Titlebook: 7th EAI International Conference on Robotic Sensor Networks; EAI ROSENET 2023 Ömer Melih Gül,Paolo Fiorini,Seifedine Nimer Kadry Conference

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书目名称7th EAI International Conference on Robotic Sensor Networks影响因子(影响力)




书目名称7th EAI International Conference on Robotic Sensor Networks影响因子(影响力)学科排名




书目名称7th EAI International Conference on Robotic Sensor Networks网络公开度




书目名称7th EAI International Conference on Robotic Sensor Networks网络公开度学科排名




书目名称7th EAI International Conference on Robotic Sensor Networks被引频次




书目名称7th EAI International Conference on Robotic Sensor Networks被引频次学科排名




书目名称7th EAI International Conference on Robotic Sensor Networks年度引用




书目名称7th EAI International Conference on Robotic Sensor Networks年度引用学科排名




书目名称7th EAI International Conference on Robotic Sensor Networks读者反馈




书目名称7th EAI International Conference on Robotic Sensor Networks读者反馈学科排名

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https://doi.org/10.1007/978-3-7091-4463-3ge. Additionally, the challenge associated with developing analytical models for soft robotic arms is compounded by the often unpredictable variability of relevant mechanical properties inherent in these systems. Recent efforts in this domain have focused on exploring the potential of employing neur

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https://doi.org/10.1007/978-3-7091-4463-3rks published from 2019 onward. The implementation of network slicing plays a crucial role in addressing the varied service requirements of 5G, encompassing improved mobile broadband, highly reliable low-latency communication, and extensive machine-type communication. The classification of papers in

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Die Schubfestigkeit des Betons,on. Monocular depth estimation from video can be improved by incorporating temporal information across frames. The recently introduced sequence modeling techniques of recurrent long-short-term memory (LSTM) networks and Transformer architectures provide two potential approaches for aggregating tempo

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,Note sur les grands ponts en béton armé,ge recognition among scientists. One of the difficulties in image recognition is obtaining a sufficiently large and well-balanced dataset to improve the effectiveness, robustness, and reliability of machine learning models. This paper explores a research approach that addresses this issue by style t

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