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In this paper we introduce the genetic algorithm as an improved method of signal reconstruction in wavelet-based compressive sensing. Instead of using basis pursuit for reconstruction, a population of initial guesses is created and a genetic algorithm is applied to find the sparsest solution. The paper investigates the different possible objective functions and constraints to give the best performance...
In this paper parallel processing is employed to improve the performance of compressive sensing techniques for wideband spectrum edge detection. By taking different sets of random sub-Nyquist samples of the spectrum and processing them in parallel then combining the edges detected by using each set we can get a better estimate of the location of the occupied bands. The compressive sensing technique...
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