Top Downloaded Article on Wiley

Our paper has been chosen as one of the most downloaded article on Wiley during its first 12 months publication in the International Journal of RF and Microwave Computer-aided Engineering.First published: 27 July 2020Citations on Google Scholar: 25 A review on the design and optimization of antennas using machine learning algorithms and techniquesDownload

Machine Learning-Aided Design of Dielectric-Filled Slotted Waveguide Antennas With Specified Sidelobe Levels

This paper presents the use of machine learning (ML) to facilitate the design of dielectric-filled Slotted Waveguide Antennas (SWAs) with specified sidelobe level ratios (SLR). Conventional design methods for air-filled SWAs require the simultaneous solving of complex equations to deduce the antenna’s design parameters, which typically requires further manual simulation-based optimization to reach the desired... Continue Reading →

A Deep Learning Framework for Breast Tumor Detection and Localization from Microwave Imaging Data

Breast Microwave Imaging (BMI) has emerged as a viable alternative to conventional breast cancer screening techniques due to its favorable features and a higher rate of detection. This paper presents a deep learning framework consisting of deep neural networks with convolutional layers to facilitate the process of tumor detection, localization, and characterization from scattering parameter... Continue Reading →

Design Procedure for Planar Slotted Waveguide Antenna Arrays with Controllable Sidelobe Level Ratio for High Power Microwave Applications

Summary: This article presents a complete design procedure for planar slotted waveguide antennas (SWA). For a desired sidelobe level ratio (SLR), the proposed method provides a pencil shape pattern with a narrow half power beamwidth, which makes the proposed system suitable for high power microwave applications. The proposed planar SWA is composed of only two... Continue Reading →

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