Results 51 to 60 of about 1,492,451 (279)

Bayesian Compressive Sensing Via Belief Propagation [PDF]

IEEE Transactions on Signal Processing, 2010
Compressive sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable, sub-Nyquist signal acquisition. When a statistical characterization of the signal is available, Bayesian inference can complement conventional CS methods based on linear ...
Dror Baron, Shriram Sarvotham, Richard G. Baraniuk   +2 more
openaire   +2 more sources

Photonics-enabled sub-Nyquist radio frequency sensing based on temporal channelization and compressive sensing [PDF]

, 2014
A novel approach to sensing broadband radio frequency (RF) spectrum beyond the Nyquist limit based on photonic temporal channelization and compressive sensing is proposed.
Wang, Chao, Chao Wang, Gomes, Nathan J., Nathan J. Gomes   +3 more
core   +1 more source

Multi‐contrast reconstruction with Bayesian compressed sensing [PDF]

Magnetic Resonance in Medicine, 2011
AbstractClinical imaging with structural MRI routinely relies on multiple acquisitions of the same region of interest under several different contrast preparations. This work presents a reconstruction algorithm based on Bayesian compressed sensing to jointly reconstruct a set of images from undersampled k‐space data with higher fidelity than when the ...
Bilgic, Berkin, Adalsteinsson, Elfar, Goyal, Vivek K.   +2 more
openaire   +4 more sources

Fast monostatic scattering analysis based on Bayesian compressive sensing [PDF]

, 2017
In conjugation with the method of moments, the Bayesian compressive sensing algorithm is utilized to fast analyze the monostatic electromagnetic scattering problem.
Wei E. I. Sha, Sha, Wei E.I., Jiang, Li (Lijun) Jun, Zhang, Huan Huan, Li Jun Jiang, Huan Huan Zhang   +5 more
core   +1 more source

Frequency-difference sparse Bayesian learning for unambiguous direction-of-arrival estimation [PDF]

JASA Express Letters
The frequency-difference (FD) method uses the FD Hadamard product, comprising auto-products to model below-band acoustic fields and unintended cross-products, for efficient direction-of-arrival (DOA) estimation under spatial aliasing.
Ze Yuan, Haiqiang Niu, Zhenglin Li, Wenyu Luo   +3 more
doaj   +1 more source

Robust Bayesian compressed sensing with outliers

Signal Processing, 2017
Abstract We consider the problem of robust compressed sensing where the objective is to recover a high-dimensional sparse signal from compressed measurements partially corrupted by outliers. A new sparse Bayesian learning method is developed for this purpose.
Qian Wan 0003, Huiping Duan, Jun Fang 0001, Hongbin Li 0001, Zhengli Xing   +4 more
openaire   +3 more sources

Bayesian Compressed Sensing with Heterogeneous Side Information [PDF]

2016 Data Compression Conference (DCC), 2016
The classical compressed sensing (CS) paradigm can be modified so as to leverage a signal correlated to the signal of interest, called side information, which is assumed to be provided a priori at the decoder in order to aid reconstruction. In this work, we propose a novel CS reconstruction method based on belief propagation principles, which manages ...
Evangelos Zimos, João F. C. Mota, Miguel R. D. Rodrigues, Nikos Deligiannis   +3 more
openaire   +2 more sources

A Novel Strategy for Radar Imaging Based on Compressive Sensing [PDF]

, 2010
Radar data have already proven to be compressible with no significant losses for most of the applications in which it is used. In the framework of information theory, the compressibility of a signal implies that it can be decomposed onto a reduced set of
López-Dekker, Paco, Mallorquí Franquet, Jordi Joan, Paco Lopez-Dekker, Tello Alonso, Marivi, Mallorquí, Jordi J., Jordi J Mallorqui, López Dekker, Francisco J., Marivi Tello Alonso, Tello Alonso, Mª Victoria   +8 more
core   +1 more source

Compressed Sensing with uncertainty - the Bayesian estimation perspective

2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015
The Compressed Sensing (CS) framework outperforms the sampling rate limits given by Shannon's theory. This gap is possible since it is assumed that the signal of interest admits a linear decomposition of few vectors in a given sparsifying Basis (Fourier, Wavelet, …).
Bernhardt, Stéphanie, Boyer, Remy, Marcos, Sylvie, Larzabal, Pascal   +3 more
openaire   +3 more sources

A Decentralized Bayesian Algorithm For Distributed Compressive Sensing in Networked Sensing Systems [PDF]

, 2015
Compressive sensing (CS), as a new sensing/sampling paradigm, facilitates signal acquisition by reducing the number of samples required for reconstruction of the original signal, and thus appears to be a promising technique for applications where the ...
Ian J. Wassell, Wei Chen
core   +2 more sources