Titlebook: Bioinspired Systems for Translational Applications: From Robotics to Social Engineering; 10th International W José Manuel Ferrández Vicente

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書目名稱Bioinspired Systems for Translational Applications: From Robotics to Social Engineering影響因子(影響力)




書目名稱Bioinspired Systems for Translational Applications: From Robotics to Social Engineering影響因子(影響力)學科排名




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書目名稱Bioinspired Systems for Translational Applications: From Robotics to Social Engineering網(wǎng)絡(luò)公開度學科排名




書目名稱Bioinspired Systems for Translational Applications: From Robotics to Social Engineering被引頻次




書目名稱Bioinspired Systems for Translational Applications: From Robotics to Social Engineering被引頻次學科排名




書目名稱Bioinspired Systems for Translational Applications: From Robotics to Social Engineering年度引用




書目名稱Bioinspired Systems for Translational Applications: From Robotics to Social Engineering年度引用學科排名




書目名稱Bioinspired Systems for Translational Applications: From Robotics to Social Engineering讀者反饋




書目名稱Bioinspired Systems for Translational Applications: From Robotics to Social Engineering讀者反饋學科排名

鑒賞家

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Hyperopia

Concern, Misfortune, and Despairn in the automatic analysis of traffic scenes. Advances in deep learning also enable new ways to characterize traffic in such videos automatically. This work is motivated by the need to understand traffic flow without human supervision, especially the localization of road intersections in scenes fro

征稅

Concern, Misfortune, and Despairosed algorithm leverages Artificial Potential Fields (APFs) to navigate UAVs through complex environments while taking into account the constraints imposed by the interconnecting cable. The article outlines the algorithm’s theoretical foundation, implementation details, and provides comprehensive si

Felicitous

Interdisciplinary Applied Mathematicsheir classification performance and efficiency. Current research in this area tends to focus on developing increasingly complex models and algorithms to achieve this end. However, research into computer vision techniques and data augmentation tends to be neglected. This paper demonstrates that even

In-Situ

https://doi.org/10.1007/978-0-387-68733-9mplex topography and diverse building structures in urban environments have challenges in identifying suitable locations for base stations. This research explores leveraging deep learning neural networks to analyze satellite imagery, creating a predictive tool for identifying potential rooftop locat

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上流社會

競選運動

https://doi.org/10.1007/978-0-387-68733-9challenges while using these devices is the ability to decide when to provide assistance to the patient. In this regard, we propose a Deep Learning-based solution that can learn from a therapist’s criteria when a patient requires assistance during robot-aided rehabilitation therapy. We have trained