Abstract-In this paper we propose an optimal routing method for cars in car navigation system. The proposed method finds the paths with a combination of Divide and Conquer method and Ant Colony algorithm. In order to do this, the road network is divided to small areas. Then the learning operation is done in these small areas. Then different learnt paths are combined together to make the complete paths. This method causes balance and reduces the traffic in lanes of the pathes, because it not only consider lengths of the paths for learning operations, it also considers the other factor which is traffic condition of the lanes of the paths. Consequently this method reduces the average triptime of the cars in comparison with existing Ant Colony, Genetic and Dijkstra algorithms by reducing and balancing of road traffic. Therefore, some improvements in the average traveling time of vehicles are achieved. Also, this results in short and precise paths and learning stage becomes faster.

Among the large number of Synthetic Aperture Radar (SAR) image despeckling approaches existing in literature, Non-Local (NL) filters have received a desirable boost. However, often NL approaches define the similarity critirion based on model assumptions, such as a fully developed speckle model. This assumption may not be verified in high-resolution images of urban environments. To address this issue, a stand-alone model-free despeckling framework is proposed in this paper. The presented approach provides a generic framework for denoising a variety of SAR products, from a single intensity/amplitude image to polarimetric and interferometric SAR data. In particular, the method is based on the empirical distributional similarity between the patch containing the pixel to be recovered and the patch containing a similar candidate pixel. To decide whether the patches follow a similar distribution, the Kolmogorov-Smirnov test is adapted. Finally, the restoration process aggregates the selected similar pixels based on their relative importance derived from their distribution similarities. To mitigate the blurring effect and preserve the resolution, the inhomogeneity of the ratio image is used to perform the bias reduction step. The designed generic despeckling filter was tested on different products of SAR data. The results show that the method proves to be an unbiased restoration approach and is able to preserve structures and textures. It works fully automatically and efficiently with single and multi-look (and multi-channel) images.

Abstract-Decision making is one of the essential and important fieldsnowadays, by integrating decision making on multi criteria and fuzzy logic, there will be a satisfactory theoretical framework for system evaluation. There exist several methods for multi parametric decision making. In this paper we will introduce a new way and optimal method for multi parametric problems by taking Fuzzy AHP method into consideration. In this method, it is planned to calculate the weight of criteria by the help of AHP analytical steps. Then we will identify the best alternative by the help of the proposed method.Index Terms-Multi criteria decision making (MCDM), AHP, fuzzy AHP.

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Synthetic aperture radar (SAR) interferometry (InSAR) has shown great potential in the monitoring of Earth's surface and detection of the possible slow temporal deformations. Within the framework of multibaseline SAR interferometry, the availability of multiple interferograms obtained from multipass satellite observations can significantly improve the accuracy of the estimated target parameters, i.e., the residual height and the mean deformation velocity. The parameters can be estimated in the maximum likelihood (ML) sense and through the data covariance matrix. However, the presence of artifact and outliers may impair the parameter estimation, specifically when the candidate cells are subject to temporal decorrelation and atmospheric phase noise effects. In this letter, the exploitation of contextual spatial information is proposed to reduce the possible ambiguity and improve the accuracy of ML-based parameter estimation. The proposed approach adds a regularization term (or a constraint) to the ML's model in order to include the information about the scene velocity variation. Hence, the resulted nonconvex optimization is resolved using the graph-cut concept. The method is evaluated using the simulated and two real data sets acquired by Constellation of Small Satellites for Mediterranean basin Observation (COSMO-SkyMed) and Sentinel-1A sensors over Tehran, Iran; and the results are validated using the global positioning system-based measurements.