Design of integrated power systems requires prototype-less approaches. Accurate simulations are necessary for analysis and verification purposes. Simulation relies on component models and associated parameters. The paper focuses on a step-by-step extraction procedure for the design parameters of a one-dimensional finite-element-method (FEM) model of the PiN diode. The design parameters are also available for diverse physics-based analytical models. The PiN diode remains a complex device to model particularly during switching transients. The paper demonstrates that a simple FEM model may be considered unknowingly of the device exact technology. Heterogeneous simulation is illustrated. The state-of-art of parameter extraction methods is briefly recalled. The proposed procedure is detailed. The diode model and extracted parameters are systematically validated from electro-thermal point-of-view. Validity domains are discussed.

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The paper addresses a simple and fast new approach to implement Artificial Neural Networks (ANN) models for the MOS transistor into SPICE. The proposed approach involves two steps, the modeling phase of the device by NN providing its input/output patterns, and the SPICE implementation process of the resulting model. Using the Taylor series expansion, a neural based small-signal model is derived. The reliability of our approach is validated through simulations of some circuits in DC and small-signal analyses

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Abstract-Image registration is a key, essential element in analysis of Remote sensing images. Registration is critical both for initial processing and for end-user processing of those image products for data fusion, and change detection. This paper focused on the feature-based category of image registration algorithms. Many techniques for the detection and description of images' local characteristics have been proposed to register a set of images without user intervention. However, it is unclear which descriptors are more appropriate. The descriptors should be distinctive and at the same time robust to changes in viewing conditions as well as to errors of the sensor. In our evaluation, we have separated the detector from the descriptor as their performance depends on the interest point detector used. The descriptors are compared according to their recall and runtime efficiency and this deals with several geometric and photometric changes. We also propose an extension to the SURF descriptor and the results show the effectiveness of proposed improvements compared to base SURF method. Furthermore, we observe that the SURF descriptor outperforms the others' descriptors. Finally, based on the test results, we propose an approach to register automatically remotely sensed images.