Compressive Sensing [Lecture Notes]

3,512 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Privacy-Preserving Distributed Multi-Task Learning with Asynchronous Updates (2017, methodology): “As the gradients are linear transformations of the data matrix, it is also possible to recover the original data after collecting enough gradient vectors with controlled inputs (i.e., p t ; q t in our case), by using sensing techniques [3].”
Iteratively reweighted least squares minimization for sparse recovery (2008, background): “We refer the reader to the survey papers [1, 4].”
Compressive sensing of the Tohoku‐Oki Mw 9.0 earthquake: Frequency‐dependent rupture modes (2011, background): “Unfortunately, this problem is non‐convex, hard to solve, and unstable in the presence of noise [Baraniuk, 2007].”
Compressed Sensing for Wireless Communications: Useful Tips and Tricks (2015, background): “Short summary of CS is presented by Baraniuk [7].”
Distributed Compressive Sensing: A Deep Learning Approach (2015, background): “In the general CS framework, instead of acquiring N samples of a signal x ∈ N × 1 , M random measurements are acquired where M N .”
Compressed sensing imaging techniques for radio interferometry (2008, methodology): “Beyond the Nyquist-Shannon theorem, the emerging theory of compressed sensing aims at merging data acquisition and compression (Candès et al. 2006a,b; Candès 2006; Donoho 2006; Baraniuk 2007).”