The objective of this book is to provide the reader with a comprehensive survey of the topic compressed sensing in information retrieval and signal detection with privacy preserving functionality without compro
Exponential Wavelet Iterative Shrinkage Thresholding Algorithm for compressed sensing magnetic resonance imaging 2015, Information Sciences Citation Excerpt : Their experiments of multi-planar reconstruction and improvement of the depth in focus further validated the feasibility and flexibility of AIST algorithm....
In multiple-input–multiple-output (MIMO) broadcast channel the throughput can be enhanced by channel state information (CSI) feedback, but it is resources and feedback expensive. We propose a compressed sensing (CS) feedback scheme for zero-forcing beamforming (ZFBF) in MIMO broadcast channel,...
The electrocardiogram signal consists in a character of smaller amplitude together with a larger interference range and the reconstructed signal, according to the classical compressed sensing theory, cannot be accurately conveyed by the signal. To solve this problem, compressed sensing based on the ...
Compressed sensing (CS) or compressive sampling has shown an enormous potential to reconstruct a signal from its highly under﹕ampled observations. A high dimensional image processing system can adopt the CS paradigm to reduce the storage and the transmission burden. However, a large sensing system...
In recent years, compressed sensing (CS)[1–4] has been a new and popular paradigm of signal acquisition and compression in applied science and engineering such as image processing, wireless communication, magnetic resonance imaging (MRI) and so on. In contrast with the conventional Nyquist sampli...
Compressed sensing theory Performance guarantees Compressed sensing (CS) theory [12,16,17] addresses the accurate recovery of unknown sparse signals from underdetermined linear measurements and has become one of the main research topics in the signal processing area for the last two decades [18,19,...
Theodoridis S, Kopsinis Y, Slavakis K. Sparsity-aware learn- ing and compressed sensing: an overview. Academic Press Library in Signal Processing. New York: Academic Press, 2012. 1271-1377S. Theodoridis, Y. Kopsinis, and K. Slavakis. Sparsity-aware learning and compressed sensing: An ...
graphs. In Section3, we present two algorithms for constructing signature matrices, which we use to obtain nearly equiangular frames. In Section4, we use the proposed frames as sensing matrices for compressed sensing and spreading sequences for synchronous CDMA systems. Conclusions are drawn in ...
Supplementary information Reporting Summary Supplementary Tables Excel workbook with Supplementary Tables 1–10 Rights and permissions Reprints and permissions About this article Cite this article Cleary, B., Simonton, B., Bezney, J. et al. Compressed sensing for highly efficient imaging transcriptomics....