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Titlebook: 6GN for Future Wireless Networks; 6th EAI Internationa Jingchao Li,Bin Zhang,Yulong Ying Conference proceedings 2024 ICST Institute for Com

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發(fā)表于 2025-3-21 17:45:22 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱(chēng)6GN for Future Wireless Networks
期刊簡(jiǎn)稱(chēng)6th EAI Internationa
影響因子2023Jingchao Li,Bin Zhang,Yulong Ying
視頻videohttp://file.papertrans.cn/102/101052/101052.mp4
學(xué)科分類(lèi)Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engi
圖書(shū)封面Titlebook: 6GN for Future Wireless Networks; 6th EAI Internationa Jingchao Li,Bin Zhang,Yulong Ying Conference proceedings 2024 ICST Institute for Com
影響因子.This 2-volume set constitutes the proceedings of the 6th EAI International Conference on 6GN for Future Wireless Networks, 6GN 2023, held in Shanghai, China, in October 7-8, 2023.. The 60 full papers were selected from 151 submissions and present the state of the art and practical applications of 6G technologies. The papers are arranged thematically in tracks as follows: intelligent systems; big data mining, D2D communication, security and privacy?for 6G networks; artificial intelligent techniques for 6G networks; power and energy systems I; power and energy system; power and energy systems; image, video, and signal processing; image, video, and signal processing & software engineering; communications systems and networking & control?and automation systems; computer systems and applications..
Pindex Conference proceedings 2024
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https://doi.org/10.1007/978-3-031-53401-0Artificial intelligence; Intelligent systems; Big data mining; D2D Communication; Security and privacy; A
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https://doi.org/10.1007/978-3-663-04953-1r effective communication. The system integrates speech recognition, morphological analysis, and image generation components, implemented on an assistive robot platform. Experiments were conducted to validate the system’s effectiveness, including speech recognition, morphological analysis, and image
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https://doi.org/10.1007/978-3-663-04954-8r’s intentions by using a speech recognition system. When the user in-structs the tidying-up robot to tidy up an object using voice commands, the robot detects and recognizes the object. To detect and recognize multiple objects, we employ an instance segmentation method using deep learning. This met
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