Classification with electromagnetic waves

Abstract

The integration of neural networks and machine learning techniques has ushered in a revolution in various fields, including electromagnetic inversion, geophysical exploration, and microwave imaging. While these techniques have significantly improved image reconstruction and the resolution of complex inverse scattering problems, this paper explores a different question: Can near-field electromagnetic waves be harnessed for object classification? To answer this question, we first create a dataset based on the MNIST dataset, where we transform the grayscale pixel values into relative electrical permittivity values to form scatterers and calculate the electromagnetic waves scattered from these objects using a 2D electromagnetic finite-difference frequency-domain solver. Then, we train various machine learning models with this dataset to classify the objects. When we compare the classification accuracy and efficiency of these models, we observe that the neural networks outperform others, achieving a 90% classification accuracy solely from the data without a need for projecting the input data into a latent space. The impacts of the training dataset size, the number of antennas, and the location of antennas on the accuracy and time spent during training are also investigated. These results demonstrate the potential for classifying objects with near-field electromagnetic waves in a simple setup and lay the groundwork for further research in this exciting direction.