Application of deep neural networks for inferring pressure in polymeric acoustic transponders/sensors
Passive sensor-transponders have raised interest for the last few decades, due to their capability of low-cost remote monitoring without the need for energy storage. Their operating principle includes receiving a signal from a source and then reflecting the signal. While well-established transponder...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Machine Learning with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666827023000300 |
| _version_ | 1827905951097683968 |
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| author | Seyedhamidreza Alaie Subhi J. Al’Aref |
| author_facet | Seyedhamidreza Alaie Subhi J. Al’Aref |
| author_sort | Seyedhamidreza Alaie |
| collection | DOAJ |
| description | Passive sensor-transponders have raised interest for the last few decades, due to their capability of low-cost remote monitoring without the need for energy storage. Their operating principle includes receiving a signal from a source and then reflecting the signal. While well-established transponders operate through electromagnetic antennas, those with a fully acoustic design have advantages such as lower cost and simplicity. Therefore, detection of pressures using the ultrasound signal that is backscattered from an acoustic resonator has been of interest recently. In order to infer the pressure from the backscattered signal, the established approach has been based upon the principle of detection of the shift to the frequency of resonance. Nevertheless, regression of the pressure from the signal with a small error is challenging and has been subject to research. Here in this paper, we explore an approach that employs deep learning for inferring pressure from the ultrasound reflections of polymeric resonators. We assess if neural network regressors can efficiently infer pressure reflected from a fully acoustic transponder. For this purpose, we compare the performance of several regressors such as a convolutional neural network, a network inspired by the ResNet, and a fully connected neural network. We observe that deep neural networks are advantageous in inferring pressure information with a minimal need for analyzing the signal. Our work suggests that a deep learning approach has the potential to be integrated with or replace other traditional approaches for inferring pressure from an ultrasound signal reflected from fully acoustic transponders or passive sensors. |
| first_indexed | 2024-03-13T00:49:50Z |
| format | Article |
| id | doaj.art-e68ffcb1ee174a1f9e357e28194918fa |
| institution | Directory Open Access Journal |
| issn | 2666-8270 |
| language | English |
| last_indexed | 2024-03-13T00:49:50Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Machine Learning with Applications |
| spelling | doaj.art-e68ffcb1ee174a1f9e357e28194918fa2023-07-08T04:19:20ZengElsevierMachine Learning with Applications2666-82702023-09-0113100477Application of deep neural networks for inferring pressure in polymeric acoustic transponders/sensorsSeyedhamidreza Alaie0Subhi J. Al’Aref1Department of Mechanical & Aerospace Engineering, New Mexico State University, Las Cruces, NM, USA; Corresponding author.Department of Internal Medicine — Division of Cardiovascular Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USAPassive sensor-transponders have raised interest for the last few decades, due to their capability of low-cost remote monitoring without the need for energy storage. Their operating principle includes receiving a signal from a source and then reflecting the signal. While well-established transponders operate through electromagnetic antennas, those with a fully acoustic design have advantages such as lower cost and simplicity. Therefore, detection of pressures using the ultrasound signal that is backscattered from an acoustic resonator has been of interest recently. In order to infer the pressure from the backscattered signal, the established approach has been based upon the principle of detection of the shift to the frequency of resonance. Nevertheless, regression of the pressure from the signal with a small error is challenging and has been subject to research. Here in this paper, we explore an approach that employs deep learning for inferring pressure from the ultrasound reflections of polymeric resonators. We assess if neural network regressors can efficiently infer pressure reflected from a fully acoustic transponder. For this purpose, we compare the performance of several regressors such as a convolutional neural network, a network inspired by the ResNet, and a fully connected neural network. We observe that deep neural networks are advantageous in inferring pressure information with a minimal need for analyzing the signal. Our work suggests that a deep learning approach has the potential to be integrated with or replace other traditional approaches for inferring pressure from an ultrasound signal reflected from fully acoustic transponders or passive sensors.http://www.sciencedirect.com/science/article/pii/S2666827023000300Pressure sensorNeural networksDeep learningUltrasoundTransponderPassive sensor |
| spellingShingle | Seyedhamidreza Alaie Subhi J. Al’Aref Application of deep neural networks for inferring pressure in polymeric acoustic transponders/sensors Machine Learning with Applications Pressure sensor Neural networks Deep learning Ultrasound Transponder Passive sensor |
| title | Application of deep neural networks for inferring pressure in polymeric acoustic transponders/sensors |
| title_full | Application of deep neural networks for inferring pressure in polymeric acoustic transponders/sensors |
| title_fullStr | Application of deep neural networks for inferring pressure in polymeric acoustic transponders/sensors |
| title_full_unstemmed | Application of deep neural networks for inferring pressure in polymeric acoustic transponders/sensors |
| title_short | Application of deep neural networks for inferring pressure in polymeric acoustic transponders/sensors |
| title_sort | application of deep neural networks for inferring pressure in polymeric acoustic transponders sensors |
| topic | Pressure sensor Neural networks Deep learning Ultrasound Transponder Passive sensor |
| url | http://www.sciencedirect.com/science/article/pii/S2666827023000300 |
| work_keys_str_mv | AT seyedhamidrezaalaie applicationofdeepneuralnetworksforinferringpressureinpolymericacoustictransponderssensors AT subhijalaref applicationofdeepneuralnetworksforinferringpressureinpolymericacoustictransponderssensors |