Online Denoising Based on the Second-Order Adaptive Statistics Model.

Yi, Sheng-Lun; Jin, Xue-Bo; Su, Ting-Li; Tang, Zhen-Yun; Wang, Fa-Fa; Xiang, Na; Kong, Jian-Lei

Yi, Sheng-Lun; Jin, Xue-Bo; Su, Ting-Li; Tang, Zhen-Yun; Wang, Fa-Fa; Xiang, Na; Kong, Jian-Lei.

Afiliação

Yi SL; School of Computer Information and Engineering, Beijing Technology and Business University, Beijing 100048, China. yishenglun@st.btbu.edu.cn.
Jin XB; Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China. yishenglun@st.btbu.edu.cn.
Su TL; School of Computer Information and Engineering, Beijing Technology and Business University, Beijing 100048, China. jinxuebo@btbu.edu.cn.
Tang ZY; Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China. jinxuebo@btbu.edu.cn.
Wang FF; School of Computer Information and Engineering, Beijing Technology and Business University, Beijing 100048, China. sutingli@btbu.edu.cn.
Xiang N; Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China. sutingli@btbu.edu.cn.
Kong JL; The Key Laboratory of Urban Security and Disaster Engineering, Ministry of Education, Beijing University of Technology, Beijing 100124, China. tzy@bjut.edu.cn.

Sensors (Basel) ; 17(7)2017 Jul 20.

Article em En | MEDLINE | ID: mdl-28726729

Palavras-chave

Kalman filter; YuleWalker algorithm; online denoising; real-time data processing; the second-order adaptive statistics model

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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2017 Tipo de documento: Article País de afiliação: China

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