# VIT Syllabus Computer Science Engineering 7th Semester

Digital Signal Processing

Objectives:

1. To learn the fundamental concepts of Digital Signal Processing.
2. To explore the properties of DFT in mathematical problem-solving.
3. To illustrate FFT calculations mathematically and develop FFT based DSP algorithms.
4. To introduce DSP processor for real-time signal processing application

Outcomes: Learner will be able to…

1. To understand the concept of DT Signal and perform signal manipulation
2. To perform analysis of DT system in time domain
3. To develop FFT flow-graph and Fast DSP Algorithms.
4. To design DSP system for Real-Time Signal Processing.

Unit-1

Discrete-Time Signal – 1.1 Introduction to Digital Signal Processing, Discrete-Time Signals, Sampling and Reconstruction, Standard DT Signals, Concept of Digital Frequency, Representation of DT signal using Standard DT Signals, Signal Manipulations(shifting, addition, subtraction, multiplication), Classification of Signals, Linear Convolution formulation(without mathematical proof), Circular Convolution formulation(without mathematical proof), Matrix Representation of Circular Convolution, Linear by Circular Convolution. Auto and Cross Correlation formula evaluation.

Unit-2

Discrete-Time System – 2.1 Introduction to Discrete-Time System, Classification of DT Systems (Linear/ Non-Linear, Causal/ Non-Causal, Time-Invariant/Time Variant Systems, Stable/ Unstable), BIBO Time Domain Stability Criteria. LTI system, Concept of Impulse Response and Step Response.

2.2 Concept of IIR System and FIR System, Output of IIR and FIR DT system using Time Domain Linear Convolution formula Method.

Unit-3

Discrete Fourier Transform – 3.1 Introduction to DTFT, DFT, Relation between DFT and DTFT, Properties of DFT without mathematical proof (Scaling and Linearity, Periodicity, Time Shift and Frequency Shift, Time Reversal, Convolution Property and Parseval’s’ Energy Theorem). DFT computation using DFT properties.

3.2 Transfer function of DT System in the frequency domain using DFT. Linear and Circular Convolution using DFT.

Unit-4

Fast Fourier Transform – 4.1 Radix-2 DIT-FFT algorithm, DIT-FFT Flowgraph for N=4, 6 & 8, Inverse FFT algorithm. Spectral Analysis using FFT, Comparison of complex and real, multiplication and additions of DFT and FFT.

Unit-5

DSP Algorithms – 5.1 Carls’ Correlation Coefficient Algorithm, Fast Circular Convolution Algorithm, Fast Linear Convolution Algorithm, Linear FIR filtering using Fast Overlap Add Algorithm and Fast Overlap Save Algorithm.

Unit-6

DSP Processors and Application of DSP – 6.1 Need for the Special architecture of DSP processor, Difference between DSP processor & microprocessor, A general DSP processor TMS320C54XX series, Case study of Real-Time DSP applications to Speech Signal Processing and Biomedical Signal Processing.

List of Experiments:

1. Develop a program to sample a continuous time signal and convert it to Discrete-Time Signal.
2. To study mathematical operation Correlation and measure a degree of similarity between two signals.
3. The aim of this experiment is to study mathematical operation such as Linear convolution, Circular
convolution, Linear convolution using circular convolution.
4. The aim of this experiment is to study magnitude spectrum of the DT signal.
5. To implement computationally fast algorithms.
6. To perform filtering of Long Data Sequence using Overlap Add Method and Overlap Save Method.
7. To perform real-time signal processing using TMS320 Processor.
8. To implement any Signal Processing operation on one-dimensional signal.

Text Books :

1. Ashok Ambardar, ‘Digital Signal Processing’, Cengage Learning, 2007, ISBN : 978-81-315-0179-5.
2. Digital Signal Processing: A Practical Approach”, Pearson Education
3. S. Salivahanan, A. Vallavaraj, C. Gnanapriya, ‘Digital Signal Processing’ TataMcgraw Hill Publication
4. Avtar Signh, S.Srinivasan,”Digital Signal Processing’, Thomson Brooks/Cole, ISBN : 981-243-254-4

Reference Books :

1. B. Venkatramani, M. Bhaskar ,”Digital Signal Processor’, TataMcGraw Hill, Second Edition
2. Sanjit Mitra, ‘Digital Signal Processing : A Computer Based Approach’ , TataMcGraw Hill, Third Edition
3. Dr, Shaila Apte, “Digital Signal Processing,”, Wiley India, Second Edition,2013 ISBN : 978-81-2652142-5
4. Proakis Manolakis, ‘Digital Signal Processing : Principles, Algorithms and Applications’ Fourth 2007, Pearson Education, ISBN 81-317-1000-9.
5. Monson H. Hayes, “Schaums Outline of Digital Signal Processing’ McGraw Hill International second edition.

Cryptography and System Security

Objectives:

1. To provide students with contemporary knowledge in Cryptography and Security.
2. To understand how crypto can be used as an effective tool in providing assurance concerning privacy and integrity of information.
3. To provide skills to design security protocols for recognizing security problems.

Outcomes:

1. Understand the principles and practices of cryptographic techniques.
2. Understand a variety of generic security threats and vulnerabilities, and identify & analyze particular security problems for given application.
3. Appreciate the application of security techniques and technologies in solving real life security problems in practical systems.
4. Apply appropriate security techniques to solve security problem
5. Design security protocols and methods to solve the specific security problems.
6. Familiar with current research issues and directions of security.

Unit-1

Introduction – 1.1 Security Attacks, Security Goals, Computer criminals, Methods of defense, Security Services, Security Mechanisms.

Unit-2

Basics of Cryptography – 2.1 Symmetric Cipher Model, Substitution Techniques, Transportation Techniques, Other Cipher Properties- Confusion, Diffusion, Block and Stream Ciphers.

Unit-3

Secret Key Cryptography – 3.1 Data Encryption Standard(DES), Strength of DES, Block Cipher Design Principles and Modes of Operations, Triple DES, International Data Encryption algorithm, Blowfish, CAST-128.

Unit-4

Public Key Cryptography – 4.1 Principles of Public Key Cryptosystems, RSA Algorithm, Diffie-
Hellman Key Exchange.

Unit-5

Cryptographic Hash Functions – 5.1 Applications of Cryptographic Hash Functions, Secure Hash Algorithm, Message Authentication Codes – Message Authentication Requirements and Functions, HMAC, Digital signatures, Digital
Signature Schemes, Authentication Protocols, Digital Signature Standards.

Unit-6

Authentication Applications 6.1 Kerberos, Key Management and Distribution, X.509 Directory Authentication service, Public Key Infrastructure, Electronic Mail Security: Pretty Good Privacy, S/MIME.

Unit-7

7.1 Program Security – Secure programs, Nonmalicious Program Errors, Malicious Software Types, Viruses, Virus Countermeasures, Worms, Targeted Malicious Code, Controls against Program Threats.

7.2 Operating System Security Memory and Address protection, File Protection Mechanism, User Authentication.

7.3 Database Security – Security Requirement, Reliability and Integrity, Sensitive data, Inference, Multilevel Databases

7.4 IDS and Firewalls – Intruders, Intrusion Detection, Password Management, Firewalls- Characteristics, Types of Firewalls, Placement of Firewalls, Firewall Configuration, Trusted systems.

Unit-8

8.1 IP Security – Overview, Architecture, Authentication Header, Encapsulating Security Payload, Combining security Associations, Internet Key Exchange, Web Security: Web Security Considerations, Secure Sockets Layer, and Transport Layer Security, Electronic Payment.

8.2 Non-cryptographic protocol Vulnerabilities DoS, DDoS, Session Hijacking and Spoofing, Software Vulnerabilities- Phishing, Buffer Overflow, Format String Attacks, SQL Injection.

Text Books:

1. Cryptography and Network Security: Principles and Practice 5th edition, William Stallings, Pearson.
2. Network Security and Cryptography 2nd edition, Bernard Menezes, Cengage Learning.
3. Cryptography and Network, 2nd edition, Behrouz A Fourouzan, Debdeep Mukhopadhyay, TMH.

Reference Books:

1. Cryptography and Network Security by Behrouz A. Forouzan, TMH
2. Security in Computing by Charles P. Pfleeger, Pearson Education.
3. Computer Security Art and Science by Matt Bishop, Addison-Wesley.

Artificial Intelligence

Objectives:

1. To conceptualize the basic ideas and techniques underlying the design of intelligent systems.
2. To make students understand and Explore the mechanism of mind that enable intelligent thought and action.
3. To make students understand advanced representation formalism and search techniques.
4. To make students understand how to deal with uncertain and incomplete information.

Outcomes:

1. Ability to develop a basic understanding of AI building blocks presented in intelligent agents.
2. Ability to choose an appropriate problem-solving method and knowledge representation technique.
3. Ability to analyze the strength and weaknesses of AI approaches to knowledge-intensive problem-solving.
4. Ability to design models for reasoning with uncertainty as well as the use of unreliable information.
5. Ability to design and develop the AI applications in real-world scenario.

Unit-1

Introduction to Artificial Intelligence – 1.1 Introduction, History of Artificial Intelligence, Intelligent Systems: Categorization of Intelligent System, Components of AI Program, Foundations of AI, Sub-areas of AI,
Applications of AI, Current trends in AI.

Unit-2

Intelligent Agents – 2.1 Agents and Environments, The concept of rationality, The nature of the environment, The structure of Agents, Types of Agents, Learning Agent.

Unit-3

Problem-solving – 3.1 Solving problem by Searching: Problem Solving Agent, Formulating Problems, Example Problems.

3.2 Uninformed Search Methods: Breadth First Search (BFS), Depth First Search (DFS) , Depth-Limited Search, Depth First Iterative Deepening(DFID), Informed Search Methods: Greedy best first Search ,A Search , Memory bounded heuristic Search.

3.3 Local Search Algorithms and Optimization Problems: Hillclimbing Genetic algorithms.

3.4 Adversarial Search: Games, Optimal strategies, The minimax algorithm, Alpha-Beta Pruning.

Unit-4

Knowledge and Reasoning – 4.1 Knowledge-based Agents, The Wumpus World, The Propositional logic, First Order Logic: Syntax and Semantic, Inference in FOL, Forward chaining, Backward Chaining.

4.2 Knowledge Engineering in First-Order Logic, Unification, Resolution, Introduction to logic programming (PROLOG).

4.3 Uncertain Knowledge and Reasoning: Uncertainty, Representing knowledge in an uncertain domain, The semantics of belief network, Inference in the belief network.

Unit-5

Planning and Learning – 5.1The planning problem, Planning with state space search, Partial order planning, Hierarchical planning, Conditional Planning.

5.2 Learning: Forms of Learning, Inductive Learning, Learning Decision Tree.

5.3 Expert System: Introduction, Phases in building Expert Systems, ES Architecture, ES vs Traditional System.

Unit-6

Applications – 6.1 Natural Language Processing(NLP), Expert Systems.

List of Experiments:

1. One case study on NLP/Expert system based papers published in IEEE/ACM/Springer or any prominent journal.
2. Program on uninformed and informed search methods.
3. Program on Local Search Algorithm.
4. Program on Optimization problem.
6. Program on Wumpus world.
7. Program on unification.
8. Program on Decision Tree.

Text Books:

1. Stuart J. Russell and Peter Norvig, “Artificial Intelligence A Modern Approach. Pearson Education.
2. Saroj Kaushik “Artificial Intelligence”  Cengage Learning.
3. George F Luger “Artificial Intelligence” Low Price Edition, Pearson Education., Fourth edition.

Reference Books:

1. Ivan Bratko “PROLOG Programming for Artificial Intelligence”, Pearson Education, Third Edition.
2. Elaine Rich and Kevin Knight “Artificial Intelligence” Third Edition
3. Davis E.Goldberg, “Genetic Algorithms: Search, Optimization and Machine Learning”, Addison Wesley, N.Y.
4. Hagan, Demuth, Beale, “Neural Network Design” CENGAGE Learning, India Edition.
5. Patrick Henry Winston , “Artificial Intelligence”, Addison-Wesley, Third Edition.
6. Han Kamber, “Data Mining Concepts and Techniques”, Morgann Kaufmann Publishers.
7. N.P.Padhy, “Artificial Intelligence and Intelligent Systems”, Oxford University Press.

Objectives:

1. To teach fundamentals of analysis of algorithm at depth
2. To provide in-depth study of advanced data structures and its uses
3. To teach analysis of problems from different domains

Outcomes:

1. Identify and use suitable data structures for given problem from different domains
2. Appreciate the role of Graph algorithms in solving variety of problems
3. Appreciate the role of Optimization by using linear programming
4. Analyze the various algorithms from different domains.

Unit-1

Introduction – 1.1 Asymptotic notations Big O, Big Q,Big W,o ,w  notations , Proofs of master theorem, applying theorem to solve problems

Unit-2

Advanced Data Structures – 2.1 Red-Black Trees: properties of red-black trees, Insertions, Deletions

2.2 B-Trees and its operations

2.3 Binomial Heaps: Binomial trees and binomial heaps, Operation on Binomial heaps

Unit-3

Dynamic Programming – 3.1 matrix chain multiplication, cutting rod problem and its analysis

Unit-4

Graph algorithms – 4.1 Bellman Ford algorithm, Dijkstra algorithm, Johnson’s All pair shortest path algorithm for sparse graphs.

Unit-5

Maximum Flow – 5.1 Flow networks, the ford Fulkerson method , max bipartite matching, push Relabel Algorithm, The relabel to front algorithm

Unit-6

Linear Programming- 6.1 Standard and slack forms, Formulating problems as linear programs, simplex algorithm, Duality, Initial basic feasible solution.

Unit-7

Computational Geometry – 7.1 Line Segment properties, Determining whether any pair of segment intersects, finding the convex hull, Finding the closest pair of points.

List of Experiments:

1. Red – black trees and its various operations
2. Binomial Heaps and its various operations
3. Dynamic programming: matrix chain multiplication, cutting rod example
4. Bellman ford, Johnson’s algorithm for sparse graphs
5. Ford Fulkerson algorithm, push-relabel to front methods
6. Finding closest pair of points, Determining the convex hull
7. Implementation of Simplex algorithm

Text Books:

1. T.H. Coreman , C.E. Leiserson, R.L. Rivest, and C. Stein, “Introduction to Algorithms”, PHI publication
2. Ellis Horowitz , Sartaj Sahni , S. Rajsekaran. “Fundamentals of computer algorithms” University press.

Image Processing

Objectives:

1. To learn the fundamental concepts of Digital Image Processing and Video Processing.
2. To understand basic image enhancement and segmentation techniques.
3. To illustrate Image Transform calculations mathematically and develop fast transform algorithm
4. To learn Image Compression and Decompression Techniques

Outcomes:

1. Understand the concept of Digital Image and Video Image.
2. Explain image enhancement and Segmentation technique.
3. Develop fast image transform flowgraph
4. Solve Image compression and decompression techniques
5. Perform Binary Image Processing Operations

Unit-1

Digital Image and Video Fundamentals – 1.1 Introduction to Digital Image, Digital Image Processing System,
Sampling and Quantization, Representation of Digital Image, Connectivity, Image File Formats: BMP, TIFF, and JPEG. Colour Models (RGB, HSI, YUV) Introduction to Digital Video, Chroma Sub-sampling, CCIR standards for Digital Video.

Unit-2

Image Enhancement – 2.1 Gray Level Transformations, Zero Memory Point Operations, Histogram Processing, neighborhood Processing, Spatial Filtering, Smoothing and Sharpening Filters. Homomorphic Filtering

Unit-3

Image Segmentation and Representation – 3.1 Detection of Discontinuities, Edge Linking using Hough Transform,
Thresholding, Region-based Segmentation, Split and Merge Technique, Image Representation, and Description, Chain Code, Polygonal Representation, Shape Number, Moments.

Unit-4

Image Transform – 4.1 Introduction to Unitary Transform, Discrete Fourier Transform(DFT), Properties of DFT, Fast Fourier Transform(FFT), Discrete Hadamard Transform(DHT), Fast Hadamard Transform(FHT), Discrete Cosine Transform(DCT), Discrete Wavelet Transform(DWT),

Unit-5

Image Compression – 5.1 Introduction, Redundancy, Fidelity Criteria,

5.2 Lossless Compression Techniques: Run

5.3 Lossy Compression Techniques: Improved Gray Scale Quantization, Vector Quantization, JPEG, MPEG-1.

Unit-6

Binary Image Processing – 6.1 Binary Morphological Operators, Hit-or-Miss Transformation, Boundary Extraction, Region Filling, Thinning and Thickening, Connected Component Labeling, Iterative Algorithm and Classical Algorithm.

Text Books :

1. Rafel C. Gonzalez and Richard E. Woods, ‘Digital Image Processing’, Pearson Education Asia, Third Edition, 2009,
2. S. Jayaraman, E.Esakkirajan and T.Veerkumar, “Digital Image Processing” TataMcGraw Hill Education Pvt Ltd
3. Anil K. Jain, “Fundamentals and Digital Image Processing”, Prentice Hall of India Private Ltd, Third Edition
4. S. Sridhar, “Digital Image Processing”, Oxford University Press, Second Edition, 2012.
5. Robert Haralick and Linda Shapiro, “Computer and Robot Vision”, Vol I, II, Addison Wesley, 1993.

Reference Books:

1. Dwayne Phillps, “Image Processing in C”, BPB Publication, 2006
2. B. Chandra and D.Dutta Majumder, “Digital Image Processing and Analysis”, Prentice Hall of India Private Ltd
3. Malay K. Pakhira, “Digital Image Processing and Pattern Recognition”, Prentice Hall of India Private Ltd
4. Fred Halshall, “Multimedia Communications: Applications, Networks Protocols and Standards,” Pearson Education
5. David A. Forsyth, Jean Ponce, “Computer Vision: A Modern Approach”, Pearson Education, Limited, 2011

Software Architecture

Outcomes:

Software architecture is foundational to the development of large, practical software-intensive applications

After successful completion of this course learner will be able to:

1. Visualize the architectural concepts in the development of large, practical software-intensive applications.

2. Rather than focusing on one method, notation, tool, or process, this new course widely surveys software architecture techniques, enabling us to choose the right tool for the job at hand.

Unit-1

Basic Concepts
1.1 Concepts of Software Architecture
1.2 Models.
1.3 Processes.
1.4 Stakeholders

Unit-2

Designing Architectures
2.1 The Design Process.
2.2 Architectural Conception.
2.3 Refined Experience in Action: Styles and Architectural Patterns.
2.4 Architectural Conception in Absence of Experience.

Unit-3

Connectors
3.1 Connectors in Action: A Motivating Example.
3.2 Connector Foundations.
3.3 Connector Roles.
3.4 Connector Types and Their Variation Dimensions.
3.5 Example Connectors.

Unit-4

Modeling
4.1 Modeling Concepts.
4.2 Ambiguity, Accuracy, and Precision.
4.3 Complex Modeling: Mixed Content and Multiple Views.
4.4 Evaluating Modeling Techniques.
4.5 Specific Modeling Techniques.

Unit-5

Analysis
5.1 Analysis Goals.
5.2 Scope of Analysis.
5.3 Architectural Concern being Analyzed.
5.4 Level of Formality of Architectural Models.
5.5 Type of Analysis.
5.6 Analysis Techniques.

Unit-6

Implementation and Deployment
6.1 Concepts.
6.2 Existing Frameworks.
6.3 Software Architecture and Deployment.
6.4 Software Architecture and Mobility.

Unit-7

Conventional Architectural styles
7.1 Pipes and Filters
7.2 Event-based, Implicit Invocation
7.3 Layered systems
7.4 Repositories
7.5 Interpreters
7.6 Process control

Unit-8

Applied Architectures and Styles
8.1 Distributed and Networked Architectures.
8.2 Architectures for Network-Based Applications.
8.3 Decentralized Architectures.
8.4 Service-Oriented Architectures and Web Services.

Unit-9

Designing for Non-Functional Properties
9.1 Efficiency.
9.2 Complexity.
9.3 Scalability and Heterogeneity.
9.5 Dependability.

Unit-10

Domain-Specific Software Engineering
10.1 Domain-Specific Software Engineering in a Nutshell.
10.2 Domain-Specific Software Architecture.
10.3 DSSAs, Product Lines, and Architectural Styles.

List of Experiments:

2. Analysis – Case study
4. Integrate software components using a middleware
5. Use middleware to implement connectors
6. Wrapper to connect two applications with different architectures
7. Creating web service
8. Architecture for any specific domain

Text Books:
1. “Software Architecture: Foundations, Theory, and Practice” by Richard N. Taylor, Nenad Medvidovic, Eric Dashofy
2. M. Shaw: Software Architecture Perspectives on an Emerging Discipline, Prentice-Hall.
3. Len Bass, Paul Clements, Rick Kazman: Software Architecture in Practice, Pearson.

References Books:

1. “Pattern-Oriented Software Architecture” by Frank Buchnan et al, Wiley India.
2. “The Art of Software Architecture” by Stephen T. Albin.

Soft Computing

Objectives:

1. To Conceptualize the working of human brain using ANN.
2. To become familiar with neural networks that can learn from available examples and generalize to form appropriate rules for inference systems.
3. To introduce the ideas of fuzzy sets, fuzzy logic and use of heuristics based on human experience.
4. To provide the mathematical background for carrying out the optimization and familiarizing genetic algorithm for seeking global optimum in self-learning situation.

Outcomes:

1. Ability to analyze and appreciate the applications which can use fuzzy logic.
3. Ability to understand the difference between learning and programming and explore practical applications of Neural Networks (NN).
4. Ability to appreciate the importance of optimizations and its use in computer engineering fields and other domains.
5. Students would understand the efficiency of a hybrid system and how Neural Network and fuzzy logic can be hybridized to form a Neuro-fuzzy network and its various applications.

Unit-1

Introduction to Soft Computing – 1.1 Soft computing Constituents, Characteristics of Neuro-Computing and Soft Computing, Difference between Hard Computing and Soft Computing, Concepts of Learning and Adaptation.

Unit-2

Neural Networks – 2.1 Basics of Neural Networks: Introduction to Neural Networks, Biological Neural Networks, McCulloch Pitt model

2.2 Supervised Learning algorithms: Perceptron (Single Layer, Multilayer), Linear separability, Delta learning rule, Back Propagation algorithm,

2.3 Un-Supervised Learning algorithms: Hebbian Learning, Winner take all Self Organizing Maps, Learning Vector
Quantization.

Unit-3

Fuzzy Set Theory – 3.1 Classical Sets and Fuzzy Sets, Classical Relations and Fuzzy Relations, Properties of the membership function, Fuzzy extension principle, Fuzzy Systems- fuzzification, defuzzification and fuzzy controllers.

Unit-4

Hybrid system – 4.1 Introduction to Hybrid Systems, Adaptive Neuro-Fuzzy Inference System(ANFIS).

Unit-5

Introduction to Optimization Techniques – 5.1 Derivative based optimization- Steepest Descent, Newton method.

5.2 Derivative-free optimization- Introduction to Evolutionary Concepts.

Unit-6

Genetic Algorithms and its applications: 6.1 Inheritance Operators, Crossover types, inversion and Deletion, Mutation Operator, Bit-wise Operators, Convergence of GA, Applications of GA.

List of Experiments:

1. One case study on Fuzzy/Neural/GA based papers published in IEEE/ACM/Springer or any prominent journal.
2. To implement Fuzzy Sets.
3. To implement Fuzzy Relations.
4. To implement Fuzzy Controllers.
5. To implement Basic Neural Network learning rules.
6. To implement any Supervised Learning algorithm.
7. To implement any Unsupervised Learning algorithm.
8. To implement a simple application using Genetic Algorithm.

Text Books:

1. Timothy J.Ross “Fuzzy Logic With Engineering Applications” Wiley.
2. S.N.Sivanandam, S.N.Deepa “Principles of Soft Computing” Second Edition, Wiley Publication.
3. S.Rajasekaran and G.A.Vijayalakshmi Pai “Neural Networks, Fuzzy Logic and Genetic Algorithms” PHI Learning.
4. J.-S.R.Jang “Neuro-Fuzzy and Soft Computing” PHI 2003.
5. Jacek.M.Zurada “Introduction to Artificial Neural Sytems” Jaico Publishing House.

Reference Books:

1. Satish Kumar “Neural Networks A Classroom Approach” Tata McGrawHill.
2. Zimmermann H.S “Fuzzy Set Theory and its Applications” Kluwer Academic Publishers.
3. Davis E.Goldberg, “Genetic Algorithms: Search, Optimization and Machine Learning”, Addison Wesley, N.Y.
4. Hagan, Demuth, Beale, “Neural Network Design” CENGAGE Learning, India Edition.

Enterprise Resource Planning and Supply Chain Management (ERP & SCM)

Objectives:
1. To understand the technical aspects of ERP and SCM systems.
2. To understand the steps and activities in the ERP and SCM life cycle.
3. To identify and describe typical functionality in an ERP and SCM system.
4. To understand tools and methodology used for designing ERP and SCM for an Enterprise.

Outcomes:

1. To conceptualize the basic structure of ERP and SCM
2. To identify implementation strategy used for ERP and SCM.
3. To apply design principles for a various business module in ERP and SCM.
4. To apply different emerging technologies for implementation of ERP and SCM.

Enterprise Resource Planning :

Unit-1

Introduction – 1.1 What is an Enterprize, Introduction to ERP, Need for ERP, Structure of ERP, Scope and Benefits, Typical business processes.

Unit-2

ERP and Technology – 2.1 ERP and related technologies, Business Intelligence, E-business and E-commerce, Business Process Reengineering.

Unit-3

ERP and Implementation – 3.1 ERP implementation and strategy, Implementation Life cycle, Pre-implementation task, requirement definition , implementation methodology.

Unit-4

ERP Business Modules – 4.1 Modules: Finance, manufacturing, human resources, quality management, material management, marketing. Sales distribution and service.

Unit-5

Extended ERP – 5.1 Enterprise application Integration (EAI), open source ERP, cloud ERP.

Supply Chain Management (SCM) :

Unit-6

Introduction and strategic decisions in SCM – 6.1 Introduction to SCM, Generic Types of supply chain, Major
Drivers of Supply chain, Strategic decisions in SCM, Business Strategy, CRM strategy, SRM strategy, SCOR model.

Unit-7

Information Technology in SCM – 7.1 Types of IT Solutions like Electronic Data Interchange (EDI), Intranet/ Extranet, Data Mining/ Data Warehousing and Data Marts, E-Commerce, E-Procurement, Barcoding, RFID, QR
code.

Unit-8

Mathematical modelling for SCM – 8.1 Introduction, Considerations in modelling SCM systems, Structuring the logistics chain, an overview of models: models on transportation problem, assignment problem, vehicle routing problem, Model for vendor analysis, Make versus buy model.

Unit-9

Agile Supply Chain – 9.1 Introduction, Characteristics of Agile Supply Chain, Achieving Agility in Supply Chain.

Unit-10

Cases of Supply Chain – 10.1 Cases of Supply Chain like News Paper Supply Chain, Disaster management, Organic
Food, Fast Food.

List of Experiments:

1. Simulating business processes of an Enterprise.
2. Designing a web portal for an Enterprise using E-business Models.
3. E-procurement model.
4. Open source ERP
5. Cloud ERP
7. SCM model.
9. Any other relevant topics covering the syllabus.

Text Books:

1. Enterprise Resource Planning: concepts & practices, by V.K. Garg & N.K. Venkatakrishnan ; PHI.
2. Supply Chain Management Theories & Practices: R. P. Mohanty, S. G. Deshmukh, Dreamtech Press.
3. ERP Demystified: II Edition, by Alexis Leon,McGraw Hill .
4. Enterprise-wide resource planning: Theory & practice: by Rahul Altekar,PHI.

Reference Books:

1. ERP to E2 ERP: A Case study approach, by Sandeep Desai, Abhishek Srivastava, PHI.
2. Managerial Issues of ERP system, by David Olson, McGraw Hill.

Computer Simulation and Modeling

Outcomes:

1. Apply simulation concepts to achieve in business, science, engineering, industry and services goals
2. Demonstrate formulation and modeling skills.
3. Perform a simulation using spreadsheets as well as simulation language/package
4. Generate pseudorandom numbers using the Linear Congruential Method
5. Evaluate the quality of a pseudorandom number generator using statistical tests
6. Analyze and fit the collected data to different distributions

Unit-1

Introduction to Simulation. Simulation Examples. General Principles

Unit-2

Statistical Models in simulation. Queuing Models

Unit-3

Random Number Generation. Testing random numbers (Refer to Third edition) Random Variate Generation: Inverse transform technique, Direct Transformation for the Normal Distribution, Convolution Method, Acceptance-Rejection Technique (only Poisson Distribution).

Unit-4

Analysis of simulation data : Input Modeling ,Verification, Calibration and Validation of Simulation , Models , Estimation of absolute performance.

Unit-5

Application : Case study on 1. Processor and Memory simulation 2. Manufacturing & Material handling.

Text Books:

1. Discrete Event System Simulation; Jerry Banks, John Carson, Barry Nelson, and David M. Nicol, Prentice-Hall
2. Discrete Event System Simulation;  Jerry Banks, John Carson, Barry Nelson, and David M. Nicol, Prentice-Hall

References Books:

1. System Modeling & Analysis; Averill M Law, 4th Edition TMH.
2. Principles of Modeling and Simulation; Banks C M , Sokolowski J A; Wiley
3. System Simulation ; Geoffrey Gordon ; EEE
4. System Simulation with Digital Computer; Narsing Deo, PHI