Compressed Sensing Reconstruction Approach using Self Adaptive Butterfly Optimization Algorithm for Bio-Signals

Abstract

Various applications require competent reprocessing and data representation in signal processing. To efficiently represent the signal the compression is represented as a standard technique. Nowadays, numerous novel approaches are adopted for compression at the sensing level. Compressed Sensing (CS) is represented as a growing domain that is based on revelation, and it gathers a sparse signal linear projection such as sufficient information for reconstruction. Using the CS, signal sampling is performed at a rate under the Nyquist sampling rate when relying on signals sparsity. In addition, original signal reconstruction from a few compressive measurements could be authentically used by CS deviated reconstruction approaches. The major objective of this work is to use a novel CS approach to reconstruct signals in biomedical data. Therefore, by performing three phases the signal can be compressed such as measurement matrix design, signal reconstruction, and signal compression. Here, the compression phase involves a novel working technique that follows three operations such as the transformation of signal,  evaluation and normalization. In this work, the Haar wavelet function is exploited for the evaluation of the theta. Furthermore, this work assures the superiority of the developed model by using the optimization process with the estimation process. The Haar wavelet function vector coefficient is optimally chosen by exploiting a novel optimization approach named Self Adaptive Butterfly Optimization Algorithm (BOA) algorithm. At last, the adopted model performance is evaluated with the conventional techniques and the outcomes reveal the betterment of the proposed model.