Identification of Ultra High Frequency Acoustic Coda Waves Using Deep Neural Networks
| Autor: | Venu Babu Thati, Nikolay Smagin, Julien Carlier, Hatem Dahmani, Ihsen Alouani |
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| Přispěvatelé: | Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Matériaux et Acoustiques pour MIcro et NAno systèmes intégrés - IEMN (MAMINA - IEMN), Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520 (IEMN-DOAE), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université Polytechnique Hauts-de-France (UPHF), COMmunications NUMériques - IEMN (COMNUM - IEMN), The authors would like to thank all the members of the CMNF platform at IEMN, UMR CNRS 8520., Renatech Network, Université catholique de Lille (UCL)-Université catholique de Lille (UCL), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA) |
| Rok vydání: | 2021 |
| Předmět: |
data processing and augmentation
Computer science Acoustics Chaotic 01 natural sciences Physics::Geophysics Coda [SPI]Engineering Sciences [physics] Sensitivity (control systems) Electrical and Electronic Engineering signal processing Instrumentation [PHYS]Physics [physics] Signal processing Artificial neural network Coda waves business.industry Deep learning 010401 analytical chemistry deep neural network source identification 0104 chemical sciences Ultra high frequency Ultrasonic sensor Artificial intelligence business |
| Zdroj: | IEEE Sensors Journal IEEE Sensors Journal, Institute of Electrical and Electronics Engineers, 2021, 21 (18), pp.20640-20647. ⟨10.1109/JSEN.2021.3099078⟩ IEEE Sensors Journal, 2021, 21 (18), pp.20640-20647. ⟨10.1109/JSEN.2021.3099078⟩ |
| ISSN: | 2379-9153 1530-437X |
| DOI: | 10.1109/jsen.2021.3099078 |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
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