Zhang Jie, Zhou Kexin
School of Computer Science & Technology, Xi'an University of Posts & Telecommunications, Xi'an 710121, China.
School of Information Science and Technology, Northwest University, Xi'an 710127, China.
Entropy (Basel). 2023 Aug 5;25(8):1170. doi: 10.3390/e25081170.
Material identification is playing an increasingly important role in various sectors such as industry, petrochemical, mining, and in our daily lives. In recent years, material identification has been utilized for security checks, waste sorting, etc. However, current methods for identifying materials require direct contact with the target and specialized equipment that can be costly, bulky, and not easily portable. Past proposals for addressing this limitation relied on non-contact material identification methods, such as Wi-Fi-based and radar-based material identification methods, which can identify materials with high accuracy without physical contact; however, they are not easily integrated into portable devices. This paper introduces a novel non-contact material identification based on acoustic signals. Different from previous work, our design leverages the built-in microphone and speaker of smartphones as the transceiver to identify target materials. The fundamental idea of our design is that acoustic signals, when propagated through different materials, reach the receiver via multiple paths, producing distinct multipath profiles. These profiles can serve as fingerprints for material identification. We captured and extracted them using acoustic signals, calculated channel impulse response (CIR) measurements, and then extracted image features from the time-frequency domain feature graphs, including histogram of oriented gradient (HOG) and gray-level co-occurrence matrix (GLCM) image features. Furthermore, we adopted the error-correcting output code (ECOC) learning method combined with the majority voting method to identify target materials. We built a prototype for this paper using three mobile phones based on the Android platform. The results from three different solid and liquid materials in varied multipath environments reveal that our design can achieve average identification accuracies of 90% and 97%.
材料识别在工业、石化、采矿等各个领域以及我们的日常生活中发挥着越来越重要的作用。近年来，材料识别已被用于安全检查、垃圾分类等。然而，当前的材料识别方法需要与目标直接接触，并且需要昂贵、笨重且不易携带的专业设备。过去解决这一限制的提议依赖于非接触式材料识别方法，如基于Wi-Fi和基于雷达的材料识别方法，这些方法可以在不进行物理接触的情况下高精度地识别材料；然而，它们不易集成到便携式设备中。本文介绍了一种基于声学信号的新型非接触式材料识别方法。与以往的工作不同，我们的设计利用智能手机的内置麦克风和扬声器作为收发器来识别目标材料。我们设计的基本思想是，声学信号在通过不同材料传播时，会通过多条路径到达接收器，产生独特的多径分布。这些分布可以作为材料识别的指纹。我们使用声学信号捕获并提取它们，计算信道冲激响应（CIR）测量值，然后从时频域特征图中提取图像特征，包括方向梯度直方图（HOG）和灰度共生矩阵（GLCM）图像特征。此外，我们采用纠错输出码（ECOC）学习方法并结合多数投票方法来识别目标材料。我们基于安卓平台使用三部手机为本文构建了一个原型。在不同多径环境中对三种不同固体和液体材料的测试结果表明，我们的设计可以实现90%和97%的平均识别准确率。
