Graduate (List)

The purpose of this course is to provide the basic principles and technological developments in semiconductor optoelectronic devices and their applications. This lecture covers optical properties of semiconductor materials, operating principles of semiconductor light sources, photodetectors, and image sensing devices along with recent research trends as well as their system applications, such as high-speed optoelectronic signal processing and passive/active optical image sensing.(Prerequisite: EE362)

This course deals with fundamental concepts of multiple view geometry for 3D computer vision, such as projective geometry, transformation, estimation of the transformation parameters, camera model and camera matrix, epipolar geometry, fundamental matrix, trifocal tensor, and 3D Structure computation, and so on.

The course will essentially provide the basis of marriage between social sciences and engineering capabilities of students, hence: 1)Link understanding between future-oriented Business and Technology Strategies in Media and Broadcasting, 3)Emphasize the importance of user considerations when identifying and designing disruptive technological solutions for future media society

As the hardware complexity and power consumption of a digital system are critically affected by computer arithmetic methods, it is important in VLSI design to understand arithmetic processing methods. This class deals with diverse number systems, hardware computing structures, and detailed arithmetic methods for high-speed and low-power operations

This course deals with advanced levels of analog circuits emphasis on CMOS. The topics include wideband operational amplifiers, comparators, Switched capacitor filters, ADC, DAC, continuous time filters, etc.
(Prerequisite: EE571)

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)