Machine Learning-Based Microwave Techniques for Dielectric Material Classification

Alsaleh, Nawal; Pomorski, Denis; Sebbache, Mohamed; Haddadi, Kamel

Document type :

Communication dans un congrès avec actes

DOI :

10.1109/ISWTA62130.2024.10651951

Title :

Machine Learning-Based Microwave Techniques for Dielectric Material Classification

Author(s) :

Alsaleh, Nawal [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Pomorski, Denis [Auteur]

Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Sebbache, Mohamed [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Circuits Systèmes Applications des Micro-ondes - IEMN [CSAM - IEMN ]
Haddadi, Kamel [Auteur]

Circuits Systèmes Applications des Micro-ondes - IEMN [CSAM - IEMN ]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]

Conference title :

2024 IEEE Symposium on Wireless Technology & Applications (ISWTA)

City :

Kuala Lumpur

Country :

Malaisie

Start date of the conference :

2024-07-20

Publisher :

IEEE

HAL domain(s) :

Physique [physics]
Sciences de l'ingénieur [physics]

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL

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