimension. Occam learning, accuracy and confidence boosting

Text Books And Reference Books:
  1. Christopher Bishop, “Pattern Recognition and Machine Learning” Springer, 2006
  2. Kevin P. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2012
  3. Ethem Alpaydin, “Introduction to Machine Learning”, Prentice Hall of India, 2005
  4. Hastie, Tibshirani, Friedman, “The Elements of Statistical Learning” (2nd ed)., Springer, 2008
  5. Stephen Marsland, “Machine Learning –An Algorithmic Perspective”, CRC Press, 2009
Essential Reading / Recommended Reading

1.      Tom Mitchell, "Machine Learning", McGraw-Hill, 1997.

Evaluation Pattern

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

of 100 marks)

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

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

MTCS333E01 - SOFTWARE PROJECT MANAGEMENT (2018 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:3

Course Objectives/Course Description

 
  • To provide students a systematic approach to initiate, plan, execute, control and close a software project.
  • To develop a good understanding of the nine project management areas, and  the role of a typical PM.
  • To equip students with understanding of the best practices, and techniques used in project management processes.
  • To enable students to gain a working knowledge of  ISO 9000 and CMMI, and process improvement techniques.

Learning Outcome

Explain and practice the process of project management and its application in delivering successful IT projects.

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

Interpret and use risk management analysis techniques that identify the factors that put a project at risk and to quantify the likely effect of risk on project timescales.

Identify the resources required for a project and to produce a work plan and resource Schedule.

Monitor and evaluate the progress of a project and to assess the risk of slippage, revising targets or counteract drift.

Distinguish between the different types of project and follow the stages needed to negotiate an appropriate contract.

Unit-1
Teaching Hours:9
Project Evaluation and Project Planning
 

Importance of Software Project Management, Activities Methodologies, Categorization of Software Projects , Setting objectives , Management Principles, Management Control, Project portfolio Management, Cost-benefit evaluation technology, Risk evaluation, Strategic program Management, Stepwise Project Planning.

 

Unit-2
Teaching Hours:9
Project Life Cycle and Effort
 

Software process and Process Models, Choice of Process models, mental delivery, Rapid Application development, Agile methods, Extreme Programming, SCRUM, Managing interactive processes, Basics of Software estimation, Effort and Cost estimation techniques, COSMIC Full function points, COCOMO II A Parametric Productivity Model, Staffing Pattern.

Unit-3
Teaching Hours:9
Activity Planning and Risk Management
 

Objectives of Activity planning, Project schedules, Activities, Sequencing and scheduling, Network Planning models, Forward Pass & Backward Pass techniques, Critical path (CRM) method, Risk identification, Assessment, Monitoring, PERT technique, Monte Carlo simulation, Resource Allocation, Creation of critical patterns, Cost schedules.

 

Unit-4
Teaching Hours:9
Project Management and Control
 

Framework for Management and control, Collection of data Project termination, Visualizing progress, Cost monitoring, Earned Value Analysis-Project tracking, Change control-Software Configuration Management, Managing contracts, Contract Management.

Unit-5
Teaching Hours:9
Staffing In Software Projects
 

Managing people, Organizational behaviour, Best methods of staff selection, Motivation, The Oldham - Hackman job characteristic model, Ethical and Programmed concerns, Working in teams, Decision making, Team structures, Virtual teams, Communications genres , Communication plans.

Text Books And Reference Books:
  1. Managing the Software Process by Watts S. Humphrey, published by Pearson Education 2012.
  2. Software Project Management, by Walker Royce, published by Pearson Education 2010.
Essential Reading / Recommended Reading
  1. An Introduction to the Team Software Process, by Watts S. Humphrey, Pearson Education 2012.
  2. A Discipline to Software Engineering by Watts S. Humphrey Pearson Education 2016.

      3.   Software Project Management in Practice by Pankaj Jalote, Pearson Education 2010.

      4.   Software Project Management Readings and Cases by Chris Kemerer 2010.

 

  • ISO standard. http://www.iso.ch
  • SEI.CMMI Tutorial, www.sei.cmu.edu/cmmi/publications/stc.presentations/tutorial.html
Evaluation Pattern

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

of 100 marks)

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

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

MTCS371 - PROJECT WORK (PHASE I) (2018 Batch)

Total Teaching Hours for Semester:180
No of Lecture Hours/Week:12
Max Marks:100
Credits:3

Course Objectives/Course Description

 

During this seminar session, each student is expected to preparand present a topic on engineering/ technology, itis 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.

Learning Outcome

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

The course will broadly cover the following aspects:

  • Teachingskills
  • Laboratoryskills andother professional activities
  • Researcmethodology

Unit-1
Teaching Hours:45
UNIT-1
 
COURSE NOTICES

 

 

 

 

 

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

 

MAKEUPPOLICY All students are required to attend all the lectureand presentations in the panel.Participation and cooperation will also be taken intaccount in the finaevaluation. Requests for makeup should normallbavoided. However,in genuine cases,panel will decide action on a case bcase basis.

 

NOTE:Seminar shall be presented in the department in presencof a committee (Batch of Teachers)constituted by HOD.The seminar markare to be awardeby thcommittee. Students shall submit the seminareport in the prescribeStandard 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

§  Continuous Internal Assessment:100 Marks

¨      Presentation assessed by Panel Members

¨      Guide

¨      Mid semester Project Report

MTCS373 - INTERNSHIP (2018 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
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:

 

  • Get an inside view of an industry and organization/company
  • Gain valuable skills and knowledge
  • Make professional connections and enhance student's network
  • Get experience in a field to allow the student  to make a career transition

Learning Outcome

  • Get an inside view of an industry and organization/company
  • Gain valuable skills and knowledge
  • Make professional connections and enhance student's network
  • 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

MTCS471 - PROJECT WORK (PHASE-II) AND DISSERTATION (2018 Batch)

Total Teaching Hours for Semester:300
No of Lecture Hours/Week:20
Max Marks:300
Credits:9

Course Objectives/Course Description

 

Objective of this course is to encourage students to do research oriented project.

Learning Outcome

The students are expected to comeout with the product implemetation with dissertation details.

Unit-1
Teaching Hours:120
Assessment of Project Work(Phase II) and Dissertation