Results 31 to 40 of about 70,734 (340)
Compressed sensing and Bayesian experimental design [PDF]
Proceedings of the 25th international conference on Machine learning - ICML '08, 2008 We relate compressed sensing (CS) with Bayesian experimental design and provide a novel efficient approximate method for the latter, based on expectation propagation. In a large comparative study about linearly measuring natural images, we show that the simple standard heuristic of measuring wavelet coefficients top-down systematically outperforms CS ...
Seeger, M., Nickisch, H.
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Complex multitask compressive sensing using Laplace priors
Electronics Letters, 2021 Most existing Bayesian compressive sensing (BCS) algorithms are developed in real numbers. This results in many difficulties in applying BCS to solve complex‐valued problems.
Qilei Zhang, Zhen Dong, Yongsheng Zhang
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Bayesian Compressive Sensing Wideband Spectrum Detection Based on Energy Efficiency [PDF]
Jisuanji gongcheng, 2016 In Cognitive Radio Network(CRN),wideband spectrum detection based on Compressive Sensing(CS) only focuses on spectral efficiency.Energy efficiency is hardly considered in spectrum detection phase,which results in larger energy consumption with the ...
WANG Zan,XU Xiaorong,YAO Yingbiao
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Low-Rank and Sparse Matrix Recovery for Hyperspectral Image Reconstruction Using Bayesian Learning
Sensors, 2022 In order to reduce the amount of hyperspectral imaging (HSI) data transmission required through hyperspectral remote sensing (HRS), we propose a structured low-rank and joint-sparse (L&S) data compression and reconstruction method.
Yanbin Zhang +4 more
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Bayesian compressed sensing: Improving inference [PDF]
2013 IEEE China Summit and International Conference on Signal and Information Processing, 2013 In this paper we present a set of theoretical results regarding inference algorithms for hierarchical Bayesian networks. More specifically we focus on a specific type of networks which result in highly sparse models for the input. Bayesian inference in these networks usually is based on optimising a non-convex cost function of the model parameters.
Evripidis Karseras, Kin Leung, Wei Dai
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Electronics Letters, 2021 An approach based on the Green function and the Born approximation is used for impulsive radio ultra‐wideband microwave imaging, in which a permittivity map of the illuminated scenario is estimated using the scattered fields measured at several positions.
Nicolás Zilberstein +2 more
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, 2022 The long-term structural health monitoring (SHM) provides massive data, leading to a high demand for data transmission and storage. Compressive sensing (CS) has great potential in alleviating this problem by using less samples to recover the complete ...
H. Wan, G. Dong, Yaozhi Luo, Yiqing Ni
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Complex multitask Bayesian compressive sensing [PDF]
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014 An effective complex multitask Bayesian compressive sensing (CMT-BCS) algorithm is proposed to recover sparse or group sparse complex signals. The existing multitask Bayesian compressive sensing (MT-CS) algorithm is powerful in recovering multiple real-valued sparse solutions. However, a large class of sensing problems deal with complex values.
Qisong Wu +3 more
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Bayesian compressive sensing framework for spectrum reconstruction in Rayleigh fading channels [PDF]
, 2016 Compressive sensing (CS) is a novel digital signal processing technique that has found great interest in many applications including communication theory and wireless communications.
Ghafoor, Abdul +3 more
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Frequency-difference sparse Bayesian learning for unambiguous direction-of-arrival estimation [PDF]
JASA Express LettersThe 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 +3 more
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