Search

Your search keyword '"Yang, Yu"' showing total 2,062 results
Start Over Author "Yang, Yu" Publisher ieee
2,062 results on '"Yang, Yu"'

1. Addressing PIM Challenges in Radio Base Stations: Field Issues and Testing Methods for Large-Scale Deployments

Author

Zhang, Queenie, primary, Liu, Chuanting, additional, Meier, Ingolf, additional, Choudhury, Noor, additional, Deng, Lei, additional, Yue, Xiang, additional, Xu, Bo, additional, Yang, Yu, additional, Damm, Frieso, additional, An, Hao, additional, Tuncel, Nezahat Gunenc, additional, Haun, Martin, additional, and Jiang, Xiaoying, additional

Published
2024
Full Text
View/download PDF

8. Decoding Multi-DoF Movements Using a CST-Based Force Generation Model With Single-DoF Training

Author

Yang Xu, Yang Yu, Zeming Zhao, and Xinjun Sheng

Subjects
Simultaneous and proportional control, cumulative spike train, motor neuron synergy, motor unit spike train, HD-sEMG, Medical technology, R855-855.5, Therapeutics. Pharmacology, RM1-950
Abstract

Recent developments in dexterous myoelectric prosthetics have established a hardware base for human-machine interfaces. Although pattern recognition techniques have seen successful deployment in gesture classification, their applications remain largely confined to certain specific discrete gestures. Addressing complex daily tasks demands an immediate need for precise simultaneous and proportional control (SPC) for multiple degrees of freedom (DoFs) movements. In this paper, we introduce an SPC approach for multi-DoF wrist movements using the cumulative spike trains (CSTs) of motor unit pools, merely leveraging single-DoF training. The efficacy of our proposed approach was validated offline against existing methods respectively based on non-negative matrix factorization and motor unit spike trains, using experimental data. The experimental process includes both single-DoF (for training) and multi-DoF (for testing) movements. We evaluated the performance using Pearson correlation coefficient (R) and the normalized root mean square error (nRMSE). The results reveal that our method outperforms comparative approaches in force estimation for both testing datasets (3 and 4). On average, for dataset 3, R and nRMSE of the flexion/extension DoF (the pronation/supination DoF) are 0.923±0.037 (0.901±0.040) and 12.3±3.1% (12.9±2.2%); similarly, those of dataset 4 are 0.865±0.057 (0.837±0.053) and 14.9±2.9% (15.4±2.0%), respectively. The outcomes demonstrate the effectiveness of our method in simultaneous and proportional force estimation for multi-DoF wrist movements, showing a promising potential as a neural-machine interface for SPC of dexterous myoelectric prostheses.

Published
2024
Full Text
View/download PDF

9. Multi-UAV Coverage Path Assignment Algorithm Considering Flight Time and Energy Consumption

Author

Yang Yu and Sanghwan Lee

Subjects
Cooperative unmanned aerial vehicle teams, coverage path planning problems, CPP, path planning, multiple traveling salesmen problems, MTSP, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Coverage path planning is an important research direction for unmanned aerial vehicles (UAV), which are primarily used in ground search or mapping scenarios. Although there have been a large number of studies on UAV coverage path planning, there are still some problems in the cooperative work of multi-UAV in complex environments under the constraints of energy consumption and work efficiency requirements of UAV. To solve unnecessary energy loss when multiple UAVs jointly execute complex area coverage tasks and improve the overall work efficiency of the UAV swarm. In this study, we simulated the situation of multiple UAVs performing the mission of area coverage under the constraints of the corresponding energy consumption in a work environment with no-fly zones. We propose the use of multiple UAV base stations to provide takeoff and landing services for UAVs that perform missions. We proposed two algorithms. One is the multi-base station multi-UAV cooperative coverage path planning algorithm with flexible obstacle avoidance ability (MBS-MUCCPPAFOA), which performs coverage route planning from an entire area using multiple base stations to perform multi-UAV area coverage tasks. The other algorithm is a multi-UAV cooperative coverage path planning algorithm with flexible obstacle avoidance capability based on area segmentation (MUAV-CCPPAFOA-AS), which divides the entire work area and performs coverage path planning for each sub-area to perform multi-UAV area coverage tasks. We compared the proposed algorithm with the common scanning coverage path planning algorithm, and proved the robustness of the proposed MBS-MUCCPPAFOA and MUAV-CCPPAFOA-AS algorithms.

Published
2024
Full Text
View/download PDF

50. Edge-Guided Parallel Network for VHR Remote Sensing Image Change Detection

Author

Ye Zhu, Kaikai Lv, Yang Yu, and Wenjia Xu

Subjects
Change detection (CD), convolutional neural networks (CNNs), difference features, edge-guided network, remote sensing, two-stream architecture, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809
Abstract

Change detection (CD) is an important research topic in the remote sensing field, and it has a wide range of applications, including resource monitoring, disaster assessment, urban planning, etc. Recently, deep learning (DL) has shown its advantages in CD. However, most existing DL-based methods cannot capture the complementary information between bitemporal and difference features. This article proposes an edge-guided parallel network (EGPNet) to solve this problem. First, our EGPNet extracts bitemporal and difference features simultaneously through a parallel encoding framework. During parallel encoding, we design a supplementary mechanism to enrich the difference features with bitemporal features. Second, we fuse bitemporal and difference features at each feature level to sufficiently exploit their complementarity. Finally, the edge-aware module and edge-guidance feature module are introduced to enhance the edge representation for improving blurred edges of detection results. Benefiting from the rich change-related information in difference features and detailed information in bitemporal features, our EGPNet can detect change regions entirely and accurately. Experimental results on the LEVIR-CD, SYSU-CD, and CDD datasets demonstrate that the proposed method outperforms several state-of-the-art approaches. Especially, our EGPNet can detect more precise and sharper edges than other methods.

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results