This paper proposes a denoising algorithm called truncated sparse decomposition (TSD) algorithm, which combines the advantage of the sparse decomposition with that of the minimum energy model truncation operation. E...This paper proposes a denoising algorithm called truncated sparse decomposition (TSD) algorithm, which combines the advantage of the sparse decomposition with that of the minimum energy model truncation operation. Experimental results on two real chaotic signals show that the TSD algorithm outperforms the recently reported denoising algorithmsnon-negative sparse coding and singular value decomposition based method.展开更多
The denoising problem of impure chaotic signals is addressed in this paper. A method based on sparse representation is proposed, in which the random frame dictionary is generated by a chaotic random search algorithm. ...The denoising problem of impure chaotic signals is addressed in this paper. A method based on sparse representation is proposed, in which the random frame dictionary is generated by a chaotic random search algorithm. The numerical simulation shows the proposed algorithm outperforms those recently reported alternative denoising methods.展开更多
An adaptive denoising algorithm based on local sparse representation (local SR) is proposed. The basic idea is applying SR locally to clusters of signals embedded in a high-dimensional space of delayed coordinates. ...An adaptive denoising algorithm based on local sparse representation (local SR) is proposed. The basic idea is applying SR locally to clusters of signals embedded in a high-dimensional space of delayed coordinates. The clusters of signals are represented by the sparse linear combinations of atoms depending on the nature of the signal. The algorithm is applied to noisy chaotic signals denoising for testing its performance. In comparison with recently reported leading alternative denoising algorithms such as kernel principle component analysis (Kernel PCA), local independent component analysis (local ICA), local PCA, and wavelet shrinkage (WS), the proposed algorithm is more efficient.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant No.60872123)the Joint Fund of the National Natural Science Foundation and the Guangdong Provincial Natural Science Foundation(Grant No.U0835001)+5 种基金the Doctorate Foundation of South China University of Technologythe Post-Doc Foundation of South China University of Technologythe Basic Scientific Research Fund of South China University of Technology for Youththe Natural Science Fund of South China University of Technology for Youththe Natural Science Foundation of Guangdong Province,Chinathe China Postdoctoral Science Foundation(Grant No.20100480049)
文摘This paper proposes a denoising algorithm called truncated sparse decomposition (TSD) algorithm, which combines the advantage of the sparse decomposition with that of the minimum energy model truncation operation. Experimental results on two real chaotic signals show that the TSD algorithm outperforms the recently reported denoising algorithmsnon-negative sparse coding and singular value decomposition based method.
基金Project supported by the National Natural Science Foundation of China (Grant No. 60872123)the Joint Fund of the National Natural Science Foundation and the Guangdong Provincial Natural Science Foundation (Grant No. U0835001)by the Doctorate Foundation of South China University of Technology,China
文摘The denoising problem of impure chaotic signals is addressed in this paper. A method based on sparse representation is proposed, in which the random frame dictionary is generated by a chaotic random search algorithm. The numerical simulation shows the proposed algorithm outperforms those recently reported alternative denoising methods.
基金Supported by the National Natural Science Foundation of China ( No. 60872123 ) and the Guangdong Provincial Natural Science Foundation (No. U0835001 ).
基金Supported by the National Natural Science Foundation of China under Grant Nos 60572025 and 60872123, the Joint Fund of the National Natural Science Foundation and the Guangdong Provincial Natural Science Foundation,China,under Grant U0835001.The authors thank S. Q. Zheng and her research team for sharing their software for the purpose of comparison.
文摘An adaptive denoising algorithm based on local sparse representation (local SR) is proposed. The basic idea is applying SR locally to clusters of signals embedded in a high-dimensional space of delayed coordinates. The clusters of signals are represented by the sparse linear combinations of atoms depending on the nature of the signal. The algorithm is applied to noisy chaotic signals denoising for testing its performance. In comparison with recently reported leading alternative denoising algorithms such as kernel principle component analysis (Kernel PCA), local independent component analysis (local ICA), local PCA, and wavelet shrinkage (WS), the proposed algorithm is more efficient.