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Blind image deblurring, i.e., estimating a blur kernel from a single input blurred image is a severely ill-posed problem. In this paper, we show how to effectively apply low rank prior to blind image deblurring and then propose a new algorithm which combines salient edges and low rank prior. Salient edges provide reliable edge information for kernel estimation, while low rank prior provides data-authentic...
Fuzzy c-regression models (FCRM) give us multiple clusters and regression models of each cluster simultaneously, while support vector regression models (SVRM) involve kernel methods which enable us to analyze non-linear structure of the data. We combine these two concepts and propose the united fuzzy c-support vector regression models (FC-SVRM). In case that c is unknown, we introduce sequential regression...
Vast amounts of valuable historical documents exist in libraries and in various National Archives that have not been exploited electronically. The analysis of historical documents presents specific difficulties with respect to other types of handwritten documents. Because of the low quality and the complexity of these documents, the document analysis remains an open research field. One of the major...
For the information system, Database acts an important role in it. How to find a way to audit the operation of the database is becoming more and more important. An effective database auditing system can not only reduce the potential security risk, also make it possible to trace the source when errors happen. In this paper, we design and implement an effective audit framework. To avoid causing any...
Karyote segmentation, a significant procedure to computer aided leukemia diagnosis, constitutes a difficult problem due to the morphological complexity, categorical diversity and distributional density of bone marrow cells. Most of the current techniques are based on the internal consistency of the karyote's intensities with little or none considerations on color or texture properties. However, neither...
In this paper, we propose a fast online learning framework for landmark recognition based on single hidden layer feedforward neural networks (SLFNs). Conventional landmark recognition frameworks generally assume that all images are available at hand to train the classifier. However, in real world applications, people may encounter the issue that the classifier built on the existing landmark dataset...
In recent 15 years, the biodiversity of Chongming Dongtan national nature reserve has been dramatically reduced by invasive plants, especially by spartina alterniflora. How to obtain and monitor spatio-temporal change of spartina alterniflora has great practical significance in managing and protecting Chongming Dongtan. Therefore, the main purpose of this paper was to build up a method to recognize...
Image edge detection is one of the most important foundations of image analysis and comprehension. Traditional edge detectors are not robust enough to illumination variations. In last decade, detector based on Phase Congruency (PC) model was proposed and invariant to illumination variations. In this paper, we introduce the quaternion Wavelet Transform to PC model and propose a new edge metric of quaternion...
Traditional OTA (Online Travel Agent) is challenged by the Internet and mobile business with the evolution of information. The precise forecasting of ticket sales in OTA companies is beneficial to budget control and service quality. The paper develops an integrated forecasting model by combining the internal factors immediately influencing the ticket sales and the external factors reflecting the ticket...
Volterra series are used for modelling nonlinear systems with memory effects. The nth-order impulse response and the kernels in the series can be determined with Fréchet derivatives of Volterra series operators. Consequently, we can determine the kernels of composite systems by taking higher-order Fréchet derivatives of composite series. The generalisation of the higher-order chain rule, Faà di Bruno's...
Conventional PCA method for fault detection and diagnosis has a high rate of false positives and false negatives due to most industrial processes with nonlinear and non-Gaussian characteristics. For a class of the problem of fault detection and diagnosis associated with nonlinear and non-Gaussian, an improved wavelet kernel KPCA method is proposed, It adopts kernel principal component analysis methods...
Paraphrase detection has several important applications in natural language processing. Examples of such applications include language translation, text summarization, question answering, plagiarism detection, and online information retrieval. A number of metrics have been proposed in the literature to quantify the textual similarity between two sentences. However, the accuracy of utilizing each similarity...
We propose a new primal-dual interior-point algorithm for semidefinite optimization(SDO) based on an eligible barrier function. New search directions and proximity measures are proposed based on the barrier function. We show that the algorithm has Ο(n log(n/ε)) and Ο(√n(log n)log(n/ε)) complexity results for small- and large-update methods, respectively. These are the best known complexity results...
Solving a multiple-valued problem means to assign values to a given set of multiple-valued variables such that certain conditions are satisfied. The solution of a multiplevalued problem is a subset of vk v-valued tuples of the length k, where k is the number of variables and v is the number of their possible values. This paper compares several approaches which solve such problems. These approaches...
Ship motion prediction plays a prominent role in the whole ship motion process. This paper presents a new approach for ship motion prediction. In order to obtain more effective prediction result, the paper studied the BP neural network and Volterra series model, and the chaos characteristics of ship motion time series. A novel method of single-output three-layer BP neural network to identify Volterra...
We investigated the nonstationary properties of cerebral hemodynamics by examining the relationship between mean arterial blood pressure (MABP), end-tidal CO2 tension (Petco2) and middle cerebral artery blood flow velocity (CBFV) during resting and hypercapnic conditions. To this end, we employed univariate and multivariate time-varying Laguerre-Volterra models as well as a Recursive Least Squares...
Due to process disturbances and some uncertainties, the process operating performance will deviate from the optimal operating point along with time, so it is very important to develop strategies for online operating performance assessment on optimality. However, a little work has been published in this research area to our knowledge. In this study, a new online operating optimality assessment method...
One-class support vector algorithms such as OCSVM and SVDD have been successfully applied to many One-Class Classification (OCC) problems. Many authors assume that kernels like the ones used in standard binary SVM classification are also appropriate to one-class classification. However, a review of the literature indicated that in general, only the Gaussian RBF kernel gives satisfactory results in...
The task of One-Class Classification (OCC) is to characterise a single class that is well described by the training data and distinguish it from all others; this is in contrast to the more common approach of binary classification or multi-class classification, in which all classes are well described by the training data. One-class support vector machine algorithms such as OCSVM and SVDD have been...
Coarse-Grained Reconfigurable Architecture (CGRAs) are a promising parallel architecture with both high performance and high power-efficiency. Inner loop pipelining and outer loop merging techniques are usually used to improve the execution performance when mapping loops ontoCGRA. However, the number of concurrently executable operators (CEOs) from the kernel still can not make the best of PEs in...
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