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Weak Signal Detection Based on Combination of Sparse Representation and Singular Value Decomposition
Applied Sciences, 2022 Due to the inevitable acquisition system noise and strong background noise, it is often difficult to detect the features of weak signals. To solve this problem, sparse representation can effectively extract useful information according to the sparse ...
Huijie Ma +3 more
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A Survey of Sparse Representation: Algorithms and Applications
IEEE Access, 2015 Sparse representation has attracted much attention from researchers in fields of signal processing, image processing, computer vision, and pattern recognition.
Zheng Zhang +4 more
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Sparse Representations of Random Signals [PDF]
Mathematical Methods in the Applied Sciences, 2021 Sparse (fast) representations of deterministic signals have been well studied. Among other types there exists one called adaptive Fourier decomposition (AFD) for functions in analytic Hardy spaces. Through the Hardy space decomposition of the $L^2$ space the AFD algorithm also gives rise to sparse representations of signals of finite energy.
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High-Resolution Azimuth Missing Data SAR Imaging Based on Sparse Representation Autofocusing
Remote Sensing, 2023 Due to significant electromagnetic interference, radar interruptions, and other factors, Azimuth Missing Data (AMD) may occur in Synthetic Aperture Radar (SAR) echo, resulting in severe defocusing and even false targets.
Nan Jiang +6 more
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Identification of Underwater Targets Based on Sparse Representation
IEEE Access, 2020 We consider using sparse representations to identify underwater targets, since underwater acoustic signal have sparse characteristics. We consider the identification problem as one of the identifying among multiple linear regression models and believe ...
Lu Yao, Xiujuan Du
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Application of Sparse Representation in Bioinformatics
Frontiers in Genetics, 2021 Inspired by L1-norm minimization methods, such as basis pursuit, compressed sensing, and Lasso feature selection, in recent years, sparse representation shows up as a novel and potent data processing method and displays powerful superiority.
Shuguang Han +8 more
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AUDIO SIGNAL REPRESENTATIONS FOR FACTORIZATION IN THE SPARSE DOMAIN [PDF]
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011 In this paper, a new class of audio representations is introduced, together with a corresponding fast decomposition algorithm. The main feature of these representations is that they are both sparse and approximately shift-invariant, which allows similarity search in a sparse domain. The common sparse support of detected similar patterns is then used to
Moussallam, Manuel +2 more
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Denoising Method of Nuclear Signal Based on Sparse Representation
Frontiers in Energy Research, 2022 Nuclear signals are sensitive to noise which may affect final monitoring results significantly. In order to suppress the nuclear signal noise, a sparse representation method, which is based on the sparse representation of signals and a matching pursuit ...
San-Jun He +4 more
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Archetypal Analysis and Structured Sparse Representation for Hyperspectral Anomaly Detection
Remote Sensing, 2021 Hyperspectral images (HSIs) often contain pixels with mixed spectra, which makes it difficult to accurately separate the background signal from the anomaly target signal.
Genping Zhao +4 more
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Sparse Representation of a Polytope and Recovery of Sparse Signals and Low-Rank Matrices [PDF]
IEEE Transactions on Information Theory, 2014 This paper considers compressed sensing and affine rank minimization in both noiseless and noisy cases and establishes sharp restricted isometry conditions for sparse signal and low-rank matrix recovery. The analysis relies on a key technical tool which represents points in a polytope by convex combinations of sparse vectors.
T. Tony Cai, Anru Zhang
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