Power characteristics of dielectric optical piet hein waveguides

A fairly rigorous analytical treatment of the power characteristics of dielectric optical waveguides with Piet Hein core-cross sectional geometry is presented in this paper. This kind of wareguide structure would be advantageous owing to the absence of corners, which are found in rectangular guides, resulting in undesirable loss (hit to the scattering of light. In order to simplify this theoretical approach. em approximation of vanishing refractive index difference between the guiding and the non-guiding sections is implemented. The variation eJ logarithmic power is shown for different dimensions of the core, corresponding to different azimuthal modal indices. It is found that the nutlet with higher index values carry less logaritlunic power in the lower tail of the propagation 's constant range, and this feature affects the higher tail. A better kind of uniformity of the power distribution is observed near the higher tail of the range of propagation Constants

ON APPROXIMATION METHODS FOR UNBOUNDED OPERATORS

Department of Mathematics, Cochin University of Science and Technology

An approximation method for evaluating centrifugal distortion constants in XH2 bent symmetric molecules

University of Cochin

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.