This paper describes a scheme of model-based remote diagnosis which decreases the onboard computational costs of the diagnostic algorithm by sending I/O-signals over a data network to an off-board component with suffi...
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This paper describes a scheme of model-based remote diagnosis which decreases the onboard computational costs of the diagnostic algorithm by sending I/O-signals over a data network to an off-board component with sufficient computing power. The concept is based on the decomposition of the fault diagnosis task into fault detection and fault identification. In the proposed scheme, the fault detection task is carried out by the onboard component, whereas fault identification is accomplished by the off-board component. The paper describes an experimental evaluation of typical communication restrictions imposed by the data network, like data losses and transmission delay. For discrete-event systems, a new diagnostic algorithm for the off-board component is proposed, which is tolerant against the loss of transmitted data.
A Modified Direct Method for the computation of the Zernike moments is presented in this paper. The presence of many factorial terms, in the direct method for computing the Zernike moments, makes their computation pro...
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In this paper, we propose a novel classification algorithm, called geometrical probability covering (GPC) algorithm, to improve classification ability. On the basis of geometrical properties of data, the proposed algo...
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The paper presents a direct adaptive fuzzy approach for parameter identification and control of unknown nonlinear systems. To prove the performance of the proposed method an autonomous underwater vehicle (AUV) is mode...
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Recently, many new flow control mechanisms derived from classic Kelly model are proposed to solve network congestion problem. They perform well in stability, fairness or robustness. However, Most of them convergence r...
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Biometric Person Authentication such as face, fingerprint, palmprint and signature depends on the quality of image processing. When it needs to be done under a low-resolution image, the accuracy will be impaired. So h...
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The goal of this paper is to present the development of a new image restoration methodology for extracting Magnetic Resonance Images (MRI) from reduced scans in k-space. The proposed approach considers the combined us...
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ISBN:
(纸本)1424402514
The goal of this paper is to present the development of a new image restoration methodology for extracting Magnetic Resonance Images (MRI) from reduced scans in k-space. The proposed approach considers the combined use of Support Vector Machine (SVM) models in a time delayed Bayesian priors framework and Bayesian restoration, in the problem of MR image reconstruction from sparsely sampled k-space, following several different sampling schemes, including spiral and radial. Effective solutions to this problem are indispensable especially when dealing with MRI of dynamic phenomena since then, rapid sampling in k-space is required. The goal in such a case is to make measurement time smaller by reducing scanning trajectories as much as possible. In this way, however, underdetermined equations are introduced and poor image extraction follows. It is suggested here that significant improvements could be achieved, concerning quality of the extracted image, by judiciously applying time delayed SVM priors and Bayesian estimation methods to the k-space data. More specifically, it is demonstrated that SVM neural network techniques could construct efficient time delayed Bayesian priors and introduce them in the procedure of Bayesian restoration. These time delayed Bayesian Priors are independent of specific image properties and probability distributions. They are based on training SVM neural filters to estimate the missing samples of complex k-space and thus, to improve k-space information capacity. Such a neural filter based time delayed Bayesian prior is integrated to the maximum likelihood procedure involved in the Bayesian reconstruction. It is found that the proposed methodology leads to enhanced image extraction results favorably compared to the ones obtained by the Integrated Bayesian MRI reconstruction approach involving simple and non time delayed SVM models priors [7], by the traditional Bayesian MRI reconstruction approach [1] as well as by the pure Neural Network (NN)
This paper presents a cognitive vision based approach to recognize polygons on a natural image. The approach is based on the Visual Feature Array (VFA), which is a cognitive computational model of the mammalian primar...
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