Next Article in Journal
A Low Complexity System Based on Multiple Weighted Decision Trees for Indoor Localization
Next Article in Special Issue
The Different Sensitive Behaviors of a Hydrogen-Bond Acidic Polymer-Coated SAW Sensor for Chemical Warfare Agents and Their Simulants
Previous Article in Journal
Biosensing with Förster Resonance Energy Transfer Coupling between Fluorophores and Nanocarbon Allotropes
Previous Article in Special Issue
Passive Acoustic Source Localization at a Low Sampling Rate Based on a Five-Element Cross Microphone Array
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(6), 14788-14808; doi:10.3390/s150614788

Design of a Thermoacoustic Sensor for Low Intensity Ultrasound Measurements Based on an Artificial Neural Network

1
and
1,2,*
1
Faculty of Engineering, University of Alberta, Edmonton, AB T6G 2V4, Canada
2
Canadian National Research Council National Institute for Nanotechnology, Edmonton, AB T6G 2M9, Canada
*
Author to whom correspondence should be addressed.
Academic Editor: Gerhard Lindner
Received: 12 March 2015 / Revised: 11 June 2015 / Accepted: 17 June 2015 / Published: 23 June 2015
(This article belongs to the Special Issue Acoustic Waveguide Sensors)
View Full-Text   |   Download PDF [1232 KB, uploaded 23 June 2015]   |  

Abstract

In therapeutic ultrasound applications, accurate ultrasound output intensities are crucial because the physiological effects of therapeutic ultrasound are very sensitive to the intensity and duration of these applications. Although radiation force balance is a benchmark technique for measuring ultrasound intensity and power, it is costly, difficult to operate, and compromised by noise vibration. To overcome these limitations, the development of a low-cost, easy to operate, and vibration-resistant alternative device is necessary for rapid ultrasound intensity measurement. Therefore, we proposed and validated a novel two-layer thermoacoustic sensor using an artificial neural network technique to accurately measure low ultrasound intensities between 30 and 120 mW/cm2. The first layer of the sensor design is a cylindrical absorber made of plexiglass, followed by a second layer composed of polyurethane rubber with a high attenuation coefficient to absorb extra ultrasound energy. The sensor determined ultrasound intensities according to a temperature elevation induced by heat converted from incident acoustic energy. Compared with our previous one-layer sensor design, the new two-layer sensor enhanced the ultrasound absorption efficiency to provide more rapid and reliable measurements. Using a three-dimensional model in the K-wave toolbox, our simulation of the ultrasound propagation process demonstrated that the two-layer design is more efficient than the single layer design. We also integrated an artificial neural network algorithm to compensate for the large measurement offset. After obtaining multiple parameters of the sensor characteristics through calibration, the artificial neural network is built to correct temperature drifts and increase the reliability of our thermoacoustic measurements through iterative training about ten seconds. The performance of the artificial neural network method was validated through a series of experiments. Compared to our previous design, the new design reduced sensing time from 20 s to 12 s, and the sensor’s average error from 3.97 mW/cm2 to 1.31 mW/cm2 respectively. View Full-Text
Keywords: Low Intensity Pulsed Ultrasound (LIPUS); thermoacoustic sensor; ultrasound intensity measurement; artificial neural network Low Intensity Pulsed Ultrasound (LIPUS); thermoacoustic sensor; ultrasound intensity measurement; artificial neural network
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Xing, J.; Chen, J. Design of a Thermoacoustic Sensor for Low Intensity Ultrasound Measurements Based on an Artificial Neural Network. Sensors 2015, 15, 14788-14808.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top