In-situ characterization of soil moisture content using a monopole probe

Sagnard, Florence; Guilbert, Vincent; Fauchard, Cyrille

doi:10.1016/j.jappgeo.2008.11.008

Cited by 17 publications

(16 citation statements)

References 33 publications

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“…Monopoles have been used as probes for the in-situ characterization of soil moisture content [6], sensors for snow and soil wetness [5], and for the measurement of electrical properties of general materials [3]. The input impedance of the monopole probe immersed in a dielectric medium depends on the electrical properties of the medium, and thus the measured reflection coeffi cient should provide sufficient information for extracting the com plex permittivity.…”

Section: The Monopole Antenna Modelmentioning

confidence: 99%

“…The size of the ground plane of the monopole, indicated by diameter d in Figure I, needs to be at least 4A to be assumed infi nite [6]. This assumption about the ground plane makes the appli- In this research we have used a 10 mm long monopole probe, which corresponds to the first resonant frequency of '" 7 GHz in air.…”

Section: The Monopole Antenna Modelmentioning

confidence: 99%

“…In the method reported in [3], the nor malized impedance of the monopole probe was expressed as a rational function of order three, and the coefficients of the function were determined based on the profile of a standard medium. Based on the rigorous expression for the impedances of monopole anten nas in half space by Gooch et al [I], various in-situ procedures have been reported in the literature for solving the nonlinear inverse problem of calculating complex permittivities [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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Measurement of Complex Permittivity using Artificial Neural Networks

Hasan

Peterson

2011

IEEE Antennas Propag. Mag.

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In this paper, a neural-network-based methodology is presented to measure the complex permittivity of materials using monopole probes. A multilayered artificial neural network, using the Levenberg Marquardt back-propagation algorithm, is used to back solve the complex permittivity of the medium. The proposed network can be trained using an analytical model, numerical model, or measurement data spread over the complete range of parameters of interest. The input training data for the nonlinear inverse problem of reconstructing the complex permittivity comprises the complex reflection coefficient of the monopole probe. For the results presented in this paper, the network was trained using the analytical model for impedances of monopole antennas in a half space by Gooch et al. [1]. In addition to computational efficiency, the proposed approach gave 99% accurate results in the frequency range of 2.5-5 GHz, with the values of permittivity varying across a range of 3-10 for the real part, and 0-0.5 for the imaginary part. The accuracy and the effective range of real and imaginary components of the complex permittivity that can be reconstructed using this approach depend upon the accuracy and robustness of the model/system used to generate the training data. The analytical model used in this paper had a limited range for the values of loss tangent that it can model accurately. However, the performance of the back-solving algorithm remains independent from any specific model, and the scheme can be successfully applied using any reliable analytical or numerical model, or reflection coefficient training data generated through a series of measurements. The methodology is likely to be employed for experimental measurements of complex permittivity of dissipative media.

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Section: The Monopole Antenna Modelmentioning

confidence: 99%

Section: The Monopole Antenna Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Measurement of Complex Permittivity using Artificial Neural Networks

Hasan

Peterson

2011

IEEE Antennas Propag. Mag.

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“…If we want error percentage less than MHz. It should be noted that the accuracy depends on what you measured whether the sample is low loss material or not [11][12][13][14][15][16][17].…”

Section: Experimental Uncertaintymentioning

confidence: 99%

Compact, printed mobile‐unit antenna for 2.4 and 5 GHz WLAN applications

Su¹,

Chang²

2010

Micro & Optical Tech Letters

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A simple yet effective, printed mobile-unit antenna aimed at providing dual-WLAN-band operation in the 2.4 and 5 GHz bands and with a small form factor is presented. The antenna comprises a Figure 11 Relative total uncertainty of e 0 . (a) (100-200 MHz) and (b) (200 MHz-3 GHz)folded monopole and a small antenna ground, all printed in a singlelayered, FR4 substrate with the overall dimensions 8 mm Â 25 mm. The frequency ratio of the dual-resonant (the upper over the lower) modes can easily be manipulated by adjusting the antenna tuning portion. In addition, the antenna can be fed by utilizing a common mini-coaxial cable, allowing it to be flexibly deployed inside versatile wireless electronic devices as an internal antenna. Details of a design prototype are described and discussed in the article.

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“…The finite diameter d of the ground plane can be neglected for diameters greater than 4\k [6], enabling the analytical model of Wu [3] to be used for the input impedance and reflection coefficient calculations. The validity of the model depends upon the ratio of monopole length to wavelength (h/k), but the model is not limited to small values of h/k.…”

Section: The Monopole Antenna Modelmentioning

confidence: 99%

Modified Particle Swarm Optimization for Patch Antenna Design Based on IE3D

Liu

Chen

Jiao

et al. 2007

Journal of Electromagnetic Waves and Applications

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9. M. Ohira, H. Deguchi, M. Tsuji, and H. Shigesawa, Multiband single-layer frequency selective surface designed by combination of genetic algorithm and geometry-refinement technique, IEEE Trans ABSTRACT: A procedure is described to enhance the accuracy of microwave measurements of the complex permittivity of a dissipative medium. Monopole probe measurements are used in conjunction with two real-valued neural networks, which are integrated together to reconstruct the complex permittivity from the measured reflection coefficients. The approach is tested over the frequency range from 2.5 to 5 GHz, for the real part of the permittivity in the range 3-10 and the imaginary part in the range 0-0.5. The performance of the network is also demonstrated for a reduced frequency range from 3.5 to 5 GHz. Less than 4% error was observed in the presence of white Gaussian noise with an SNR of 10dB.

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In-situ characterization of soil moisture content using a monopole probe

Cited by 17 publications

References 33 publications

Measurement of Complex Permittivity using Artificial Neural Networks

Measurement of Complex Permittivity using Artificial Neural Networks

Compact, printed mobile‐unit antenna for 2.4 and 5 GHz WLAN applications

Modified Particle Swarm Optimization for Patch Antenna Design Based on IE3D

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