2013/09/30 by Benyuan Liu, Zhilin Zhang, Liu, Benyuan +7 · 2 citations
Computer Science · Engineering · Mathematics · #Algorithm #Analog and Mixed-Signal Circuit Design #Artificial intelligence #Blind Source Separation Techniques #Compressed sensing #Computer science #Data compression #Electrical engineering #Embedded system #Energy (signal processing) #Energy consumption #Engineering #FOS: Computer and information sciences #Field-programmable gate array #Information Theory (cs.IT) #Real-time computing #Sparse and Compressive Sensing Techniques #Telecommunications #Wavelet #Wireless #cs.IT #math.IT
paper · pdf · doi:10.48550/arxiv.1309.7843
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2013/09/30 · arxiv created 2014/03/05 · arxiv updated 2014/03/06 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28
Wireless telemonitoring of physiological signals is an important topic in eHealth. In order to reduce on-chip energy consumption and extend sensor life, recorded signals are usually compressed before transmission. In this paper, we adopt compressed sensing (CS) as a low-power compression framework, and propose a fast block sparse Bayesian learning (BSBL) algorithm to reconstruct original signals. Experiments on real-world fetal ECG signals and epilepsy EEG signals showed that the proposed algorithm has good balance between speed and data reconstruction fidelity when compared to state-of-the-art CS algorithms. Further, we implemented the CS-based compression procedure and a low-power compression procedure based on a wavelet transform in Filed Programmable Gate Array (FPGA), showing that the CS-based compression can largely save energy and other on-chip computing resources.