Investigations On A Microstrip Excited Rectangular Dielectric Resonator Antenna

The thesis relates to the investigations carried out on Rectangular Dielectric Resonator Antenna configurations suitable for Mobile Communication applications. The main objectives of the research are to: - numerically compute the radiation characteristics of a Rectangular DRA - identify the resonant modes - validate the numerically predicted data through simulation and experiment 0 ascertain the influence of the geometrical and material parameters upon the radiation behaviour of the antenna ° develop compact Rectangular DRA configurations suitable for Mobile Communication applications Although approximate methods exist to compute the resonant frequency of Rectangular DRA’s, no rigorous analysis techniques have been developed so far to evaluate the resonant modes. In this thesis a 3D-FDTD (Finite Difference Time Domain) Modeller is developed using MATLAB® for the numerical computation of the radiation characteristics of the Rectangular DRA. The F DTD method is a powerful yet simple algorithm that involves the discretimtion and solution of the derivative form of Maxwell’s curl equations in the time domain.

Octagonal Microstrip Patch Antenna For Dual Band Applications

A dual port dual polarized octagonal microstrip patch antenna suitable for dual band applications is discussed theoretically and experimentally. The antenna exhibits good impedance bandwidth, gain and broad radiation patterns. Parameters predicted by the Conformal Finite Difference Time Domain algorithm show good agreement with the simulated results and experimental observations

ARMA modeling of time series based on rational approximation of spectral density function

This study is concerned with Autoregressive Moving Average (ARMA) models of time series. ARMA models form a subclass of the class of general linear models which represents stationary time series, a phenomenon encountered most often in practice by engineers, scientists and economists. It is always desirable to employ models which use parameters parsimoniously. Parsimony will be achieved by ARMA models because it has only finite number of parameters. Even though the discussion is primarily concerned with stationary time series, later we will take up the case of homogeneous non stationary time series which can be transformed to stationary time series. Time series models, obtained with the help of the present and past data is used for forecasting future values. Physical science as well as social science take benefits of forecasting models. The role of forecasting cuts across all fields of management-—finance, marketing, production, business economics, as also in signal process, communication engineering, chemical processes, electronics etc. This high applicability of time series is the motivation to this study.