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 →

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