Results 161 to 170 of about 5,326,339 (296)

Dealing with Topological Information within a Fully Convolutional Neural Network

, 2018
International audienceA fully convolutional neural network has a receptive field of limited size and therefore cannot exploit global information, such as topological information.
Lay, Bruno, Baldeweck, Thérèse, Chen, Juanjuan, Gauthier, Gervais, Decencière, Etienne, Min, Fu, Burdin, Hélène, Bornschloegl, Thomas, Velasco-Forero, Santiago   +8 more
core   +1 more source

3D Printing of Soft Robotic Systems: Advances in Fabrication Strategies and Future Trends

Advanced Robotics Research, EarlyView.
Collectively, this review systematically examines 3D‐printed soft robotics, encompassing material selections, function integration, and manufacturing methodologies. Meanwhile, fabrication strategies are analyzed in order of increasing complexity, highlighting persistent challenges with proposed solutions.
Changjiang Liu, Bin Cui, Xumin Huang, Xiaoyu (Rayne) Zheng, T. Thang Vo‐Doan, Ruirui Qiao   +5 more
wiley   +1 more source

A Fully Convolutional Network-Based Tube Contour Detection Method Using Multi-Exposure Images. [PDF]

Sensors (Basel), 2021
Cheng X, Sun J, Zhou F.
europepmc   +1 more source

Fully convolutional neural network for removing background in noisy images of uranium bearing particles

, 2017
Removal of background signal with near 99% accuracy from particle SIMS images using a fully convolutional neural network.
Benjamin E. Naes, Lamar Butler, David Willingham, Keeyahna Foster, Caleb M. Gumbs, Jay G. Tarolli, Andrea L. Howard   +6 more
core   +1 more source

A Review on Sensor Technologies, Control Approaches, and Emerging Challenges in Soft Robotics

Advanced Robotics Research, EarlyView.
This review provides an introspective of sensors and controllers in soft robotics. Initially describing the current sensing methods, then moving on to the control methods utilized, and finally ending with challenges and future directions in soft robotics focusing on the material innovations, sensor fusion, and embedded intelligence for sensors and ...
Ean Lovett, Maxwell Hammond, Niloufar Seyfi, Amirreza Fahim Golestaneh, Venanzio Cichella, Caterina Lamuta   +5 more
wiley   +1 more source

Optimising a Simple Fully Convolutional Network for Accurate Brain Age Prediction in the PAC 2019 Challenge. [PDF]

Front Psychiatry, 2021
Gong W, Beckmann CF, Vedaldi A, Smith SM, Peng H.   +4 more
europepmc   +1 more source

Improving the Robustness of Visual Teach‐and‐Repeat Navigation Using Drift Error Correction and Event‐Based Vision for Low‐Light Environments

Advanced Robotics Research, EarlyView.
Visual teach‐and‐repeat (VTR) navigation allows robots to learn and follow routes without building a full metric map. We show that navigation accuracy for VTR can be improved by integrating a topological map with error‐drift correction based on stereo vision.
Fuhai Ling, Ze Huang, Tony J. Prescott
wiley   +1 more source

Fully convolutional network-based multi-output model for automatic segmentation of organs at risk in thorax. [PDF]

Sci Prog, 2021
Zhang J, Yang Y, Shao K, Bai X, Fang M, Shan G, Chen M.   +6 more
europepmc   +1 more source

Multimodal Engagement Assessment in Children During Invented Story Paradigm With a Social Robot

Advanced Robotics Research, EarlyView.
A multimodal framework is proposed to assess children's engagement during storytelling interactions with a social robot. Gaze, physiological, and behavioral data are combined and validated against observer ratings. An automated gaze‐labeling strategy is introduced, and supervised classifiers achieve high accuracy. The study supports scalable engagement
Laura Fiorini, Elena Adelucci, Stefano Scatigna, Lorenzo Pugi, Alice Bruni, Benedetta Carotenuto, Chiara Pecini, Filippo Cavallo   +7 more
wiley   +1 more source

Multimodal Human–Robot Interaction Using Human Pose Estimation and Local Large Language Models

Advanced Robotics Research, EarlyView.
A multimodal human–robot interaction framework integrates human pose estimation (HPE) and a large language model (LLM) for gesture‐ and voice‐based robot control. Speech‐to‐text (STT) enables voice command interpretation, while a safety‐aware arbitration mechanism prioritizes gesture input for rapid intervention.
Nasiru Aboki, Ilche Georgievski, Marco Aiello   +2 more
wiley   +1 more source