The Experts below are selected from a list of 8307 Experts worldwide ranked by ideXlab platform
A. Yongacoglu - One of the best experts on this subject based on the ideXlab platform.
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Flexible tree-search based Orthogonal Matching Pursuit algorithm
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 2005Co-Authors: G.z. Karabulut, L. Moura, D. Panario, A. YongacogluAbstract:The Orthogonal Matching Pursuit (OMP) algorithm is an adaptive nonlinear algorithm for signal decomposition using an overcomplete dictionary. A tree-search based Orthogonal Matching Pursuit (TB-OMP) has been proposed (Cotter et al. (2001)). Although the TB-OMP algorithm improves the approximation performance, its computation time requirement increases exponentially making the algorithm impractical for certain applications. In this paper, we propose the flexible tree-search based Orthogonal Matching Pursuit (FTB-OMP). The algorithm provides design parameters that give flexibility to establish a tradeoff between approximation performance and experimental time complexity. Sparse signal representations are frequently required in problems related to signal processing and communication areas. The proposed FTB-OMP algorithm is a promising solution for such problems.
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ICASSP (4) - Flexible tree-search based Orthogonal Matching Pursuit algorithm
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 2005Co-Authors: G.z. Karabulut, L. Moura, D. Panario, A. YongacogluAbstract:The Orthogonal Matching Pursuit (OMP) algorithm is an adaptive nonlinear algorithm for signal decomposition using an overcomplete dictionary. A tree-search based Orthogonal Matching Pursuit (TB-OMP) has been proposed (Cotter et al. (2001)). Although the TB-OMP algorithm improves the approximation performance, its computation time requirement increases exponentially making the algorithm impractical for certain applications. In this paper, we propose the flexible tree-search based Orthogonal Matching Pursuit (FTB-OMP). The algorithm provides design parameters that give flexibility to establish a tradeoff between approximation performance and experimental time complexity. Sparse signal representations are frequently required in problems related to signal processing and communication areas. The proposed FTB-OMP algorithm is a promising solution for such problems.
G.z. Karabulut - One of the best experts on this subject based on the ideXlab platform.
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Flexible tree-search based Orthogonal Matching Pursuit algorithm
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 2005Co-Authors: G.z. Karabulut, L. Moura, D. Panario, A. YongacogluAbstract:The Orthogonal Matching Pursuit (OMP) algorithm is an adaptive nonlinear algorithm for signal decomposition using an overcomplete dictionary. A tree-search based Orthogonal Matching Pursuit (TB-OMP) has been proposed (Cotter et al. (2001)). Although the TB-OMP algorithm improves the approximation performance, its computation time requirement increases exponentially making the algorithm impractical for certain applications. In this paper, we propose the flexible tree-search based Orthogonal Matching Pursuit (FTB-OMP). The algorithm provides design parameters that give flexibility to establish a tradeoff between approximation performance and experimental time complexity. Sparse signal representations are frequently required in problems related to signal processing and communication areas. The proposed FTB-OMP algorithm is a promising solution for such problems.
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Optical CDMA detection by Orthogonal Matching Pursuit
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 2005Co-Authors: T. Kurt, G.z. Karabulut, A. YongapgluAbstract:In 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, and high detection performance. It is also shown to be more robust to low SNR and near-far effect when compared to most of the well-known optical CDMA receiver architectures.
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ICASSP (4) - Flexible tree-search based Orthogonal Matching Pursuit algorithm
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 2005Co-Authors: G.z. Karabulut, L. Moura, D. Panario, A. YongacogluAbstract:The Orthogonal Matching Pursuit (OMP) algorithm is an adaptive nonlinear algorithm for signal decomposition using an overcomplete dictionary. A tree-search based Orthogonal Matching Pursuit (TB-OMP) has been proposed (Cotter et al. (2001)). Although the TB-OMP algorithm improves the approximation performance, its computation time requirement increases exponentially making the algorithm impractical for certain applications. In this paper, we propose the flexible tree-search based Orthogonal Matching Pursuit (FTB-OMP). The algorithm provides design parameters that give flexibility to establish a tradeoff between approximation performance and experimental time complexity. Sparse signal representations are frequently required in problems related to signal processing and communication areas. The proposed FTB-OMP algorithm is a promising solution for such problems.
D. Panario - One of the best experts on this subject based on the ideXlab platform.
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Flexible tree-search based Orthogonal Matching Pursuit algorithm
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 2005Co-Authors: G.z. Karabulut, L. Moura, D. Panario, A. YongacogluAbstract:The Orthogonal Matching Pursuit (OMP) algorithm is an adaptive nonlinear algorithm for signal decomposition using an overcomplete dictionary. A tree-search based Orthogonal Matching Pursuit (TB-OMP) has been proposed (Cotter et al. (2001)). Although the TB-OMP algorithm improves the approximation performance, its computation time requirement increases exponentially making the algorithm impractical for certain applications. In this paper, we propose the flexible tree-search based Orthogonal Matching Pursuit (FTB-OMP). The algorithm provides design parameters that give flexibility to establish a tradeoff between approximation performance and experimental time complexity. Sparse signal representations are frequently required in problems related to signal processing and communication areas. The proposed FTB-OMP algorithm is a promising solution for such problems.
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ICASSP (4) - Flexible tree-search based Orthogonal Matching Pursuit algorithm
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 2005Co-Authors: G.z. Karabulut, L. Moura, D. Panario, A. YongacogluAbstract:The Orthogonal Matching Pursuit (OMP) algorithm is an adaptive nonlinear algorithm for signal decomposition using an overcomplete dictionary. A tree-search based Orthogonal Matching Pursuit (TB-OMP) has been proposed (Cotter et al. (2001)). Although the TB-OMP algorithm improves the approximation performance, its computation time requirement increases exponentially making the algorithm impractical for certain applications. In this paper, we propose the flexible tree-search based Orthogonal Matching Pursuit (FTB-OMP). The algorithm provides design parameters that give flexibility to establish a tradeoff between approximation performance and experimental time complexity. Sparse signal representations are frequently required in problems related to signal processing and communication areas. The proposed FTB-OMP algorithm is a promising solution for such problems.
L. Moura - One of the best experts on this subject based on the ideXlab platform.
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Flexible tree-search based Orthogonal Matching Pursuit algorithm
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 2005Co-Authors: G.z. Karabulut, L. Moura, D. Panario, A. YongacogluAbstract:The Orthogonal Matching Pursuit (OMP) algorithm is an adaptive nonlinear algorithm for signal decomposition using an overcomplete dictionary. A tree-search based Orthogonal Matching Pursuit (TB-OMP) has been proposed (Cotter et al. (2001)). Although the TB-OMP algorithm improves the approximation performance, its computation time requirement increases exponentially making the algorithm impractical for certain applications. In this paper, we propose the flexible tree-search based Orthogonal Matching Pursuit (FTB-OMP). The algorithm provides design parameters that give flexibility to establish a tradeoff between approximation performance and experimental time complexity. Sparse signal representations are frequently required in problems related to signal processing and communication areas. The proposed FTB-OMP algorithm is a promising solution for such problems.
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ICASSP (4) - Flexible tree-search based Orthogonal Matching Pursuit algorithm
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 2005Co-Authors: G.z. Karabulut, L. Moura, D. Panario, A. YongacogluAbstract:The Orthogonal Matching Pursuit (OMP) algorithm is an adaptive nonlinear algorithm for signal decomposition using an overcomplete dictionary. A tree-search based Orthogonal Matching Pursuit (TB-OMP) has been proposed (Cotter et al. (2001)). Although the TB-OMP algorithm improves the approximation performance, its computation time requirement increases exponentially making the algorithm impractical for certain applications. In this paper, we propose the flexible tree-search based Orthogonal Matching Pursuit (FTB-OMP). The algorithm provides design parameters that give flexibility to establish a tradeoff between approximation performance and experimental time complexity. Sparse signal representations are frequently required in problems related to signal processing and communication areas. The proposed FTB-OMP algorithm is a promising solution for such problems.
Mike E Davies - One of the best experts on this subject based on the ideXlab platform.
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in greedy Pursuit of new directions nearly Orthogonal Matching Pursuit by directional optimisation
European Signal Processing Conference, 2007Co-Authors: Thomas Blumensath, Mike E DaviesAbstract:Matching Pursuit and Orthogonal Matching Pursuit are greedy algorithms used to obtain sparse signal approximations. Orthogonal Matching Pursuit is known to offer better performance, but Matching Pursuit allows more efficient implementations. In this paper we propose novel greedy Pursuit algorithms based on directional updates. Using a conjugate direction, the algorithm becomes a novel implementation of Orthogonal Matching Pursuit, with computational requirements similar to current implementations based on QR factorisation. A significant reduction in memory requirements and computational complexity can be achieved by approximating the conjugate direction. Further computational savings can be made by using a steepest descent direction. The two resulting algorithms are then comparable to Matching Pursuit in their computational requirements, their performance is however shown to be closer to that of Orthogonal Matching Pursuit with the (slightly slower) approximate conjugate direction based approach outperforming the gradient descent method.
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EUSIPCO - In greedy Pursuit of new directions: (Nearly) Orthogonal Matching Pursuit by directional optimisation
2007Co-Authors: Thomas Blumensath, Mike E DaviesAbstract:Matching Pursuit and Orthogonal Matching Pursuit are greedy algorithms used to obtain sparse signal approximations. Orthogonal Matching Pursuit is known to offer better performance, but Matching Pursuit allows more efficient implementations. In this paper we propose novel greedy Pursuit algorithms based on directional updates. Using a conjugate direction, the algorithm becomes a novel implementation of Orthogonal Matching Pursuit, with computational requirements similar to current implementations based on QR factorisation. A significant reduction in memory requirements and computational complexity can be achieved by approximating the conjugate direction. Further computational savings can be made by using a steepest descent direction. The two resulting algorithms are then comparable to Matching Pursuit in their computational requirements, their performance is however shown to be closer to that of Orthogonal Matching Pursuit with the (slightly slower) approximate conjugate direction based approach outperforming the gradient descent method.
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on the difference between Orthogonal Matching Pursuit and Orthogonal least squares
2007Co-Authors: Thomas Blumensath, Mike E DaviesAbstract:Greedy algorithms are often used to solve under- determined inverse problems when the solution is constrained to be sparse, i.e. the solution is only expected to have a relati vely small number of non-zero elements. Two different algorithms have been suggested to solve such problems in the signal pro- cessing and control community, Orthogonal Matching Pursuit and Orthogonal Least Squares respectively. In the current literature, there exist a great deal of confusion between the two strategies. For example, the later strategy has often be called Orthogonal Matching Pursuit and has repeatedly been "re-discovered" in several papers. In this communication we try to pull together some of the literature and clarify the difference between the methods. x and a matrix � ∈ R N x×Ns , find a vector s such that the squared error is small, while s has only a small number of non-zero elements. For the discussion here, we use the term algorithm to mean any computational procedure that gives a particular result, i.e. we here discuss two different algor ithms, which can be implemented using different computational steps.
