Syllabus for
Master of Technology (Computer Science and Engineering)
Academic Year  (2022)

The 2 year Post graduate program M.Tech in Computer Science and Engineering.started in 2009 . The course was started mainly to cater to the increasing demand for higher studies in the country. A growing intake with students from across the nation shows the popularity of the program. The Department strives to give skills essential to practicing engineering professionals; it is also an objective to provide experience in leadership, management, planning, and organization. The department understands its role in developing and evaluating methods that encourage students to continue to learn after leaving the university. We believe that the student opportunities and experiences should lead to an appreciation of the holistic development of individual. We also try to pass to our students our passion for what we do, and to have the students comprehend that we also desire to continue to learn.

Programme Outcome/Programme Learning Goals/Programme Learning Outcome:

PO2: Analyze complex engineering problems critically, apply independent judgment for synthesizing information to make intellectual and/or creative advances for conducting research in a wider theoretical, practical and policy context.

PO3: Think laterally and originally, conceptualize and solve engineering problems, evaluate a wide range of potential solutions for those problems and arrive at feasible, optimal solutions after considering public health and safety, cultural, societal and environmental factors in the core areas of expertise.

PO4: Develop and design real time projects more efficiently using math, statistics and analytics tools to deliver quality software solutions.

PO5: Analyze and apply the needs of computing in the society to promote novel and sustainable research ideas.

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)

Course description:

The course is designed to equip the necessary awareness and command on the use of English language in writing a research paper starting from how to compile an appropriate title, language to use at different stages of a paper to make it effective and meaningful. 

Course objectives:

• Understand that how to improve your writing skills and level of readability
• Learn about what to write in each section.
• Understand the skills needed when writing a Title and ensure the good quality of paper at very first-time submission

          -       Useful phrases to ensure the quality of the paper

Adrian Wallwork, English for Writing Research Papers, Springer New York Dordrecht Heidelberg London, 2011

Highman N (1998), Handbook of Writing for the Mathematical Sciences, SIAM. Highman’sbook.

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. 

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. 

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. 

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

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.

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.

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

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

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.

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

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

Components of the CIA

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

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

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

     Attendance                                                                            : 05 marks

     Total                                                                                       : 50 marks

The students will learn the fundamental concepts of Image Processing.

The students will learn image enhancement techniques in the spatial & frequency domains

The students will learn the restoration & compression models.

Help the students with segmentation and representation techniques for the region of interest.

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

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

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

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

Representation schemes- Boundary descriptors- Regional descriptors - Relational Descriptors

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

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

·         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)

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

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

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.

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

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

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

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.

1.       1. Tom White, "Hadoop: The Definitive Guide", 4^th  Edition, O'Reilley, 2012.

2. Eric Sammer, "Hadoop Operations",1^st Edition, O'Reilley, 2012.

1.    1. VigneshPrajapati, Big data analytics with R and Hadoop, SPD 2013.

2.    2. E. Capriolo, D. Wampler, and J. Rutherglen, "Programming Hive", O'Reilley, 2012.

3.   3.  Lars George, "HBase: The Definitive Guide", O'Reilley, 2011.

4. Alan Gates, "Programming Pig", O'Reilley, 2011.

End Semester Examination: 50 marks

● 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.

1. Implementation of Sorting Techniques like Bubble Sort, Selection Sort, Insertion Sort, Merge Sort, Quick Sort and Heap Sort and Compare their performances.
2. Implementation of Linear Search and Binary Search Algorithms and Compare their performances.
3. Mini Project Details: Selection of Topic and Research about the Selected Topic.

1. Implementation of Shortest Path Algorithm (Bellman Ford).
2. Implementation of Single Source Shortest Path in a DAG using Dijkstra's Algorithm.
3. Mini Project Details: Choosing the right Dataset for the chosen Title/Topic and Exploratory Data Analysis (EDA).

1. Implement Chinese Remainder Theorem Algorithm.
2. Implement RSA Public-Key Cryptosystem.
3. Mini Project Details: Implementation (2 Phases) - Phase 1

1. Design, develop, and write a program to solve String Matching Problem using naïve approach, the KMP and Robin Karp algorithm. Compare their performances.
2. Mini Project Details: Implementation (2 Phases) - Phase 2

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. Implement any Las Vegas Algorithm used for Randomization.
3. Mini Project Details: Performance Analysis and Report Submission

Records: 05 marks

Mid semester examination: 10 marks

Class work: 10 marks

Total: 50 marks

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

1. Display of Grayscale Images, 

1 . Implementation of various transforms and their use.

2. Implementation of Histogram Equalization, Non-linear Filtering.

1.Implementation of Edge detection using Operators, 2-D DFT and DCT.

2. Implementation of Filtering in frequency domain

1. Implementation of Segmentation using various transform.

1. Implementation of various Morphological algorithms.

2. Implementation of IEEE/ACM paper in the Digital image processing area

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

            Records                                                                          : 05 marks

            Mid semester examination                                          : 10 marks

            Class work                                                                     : 10 marks

            Total                                                                                : 50 marks

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.

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. 

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. 

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. 

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.

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.

T2. Garg, B.L., Karadia, R., Agarwal, F. and Agarwal, “An introduction to Research Methodology”, RBSA Publishers, 2002.

T3. Day, R.A., “How to Write and Publish a Scientific Paper”, Cambridge University Press, 1992.

R2. Sarah J Tracy, “Qualitative Research Methods” Wiley Balckwell- John wiley & sons, 1/e, 2013.

R3. James Hartley, “Academic Writing and Publishing”, Routledge Pub., 2008.

End Semester Examination - 50%

To achieve overall health of body and mind

To overcome stress

Definitions of Eight parts of yog. ( Ashtanga )

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

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

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.

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.

·      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.

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.    

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.

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.

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.

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).

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.

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

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

Components of the CIA

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

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

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

                   Attendance                                                                            : 05 marks

              Total                                                                                       : 50 marks

Objectives of this course are:

·        Able to apply fundamental algorithmic ideas to process data.

·        Learn to apply hypotheses and data into actionable predictions.

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

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

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.            

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

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

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

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

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

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

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

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

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

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

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

Components of the CIA

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

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

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

       Attendance                                                                                                     : 05 marks

                   Total                                                                                                                  : 50 marks

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.  

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. 

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.

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.

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.

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.

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.

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

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

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

Components of the CIA

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

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

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

  Attendance                                                                            : 05 marks

  Total                                                                                       : 50 marks

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.

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.

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.

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

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.

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

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.

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."

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

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

Components of the CIA

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

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

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

     Attendance                                                                            : 05 marks

     Total                                                                                       : 50 marks

• 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.

• 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.

            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.

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.

• 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

            End semester practical examination                                     : 50 marks

            Records                                                                                   : 05 marks

            Mid semester examination                                                    : 25 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.

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.

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

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

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

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

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.

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

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

 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)

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: 

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.

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

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

Project Work 

▪          Continuous Internal Assessment:100 Marks

♦        Presentation assessed by Panel Members

♦        Guide

♦        Mid semester Project Report

End semester Examination :100 Marks

Presentation assessed by Panel Members

♦        Guide

♦        End semester Project Report

This course aims at making the students get an understanding of the compression techniques available for multimedia applications and also get an understanding of the encryption that can be implemented along with the compression.

Special features of Multimedia – Graphics and Image Data Representations – Fundamental Concepts in Video and Digital Audio – Storage requirements for multimedia applications -Need for Compression - Taxonomy of compression techniques – Overview of source coding

Compaction techniques – Huffmann coding – Adaptive Huffmann Coding – Arithmatic coding – Shannon-Fano coding – Dictionary techniques – LZW family algorithms

Video compression techniques and standards – MPEG Video Coding I: MPEG – 1 and 2 – MPEG Video Coding II: MPEG – 4 and 7 – Motion estimation and compensation techniques – H.261 Standard

Introduction: Services, Mechanisms and Attacks, OSI security Architecture, Model for network Security; Classical Encryption Techniques:Symmetric Cipher Model, Substitution Techniques, Transposition Techniques, Rotor Machines, Stegnography;

Block Ciphers and Data Encryption Standard: Simplified DES, Block Cipher Principles, Data Encryption Standard, Strength of DES, Differential and Linear Crypt Analysis, Block Cipher Design Principles, Block Cipher Modes of Operation

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

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

 Components of the CIA

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

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

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

    Attendance                                                                            : 05 marks

            Total                                                                                       : 50 marks

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

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

Project Work 

▪ Continuous Internal Assessment:100 Marks

♦ Presentation assessed by Panel Members

♦ Assessed by Guide

♦ Mid Semester Project Report

▪ End Semester Examination:100 Marks

♦ Viva Voce

♦ Demonstration

♦ Project Report

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

♦ Internal Review : 50 Marks

♦ External review : 50 Marks