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Since the traditional wavelet and wavelet packet coefficients do not exactly represent the strength of signal components at the very time(space)-frequency tilling, group- normalized wavelet packet transform (GNWPT), is presented f...
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Since the traditional wavelet and wavelet packet coefficients do not exactly represent the strength of signal components at the very time(space)-frequency tilling, group- normalized wavelet packet transform (GNWPT), is presented for nonlinear signal filtering and extraction from the clutter or noise, together with the space(time)-frequency masking technique. The extended F-entropy improves the performance of GNWPT. For perception-based image, soft-logic masking is emphasized to remove the aliasing with edge preserved. Lawton's method for complex valued wavelets construction is extended to generate the complex valued compactly supported wavelet packets for radar signal extraction. This kind of wavelet packets are symmetry and unitary orthogonal. Well-defined wavelet packets are chosen by the analysis remarks on their time-frequency characteristics. For real valued signal processing, such as images and ECG signal, the compactly supported spline or bi- orthogonal wavelet packets are preferred for perfect de- noising and filtering qualities.
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We present the interval interpolating wavelet transform for fast image compression. Comparing with the common used wavelet coding, this method is more systematically stable, with less computing complexity and the inherent parallel...
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We present the interval interpolating wavelet transform for fast image compression. Comparing with the common used wavelet coding, this method is more systematically stable, with less computing complexity and the inherent parallel processing. In theory, it has the nearly optimal minimax compression characteristics. The simulation shows that the interval interpolating wavelet transform are more qualified and remove the artificial blocking effects. The interval wavelet is introduced to deal with the boundary points of the finite localized image. This method does not only improve the compress rate, but also deletes the quantization aliasing of the boundary pixels.
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Recently, blockchain was introduced into the cyber-physical systems, which provides services of privacy and trust. However, reliability and system performance issues exist when blockchain and cyber-physical systems are integrated....
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Recently, blockchain was introduced into the cyber-physical systems, which provides services of privacy and trust. However, reliability and system performance issues exist when blockchain and cyber-physical systems are integrated. In this paper, we design a blockchain-enabled cyber-physical system, where a new blockchain consensus is used to solve the problems of reliability and system performance. Firstly, an autonomous consensus mechanism called Proof-of-Weighted-Planned-Behavior is established based on the theory of planned behavior. Then, the behavior of consensus participants gets further explained by introducing credit evaluation and vulnerable node analysis. Moreover, considering the Jain fairness index, a dynamic authorizer group mechanism that coordinates reliability and decentralization is developed. By optimizing the credit threshold of the authorization group, the security and reliability of our designed mechanism are guaranteed. Finally, the experimental simulation results prove that compared with the traditional consensus, our proposed consensus improves the reliability and the system performance of the blockchain-enabled cyber-physical systems.
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In this paper, we establish a data model for the feature extraction of point scatterers in the presence of motion through resolution cell (MTRC) errors and unknown noise, the data model is a sum of 2-dimensional sinusoidal signals...
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In this paper, we establish a data model for the feature extraction of point scatterers in the presence of motion through resolution cell (MTRC) errors and unknown noise, the data model is a sum of 2-dimensional sinusoidal signals with quadratic phase errors, which are caused by 'range walk' and 'variable range rate' respectively. Based on the data model, we propose a parametric RELAX-based algorithm to extract the target features when there are MTRC errors in radar imaging. The algorithm minimizes a complicated nonlinear least-squares (NLS) cost function, and it is performed alternately by letting only the parameters and errors of one scatterer vary and freezing all others at their most recently determined values. The Cramer-Rao bounds (CRB's) for the parameters of the data model are also derived. We compare the performance of the proposed algorithm with the CRB's by simulation. And the results show that the mean squared errors of the parameter estimates obtained by the algorithm can approach the corresponding CRB's. Then we apply the algorithm to the simulated radar data with MTRC errors. The proposed algorithm generates 'focused' point image with higher resolution, which conforms the algorithm and the data model.
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Abstract: In this paper, we establish a data model for the feature extraction of point scatterers in the presence of motion through resolution cell (MTRC) errors and unknown noise, the data model is a su...
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Abstract: In this paper, we establish a data model for the feature extraction of point scatterers in the presence of motion through resolution cell (MTRC) errors and unknown noise, the data model is a sum of 2-dimensional sinusoidal signals with quadratic phase errors, which are caused by 'range walk' and 'variable range rate' respectively. Based on the data model, we propose a parametric RELAX-based algorithm to extract the target features when there are MTRC errors in radar imaging. The algorithm minimizes a complicated nonlinear least-squares (NLS) cost function, and it is performed alternately by letting only the parameters and errors of one scatterer vary and freezing all others at their most recently determined values. The Cramer-Rao bounds (CRB's) for the parameters of the data model are also derived. We compare the performance of the proposed algorithm with the CRB's by simulation. And the results show that the mean squared errors of the parameter estimates obtained by the algorithm can approach the corresponding CRB's. Then we apply the algorithm to the simulated radar data with MTRC errors. The proposed algorithm generates 'focused' point image with higher resolution, which conforms the algorithm and the data model. !7
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To resolve the problem of how to determine the proper number of the mixture models for radar high-resolution range profile (HRRP) target recognition. This paper develops a variational Bayesian mixture of factor analyzers (VBMFA) m...
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To resolve the problem of how to determine the proper number of the mixture models for radar high-resolution range profile (HRRP) target recognition. This paper develops a variational Bayesian mixture of factor analyzers (VBMFA) model. This method can automatically determine the optimal number of models by birth-death moves and can accurately describe the statistical characteristics of HRRP. So the VBMFA method should have better recognition performance than factor analysis and mixtures of factor analyzers method, and experimental results for measured data proved this conclusion.
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A region-based variational model for color image segmentation is proposed using the chromaticity-brightness decomposition. By this decomposition, we extend the Wasserstein distance based method to color images. The chromaticity te...
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A region-based variational model for color image segmentation is proposed using the chromaticity-brightness decomposition. By this decomposition, we extend the Wasserstein distance based method to color images. The chromaticity term of the proposed functional follows the data term of the color Chan-Vese model with constraint on unit sphere, and the brightness term is formulated by the Wasserstein distance between the computed probability density function in the local windows (e.g. 3 by 3 or 5 by 5 window) and its estimated counterparts in classified regions. Experimental results on synthetic and real color images show that the proposed method performs well for the segmentation of different image regions.
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A region-based variational model for color image segmentation is proposed using the chromaticity-brightness decomposition. By this decomposition, we extend the Wasserstein distance based method to color images. The chromaticity te...
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A region-based variational model for color image segmentation is proposed using the chromaticity-brightness decomposition. By this decomposition, we extend the Wasserstein distance based method to color images. The chromaticity term of the proposed functional follows the data term of the color Chan-Vese model with constraint on unit sphere, and the brightness term is formulated by the Wasserstein distance between the computed probability density function in the local windows (e.g. 3 by 3 or 5 by 5 window) and its estimated counterparts in classified regions. Experimental results on synthetic and real color images show that the proposed method performs well for the segmentation of different image regions.
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The noise in natural images sometimes changes according to imaging mechanism or local image information. This is called spatially varying noise. It is obvious that classical variational denoising algorithms such as the Rudin-Osher...
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The noise in natural images sometimes changes according to imaging mechanism or local image information. This is called spatially varying noise. It is obvious that classical variational denoising algorithms such as the Rudin-Osher-Fatemi model are not suitable for this kind of noise. We propose a variational method to remove this spatially varying noise based on the estimation of local variance for a given image, such that high noise regions are smoothed meanwhile the textures and certain details in low noise regions are preserved. Moreover, we give the proof of existence of the minimizer of our proposed functional. The experimental results show visual improvement and high signal-to-noise ratio over other variational denoising models.
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摘要 :
The noise in natural images sometimes changes according to imaging mechanism or local image information. This is called spatially varying noise. It is obvious that classical variational denoising algorithms such as the Rudin-Osher...
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The noise in natural images sometimes changes according to imaging mechanism or local image information. This is called spatially varying noise. It is obvious that classical variational denoising algorithms such as the Rudin-Osher-Fatemi model are not suitable for this kind of noise. We propose a variational method to remove this spatially varying noise based on the estimation of local variance for a given image, such that high noise regions are smoothed meanwhile the textures and certain details in low noise regions are preserved. Moreover, we give the proof of existence of the minimizer of our proposed functional. The experimental results show visual improvement and high signal-to-noise ratio over other variational denoising models.
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