Results 31 to 40 of about 279,028 (324)
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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Sparse polynomial interpolation: sparse recovery, super-resolution, or Prony? [PDF]
Advances in Computational Mathematics, 2019 We show that the sparse polynomial interpolation problem reduces to a discrete super-resolution problem on the $n$-dimensional torus. Therefore the semidefinite programming approach initiated by Cand s \\& Fernandez-Granda \cite{candes\_towards\_2014} in the univariate case can be applied.
Josz, Cédric +2 more
openaire +3 more sources
A Review of Sparse Recovery Algorithms
IEEE Access, 2019 Nowadays, a large amount of information has to be transmitted or processed. This implies high-power processing, large memory density, and increased energy consumption.
Elaine Crespo Marques +4 more
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Compressed Sensing over the Grassmann Manifold: A Unified Analytical Framework [PDF]
, 2009 It is well known that compressed sensing problems reduce to finding the sparse solutions for large under-determined systems of equations. Although finding the sparse solutions in general may be computationally difficult, starting with the seminal work of
Hassibi, Babak, Xu, Weiyu
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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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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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Continuous-Time Sparse Signal Recovery
IEEE AccessThis study investigates a continuous-time method for sparse signal recovery, which is suitable for analog optical circuit implementation. The proposed method is defined by a nonlinear ordinary differential equation (ODE) derived from the gradient flow ...
Tadashi Wadayama, Ayano Nakai-Kasai
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Jointly Iterative Adaptive Approach Based Space Time Adaptive Processing Using MIMO Radar
IEEE Access, 2018 To solve the problem of large training samples requirement of space time adaptive processing (STAP), a jointly sparse matrices recovery-based method is proposed for clutter plus noise covariance matrix estimation by exploiting the transmitting waveform ...
Weike Feng +4 more
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Deep Unfolded Gridless DOA Estimation Networks Based on Atomic Norm Minimization
Remote Sensing, 2022 Deep unfolded networks have recently been regarded as an essential way to direction of arrival (DOA) estimation due to the fast convergence speed and high interpretability. However, few consider gridless DOA estimation.
Hangui Zhu +4 more
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