On-demand Reconstruction for Compressively Sensed Problematic Signals

In this paper, to achieve low-complexity on-demand compressed sensing (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.

Recommended citation:

C.-Y. Chou, K.-C. Hsu, B.-H. Cho, K.-C. Chen and A.-Y. (ANDY) Wu, ‘‘Low-Complexity On-demand Reconstruction for Compressively Sensed Problematic Signals,’’ in IEEE Trans. Signal Process., doi: 10.1109/TSP.2020.3006766.