Fall 2003 Courses

Registration begins April 18 and will continue through September 8, 2003. New students to the program can begin their registration for fall beginning in late July. See registration materials for more information.

Class recording begins on September 2, 2003.

This page contains links to course descriptions, course outlines (syllabi) and faculty homepages.  

Course Number

Course Name

Instructor

CS 542 - PROGRAMMING DESIGN PATTERNS, Prof. Leslie Lander - 3 cr.

Fundamentals of object-oriented programming using the Smalltalk language and programming environment.  Patterns for program design, examples of patterns used in existing software libraries such as a windowing toolkit.  Exercises in programming with design patterns and communicating designs to other programmers using the language of patterns.  UML design notation.  Project using a compiled object oriented programming language.  Prerequisites:   CS 571 or CS 471-Programming Languages or equivalent experience.

Course Syllabus

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Course Syllabus

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CS 562 - Neural Networks/genetic optimization applications, Walker Land -  3 cr.

Emphasis on tool building and applications.  Neural networks:  multi-layer propagation, multi-temporal paradigms, pre-and post-processing, training.  Real domain neural networks; network sizing.  Evolutionary computing.  Genetic optimization: coding, fitness functions, reproduction and convergence.  Comparison with gradient methods, iterated search and simulated annealing.  Implementation in an object-oriented language using libraries of object-oriented reusable components.   Prerequisites:  CS 333 - Algorithms and MATH 304 - Linear Algebra.

Course Syllabus

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CS 571 - PROGRAMMING LANGUAGES, Prof. Leslie Lander- 3 Cr.  

Selected topics in programming languages and alternative programming paradigms. Functional and imperative languages. Logic programming and object-oriented programming paradigms. Languages for concurrent computation. Semantics of programming languages. Prerequisite: CS 471-Programming Languages.

Course Syllabus

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CS 575 -  DESIGN AND ANALYSIS OF COMPUTER ALGORITHMS, 
Prof. Patrick Madden - 3 CR.  

Analysis of programs and review of design techniques. Lower bound theory and NP-completeness. Heuristic, approximation, probabilistic and parallel algorithms. Prerequisites: CS 373-Automata Theory and Formal Languages and CS333--Algorithms.

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EE 505 - Analysis and Design of Control Systems,
Prof. Eva Wu - 3 cr.

Advanced techniques for analysis and design of analog linear and non-linear control systems.  Topics include oncventional and state variable techniques for the mathematical description of control systems, stability analysis conventional and modern design techniques.  Prerequisites:  EE 361 - control Systems or equivalent.

Course Syllabus

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Conventional and state variable techniques for the analysis and design of digital and analog control systems.  Z-transform.  Sampled data systems.  Discrete state variable.  Prerequisite:  EE 361 - Control Systems and approval of the graduate adviser.

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EE 533 - Electromagnetic Compatibility, Prof. James Constable - 3 CR.

Signal paths; conductive, inductive, capacitive, electromagnetic.  Shielding and grounding concepts.  Methods of measurement.  EMC specifications and standards.  Prerequisite: EE 423-Electromagnetic or equivalent.

Course Syllabus

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EE 534 - Signal Transmission in Design, Prof. Harry Kroger - 3 CR.

General transmission line theory as applied to electronics packaging; digital signal transmission; interconnections; transient analysis of transmission lines by Laplace transform.  Prerequisites: EE 423-Electromagnetic or equivalent

Course Syllabus

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ME 506 - Vehicle Control and Simulation, Prof. Frank Cardullo - 3 CR.

Concepts of modeling and simulation of vehicle dynamics are developed with particular emphasis on real-time simulation.  The digital simulation of the continuous system is developed as a discrete dynamic system that may be filtered, tuned, stabilized, controlled.  Prerequisites: BS degree in engineering or physics, or consent of department chair.

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A survey and discussion of some of the most important and useful analytical methods for analyzing a wide variety of engineering and scientific problems.  Topics include solution of partial differential equations, including methods for linear equations; eigenfunction expansions and separation of variables.  Prerequisites:  ordinary differential equations, ME 302 - Engineering Analysis or equivalent.

Course Syllabus 

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Includes 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.  Prerequisite:  one year of calculus.

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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; contigency tables, goodness of fit, non-parametric statistics, regression and correlation.  Prerequisite:  one year of calculus.

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Production scheduling and control.  Design/production interface, bills of material, engineering revision control and general concepts of production planning and control for the engineer.  Prerequisite:  SSIE 510 - The Science of Manufacturing or consent of department chair.

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Covers relatively new approaches to machine intelligence known collectively as soft computing.  Introduces various types of fuzzy inference systems, neural networks and genetic algorithms, along with several synergistic approaches for combining them as hybrid intelligent systems.  The emphasis is on applications, including modeling, prediction, design, control, databases and data mining.  Prerequisites:  senior standing, basic knowledge of calculus and discrete mathematics, and competence in at least one programming language, or consent of the instructor.

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SSIE 561- QUALITY ASSURANCE FOR ENGINEERS, Prof. Nagen Nagarur - 3 CR.

Statistical quality control, designing for quality, process control, vendor and customer quality issues, quality costs and production. Prerequisites: BS in engineering (any field), and probability and statistics coursework, or consent of department chair.

Course Syllabus (requires Adobe Acrobat Reader - Free Download)

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SSIE 578 - Processes for Electronic Manufacturing, Prof. Hari Srihari - 3 CR.

The electrical content of manufactured products is increasing in all areas.  To prepare the engineer for manufacturing these electrical assemblies, this course has been structured to cover topics in soldering, wire bonding, TAB, printed wiring board production, PCB assembly and population processes (through hole and SMT), and associated environmental issues.   Prerequisite:  Undergraduate course in manufacturing processes, related experience, or consent of department chair.

Course Syllabus (requires Adobe Acrobat Reader - Free Download)

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SSIE 631 - Foundations of Neural Networks, Prof. Sarah Lam - 3 CR.

This course is designed to introduce students to the theory and practical applications of artificial neural networks.  Topics include simple learning rules, back propagation networks, associative networks, competitive networks, adaptive resonance theory, radial basis function networks, Hopfield networks and others.  Validation and modeling building issues will also be discussed. Prerequisite:  SSIE 505 - Applied Probability and Statistics and SSIE 520 - Modeling and Simulation or equivalents

Course Syllabus

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