Compressive sensing (CS) is a promising solution for physiological signals acquisition and wireless healthcare systems. It enables new reduced-complexity designs of sensor nodes and helps to save overall transmission power in wireless sensor network. However, in practical applications, measurement noise from non-ideality of CS sensor destroy the signal sparsity, thus drastically degrading the quality of the reconstructed signals. We find that sparsity information/estimation is the key factor to cope with measurement noise. However, prior data information, such as sparsity level or noise level, is usually unavailable in the CS-based wireless healthcare system. In this talk, we will first give an overview of the CS techniques, including the fundamentals and the popular reconstruction algorithms (BP, OMP). Then, we present a robust mechanism of sparsity estimation, called Sparsity Estimation-Subspace Pursuit (SE-SP). It can reconstruct compressively-sensed physiological signals in the presence of measurement noise. The experimental results show that the SE-SP shows superior robustness against measurement noise. A high-efficient CS reconstruction chip is also implemented based on the SE-SP algorithm, which was presented in 2018 ISSCC. We will exploit the embedded security feature of CS-based transmission. CS sensors can generate randomized measurement data during the sub-sampling process. Hence, it bears natural security for those sensed data. In this talk, we will present a new mechanism to enhance the security level of CS data. The two topics presented in this talk are applicable to emerging CS-based wireless healthcare systems.
An-Yeu (Andy) Wu (IEEE M’96-SM’12-F’15) received the B.S. degree from National Taiwan University in 1987, and the M.S. and Ph.D. degrees from the University of Maryland, College Park in 1992 and 1995, respectively, all in Electrical Engineering.
In August 2000, he joined the faculty of the Department of Electrical Engineering and the Graduate Institute of Electronics Engineering, National Taiwan University (NTU), where he is currently a Professor. His research interests include low-power/high-performance VLSI architectures for DSP and communication applications, adaptive/ bio-medical signal processing, reconfigurable broadband access systems and architectures, and System-on-Chip (SoC)/Network-on-Chip (NoC) platform for software/hardware co-design. He has published more than 250 refereed journal and conference papers in above research areas, together with five book chapters and 20 granted US patents.
From August 2007 to Dec. 2009, he was on leave from NTU and served as the Deputy General Director of SoC Technology Center (STC), Industrial Technology Research Institute (ITRI), Hsinchu, TAIWAN, supervising WiMAX, Parallel Core Architecture (PAC) VLIW DSP Processor, and Android-based Multicore SoC platform projects. From March 2014 to September 2017, Dr. Wu is in charge of the overall talent cultivation program office in National Program for Intelligent Electronics (NPIE), under sponsorship of Ministry of Education in Taiwan.
In 2015, Prof. Wu is elevated to IEEE Fellow for his contributions to “DSP algorithms and VLSI designs for communication IC/SoC.” He now serves as a Board of Governor (BoG) Member in IEEE Circuits and Systems Society (CASS) for the term of 2016-2018. Starting from August 2016, he serves as the Director of Graduate Institute of Electronics Engineering (GIEE), National Taiwan University.