Results 31 to 40 of about 1,492,451 (279)
Bayesian Inference and Compressed Sensing
, 2017 This chapter provides the use of Bayesian inference in compressive sensing (CS), a method in signal processing. Among the recovery methods used in CS literature, the convex relaxation methods are reformulated again using the Bayesian framework and this method is applied in different CS applications such as magnetic resonance imaging (MRI), remote ...
Solomon A. Tesfamicael, Faraz Barzideh
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Advanced channel estimation in OTFS and NOMA using deep bayesian gaussian processes and compressive sensing [PDF]
Scientific ReportsFor high-mobility wireless communications in Orthogonal Time Frequency Space (OTFS) and Non-Orthogonal Multiple Access (NOMA) systems, accurate Channel Estimation (CE) is mandatory. Conventional pilot-based methods, such as Least Squares (LS) and Minimum
Nitha Anilkumar, Sudhakar Sengan
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Bayesian Compressive Sensing for clustered sparse signals
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011 In traditional framework of Compressive Sensing (CS), only sparse prior on the property of signals in time or frequency domain is adopted to guarantee the exact inverse recovery. Besides sparse prior, cluster prior is introduced in this paper in order to investigate a class of structural sparse signals, called clustered sparse signals.
Yu, Lei +3 more
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3D Imaging Algorithm for Down-Looking MIMO Array SAR Based on Bayesian Compressive Sensing [PDF]
International Journal of Antennas and Propagation, 2014 Down-looking MIMO array SAR can reconstruct 3D images of the observed area in the inferior of the platform of the SAR and has wide application prospects.
Xiaozhen Ren, Lina Chen, Jing Yang
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A nonparametric Bayesian compressive sensing classification [PDF]
, 2020 This paper presents a novel non-parametric back-propagation Bayesian compressive sensing (BBCS) classification approach. While the state-of-the-art parametric classifiers such as logistic regression require model training and can result in inadequate models, the developed approach does not require model training.
Chen, R., Hawes, M., Mihaylova, L.
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Bayesian online compressed sensing [PDF]
Physical Review E, 2016 In this paper, we explore the possibilities and limitations of recovering sparse signals in an online fashion. Employing a mean field approximation to the Bayes recursion formula yields an online signal recovery algorithm that can be performed with a computational cost that is linearly proportional to the signal length per update.
Paulo V. Rossi +2 more
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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, Matthias, Nickisch, Hannes
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Bayesian compressive sensing and projection optimization [PDF]
Proceedings of the 24th international conference on Machine learning, 2007 This paper introduces a new problem for which machine-learning tools may make an impact. The problem considered is termed "compressive sensing", in which a real signal of dimension N is measured accurately based on ...
Shihao Ji 0001, Lawrence Carin
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Bayesian compressive sensing for phonetic classification [PDF]
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010 In this paper, we introduce a novel bayesian compressive sensing (CS) technique for phonetic classification. CS is often used to characterize a signal from a few support training examples, similar to k-nearest neighbor (kNN) and Support Vector Machines (SVMs). However, unlike SVMs and kNNs, CS allows the number of supports to be adapted to the specific
Tara N. Sainath +3 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 +2 more
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