bradycardia
发表于 2025-3-21 19:19:06
书目名称Communications, Signal Processing, and Systems影响因子(影响力)<br> http://impactfactor.cn/2024/if/?ISSN=BK0230550<br><br> <br><br>书目名称Communications, Signal Processing, and Systems影响因子(影响力)学科排名<br> http://impactfactor.cn/2024/ifr/?ISSN=BK0230550<br><br> <br><br>书目名称Communications, Signal Processing, and Systems网络公开度<br> http://impactfactor.cn/2024/at/?ISSN=BK0230550<br><br> <br><br>书目名称Communications, Signal Processing, and Systems网络公开度学科排名<br> http://impactfactor.cn/2024/atr/?ISSN=BK0230550<br><br> <br><br>书目名称Communications, Signal Processing, and Systems被引频次<br> http://impactfactor.cn/2024/tc/?ISSN=BK0230550<br><br> <br><br>书目名称Communications, Signal Processing, and Systems被引频次学科排名<br> http://impactfactor.cn/2024/tcr/?ISSN=BK0230550<br><br> <br><br>书目名称Communications, Signal Processing, and Systems年度引用<br> http://impactfactor.cn/2024/ii/?ISSN=BK0230550<br><br> <br><br>书目名称Communications, Signal Processing, and Systems年度引用学科排名<br> http://impactfactor.cn/2024/iir/?ISSN=BK0230550<br><br> <br><br>书目名称Communications, Signal Processing, and Systems读者反馈<br> http://impactfactor.cn/2024/5y/?ISSN=BK0230550<br><br> <br><br>书目名称Communications, Signal Processing, and Systems读者反馈学科排名<br> http://impactfactor.cn/2024/5yr/?ISSN=BK0230550<br><br> <br><br>
单挑
发表于 2025-3-21 21:11:09
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flammable
发表于 2025-3-22 03:08:34
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AV-node
发表于 2025-3-22 06:14:38
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摘要记录
发表于 2025-3-22 10:15:17
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偶然
发表于 2025-3-22 14:59:08
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偶然
发表于 2025-3-22 18:49:45
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Alcove
发表于 2025-3-22 21:19:13
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白杨
发表于 2025-3-23 03:49:35
,Cross-Modal Two-Stream Target Focused Network for Video Anomaly Detection, video. Training an unsupervised learning model on normal data is one of the most prevalent VAD paradigms owning to rare abnormal snippets. However, anomaly related and irrelated information sharing feature space will degrade the representation ability of the model for abnormal events. In this artic
骂人有污点
发表于 2025-3-23 06:39:28
,Fall Detection in the Wild: An Intelligent Emergency Assistance System,s of serious injuries caused by falls. In this paper, we propose an IEAS, a framework that integrates deep learning-based fall detection and edge computing-based local processing. First, we introduce a novel challenging benchmark dataset, called WildFall, to better evaluate fall detection models in