Distributed Signal Processing Algorithms for Wireless Sensor Networks

Ashwini S Chiwarkar
{"title":"Distributed Signal Processing Algorithms for Wireless Sensor Networks","authors":"Ashwini S Chiwarkar","doi":"10.24113/ijoscience.v7i8.404","DOIUrl":null,"url":null,"abstract":"Wireless Body Area Networks (WBAN), in particular in the field of wearable health monitoring system (WMB), such as electromagnetic cardiograms (ECG) data collecting system via WBANs in e-health applications, is becoming increasingly important for future communication systems. Compressive sensing (CS), on the other hand, has been shown to consume less power compared classic transform-coding-based approaches. We propose a new low-rank sparse deep signal recovery algorithm for recovering ECG data in the context of CS (Compressive sensing) because the spatial and temporal data collected by a WBAN have some closely correlated structures in certain wavelet domains e.g., the discrete wavelet transform (DWT) domain","PeriodicalId":429424,"journal":{"name":"SMART MOVES JOURNAL IJOSCIENCE","volume":"272 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SMART MOVES JOURNAL IJOSCIENCE","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.24113/ijoscience.v7i8.404","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

Wireless Body Area Networks (WBAN), in particular in the field of wearable health monitoring system (WMB), such as electromagnetic cardiograms (ECG) data collecting system via WBANs in e-health applications, is becoming increasingly important for future communication systems. Compressive sensing (CS), on the other hand, has been shown to consume less power compared classic transform-coding-based approaches. We propose a new low-rank sparse deep signal recovery algorithm for recovering ECG data in the context of CS (Compressive sensing) because the spatial and temporal data collected by a WBAN have some closely correlated structures in certain wavelet domains e.g., the discrete wavelet transform (DWT) domain
无线传感器网络的分布式信号处理算法
无线体域网络(WBAN),特别是在可穿戴健康监测系统(WMB)领域,如电子医疗应用中通过无线体域网络的电磁心电图(ECG)数据采集系统,对未来的通信系统变得越来越重要。另一方面,与经典的基于变换编码的方法相比,压缩感知(CS)已被证明消耗更少的功率。由于WBAN采集的时空数据在某些小波域(如离散小波变换域)中具有密切相关的结构,因此提出了一种新的低秩稀疏深度信号恢复算法,用于压缩感知下的心电数据恢复
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信