Graduate (List)

This course is designed to expose students to the important issues in high performance CMOS circuit design. This course covers the data path design in full custom design methodology, clocking strategy, and the state-of-the art CMOS logic styles.

This course is intended to present the fundamental result of analysis and design of nonlinear control systems. Especially, this course is concerned with the analysis tools for nonlinear dynamical systems and the design techniques for nonlinear control systems. (Prerequisite: EE581)

Among the various well-known intelligent control techniques, the methods of fuzzy control and neural net-based learning control are first introduced to allow for handling ambiguous/uncertain situations and effective supervised learning, respectively. Specifically, the theory of fuzzy sets and fuzzy logic-based inference mechanism are studied and the design techniques of fuzzy control are introduced. Then, the neural net learning structure is discussed and the control system based on artificial neural nets is studied. Fuzzy-neuro systems are also considered. In the second part of the course work, some other computational intelligence techniques such as GA and the rough set are briefly covered and then the basic machine learning techniques and the reinforcement learning method are studied in conjunction with their use in control system design. (Prerequisite: EE581)

This course is intended to cover kinematics, dynamics and control algorithm of a robot manipulator. After covering homogeneous transformations, kinematics equations, motion trajectory planning, we will handle various control methods. We will compare the utilization of these control methods through simulation.

This course deals with the derivation of maximum principle and the design of optimal control system. It includes an optimal design method for minimum time and energy along with dynamic programming and discrete maximum principle. Also advanced topics of optimal control are introduced. (Prerequisite: EE581)

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 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 is designed to provide fundamental principles of EMI / EMC with numerous design practices of high-performance circuit, module, and system to meet EMI / EMC compliant specifications.
(Prerequisite: EE204, EE304)