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.
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.
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)
Recommend
This course aims to learn fundamental technologies for signal modeling and estimation and covers deterministic and random signal modeling, lattice filter realization, parameter, and signal estimation, Wiener and Kalman filter design, parametric and nonparametric spectrum estimation, and adaptive filtering. (Prerequisite: EE432, EE528)
The primary objective of this course is to discuss what NeuroImaging methods are available to study the brain. The focus of the course will be on modern tools capable of whole-brain imaging (mostly MRI), but we will also discuss non-MRI techniques as well. As part of the term project, students will be asked to propose novel acquisition and/or analysis method that is likely to facilitate our ability to understand the brain.
This course provides basic theory and techniques for the representation and processing of digital video. Topics include digital video formats, video spatiotemporal Sampling, 2-D/3-D motion estimation, motion segmentation, digital video filtering, video enhancement, video compression, and digital video system. In addition to the theory, students suppose to participate in experiments that are related to the above topics.
