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Optical CDMA Detection by Orthogonal Matching Pursuit
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 2006In this paper, we present a novel optical CDMA multi-user detector employing the orthogonal matching pursuit algorithm. The proposed system is compared with most of the receiver structures in the literature. It is shown by simulation results that the proposed detection architecture is a very promising candidate with its low computational complexity ...
Tolga Kurt +2 more
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Orthogonal Matching Pursuit with correction
2016 IEEE 12th International Colloquium on Signal Processing & Its Applications (CSPA), 2016Orthogonal Matching Pursuit (OMP) is the most popular greedy algorithm that has been developed to find a sparse solution vector to an under-determined linear system of equations. OMP follows the projection procedure to identify the indices of the support of the sparse solution vector.
Nasser Mourad +2 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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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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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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Probabilistic Orthogonal Matching Pursuit
2022 IEEE International Conference on Big Data (Big Data), 2022Ghazal Fazelnia, John W. Paisley
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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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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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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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