题目:  Compressive Sensing in Wireless Health Monitoring: From Theory to Hardware Implementations 报告人：Dr. Fengbo Ren    时间：2016年7月29日下午2:00-3:00 地点：玉泉校区行政楼三楼第三会议室

Abstract:

Digital electronic industry today relies on Nyquist sampling theorem, which requires doubling the size (sampling rate) of the signal representation on the Fourier basis to avoid information loss. However, most natural signals have very sparse representations on some other orthogonal (non-Fourier) basis. This mismatch implies a large redundancy in Nyquist-sampled data, making compression a necessity prior to storage or transmission. Recent advances in compressive sensing theory offer us an alternative data acquisition framework. Compressive sensing techniques provide a universal approach to sample compressible signals at a rate significantly below the Nyquist rate with limited information loss. Therefore, Compressive sensing is a promising technology for realizing configurable, cost-effective, miniaturized, and ultra-low-power data acquisition devices for mobile and wearable applications. In this talk, I will talk about compressive sensing from the perspectives of theory, applications, and hardware implementations to showcase our research efforts in bringing compressive sensing technology into real-life applications for wireless health monitoring. 

Biography:

Fengbo Ren received the B.Eng. degree in Electrical Engineering from Zhejiang University in 2008, and the M.S. and Ph.D. degree in Electrical Engineering from University of California, Los Angeles, in 2010 and 2014, respectively. He joined the School of Computing, Informatics and Decision Systems Engineering at Arizona State University as an Assistant Professor in January 2015. He is directing the Parallel Systems and Computing Laboratory (PSCLab), and he is affiliated with the National Science Foundation Industry/University Cooperative Research Center for Embedded Systems. His current research interests are focused on hardware acceleration and parallel computing solutions for data analytics and information processing, with emphasis on compressive sensing and deep learning frameworks. He is a recipient of the 2012-2013 Broadcom Fellowship. He is a member of the Technical Committees of Digital Signal Processing and VLSI Systems & Applications in the IEEE Circuits and Systems Society.