Graduate (List)

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.

In this advanced research course, we study the various aspects that enable mobile ubiquitous computing; network architectures, cloud computing, on-device machine learning, wearables, applications, security, privacy, and interaction methods. We will read and present seminal and state-of-the-art papers on each topic. Students will also design and demonstrate class projects in mobile applications or IoT services.

Mobile computing and wireless networking has become an essential part of our daily lives. In this advanced, research oriented course, we study the various research aspects that enable intelligent mobile computing; wireless connectivity, network architectures, protocols, systems, services, applications, security, privacy, and even machine learning. Students will design class projects that develop interesting mobile applications or IoT services. EE323 and EE202 are strongly encouraged, but are not required prerequisites. Proficiency in programming is expected.

This course deals with security issues related to hardware computer architecture and microarchitecture. Specifically, students will learn the fundamental knowledge of recent microarchitectural vulnerabilities and explore various attacks and defenses on CPU, memory, and interconnect architectures.

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 Big data – AI Integration, which is at the intersection of data management, machine learning, and systems. Each week, students will take turns presenting recent papers in each topic in a seminar format or submit short reviews. Students will also write surveys on recent papers of interest and implement state-of-the-art methods based on them.