Technology for Silicon Semiconductor IC (Integrated Circuit) chip which is the basis of modern electronic systems, will be covered, focusing on its historical background, structures of modern semiconductor devices, and fabrication processes. Current and future trends of semiconductor IC technology will also be discussed.
(Prerequisite: EE211, EE362)
This course will teach students the fundamental principles and concepts for an electric power system with an emphasis on renewable energy technologies that are important from the perspectives of electrical engineering.
This course will introduce elementary concepts of biomedical electronics and guide students on how to apply their electrical engineering skills to solve problems in medicine and biology. Topics include biomedical sensors, nano-biosensors, nano-bio actuators, bio-inspired devices for medicine, non-invasive and ubiquitous body sensing, and their clinical applications.
This course introduces students to a variety of media elements including text, graphics, sound, video, hardware and software components and the necessity for interactivity in multimedia as well. By introducing associate fundamental technologies, the course aims to help and encourage students to develop their imaginative and creative skills using multimedia. (Prerequisite: EE202)
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 is on the design and implementation of database and big data systems. The first part covers database design and usage. The second part covers the internals of database systems and recent NoSQL and NewSQL systems.
In this course, a broad and practical overview for robotics is given in a multi-disciplinary perspective. Key principles such as coordinate transformation, navigation, control, motion planning, and decision making are taught. Recent advances in drons, self-driving cars, and AI for robotics are also introduced.
This course surveys scientific computing and data science methods relevant for physical electronics. First, traditional numerical analysis methods for the solution of ordinary and partial differential equations will be presented. Next, machine learning approaches and their mathematical basis will be surveyed in view of a modern numerical analysis framework.
The course begins with the quantum logic and aims to deliver how quantum advantages can be achieved in communication and computational tasks. Examples of quantum algorithms and quantum protocols are provided. Known approaches to implement quantum information processing are explained.
Two major themes of this course are ‘Modern Control System’ and ‘Computational Intelligence’. Each lecture will address a balanced emphasis on the theory about the control system and its applications in practice. The first part of this course includes digital control system design and state-space methods for control system design. The basic system identification scheme will also be included, considering the control of unknown systems. Once background knowledge of the modern control system is established, this course will then focus on the second part composed of computational intelligence using fuzzy logic, artificial neural network, and evolutionary computation as main topics to introduce recent trend in intelligent control. Term projects will be assigned to test the algorithms to the given problems. (Prerequisites: EE381)