检索结果-内蒙古大学图书馆

A Defective Ground Structure Design for Reducing Coupling between Microstrip Antenna Arrays

Progress in Electromagnetics Research C 2025年 151卷 123-130页

作者： Xu, Junhao Wang, Yafei Wang, Xiaozhe Li, Zhensong Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing100101 China Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100101 China

In order to reduce the coupling between dense antenna arrays in multiple input multiple output (MIMO) systems, this paper proposes a method to reduce the coupling between microstrip antenna arrays by utilizing a defected ground structure (DGS), which consists of "W"-shaped slits etched in the ground plane. Simulation results show that the structure can achieve −46.6 dB isolation between antenna arrays. To verify the effectiveness of the structure, the microstrip antenna arrays loaded with this DGS are fabricated and measured, and the measured results are consistent with the simulated ones, which verifies the effectiveness of the structure. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Antenna arrays

来源：评论

学校读者我要写书评

暂无评论

Latency minimization for multiuser computation offloading in fog-radio access networks

引用

Digital communications and Networks 2025年第1期11卷 160-171页

作者： Wei Zhang Shafei Wang Ye Pan Qiang Li Jingran Lin Xiaoxiao Wu School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu611731China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing100089China

Recently,the Fog-Radio Access Network(F-RAN)has gained considerable attention,because of its flexible architecture that allows rapid response to user *** this paper,computational offloading in F-RAN is considered,where multiple User Equipments(UEs)offload their computational tasks to the F-RAN through fog *** UE can select one of the fog nodes to offload its task,and each fog node may serve multiple *** tasks are computed by the fog nodes or further offloaded to the cloud via a capacity-limited fronhaul *** order to compute all UEs'tasks quickly,joint optimization of UE-Fog association,radio and computation resources of F-RAN is proposed to minimize the maximum latency of all *** min-max problem is formulated as a Mixed Integer Nonlinear Program(MINP).To tackle it,first,MINP is reformulated as a continuous optimization problem,and then the Majorization Minimization(MM)method is used to find a *** MM approach that we develop is unconventional in that each MM subproblem is solved inexactly with the same provable convergence guarantee as the exact MM,thereby reducing the complexity of MM *** addition,a cooperative offloading model is considered,where the fog nodes compress-and-forward their received signals to the *** this model,a similar min-max latency optimization problem is formulated and tackled by the inexact *** results show that the proposed algorithms outperform some offloading strategies,and that the cooperative offloading can exploit transmission diversity better than noncooperative offloading to achieve better latency performance.

关键词： Fog-radio access network Fog computing Majorization minimization WMMSE

来源：评论

学校读者我要写书评

暂无评论

Joint Task Offloading and Resource Allocation Strategy for Space-Air-Ground Integrated Vehicular Networks

引用

清华大学学报自然科学版（英文版） 2025年第3期30卷 1027-1043页

作者： Yuanshuo Gang Yuexia Zhang Zhihai Zhuo School of Information and Communication Engineering Beijing Information Science and Technology UniversityBeijing 100101China Key Laboratory of Modern Measurement and Control Technology Ministry of EducationBeijing Information Science and Technology UniversityBeijing 100101China

Space-Air-Ground integrated Vehicular Network(SAGVN)aims to achieve ubiquitous connectivity and provide abundant computational resources to enhance the performance and efficiency of the vehicular ***,there are still challenges to overcome,including the scheduling of multilayered computational resources and the scarcity of spectrum *** address these problems,we propose a joint Task Offloading(TO)and Resource Allocation(RA)strategy in SAGVN(namely JTRSS).This strategy establishes an SAGVN model that incorporates air and space networks to expand the options for vehicular TO,and enhances the edge-computing resources of the system by deploying edge *** minimize the system average cost,we use the JTRSS algorithm to decompose the original problem into a number of subproblems.A maximum rate matching algorithm is used to address the channel allocation and the Lagrangian multiplier method is employed for computational *** acquire the optimal TO decision,a differential fusion cuckoo search algorithm is *** simulation results demonstrate the significant superiority of the JTRSS algorithm in optimizing the system average cost.

关键词： edge computing Resource Allocation(RA) Space-Air-Ground integrated Vehicular Network(SAGVN) Task Offloading(TO)

来源：评论

学校读者我要写书评

暂无评论

Delayed interference-based broadband optical chaos generation in a mutual coupled RSOA and DFB system

引用

Optics Letters 2025年第10期50卷 3381-3384页

作者： Qian, Jiahao Jiang, Ning Li, Anran Zhang, Yiqun Xu, Bingjie Huang, Wei Qiu, Kun School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China Tianfu Jiangxi Laboratory Chengdu641419 China National Key Laboratory of Security Communication Chengdu610041 China

In this Letter, a delayed interference (DI)-based chaos generation scheme in a mutually coupled system consisting of a reflective semiconductor optical amplifier (RSOA) and a distributed feedback (DFB) semiconductor laser is proposed. We experimentally demonstrate its capability of generating broadband chaos with a large effective bandwidth and a flat power spectrum, and the influences of relevant parameters on the system performance are thoroughly analyzed. Compared to the conventional optical feedback-based chaos generation schemes, the proposed system can simultaneously output two chaotic signals with enhanced bandwidth and high spectral flatness. Moreover, the time-delay signature (TDS) of chaotic signals generated by this configuration is also effectively suppressed. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Semiconductor optical amplifiers

来源：评论

学校读者我要写书评

暂无评论

Research on Radar Anti-deceptive-jamming Method based on Characterization 16

Research on Radar Anti-deceptive-jamming Method based on Cha...

引用

16th International Conference on Signal Processing Systems, ICSPS 2024

作者： Lei, Lei Jiang, Meiqiu Li, JiaChen Guo, Shisheng Cui, Guolong Xia, Bijun Zhu, Lei School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China National Key Laboratory of Electromagnetic Space Security Chengdu China

ISBN: (纸本)9781510689251

Deceptive jamming plays an increasingly important role in radar electronic countermeasures, and there is an urgent need to study new radar anti-deceptive jamming methods. In this paper, four characteristic factors, namely, amplitude variance, amplitude ratio, power ratio and number of correlated signals, are extracted based on the intra pulse characteristic differences between the target and deceptive jamming signals. Based on these four factors, a multi-domain cross-coupled neural network is proposed to distinguish between real targets and deceptive jamming. Finally, the simulation results show that this method has a significant effect on anti-deceptive jamming. © 2025 SPIE.

关键词： Radar countermeasures

来源：评论

学校读者我要写书评

暂无评论

Multi-scale network for single image deblurring based on ensemble learning module

引用

Multimedia Tools and Applications 2025年第11期84卷 9045-9064页

作者： Wu, WenBo Pan, Yun Su, Na Wang, JingTao Wu, ShaoChuan Xu, ZeKun Yu, YouJian Liu, YaPeng State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China School of Computer and Cyberspace Security Communication University of China Beijing100024 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China College of Computer and Control Engineering Northeast Forestry University Harbin150040 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China School of Computer Science and Technology Anhui University Hefei230601 China

In this paper, we have identified two primary issues with current multi-scale image deblurring methods. On the one hand, the blurring scale is ignored. On the other hand, the context information of images is not fully utilized, and the spatial detail information of images is lost. To better solve the above problems, on top of that, this paper proposes a multi-scale network for single image deblurring. The designed network contains two parts: 1) Ensemble Learning Model: The Weak Learning Model in this module connects multiple weak learners in series to increase network depth. Moreover, through multi-scale feature ensemble module, the output features and intermediate features of different weak learner are integrated, and finally strong learner is used to enhance the features with semantic information at different scales;2) Spatial Feature Enhancement Module: This module can preserve spatial detail information of images and compensate for the disadvantages of losing image spatial details due to continuous downsampling in the Ensemble Learning Model structure. Our proposed deblurring model has been extensively evaluated on several benchmark datasets, and the model achieves superior performance compared to the most advanced deblurring methods. In particular, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values are 33.57 dB and 0.965 on the GoPro dataset, respectively, with PSNR and SSIM improving by 0.49 dB and 0.002 over NAFNet, and 0.65 dB and 0.004 over Restormer. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

Dynamic Charging and Path Planning for UAV-Powered Rechargeable WSNs Using Multi-Agent Deep Reinforcement Learning

引用

IEEE Transactions on Automation science and Engineering 2025年 22卷 15610-15626页

作者： Betalo, Mesfin Leranso Leng, Supeng Seid, Abegaz Mohammed Abishu, Hayla Nahom Erbad, Aiman Bai, Xiaoshan Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Unmanned Aerial Vehicle (UAV)-powered 5G/6G networks integrated with rechargeable wireless sensor networks (RWSNs) offer promising solutions for extending system lifetime, collecting data, and providing computing services and power to sensor nodes (SNs). UAVs offer significant advantages, including exceptional mobility, cost-effective deployment, and the ability to be easily reprogrammed for a wide range of missions. However, the limited onboard power capacity of UAVs, coupled with the lack of dynamic and intelligent charging station (CS) management and inefficient path planning, can lead to SN failure in dynamic mobile environments. To address these challenges, we propose an energy-efficient laser-charged UAV (LCU)-enabled RWSN environment, wherein UAVs, powered by laser beams from ground-based stations, provide services, collect data, and transfer energy to SNs. We formulate a joint optimization problem involving power allocation, dynamic charging strategy (DCS), and path planning to minimize task completion time and sensor node death time. Given the NP-hard nature of the problem, we employ a stochastic game model based on a Markov decision process (MDP) for its solution. To solve this problem, we propose a deep reinforcement learning (DRL) based algorithm that enables real-time charging scheduling decisions while optimizing network performance. We introduce a multi-agent double deep Q-network (MA-DDQN) model to determine the optimal trajectories for all UAVs in large and complex environments. Simulation results demonstrate that the MA-DDQN approach outperforms state-of-the-art techniques, showing significant improvements in terms of average delay, energy consumption, and task completion time. © 2004-2012 IEEE.

关键词： Stochastic models

来源：评论

学校读者我要写书评

暂无评论

MMH-FE:AMulti-Precision and Multi-Sourced Heterogeneous Privacy-Preserving Neural Network Training Based on Functional Encryption

引用

Computers, Materials & Continua 2025年第3期82卷 5387-5405页

作者： Hao Li Kuan Shao Xin Wang Mufeng Wang Zhenyong Zhang The State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science CenterQilu University of Technology(Shandong Academy of Sciences)Jinan250014China China Industrial Control Systems Cyber Emergency Response Team Beijing100040China

Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model ***,dishonest clouds may infer user data,resulting in user data *** schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing *** address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training ***,we design a multi-precision functional encryption computation based on Euclidean ***,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced ***,we conduct experiments on three *** results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.

关键词： Functional encryption multi-sourced heterogeneous data privacy preservation neural networks

来源：评论

学校读者我要写书评

暂无评论

Radio Signal Modulation Pattern Recognition Based on Time-Frequency Adaptive Decomposition and Hybrid Neural Network

引用

IEEE Internet of Things Journal 2025年

作者： Dai, Shaogang Xu, Jianwei Zhou, Huaji Hangzhou Dianzi University School of communication engineering Hangzhou310018 China Science and Technology on Communication Information Security Control Laboratory Jiaxing314000 China

Automatic modulation classification of electromagnetic signals has an important role in the field of signal processing. The current methods for identifying the modulation pattern of radio signals mainly used end-to-end neural network models, which suffered from the problems such as network redundancy and non-interpretability. In order to solve the above problems, this paper proposes a radio signal modulation pattern recognition method based on time-frequency adaptive decomposition and hybrid neural network. The discrete wavelet decomposition is used to decompose the original radio signals into adaptive high and low frequencies. Then feed the resulting high and low frequency portions into a multi-channel hybrid neural network (MHNN) for training to obtain the classification results. In the discrete wavelet decomposition process, the number of decomposition layers and the decomposition threshold are determined adaptively according to the difference of SNRs. In the design of multi-path hybrid neural network structure, the signal characteristics of high and low frequency parts are considered comprehensively. The network channels with different convolutional kernel scales and different layers are constructed to reduce the number of model parameters and training time while ensuring the classification accuracy. Using RadioML2016.10a dataset for experiments, the recognition accuracy of MHNN is prior to the end-to-end deep learning methods, which can achieve the accuracy of 92.30% and the F1 of 91.84% when SNR=2dB. It shows that the proposed radio signal modulation pattern recognition method based on the time-frequency adaptive decomposition and hybrid neural network can balance the accuracy and lightweight. Moreover, it has certain interpretability for the original input data, distinguishing the main body and details of the signal by dividing the signal into high and low frequencies, which provides a new idea for the interpretability of deep convolutional neural networks. ©

关键词： Signal modulation

来源：评论

学校读者我要写书评

暂无评论

Underwater arbitrary ranging based on chirped pulse interferometry with a mode-locked laser

引用

Journal of Lightwave technology 2025年第11期43卷 5235-5243页

作者： Shi, Haonan Zhao, Haihan Xu, Xinyang Zhu, Zhiwei Zhai, Jingsheng Li, Xiaobo Tianjin University School of Marine Science and Technology Tianjin300072 China Research Institute of China Electronics Technology Group Corporation Science and Technology on Electro-Optical Information Security Control Laboratory No. 53 Tianjin300308 China The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Hong Kong999077 Hong Kong Nanjing University of Aeronautics and Astronautics Key Laboratory of Space Photoelectric Detection and Perception Ministry of Industry and Information Technology Nanjing211106 China

Underwater distance measurement and length sensing, as fundamental functions of geoscience, are integrated into underwater robots and vessels in various forms of sensor payloads and play a crucial role in a wide range of remote sensing applications. Advanced laser-based detection systems and algorithms are essential for underwater tasks, such as detecting damage in underwater pipelines and conducting detailed structural health assessments. In this paper, we experimentally demonstrated an arbitrary underwater ranging system by the chirped pulse interferometry with a mode-locked laser. Water's inherent characteristic introduces dispersion, enabling unique distance discrimination. Adjusting the repetition frequency allows us to acquire the interference spectrum. A long fiber delay line, stabilized by the servo-controlled system, eliminates the ranging dead zone. The widest fringe of the interference spectrum, corresponding to an instantaneous repetition frequency, indicates the arbitrary measured distance. Leveraging a charge-coupled device detection module, we achieved an update rate of 13.5 Hz for the interference spectrum. Experimental results show a precise distance difference within 59.8 μm at the 5 m range underwater without the dead zone. © 2025 IEEE. All rights reserved.

关键词： Laser mode locking

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：