检索结果-内蒙古大学图书馆

Federated Multi-Agent Reinforcement Learning for Resource Allocation in NR-V2X Mode 2

IEEE Internet of Things Journal 2025年第13期12卷 23402-23417页

作者： Saad, Malik Muhammad Tariq, Muhammad Ashar Ajmal, Mahnoor Kim, Dongkyun Srivastava, Gautam Kyungpook National University School of Computer Science and Engineering Dongseo University Republic of Korea and the School of Computer Science Busan Daegu Korea Republic of Kyungpook National University School of Computer Science and Engineering Daegu Korea Republic of Brandon University Department of Math and Computer Science BrandonMBR7A 6A9 Canada China Medical University Research Centre for Interneural Computing Taichung40402 Taiwan Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India

The Third Generation Partnership Project (3GPP) introduced Cellular Vehicle-to-Everything (C-V2X) for vehicular communications. In the standard, C-V2X Mode 4 is defined for the distributed resource selection. Subsequently, in 3GPP Release 16, NR-V2X is introduced with Mode 1 and Mode 2 for vehicular communications. Likewise C-V2X Mode 4, NR-V2X Mode 2 is used for decentralized resource scheduling. The vehicles select the resources based on their local observations by utilizing the Semi-persistent Scheduling (SPS). Since, the vehicles select the resources based on the local observation, sensing nature of SPS is challenged by the hidden node problem that lead to resource conflict. To resolve the contention, 3GPP also introduced the Physical Sidelink Feedback Channel (PSFCH) to assist the distributive resource scheduling based on the receiver feedback. However, this incurred a signaling overhead. In this work, federated learning is exploited for distributive training via offline method and distributive multi-agent-based resource scheduling is performed following the principles of NR-V2X Mode 2. Distributed training favors the model accuracy by accommodating the varying affect of the environment due to the high mobile dynamics. Simulation is conducted by integrating SUMO in conjunction with 3GPP NR-V2X standard. Performance results demonstrate a substantial improvement compared to other deep learning methods, where centralized training and random resource selection procedures are employed. This research marks a significant stride towards efficient and conflict-resilient resource allocation in vehicular communications. © 2014 IEEE.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

Advanced Video-Based Deep Learning Framework for Comprehensive Detection, Diagnosis, and Classification of Dermatological Conditions in Real-Time Datasets 6

Advanced Video-Based Deep Learning Framework for Comprehensi...

引用

6th International Conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2024

作者： Ahmed, Syed Thouheed Guthur, Amogh S. Rai, Pratyush Kumar Pranava Swaroop, N. School of Computing and Information Technology REVA University Bengaluru India Department of Computer Science and Engineering Sri Venkateshwara College of Engineering VTU Bengaluru India School of Computer Science and Engineering Presidency University Bengaluru India

The advanced acne detection model showcased in this project utilizes deep learning methods to accurately classify skin conditions, including blackheads, dark areas, blemishes, and creases. It employs a YOLOv5 format annotation scheme to analyze spatial and temporal information from video sequences, resulting in exceptional performance in detecting seven distinct classes. The model's resilient performance indicates high accuracy, with a mean Average Precision (mAP) of about 0.85-0.9 at an IoU threshold of 0.5. It also demonstrates generalization and robustness with an mAP of 0.5-0.55 across IoU thresholds from 0.5 to 0.95, making it suitable for real-world dermatological assessments. The proposed method enables early detection and more effective treatment by monitoring skin conditions over time, significantly impacting dermatological image analysis. The goal is to improve patient outcomes and provide personalized skincare recommendations using deep learning techniques, benefiting clinicians and researchers in analyzing and categorizing skin conditions. Additionally, incorporating additional data sources like clinical images or medical histories can enhance the model's diagnostic capabilities and accuracy. Expanding the dataset will enhance the model's generalizability and robustness for new skin conditions. © 2025 The Author(s). Published by Elsevier B.V.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Digital Twins and Cyber-Physical systems:A New Frontier in computer Modeling

引用

computer Modeling in engineering & sciences 2025年第4期143卷 51-113页

作者： Vidyalakshmi G S Gopikrishnan Wadii Boulila Anis Koubaa Gautam Srivastava Department of Computer Science and Engineering SRM University APAmaravati522240India School of Computer Science and Engineering VIT-AP UniversityAmaravathi522240India Robotics and Internet-of-Things Laboratory Prince Sultan UniversityRiyadh12435Saudi Arabia Department of Math and Computer Science Brandon UniversityBrandonMB R7A6A9Canada Research Centre for Interneural Computing China Medical UniversityTaichung40402Taiwan

Cyber-Physical systems(CPS)represent an integration of computational and physical elements,revolutionizing industries by enabling real-time monitoring,control,and optimization.A complementary technology,Digital Twin(DT),acts as a virtual replica of physical assets or processes,facilitating better decision making through simulations and predictive *** and DT underpin the evolution of Industry 4.0 by bridging the physical and digital *** survey explores their synergy,highlighting how DT enriches CPS with dynamic modeling,realtime data integration,and advanced simulation *** layered architecture of DTs within CPS is examined,showcasing the enabling technologies and tools vital for seamless *** study addresses key challenges in CPS modeling,such as concurrency and communication,and underscores the importance of DT in overcoming these *** in various sectors are analyzed,including smart manufacturing,healthcare,and urban planning,emphasizing the transformative potential of CPS-DT *** addition,the review identifies gaps in existing methodologies and proposes future research directions to develop comprehensive,scalable,and secure CPSDT *** synthesizing insights fromthe current literature and presenting a taxonomy of CPS and DT,this survey serves as a foundational reference for academics and *** findings stress the need for unified frameworks that align CPS and DT with emerging technologies,fostering innovation and efficiency in the digital transformation era.

关键词： Cyber physical systems digital twin efficiency Industry 4.0 robustness and intelligence

来源：评论

学校读者我要写书评

暂无评论

Multi-Focus Image Fusion via Explicit Defocus Blur Modelling 39

Multi-Focus Image Fusion via Explicit Defocus Blur Modelling

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Quan, Yuhui Wan, Xi Tang, Zitao Liang, Jinxiu Ji, Hui School of Computer Science and Engineering South China University of Technology China School of Computer Science Peking University China Department of Mathematics National University of Singapore Singapore

ISBN: (纸本)157735897X

Multi-focus image fusion (MFIF) enhances depth of field in photography by generating an all-in-focus image from multiple images captured at different focal lengths. While deep learning has shown promise in MFIF, most existing methods overlooked the physical properties of defocus blurring in their network design, limiting their interoperability and generalization. This paper introduces a novel framework that integrates explicit defocus blur modelling into the MFIF process, improving both interpretability and performance. Using an atom-based spatially-varying parameterized defocus blurring model, our approach calculates pixel-wise defocus descriptors and initial focused images from multi-focus source images in a scale-recurrent manner to estimate soft decision maps. Fusion is then performed using masks derived from these decision maps, with special treatment for pixels likely defocused in all source images or near boundaries of defocused/focused regions. The model is trained with a fusion loss and a cross-scale defocus estimation loss. Extensive experiments on benchmark datasets demonstrated the effectiveness of our approach. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). 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

Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

引用

Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Detecting Fire in Color Images using Convolutional Neural Network Architectures 8

Detecting Fire in Color Images using Convolutional Neural Ne...

引用

8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Tanmayi, Doddapaneni Udaya, Ediga Pati, Peeta Basa Amrita School of Engineering Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350334012

Fire accidents are the most prevalent disasters both indoors and outdoors. Early detection of such accidents helps prevent loss of property and lives. Approaches to detect fire are mostly based on environmental sensors or visual signals. Camera captured images and video sequences are utilized to detect smoke and fires by employing statistical & structural techniques as well as deep learning *** this work, we have employed various standard and custom CNN architectures for detecting fires in both indoor and outdoor environments. We have used 2 different publicly available datasets to train & test our models. These results are compared against already reported results. We find that AlexNet, though a simple architecture, performs the best at 86% accuracy in detecting fire in both indoor and outdoor environments. © 2023 IEEE.

关键词： Fires

来源：评论

学校读者我要写书评

暂无评论

Decentralized Identity Management Using Self-Sovereign Identity Approach Through Blockchain 1

引用

8th International Conference on Inventive Communication and Computational Technologies, ICICCT 2024

作者： Anas, Khan Mohammad Rizvi, Sheenu Arora, Deepak Tiwari, Shivam Department of Computer Science and Engineering Amity School of Engineering & Technology Lucknow Amity University Uttar Pradesh Lucknow India Department of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (数字)9789819777105

ISBN: (纸本)9789819777099

Exploring the transformative potential of Self-Sovereign Identity (SSI) in decentralized identity management, utilizing blockchain technology, this research analyzes the evolution from centralized to user-centric online identity paradigms. Addressing issues related to centralized authorities, SSI emerges as a solution. Recent statistics reveal a surge in identity-related fraud and privacy concerns amid escalating data breaches and cyber threats. The methodology involves in-depth analysis and visualization of SSI components like decentralized identifiers, verifiable credentials, and presentations, alongside the integration of a decentralized key management system, identifying the critical role of trust infrastructure. Comparative analysis of established SSI systems and decentralized identifier (DID) methods contributes to the development of secure, user-centric systems, countering risks associated with centralized identity management and laying a foundation for robust decentralized identity infrastructures in the digital field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decentralized finance

来源：评论

学校读者我要写书评

暂无评论

ML Based Light-Weight Security Protocol for IoFT Device 6

ML Based Light-Weight Security Protocol for IoFT Device

引用

6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Khan, Awais Aziz, Ayesha Tariq, Muhamad Kamal, Mohsin National Univeristy of Computer and Emerging Sciences Department of Electrical Engineering Islamabad Pakistan School of Computer Science National Univeristy of Computer and Emerging Sciences Islamabad Pakistan School of Electrical Engineering and Computer Science National Univeristy Science and Technology Islamabad Pakistan

ISBN: (纸本)9798350355703

Forest plays a vital role in environmental change and the protection of forests is the utmost important thing. The study attempts to suggest a solution for a smart forest (Internet of Forest Things). As nowadays, the Internet of Things is emerging very vastly and a large number of researchers are working towards the field of Internet of Forest Things (loFT). The protection of smart forests is of utmost importance because the infrastructure is physically unprotected and they generate huge data about the forest. Due to scalability, smart forest controllers are small-sized and low-cost devices with less computational capabilities. Keeping it in mind, the designed algorithms should be lightweight so that they can meet the requirements. To address this issue, this paper proposes a machine learning approach to develop a lightweight security solution to address data altering and man-in-the-middle attacks. The algorithm generates a link fingerprint of the received signal strength indicator (RSSI) value of the communicating IoFT node. The packet is sent to the server unit and it is decoded and matched with the table to identify the path. A trained machine learning model at the server gets the RSSI value from the packet and successfully classify about the presence of an attack. © 2024 IEEE.

关键词： k-nearest neighbors

来源：评论

学校读者我要写书评

暂无评论

Detection of Phishing in Internet-of-Things Using Hybrid Deep Belief Network

引用

Intelligent Automation & Soft Computing 2023年第6期36卷 3043-3056页

作者： S.Ashwini S.Magesh Kumar Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences(Deemed to be University)ChennaiTamilnaduIndia

Increase in the use of internet of things owned devices is one of the *** publicly available network many kinds of phishing attacks are able to enter into the mobile devices and corrupt the existing *** Phishing is the slow and resilient attack stacking techniques probe the *** proposed model is focused on detecting phishing attacks in internet of things enabled devices through a robust algorithm called Novel Watch and Trap Algorithm(NWAT).Though Predictive mapping,Predictive Validation and Predictive analysis mechanism is *** the test purpose Canadian Institute of cyber security(CIC)dataset is used for creating a robust prediction *** attack generates a resilience corruption works that slowly gathers the credential information from the *** proposed Predictive analysis model(PAM)enabled NWAT algorithm is used to predict the phishing probes in the form of suspicious process happening in the IoT *** prediction system considers the peer-to-peer communication window open for the established communication,the suspicious process and its pattern is identified by the new *** proposed model is validated by finding thepredictionaccuracy,Precision,recallsF1score,errorrate,Mathew’sCorre-lationCoefficient(MCC)andBalancedDetectionRate(BDR).Thepresented approach is comparatively analyzed with the state-of-the-art approach of existing system related to various types of Phishing probes.

关键词： Cyber security internet of things phishing attacks fault-tolerant devices smart devices cyber security attacks

来源：评论

学校读者我要写书评

暂无评论

A 0.75 to 1.5 GHz Reconfigurable Diplexer With Independently Controllable Center Frequency and Insertion Phase

引用

IEEE Transactions on Circuits and systems II: Express Briefs 2024年第4期71卷 1924-1928页

作者： Yi, Chengfei Chen, Xiong Chi, Pei-Ling Yang, Tao The School of Electronic Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China The Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu300 Taiwan

A novel reconfigurable diplexer with independently controllable center frequency and insertion phase is proposed in this brief. It simply consists of six coupled resonators and two non-resonating-nodes (NRNs). These six resonators are arranged in two triplet topologies and construct two typical third-order filters, forming the lower and upper channels in the diplexer, respectively. The NRNs are loaded at the receiver (RX) and transmitter (TX) ports. With the utilization of NRNs, the insertion phases of the upper and lower channels in the diplexer can be independently controlled. Optimization technique is used to obtain the coupling matrix of the diplexer, in which the NRN is considered to achieve a prespecified insertion phase. To validate the proposed idea, a microstrip prototype is fabricated and measured. Experiment results show that the center frequencies of lower and upper channels can be tuned from 0.75 GHz to 0.98 GHz, and from 1.23 GHz to 1.5 GHz, respectively. The insertion phases of both channels can be independently tuned from 0 to 120 degrees at each frequency tuning state. © 2023 IEEE.

关键词： Resonators

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：