Auto-Disturbance Rejection Controller (ADRC) has been proved to be a capable replacement of PID with unmistakable advantage in performance and practicality. But it is difficult to obtain a set of optimal parameters, f...
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The existing high-speed wire production line of Wuhan Iron and Steel Group Corporation has the shortage that reliability of water-cooling control system is poor, the temperature of rolling line fluctuation range is la...
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An unsupervised change detection method based on spectral clustering and difference image methods for multitemporal single-channel single-polarization synthetic aperture radar (SAR) images is proposed. The difference ...
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An unsupervised change detection method based on spectral clustering and difference image methods for multitemporal single-channel single-polarization synthetic aperture radar (SAR) images is proposed. The difference image is generated by integrating the typical difference image method with Non-Local Filter, which exploits both the spatial neighborhood information and gray similarity information, and can well reduce the speckle noises of SAR images. The spectral clustering algorithm is employed to cluster the difference image into two clusters and get the change map. Compared with traditional clustering algorithms, such as A-means, SC can recognize the clusters of unusual shapes and obtain the globally optimal solutions. Experimental results confirm the effectiveness of the proposed techniques.
For the special different nature images, we could hardly find particularly desirable approach, and there always exist Gibbs-type artifacts in the results of most methods. A novel Partial Differential Equation (PDE) mo...
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For the special different nature images, we could hardly find particularly desirable approach, and there always exist Gibbs-type artifacts in the results of most methods. A novel Partial Differential Equation (PDE) model is proposed based on image feature for images denoising. The PDE model is adaptive within each region according to the details of the image feature to adjust the size of the diffusion coefficient. So it can be disposed the high gradient noise at the same time better to retain the edge information. We also analyze the performance of the PDE model method. Numerical results show that our algorithm competes favorably with state of the-art TV projection methods to eliminate noise and reduce Gibbs-type artifacts.
In this paper, a novel method for image denoising is proposed which adopts multiscale geometry tool. Firstly the image is decomposed by discrete shearlet transform. The shearlet coefficients of each direction approach...
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In this paper, a novel method for image denoising is proposed which adopts multiscale geometry tool. Firstly the image is decomposed by discrete shearlet transform. The shearlet coefficients of each direction approach the generalized Gaussian distribution. We use the principal component analysis (PCA) for every similarity window of shearlet coefficients. Then we use Generalized Gaussian model of non-local means method to handle the shearlet coefficients. Finally, we reconstruct image with the new shearlet coefficients to obtain the result. Numerical results show that our algorithm competes favorably with nonlocal means algorithms in the case of high noise.
This paper presents a wavelet-based multiscale products scheme for synthetic aperture radar (SAR) image despeckling. A compactly supported quadratic spline function that approximates the first derivative of Gaussian ...
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This paper presents a wavelet-based multiscale products scheme for synthetic aperture radar (SAR) image despeckling. A compactly supported quadratic spline function that approximates the first derivative of Gaussian is employed in the scheme to decompose log-transformed SAR images. The multiplied results of the decomposed coefficients of adjacent scales consist of multiscale products. The multiscale products can sharp the important structures while weakening noise. A spatially selective neighborhood technique by iteratively selecting neighborhood system in the multiscale products is introduced in searching the important structure information. The influence of the spatial information is imposed on the multiscale products, instead of on the wavelet coefficients, which improves the capability of identifying important features. Experiments show that the proposed scheme is better in SAR image despeckling and preserving edges and detail information than other waveletbased multiscale products methods.
In this paper, we focus on the distribution of eigenvalues, and based on Gaussian assumption, then we do an analysis of the eigenvalues potential for POL-SAR classification. Generally, we use Gaussian mixture model to...
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In this paper, we focus on the distribution of eigenvalues, and based on Gaussian assumption, then we do an analysis of the eigenvalues potential for POL-SAR classification. Generally, we use Gaussian mixture model to describe the distribution of the eigenvalue and Bayesian classifier to achieve the POL-SAR pixel classification. The method is tested with the NASA/JPL AIRSAR data.
The capacity for walking is an important assessment to reflect the ability about how the patients who have movement disorders to control their lower limbs. Electroencephalography (EEG), which can describe brain activi...
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In this paper, a nonlinear chaotic system is presented, which is derived from the chua's circuit with a memristor. This is a four-dimensional autonomous circuit which exhibits chaotic behavior. The chaotic system ...
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作者:
Jing ZhongWenzhong LiuZhongzhou DuPaulo C′esar de MoraisQing XiangQingguo XieDepartment of Control Science and Engineering
Huazhong University of Science and TechnologyWuhan 430074People''s Republic of China Key Laboratory of Image Processing and Intelligent ControlHuazhong University of Science and TechnologyWuhan 430074People''s Republic of China Department of Control Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074People''s Republic of China Universidade de BrasiliaInstituto de FisicaNucleo de Fisica AplicadaBrasilia DF 70910-900Brazil School of Life Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074People''s Republic of China
is study describes an approach for remote measuring of on-site temperature and particleconcentration using magnetic nanoparticles (MNPs) via simulation and also *** sensor model indicates that under different applied ...
is study describes an approach for remote measuring of on-site temperature and particleconcentration using magnetic nanoparticles (MNPs) via simulation and also *** sensor model indicates that under different applied magnetic fields, the magnetizationequation of the MNPs can be discretized to give a higher-order nonlinear equation in twovariables that consequently separates information regarding temperature and particleconcentration. As a result, on-site tissue temperature or nanoparticle concentration can bedetermined using remote detection of the magnetization. In order to address key issues in thehigher-order equation we propose a new solution method of the first-order model from theperspective of the generalized inverse matrix. Simulations for solving the equation, as well asto optimize the solution of higher equations, were carried out. In the final section we describea prototype experiment used to investigate the measurement of the temperature in which weused a superconducting magnetometer and commercial MNPs. The overall error after ninerepeated measurements was found to be less than 0.57 K within 310–350 K, with acorresponding root mean square of less than 0.55 K. A linear relationship was also foundbetween the estimated concentration of MNPs and the sample’s mass.
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