Graduate (List)

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)

This course covers the core concept of information theory, including the fundamental source and channel coding theorems, coding theorem for Gaussian channel, rate-distortion theorem, vector quantization, multiple user channel, and multiple access channels.
(Prerequisite: CC511, EE528)

This course deals with cellular communication systems, the structure of cell phone systems, access technology, wireless communication radio, fading issues, diversity, link analysis, CDMA diffuse spectrum system, physical/data link/network layers, traffic control, mobile network structure and 3rd generation mobile communication systems.

This course is meant to provide a strong foundation for graduate study and research in the area of communications. The main objective of this course is to fortify the understanding of advanced communication theories required to design and analyze digital communication systems, especially for memory channels

This course focuses on advanced techniques for control, modeling and performance analysis of high-speed communication networks and the Internet. Traffic, network queueing, quality of services, various network algorithms and protocols are quantitatively analyzed and discussed.

Video compression is very important and widely deployed in Smart Phones, DTV/UHDTV, Digital Cameras/Camcorders, etc. This class aims at providing students with a comprehensive overview of the principles and algorithms employed in image and video compression. A particular course objective is an in-depth understanding of the rationale behind the frame-based video coding such as H.264/AVC (Advanced Video Coding) as well as HEVC (High-Efficiency Video Coding). (Prerequisite: EE432)

This course deals with the efficient coding of still image and video sequence and the international standards for transmission and storage of image information. Topics cover the representation of image signals, sampling, quantization, entropy coding, predictive coding, transform coding, subband coding, vector quantization, motion estimation, motion-compensated coding, segmentation-based coding, various international standards for bi-level image coding, still image coding and video coding.