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Titlebook: Embedded System Technology; 13th National Confer Xing Zhang,Zhonghai Wu,Xingmian Sha Conference proceedings 2015 Springer Science+Business

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书目名称Embedded System Technology
副标题13th National Confer
编辑Xing Zhang,Zhonghai Wu,Xingmian Sha
视频video
概述Includes supplementary material:
丛书名称Communications in Computer and Information Science
图书封面Titlebook: Embedded System Technology; 13th National Confer Xing Zhang,Zhonghai Wu,Xingmian Sha Conference proceedings 2015 Springer Science+Business
描述.This book constitutes the refereed proceedings of the 13thNational Conference on Embedded System Technology, ESTC 2015, held in Beijing,China, in October 2015..The 18 revised full papers presented werecarefully reviewed and selected from 63 papers. The topics cover a broad rangeof fields focusing on research about embedded system technologies, such assmart hardware, system and network, applications and algorithm..
出版日期Conference proceedings 2015
关键词Embedded system; wireless sensor networks; data acquisition and communication; electronic circuit desig
版次1
doihttps://doi.org/10.1007/978-981-10-0421-6
isbn_softcover978-981-10-0420-9
isbn_ebook978-981-10-0421-6Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Science+Business Media Singapore 2015
The information of publication is updating

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Communications in Computer and Information Sciencehttp://image.papertrans.cn/e/image/307943.jpg
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978-981-10-0420-9Springer Science+Business Media Singapore 2015
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Soft Computing for Metamaterial Structures,per. The EAKFA method selects adaptive Kalman filter algorithm as a fading factor. Furthermore, the fading factor is optimized by evolutionary algorithms. Finally, the effectiveness of EAKFA is validated by comparing EAKFA with a general adaptive Kalman filter algorithm. The results show that the er
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https://doi.org/10.1007/978-94-011-5978-4++ is a kind of effective single model recommendation algorithm, which is based on the matrix decomposition combined with the neighborhood model. On the Spark, using the Stochastic Gradient Descent, this paper realized the distributed SVD++ algorithm through the Scala, deployed and applied the algor
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