Fall 2011 courses

Course Number Course Name Instructor CRN Number



Computer Science

CS 555  Introduction to Visual Information Processing  Prof. LiJun Yin  

Electrical and Computer Engineering   

EECE 506  Mathematical Methods in EE  Prof. David Klotzkin  
EECE 515  Analysis & Des of Control Systems  Prof. Eva Wu  
EECE 545  Digital Communication Systems  Prof. Edward Li  
EECE 530/474  Electro-Optics  Prof. Vladimir Nikulin  
EECE 680B  Convex Optimization for EE & CoE  Prof. Eva Wu  

Systems Science and Industrial Engineering   

SSIE 501  Introduction to Systems Science  Prof. Harold Lewis  
SSIE 505  Introduction to Applied Probability and Statistics  Prof. Sangwon Yoon  
SSIE 510  Enterprise Systems Engineering  Prof. Nagen Nagarur  
SSIE 519  Applied Soft Computing  Prof. Harold Lewis  
SSIE 525  Principles of Systems Engineering    
SSIE 578  Processes for Electronic Manufacturing  Prof. Peter Borgensen  
SSIE 605  Appl'd Multivar. Data Analysis  Prof. Susan Lu  
SSIE 644  Found of Adaptive Optimization  Prof. Sarah Lam  

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.

Course Syllabus

Electrical and Computer Engineering

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.

EECE 515 - Analysis & Des of Control Systems, Prof. Eva Wu - 3 cr.

Advanced techniques for analysis and design of analog linear and non-linear control systems. Topics include conventional and state variable techniques for the mathematical description of control systems, stability analysis, conventional and modern design techniques, numerical simulation and computer-aided design of control systems. Prerequisites: EECE 361 or equivalent.

EECE 530/474 - Electro-Optics, Prof. Vladimir Nikulin - 3 cr.

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.

EECE 545 - Digital Communication Systems, Prof. Edward Li - 3 cr.

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 680B - Convex Optimization for EE & CoE - Prof. Eva Wu - 3 cr.

Basics of convex analysis. linear, quadratic, and semi-definite programs, duality theory. Interior-point, cutting-plane, and ellipsoid methods. Matlab-based tool for convex optimization. Applications of convex optimization to fields in electrical and computer engineering.

Prerequisites: EECE


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 - Applied Probability and Statistics, Prof. Susan Lu - 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. Nagen Nagarur - 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 519 - Applied Soft Computing, Prof. Harold Lewis - 3 cr.

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.

SSIE 525 - Principles of Systems Engineering, Professor TBA - 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 578 - Processes for Electronic Manufacturing,Prof. Peter Borgensen - 3 cr.

The purpose is for the students to gain a broad knowledge and understanding of the basics of printed circuit board manufacturing and assembly. The course offers an introduction to surface mount and insertion mount components, materials and processes as well as to PCB design and manufacturing. Lectures will cover an introduction to assembly process flows and components types, PCB construction and defects, solder paste printing and equipment, dispensing, placement processes and equipment, reflow and ovens, flip chip assembly and underfilling, inspection methods, defects and mitigation, rework and repair, reliability optimization and testing. Efforts will be made to include visits to local industrial assembly facilities as well as equipment on campus. The overall goal is to provide the students with a basis for communicating and working with subject matter experts. The final grade will be determined by class participation and performance on the final exam. No books are required or recommended. All required information will be included on slides posted on Blackboard. Prerequisite: Undergraduate course in manufacturing processes, related experience, or consent of instructor.

SSIE 605 - Applied Multivar. 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. Parerequisite: SSIE 505 or its equivalent.

SSIE 644 - Found of Adaptive Optimization, Prof. Sarah Lam - 3 cr.

Prerequisite: SSIE 520 or equivalent and knowledge of at least one programming language.


Last Updated: 11/26/13