Current Issue : July - September Volume : 2012 Issue Number : 3 Articles : 5 Articles
We show that Brouwer�s fixed point theorem with isolated fixed points is equivalent to Brouwer�s fan theorem....
Construction of three-dimensional structures from video sequences has wide applications for intelligent\r\nvideo analysis. This paper summarizes the key issues of the theory and surveys the recent\r\nadvances in the state of the art. Reconstruction of a scene object from video sequences often takes\r\nthe basic principle of structure from motion with an uncalibrated camera. This paper lists the\r\ntypical strategies and summarizes the typical solutions or algorithms for modeling of complex\r\nthree-dimensional structures. Open difficult problems are also suggested for further study....
This paper investigates the problem on master-salve synchronization for stochastic neural\r\nnetworks with both time-varying and distributed time-varying delays. Together with the driveresponse\r\nconcept, LMI approach, and generalized convex combination, one novel synchronization\r\ncriterion is obtained in terms of LMIs and the condition heavily depends on the upper and lower\r\nbounds of state delay and distributed one. Moreover, the addressed systems can include some\r\nfamous network models as its special cases, which means that our methods extend those present\r\nones. Finally, two numerical examples are given to demonstrate the effectiveness of the presented\r\nscheme....
Medical images often consist of low-contrast objects corrupted by random noise arising in the image acquisition process. Thus,\r\nimage denoising is one of the fundamental tasks required by medical imaging analysis. Nonlocal means (NL-means) method\r\nprovides a powerful framework for denoising. In this work, we investigate an adaptive denoising scheme based on the patch NLmeans\r\nalgorithm for medical imaging denoising. In contrast with the traditional NL-means algorithm, the proposed adaptive\r\nNL-means denoising scheme has three unique features. First, we use a restricted local neighbourhood where the true intensity\r\nfor each noisy pixel is estimated from a set of selected neighbouring pixels to perform the denoising process. Second, the weights\r\nused are calculated thanks to the similarity between the patch to denoise and the other patches candidates. Finally, we apply the\r\nsteering kernel to preserve the details of the images. The proposed method has been compared with similar state-of-art methods\r\nover synthetic and real clinical medical images showing an improved performance in all cases analyzed....
Permeability is a key parameter associated with the characterization of any hydrocarbon reservoir.\r\nIn fact, it is not possible to have accurate solutions to many petroleum engineering problems without\r\nhaving accurate permeability value. The conventional methods for permeability determination\r\nare core analysis and well test techniques. These methods are very expensive and time consuming.\r\nTherefore, attempts have usually been carried out to use artificial neural network for identification\r\nof the relationship between the well log data and core permeability. In this way, recent works on\r\nartificial intelligence techniques have led to introduce a robust machine learning methodology\r\ncalled support vector machine. This paper aims to utilize the SVM for predicting the permeability\r\nof three gas wells in the Southern Pars field. Obtained results of SVM showed that the correlation\r\ncoefficient between core and predicted permeability is 0.97 for testing dataset. Comparing the\r\nresult of SVM with that of a general regression neural network GRNN revealed that the SVM\r\napproach is faster and more accurate than the GRNN in prediction of hydrocarbon reservoirs\r\npermeability....
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