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Intelligent Sensing Using Multiple Sensors for Material Characterization Albishi, Ali M.; Mirjahanmardi, Seyed H.; Ali, Abdulbaset M.; Nayyeri, Vahid; Wasly, Saud M.; Ramahi, Omar M.

Abstract

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.

Item Metadata

Title
Intelligent Sensing Using Multiple Sensors for Material Characterization
Creator
Albishi, Ali M.; Mirjahanmardi, Seyed H.; Ali, Abdulbaset M.; Nayyeri, Vahid; Wasly, Saud M.; Ramahi, Omar M.
Publisher
Multidisciplinary Digital Publishing Institute
Date Issued
2019-11-02
Description
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.
Subject
artificial intelligence; complementary split-ring resonators; electrically-small resonators; fluid characterization; material measurements; neural networks; sensors
Genre
Article
Type
Text
Language
eng
Date Available
2019-11-12
Provider
Vancouver : University of British Columbia Library
Rights
CC BY 4.0
DOI
10.14288/1.0385173
URI
http://hdl.handle.net/2429/72261
Affiliation
Applied Science, Faculty of; Non UBC
Citation
Sensors 19 (21): 4766 (2019)
Publisher DOI
10.3390/s19214766
Peer Review Status
Reviewed
Scholarly Level
Faculty
Rights URI
https://creativecommons.org/licenses/by/4.0/
Aggregated Source Repository
DSpace

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CC BY 4.0