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Open AccessArticle

Intelligent Sensing Using Multiple Sensors for Material Characterization

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Department of Electrical Engineering, King Saud University, Riyadh 11451, Saudi Arabia
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Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
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Faculty of Applied Science, University of British Columbia, Kelowna, BC V1V 1V7, Canada
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School of Advanced Technologies, Iran University of Science and Technology, Tehran 1684613114, Iran
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Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(21), 4766; https://doi.org/10.3390/s19214766
Received: 23 September 2019 / Revised: 24 October 2019 / Accepted: 31 October 2019 / Published: 2 November 2019
(This article belongs to the Special Issue Metamaterials for Near-Field Microwaves Sensing)
This paper presents a concept of an intelligent sensing technique based on modulating the frequency responses of microwave near-field sensors to characterize material parameters. The concept is based on the assumption that the physical parameters being extracted such as fluid concentration are constant over the range of frequency of the sensor. The modulation of the frequency response is based on the interactions between the material under test and multiple sensors. The concept is based on observing the responses of the sensors over a frequency wideband as vectors of many dimensions. The dimensions are then considered as the features for a neural network. With small datasets, the neural networks can produce highly accurate and generalized models. The concept is demonstrated by designing a microwave sensing system based on a two-port microstrip line exciting three-identical planar resonators. For experimental validation, the sensor is used to detect the concentration of a fluid material composed of two pure fluids. Very high accuracy is achieved. View Full-Text
Keywords: artificial intelligence; complementary split-ring resonators; electrically-small resonators; fluid characterization; material measurements; neural networks; sensors artificial intelligence; complementary split-ring resonators; electrically-small resonators; fluid characterization; material measurements; neural networks; sensors
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Albishi, A.M.; Mirjahanmardi, S.H.; Ali, A.M.; Nayyeri, V.; Wasly, S.M.; Ramahi, O.M. Intelligent Sensing Using Multiple Sensors for Material Characterization. Sensors 2019, 19, 4766.

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