Ph.D. Course Descriptions
This course includes analytic and descriptive survey designs, pilot work, selection methods for data collection, evidence interpretation and reporting methods as preliminary to a research project to be prepared by the student under the surveillance of a faculty member. The project provides an opportunity to prepare and present an integrated technology oriented project of significant importance to the student’s organization […]
The aim in this course is to introduce systems engineering methodology and some of its applications to graduate students. The subject matter will be treated through analytical tools, wherever appropriate, and will cover the following material: introductory concepts; systems thinking; system life cycle; system modeling for analysis and design; life cycle costing; system realibility and effectiveness ; solution design; solution […]
The aim in this corse is to present modeling and analysis of real dynamic phenomena through the use of systems theory. Both discerete and continuous time systems will be covered, emphasis being on the former. The following topics are included in the course: models of multivariable systems; state equations and their solutions; concepts of stability, controllability, and observability; nonlinear systems […]
In this course, the student carries out an independent study to prepare for the qualifying exam. At the end of the course, the student takes a written and oral qualifying exam to demonstrate that he/she has sufficient knowledge about the fundamental subjects in his/her field and that he/she is capable of conducting scientific reseach towards writing a Ph.D. thesis.
The course is designed to give students opportunity to do complex and advance analysis and to create new research models based on the data obtained from primary or secondary sources.
This course focuses on the design and analysis of combinatorial optimization algorithms. Computational complexity of P and NP problem classes, NP-Completeness, neighborhood, local and global optimality, and analysis of algorithms are among topics that are covered. In addition to exact algorithms developed for problems that can be solved in polynomial time, the course also introduces local search algorithms and metaheuristic […]
Maximum-likelihood estimation. Unbiasedness, consistency, sufficiency, completeness and uniqueness. Minimum-variance unbiased estimators. Fisher information and Rao-Cramer lower bound. Efficiency. Exponential class. Bayesian estimation. Best tests.Uniformly most powerful tests. Likelihood-ratio tests. Sequential probability ratio tests. Prerequisite: A background in statistics at the level of ISE 254.
Stationarity. Autocovariance and autocorrelation functions. General linear process. Stationary models: AR, MA, ARMA. Model identification. Estimation. Diagnostic cecks. Nonstationary models: ARIMA. Seasonal models. Forecasting. Statistical package applications. Prerequisite: A background in statistics at the level of ISE 254.
Structure of the automation systems: the machine, the cell and plant level control. Control of large-scale industrial systems: Hierarchical control, multilayer control and its optimization. Dedicated computer structures for process control and automation. Control systems and applications in industrial automation. Advanced programming techniques for Programmable Controllers (PLC): Structured Control Language (SCL) and Function Block Diagram (FBD). Industrial data communication systems. […]