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IntSHU: A Security-Enabled Intelligent Soft Handover Approach for UAV-aided 5G and Beyond

IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Karmakar, Raja Kaddoum, Georges Akhrif, Ouassima Heritage Institute of Technology Department of Computer Science and Engineering IoT and Cyber Security including Blockchain Technology Kolkata700107 India University of Quebec Department of Electrical Engineering ETS Montreal Canada Lebanese American University Cyber Security Systems and Applied AI Research Center Beirut Lebanon

Unmanned aerial vehicles (UAVs) play a significant role in serving as gNodeBs (gNBs), or user devices (UDs). However, considering the high data rates and low latency required by fifth generation (5G) and beyond, it is essential to maintain a reliable connection at UAVs, particularly due to frequent handovers. To minimize handover failures, the hysteresis margin and time-to-trigger (TTT) should be dynamically adjusted. Furthermore, to prevent vulnerabilities to threats, UAV and gNB authentication and secure communication should be addressed during handovers. However, available literature uses a learning-based approach to handover management in UAV-aided 5G networks, thereby neglecting security, or offers a handover authentication mechanism, but lacks a novel handover strategy. To fill this gap in the literature, in this study, we design an Intelligent Soft Handover for UAV-enabled cellular networks (IntSHU), a generative adversarial network (GAN)-based scheme designed for intelligent soft handovers in UAV-enabled 5G and beyond. IntSHU uses adapted hysteresis margins and TTT values, dynamically generated through an -greedy policy. In addition, we implement a lightweight, physically unclonable function (PUF)-based message encryption and authentication scheme via blockchain for streamlined access control during handovers. The results of our simulation indicate that IntSHU significantly enhances network performance by facilitating reliable soft handovers. © 2015 IEEE.

关键词： 5G mobile communication systems

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Intelligent larval zebrafish phenotype recognition via attention mechanism for high-throughput screening

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computers in Biology and Medicine 2025年 188卷 109892-109892页

作者： Wang, Baihua Sun, Qi Liu, Yujia Zhang, Jiheng Li, Gaozheng Wu, Sifang Zheng, Houbing Ye, Jialin Zhou, Meihua Zheng, Haisu Yu, Yongqiang Zhong, Yi Wu, Yuanzi Huang, Da Wang, Biao Weng, Zuquan College of Biological Science and Engineering Fuzhou University Fujian Fuzhou China Department of Plastic and Cosmetic Surgery The First Affiliated Hospital of Fujian Medical University Fujian Fuzhou China The Center for Big Data Research in Burns and Trauma College of Computer and Data Science/College of Software Fuzhou University Fujian China

Background: Larval zebrafish phenotypes serve as critical research indicators in fields such as ecotoxicology and safety assessment since phenotypic defects are closely related to alterations of underlying pathway. However, identifying these defects is time-consuming and requires specialized knowledge. Method: We proposed a deep network model called RECNet, which combines attention mechanisms and residual structures. In terms of data processing, we applied the mixup data augmentation technique and accumulated a collection of 6805 larval zebrafish phenotype images, mostly generated from our laboratory. Our proposed model was deployed to execute two distinct tasks, including a four-classification of zebrafish phenotypes and a seven-classification involving mixed labels for abnormalities. Results: In the four-class classification task, the RECNet model achieved an accuracy of 0.949, with a mean area under the curve of 0.986 and an F1-score of 0.966. Through interpretable research, attention mechanisms enable the model to focus more accurately on regions of interest. In the mixed-label seven-classification task for anomalies, our model achieved an accuracy of 0.913 and a mean average precision value of 0.847 by employing the weighted loss function (DFBLoss). Furthermore, in a new test dataset, the RECNet model achieved accuracy rates of 0.924 and 0.876 for the two tasks, respectively. Our RECNet model was trained by orders of magnitude larger dataset than previous studies and also showed better accuracy rates. Conclusions: Our method holds promise for diverse applications within zebrafish laboratories and fields such as toxicology, providing indispensable support to scientific research. © 2025 Elsevier Ltd

关键词： Invertebrates

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Lightweight Authenticated Task Offloading in 6G-Cloud Vehicular Twin Networks

Lightweight Authenticated Task Offloading in 6G-Cloud Vehicu...

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IEEE Conference on Wireless Communications and Networking

作者： Sarah Al–Shareeda Fusun Ozguner Keith Redmill Trung Q. Duong Berk Canberk Electrical and Computer Engineering Department Center for Automotive Research (CAR) The Ohio State University USA BTS Group Turkey Department of AI and Data Engineering Istanbul Technical University Turkey School of Electronics Electrical Engineering and Computer Science Queen's University Belfast UK Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John's NL Canada School of Computing Engineering and The Built Environment Edinburgh Napier University UK

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Task offloading management in 6G vehicular net-works is crucial for maintaining network efficiency, particularly as vehicles generate substantial data. Integrating secure communication through authentication introduces additional computational and communication overhead, significantly impacting offloading efficiency and latency. This paper presents a unified framework incorporating lightweight Identity-Based Cryptographic (IBC) authentication into task offloading within cloud-based 6G Vehicular Twin Networks (VTNs). Utilizing Proximal Policy Optimization (PPO) in Deep Reinforcement Learning (DRL), our approach optimizes authenticated offloading decisions to minimize latency and enhance resource allocation. Performance evaluation under varying network sizes, task sizes, and data rates reveals that IBC authentication can reduce offloading efficiency by up to 50 % due to the added overhead. Besides, increasing network size and task size can further reduce offloading efficiency by up to 91.7%. As a countermeasure, increasing the transmission data rate can improve the offloading performance by as much as 63%, even in the presence of authentication overhead. The code for the simulations and experiments detailed in this paper is available on GitHub for further reference and reproducibility [1].

关键词： 6G mobile communication Performance evaluation Identity-based encryption Authentication Deep reinforcement learning Reproducibility of results Resource management Cryptography Optimization Software development management

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A weakly supervised pavement crack segmentation based on adversarial learning and transformers

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Multimedia Systems 2025年第3期31卷 1-21页

作者： Apedo, Yvon Tao, Huanjie School of Computer Science Northwestern Polytechnical University Xi’an People’s Republic of China Engineering and Research Center of Embedded Systems Integration (Northwestern Polytechnical University) Ministry of Education Xi’an People’s Republic of China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an People’s Republic of China

While pixel-level crack segmentation has demonstrated significant potential in infrastructure inspections, its reliance on detailed annotations poses challenges for widespread adoption due to the time-consuming and expensive nature of such labeling. In response, weakly supervised crack segmentation has garnered attention as it eliminates the need for pixel-level annotations. However, existing methods, primarily based on class activation maps (CAM), involve complex training processes and suffer from poor performance due to misalignment between CAM-generated labels and the true target in the image. To address these challenges, we propose a weakly supervised approach for crack segmentation that simultaneously generates synthetic crack images and performs segmentation through adversarial learning. Unlike traditional methods that rely on coarse labels, our approach leverages synthetic crack images with corresponding labels, effectively eliminating issues of misalignment and noisy pseudo-labels. Our method introduces an encoder-decoder architecture for the segmentation model, incorporating a Transformer-based Feature Enhancement module (TFE) in the encoder. This module is specifically designed to capture long-range dependencies and efficiently integrate both high- and low-level features. Additionally, the model includes a Hilo block to extract both high- and low-frequency information from the images, along with a Progressive Shrinking Decoder (PSD) to aggregate and refine adjacent feature maps. Extensive experiments were conducted, and our model achieved an ODS of 67.12% on the CrackForest dataset, 60.11% on the Crack500 dataset, and 44.80% on the AEL dataset. These results demonstrate that our model outperforms several existing weakly supervised pavement segmentation methods across these datasets.

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DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series

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IEEE Transactions on Knowledge and data engineering 2025年

作者： Darban, Zahra Zamanzadeh Yang, Yiyuan Webb, Geoffrey I. Aggarwal, Charu C. Wen, Qingsong Pan, Shirui Salehi, Mahsa Monash University Department of Data Science and AI Melbourne Australia University of Oxford Department of Computer Science Oxford OX1 3SA United Kingdom Monash University Department of Data Science and AI Australia IBM T. J. Watson Research Center Yorktown Heights United States Squirrel Ai Learning Bellevue United States Griffith University School of ICT Australia

In time series anomaly detection (TSAD), the scarcity of labeled data poses a challenge to the development of accurate models. Unsupervised domain adaptation (UDA) offers a solution by leveraging labeled data from a related domain to detect anomalies in an unlabeled target domain. However, existing UDA methods assume consistent anomalous classes across domains. To address this limitation, we propose a novel Domain Adaptation Contrastive learning model for Anomaly Detection in multivariate time series (DACAD), combining UDA with contrastive learning. DACAD utilizes an anomaly injection mechanism that enhances generalization across unseen anomalous classes, improving adaptability and robustness. Additionally, our model employs supervised contrastive loss for the source domain and self-supervised contrastive triplet loss for the target domain, ensuring comprehensive feature representation learning and domain-invariant feature extraction. Finally, an effective center-based Entropy Classifier (CEC) accurately learns normal boundaries in the source domain. Extensive evaluations on multiple real-world datasets and a synthetic dataset highlight DACAD's superior performance in transferring knowledge across domains and mitigating the challenge of limited labeled data in TSAD. © 1989-2012 IEEE.

关键词： Anomaly Detection Contrastive Learning Deep Learning Domain Adaptation Self-Supervised Learning Time Series

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Path optimization method for the sign problem caused by fermion determinant

arXiv

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arXiv 2025年

作者： Hisayoshi, Kazuki Kashiwa, Kouji Namekawa, Yusuke Takase, Hayato Department of Computer Science and Engineering Faculty of Information Engineering Fukuoka Institute of Technology Fukuoka811-0295 Japan Education and Research Center for Artificial Intelligence and Data Innovation Hiroshima University Hiroshima730-0053 Japan

The path optimization method with machine learning is applied to the one-dimensional massive lattice Thirring model, which has the sign problem caused by the fermion determinant. This study aims to investigate how the path optimization method works for the sign problem. We show that the path optimization method successfully reduces statistical errors and reproduces the analytic results. We also examine an approximation of the Jacobian calculation in the learning process and show that it gives consistent results with those without an approximation. Copyright © 2025, The Authors. All rights reserved.

关键词： Fermions

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Multi-agent Collaboration for Vehicular Task Offloading Using Federated Deep Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年

作者： Chen, Xing Xiao, Bohuai Lin, Xinyu Chen, Zheyi Min, Geyong Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science ExeterEX4 4QF United Kingdom

Mobile Edge Computing (MEC) distributes resources such as computing, storage, and bandwidth to the side close to users, which can provide low-latency services to in-vehicle users, thus promising a more efficient and safer driving environment. However, due to the dynamic scale of vehicle and the variability of resource requirements, it is a significant challenge to quickly obtain effective task offloading in large-scale vehicle scenarios. The existing studies generally adopt the centralized decision-making method, with long decision-making time and high computational overhead, which cannot effectively achieve good offloading decisions in large-scale scenarios. To address these problems, we propose a Multi-agent Collaborative Method for vehicular task offloading using Federated Deep Reinforcement Learning called MCM-FDRL. First, each vehicle as an agent, independently makes offloading decisions based on local information. Next, the offloading decision model of each vehicle is obtained through federated reinforcement learning training. At runtime, an effective vehicle offloading plan can be gradually developed through multi-agent collaboration. Using two real-world datasets, experiments show that the MCM-FDRL has good adaptability and scalability. Moreover, compared to the state-of-the-art methods, the task's average response time of the MCM-FDRL is reduced by 9.75%-64.90%, respectively. © 2002-2012 IEEE.

关键词： Federated learning

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ATCM: Aerial-Terrestrial LiDAR-based Collaborative Simultaneous Localization and Mapping

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Xu, Yuhang Chen, Chi Yang, Bisheng Wu, Weitong Sun, Shangzhe Wang, Zhiye Li, Liuchun Zou, Qin Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Engineering Research Center for SpatioTemporal Data Smart Acquisition and Application Ministry of Education of China Institute of Geo-Spatial Intelligence Wuhan430079 China Hohai University School of Earth Sciences and Engineering Nanjing211100 China Wuhan University School of Computer Science Wuhan430079 China

Multi-robot collaborative simultaneous localization and mapping (C-SLAM) offers precise scene reconstruction over single-robot SLAM and enables the data fusion from heterogeneous robots. However, heterogeneous C-SLAM faces challenges in both accurate inter-robot loop closure detection and globally consistent data fusion due to inherent viewpoint disparities and heterogeneous data characteristics. This paper introduces ATCM, an Aerial-Terrestrial LiDAR-based C-SLAM method designed for heterogeneous robots without priori initial relative position. ATCM comprises three modules: single-robot front-end employing diverse SLAM methods, multi-robot loop closure detection, and global pose graph optimization. A novel LiDAR-based cross-view global loop descriptor is proposed for scan-to-scan heterogeneous inter-robot loop closure detection. By uniformly mapping cross-view information into the height domain and integrating dynamic height, the loop descriptor automatically achieves viewpoint correction. Additionally, we introduce a bidirectional loop detection algorithm that validates inter-robot loop closures through both forward and reverse detections. Finally, the two-stage global pose graph optimization integrates multi-source measurements, ensuring globally consistent mapping and localization with cross-view data. We have validated the effectiveness of ATCM on campus scenario datasets and the KITTI dataset, achieving a remarkable 21.95% improvement in trajectory accuracy and a 17.00% enhancement in map precision compared to high-precision point cloud maps, surpassing state-of-the-art LiDAR-based odometry methods. In the ablation experiments, the proposed loop descriptor achieved 97% accuracy in recognizing heterogeneous inter-robot loop closures. Moreover, compared to the traditional unidirectional method, the bidirectional loop detection method demonstrates up to a 31.2% improvement in loop closure accuracy. © 2025 IEEE.

关键词： SLAM robotics

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Five-user quantum virtual local area network with an AlGaAs entangled photon source

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science China(Physics,Mechanics & Astronomy) 2025年第3期68卷 61-73页

作者： Cheng Qian Hongkai Tian Xu Jing Yichen Liu Zhe Chen Huiran Luo Yuan Du Xiaodong Zheng Tangsheng Chen Yuechan Kong Hua-Lei Yin Dong Jiang Bin Niu Liangliang Lu Key Laboratory of State Manipulation and Advanced Materials in Provincial Universities School of Physical Science and TechnologyNanjing Normal UniversityNanjing210023China School of Internet Anhui UniversityHefei230039China National Key Laboratory of Solid-State Microwave Devices and Circuits Nanjing Chip Valley Industrial Technology InstituteNanjing Electronic Devices InstituteNanjing210016China Research Center for Quantum Optics and Quantum Communication School of ScienceQingdao University of TechnologyQingdao266520China Department of Physics and Beijing Key Laboratory of Opto-electronic Functional Materials and Micro-nano Devices Key Laboratory of Quantum State Construction and Manipulation(Ministry of Education)Renmin University of ChinaBeijing100872China Beijing Academy of Quantum Information Sciences Beijing100193China National Engineering Research Center of Agro-Ecological Big Data Analysis and Application Anhui UniversityHefei230601China National Laboratory of Solid-State Microstructures and School of Physics Nanjing UniversityNanjing210093China Hefei National Laboratory Hefei230088China

The promotion of quantum network applications demands the scalable connection of quantum *** is preferable to set up multiple logical networks coexisting on a single physical network infrastructure to accommodate a larger number of *** we present a quantum virtual network architecture that offers this level of scalability,without being constrained to a fixed physical-layer network relying solely on passive multiplexing *** architecture can be understood as arising from the superposition of a fully connected entanglement distribution network and port-based virtual local area network,which group multiusers by access *** terms of hardware,we leverage a semiconductor chip with a high figure-of-merit modal overlap to directly generate high-quality polarization entanglement,and a streamlined polarization analysis module,which requires only one single-photon detector for each end *** experimentally perform the BBM92 QKD protocol on the five-user quantum virtual network and demonstrate voice and image encryption on a campus area *** results may provide insights into the realization of large-scale quantum networks with integrated and cost-efficient photonic architecture.

关键词： quantum network quantum entanglement distribution integrated photonics image and voice encryption

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Predicting optical properties of NiO films fabricated by RF magnetron sputtering: A machine learning approach

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Optik 2025年 321卷

作者： Yüksek, Ahmet Gürkan Horoz, Sabit Altuntaş, İsmail Demır, İlkay Tüzemen, Ebru Ş. Nanophotonics Research and Application Center Sivas Cumhuriyet University Sivas58140 Turkey Computer Engineering Department Sivas Cumhuriyet University Sivas58140 Turkey Department of Fundamental Engineering Sciences Sivas University of Science and Technology Sivas58140 Turkey Department of Nanotechnology Engineering Sivas Cumhuriyet University Sivas58140 Turkey Department of Physics Sivas Cumhuriyet University Sivas58140 Turkey

NiO films with different thicknesses (100, 150, 200, 250, 300 and 400 nm) were grown on glass substrates using the RF Magnetron sputtering method and their optical transmittance properties were analysed with a spectrophotometer. An innovative aspect of this work was the application of machine learning techniques used to derive new insights from experimental data. Four different machine learning algorithms -ANFIS, Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Gaussian Process Regression (GPR)- were tested. While the models were trained using films of different thicknesses, a randomly selected 75 % of the whole dataset was used for model testing and the remaining 25 % of the films were used for testing the models. Among these, ANN and GPR models were found to be the most successful models. Using these models, the energy band gaps were estimated at 1 nm intervals and the values ranged from approximately 3.50 eV to 3.76 eV. © 2024 Elsevier GmbH

关键词： Magnetron sputtering

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