. Improving the performances of a high Tc superconducting circular microstrip antenna with multilayered configuration and anisotropic dielectrics. Progress In Electromagnetics Research, EMW Publishing, 2010, vol. 18, pp 169-183. Progress In Electromagnetics Research C, Vol. 18, 169-183, 2011 IMPROVING Abstract-The moment method technique has been improved to investigate the scattering properties of high T c superconducting circular antennas with anisotropic substrate in multi-layered configuration. In this method, the electric field integral equation for a current element on a grounded dielectric slab of infinite extent was developed by basis functions involving Chebyshev polynomials. An improved analytical model is presented taking into account anisotropic substrate, superconducting material for the circular patch and multilayered structure. To validate the theoretical results, an experimental study has been performed for a perfectly conducting circular patch on a single layer, with and without air gap. Good agreements were obtained between our theory and measurements. Effects of temperature and thickness of a superconducting film are also reported and discussed. The performances of high T c superconducting circular antennas were improved by the use of uniaxial anisotropy substrate and multilayer configuration.
ABSTRA CT:Closed-form expressions for two kinds of Hankel transform integrals, which are encountered in the spectral moment method solution of a circular patch, are obtained. Application of the newly obtained formulas alleviates dramatically the algebraic work for determining the Hankel transforms of the current basis functions involving Chebyshev polynomials and edge condition. Computed moment method results using these expressions are presented. The effects of both uniaxial anisotropy in the substrate on the resonant frequency and bandwidth are investigated.
Feed for word neural networks (FFNN) have attracted a great attention, in digital communication area. Especially they are investigated as nonlinear equalizers at the receiver, to mitigate channel distortions and additive noise. The major drawback of the FFNN is their extensive training. We present a new approach to enhance their training efficiency by adapting the activation function. Adapting procedure for activation function extensively increases the flexibility and the nonlinear approximation capability of FFNN. Consequently, the learning process presents better performances, offers more flexibility and enhances nonlinear capability of NN structure thus the final state kept away from undesired saturation regions. The effectiveness of the proposed method is demonstrated through different challenging channel models, it performs quite well for nonlinear channels which are severe and hard to equalize. The performance is measured throughout, convergence properties, minimum bit error achieved. The proposed algorithm was found to converge rapidly, and accomplish the minimum steady state value. All simulation shows that the proposed method improves significantly the training efficiency of FFNN based equalizer compared to the standard training one.
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