channel status information (CSI), wireless fidelity(WiFi), material identification, randomness ,"/> MateFi: 基于 WiFi 设备的材料识别系统

吉林大学学报(信息科学版) ›› 2023, Vol. 41 ›› Issue (2): 299-305.

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MateFi: 基于 WiFi 设备的材料识别系统

戴泽淼1,2   

  1.  (1. 安徽国防科技职业学院 信息技术学院, 安徽 六安 237011; 2. 合肥工业大学 计算机与信息学院, 合肥 230601) 
  • 收稿日期:2022-07-27 出版日期:2023-04-13 发布日期:2023-04-16
  • 作者简介:戴泽淼(1982— ), 女, 安徽六安人, 安徽国防科技职业学院副教授, 主要从事人工智能研究, ( Tel) 86-18110693660 (E-mail)657410540@ qq. com
  • 基金资助:
     国家重点研发计划基金资助项目(2018YFB0803403); 安徽省 2021 年质量工程基金资助项目(2021tszy015); 安徽省 2020 年高校优秀青年骨干教师国内访问研修基金资助项目(gxgnfx2020172) 

MateFi: Material Identification System Based on WiFi Equipment

 DAI Zemiao 1,2   

  1. (1. School of Information Technology, Anhui Vocational College of Defense Technology, Liu’an 237011, China; 2. School of Computer and Information, Hefei University of Technology, Hefei 230601, China) 
  • Received:2022-07-27 Online:2023-04-13 Published:2023-04-16

摘要:  由于目前的材料识别方法 X 射线技术依赖专用设备传输高频信号且放射性强, 超声波技术设备笨重且 携带不便, 基于射频技术主要依赖成本较高的 RFID(Radio Frequency Identification Devices)设备。 因此, 为满足 日常家居和办公场景使用, 提出基于 WiFi(Wireless Fidelity)实现材料识别的 MateFi 系统, 同时建立一个新的 理论模型, 能更具体地描述电磁波在穿透不同材料时的衰减状态。 进而, 利用该理论模型结合材料本身特征与 机器学习技术, 搭建了鲁棒性更强、 精确性更高的材料识别系统。 针对 MateFi 系统在真实场景下的性能, 进行 了测试和验证。 实验结果表明, MateFi 可以识别出木板、 纸板、 镍、 薄木片、 铁、 钛 6 种不同的材料, 平均识别 准确率可到达 96. 70% , 说明该系统具有精准的材料识别性能。

关键词: 信道状态信息, 无线保真(WiFi), 材料识别

Abstract:  Current material identification methods are mainly based on X-ray technology, ultrasound technology and radio frequency technology. However, X-ray technology relies on special equipment to transmit high frequency signals and is highly radioactive; ultrasonic technology equipment is bulky and inconvenient to carry; and RF(Radio Frequency) technology mainly relies on costly RFID(Radio Frequency Identification Devices). In order to meet the daily use in home and office scenarios, MateFi system for material identification is proposed based on WiFi(Wireless Fidelity), aiming to establish a new theoretical model to describe more specifically the attenuation state of electromagnetic waves as they penetrate different materials. The theoretical model is used to build a more robust and accurate material recognition system by combining material characteristics with machine learning techniques. The performance of the MateFi system is tested and validated in real-life scenarios. The experiments show that MateFi can recognise six different materials: wood, cardboard, nickel, thin wood, iron and titanium, with an average recognition accuracy of 96. 70% , demonstrating the system’s ability to identify materials accurately. 

Key words: channel status information (CSI)')">

channel status information (CSI), wireless fidelity(WiFi), material identification, randomness

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