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arXiv 2025年

作者： Nikolaidis, Panagiotis Mostafavi, Samie Gross, James Baras, John Department of Electrical and Computer Engineering The Institute for Systems Research University of Maryland United States School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

Augmented reality applications are bitrate intensive, delay-sensitive, and computationally demanding. To support them, mobile edge computing systems need to carefully manage both their networking and computing resources. To this end, we present a proof of concept resource management scheme that adapts the bandwidth at the base station and the GPU frequency at the edge to efficiently fulfill roundtrip delay constrains. Resource adaptation is performed using a Multi-Armed Bandit algorithm that accounts for the monotonic relationship between allocated resources and performance. We evaluate our scheme by experimentation on an OpenAirInterface 5G testbed where the considered application is OpenRTiST. The results indicate that our resource management scheme can substantially reduce both bandwidth usage and power consumption while delivering high quality of service. Overall, this work demonstrates that intelligent resource control can potentially establish systems that are not only more efficient but also more sustainable. © 2025, CC BY.

关键词： Quality of service

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So Far Yet So Near: Time Series Data Augmentation with Exploring non-Semantic Boundaries based on Reinforcement Learning

So Far Yet So Near: Time Series Data Augmentation with Explo...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Haoran Zhang, Zhibo Kang, Jiarong Jiang, Xun Gong, Xiaoli Zhang, Jin Sun, Zhe Cichocki, Andrzej College of Computer Science Nankai University Tianjin China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Medicine Faculty of Health Data Science Juntendo University Japan Systems Research Institute Polish Academy of Science Warsaw Poland

ISBN: (纸本)9798350368741

Data augmentation effectively expands feature distribution in time series classification, enhancing downstream task performance. However, existing techniques often fail to maintain semantic consistency between augmented and original time series data, causing label noise and thereby degrading downstream task performance. We argue that data augmentation should preserve time series semantic consistency and expand the non-semantic information space. In this paper, we reformulate data augmentation as a semantic path planning problem between original data and augmented data, modeled as a Markov Decision Process (MDP). We propose a reinforcement learning-based algorithm (RL) named FreqSYN, where the action space is defined by a set of learnable Gaussian kernels that perturbs the frequency domain of the original data to generate augmented samples. The confidence coefficients of augmented data in semantically relevant classification tasks are used as a reward to iteratively refine the FreqSYN. Our method is validated across four datasets, achieving state-of-the-art performance, with a 2% improvement in F1 score over the SimPSI method. The code and models are available at https://***/NKU-EmbeddedSystem/FreqSYN. © 2025 IEEE.

关键词： Data argumentation frequency domain augmentation Gaussian kernel prediction time series

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An Intelligent Reinforcement Learning–Based Method for Threat Detection in Mobile Edge Networks

An Intelligent Reinforcement Learning–Based Method for Thre...

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作者： Saeed, Muhammad Yousaf He, Jingsha Zhu, Nafei Farhan, Muhammad Dev, Soumyabrata Gadekallu, Thippa Reddy Almadhor, Ahmad College of Computer Science Beijing University of Technology Beijing China Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal Pakistan School of Computer Science University College Dublin Dublin Ireland Centre of Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India The College of Mathematics and Computer Science Zhejiang A&F University Hangzhou China Division of Research and Development Lovely Professional University Phagwara India Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakaka Saudi Arabia

Traditional techniques for detecting threats in mobile edge networks are limited in their ability to adapt to evolving threats. We propose an intelligent reinforcement learning (RL)–based method for real-time threat detection in mobile edge networks. Our approach enables an agent to continuously learn and adapt its threat detection capabilities based on feedback from the environment. Through experiments, we demonstrate that our technique outperforms traditional methods in detecting threats in dynamic edge network environments. The intelligent and adaptive nature of our RL-based approach makes it well suited for securing mission-critical edge applications with stringent latency and reliability requirements. We provide an analysis of threat models in multiaccess edge computing and highlight the role of on-device learning in enabling distributed threat intelligence across heterogeneous edge nodes. Our technique has the potential, significantly enhancing threat visibility and resiliency in next-generation mobile edge networks. Future work includes optimizing sample efficiency of our approach and integrating explainable threat detection models for trustworthy human–AI collaboration. © 2024 John Wiley & Sons Ltd.

关键词： Reinforcement learning

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Adaptive Quantum and Graph-Attentive Security Framework for DoS Attack Mitigation in VANETs 2

Adaptive Quantum and Graph-Attentive Security Framework for ...

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2nd International Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Anjum, Asma Rao, P. S. V. Srinivasa Sindhu, M. Bhatt, Pooja Priya, M.R. Maranan, Ramya HKBK College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru560045 India Joginpally B R Engineering College Department of Computer Science and Engineering Telangana Hyderabad500075 India R.M.K Engineering College Department of Computer Science and Business Systems Tamil Nadu Thiruvallur601206 India Parul institute of Engineering and Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology Post Limda Gujarat 391760 India NIE First Grade College Department of Computer Science Karnataka Mysuru570008 India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331505745

Vehicular Ad Hoc Networks (VANETs) are considered crucial for real-time vehicle-to-vehicle communication, which in turn enhances the efficiency of traffic and road safety. VANETs are very vulnerable to Denial-of-Service (DoS) attacks, which seriously disrupt communication and degrade network performance. This paper introduces a high-performance neural network-based detection and prevention mechanism that can effectively counter DoS attacks. The proposed method is initiated with the real-time monitoring of the network. Data collection and preprocessing through the Adaptive Self-Guided Loop Filter (ASGLF) are used to filter out noise from the data. The Fast Hybrid Vision Transformer (FHVT) conducts feature extraction for network traffic to effectively preserve the spatial and temporal relationships. Using the Reflecting Equivariant Quantum Neural Network (REQNN) integrated with Heterogeneous Edge-Enhanced Graph Attention Network (HEEGAT) ensures timely anomaly detection accuracy of attacks. Subtraction-Average-Based Optimizer (SAO) optimizes the detection parameters to effectively utilize resources. The experimental results have shown accuracy in the detection of DoS attacks as 99.9% with a PDR of 99.9% and also improved throughput efficiency by 99.8%. These results validate the effectiveness of the proposed model in securing VANETs from DoS attacks, offering uninterrupted network availability and improved vehicular communication security for intelligent transportation systems. © 2025 IEEE.

关键词： Advanced driver assistance systems

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SSCCPC-Net: Simultaneously Learning 2D and 3D Features with CLIP for Semantic Scene Completion on Point Cloud 41st

SSCCPC-Net: Simultaneously Learning 2D and 3D Features with...

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41st computer Graphics International Conference, CGI 2024

作者： Duan, Wantong Bao, Yongtang Qi, Yue State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Shandong University of Science and Technology Qingdao266590 China Jingdezhen Research Institute of Beihang University Jingdezhen333000 China Qingdao Research Institute of Beihang University Qingdao266000 China

ISBN: (纸本)9783031820236

Compare to traditional Scene Completion (SC), Semantic Scene Completion (SSC) is a challenging task that aims to generate complete and semantically consistent 3D scene from partial and sparse input data, which is fundamental to fully understanding the scene and being able to interact with it. Consequently, the SSC task has received much attention in recent years. Most of the methods are voxel-based approaches, but they have high computational and memory requirements. A few works based on point cloud do not sufficiently exploit the correlation between semantic segmentation and geometric completion subtasks, while focusing too much on point cloud shape features and ignoring the rich texture information that RGB images can provide. In this paper, we present SSCCPC-Net (Semantic Scene Completion with CLIP on Point Cloud-Net), a novel network architecture for point cloud semantic scene completion using a combination of 2D and 3D features. Inspired by recent works of large pretrained vision-language models in semantic segmentation, we explore to accomplish SSC task with the help of Contrastive Language-Image Pre-Training (CLIP) model. Specifically, we use the CLIP features for guidance to fuse the 2D features extracted from the RGB image and the 3D features extracted from the point cloud. The fused features are then fed into our designed Semantic-Completion Decoder for per-point semantic prediction and semantic labeling-assisted point cloud completion. Finally, we obtain the complete semantically point cloud. Numerous experiments have demonstrated that our method has higher effectiveness and generalizability compared to state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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Prioritizing locations for renewable hydrogen production in Algeria

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International Journal of Hydrogen Energy 2025年 130卷 480-502页

作者： Ramezani, Mohammad Himri, Youcef Sedaghat, Ahmad Mishra, Phoolenrda Goodarzian, Fariba Mostafaeipour, Ali Industrial Engineering Department Yazd University Yazd Iran Faculté des Sciences Exactes Bechar Algeria Higher National School of Renewable Energies Environment & Sustainable Development: Laboratory of Renewable Energy Energy Efficiency and Smart Systems Batna Algeria Department of Mechanical Engineering College of Engineering Australian University West Mishref Safat13015 Kuwait Department of Civil and Environmental Engineering California State University FullertonCA United States Edinburgh Business School Heriot-watt University Scotland United Kingdom Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Engineering & Technology Duy Tan University Da Nang Viet Nam

Algeria has enormous potential in terms of developing hybrid wind-solar sources for hydrogen production. In this research, the suitable locations for hydrogen production from wind-solar hybrid power plants in Algeria are investigated. A Novel ARAS(Additive Ratio Assessment) method is implemented using seventeen criteria which is thoroughly examined for evaluating and prioritizing eight states in Algeria. To assess the results for the hydrogen production, three different validating methods are examined in parallel. Then, the economic evaluation is carried out for selecting optimal number of wind turbines and solar PV panels for hydrogen production. The findings suggest that Algeries, Adrar, and Oran are ranked with the highest priority cities for constructing wind-solar hybrid power plants for hydrogen production. Next, techno-economic evaluation is explored on NPV (Net Present Value) basis, which determines nominal power production of 100 kW and 500 kW from wind turbines and solar panels, respectively. Considering 20 years of useful life for the hybrid wind-solar system, the project is economically evaluated feasible with $261,049,800 NPV. Hydrogen production revenue and CO2 emission saving are equal to $71,473,524 and $637.524, respectively. Considering that the efficiency of the RES decreases by 2 % every year, the amount of hydrogen and the CO2 emitted drops by about 2 %. This study provides planners with a detailed insight for hydrogen production from renewable sources in Algeria. © 2025 Hydrogen Energy Publications LLC

关键词： Windmill

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Reconstruction of Thermal Protection System Aeroheating Using a Green’s Function Approach

Reconstruction of Thermal Protection System Aeroheating Usin...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： McAfee, Kenneth Rabin, Oded Alpert, Hannah S. Department of Aerospace Engineering University of Maryland College ParkMD20742 United States Department of Aerospace Engineering Institute for Research in Electronics and Applied Physics University of Maryland College ParkMD20742 United States Entry Systems and Vehicle Development Branch NASA Ames Research Center Moffett FieldCA94035 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Inverse heat transfer (IHT) techniques are often used to reconstruct the surface heating conditions on spacecraft thermal protection systems (TPS) during atmospheric entry. Current IHT techniques for entry spacecraft applications, however, demand substantial computational resources, and are impractical for analyses such as uncertainty quantification and real-time health monitoring. In this paper, a Green’s function sensor fusion approach is used to reconstruct the TPS surface aeroheating conditions on experimental spaceflight and ground test systems from collocated temperature and heat flux sensors embedded in the TPS. The algorithm leverages Green’s functions to model the heat conduction within the spacecraft TPS and stabilizes the recovery of the surface heat flux boundary condition using the direct heat flux sensor measurement. The algorithm is validated using arc-jet ground test data and applied to the reconstruction of the Mars 2020 backshell heating during Martian atmospheric entry. The performance of the algorithm is benchmarked against a current state-of-the-art IHT framework, FIAT_Opt. The Green’s function-based reconstruction algorithm recovers the net hot-wall heat flux absorbed by the TPS and the incident heat flux from the atmospheric entry environment in close agreement with FIAT_Opt. Notably, computation of the surface heating conditions is completed in three orders of magnitude less time with the Green’s function sensor fusion approach using a consumer-grade PC, versus with FIAT_Opt running on a high-performance computer cluster. The efficiency of the algorithm is leveraged to compute the uncertainty contributions of input parameters to the total uncertainty of reconstructed Mars 2020 backshell heating conditions for the full atmospheric entry heat pulse. The sensitivity analysis uncovers that, at different times throughout the entry heat pulse, uncertainties in the TPS specific heat, thermal conductivity, and emissivity are all dominant drivers of t

关键词： Sensitivity analysis

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A Data-and-Model-Driven Acceleration Approach for Large-Scale Network-Constrained Unit Commitment Problem With Uncertainty

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IEEE Transactions on Sustainable Energy 2025年

作者： Zhou, Yuzhou Han, Zhihan Zhai, Qiaozhu Wu, Lei Cao, Xiaoyu Guan, Xiaohong Xi'an Jiaotong University Systems Engineering Institute MOEKLINNS Lab Xi'an710049 China Sichuan Digital Economy Industry Development Research Institute Sichuan Chengdu610037 China Xi'an Jiaotong University Systems Engineering Institute Xi'an710049 China Stevens Institute of Technology Department of Electrical and Computer Engineering Hoboken07030 United States Tsinghua University Department of Automation Center for Intelligent and Networked Systems Beijing100084 China

Numerous binary variables and constraints greatly affect the computation of large-scale network-constrained unit commitment (NCUC) problems. With the integration of massive renewable energy and distributed loads, binary variables and network power flows face more frequent changes to cope with uncertainties. How to ensure the calculation efficiency and accuracy of NCUC problems against uncertainties and frequent changes becomes a common challenge for academia and industry. Inspired by data-driven and model-driven technologies, this paper presents a hybrid reduction approach for variables and constraints to accelerate the solving of large-scale NCUC problems, which achieves "one plus one much greater than two" effect. Specifically, an offline database of nodal net load clusters and the corresponding NCUC solutions is established, which also captures the impacts of changes in renewables, loads, topology, etc. on UC solutions. With this database, a data-and-modeldriven reduction method is proposed to fix binary variables and reduce constraints, while considering the changes of prices and net loads in online NCUC instances. Moreover, the reduction method was further developed to accelerate various NCUC problems, including scenario-based methods, robust optimization methods, etc. Thus, the computation burden of online large-scale NCUC problems is greatly mitigated while ensuring good solution quality. Numerical tests implemented on IEEE 24-bus system, 118-bus system, and Polish 2383-bus system illustrate the efficiency of the proposed approach. © 2025 IEEE. All rights reserved.

关键词： Mendelevium

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Experimental implementation of chaos-based video encryption using visible light communication technology

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Applied Optics 2025年第9期64卷 C101-C118页

作者： Fotso, Sorel Bagio Nono Mbé, Jimmi Hervé Talla Takotue, Jean Ernest Takougang, Armand Cyrille Nzeukou Ndjiongue, Alain Richard Research Unit of Condensed Matter Electronics and Signal Processing Department of Physics University of Dschang P.O. Box 67 Dschang Cameroon Research Unit of Automation and Applied Computer Department of Electrical Engineering IUT-FV Bandjoun University of Dschang P.O. Box: 134 Bandjoun Cameroon Laboratory of Industrial Systems and Environmental Engineering Fotso Victor University Institute of Technology University of Dschang P.O. Box 134 Bandjoun Cameroon School of Electrical and Information Engineering Faculty of Engineering and the Built Environment University of the Witwatersrand Johannesburg South Africa

Simulations are commonly used to develop and evaluate video encryption algorithms. Although these approaches are useful for demonstrating theoretical feasibility and algorithm performance, they neglect practical challenges and real-world communication conditions. This oversight creates a critical gap in evaluating their effectiveness and security in practical applications. In this paper, we describe a novel, to the best of our knowledge, video encryption scheme that employs a modified chaotic Colpitts oscillator, a customized Josephus problem, and ribonucleic acid (RNA) operations, and is implemented in visible light communication (VLC) environment. The modified Colpitts oscillator reveals significant phenomena, the most prominent for this study being chaos. The encryption scheme involves block scrambling and pixel-level permutation employing a customized Josephus problem and a column and row shifting technique. Diffusion is then achieved through a custom Josephus problem-oriented RNA operation combined with basic RNA operations and cipher block chaining (CBC). Based on the results, we found that the customized Josephus problem-based permutation performed more efficiently than traditional methods, whereas the column and row shifting permutation was more robust. By eliminating key sequence coding and item-to-item operations, the proposed RNA operation reduces computational overhead. This video encryption scheme is proven to be effective and resilient at securing video data transmitted over the VLC channel, and it constitutes a significant advancement in areas such as surveillance, medical imaging, and military communication requiring robust secured video data. © 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： Medical imaging

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Decoupling While Coupling: Towards More Accurate Stereo Image Sand Removal Beyond Certainty

Decoupling While Coupling: Towards More Accurate Stereo Imag...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wei, Bingcai Liu, Hui Qian, Chuang Jia, Yi Wu, Wangyu Li, Zhishan School of Computer Science Wuhan University Wuhan China GNSS Research Center Wuhan Unicersity Wuhan China Intelligent Transport Systems Research Center Wuhan University of Technology Wuhan China College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin China University of Liverpool Liverpool United Kingdom School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798350368741

Stereo image sand removal is crucial to improve the perceptual quality for autonomous driving perception. Existing methods often fall short in accurately estimating the uncertainty inherent in degraded images, leading to suboptimal outcomes. To address this, we introduce a novel framework named Decoupling While Coupling(DWC). DWC pioneers the integration of inter-view uncertainty estimation, cross-view uncertainty-aware interaction and block-wise uncertainty representation for superior stereo image sand removal. For cross-view information interaction, we propose an Uncertainty-aware Cross-view Attentive Interaction module(UCAI) to cope with the lack of uncertainty estimation ability in the existing cross-view information interaction mechanism. For the uncertainty perception and information interaction within the inter-view, we propose a Distribution Modeling Coupling Block(DMCB), which transmits the representation of uncertainty between each backbone module. For block-wise uncertainty estimation, we use our proposed Uncertainty-aware Distribution Feature Modulator(UDFM) as the backbone of DWC to modulate the uncertainty inside the neural network itself. Extensive experimental validations on our proposed stereo image sand removal dataset SandST confirm the efficacy of DWC. Our method not only achieves higher PSNR and SSIM, but also exhibits enhanced robustness against various sand degrees and patterns. © 2025 IEEE.

关键词： Cross-view Inter-view Sand removal Stereo image Uncertainty estimation

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