检索结果-内蒙古大学图书馆

Multi-agent Collaboration for Vehicular Task Offloading Using Federated Deep Reinforcement Learning

IEEE Transactions on Mobile computing 2025年

作者： Chen, Xing Xiao, Bohuai Lin, Xinyu Chen, Zheyi Min, Geyong Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science ExeterEX4 4QF United Kingdom

Mobile Edge computing (MEC) distributes resources such as computing, storage, and bandwidth to the side close to users, which can provide low-latency services to in-vehicle users, thus promising a more efficient and safer driving environment. However, due to the dynamic scale of vehicle and the variability of resource requirements, it is a significant challenge to quickly obtain effective task offloading in large-scale vehicle scenarios. The existing studies generally adopt the centralized decision-making method, with long decision-making time and high computational overhead, which cannot effectively achieve good offloading decisions in large-scale scenarios. To address these problems, we propose a Multi-agent Collaborative Method for vehicular task offloading using Federated Deep Reinforcement Learning called MCM-FDRL. First, each vehicle as an agent, independently makes offloading decisions based on local information. Next, the offloading decision model of each vehicle is obtained through federated reinforcement learning training. At runtime, an effective vehicle offloading plan can be gradually developed through multi-agent collaboration. Using two real-world datasets, experiments show that the MCM-FDRL has good adaptability and scalability. Moreover, compared to the state-of-the-art methods, the task's average response time of the MCM-FDRL is reduced by 9.75%-64.90%, respectively. © 2002-2012 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

PD-SDF: Dynamic Surface Reconstruction Based on Plane Decomposition for Single View RGB-D Videos

PD-SDF: Dynamic Surface Reconstruction Based on Plane Decomp...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Jinwen Xie, Weixing Yao, Junfeng Wu, Shaoqi Peng, Youhong Ge, Mengyuan Dong, Xiao Center for Digital Media Computing School of Film School of Informatics Xiamen University China National Institute for Data Science in Health and Medicine Xiamen University China Jiujiang Research Institute of Xiamen University Jiujiang China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism China Guangdong Provincial/Zhuhai Key Laboratory of IRADS BNU-HKBU United International College Zhuhai China

ISBN: (纸本)9798350368741

Surface reconstruction of dynamic scenes from single view videos is a challenging task due to the highly ill-posed and under-constrained nature. Existing single view reconstruction methods suffer from severe quality issues, such as surface distortion and mesh adherison. In this paper, we propose an efficient dynamic representation network, PD-SDF, which consists of a 4D motion field and a 3D geometry field. The explicit disentanglement of motion and geometry based on planar factorization guarantees the mesh consistency across frames. Specifically, to address the mesh adhesion problem, we design a depth-guided sampling strategy to focus on optimizing the SDF field near the object surface. Due to insufficient geometric cues, we design various regularization strategies to constrain smoothness and topological correcness of the scene geometry. Extensive experiments show that our method outperforms existing methods in both appearance and geometry reconstruction. The project page: https://***/. © 2025 IEEE.

关键词： Dynamic Surface Reconstruction Neural SDF Field Single View Reconstruction

来源：评论

学校读者我要写书评

暂无评论

DMSACNN: Deep Multiscale Attentional Convolutional Neural Network for EEG-Based Motor Decoding

引用

IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Liu, Ke Xing, Xin Yang, Tao Yu, Zhuliang Xiao, Bin Wang, Guoyin Wu, Wei Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing China Chongqing University of Posts and Telecommunications Key Laboratory of Cyberspace Big Data Intelligent Security of Ministry of Education China South China University of Technology College of Automation Science and Engineering Guangzhou510641 China Chongqing Normal University National Center for Applied Mathematics in Chongqing Chongqing401331 China Shanghai Jiao Tong University School of Medicine Songjiang Hospital Songjiang Research Institute Shanghai Key Laboratory of Emotions and Affective Disorders Shanghai201600 China

Objective: Accurate decoding of electroencephalogram (EEG) signals has become more significant for the brain-computer interface (BCI). Specifically, motor imagery and motor execution (MI/ME) tasks enable the control of external devices by decoding EEG signals during imagined or real movements. However, accurately decoding MI/ME signals remains a challenge due to the limited utilization of temporal information and ineffective feature selection methods. Methods: This paper introduces DMSACNN, an end-to-end deep multiscale attention convolutional neural network for MI/ME-EEG decoding. DMSACNN incorporates a deep multiscale temporal feature extraction module to capture temporal features at various levels. These features are then processed by a spatial convolutional module to extract spatial features. Finally, a local and global feature fusion attention module is utilized to combine local and global information and extract the most discriminative spatiotemporal features. Main results: DMSACNN achieves impressive accuracies of 78.20%, 96.34% and 70.90% for hold-out analysis on the BCI-IV-2a, High Gamma and OpenBMI datasets, respectively, outperforming most of the state-of-the-art methods. Conclusion and significance: These results highlight the potential of DMSACNN in robust BCI applications. Our proposed method provides a valuable solution to improve the accuracy of the MI/ME-EEG decoding, which can pave the way for more efficient and reliable BCI systems. The source code for DMSACNN is available at https://***/xingxin-99/***. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Meta-Transfer Learning Based Cross-Domain Gesture Recognition Using WiFi Channel State Information

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Dai, Penglin Zhou, Junfei Ma, Jialong Zhang, Hao Wu, Xiao Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China Ministry of Education Engineering Research Center of Sustainable Urban Intelligent Transportation China Tangshan Institute of Southwest Jiaotong University Tangshan063000 China Chongqing University of Posts and Telecommunications College of Computer Science and Technology Key Laboratory of Data Engineering and Visual Computing Chongqing400065 China

Gesture recognition plays a crucial role in a wide range of consumer electronics applications, including human-computer interaction and virtual reality, by enabling the identification and interpretation of human gestures. In recent times, WiFi-based gesture recognition has garnered significant attention due to its privacy protection and unobtrusive nature. However, this approach heavily depends on neural network-based models and is notably influenced by environmental conditions, such as specific location and orientation. To address these environmental impacts, we propose a network framework that leverages transfer learning and meta-learning. The primary focus is on utilizing transfer learning to train a convolutional neural network (ResNet) for feature extraction, enabling the extraction of domain-independent features specifically related to gestures, rather than being influenced by environmental factors. Additionally, we employ the meta-learning algorithm MAML to train the fully connected network for gesture classification. Following training, a small set of samples can be utilized for rapid adaptation to diverse domains, maintaining high accuracy across different domains to achieve cross-domain gesture recognition. Our performance evaluation is based on the Widar 3.0 dataset, encompassing both in-domain and cross-domain scenarios. The simulation results demonstrate that our proposed algorithm surpasses Widar3.0 and WiGRUNT by approximately 7.9% and 1.12% in gesture recognition accuracy across all scenarios. © 2025 IEEE.

关键词： Wi-Fi

来源：评论

学校读者我要写书评

暂无评论

RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling Under Continuous Representation 15th

RobNODDI: Robust NODDI Parameter Estimation with Adaptive S...

引用

15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Xiao, Taohui Cheng, Jian Fan, Wenxin Yang, Jing Li, Cheng Dong, Enqing Wang, Shanshan School of Mechanical Electrical and Information Engineering Shandong University Weihai264209 China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Zhejiang Hangzhou China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Peng Cheng Laboratory Guangdong Shenzhen China

ISBN: (纸本)9783031869198

Neurite Orientation Dispersion and Density Imaging (NODDI) is an important imaging technology used to evaluate the microstructure of brain tissue, which is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods perform parameter estimation through diffusion magnetic resonance imaging (dMRI) with a small number of diffusion gradients. These methods speed up parameter estimation and improve accuracy. However, the diffusion directions used by most existing deep learning models during testing needs to be strictly consistent with the diffusion directions during training. This results in poor generalization and robustness of deep learning models in dMRI parameter estimation. In this work, we first verify that the parameter estimation performance of current mainstream methods will significantly decrease when the testing diffusion directions and the training diffusion directions are inconsistent. A robust NODDI parameter estimation method with adaptive sampling under continuous representation (RobNODDI) is proposed. Furthermore, long short-term memory (LSTM) units and fully connected layers are selected to learn continuous representation signals. To this end, we use a total of 100 subjects to conduct experiments based on the Human Connectome Project (HCP) dataset, of which 60 are used for training, 20 are used for validation, and 20 are used for testing. The test results indicate that RobNODDI improves the generalization performance and robustness of the deep learning model, enhancing the stability and flexibility of deep learning NODDI parameter estimatimation applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Provably Secure Authenticated Key-Management Mechanism for e-Healthcare Environment

引用

IEEE Internet of Things Journal 2025年

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Ghaffar, Zahid Xie, Yong Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Douliu64002 Taiwan Qinghai Institute of Technology School of Computer and Information Science China Qinghai Provincial Laboratory for Intelligent Computing and Application China Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan250353 China

The Internet of Things (IoT) is rapidly permeating all aspects of human life, involving a network of devices that share sensitive data. A notable application is the e-healthcare systems, which employ connected sensors, medical servers, and wearable devices. However, the public nature of communication in e-healthcare systems poses challenges such as security, privacy, and authentication of participating entities. Recently, many authentication protocols have been introduced to address these challenges. However, most of these protocols remain vulnerable to various security attacks, including device or medical server impersonation, denial of service, physical or cloning, and de-synchronization attacks. Therefore, we introduce an authenticated key-management protocol utilizing hash functions and Cipher-Block Chaining-Advanced Encryption Standard encryption (CBC-AES) encryption. The proposed protocol also employs the Physical Unclonable Function (PUF), which makes it more robust and efficient in resisting physical or cloning attacks. Additionally, the proposed scheme resists various security threats, including impersonation, session key leakage, ephemeral secret leakage, and de-synchronization attacks. We analyze the scheme’s security and reliability through formal and informal analysis. The informal analysis demonstrates that the scheme encompasses crucial security features, while the formal analysis substantiates. Moreover, performance analysis of the proposed protocol with various competing results indicates that our protocol achieves an average reduction in communication and computation overheads by 36.03.% and 41.79%, respectively. © 2014 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

LDG: Lightweight Deformable 3D Gaussians for Single-View Dynamic Scene Reconstruction

LDG: Lightweight Deformable 3D Gaussians for Single-View Dyn...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Youhong Peng Weixing Xie Jinwen Li Shaoqi Wu Zefeng Wang Bingbing Hu Junfeng Yao Center for Digital Media Computing School of Film School of Informatics Xiamen University China National Institute for Data Science in Health and Medicine Xiamen University China Ministry of Culture and Tourism Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent deformable 3D Gaussians methods achieve high-quality reconstruction and real-time rendering. However, they require multi-view information and are not applicable to single-view dynamic scenes captured from mobile phones. Additionally, the high-dimensional hidden layer of deformation MLP and the excessive number of Gaussian primitives and attributes impose significant storage pressure, greatly limiting their practical application. To address the issues, we propose a novel Lightweight Deformable 3D Gaussians teacher-student framework. Specifically, we initialize Gaussian primitives with an initialization strategy designed for single-view scenes, and then optimize the teacher model using color and depth information. For the trained teacher model, we distill deformation MLP, prune Gaussian primitives and Gaussian attributes, and finally obtain a student model with low storage and high efficiency. Public benchmark experiments demonstrate the effectiveness of our framework, showing a compression rate exceeding 4× while maintaining satisfactory rendering quality. Project page: https://***/ldg/.

关键词： Deformable models Training Three-dimensional displays Deformation Memory management Color Rendering (computer graphics) Real-time systems Mobile handsets Speech processing

来源：评论

学校读者我要写书评

暂无评论

LP-Gaussians: Learnable Parametric Gaussian Splatting for Efficient Dynamic Reconstruction of Single-View Scenes

LP-Gaussians: Learnable Parametric Gaussian Splatting for Ef...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shaoqi Wu Weixing Xie Youhong Peng Jinwen Li Jiawei Yao Junfeng Yao Center for Digital Media Computing School of Film School of Informatics Xiamen University China National Institute for Data Science in Health and Medicine Xiamen University China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

With the popularity of short video platforms, the number of single-view videos has increased significantly. Existing NeRF-based methods can reconstruct dynamic scenes in a single-view setting, but slow rendering speed and low rendering quality limit their practical applications. To address these challenges, we propose a fast single-view scenes reconstruction framework based on 3D Gaussian Splatting. Our method uses point clouds obtained with depth priors as the Gaussian initialization and introduces learnable parametric functions to model the time-dependent deformation of Gaussians. The explicit deformation modeling for Gaussians significantly reduces training and rendering time. Furthermore, to improve the rendering quality of challenging areas, we adopt an adaptive sampling strategy to densify Gaussians. For occlusion problems from single-view videos, we design a smooth loss function to restore the color of the occluded areas. Experimental results demonstrate that our method significantly reduces training time, enhances rendering quality, and accelerates rendering speed. Project page: https://***/LPGaussians.

关键词： Training Point cloud compression Deformable models Adaptation models Three-dimensional displays Deformation Streaming media Signal processing Rendering (computer graphics) Speech processing

来源：评论

学校读者我要写书评

暂无评论

Single-cell technologies: current and near future

引用

science China(Life sciences) 2025年第1期68卷 1-4页

作者： Chenfei Wang Qi Liu Xiaohui Fan Tieliu Shi KeyLaboratory of Spine and Spinal CordInjury Repairand Regeneration Ministry of EducationOrthopedics DepartmentTongji HospitalBioinformatics DepartmentSchoolofLifeSciencesandTechnologyTongji UniversityShanghai 200082China Frontier Science Center for Stem Cells School of Life Sciences and TechnologyTongji UniversityShanghai 200092China Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Research Institute of Intelligent Computing Zhejiang LabHangzhou 311121China Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314103China Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Key Laboratory of Advanced Theory and Application in Statistics and Data Science(MOE) School of StatisticsEast China Normal UniversityShanghai 200062China

Single-cell technologies enable the indepth exploration of multiple biological hierarchies at the scale of individual cells,which have deepened our knowledge of cellular diversity,tissue organization,and overall organism function(Sun et al.,2024).In 2009,single-cell RNA sequencing(scRNA-seq)was first developed as a powerful tool to dissect gene expression and uncover transcriptional *** the rapid advancement of technology,single-cell approaches have expanded to encompass other omics,such as genomics,epigenomics,proteomics,and ***,multimodal technologies including spatially resolved transcriptomics and clustered regularly interspaced short palindromic repeats(CRISPR)screening at the single-cell level further advanced our ability to comprehensively understand cellular ***-cell methods provide deeper insights into disease mechanisms across various conditions,including cancer,developmental disorders,and aging-associated *** this issue,we focus on topics related to single-cell methods,covering the following four aspects:(i)single-cell multi-omic sequencing technology;(ii)single-cell spatial technology;(iii)singlecell CRISPR screening technology;(iv)applications of single-cell technology.

关键词： expanded enable insight

来源：评论

学校读者我要写书评

暂无评论

NOBGP: A Novel Optimized Balanced Graph Partitioning Algorithm 19th

NOBGP: A Novel Optimized Balanced Graph Partitioning Algorit...

引用

19th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2024

作者： Chen, Jiebin Hu, Ziqiang Ye, Renjie Zhang, Qishan Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China Xianda College of Economics and Humanities Shanghai International Studies University Shanghai China

ISBN: (纸本)9789819623723

Large-scale graphs have become prevalent with the advent of the big data era. Distributed graph computing systems are commonly used for processing and analyzing large-scale graphs, with graph partitioning being a key prerequisite for their efficient computation. Graph partitioning aims to balance the load across partitions while minimizing the number of cut-edges. Moreover, it should achieve high efficiency and scalability. However, the existing popular graph partitioning algorithms do not fully take into account the internal topology of real-world graphs, which affects the final partition quality and convergence. Meanwhile, they easily fall into the local optimum due to partition load constraints. This paper introduces a Novel Optimized Balanced Graph Partitioning algorithm (NOBGP). First, we propose an initialization strategy based on label propagation of core vertices to achieve initial partitions with good locality and accelerate convergence. Second, we optimize the label propagation process to ensure balanced partitions and propose a probability-based disruption strategy to avoid the local optimum. We implement NOBGP on the distributed graph computing framework GraphX. Extensive experimental results on real-world graphs show that the proposed algorithm is scalable and performs better than the existing algorithms. We also run PageRank and Louvain applications using the graph partitioning results to demonstrate the efficiency of our algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph algorithms

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：