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Compressive Sensing (CS) asserts that one can recover a sparse signal from a limited number of random or deterministic projections exactly if the measurement matrix satisfies the so-called RIP. People try to design deterministic matrices for CS because of the small storage, high efficiency and low complexity compared with the random matrices in practical applications. Recent works explore expander...
Development of wearable sensor node for continuous monitoring of respiratory sounds presents several research challenges. In this article we analysed power consumption of such node in scenarios of on-node signal processing, and compressive sampling with low frequency data streaming. Onset of requirements defined by each scenario, we reviewed state of the art of recent commercial off-the-shelf (COTS)...
The sparse signal reconstruction problem has been the subject of extensive research in several different communities. Tractable reconstruction algorithm is a crucial and fundamental theme of compressive sensing, which has drawn significant interest in the last few years. In this paper, firstly a novel approach was proposed to improve the original IHT algorithm, which is called Orthogonal Iterative...
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