Distributed Compressed Sensing of Jointly Sparse Signals

By Marco F. Duarte, Shriram Sarvotham, Dror Baron, Michael B. Wakin, Richard Baraniuk. Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005., 2005

596 citations recorded by the source corpus.

Subfields: Acoustics and Ultrasonics, Biomedical Engineering, Computational Mechanics

View Paper: https://www.semanticscholar.org/paper/af2eff7bf92a2bf16be5dbfbd9f197246f0fac2e

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In Acoustics and Ultrasonics

In Biomedical Engineering

In Computational Mechanics

Citations in the corpus

Position and Orientation Estimation Through Millimeter-Wave MIMO in 5G Systems (2017, unknown): “For the sake of completeness, the steps of DCS-SOMP can be found in Algorithm 1.”
An efficient algorithm for total variation regularization with applications to the single pixel camera and compressive sensing (2010, background): “Many applications have been studied, such as reconstruction or denoising of Magnetic Resonance Images (MRI) [29, 30], analog-to-information conversion [31], sensor networks [34, 35], and even homeland security [68].”
Distributed Compressive Spectrum Sensing in Cooperative Multihop Cognitive Networks (2011, background): “Similar signal models with sparse innovations are discussed in [18] and [19] for other signal processing applications, but truly decentralized fusion solutions are lacking.”
An efficient augmented Lagrangian method with applications to total variation minimization (2013, background): “Nowadays, CS has been widely studied and applied to various ﬁelds (see [10, 13, 22–25] for example).”
Restricted isometry properties and nonconvex compressive sensing (2007, background): “Such measurements of a signal can then be regarded as a compression of the original signal, which can be recovered if it is sufficiently compressible.”
Iteratively reweighted algorithms for compressive sensing (2008, background): “A few of the many potential applications are medical image reconstruction [7], image acquisition [8], and sensor networks [9].”
On Nonconvex Decentralized Gradient Descent (2016, background): “Some typical applications include: (i) the distributed compressed sensing problems [14], [30], [39], [45], [49]; (ii) distributed consensus [9], [29], [55], [58], [61], [69]; (iii) distributed and parallel machine learning [15], [21], [33], [43], [55].”
Distributed Compressive Sensing (2009, background): “Preliminary versions of this work have appeared at the 43rd Allerton Conference on Communication, Control, and Computing [1], the 39th Asilomar Conference on Signals, Systems and Computers [2], and the 19th Conference on Neural Information Processing Systems [3].”
Linear Convergence of Iterative Soft-Thresholding (2007, unknown): “First, we consider the situation of so-called joint sparsity for vector-valued problems, see [ 10 , 17, 32].”