Compressed Sensing: A Simple Deterministic Measurement Matrix and a Fast Recovery Algorithm


Compressed sensing (CS) could be a technique that's appropriate for compressing and recovering signals having sparse representations in bound bases. CS has been widely used to optimize the measurement process of bandwidth and power constrained systems like wireless body sensor network. The central problems with CS are the development of measurement matrix and the development of recovery algorithm. In this paper, we tend to propose a straightforward deterministic measurement matrix that facilitates the hardware implementation. To management the sparsity level of the signals, we tend to apply a thresholding approach within the discrete cosine remodel domain. We have a tendency to propose a fast and simple recovery algorithm that performs the proposed thresholding approach. We validate the proposed method by compressing and recovering electrocardiogram and electromyogram signals. We have a tendency to implement the proposed measurement matrix in a MSP-EXP430G2 LaunchPad development board. The simulation and experimental results show that the proposed measurement matrix features a better performance in terms of reconstruction quality compared with random matrices. Depending on the compression ratio, it improves the signal-to-noise ratio of the reconstructed signals from six to 20 dB. The obtained results additionally confirm that the proposed recovery algorithm is, respectively, 23 and 12 times faster than the orthogonal matching pursuit (OMP) and stagewise OMP algorithms.

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