检索结果-内蒙古大学图书馆

6th International Conference on Advanced Communication Technologies and Networking, CommNet 2023

作者： Bouras, Christos Chatzigeorgiou, Charalampos Kokkinos, Vasileios Gkamas, Apostolos Pouyioutas, Philippos University of Patras Computer Engineering & Informatics Dept Greece University Ecclisiastical Academy of Vella Computer Science Dept Greece University of Nicosia Computer Science Dept Cyprus

ISBN: (纸本)9798350329391

This research paper presents a comprehensive investigation into the optimization of resource allocation in 5G networks through the technique of Downlink and Uplink Decoupling (DUDe). With the growing need for accurate modeling and scenario planning in 5G systems, DUDe allows for the configuration of an additional, lower frequency signal on the uplink to complement the existing uplink signal, rebalancing the uplink/downlink difference at the cell edge and enhancing coverage and network capacity. Drawing on extensive literature and industry trends, this study explores the benefits and challenges of DUDe, considering its impact on network performance, user experience, and future developments. The paper also introduces a simulation-based methodology and experiments to evaluate the effectiveness of DUDe in improving coverage, capacity, latency, and energy efficiency. The findings contribute to the understanding of DUDe's potential in optimizing 5G networks, providing valuable insights for researchers and network operators in designing and deploying efficient resource allocation strategies for enhanced network performance in various scenarios. © 2023 IEEE.

关键词： Network performance

来源：评论

学校读者我要写书评

暂无评论

BEDiff: denoising diffusion probabilistic models for building extraction

引用

Optoelectronics Letters 2025年第5期21卷 298-305页

作者： LEI Yanjing WANG Yuan CHAN Sixian HU Jie ZHOU Xiaolong ZHANG Hongkai College of Computer Science and Technology Zhejiang University of Technology Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information China Three Gorges University Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province Wenzhou University College of Electrical and Information Engineering Quzhou University School of Electronic and Information Engineering Anhui Jianzhu University

Accurately identifying building distribution from remote sensing images with complex background information is challenging. The emergence of diffusion models has prompted the innovative idea of employing the reverse denoising process to distill building distribution from these complex backgrounds. Building on this concept, we propose a novel framework, building extraction diffusion model(BEDiff), which meticulously refines the extraction of building footprints from remote sensing images in a stepwise fashion. Our approach begins with the design of booster guidance, a mechanism that extracts structural and semantic features from remote sensing images to serve as priors, thereby providing targeted guidance for the diffusion process. Additionally, we introduce a cross-feature fusion module(CFM) that bridges the semantic gap between different types of features, facilitating the integration of the attributes extracted by booster guidance into the diffusion process more effectively. Our proposed BEDiff marks the first application of diffusion models to the task of building extraction. Empirical evidence from extensive experiments on the Beijing building dataset demonstrates the superior performance of BEDiff, affirming its effectiveness and potential for enhancing the accuracy of building extraction in complex urban landscapes.

关键词： A

来源：评论

学校读者我要写书评

暂无评论

Shared Parking Concept in the Smart City Environment 8th

Shared Parking Concept in the Smart City Environment

引用

8th International Congress on Information and Communication technology, ICICT 2023

作者： Špitálová, Zuzana Mandová, Lucia Opatovský, Martin Faculty of Informatics and Information Technology Institute of Computer Engineering and Applied Informatics Slovak University of Technology Bratislava Slovakia

ISBN: (纸本)9789819930906

With the modern city infrastructure and increasing number of subjects in the traffic environment, there is a need for increased number of parking places. As the parking place is usually not used for a whole day, it could be shared with the others in the certain community. As it eliminates the useless driving due to the finding of free parking place, the traffic becomes more effective and produces less emissions. We designed the application for smart city environment, which helps to achieve the described situation. It consists of two parts, the frontend and backend. The data processed by the backend part can be further used for the city infrastructure improvements. The front-end part, designed in React Native, represents the graphical interface. The back-end part, designed in PostgreSQL, represents the database. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smart city

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence in physiological characteristics recognition for internet of things authentication

引用

Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

来源：评论

学校读者我要写书评

暂无评论

Introduction to Heterogeneous Robotic Systems in 5G and Beyond Networks

引用

IEEE WIRELESS COMMUNICATIONS 2023年第2期30卷 64-65页

作者： Lu, Huimin Ho, Pin-Han Khalfi, Bassem Verikoukis, Christos Kyushu Institute of Technology Japan Department of Electrical and Computer Engineering The University of Waterloo Canada Oregon State University CorvallisOR United States University of Patras Department of Computer Engineering & Informatics Greece

Remote intelligence in the application of robotics and the autonomous system relies heavily on seamless wireless connections. The 5G mobile network technology meets traditional manufacturing enterprises' application requirements for wireless networks based on robot transformation and upgrading, robot interconnection, and remote interactive applications in production. However, there exists many challenging 5G communication issues, such as different communication protocols in the system varies with different robots and no dis-ruptive changes to the physical layer.

关键词： Special issues and sections Robots 5G mobile communication Physical layer Autonomous systems Heterogeneous networks

来源：评论

学校读者我要写书评

暂无评论

Learning to bag with a simulation‐free reinforcement learning framework for robots

引用

IET Cyber-Systems and Robotics 2024年第2期6卷 52-66页

作者： Francisco Munguia-Galeano Jihong Zhu Juan David Hernández Ze Ji Cooper Group University of LiverpoolLiverpoolUK School of Physics Engineering and Technology University of YorkYorkUK School of Computer Science and Informatics Cardiff UniversityCardiffUK School of Engineering Cardiff UniversityCardiffUK

Bagging is an essential skill that humans perform in their daily ***,deformable objects,such as bags,are complex for robots to manipulate.A learning-based framework that enables robots to learn bagging is *** novelty of this framework is its ability to learn and perform bagging without relying on *** learning process is accomplished through a reinforcement learning(RL)algorithm introduced and designed to find the best grasping points of the bag based on a set of compact state *** framework utilises a set of primitive actions and represents the task in five *** our experiments,the framework reached 60% and 80% success rates after around 3 h of training in the real world when starting the bagging task from folded and unfolded states,***,the authors test the trained RL model with eight more bags of different sizes to evaluate its generalisability.

关键词： reinforcement learning robot learning robotics

来源：评论

学校读者我要写书评

暂无评论

Feature-preserving color pencil drawings from photographs

引用

Computational Visual Media 2023年第4期9卷 807-825页

作者： Dong Wang Guiqing Li Chengying Gao Shengwu Fu Yun Liang College of Mathematics and Informatics South China Agricultural UniversityGuangzhouChina School of Computer Science&Engineering South China University of TechnologyGuangzhouChina School of Data and Computer Science Sun Yat-sen UniversityGuangzhouChina

Color pencil drawing is well-loved due to its rich *** paper proposes an approach for generating feature-preserving color pencil drawings from *** mimic the tonal style of color pencil drawings,which are much lighter and have relatively lower saturation than photographs,we devise a lightness enhancement mapping and a saturation reduction *** lightness mapping is a monotonically decreasing derivative function,which not only increases lightness but also preserves input photograph *** saturation is usually related to lightness,so we suppress the saturation dependent on lightness to yield a harmonious ***,two extremum operators are provided to generate a foreground-aware outline map in which the colors of the generated contours and the foreground object are *** experiments show that color pencil drawings generated by our method surpass existing methods in tone capture and feature preservation.

关键词： non-photorealistic rendering pencil drawings image editing feature preservation

来源：评论

学校读者我要写书评

暂无评论

An Ensemble-Based Machine Learning Approach to Identify SARS-CoV-2 Virus Infection by Analyzing S Protein Sequences 2nd

An Ensemble-Based Machine Learning Approach to Identify SARS...

引用

2nd International Conference on Big Data, IoT and Machine Learning, BIM 2023

作者： Moni, Raka Zahid Hasan, Md. Shahriar Shakil, Md. Ferdous, Most. Jannatul Arefin, Mohammad Shamsul Bhuiyan, Touhid Health Informatics Research Lab Computer Science and Engineering Daffodil International University Dhaka Bangladesh Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh

ISBN: (纸本)9789819989362

Coronavirus disease (COVID-19), an effect of the SARS-CoV-2 virus, is an emerging infectious disease that infects humans due to interspecies transmission. Typically, Spike (S) protein plays a crucial role in entering host cells and beginning infection in contrast to the Membrane (M) and Envelope (E) proteins which are primarily involved in virus assembly. The purpose of this research is to examine the S protein sequences of the human virus to forecast SARS-CoV-2 infection potential. Using computational modeling and bioinformatics approaches, we analyzed the dataset taken from the Virus Pathogen Database except for 47 SARS2-CoV sequences from the SARS2-CoV Database containing aligned sequences of lengths 2396. This study suggests using several machine learning methods to determine infectivity based on the sequence of the coronavirus spike protein. The classical machine learning approach such as k-nearest neighbors (KNNs), Gaussian Naive Bayes (GaussianNB), support vector machine (SVM), decision tree (DT) is used. Bagging Classifier, Gradient Boosting algorithm, and Random Forest are used as the ensemble machine learning algorithm. Artificial Neural Network (ANN) is also applied as a deep learning model. The Gradient Boosting algorithm demonstrates the best accuracy in determining infectivity status. According to our research, the outcome of this study has a chance to accurately forecast coronavirus infectivity from genetic information. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Coronavirus

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Approach for Hand Gesture Recognition:Applications in Deaf Communication and Healthcare

引用

computers, Materials & Continua 2024年第1期78卷 127-144页

作者： Khursheed Aurangzeb Khalid Javeed Musaed Alhussein Imad Rida Syed Irtaza Haider Anubha Parashar Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Kingdom of Saudi Arabia Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Laboratory Biomechanics and Bioengineering University of Technology of CompiegneCompiegne60200France Department of Computer Science and Engineering Manipal University JaipurJaipur303007India

Hand gestures have been used as a significant mode of communication since the advent of human *** facilitating human-computer interaction(HCI),hand gesture recognition(HGRoc)technology is crucial for seamless and error-free *** technology is pivotal in healthcare and communication for the deaf *** significant advancements in computer vision-based gesture recognition for language understanding,two considerable challenges persist in this field:(a)limited and common gestures are considered,(b)processing multiple channels of information across a network takes huge computational time during discriminative feature ***,a novel hand vision-based convolutional neural network(CNN)model named(HVCNNM)offers several benefits,notably enhanced accuracy,robustness to variations,real-time performance,reduced channels,and ***,these models can be optimized for real-time performance,learn from large amounts of data,and are scalable to handle complex recognition tasks for efficient human-computer *** proposed model was evaluated on two challenging datasets,namely the Massey university Dataset(MUD)and the American Sign Language(ASL)Alphabet Dataset(ASLAD).On the MUD and ASLAD datasets,HVCNNM achieved a score of 99.23% and 99.00%,*** results demonstrate the effectiveness of CNN as a promising HGRoc *** findings suggest that the proposed model have potential roles in applications such as sign language recognition,human-computer interaction,and robotics.

关键词： computer vision deep learning gait recognition sign language recognition machine learning

来源：评论

学校读者我要写书评

暂无评论

Adaptive Autopilot: Constrained Drl for Diverse Driving Behaviors 27

Adaptive Autopilot: Constrained Drl for Diverse Driving Beha...

引用

27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Selvaraj, Dinesh Cyril Vitale, Christian Panayiotou, Tania Kolios, Panayiotis Chiasserini, Carla Fabiana Ellinas, Georgios University of Cyprus Kios Research and Innovation Center of Excellence Department of Electrical and Computer Engineering Nicosia Cyprus University of Cyprus Kios Research and Innovation Center of Excellence Department of Computer Science Nicosia Cyprus CARS@Polito and Politecnico di Torino Torino Italy Chalmers University of Technology Sweden

ISBN: (纸本)9798331505929

In pursuit of autonomous vehicles, achieving human-like driving behavior is vital. This study introduces adaptive autopilot (AA), a unique framework utilizing constrained-deep reinforcement learning (C-DRL). AA aims to safely emulate human driving to reduce the necessity for driver intervention. Focusing on the car-following scenario, the process involves (i) extracting data from the highD natural driving study and categorizing it into three driving styles using a rule-based classifier;(ii) employing deep neural network (DNN) regressors to predict human-like acceleration across styles;and (iii) using C-DRL, specifically the soft actor-critic Lagrangian technique, to learn human-like safe driving policies. Results indicate effectiveness in each step, with the rule-based classifier distinguishing driving styles, the regressor model accurately predicting acceleration, outperforming traditional car-following models, and C-DRL agents learning optimal policies for humanlike driving across styles. © 2024 IEEE.

关键词： Deep neural networks

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：