Results 311 to 320 of about 70,734 (340)

Distributed Bayesian Compressive Sensing using Gibbs sampler

2012 International Conference on Wireless Communications and Signal Processing (WCSP), 2012
Bayesian Compressive Sensing (BCS) observes s-parse signal from the statistics viewpoint. In BCS, a Bayesian hierarchy is established utilizing Bayesian inference, thus gives the reconstruction algorithm plenty of robust and flexibility. When dealing with distributed scenario, Bayesian hierarchy is also an effective method. Not only can statistic model
Hua Ai, Yang Lu, Wenbin Guo
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Bayesian compressed sensing using generalized Cauchy priors

2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010
Compressed sensing shows that a sparse or compressible signal can be reconstructed from a few incoherent measurements. Noting that sparse signals can be well modeled by algebraic-tailed impulsive distributions, in this paper, we formulate the sparse recovery problem in a Bayesian framework using algebraic-tailed priors from the generalized Cauchy ...
Rafael E. Carrillo, Tuncer C. Aysal, Kenneth E. Barner   +2 more
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Transmission-Efficient Grid-Based Synchronized Model for Routing in Wireless Sensor Networks Using Bayesian Compressive Sensing

SN Computer Science, 2023
Deepa Devasenapathy, P. Madhumathy, R. Umamaheshwari, B. Pandey, Digvijay Pandey   +4 more
semanticscholar   +1 more source

Hierarchical Bayesian Compressed Sensing of Sparse Signals

2020
Compressed sensing (CS) is a new concept in signal processing where one seeks to minimize the number of measurements to be taken from signals while still retaining the information necessary to approximate them well. Conventional approaches to sampling signals or images follow Shannon’s celebrated theorem: the sampling rate must be at least twice the ...
Shruti Sharma, Khyati Chopra, Rasveen Bholan   +2 more
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A nonparametric seismic reliability analysis method based on Bayesian compressive sensing – Stochastic harmonic function method and probability density evolution method

Mechanical systems and signal processing, 2023
Jingran He, Ruofan Gao, Hao Zhou
semanticscholar   +1 more source

An active learning reliability analysis method using adaptive Bayesian compressive sensing and Monte Carlo simulation (ABCS-MCS)

Reliability Engineering & System Safety, 2022
Peiping Li, Yu Wang
semanticscholar   +1 more source

Non-parametric generation of multivariate cross-correlated random fields directly from sparse measurements using Bayesian compressive sensing and Markov chain Monte Carlo simulation

Stochastic environmental research and risk assessment (Print), 2023
Peiping Li, Yu Wang, Zheng Guan
semanticscholar   +1 more source

An adaptive sparse polynomial dimensional decomposition based on Bayesian compressive sensing and cross-entropy

Structural And Multidisciplinary Optimization, 2022
Wanxin He, Gang Li, Zhaokun Nie
semanticscholar   +1 more source

Efficient basis-adaptive Bayesian compressive sensing with fast leave-one-out cross-validation for reliability analysis of geotechnical engineering systems

Computers and geotechnics
Peiping Li, C. Shi
semanticscholar   +1 more source

Grid Matching in Monte Carlo Bayesian Compressive Sensing

2019
HAVELSAN, METEKSAN SAVUNMA, TUBITAK, METRON Sci Solut, Off Naval Res Global Sci & Technol, Ankara Univ, Sabanci Univ, STM, TAI, ASELSAN, Koc Bilgi Savunma Teknolojileri A S, Kale Havacilik, Int Soc Infromat Fus, IEEE ...
Kyriakides, Ioannis, Pribic, Radmila, Sar, Hüseyin, At, Nuray   +3 more
openaire   +2 more sources