Microwave Method for Measuring Electrical Properties of the Materials

Gorst, Aleksandr; Zavyalova, Kseniya; Shipilov, S. É.; Yakubov, V. P.; Mironchev, Aleksandr

doi:10.3390/app10248936

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…The values of acetone affected the dielectric properties of the phantoms, so its amount was precisely adjusted to achieve the required values of the real part of the dielectric permittivity. The obtained flat materials were measured using a setup for measuring electrophysical parameters [17], as shown in Figure 7. Based on the measurement results, graphs of the real parts of the dielectric permittivity versus frequency were built for each of the materials (Figures 8 and 9).…”

Section: Creation Of a Sensor And A Phantom Of Human Biological Tissuesmentioning

confidence: 99%

Non-Invasive Determination of Glucose Concentration Using a Near-Field Sensor

2021

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The article presents a model of a near-field sensor for non-invasive glucose monitoring. The sensor has a specific design and forms a rather extended near-field. Due to this, the high penetration of electromagnetic waves into highly absorbing media (biologic media) is achieved. It represents a combined slot antenna based on a flexible RO3003 substrate. Moreover, it is small and rather flat (25 mm in diameter, 0.76 mm thick). These circumstances are the distinguishing features of this sensor in comparison with microwave sensors of other designs. The article presents the results of numerical modeling and experimental verification of a near-field sensor. Furthermore, a phantom of human biological media (human hand) was created for experimentation. In the case of numerical modeling, the sensor is located close to the hand model. In a full-scale experiment, it is located close to the phantom of the human hand for the maximum interaction of the near-field with biological materials. As a result of a series of measurements for this sensor, the reflection coefficient is measured, and the dependences of the reflected signal on the frequency are plotted. According to the results of the experiments carried out, there is a clear difference in glucose concentrations. At the same time, the accuracy of determining the difference in glucose concentrations is high. The values of the amplitude of the reflected signal with a change in concentration differ by 0.5–0.8 dB. This sensor can be used for developing a non-invasive blood glucose measurement procedure.

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Section: Creation Of a Sensor And A Phantom Of Human Biological Tissuesmentioning

confidence: 99%

Non-Invasive Determination of Glucose Concentration Using a Near-Field Sensor

2021

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“…In the free space measurement methods, the relative permeability is estimated by observing the distortion introduced by a piece of material placed between transmitter and receiver [34]. In the transmission reflection methods, the relative permittivity is obtained by observing the reflection coefficient [35][36][37][38][39]. The resonant methods explore the resonant properties of the material [40] or the perturbation of a piece of material placed in the empty resonator.…”

Section: Relative Permittivity Estimationmentioning

confidence: 99%

Framework for the Machine Learning Based Wireless Sensing of the Electromagnetic Properties of Indoor Materials

et al. 2021

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Available digital maps of indoor environments are limited to a description of the geometrical environment, despite there being an urgent need for more accurate information, particularly data about the electromagnetic (EM) properties of the materials used for walls. Such data would enable new possibilities in the design and optimization of wireless networks and the development of new radio services. In this paper, we introduce, formalize, and evaluate a framework for machine learning (ML) based wireless sensing of indoor surface materials in the form of EM properties. We apply the radio-environment (RE) signatures of the wireless link, which inherently contains environmental information due to the interaction of the radio waves with the environment. We specify the content of the RE signature suitable for surface-material classification as a set of multipath components given by the received power, delay, phase shift, and angle of arrival. The proposed framework applies an ML approach to construct a classification model using RE signatures labeled with the environmental information. The ML method exploits the data obtained from measurements or simulations. The performance of the framework in different scenarios is evaluated based on standard ML performance metrics, such as classification accuracy and F-score. The results of the elementary case prove that the proposed approach can be applied for the classification of the surface material for a plain environment, and can be further extended for the classification of wall materials in more complex indoor environments.

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“…Inside these two groups are found six main techniques [18]- [20] according to the application domain and the state or kind of material to be characterized [21]. These six methods are Free-space [9], [22], [23], resonant cavity [24]- [26], capacitive or parallel plates capacitor [27], inductance [11], [28], probes [29], [30], and transmission line [31]- [33].…”

Section: Introductionmentioning

confidence: 99%

Material Relative Permittivity Determination From the Inhomogeneous Transmission-Line Secondary Parameters

2022

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After further transmission line technique investigation, we propose a new approach to material characterization based on the combination of the propagation constant and the characteristic impedance of the transmission-line. Three main elements constitute the approach novelty's root: the determination of the propagation constant without using the eigenvalue principle, the improved mathematical expression of the characteristic impedance, and the automatically corrected coefficient. The two-line technique is based on three required measures, where the most extended fixture is partially filled with the specimen to be tested. As a result, the discontinuities caused by the geometric change of access interfaces and the waveguide dimensions have been solved. The characteristic impedance is determined straight and amended by a third polynomial function degree. The polynomial function and the amended correction coefficient determination are the facilitation parameters of the new approach technique. This new method allows the extraction of the material's complex relative permittivity. The procedure has been validated with the Rogers RO4003C, Alumina 99.6%, and Vinifera (based on the dielectric materials available in our laboratory) through the microstrip fixture in the scanned frequency (0.08 -8) GHz by extracting its electric intrinsic parameters. The dielectric constants range from 1.1-10.4, where the thicknesses are 1.54 mm, 2.067 mm, and 1.078 mm have been used. Two identical microstrip test cells with the same geometric dimensions but various lengths have been manufactured. That promotes the feasibility of two measures simultaneously.

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Microwave Method for Measuring Electrical Properties of the Materials

Cited by 7 publications

References 28 publications

Non-Invasive Determination of Glucose Concentration Using a Near-Field Sensor

Non-Invasive Determination of Glucose Concentration Using a Near-Field Sensor

Framework for the Machine Learning Based Wireless Sensing of the Electromagnetic Properties of Indoor Materials

Material Relative Permittivity Determination From the Inhomogeneous Transmission-Line Secondary Parameters

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