Curriculum

This course provides theory and technique for design and analysis of combinational/sequential digital circuits using discrete mathematics. Topics include: basics for set, relation and lattices; switching and boolean algebra, and switching function; combinational logic synthesis by functional decomposition; fault detection in combinational / sequential circuits; structure of finite state automata; automata-to-machine transformation; state and machine identification; properties of finite state machine with memory span; inverse machine; communicating finite state machine and systems verification; binary decision diagram and its application. (Prerequisite: EE303)

We Study introductory mathematical and programming tools for big data analytics, in particular focusing on recently successful real-world applications, e.g., web search, spam filtering, crowd-sourcing, visualization, and recommendation system

In this lecture, various hardware and software components and system implementation aspects of an embedded system are covered. Covered topics include bus-based expandable ARM processor-based board, open-source embedded Linux operating system, PC-based software development environment, digital and analog interface techniques, ARM assembly language, device drivers. Hands-on experience is gained to enhance firm understanding.
(Prerequisite: EE303)

This course provides students with the knowledge and skills necessary to build a foundation in system programmings for Electrical Engineering, especially focused on operating systems and implementation. Topics include an overview of the components of an OS, concurrency, synchronization, processes, memory management, I/O devices, and file systems.

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.

As more industries are adopting artificial intelligence (AI) and machine learning (ML) technology, we are facing fast-growing demands for new types of hardware that enable faster and more energy efficient processing in relevant workloads. In this class, we will overview recent advances in AI/ML models, and study various AI silicon systems from both academia and industry

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)

This introductory course is intended to familiarize students with underlying principles of fiber optic communication systems. Topics include an overview of fiber optic communication systems, optics review, lightwave fundamentals, light detectors, noise analysis, and system design, etc.