Terahertz tag identifiable through shielding materials using machine learning
Terahertz tag identifiable through shielding materials using machine learning
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DOI：
10.1364/oe.384195
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发表时间：
2020-02-03
期刊：
影响因子：
3.8
通讯作者：
Kawase, Kodo
中科院分区：
文献类型：
--
作者：
Mitsuhashi, Ryoya;Murate, Kosuke;Kawase, Kodo
近年来，人们对工作在太赫兹（THz）频率范围内的无芯片射频识别（RFID）设备产生了极大的兴趣。尽管RFID技术取得了进步，但由于与屏蔽材料相关的成本和检测精度问题，其在THz范围内的实际应用尚未实现。在这项研究中，我们提出了两种类型的低成本太赫兹标签，一种是基于涂层聚乙烯的厚度变化，另一种是基于试剂的指纹光谱。在所提出的方法中，机器学习，特别是深度学习方法，用于高精度的标签识别，即使信号很弱，或者当频谱被屏蔽材料干扰时。我们实现了几乎100%的识别精度，尽管使用一个廉价的标签放置在厚屏蔽材料下，衰减率约为-50 dB。此外，通过结合多波长注入种子太赫兹参数发生器和卷积神经网络，实时标签识别被证明。(C)根据OSA开放获取出版协议的条款，2020年美国光学学会
In recent years, there has been great interest in chipless radio-frequency identification (RFID) devices that work in the terahertz (THz) frequency range. Despite advances in RFID technology, its practical use in the THz range has yet to be realized, due to cost and detection accuracy issues associated with shielding materials. In this study, we propose two types of low-cost THz-tags; one is based on the thickness variation of coated polyethylene and the other on the fingerprint spectra of reagents. In the proposed approach, machine learning, specifically a deep-learning method, is used for high-precision tag identification even with weak signals, or when the spectrum is disturbed by passing through shielding materials. We achieved almost 100% identification accuracy despite using an inexpensive tag placed under thick shielding materials with an attenuation rate of about -50 dB. Furthermore, real-time tag identification was demonstrated by combining a multiwavelength injection-seeded THz parametric generator and a convolutional neural network. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement