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Compressive Sensing (CS) shows that sparse signals can be exactly recovered from a limited number of random or deterministic projections when the measurement mode satisfies some specified conditions. Random matrices, with the drawbacks of large storage, low efficiency and high complexity, are hard to use in practical applications. Recent works explore expander graphs for efficient CS recovery, but...
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