Fields and coupling between coils embedded in conductive environments

Chu, Son; Vallecchi, A.; Stevens, Christopher J.; Shamonina, E.

doi:10.1051/epjam/2017012

“…Localizing the object for intermediate positions, i.e., between two adjacent cells, would require a more comprehensive model that takes into account the impact of the conductive object on the mutual inductance M, which was discussed in [33], [34], [35]. This is neglected in this article, and satisfying results for discrete localization (when the object is above centres of the cells) have been obtained.…”

Section: B Metamaterials Localization -Miw Reflectance and Input Impementioning

confidence: 88%

A Metamaterial Position Sensor Based on Magnetoinductive Waves

Yan

¹

,

Stevens

²

,

Shamonina

³

2021

IEEE Open J. Antennas Propag.

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In this article we report our first investigations of a new contactless localizing sensor based on the propagation of slow waves in metamaterials. Using the properties of magnetoinductive waves in a one dimensional metamaterial we are able to unambiguously locate a nearby object. This works because when an object impinges on the near field of the metamaterial's meta-atoms, it introduces a local defect resulting in the reflections of magnetoinductive waves. Key performance metrics are investigated and the ultimate horizontal range of the sensor is demonstrated to be directly linked to the metamaterials quality. An algorithm is devised based on the standing waves modes. The effect of terminating the structure with a matching impedance is discussed. Unambiguous localization of a single object is possible using a low-complexity algorithm, when the object interacts strongly with the metamaterial structure.

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“…Localizing the object for intermediate positions, i.e., between two adjacent cells, would require a more comprehensive model that takes into account the impact of the conductive object on the mutual inductance M, which was discussed in [33], [34], [35]. This is neglected in this article, and satisfying results for discrete localization (when the object is above centres of the cells) have been obtained.…”

Section: B Metamaterials Localization -Miw Reflectance and Input Impe...mentioning

confidence: 88%

A Meta-Material Position Sensor Based on Magneto-Inductive Waves

Stevens

¹

,

Yan

²

,

Shamonina³

2019

2019 Thirteenth International Congress on Artificial Materials for Novel Wave Phenomena (Metamaterials)

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In this article we report our first investigations of a new contactless localizing sensor based on the propagation of slow waves in metamaterials. Using the properties of magnetoinductive waves in a one dimensional metamaterial we are able to unambiguously locate a nearby object. This works because when an object impinges on the near field of the metamaterial's meta-atoms, it introduces a local defect resulting in the reflections of magnetoinductive waves. Key performance metrics are investigated and the ultimate horizontal range of the sensor is demonstrated to be directly linked to the metamaterials quality. An algorithm is devised based on the standing waves modes. The effect of terminating the structure with a matching impedance is discussed. Unambiguous localization of a single object is possible using a low-complexity algorithm, when the object interacts strongly with the metamaterial structure.

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“…Metamaterials do not escape the need for optimization [15,17,18], in particular with the prowess achieved by artificial intelligence, in the broadest sense, and more precisely by neural networks [18]. Actually, only a few papers deal with the application of neural networks to metamaterials.…”

Section: Introductionmentioning

confidence: 99%

Morphing for faster computations with finite difference time domain algorithms

Aznavourian

¹

,

Guenneau

²

,

Ungureanu

³

et al. 2022

EPJ Appl. Metamat.

0

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In the framework of wave propagation, finite difference time domain (FDTD) algorithms, yield high computational time. We propose to use morphing algorithms to deduce some approximate wave pictures of their interactions with fluid-solid structures of various shapes and different sizes deduced from FDTD computations of scattering by solids of three given shapes: triangular, circular and elliptic ones. The error in the L2 norm between the FDTD solution and approximate solution deduced via morphing from the source and destination images are typically less than 1% if control points are judiciously chosen. We thus propose to use a morphing algorithm to deduce approximate wave pictures: at intermediate time steps from the FDTD computation of wave pictures at a time step before and after this event, and at the same time step, but for an average frequency signal between FDTD computation of wave pictures with two different signal frequencies. We stress that our approach might greatly accelerate FDTD computations as discretizations in space and time are inherently linked via the Courant–Friedrichs–Lewy stability condition. Our approach requires some human intervention since the accuracy of morphing highly depends upon control points, but compared to the direct computational method our approach is much faster and requires fewer resources. We also compared our approach to some neural style transfer (NST) algorithm, which is an image transformation method based on a neural network. Our approach outperforms NST in terms of the L2 norm, Mean Structural SIMilarity, expected signal to error ratio.

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Fields and coupling between coils embedded in conductive environments

Cited by 8 publications

References 22 publications

A Metamaterial Position Sensor Based on Magnetoinductive Waves

A Metamaterial Position Sensor Based on Magnetoinductive Waves

A Meta-Material Position Sensor Based on Magneto-Inductive Waves

Morphing for faster computations with finite difference time domain algorithms

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