Results 41 to 50 of about 70,734 (340)
A Max-Product EM Algorithm for Reconstructing Markov-tree Sparse Signals from Compressive Samples [PDF]
, 2013 We propose a Bayesian expectation-maximization (EM) algorithm for reconstructing Markov-tree sparse signals via belief propagation. The measurements follow an underdetermined linear model where the regression-coefficient vector is the sum of an unknown ...
Dogandžić, Aleksandar +2 more
core +4 more sources
Info-Greedy sequential adaptive compressed sensing [PDF]
, 2015 We present an information-theoretic framework for sequential adaptive compressed sensing, Info-Greedy Sensing, where measurements are chosen to maximize the extracted information conditioned on the previous measurements.
Braun, Gabor +2 more
core +1 more source
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
openaire +1 more source
Leida xuebao, 2016 Nowadays, high-speed sampling and transmission is a foremost challenge of radar system. In order to solve this problem, a compressive sensing approach is proposed for radar target signals in this study.
Zhong Jinrong, Wen Gongjian
doaj +1 more source
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, Lawrence Carin
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Dynamic Compressive Sensing of Time-Varying Signals via Approximate Message Passing [PDF]
, 2013 In this work the dynamic compressive sensing (CS) problem of recovering sparse, correlated, time-varying signals from sub-Nyquist, non-adaptive, linear measurements is explored from a Bayesian perspective.
Justin Ziniel +3 more
core +2 more sources
A Noise-Robust Fast Sparse Bayesian Learning Model [PDF]
, 2020 This paper utilizes the hierarchical model structure from the Bayesian Lasso in the Sparse Bayesian Learning process to develop a new type of probabilistic supervised learning approach.
Helgøy, Ingvild M., Li, Yushu
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A Bayesian Compressive Sensing Approach to Robust Near-Field Antenna Characterization [PDF]
IEEE Transactions on Antennas and Propagation, 2021 A novel probabilistic sparsity-promoting method for robust near-field (NF) antenna characterization is proposed. It leverages on the measurements-by-design (MebD) paradigm and it exploits some a-priori information on the antenna under test (AUT) to ...
M. Salucci +3 more
semanticscholar +1 more source
Achievable performance of Bayesian compressive sensing based spectrum sensing
2014 IEEE International Conference on Ultra-WideBand (ICUWB), 2014 In wideband spectrum sensing compressive sensing approaches have been used at the receiver side to decrease the sampling rate if the wideband signal can be represented as sparse in a given domain. While most studies consider the reconstruction of primary user's signal accurately it is indeed more important to analyze the presence or absence of the ...
Basaran, Mehmet +2 more
openaire +3 more sources
Defence Technology, 2022 In this paper, we propose a compressive sampling and reconstruction system based on the shift-invariant space associated with the fractional Gabor transform.
Qiang Wang, Chen Meng, Cheng Wang
doaj +1 more source