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Field Robotics 2025年 3卷 435-459页

作者： Banghe Wu Chengzhong Xu Hui Kong School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing Jiangsu China The State Key Laboratory of Internet of Things for Smart City (SKL-IOTSC) Department of Computer Science University of Macau Macau The State Key Laboratory of Internet of Things for Smart City (SKL-IOTSC) Department of Electromechanical Engineering (EME) University of Macau Macau

In this work, we propose the LiDAR Road-Atlas, a compact and efficient 3D map representation, for autonomous robot or vehicle navigation in a general urban environment. The LiDAR Road-Atlas can be generated by an online mapping framework which incrementally merges local 2D occupancy grid maps (2D-OGMs). Specifically, the contributions of our method are threefold. First, we solve the challenging problem of creating local 2D-OGMs in nonstructured urban scenes based on a real-time delimitation of traversable and curb regions in a LiDAR point cloud. Second, we achieve accurate 3D mapping in multiple-layer urban road scenarios by a probabilistic fusion scheme. Third, we achieve a very efficient 3D map representation of a general environment thanks to the automatic local-OGM-induced traversable-region labeling and a sparse probabilistic local point-cloud encoding. Given the LiDAR Road-Atlas, one can achieve accurate vehicle localization, path planning, and some other tasks. Our map representation is insensitive to dynamic objects which can be filtered out in the resulting map based on a probabilistic fusion. Empirically, we compare our map representation with a couple of popular map representations in robotics society, and our map representation is more favorable in terms of efficiency, scalability, and compactness. Additionally, we also evaluate localization performance given the LiDAR Road-Atlas representations on two public datasets. With a 16-channel LiDAR sensor, our method achieves an average global localization error of 0.26 m (translation) and 1.07° (rotation) on the Apollo dataset, and 0.89 m (translation) and 1.29° (rotation) on the MulRan dataset, respectively, at 10 Hz, which validates its promising performance. The code for this work is open-sourced at https://***/IMRL/Lidar-road-atlas.

关键词： Three-dimensional displays Roads Laser radar Robots Navigation Semantics Location awareness Autonomous vehicles Robot kinematics Cameras

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A model parameter update strategy for enhanced asynchronous federated learning algorithm 3

A model parameter update strategy for enhanced asynchronous ...

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3rd IEEE International Conference on computer Systems, ICCS 2023

作者： Sun, Yu Gao, Xin Shen, Ying Xie, Jianfei Yang, Jiandong Si, Nong Beijing University of Technology Faculty of Information Technology Beijing China North China Institute of Science & Technology Department of Internet of Things Engineering Hebei China Capinfo Technology Development Co. Ltd. Beijing China

ISBN: (纸本)9798350313666

Federated learning addresses the 'data islands' issue resulting from privacy concerns, enabling multiple users to train a model collaboratively without sharing their private datasets. However, in the model training process of federated learning, effectively coordinating the communication between participants and server is essential to improve the training efficiency. Traditional synchronous and asynchronous federated learning algorithms cannot meet the requirements of convergence efficiency and precision stability at the same time. In order to balance algorithm efficiency and model stability, this paper proposes a model parameter update strategy for an enhanced asynchronous federated learning algorithm (EAFL). This strategy requires the server to store the latest global model parameters, the last round of global model parameters, and the update part of both, and judge the issued model type according to the sensitivity difference of the client. The client executes different local update processes according to different model types to improve the training efficiency and maintain the stability of the training accuracy. The experimental results show that the EAFL algorithm achieves better training results on MNIST and CIFAR-10 data sets than the traditional federated learning parameter update algorithm. This improvement in training results can provide new thoughts to solve computer vision tasks such as object detection with scattered devices. © 2023 IEEE.

关键词： Learning algorithms

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The Influence of Human Factors in Ensuring Cybersecurity in Organizations: Effectiveness of PDPA and GDPR Implementation 3

The Influence of Human Factors in Ensuring Cybersecurity in ...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Sharifi, Ahmad Zia Walid, Md. Abul Ala Momand, Mohammad Dawood Safi, Ihsan Ul Haq Safi, Hamidullah Khanzai Hossain, Sadia Nangarhar University Faculty of Computer Science Department of Networking and Communication Afghanistan Bangabandhu Sheikh Mujibur Rahman Digital University Department of Data Science and Engineering Gazipur Bangladesh Lautsprecher Teufel GMbH Berlin Germany Bangabandhu Sheikh Mujibur Rahman Digital University Department of Internet of Things and Robotics Engineering Gazipur Bangladesh

ISBN: (纸本)9798331518592

Modern organizations face rising levels of cyber risks, making cybersecurity a top priority for safeguarding sensitive data and maintaining operational continuity. Recent studies find human factors as the most critical contributors to cyber risks. These human-related risks include user misuse, user mistakes, and user malice. A large proportion of cyber risks arise when hackers use social engineering or phishing techniques to manipulate employees into disclosing confidential information. While some employees knowingly engage in these actions, some incidents occur accidentally due to a lack of awareness about cybersecurity protocols. As a result, governments around the world are enacting data protection legislation aimed at enhancing employees' cyber risk. This study explores the perception of US employees on the effectiveness of data protection legislation in countering cyber threats. It surveyed 30 randomly selected employees from various industries. The results showed that employees perceive data protection legislation as effective in mitigating employeerelated cyber risks. © 2024 IEEE.

关键词： Phishing

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Message Verification Protocol Based on Bilinear Pairings and Elliptic Curves for Enhanced Security in Vehicular Ad Hoc Networks

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computers, Materials & Continua 2024年第10期81卷 1029-1057页

作者： Vincent Omollo Nyangaresi Arkan A.Ghaib Hend Muslim Jasim Zaid Ameen Abduljabbar Junchao Ma Mustafa A.Al Sibahee Abdulla J.Y.Aldarwish Ali Hasan Ali Husam A.Neamah Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennaiTamilnadu602105India Information Technology Department Management Technical CollegeSouthern Technical UniversityBasrah61004Iraq Department of Computer Science College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq College of Big Data and Internet Shenzhen Technology UniversityShenzhen518118China Huazhong University of Science and Technology Shenzhen InstituteShenzhen518000China National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen518060China Computer Technology Engineering Department Iraq University CollegeBasrah61004Iraq Department of Mathematics College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq Institute of Mathematics University of DebrecenDebrecenH-4002Hungary Department of Electrical Engineering and Mechatronics Faculty of EngineeringUniversity of DebrecenDebrecen4028Hungary

Vehicular ad hoc networks(VANETs)provide intelligent navigation and efficient route management,resulting in time savings and cost reductions in the transportation ***,the exchange of beacons and messages over public channels among vehicles and roadside units renders these networks vulnerable to numerous attacks and privacy *** address these challenges,several privacy and security preservation protocols based on blockchain and public key cryptography have been proposed ***,most of these schemes are limited by a long execution time and massive communication costs,which make them inefficient for on-board units(OBUs).Additionally,some of them are still susceptible to many *** such,this study presents a novel protocol based on the fusion of elliptic curve cryptography(ECC)and bilinear pairing(BP)*** formal security analysis is accomplished using the Burrows–Abadi–Needham(BAN)logic,demonstrating that our scheme is verifiably *** proposed scheme’s informal security assessment also shows that it provides salient security features,such as non-repudiation,anonymity,and ***,the scheme is shown to be resilient against attacks,such as packet replays,forgeries,message falsifications,and *** the performance perspective,this protocol yields a 37.88%reduction in communication overheads and a 44.44%improvement in the supported security ***,the proposed scheme can be deployed in VANETs to provide robust security at low overheads.

关键词： Attacks bilinear elliptic curve cryptography(ECC) privacy security vehicular ad hoc network(VANET)

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Expeditious Diagnosis of Breast Cancer from Magnetic Resonance Images Using Grey Wolf Optimization based Improved K-means Algorithm

Expeditious Diagnosis of Breast Cancer from Magnetic Resonan...

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2024 International Conference on Recent Innovation in Smart and Sustainable technology, ICRISST 2024

作者： Krishna Narayanan, S. Ganesh C S, Sundar Petchinathan, G. Ramaraj, Kottaimalai Murugan, Pallikonda Rajasekaran Thilagaraj, M. Mvj College of Engineering Department of Computer Science and Engineering Bengaluru India Karpagam College of Engineering Department of Electrical and Electronics Engineering Tamilnadu Coimbatore India Sri Shanmugha College of Engineering and Technology Department of Biomedical Engineering Tamilnadu Tiruchengode India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamilnadu Krishnankoil India Mvj College of Engineering Department of Industrial Internet of Things Bengaluru India

ISBN: (纸本)9798350309140

Considering early recognition can improve patient survival rates, it is one of the most crucial concerns that ought to be addressed globally. Promptly identifying any malignant cell or cells has the potential to prevent millions of deaths. Early detection of breast cancer has been achieved by the use of MRIs and mammograms;if the disease is found early, the cost of therapy can be significantly lowered. Methods for segmentation are necessary for the identification of breast cancers. In analyzing images, segmentation - which comprises identification, extraction of features, categorization, and treatment - plays a crucial role. In order to plan treatments, segmentation assists doctors in measuring the amount of tissue in the breast. Utilizing image classification techniques, one may determine the degree of cancer by analyzing the form of the malignant cells. The application of unsupervised K-Means clustering is employed to distinguish the region of interest from the background. By utilizing K-centroids, the data is segmented into K-clusters. To enhance segmentation outcomes, the Grey Wolf Optimization (GWO) is integrated with K-means. GWO is a contemporary metaheuristic swarm intelligence technique known for its notable advantages over traditional methods. Notably, it excels in the absence of derivative information during the initial search and involves minimal parameters. As a result, GWO is widely utilized for optimizing various ***, it is straightforward, user-friendly, adaptable, scalable, and has the unique ability to manage exploration and exploitation throughout the search in a way that promotes optimal convergence. When tested on the MIAS breast cancer dataset, the hybrid GWO based enhanced K-means (GWO_iK-means) algorithm scored 93% dice score value on segmenting the infected region. © 2024 IEEE.

关键词： Diseases

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Blockchain-Based Anomaly Detection and Intrusion Prevention in IoT Networks 8th

Blockchain-Based Anomaly Detection and Intrusion Prevention ...

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8th International Conference on internet of things and Connected Technologies, ICIoTCT 2023

作者： Ganesh Kumar, G. Kanakaprabha, S. Venu Gopal, Gaddam Shaik, Riaz Senthil Kumar, T. Udhaya Kumar, T. Department of Information Technology Hindusthan Institute of Technology Tamilnadu Coimbatore641032 India Department of Computer Science and Engineering Rathinam Technical Campus Anna University Coimbatore India B V Raju Institute of Technology Telangana Narsapur Medak502313 India Department of Internet of Things KL Deemed to be University Vijayawada India Department of Information Technology Hindusthan Institute of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Hindusthan Institute of Technology Coimbatore India

ISBN: (纸本)9789819757855

The increasing adoption of IoT devices has raised significant concerns about security and privacy. This paper proposes a blockchain-based approach for anomaly detection and intrusion prevention in IoT networks. The methodology combines blockchain technology with anomaly detection algorithms to enhance the security and trustworthiness of IoT systems. Through simulations and experiments using real-world IoT datasets, the effectiveness of the approach is evaluated. The results demonstrate improved accuracy in detecting anomalies and reduced false positives compared to traditional methods. The blockchain-based solution also provides tamper-proof and decentralized data storage, ensuring the integrity and availability of IoT network information. This research highlights the potential of blockchain technology as a robust solution for enhancing anomaly detection and intrusion prevention in IoT networks, addressing critical security challenges in the IoT ecosystem. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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Coseismic Landslide Mapping Based on Trans-UNet and Transfer Learning

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 13074-13086页

作者： Ren, Tianhe Gong, Wenping Chen, Jun Gao, Liang Wu, Jiahao Xiang, Xuyang China University of Geosciences Faculty of Engineering Wuhan430074 China China University of Geosciences School of Automation Wuhan430074 China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Ocean Science and Technology 999078 China Changjiang Institute of Survey Technical Research MWR Wuhan430074 China

Coseismic landslides often cause significant property damage and loss of life, necessitating timely and accurate detection for emergency response and hazard mitigation. Optical remote sensing imagery plays a critical role in landslide identification;however, sufficient high-resolution images might not be accessible after an earthquake. Traditional detection methods struggle with the coarse spatial details of low-resolution images, limiting their reliability in such scenarios. This study introduces a novel landslide mapping method leveraging the Trans-UNet model and a transfer learning strategy to overcome these challenges. The Trans-UNet model integrates a UNet-like encoder-decoder structure with a Transformer module to enhance global context extraction, a U-shaped full-scale feature extraction module to preserve multi-scale spatial details, and a convolutional decoder for effective feature fusion and resolution restoration. During model training, a transfer learning strategy is employed to enhance the performance of the model on low-resolution images by utilizing rich feature information obtained from high-resolution images. Additionally, this study constructs an open dataset of coseismic landslides triggered by the 2022 MS 6.8 Luding earthquake, using the Gaofen-2 images with a one-meter resolution and the Sentinel-2 images with a ten-meter resolution. The effectiveness and superiority of the proposed method are validated through its application to the Luding Landslide Dataset and comparisons with other state-of-the-art models. This method provides a reliable solution for rapid post-earthquake assessment in resource-constrained environments. © 2008-2012 IEEE.

关键词： Optical remote sensing

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Spike-VAD: Efficient and Robust Spiking Neural Network for Voice Activity Detection

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Shi, Kexin Hou, Mengshu Luo, Xiaoling Zhang, Dehao Liu, Hanwen Wang, Jingya University of Electronic Science and Technology of China Department of Computer Science and Engineering China Sichuan University of Science and Engineering Key Laboratory of Higher Education of Sichuan Province for Enterprise Informationalization and Internet of Things China

In modern speech applications, achieving both low power consumption and noise robustness is critically essential. A well-designed Voice Activity Detection (VAD) front-end minimizes processing demands. Spiking Neural Networks (SNNs), a cutting-edge paradigm in brain-inspired computing, excel in energy efficiency due to their spike-based processing mechanisms. This makes them a promising solution for developing more efficient VAD models. In recent years, researchers have achieved notable advancements in applying SNNs to VAD, particularly in energy efficiency and performance. However, current SNN-based VAD models still struggle to achieve sufficient robustness and fail to fully exploit the low-power potential of SNNs. To address this challenge, we propose an energy-efficient and highly robust spike-based VAD model, called Spike-VAD. Spike-VAD leverages an energy-saving resonate-and-fire frequency (RF-FRE) spike encoding scheme, eliminating the need for computation-intensive Fourier Transform (FT) operations. Inspired by the human auditory frequency preference, a spike-based attention module is designed to refine the encoded spike features and enhance robustness. Furthermore, an Adaptive Memory Modulation Strategy (AMMS) is introduced to dynamically modulate historical information from the audio, facilitating more effective decision-making. Experiments on the QUT-NOISE-TIMIT dataset indicate that compared with previous SNN-based VAD models, our model achieves state-of-the-art (SOTA) performance in both robustness and energy consumption. © 2016 IEEE.

关键词： Energy utilization

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Computing on Surface: A Multi-Task Multi-Access Offloading Scheme in Maritime Edge Networks

Computing on Surface: A Multi-Task Multi-Access Offloading S...

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2024 International Conference on Cloud and Network Computing, ICCNC 2024

作者： Dai, Minghui Dou, Chenglong Wu, Yuan Qian, Liping Lin, Bin Su, Zhou Shen, Xuemin Sherman University of Macau State Key Laboratory of Internet of Things for Smart City China Zhuhai UM Science and Technology Research Institute Zhuhai China Zhejiang University of Technology College of Information Engineering Hangzhou China Dalian Maritime University Department of Communication Engineering Dalian China Xi'An Jiaotong University School of Cyber Science and Engineering Xi'an China University of Waterloo Department of Electrical and Computer Engineering Waterloo Canada

ISBN: (纸本)9798350367805

The recent development of unmanned aerial vehicles (UAVs) technology has been envisioned as a promising paradigm to cater for the growing maritime activities. However, the increasing growth of marine services poses challenges for processing maritime data. In this paper, we propose a surface computing paradigm in maritime networks, in which multi-Task sensed by UAVs can be offloaded to multiple beacon stations. Multiple UAVs can process their workloads locally or offload to surface computing. Taking the system welfare and energy consumption into consideration, we present an optimization problem to determine the selection of beacon stations and the offloading decision, with the objective of maximizing the system welfare. We propose a hybrid auction and convex optimization approach to address the formulated problem. Finally, simulation results demonstrate the effectiveness of our proposed algorithms compared to several baselines. © 2024 IEEE.

关键词： Convex optimization

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Hybrid Artificial Bee Colony and Adaptive Fuzzy K-Means for Detecting Abnormalities in Brain MR Images 5

Hybrid Artificial Bee Colony and Adaptive Fuzzy K-Means for ...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Ts, Kiran Babu Arun Francis, G. Sathya, R. Vijayalakshmi, A. Ramaraj, Kottaimalai Thilagaraj, M. MVJ College of Engineering Department of Computer Science and Engineering Bengaluru India Karpagam College of Engineering Department of Electronics and Communication Engineering Coimbatore India Rajalakshmi Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India Hindusthan College of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamilnadu Krishnankoil India MVJ College of Engineering Department of Industrial Internet of Things Bengaluru India

ISBN: (纸本)9798331530013

A brain tumor is an abnormal mass of cells in the brain and can cause issues by pressing on the brain or infiltrating brain tissue. Not all brain tumors are cancerous;grades 1 and 2 are benign but may recur and become malignant. Symptoms vary depending on the affected area of the brain, as different regions control different functions. Medical image processing is the need for the investigation of 3D images of the humans' internal organs, typically collected from a Magnetic Resonance Imaging or Computed Tomography, to identify disorders, assist therapeutic procedures like incision preparation and in academic research. The multiparametric MR images of individuals with low- and high-grade gliomas that were manually segregated by skilled assessors constitute up the BraTS data set. Human separation of tumors is a laborious, time-consuming process that necessitates the delineation of sections by a human expert. Segmenting gliomas using autonomous algorithms for learning has been explored as a solution to these inadequacies. For glioma segmentation, the suggested method combines artificial bee colony (ABC) optimization and adaptive fuzzy k-means clustering (AFKM) that may be readily integrated into the medical procedure. ABC optimization efficiently finds optimal initial cluster centroids, overcoming Fuzzy K-Means' limitation of poor initialization and suboptimal results. ABC optimization balances global search and local refinement for efficient clustering, while Adaptive Fuzzy K-Means dynamically adjusts clusters, with ABC fine-tuning for optimal fit. The efficacy of the suggested method was validated with the help of metric parameters. © 2024 IEEE.

关键词： Magnetic resonance imaging

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