Graduate (List)

This course aims to learn fundamental technologies for signal modeling and estimation and covers deterministic and random signal modeling, lattice filter realization, parameter, and signal estimation, Wiener and Kalman filter design, parametric and nonparametric spectrum estimation, and adaptive filtering. (Prerequisite: EE432, EE528)

The primary objective of this course is to discuss what NeuroImaging methods are available to study the brain. The focus of the course will be on modern tools capable of whole-brain imaging (mostly MRI), but we will also discuss non-MRI techniques as well. As part of the term project, students will be asked to propose novel acquisition and/or analysis method that is likely to facilitate our ability to understand the brain.

This course provides basic theory and techniques for the representation and processing of digital video. Topics include digital video formats, video spatiotemporal Sampling, 2-D/3-D motion estimation, motion segmentation, digital video filtering, video enhancement, video compression, and digital video system. In addition to the theory, students suppose to participate in experiments that are related to the above topics.

This course offers the basic mathematical backgrounds and implementation techniques of not only recent mobile speech coding methods including CELP but also audio coding techniques such as MP3 and AAC. In addition, we study the trends for convergence of speech and audio coding techniques. (Prerequisite: EE432)

The primary objective of this course is to explore the synthetic modeling approach to understand brain-based mechanisms for learning and generating sensory-motor behaviors. For this purpose, the course will offer introduction of neuro-robotics studies as well as neuroscience literature related to brain mechanisms for sensory perception and behavior generation. In addition, the course will offer hands-on experiences on experimenting neuro-driven learnable robots in the instructor’s lab. The course will gain a good understanding of mechanisms for learning and generating cognitive behaviors both in biological brains and artifacts. Evaluation is based on quizzes during class, term project, and active class participation.

Key elements of microwave/RF ICs for wireless systems including mobile communications and radars are covered. Subcircuits including low noise amplifier, mixer, VOC, power amplifier, switches, phase shifter, and digital RF blocks are studied with their design methods, modeling methods, and characterizing methods.
(Prerequisite: EE204, EE304)

This course is designed to provide graduate students with design capability of the millimeter-wave and terahertz integrated circuits and application systems. The course starts with the active/passive device models for active and passive circuits on silicon. On-chip antenna, Beam-forming and radar blocks will be also studied for multi-Gbps wireless communication and wireless sensor applications.

RF signals in modern wireless systems are basically based on digital techniques. To understand the architectures and be able to specify the parameters of the modern RF transceiver systems, fundamental concepts both on digital and RF are necessarily understood. This course gives the basic concepts and technologies related to modern digital radio transceiver systems.

The course will cover the photonic properties of nanoscale structures and devices. Basic principles and their applications are introduced.

The course covers parallel and distributed algorithms for optimization problems with special emphasis on the application of these algorithms to various communication network algorithms such as distributed power control, flow control and routing. In particular, asynchronous algorithmic models are emphasized.