Graduate Program

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Graduate Program

Graduate Program

Adaptive Signal Processing

Subject No.

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 is the advanced course dealing with methods for correcting and detecting errors in data and covers finite field theory, cyclic code, BCH code, Reed-Solomon code, convolutional code, trellis-coded modulation, turbo code, LDPC code, space-time code, and adaptive coding. (Prerequisite: EE522, EE528)

Communication, Signal

The purpose of this course is to provide the fundamental background behind detection and estimation theories based on likelihood functions as well as on Bayesian principles. Topics to be covered are decision theory, hypothesis testing, performance analysis, detection and estimation from waveform observation, linear and nonlinear parameter estimations. (Prerequisite: EE528 recommended)

Communication, Signal

This course covers the core concept of information theory, including the fundamental source and channel coding theorems, coding theorem for Gaussian channel, rate-distortion theorem, vector quantization, multiple user channel, and multiple access channels.
(Prerequisite: CC511, EE528)


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)