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MLP_DoA module, being an integral part of the smart TWAA DoA subsystem, intended for fast DoA estimation is proposed. Multilayer perceptron network is used to create the MLP_DoA module that provides a radio gateway location in azimuthal plane at its output when a spatial correlation matrix, found by receiving the radio gateway signal using two-element textile wearable antenna array, is on its input. MLP_DoA network training with monitoring the generalization capabilities on the validation set of samples is applied. The accuracy of the proposed modeling approach is compared to the classical approach in MLP_DoA module training previously developed by the authors. Comparison of the presented ANN model with the root MUSIC algorithm in terms of accuracy and program execution time is also done.

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In this paper a lumped element model of RF MEMS capacitive switches which is scalable with the lateral dimensions of the bridge is proposed. The dependence of the elements of the model on the bridge dimensions is introduced by using one or more artificial neural networks to model the relationship between the bridge dimensions and the inductive and resistive elements of the lumped element model. The achieved results show that the developed models have a good accuracy over the whole considered range of the bridge dimension values.