Material Identification Using a Microwave Sensor Array and Machine Learning
In this paper, a novel methodology is proposed for material identification. It is based on the use of a microwave sensor array with the elements of the array resonating at various frequencies within a wide range and applying machine learning algorithms on the collected data. Unlike the previous micr...
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MDPI AG
2020-02-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/9/2/288 |
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doaj-8e90f448398041f89d0bcac11a253aab2020-11-25T01:27:38ZengMDPI AGElectronics2079-92922020-02-019228810.3390/electronics9020288electronics9020288Material Identification Using a Microwave Sensor Array and Machine LearningLuke Harrsion0Maryam Ravan1Dhara Tandel2Kunyi Zhang3Tanvi Patel4Reza K. Amineh5Department of Electrical and Computer Engineering, New York Institute of Technology, New York, NY 10023, USADepartment of Electrical and Computer Engineering, New York Institute of Technology, New York, NY 10023, USADepartment of Electrical and Computer Engineering, New York Institute of Technology, New York, NY 10023, USADepartment of Electrical and Computer Engineering, New York Institute of Technology, New York, NY 10023, USADepartment of Electrical and Computer Engineering, New York Institute of Technology, New York, NY 10023, USADepartment of Electrical and Computer Engineering, New York Institute of Technology, New York, NY 10023, USAIn this paper, a novel methodology is proposed for material identification. It is based on the use of a microwave sensor array with the elements of the array resonating at various frequencies within a wide range and applying machine learning algorithms on the collected data. Unlike the previous microwave sensing systems which are mainly based on a single resonating sensor, the proposed methodology allows for material characterization over a wide frequency range which, in turn, improves the accuracy of the material identification procedure. The performance of the proposed methodology is tested via the use of easily available materials such as woods, cardboards, and plastics. However, the proposed methodology can be extended to other applications such as industrial liquid identification and composite material identification, among others.https://www.mdpi.com/2079-9292/9/2/288machine learningmaterial identificationmicrowave sensor array |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Luke Harrsion Maryam Ravan Dhara Tandel Kunyi Zhang Tanvi Patel Reza K. Amineh |
| spellingShingle |
Luke Harrsion Maryam Ravan Dhara Tandel Kunyi Zhang Tanvi Patel Reza K. Amineh Material Identification Using a Microwave Sensor Array and Machine Learning Electronics machine learning material identification microwave sensor array |
| author_facet |
Luke Harrsion Maryam Ravan Dhara Tandel Kunyi Zhang Tanvi Patel Reza K. Amineh |
| author_sort |
Luke Harrsion |
| title |
Material Identification Using a Microwave Sensor Array and Machine Learning |
| title_short |
Material Identification Using a Microwave Sensor Array and Machine Learning |
| title_full |
Material Identification Using a Microwave Sensor Array and Machine Learning |
| title_fullStr |
Material Identification Using a Microwave Sensor Array and Machine Learning |
| title_full_unstemmed |
Material Identification Using a Microwave Sensor Array and Machine Learning |
| title_sort |
material identification using a microwave sensor array and machine learning |
| publisher |
MDPI AG |
| series |
Electronics |
| issn |
2079-9292 |
| publishDate |
2020-02-01 |
| description |
In this paper, a novel methodology is proposed for material identification. It is based on the use of a microwave sensor array with the elements of the array resonating at various frequencies within a wide range and applying machine learning algorithms on the collected data. Unlike the previous microwave sensing systems which are mainly based on a single resonating sensor, the proposed methodology allows for material characterization over a wide frequency range which, in turn, improves the accuracy of the material identification procedure. The performance of the proposed methodology is tested via the use of easily available materials such as woods, cardboards, and plastics. However, the proposed methodology can be extended to other applications such as industrial liquid identification and composite material identification, among others. |
| topic |
machine learning material identification microwave sensor array |
| url |
https://www.mdpi.com/2079-9292/9/2/288 |
| work_keys_str_mv |
AT lukeharrsion materialidentificationusingamicrowavesensorarrayandmachinelearning AT maryamravan materialidentificationusingamicrowavesensorarrayandmachinelearning AT dharatandel materialidentificationusingamicrowavesensorarrayandmachinelearning AT kunyizhang materialidentificationusingamicrowavesensorarrayandmachinelearning AT tanvipatel materialidentificationusingamicrowavesensorarrayandmachinelearning AT rezakamineh materialidentificationusingamicrowavesensorarrayandmachinelearning |
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1725104160259440640 |