Model-Based Compressive Sensing for Signal Ensembles
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Over the past decade researches in applied mathematics, signal processing and communications have introduced compressive sampling (CS) as an alternative to the Shannon sampling theorem. The two key observations making CS theory widely applicable to numerou ...
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Sampling moiré effects occur due to aliasing (foldover) when a continuous periodic signal g(x) is sampled using a sampling frequency that does not respect the Nyquist condition. However, visible beating artifacts may also occur when g(x) is sampled using s ...
Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for acquisition of sparse or compressible signals; instead of taking periodic samples, we measure inner products with All < N random vectors and then recover the signal via a sparsity-s ...
Compressed sensing is a new trend in signal processing for efficient sampling and signal acquisition. The idea is that most real-world signals have a sparse representation in an appropriate basis and this can be exploited to capture the sparse signal by ta ...
Scanning large bandwidths (spectrum sensing) pushes today’s analog hardware to its limits since periodic sampling at Nyquist rate with sufficient resolution is often prohibitively complex. In this paper, we consider a scenario where the signal to be acquir ...
Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for acquisition of sparse or compressible signals that can be well approximated by just K < N elements from an N-dimensional basis. Instead of taking periodic samples, we measure inner ...
Institute of Electrical and Electronics Engineers2010
