Design of Circular and Rectangular Patch Antennas Using Teflon and Rogers Substrates for 5G Applications at 25 GHz

2024-04-16T06:36:32+00:00

This paper presents a comprehensive study on the design and performance evaluation of circular and rectangular microstrip patch antennas tailored for 5G applications, operating at 25 GHz. Utilizing Teflon and Rogers substrates with superior dielectric properties, the antennas are meticulously crafted and rigorously analyzed. The study explores the design methodology, and simulation outcomes, and compares circular and rectangular patch antennas, focusing on their suitability for 5G systems. Through iterative simulations and optimizations, the antennas are fine-tuned to achieve optimal performance parameters, including impedance matching, radiation efficiency, and bandwidth, ensuring their efficacy in high-frequency communication environments.

The design methodology involves precise calculations for optimal antenna dimensions, considering substrate material properties, dielectric constants, and desired radiation characteristics. Performance metrics such as peak directivity, gain, efficiency, and bandwidth are comprehensively evaluated and compared for both Teflon and Rogers substrates. This investigation contributes to advancing microstrip patch antenna design for 5G, elucidating the interplay between substrate materials, antenna geometries, and performance characteristics. The findings not only enhance our understanding of antenna design principles but also lay the groundwork for further optimization and refinement of microstrip patch antennas for future high-frequency communication systems.

Comparative Analysis of Machine Learning Approaches for Optimizing Rainfall Prediction for Enhanced Agricultural Sustainability

2024-03-29T11:05:41+00:00

This study focuses on improving and enhancing agricultural output and reducing the negative effects of unpredictability in the weather on food and water security, the purpose of this study is to develop methods for predicting daily rainfall. The purpose of this research is to discover major atmospheric factors that influence rainfall and to forecast its daily occurrence. The implementation of machine learning and deep learning strategies will be utilized to achieve this goal. Data from Kaggle, specifically the "Rainfall in India from 2005-2020" dataset, is utilized to evaluate the effectiveness of Multivariate Linear Regression, Random Forest, and Artificial Neural Network algorithms. The Pearson correlation technique aids in selecting relevant environmental variables for model inputs. The performance of the model is evaluated using evaluation metrics such as root mean squared error and mean absolute error, and the results indicate that the Artificial Neural Network method has a superior predictive capability. This study not only contributes to a better understanding of the complex atmospheric dynamics that are responsible for the patterns of rainfall, but it also has practical implications for improving the management of water resources and enhancing the resilience of agricultural systems in the face of climate change.

Journal of Advance Electrical Engineering and Devices

Design of Circular and Rectangular Patch Antennas Using Teflon and Rogers Substrates for 5G Applications at 25 GHz

Comparative Analysis of Machine Learning Approaches for Optimizing Rainfall Prediction for Enhanced Agricultural Sustainability