The latest developments in Markov models' theory and their corresponding computational techniques have opened new rooms for image and signal modeling. In particular, the use of Dempster-Shafer theory of evidence within Markov models has brought some keys to several challenging difficulties that the conventional hidden Markov models cannot handle. These difficulties are concerned mainly with two situations: multisensor data, where the use of the Dempster-Shafer fusion is unworkable; and nonstationary data, due to the mismatch between the estimated stationary model and the actual data. For each of the two situations, the Dempster-Shafer combination rule has been applied, thanks to the triplet Markov models' formalism, to overcome the drawbacks of the standard Bayesian models. However, so far, both situations have not been considered in the same time. In this article, we propose an evidential Markov chain that uses the Dempster-Shafer combination rule to bring the effect of contextual information into segmentation of multisensor nonstationary data. We also provide the Expectation-Maximization parameters' estimation and the maximum posterior marginal's restoration procedures. To validate the proposed model, experiments are conducted on some synthetic multisensor data and noised images. The obtained segmentation results are then compared to those obtained with conventional approaches to bring out the efficiency of the present model.

Abstract-Hidden Markov models are very robust and have been widely used in a wide range of application fields; however, they can prove some limitations for data restoration under some complex situations. These latter include cases when the data to be recovered are nonstationary. The recent triplet Markov models have overcome such difficulty thanks to their rich formalism, that allows considering more complex data structures while keeping the computational complexity of the different algorithms linear to the data size. In this letter, we propose a new triplet Markov chain that allows the unsupervised restoration of random discrete data hidden with switching noise distributions. We also provide genuine parameters estimation and MPM restoration algorithms. The new model is validated through experiments conducted on synthetic data and on real images, whose results show its interest with respect to the standard hidden Markov chain.

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Z i N et wo rk S ou rce Z 0 Z 1 N o d al 0 N o d a l i N o d a l 1 A ft e r t h e fi lt e r Be fo r e t h e f il te r Z lAbstract-Power by wire concept is a novel trend in modern electrical systems. Hence, system networks have become more and more sophisticated, and their modeling is no longer an easy task. In this paper, we propose an analytical analysis of a global EMC of a modern electrical network. Explicitly, we develop an analytical model to evaluate differential mode impedance at different sites of a complex network. To this end, we use a Transmission Line "TL" theory applied in the case of bifilar cables. Bifilar cable parameters, such as the propagation constant and the characteristic impedance, are identified by measurement via the impedance analyzer Agilent 4294A. Such parameters are included into the analytical model for the computation process. The impedances, computed at different sites are compared to each other, and on the same time, to the impedance of Line Impedance Stabilization Network (LISN) in accordance with the CISPR 22 standard. To assess the model performance, we have carried out an experimental test bench. The measured impedances agree with those computed analytically.

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