Bioengineering

Computer Science

Electrical and Computer Engineering   

Systems Science and Industrial Engineering   

Bioengineering

BME 572 - Multivariate Statistics, Prof. Walker Land - 3 cr.

A major focus of the BME program is on developing in the students an understanding of complex systems and how to analyze experimental data obtained from such systems. Analysis biological/medical experimental data invariably requires a multivariate approach, therefore, this course will be required of all graduate students in BME.

Computer Science

CS 555 - Introduction To Visual Information Processing, Prof. LiJun Yin - 3 cr.

The course focuses on fundamental topics, including visual information acquisition, representation, description, enhancement, restoration, transformations and compressions, and reconstruction from projections. The second focus is on Computer Science applications, including algorithms developed in applications such as statistical and syntactic pattern recognition, robotic vision, multimedia indexing, visual data mining, and bio-informatics. Prerequisite: CS 333.

CS 580Z - z/VM Virtualization, Prof. Merwyn Jones - 3 cr.

The course provides students with the background knowledge and skills necessary to begin using the functions and features of z/VM, a mainframe OS. Topics include: Virtualization, Control Program (CP), Conversations Monitoring System (CMS), TEXX, Network and performance systems management. Students are assumed to be familiar with PC computing and have had some computer science or information systems background.

Prerequisite: CS 350.

Electrical and Computer Engineering

EECE502 - Electrical Power Systems, Prof. Eva Wu - 3 cr.

This course will cover the basics of electric power systems including developments related tot he more widespread use of intermittent renewal energy sources. Topics in the course will include a review of fundamental circuit principles related to power system networks, principles of magnetic theory related to power systems, transformers, synchronous generators, AC and DC transmission lines, power flow, stability and control in interconnected power systems, power fault analysis, and other general characteristics of electric power systems.

Prerequisites: Course in electrical circuits and a course in electromagnetics.

EECE 506 - Mathematical Methods in Electrical Engineering, Prof. David Klotzkin - 3 cr.

Selected topics in the advanced engineering mathematics, with special focus on their electrical engineering applications. Topics include ordinary and partial differential equations, Laplace transform, Fourier transform, linear algebra, matrix theory, numerical methods, complex analysis, optimization, probability and statistics. Prerequisites: calculus and differential equations.

Transmission of information in digital form; coding; packets; error detection, correction; carriers; multipath and intersymbol interference, spread spectrum.

Prerequisites: EECE 377 - Communications or equivalent.

EECE 549 - Free-Space Laser Communication, Prof. Vladimir Nikulin - 3 cr.

Introduction to the phenomena related to optical communications. Laser crosslinks, optomechanical, laser and detector technologies, acquisition and tracking. System configuration and design. Prerequisite: familiarity with electromagnetic theory, basic concepts of optics and electronics, elementary differential equations and fundamental principles of communications theory.

EECE 552 - Computer Design, Prof. Aaron Carpenter - 3 cr.

Computer architectures, virtual memory organization, input-output, microprogramming, multiprocessor systems, memory hierarchies, pipelined architecture, RISC machines, fault-tolerant machines.

Prerequisites: EECE 352 or equivalent.

EECE 619 - Control of Networked Systems, Prof. Eva Wu - 3 cr.

The course details the techniques for modeling, control and performance analysis of asynchronous systems driven by random events. Main topics include Markov chain models, discrete event simulations, design optimization and optimal control of networked systems, such as computer and sensor networks.

Prerequisites: A course in probability.

System Science and Industrial Engineering

SSIE 501 - Introduction to Systems Science, Prof. Harold Lewis - 3 cr.

Course will include the following: a general characterization of systems science as a field of study; intellectual roots, philosophical assumptions, and historical development of the field; an overview of fundamental systems concepts, principles, and laws; and a survey of application areas of systems science and its implications for other fields of study.

SSIE 505 - Introduction to Applied Probability and Statistics, Prof. Sangwon Yoon - 3 cr.

Basic concepts in probability and statistics required in the modeling of random processes and uncertainty. Bayes' formula. Bayesian statistics, independent events; random variables and their descriptive statistics; distribution functions; Bernoulli, Binomial, Hyper geometric, poisson, normal, exponential, gamma. Weibull and multinomial distributions; Chebyshev's theorem; central limit theorem; joint distributions; hypothesis testing; contingency tables, goodness of fit, non-parametric statistics, regression and correlation. Prerequisite: one year of calculus.

SSIE 510 - Enterprise Systems Engineering, Prof. Krishnaswami Srihari - 3 cr.

Manufacturing has become increasingly critical to standard of living and competitive market position. Little has really been published and analyzed as to the underlying science of manufacturing. Course studies the manufacturing literature and the manufacturing process and investigates the underlying principles that govern manufacturing.

Prerequisites: SSIE 505 - Introduction to Applied Probability and Statistics or equivalent

SSIE 525 - Principles of Systems Engineering, Prof. Nagen Nagarur - 3 cr.

Basic principles of systems engineering applied in transforming client requirements into an operational system. Topics cover the full system life cycle: planning, integrated product/process development, system architecture and design, modeling, requirements analysis, development, integration, test and evaluation. Specialized concepts involved in engineering complex systems are reinforced through case studies and student exercised.

Prerequisite: graduate standing or consent of instructor.

SSIE 533 - Human Factors Engineering and Design, Prof. Mohammad Khasawneh - 3 cr.

Introduction to Human Factors and systems: design for human use; Human Factors research methodologies; information about human performance, abilities, and limitations will be surveyed and applied: physical work and manual handling, applied anthropometry and workplace design, human control of systems, control and date entry devices, and environmental conditions; Human Factors applications including human error, accidents, safety, Human Factors and the automobile, and Human Factors in Systems Design.

Prerequisites: Basic course in probability and statistics or permission of the instructor.

SSIE 562 - Reliability, Prof. Susan Lu - 3 cr.

Reliability networks, failure mode and effect analysis, apportionment, fault trees and human reliability.

Prerequisites: SSIE 561 and probability and statistics, or consent of department chair

SSIE 612 - Advanced Topics in Integrated Manufacturing, Prof. Nagen Nagarur - 3 cr.

The continual need to improve quality and productivity and remain competitive in a global market requires the comprehensive integration of people, equipment, computers and information within a manufacturing systems engineering framework. Course studies manufacturing integration issues with a special focus on integrating elements such as process planning, group technology, concurrent engineering, product quality, cost analysis, flexible manufacturing, inventory control, information flow and management, and global computer-integrated manufacturing (CIM) concept.

Prerequisite: SSIE 512 or equivalent or consent of department chair.

SSIE 644 - Foundations of Neural Networks, Prof. Sarah Lam - 3 cr.

This course is a survey of the newer, most common adaptive search methods. This is a project and research oriented course designed to give graduate students a foundation from which to explore areas of their own interest. Focused topics include simulated annealing, genetic algorithms, evolution strategies, tabu search, ant colony methods, and particle swarm optimization. Other search methods such as genetic programming, evolutionary programming, neural networks and random search methods will be briefly covered. Major emphasis is on NP complete combinatorial problems found in engineering. Issues such as solution encodings, stochastic convergence, selection methods, and local and global search methods are discussed.

Prerequisite: SSIE 505 or equivalent, and knowledge of at least one programming language.