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1 Semester - 2021 - Batch | Course Code |
Course |
Type |
Hours Per Week |
Credits |
Marks |
MTAC121 | ENGLISH FOR RESEARCH PAPER WRITING | Skill Enhancement Courses | 1 | 2 | 0 |
MTCS112 | PROFESSIONAL PRACTICE - I | Skill Enhancement Courses | 2 | 1 | 50 |
MTCS133 | ADVANCED ALGORITHMS | Core Courses | 3 | 3 | 100 |
MTCS135 | ADVANCED DIGITAL IMAGE PROCESSING | Core Courses | 3 | 3 | 100 |
MTCS141E03 | SOFTWARE PROJECT MANAGEMENT | Core Courses | 3 | 3 | 100 |
MTCS142E01 | BIG DATA ANALYTICS | Core Courses | 3 | 3 | 100 |
MTCS151 | ADVANCED ALGORITHMS LAB | Core Courses | 4 | 2 | 50 |
MTCS152 | ADVANCED DIGITAL IMAGE PROCESSING LAB | Core Courses | 4 | 2 | 50 |
MTMC125 | RESEARCH METHODOLOGY AND IPR | Core Courses | 3 | 3 | 100 |
2 Semester - 2021 - Batch | Course Code |
Course |
Type |
Hours Per Week |
Credits |
Marks |
MTAC226 | STRESS MANAGEMENT BY YOGA | Skill Enhancement Courses | 2 | 0 | 0 |
MTCS213 | PROFESSIONAL PRACTICE - II | Skill Enhancement Courses | 2 | 1 | 50 |
MTCS231 | COMPUTER COMMUNICATION NETWORKS | Core Courses | 3 | 3 | 100 |
MTCS232 | DATA SCIENCE | Core Courses | 3 | 3 | 100 |
MTCS243E01 | CLOUD COMPUTING | Discipline Specific Elective Courses | 3 | 3 | 100 |
MTCS244E01 | INTERNET OF THINGS | Discipline Specific Elective Courses | 3 | 3 | 100 |
MTCS251 | NETWORKING LAB | Core Courses | 4 | 2 | 50 |
MTCS252 | DATA SCIENCE LAB | Core Courses | 4 | 2 | 50 |
3 Semester - 2020 - Batch | Course Code |
Course |
Type |
Hours Per Week |
Credits |
Marks |
MTCS345E06 | MULTIMEDIA SYSTEMS | - | 3 | 3 | 100 |
MTCS381 | INTERNSHIP | - | 4 | 2 | 50 |
MTCS382 | DISSERTATION PHASE - I | - | 20 | 10 | 200 |
MTEC361 | ADVANCED COMMUNICATION NETWORKS | - | 3 | 3 | 100 |
4 Semester - 2020 - Batch | Course Code |
Course |
Type |
Hours Per Week |
Credits |
Marks |
MTCS483 | DISSERTATION PHASE-II | - | 32 | 16 | 200 |
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Introduction to Program: | |
The 2 year Post graduate program M.Tech in Computer Science and Engineering.started in 2011 . The course was started mainly to cater to the increasing demand for higher studies in the country. A growing intake with students from across the nation shows the popularity of the program. The Department strives to give skills essential to practicing engineering professionals; it is also an objective to provide experience in leadership, management, planning, and organization. The department understands its role in developing and evaluating methods that encourage students to continue to learn after leaving the university. We believe that the student opportunities and experiences should lead to an appreciation of the holistic development of individual. We also try to pass to our students our passion for what we do, and to have the students comprehend that we also desire to continue to learn. | |
Programme Outcome/Programme Learning Goals/Programme Learning Outcome: PO4: An ability to conduct experiments to investigate problems based on changing requirements, analyze and interpret results.PO5: An ability to create, select, adapt appropriate techniques and use of the modern computational tools, techniques and skills, and best of engineering practices. PO6: To understand the impact of contextual knowledge on social aspects and cultural issues. PO7: An ability to understand contemporary issues related to social & environmental context for sustainable development of engineering solutions. PO8: An ability to understand professional & ethical responsibility to contribute for societal and national needs. PO9: An ability to function and coordinate effectively as an individual, as a member or leader in diverse, multicultural& multidisciplinary teams PO10: An ability to communicate effectively. PO11: An understanding of computer science and engineering & management principles to manage software projects. PO12: A recognition and realization of the need for, and an ability to engage in lifelong learning | |
Assesment Pattern | |
Components of the CIA CIA I : Mid Semester Examination (Theory) : 25 marks CIA II : Assignments : 10 marks CIA III: Quizzes/Seminar/Case Studies/Project Work : 10 marks Attendance : 05 marks Total : 50 marks For subjects having practical as part of the subject End semester practical examination : 25 marks Records : 05 marks Mid semester examination : 10 marks Class work : 10 marks Total : 50 marks | |
Examination And Assesments | |
Assessment of each paper · Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks) · End Semester Examination(ESE) : 50% (50 marks out of 100 marks) |
MTAC121 - ENGLISH FOR RESEARCH PAPER WRITING (2021 Batch) | |
Total Teaching Hours for Semester:15 |
No of Lecture Hours/Week:1 |
Max Marks:0 |
Credits:2 |
Course Objectives/Course Description |
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Students will be able to:
Understand that how to improve your writing skills and level of readability
· Learn about what to write in each section Understand the skills needed when writing a Title and ensure the good quality of paper at very first-time submission |
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Course Outcome |
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C01: Write research paper which will have higher level of readability C02: Demonstrate what to write in each section C03: To write appropriate Title for the research paper CO4: Write concise abstract C05: Write conclusions clearly explaining the outcome of the research work |
Unit-1 |
Teaching Hours:3 |
Planning and Preparation
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Word Order, Breaking up long sentences, Structuring Paragraphs and Sentences, Being Concise and Removing Redundancy, Avoiding Ambiguity and Vagueness | |
Unit-2 |
Teaching Hours:3 |
Clarifying Who Did What
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Highlighting Your Findings, Hedging and Criticising, Paraphrasing and Plagiarism, Sections of a Paper, Abstracts. Introduction | |
Unit-3 |
Teaching Hours:3 |
Review of the Literature
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Methods, Results, Discussion, Conclusions, The Final Check | |
Unit-4 |
Teaching Hours:3 |
Skills
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Skills are needed when writing a Title, key skills are needed when writing an Abstract, key skills are needed when writing an Introduction, skills needed when writing a Review of the Literature, | |
Unit-5 |
Teaching Hours:3 |
Skills for Writing the Methods
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Skills needed when writing the Results, skills are needed when writing the Discussion, skills are needed when writing the Conclusions useful phrases, how to ensure paper is as good as it could possibly be the first- time submission | |
Text Books And Reference Books: Goldbort R (2006) Writing for Science, Yale University Press (available on Google Books) Day R (2006) How to Write and Publish a Scientific Paper, Cambridge University Press | |
Essential Reading / Recommended Reading Highman N (1998), Handbook of Writing for the Mathematical Sciences, SIAM. Highman’sbook . | |
Evaluation Pattern It is Audit Course | |
MTCS112 - PROFESSIONAL PRACTICE - I (2021 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:2 |
Max Marks:50 |
Credits:1 |
Course Objectives/Course Description |
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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. |
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Course Outcome |
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CO 1: Demonstrate the concepts of Teaching through Black board and ICT techniques CO 2: To apply and analyze the Newer Research directions in areas of Computer science and Engineering |
Unit-1 |
Teaching Hours:30 |
na
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na | |
Text Books And Reference Books: -https://www.scholarify.in/teaching-support-system | |
Essential Reading / Recommended Reading -https://testbook.com/learn/ict-based-teaching/ | |
Evaluation Pattern Head of the Department will assign a suitable instructor/faculty member to each student. Students and faculty members covering a broad area will be grouped in a panel consisting of 4-5 students and 4-5 faculty members. Within one week after registration, the student should plan the details of the topics of lectures, laboratory experiments, developmental activities and broad topic of research etc in consultation with the assigned instructor/faculty. The student has to submit two copies of the written outline of the total work to the instructor within one week.
In a particular discipline, Instructors belonging to the broad areas will form the panel and will nominate one of them as the panel coordinator. The coordinator together with the instructors will draw a complete plan of lectures to be delivered by all students in a semester. Each student will present 3- 4 lectures, which will be attended by all other students and Instructors. These lectures will be evenly distributed over the entire semester. The coordinator will announce the schedule for the entire semester and fix suitable meeting time in the week.
Each student will also prepare one presentation about his findings on the broad topic of research. The final report has to be submitted in the form of a complete research proposal. The References and the bibliography should be cited in a standard format. The research proposal should contain a) Topic of research b) Background and current status of the research work in the area as evident from the literature review c) Scope of the proposed work d) Methodology e) References and bibliography.
A report covering laboratory experiments, developmental activities and code of professional conduct and ethics in discipline has to be submitted by individual student.
The panel will jointly evaluate all the components of the course throughout the semester and the mid semester grade will be announced by the respective instructor to his student. A comprehensive viva/test will be conducted at the end of the semester jointly, wherever feasible by all the panels in a particular academic discipline/department, in which integration of knowledge attained through various courses will be tested and evaluated. Wherever necessary and feasible, the panel coordinator in consultation with the concerned group may also seek participation of the faculty members from other groups in lectures and comprehensive viva. Mid semester report and final evaluation report should be submitted in the 9th week and 15th week of the semester respectively. These should contain the following sections: ● Section (A): Lecture notes along with two question papers each of 180 min duration, one quiz paper (CIA-I) of 120 min duration on the topics of lectures. The question paper should test concepts, analytical abilities and grasp of the subject. Solutions of questions also should be provided. All these will constitute lecture material. ● Section (B): Laboratory experiments reports and professional work report. Section (C): Research proposal with detailed references and bibliography in a standard format. Wherever necessary, respective Head of the Departments could be approached by Instructors/panel coordinators for smooth operation of the course. Special lectures dealing with professional ethics in the discipline may also be arranged by the group from time to time. | |
MTCS133 - ADVANCED ALGORITHMS (2021 Batch) | |
Total Teaching Hours for Semester:45 |
No of Lecture Hours/Week:3 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
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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 |
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Course Outcome |
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CO1: Summarize the properties of advanced data structures. CO2: Design algorithms and employ appropriate advanced data structures for solving computing problems efficiently. CO3: Analyze and compare the efficiency of algorithms. CO4: Design and implement efficient algorithms for solving computing problems in a high-level object-oriented programming language. CO5: Compare, contrast, and apply algorithmic trade-offs : time vs. space, deterministic vs. randomized, and exact vs. approximate |
Unit-1 |
Teaching Hours:9 |
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INTRODUCTION
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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 |
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GRAPH ALGORITHMS AND POLYNOMIALS
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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 |
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NUMBER THEORETIC ALGORITHMS
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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 |
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STRING MATCHING ALGORITHMS
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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 |
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PROBABILISTIC ALGORITHMS
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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:
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Essential Reading / Recommended Reading
3. Mark Allen Weiss, “Data Structures and Algorithm Analysis in Java”, 3rd edition,Pearson Education, 2012. 4. Aho, Hopcroft, Ullman, “Data Structures and Algorithms”, Pearson Education, 2009. | ||
Evaluation Pattern Assessment of each paper · Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks) · End Semester Examination(ESE) : 50% (50 marks out of 100 marks) Components of the CIA
CIA I : Quizzes/Seminar/Case Studies/Project Work /Assignments : 10 marks
CIA II : Mid Semester Examination (Theory) : 25 marks
CIA III : Quizzes/Seminar/Case Studies/Project Work /Assignments : 10 marks
Attendance : 05 marks
Total : 50 marks
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MTCS135 - ADVANCED DIGITAL IMAGE PROCESSING (2021 Batch) | ||
Total Teaching Hours for Semester:45 |
No of Lecture Hours/Week:3 |
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Max Marks:100 |
Credits:3 |
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Course Objectives/Course Description |
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Course Outcome |
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CO1: Apply the image fundamentals and mathematical transformations necessary for image processing CO2: Analyze image enhancement techniques in Spatial &frequency domain CO3: Apply restoration models and compression models for image processing CO4: Analyze and synthesis image using segmentation and representation techniques CO5: Analyze and extract potential features of interest from the image CO6: Design object recognition systems using pattern recognition techniques
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Unit-1 |
Teaching Hours:9 |
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DIGITAL IMAGE FUNDAMENTALS
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Image formation, Image transforms – Fourier transforms, Walsh, Hadamard, Discrete cosine, Hotelling transforms | ||
Unit-2 |
Teaching Hours:9 |
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IMAGE ENHANCEMENT & RESTORATION
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Histogram modification techniques - Image smoothening - Image Sharpening - Image Restoration - Degradation Model – Noise models - Spatial filtering – Frequency domain filtering | ||
Unit-3 |
Teaching Hours:9 |
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IMAGE COMPRESSION & SEGMENTATION
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Compression Models - Elements of information theory - Error free Compression -Image segmentation –Detection of discontinuities – Region based segmentation – Morphology | ||
Unit-4 |
Teaching Hours:9 |
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REPRESENTATION AND DESCRIPTION
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Representation schemes- Boundary descriptors- Regional descriptors - Relational Descriptors | ||
Unit-5 |
Teaching Hours:9 |
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OBJECT RECOGNITION AND INTERPRETATION
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Patterns and pattern classes - Decision-Theoretic methods - Structural methods-Case studies | ||
Text Books And Reference Books:
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Essential Reading / Recommended Reading
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Evaluation Pattern Assessment of each paper · Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks) · End Semester Examination(ESE) : 50% (50 marks out of 100 marks) Components of the CIA CIA II : Mid Semester Examination (Theory) : 25 marks CIA I : Assignments : 10 marks CIA III : Quizzes/Seminar/Case Studies/Project Work : 10 marks Attendance : 05 marks Total : 50 marks | ||
MTCS141E03 - SOFTWARE PROJECT MANAGEMENT (2021 Batch) | ||
Total Teaching Hours for Semester:45 |
No of Lecture Hours/Week:3 |
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Max Marks:100 |
Credits:3 |
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Course Objectives/Course Description |
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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.
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Course Outcome |
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CO1: Understanding the specific roles within a Conventional Software Management organization as related to the project. CO2: Describe and determine the purpose and importance of project management from the perspectives of planning, cost, tracking and completion of project. CO3: Evaluate a project to develop the scope of work, provide accurate cost estimates and to plan the various activities. CO4: Implement a project to manage project schedule, expenses and resources with the application of suitable protect management tools. CO5: Identify organization structures, project structures, resources required for a project and to produce a work plan and resource Schedule. |
Unit-1 |
Teaching Hours:9 |
Unit-1
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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
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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
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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
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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
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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:
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Essential Reading / Recommended Reading
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Evaluation Pattern Assessment of each paper · Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks) · End Semester Examination(ESE) : 50% (50 marks out of 100 marks) Components of the CIA CIA I : Mid Semester Examination (Theory) : 25 marks CIA II : Assignments : 10 marks CIA III : Quizzes/Seminar/Case Studies/Project Work : 10 marks Attendance : 05 marks Total : 50 marks | |
MTCS142E01 - BIG DATA ANALYTICS (2021 Batch) | |
Total Teaching Hours for Semester:45 |
No of Lecture Hours/Week:3 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
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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 |
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Course Outcome |
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CO1: Describe big data and use cases from selected business domains CO2: Discuss open source technologies CO3: Explain NoSQL big data management CO4: Discuss basics of Hadoop and HDFS CO5:
CO5: Discuss map-reduce analytics using Hadoop and Use of Hadoop related tools such as HBase, Cassandra, Pig, and Hive for big data Analytics
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Unit-1 |
Teaching Hours:9 |
UNDERSTANDING BIG DATA
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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
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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
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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
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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
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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:
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Essential Reading / Recommended Reading
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Evaluation Pattern Assessment of each paper · Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks) Components of the CIA CIA I : Quizzes/Seminar/Case Studies/Project Work /Assignments : 10 marks CIA II : Mid Semester Examination (Theory) : 25 marks CIA III : Quizzes/Seminar/Case Studies/Project Work /Assignments : 10 marks Attendance : 05 marks Total : 50 marks · End Semester Examination(ESE) : 50% (50 marks out of 100 marks)
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MTCS151 - ADVANCED ALGORITHMS LAB (2021 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
Max Marks:50 |
Credits:2 |
Course Objectives/Course Description |
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● To increase the knowledge of advanced paradigms of algorithm design. |
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Course Outcome |
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CO1: Effective use of mathematical techniques to construct robust algorithms. CO2:
· Effective use of mathematical techniques to construct robust algorithms.
Assess and to make critical judgment on the choices of algorithms for modern computer systems.
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Unit-1 |
Teaching Hours:6 |
List of Experiments
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Unit-2 |
Teaching Hours:6 |
Programs on Data Structures and Algorithms
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Unit-3 |
Teaching Hours:6 |
Programs on Cloud Computing
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Unit-4 |
Teaching Hours:6 |
Programs on Cloud Computing
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Unit-5 |
Teaching Hours:6 |
Programs on Advanced Computer Architecture
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Text Books And Reference Books:
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Essential Reading / Recommended Reading
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Evaluation Pattern End semester practical examination : 25 marks Records : 05 marks Mid semester examination : 10 marks Class work : 10 marks Total : 50 marks | |
MTCS152 - ADVANCED DIGITAL IMAGE PROCESSING LAB (2021 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
Max Marks:50 |
Credits:2 |
Course Objectives/Course Description |
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Students are expected to implement the image processing algorithms and techniques to solve the real life problems. |
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Course Outcome |
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CO1: Apply principles and techniques of digital image processing in applications related to digital imaging system design and analysis CO2: Analyze and implement image processing algorithms CO3: Understand software tools for processing digital images CO4: Experiment image processing problems and techniques CO5: Examine image processing algorithms on computers CO6: Demonstrate algorithms to solve image processing problems |
Unit-1 |
Teaching Hours:12 |
unit 1
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Unit-2 |
Teaching Hours:12 |
unit 2
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Unit-3 |
Teaching Hours:12 |
unit 3
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Unit-4 |
Teaching Hours:12 |
unit 4
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Unit-5 |
Teaching Hours:12 |
unit 5
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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
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Essential Reading / Recommended Reading
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Evaluation Pattern End semester practical examination : 25 marks Records : 05 marks Mid semester examination : 10 marks Class work : 10 marks Total : 50 marks | |
MTMC125 - RESEARCH METHODOLOGY AND IPR (2021 Batch) | |
Total Teaching Hours for Semester:45 |
No of Lecture Hours/Week:3 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
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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. |
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Course Outcome |
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CO1: Explain the principles and concepts of research methodology. CO2: Understand the different methods of data collection CO3: Apply appropriate method of data collection and analyze using statistical/software tools. CO4: Present research output in a structured report as per the technical and ethical standards. CO5: Create research design for a given engineering and management problem /situation. |
Unit-1 |
Teaching Hours:9 |
Introduction to Research Methodology
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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.
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Unit-2 |
Teaching Hours:9 |
Literature Review and Research Problem Identification
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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.
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Unit-3 |
Teaching Hours:9 |
Data Collection & Analysis
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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
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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
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Unit-5 |
Teaching Hours:9 |
IPR and Research Writing
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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:
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Essential Reading / Recommended Reading 1. Bjorn Gustavii, “How to Write and Illustrate Scientific Papers “ Cambridge University Press, 2/e. 2. Sarah J Tracy, “Qualitative Research Methods” Wiley Balckwell- John wiley & sons, 1/e, 2013. 3. .James Hartley, “Academic Writing and Publishing”, Routledge Pub., 2008. | |
Evaluation Pattern Assessment of each paper · Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks) · End Semester Examination(ESE) : 50% (50 marks out of 100 marks) Components of the CIA CIA I : Quizzes/Seminar/Case Studies/Project Work /Assignments : 10 marks CIA II : Mid Semester Examination (Theory) : 25 marks CIA III : Quizzes/Seminar/Case Studies/Project Work /Assignments : 10 marks Attendance : 05 marks Total : 50 marks | |
MTAC226 - STRESS MANAGEMENT BY YOGA (2021 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:2 |
Max Marks:0 |
Credits:0 |
Course Objectives/Course Description |
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To achieve overall health of body and mind To overcome stress |
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Course Outcome |
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CO1: Explain the effectiveness of stress management techniques through Yoga. CO2: Apply various Yoga Techniques. CO3: Assess and analyze the symptoms, causes and effects of personal and academic stressors in order to implement appropriate stress management techniques. |
Unit-1 |
Teaching Hours:8 |
Unit-1
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Definitions of Eight parts of yog. ( Ashtanga ) | |
Unit-2 |
Teaching Hours:8 |
unit-2
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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
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Asan and Pranayam i) Various yog poses and their benefits for mind & body ii)Regularization of breathing techniques and its effects-Types of pranayam | |
Text Books And Reference Books: Yogic Asanas for Group Tarining-Part-I” :Janardan Swami YogabhyasiMandal, Nagpur | |
Essential Reading / Recommended Reading “Rajayoga or conquering the Internal Nature” by Swami Vivekananda, AdvaitaAshrama (Publication Department), Kolkata | |
Evaluation Pattern NA | |
MTCS213 - PROFESSIONAL PRACTICE - II (2021 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:2 |
Max Marks:50 |
Credits:1 |
Course Objectives/Course Description |
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Duringtheseminarsessioneachstudentisexpectedtoprepare and presentatopicon engineering/ technology, itis designed to:
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Course Outcome |
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CO 1: To implement a case study in the area of study CO 2: To analyze the results obtained by implementation of case study in the area of study. |
Unit-1 |
Teaching Hours:30 |
COURSE NOTICES
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Notices pertaining to this course will be displayed on the respective departmental notice boards by the panel coordinator/instructor.Students may also check the exam notice board for notices issued by the exam division.
MAKEUPPOLICY: All students are required to attend all the lectures and presentations in the panel. Participation and cooperation will also be taken into account in the final evaluation. Requests for makeup should normally be avoided. However,in genuine cases,panel will decide action on a case by case basis.
NOTE:Seminar shall be presented in the department in presence of a committee (Batch of Teachers)constituted by HOD.The seminar marks are to be awarded by the committee. Students shall submit the seminar report in the prescribed Standard format. | |
Text Books And Reference Books: Selected domain related text book will be sugessted. | |
Essential Reading / Recommended Reading Research papers for the selected domain | |
Evaluation Pattern - | |
MTCS231 - COMPUTER COMMUNICATION NETWORKS (2021 Batch) | |
Total Teaching Hours for Semester:45 |
No of Lecture Hours/Week:3 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
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· 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. |
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Course Outcome |
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CO1: Recognize the basic requirements of building of network and layering of protocols. CO2: Distinguish the concept of internetworking and routing through internet protocol addressing. CO3: Discuss the role of different protocols in internetworking. CO4: Examine the security issues and congestion control in the networks CO5: Determine the features and operations of various application layer protocols. |
Unit-1 |
Teaching Hours:9 |
INTRODUCTION
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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.
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Unit-2 |
Teaching Hours:9 |
INTERNETWORKING- I
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Switching and Bridging, Datagram’s, Virtual Circuit Switching, Source Routing, Bridges and LAN Switches, Basic Internetworking (IP), Service Model, Global Addresses, Datagram Forwarding in IP, subnetting and classless addressing, Address Translation(ARP), Host Configuration(DHCP), Error Reporting(ICMP), Virtual Networks and Tunnels. | |
Unit-3 |
Teaching Hours:9 |
INTERNETWORKING- II
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Network as a Graph, Distance Vector (RIP), Link State (OSPF), Metrics, The Global Internet, Routing Areas, Routing among Autonomous systems (BGP), IP Version 6(IPv6), Mobility and Mobile IP. | |
Unit-4 |
Teaching Hours:9 |
NETWORK SECURITY
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Simple Demultiplexer (UDP), Reliable Byte Stream(TCP), End-to-End Issues, Segment Format, Connecting Establishment and Termination, Sliding Window Revisited, Triggering Transmission, Adaptive Retransmission, Record Boundaries, TCP Extensions, Queuing Disciplines, FIFO, Fair Queuing, TCP Congestion Control, Additive Increase/Multiplicative Decrease, Slow Start, Fast Retransmit and Fast Recovery. | |
Unit-5 |
Teaching Hours:9 |
APPLICATIONS
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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.
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Essential Reading / Recommended Reading | |
Evaluation Pattern Assessment of each paper · Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks) · End Semester Examination(ESE) : 50% (50 marks out of 100 marks) Components of the CIA
CIA I : Quizzes/Seminar/Case Studies/Project Work /Assignments : 10 marks
CIA II : Mid Semester Examination (Theory) : 25 marks
CIA III : Quizzes/Seminar/Case Studies/Project Work /Assignments: 10 marks
Attendance : 05 marks
Total : 50 marks
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MTCS232 - DATA SCIENCE (2021 Batch) | |
Total Teaching Hours for Semester:45 |
No of Lecture Hours/Week:3 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
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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. |
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Course Outcome |
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CO1: Understand the foundations of data processing CO2: Apply the clustering and Classification methods for modelling the data CO3: Analysis of Statistical models and data distributions using Python Programming. CO4: Analysis of distributed file system and Data Processing using Spark CO5: Evaluating the results of data science experiment using Power BI. |
Unit-1 |
Teaching Hours:9 |
INTRODUCTION AND THE DATA SCIENCE
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Data science process – roles, stages in data science project – working with data from files –relational andNon-Relational databases – exploring data – managing data – cleaning and sampling for modeling and validation – Data preprocessing-Statistics for Data Science-Data Distributions | |
Unit-2 |
Teaching Hours:9 |
MODELING METHODS
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Choosing and evaluating models – mapping problems to machine learning, evaluating clustering models, validating models – cluster analysis – K-means algorithm, Naïve Bayes – Memorization Methods – Linear and logistic regression – unsupervised methods. | |
Unit-3 |
Teaching Hours:9 |
ANALYTICS WITH PYTHON
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Data Analysis with Numpy andPandas – Visualization with SeabornMatplotlib, Plotly and Cufflinks – Scikit-learn –Regression, KNN, PCA and SVM in Python– Recommender systems – NLP with NLTK – Neural Nets and Deep Learning with Tensor Flow | |
Unit-4 |
Teaching Hours:9 |
SPARK SYSTEMS
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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.
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Unit-5 |
Teaching Hours:9 |
DELIVERING RESULTS with POWER BI
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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.
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Text Books And Reference Books: 1. William McKinney- Python for Data Analysis: Data Wrangling with Pandas, NumPy, and Ipython, O'Reilly; Second edition, 2017 2. Sandy Ryza, Uri Laserson. Advanced Analytics with Spark: Patterns for Learning from Data at Scale – O'Reilly 2017 3. Brett Powell Mastering Microsoft Power Bi, Packt Publishing, 2018 | |
Essential Reading / Recommended Reading 1. Jake VanderPlas. Python Data Science Handbook: Essential Tools for Working with Data O'Reilly 2016. 2. Holden Karau, Andy Konwinski, Learning Spark: Lightning-Fast Big Data Analysis, O'Reilly 2015 3. Tony Ojeda, Sean Patrick Murphy, Benjamin Bengfort, AbhijitDasgupta, "Practical Data Science Cookbook", Packt Publishing Ltd., 2014. 4. AurÈlienGÈron Hands-On Machine Learning with Scikit-Learn and Tensor Flow: Concepts, Tools, and Techniques to Build Intelligent Systems O'Reilly2017. 5. Devin Knight, Brian Knight. Microsoft Power BI Quick Start Guide: Build dashboards and visualizations to make your data come to life, Packt Publishing, 2018. | |
Evaluation Pattern Assessment of each paper · Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks) · End Semester Examination(ESE) : 50% (50 marks out of 100 marks) Components of the CIA CIA I : Quizzes/Seminar/Case Studies/Project Work : 10 marks CIA II : Mid Semester Examination (Theory) : 25 marks CIA III : Quizzes/Seminar/Case Studies/Project Work : 10 marks Attendance : 05 marks Total : 50 marks | |
MTCS243E01 - CLOUD COMPUTING (2021 Batch) | |
Total Teaching Hours for Semester:45 |
No of Lecture Hours/Week:3 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
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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. |
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Course Outcome |
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CO1: Understand the fundamentals of Cloud Storage, Cloud Architecture and Cloud Computing CO2: Explain Cloud Computing technologies with respect to platforms, services, network, security and applications CO3: Analyze Virtualization techniques, Virtual machines provisioning and Migrating services. CO4: Examine Work flow and Map-reduce programming models CO5: Assess various Cloud applications, Security and Performance issues |
Unit-1 |
Teaching Hours:9 |
UNDERSTANDING CLOUD COMPUTING
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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
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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
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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
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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
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Case Study: Business and Consumer Applications: CRM and ERP, Social Networking, Multiplayer Online Gaming – Technologies for Data Security in Cloud Computing – Cloud Computing and Data Security Risk- The Cloud, Digital Identity, and Data Security–Content Level Security-Data Privacy and Security Issues – HPC in the Cloud: Performance related Issues. | |
Text Books And Reference Books: 1. RajkumarBuyya, Vecchiola, Selvi, “Mastering Cloud Computing”, McGraw Hill. 2013. 2. Anthony Velte, Toby Velte, and Robert Elsenpeter. “Cloud Computing – A Practical Approach”, McGraw Hill. 2010. 3. RajkumarBuyya, James Broberg, Andrzej M. Goscinski, “Cloud Computing: Principles and Paradigms”, Wiley 2013. | |
Essential Reading / Recommended Reading 1. Massimo Cafaro and Giovanni Aloisio. “Grids, Clouds and Virtualization”. Springer 2012. 2. Michael Miller, “Cloud Computing: Web-Based Applications That Change the Way You Work and Collaborate Online”, Que Publishing, August 2008. | |
Evaluation Pattern Assessment of each paper · Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks) · End Semester Examination(ESE) : 50% (50 marks out of 100 marks) Components of the CIA
CIA I : Quizzes/Seminar/Case Studies/Project Work /Assignments : 10 marks CIA II : Mid Semester Examination (Theory) : 25 marks CIA III : Quizzes/Seminar/Case Studies/Project Work /Assignments : 10 marks Attendance : 05 marks Total : 50 marks
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MTCS244E01 - INTERNET OF THINGS (2021 Batch) | |
Total Teaching Hours for Semester:45 |
No of Lecture Hours/Week:3 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
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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. |
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Course Outcome |
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CO1: Explain the fundamental building blocks of an IoT environment from a logical and physical perspective.
CO2: Experiment with Arduino and Raspberry Pi to choose the appropriate hardware for different IoT projects. CO3: Summarize various IoT protocols in Application and Network layers by outlining their advantages and disadvantages. CO4: Develop IoT solutions using Arduino and Raspberry Pi to solve real life problems. CO5: Analyze the IoT design and cloud incorporation. |
Unit-1 |
Teaching Hours:9 |
INTRODUCTION AND BACKGROUND
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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
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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
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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
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Arduino Programming: Serial Communications, Getting input from sensors, Visual, Physical and Audio Outputs, Remotely Controlling External Devices, Wireless Communication. Programming with Raspberry Pi: Basics of Python Programming, Python packages of IoT, IoT Programming with CDAC IoT devices. | |
Unit-5 |
Teaching Hours:9 |
IOT DESIGN AND CLOUD INCORPORATION
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Case Studies- IoT Design and Cloud incorporation: Introduction to IOT Design, Home Automation, Smart Lighting , Home Intrusion Detection, Cities , Smart Parking , Environment , Weather Monitoring System , Weather Reporting Bot , Air Pollution Monitoring , Forest Fire Detection, Agriculture, Smart Irrigation, Productivity Applications , IoT Printer.. | |
Text Books And Reference Books: 1. Vijay Madisetti and ArshdeepBahga, “Internet of Things (A Hands-on-Approach)”, 1st Edition, VPT, 2014. 2. Margolis, Michael. “Arduino Cookbook: Recipes to Begin, Expand, and Enhance Your Projects. " O'Reilly Media, Inc.", 2011. 3. Monk, Simon. Raspberry Pi cookbook: Software and hardware problems and solutions. " O'Reilly Media, Inc.", 2016. | |
Essential Reading / Recommended Reading 1. The Internet of Things: Applications to the Smart Grid and Building Automation by – Olivier Hersent, Omar Elloumi and David Boswarthick – Wiley Publications -2012. 2. Honbo Zhou, “The Internet of Things in the Cloud: A Middleware Perspective”, CRC Press, 2012. 3. David Easley and Jon Kleinberg, “Networks, Crowds, and Markets: Reasoning About a Highly Connected World”, Cambridge University Press, 2010. 4. Al-Fuqaha, Ala, et al. "Internet of things: A survey on enabling technologies, protocols, and applications." IEEE Communications Surveys & Tutorials 17.4 (2015): 2347-2376. 5. Tsitsigkos, Alkiviadis, et al. "A case study of internet of things using wireless sensor networks and smartphones." Proceedings of the Wireless World Research Forum (WWRF) Meeting: Technologies and Visions for a Sustainable Wireless Internet, Athens, Greece. Vol. 2325. 2012. Ye, Mengmei, et al. "Security Analysis of Internet-of-Things: A Case Study of August Smart Lock." | |
Evaluation Pattern Assessment of each paper · Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks) · End Semester Examination(ESE) : 50% (50 marks out of 100 marks) Components of the CIA
CIA I : Quizzes/Seminar/Case Studies/Project Work /Assignments : 10 marks
CIA II : Mid Semester Examination (Theory) : 25 marks
CIA III : Quizzes/Seminar/Case Studies/Project Work /Assignments : 10 marks
Attendance : 05 marks
Total : 50 marks
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MTCS251 - NETWORKING LAB (2021 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
Max Marks:50 |
Credits:2 |
Course Objectives/Course Description |
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Course Outcome |
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CO1: Examine the performances of Routing protocol CO2: Experiment with different application layer protocols CO3: Experiment with different security techniques over peer to peer medium. |
Unit-1 |
Teaching Hours:60 |
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Design, develop the project to implement following areas in networks
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Text Books And Reference Books:
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Essential Reading / Recommended Reading | |||||||||||
Evaluation Pattern End semester practical examination : 25 marks Records : 05 marks Mid semester examination : 10 marks Class work : 10 marks Total : 50 marks Mid semester practical examination will be conducted during regular practical hour with prior intimation to all candidates. End semester practical examination will have two examiners an internal and external examiner. | |||||||||||
MTCS252 - DATA SCIENCE LAB (2021 Batch) | |||||||||||
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
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Max Marks:50 |
Credits:2 |
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Course Objectives/Course Description |
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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. |
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Course Outcome |
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CO1: Apply and evaluate the clustering and Classification methods for modeling the data CO2: Apply the Statistical models and data distributions using Python Programming. |
Unit-1 |
Teaching Hours:60 |
List of Experiments
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Text Books And Reference Books: - | |
Essential Reading / Recommended Reading - | |
Evaluation Pattern For subjects having practical as part of the subject End semester practical examination : 25 marks Records : 05 marks Mid semester examination : 10 marks Class work : 10 marks Total : 50 marks Mid semester practical examination will be conducted during regular practical hour with prior intimation to all candidates. End semester practical examination will have two examiners an internal and external examiner. | |
MTCS345E06 - MULTIMEDIA SYSTEMS (2020 Batch) | |
Total Teaching Hours for Semester:45 |
No of Lecture Hours/Week:3 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
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Course Outcome |
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CO1: Demonstrate the different QoS CO2: Illustrate the various RTS CO3: Discuss different methods in File systems and networks CO4: Establish different communication methods CO5: Examine the synchronization mechanisms |
Unit-1 |
Teaching Hours:9 |
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INTRODUCTION AND QOS
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Introduction-QOS Requirements and Constraints-Concepts-Resources- Establishment Phase-Run-Time Phase-Management Architectures. | |||||||||||||||||||||||||||||||
Unit-2 |
Teaching Hours:9 |
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OPERATING SYSTEMS
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Real-Time Processing-Scheduling-Interprocess Communication-Memory and Management-Server Architecture-Disk Management. | |||||||||||||||||||||||||||||||
Unit-3 |
Teaching Hours:9 |
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FILE SYSTEMS AND NETWORKS
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Traditional and Multimedia File Systems-Caching Policy-Batching-Piggy backing-Ethernet-Gigabit Ethernet-Token Ring-100VG AnyLAN-Fiber Distributed Data Interface (FDDI)- ATM Networks-MAN-WAN. | |||||||||||||||||||||||||||||||
Unit-4 |
Teaching Hours:9 |
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COMMUNICATION
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Transport Subsystem-Protocol Support for QOS-Transport of Multimedia-Computer Supported Cooperative Work-Architecture-Session Management-MBone Applications. | |||||||||||||||||||||||||||||||
Unit-5 |
Teaching Hours:9 |
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SYNCHRONIZATION
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Synchronization in Multimedia Systems-Presentation-Synchronization Types-Multimedia Synchronization Methods-Case Studies-MHEG-MODE-ACME. | |||||||||||||||||||||||||||||||
Text Books And Reference Books: 1. Ralf Steinmetz and Klara Nahrstedt, “Multimedia Systems”, Springer, I Edition 2004. (Latest edition/ reprint available in market). 2. K. R. Rao, Zoran S. Bojkovic, Dragorad A. Milovacovic, D. A. Milovacovic , “Multimedia Communication Systems: Techniques, Standards, and Networks”, Prentice Hall, 1st Edition, 2002 (Latest edition/ reprint available in market) 3. Ze-Nian Li and Mark S. Drew, “Fundamentals of Multimedia”, Pearson, 2004. (Latest edition/ reprint available in market) | |||||||||||||||||||||||||||||||
Essential Reading / Recommended Reading 1. Ralf Steinmetz and Klara Nahrstedt , “Media Coding and Content Processing”, Prentice hall, 2002. 2. Vaughan T, “Multimedia”, 9th Edition, Tata McGraw Hill, 1999. 3. Mark J.B., Sandra K.M., “Multimedia Applications Development using DVI technology”, McGraw Hill, 1992. | |||||||||||||||||||||||||||||||
Evaluation Pattern
Assessment of each paper · Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks) End Semester Examination(ESE) : 50% (50 marks out of 100 marks)
Components of CIA CIA 1 Assignment and MCQ - 10 marks CIA 2 Mid Semester Examination - 25 marks CIA 3 MOOC Course and Closed book test - 10 marks Attendance - 5 marks
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MTCS381 - INTERNSHIP (2020 Batch) | |||||||||||||||||||||||||||||||
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
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Max Marks:50 |
Credits:2 |
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Course Objectives/Course Description |
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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: |
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Course Outcome |
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CO1: Explain inside view of an industry and organization/company. CO2: Make use of professional connections and enhance student's network. CO3: Illustrate how to get experience in a field to allow the student to make a career transition. |
Unit-1 |
Teaching Hours:60 |
Regulations
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1.The student shall undergo an Internship for30 days starting from the end of 2nd semester examination and completing it during the initial period of 3rd semester. 2.The department shall nominate a faculty as a mentor for a group of students to prepare and monitor the progress of the students 3. The students shall report the progress of the internship to the mentor/guide at regular intervals and may seek his/her advise. 4. The Internship shall be completed by the end of 2nd semesters. 5. The students are permitted to carry out the internship outside India with the following conditions, the entire expenses are to be borne by the student and the University will not give any financial assistance. 6. Students can also undergo internships arranged by the department during vacation. 7. After completion of Internship, students shall submit a report to the department with the approval of both internal and external guides/mentors.
8. There will be an assessment for the internship for 2 credits, in the form of report assessment by the guide/mentor and a presentation on the internship given to department constituted panel. | |
Text Books And Reference Books: Related to the Internship domain text books are sugessted. | |
Essential Reading / Recommended Reading Readings Related to the Internship domain | |
Evaluation Pattern Internal 50 Marks | |
MTCS382 - DISSERTATION PHASE - I (2020 Batch) | |
Total Teaching Hours for Semester:200 |
No of Lecture Hours/Week:20 |
Max Marks:200 |
Credits:10 |
Course Objectives/Course Description |
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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:
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Course Outcome |
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CO1: Analyze literature review, domain identification CO2: Demonstrate the concept of framing the research problem CO3: Apply the project design and analysis concepts. |
Unit-1 |
Teaching Hours:200 |
DISSERTATION PHASE -1
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Project Work | |
Text Books And Reference Books: Journal article, industry white papers text books basedon the domain on which the student will be doing his/her work. | |
Essential Reading / Recommended Reading Recommendation will be given Based on the domian in which student will be interested and planning to do the dissertation work | |
Evaluation Pattern ❖ Assessment of Project Work(Phase I) ▪ Continuous Internal Assessment:100 Marks ♦ Presentation assessed by Panel Members ♦ Guide ♦ Mid semester Project Report End semester Examination :100 Marks Presentation assessed by Panel Members ♦ Guide ♦ End semester Project Report | |
MTEC361 - ADVANCED COMMUNICATION NETWORKS (2020 Batch) | |
Total Teaching Hours for Semester:45 |
No of Lecture Hours/Week:3 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
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This course aims at making the students understand the different communication protocols, understand advanced concepts in communication networking and also the concept of QoS in communication networks |
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Course Outcome |
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CO1: Understand advanced concepts in Congestion Control and Flow control including algorithms CO2: Describe the quality of service requirements in the internet with the integrated services model and protocols CO3: Understand the scheduling requirement in Internet including scheduler, classifier and IP addressing CO4: Explain the admission control in internet protocol including the algorithms and differentiated services model for quality of service requirement CO5: Understand the network interconnection models and protocols including MPLS |
Unit-1 |
Teaching Hours:9 |
Unit - 1
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Overview of -ATM. TCP/IP Congestion and Flow Control in Internet-Throughput analysis of TCP congestion control. TCP for high bandwidth delay networks. Fairness issues in TCP | |
Unit-2 |
Teaching Hours:9 |
Unit -2
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Real Time Communications over Internet. Adaptive applications. Latency and throughput issues. Integrated Services Model (intServ). Resource reservation in Internet. RSVP, Characterization of Traffic by Linearly Bounded Arrival Processes (LBAP). Leaky bucket algorithm and its properties | |
Unit-3 |
Teaching Hours:9 |
Unit 3
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Packet Scheduling Algorithms-requirements and choices. Scheduling guaranteed service Connections, IP address lookup-challenges. Packet classification algorithms, IPV4 and IPv6 address | |
Unit-4 |
Teaching Hours:9 |
Unit -4
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Admission control in Internet. Concept of Effective bandwidth. Measurement based admission control. Differentiated Services in Internet (DiffServ). DiffServ architecture and framework | |
Unit-5 |
Teaching Hours:9 |
Unit 5
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IP switching and MPLS, Overview of IP over ATM and its evolution to IP switching. MPLS architecture and framework. MPLS Protocols. Traffic engineering issues in MPLS | |
Text Books And Reference Books:
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Essential Reading / Recommended Reading
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Evaluation Pattern Assessment of each paper Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks) End Semester Examination(ESE) : 50% (50 marks out of 100 marks) Components of the CIA CIA I : Quizzes/Seminar/Case Studies/Project Work /Assignments : 10 marks CIA II : Mid Semester Examination (Theory) : 25 marks CIA III : Quizzes/Seminar/Case Studies/Project Work /Assignments : 10 marks Attendance : 05 marks Total : 50 marks
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MTCS483 - DISSERTATION PHASE-II (2020 Batch) | |
Total Teaching Hours for Semester:480 |
No of Lecture Hours/Week:32 |
Max Marks:200 |
Credits:16 |
Course Objectives/Course Description |
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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:
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Course Outcome |
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CO1: Interpret Teaching, Laboratory & Research outcome by Academic participation CO2: Impart research Focus based on Study of Journal publications. CO3: Analyze domain identification, framing the research problem and Project design analysis |
Unit-1 |
Teaching Hours:480 |
DISSERTATION PHASE -II
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Project Work | |
Text Books And Reference Books: Journal article, industry white papers text books basedon the domain on which the student will be doing his/her work. | |
Essential Reading / Recommended Reading Recommendation will be given Based on the domian in which student will be interested and planning to do the dissertation work | |
Evaluation Pattern Assessment of Project Work(Phase II) and Dissertation ▪ Continuous Internal Assessment:100 Marks ♦ Presentation assessed by Panel Members ♦ Assessed by Guide ♦ Mid Semester Project Report ▪ End Semester Examination:100 Marks ♦ Viva Voce ♦ Demonstration ♦ Project Report ▪ Dissertation (Exclusive assessment of Project Report): 100 Marks ♦ Internal Review : 50 Marks ♦ External review : 50 Marks |