Identification of ultra high frequency ...
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
Identification of ultra high frequency acoustic coda waves using deep neural networks
Auteur(s) :
Thati, Venu Babu [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Smagin, Nikolay [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Université Polytechnique Hauts-de-France [UPHF]
Dahmani, Hatem [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Carlier, Julien [Auteur]
Matériaux et Acoustiques pour MIcro et NAno systèmes intégrés - IEMN [MAMINA - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Alouani, Lihsen [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Smagin, Nikolay [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Université Polytechnique Hauts-de-France [UPHF]
Dahmani, Hatem [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Carlier, Julien [Auteur]
Matériaux et Acoustiques pour MIcro et NAno systèmes intégrés - IEMN [MAMINA - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Alouani, Lihsen [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Titre de la revue :
IEEE Sensors Journal
Pagination :
20640-20647
Éditeur :
Institute of Electrical and Electronics Engineers
Date de publication :
2021-09-15
ISSN :
1530-437X
Mot(s)-clé(s) en anglais :
Coda waves
deep neural network
source identification
data processing and augmentation
signal processing
deep neural network
source identification
data processing and augmentation
signal processing
Discipline(s) HAL :
Physique [physics]
Sciences de l'ingénieur [physics]
Sciences de l'ingénieur [physics]
Résumé en anglais : [en]
Due to the multi-path propagation and extreme sensitivity to minor changes in the propagation medium, the coda waves open new fascinating possibilities in the non-destructive evaluation and acoustic imaging. However, their ...
Lire la suite >Due to the multi-path propagation and extreme sensitivity to minor changes in the propagation medium, the coda waves open new fascinating possibilities in the non-destructive evaluation and acoustic imaging. However, their noise-like structure and high spurious sensitivity for ambient conditions (temperature, humidity, and others) make it challenging to perform localized inspection in the overall coda wave evolution. While existing deterministic solutions reach their limit in handling complex data, emerging techniques such as deep learning-based algorithms have shown a promising potential to overcome these limitations. This paper proposes a deep neural network that paves the way to make the complex features of coda waves more handleable to reliably exploit coda waves in several applications even in a changing or unstable environment. Specifically, designed a Coda-Convolutional Neural Network that is able to identify coda waves with 95.65% precision in a silicon chaotic cavity including 5 emitters and 16 receivers using ultra high frequency ultrasonic coda waves.Lire moins >
Lire la suite >Due to the multi-path propagation and extreme sensitivity to minor changes in the propagation medium, the coda waves open new fascinating possibilities in the non-destructive evaluation and acoustic imaging. However, their noise-like structure and high spurious sensitivity for ambient conditions (temperature, humidity, and others) make it challenging to perform localized inspection in the overall coda wave evolution. While existing deterministic solutions reach their limit in handling complex data, emerging techniques such as deep learning-based algorithms have shown a promising potential to overcome these limitations. This paper proposes a deep neural network that paves the way to make the complex features of coda waves more handleable to reliably exploit coda waves in several applications even in a changing or unstable environment. Specifically, designed a Coda-Convolutional Neural Network that is able to identify coda waves with 95.65% precision in a silicon chaotic cavity including 5 emitters and 16 receivers using ultra high frequency ultrasonic coda waves.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Source :