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Binary generalized orthogonal matching pursuit
Japan Journal of Industrial and Applied Mathematics, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Haifeng Li, Hao Ying, Xiaoli Liu
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High-Dimensional Generalized Orthogonal Matching Pursuit With Singular Value Decomposition
IEEE Geoscience and Remote Sensing Letters, 2023Matching pursuit (MP) is an algorithm, which can reconstruct signal accurately, and is widely used in signal processing. However, MP algorithm has efficiency problem in processing large amount of data, such as five-dimensional (5-D) seismic data ...
Z. Zong, Ting Fu, Xingyao Yin
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IEEE Sensors Journal, 2022
Convolutional sparse representation based on local orthogonal matching pursuit (SR-LocOMP) can recover wheelset-bearing fault impulses without being affected by random slippage and plays an important role in wheelset-bearing fault diagnosis. However, the
Cai Yi +6 more
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Convolutional sparse representation based on local orthogonal matching pursuit (SR-LocOMP) can recover wheelset-bearing fault impulses without being affected by random slippage and plays an important role in wheelset-bearing fault diagnosis. However, the
Cai Yi +6 more
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Ordered Orthogonal Matching Pursuit
2012 National Conference on Communications (NCC), 2012Compressed Sensing deals with recovering sparse signals from a relatively small number of linear measurements. Several algorithms exists for data recovery from the compressed measurements, particularly appealing among these is the greedy approach known as Orthogonal Matching Pursuit (OMP). In this paper, we propose a modified OMP based algorithm called
Deepak Baby, Sibi Raj B Pillai
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A defect localization method based on self-sensing and orthogonal matching pursuit.
Ultrasonics, 2022In conventional structural health monitoring (SHM), a sensor array enables to localize a potential defect by using at least three lead zirconate titanate (PZT) patches.
Yuqing Gao +3 more
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Subspace pursuit embedded in Orthogonal Matching Pursuit
TENCON 2012 IEEE Region 10 Conference, 2012Orthogonal Matching Pursuit (OMP) is a popular greedy pursuit algorithm widely used for sparse signal recovery from an undersampled measurement system. However, one of the main shortcomings of OMP is its irreversible selection procedure of columns of measurement matrix.
Sooraj K. Ambat +2 more
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Generalized Orthogonal Matching Pursuit With Singular Value Decomposition
IEEE Geoscience and Remote Sensing Letters, 2022Matching pursuit (MP) is an algorithm that can represent signal sparsely, and this advantage makes MP popular in signal processing. However, MP algorithm is a greedy algorithm which means it cannot deal with a large family of signals like seismic data ...
Ting Fu, Z. Zong, Xingyao Yin
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Online search Orthogonal Matching Pursuit
2012 IEEE Statistical Signal Processing Workshop (SSP), 2012The recovery of a sparse signal x from y= Φx, where Φ is a matrix with more columns than rows, is a task central to many signal processing problems. In this paper we present a new greedy algorithm to solve this type of problem. Our approach leverages ideas from the field of online search on state spaces.
Alejandro J. Weinstein, Michael B. Wakin
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IEEE Sensors Journal, 2021
Sparse representation has been widely used in bearing fault impact detection, which can find the impact that best matches the fault waveform from the pre-defined dictionary and recover the fault impulse waveform. However, the current dictionary of sparse
Qiuyang Zhou +5 more
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Sparse representation has been widely used in bearing fault impact detection, which can find the impact that best matches the fault waveform from the pre-defined dictionary and recover the fault impulse waveform. However, the current dictionary of sparse
Qiuyang Zhou +5 more
semanticscholar +1 more source
Randomized simultaneous orthogonal matching pursuit
2015 23rd European Signal Processing Conference (EUSIPCO), 2015In this paper, we develop randomized simultaneous orthogonal matching pursuit (RandSOMP) algorithm which computes an approximation of the Bayesian minimum mean-squared error (MMSE) estimate of an unknown rowsparse signal matrix. The approximation is based on greedy iterations, as in SOMP, and it elegantly incorporates the prior knowledge of the ...
Aqib Ejaz, Esa Ollila, Visa Koivunen
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