ABSTRACT
The field of compressive sensing (CS) is related to several areas in signal processing and computational mathematics, such as underdetermined linearsystems, group testing, heavy hitters, multiplexing, sparse sampling, sparse coding, and finite rate of innovation. The potential of the emerging CS signal acquisition compression paradigm is quantified for low-complexity energy efficient ECG compression on the state-of-the-art Shimmer wireless body sensor network (WBSN) mote. The achievement of truly WBSN-enabled ambulatory monitoring systems requires more breakthroughs not only in terms of ultra-low-power read-out electronics and radios, but also increasingly in terms of ultra-low-power-dedicated digital processors and associated embedded feature extraction and data compression algorithms. Being an iterative reconstruction technique, CS magnetic resonance imaging (MRI) reconstruction can be more time-consuming than traditional inverse Fourier reconstruction. CS MRI reconstruction is accelerated by factors of up to 27 by using a split Bregman solver combined with a graphics processing unit computing platform. CS-based image fusion has a number of advantages over conventional imagefusion algorithms.
