https://scholars.lib.ntu.edu.tw/handle/123456789/611217
標題: | Low-Complexity On-Demand Reconstruction for Compressively Sensed Problematic Signals | 作者: | AN-YEU(ANDY) WU | 關鍵字: | compressed learning; Compressed sensing; hardware sharing; on-demand reconstruction; sparse transform | 公開日期: | 2020 | 卷: | 68 | 起(迄)頁: | 4094-4107 | 來源出版物: | IEEE Transactions on Signal Processing | 摘要: | Compressed Sensing (CS) is a revolutionary technology for realizing low-power sensor nodes through sub-Nyquist sampling, and the CS reconstruction engines have been widely studied to fulfill the energy efficiency for real-time processing. However, in most cases, we only want to analyze the problematic signals which account for a very low percentage. Therefore, large efforts will be wasted if we recover uninterested signals. On the other hand, in order to identify the high-risk signals, additional hardware and computation overhead are required for classification other than CS reconstruction. In this paper, to achieve low-complexity on-demand CS reconstruction, we propose a two-stage classification-aided reconstruction (TS-CAR) framework. The compressed signals can be classified with a sparse coding based classifier, which provides the hardware sharing potential with reconstruction. Furthermore, to accelerate the reconstruction speed, a cross-domain sparse transform is applied from classification to reconstruction. TS-CAR is implemented in electrocardiography based atrial fibrillation (AF) detection. The average computational cost of TS-CAR is 2.25× fewer compared to traditional frameworks when AF percentage is among 10% to 50%. Finally, we implement TS-CAR in TSMC 40 nm technology. The post-layout results show that the proposed intelligent CS reconstruction engine can provide a competitive area- and energy-efficiency compared to state-of-the-art CS and machine learning engines. © 1991-2012 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089303594&doi=10.1109%2fTSP.2020.3006766&partnerID=40&md5=02cb607776f5fe7f966a53f8f7e4551c https://scholars.lib.ntu.edu.tw/handle/123456789/611217 |
ISSN: | 1053587X | 其他識別: | ITPRE | DOI: | 10.1109/TSP.2020.3006766 | SDG/關鍵字: | Compressed sensing; Energy efficiency; Engines; Learning systems; Sensor nodes; Signal reconstruction; Atrial fibrillation; Compressive sensing; Computation overheads; Computational costs; Realtime processing; Reconstruction speed; Revolutionary technology; Sub-Nyquist sampling; Biomedical signal processing |
顯示於: | 電機工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。