B.E. Electronics(Instrumentation Control):Simulation and Modeling

First Year: Semester I

Mathematics I
Engineering graphics
Computer Programming
Physics
Solid Mechanics
Communication Skills

First year: Semester II

Mathematics II
Manufacturing Process
Chemistry
Electrical and Electronic Science
Thermodynamics
Organizational Behavior

Second year: Semester I

Electromagnetic Fields
Human Values, Ethics and IPR
Semiconductor Devices
Measurement Science and Techniques
Circuit Theory
Digital Electronic Circuits
Electrical Machines


Second year: Semester II

Fluid Mechanics
Computer System Architecture
Optimization Techniques
Analog Electronic Circuits
Numerical and Statistical Methods
Electrical and Electronic Measurements
Environmental Studies


Third year: Semester I

Elements and Analysis of Instrumentation System
Analytical Instrumentation
Signals and Systems
Power Electronics
Microprocessors
Biomedical instrumentation
Summer Training

Third year: Semester II

Data Acquisition Systems
Industrial Measurements
Process Dynamics and Control
Control Systems
Total Quality Management


Fourth year: Semester I

Advance Process Control
Virtual Instrumentation
Instrumentation System Design
Engineering Economics
Microelectronics and ICs

Fourth year: Semester II

Project Semester
Project
Industrial Training(6 weeks)

Simulation and Modeling

Introduction: Objectives of Modeling, System Theory and State Variables.

Type of Models: Analytic, Simulation, Measurement, Analytic Modeling, Probability Theory, Random Variables, Poisson Process, Markov Chains.

Queuing Theory: Little?s Law, M/M/1, M/M/1/k, M/M/C Queuing Models, M/G/1 [Impact Variation in Service Times].

Petrinets: Stochastic Petrinets [ SPN ] , GSPN .

Simulation Modeling: Continuous & discrete Event Simulation, Monte Carlo Simulation, Pseudo Random Number Generation, Non-Uniform Random Variable Generation, Simulation Language Features : Simpack, GPSS, GASP IV, CSIM, Estimation of Simulation Outputs/Output Matrix, Confidence Intervals, Regenerative Simulation, Method of Batch Means.

Case Studies: Analytic vs. Simulation Models, Application to Operating Systems, Data Bases, Networks Architecture.

Laboratory work: Write programs to generate random numbers using seed method, to test randomness of two numbers generated using frequency test, uniformity test, to implement queue using M/M/1 and M/M/1/K models, to implement bomber fighter problem, to implement inventory control system keeping storage cost, lost business etc in mind, to implement a chemical reactor problem, to implement various features of various simulation languages such as GPSS, GASP, CSIM. .