BCO 001A SOFTWARE ENGINEERING 3-0-0 [3] Objective To learn about generic models of software development process. To understand fundamental concepts of requirements engineering and Analysis Modeling. To understand the different design techniques and their implementation. To learn various testing and maintenance measures UNIT 1 Introduction- Introduction to Software Engineering, Software Components, Software Characteristics, Software Crisis, Engineering aspects of Software production – necessity of automation .Job responsibilities of Programmers and Software Engineers as Software developers.Software Development Life Cycle (SDLC) UNIT 2 Process Models and Program Design Techniques- Software Development Process Models – Code & Fix model, Waterfall model, Incremental model, Rapid Prototyping model, Spiral (Evolutionary) model.Software Requirement Specifications (SRS), Management of User Needs, Data Flow Diagrams, Entity Relationship Diagrams, Decision Tables, SRS Document, Design Techniques – Structured Programming, Coupling and Cohesion, Abstraction and Information Hiding, SoftwareModeling Tools –Data flow Diagrams, UML and XML. UNIT 3 Software Testing: Testing Objectives, Unit Testing, Integration Testing, Acceptance Testing, Regression Testing, Verification and Validation: Testing of Software Products – Black-Box Testing and White-Box Testing, Static Analysis, Symbolic Execution and Control Flow Graphs –Cyclomatic Complexity. UNIT 4 Software Project Management: Management Functions and Processes, Project Planning and Control, Organization and Intra-team Communication, Risk Management. Software Cost Estimation – underlying factors of critical concern. Metrics for estimating costs of software products – Function Points. Techniques for software cost estimation –Expert judgment, Work break-down structure and Process breakdown structure, COCOMO and COCOMO-II. UNIT 5 Software Maintenance,Need for Maintenance, Categories of Maintenance, An Overview of CASE Tools. Advanced Topics: Support environment for Development of Software Products. Representative Tools for Editors, Linkers, Interpreters, Code Generators, Debuggers. Tools for Decision Support and Synthesis, Configuration control and Engineering Databases. Course Outcome (CO):
CO4 L M L BCO 005A DATA STRUCTURE AND ALGORITHMS LAB 0-0-2 [2] List of Experiments 1.Write a program to implement following searching algorithms using array data structure 1.1 Matrix Addition and Subtraction 1.2 Matrix Multiplication and Transpose 2.Write a program to implement following searching algorithms using array data structure 2.1. Linear Search 2.2. Binary Search 3. Write a program to implement following searching algorithms using array data structure 3.1. Insertion Sort 3.2 Bubble Sort 4. Write a program to implement following searching algorithms using array data structure 4.1. Selection Sort 4.2 Quick Sort 5. Write a program to implement following operations on stack using array data structure. 5.1 Traversing 5.2 Push 5.3 POP 6. Write a program to implement following examples of recursion 6.1 Fibonacci Series 6.2 Factorial Function 6.3 Tower of Hanoi 7. Write a program to implement Merge Sort. 8. Write a program to implement following operations on Queue using array data structure. 8.1 Insertion8.2 Deletion8.3 Traversing
TEXT BOOKS William Stallings, “ISDN and Broadband ISDN with Frame Relay and ATM”, of India, Fourth edition, 2004. William Stallings, “High Speed Networks and Internets”, Pearson Education, Secondedition, 2002. REFERENCES Greg Bemstein, Bala Rajagopalan and Debanjan Saha, “Optical Network Control Protocols and Standards”, Pearson Education, 2004. Behrouz A Forouzan, “Data Communications and Networking”, Tata McGraw-Hill, Fourthedition, 2006. Behrouz A. Forouzan and Sophia Chung Fegan, “Local Area Networks”, Tata McGraw- Hill, 2001. Rajiv Ramaswami and Kumar N. Sivarajan, “Optical Networks: A Practical Perspective”, Morgan Kaufmann, Second edition (Elsevier Indian Edition), 2004. (UNITs IV & V) Uless Black, “Optical Networks - Third Generation Transport Systems”, Pearson Education, 2002. C. Siva Ram Murthy and Mohan Gurusamy, “WDM Optical Networks: Concepts, Designand Algorithms”, Prentice-Hall of India, 2002. Fred Halsall, “Multimedia Communications – Applications, Networks, Protocols”, PearsonEdition, 2001. BCO 045A OPERATIONS RESEARACH 4:0:0 [4] Objective: Students will get an introduction to use quantities methods and techniques for effective decisions making; Students will get introduction about model formulation and applications that are used in solving business decision problems. UNIT 1 Introduction: Definition and scope of operations research (OR), OR model, solving the OR model, art of modeling, phases of OR study. Linear Programming: Two variable Linear Programming model and Graphical method of solution, Simplex method, Dual Simplex method, special cases of Linear Programming, duality, sensitivity analysis. UNIT 2 Transportation Problems: Types of transportation problems, mathemataical models , transportation algorithms, Assignment: Allocation and assignment problems and models, processing of job through machines
Course Outcome (CO) of Simulation and Modeling At the end of this course students will have: CO1: To provide an in-depth understanding of the terminology of modeling and simulation CO2: To equip our students with technical concept of simulation, simulation models and gives practical examples for each category CO3: Develop a modeling strategy for a real world engineering system, which considers prediction and evaluation against design criteria, and integrates any required sub-system models. CO4: Interpret the simulation results of an engineering system model, within the context of its capabilities and limitations, to address critical issues in an engineering project MAPPING COURSE OUTCOMES LEADING TO THE ACHIEVEMENT OF PROGRAM OUTCOMES AND PROGRAM SPECIFIC OUTCOMES: Course Outcome Program Outcome Program Specifice Outcome PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3 CO1 H H CO2 L H M CO3 H M M M L CO4 M L H = Highly Related; M = Medium L = Low Text Books: 1.Jerry Banks, John S. Carson II, Barry L. Nelson and David M. Nicol, Discrete-Event System and Simulation,Prentice Hall of India, New Delhi, 2005 2.Averill M. Law, Simulation modeling and analysi (SIE), Tata McGraw Hill India, 20073.David Cloud, Larry Rainey, Applied Modeling and Simulation, Tata McGraw Hill, India. Reference Books: 1.Gabriel A. Wainer, Discrete-event modeling and simulation: a practitioner's approach, CRC Press, 2009. 2. Bernard P. Zeigler, Herbert Praehofer, Tag Gon Kim, Theory of modeling and simulation: integrating discrete event and continuous complex dynamic systems, Academic Press, 2000. 3. Averill M. Law, W. David Kelton, Simulation modeling and analysis, McGraw Hill, 2000. 4. Walter J. Karplus, George A. Bekey, Boris YakobKogan, Modeling and simulation: theory and practice, Springer, 2003. 5.StanislawRaczynski, Modeling and simulation: the computer science of illusion, Wiley, 2006. 6.MohammadSalamehObaidat, Georgios I. Papadimitriou, Applied system simulation: methodologies an application, Springer, 2003.
Gilles Brassard & Paul Bratley, Fundamental Algorithms, Prentice-Hall. 1998 BCO 024A ADVANCED COMPUTER ARCHITECTURE 3-0-0 [3] OBJECTIVES:- Tocover the underlying concepts and techniques used in Advance Computer Architecture. To discusses principles of parallel algorithmsdesign and different parallel programming models To have general idea of Computer Organization. In addition, a familiaritywith Memory organization, Computational models required. UNIT 1 Introduction - What is computer architecture? Software-hardware interface. Performance andPower.Performancemetrics.Performancemeasurement.Benchmarkprograms. UNIT 2 Instructions- Instruction Set. Operations. Operands and addressing modes.Roleof compilers and system software.Understanding implementation of function calls andreturns, array references, pointers. UNIT 3 Computer Arithmetic- Signed integers. Floating point.Rounding and accuracy.Addition and Subtraction.Multiplication.Division; Processor - Data path elements.Data pathcontrol. UNIT 4: Pipelining Speedup. Pipeline hazards.Stalling.Forwarding.Branchprediction.Exceptions.Speculation.Multiple issue.Dynamic scheduling; Cache memory- Locality ofreference.Cache organization and access. Multilevelcaches.Performance.Cache coherence. UNIT 5 Virtual memory- Hardware support for address translation, page fault handling.Translation look aside buffer. Hardware-software interface. OUTCOMES:-Upon completion of the syllabus the students will be able to know : Understand the concepts and techniques used in Advance Computer Architecture. Use of the principles of parallel algorithmsdesign and different parallel programming models. Familiar with general idea of Computer Organization. In addition, a familiarity with Memory organization, Computational models required MAPPING COURSE OUTCOMES LEADING TO THE ACHIEVEMENT OF PROGRAM OUTCOMES AND PROGRAM SPECIFIC OUTCOMES: Course Program OutComes Program Specific Outcomes
CO5 H = Highly Related; M = Medium L = Low References: 1. Digital Image Processing 2ndEdition, Rafael C. Gonzalvez and Richard E. Woods. Published by: Pearson Education. 2. Digital Image Processing and Computer Vision, R.J. Schalkoff. Published by: John Wiley and Sons, NY. 3. Fundamentals of Digital Image Processing, A.K. Jain. Published by Prentice Hall, Upper Saddle River, NJ. BCO 086A MACHINE LEARNING 4-0-0 [4] Course Objectives To understand the basic concepts of learning and decision trees. To understand the neural networks and genetic algorithms To understand the Bayesian techniques To understand the instant based learning To understand the analytical learning and reinforced learning UNIT I INTRODUCTION, CONCEPT LEARNING AND DECISION TREES Learning Problems – Designing Learning systems, Perspectives and Issues – Concept Learning – Version Spaces and Candidate Elimination Algorithm – Inductive bias – Decision Tree learning – Representation – Algorithm – Heuristic Space Search. UNIT II NEURAL NETWORKS AND GENETIC ALGORITHMS Neural Network Representation – Problems – Perceptrons – Multilayer Networks and Back Propagation Algorithms – Advanced Topics – Genetic Algorithms – Hypothesis Space Search – Genetic Programming – Models of Evolution and Learning. UNIT III BAYESIAN AND COMPUTATIONAL LEARNING Bayes Theorem – Concept Learning – Maximum Likelihood – Minimum Description Length Principle – Bayes Optimal Classifier – Gibbs Algorithm – Naïve Bayes Classifier – Bayesian Belief Network – EM Algorithm – Probably Learning – Sample Complexity for Finite and Infinite Hypothesis Spaces – Mistake Bound Model. UNIT IV INSTANT BASED LEARNING AND LEARNING SET OF RULES K- Nearest Neighbor Learning – Locally Weighted Regression – Radial Basis Functions – Case-Based Reasoning – Sequential Algorithms – Learning Rule Sets – Learning First Order Rules – Learning Sets of First Order Rules – Induction as Inverted Deduction – Inverting Resolution UNIT V ANALYTICAL LEARNING AND REINFORCED LEARNING Perfect Domain Theories – Explanation Based Learning – Inductive-Analytical Approaches - FOCL Algorithm – Reinforcement Learning – Task – Q-Learning – Temporal Difference Learning OUTCOME:On Completion of the course, the students will be able to
and classes as strings, Tree-based representations, Frequentitemsets for representing classes and clusters, Patterns and classes as logical formulas. UNIT 3 Proximity Measures- Dissimilarity measures, metrics, similarity measures, Edit distance, Hausdorff metric between point sets, Kernel functions, Contextual and conceptual similarity between points. UNIT 4 Dimensionality Reduction- Feature selection: Branch and bound, Sequential feature selection, Feature extraction: Fisher's linear discriminant, Principal components as features; Nearest Neighbor Classifiers- Nearest neighbor classifier, Soft nearest neighbor classifiers, Efficient algorithms for nearest neighbor classification, K-nearest neighbor classifier, minimal distance classifier, condensed nearest neighbor classifier and its modifications. UNIT 5 Bayes Classifier- Bayes classifier, naïve Bayes classifier, Belief net; Decision Trees- Axis-parallel and oblique decision trees, Learning decision trees, Information gain and Impurity measures. OUTCOMES: : At the end of the course, the student should be able to: Concepts of Pattern Identification in Data Mining. Understanding algorithms for Classification. Use of Classifiers. MAPPING COURSE OUTCOMES LEADING TO THE ACHIEVEMENT OF PROGRAM OUTCOMES AND PROGRAM SPECIFIC OUTCOMES: H = Highly Related; M = Medium L = Low Text Books: 1.V. Susheela Devi and M. NarasimhaMurty, Pattern Recognition: An Introduction, Universities Press,Hyderabad, 2011. Reference Books: 1.R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification, John Wiley and Sons, 2000. 2.M. NarasimhaMurty and V. Susheela Devi, Pattern Recognition, NPTEL Web Course, 2011 ().
CO5 H = Highly Related; M = Medium L = Low Text Books: 1.Perry Edwards, “System analysis and design”, McGraw Hill international edition, 1993. 2. Len Fertuck, “System analysis and design with CASE tools”, Wm C. Brown Publishers, 1992. Reference Books: 1. Er. V.K. Jain, “System analysis and design “, Dreamtech Press. 2. Kenneth E.Kendall and Julie E.Kendall, “System analysis and design”, Prentice Hall, India, 2007.