Compressive signaling is a new approach of sampling theory, which assumes that signal can be exactly recovered from incomplete information. It relies on properties such as incoherence, signal sparsity and compressibility, and does not follow traditional acquisition process based on transform coding. Sensing procedure is a nonadaptive method that employs linear projections of signal onto test functions. Set of test functions is arranged in the measurement matrix that allows acquiring random samples of original signal. Signal reconstruction is achieved from small amount of data by an optimization process which has the aim to find the sparsest vector with transform coefficients among all possible solutions. Learn more from the researcher himself.
Friday, November 9 at 3:00 PM to 4:00 PM
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