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Subwavelength micro-structure probing by binary-specialized methods: contrast source and convolutional neural networks

Abstract : Time-harmonic transverse-magnetic electromagnetic probing of a grid-like, finite set of infinitely long circular cylindrical dielectric rods affected by missing ones is investigated. Sub-wavelength distances between adjacent rods and subwavelength rod diameters are assumed and it leads to a severe challenge due to need of super-resolution within the present micro-structure, far beyond the Rayleigh criterion. A binary case is focused onto herein: all rods have same permittivity, but an unknown number of them is missing, the aim being to detect those within the resulting damaged micro-structure from far-field data. Two binary-specialized methods are developed to that effect and discussed in depth. One builds upon the iterative contrast source inversion (CSI) with enforcing a binary contrast inside it. The other is set within a machine learning framework and it uses convolutional neural networks (CNN). The CSI version is mostly used as reference for the CNN one. Comprehensive numerical simulations in configurations of interest in terms of organization of the micro-structure, missing rods, frequency of observation, data acquisition and noise are proposed. The binary-specialized CNN method appears powerful, upon proper training as expected, and outperforms the binary-specialized CSI method in terms of computational burden, quality of the probing and versatility.
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https://hal-centralesupelec.archives-ouvertes.fr/hal-02472522
Contributor : Dominique Lesselier <>
Submitted on : Monday, February 10, 2020 - 12:01:40 PM
Last modification on : Saturday, October 3, 2020 - 4:22:14 AM

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Peipei Ran, Yingying Qin, Dominique Lesselier, Mohammed Serhir. Subwavelength micro-structure probing by binary-specialized methods: contrast source and convolutional neural networks. IEEE Transactions on Antennas and Propagation, 2020, IEEE Early Access, ⟨10.1109/TAP.2020.3016175⟩. ⟨hal-02472522⟩

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