检索结果-内蒙古大学图书馆

A privacy-preserving access control protocol for 6G supported intelligent UAV networks

Vehicular Communications 2025年 54卷

作者： Mahmood, Khalid Shamshad, Salman Anisi, Mohammad Hossein Brighente, Alessandro Saleem, Muhammad Asad Das, Ashok Kumar School of Engineering and Computing University of Lancashire Preston PR1 2HE United Kingdom of Intelligent Data Science National Yunlin University of Science and Technology Douliu 64002 Taiwan Department of Software Engineering The University of Lahore Lahore 54590 Pakistan School of Computer Science and Electronic Engineering University of Essex Colchester CO4 3SQ United Kingdom Department of Mathematics and HIT research center Univ. of Padova Italy School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 611731 China Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad 500032 India Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 South Korea

Due to their autonomous operation, high mobility, and real-time communication capabilities, 6G-supported Unmanned Aerial Vehicles (6G-UAVs) (i.e., drones) are increasingly being utilized to enhance data collection and management in Intelligent Transportation Systems (ITSs). Despite their manifold benefits, 6G-supported UAV-based ITS (6G-U-ITS) faces unique security challenges beyond conventional cyber and physical threats. These include real-time authentication, impersonation attacks, physical tampering or cloning and protection against identity spoofing in highly dynamic environments. For instance, an attacker may steal a drone and use its identity to send authenticated malicious messages to the ITS, causing road accidents. Therefore, a secure authentication scheme must ensure resilience against UAV identity theft and unauthorized access while maintaining low-latency and computational efficiency to support the stringent real-time security requirements of 6G-U-ITS. Existing authentication schemes are not specifically designed to address these challenges, making it imperative to develop a lightweight and robust authentication mechanism tailored for 6G-U-ITS. Moreover, most of the existing protocols are vulnerable to physical tampering and impersonation attacks and also require high computation overhead. In this paper, to mitigate these limitations and satisfy the aforementioned requirements, we propose a secure access control protocol for 6G-U-ITS. To the best of our knowledge, this is the first security solution in the literature that can achieve security against UAVs physical attacks. Furthermore, we justify the robustness of the designed protocol against potential attacks through detailed formal and informal security assessment. Via testbed experiments, we show that our protocol achieves 20.66% and 22.82% higher efficiency on communication and computation overhead, respectively, compared to other contemporary competing protocols. © 2025 Elsevier Inc.

关键词： Authentication protocol Intelligent transport systems Key agreement Security protocol UAV

来源：评论

学校读者我要写书评

暂无评论

Correcting rural building annotations in OpenStreetMap using convolutional neural networks

arXiv

引用

arXiv 2019年

作者： Vargas-Muñoz, John E. Lobry, Sylvain Falcão, Alexandre X. Tuia, Devis Laboratory of Image Data Science Institute of Computing University of Campinas Campinas Brazil Laboratory of Geo-information Science and Remote Sensing Wageningen University & Research Netherlands

Rural building mapping is paramount to support demographic studies and plan actions in response to crisis that affect those areas. Rural building annotations exist in OpenStreetMap (OSM), but their quality and quantity are not sufficient for training models that can create accurate rural building maps. The problems with these annotations essentially fall into three categories: (i) most commonly, many annotations are geometrically misaligned with the updated imagery;(ii) some annotations do not correspond to buildings in the images (they are misannotations or the buildings have been destroyed);and (iii) some annotations are missing for buildings in the images (the buildings were never annotated or were built between subsequent image acquisitions). First, we propose a method based on Markov Random Field (MRF) to align the buildings with their annotations. The method maximizes the correlation between annotations and a building probability map while enforcing that nearby buildings have similar alignment vectors. Second, the annotations with no evidence in the building probability map are removed. Third, we present a method to detect non-annotated buildings with predefined shapes and add their annotation. The proposed methodology shows considerable improvement in accuracy of the OSM annotations for two regions of Tanzania and Zimbabwe, being more accurate than state-of-the-art baselines. Copyright © 2019, The Authors. All rights reserved.

关键词： Buildings

来源：评论

学校读者我要写书评

暂无评论

Robot Simulation and Reinforcement Learning Training Platform Based on Distributed Architecture 4th

Robot Simulation and Reinforcement Learning Training Platfor...

引用

4th International Conference on Cognitive Systems and information Processing, ICCSIP 2018

作者： Jiang, Yun-Liang Huang, Zeng-Qiang Cao, Jun-Jie Liu, Yong Ma, Xinqiang Huang, Yi School of Computer Science Hangzhou Dianzi University Hangzhou310018 China School of Information Engineering Huzhou University Huzhou313000 China Institute of Cyber Systems and Control Zhejiang University Hangzhou310027 China Institute of Intelligent Computing and Visualization Based on Big Data Chongqing University of Arts and Sciences Chongqing402160 China

ISBN: (纸本)9789811379826

In recent years, reinforcement learning, which enables robots to learn previously missing abilities, plays an increasingly important role in robotics, such as learning hard-to-code behaviors or optimizing problems without an accepted closed solution. The main problem of RL in robotics is that it is expensive and takes a long time to learn and operate. Another problem: advanced robot simulators like Gazebo are inefficient and time-consuming. In order to cope with these problems, a hybrid computing platform based on traditional robot simulation architecture and distributed architecture (hereinafter referred to as RDTP) is proposed in this paper, which helps to save cost, shorten time and speed up simulation and training. Additionally, the platform is optimized to a certain extent in terms of ease of use and compatibility. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

BASSET: Bandpass-Adaptive Single-pulse SEarch Toolkit – Optimized Sub-Band Pulse Search Strategies for Faint Narrow-Band FRBs

arXiv

引用

arXiv 2025年

作者： Cao, J.H. Wang, P. Li, D. Pan, Q.H. Mao, K. Niu, C.H. Zhang, Y.K. Qu, Q.Y. Lu, W.J. Zhang, J.S. Zhu, Y.H. Wang, Y.D. Chen, H.X. Chen, X.L. Gügercinoğlu, E. Fang, J.H. Feng, Y. Gao, H. Huang, Y.F. Li, J. Miao, C.C. Tsai, C.W. Yao, J.M. You, S.P. Zhao, R.S. Liu, Q.Z. Weng, S.M. Yew, S.H. Zhang, J. Zhang, L. Zhou, D.K. Zhu, W.W. National Astronomical Observatories Chinese Academy of Sciences 20A Datun Road Chaoyang District Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China Institute for Frontiers in Astronomy and Astrophysics Beijing Normal University Beijing102206 China Department of Astronomy Tsinghua University Beijing100084 China Zhejiang Lab Zhejiang Hangzhou311121 China Central China Normal University Wuhan430079 China Institute for Astronomy School of Physics Zhejiang University Hangzhou310027 China Department of Astronomy Beijing Normal University Beijing100875 China School of Astronomy and Space Science Nanjing University Nanjing210023 China Ministry of Education Nanjing210023 China Department of Astronomy School of Physical Sciences University of Science and Technology of China Hefei230026 China Xinjiang Astronomical Observatory Chinese Academy of Sciences Urumqi830011 China Key Laboratory of Information and Computing Science Guizhou Province Guizhou Normal University Guiyang550001 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China Purple Mountain Observatory Chinese Academy of Sciences Nanjing210023 China College of Physics and Electronic Engineering Qilu Normal University Jinan250200 China School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Centre for Astrophysics and Supercomputing Swinburne University of Technology P.O. Box 218 HawthornVIC3122 Australia

The existing single-pulse search algorithms for fast radio bursts (FRBs) do not adequately consider the frequency bandpass pattern of the pulse, rendering them incomplete for the relatively narrow-spectrum detection of pulses. We present a new search algorithm for narrow-band pulses to update the existing standard pipeline, Bandpass-Adaptive Single-pulse SEarch Toolkit (BASSET). The BASSET employs a time-frequency correlation analysis to identify and remove the noise involved by the zero-detection frequency band, thereby enhancing the signal-to-noise ratio (SNR) of the pulses. The BASSET algorithm was implemented on the FAST real dataset of FRB 20190520B, resulting in the discovery of additional 79 pulses through reprocessing. The new detection doubles the number of pulses compared to the previously known 75 pulses, bringing the total number of pulses to 154. In conjunction with the pulse calibration and the Markov Chain Monte Carlo (MCMC) simulated injection experiments, this work updates the quantified parameter space of the detection rate. Moreover, a parallel-accelerated version of the BASSET code was provided and evaluated through simulation. BASSET has the capacity of enhancing the detection sensitivity and the SNR of the narrow-band pulses from the existing pipeline, offering high performance and flexible applicability. BASSET not only enhances the completeness of the low-energy narrow-band pulse detection in a more robust mode, but also has the potential to further elucidate the FRB luminosity function at a wider energy scale. Copyright © 2025, The Authors. All rights reserved.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Understanding urban landuse from the above and ground perspectives: a deep learning, multimodal solution

arXiv

引用

arXiv 2019年

作者： Srivastava, Shivangi Vargas-Muñoz, John E. Tuia, Devis Laboratory of Geo-information Science and Remote Sensing Wageningen University & Research Netherlands Laboratory of Image Data Science Institute of Computing University of Campinas Campinas Brazil

This is the pre-acceptance version, to read the final version published in the journal Remote Sensing of Environment, please go to: https://***/10.1016/***.2019.04.014 Landuse characterization is important for urban planning. It is traditionally performed with field surveys or manual photo interpretation, two practices that are time-consuming and labor-intensive. Therefore, we aim to automate landuse mapping at the urban-object level with a deep learning approach based on data from multiple sources (or modalities). We consider two image modalities: overhead imagery from Google Maps and ensembles of ground-based pictures (side-views) per urban-object from Google Street View (GSV). These modalities bring complementary visual information pertaining to the urban-objects. We propose an end-to-end trainable model, which uses OpenStreetMap annotations as labels. The model can accommodate a variable number of GSV pictures for the ground-based branch and can also function in the absence of ground pictures at prediction time. We test the effectiveness of our model over the area of Île-de-France, France, and test its generalization abilities on a set of urban-objects from the city of Nantes, France. Our proposed multimodal Convolutional Neural Network achieves considerably higher accuracies than methods that use a single image modality, making it Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

A short-term output power prediction model of wind power based on deep learning of grouped time series

引用

European Journal of Electrical Engineering 2020年第1期22卷 29-38页

作者： Wang, Yongsheng Gao, Jing Xu, Zhiwei Li, Leixiao College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot010018 China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application for Agriculture and Animal Husbandry Hohhot010018 China College of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service Hohhot010080 China Institute of Computing Technology Chinese Academy of Sciences Beijing100080 China

The output power prediction of wind power plants is an important guarantee to improve the utilization rate of wind energy and reduce wind curtailment. However, due to the strong randomness of wind energy, the ultra-short-term prediction accuracy of wind power output is poor. In view of the problem above, a prediction model based on deep learning of grouped time series (LW-CLSTM) was proposed in this paper. Based on this model, the authors attempted to explore a prediction method of wind power output. For this, first the multivariate data of wind power was fused, cleaned, dimension-reduced, and standardized, and the time period characteristics of the output power itself were extracted. Afterwards, it proposes a time sliding window (TSW) algorithm, and constructs a neural network input data set. Then a deep neural network prediction model combining the Convolutional Neutral Network (CNN) and Long-Short Term Memory (LSTM) was established, and the regression evaluation criteria for output power forecast accuracy in wind power production were designed, to compare the proposed model with four other prediction models. Finally, the experiments on the TensorFlow platform using real data show that this model has better prediction accuracy than the other four models, reaching a prediction accuracy rate of 92.5%, which verified the effectiveness of this prediction method. © 2020 International information and Engineering Technology Association. All rights reserved.

关键词： Wind power

来源：评论

学校读者我要写书评

暂无评论

LET'S PLAY POLO: DIRECT OPTIMIZATION OF THE PROBABILITY OF LESION ORIGIN IN PROTON TREATMENT PLANNING FOR LOW-GRADE GLIOMA PATIENTS

International Journal of Particle Therapy

引用

International Journal of Particle Therapy 2024年 12卷

作者： Tim Ortkamp Habiba Sallem Semi Harrabi Martin Frank Oliver Jäkel Julia Bauer Niklas Wahl Scientific Computing Center (scc) Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Helmholtz Information and Data Science School for Health (HIDSS4Health) Karlsruhe Germany Medical Physics In Radiation Oncology (e040) German Cancer Research Center (DKFZ) Heidelberg Germany Department Of Radiation Oncology University Hospital Heidelberg Heidelberg Germany Heidelberg Institute For Radiation Oncology (hiro) National Center for Radiation Research in Oncology (NCRO) Heidelberg Germany Heidelberg Ion Beam Therapy Center (HIT) Heidelberg Germany

来源：评论

学校读者我要写书评

暂无评论

Epitopedia: identifying molecular mimicry between pathogens and known immune epitopes

引用

ImmunoInformatics 2023年 9卷

作者： Christian A Balbin Janelle Nunez-Castilla Vitalii Stebliankin Prabin Baral Masrur Sobhan Trevor Cickovski Ananda Mohan Mondal Giri Narasimhan Prem Chapagain Kalai Mathee Jessica Siltberg-Liberles Department of Biological Sciences College of Arts Science and Education Florida International University Miami United States Bioinformatics Research Group (BioRG) Knight Foundation School of Computing and Information Sciences Florida International University Miami United States Department of Physics College of Arts Science and Education Florida International University Miami United States Machine Learning and Data Analytics Group (MLDAG) Knight Foundation School of Computing and Information Sciences Florida International University Miami United States Biomolecular Sciences Institute Florida International University Miami United States Department of Human and Molecular Genetics Herbert Wertheim College of Medicine Florida International University Miami United States

Upon infection, foreign antigenic proteins stimulate the host's immune system to produce antibodies targeting the pathogen. These antibodies bind to regions on the antigen called epitopes. Structural similarity (molecular mimicry) of epitopes between an infecting pathogen and host proteins or other pathogenic proteins the host has previously encountered can impact the host immune response to the pathogen and may lead to cross-reactive antibodies. The ability to identify potential regions of molecular mimicry in a pathogen can illuminate immune effects which are especially important to pathogen treatment and vaccine design. Here we present Epitopedia, a software pipeline that facilitates the identification of regions that may exhibit potential three-dimensional molecular mimicry between an antigenic pathogen protein and known immune epitopes as catalogued by the Immune Epitope database (IEDB). Epitopedia is open-source software released under the MIT license and is freely available on GitHub, including a Docker container with all other software dependencies preinstalled. We performed an analysis describing how various secondary structure states, identity between pentapeptide pairs, and identity between the parent sequences of pentapeptide pairs affects RMSD. We found that pentapeptides pairs in a helical conformation had considerably lower RMSD values than those in extended or coil conformations. We also found that RMSD is significantly increased when pentapeptide pairs are from non-homologous sequences.

关键词：

来源：评论

学校读者我要写书评

暂无评论

The EMPOWER blended digital intervention for relapse prevention in schizophrenia: a feasibility cluster randomised controlled trial in Scotland and Australia

引用

The Lancet Psychiatry 2022年第6期9卷 477-486页

作者： Gumley, Andrew I Bradstreet, Simon Ainsworth, John Allan, Stephanie Alvarez-Jimenez, Mario Aucott, Lorna Birchwood, Maximillian Briggs, Andrew Bucci, Sandra Cotton, Sue M Engel, Lidia French, Paul Lederman, Reeva Lewis, Shôn Machin, Matthew MacLennan, Graeme McLeod, Hamish McMeekin, Nicola Mihalopoulos, Cathy Morton, Emma Norrie, John Schwannauer, Matthias Singh, Swaran P Sundram, Suresh Thompson, Andrew Williams, Chris Yung, Alison R Farhall, John Gleeson, John Glasgow Institute of Health and Wellbeing University of Glasgow Glasgow United Kingdom Division of Psychology and Mental Health School of Health Sciences Faculty of Biology Medicine and Health Manchester Academic Health Science Centre University of Manchester Manchester United Kingdom Division of Informatics Imaging and Data Sciences School of Health Sciences Faculty of Biology Medicine and Health Manchester Academic Health Science Centre University of Manchester Manchester United Kingdom Orygen Melbourne Melbourne VIC Australia Centre for Youth Mental Health University of Melbourne Melbourne VIC Australia School of Computing and Information Systems Faculty of Engineering and Information Technology University of Melbourne Melbourne VIC Australia Centre for Healthcare Randomised Trials (CHaRT) University of Aberdeen Aberdeen United Kingdom Centre for Mental Health and Wellbeing Research Warwick Medical School University of Warwick Warwick United Kingdom Department of Health Services Research and Policy London School of Hygiene & Tropical Medicine London United Kingdom Greater Manchester Mental Health NHS Foundation Trust Manchester United Kingdom School of Public Health and Preventive Medicine Monash University Melbourne VIC Australia Department of Psychiatry School of Clinical Sciences Monash University Melbourne VIC Australia School of Medicine Deakin University Melbourne VIC Australia Department of Psychiatry Manchester Metropolitan University Manchester United Kingdom Department of Psychiatry University of British Columbia Vancouver BC Canada The Usher Institute University of Edinburgh Edinburgh United Kingdom School for Health in Social Sciences University of Edinburgh Edinburgh United Kingdom Mental Health Program Monash Health Melbourne VIC Australia Department of Psychology and Counselling La Trobe University Melbourne VIC Australia NorthWestern Mental Health The Royal Melbourne Hospital Epping VIC Australia Healthy Brain and Mi

Background: Early warning signs monitoring by service users with schizophrenia has shown promise in preventing relapse but the quality of evidence is low. We aimed to establish the feasibility of undertaking a definitive randomised controlled trial to determine the effectiveness of a blended digital intervention for relapse prevention in schizophrenia. Methods: This multicentre, feasibility, cluster randomised controlled trial aimed to compare Early signs Monitoring to Prevent relapse in psychosis and prOmote Well-being, Engagement, and Recovery (EMPOWER) with treatment as usual in community mental health services (CMHS) in Glasgow and Melbourne. CMHS were the unit of randomisation, selected on the basis of those that probably had five or more care coordinators willing to participate. Participants were eligible if they were older than 16 years, had a schizophrenia or related diagnosis confirmed via case records, were able to provide informed consent, had contact with CMHS, and had had a relapse within the previous 2 years. Participants were randomised within stratified clusters to EMPOWER or to continue their usual approach to care. EMPOWER blended a smartphone for active monitoring of early warning signs with peer support to promote self-management and clinical triage to promote access to relapse prevention. Main outcomes were feasibility, acceptability, usability, and safety, which was assessed through face-to-face interviews. App usage was assessed via the smartphone and self-report. Primary end point was 12 months. Participants, research assistants and other team members involved in delivering the intervention were not masked to treatment conditions. Assessment of relapse was done by an independent adjudication panel masked to randomisation group. The study is registered at ISRCTN (99559262). Findings: We identified and randomised eight CMHS (six in Glasgow and two in Melbourne) comprising 47 care coordinators. We recruited 86 service users between Jan 19 and Au

关键词：

来源：评论

学校读者我要写书评

暂无评论

DLEDNet: A deep learning-based image encryption and decryption network for internet of medical things

arXiv

引用

arXiv 2020年

作者： Ding, Yi Wu, Guozheng Chen, Dajiang Zhang, Ning Gong, Linpeng Cao, Mingsheng Qin, Zhiguang Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu Sichuan610054 China Institute of Electronic and Information Engineering of UESTC in Guangdong Dongwan Guangdong China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu Sichuan610054 China National Natural Science Foundation of China Beijing China Department of Computing Science Texas A&M University-Corpus Christi Corpus ChristiTX78412 United States

With the rapid development of Internet of Medical Things (IoMT) technology, many medical imaging equipments are connected to the medical information network to facilitate the process of diagnosing and treating for doctors. As medical image contains sensitive information, it is of importance yet very challenging to encrypt the medical image to safeguard the privacy or security of the patient. In this work, a deep learning based encryption and decryption network (DLEDNet) is proposed to fulfill the process of encrypting and decrypting the medical image. Specifically, in DLEDNet, the Cycle-GAN network is employed as the main learning network to transfer the medical image from its original domain to the target domain. Target domain is regarded as a "Hidden Factors" to guide the learning model to realize the encryption process. The encrypted image is restored to the original (plaintext) image through a reconstruction network to achieve an image decryption process. In order to realize the data mining process direct from the privacy-protected environment, a ROI (Region of interest)-mining-network is proposed to extract the interested object directly from the encrypted image. The proposed DLEDNet is evaluated on the chest X-ray dataset. Extensive experimental results show that the proposed method can achieve a high level of security with a good performance in efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：