Dima Georgiana, Stevens Christopher John
Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK.
Sci Rep. 2024 Oct 15;14(1):24156. doi: 10.1038/s41598-024-75218-2.
In this study, we introduce a two-dimensional metasurface sensor designed to detect, locate and distinguish between different objects placed in its near field. When an object is placed on the metasurface, local changes can be detected in one or more of the structure's meta-atoms. This interaction generally modifies the inductance of the meta-atom, resulting in changes to the overall input impedance of the surface. We derive the properties of the structure and its behaviour in terms of superposition and demonstrate that observing the meta-surface from a single point is sufficient for unambiguous localisation and identification. To model these changes effectively and identify the position of an object, we employ a neural network machine learning algorithm. Our approach enables accurate localisation of all studied objects, with a precision exceeding . Additionally, the distinct signatures of the objects allow for separation between them with an accuracy of over . The potential applications of this platform extend to foreign object detection on metasurfaces for wireless power transfer, providing proximity detection for many surfaces such as clothing, car bodies and robotic carapaces. Furthermore, our research suggests the feasibility of implementing a touchscreen type interface requiring only a single waveguide connection.
在本研究中，我们介绍了一种二维超表面传感器，其设计目的是检测、定位并区分放置在其近场中的不同物体。当一个物体放置在超表面上时，可以在结构的一个或多个超原子中检测到局部变化。这种相互作用通常会改变超原子的电感，从而导致表面整体输入阻抗的变化。我们根据叠加原理推导了该结构的特性及其行为，并证明从单个点观察超表面足以进行明确的定位和识别。为了有效地模拟这些变化并识别物体的位置，我们采用了一种神经网络机器学习算法。我们的方法能够对所有研究的物体进行精确的定位，精度超过 。此外，物体的独特特征使得它们之间的分离精度超过 。该平台的潜在应用扩展到用于无线电力传输的超表面上的异物检测，为许多表面（如衣物、车身和机器人外壳）提供接近检测。此外，我们的研究表明了仅需单个波导连接即可实现触摸屏式界面的可行性。
