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arXiv 2024年

作者： Cheng, Jintao Zeng, Kang Huang, Zhuoxu Tang, Xiaoyu Wu, Jin Zhang, Chengxi Chen, Xieyuanli Fan, Rui The School of Electronic and Information Engineering South China Normal University Foshan528225 China The Department of Computer Science Aberystwyth University AberystwythSY23 3DB United Kingdom The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The School of Internet of Things Engineering Jiangnan University Wuxi China The College of Intelligence Science and Technology National University of Defense Technology Changsha China The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China

Moving object segmentation (MOS) provides a reliable solution for detecting traffic participants and thus is of great interest in the autonomous driving field. Dynamic capture is always critical in the MOS problem. Previous methods capture motion features from the range images directly. Differently, we argue that the residual maps provide greater potential for motion information, while range images contain rich semantic guidance. Based on this intuition, we propose MF-MOS, a novel motion-focused model with a dual-branch structure for LiDAR moving object segmentation. Novelly, we decouple the spatial-temporal information by capturing the motion from residual maps and generating semantic features from range images, which are used as movable object guidance for the motion branch. Our straightforward yet distinctive solution can make the most use of both range images and residual maps, thus greatly improving the performance of the LiDAR-based MOS task. Remarkably, our MF-MOS achieved a leading IoU of 76.7% on the MOS leaderboard of the SemanticKITTI dataset upon submission, demonstrating the current state-of-the-art performance. The implementation of our MF-MOS has been released at https://***/SCNU-RISLAB/MF-MOS. © 2024, CC BY.

关键词： Semantics

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Secure Relay Selection for UAV-empowered Two Way Untrusted Relaying Networks

Secure Relay Selection for UAV-empowered Two Way Untrusted R...

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Mobile Internet, Cloud Computing and Information Security (MICCIS), International Conference on

作者： Kan Yu Zhiyong Feng Lin Yang Dianxia Chen Dong Li Xiaoshan Ma The School of Computer Science and Engineering Macau University of Science and Technology Taipa P. R. China The Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing P.R. China School of Computer Science and Engineering Macau University of Science and Technolog China The National Key Laboratory of Science and Technology on Information System Security Systems Engineering Institute AMS PLA Beijing P.R. China The Education Tianjin Ren’ai College Tianjin P.R. China The School of Computer Science and Engineering Macau University of Science and Technology P. R. China The Academy of Military Sciences of PLA Beijing P.R. China

ISBN: (数字)9798350389906

ISBN: (纸本)9798350389913

In this paper, we examine the security aspects of two-way untrusted relaying networks (TWURNs) through the lens of physical layer security (PLS). These networks involve the exchange of confidential information between two multiple-antenna base stations (BSs) with the aid of unmanned aerial vehicle (UAV)-enabled untrusted relays equipped with a single antenna. Given the shortcomings observed in existing single untrusted relay selection methods, we introduce an enhanced approach grounded in the min-min criterion (MMC) based on received signal strength. Additionally, we present a novel closed-form expression for the secrecy outage probability (SOP), a fundamental metric within the domain of PLS, derived from the cumulative distribution function (CDF) and probability density function (PDF) of received signal strengths. Through simulations, we substantiate the validity of our analytical findings, demonstrating the superior efficacy of the proposed MMC compared to prevailing schemes.

关键词： Measurement Probability density function Probability Physical layer security Minimax techniques Power system reliability Relays

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Multi-scale Frequency Enhancement Network for Blind Image Deblurring

arXiv

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arXiv 2024年

作者： Xiang, Yawen Zhou, Heng Li, Chengyang Li, Zhongbo Xie, Yongqiang The Institute of Systems Engineering Academy of Military Science Beijing100141 China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China Beijing100100 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China Beijing100100 China

Image deblurring is an essential image preprocessing technique, aiming to recover clear and detailed images form blurry ones. However, existing algorithms often fail to effectively integrate multi-scale feature extraction with frequency enhancement, limiting their ability to reconstruct fine textures. Additionally, non-uniform blur in images also restricts the effectiveness of image restoration. To address these issues, we propose a multi-scale frequency enhancement network (MFENet) for blind image deblurring. To capture the multi-scale spatial and channel information of blurred images, we introduce a multi-scale feature extraction module (MS-FE) based on depthwise separable convolutions, which provides rich target features for deblurring. We propose a frequency enhanced blur perception module (FEBP) that employs wavelet transforms to extract high-frequency details and utilizes multi-strip pooling to perceive non-uniform blur, combining multiscale information with frequency enhancement to improve the restoration of image texture details. Experimental results on the GoPro and HIDE datasets demonstrate that the proposed method achieves superior deblurring performance in both visual quality and objective evaluation metrics. Furthermore, in downstream object detection tasks, the proposed blind image deblurring algorithm significantly improves detection accuracy, further validating its effectiveness androbustness in the field of image deblurring. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

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Benchmarking Semantic Communications for Image Transmission Over MIMO Interference Channels

arXiv

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arXiv 2024年

作者： Wang, Yanhu Guo, Shuaishuai Dong, Anming Zhao, Hui School of Control Science and Engineering Shandong Key Laboratory of Wireless Communication Technologies Shandong University Jinan250061 China The School of Computer Science and Technology Qilu University of Technology Jinan250353 China The Communication Systems Department EURECOM Sophia Antipolis 06410 France

Semantic communications offer promising prospects for enhancing data transmission efficiency. However, existing schemes have predominantly concentrated on point-to-point transmissions. In this paper, we aim to investigate the validity of this claim in interference scenarios compared to baseline approaches. Specifically, our focus is on general multiple-input multiple-output (MIMO) interference channels, where we propose an interference-robust semantic communication (IRSC) scheme. This scheme involves the development of transceivers based on neural networks (NNs), which integrate channel state information (CSI) either solely at the receiver or at both transmitter and receiver ends. Moreover, we establish a composite loss function for training IRSC transceivers, along with a dynamic mechanism for updating the weights of various components in the loss function to enhance system fairness among users. Experimental results demonstrate that the proposed IRSC scheme effectively learns to mitigate interference and outperforms baseline approaches, particularly in low signal-to-noise (SNR) regimes. © 2024, CC BY.

关键词： Channel state information

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Unrevealed Threats: A Comprehensive Study of the Adversarial Robustness of Underwater Image Enhancement Models

arXiv

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arXiv 2024年

作者： Zhai, Siyu He, Zhibo Cong, Xiaofeng Hou, Junming Gui, Jie Wei You, Jian Gong, Xin Tin-Yau Kwok, James Tang, Yuan Yan School of Cyber Science and Engineering Southeast University Nanjing210000 China Information Management and Information Systems Xian Jiaotong Liverpool University Suzhou215123 China The State Key Laboratory of Millimeter Waves School of Information Science and Engineering Southeast University Nanjing210096 China The School of Cyber Science and Engineering Southeast University Purple Mountain Laboratories Nanjing210000 China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong999077 Hong Kong Department of Computer and Information Science University of Macau Macau999078 China

Learning-based methods for underwater image enhancement (UWIE) have undergone extensive exploration. However, learning-based models are usually vulnerable to adversarial examples so as the UWIE models. To the best of our knowledge, there is no comprehensive study on the adversarial robustness of UWIE models, which indicates that UWIE models are potentially under the threat of adversarial attacks. In this paper, we propose a general adversarial attack protocol. We make a first attempt to conduct adversarial attacks on five well-designed UWIE models on three common underwater image benchmark datasets. Considering the scattering and absorption of light in the underwater environment, there exists a strong correlation between color correction and underwater image enhancement. On the basis of that, we also design two effective UWIE-oriented adversarial attack methods Pixel Attack and Color Shift Attack targeting different color spaces. The results show that five models exhibit varying degrees of vulnerability to adversarial attacks and well-designed small perturbations on degraded images are capable of preventing UWIE models from generating enhanced results. Further, we conduct adversarial training on these models and successfully mitigated the effectiveness of adversarial attacks. In summary, we reveal the adversarial vulnerability of UWIE models and propose a new evaluation dimension of UWIE models. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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A Generic Load Flow Solution for Droop-Controlled AC Microgrids Considering Electric Vehicle Loads

A Generic Load Flow Solution for Droop-Controlled AC Microgr...

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International Conference on Power Electronic Drives

作者： Ghanshyam Meena Akhilesh Mathur V. P. Singh Krishna Murari Sukumar Kamalasadan Department of Electrical Engineering Malaviya National Institute of Technology Jaipur Rajasthan India Electrical Engineering and Computer Science Department The University of Toledo Toledo Ohio USA Department of Electrical Engineering Power Energy and Intelligent Systems Laboratory EPIC University of North Carolina at Charlotte Charlotte NC USA

ISBN: (数字)9798350372472

ISBN: (纸本)9798350372489

Microgrids (MGs) have become more unpredictable due to integration of renewable generation sources. Several methods are used to solve load flow problems in MGs. Load flow analysis is a complex problem for islanded MGs due to the absence of slack bus and dependency of active power generation of droop-controlled DGs on frequency. In this paper, a Modified Newton Raphson (MNR) approach is used to perform load flow analysis of isolated MGs considering various types of electric vehicle (EV) loads. The presented method has been validated on 6-bus and 38-bus test systems of the islanded microgrid to give an accurate and simple approach for computing the load flow solutions.

关键词： Renewable energy sources Microgrids Drives Electric vehicles Software Power electronics Distributed power generation Planning MATLAB Load flow analysis

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GaRField++: Reinforced Gaussian Radiance Fields for Large-Scale 3D Scene Reconstruction

arXiv

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arXiv 2024年

作者： Zhang, Hanyue Yang, Zhiliu Zuo, Xinhe Tong, Yuxin Long, Ying Liu, Chen School of Information Science and Engineering Yunnan University Yunnan Kunming650500 China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Yunnan Kunming650500 China Department of Electrical and Computer Engineering Clarkson University PotsdamNY13699 United States

This paper proposes a novel framework for large-scale scene reconstruction based on 3D Gaussian splatting (3DGS) and aims to address the scalability and accuracy challenges faced by existing methods. For tackling the scalability issue, we split the large scene into multiple cells, and the candidate point-cloud and camera views of each cell are correlated through a visibility-based camera selection and a progressive point-cloud extension. To reinforce the rendering quality, three highlighted improvements are made in comparison with vanilla 3DGS, which are a strategy of the ray-Gaussian intersection and the novel Gaussians density control for learning efficiency, an appearance decoupling module based on ConvKAN network to solve uneven lighting conditions in large-scale scenes, and a refined final loss with the color loss, the depth distortion loss, and the normal consistency loss. Finally, the seamless stitching procedure is executed to merge the individual Gaussian radiance field for novel view synthesis across different cells. Evaluation of Mill19, Urban3D, and MatrixCity datasets shows that our method consistently generates more high-fidelity rendering results than state-of-the-art methods of large-scale scene reconstruction. We further validate the generalizability of the proposed approach by rendering on self-collected video clips recorded by a commercial drone. © 2024, CC BY.

关键词： 3D reconstruction

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MRCap: Multi-modal and Multi-level Relationship-based Dense Video Captioning

MRCap: Multi-modal and Multi-level Relationship-based Dense ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Wei Chen Jianwei Niu Xuefeng Liu State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

Dense video captioning, with the objective of describing a sequence of events in a video, has received much attention recently. As events in a video are highly correlated, leveraging relationships among events helps generate coherent captions. To utilize relationships among events, existing methods mainly enrich event representations with their context, either in the form of vision (i.e., video segments) or combining vision and language (i.e., captions). However, these methods do not explicitly exploit the correspondence between these two modalities. Moreover, the video-level context spanning multiple events is not fully exploited. In this paper, we propose MRCap, a novel relationship-based model for dense video captioning. The key of MRCap is a multi-modal and multi-level event relationship module (MMERM). MMERM exploits the correspondence between vision and language at both the event level and the video level via contrastive learning. Experiments on ActivityNet Captions and YouCook2 datasets demonstrate that MRCap achieves state-of-the-art performance.

关键词：

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A Data-Informed Analysis of Scalable Supervision for Safety in Autonomous Vehicle Fleets

A Data-Informed Analysis of Scalable Supervision for Safety ...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Cameron Hickert Zhongxia Yan Cathy Wu Institute for Data Systems and Society Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Laboratory for Information & Decision Systems Massachusetts Institute of Technology Cambridge MA USA Department of Civil and Environmental Engineering Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Autonomous driving is a highly anticipated approach toward eliminating roadway fatalities. At the same time, the bar for safety is both high and costly to verify. This work considers the role of remotely-located human operators supervising a fleet of autonomous vehicles (AVs) for safety. Such a ‘scalable supervision’ concept was previously proposed to bridge the gap between still-maturing autonomy technology and the pressure to begin commercial offerings of autonomous driving. The present article proposes DISCES, a framework for Data-Informed Safety-Critical Event Simulation, to investigate the practicality of this concept from a dynamic network loading standpoint. With a focus on the safety-critical context of AVs merging into mixed-autonomy traffic, vehicular arrival processes at 1,097 highway merge points are modeled using microscopic traffic reconstruction with historical data from interstates across three California counties. Combined with a queuing theoretic model, these results characterize the dynamic supervision requirements and thereby scalability of the teleoperation approach. Across all scenarios we find reductions in operator requirements greater than 99% as compared to in-vehicle supervisors for the time period analyzed. The work also demonstrates two methods for reducing these empirical supervision requirements: (i) the use of cooperative connected AVs — which are shown to produce an average 3.67 orders-of-magnitude system reliability improvement across the scenarios studied — and (ii) aggregation across larger regions.

关键词： Road transportation Scalability System performance Predictive models Safety Reliability Vehicle dynamics Autonomous vehicles Queueing analysis Load modeling

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Efficient Evaluation of Short-Circuit Faults in Active Distribution Networks

Efficient Evaluation of Short-Circuit Faults in Active Distr...

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International Conference on Power Electronic Drives

作者： Ghanshyam Meena Akhilesh Mathur V. P. Singh Krishna Murari Sukumar Kamalasadan Department of Electrical Engineering Malaviya National Institute of Technology Jaipur Rajasthan India Electrical Engineering and Computer Science Department The University of Toledo Toledo Ohio USA EPIC and Department of Electrical Engineering Power Energy and Intelligent Systems Laboratory University of North Carolina at Charlotte Charlotte NC USA

ISBN: (数字)9798350372472

ISBN: (纸本)9798350372489

Short-circuit problems in unbalanced radial distribution networks have become more common as a result of distributed generation connectivity and smart grid advancements. An accurate and effective strategy for assessing these faults due to short circuits is presented in this study. In contrast to traditional methods, this approach uses relationship matrices that are created expressly to take use of distribution network's radial characteristics. Through the examination of variations in bus voltages, bus current injections, and branch currents during fault conditions, the suggested method formulates the analysis of various fault types. The accuracy of developed method has been validated on IEEE-123 bus modified test network by comparing the obtained results with PSCAD/EMTDC simulation results.

关键词： Accuracy Simulation Distribution networks Drives Power electronics Distributed power generation Smart grids Circuit faults Active distribution networks

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