Academics

Graduate Program

Academics

Graduate Program

Graduate Program

Advanced Digital Signal Processing

Subject No.
Research
Credit
Classification
Prerequisite

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)

Recommend

Computer, Communication, Signal
EE523

The main goal of this course is to present advanced topics of convex optimization which are essential for researches in communications and networks, estimation and signal processing, data analysis and modeling, statistics and finance, electronic circuit design, automatic control, and industrial engineering and to deal with their application areas. We study the primal-dual interior point method, semi-definite programs, and second-order cone programs.

Computer, Communication, Signal
EE528

In this course, based on the fundamental concepts and knowledge addressed in EE210, we discuss advanced topics in probability and random processes for applications in engineering. Topics include algebra of sets, limit events, random vectors, convergence, correlation functions, independent increment processes, and compound processes. (Prerequisite: {EE210} or {Approval of the Instructor})

Communication, Signal
EE581

Topics include system representation (input-output description, state variable description), solutions of linear dynamical equations, controllability and observability, irreducible realization, stability (BIBO stability, Lyapunov stability) for a rigorous treatment of linear systems. In addition, feedback linearization is to be covered.