Aileni Raluca Maria, Pașca Sever, Valderrama Carlos
{"title":"Biomedical sensors data fusion algorithm for enhancing the efficiency of fault-tolerant systems in case of wearable electronics device","authors":"Aileni Raluca Maria, Pașca Sever, Valderrama Carlos","doi":"10.1109/ROLCG.2015.7367228","DOIUrl":null,"url":null,"abstract":"The paper presents multisensor data fusion process and algorithm modeling. The multisensor system consists in a set of heterogeneous medical sensors for biomedical electronics. Multisensor data fusion is based on artificial intelligence, pattern recognition and statistical estimation. The goal of the modeling is to reduce uncertainty of the data values tracked from sensors. The big data collected from sensors can contain also noisy data and repetitive one which can lead to inefficiencies in data storage. In this case is appropriate to reduce the errors by using algorithms and data fusion.","PeriodicalId":126559,"journal":{"name":"2015 Conference Grid, Cloud & High Performance Computing in Science (ROLCG)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 Conference Grid, Cloud & High Performance Computing in Science (ROLCG)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ROLCG.2015.7367228","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
The paper presents multisensor data fusion process and algorithm modeling. The multisensor system consists in a set of heterogeneous medical sensors for biomedical electronics. Multisensor data fusion is based on artificial intelligence, pattern recognition and statistical estimation. The goal of the modeling is to reduce uncertainty of the data values tracked from sensors. The big data collected from sensors can contain also noisy data and repetitive one which can lead to inefficiencies in data storage. In this case is appropriate to reduce the errors by using algorithms and data fusion.