A. Razaque, Fathi H. Amsaad, Cai Mengjie Cherry, Lin Jiahui Linda, A. Oun
{"title":"Smart Phone as Toolbox for Height Measurement","authors":"A. Razaque, Fathi H. Amsaad, Cai Mengjie Cherry, Lin Jiahui Linda, A. Oun","doi":"10.1109/NAECON46414.2019.9058290","DOIUrl":null,"url":null,"abstract":"Many researchers focus on using smart phone as toolbox, such as measuring height via barometer on smart phone. However, none of them compared the error rate that exists in different brands of mobile phones (e.g., Samsung, HUAWEI and iPhone). This paper carries out sets of control experiments to acquire plenty of data to analyze and contrast the error rate in those mobile systems. This paper uses elementary effects (EE) method to recognize variables that are non-influential. EE method is also used to sort affecting factors such as temperature and humidity, according to factors’ significance. Furthermore, this paper minimizes the burst affecting variable random error by using a method called Filtering Burst Error and Random Error Process (FBEREP) to ensure the error rate. The validation is conducted using real smart phones. Based on the result, it can be observed that error rate is controlled within 2%.","PeriodicalId":193529,"journal":{"name":"2019 IEEE National Aerospace and Electronics Conference (NAECON)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE National Aerospace and Electronics Conference (NAECON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NAECON46414.2019.9058290","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Many researchers focus on using smart phone as toolbox, such as measuring height via barometer on smart phone. However, none of them compared the error rate that exists in different brands of mobile phones (e.g., Samsung, HUAWEI and iPhone). This paper carries out sets of control experiments to acquire plenty of data to analyze and contrast the error rate in those mobile systems. This paper uses elementary effects (EE) method to recognize variables that are non-influential. EE method is also used to sort affecting factors such as temperature and humidity, according to factors’ significance. Furthermore, this paper minimizes the burst affecting variable random error by using a method called Filtering Burst Error and Random Error Process (FBEREP) to ensure the error rate. The validation is conducted using real smart phones. Based on the result, it can be observed that error rate is controlled within 2%.