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.
 

Course Syllabus

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EECE 521 - Digital Signal Processing,  Prof. Mark Fowler -  
3 cr.

Advanced topics in digital signal processing Bandpass signals and bandpass sampling, DFT-based processing, multi-rate processing and filterbanks, andom signals and spectrum estimation.
Prerequisites:  EECE 402 - Signal Processing or equivalent and MATH 327 or ISE 261 - or equivalent.

Course Syllabus - Fall 2007 Syllabus posted
Course OUTLINE

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Electro-optic devices and systems.  Blackbody, LED and laser sources, photodetectors, modulators, fiber optics, Fourier optics.  Design of electro-optic systems.  Lecture portion meets with EECE 474.
Prerequisites:  EECE 323 - Electromagnetics or equivalent. 

Course Syllabus - Fall 2007 syllabus posted

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Transmission of information in digital form; coding; packets; error detection, correction; carriers; multipath and intersymbol interference, spread spectrum.
Prerequisites:  EECE 377 - Communications or equivalent.

Course Syllabus - Fall 2006 Course Syllabus.  Will update with  fall 2007 syllabus once received.

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Computer architectures, virtual memory organization, input-output, microprogramming, multiprocessor systems, memory hierarchies, pipelined architecture, RISC machines, fault-tolerant machines.
Prerequisites:  EECE 352 - Computer Organization and Microprocessors or equivalent.

Course Syllabus -  Fall 2007 Syllabus posted

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The MOS transistor, circuit characterization and performance estimation.  CMOS logic and structured design:  electrical design of logic circuits, clocking strategies, and design rules.  CMOS systems and RISC architectures.  Prerequisites:  EECE 351 - Digital Logic Design or equivalent

Course Syllabus  - fall 2007 syllabus posted

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Basic principles and strategies for pattern processing and recognition systems.  Parametric and non-parametric techniques incoluding Bayesian classifiers and neural networks.  Analysis of linear and nonlinear decision functions for pattern classification.  Trainable pattern classifiers with statistical data sets.  Prerequisites:  EECE 301 - Signals and Systems or equivalent and MATH 341 or ISE 261 or equivalent.

Course Syllabus - Fall 2007 Syllabus posted

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

Course Syllabus -

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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

Course Syllabus  - fall 2007 syllabus posted

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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. Offered as a dual level (graduate/undergraduate) course with ISE 419. The undergraduate students are not required to do projects on the same level as the graduate students, and are not required to place the degree of emphasis on hybrids. Prerequisites: senior standing, basic knowledge of calculus and discrete mathematics, and competence in at least one programming language, or consent of the instructor.
 

Course Syllabus  

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SSIE 537 - Industrial and Systems Engineering in Health Care,  Prof. Mohammad Knasawneh - 3 CR.

Introduction to health systems and health care delivery. The application of industrial and systems engineering principles to continuous process improvement in the health care domain will be studied. Concepts that will be addressed will include, but not be limited to, process mapping, optimization, scheduling, lean and flexible systems, quality enhancement, simulation, supply chain management, inventory control, and information management. Prerequisite: Graduate standing in the department or permission of the instructor
 

Course Syllabus

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SSIE 605 - Applied Multivariate Data Analysis, Prof. Susan Lu   - 3 cr.

Course introduces different multivariate data analysis and modeling tools, which can be used for simultaneously analyzing data with multiple dependent variables. It is designed to emphasize applied methodologies and applications in multivariate data analysis, especially in engineering fields. Topics to be covered include: multivariate regression, logistic regression, multivariate analysis of variance (MANOVA), principal components analysis, cluster analysis, canonical correlation, factor analysis, and discriminant analysis. The effective use of advanced data analysis software, such as SAS, for solving real-world engineering problems will be also addressed. Prerequisite: SSIE 505 or its equivalent.

Course Syllabus -

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The course consists of two interwoven parts. The first part covers fundamentals of fuzzy set theory and the associated fuzzy logic. The second part is devoted to applications of the theory. Topics of the theoretical part include basic concepts of fuzzy set theory and fuzzy logic; representations of fuzzy sets and fuzzy propositions; fuzzification of classical mathematical concepts; aggregation operations on fuzzy sets; fuzzy numbers, arithmetic, and calculus; fuzzy relations and fuzzy relation equations; approximate reasoning based on fuzzy logic; knowledge-based and model-based fuzzy systems; and possibility theory based on fuzzy sets. In the application part, methods of constructing fuzzy sets in various applications are overviewed and representative applications of fuzzy sets and fuzzy logic are examined. The application areas covered include: approximate reasoning in expert systems; database and information retrieval systems; pattern recognition and image processing; decision making; and various applications in engineering, science, and other areas of human affairs. Prerequisites: Basic probability theory (e.g., SSIE 505), calculus and discrete mathematics, or consent of the instructor.

course Syllabus

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