Electromagnetic Micro-Structure Non-Destructive Testing: Sparsity-Constrained and Combined Convolutional Recurrent Neural Network Methods

Abstract

How to locate missing rods within a micro-structure composed of a grid-like, finite set of infinitely long circular cylindrical dielectric rods under the sub-wavelength condition is investigated. Sub-wavelength distances between adjacent rods and sub-wavelength rod diameters require super-resolution, beyond the Rayleigh criterion. Two different methods are proposed to achieve this: One builds upon the multiple scattering expansion method (MSM), and it enforces strong sparsity-prior information. The other is a data-driven method that combines convolutional neural networks (CNN) and recurrent neural networks (RNN), and it can be applied in effect with little knowledge of the wavefield interactions involved, in much contrast with the previous one. Comprehensive numerical simulations are proposed in terms of the missing rod number, shape, the frequency of observation, and the configuration of the tested structures. Both methods are shown to achieve suitable detection, yet under more or less stringent conditions as discussed.