We address the problem of compressed sensing (CS) with prior information: reconstruct a
target CS signal with the aid of a similar signal that is known beforehand, our prior …

The reliable extraction of structural characteristics, such as modal information, from
operating structural systems allows for the formation of indicators tied to structural …

Weighted-minimization has been studied as a technique for the reconstruction of a sparse
signal from compressively sampled measurements when prior information about the signal …

Compressive sensing is used to recover a sparse signal from linear measurements. Without
any prior support information (PSI), least absolute shrinkage and selection operator …

In this paper we study the quantization stage that is implicit in any compressed sensing
signal acquisition paradigm. We propose using Sigma–Delta (ΣΔ) quantization and a …

Much research has recently been devoted to sparse signal recovery and image
reconstruction from multiple measurement vectors. The assumption that the underlying …

Compressive spectrum sensing (CSS) is regarded as one of the promising techniques to
detect wideband spectrum holes. How to formulate an accurate prior knowledge in CSS is …

In this paper, the sufficient condition in terms of the RIC and ROC for the stable and robust
recovery of signals in both noiseless and noisy settings was established via weighted l 1 …

Compressed sensing deals with the recovery of sparse signals from linear measurements.
Without any additional information, it is possible to recover an s-sparse signal using m> s log …

Approximate message passing (AMP) methods have gained recent traction in sparse signal
recovery. Additional information about the signal, or side information (SI), is commonly …