In this paper, it is attempted to approach a fast efficient algorithm for solving the famous Hallen's and Pocklington's integral equations, regarding the current distribution on a finite-length linear thin wire antenna. Here, the conventional moment method in conjunction with wavelet basis functions was used to obtain the current distribution of the antenna. The aim of this work is first to introduce the application of wavelet in electromagnetic scattering, secondly a comparison of the two method of analysis the thin wire antenna. By using the wavelet expansion, wavelets as basis and testing functions, a sparse matrix is generated from the previous moment method dense matrix. A sparsely filled matrix is easier to store and invert. The result extracted from Pocklington's integral equation gives better convergence at the feeding point, though it takes more time to be computed because of the complexity in Pocklington's equation. Results are compared to the previous work done and published, excellent results are obtained.
Artificial neural networks (ANNs) have recently been introduced in the microwave area as a fast and flexible vehicle to microwave modeling, simulation and optimization. The models are fast and can represent EM/physics behaviors it learnt which otherwise are computationally expensive. In this paper a neural network model is developed for a Pseudomorphic High Electron Mobility Transistor PHEMT (ED02AH-6x30), a transistor of 6 gate fingers, each with a width of 30
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