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發(fā)表于 2025-3-21 16:18:00 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Green, Pervasive, and Cloud Computing
編輯Zhiwen Yu,Christian Becker,Guoliang Xing
視頻videohttp://file.papertrans.cn/389/388546/388546.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: ;
出版日期Conference proceedings 2020
版次1
doihttps://doi.org/10.1007/978-3-030-64243-3
isbn_softcover978-3-030-64242-6
isbn_ebook978-3-030-64243-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
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沙發(fā)
發(fā)表于 2025-3-21 20:48:40 | 只看該作者
板凳
發(fā)表于 2025-3-22 01:47:46 | 只看該作者
Observations Concerning the Planet Venusormation in time. By virtue of concept stability, an isolated maximal clique detection approach is proposed. Experimental results shown that the proposed algorithm has a high F-measure value for detecting the isolated maximal cliques in social network.
地板
發(fā)表于 2025-3-22 07:40:49 | 只看該作者
Alexei Gvishiani,Anatoly Solovievo limit the number of output connections to compute the output effectively by removing unimportant ones. The simulation results of the chaotic time series prediction show the effectiveness of the proposed model.
5#
發(fā)表于 2025-3-22 10:05:22 | 只看該作者
Evaluating mmWave Sensing Ability of Recognizing Multi-people Under Practical Scenariosarly under multi-person application scenarios in different environments remains to be explored. In this work, we collect and evaluate over the mmWave sensing point cloud dataset from mmWave FMCW radars. Our study reveals the change of point cloud as people population and their distance varies.
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發(fā)表于 2025-3-22 13:29:36 | 只看該作者
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發(fā)表于 2025-3-22 17:14:58 | 只看該作者
Echo State Network Based on L0 Norm Regularization for Chaotic Time Series Predictiono limit the number of output connections to compute the output effectively by removing unimportant ones. The simulation results of the chaotic time series prediction show the effectiveness of the proposed model.
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發(fā)表于 2025-3-22 22:16:08 | 只看該作者
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發(fā)表于 2025-3-23 03:42:41 | 只看該作者
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發(fā)表于 2025-3-23 05:54:25 | 只看該作者
https://doi.org/10.1007/978-3-663-08220-0r, the average absolute error percentage is less than 2.0% and 3.5% for respiration and heart rate, respectively. In addition, the proposed method improves the accuracy of vital signals detection in comparison with Bandpass filter and Peak Detection (BPK).
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