检索结果-内蒙古大学图书馆

Near-field communications:characteristics,technologies,and engineering

Frontiers of Information Technology & Electronic engineering 2024年第12期25卷 1580-1626页

作者： Yajun ZHAO Linglong DAI Jianhua ZHANG Ran JI Mengnan JIAN Hao XUE Hongkang YU Yunqi SUN Yu LU Zidong WU Zhuo XU Jinke LI Haiyang MIAO Zhiqiang YUAN Pan TANG Jiayu SHEN Tierui GONG Haixia LIU Jiaqi HAN Qiang FENG Zhi CHEN Lingxiang LI Gang YANG Yong ZENG Cunhua PAN Wang LIU Kangda ZHI Weidong HU Yuanwei LIU Xidong MU Chau YUEN Mérouane DEBBAH Chongwen HUANG Long LI Ping ZHANG State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen 518055China ZTE Corporation Beijing 100192China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310007China Key Laboratory of High Speed Circuit Design and EMC of Ministry of Education School of Electronic EngineeringXidian UniversityXi’an 710071China Collaborative Innovation Center of Information Sensing and Understanding Xidian UniversityXi’an 710071China National Key Laboratory of Wireless Communications University of Electronic Science and Technology of ChinaChengdu 611731China National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China Purple Mountain Laboratories Nanjing 211111China School of Electrical Engineering and Computer Science Technical University of BerlinBerlin 10623Germany School of Integrated Circuits and Electronics Beijing Institute of TechnologyBeijing 100081China Department of Electrical and Electronic Engineering The University of Hong KongHong KongChina Centre for Wireless Innovation(CWI) School of ElectronicsElectrical Engineering and Computer ScienceQueen’s University BelfastBelfast BT39DTUK School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore KU 6G Research Center Khalifa University of Science and TechnologyAbu DhabiUnited Arab Emirates

Near-field technology is increasingly recognized due to its transformative potential in communication systems,establishing it as a critical enabler for sixth-generation(6G)telecommunication *** paper presents a comprehensive survey of recent advancements in near-field technology ***,we explore the near-field propagation fundamentals by detailing definitions,transmission characteristics,and performance ***,we investigate various near-field channel models—deterministic,stochastic,and electromagnetic information theory based models,and review the latest progress in near-field channel testing,highlighting practical performance and *** evolving channel models,traditional mechanisms such as channel estimation,beamtraining,and codebook design require redesign and optimization to align with near-field propagation *** then introduce innovative beam designs enabled by near-field technologies,focusing on non-diffractive beams(such as Bessel and Airy)and orbital angular momentum(OAM)beams,addressing both hardware architectures and signal processing frameworks,showcasing their revolutionary potential in near-field communication ***,we highlight progress in both engineering and standardization,covering the primary 6G spectrum allocation,enabling technologies for near-field propagation,and network deployment ***,we conclude by identifying promising future research directions for near-field technology development that could significantly impact system *** comprehensive review provides a detailed understanding of the current state and potential of near-field technologies.

关键词： 6G Near-field technology Channel model Codebook Non-diffractive beams Orbital angular momentum engineering and standardization

来源：评论

学校读者我要写书评

暂无评论

A Phase Estimation Algorithm for Quantum Speed-Up Multi-Party Computing

引用

computers, Materials & Continua 2021年第4期67卷 241-252页

作者： Wenbin Yu Hao Feng Yinsong Xu Na Yin Yadang Chen Zhi-Xin Yang Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Jiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Department of Computer Science and Engineering Michigan State UniversityEast Lansing48824MIUSA State Key Laboratory of Internet of Things for Smart City and Department of Electromechanical Engineering University of Macao999078Macao

Security and privacy issues have attracted the attention of researchers in the field of IoT as the information processing scale grows in sensor *** computing,theoretically known as an absolutely secure way to store and transmit information as well as a speed-up way to accelerate local or distributed classical algorithms that are hard to solve with polynomial complexity in computation or *** this paper,we focus on the phase estimation method that is crucial to the realization of a general multi-party computing model,which is able to be accelerated by quantum algorithms.A novel multi-party phase estimation algorithm and the related quantum circuit are proposed by using a distributed Oracle operator with *** proved theoretical communication complexity of this algorithm shows it can give the phase estimation before applying multi-party computing efficiently without increasing any additional ***,a practical problem of multi-party dating investigated shows it can make a successful estimation of the number of solution in advance with zero communication complexity by utilizing its special statistic *** simulations present the correctness,validity and efficiency of the proposed estimation method.

关键词： Edge computing security multi-party computing quantum algorithm phase estimation communication complexity

来源：评论

学校读者我要写书评

暂无评论

FedABC: Targeting Fair Competition in Personalized Federated Learning

arXiv

引用

arXiv 2023年

作者： Wang, Dui Shen, Li Luo, Yong Hu, Han Su, Kehua Wen, Yonggang Tao, Dacheng National Engineering Research Center for Multimedia Software School of Computer Science Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China Hubei Luojia Laboratory Wuhan China JD Explore Academy China School of Information and Electronics Beijing Institute of Technology China School of Computer Science and Engineering Nanyang Technological University Singapore

Federated learning aims to collaboratively train models without accessing their client’s local private data. The data may be Non-IID for different clients and thus resulting in poor performance. Recently, personalized federated learning (PFL) has achieved great success in handling Non-IID data by enforcing regularization in local optimization or improving the model aggregation scheme on the server. However, most of the PFL approaches do not take into account the unfair competition issue caused by the imbalanced data distribution and lack of positive samples for some classes in each client. To address this issue, we propose a novel and generic PFL framework termed Federated Averaging via Binary Classification, dubbed FedABC. In particular, we adopt the "one-vs-all" training strategy in each client to alleviate the unfair competition between classes by constructing a personalized binary classification problem for each class. This may aggravate the class imbalance challenge and thus a novel personalized binary classification loss that incorporates both the under-sampling and hard sample mining strategies is designed. Extensive experiments are conducted on two popular datasets under different settings, and the results demonstrate that our FedABC can significantly outperform the existing counterparts. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV

IEEE Transactions on Green Communications and Networking

引用

IEEE Transactions on Green Communications and networking 2025年

作者： Zhang, Zheng Wu, Qiong Fan, Pingyi Cheng, Nan Chen, Wen Letaief, Khaled B. Jiangnan University School of Internet of Things Engineering Wuxi214122 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering State Key laboratory of Space Network and Communications Tsinghua University Beijing100084 China Xidian University State Key Lab. of ISN School of Telecom-munications Engineering Xi'an710071 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China Department of Electrical and Computer Engineer-ing Hong Kong

To address communication latency issues, the Third Generation Partnership Project (3GPP) has defined Cellular-Vehicle to Everything (C-V2X) technology, which includes Vehicle-to-Vehicle (V2V) communication for direct vehicle-to-vehicle communication. However, this method requires vehicles to autonomously select communication resources based on the Semi-Persistent Scheduling (SPS) protocol, which may lead to collisions due to different vehicles sharing the same communication resources, thereby affecting communication effectiveness. Non-Orthogonal Multiple Access (NOMA) is considered a potential solution for handling large-scale vehicle communication, as it can enhance the Signal-to-Interference-plus-Noise Ratio (SINR) by employing Successive Interference Cancellation (SIC), thereby reducing the negative impact of communication collisions. When evaluating vehicle communication performance, traditional metrics such as reliability and transmission delay present certain contradictions. Introducing the new metric Age of Information (AoI) provides a more comprehensive evaluation of communication system. Additionally, to ensure service quality, user terminals need to possess high computational capabilities, which may lead to increased energy consumption, necessitating a trade-off between communication energy consumption and effectiveness. Given the complexity and dynamics of communication systems, Deep Reinforcement Learning (DRL) serves as an intelligent learning method capable of learning optimal strategies in dynamic environments. Therefore, this paper analyzes the effects of multi-priority queues and NOMA on AoI in the C-V2X vehicular communication system and proposes an energy consumption and AoI optimization method based on DRL. Finally, through comparative simulations with baseline methods, the proposed approach demonstrates its advances in terms of energy consumption and AoI. © 2017 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Cross-Modal Few-Shot Learning: a Generative Transfer Learning Framework

arXiv

引用

arXiv 2024年

作者： Yang, Zhengwei Li, Yuke Sun, Qiang Fernando, Basura Huang, Heng Wang, Zheng National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University China Hubei Key Laboratory of Multimedia and Network Communication Engineering China Centre for Frontier AI Research Agency for Science Technology and Research Singapore University of Maryland College Park United States University of Toronto Canada MBZUAI United Arab Emirates

Most existing studies on few-shot learning focus on unimodal settings, where models are trained to generalize to unseen data using a limited amount of labeled examples from a single modality. However, real-world data are inherently multi-modal, and such unimodal approaches limit the practical applications of few-shot learning. To bridge this gap, this paper introduces the Cross-modal Few-Shot Learning (CFSL) task, which aims to recognize instances across multiple modalities while relying on scarce labeled data. This task presents unique challenges compared to classical few-shot learning arising from the distinct visual attributes and structural disparities inherent to each modality. To tackle these challenges, we propose a Generative Transfer Learning (GTL) framework by simulating how humans abstract and generalize concepts. Specifically, the GTL jointly estimates the latent shared concept across modalities and the in-modality disturbance through a generative structure. Establishing the relationship between latent concepts and visual content among abundant unimodal data enables GTL to effectively transfer knowledge from unimodal to novel multimodal data, as humans did. Comprehensive experiments demonstrate that the GTL achieves state-of-the-art performance across seven multi-modal datasets across RGB-Sketch, RGB-Infrared, and RGB-Depth. © 2024, CC BY-NC-ND.

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

research on key Word Information Retrieval Based on Inverted Index 8th

Research on Key Word Information Retrieval Based on Inverted...

引用

8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Qi, Meihan Fang, Wei Zhao, Yongming Sha, Yu Sheng, Victor S. School of Computer and Software Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China State Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences Beijing100081 China China Meteorological Administration Training Center Beijing China Department of Computer Texas Tech University LubbockTX79409 United States

ISBN: (纸本)9783031067600

With the advent of the era of big data, data has penetrated into every aspect of social life and become an important production factor in various industries. However, while providing convenience to our life, massive information also brings difficulties to information retrieval, which has problems of low retrieval efficiency and poor retrieval accuracy. Therefore, how to improve the efficiency and accuracy of information retrieval has become a key technical problem to be solved. In this paper, we propose a key word information retrieval scheme based on inverted index, which uses the inverted index segmentation algorithm based on keywords to realize the block retrieval of information, improves the efficiency of information retrieval, and improves the accuracy of retrieval content through reasonable error correction of keywords. The results of evaluation indicate that compared with the mainstream information retrieval methods, the keyword information retrieval scheme based on inverted index can carry out faster and more accurate information retrieval. Experimental results show that the keyword information retrieval scheme with inverted index can further improve the efficiency and accuracy of information retrieval. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Information retrieval

来源：评论

学校读者我要写书评

暂无评论

MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining

arXiv

引用

arXiv 2024年

作者： Wang, Di Zhang, Jing Xu, Minqiang Liu, Lin Wang, Dongsheng Gao, Erzhong Han, Chengxi Guo, Haonan Du, Bo Tao, Dacheng Zhang, Liangpei School of Computer Science Wuhan University Wuhan430072 China Institute of Artificial Intelligence Wuhan University Wuhan430072 China National Engineering Research Center for Multimedia Software Wuhan University Wuhan430072 China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China School of Computer Science Faculty of Engineering The University of Sydney Australia iFlytek Co Ltd National Engineering Research Center of Speech and Language Information Processing Hefei230088 China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

Foundation models have reshaped the landscape of Remote Sensing (RS) by enhancing various image interpretation tasks. Pretraining is an active research topic, encompassing supervised and self-supervised learning methods to initialize model weights effectively. However, transferring the pretrained models to downstream tasks may encounter task discrepancy due to their formulation of pretraining as image classification or object discrimination tasks. In this study, we explore the Multi-Task Pretraining (MTP) paradigm for RS foundation models to address this issue. Using a shared encoder and task-specific decoder architecture, we conduct multi-task supervised pretraining on the SAMRS dataset, encompassing semantic segmentation, instance segmentation, and rotated object detection. MTP supports both convolutional neural networks and vision transformer foundation models with over 300 million parameters. The pretrained models are finetuned on various RS downstream tasks, such as scene classification, horizontal and rotated object detection, semantic segmentation, and change detection. Extensive experiments across 14 datasets demonstrate the superiority of our models over existing ones of similar size and their competitive performance compared to larger state-of-the-art models, thus validating the effectiveness of MTP. The codes and pretrained models will be released at https://***/ViTAE-Transformer/MTP. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Biomedical data and AI

引用

Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Selector-Enhancer: Learning Dynamic Selection of Local and Non-local Attention Operation for Speech Enhancement

arXiv

引用

arXiv 2022年

作者： Xu, Xinmeng Tu, Weiping Yang, Yuhong National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China Hubei Luojia Laboratory China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China

Attention mechanisms, such as local and non-local attention, play a fundamental role in recent deep learning based speech enhancement (SE) systems. However, natural speech contains many fast-changing and relatively brief acoustic events, therefore, capturing the most informative speech features by indiscriminately using local and non-local attention is challenged. We observe that the noise type and speech feature vary within a sequence of speech and the local and non-local operations can respectively extract different features from corrupted speech. To leverage this, we propose Selector-Enhancer, a dual-attention based convolution neural network (CNN) with a feature-filter that can dynamically select regions from low-resolution speech features and feed them to local or non-local attention operations. In particular, the proposed feature-filter is trained by using reinforcement learning (RL) with a developed difficulty-regulated reward that is related to network performance, model complexity, and "the difficulty of the SE task". The results show that our method achieves comparable or superior performance to existing approaches. In particular, Selector-Enhancer is potentially effective for real-world denoising, where the number and types of noise are varies on a single noisy mixture. © 2022, CC BY.

关键词： Speech enhancement

来源：评论

学校读者我要写书评

暂无评论

INJECTING SPATIAL INFORMATION FOR MONAURAL SPEECH ENHANCEMENT VIA KNOWLEDGE DISTILLATION

arXiv

引用

arXiv 2022年

作者： Xu, Xinmeng Tu, Weiping Yang, Yuhong National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China Hubei Luojia Laboratory China

Monaural speech enhancement (SE) provides a versatile and cost-effective approach to SE tasks by utilizing recordings from a single microphone. However, the monaural SE lags performance behind multi-channel SE as the monaural SE methods are unable to extract spatial information from one-channel recordings, which greatly limits their application scenarios. To address this issue, we inject spatial information into the monaural SE model and propose a knowledge distillation strategy to enable the monaural SE model to learn binaural speech features from the binaural SE model, which makes monaural SE model possible to reconstruct higher intelligibility and quality enhanced speeches under low signal-to-noise ratio (SNR) conditions. Extensive experiments show that our proposed monaural SE model by injecting spatial information via knowledge distillation achieves favorable performance against other monaural SE models with fewer parameters. © 2022, CC BY.

关键词： Cost effectiveness

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：