TSLNet: a hierarchical multi-head attention-enabled two-stream LSTM network for accurate pedestrian tracking and behavior recognition [0.03%]
基于层次结构的多头注意力两流LSTM网络在行人跟踪和行为识别中的应用研究
Shouye Lv,Rui He,Xiaofei Cheng et al.
Shouye Lv et al.
Accurate pedestrian tracking and behavior recognition are essential for intelligent surveillance, smart transportation, and human-computer interaction systems. This paper introduces TSLNet, a Hierarchical Multi-Head Attention-Enabled Two-St...
Adaptive-expert-weight-based load balance scheme for dynamic routing of MoE [0.03%]
基于自适应专家权重的MoE动态路由负载均衡方案
Jialin Wen,Xiaojun Li,Junping Yao et al.
Jialin Wen et al.
Load imbalance is a major performance bottleneck in training mixture-of-experts (MoE) models, as unbalanced expert loads can lead to routing collapse. Most existing approaches address this issue by introducing auxiliary loss functions to ba...
UHGAN: a dual-phase GAN with Hough-transform constraints for accurate farmland road extraction [0.03%]
基于霍夫变换约束的双重生成对抗网络算法的精准提取农田道路研究
Xinliang Wang,Yuan Ma
Xinliang Wang
Introduction: Traditional methods for farmland road extraction, such as U-Net, often struggle with complex noise and geometric features, leading to discontinuous extraction and insufficient sensitivity. To address these l...
UAV-based intelligent traffic surveillance using recurrent neural networks and Swin transformer for dynamic environments [0.03%]
基于无人机的智能交通监控：用于动态环境的循环神经网络和Swin变换器方法
Mohammed Alshehri,Ting Wu,Nouf Abdullah Almujally et al.
Mohammed Alshehri et al.
Introduction: Urban traffic congestion, environmental degradation, and road safety challenges necessitate intelligent aerial robotic systems capable of real-time adaptive decision-making. Unmanned Aerial Vehicles (UAVs), ...
End-to-end robot intelligent obstacle avoidance method based on deep reinforcement learning with spatiotemporal transformer architecture [0.03%]
基于时空变换器架构的深度强化学习端到端机器人智能避障方法
Yuwen Zhou,Weizhong Zhang
Yuwen Zhou
To enhance the obstacle avoidance performance and autonomous decision-making capabilities of robots in complex dynamic environments, this paper proposes an end-to-end intelligent obstacle avoidance method that integrates deep reinforcement ...
DWMamba: a structure-aware adaptive state space network for image quality improvement [0.03%]
DWMamba：一种结构感知自适应状态空间网络用于图像质量提升
Wenjun Fu,Xiaobin Wang,Chuncai Yang et al.
Wenjun Fu et al.
Overcoming visual degradation in challenging imaging scenarios is essential for accurate scene understanding. Although deep learning methods have integrated various perceptual capabilities and achieved remarkable progress, their high comput...
Tom Donnelly,Elena Seminati,Benjamin Metcalfe
Tom Donnelly
Introduction: Abandonment rates for myoelectric upper limb prostheses can reach 44%, negatively affecting quality of life and increasing the risk of injury due to compensatory movements. Traditional myoelectric prostheses...
Weizhen Tang,Jie Dai
Weizhen Tang
Introduction: To address the challenges of cumulative errors, insufficient modeling of complex spatiotemporal features, and limitations in computational efficiency and generalization ability in 4D trajectory prediction, t...
Correction: Pre-training, personalization, and self-calibration: all a neural network-based myoelectric decoder needs [0.03%]
纠正：预训练、个性化和自校准：基于神经网络的肌电解码器所需的一切
Chenfei Ma,Xinyu Jiang,Kianoush Nazarpour
Chenfei Ma
[This corrects the article DOI: 10.3389/fnbot.2025.1604453.]. Keywords: adaptation; deep learning; myoelectr...
Published Erratum
Frontiers in neurorobotics. 2025 Sep 19:19:1675642. DOI:10.3389/fnbot.2025.1675642 2025
RSA-TransUNet: a robust structure-adaptive TransUNet for enhanced road crack segmentation [0.03%]
鲁棒结构自适应TransformerUNET以提升路面裂缝分割性能的RSA-TransUNet方法
Liling Hou,Fei Yu,Yaowen Hu et al.
Liling Hou et al.
With the advancement of deep learning, road crack segmentation has become increasingly crucial for intelligent transportation safety. Despite notable progress, existing methods still face challenges in capturing fine-grained textures in sma...