Results 101 to 110 of about 211,113 (260)
This article develops a soft magnetic sensor array to extract 3D and distributional muscle deformations, which has highly consistent measurements in amphibious environments, robustness to hydraulic pressure, and about 200 ms faster response than an inertial measurement unit, achieving over 98% classification accuracy and below 3% phase estimation ...
Yuchao Liu +8 more
wiley +1 more source
Multi-stage refinement network for point cloud completion based on geodesic attention
The attention mechanism has significantly progressed in various point cloud tasks. Benefiting from its significant competence in capturing long-range dependencies, research in point cloud completion has achieved promising results.
Yuchen Chang, Kaiping Wang
doaj +1 more source
Riemannian Geometry for the Classification of Brain States with Intracortical Brain Recordings
Geometric machine learning is applied to decode brain states from invasive intracortical neural recordings, extending Riemannian methods to the invasive regime where data is scarcer and less stationary. A Minimum Distance to Mean classifier on covariance manifolds uses geodesic distances to outperform convolutional neural networks while reducing ...
Arnau Marin‐Llobet +9 more
wiley +1 more source
BMPCQA: Bioinspired Metaverse Point Cloud Quality Assessment Based on Large Multimodal Models
This study presents a bioinspired metaverse point cloud quality assessment metric, which simulates the human visual evaluation process to perform the point cloud quality assessment task. It first extracts rendering projection video features, normal image features, and point cloud patch features, which are then fed into a large multimodal model to ...
Huiyu Duan +7 more
wiley +1 more source
Identifying non‐small cell lung cancer (NSCLC) subtypes is essential for precision cancer treatment. Conventional methods are laborious, or time‐consuming. To address these concerns, RPSLearner is proposed, which combines random projection and stacking ensemble learning for accurate NSCLC subtyping. RPSLearner outperforms state‐of‐the‐art approaches in
Xinchao Wu, Jieqiong Wang, Shibiao Wan
wiley +1 more source
A hierarchical multimodal framework coupling a large language model for task decomposition and semantic mapping with a fine‐tuned vision‐language model for semantic perception, enhanced by GridMask, is presented. An aerial‐ground robot team exploits the semantic map for global and local planning.
Haokun Liu +6 more
wiley +1 more source
Parametric Surfaces for Elliptic and Hyperbolic Geometries
Background/Objectives: In computer graphics, virtual worlds are constructed and visualized through algorithmic processes. These environments are typically populated with objects defined by mathematical models, traditionally based on Euclidean geometry ...
László Szirmay-Kalos +3 more
doaj +1 more source
A surrogate‐model‐based framework is proposed for combining high‐fidelity finite element method and efficient physics simulations to enable fast, accurate soft robot simulation for reinforcement learning, validated through sim‐to‐real experiments. Soft robotics holds immense promise for applications requiring adaptability and compliant interactions ...
Taehwa Hong +3 more
wiley +1 more source
Logarithmic corrections for near-extremal black holes
We present the computation of logarithmic corrections to near-extremal black hole entropy from one-loop Euclidean gravity path integral around the near-horizon geometry. We extract these corrections employing a suitably modified heat kernel method, where
Nabamita Banerjee +2 more
doaj +1 more source
Determining Levels of Affective States with Riemannian Geometry Applied to EEG Signals
Emotion recognition from electroencephalography (EEG) often relies on Euclidean features that ignore the curved geometry of covariance matrices. We introduce a Riemannian-manifold pipeline which, combined with the Fisher Geodesic Minimum Distance to Mean
Agnieszka Wosiak +2 more
doaj +1 more source

