检索结果-内蒙古大学图书馆

An Investigation on Open-RAN Specifications:Use Cases,Security Threats,Requirements,Discussions

computer Modeling in engineering & Sciences 2024年第10期141卷 13-41页

作者： Heejae Park Tri-HaiNguyen Laihyuk Park Department of Computer Science and Engineering Seoul National University of Science and TechnologySeoul01811Republic of Korea Faculty of Information Technology School of TechnologyVan Lang UniversityHo Chi Minh City70000Vietnam

The emergence of various technologies such as terahertz communications,Reconfigurable Intelligent Surfaces(RIS),and AI-powered communication services will burden network operators with rising infrastructure ***,the Open Radio Access Network(O-RAN)has been introduced as a solution for growing financial and operational burdens in Beyond 5G(B5G)and 6G networks.O-RAN promotes openness and intelligence to overcome the limitations of traditional *** disaggregating conventional Base Band Units(BBUs)into O-RAN Distributed Units(O-DU)and O-RAN Centralized Units(O-CU),O-RAN offers greater flexibility for upgrades and network ***,this openness introduces new security challenges compared to traditional *** existing studies overlook these security requirements of the O-RAN *** gain deeper insights into the O-RAN system and security,this paper first provides an overview of the general O-RAN architecture and its diverse use cases relevant to B5G and 6G *** then delve into specifications of O-RAN security threats and requirements,aiming to mitigate security vulnerabilities *** providing a comprehensive understanding of O-RAN architecture,use cases,and security considerations,thisworkserves as a valuable resource for future research in O-RAN and its security.

关键词： O-RAN architecture use cases security issues security requirements security discussions

来源：评论

学校读者我要写书评

暂无评论

Generalizable Physics-Guided Convolutional Neural Network for Irregular Terrain Propagation

引用

IEEE Transactions on Antennas and Propagation 2025年第6期73卷 3975-3985页

作者： Huang, Siyi Qin, Hao Hou, Weibin Zhang, Xinyue Zhang, Xingqi University of Alberta Department of Electrical and Computer Engineering Canada University College Dublin School of Electrical and Electronic Engineering Ireland China Academy of Information and Communications Technology Beijing China

The application of split-step parabolic equation methods for radio wave propagation across irregular terrains has gained widespread attention. However, the computational intensity of these methods limits their practical use, leading to the exploration of machine learning (ML) techniques as an alternative. A significant hurdle for ML models in the field of electromagnetics is their ability to precisely forecast relevant quantities in situations not covered by their training data, which have not been considered in the current ML-assisted propagation models over irregular terrain. To that end, we propose a generalizable physics-guided propagation modeling framework of high fidelity. This framework is adept at generalizing across various terrain types and antenna configurations, showcasing extrapolation capabilities beyond its training dataset. Our approach innovates by embedding prior knowledge from deterministic models into the network architecture. Furthermore, we demonstrate that adapting the network structure to align with the electromagnetic properties of terrain propagation markedly improves the model's predictive accuracy and generalizability. © 1963-2012 IEEE.

关键词： Landforms

来源：评论

学校读者我要写书评

暂无评论

Osteoarthritis Prediction in Knee Joint Using Deep Learning Techniques 27

Osteoarthritis Prediction in Knee Joint Using Deep Learning ...

引用

27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Singh, Shubham Kumar Chouhan, Kuldeep Agrawal, Arun Prakash Sharda University Sharda School of Engineering and Technology Department of Computer Science and Applications Greater Noida India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350392319

Osteoarthritis is a disorder that transpires when the articular cartilage in the knee breaks down and changes to the underbelly of the cartilage. Normal joint fracture protection for the bones is provided by the slick substance known as articular cartilage. Early prediction can slow down the progression of the condition. Medical professionals' expertise is utilized to predict severity. In this research, we address the issue that arises when a model's validation accuracy is lower than its training accuracy. Specifically, we find that as dataset size increases, the validation accuracy of Osteoarthritis prediction falls. We used a Deep Convolutional Neural Network to develop an automatic osteoarthritis severity assessment system using the Kellgren and Lawrence (KL) grading system. With this method, X-ray images are used to predict the severity of osteoarthritis. This study's objective is to predict the severity stage of knee osteoarthritis in the form of prediction accuracy (in %) of osteoarthritis using a transfer learning and deep learning approach. As a result, EfficientNetB5 provides a prediction accuracy of over 97.45% as demonstrated in this work. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Time-Efficient Video Anomaly Detection With Parallel Computing and Twice-Reconstruction

引用

IEEE Sensors Journal 2025年第6期25卷 9887-9901页

作者： Qi, Xiaosha Chao, Xin Ji, Genlin Li, Le Changzhou Institute of Technology School of Computer Science and Information Engineering Jiangsu Changzhou213000 China Nanjing Normal University School of Geography Jiangsu Nanjing210023 China Nanjing Normal University School of Computer and Electronic Information Jiangsu Nanjing210023 China

To tackle two prevalent challenges in video anomaly detection, misclassification of abnormal frames as normal and the prolonged runtime of existing methods, this article proposes a time-efficient anomaly detection method utilizing twice-reconstruction and parallel computing. The proposed method integrates three key innovations: a twice-reconstruction model to amplify the reconstruction error for abnormal frames, a memory module to better capture and utilize normal distribution patterns, and a parallel computing strategy to significantly accelerate anomaly detection. By reconstructing frames twice, our method enhances the model's anomaly detection capabilities. Meanwhile, a memory module is incorporated to retain normal distribution patterns more effectively, reducing the likelihood of misclassifying abnormal frames. Furthermore, parallel computing is employed to minimize runtime and boost detection efficiency. Unlike existing methods, TPR-VAD achieves both high detection accuracy and time efficiency, making it well-suited for real-world applications such as intelligent surveillance systems. Experimental results on the UCSD Ped2, CUHK Avenue, and ShanghaiTech datasets show that our method achieves superior detection accuracy, reaching 99.01%, 91.21%, and 82.77%, respectively, while significantly reducing runtime. © 2001-2012 IEEE.

关键词： Normal distribution

来源：评论

学校读者我要写书评

暂无评论

A video data hiding technique based on pixel sequence, weight interpolation and quorum function

引用

Multimedia Tools and Applications 2024年第41期83卷 88881-88901页

作者： Roselinkiruba, R. Jothi, C. Saranya Koushik, C.P. Keerthika, A. Sathya, Vanlin School of Computer and Science Engineering Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai Avadi India School of Computer and Science Engineering VIT Bhopal University Bhopal India Electronics and Communication Engineering Aarupadai Veedu Institute of Technology Chennai India Celona Inc CampbellCA United States

The development of multimedia technology has increased the challenge in protecting the information. This paper proposes an effective method for data hiding in videos based on the Pixel Sequence (PS), weight interpolation to protect the secret data from intruders. Initially, the noise is removed from the video with the help of median filter and Moving Objects (MO) inside the frames having different backgrounds are accurately detected using Fast Region-based Convolution Neural Network (FR-CNN). Next, the MO and Non Moving Object (NMO) pixel from each frame are mapped with the adjacent frames to determine the weights applying PS. Then, the cover video is up-scaled with interpolated pixels based on weights of MO and NMO. Finally, the secret data are hidden inside the both interpolated and non-interpolated pixels elevate Embedding Capacity (EC). In interpolated pixels, both Most Significant Bit (MSB) and Least Significant Bit (LSB) of each frames based on their weight are utilized to hide data. Whereas, in non-interpolated pixels, Quorum Function (QF) is used to embed data. Two different data embedding techniques are adopted in each frame to enhance the security. The performance evaluation of the proposed method out-performs the existing methods in terms of precision and recall. Experimental results also show that the stego-video quality is uncompromised and is resistive to security attacks. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Pixels

来源：评论

学校读者我要写书评

暂无评论

Spatial and temporal analysis of urban heat island effect over Tiruchirappalli city using geospatial techniques

引用

Geodesy and Geodynamics 2023年第3期14卷 275-291页

作者： Ajay Badugu K.S.Arunab Aneesh Mathew P.Sarwesh Department of Civil Engineering National Institute of TechnologyTiruchirappalli620015Tamil NaduIndia School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014Tamil NaduIndia

Alterations made to the natural ground surface and the anthropogenic activity elevate the surface and air temperature in the urban areas compared with the surrounding rural areas,known as urban heat island *** remote sensors measure the radiation emitted by ground objects,which can be used to estimate the land surface temperature and are beneficial for studying urban heat island *** present study investigates the spatial and temporal variations in the effects of urban heat island over Tiruchirappalli city in India during the summer and winter *** study also identifies hot spots and cold spots within the study *** this study,a significant land surface temperature difference was observed between the urban and rural areas,predominantly at night,indicating the presence of urban heat island at *** diurnal land surface temperature fluctuations are also detected seasonally,with a relatively higher temperature intensity during the *** trend line analysis shows that the mean land surface temperature of the study area is increasing at a rate of 0.166 K/decade with p less than *** using the spatial autocorrelation method with the urban heat island index as the key parameter,hot spots with a 99 percent confidence level and a 95 percent confidence level were found within the urban area.A hot spot with 95 and 90 percent confidence level was identified outside the urban *** spike in temperature for a particular region in the rural area is due to industry and the associated built-up *** study also identified cold spots with a 90 percent confidence level within the rural ***,cold spots with a 95 and 99 percent confidence level were not identified within the study area.

关键词： Urbanization Land surface temperature Urban heat island effect Hot spots Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Two-stage IDS for IoT using layered machine- and deep-learning models

引用

Cyber-Physical Systems 2024年第1期10卷 60-83页

作者： van der Walt, André Quazi, Tahmid van Niekerk, Brett School of Engineering University of KwaZulu-Natal Durban South Africa School of Mathematics Statistics and Computer Science University of KwaZuluNatal South Africa Department of Information Technology Durban University of Technology South Africa

The ever-growing integration of Internet-of-Things (IoT) devices into our daily lives provides us with a level of convenience never before seen. However, the effects of attacks on these devices can be devastating. The discrete, low-powered nature of IoT devices makes their security a difficult problem to solve. To provide a solution, this work proposes a two-stage Intrusion Detection Systems (IDS) using layered machine and deep learning models. The potential benefits of the system are examined and the results presented show a reduction of threat detection/identification time of 0.51 s on average and an increase of threat classification F1-Score by 0.05. . © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

A Machine Learning Approach for the Identification and Classification of Cardiovascular Diseases Using ECG Signals and Sensor data

A Machine Learning Approach for the Identification and Class...

引用

2024 International Conference on Smart Devices, ICSD 2024

作者： Shaik, Nazeer Dharani, M. Kumar, Bura Vijay Gupta, Vikas Singh, Jagendra Pal, Yogesh SRIT-Autonomous Department of Computer Science Engineering Anantapur India K S R Institute for Engineering and Technology School of Electronics and Communication Engineering Tiruchengode India SR University School of Computer Science and Artificial Intelligence Warangal India Raj Kumar Goel Institute of Technology Department of Computer Science Engineering Ghaziabad India Bennett University School of Computer Science Engineering & Technology Greater Noida India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350394412

Cardiovascular diseases (CVDs) continue to be a major concern in the medical field to date. Among the many diagnostic tools, electrocardiogram (ECG) remains one of the main ways with which to detect cardiac abnormalities. Although classic ECG interpretation is powerful, it is a laborious and time-demanding task and tends to be onerous for physicians. This study suggests a means of effective machine learning to increase the accuracy and speed of diagnosing CVDs based on ECG signals and other sensor data. On the other hand, we are concerned with the application of Frequency Domain Measures in deep-learning methodologies of image classifiers. Other models include the raw ECG sent to a 1-dimensional convolutional model together with the XGBoost model for the time-series feature-based prediction. Preliminary assessment reveals that Frequency Domain Measures provide good atrial fibrillation prediction but are quite limited for other types of arrhythmias. Computational results for the XGBoost model are, however, efficient for predictions on long data but proceed at the cost of extended inference times, considering the computational demand of the pre-processing stage. This paper opens up the scope of the potential for machine learning in revolutionizing ECG-based CVD detection, and we have to keep on the protocols of refinement and research toward this usage. © 2024 IEEE.

关键词： Frequency domain analysis

来源：评论

学校读者我要写书评

暂无评论

A Neural Computing-Based Access Control Protocol for AI-Driven Intelligent Flying Vehicles in Industry 5.0-Assisted Consumer Electronics

引用

IEEE Transactions on Consumer Electronics 2024年第1期70卷 3573-3581页

作者： Mahmood, Khalid Tariq, Tayyaba Sangaiah, Arun Kumar Ghaffar, Zahid Saleem, Muhammad Asad Shamshad, Salman National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology International Graduate School of AI Douliu64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102 2801 Lebanon University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China The University of Lahore Department of Software Engineering Lahore54590 Pakistan

The continuous revolution in Artificial Intelligence (AI) has played a significant role in the development of key consumer applications, including Industry 5.0, autonomous decision-making, fault diagnosis, etc. In practice, autonomous and intelligent robots and flying vehicles (i.e., drones) are also becoming the integral part of Industry 5.0. These vehicles are attaining significant importance nowadays because of their applications in consumer electronics. However, there may be numerous privacy issues in such an infrastructure. Nevertheless, neural computing-driven security solutions have played a significant role in overcoming the security and privacy issues of AI-driven intelligent flying vehicles. Motivated by these facts, this article presents a neural computing-based access control protocol for AI-driven flying vehicles to prevent all possible security threats. A detailed security evaluation of the devised protocol is done formally using the extensively recognized Real-Or-Random (ROR) oracle model. Similarly, the informal security analysis ensures the resistance of our scheme against various potential attacks under the assumptions of CK and DY adversarial models. Moreover, detailed performance scrutiny of the devised protocol with various existing schemes shows the robustness and supremacy of our scheme in terms of computation, communication overhead, and security attributes. © 1975-2011 IEEE.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Retrieval of High-Resolution Fingerprints Based on Pore Feature

Hierarchical Retrieval of High-Resolution Fingerprints Based...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ma, Jing Xu, Yuanrong Dong, Suyu Wang, Wei Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Harbin Institute of Technology School of Computer Science and Technology Weihai China Northeast Forestry University College of Computer and Control Engineering Harbin China

ISBN: (纸本)9798350386226

Faced with an escalating number of fingerprint images, most existing retrieval approachs suffer from a common problem: diminishing computational efficiency. This paper presents a hierarchical retrieval system tailored for high-resolution fingerprint images that utilizes abundant pore features and robust recognizability to improve retrieval performance. The framework comprises two core components. Firstly, a CNN-based feature extraction network is established, incorporating an attention mechanism to capture pore features in fingerprint images comprehensively. Subsequently, a hierarchical fingerprint retrieval approach is introduced, involving connection graph construction and a hierarchy of jump table structures for efficient retrieval of query pores. Empirical experiments conducted on high-resolution fingerprint image datasets underscore the system's effectiveness. Compared with other advanced pore-based fingerprint retrieval methods, the proposed method exhibits a notable rise in the hit rate with reduced penetration rates, significantly reducing the retrieval time. © 2024 IEEE.

关键词： Image enhancement

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：