Department of CSE, REC
RAJALAKSHMI ENGINEERING COLLEGE
(An Autonomous Institution, Affiliated to Anna University, Chennai)
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
Curriculum and Syllabus, Regulations 2017
M.E. COMPUTER SCIENCE AND ENGINEERING
PROGRAMME EDUCATIONAL OBJECTIVES (PEOs)
1. To equip students with essential background in computer science, basic electronics and applied
mathematics.
2. To prepare students with fundamental knowledge in programming languages and tools and enable them to
develop applications.
3. To encourage the research abilities and innovative project development in the field of networking,
security, data mining, web technology, mobile communication and also emerging technologies for the
cause of social benefit.
4. To develop professionally ethical individuals enhanced with analytical skills, communication skills and
organizing ability to meet industry requirements.
PROGRAM OUTCOMES (POs)
A post graduate of the Computer Science and Engineering Program will demonstrate:
PO1: Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and
an engineering specialization to the solution of complex engineering problems.
PO2: Problem analysis: Identify, formulate, review research literature, and analyze complex engineering
problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and
engineering sciences.
PO3: Design/development of solutions: Design solutions for complex engineering problems and design
system components or processes that meet the specified needs with appropriate consideration for the public
health and safety, and the cultural, societal, and environmental considerations.
PO4: Conduct investigations of complex problems: Use research-based knowledge and research methods
including design of experiments, analysis and interpretation of data, and synthesis of the information to
provide valid conclusions.
PO5: Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern engineering
and IT tools including prediction and modeling to complex engineering activities with an understanding of the
limitations.
PO6: The engineer and society: Apply reasoning informed by the contextual knowledge to assess societal,
health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional
engineering practice.
PO7: Environment and sustainability: Understand the impact of the professional engineering solutions in
societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable
development.
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 1
Department of CSE, REC
PO8: Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the
engineering practice.
PO9: Individual and team work: Function effectively as an individual, and as a member or leader in diverse
teams, and in multidisciplinary settings.
PO10: Communication: Communicate effectively on complex engineering activities with the engineering
community and with society at large, such as, being able to comprehend and write effective reports and design
documentation, make effective presentations, and give and receive clear instructions.
PO11: Project management and finance: Demonstrate knowledge and understanding of the engineering and
management principles and apply these to one‘s own work, as a member and leader in a team, to manage
projects and in multidisciplinary environments.
PO12: Life-long learning: Recognize the need for, and have the preparation and ability to engage in
independent and life-long learning in the broadest context of technological change.
PROGRAM SPECIFIC OBJECTIVES (PSOs)
A post graduate of the Computer Science and Engineering Program will demonstrate:
PSO1: Foundation Skills: Ability to understand, analyze and develop computer programs in the areas
related to algorithms, system software, web design, machine learning, data analytics, and networking for
efficient design of computer-based systems of varying complexity. Familiarity and practical competence with
a broad range of programming language and open source platforms.
PSO2: Problem-Solving Skills: Ability to apply mathematical methodologies to solve computational task,
model real world problem using appropriate data structure and suitable algorithm. To understand the Standard
practices and strategies in software project development using open-ended programming environments to
deliver a quality product.
PSO3: Successful Progression: Ability to apply knowledge in various domains to identify research gaps and
to provide solution to new ideas, inculcate passion towards higher studies, creating innovative career paths to
be an entrepreneur and evolve as an ethically social responsible computer science professional.
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 2
Department of CSE, REC
CURRICULUM
SEMESTER I
Sl. COURSE COURSE TITLE CATEGORY CONTACT
No. CODE PERIODS L T P C
THEORY
1. AppliedProbability
MA17173 HS 5 3 2 0 4
And Statistics
2. Design and
CP17101 Managementof PC 3 3 0 0 3
ComputerNetworks
3. AdvancedData
CP17102 Structuresand PC 3 3 0 0 3
Algorithms
4. CP17103 Multicore Architectures PC 3 3 0 0 3
5. AdvancedOperating
CP17104 PC 3 3 0 0 3
Systems
6. Advanced Software
SE17102 PC 3 3 0 0 3
Engineering
PRACTICALS
Advanced Data
7. CP17111 PC 4 0 0 4 2
StructuresLaboratory
TOTAL 24 18 2 4 21
SEMESTER II
Sl. COURSE CONTACT
No CODE COURSE TITLE CATEGORY PERIODS L T P C
THEORY
Cloud Computing
1. CP17201 PC 3 3 0 0 3
Technologies
2. CP17202 Advanced Databases PC 3 3 0 0 3
MachineLearning
3. CP17203 PC 3 3 0 0 3
Techniques
4. CP17204 Big Data Analytics PC 3 3 0 0 3
5. Elective I PE 3 3 0 0 3
6. Elective II PE 3 3 0 0 3
PRACTICALS
Cloud Computing
7. CP17211 PC 4 0 0 4 2
Laboratory
Term Paper Writing and
8. CP17212 EEC 2 0 0 2 1
Seminar
TOTAL 24 18 0 6 21
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 3
Department of CSE, REC
SEMESTER III
Sl. COURSE CONTACT
No CODE COURSE TITLE CATEGORY PERIODS L T P C
THEORY
1. CP17301 SecurityPrinciples PC 3 3 0 0 3
2. Elective III PE 3 3 0 0 3
3. Elective IV PE 3 3 0 0 3
4. Elective V PE 3 3 0 0 3
PRACTICALS
5. CP17311 Project Work (Phase I) EEC 12 0 0 12 6
TOTAL 24 12 0 12 18
SEMESTER IV
COURSE CONTACT
Sl. No COURSE TITLE CATEGORY L T P C
CODE PERIODS
THEORY
1. CP17411 Project Work (Phase II) EEC 24 0 0 24 12
TOTAL 21 0 0 24 12
TOTAL NO. OF CREDITS: 72
PROFESSIONAL ELECTIVES(PE)
SEMESTER II
ELECTIVE – I & ELECTIVE - II
COURSE CONTACT
Sl. No COURSE TITLE CATEGORY L T P C
CODE PERIODS
Performance Evaluation of
1. CP17E21 PE 3 3 0 0 3
Computer Systems
Data Analysis and
2. CP17E22 PE 3 3 0 0 3
Business Intelligence
Image Processing
3. CP17E23 PE 3 3 0 0 3
and Analysis
Software Quality
4. CP17E24 PE 3 3 0 0 3
Assurance and Testing
5. CP17E25 Randomized Algorithms PE 3 3 0 0 3
Mobile andPervasive
6. CP17E26 PE 3 3 0 0 3
Computing
Software Requirements
7. CP17E27 PE 3 3 0 0 3
Engineering
Data Visualization
8. CP17E28 PE 3 3 0 0 3
Techniques
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 4
Department of CSE, REC
SEMESTER III
ELECTIVE – III, ELECTIVE – IV & ELECTIVE - V
COURSE CONTACT
Sl. No COURSE TITLE CATEGORY L T P C
CODE PERIODS
1. CP17E31 Bio-Informatics PE 3 3 0 0 3
2. CP17E32 Computer Vision PE 3 3 0 0 3
Design and Analysis
3. CP17E33 of Parallel PE 3 3 0 0 3
Algorithms
Speech Processing
4. CP17E34 PE 3 3 0 0 3
and Synthesis
5. CP17E35 Trusted Computing PE 3 3 0 0 3
6. CP17E36 Internet of Things PE 3 3 0 0 3
Social Network
7. CP17E37 PE 3 3 0 0 3
Analysis
8. CP17E38 Software Design PE 3 3 0 0 3
Information Storage
9. CP17E39 PE 3 3 0 0 3
Management
SUMMARY:
M.E.COMPUTER SCIENCE AND ENGINEERING
S.NO. SUBJECT AREA Credits per Semester Credits Total
I II III IV
1. HS 4 4
2. PC 17 14 3 34
3. PE 6 9 15
4. EEC 1 6 12 19
TOTAL 21 21 18 12 72
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 5
Department of CSE, REC
MP17173 APPLIED PROBABILITY AND STATISTICS L T P C
3 2 0 4
OBJECTIVES:
The student should be made to:
Understand the concept of random variable and probability distribution for solving problems.
Develop the concept of correlation and regression and apply in real life problems.
Understand the techniques of forecasting.
Develop the skills on decision making using the concept of testing of hypothesis.
UNIT I ONE DIMENSIONAL RANDOM VARIABLES 15
Random variables - Probability function – Moments – Moment generating functions and their properties –
Binomial, Poisson, Geometric, Uniform, Exponential, Gamma and Normal distributions – Functions of a
Random Variable.
UNIT II TWO DIMENSIONAL RANDOM VARIABLES 15
Joint distributions – Marginal and Conditional distributions – Functions of two dimensionalrandom variables
– Regression Curve – Correlation.
UNIT III ESTIMATION THEORY 15
Unbiased Estimators – Method of Moments – Maximum Likelihood Estimation - Curve fittingby Principle of
least squares – Regression Lines.
UNIT IV TESTING OF HYPOTHESES 15
Sampling distributions - Type I and Type II errors – Tests based on Normal, t, 2 and F distributions for
testing of mean, variance and proportions – Tests for Independence of attributes and Goodness of fit.
UNIT V MULTIVARIATE ANALYSIS 15
Random Vectors and Matrices - Mean vectors and Covariance matrices – MultivariateNormal density and its
properties - Principal components Population principal components Principalcomponentsfromstandardized
Variables.
TOTAL: 75 PERIODS
OUTCOMES:
At the end of the course, the student will be able to:
• Use the concept of MGF and probability distribution for solving problems that arise from time to
time.
• Apply the concept of correlation and regression in real life situation.
• Apply the concept of estimation theory and curve fitting for forecasting.
• Enable the students to use the concepts of Testing of Hypothesis for industrial problems
• Identify and analyze the principle components of different process.
REFERENCES:
1. Jay L. Devore, ―Probability and Statistics for Engineering and the Sciences‖, Thomson and Duxbury,
2002.
2. Richard Johnson, ―Miller& Freund‘s Probability and Statistics for Engineer‖, Prentice – Hall, Seventh
Edition, 2007.
3. Richard A. Johnson and Dean W. Wichern, ―Applied Multivariate Statistical Analysis‖, Pearson
Education, Asia, Fifth Edition, 2002.
4. Gupta S.C. and Kapoor V.K,‖ Fundamentals of Mathematical Statistics‖, Sultan and Sons, 2001.
5. Dallas E Johnson, ―Applied Multivariate Methods for Data Analysis‖, Thomson and Duxbury press,
1998.
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 6
Department of CSE, REC
CP17101 DESIGN AND MANAGEMENT OF COMPUTER NETWORKS L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
IPV4 and IPV6 protocols routing
Frame relay and ATM congestion control management
Network security and Integrated and Differentiated Services
Network management and its protocols
UNIT I INTRODUCTION TO NETWORK MANAGEMENT 9
Overview ofAnalysis, Architecture and Design Process-System Methodology, Service methodology, Service
Description-Service characteristics - Performance Characteristics -Network supportability - Requirement
analysis – User Requirements – Application Requirements–Device Requirements – Network Requirements
– Other Requirements Requirement specification and map.
UNIT II REQUIREMENTS ANALYSIS 9
Requirement Analysis Process – Gathering and Listing Requirements- Developing service metrics–
Characterizing behaviour – Developing RMA requirements – Developing delay Requirements -Developing
capacity Requirements - Developing supplemental performanceRequirements –Requirements mapping –
Developing the requirements specification.
UNIT III FLOW ANALYSIS 9
Individual and Composite Flows – Critical Flows - Identifying and developing flows – Data sources and sinks
– Flow models- Flow prioritization – Flow specification algorithms –Example Applications of Flow Analysis
UNIT IV NETWORK ARCHITECTURE 9
Architecture and design – Component Architectures – Reference Architecture – Architecture Models –
System and Network Architecture – Addressing and Routing Architecture –Addressing and Routing
Fundamentals – Addressing Mechanisms – Addressing Strategies –Routing Strategies – Network
Management Architecture – Network Management Mechanisms Performance Architecture – Performance
Mechanisms – Security and PrivacyArchitecture –Planning security and privacy Mechanisms
UNIT V NETWORK DESIGN 9
Design Concepts – Design Process - Network Layout – Design Traceability – Design Metrics–Logical
Network Design – Topology Design – Bridging, Switching and Routing ProtocolsPhysicalNetworkDesign–
SelectingTechnologiesandDevicesforCampusandEnterpriseNetworks–OptimizingNetworkDesign.
OUTCOMES: TOTAL: 45 PERIODS
At the end of the course, the student will be able to:
Independently understand basic computer network technology.
Understand and explain Data Communications System and its components.
Identify the different types of network topologies and protocols.
Enumerate the layers of the OSI model and TCP/IP. Explain the function(s) of eachlayer.
Identify the different types of network devices and their functions within a network
REFERENCES:
1. James D. McCabe, Morgan Kaufmann, Network Analysis, Architecture, and Design, Third Edition,
2007
2. Larry L. Peterson and Bruce S. Davie, Computer Networks: A Systems Approach, Elsevier,2007
3. Priscilla Oppenheimer, Top-down Network Design: [a Systems Analysis Approach to Enterprise
Network Design], Cisco Press, 3rd Edition
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 7
Department of CSE, REC
4. Heinz-GerdHegering, Sebastian Abeck, and Bernhard Neumair, Integrated Management of
Networked Systems: Concepts, Architectures, and TheirOperational Application, The Morgan
Kaufmann Series in Networking, 1999.
5. Steven T. Karris, Network Design and Management, Orchard Publications, Second Edition, 2009
6. Teresa C. Rubinson and KornelTerplan, Network Design, Management and Technical Perspective,
CRC Press,1999
7. Gilbert Held, Ethernet Networks-Design, Implementation, Operation and Management, John Wiley
and sons, Fourth Edition
8. James Kurose and Keith Ross, Computer Networking: A Top-Down Approach Featuring the
Internet,1999
CP17102 ADVANCED DATA STRUCTURES AND ALGORITHMS L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
Understand the principles of iterative and recursive algorithms.
Learn the graph search algorithms.
Study network flow and linear programming problems.
Learn the hill climbing and dynamic programming design techniques.
Develop recursive backtracking algorithms.
UNIT I ITERATIVE AND RECURSIVE ALGORITHMS 9
Iterative Algorithms: Measures of Progress and Loop Invariants-Paradigm Shift: Sequence ofActions versus
Sequence of Assertions- Steps to Develop an Iterative Algorithm-DifferentTypes of Iterative Algorithms--
Typical Errors-Recursion-Forward versus Backward- Towers ofHanoi Checklist for Recursive Algorithms-
The Stack Frame-Proving Correctness with StrongInduction Examples of Recursive Algorithms-Sorting and
Selecting Algorithms-Operations onIntegers Ackermann‘s Function- Recursion on Trees-Tree Traversals-
ExamplesGeneralizingtheProblem-HeapSortandPriorityQueues-RepresentingExpressions.
UNIT II OPTIMISATION ALGORITHMS 9
Optimization Problems-Graph Search Algorithms-Generic Search-Breadth-First Search Dijkstra‘sShortest-
Weighted-Path-Depth-FirstSearch-RecursiveDepth-FirstSearch-Linear Ordering of a Partial Order- Network
Flows and Linear Programming-Hill Climbing-PrimalDual Hill Climbing Steepest Ascent Hill Climbing-
Linear Programming-Recursive Backtracking-Developing Recursive Backtracking Algorithm- Pruning
Branches-Satisfiability.
UNIT III DYNAMIC PROGRAMMING ALGORITHMS 9
Developing a Dynamic Programming Algorithm-Subtle Points- Question for the Little BirdSubinstances and
Subsolutions -Set of Substances-Decreasing Time and Space-Number ofSolutions-Code. Reductions and NP
- Completeness – Satisfiability - Proving NP Completeness-3-Coloring- Bipartite Matching. Randomized
Algorithms - Randomness toHide Worst Cases Optimization Problems with a Random Structure.
UNIT IV SHARED OBJECTS AND CONCURRENT OBJECTS 9
Shared Objects and Synchronization -Properties of Mutual Exclusion-The Mora l- TheProducer–
Consumer Problem -The Readers–Writers Problem-Realities of ParallelizationParallel Programming-
Principles- Mutual Exclusion-Time- Critical Sections—Thread Solutions-The Filter Lock-Fairness-
Lamport‘s Bakery Algorithm-Bounded Timestamps-Lower Bounds on the Number of Locations-Concurrent
Objects- Concurrency and Correctness Sequential Objects-Quiescent Consistency- Sequential Consistency-
Linearizability- Formal Definitions- Progress Conditions- The Java Memory Model.
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 8
Department of CSE, REC
UNIT V CONCURRENT DATA STRUCTURES 9
Practice-Linked Lists-The Role of Locking-List-Based Sets-Concurrent Reasoning-
CoarseGrainedSynchronization-Fine-Grained Synchronization-Optimistic Synchronization-
LazySynchronization-Non-Blocking Synchronization-Concurrent Queues and the ABA ProblemQueues-A
Bounded Partial Queue-An Unbounded Total Queue-An Unbounded Lock-FreeQueue Memory
Reclamation and the ABA Problem- Dual Data Structures- ConcurrentStacks and Elimination- An
Unbounded Lock-Free Stack- Elimination-The Elimination Back off Stack.
TOTAL: 45 PERIODS
OUTCOMES:
At the end of the course, the student will be able to:
Design and apply iterative and recursive algorithms.
Design and implement optimization algorithms in specific applications.
Design appropriate shared objects and concurrent objects for applications.
Implement and apply concurrent linked lists, stacks, and queues.
REFERENCES:
1. Jeff Edmonds, ―How to Think about Algorithms‖, Cambridge University Press, 2008.
2. M. Herlihy and N. Shavit, ―The Art of Multiprocessor Programming‖, MorganKaufmann, 2008.
3. Steven S. Skiena, ―The Algorithm Design Manual‖, Springer, 2008.
4. Peter Brass, ―Advanced Data Structures‖, Cambridge University Press, 2008.
5. S. Dasgupta, C. H. Papadimitriou and U. V. Vazirani, ―Algorithms‖, McGraw-Hill,2008.
6. J. Kleinberg and E. Tardos, "Algorithm Design ―, Pearson Education, 2006.
7. T. H. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein, ―Introduction to Algorithms ―, PHI
Learning Private Limited, 2012.
8. Rajeev Motwani and Prabhakar Raghavan, ―Randomized Algorithms‖, Cambridge University Press,
1995.
9. A. V. Aho, J. E. Hopcroft, and J. D. Ullman, ―The Design and Analysis of ComputerAlgorithms‖,
Addison-Wesley, 1975.
10. A. V. Aho, J. E. Hopcroft, and J. D. Ullman,‖ Data Structures and Algorithms‖, Pearson, 2006.
CP17103 MULTICORE ARCHITECTURES L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
Understand the recent trends in the field of Computer Architecture and identify performance related
parameters
Appreciate the need for parallel processing
Expose the students to the problems related to multiprocessing
Understand the different types of multicore architectures
Expose the students to warehouse-scale and embedded architectures
UNIT I FUNDAMENTALS OF QUANTITATIVE DESIGN AND ANALYSIS 9
Classes of Computers – Trends in Technology, Power, Energy and Cost – Dependability –Measuring,
Reporting and Summarizing Performance – Quantitative Principles of ComputerDesign – Classes of
Parallelism - ILP, DLP, TLP and RLP - Multithreading - SMT and CMPArchitectures – Limitations of Single
Core Processors - The Multicore era – Case Studies ofMulticore Architectures.
UNIT II DLP IN VECTOR, SIMD AND GPU ARCHITECTURES 9
Vector Architecture - SIMD Instruction Set Extensions for Multimedia – Graphics ProcessingUnits- Detecting
and Enhancing Loop Level Parallelism - Case Studies.
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 9
Department of CSE, REC
UNIT III TLP AND MULTIPROCESSORS 9
Symmetric and Distributed Shared Memory Architectures – Cache Coherence Issues-Performance Issues –
Synchronization Issues – Models of Memory Consistency –Interconnection Networks – Buses, Crossbar and
Multi-Stage Interconnection Networks.
UNIT IV RLP AND DLP IN WAREHOUSE-SCALE ARCHITECTURES 9
Programming Models and Workloads for Warehouse-Scale Computers – Architectures forWarehouse-Scale
Computing – Physical Infrastructure and Costs – Cloud Computing –Case Studies.
UNIT V ARCHITECTURES FOR EMBEDDED SYSTEMS 9
Features and Requirements of Embedded Systems – Signal Processing and EmbeddedApplications – The
Digital Signal Processor – Embedded Multiprocessors - Case Studies.
TOTAL: 45 PERIODS
OUTCOMES:
At the end of the course, the student will be able to:
Identify the limitations of ILP and the need for multicore architectures
Discuss the issues related to multiprocessing and suggest solutions
Point out the salient features of different multicore architectures and how they exploitparallelism
Critically analyze the different types of inter connection networks
REFERENCES:
1. John L. Hennessey and David A. Patterson, ―Computer Architecture – A QuantitativeApproach‖,
Morgan Kaufmann / Elsevier, 5th edition, 2012.
2. Kai Hwang, ―Advanced Computer Architecture‖, Tata McGraw-Hill Education, 2003
3. Richard Y. Kain, ―Advanced Computer Architecture a Systems Design Approach‖, Prentice Hall,
2011.
4. David E. Culler, Jaswinder Pal Singh, ―Parallel Computing Architecture: A Hardware/Software
Approach‖, Morgan Kaufmann / Elsevier, 1997.
CP17104 ADVANCED OPERATING SYSTEMS L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
Learn the fundamentals of Operating Systems
Gain knowledge on Distributed operating system concepts that includes architecture, Mutual
exclusion algorithms, Deadlock detection algorithms and agreement protocols
Gain insight on to the distributed resource management components viz. thealgorithms for
Know the components and management aspects of Real time, Mobile operating systems
UNIT I FUNDAMENTALS OF OPERATING SYSTEMSM 9
Overview – Synchronization Mechanisms – Processes and Threads - Process Scheduling –Deadlocks:
Detection, Prevention and Recovery – Models of Resources – MemoryManagement Techniques.
UNIT II DISTRIBUTED OPERATING SYSTEMS 9
Issues in Distributed Operating System – Architecture – Communication Primitives –Lamport‘s Logical
clocks – Causal Ordering of Messages – Distributed Mutual Exclusion Algorithms – Centralized and
Distributed Deadlock Detection Algorithms – Agreement Protocols.
UNIT III DISTRIBUTED RESOURCE MANAGEMENT 9
Distributed File Systems – Design Issues - Distributed Shared Memory – Algorithms for Implementing
Distributed Shared memory–Issues in Load Distributing – Scheduling Algorithms –Synchronous and
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 10
Department of CSE, REC
Asynchronous Check Pointing and Recovery – Fault Tolerance – Two-Phase Commit Protocol – Non
blocking Commit Protocol – Security and Protection.
UNIT IV REAL TIME AND MOBILE OPERATING SYSTEMS 9
Basic Model of Real Time Systems - Characteristics- Applications of Real Time Systems –Real Time Task
Scheduling - Handling Resource Sharing - Mobile Operating Systems –Micro Kernel Design - Client Server
Resource Access – Processes and Threads - MemoryManagement – File system.
UNIT V CASE STUDIES 9
Linux System: Design Principles - Kernel Modules - Process Management Scheduling –Memory
Management - Input-Output Management - File System – Inter process Communication. iOS and Android:
Architecture and SDK Framework - Media Layer ServicesLayer-CoreOSLayer–FileSystem.
TOTAL: 45 PERIODS
OUTCOMES:
At the end of the course, the student will be able to:
Discuss the various synchronization, scheduling and memory management issues
Demonstrate the Mutual exclusion, Deadlock detection and agreement protocols ofdistributed
operating system
Discuss the various resource management techniques for distributed systems
Identify the different features of real time and mobile operating systems
Install and use available open source kernel
REFERENCES:
1. Mukesh Singhal and Niranjan G. Shivaratri, ―Advanced Concepts in Operating Systems –
Distributed, Database, and Multiprocessor Operating Systems‖, Tata McGraw-Hill, 2001.
2. Abraham Silberschatz; Peter Baer Galvin; Greg Gagne, ―Operating SystemConcepts‖, Seventh
Edition, John Wiley & Sons, 2004.
3. Daniel PBovet and Marco Cesati, ―Understanding the Linux kernel‖, 3rd edition, O‘Reilly, 2005.
4. Rajib Mall, ―Real-Time Systems: Theory and Practice‖, Pearson Education India,2006.
5. Neil Smyth, ―iPhone iOS 4 Development Essentials – Xcode‖, Fourth Edition, Payload media, 2011.
SE17102 ADVANCED SOFTWARE ENGINEERING L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
Have a clear understanding of Software Engineering concepts.
Gain knowledge of the Analysis and System Design concepts.
Learn how to manage change during development.
Learn the SOA and AOP concepts.
UNIT I INTRODUCTION 9
System Concepts – Software Engineering Concepts - Software Life Cycle– Development Activities –
Managing Software Development – Unified Modelling Language – Project Organization – Communication.
UNIT II ANALYSIS 9
Requirements Elicitation – Use Cases – Unified Modelling Language, Tools – Analysis Object Model
(Domain Model) – Analysis Dynamic Models – Non-functional requirements –Analysis Patterns.
UNIT III SYSTEM DESIGN 9
Overview of System Design – Decomposing the system -System Design Concepts – SystemDesign Activities
– Addressing Design Goals – Managing System Design.
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 11
Department of CSE, REC
UNIT IV IMPLEMENTATION AND MANAGING CHANGE 9
Programming languages and coding- Human computer Interaction-Reusing Pattern Solutions –specifying
Interfaces – Mapping Models to Code – Testing Rationale Management – Configuration Management –
Project Management -real time interface design (eg: mobile design)
UNIT V ASPECT ORIENTED SOFTWARE DEVELOPMENT 9
AO Design Principles -Separations of Concerns, Subject Oriented Decomposition, Traits, Aspect Oriented
Decomposition, Theme Approach, Designing Base and Crosscutting Themes, Aspect-Oriented Programming
using Aspect-J
TOTAL: 45 PERIODS
OUTCOMES:
At the end of the course, the student will be able to:
A clear understanding of Software Engineering concepts.
Knowledge gained of Analysis and System Design concepts.
Ability to manage change during development.
Basic idea of the SOA and AOP concepts.
REFERENCES:
nd
1. Bernd Bruegge, Alan H Dutoit, Object-Oriented Software Engineering, 2 Edition, Pearson
Education, 2004.
rd
2. Craig Larman, Applying UML and Patterns, 3 Edition, Pearson Education, 2005.
th
3. Stephen Schach, Software Engineering 7 Edition, McGraw-Hill, 2007.
4. Aspect J in Action, RamnivasLaddad, Manning Publications, 2003
5. Aspect-Oriented Software Development, Robert E. Filman, TzillaElrad, SiobhanClarke, and Mehmet
Aksit, October 2006.
6. Aspect-Oriented Software Development with Use Cases, (The Addison-Wesley Object Technology
Series), Ivar Jacobson and Pan-Wei Ng, December 2004
7. Aspect-Oriented Analysis and Design: The Theme Approach, (The Addison-Wesley Object
Technology Series), Siobhan Clarke and Elisa Baniassad, March 2005.
8. Mastering AspectJ: Aspect-Oriented Programming in Java, Joseph D. Gradecki and Nicholas
Lesiecki, March 2003.
CP17111 ADVANCED DATA STRUCTURES LABORATORY L T P C
0 0 4 2
OBJECTIVES:
The student should be made to:
Learn to implement iterative and recursive algorithms.
Learn to design and implement algorithms using hill climbing and dynamic programming techniques.
Learn to implement shared and concurrent objects.
Learn to implement concurrent data structures.
LAB EXERCISES:
Each student has to work individually on assigned lab exercises. It is recommended that allimplementations
are carried out in Java. If C or C++ has to be used, then the threads librarywill be required for concurrency.
Exercises should be designed to cover the following topics:
1. Implementation of graph search algorithms.
2. Implementation and application of network flow and linear programming problems.
3. Implementation of algorithms using the hill climbing and dynamic programming design techniques.
4. Implementation of recursive backtracking algorithms.
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 12
Department of CSE, REC
5. Implementation of randomized algorithms.
6. Implementation of various locking and synchronization mechanisms for concurrentlinked lists, concurrent
queues, and concurrent stacks.
7. Developing applications involving concurrency.
TOTAL: 60 PERIODS
OUTCOMES:
At the end of the course, the student will be able to:
Design and apply iterative and recursive algorithms.
Design and implement algorithms using the hill climbing and dynamic programmingand recursive
backtracking techniques.
Design and implement optimisation algorithms for specific applications.
Design and implement randomized algorithms.
Implement and apply concurrent linked lists, stacks, and queues.
CP17201 CLOUD COMPUTING TECHNOLOGIES L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
Introduce the broad perceptive of cloud architecture and model
Understand the concept of Virtualization
Be familiar with the lead players in cloud
Understand the features of cloud simulator
Apply different cloud programming model as per need
UNIT I CLOUD ARCHITECTURE AND MODEL 9
Technologies for Network-Based System– System Models for Distributed and Cloud Computing – NIST
Cloud Computing Reference Architecture. Cloud Models- Characteristics– Cloud Services – Cloud models
(IaaS, PaaS, and SaaS) – Public vs Private Cloud –CloudSolutions - Cloud ecosystem – Service management
– Computing on demand.
UNIT II VIRTUALIZATION 9
Basics of Virtualization - Types of Virtualization - Implementation Levels of Virtualization-Virtualization
Structures - Tools and Mechanisms - Virtualization of CPU, Memory, I/O Devices -Virtual Clusters and
Resource management – Virtualization for Data-centre Automation.
UNIT III CLOUD INFRASTRUCTURE 9
Architectural Design of Compute and Storage Clouds – Layered Cloud Architecture Development– Design
Challenges - Inter Cloud Resource Management – Resource Provisioning and Platform Deployment – Global
Exchange of Cloud Resources.
UNIT IV PROGRAMMING MODEL 9
Parallel and Distributed Programming Paradigms – Map Reduce, Twister and Iterative Map Reduce –
Hadoop Library from Apache – Mapping Applications - Programming Support- Google App Engine,
Amazon AWS - Cloud Software Environments -Eucalyptus, OpenNebula, OpenStack, Aneka, CloudSim.
UNIT V SECURITY IN THE CLOUD 9
Security Overview – Cloud Security Challenges and Risks – Software-as-a-Service Security–Security
Governance – Risk Management – Security Monitoring – Security Architecture Design –Data Security –
Application Security – Virtual Machine Security - Identity Management and Access Control – Autonomic
Security.
TOTAL: 45 PERIODS
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 13
Department of CSE, REC
OUTCOMES:
At the end of the course, the student will be able to:
Compare the strengths and limitations of cloud computing
Identify the architecture, infrastructure and delivery models of cloud computing
Apply suitable virtualization concept.
Choose the appropriate cloud player
Choose the appropriate Programming Models and approach.
REFERENCES:
1. Kai Hwang, Geoffrey C Fox, Jack G Dongarra, ―Distributed and Cloud Computing, From Parallel
Processing to the Internet of Things‖, MorganKaufmann Publishers, 2012.
2. John W. Rittinghouse and James F. Ransome, ―Cloud Computing: Implementation, Management, and
Security‖, CRC Press, 2010.
3. Toby Velte, Anthony Velte, Robert Elsenpeter, ―Cloud Computing, A PracticalApproach‖, TMH,
2009.
4. Kumar Saurabh, ―Cloud Computing – insights into New-Era Infrastructure‖, WileyIndia, 2011.
5. George Reese, ―Cloud Application Architectures: Building Applications and Infrastructure in the
Cloud‖ O'Reilly
6. James E. Smith, Ravi Nair, ―Virtual Machines: Versatile Platforms for Systemsand Processes‖,
Elsevier/Morgan Kaufmann, 2005.
7. Katarina Stanoevska-Slabeva, Thomas Wozniak, Santi Ristol, ―Grid and Cloud Computing – A
Business Perspective on Technology and Applications‖, Springer.
8. Ronald L. Krutz, Russell Dean Vines, ―Cloud Security – A comprehensive Guide to Secure Cloud
Computing‖, Wiley – India, 2010.
9. Rajkumar Buyya, Christian Vecchiola, S. Tamarai Selvi, ‗Mastering Cloud Computing‖, TMGH,
2013.
10. Gautam Shroff, Enterprise Cloud Computing, Cambridge University Press, 2011
11. Michael Miller, Cloud Computing, Que Publishing,2008
CP17202 ADVANCED DATABASES L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
Learn the modelling and design of databases.
Acquire knowledge on parallel and distributed databases and its applications.
Study the usage and applications of Object Oriented database
Understand the principles of intelligent databases.
Understand the usage of advanced data models.
UNIT I PARALLEL AND DISTRIBUTED DATABASES 9
Database System Architectures: Centralized and Client-Server Architectures – Server System Architectures
– Parallel Systems- Distributed Systems – Parallel Databases: I/O Parallelism – Inter and Intra Query
Parallelism – Inter and Intra operation Parallelism –Design of Parallel Systems Distributed Database Concepts
- Distributed Data Storage –Distributed Transactions – Commit Protocols – Concurrency Control –
Distributed Query Processing – Case Studies
UNIT II OBJECT AND OBJECT RELATIONAL DATABASES 9
Concepts for Object Databases: Object Identity – Object structure – Type Constructors –Encapsulation of
Operations – Methods – Persistence – Type and Class Hierarchies –Inheritance– Complex Objects – Object
Database Standards, Languages and Design: ODMG Model – ODL –OQL – Object Relational and Extended –
Relational Systems: ObjectRelational features in SQL/Oracle – Case Studies.
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Department of CSE, REC
UNIT III INTELLIGENT DATABASES 9
Active Databases: Syntax and Semantics (Starburst, Oracle, DB2)- Taxonomy- ApplicationsDesign
Principles for Active Rules- Temporal Databases: Overview of TemporalDatabasesTSQL2- Deductive
Databases: Logic of Query Languages – Datalog- Recursive Rules-Syntax and Semantics of Datalog
Languages- Implementation of Rules andRecursion- Recursive Queries in SQL- Spatial Databases-
Spatial Data Types- SpatialRelationships- Spatial Data Structures Spatial Access Methods- Spatial DB
Implementation.
UNIT IV ADVANCED DATA MODELS 9
Mobile Databases: Location and Handoff Management - Effect of Mobility on Data Management -Location
Dependent Data Distribution - Mobile Transaction Models-Concurrency Control -Transaction Commit
Protocols- Multimedia Databases- Information Retrieval- Data Warehousing Data Mining- Text Mining.
UNIT V EMERGING TECHNOLOGIES 9
XML Databases: XML-Related Technologies-XML Schema- XML Query Languages- StoringXML in
Databases-XML and SQL- Native XML Databases- Web Databases- GeographicInformation Systems-
Biological Data Management- Cloud Based Databases: Data StorageSystems on the Cloud- Cloud Storage
Architectures-Cloud Data Models- Query LanguagesIntroductiontoBigData-Storage-Analysis.
TOTAL: 45 PERIODS
OUTCOMES:
At the end of the course, the student will be able to:
Select the appropriate high performance database like parallel and distributed database
Model and represent the real world data using object oriented database
Design a semantic based database to meaningful data access
Embed the rule set in the database to implement intelligent databases
Represent the data using XML database for better interoperability
REFERENCES:
1. R. Elmasri, S.B. Navathe, ―Fundamentals of Database Systems‖, Fifth Edition, Pearson
Education/Addison Wesley, 2007.
2. Thomas Cannolly and Carolyn Begg, ―Database Systems, A Practical Approachto Design,
Implementation and Management‖, Third Edition, Pearson Education,2007.
3. Henry F Korth, Abraham Silberschatz, S. Sudharshan, ―Database System Concepts‖, Fifth Edition,
McGraw Hill, 2006.
4. C.J. Date, A. Kannan and S. Swamynathan,‖ An Introduction to Database Systems‖, Eighth Edition,
Pearson Education, 2006.
5. Raghu Ramakrishnan, Johannes Gehrke, ―Database Management Systems‖, McGraw Hill, Third
Edition 2004.
CP17203 MACHINE LEARNING TECHNIQUES L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
Understand the machine learning theory
Implement linear and non-linear learning models
Implement distance-based clustering techniques
Build tree and rule based models
Apply reinforcement learning techniques
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 15
Department of CSE, REC
UNIT I FOUNDATIONS OF LEARNING 9
Components of learning – learning models – geometric models – probabilistic models – logicmodels –
grouping and grading – learning versus design – types of learning – supervised –unsupervised – reinforcement
– theory of learning – feasibility of learning – error and noise –training versus testing – theory of
generalization – generalization bound – approximationgeneralization tradeoff – bias and variance – learning
curve
UNIT II LINEAR MODELS 9
Linear classification – univariate linear regression – multivariate linear regression –regularized regression –
Logistic regression – perceptrons – multilayer neural networks –learning neural networks structures – support
vector machines – soft margin SVM – goingbeyond linearity – generalization and overfitting – regularization
– validation
UNIT III DISTANCE-BASED MODELS 9
Nearest neighbor models – K-means – clustering around medoids – silhouttes – hierarchicalclustering – k-d
trees – locality sensitive hashing – non-parametric regression – ensemblelearning– bagging and random
forests – boosting – meta learning
UNIT IV TREE AND RULE MODELS 9
Decision trees – learning decision trees – ranking and probability estimation trees –regression trees –
clustering trees – learning ordered rule lists – learning unordered rule lists– descriptive rule learning –
association rule mining – first-order rule learning
UNIT V REINFORCEMENT LEARNING 9
Passive reinforcement learning – direct utility estimation – adaptive dynamic programming –temporal-
difference learning – active reinforcement learning – exploration – learning an action utility function –
Generalization in reinforcement learning – policy search – applications in game playing – applications in
robot control
TOTAL: 45 PERIODS
OUTCOMES:
At the end of the course, the student will be able to:
Explain theory underlying machine learning
Construct algorithms to learn linear and non-linear models
Implement data clustering algorithms
Construct algorithms to learn tree and rule-based models
Apply reinforcement learning techniques
REFERENCES:
1. Y. S. Abu-Mostafa, M. Magdon-Ismail, and H.-T. Lin, ―Learning from Data‖, AML Book
Publishers, 2012.
2. P. Flach, ―Machine Learning: The art and science of algorithms that make senseof data‖, Cambridge
University Press, 2012.
3. K. P. Murphy, ―Machine Learning: A probabilistic perspective‖, MIT Press, 2012.
4. C. M. Bishop, ―Pattern Recognition and Machine Learning‖, Springer, 2007.
5. D. Barber, ―Bayesian Reasoning and Machine Learning‖, Cambridge UniversityPress, 2012.
6. M. Mohri, A. Rostamizadeh, and A. Talwalkar, ―Foundations of Machine Learning‖, MIT Press,
2012.
7. T. M. Mitchell, ―Machine Learning‖, McGraw Hill, 1997.
8. S. Russel and P. Norvig, ―Artificial Intelligence: A Modern Approach‖, Third Edition, Prentice
Hall, 2009.
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 16
Department of CSE, REC
CP17204 BIG DATA ANALYTICS L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
Understand big data for business intelligence
Learn business case studies for big data analytics
Understand NoSQL big data management
Perform map-reduce analytics using Hadoop and related tools
UNIT I UNDERSTANDING BIG DATA 9
What is big data – why big data – convergence of key trends – unstructured data – industryexamples of big
data – web analytics – big data and marketing – fraud and big data – riskand big data – credit risk
management – big data and algorithmic trading – big data andhealthcare – big data in medicine – advertising
and big data – big data technologies –introduction to Hadoop – open source technologies – cloud and big data
– mobile businessintelligence – Crowd sourcing analytics – inter and trans firewall analytics
UNIT II NOSQL DATA MANAGEMENT 9
Introduction to NoSQL – aggregate data models – aggregates – key-value and documentdata models –
relationships – graph databases – schema less databases – materialized views – distribution models – sharding
– master-slave replication – peer-peer replication –sharding and replication – consistency – relaxing
consistency – version stamps – map reduce–partitioningandcombining–composingmap-reducecalculations
UNIT III BASICS OF HADOOP 9
Data format –analyzing data with Hadoop – scaling out – Hadoop streaming – Hadooppipes –design of
Hadoop distributed file system (HDFS) – HDFS concepts – Java interface –data flow –Hadoop I/O – data
integrity – compression – serialization – Avro – file-based datastructures
UNIT IV MAPREDUCE APPLICATIONS 9
Map Reduce workflows – unit tests with MR Unit – test data and local tests – anatomy of Map Reduce job run
– classic Map-reduce – YARN – failures in classic Map-reduce andYARN –job scheduling – shuffle and sort
– task execution – Map Reduce types – input formats – output formats
UNIT V HADOOP RELATED TOOLS 9
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 PigLatin scripts-Hive – data types and file formats – HiveQL data
definition – HiveQL data manipulation – HiveQL queries.
TOTAL: 45 PERIODS
OUTCOMES:
At the end of the course, the student will be able to:
Describe big data and use cases from selected business domains
Explain NoSQL big data management
Install, configure, and run Hadoop and HDFS
Perform map-reduce analytics using Hadoop
Use Hadoop related tools such as HBase, Cassandra, Pig, and Hive for big data analytics
REFERENCES:
1. Michael Minelli, Michelle Chambers, and Amiga Hiram, ―Big Data, Big Analytics: Emerging
Business Intelligence and Analytic Trends for Today‘s Businesses‖, Wiley,2013.
2. P. J. Sadalage and M. Fowler, "NoSQL Distilled: A Brief Guide to the Emerging World ofPolyglot
Persistence", Addison-Wesley Professional, 2012.
3. Tom White, "Hadoop: The Definitive Guide", Third Edition, O'Reilley, 2012.
4. Eric Sammer, "Hadoop Operations", O'Reilley, 2012.
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 17
Department of CSE, REC
5. E. Capriolo, D. Wampler, and J. Rutherglen, "Programming Hive", O'Reilley, 2012.
6. Lars George, "HBase: The Definitive Guide", O'Reilley, 2011.
7. Eben Hewitt, "Cassandra: The Definitive Guide", O'Reilley, 2010.
8. Alan Gates, "Programming Pig", O'Reilley, 2011.
CP17211 CLOUD COMPUTING LABORATORY L T P C
0 0 4 2
OBJECTIVES:
The student should be made to:
Appreciate cloud architecture
Create and run virtual machines on open source OS
Implement Infrastructure, storage as a Service.
Install and appreciate security features for cloud
EXPERIMENTS:
1. Study of Cloud Computing & Architecture
2. Virtualization in Cloud
3. Study and implementation of Infrastructure as a Service
4. Study and installation of Storage as Service
5. Implementation of identity management
6. Write a program for web feed
7. Study and implementation of Single-Sing-On
8. Securing Servers in Cloud
9. User Management in Cloud
10. Case study on Amazon EC2
11. Case study on Microsoft azure
12. Mini project
TOTAL: 60 PERIODS
CP17212 TERM PAPER WRITING AND SEMINAR L T P C
0 0 2 1
In this course, students will develop their scientific and technical reading and writing skills that they need to
understand and construct research articles. A term paper requires a student to obtain information from a
variety of sources (i.e., Journals, dictionaries, reference books) and then place it in logically developed ideas.
The work involves the following steps:
1. Selecting a subject, narrowing the subject into a topic
2. Stating an objective.
3. Collecting the relevant bibliography (at least 15 journal papers)
4. Preparing a working outline.
5. Studying the papers and understanding the author‘s contributions and critically analysing each paper.
6. Preparing a working outline
7. Linking the papers and preparing a draft of the paper.
8. Preparing conclusions based on the reading of all the papers.
9. Writing the Final Paper and giving final Presentation Please keep a file where the work carried out by
you is maintained.
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 18
Department of CSE, REC
10. Activities to be carried out
Activity Instructions Submission Evaluation
week
nd
Selection of area of You are requested to select an area of 2 week 3 % Based on clarity of
interest and Topic interest, topic and state an objective thought, current relevance
Stating an in and clarity in writing
writing Objective
rd
Collecting 1. List 1 Special Interest Groups or 3 week 3% ( the selected
Information about professional society information must be area
your area & topic 2. List 2 journals specific and of
3. List 2 conferences, symposia or international and national
workshops standard)
4. List 1 thesis title
5. List 3 web presences (mailing lists,
forums, news sites)
6. List 3 authors who publish regularly
in your area
7. Attach a call for papers (CFP) from
your area.
th
Collection of You have to provide a 4 week 6% ( the list of standard
Journal papers in complete list of references papers and reason for
the topic in the you will be using- Based on selection)
context of the your objective -Search various
objective – collect digital libraries and Google
20 & then filter Scholar When picking papers
to read - try to:
Pick papers that are related to
each
other in some ways and/or
that are in the same field so
that you can write a
meaningful survey out of
them, Favor papers from well-
known
journals and conferences,
Favour ―first or
―foundational papers in the
field (as indicated in other
people ‗s survey paper),
Favour more recent papers,
Pick a recent survey of the
field so you can quickly gain
an overview,
Find relationships with
respect to each other and to
your topic area (classification
scheme/categorization)
Mark in the hard copy of
papers whether complete
work or section/sections of the
paper are being considered
th
Reading and notes Reading Paper Process 5 week 8% ( the table given
for first 5 papers For each paper form a Table should indicate your
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 19
Department of CSE, REC
answering the following understanding of the
questions: paper and the evaluation
What is the main topic of the is based on your
article? conclusions about each
What was/were the main paper)
issue(s) the author said they
want to discuss?
Why did the author claim it
was important?
How does the work build on
other ‗s work, in the author ‗s
opinion?
What simplifying assumptions
does the author claim to be
making?
What did the author do?
How did the author claim they
were going to evaluate their
work and compare it to
others?
What did the author say were
the limitations of their
research?
What did the author say were
the important directions for
future research?
Conclude with limitations/issues not
addressed by the paper ( from the
perspective of your survey)
th
Reading and notes Repeat Reading Paper Process 6 week 8% ( the table given
for next5 papers should indicate your
understanding of the
paper and the evaluation
is based on your
conclusions about each
paper)
th
Reading and notes Repeat Reading Paper Process 7 week 8% ( the table given
for final 5 papers should indicate your
understanding of the
paper and the evaluation
is based on your
conclusions about each
paper)
th
Draft outline 1 and Prepare a draft Outline, your survey 8 week 8% ( this component will
Linking papers goals, along with a classification / be evaluated based on the
categorization diagram linking and classification
among the papers)
th
Abstract Prepare a draft abstract and give a 9 week 6% (Clarity, purpose and
presentation conclusion) 6%
Presentation & Viva Voce
th
Introduction Write an introduction and background 10 week 5% ( clarity)
Background sections
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 20
Department of CSE, REC
th
Sections of the Write the sections of your paper based 11 week 10% (this component will
paper on the classification / categorization be evaluated based on the
diagram in keeping with the goals of linking and classification
your survey among the papers)
th
Your conclusions Write your conclusions and future 12 week 5% ( conclusions – clarity
work and your ideas)
th
Final Draft Complete the final draft of your paper 13 week 10% (formatting, English,
Clarity and linking) 4%
Plagiarism Check Report
th
th
Seminar A brief 15 slides on your paper 14 & 15 10% (based on
week presentation and Viva-
voce)
TOTAL: 30 PERIODS
CP 17301 SECURITY PRINCIPLES L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
Understand the mathematical foundations of security principles
Appreciate the different aspects of encryption techniques
Understand the role played by authentication in security
Understand various key management techniques
Appreciate the current trends of security practices
UNIT I CLASSICAL CIPHERS 9
Classical Cryptography- Shift Cipher - Substitution Cipher - Affine Cipher – Cryptanalysis -Cryptanalysis of
the Affine Cipher - Cryptanalysis of the Substitution Cipher - Cryptanalysis of the Vigenere Cipher –
Shannon‘s Theory
UNIT II SYMMETRIC CIPHERS AND HASH FUNCTIONS 9
Substitution-Permutation Networks - Linear Cryptanalysis - Differential Cryptanalysis – DataEncryption
Standard - Advanced Encryption Standard - Modes of Operation -Cryptography HashFunction - Hash
Function and Data Integrity - Security of Hash Function - Iterated Hash Functions- Message Authentication
Codes
UNIT III PUBLIC-KEY ENCRYPTION TECHNIQUES 9
Introduction to Public–key Cryptography - Number theory - RSA Cryptosystem - Attacks on RSA –El-Gamal
Cryptosystem - Shanks Algorithm - Elliptic Curves over the Reals - Elliptical CurvesModulo a Prime -
Signature Scheme – Digital Signature Algorithm
UNIT IV KEY MANAGEMENT 9
Identification Scheme and Entity Attenuation - Challenge and Response in the Secret-key Setting -Challenge
and Response in the Public Key Setting - Schnorr Identification Scheme – Key distribution - Diffie-Hellman
Key - Pre-distribution - Unconditionally Secure key Pre-distribution -Key Agreement Scheme - Diffie-
Hellman Key agreement - Public key infrastructure – Certificates - Trust Models.
UNIT V SECURITY PRACTICES 9
Transport-Level Security – SSL – TLS - HTTPS – SSH - Electronic Mail Security - Pretty GoodPrivacy - IP
Security - IP Security Architecture – Authentication Header – Encapsulating SecurityPayload – Key
Management - Legal and Ethical Issues
TOTAL: 45 PERIODS
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 21
Department of CSE, REC
OUTCOMES:
At the end of the course, the student will be able to:
Use the mathematical foundations in security principles
Identify the features of encryption and authentication
Implement the public key encryption techniques
Identify and distribute the keys.
Discuss available security practices
TEXT BOOKS:
1. Douglas R. Stinson, ―Cryptography Theory and Practice‖, Third Edition, Chapman & Hall/CRC,2006.
2. William Stallings, ―Cryptography and Network Security: Principles and Practices‖, Sixth Edition, Pearson
Education, 2013.
REFERENCES:
1. Wenbo Mao, ―Modern Cryptography – Theory and Practice‖, Pearson Education, 2003.
2. Charles B. Pfleeger, Shari Lawrence Pfleeger, ―Security in Computing‖, Fourth Edition, Pearson Education,
2007.
3. Wade Trappe and Lawrence C. Washington, ―Introduction to Cryptography with Coding Theory‖, Second
Edition, Pearson Education, 2007.
PROFESSIONAL ELECTIVES(PE)
SEMESTER II
ELECTIVE I
CP17E21 PERFORMANCE EVALUATION OF COMPUTER SYSTEMS L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
Understand the mathematical foundations needed for performance evaluation of computer systems
Understand the metrics used for performance evaluation
Understand the analytical modeling of computer systems
Enable the students to develop new queuing analysis for both simple and complex systems
Appreciate the use of smart scheduling and introduce the students to analytical techniques for
evaluating scheduling policies
UNIT I OVERVIEW OF PERFORMANCE EVALUATION 9
Need for Performance Evaluation in Computer Systems – Overview of Performance Evaluation Methods –
Introduction to Queuing – Probability Review – Generating Random Variables for Simulation – Sample
Paths, Convergence and Averages – Little ‗s Law and other Operational Laws – Modification for Closed
Systems - Modification Analysis Examples
UNIT II MARKOV CHAINS AND SIMPLE QUEUES 9
Discrete-Time Markov Chains – Ergodicity Theory – Real World Examples – Google, Aloha – Transition to
Continuous-Time Markov Chain – M/M/1 and PASTA.
UNIT III MULTI-SERVER AND MULTI-QUEUE SYSTEMS 9
Server Farms: M/M/k and M/M/k/k – Capacity Provisioning for Server Farms – Time Reversibility and Burke
‗s Theorem – Networks of Queues and Jackson Product Form – Classed and Closed Networks of Queues.
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 22
Department of CSE, REC
UNIT IV REAL-WORLD WORKLOADS 9
Case Study of Real-world Workloads – Phase-Type Distributions and Matrix-Analytic Methods – Networks
with Time-Sharing Servers – M/G/1 Queue and the Inspection Paradox – Task Assignment Policies for Server
Farms-Transform Analysis.
UNIT V SMART SCHEDULING IN THE M/G/1 9
Performance Metrics – Scheduling Non-Preemptive and Preemptive Non-Size-Based Policies - Scheduling
Non-Preemptive and Preemptive Size-Based Policies – Scheduling - SRPT and Fairness - Comparisons with
Other Policies
TOTAL: 45 PERIODS
OUTCOMES:
At the end of the course, the student will be able to:
Identify the need for performance evaluation and the metrics used for it
Distinguish between open and closed queuing networks
Use Little‘s law and other operational laws and apply to open and closed systems
Use discrete-time and continuous-time Markov chains to model real world systems
Develop analytical techniques for evaluating scheduling policies
TEXT BOOK:
1. MorHarchol, Balter, Performance Modeling and Design of Computer Systems – Queueing Theory in
Action, Cambridge University Press, 2013.
REFERENCES:
1. K. S. Trivedi, Probability and Statistics with Reliability, Queueing and Computer Science
Applications, John Wiley and Sons, 2001.
2. Krishna Kant, Introduction to Computer System Performance Evaluation, McGraw-Hill, 1992.
3. Lieven Eeckhout, Computer Architecture Performance Evaluation Methods, Morgan and Claypool
Publishers, 2010.
4. Paul J. Fortier and Howard E. Michel, Computer Systems Performance Evaluation and Prediction‖,
Elsevier, 2003.
5. Raj Jain, The Art of Computer Systems Performance Analysis: Techniques for Experimental Design,
Measurement, Simulation and Modeling, Wiley-Inter science, 1991.
CP17E22 DATA ANALYSIS AND BUSINESS INTELLIGENCE L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
Understand linear regression models
Understand logistic and generalized linear models
Understand simulation and causal inference using regression models
Understand multilevel regression
Understand data collection and model understanding
UNIT I LINEAR REGRESSION 9
Introduction to data analysis – Statistical processes – statistical models – statistical inference – review of
random variables and probability distributions – linear regression – one predictor – multiple predictors –
prediction and validation – linear transformations – centering and standardizing – correlation – logarithmic
transformations – other transformations – building regression models – fitting a series of regressions.
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 23
Department of CSE, REC
UNIT II LOGISTIC AND GENERALIZED LINEAR MODELS 9
Logistic regression – logistic regression coefficients – latent-data formulation – building a logistic regression
model – logistic regression with interactions – evaluating, checking, and comparing fitted logistic regressions
– identifiability and separation – Generalized linear models: Poisson regression – logistic-binomial model –
Probit regression – multinomial regression – robust regression using t model – building complex generalized
linear models – constructive choice models.
UNIT III SIMULATION AND CAUSAL INFERENCE 9
Models and Statistical inference: Simulation of probability models – summarizing linear regressions –
simulation of non-linear predictions – predictive simulation for generalized linear models –Statistical
Procedures and model Fits: - fake-data simulation – simulating and comparing to actual data – predictive
simulation to check the fit of a time-series model – Causal inference: – Randomized experiments –
observational studies – causal inference using advanced models – matching – instrumental variables.
UNIT IV MULTILEVEL REGRESSION 9
Multilevel structures: – clustered data – multilevel linear models - Repeated measurements - Indicator
variables and fixed or random effects - Costs and benefits – Multilevel linear models: - partial pooling –
Quickly fitting multilevel models in R - group-level predictors – model building and statistical significance –
varying intercepts and slopes – scaled inverse-Wishart distribution – non-nested models – multi-level logistic
regression – multi-level generalized linear models.
UNIT V DATA COLLECTION AND MODEL UNDERSTANDING 9
Design of data collection – classical power calculations – multilevel power calculations for cluster sampling –
– multilevel power calculation using fake-data simulation – understanding and summarizing fitted models –
2
uncertainty and variability – variances – R and explained variance – multiple comparisons and statistical
significance – Analysis of Variance: Classical analysis of variance – ANOVA and multilevel linear and
general linear models – Causal inference using multilevel models.
TOTAL: 45 PERIODS
OUTCOMES:
At the end of the course, the student will be able to:
Build and apply linear regression models.
Build and apply logistic and generalized linear models.
Perform simulation and casual inference from data using regression models.
Build and apply multilevel regression models.
Perform data collection and variance analysis.
TEXT BOOKS:
1. Andrew Gelman and Jennifer Hill, "Data Analysis using Regression and multilevel/Hierarchical
Models", Cambridge University Press, 2006.
REFERENCES:
1. Philipp K. Janert, "Data Analysis with Open Source Tools", O'Reilley, 2010.
2. Wes McKinney, "Python for Data Analysis", O'Reilley, 2012.
3. Davinderjit Sivia and John Skilling, "Data Analysis: A Bayesian Tutorial", Second Edition, Oxford
University Press, 2006.
4. Robert Nisbelt, John Elder, and Gary Miner, "Handbook of statistical analysis and data mining
applications", Academic Press, 2009.
5. Michael Minelli, Michelle Chambers, and Ambiga Dhiraj, "Big Data, Big Analytics: Emerging
Business Intelligence and Analytic Trends for Today's Businesses", Wiley, 2013.
6. John Maindonald and W. John Braun, "Data Analysis and Graphics Using R: An Examplebased
Approach", Third Edition, Cambridge University Press, 2010.
7. David Ruppert, "Statistics and Data Analysis for Financial Engineering", Springer, 2011.
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 24
Department of CSE, REC
CP17E23 IMAGE PROCESSING AND ANALYSIS L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
understand the image processing concepts and analysis
understand the image processing techniques
familiarize the image processing environment and their applications,
appreciate the use of image processing in various applications
understand the image Registration and Visualization
UNIT I IMAGE PROCESSING FUNDAMENTALS 9
Introduction – Elements of visual perception, Steps in Image Processing Systems – Digital Imaging System -
Image Acquisition – Sampling and Quantization – Pixel Relationships – File Formats – colour images and
models - Image Operations – Arithmetic, logical, statistical and spatial operations.
UNIT II IMAGE ENHANCEMENT AND RESTORATION 9
Image Transforms -Discrete and Fast Fourier Transform and Discrete Cosine Transform, Spatial Domain -
Gray Level Transformations Histogram Processing Spatial Filtering – Smoothing and Sharpening. Frequency
Domain: Filtering in Frequency Domain – Smoothing and Sharpening filters.
UNIT III IMAGE SEGMENTATION AND MORPHOLOGY 9
Detection of Discontinuities – Edge Operators – Edge Linking and Boundary Detection – Thresholding –
Region Based Segmentation – Motion Segmentation, Image Morphology: Binary and Gray level morphology
operations - Erosion, Dilation, Opening and Closing Operations Distance Transforms- Basic Morphological
Algorithms. Features – Textures - Boundary representations and Descriptions- Component Labeling.
UNIT IV IMAGE ANALYSIS AND CLASSIFICATION 9
Image segmentation- pixel based, edge based, region based segmentation. Active contour models and Level
sets for medical image segmentation, Image representation and analysis, Feature extraction and
representation, Statistical, Shape, Texture, feature and statistical image classification.
UNIT V IMAGE REGISTRATION AND VISUALIZATION 9
Rigid body visualization, Principal axis registration, Interactive principal axis registration, Feature based
registration, Elastic deformation based registration, Image visualization – 2D display methods, 3D display
methods, virtual reality based interactive visualization.
TOTAL: 45 PERIODS
OUTCOMES:
At the end of the course, the student will be able to:
Design and implement algorithms for image processing applications that incorporates different
concepts of medical Image Processing
Familiar with the use of MATLAB and its equivalent open source tools
Critically analyze different approaches to image processing applications
Explore the possibility of applying Image processing concepts in various applications
TEXT BOOK:
1. Rafael C.Gonzalez and Richard E.Woods, Digital Image Processing, Third Edition, Pearson Education,
2008, New Delhi
REFERENCES:
1. Alasdair McAndrew, Introduction to Digital Image Processing with Matlab, Cengage Learning 2011
2. Anil J Jain, Fundamentals of Digital Image Processing, PHI, 2006.
3. Kavyan Najarian and Robert Splerstor, Biomedical signals and Image processing, CRC – Taylor and
Francis, New York, 2006
4. S.Sridhar, Digital Image Processing, Oxford University Press, 2011
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 25
Department of CSE, REC
CP17E24 SOFTWARE QUALITY ASSURANCE AND TESTING L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
Learn the software quality assurance, components, metrics, defect prevention techniques.
Learn the techniques for quality assurance and applying for applications.
Understand the basics concepts of testing, planning and testing team formation.
Learn the concepts of different software testing strategies.
Build design concepts for system testing and execution.
UNIT I SOFTWARE QUALITY 9
Introduction to Software Quality - The Software Quality challenge- Software Quality factors- The
components of Software Quality Assurance System-People‘s Quality Expectations- Frameworks and ISO-
9126-McCall ‗s Quality Factors and Criteria – Relationship. Quality Metrics- Quality Characteristics ISO
9000:2000- Software Quality Standard-Maturity models- Test Process Improvement- Testing Maturity Model.
UNIT II SOFTWARE QUALITY ASSURANCE 9
SQA Components in Project Life Cycle -SQA Defect Removal Policies – Reviews. Quality Assurance - Root
Cause Analysis- modeling- technologies-standards and methodologies for defect prevention- Fault Tolerance
and Failure Containment - Safety Assurance and Damage Control- Hazard analysis using fault-trees and
event-trees- Comparing Quality Assurance Techniques and Activities- QA Monitoring and Measurement-
Risk Identification for Quantifiable Quality Improvement- Case Study: FSM-Based Testing of Web-Based
Applications.
UNIT III SOFTWARE TESTING BASIC CONCEPTS 9
Objectives of Testing- Role of Testing, Verification and Validation- Failure- Error- Fault- and Defect- Central
Issue in Testing- Testing Activities Classifications- White-Box and Black- box-test Planning and design-
Monitoring and Measuring Test Execution-Test Tools and Automation- Test Team Organization and
Management-Test Groups- Software Quality Assurance Group -System Test Team Hierarchy-Team Building.
UNIT IV SOFTWARE TESTING STRATEGIES 9
System Integration testing - System Integration Techniques- Software and Hardware Integration-Test Plan for
System Integration- Off-the-Shelf Component Integration-Functional testing – Concepts- Complexities-
Equivalence Class Partitioning-Boundary Value Analysis-Decision Tables- Acceptance testing – Types-
Selection of Acceptance Criteria- Acceptance Test Plan- Test Execution-Test Report- Acceptance Testing in
extreme Programming.
UNIT V SYSTEM TESTING 9
Taxonomy of System Tests- Functionality Tests- Robustness Tests- Interoperability Tests- Scalability Tests-
Stress Tests- Load and Stability Tests- Reliability Tests- Regression Tests- Regulatory Tests- Test Design
Factors- Requirement Identification-modeling a Test Design Process- Test Design Preparedness- Metrics
andTest Case Design Effectiveness- Modeling Defects- Metrics for Tracking System Test-Defect Causal
Analysis- Beta testing- measuring Test Effectiveness.
TOTAL: 45 PERIODS
OUTCOMES:
At the end of the course, the student will be able to:
Identify defect prevention techniques and software quality assurance metrics.
Apply techniques of quality assurance for typical applications.
Understand various software testing strategies.
Understand system testing and test execution process.
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 26
Department of CSE, REC
TEXT BOOKS:
1. Software Quality Assurance – From Theory to Implementation, Daniel Galin, Pearson Education,
2009.
2. Software Testing and Quality Assurance-Theory and Practice, Kshirasagar Nak Priyadarshi Tripathy,
John Wiley & Sons Inc, 2008.
REFERENCES:
1. Software Quality Engineering: Testing, Quality Assurance, and Quantifiable Improvement, Jeff Tian,
John Wiley & Sons, Inc., Hoboken, New Jersey. 2005.
2. Software Quality Assurance, Milind Limaye, TMH, New Delhi, 2011.
3. Ron Patton, ―Software Testing‖, Second Edition, Pearson Education, 2007.
CP17E25 RANDOMIZED ALGORITHMS L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
Understand the mathematical foundations needed for understanding and designing randomized
algorithms
Appreciate the need for randomized algorithms
Expose the students to probabilistic methods
Understand the concept of random walk
Expose the students to different types of applications of randomized algorithms
UNIT I INTRODUCTION TO RANDOMIZED ALGORITHMS 9
Introduction to Randomized Algorithms - Min-cut – Elementary Probability Theory – Models of Randomized
Algorithms – Classification of Randomized Algorithms – Paradigms of the Design of Randomized
Algorithms - Game Theoretic Techniques – Game Tree Evaluation – Minimax Principle – Randomness and
Non Uniformity.
UNIT II PROBABILISTIC METHODS AND ALGORITHM 9
Moments and Deviations – occupancy Problems – Markov and Chebyshev Inequalities – Randomized
Selection – Two Point Sampling – The Stable Marriage Problem – The Probabilistic Method – Maximum
Satisfiability – Expanding Graphs – Method of Conditional Probabilities – Markov Chains and Random
Walks – 2-SAT Example – Random Walks on Graphs – Random Connectivity-Probabilistic algorithms-
Interactive proofs-Randomized Verifier- Amplification-Math Prerequisite-Proof of Amplification Lemma
UNIT III ALGEBRAIC TECHNIQUES AND APPLICATIONS 9
Fingerprinting Techniques – Verifying Polynomial Identities – Perfect Matching in Graphs – PatternMatching
– Verification of Matrix Multiplication - Data Structuring Problems – Random Treaps – Skip Lists – Hash
Tables.
UNIT IV GEOMETRIC AND GRAPH ALGORITHMS 9
Randomized Incremental Construction – Convex Hulls – Duality – Trapezoidal Decompositions –Linear
Programming – Graph Algorithms – Min-cut – Minimum Spanning Trees.
UNIT V HASHING AND ONLINE ALGORITHMS 9
Hashing – Universal Hashing - Online Algorithms – Randomized Online Algorithms – Online Paging –
Adversary Models – Relating the Adversaries – The k-server Problem Hashing Algorithm -Collision
Resolution-Separate chaining--Linear probing—Double Hashing
TOTAL: 45 PERIODS
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 27
Department of CSE, REC
OUTCOMES:
At the end of the course, the student will be able to:
Identify the need for randomized algorithms
Discuss the classification of randomized algorithms
Present the various paradigms for designing randomized algorithms
Discuss the different probabilistic methods used for designing randomized algorithms
Apply the techniques studied to design algorithms for different applications like matrix multiplication,
hashing, linear programming
TEXT BOOK:
1. Rajeev Motwani and Prabhakar Raghavan, ―Randomized Algorithms‖, Cambridge University Press,
1995.
REFERENCES:
1. Juraj Hromkovic,‖Design and Analysis of Randomized Algorithms‖, Springer, 2010.
2. Michael Mitzenmacher and Eli Upfal, ―Probability and Computing – Randomized Algorithms and
Probabilistic Analysis‖, Cambridge University Press, 2005.
CP17E26 MOBILE AND PERVASIVE COMPUTING L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
Learn the basic architecture and concepts of tele communication systems.
Understand the latest 4G Telecommunication System Principles.
Study the overview of pervasive concepts and elements.
Explore the HCI in Pervasive computing and its scenario,
Apply the pervasive concepts in mobile environment.
UNIT I INTRODUCTION 9
Applications – Telecommunication systems: GSM – DECT – TETRA – UMTS – IMT – 2000 – Satellite
systems - Wireless LAN- IEEE 802.11-HIPERLAN- Blue tooth-Wi-Fi- WiMAX- Mobile IP -WAP- Data
networks – SMS – GPRS – EDGE – Hybrid Wireless100 Networks – ATM – Wireless ATM.
UNIT II OVERVIEW OF A MODERN 4G TELECOMMUNICATIONS SYSTEM 9
Introduction- LTE-A System Architecture- LTE RAN- OFDM Air Interface- Evolved Packet Core- LTE
Requirements- LTE-Advanced- LTE-A in Release 11- OFDMA- Introduction- OFDM Principles- LTE
Uplink- SC-FDMA- Summary of OFDMA.
UNIT III PERVASIVE CONCEPTS AND ELEMENTS 9
Technology Trend Overview - Pervasive Computing: Concepts - Challenges – Technology- The Structure and
Elements of Pervasive Computing Systems-Infrastructure and Devices- Middleware- Pervasive Computing
Environments - Smart Car Space - Intelligent Campus. Context Collection- User Tracking- and Context
Reasoning.
UNIT IV HCI IN PERVASIVE COMPUTING 9
Overview- HCI Service and Interaction Migration -Context- Driven HCI Service Selection -Interaction
Service Selection Overview - User Devices -Context Manager- Local Service Matching- Global Combination
selection - Scenario Study- Video Calls at a Smart Office - Scenario Description - HCI Migration Request-
Context Format- Device Profile.
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 28
Department of CSE, REC
UNIT V PERVASIVE MOBILE TRANSACTIONS 9
Introduction to Pervasive Transactions - Mobile Transaction Framework - Context-Aware Pervasive
Transaction Model -Dynamic Transaction Management -Formal Transaction Verification- Case Studies-
campus Prototype-IPSpace- An IPv6- Enabled Intelligent Space.
TOTAL PERIODS: 45
OUTCOMES:
At the end of the course, the student will be able to:
Obtain a thorough understanding of Basic architecture and concepts of till Third Generation
Communication systems.
Explain the latest 4G Telecommunication System Principles.
Incorporate the pervasive concepts.
Implement the HCI in Pervasive environment.
Work on the pervasive concepts in mobile environment.
TEXT BOOKS:
1. J.Schiller, Mobile Communication, Addison Wesley, 2000.
2. Juha Korhonen, Introduction to 4G Mobile Communications , Artech House Publishers, 2014.
REFERENCES
1. MinyiGuo, Jingyu Zhou, Feilong Tang, Yao Shen, Pervasive Computing: Concepts, Technologies
and Applications, CRC Press, 2016
2. Alan Colman, Jun Han, and Muhammad Ashad Kabir, Pervasive Social Computing Socially-Aware
Pervasive Systems and Mobile Applications, Springer, 2016.
3. Kolomvatsos, Kostas, Intelligent Technologies and Techniques for Pervasive Computing, IGI Global,
2013.
4. M. Bala Krishna, Jaime Lloret Mauri, Advances in Mobile Computing and Communications:
Perspectives and Emerging Trends in 5G Networks, CRC 2016
CP17E27 SOFTWARE REQUIREMENTS ENGINEERING L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
Understand the basics of requirements engineering
Learn different techniques used for requirements elicitation
Know the role played by requirements analysis in requirement integration
Appreciate the use of various methodologies for requirements development
Study the current trends in requirements prioritization and validation.
UNIT I REQUIREMENTS ENGINEERING OVERVIEW 9
Software Requirement Overview – Software Development Roles –Software Development Process Kernels –
Commercial Life Cycle Model – Vision Development – Stakeholders Needs & Analysis – Stakeholder needs
–Stakeholder activities.
UNIT II REQUIREMENTS ELICITATION 9
The Process of Requirements Elicitation – Requirements Elicitation Problems – Problems of Scope –
Problems of Understanding – Problems of Volatility – Current Elicitation Techniques – Information
Gathering – Requirements Expression and Analysis – Validation – An Elicitation Methodology Framework –
A Requirements Elicitation Process Model – Methodology over Method – Integration of Techniques – Fact-
Finding – Requirements Gathering – Evaluation and Rationalization.
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 29
Department of CSE, REC
UNIT III REQUIREMENTS ANALYSIS 9
Identification of Functional and Non Functional Requirements – Identification of Performance Requirements
– Identification of safety Requirements – Analysis – Feasibility and Internal Compatibility of System
Requirements.
UNIT IV REQUIREMENTS DEVELOPMENT 9
Requirements analysis – Requirements Documentation – Requirements Development Workflow –
Fundamentals of Requirements Development – Requirements Attributes Guidelines Document –
Supplementary Specification Document – Use Case Specification Document – Methods for Software
Prototyping – Evolutionary prototyping.
UNIT V REQUIREMENTS VALIDATION 9
Validation objectives – Analysis of requirements validation – Activities – Properties – Requirement reviews –
Requirements testing – Case tools for requirements engineering.
TOTAL:45 PERIODS
OUTCOMES:
At the end of the course, the student will be able to:
Elicit and document requirements for software systems
Understand the implications of requirements on the broader software development life-cycle.
Prepare SRS including the details of requirements engineering
Describe the stages of requirements elicitation
Analyze software requirements gathering
TEXT BOOK:
1. Ian Sommerville, Pete Sawyer, Requirements Engineering: A Good Practice Guide, Sixth Edition,
Pearson Education, 2004
REFERENCES:
1. Dean Leffingwe, Don Widrig, Managing Software Requirements a Use Case Approach, Second
Addition, Addison Wesley, 2003
2. Karl Eugene Wiegers, Software Requirements, Word Power Publishers, 2000
3. Ian Graham, Requirements Engineering and Rapid Development, Addison Wesley, 1998
4. Wiegers, Karl, Joy Beatty, Software requirements, Pearson Education, 2013
CP17E28 DATA VISUALIZATION TECHNIQUES L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
Develop skills to both design and critique visualizations.
Introduce visual perception and core skills for visual analysis.
Understand visualization for time-series, ranking analysis and deviation analysis.
Understand visualization for distribution, correlation analysis and multivariate analysis.
Understand issues and best practices in information dashboard design.
UNIT I CORE SKILLS FOR VISUAL ANALYSIS 9
Information visualization – effective data analysis – traits of meaningful data – visual perception –making
abstract data visible – building blocks of information visualization – analytical interaction – analytical
navigation – optimal quantitative scales – reference lines and regions – trellises and crosstabs – multiple
concurrent views – focus and context – details on demand – over-plotting reduction – analytical patterns –
pattern examples.
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 30
Department of CSE, REC
UNIT II TIME-SERIES, RANKING, AND DEVIATION ANALYSIS 9
Time-series analysis – time-series patterns – time-series displays – time-series analysis techniques and best
practices – part-to-whole and ranking patterns – part-to-whole and ranking displays – part-to-whole and
ranking techniques and best practices – deviation analysis – deviation analysis displays – deviation analysis
techniques and best practices.
UNIT III DISTRIBUTION, CORRELATION, AND MULTIVARIATE ANALYSIS 9
Distribution analysis – describing distributions – distribution patterns – distribution displays – distribution
analysis techniques and best practices – correlation analysis – describing correlations – correlation patterns –
correlation displays – correlation analysis techniques and best practices – multivariate analysis – multivariate
patterns – multivariate displays – multivariate analysis techniques and best practices.
UNIT IV INFORMATION DASHBOARD DESIGN I 9
Information dashboard – Introduction– dashboard design issues and assessment of needs – Fundamental
Considerations for designing dashboard- Power of Visual perception – Achieving Eloquence through
simplicity.
UNIT V INFORMATION DASHBOARD DESIGN II 9
Advantages of Graphics - An Ideal Library of Graphs – Designing Bullet Graphs – Designing Sparklines –
Dashboard Display Media –Critical Design Practices – Putting it all together – imaging to Unveiling.
TOTAL: 45 PERIODS
OUTCOMES:
At the end of the course, the student will be able to:
Explain principles of visual perception
Apply core skills for visual analysis
Apply visualization techniques for time-series, ranking analysis and deviation analysis
Apply visualization techniques for distribution, correlation analysis and multivariate analysis
Design information dashboard
TEXT BOOK:
1. Stephen Few, "Information dashboard design: Displaying data for at-a-glance monitoring", second
edition, Analytics Press, 2013.
REFERENCES:
1. Ben Fry, "Visualizing data: Exploring and explaining data with the processing environment",
O'Reilly, 2008.
2. Edward R. Tufte, "The visual display of quantitative information", Second Edition, Graphics Press,
2001.
3. Evan Stubbs, "The value of business analytics: Identifying the path to profitability", Wiley, 2011.
4. Gert H. N. Laursen and Jesper Thorlund, "Business Analytics for Managers: Taking business
intelligence beyond reporting", Wiley, 2010.
5. Nathan Yau, "Data Points: Visualization that means something", Wiley, 2013.
6. Stephen Few, "Now you see it: Simple Visualization techniques for quantitative analysis", Analytics
Press, 2009.
7. Tamara Munzner, ―Visualization Analysis and Design‖, AK Peters Visualization Series, CRC Press,
Nov. 2014
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 31
Department of CSE, REC
SEMESTER III
ELECTIVES
CP17E31 BIO INFORMATICS L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
Get exposed to the fundamentals of bioinformatics.
Learn bio-informatics algorithm and phylogenetic concept.
Understand open problems and issues in replication and molecular clocks.
Learn assemble genomes and corresponding theorem.
Study and exposed to the domain of human genomics.
UNIT I INTRODUCTION AND FUNDAMENTALS 9
Fundamentals of genes, genomics, molecular evolution – genomic technologies – beginning of bioinformatics
- genetic data –sequence data formats – secondary database – examples – data retrieval systems – genome
browsers.
UNIT II BIOINFORMATICS ALGORITHM AND ANALYSIS 9
Sequence alignment and similarity searching in genomic databases: BLAST and FASTA – additional
bioinformatics analysis involving nucleic acid sequences-additional bioinformatics analysis involving protein
sequences – Phylogenetic Analysis.
UNIT III DNA REPLICATION AND MOLECULAR CLOCKS 9
Beginning of DNA replication – open problems – multiple replication and finding replication – computing
probabilities of patterns in a string-the frequency array-converting patterns solving problems- finding
frequents words-Big-O notation –case study-The Tower of Hanoi problem.
UNIT IV ASSEMBLE GENOMES AND SEQUENCES 9
Methods of assemble genomes – string reconstruction – De Bruijn graph – Euler ‗s theorem – assembling
genomes –DNA sequencing technologies – sequence antibiotics – Brute Force Algorithm – Branch and Bound
algorithm – open problems – comparing biological sequences- Case Study –Manhattan tourist Problem.
UNIT V HUMAN GENOME 9
Human and mouse genomes-random breakage model of chromosome evolution – sorting by reversals –
greedy heuristic approach – break points- rearrangements in tumor and break point genomes-break point
graps- synteny block construction -open problems and technologies.
TOTAL: 45 PERIODS
OUTCOMES:
At the end of the course, the student will be able to:
Deploy the genomics technologies in Bioinformatics.
Able to distinct efficient algorithm and issues.
Deploy the replication and molecular clocks in bioinformatics.
Work on assemble genomes and sequences.
Use the Microarray technologies for genome expression.
TEXT BOOKS:
1. Supratim Choudhuri, ―Bioinformatics for Beginners‖, Elsevier, 2014.
2. Philip Compeau and Pavel pevzner, ―Bioinformatics Algorithms: An Active Learning Approach‖,
Second edition volume I, Cousera, 2015.
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 32
Department of CSE, REC
REFERENCES:
1. Ion Mandoiu and Alexander Zelikovsky, ―Computational Methods for Next Generation Sequencing
Data Analysis‖, Wiley series 2016.
2. Istvan Miklos, RenyiInstitutue, ―Introduction to algorithms in bioinformatics‖,Springer 2016
CP17E32 COMPUTER VISION L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
Review image processing techniques for computer vision
Understand shape and region analysis
Understand Hough Transform and its applications to detect lines, circles, ellipses
Understand three-dimensional image analysis and motions.
Study some applications of computer vision algorithms.
UNIT I IMAGE PROCESSING FOUNDATIONS 9
Introduction-Image Processing Operations– Basic Image filtering operations: Noise Suppression by Gaussian
Smoothing- Median Filters- Mode Filters- Rank Order Filters- The Role of Filters in Industrial Applications
of Vision-Thresholding- Adaptive Thresholding-Edge detection techniques – corner and interest point
detection – mathematical morphology – Some Basic Approaches to Texture Analysis.
UNIT II SHAPES AND REGIONS 9
Binary shape analysis – Connectedness – Object labeling and counting – Size filtering – Distance functions –
Skeletons and thinning –Other Measures for Shape Recognition – Boundary tracking procedures – Boundary
Pattern Analysis- Centroidal profiles – Problems- Plot- Handling occlusion- Accuracy of boundary length
measures.
UNIT III THE HOUGH TRANSFORM 9
Line detection- Application of Hough Transform (HT) for line detection – The Foot-of-normal method –
Longitudinal line localization – Final line fitting – Using RANSAC for straight line detection Circle and
Ellipse Detection: HT for circular object detection – accurate center location – speed problem – ellipse
detection – Case study- Human Iris location – hole detection- Generalized Hough Transform (GHT) – Spatial
matched filtering – Use of the GHT for Ellipse Detection.
UNIT IV 3D VISION AND MOTION 9
3-D Vision - Methods for 3D vision – projection schemes – shape from shading – photometric stereo –
Surface Smoothness– shape from texture – use of structured lighting- three dimensional object recognition
schemes- Image Transformations and Camera Calibration- Motion: Optical Flow- Interpretation- Time-to-
Adjacency Analysis- Difficulties- Stereo from Motion- The Kalman Filter.
UNIT V APPLICATION 9
Automated Visual Inspection: Process- Types- Application: Photo album – Face detection – Face recognition
– Eigen faces – Active appearance and 3D shape models of faces Application- Surveillance-foreground-
background separation – particle filtres– Chamfer matching- tracking- and occlusion – combining views from
multiple cameras – human gait analysis Application- In-vehicle vision system: locating roadway – road
markings – road signs – locating pedestrians.
TOTAL: 45 PERIODS
OUTCOMES:
At the end of the course, the student will be able to:
Implement fundamental image processing techniques required for computer vision
Perform shape analysis and able to implement boundary tracking techniques
Apply Hough Transform for line, circle, and ellipse detections
Apply 3D vision techniques and to implement motion related techniques
Develop applications using computer vision techniques
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 33
Department of CSE, REC
TEXT BOOK:
1. E. R. Davies, ―Computer & Machine Vision‖, Fourth Edition, Academic Press, 2012.
REFERENCES:
1. R. Szeliski, ―Computer Vision: Algorithms and Applications‖, Springer 2011.
2. Simon J. D. Prince, ―Computer Vision: Models, Learning, and Inference‖, Cambridge University
Press, 2012.
3. Mark Nixon and Alberto S. Aquado, ―Feature Extraction & Image Processing for Computer Vision‖,
Third Edition, Academic Press, 2012.
4. D. L. Baggio et al., ―Mastering OpenCV with Practical Computer Vision Projects‖, Packt Publishing,
2012.
5. Jan Erik Solem, ―Programming Computer Vision with Python: Tools and algorithms for analyzing
images‖, O'Reilly Media, 2012.
CP17E33 DESIGN AND ANALYSIS OF PARALLEL ALGORITHMS L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
Understand the need for parallel algorithms
Expose the students to different models of parallel computation
Expose the students to parallel sorting and searching algorithms
Understand the application of the concepts studied to different types of problems
Analyze parallel algorithms
UNIT I INTRODUCTION 9
Introduction to Parallel Algorithms – Models of Parallel Computation – Sorting on an EREW-SIMD PRAM
Computer – Relation between PRAM Models – SIMD Algorithms – MIMD Algorithms – Selection –
Desirable Properties for Parallel Algorithms - Parallel Algorithm for Selection – Analysis of Parallel
Algorithms.
UNIT II SORTING AND SEARCHING 9
Merging on the EREW and CREW Models - A better Algorithm for the EREW Model - Sorting Networks –
Sorting on a Linear Array – Sorting on CRCW- CREW- EREW Models – Searching a Sorted Sequence –
Searching a Random Sequence.
UNIT III ALGEBRAIC AND NUMERICAL PROBLEMS 9
Generating Permutations and Combinations in Parallel – Matrix Transpositions – Matrix by
MatrixMultiplications – Matrix by Vector multiplication - Solving Systems of Linear Equations - Finding
Roots of Nonlinear Equations - Solving Partial Differential Equations.
UNIT IV GRAPH THEORY AND COMPUTATIONAL GEOMETRY PROBLEMS 9
Connectivity Matrix – Connected Components – All Pairs Shortest Paths – Minimum Spanning Trees – Point
Inclusion – Intersection- Proximity and Construction Problems - Sequential Tree Traversal - Basic Design
Principles – Algorithm – Analysis.
UNIT V DECISION AND OPTIMIZATION PROBLEMS 9
Computing Prefix Sums – Applications - Job Sequencing with Deadlines – Knapsack Problem- The Bit
Complexity of Parallel Computations.
TOTAL: 45 PERIODS
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 34
Department of CSE, REC
OUTCOMES:
At the end of the course, the student will be able to:
Identify the need for parallel algorithms.
Discuss the classification of parallel architectures and identify suitable programming models
Perform sorting on Sorting on CRCW, CREW, EREW Models
Search a sorted as well as random sequence
Develop and analyze algorithms for different applications like matrix multiplication, shortest path, job
sequencing and the knapsack problem.
TEXT BOOK:
1. Selim G. Akl, ―The Design and Analysis of Parallel Algorithms‖, Prentice Hall, New Jersey, 1989
REFERENCES:
1. Michael J. Quinn, ―Parallel Computing: Theory & Practice‖, Tata McGraw Hill Edition, 2003.
2. Justin R. Smith, ―The Design and Analysis of Parallel Algorithms‖, Oxford University Press,USA,
1993.
3. Joseph JaJa, ―Introduction to Parallel Algorithms‖, Addison-Wesley, 1992.
4. Justin R. Smith, ―The Design and Analysis of Parallel Algorithms‖, Oxford University,1993
CP17E34 SPEECH PROCESSING AND SYNTHESIS L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
Understand the fundamentals needed for speech processing.
Understand the basic concepts of digital signal processing and speech signal representations.
Familiarize the students with the various speech recognition models.
Study the basic concepts of test to speech systems.
Familiarize the students with the basic concepts of speech synthesis.
UNIT I FUNDAMENTAL THEORY 9
Motivations – Spoken Language System Architecture- Spoken Language Structure- Sound and Human
Speech Systems– Phonetics and Phonology – Syllables and Words – Syntax and Semantics - Probability
Theory – Estimation Theory – Significance Testing – Information Theory.
UNIT II SPEECH PROCESSING 9
Digital Signals and Systems- Continuous-Frequency, Discrete - Frequency Transforms-Speech Signal
Representations- Short time Fourier Analysis – Acoustic Model of Speech Production – Linear Predictive
Coding – Cepstral Processing – Perceptually -Motivated Representations – Formant Frequencies – The Role
of Pitch-Speech Coding – CELP – LPC Coder.
UNIT III SPEECH RECOGNITION 9
The Markov Chain – Definition– Continuous and semicontinuous HMMs – Practical Issues – Limitations.
Acoustic Modeling- Variability in the Speech Signal – Extracting Features – Phonetic Modeling – Acoustic
Modeling – Adaptive Techniques – Confidence Measures – Other Techniques.
UNIT IV TEXT -TO- SPEECH SYSTEMS 9
Lexicon – Document Structure Detection – Text Normalization – Linguistic Analysis – Homograph
Disambiguation – Morphological Analysis – Letter-to-sound Conversion- Generation schematic – Speaking
Style – Symbolic Prosody – Duration Assignment – Pitch Generation– Prosody Markup Languages – Prosody
Evaluation.
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 35
Department of CSE, REC
UNIT V SPEECH SYNTHESIS 9
Attribues– Formant Speech Synthesis – Concatenative Speech Synthesis – Prosodic Modification of Speech –
Source-filter Models for Prosody Modification – Evaluation of TTS Systems.
TOTAL: 45 PERIODS
OUTCOMES:
At the end of the course, the student will be able to:
Identify the various temporal, spectral and cepstral features required for identifying speech units –
phoneme, syllable and word
Determine and apply Mel-frequency cepstral coefficients for processing all types of signals
Justify the use of formant and concatenative approaches to speech synthesis
Identify the apt approach of speech synthesis depending on the language to be processed
TEXT BOOK:
1. Xuedong Huang, Alex Acero, Hsiao-Wuen Hon, Spoken Language Processing – A guide to Theory,
Algorithm and System Development‖, Prentice Hall PTR, 2001.
REFERENCES:
1. Joseph Mariani, Language and Speech Processing‖, Wiley, 2009.
2. Lawrence Rabiner and Biing-Hwang Juang, Fundamentals of Speech Recognition, Prentice Hall
Signal Processing Series, 1993.
3. Sadaoki Furui, Digital Speech Processing: Synthesis, and Recognition, Second Edition, (Signal
Processing and Communications), Marcel Dekker, 2000.
4. Thomas F.Quatieri, Discrete-Time Speech Signal Processing‖, Pearson Education, 2002.
CP17E35 TRUSTED COMPUTING L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
Learn the concepts of trust categories
Understand trust architecture and formalization of security properties
Understand about programming interfaces
Learn about core service and storage
Understand trusted computing and administration
UNIT I INTRODUCTION 9
Introduction – Trust and Computing – Instantiations– Design and Applications – Progression – Motivating
scenarios – Attacks - Design goals of the trusted platform modules - Introduction to simulators –
Implementation of attacks
UNIT II ARCHITECTURE, VALIDATION AND APPLICATION CASE STUDIES 9
Foundations – Design challenges – Platform Architecture – Security architecture – erasing secrets – sources –
software threats – codeintegrity and code loading - Outbound Authentication – Problem – Theory – Design
and Implementation -Validation – Process – strategy – Formalizing securityproperties – Formal verification –
other validation tasks – reflection- Application casestudies – Basic building blocks – Hardened web servers –
Right‘s management for Big Brother‘s computer – Private Information – Other projects. TCPA/TCG
UNIT III PROGRAMMING INTERFACES TO TCG 9
Experimenting with TCPA/TCG – Desired properties - Lifetime mismatch –Architecture Implementation –
Applications - Writing a TPM device driver – Lowlevel software – Trusted boot – TCG software stack –
Using TPM keys-Implementation using simulator tools—SCE tools--Deploying and Starting the XDMS
Simulator--Using the XCAP Interface to Populate and Query the XDMS
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 36
Department of CSE, REC
UNIT IV TSS CORE SERVICE AND SECURE STORAGE 9
TSS core service – Public key cryptography standard– Architecture – Trusted computing and secure storage –
Linking to encryption algorithms – encrypting files and locking data to specificPCs-content protection –
secure printing and faxing - Simulation analysis of symmetric and public key cryptographic standards-
performance evaluation of these trust models.
UNIT V TRUSTED COMPUTING AND SECURE IDENTIFICATION 9
Trusted Computing and secure identification – Administration of trusted devices –Secure /backup
maintenance – assignment of key certificates-secure time reporting-key recovery – TPM tools- Ancillary
hardware -Security of Trusted computing on Cloud-Build Trusted Cloud Computing System Using TCP
TOTAL: 45 PERIODS
OUTCOMES:
At the end of the course, the student will be able to:
Identify the need for trust categories
Analyze the trust architecture, and validating the process strategy
Discuss about programming interfaces
Develop core service and storage
Analyze the secure identification
TEXT BOOKS:
1. Sean W.Smith, ―Trusted Computing Platforms: Design and Applications‖, Springer Science and
Business media, 2005.
2. Challener D., Yoder K., Catherman R., Safford D., V an DoornL.. ―A Practical Guide to Trusted
Computing‖, IBM press, 2008.
REFERENCES
1. Xujan Zhou, Yue Xu, Yuefeng Li, AudunJøsang, and Clive Cox. ―The state-of- the-art in
personalized recommender systems for social networking‖, Artificial Intelligence Review, Issue C,
pp. 1-14, Springer, 2
2. ZhidongShen ,Qiang Tong ―The Security of Cloud Computing System enabled by Trusted Computing
Technology‖ 2nd International Conference on Signal Processing Systems (ICSPS) ,2010
CP17E36 INTERNET OF THINGS L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
Understand the fundamentals of Internet of Things
Learn about different IoT architecture
Learn about the basics of IoT protocols
Build a small low cost embedded system using Raspberry Pi.
Apply the concept of Internet of Things in the real world scenario.
UNIT I INTRODUCTION TO IoT 9
Internet of Things - Physical Design- Logical Design- IoT Enabling Technologies - IoT Levels & Deployment
Templates - Domain Specific IoTs - IoT and M2M - IoT System Management with NETCONF-YANG- IoT
Platforms Design Methodology
UNIT II IoT ARCHITECTURE 9
M2M high-level ETSI architecture - IETF architecture for IoT - OGC architecture - IoT reference model -
Domain model - information model - functional model - communication model – IoT reference architecture
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 37
Department of CSE, REC
UNIT III IoT PROTOCOLS 9
Web of Things versus Internet of Things - Protocol Standardization for IoT – Efforts – M2M and WSN
Protocols – SCADA and RFID Protocols – Unified Data Standards – Protocols – IEEE 802.15.4 – BACNet
Protocol – Modbus– Zigbee Architecture – Network layer – 6LowPAN - CoAP – Security
UNIT IV BUILDING IoT WITH RASPBERRY PI & ARDUINO 9
Building IOT with RASPBERRY PI- IoT Systems - Logical Design using Python – IoT Physical Devices &
Endpoints - IoT Device -Building blocks -Raspberry Pi -Board - Linux on Raspberry Pi - Raspberry Pi
Interfaces -Programming Raspberry Pi with Python - Other IoT Platforms - Arduino.
UNIT V CASE STUDIES AND REAL-WORLD APPLICATIONS 9
Real world design constraints - Applications - Asset management, Industrial automation, smart grid,
Commercial building automation, Smart cities - participatory sensing - Data Analytics for IoT – Software &
Management Tools for IoT Cloud Storage Models & Communication APIs – Cloud for IoT - Amazon Web
Services for IoT.
TOTAL: 45 PERIODS
OUTCOMES:
At the end of the course, the student will be able to:
Analyze various protocols for IoT
Develop web services to access/control IoT devices.
Design a portable IoT using Rasperry Pi
Deploy an IoT application and connect to the cloud.
Analyze applications of IoT in real time scenario
TEXT BOOK:
1. Olivier Hersent, David Boswarthick, Omar Elloumi , ―The Internet of Things – Key applications and
Protocols‖, Wiley, 2012
REFERENCES:
1. ArshdeepBahga, Vijay Madisetti, ―Internet of Things – A hands-on approach‖, Universities Press,
2015
2. Dieter Uckelmann, Mark Harrison, Michahelles, Florian (Eds), ―Architecting the Internet of Things‖,
Springer, 2011.
3. Honbo Zhou, ―The Internet of Things in the Cloud: A Middleware Perspective‖, CRC Press, 2012.
4. Jan Ho¨ ller, VlasiosTsiatsis, Catherine Mulligan, Stamatis,Karnouskos, Stefan Avesand. David
Boyle, "From Machine-to-Machine to the Internet of Things - Introduction to a New Age of
Intelligence", Elsevier, 2014.
CP17E37 SOCIAL NETWORK ANALYSIS L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
Understand the components of the social network.
Model and visualize the social network.
Mine the users in the social network.
Understand the evolution of the social network.
Know the applications in real time systems.
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 38
Department of CSE, REC
UNIT I INTRODUCTION 9
Introduction to Semantic Web - Limitations of current Web – Development of Semantic Web – Emergence of
the Social Web – Social Network analysis - Development of Social Network Analysis - Key concepts and
measures in network analysis - Electronic sources for network analysis - Blogs and online communities -
Web-based networks.
UNIT II MODELING AND VISUALIZATION 9
Statistical Properties of Social Networks - Static &Dynamic Properties - Random Walks on Graphs -
Background&Applications - Evaluation and datasets Visualizing Social Networks - A Taxonomy of
Visualizations - The Convergence of Visualization- Interaction and Analytics.
UNIT III COMMUNITY, NODE AND DATA MINING 9
Communities in Context - Core Methods - Emerging Fields and Problems - Community Discovery in different
networks - Node Classification- Problem Formulation - Methods using Local Classifiers - Random Walk
based Method - Applying Node Classification to Large Social Networks - Data Mining- Introduction - Data
Mining Methods - Related Efforts
UNIT IV EVOLUTION 9
Evolution in Social Networks – Framework - Challenges of Social Network Streams - Tracing Smoothly
Evolving Communities - Laws of Evolution in Social Networks - Models and Algorithms for Social Influence
Analysis- Influence Related Statistics – Social Similarity and Influence - Influence Maximization in Viral
Marketing - Algorithms and Systems for Expert Location in Social Networks- Expert Location without Graph
Constraints - with Score Propagation – Expert Team Formation - Link Prediction in Social Networks: Feature
based Link Prediction – Bayesian Probabilistic Models - Probabilistic Relational Models.
UNIT V APPLICATIONS 9
A Learning Based Approach for Real Time Emotion Classification of Tweets - A New Linguistic Approach to
Assess the Opinion of Users in Social Network Environments- Visual Analysis of Topical Evolution in
Unstructured Text: Design and Evaluation of Topic Flow - Explaining Scientific and Technical Emergence
Forecasting - Social Network Analysis for Biometric Template Protection
TOTAL: 45 PERIODS
OUTCOMES:
At the end of the course, the student will be able to:
Work on the internals components of the social network
Model and visualize the social network
Mine the behavior of the users in the social network
Predict the possible next outcome of the social network
Apply social network in real time applications
TEXT BOOKS:
1. Peter Mika, ―Social Networks and the Semantic Web‖, Springer, 1st edition, 2007.
2. PrzemyslawKazienko, Nitesh Chawla, ―Applications of Social Media and Social Network Analysis‖,
Springer,2015
REFERENCES:
1. Ajith Abraham, Aboul Ella Hassanien, VáclavSnasel, ―Computational Social Network Analysis:
Trends, Tools and Research Advances‖, Springer, 2012
2. BorkoFurht, ―Handbook of Social Network Technologies and Applications‖, Springer, 1st edition,
2011
3. Charu C. Aggarwal, ‖Social Network Data Analytics‖, Springer; 2014
4. Giles, Mark Smith, John Yen, ―Advances in Social Network Mining and Analysis‖, Springer, 2010.
5. GuandongXu ,Yanchun Zhang and Lin Li, ‖Web Mining and Social Networking Techniques and
applications‖, Springer, 1st edition, 2012
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 39
Department of CSE, REC
CP17E38 SOFTWARE DESIGN L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
Understand software modeling and Architectural Concepts
Understand and apply UML notations in designing software
Gain knowledge about Static and Dynamic modeling
Understand the importance of Software Quality attributes in Software Design
Solving various Case studies using the concepts learnt and understood in this course
UNIT I INTRODUCTION 9
Software Modeling – Object oriented Methods and UML- Software Architectural design – Method and
Notation – Evolution of Software Modeling and Design Methods - Overview of UML Notations – Software
Life cycles and UML Processes – Software Life cycle and Models – Design Verification and Validation –
Software Design and Architectural Concepts – OO Concepts – Information Hiding - Inheritance and
Generalization- Concurrent Processing – Design Patterns
UNIT II SOFTWARE MODELING 9
Use case Modeling – Static Modeling – Association between classes- Composition and Classification
Hierarchies – Constraints – Static Modeling and the UML – Categorization of classes using UML stereotypes
– Modeling External Classes – Static Modeling of Entity Classes – Object and class Structuring
UNIT III DETAILED DESIGN 9
Dynamic Interaction Modeling – Object Interaction Modeling – Message Sequence Numbering on Interaction
Diagram – Dynamic Interaction Modeling – Stateless Dynamic Interaction Modeling– Finite State Machines
and State Transitions – Events, Guard Conditions and Actions – Hierarchical State charts – Guidelines for
designing State Charts – Steps in State Dependent Dynamic Interaction Modeling.
UNIT IV ARCHITECTURAL DESIGN 9
Software Architecture and Component Based Software Architecture – Multiple views of Software
Architecture and Patterns – Documenting Software Architecture – Interface Design – Designing Software
Architecture – Software Sub System Architectural Design – Designing Object Oriented Software Architecture
– Designing Component Based Software Architecture
UNIT V CASE STUDIES 9
Designing Concurrent and Real Time Software Architectures – Designing Software Product Line
Architectures – Software Quality Attributes – Case Studies - Client – Server Software Architecture Case
Study - Component Based Software Architecture Case Study.
TOTAL: 45 PERIODS
OUTCOMES:
At the end of the course, the student will be able to:
Software modeling and Architectural Concepts
Apply UML notations in designing software
Gain knowledge about Static and Dynamic modeling
Importance of Software Quality attributes in Software Design
Solving various Case studies
TEXT BOOK:
st
1. Hassan Gomma, ―Software Modeling and design with UML‖, Cambridge University Press, 1
edition, 2011.
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 40
Department of CSE, REC
REFERENCES
1. David Budgen, ―Software Design‖, Addison-Wesley, 2007.
2. Christopher Fox, ―Introduction to Software Engineering Design: Processes, Principles and Patterns
with UML2‖, Pearson, 2007.
3. Michael Bigrigg, ―Software Design Specification with UML‖, Addison- Wesley, 2007.
CP17E39 INFORMATION STORAGE MANAGEMENT L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
Understand the basics of information storage management.
Familiarize the students with the various aspects of storage system.
Study the different storage networking technologies.
Learn to establish & manage datacenter.
Learn security aspects of storage & virtualization.
UNIT I INTRODUCTION 9
Information Storage - Evolution of Storage Technology and Architecture- Data Center Infrastructure - Key
Challenges in Managing Information - Information Lifecycle.
UNIT II STORAGE SYSTEMS 9
Components of a Storage System Environment- Disk Drive Components- Disk Drive Performance - Logical
Components of the Host -Application Requirements and Disk Performance-Data Protection-RAID -
Implementation- Array Components- Levels- Comparison- Impact on Disk Performance- Hot Spares-
Intelligent Storage System- Components- Intelligent Storage Array.
UNIT III STORAGE NETWORKING TECHNOLOGIES 9
DAS Types- Benefits and Limitations- Introduction to Parallel SCSI- Storage Area Networks- The SAN and
its evolution- Components- FC Connectivity- Ports- Architecture- Topologies-Network Attached Storage-
Benefits- Components- Implementations- and Protocols- Operations- IPSAN-ISCSI- CAS- Types- Features
and Benefits- Architecture- Examples.
UNIT IV MONITORING & MANAGING DATACENTERS 9
Business Continuity - Information Availability-Terminology- Lifecycle- Failure Analysis. Backup and
Recovery- Purpose – Considerations – Granularity – Methods – Process – Operations-Topologies-
Technologies - Data consistency- Local Replication Technologies - Modes of Remote Replication-Remote
Replication Technologies.
UNIT V STORAGE SECURITY AND VIRTUALIZATION 9
Storage Security Framework- Risk Triad- Storage Security Domains- Security Implementations-Monitoring
the Storage Infrastructure - Storage Management Activities - Challenges- Storage Virtualization.
TOTAL: 45 PERIODS
OUTCOMES:
At the end of the course, the student will be able to:
Select from various storage technologies to suit for required application.
Understand storage networking technologies.
Apply security measures to safeguard storage & farm.
Analyse QoS on Storage and virtualization.
TEXT BOOK:
1. EMC Corporation, "Information Storage and Management: Storing, Managing, and Protecting
Digital Information", Wiley India, 2010
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 41
Department of CSE, REC
REFERENCES
1. Marc Farley―Building Storage Networks, Tata McGraw Hill, Osborne, 2001.
2. Robert Spalding-Storage Networks: The Complete Reference― Tata McGraw Hill, Osborne, 2003.
Curriculum and Syllabus | M.E. Computer Science and Engineering | R2017 Page 42
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