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Compressive Sensing (CS) is a new paradigm for signal acquisition and processing, which integrates sampling, compression, dimensionality reduction and optimization, which has caught the attention of a many researchers; SC allows the reconstruction of dispersed signals in a given domain from a set of measurements could be described as incomplete, due to that the rate at which the signal is sampled is much smaller than Nyquist's rate. This article presents an approach to address methodologicaldoi:10.15446/dyna.v82n192.45512 fatcat:zm2krs7c7beb5kpazlxigqdqne