课程号：AI206006

课程名：压缩感知和稀疏恢复

英文课程名：Compressed sensing and sparse recovery

开课单位：人工智能学院

授课教师：Trac D. Tran

责任教师：林杰  

学分/学时：1学分/16学时

教师简介：教师简介Trac D. Tran

Trac D. Tran教授现就职于美国约翰.霍普金斯大学电子与计算机工程系。Trac教授分别于1993年和1994年在麻神理工学院取得电子工程专业学士学位和硕士学位，并于1998年在威斯康星大学麦迪逊分校取得电子工程专业博士学位。主要研究方向为DSP，稀疏表示，稀疏矩阵恢复，多速率系统，滤波器以及小波变换。Trac教授于2001年取得美国国家基金事业奖（NSF CAREER award），并于2009年荣获卡泊斯&马里昂.麦当劳卓越顾问奖项。

Trac D. Tran S’94-M’98-SM’08 received the B.S. and M.S. degrees from the Massachusetts Institute of Technology, Cambridge, in 1993 and 1994, respectively, and the PhD degree from the University of Wisconsin, Madison, in 1998—all in Electrical Engineering. In July of 1998, Dr. Tran joined the Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, where he was recently promoted to the rank of Professor. His research interests are in the field of digital signal processing, particularly in sparse representation, sparse recovery, sampling, multi-rate systems, filter banks, transforms, wavelets, and their applications in signal analysis, compression, processing, and communications. Prof. Trac is an IEEE Fellow. And he won the Capers and Marion McDonald Award for Excellence in Mentoring and Advising and the NSF Career Award.

课程简介：课程简介:

稀疏性是近年来应用数学中一个非常重要的概念，特别是在数学信号和图像处理以及逆问题中。本课程基于稀疏性，让学生学习如何挖掘和利用信号和图像的先验知识，对信号和图像进行更准确的处理。本课程提供了压缩感知领域的完整覆盖，如果真正的信号是稀疏的开始，准确、鲁棒、甚至完美的信号恢复可以从仅仅几个随机测量。本课程将重点描述稀疏恢复中出现的新思想，重点介绍理论基础、实际的数值算法和各种相关的信号处理应用。

Sparsity has become a very important concept in recent years in applied mathematics, especially in mathematical signal and image processing, as in inverse problems. The key idea is that many classes of natural signals can be described by only a small number of significant degrees of freedom. This course offers a complete coverage of the recently emerged field of compressed sensing, which asserts that, if the true signal is sparse to begin with, accurate, robust, and even perfect signal recovery can be achieved from just a few randomized measurements. The focus is on describing the novel ideas that have emerged in sparse recovery with emphasis on theoretical foundations, practical numerical algorithms, and various related signal processing applications.

先修课程：信号与系统，数字信号处理，线性代数，Matlab编程