This course is to discuss some important advanced topics in the area of signal detection theory. Topics may vary: In Fall 2005, the main topic will be locally-optimum detection of weak signals.
(Prerequisite: {EE528 and EE622} or {Approval of the Instructor})
This course provides a performance analysis of the existing and future network according to the ISO/OSI 7 layer model. We focus on the performance of network systems (switch, router, server/gateway, wireless) and their protocols. Topics include mathematical approaches on flow control, routing, polling, and scheduling algorithm by using queueing theory. Operational analysis and OPNET simulation are compared with numerical results.
Recommend
Discrete Event System Modeling and Simulation
Distributed Computing Systems
Service Oriented Computing Systems
Cellular Communication Systems and Protocols
Performance Analysis of Communication Networks
Optimization in Communication Network
Economics in Communication Network
Local Area Network/Metropolitan Area Network (LAN/MAN)
Queueing theory with applications
Wireless Communication Protocols and Analysis
Parallel and Distributed Computation in Communication Network
The course covers fundamental theories and key techniques for applications in adaptive signal processing. More details are signal modeling, optimal estimation theory, Wiener and Kalman filters, eigen-filters, LMS/RLS algorithms, and their variants. We also deal with advanced topics such as adaptive equalization, adaptive beam-forming, and adaptive interference cancellations. (Prerequisite: EE432, EE528)
This course introduces fundamentals of multirate digital signal processing, such as decimation, expansion, theory, and design of multirate filter banks, wavelet transform, and applications of multirate signal processing.
(Prerequisite: EE432)
This course explores the theory and methodologies used to interpret images and videos in terms of semantic content. Techniques from pattern recognition are introduced and discussed to explain how to apply them for image understanding. (Prerequisite: EE535)
This course will explore the principles, models, and applications of computer vision. The course consists of five parts: image formations and image models; generic features, such as edges and corners, from images; the multiple view analysis to recover three-dimensional structure from images; segmentation of images and tracking; the object recognition methodologies. (Prerequisite: EE535)
This course is designed to introduce several medical image systems and related applications based on various image processing techniques. Topics include image reconstruction algorithms, X-ray CT, single photon emission CT, positron emission tomography, magnetic resonance imaging, ultrasound imaging, and related post-processing techniques.
The goal of this course is to provide the theoretical and technical basis required to design and implement speech recognition algorithms or systems. The topics include acoustic-phonetic characterization, speech processing techniques for speech recognition, pattern comparison techniques, theory and implementation of HMMs, searching techniques for continuous speech recognition, and other related implementation issues. (Prerequisite: EE432)
This course discusses cognitive information processing mechanism in our brain and computational models for human-like cognitive systems. We will first discuss neural data representation and move to the models of perception, attention, socialization, memory, learning, reasoning, and problem-solving.
This course is designed for introducing ray analysis to analyze electromagnetic scattering problems. As one of the ray analyses, GTD (Geometrical Theory of Diffraction) is explained and employed to solve various electromagnetic scattering problems.
