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In this article, a miniaturized rectangular printed antenna that meets the ultrawideband (UWB) characteristics in terms of bandwidth and reflection coefficient is proposed. The designed antenna with dimension of 25×16 mm2 (L × W) was fabricated on FR-4 epoxy dielectric with relative permittivity of 4.4. SMA female connector is used for feeding with characteristic impedance of 50 Ω. The antenna exhibits good UWB characteristics and has the capability of operating between 3.92 to 11.32 GHz. The proposed antenna has omnidirectional radiation pattern on most of the operating band. The measured reflection coefficient of the proposed antenna is compared with the simulated one; good agreement is observed. Also, radiation pattern and gain of the antenna are presented. All simulations are carried out using the EM commercial simulators, HFSS, and CST. © 2015 Wiley Periodicals, Inc. Microwave Opt Technol Lett 57:460–463, 2015

In this paper, a simple waveguide measurement technique is presented to determine the complex permittivity of a dielectric material. The dielectric sample is loaded in a short-circuited rectangular waveguide. The reflection coefficient of the waveguide is measured by Network analyser and calculated as a function of the complex permittivity using theory of transmissions lines. The Newton Raphson method is then used to determine de complex permittivity by matching the calculated value with the measured value. The results obtained using Newton Raphson are in good agreement with the results obtained by the Nicholson-Ross technique.

This paper proposes a generalized radial basis function (GRBF) network to accurately describe drain to source current nonlinearity for intermodulation distortion (IMD) prediction of MESFETs and HEMTs applications in their saturated region. Trying to analytically reproduce the nonlinearities second and third order Taylor-series coefficients, responsible for IMD performance in these devices, may result in a quite difficult task. Neural networks were introduced as a robust alternative for microwave modeling, mostly employing the black-box model type approach of the multilayer perceptron network. The GRBF network we consider is a generalization of the RBF network, which takes advantage of problem dependent information. Allowing different variances for each dimension of input space, the GRBF network makes use of soft nonlinear dependence of the drain to source current derivatives with drain to source voltage for improving accuracy at reduced cost. The network structure and its learning algorithm are presented. Results of its performance are compared to other structures with similar amounts of parameters. Carrier to intermodulation (C/I) predictions validate this approach for precise IMD control versus bias and load in class A amplifiers applications. ©1999 John Wiley & Sons, Inc. Int J RF and Microwave CAE 9: 261–276, 1999.