Graduate (List)

The lecture on network management will introduce the key issues in the communications network management and will cover a new paradigm encountered in managing communications network.

This course covers mathematical theories associated with computation, convergence, communication, and synchronization of parallel and distributed algorithms which often appear in a network, communication, control, signal processing and OR problems, focusing on asynchronous parallel and distributed algorithms. A system of equations, nonlinear optimization, variational inequality problem, shortest path problem, dynamic programming, and network flow problem will be addressed as applications with many real-world examples.

The design and implementation of the physical layer, data link layer, and network layer protocols are explained. Also, client/server programming using UNIX and windows sockets is studied. Moreover, the architecture of SDR based terminal is investigated. Finally, this course involves protocol design, verification, and optimization.
(Prerequisite: EE527)

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.

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.