检索结果-内蒙古大学图书馆

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

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

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

Correction to: An advanced reinforcement learning control method for quadruped robots in typical urban terrains (International Journal of Machine Learning and Cybernetics, (2024), 10.1007/s13042-024-02478-9)

引用

International Journal of Machine Learning and Cybernetics 2025年 1-2页

作者： Yan, Chi Wang, Ning Gao, Hongbo Wang, Xinmiao Tang, Chao Zhou, Lin Li, Yuehua Wang, Yue School of Information Science and Technology University of Science and Technology of China No. 96 Jinzhai Road Anhui Hefei230026 China School of Information and Security Chongqing College of Mobile Communication No. 36 Dengying Avenue Qijiang District Sichuan Chongqing401520 China Institute of Advanced Technology University of Science and Technology of China No. 5089 Wangjiang West Road Anhui Hefei230088 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Nanyang639798 Singapore Zhejiang Lab Kechuang Avenue Zhongtai Sub-District Zhejiang Hangzhou311121 China College of Control Science and Engineering Zhejiang University Zhejiang Hangzhou310027 China

In this article, the affiliation details for Authors "Ning Wang2, Hongbo Gao1,3,4, Yuehua Li5, Yue Wang6" were interchanged and it is corrected now. Incorrect affiliation: 2Institute of Advanced technology, University of science and technology of China, No. 5089 Wangjiang West Road, Hefei 230088, Anhui, China 3School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798 Nanyang, Singapore 4Zhejiang Lab, Kechuang Avenue, Zhongtai Sub-District, Hangzhou 311121, Zhejiang, China 5College of control science and Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China 6School of information and security, Chongqing College of Mobile communication, No. 36 Dengying Avenue, Qijiang District, Chongqing 401520, Sichuan, China Correct affiliation: 2School of information and security, Chongqing College of Mobile communication, No. 36 Dengying Avenue, Qijiang District, Chongqing 401520, Sichuan, China 3Institute of Advanced technology, University of science and technology of China, No. 5089 Wangjiang West Road, Hefei 230088, Anhui, China 4School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798 Nanyang, Singapore 5Zhejiang Lab, Kechuang Avenue, Zhongtai Sub-District, Hangzhou 311121, Zhejiang, China 6College of control science and Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China The original article has been corrected. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词：

来源：评论

学校读者我要写书评

暂无评论

OS-SEI: Open-Set Specific Emitter Identification Based on Outlier Exposure and Label Smoothing

引用

IEEE Internet of Things Journal 2025年

作者： Wang, Juzhen Wang, Hanhong Wang, Wei Zha, Haoran Hangzhou Dianzi University School of Communication Engineering Hangzhou310018 China Binjiang Institute of Artificial Intelligence of Zhejiang University of Technology Hangzhou310052 China Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China Harbin Engineering University College of Information and Communication Engineering Harbin150009 China

Specific Emitter Identification (SEI), based on the inevitable hardware imperfections of emitters, plays a crucial role in physical layer security, independent of upper-layer cryptographic methods. In recent years, SEI combined with deep learning has demonstrated exceptional performance and considerable potential. However, most SEI methods are designed for closed, independent identically distributed environments, which contrasts with real-world open scenarios. This discrepancy leads to significant security vulnerabilities when deploying models in practical applications. To address this challenge, we propose a novel Open-Set SEI (OS-SEI) framework, Outlier Exposure with Label Smoothing (OELS), which utilizes partial exposure to auxiliary anomalous data during the training phase and introduces specific loss functions to mitigate the open-set risk. By constructing smooth decision boundaries through label smoothing and applying thresholds to class confidence, our SEI framework demonstrates robust open-set authentication capabilities. Extensive experiments conducted on ADS-B and WiFi datasets show that the OELS method achieves outstanding open-set recognition performance, with F1 scores of 83.77% and 93.14% at an openness level of 0.10557. Furthermore, ablation experiments confirm the effectiveness and potential of our framework’s components. By increasing the diversity of auxiliary unknown data, we further improve performance in OS-SEI tasks. © 2014 IEEE.

关键词： Hardware security

来源：评论

学校读者我要写书评

暂无评论

Breather dynamics in an optomechanical array with staggered coupling

引用

Physical Review A 2025年第3期111卷 033502-033502页

作者： Yuan Zhao Yiheng Hou Yu Wang Zhuo Fan Wei Peng Qin Zhou Siliu Xu Shihua Chen Key Laboratory of Optoelectronic Sensing and Intelligent Control Hubei University of Science and Technology Xianning 437100 China School of Biomedical Engineering and Imaging Xianning Medical College Hubei University of Science and Technology Xianning 437100 China School of Electronic and Information Engineering Hubei University of Science and Technology Xianning 437100 China Research Center of Nonlinear Science School of Mathematics and Physical Sciences Wuhan Textile University Wuhan 430200 China School of Physics and Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing 211189 China

We present an approach toward generating highly controllable breathers, using an array of staggered optomechanical cavities that feature alternating coupling strengths. Four different types of breather states as well as stable two-soliton states are demonstrated to form in such an optomechanical array, thanks to the interplay between the photon-hopping induced tunneling effect and the photon-phonon coupling in the optomechanical cell. It is seen that both their photonic and phononic wave components manifest quasi-periodic oscillatory patterns and coherent localization. We unveil that such breather dynamics can be well controlled by adjusting the coupling strength, the cavity field decay, and the number of array cells. As a result, we observe an unusual odd-even lattice effect in that stable breathers are only available when the cell number is even. We anticipate that these findings from the staggered optomechanical configuration may expand the realm of cavity optomechanics, yielding promising applications such as on-chip manipulation of light propagation.

关键词： Solitons

来源：评论

学校读者我要写书评

暂无评论

A robust TDOA based solution for source location using mixed Huber loss

引用

Journal of Systems Engineering and Electronics 2021年第6期32卷 1375-1380页

作者： YOU Mingyi LU Annan Science and Technology on Communication Information Security Control Laboratory No.36 Research Institute of China Elec-tronics Technology Group CorporationJiaxing 314033China No.36 Research Institute of China Electronics Technology Group Corporation Jiaxing 314033China

This paper proposes a source localization solution robust to measurement outliers in time differences of arrivals(TDOA)*** solution uses a piecewise loss function named as mixed Huber loss(MHL)proposed based on the classical Huber loss(HL)and its refined *** MHL is able to effectively mitigate the impact of all levels of measurement outliers by setting two triggering *** practice,appropriate triggering threshold values can be obtained through simulation given the level of measurement noise and a rough range of potential measurement outliers.A clustering based approach is proposed to further improve the robustness of localization solution against reference sensor related *** are included to examine the solution's performance and compare it with several *** proposed MHL based solution is shown to be superior to the classical solution and the *** solution is shown to be even robust to multiple measurement ***,the influence of range measurement outliers in the reference sensor can be effectively mitigated by the clustering based approach.

关键词： time differences of arrivals(TDOA) robust localization Huber loss(HL) outlier mitigation

来源：评论

学校读者我要写书评

暂无评论

Sensing With Random communication Signals

引用

IEEE Network 2025年

作者： Lu, Shihang Liu, Fan Xiong, Yifeng Du, Zhen Cui, Yuanhao Li, Shuangyang Yuan, Weijie Yang, Jie Jin, Shi Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China Nanjing University of Information Science and Technology School of Electronic and Information Engineering Nanjing210044 China Technical University of Berlin Faculty of Electrical Engineering and Computer Science Berlin10587 Germany Shenzhen518055 China Frontiers Science Center for Mobile Information Communication and Security China Southeast University MOE Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing211189 China

communication-centric Integrated Sensing and communication (ISAC) has been recognized as a promising methodology to implement wireless sensing functionality over existing network architectures, due to its cost-effectiveness and backward compatibility to legacy cellular systems. However, the inherent randomness of the communication signal may incur huge fluctuations in sensing capabilities, leading to unfavorable detection and estimation performance. To address this issue, we elaborate on random ISAC signal processing methods in this article, aiming at improving the sensing performance without unduly deteriorating the communication functionality. Specifically, we commence by discussing the fundamentals of sensing with random communication signals, including the performance metrics and optimal ranging waveforms. Building on these concepts, we then present a general framework for random ISAC signal transmission, followed by an in-depth exploration of time-domain pulse shaping, frequency-domain constellation shaping, and spatial-domain precoding methods. We provide a comprehensive overview of each of these topics, including models, results, and design guidelines. Finally, we conclude this article by identifying several promising research directions for random ISAC signal transmission. © 2025 IEEE.

关键词： Mobile telecommunication systems

来源：评论

学校读者我要写书评

暂无评论

Sensing With Random communication Signals

arXiv

引用

arXiv 2025年

作者： Lu, Shihang Liu, Fan Xiong, Yifeng Du, Zhen Cui, Yuanhao Li, Shuangyang Yuan, Weijie Yang, Jie Jin, Shi National Mobile Communications Research Laboratory Southeast University Nanjing210096 China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing210044 China Faculty of Electrical Engineering and Computer Science Technical University of Berlin Berlin10587 Germany Southern University of Science and Technology Shenzhen518055 China Frontiers Science Center for Mobile Information Communication and Security and also The Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Nanjing211189 China

关键词： Signaling

来源：评论

学校读者我要写书评

暂无评论

Analysis of Frequency Offset for Satellite Navigation Receiver Using Carrier-Aided Code Tracking Loop 20

Analysis of Frequency Offset for Satellite Navigation Receiv...

引用

20th IEEE International Conference on communication technology, ICCT 2020

作者： Gao, Teng Wang, Lijun Jin, Xiaofan No.36 Research Institute of Cetc Science and Technology on Communication Information Security Control Laboratory Jiaxing China

ISBN: (纸本)9781728181417

Carrier-aided code tracking loop is widely used in satellite navigation receivers. This kind of loop structure can reduce code tracking noise by narrowing the bandwidth of code tracking loop. The performance of carrier-aided code tracking loop in receivers is affected by frequency deviation of reference clock source. This paper analyzes the influence of carrier frequency offset and sampling frequency offset on carrier-aided code tracking loop due to reference clock offset. The results show that large frequency offset can cause code tracking loop lose lock, code tracking loop is more sensitive to sampling frequency deviation and increasing the loop bandwidth can reduce the effects of frequency offset. This analysis provides reference for receiver tracking loop design. © 2020 IEEE.

关键词： Bandwidth

来源：评论

学校读者我要写书评

暂无评论

RainGaugeNet: CSI-Based Sub-6 GHz Rainfall Attenuation Measurement and Classification for ISAC Applications

arXiv

引用

arXiv 2025年

作者： Li, Yan Yang, Jie Huang, Yixuan Yang, Tao Wen, Chao-Kai Jin, Shi The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Frontiers Science Center for Mobile Information Communication and Security Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China The National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety Institute of Water Science and Technology Hohai University Nanjing210098 China The Institute of Communications Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan The National Mobile Communications Research Laboratory Southeast University Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China

Rainfall impacts daily activities and can lead to severe hazards such as flooding. Traditional rainfall measurement systems often lack granularity or require extensive infrastructure. While the attenuation of electromagnetic waves due to rainfall is well-documented for frequencies above 10 GHz, sub-6 GHz bands are typically assumed to experience negligible effects. However, recent studies suggest measurable attenuation even at these lower frequencies. This study presents the first channel state information (CSI)-based measurement and analysis of rainfall attenuation at 2.8 GHz. The results confirm the presence of rain-induced attenuation at this frequency, although classification remains challenging. The attenuation follows a power-law decay model, with the rate of attenuation decreasing as rainfall intensity increases. Additionally, rainfall onset significantly increases the delay spread. Building on these insights, we propose RainGaugeNet, the first CSI-based rainfall classification model that leverages multipath and temporal features. Using only 20 seconds of CSI data, RainGaugeNet achieved over 90% classification accuracy in line-of-sight scenarios and over 85% in non-line-of-sight scenarios, significantly outperforming state-of-the-art methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel state information

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：