Compressive sensing and processing delivers high resolution data using reduced sampling rates and computational effort compared to Nyquist sensing and processing. Compressive proc...
The recently developed compressive sensing (CS) framework enables the design of sub-Nyquist analog-to-digital converters. Several architectures have been proposed for the acquisit...
John P. Slavinsky, Jason N. Laska, Mark A. Davenpo...
Compressive sensing accurately reconstructs a signal that is sparse in some basis from measurements, generally consisting of the signal’s inner products with Gaussian random vec...
Abstract. Compressive sensing (CS) is a new approach for the acquisition and recovery of sparse signals and images that enables sampling rates significantly below the classical Ny...
The theory of compressive sampling involves making random linear projections of a signal. Provided signal is sparse in some basis, small number of such measurements preserves the ...