Convex Optimization Techniques

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.

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  • The aims of this course are to make the student understand the principles and fundamental concepts of circuit analysis; to develop the student's familiarity and understanding in modeling and analyzing circuits through a variety of real-world examples. Another important aim is to extend the student's ability to apply system analysis to other branches of engineering.

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  • This course is an introduction to continuous-time and discrete-time signals and systems. The course covers Fourier series, Fourier transform, Laplace transform, and z-transform. Various types of systems with emphasis on linear time invariant system is studied.

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  • This course covers introductory electromagnetic fields and waves. Static electric fields and static magnetic fields are discussed. Time-varying fields and Maxwell's equations are introduced. Waves and transmission lines are studied.

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  • This course covers data structures, algorithms, JAVA for electron electronics engineering. We study object-oriented programming techniques and use programming language C, JAVA.

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  • Experiments related to electronics are performed. Focus is made for both hands-on experience and design practice. (Prerequisite: EE201, EE304)

     

  • In this design experiment laboratory, knowledge learned in many other courses in this division are brought to bear on performing a project combining analog/digital and hardware/software. Hence, a chip stone design experiment will be performed, which establishes a synthesized application of undergraduate theory courses. For example, analog AM radio will be designed using various analog circuits, and voice recorder will be designed using Linux based embedded system.

  • In this course, we discuss such various topics in probability theory and introductory random processes as probability, random variables, expectations, characteristic functions, random vectors, random processes, correlation functions, and power spectrum. From time to time, homework problems will be assigned, usually not for mandatory submission.

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  • This course is a brief introduction to random processes. Topics include Basic operating principles and circuits of AM, FM, and SSB modulation/demodulation, PLLs, mixers, and ADCs; Noise performance of communication systems; Introduction to digital communication techniques such as BPSK, FSK and QAM keying/detections. Issues related to multiple access techniques are covered. (Prerequisite: EE202)

  • This lecture provides a short introduction to essential topics in information theory for communication engineers. The topics include 1) measures of information and source, 2) Data compression, 3) Channel Capacity and Error Control Codes, 4) a very short description of rate-distortion theory.

  • This course emphasizes practical implementation aspects of digital communication systems. A physical-layer software implementation project will be assigned for a selected commercially-deployed communication system. Topics covered in this digital communication course include (1) Digital modulation and demodulation, Optimum receivers, (2) Adaptive equalization and Synchronization, (3) Channel capacity, Error control codes.
    (Prerequisite: EE321)

  • The primary objective of this course is to present fundamental concepts and basic techniques of optimization with possible applications, which are essential for researches in circuit design, communications, signal processing, and control engineering. Topics include linear vector spaces and linear operators, linear estimation and filtering, functional analysis, optimal control, linear programming, nonlinear programming, dynamic programming, genetic programming (evolutionary computation), and neural networks.

  • This course teaches the principles of wireless network access techniques and system applications. The main focus of contents covers wireless medium access techniques, multiple access control and scheduling, system capacity optimization, and their applications to WiFi, WiMax, and ad-hoc sensor networks.

  • This course studies the representation, analysis, and design of discrete-time signals and systems. Topics include a review of the z-transform and the discrete Fourier transform, the fast Fourier transform, digital filter structures, digital filter design techniques, analog-to-digital, and digital-to-analog data conversion, rate conversion, sampling and aliasing issues. (Prerequisite: EE202)

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