• This course introduces electric vehicles consisting of two major subtopics: general knowledge of vehicles (chassis, drivetrains, electronic control units, and etc.) and electric vehicle E/E (electrical and electronics) architectures (electric motors, drivers, batteries, BMS, etc.).

  • This course covers the design and analysis of the topology about the DC / DC converter, PFC (Power Factor Correction) circuit and control method in that topology. Also, the topology such as an inverter, resonant converter, and active power filter is introduced, and the control algorithm of that topology is studied in this course. Finally, the state of the art in power conversion system is discussed, and every student carries out a term project about design and modeling of a power supply. On completion of this course, students will have built confidence in their ability to design and analyze the power conversion system.
    (Prerequisite: EE391)

  • This course covers topics of interest in Electrical and Computer Engineering at the graduate level. The course content is specifically designed by the instructor.

  • This course provides theory and practice for modeling and simulation of discrete event systems which include communication networks, manufacturing systems, and high-level computer systems. Topics include system taxonomy and discrete event systems (DES) characteristics; three entities in modeling and simulation; model representation and formalism construction; DEVS (Discrete Event systems Specification) formalism and DES modeling; simulation algorithm for DES; Petri Net modeling and analysis; statistics for modeling, simulation and analysis; model validation; output analysis and performance evaluation; advanced topics in DES modeling and simulation.

  • Distributed computing systems have become pervasive. From clusters to internet-worked computers, to mobile machines, distributed systems are being used to support a wide variety of applications. This course introduces key concepts and techniques underlying the design and engineering of distributed computing systems. The following are the objectives of this course:
    - In-depth understanding of core concepts of distributed computing.
    - Construction of applications and supporting system components by doing project work.

  • Many of key technique now being applied in building services and service-based applications were developed in the areas of databases, distributed computing, and multiagent systems. These are generally established bodies of work that can be readily adapted for service composition. Lecture on service-oriented computing will cover the principles and practice of service-oriented computing. Especially, it introduces architecture, theories, techniques, standards, and infrastructure necessary for employing services.

  • This course covers advanced research topics in computer networking and cloud computing. The course is designed to cover various topics in the broad areas of computer systems, networking, cloud and mobile computing, including issues such as wide-area networking, congestion control, data center networking, software-defined networking, network functions virtualization, distributed systems, systems for machine learning, and data intensive computing.

  • The focus of this course is to understand the mathematical foundations of this methodology in light of the convergence, degree of suboptimality, computational complexity and sample efficiency of different algorithms.

  • 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)

  • 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)