MODEL PRACTICAL EXAMINATION II Learning Resources

Text Book

1. Herbert Schildt – “Java The Complete Reference” Tata McGrawHill Fifth Edition 2005.

Reference Books

1. E.BalaGurusamy – “ Programming in Java” Tata McGrawHill Second Edition 2. H.M.Deitel and P.J.Deitel –“Java How to Program” Pearson Prentice Hall Sixth Edition

3. Bruce Eckel – “Thinking in Java” Pearson Prentice Hall Third Edition Online Resources

http://www.tutorialspoint.com/java/

http://wp.mykau.com/wp-content/uploads/2010/06/cpit305-lab-manual.pdf

99

SEMESTER IV

SUB.CODE SUBJECTS L T P C

THEORY

U4MAB05 Probability and Queuing Theory 3 1 0 4

U4CSB07 Design and Analysis of Algorithms 3 1 0 4

U4CSB08 Theory of Computation 3 0 0 3

U4ECB14 Microprocessors & Microcontrollers 3 0 0 3

U4CSB09 Database management System 3 0 0 3

U4CSB10 Operating System 3 0 0 3

PRACTICAL

U4CSB11 Operating System Lab 0 0 3 2

U4ECB17 Microprocessors and Microcontrollers Lab 0 0 3 2

U4CSB12 Database Management System Lab 0 0 3 2

Total Credits 25

L – Lecture; T – Tutorial; P – Practical; C – Credit

Course Code :U4MAB05

Course Name: Probability And Queuing Theory Course Educational Objectives :

 Providing the students with fundamental knowledge of basic probability theory.

 Equipping the students with a fair knowledge of standard distributions with application to real life phenomena.

 Preparing the students to handle situations involving two or more random variables and functions of random variables.

 Enabling the students to model the phenomena which evolve with respect to time (discrete or continuous) in a probabilistic manner.

 Exposing to the students the basics and applications of white noise, telegraph processes in communication engineering.

 Developing skills in the students to model and analyse queuing problems in computer science and engineering.

Course Outcomes :

Upon the successful completion of the course, learners will be able to CO

Determine the probability distributions of different types of random variables and work binomial, Poisson, geometric, uniform, exponential, normal distribution and their statistical measures.

K3

 CO2 Calculate Probabilities, correlation co-efficient and regression

lines of two dimensional random variables. K2

 CO3 Identify the nature of the process namely Markov and Poisson

processes and calculate Stationary and transition probabilities. K3

 CO4 Apply the concept of Markovian Queueing models for obtaining measures of performance of real-time problems under steady state conditions.

K3

 CO5

Apply the concept of non-Markovian queues and networks of queues for obtaining measures of performance of real-time problems under

steady state conditions. K3

Prerequisite:

 Mathematics-I

 Mathematics-II

 Transforms and partial differential equations

 Basic probability concepts.

Course Content :

UNIT I ONE DIMENSIONAL RANDOM VARIABLES L- 9 + T-3

Discrete and continuous random variables – moments – moment generating functions and their properties – binomial, Poisson, geometric, uniform, exponential, normal distributions.

101

L T P C 3 1 0 4

UNIT II TWO DIMENSIONAL RANDOM VARIABLES L- 9 + T-3

Joint distributions – marginal and conditional distributions – covariance – correlation and regression – transformation of random variables – central limit theorem (for IID random variables)

UNIT III MARKOV PROCESSES AND MARKOV CHAINS L- 9 + T-3

Classification – stationary process (wide sense and strict sense) – Markov process – Markov chains – transition probabilities – limiting distributions – Poisson process.

UNIT IV QUEUEING THEORY L- 9 + T-3

Markovian models – birth and death queuing models – steady state results: single and multiple server queueing models – queues with finite waiting rooms – finite source models – Little’s formula.

UNIT V NON-MARKOVIAN QUEUES AND QUEUE NETWORKS L- 9 + T-3 M/G/1 queue – Pollaczek-Khintchine formula – series queues – open and closed networks.

Total: 60 Periods Learning Resources

i. Text Books:

1. O. C. Ibe, Fundamentals of Applied Probability and Random Processes, Elsevier, Indian Reprint 2007.

2. O. C. Ibe, Markov Processes for Stochastic Modeling, Elsevier, 2009.

3. D. Gross, John F.Shortle, James M. Thompson, Carl. M. Harris, Fundamentals of Queueing Theory (4^th Edition), Wiley Student Edition, 2013.

ii. References Books:

1. S. Asmussen, Applied Probability and Queueing Theory (2^nd Edn) Springer, Berlin, 2003.

2. W. C. Chan, Performance analysis of telecommunications and local area networks, Kluwer Academic Publishers, 2002.

3. R. B. Cooper, Introduction to Queueing Theory, (2^nd Edn), North Holland, New York,1981 4. S. Karlin and H. M. Taylor, A First course in Stochastic processes (2^nd Edition) Elsevier,

USA, 1975.

5. J. Medhi, Stochastic Processes, New Age Publishers, New Delhi, 1994.

6. J. Medhi, Stochastic models in Queuing theory (2^nd Edition) Elsevier Science, USA, 2003.

7. T. G. Robertazzi, Computer Networks and Systems: Queueing Theory and Performance Evaluation (3^rd Edn) Springer, Berlin, 2000.

8. S. M. Ross, Stochastic Processes, (2^nd Edn.), John Wiley & Sons, New Delhi, 2004.

Course Code: U4CSB07

Course Name: Design and Analysis of Algorithms Pre-requisites:

 Basics of Computing and C platform

 C programming lab

 Data Structure lab

Course Educational Objectives:

Students undergoing this course are exposed to

 Basic paradigms and data structures used to solve algorithmic problems

 Estimate the time & space complexity of algorithms and will be able to analyze the performance of algorithms across the domains

 Represent the complexity using asymptotic notations

 Use of appropriate algorithm design methodology to develop algorithms for a given problem

Course Outcomes:

Students undergoing this course are able to:

CO

Nos. Course Outcomes Level of learning

domain (Based on revised Bloom’s) CO1 Explain various asymptotic notations and Compute the

efficiency of given algorithms K3

 CO2 Apply the brute force technique to solve the given problem K3

 CO3 Use DAC technique to solve a given problem. K3

 CO4 Compute optimum solutions for the given recurrence equation. K2 CO5 Discuss the improvement of computational efficiency using

iterative approaches K2

CO6 Illustrate the NP completeness and NP hard problem K2 Course Content:

UNIT I INTRODUCTION L-9 + T-3

Notion of an Algorithm – Fundamentals of Algorithmic Problem Solving – Important Problem Types – Fundamentals of the Analysis of Algorithm Efficiency – Analysis Framework – Asymptotic Notations and its properties – Mathematical analysis for Recursive and Non-recursive algorithms.

UNIT II BRUTE FORCE AND DIVIDE-AND-CONQUER L-9+T- 3 Brute Force - Closest-Pair and Convex-Hull Problems-Exhaustive Search - Traveling Salesman Problem - Knapsack Problem - Assignment problem. Divide and conquer methodology – Merge sort – Quick sort – Binary search.