Results 1 to 10 of about 66,013 (262)
Compressed Sensing of Extracellular Neurophysiology Signals: A Review
Frontiers in Neuroscience, 2021 This article presents a comprehensive survey of literature on the compressed sensing (CS) of neurophysiology signals. CS is a promising technique to achieve high-fidelity, low-rate, and hardware-efficient neural signal compression tasks for wireless ...
Biao Sun, Wenfeng Zhao
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Sparse recovery for discrete tomography [PDF]
2010 IEEE International Conference on Image Processing, 2010 Discrete tomography (DT) focuses on the reconstruction of a discrete valued image from few projection angles. Prior knowledge about the image can greatly increase the quality of the reconstructed image, especially when a small number of projections are available. In this paper, we show that DT can be formulated as a sparse signal recovery problem.
Yen-ting Lin +2 more
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A Space-time Adaptive Processing Algorithm Based on Joint Sparse Recovery
Leida xuebao, 2014 Sparse recovery Space-Time Adaptive Processing (STAP) methods for obtaining the clutter spectrum require few training samples and can effectively suppress clutter in nonhomogeneous clutter environments.
Duan Ke-qing +4 more
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Block-Sparse Tensor Recovery [PDF]
IEEE Transactions on Information TheoryAccepted by IEEE Transactions on Information ...
Liyang Lu +4 more
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IEEE Access, 2023 Reconfigurable intelligent surfaces (RIS) are passive controllable arrays of small reflectors that direct electromagnetic energy towards or away from the target nodes, thereby allowing better management of signals and interference in a wireless network ...
Bharath Shamasundar +2 more
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Sequential Testing for Sparse Recovery [PDF]
IEEE Transactions on Information Theory, 2014 This paper studies sequential methods for recovery of sparse signals in high dimensions. When compared to fixed sample size procedures, in the sparse setting, sequential methods can result in a large reduction in the number of samples needed for reliable signal support recovery.
Matthew Malloy, Robert D. Nowak
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Recovery of sparse urban greenhouse gas emissions [PDF]
Geoscientific Model Development, 2022 To localize and quantify greenhouse gas emissions from cities, gas concentrations are typically measured at a small number of sites and then linked to emission fluxes using atmospheric transport models. Solving this inverse problem is challenging because
B. Zanger +4 more
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Saliency Detection with Sparse Prototypes: An Approach Based on Multi-Dictionary Sparse Encoding
MATEC Web of Conferences, 2018 This paper proposes a bottom-up saliency detection algorithm based on multi-dictionary sparse recovery. Firstly, the SLIC algorithm is used to segment the image into superpixels in multilevel and atoms with a high background possibility are selected from
Wang Jun, Wu Zemin, Tian Chang, Hu Lei
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Nonconvex Penalized Regularization for Robust Sparse Recovery in the Presence of
IEEE Access, 2018 Nonconvex penalties have recently received considerable attention in sparse recovery based on Gaussian assumptions. However, many sparse recovery problems occur in the presence of impulsive noises. This paper is concerned with the analysis and comparison
Yunyi Li +5 more
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