Mostafa Refat A. Ismail

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For the first time, we present modeling of microwave circuits using artificial neural networks (ANN’s) based on space-mapping (SM) technology. SM-based neuromodels decrease the cost of training, improve generalization ability, and reduce the complexity of the ANN topology with respect to the classical neuromodeling approach. Five creative techniques are(More)
We propose, for the first time, neural space-mapping (NSM) optimization for electromagnetic-based design. NSM optimization exploits our space-mapping (SM)-based neuromodeling techniques to efficiently approximate the mapping. A novel procedure that does not require troublesome parameter extraction to predict the next point is proposed. The initial mapping(More)
A comprehensive framework to engineering device modeling, which we call generalized space mapping (GSM) is introduced in this paper. GSM permits many different practical implementations. As a result, the accuracy of available empirical models of microwave devices can be significantly enhanced. We present three fundamental illustrations: a basic(More)
We present neural inverse space mapping (NISM) optimization for electromagnetics-based design of microwave structures. The inverse of the mapping from the fine to the coarse model parameter spaces is exploited for the first time in a space mapping algorithm. NISM optimization does not require up-front EM simulations, multipoint parameter extraction, or(More)
We present a novel design framework for microwave circuits. We calibrate coarse models (circuit based models) to align with fine models (full-wave electromagnetic simulations) by allowing some preassigned parameters (which are not used in optimization) to change in some components of the coarse model. Our expanded space-mapping design-framework (ESMDF)(More)
We present a simple new approach to EMbased microwave modeling and design. It is a special case of a novel concept we call Implicit Space Mapping. We propose to calibrate a suitable coarse model against a fine model (full wave EM simulation) by relaxing certain coarse model preassigned parameters. Our algorithm updates these preassigned parameters through(More)
A main challenge facing the law-enforcement and intelligence-gathering environment is accurately and efficiently analyzing the huge volumes of data. Analyzing crime data can be difficult because of recognizing key features and transactions among the large amounts of data, of which only a small section is relevant to illegal process. An intelligent forensic(More)
We present a new computer-aided modeling methodology to develop physicsbased models for passive components. We coherently integrate full-wave electromagnetic simulators, artificial neural networks, multivariable rational functions, dimensional analysis, and frequency mapping to establish broadband models. We consider both frequency-independent and(More)
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