Machine Learning-Based Microwave Techniques for Dielectric Material Classification - Archive ouverte HAL
Communication Dans Un Congrès Année : 2024

Machine Learning-Based Microwave Techniques for Dielectric Material Classification

Résumé

This paper presents two innovative Microwave Non-Destructive Testing and Evaluation (MNDT&E) techniques designed specifically for characterizing planar dielectric materials, regardless of their thickness. These techniques involve measuring the reflection coefficient parameters S11 of the materials using two separate microwave characterization instruments: a monostatic free-space radar and an open-ended rectangular waveguide (OERW). Our objective is to develop a compact, low-power, fast instrument for classifying and evaluating the materials sensed by microwaves across a frequency range varying from 3.95 to 5.85 GHz. These approaches coupled with machine learning (ML) models, are employed and validated within two distinct environmental settings: controlled laboratory conditions and more challenging real-world noisy conditions. Furthermore, a comparative performance analysis is conducted between the two proposed techniques.
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Dates et versions

hal-04692092 , version 1 (01-10-2024)

Identifiants

Citer

Nawal Alsaleh, Denis Pomorski, Mohamed Sebbache, Kamel Haddadi. Machine Learning-Based Microwave Techniques for Dielectric Material Classification. 2024 IEEE Symposium on Wireless Technology & Applications (ISWTA), Jul 2024, Kuala Lumpur, Malaysia. pp.250-253, ⟨10.1109/ISWTA62130.2024.10651951⟩. ⟨hal-04692092⟩
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