3rd EAI International Conference on Robotic Sensor Networks, 1st ed. 2021 ROSENET 2019 EAI/Springer Innovations in Communication and Computing Series

This proceedings presents the papers of the 3rd EAI International Conference on Robotic Sensor Networks (ROSENET 2019).  The conference explores the integration of networks and robotic technologies, which has become a topic of increasing interest for both researchers and developers from academic fields and industries worldwide. The authors posit that big networks will be the main approach to the next generation of robotic research, The book discusses how the explosive number of network models and increasing computational power of computers significantly extends the number of potential applications for robotic technologies while also bringing new challenges to each network's community. The conference provided a platform for researchers to share up-to-date scientific achievements in this field. The conference took place August 17, 2019, Kitakyushu, Japan.

• Presents the proceedings of the 3rd EAI International Conference on Robotic Sensor Networks (ROSENET 2019),August 17, 2019, Kitakyushu, Japan
• Features papers on robotic technologies for healthcare, medicine, military and more
• Includes perspectives from a multi-disciplinary selection of global researchers, academics, and professionals

Introduction.-  Blood Vessels Image Segmentation Based on Clifford Algebra and Voronoi Diagram.- Reinforcement Learning Based Cell Intelligent Multi-mode Frequency Reuse Method.- Image Registration Method for Temporal Substraction Based on Salient Region Features.- Extreme ROS Reality: A Representation Framework for Robots Using Image Dehazing and VR.- Double-Blinded Finder: A Two-Side Privacy-Preserving Approach for Finding Missing Children.- Complex Object Illumination Transfer through Semantic and Material Parsing and Composition.- Global-best Leading Artificial Bee Colony Algorithms.- Position Control of Ultrasonic Motor using PID Control Combined with Artificial Bee Colony Type Neural Networks.- An Adaptable Feature Synthesis for Camouflage.- Conclusion.