Results 21 to 30 of about 6,810,122 (281)
Physics-constrained bayesian neural network for fluid flow reconstruction with sparse and noisy data
Theoretical and Applied Mechanics Letters, 2020 : In many applications, flow measurements are usually sparse and possibly noisy. The reconstruction of a high-resolution flow field from limited and imperfect flow information is significant yet challenging. In this work, we propose an innovative physics-
Luning Sun, Jian-Xun Wang
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Entropy, 2023 Compressive sensing is a sub-Nyquist sampling technique for efficient signal acquisition and reconstruction of sparse or compressible signals. In order to account for the sparsity of the underlying signal of interest, it is common to use sparsifying ...
Mohammad Shekaramiz, Todd K. Moon
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An Improved Iterative Reweighted STAP Algorithm for Airborne Radar
Remote Sensing, 2022 In recent years, sparse recovery-based space-time adaptive processing (SR-STAP) technique has exhibited excellent performance with insufficient samples.
Weichen Cui +3 more
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Bayesian Orthogonal Component Analysis for Sparse Representation [PDF]
, 2010 This paper addresses the problem of identifying a lower dimensional space where observed data can be sparsely represented. This undercomplete dictionary learning task can be formulated as a blind separation problem of sparse sources linearly mixed with ...
Nicolas Dobigeon +3 more
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A Bayesian Lasso based sparse learning model
Communications in Statistics - Simulation and Computation, 2023 The Bayesian Lasso is constructed in the linear regression framework and applies the Gibbs sampling to estimate the regression parameters. This paper develops a new sparse learning model, named the Bayesian Lasso Sparse (BLS) model, that takes the hierarchical model formulation of the Bayesian Lasso. The main difference from the original Bayesian Lasso
Ingvild M. Helgøy, Yushu Li
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Variational Bayesian Sparse Signal Recovery With LSM Prior
IEEE Access, 2017 This paper presents a new sparse signal recovery algorithm using variational Bayesian inference based on the Laplace approximation. The sparse signal is modeled as the Laplacian scale mixture (LSM) prior.
Shuanghui Zhang +3 more
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A Robust Sparse Bayesian Learning-Based DOA Estimation Method With Phase Calibration
IEEE Access, 2020 Usually, the array manifolds are assumed to be known perfectly in the radar systems, but the imprecise knowledge substantially degrades the performance of estimating the direction of arrival (DOA).
Zhimin Chen +3 more
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ISAR Imaging Algorithm for Parameter Iterative Minimization Sparse Signal Recovery [PDF]
Jisuanji gongcheng, 2018 In order to obtain the robustness Inverse Synthetic Aperture Radar(ISAR) image,an iterative minimization Bayesian learning sparse signal recovery algorithm is proposed.Firstly,ISAR imaging is established,and the imaging problem is converted to sparse ...
FENG Junjie,ZHANG Gong
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IEEE Access, 2021 This work discusses a variational Bayesian learning approach towards decentralized blind deconvolution of seismic signals within a sensor network. Blind seismic deconvolution is cast into a probabilistic framework based on Sparse Bayesian learning ...
Dmitriy Shutin, Ban-Sok Shin
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Relevance Vector Machines for Enhanced BER Probability in DMT-Based Systems
Journal of Electrical and Computer Engineering, 2010 A new channel estimation method for discrete multitone (DMT) communication system based on sparse Bayesian learning relevance vector machine (RVM) method is presented.
Ashraf A. Tahat, Nikolaos P. Galatsanos
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