Title :

Toward a Reliable Estimation of Fluid Concentration using High Frequency Acoustic Waves: A Machine Learning Approach

Author(s) :

Thati, Venu Babu [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Zaaroura, Ibrahim [Auteur]
Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201 [LAMIH]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Toubal, Malika [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]
Harmand, Souad [Auteur]
Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201 [LAMIH]
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]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]

Journal title :

IEEE Sensors Journal

Pages :

8772 - 8780

Publisher :

Institute of Electrical and Electronics Engineers

Publication date :

2022-05-01

ISSN :

1530-437X

English keyword(s) :

Acoustic waves
Machine learning
Fluid concentration estimation
Data processing & augmentation
Deep neural network

HAL domain(s) :

Sciences de l'ingénieur [physics]

English abstract : [en]

Using acoustic waves to estimate fluid concentration is a promising technology due to its practicality and non-intrusive aspect, especially for medical applications. The existing approaches are exclusively based on the ...
Show more >Using acoustic waves to estimate fluid concentration is a promising technology due to its practicality and non-intrusive aspect, especially for medical applications. The existing approaches are exclusively based on the correlation between the reflection coefficient and the concentration. However, these techniques are limited by the high sensitivity of the reflection coefficient to environment conditions changes, even slight ones. This introduces inaccuracies that cannot be tolerated in medical applications. This paper proposed a deep learning model, Fluid Concentration Estimation Convolutional Neural Network (FCE-CNN), to estimate fluid concentration. Instead of using only the reflection coefficient, we train our model to detect concentration-related patterns based on the whole received acoustic signal. FCE-CNN shows promising results that overcome the state-of-the-art limitations. Specifically, our model that is able to estimate fluid concentration with 98.5% accuracy using ultra high frequency acoustic waves.Show less >

Language :

Anglais

Popular science :

Non

Comment :

dataset associé: This article includes datasets hosted on IEEE DataPort(TM) DOI: 10.21227/8nnk-kd35dataset name: FLUID CONCENTRATION ESTIMATION

Collections :

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

Source :

Harvested from HAL