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Compressed sensing seeks to recover a sparse or compressible signal from a small number of linear and non-adaptive measurements. While most of the studies so far focus on the prominent Gaussian random measurements, we investigate the performances of matrices with Bernoulli distribution. As extensions of symmetric signs ensemble, random binary ensemble and(More)
Compressed sensing (CS) is a new signal acquisition technology which seeks to recover the signal using incomplete linear projections acquired by a projection matrix. Semi-Hadamard matrices and their simplifications are proposed as a kind of feasible projection matrix with binary structure in CS frame work. Basic definitions of semi-Hadamard and their(More)
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