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In WDM optical networks, the fiber links may share some common physical resources (e.g., cables, conduits) and the consequence is that they have the Correlated Link Failure Probability (CLFP), which denotes the probability of link l failure after link f fails. Based on CLFP, we propose a new dynamic survivable algorithm, called Differentiated Path Shared Protection (DPSP), to protect the double-link failures in WDM optical networks. In DPSP, each connection request can be assigned one working path and additional backup paths according to the differentiated reliable requirements of users. Compared to previous work, DPSP can obtain better performances in resource utilization ratio and blocking probability. Keywords: WDM networks, differentiated reliability, Correlated Link Failure Probability (CLFP), double-link failures, path shared protection. [ABSTRACT FROM AUTHOR]

In this paper, a robust image segmentation method is proposed. The relationship between pixel intensities and distance between pixels are introduced to the traditional neighbourhood potential function To perform an unsupervised segmentation, the Bayes Information Criterion (BIC) is used to determine the class number, the K-means is employed to initialise the classification and calculate the mean values and variances of the classes. The segmentation is transformed to maximize a posteriori(MAP) procedure. Then, the Iterative Conditional Model (ICM) is employed to solve the MAP problem. In the experiments, the proposed method is compared with other segmentation techniques, for noisy image segmentation applying on synthetic and real images. The experiment results shows that the proposed algorithm is the better choice. [ABSTRACT FROM AUTHOR]

On the basis of Markov Random Field (MRF), which uses context information, in this paper, a robust image segmentation method is proposed. The relationship between observed pixel intensities and distance between pixels are introduced to the traditional neighbourhood potential function, which described the probability of pixels being classified into one class. To perform an unsupervised segmentation, the Bayes Information Criterion (BIC) is used to determine the class number. The K-means is employed to initialise the classification and calculate the mean values and variances of the classes. The segmentation is transformed to maximize a posteriori (MAP) procedure. Then, the Iterative Conditional Model (ICM) is employed to solve the MAP problem. In the experiments, the proposed method is adopted with K-means, traditional Expectation-Maximization (EM) and MRF image segmentation techniques, for noisy image segmentation applying on synthetic and real images. The experiment results and the histogram of signal to noise ratio (SNR)-miss classification ratio (MCR) showed that the proposed algorithm is the better choice. [ABSTRACT FROM AUTHOR]