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Graduate Program

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Academics

Graduate Program

Graduate Program

Curriculum

Communication
EE529

This course, as an advanced course of EE 421, aims at providing a strong foundation for research in the are of wireless communications. The course will address (1) wireless channel models from a system theoretic viewpoint, (2) modulation and demodulation in wireless channels, (3) coding techniques for wireless channels, (4) various equalization techniques for ISI channels, (5) multicarrier transmission techniques including OFDM, (6) spread spectrum technique (DS, FH), and (7) MIMO communications with a focus on single-user MIMO.

Introduce students the fundamental concepts and intuition behind modern machine learning techniques and algorithms, beginning with topics such as perceptron to more recent topics such as boosting, support vector machines and Bayesian networks. Statistical inference will be the foundation for most of the algorithms covered in the course.

 

This course is to allow the students majoring in the general areas of communications and signal processing (and those in other areas also) to obtain the basic and advanced knowledge of statistical techniques for signal processing. Topics include multivariate distributions, order statistics, and their applications. The key concepts, theory, and methodology of nonlinear techniques for statistical signal processing are studied.
(Prerequisite: EE528 recommended)

Recommend

This course is designed to treat electromagnetic theory with applications in waveguides and antennas. The course will start with Maxwell’s equations and show how to apply Maxwell’s equations to the basic electromagnetic wave phenomena.

This course is designed to provide in-depth understanding and knowledge on the theory and applications of microwave circuits, components, and systems used in Microwave and RF wireless communication systems.
(Prerequisite: EE204)