The Experts below are selected from a list of 263148 Experts worldwide ranked by ideXlab platform
Arye Nehorai - One of the best experts on this subject based on the ideXlab platform.
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joint sparse Recovery Method for compressed sensing with structured dictionary mismatches
IEEE Transactions on Signal Processing, 2014Co-Authors: Zhao Tan, Peng Yang, Arye NehoraiAbstract:In traditional compressed sensing theory, the dic- tionary matrix is given ap riori, whereas in real applications this matrix suffers from random noise and fluctuations. In this paper, we consider a signal model where each column in the dictionary matrix is affected by a structured noise. This formulation is common in direction-of-arrival (DOA) estimation of off-grid targets, encountered in both radar systems and array processing. We propose to use joint sparse signal Recovery to solve the com- pressed sensing problem with structured dictionary mismatches and also give an analytical performance bound on this joint sparse Recovery. We show that, under mild conditions, the reconstruction error of the original sparse signal is bounded by both the sparsity and the noise level in the measurement model. Moreover, we implement fast first-order algorithms to speed up the computing process. Numerical examples demonstrate the good performance of the proposed algorithm and also show that the joint-sparse Recovery Method yields a better reconstruction result than existing Methods. By implementing the joint sparse Recovery Method, the accuracy and efficiency of DOA estimation are improved in both passive and active sensing cases.
Zhao Tan - One of the best experts on this subject based on the ideXlab platform.
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joint sparse Recovery Method for compressed sensing with structured dictionary mismatches
IEEE Transactions on Signal Processing, 2014Co-Authors: Zhao Tan, Peng Yang, Arye NehoraiAbstract:In traditional compressed sensing theory, the dic- tionary matrix is given ap riori, whereas in real applications this matrix suffers from random noise and fluctuations. In this paper, we consider a signal model where each column in the dictionary matrix is affected by a structured noise. This formulation is common in direction-of-arrival (DOA) estimation of off-grid targets, encountered in both radar systems and array processing. We propose to use joint sparse signal Recovery to solve the com- pressed sensing problem with structured dictionary mismatches and also give an analytical performance bound on this joint sparse Recovery. We show that, under mild conditions, the reconstruction error of the original sparse signal is bounded by both the sparsity and the noise level in the measurement model. Moreover, we implement fast first-order algorithms to speed up the computing process. Numerical examples demonstrate the good performance of the proposed algorithm and also show that the joint-sparse Recovery Method yields a better reconstruction result than existing Methods. By implementing the joint sparse Recovery Method, the accuracy and efficiency of DOA estimation are improved in both passive and active sensing cases.
Sung Soon Park - One of the best experts on this subject based on the ideXlab platform.
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iscsi multi connection and error Recovery Method for remote storage system in mobile appliance
Lecture Notes in Computer Science, 2006Co-Authors: Shaikh Muhammad Allayear, Sung Soon ParkAbstract:The continued growth of both mobile appliance and wireless Internet technologies is bringing a new telecommunication revolution and has extended the demand of various services with mobile appliance. However, during working with wireless access devices, users have a limited amount of storage available to them due to their limited size and weight. To relieve this problem iSCSI (Internet Small Computer Interface) remote storage system would be one solution but the question is high availability and performance. In this paper, we propose a new approach of Multi-Connection in one session based remote storage system for mobile appliance with error Recovery Method that avoids drastic reduction of transmission rate from TCP congestion control in wireless environment as compared to traditional iSCSI.
Wang Huijie - One of the best experts on this subject based on the ideXlab platform.
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A spectral Recovery Method for Raman spectroscopy utilizing prior datasets
Spectrochimica acta. Part A Molecular and biomolecular spectroscopy, 2019Co-Authors: Sun Xueqing, Wang HuijieAbstract:Spectral-based Method has been widely used for the qualitative and quantitative analysis of different substances in various fields. The spectral Recovery Method is a crucial role in the spectral-based Method, which can save the measurement cost and computation time in measuring. In this paper, we introduce a simple and reliable spectral Recovery Method base on prior datasets, which can tolerate substantial spectral noise. The Method has been successfully applied in the quantitative analysis of the pharmaceutical mixture. The SNR of the Recovery spectra can be increased by ~100 times.
Peng Yang - One of the best experts on this subject based on the ideXlab platform.
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joint sparse Recovery Method for compressed sensing with structured dictionary mismatches
IEEE Transactions on Signal Processing, 2014Co-Authors: Zhao Tan, Peng Yang, Arye NehoraiAbstract:In traditional compressed sensing theory, the dic- tionary matrix is given ap riori, whereas in real applications this matrix suffers from random noise and fluctuations. In this paper, we consider a signal model where each column in the dictionary matrix is affected by a structured noise. This formulation is common in direction-of-arrival (DOA) estimation of off-grid targets, encountered in both radar systems and array processing. We propose to use joint sparse signal Recovery to solve the com- pressed sensing problem with structured dictionary mismatches and also give an analytical performance bound on this joint sparse Recovery. We show that, under mild conditions, the reconstruction error of the original sparse signal is bounded by both the sparsity and the noise level in the measurement model. Moreover, we implement fast first-order algorithms to speed up the computing process. Numerical examples demonstrate the good performance of the proposed algorithm and also show that the joint-sparse Recovery Method yields a better reconstruction result than existing Methods. By implementing the joint sparse Recovery Method, the accuracy and efficiency of DOA estimation are improved in both passive and active sensing cases.
