Natalia Vallejo Montoya, Daniel Rodriguez, Changzhi Li
{"title":"基于监督学习的牛奶掺假检测无线传输传感方法","authors":"Natalia Vallejo Montoya, Daniel Rodriguez, Changzhi Li","doi":"10.1109/RWS53089.2022.9719981","DOIUrl":null,"url":null,"abstract":"With the increasing demand for wireless sensors due to the growing Internet of Things (IoT) industry, it becomes desirable to use existing technologies to realize new sensing functions. As wireless power transfer (WPT) becomes a standard feature in smartphones, this paper studies the non-invasive classification of liquid solutions with different concentrations, based on the WPT technology already deployed in mobile devices. Average accuracies of up to 97.6% were achieved utilizing supervised machine learning for the classification of milk adulterated with different water volumes. For these experiments, milk concentrations of 100%, 80%, 60%, and 40% were used for classification. Additionally, singular value decomposition and boxplot analysis were used to reduce the radio frequency bandwidth needed for classification to 9.45 MHz, leading to a drastic reduction in hardware complexity and computational cost.","PeriodicalId":113074,"journal":{"name":"2022 IEEE Radio and Wireless Symposium (RWS)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Wireless Power Transfer Sensing Approach for Milk Adulteration Detection Using Supervised Learning\",\"authors\":\"Natalia Vallejo Montoya, Daniel Rodriguez, Changzhi Li\",\"doi\":\"10.1109/RWS53089.2022.9719981\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the increasing demand for wireless sensors due to the growing Internet of Things (IoT) industry, it becomes desirable to use existing technologies to realize new sensing functions. As wireless power transfer (WPT) becomes a standard feature in smartphones, this paper studies the non-invasive classification of liquid solutions with different concentrations, based on the WPT technology already deployed in mobile devices. Average accuracies of up to 97.6% were achieved utilizing supervised machine learning for the classification of milk adulterated with different water volumes. For these experiments, milk concentrations of 100%, 80%, 60%, and 40% were used for classification. Additionally, singular value decomposition and boxplot analysis were used to reduce the radio frequency bandwidth needed for classification to 9.45 MHz, leading to a drastic reduction in hardware complexity and computational cost.\",\"PeriodicalId\":113074,\"journal\":{\"name\":\"2022 IEEE Radio and Wireless Symposium (RWS)\",\"volume\":\"54 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE Radio and Wireless Symposium (RWS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RWS53089.2022.9719981\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE Radio and Wireless Symposium (RWS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RWS53089.2022.9719981","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Wireless Power Transfer Sensing Approach for Milk Adulteration Detection Using Supervised Learning
With the increasing demand for wireless sensors due to the growing Internet of Things (IoT) industry, it becomes desirable to use existing technologies to realize new sensing functions. As wireless power transfer (WPT) becomes a standard feature in smartphones, this paper studies the non-invasive classification of liquid solutions with different concentrations, based on the WPT technology already deployed in mobile devices. Average accuracies of up to 97.6% were achieved utilizing supervised machine learning for the classification of milk adulterated with different water volumes. For these experiments, milk concentrations of 100%, 80%, 60%, and 40% were used for classification. Additionally, singular value decomposition and boxplot analysis were used to reduce the radio frequency bandwidth needed for classification to 9.45 MHz, leading to a drastic reduction in hardware complexity and computational cost.