AI Article Synopsis
- The paper introduces an intelligent sensing technique using microwave near-field sensors to analyze material parameters by modulating their frequency responses.
- The method assumes that physical parameters, like fluid concentration, remain constant across the sensor's frequency range and utilizes multiple sensors to observe their responses in a multi-dimensional vector format.
- A neural network processes these diverse dimensions to create accurate models, which is validated through experiments that detect fluid concentrations with high precision using a specially designed microwave sensing system.
Article 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.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6864703 | PMC |
| http://dx.doi.org/10.3390/s19214766 | DOI Listing |
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