Electromagnetic Classification
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Abstract
Rather than reconstructing the properties or parameters of a medium as it is done in electromagnetic inversion, this work aims to classify objects with neural networks that are trained with scattered field data and labels (classes). The study demonstrates the feasibility of achieving an 86% accuracy, showcasing potential applications in robotics and environmental perception.