Fluid concentration estimation
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 environmental 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.
The entire data is splited into two zip files
FCECNN_1 has 564 files
FCE_CNN_2 has 500 files
total 1064 files
- First 564 files of data from 1064 files FCECNN_1.zip (22.83 MB)
- rest 500 files of 1064 files FCECNN_2.zip (21.21 MB)