Abstract-The circular disc monopole (CDM) antenna has been reported to yield wide-impedance bandwidth. Experiments have been carried out on a CDM that has twice the diameter of the reported disc with similar results. New configurations are proposed such as elliptical (with different ellipticity ratios), square, rectangular, and hexagonal disc monopole antennas. A simple formula is proposed to predict the frequency corresponding to the lower edge of the bandwidth for each of these configurations. The elliptical disc monopole (EDM) with ellipticity ratio of 1.1 yields the maximum bandwidth from 1.21 GHz to more than 13 GHz for voltage standing wave ratio (VSWR) < 2.Index Terms-Microstrip antennas.

This paper presents the design equations for lower band-edge frequency for all the regular shapes of printed monopole antennas with various feed positions. The length of the feed transmission line is a critical design parameter of these monopole antennas. Design curves for the length of the feed transmission line for various lower band-edge frequencies for all these regular shaped monopoles have been generated. A systematic study has been presented to explain the ultra-wide bandwidth obtained from these antennas with an example of elliptical monopole antenna.

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The time-series forecasting makes a substantial contribution in timely decision-making. In this article, a recently developed eigenvalue decomposition of Hankel matrix (EVDHM) along with the autoregressive integrated moving average (ARIMA) is applied to develop a forecasting model for nonstationary time series. The Phillips-Perron test (PPT) is used to define the nonstationarity of time series. EVDHM is applied over a time series to decompose it into respective subcomponents and reduce the nonstationarity. ARIMA-based model is designed to forecast the future values for each subcomponent. The forecast values of each subcomponent are added to get the final output values. The optimized value of ARIMA parameters for each subcomponent is obtained using a genetic algorithm (GA) for minimum values of Akaike information criterion (AIC). Model performance is evaluated by estimating the future values of daily new cases of the recent pandemic disease COVID-19 for India, USA, and Brazil. The high efficacy of the proposed method is convinced with the results. Index Terms-Autoregressive integrated moving average (ARIMA), COVID-19, eigenvalue decomposition of Hankel matrix (EVDHM), Phillips-Perron test (PPT), time-series forecasting. I. INTRODUCTIONF UTURE value forecasting is a crucial field of data science and automated systems in which a model is developed based on the past observations. The model is utilized to extrapolate the future values. Many methodologies have been developed in past for time-series forecasting. Previously, the autoregressive integrated moving average (ARIMA) has been used in several fields for statistical analysis and data prediction, such as electricity price [1], energy demand [2], vehicle velocity forecasting [3], and stock market price prediction [4]. Other methods, such as deep learning-based method, is also utilized for the time-series forecasting in [5]. Deep-learningbased methodologies need a huge amount of data for training Manuscript

This paper describes a broadband planar rectangular monopole antenna. Both theoretical and experimental investigations have been carried out to draw an analogy between this monopole antenna and a rectangular microstrip antenna. Parametric studies have been carried out to optimize the bandwidth of the planar rectangular monopole antenna. There is good agreement between the theoretical and experimental results. © 2001 John Wiley & Sons, Inc. Microwave Opt Technol Lett 28: 55–59, 2001.