Material Identification Using a Microwave Sensor Array and Machine Learning

Luke Harrsion, Maryam Ravan, Dhara Tandel, Kunyi Zhang, Tanvi Patel, Reza Khalaj Amineh

Electronics 2020 article cited by 31

Source Open-access PDF More in Engineering & Materials

importance 3/10 partially solved Microwave and Dielectric Measurement Techniques materials sensing stage: research

Problem

Identifying materials via microwave sensing, where single-resonator systems limit characterization to narrow frequency ranges and reduce identification accuracy.

Why it matters

Accurate, wide-range material identification matters for industrial and composite material applications.

What the AI did

Machine learning algorithms were applied to data from a novel microwave sensor array whose elements resonate at various frequencies, enabling material characterization over a wide frequency range.

Results

not stated in source

Impact

Method tested on woods, cardboards, and plastics; abstract claims improved accuracy but gives no figures.

Record

Venue	Electronics
Published	2020
Work type	article
Authors	Luke Harrsion, Maryam Ravan, Dhara Tandel, Kunyi Zhang, Tanvi Patel, Reza Khalaj Amineh
Citations	31 (OpenAlex, at import time)
References	24
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.3390/electronics9020288
Open access	gold
Keywords	microwave sensing, sensor array, machine learning, material identification

Importance rating

3 / 10

incremental / niche

Wide-frequency material identification for industrial liquids and composites; real but narrow technical advance; no scale stated.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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