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    Fast and accurate solutions of scattering problems involving dielectric objects with moderate and low contrasts
    (IEEE, 2007-08) Ergül, Özgür; Gürel, Levent
    We consider the solution of electromagnetic scattering problems involving relatively large dielectric objects with moderate and low contrasts. Three-dimensional objects are discretized with Rao-Wilton-Glisson functions and the scattering problems are formulated with surface integral equations. The resulting dense matrix equations are solved iteratively by employing the multilevel fast multipole algorithm. We compare the accuracy and efficiency of the results obtained by employing various integral equations for the formulation of the problem. If the problem size is large, we show that a combined formulation, namely, electric-magnetic current combined-field integral equation, provides faster iterative convergence compared to other formulations, when it is accelerated with an efficient block preconditioner. For low-contrast problems, we introduce various stabilization procedures in order to avoid the numerical breakdown encountered in the conventional surface formulations. © 2007 IEEE.
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    Synthetic TEC mapping with ordinary and universal kriging
    (IEEE, 2007-06) Sayın, I.; Arıkan, F.; Arıkan, Orhan
    Spatiotemporal variations in the ionosphere affects the HF and satellite communications and navigation systems. Total Electron Content (TEC) is an important parameter since it can be used to analyze the spatial and temporal variability of the ionosphere. In this study, the performance of the two widely used Kriging algorithms, namely Ordinary Kriging (OrK) and Universal Kriging (UnK), is compared over the synthetic data set. In order to represent various ionospheric states, such as quiet and disturbed days, spatially correlated residual synthetic TEC data with different variances is generated and added to trend functions. Synthetic data sampled with various type of sampling patterns and for a wide range of sampling point numbers. It is observed that for small sampling numbers and with higher variability, OrK gives smaller errors. As the sample number increases, UnK errors decrease faster. For smaller variances in the synthetic surfaces, UnK gives better results. For increasing variance and decreasing range values, usually, the errors increase for both OrK and UnK. © 2007 IEEE.