Results 171 to 180 of about 3,975 (201)
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Dispersion curve recovery with orthogonal matching pursuit
The Journal of the Acoustical Society of America, 2014Dispersion curves characterize many propagation mediums. When known, many methods use these curves to analyze waves. Yet, in many scenarios, their exact values are unknown due to material and environmental uncertainty. This paper presents a fast implementation of sparse wavenumber analysis, a method for recovering dispersion curves from data.
Joel B, Harley, José M F, Moura
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FPGA implementation of Orthogonal Matching Pursuit algorithm
2016 13th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), 2016In this paper, the Orthogonal Matching Pursuit (OMP) algorithm is implemented on a Field Programmable Gate Array (FPGA) to obtain the number and position of the atoms in a dictionary. The dictionary, obtained by K-Singular Value Decomposition (K-SDV) algorithm and developed in Matlab, reconstructs a signal from its Sparse representation. With the atoms
Carlos Morales-Perez +4 more
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Reduced Look Ahead Orthogonal Matching Pursuit
2014 Twentieth National Conference on Communications (NCC), 2014Compressed Sensing (CS) is an elegant technique to acquire signals and reconstruct them efficiently by solving a system of under-determined linear equations. The excitement in this field stems from the fact that we can sample at a rate way below the Nyquist rate and still reconstruct the signal provided some conditions are met.
Prateek Basavapur Swamy +3 more
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Probabilistic Orthogonal Matching Pursuit
2022 IEEE International Conference on Big Data (Big Data), 2022Ghazal Fazelnia, John W. Paisley
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Orthogonal Matching Pursuit for Sparse Quantile Regression
2014 IEEE International Conference on Data Mining, 2014We consider new formulations and methods for sparse quantile regression in the high-dimensional setting. Quantile regression plays an important role in many data mining applications, including outlier-robust exploratory analysis in gene selection. In addition, the sparsity consideration in quantile regression enables the exploration of the entire ...
Aleksandr Y. Aravkin +3 more
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Hierarchical orthogonal matching pursuit for face recognition
The First Asian Conference on Pattern Recognition, 2011This paper tries to exploit the joint group intrinsics in face recognition problem by using sparse representation with multiple features. We claim that different feature vectors of one test face image share the same sparsity pattern at the higher group level, but not necessarily at the lower (inside the group) level. This means that they share the same
Huaping Liu 0001, Fuchun Sun 0001
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Sorted Random Matrix for Orthogonal Matching Pursuit
2010 International Conference on Digital Image Computing: Techniques and Applications, 2010Orthogonal Matching Pursuit (OMP) algorithm is widely applied to compressive sensing (CS) image signal recovery because of its low computation complexity and its ease of implementation. However, OMP usually needs more measurements than some other recovery algorithms in order to achieve equal-quality reconstructions. This article firstly illustrates the
Zhenglin Wang, Ivan Lee 0001
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Perturbation analysis of simultaneous orthogonal matching pursuit
Signal Processing, 2015The theory of compressed sensing (CS) indicates that a sparse vector lying in a high dimensional space can be accurately recovered from only a small set of linear measurements, under appropriate conditions on the measurement matrix. For multiple sparse signals that share common locations of the nonzero entries, simultaneous orthogonal matching pursuit (
Wenbo Xu 0003 +4 more
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Structured orthogonal matching pursuit for feature selection
Neurocomputing, 2019Abstract Feature selection techniques are widely adopted to deal with high-dimensional data. One of the most popular algorithms is orthogonal matching pursuit (OMP). OMP tends to select only one from correlated features, because the next selected feature relies on a residual that is orthogonal to previous selected features.
Xiaoshuang Shi +5 more
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Orthogonal Matching Pursuit Algorithm Via Improved Matching Criterion
Proceedings of the 2nd International Conference on Digital Signal Processing, 2018Better support set Selected is the key step in Compressed Sensing to improve the reconstruction effect. The inner product matching criterion adopted in orthogonal matching pursuit algorithm fails to fully consider the correlation between the residuals and the atoms, which leads to greater error in the reconstruction process.
Jianhong Xiang, HuiHui Yue, Xiangjun Yin
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