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arXiv 2025年

作者： Cao, Yuqin Min, Xiongkuo Gao, Yixuan Sun, Wei Zhai, Guangtao Institute of Image Communication and Network Engineering Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai Jiao Tong University Shanghai China

Many video-to-audio (VTA) methods have been proposed for dubbing silent AI-generated videos. An efficient quality assessment method for AI-generated audio-visual content (AGAV) is crucial for ensuring audio-visual quality. Existing audio-visual quality assessment methods struggle with unique distortions in AGAVs, such as unrealistic and inconsistent elements. To address this, we introduce AGAVQA, the first large-scale AGAV quality assessment dataset, comprising 3, 382 AGAVs from 16 VTA methods. AGAVQA includes two subsets: AGAVQA-MOS, which provides multi-dimensional scores for audio quality, content consistency, and overall quality, and AGAVQA-Pair, designed for optimal AGAV pair selection. We further propose AGAV-Rater, a LMM-based model that can score AGAVs, as well as audio and music generated from text, across multiple dimensions, and selects the best AGAV generated by VTA methods to present to the user. AGAV-Rater achieves state-of-the-art performance on AGAVQA, Text-to-Audio, and Text-to-Music datasets. Subjective tests also confirm that AGAV-Rater enhances VTA performance and user experience. The project page is available at https://***. Copyright © 2025, The Authors. All rights reserved.

关键词： Subjective testing

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An Enhancing Stability Control Strategy of VSG-Based Power Source Under PWM Rectifier Load

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Gaoxiang Liu, Bowen Song, Wenhao Jining, Chen Liu, Xiao Guangxi University Guangxi Key Laboratory of Power System Optimization and Energy Technology School of Electrical Engineering Nanning530004 China Guangxi Zhuang Autonomous Region Information Center Guangxi Key Laboratory of Digital Infrastructure Nanning530200 China Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi Colleges and Universities Key Laboratory of Multimedia Communications and Information Processing School of Computer Electronics and Information Nanning530004 China

Under PWM rectifier load, the power source based on virtual synchronous generator (VSG) is prone to cause mid-frequency oscillation. Firstly, the system oscillation mechanism and characteristic of the VSG-based power source are analyzed based on impedance method. Due to the negative resistance and capacitive characteristic in the middle frequency range, the PWM rectifier load is prone to interact with the VSG-based power source, which can lead to impedance resonance. Secondly, to improve the stability of the VSG-based power source under the PWM rectifier load, a voltage differentiation feedforward control is proposed. In addition, since the mid-frequency impedance magnitude decrease obviously, the improved VSG-based power source is closer to the ideal voltage source, which can automatically suppress the middle harmonics induced by uncontrollable rectifier load. Finally, the proposed control and the corresponding theoretical analysis are verified by simulations and experiments. © 1975-2011 IEEE.

关键词： Synchronous generators

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On Positive-Unlabeled Classification from Corrupted Data in GANs

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Wang, Yunke Xu, Chang Guo, Tianyu Du, Bo Tao, Dacheng Wuhan University School of Computer Science Institute of Artificial Intelligence National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan430072 China The University of Sydney School of Computer Science Faculty of Engineering DarlingtonNSW2008 Australia Peking University Key Laboratory of Machine Perception Shenzhen Graduate School China

This paper defines a positive and unlabeled classification problem for standard GANs, which then leads to a novel technique to stabilize the training of the discriminator in GANs and deal with corrupted data. Traditionally, real data are taken as positive while generated data are negative. This positive-negative classification criterion was kept fixed all through the learning process of the discriminator without considering the gradually improved quality of generated data, even if they could be more realistic than real data at times. In contrast, it is more reasonable to treat the generated data as unlabeled, which could be positive or negative according to their quality. The discriminator is thus a classifier for this positive and unlabeled classification problem, and we derive a new Positive-Unlabeled GAN (PUGAN). We theoretically discuss the global optimality the proposed model will achieve and the equivalent optimization goal. Empirically, we find that PUGAN can achieve comparable or even better performance than those sophisticated discriminator stabilization methods. Considering the potential corrupted data problem in real-world scenarios, we further extend our approach to PUGAN-C, which treats real data as unlabeled that accounts for both clean and corrupted instances, and generated data as positive. The samples from generator could be closer to those corrupted data within unlabeled data at first, but within the framework of adversarial training, the generator will be optimized to cheat the discriminator and produce samples that are similar to those clean data. Experimental results on image generation from several corrupted datasets demonstrate the effectiveness and generalization of PUGAN-C. © 1979-2012 IEEE.

关键词： Labeled data

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UniUIR: Considering Underwater Image Restoration as An All-in-One Learner

arXiv

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arXiv 2025年

作者： Zhang, Xu Zhang, Huan Wang, Guoli Zhang, Qian Zhang, Lefei Du, Bo The Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan430072 China The Hubei Luojia Laboratory Wuhan China The National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China The School of Information Engineering Guangdong University of Technology Guangzhou510006 China The Horizon Robotics Beijing100083 China

Existing underwater image restoration (UIR) methods generally only handle color distortion or jointly address color and haze issues, but they often overlook the more complex degradations that can occur in underwater scenes. To address this limitation, we propose a Universal Underwater Image Restoration method, termed as UniUIR, considering the complex scenario of real-world underwater mixed distortions as an all-in-one manner. To decouple degradation-specific issues and explore the inter-correlations among various degradations in UIR task, we designed the Mamba Mixture-of-Experts module. This module enables each expert to identify distinct types of degradation and collaboratively extract task-specific priors while maintaining global feature representation based on linear complexity. Building upon this foundation, to enhance degradation representation and address the task conflicts that arise when handling multiple types of degradation, we introduce the spatial-frequency prior generator. This module extracts degradation prior information in both spatial and frequency domains, and adaptively selects the most appropriate task-specific prompts based on image content, thereby improving the accuracy of image restoration. Finally, to more effectively address complex, region-dependent distortions in UIR task, we incorporate depth information derived from a large-scale pre-trained depth prediction model, thereby enabling the network to perceive and leverage depth variations across different image regions to handle localized degradation. Extensive experiments demonstrate that UniUIR can produce more attractive results across qualitative and quantitative comparisons, and shows strong generalization than state-of-the-art methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Image reconstruction

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Mixed Data Augmentation Through the Integration of Mode Decomposition and Time-Series Transformation for Radio Signal Classification

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IEEE Transactions on Cognitive communications and networking 2025年

作者： Wang, Bin Yuan, Zhuang Lu, Jun Zhang, Xianchao Xi'an University of Science and Technology School of Communication Engineering Xi'an710699 China Xi'an Key Laboratory of Heterogeneous Network Convergence Communication Xi'an710054 China Jiaxing University Key Laboratory of Multimodal Sensing and Intelligent Systems of Zhejiang Province Jiaxing314001 China Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province Jiaxing314001 China

The rapid development of deep learning has provided new solutions for radio signal recognition in complex electromagnetic environments, demonstrating excellent performance and being widely applied. The data-driven based radio signal recognition methods rely on large amounts of labeled data for training. However, in non-cooperative communication scenarios, the limited availability of labeled samples can lead to model overfitting, which in turn degrades the recognition performance of the model. Therefore, this paper first combines the variational mode decomposition with time series transformation to propose a mixed data augmentation method for improving the feature extraction efficiency of the model. Secondly, considering the implementation of lightweight network structures on devices with limited computing power and storage while ensuring model recognition performance, a sparse residual network (SRNet) is designed based on a hybrid attention mechanism and sparse coding structure. Finally, comparative experiments and performance analysis are conducted on simulated and real-measured data. The simulation and experiment results indicate that the proposed method can significantly improve recognition accuracy while achieving model lightweighting. © 2015 IEEE.

关键词： Cooperative communication

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Parameterized TDOA: TDOA Estimation for Mobile Target Localization in a Time-Division Broadcast Positioning System

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IEEE Internet of Things Journal 2025年

作者： Tu, Chenxin Cui, Xiaowei Liu, Gang Zhao, Sihao Lu, Mingquan Tsinghua University Department of Electronic Engineering Beijing100084 China Tsinghua University State Key Laboratory of Space Network and Communication Department of Electronic Engineering Beijing100084 China independent researcher AB Canada

In a time-division broadcast positioning system (TDBPS), localizing mobile targets using classical time difference of arrival (TDOA) methods poses significant challenges. Concurrent TDOA measurements are infeasible because targets receive signals from different anchors and extract their transmission times at different reception times, as well as at varying positions. Traditional TDOA estimation schemes implicitly assume that the target remains stationary during the measurement period, which is impractical for mobile targets exhibiting high dynamics. Existing methods for mobile target localization are mostly specialized and rely on motion modeling and do not rely on the concurrent TDOA measurements. This issue limits their direct use of the well-established classical TDOA-based localization methods and complicating the entire localization process. In this paper, to obtain concurrent TDOA estimates at any instant out of the sequential measurements for direct use of existing TDOA-based localization methods, we propose a novel TDOA estimation method, termed parameterized TDOA (P-TDOA). By approximating the time-varying TDOA as a polynomial function over a short period, we transform the TDOA estimation problem into a model parameter estimation problem and derive the desired TDOA estimates thereafter. Theoretical analysis shows that, under certain conditions, the proposed P-TDOA method closely approaches the Cramér-Rao Lower Bound (CRLB) for TDOA estimation in concurrent measurement scenarios, despite measurements being obtained sequentially. Extensive numerical simulations validate our theoretical analysis and demonstrate the effectiveness of the proposed method, highlighting substantial improvements over existing approaches across various scenarios. © 2014 IEEE.

关键词： Cramer-Rao bounds

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A Robust Indoor Vehicle Localization Strategy based on UWB and INS

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IEEE Transactions on Vehicular Technology 2025年

作者： Cheng, Long Cui, Chen Zhang, Hao Northeastern University Department of Computer and Communication Engineering Qinhuangdao066004 China Northeastern University Hebei Key Laboratory of Marine Perception Network and Data Processing Hebei Qinhuangdao066004 China

High-precision indoor vehicle localization technology has recently been a research hotspot for indoor location services. This paper investigate the indoor combined positioning technology based on Ultra wide band (UWB) and inertial navigation system(INS), aiming to overcome the limitations of UWB in non-line of sight (NLOS) environment and cumulative errors of INS. For dealing with NLOS errors conveniently, we propose a two-layer NLOS error weakening method. Firstly, k-mediods clustering method is used to preprocess the obtained distance measurements, and some of the measurements containing NLOS errors are replaced with the measurements undisturbed by NLOS transmission. The processed result is processed again with gray Kalman filter, which reduces the process noise and NLOS error. A hybrid Gaussian fitting approach is suggested to determine the propagation conditions and simulate the likelihood of propagation through the residual after calculating the difference between the distance that was filtered and the distance that was measured. The two-sided correction technique changed the findings of the fitting method and the improved particle swarm optimization method. The adaptive Kalman filter was then used to combine the INS and UWB location results to get the final location data. Simulation and experimental results show that the proposed algorithm owns higher localization accuracy and dependability. © 1967-2012 IEEE.

关键词： Inertial navigation systems

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Joint phase-frequency modulation with binary amplitudes for optical camera communication

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Optics Letters 2025年第10期50卷 3217-3220页

作者： Ke Dong Weiqiang Su Mingjun Wang Dan Chen School of Automation and Information Engineering Xi’an University of Technology Shaanxi Xi’an 710048 China Xi’an Key Laboratory of Wireless Optical Communication and Network Research Shaanxi Xi’an 710049 China

Constrained by the imaging mechanisms of cameras, the data transmission rate in optical camera communication (OCC) is considerably lower than that in traditional visible light communication (VLC). This Letter introduces a new, to the best of our knowledge, binary modulation scheme designed to increase OCC transmission rates, with the transmitter performing high-order modulation across frequency and phase dimensions and the receiver employing a frequency-domain demodulation approach. This proposed scheme exhibits better scalability and lower implementation complexity than existing schemes. Additionally, the influence of exposure time and readout time parameters of rolling shutter cameras on communication performance has been dissected and confirmed through numerical simulations and experimental validation, laying a theoretical foundation for the design and optimization of the OCC system.

关键词： Frequency modulation Modulation techniques Numerical simulation Signal processing Systems design Visible light communications

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A Survey of Adversarial Examples in Computer Vision:Attack,Defense,and Beyond

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Wuhan University Journal of Natural Sciences 2025年第1期30卷 1-20页

作者： XU keyizhi LU Yajuan WANG Zhongyuan LIANG Chao School of Computer Science Wuhan UniversityWuhan 430072HubeiChina National Engineering Research Center for Multimedia Software(NERCMS) Wuhan UniversityWuhan 430072HubeiChina Key Laboratory of Multimedia and Network Communication Engineering Hubei ProvinceWuhan UniversityWuhan 430072HubeiChina School of Cyber Science and Engineering Wuhan UniversityWuhan 430072HubeiChina

Recent years have witnessed the ever-increasing performance of Deep Neural networks(DNNs)in computer vision ***,researchers have identified a potential vulnerability:carefully crafted adversarial examples can easily mislead DNNs into incorrect behavior via the injection of imperceptible modification to the input *** this survey,we focus on(1)adversarial attack algorithms to generate adversarial examples,(2)adversarial defense techniques to secure DNNs against adversarial examples,and(3)important problems in the realm of adversarial examples beyond attack and defense,including the theoretical explanations,trade-off issues and benign attacks in adversarial ***,we draw a brief comparison between recently published surveys on adversarial examples,and identify the future directions for the research of adversarial examples,such as the generalization of methods and the understanding of transferability,that might be solutions to the open problems in this field.

关键词： computer vision adversarial examples adversarial attack adversarial defense

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SRv6-INT Enabled network Monitoring and Measurement: Toward High-Yield network Observability for Digital Twin 3rd

SRv6-INT Enabled Network Monitoring and Measurement: Toward...

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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Zhang, Xu Feng, Chuan Han, Pengchao Liu, Wei Gong, Xiaoxue Zhang, Qiupei Guo, Lei School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China School of Information Engineering Guangdong University of Technology Guangzhou510006 China

ISBN: (纸本)9789819616978

network monitoring and measurement is an important part of realizing the network digital twin. However, it introduces the problem of high cost when obtaining the status data of physical networks. Therefore, to efficiently achieve network observability, we first introduce potential techniques and network scenarios for implementing network measurements. Then, a High-Yield Segment Routing over IPv6 based In-band network Telemetry (HY-SRv6-INT) system is proposed in this paper. In particular, we design the HY-SRv6-INT system architecture. It consists of IPv6 switches implemented by the P4 language and a Software-Defined networking (SDN) controller containing the HY-SRv6-INT application. Next, we experimentally demonstrate a packet-level path-associated network measurement process. In addition, the performance of the HY-SRv6-INT system is also quantitatively evaluated to verify its advantages. Finally, we discuss some research challenges and open issues for future network telemetry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Internet protocols

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