Akte-Liquid: Acoustic-based Liquid Identification with Smartphones
Akte-Liquid: Acoustic-based Liquid Identification with Smartphones
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DOI：
10.1145/3551640
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发表时间：
2022-08
影响因子：
4.1
通讯作者：
Xue Sun;Wenwen Deng;Xudong Wei;Dingyi Fang;Baochun Li;Xiaojiang Chen
中科院分区：
文献类型：
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作者：
Xue Sun;Wenwen Deng;Xudong Wei;Dingyi Fang;Baochun Li;Xiaojiang Chen
液体识别在我们的日常生活中起着至关重要的作用。然而，现有的RF感测方法仍然需要专用硬件，例如RFID读取器和UWB收发器，这对于大多数用户来说并不容易获得。在这篇文章中，我们提出了Akte-Liquid，它利用智能手机上的扬声器来传输声学信号，并利用智能手机上的麦克风来接收反射信号，以识别液体类型并分析液体浓度。我们的工作源于液体的声学固有阻抗特性，即不同的液体具有不同的固有阻抗，导致液体的反射声信号不同。然后，我们发现反射信号的幅频特性可以用来表征液体的特征。有了这个见解，我们提出了新的机制，以消除硬件和多径传播效应所造成的干扰，以提取液体特征。此外，我们设计了一种新的基于Siamese网络的结构，具有特定的训练样本选择机制，将提取的特征重构为与容器无关的特征。我们的实验评估表明，Akte-Liquid能够以更高的准确度区分20种液体，并以92.3%的准确度识别食品添加剂和测量人工尿液中的蛋白质浓度，低于1 mg/100 mL。
Liquid identification plays an essential role in our daily lives. However, existing RF sensing approaches still require dedicated hardware such as RFID readers and UWB transceivers, which are not readily available to most users. In this article, we propose Akte-Liquid, which leverages the speaker on smartphones to transmit acoustic signals, and the microphone on smartphones to receive reflected signals to identify liquid types and analyze the liquid concentration. Our work arises from the acoustic intrinsic impedance property of liquids, in that different liquids have different intrinsic impedance, causing reflected acoustic signals of liquids to differ. Then, we discover that the amplitude-frequency feature of reflected signals may be utilized to represent the liquid feature. With this insight, we propose new mechanisms to eliminate the interference caused by hardware and multi-path propagation effects to extract the liquid features. In addition, we design a new Siamese network-based structure with a specific training sample selection mechanism to reconstruct the extracted feature to container-irrelevant features. Our experimental evaluations demonstrate that Akte-Liquid is able to distinguish 20 types of liquids at a higher accuracy, and to identify food additives and measure protein concentration in the artificial urine with a 92.3% accuracy under 1 mg/100 mL as well.