Xinyi Li, Chao Feng, Nana Ding, Ju Wang, Jie Xiong, Yuhui Ren, Xiaojiang Chen, Dingyi Fang
{"title":"Target Material Identification with Commodity RFID Devices","authors":"Xinyi Li, Chao Feng, Nana Ding, Ju Wang, Jie Xiong, Yuhui Ren, Xiaojiang Chen, Dingyi Fang","doi":"10.1145/3131348.3131352","DOIUrl":null,"url":null,"abstract":"Target material identification plays an important role in many real-life applications. This paper introduces a system that can identify the material type with cheap commercial off-the-shelf (COTS) RFID devices. The key intuition is that different materials cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. However, without knowing either material type, trying to obtain the information is challenging. We propose a method to address this challenge and evaluate the method's performance in real-world environment. The results show that we achieve higher than 94% material identification accuracies for 10 liquids and differentiate even very similar objects such as Coke and Pepsi.","PeriodicalId":62224,"journal":{"name":"世界中学生文摘","volume":"38 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2017-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"世界中学生文摘","FirstCategoryId":"90","ListUrlMain":"https://doi.org/10.1145/3131348.3131352","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Target material identification plays an important role in many real-life applications. This paper introduces a system that can identify the material type with cheap commercial off-the-shelf (COTS) RFID devices. The key intuition is that different materials cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. However, without knowing either material type, trying to obtain the information is challenging. We propose a method to address this challenge and evaluate the method's performance in real-world environment. The results show that we achieve higher than 94% material identification accuracies for 10 liquids and differentiate even very similar objects such as Coke and Pepsi.