Computer Science

Electrical and Computer Engineering   

Mechanical Engineering

Systems Science and Industrial Engineering   

Computer Science

CS 554 - introduction Real-time Embedded System - 96196, Prof. Kyuong Don Kang - 3 cr

This course covers important topics related to developing next generation real-time embedded systems such as real-time scheduling, real-time operating systems, and sensor networks. It will begin with an introduction to classical real-time systems. Following the introduction, research papers envisioning future real-time systems will be discussed. The class will include not only lectures but also discussions and brainstorming. In this way, students can develop a solid understanding of real-time computing, while improving research skills.

CS 580W - Introduction to Mainframe Hardware Manager - 98353, Prof. Merwyn Jones - 3 cr.

This course is a Mainframe course that will introduce the student to Mainframe Hardware Systems Management. Specifically, this course will provide the student with the knowledge and skills necessary to control, configure, update and manage mainframe hardware using the Hardware Management Console (HMC) and Support Elements (SE). A new IBM Redbook, “Introduction to the Mainframe: The Hardware Management Console (HMC)”, will be the guide for the course. It is part of a series of textbooks designed to introduce students to mainframe and enterprise system concepts. The HMC will be extensively used in the labs and assignments associated with this course. The specific topics covered in the course include: the HMC and SE User Interface and User Management; System z First Failure Data Capture and Serviceability functions including: Problem Analysis, Repair and Verify (R/V), Remote Support Facility (RSF), and Code Update; Monitoring the System z performance using the HMC System Activity Display (SAD); implementing the networking capabilities of the HMC; managing the System z Partitions (LPAR) and Partition resource movement and sharing; and enabling and customizing the System z Customer Upgrade on Demand advanced functionality. This course will involve an extensive set of assignments in the HMC environment.

Prerequisite: CS 350. Also students are assumed to be familiar with PC computing and have had some computer science or information systems background.

Course Syllabus

CS 580Y - Z/VM Advance Topics and Techniques - 97485, Prof. Merwyn Jones - 3 cr.

This course is a z/VM Power User Class that will introduce the student to z/VM System Programmer tasks. The course will build the student proficiency in the knowledge and skills necessary for effectively executing the tasks associated with a z/VM System Programmer role (which will be used extensively in the follow-on z/VM System Programmer course). The z/VM operating system will be extensively used in the labs and assignments associated with this course. The specific topics covered in the course include: z/VM Packaging, Installation & Service, Advanced XEDIT facilities, REXX, PIPELINES, Assembler, CMS Program Management, z/VM automation, z/VM System Customization, and z/VM server Connectivity configuration. This course will involve an extensive set of assignments in the z/VM environment.

Prerequisites: CS 350, CS480Z/580Z

Course Syllabus

CS 660 - Advances Computer Graphics - 97792, Prof. LiJun Yin - 3 cr.

A comprehensive review of the techniques needed to produce computer-generated shaded images of three-dimensional scenes. Recent research results are presented. Students design and implement portions of a three-dimensional graphics package. Topics selected from modern graphics standards (PHIGS, X-Windows), user interface issues, 3-D viewing, geometric modeling, image synthesis, image manipulation, animation, scientific visualization.

Prerequisites: CS 560 - Computer Graphics

Course Syllabus

Electrical and Computer Engineering

EECE 503 - Electric Drives - 98224, Prof. Vladimir Nikulin - 3 cr.

Fundamentals of electric drive systems with applications emphasis. The course offers an integrative treatment of multiple components that make up electric drives, including electrical machines, power-electronics-based converters, mechanical systems, feedback controller design, and the interaction of the drives with the utility grid.

Prerequisites: EECE 260 Circuits, EECE 301 Signals and Systems, EECE 323 Electromagnetics.

Advanced topics in digital signal processing. Bandpass signals and bandpass sampling, DFT-based processing, multi-rate processing and filterbanks, random signals and spectrum estimation.

Prerequisites: EECE 402 or equivalent and MATH 327 or ISE 361 or equivalent.

EECE 523 - Data Compression - 95001, Prof. Mark Fowler - 3 cr.

Discusses the theory and practice of data compression of signals, images, and video. Techniques covered include: Quantization, Vector Quantization, Differential Schemes, Filterbanks and Subband Coding, Wavelet Transform, JPEG 2000 MPEG.

Prerequisites: EECE 402 or equivalent and MATH 341 or ISE 361 or equivalent.

EECE 527 - Information Theory - 94706, Prof. Scott Craver - 3 cr.

An introduction to information theory for signal processing and communication theory. Entropy, mutual information, divergence, channel capacity, multi-user communications, hypothesis testing and types.

Prerequisites: EECE 301 or equivalent.

Course Syllabus

EECE 575 - VLSI System Design - 94714, Prof. Qinru Qiu - 3 cr.

Gate level and physical level design of a complex system, such as RISC processor, is discussed. Advanced topics covered in logic-level design, such as high performance design, dynamic logic, low-power design, asynchronous logic, interconnect analysis, cross-talk issues, bus architecture, layout floor planning, and placement and routing. Students will be asked to use Cadence physical design, analysis and simulation tools.

Prerequisites: EECE 574 - VLSI Circuit Design Architectures

Course Syllabus

EECE 629 - Machine Pattern Classification - 93230, Prof. Stephen Zahorian - 3 cr.

Basic principles and strategies for pattern processing and recognition systems. Parametric and non-parametric techniques including Bayesian classifiers and neural networks. Analysis of linear and nonlinear decision functions for pattern classification. Trainable pattern classifiers with statistical data sets. Extensive use of software simulations in a high-level language such as Matlab.

Prerequisites: EECE 520, Digital Signal Processing I, or EECE521, Digital Signal Processing, and general background in probability theory

Course Syllabus

Mechanical Engineering

WSTN 581 - Electronics Packaging Systems - 94039, Prof. Bill Infantolino - 1 cr.

Exposes the student to the latest developments in the field of electronics packaging by using a variety of academic and industrial experts. Provides a broad perspective on the electronics packaging concepts, terminology, industry and recent developments. Addresses design, materials and manufacturing aspects of electronics packages.

Prerequisites: graduate standing.

WTSN 582 - Fundamentals of Electronics Packaging - 94041, Prof. Bahgat Sammakia - 3 cr.

Provides students with a general overview of electronics packaging, including the definition of electronics packaging, the latest developments in packaging technology and applications and roadmaps for the future of packaging. An almost equal emphasis is given to both the fundamentals as well as the applications aspects of electronics packaging. A few applications are selected and discussed; then, the fundamental sciences used for analyzing those applications are presented. Using a multi-disciplinary approach, the course is taught by world-class researchers from mechanical, electrical and industrial engineering, chemistry and physics.

Course Syllabus

Systems Science and Industrial Engineering

SSIE 506 - Systems Problem Solving - 93974, Prof. Doug Elias - 3 cr.

A comprehensive conceptual framework for systems problem solving is introduced. Discusses methods applicable to broad classes of problems.

Prerequisites: SSIE 505—Introduction Applied Probability and Statistics or equivalent, or consent of instructor.

Course Syllabus

SSIE 515 - Operations Management of Supply Chain - 94641, Prof. Nagen Nagarur - 3 cr.

Deals with management of supply chains -- in particular, with the operational aspects. A broad overview of supply chains of a company is introduced, together with performance measures and needed critical success factors. Concentrates on supplies, inventories, manufacturing and logistics of distribution. Managerial aspects as well as mathematical modeling for better planning and control are covered. Enabling the supply chains through Enterprise Resource Planning modules and e-commerce is also discussed. This is offered as a dual-level course with ISE 415.

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

SSIE 520 - Modeling and Simulation - 94640, Prof. Sarah Lam - 3 cr.

Stochastic processes, review of probability and statistics, covariance, input data selection, random number generators, non-parametric tests for randomness, generation of random variates, output data analysis, terminating and non-terminating simulations, model validation, comparison of alternatives, variance reduction techniques, sensitivity analysis, experimental design and predictive models.

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

SSIE 553 - Operations Research - 99041, Prof. SangWon Yoon - 3 cr.

Operations Research (OR) is devoted to the determination of the best course of action of a decision problem given resource restrictions. This course is intended to provide the engineer with a firm grounding in the use of OR (mathematical) techniques devoted to the modeling and analysis of decision problems. Techniques will include the following: Decision Modeling; Linear Integer, and Dynamic Programming; Emerging Optimization Techniques (e.g., Genetic Algorithms, Simulated Annealing, etc.); Game Theory; Queuing Theory. Problem areas will include the following: Transportation Models; Project/Production Scheduling; Inventory Models; Assignment Problems.

SSIE 561 - Quality Assurance for Engineers - 94639, Prof. Daryl Santos - 3 cr.

Quality has become a critical issue for competition throughout the world. Quality starts with the engineering design process and goes through the product manufacture. Course covers basic elements of statistical quality control, designing for quality, process control, vendor and customer quality issues, quality costs and the production of a quality product.

Prerequisites: An introduction to probability and statistics or consent of department chair.

SSIE 566 - Designing with Experiments - 97343, Prof. Susan Lu - 3 cr.

Basics of applying statistical design, and the design function, statistical experimental design, control of experimental setting, Taguchi methods and analysis of results.

Prerequisites: SSIE 561 and 505 or equivalents, or approval of department chair.

Course Syllabus

SSIE 616 - Advance Topics Applied Soft Computing - 98930, Prof. Harold Lewis - 3 cr.

Course is designed to follow a currently offered course, SSIE 519, Applied Soft Computing. Both courses are designed to cover relatively new approaches to machine intelligence and systems analysis known collectively as soft computing. The 519 course already introduces various types of fuzzy inference systems, neural networks, and genetic algorithms, along with several synergistic approaches for combining them, including "neuro & fuzzy" techniques, neuro-fuzzy models, the use of neural models in fuzzy systems design, genetic auto-tuning techniques, genetic training of neural nets, fuzzified neural nets, and neural genetic fuzzy models. Naturally, with so many new approaches developing in this field, it is possible in an entry-level graduate course only to cover the main topics in depth and to offer only a general overview on the more advanced hybrid approaches. The purpose of SSIE 616 is to allow students to pursue these advanced approaches to a much greater depth. The emphasis will be on applications, including modeling, prediction, design, control, databases, and data mining, just as is already the case in the 519 course.

Prerequisite: SSIE 519 - Applied Soft Computing

SSIE 633 - Advanced Human Factors - 98928, Prof. Mohammad Khasawneh - 3 cr.

This course is intended as an advanced course in human factors/ergonomics engineering. The course is project/research oriented in nature to provide the graduate students a foundation from which they can explore areas of their own interests. Focused topics include fundamentals and dynamics of human performance modeling, particularly using digital humans; advanced human factors research and development, including ethics, methods, and analysis tools; human-machine systems modeling and design; human reliability analysis; adaptive hybrid systems; and control theory for humans. Application areas will include quality/process control in manufacturing, healthcare, transportation, aviation, and military systems.

Prerequisites: SSIE 533 (or equivalent) or permission of the instructor

Course Syllabus