检索结果-内蒙古大学图书馆

2024 IEEE Conference on Advanced Topics on Measurement and Simulation, ATOMS 2024

作者： Adam, Ishq Vassos, Evangelos Ahmed, Qasim Schofield, Nigel Sambell, Alistair Zaharis, Zaharias D. Poulkov, Vladimir Lazaridis, Pavlos I. University of Huddersfield School of Computing and Engineering HuddersfieldHD1 3DH United Kingdom Aristotle University of Thessaloniki Department of Electrical and Computer Engineering 54124 Greece Technical University of Sofia Faculty of Telecommunications Sofia1756 Bulgaria

ISBN: (纸本)9798350358377

This study compares several variants of Yagi-Uda antennas for television (TV) reception that can reject ultrahigh frequency (UHF) 5G mobile communication band transmissions. The CST software is used for modeling and simulation. Several design parameters which include radiating element spacings, lengths, corner grid reflector types, and bow-tie directors are considered. These parameters affect the performance of the antenna and its capacity to reject signals in the (UHF) 5G mobile communication band without the use of an external filter. The antenna has a high realized gain of over 16 dBi in the 470 and 694 MHz band of UHF TV broadcasting. © 2024 IEEE.

关键词： 5G mobile communication systems

来源：评论

学校读者我要写书评

暂无评论

Convergence of Blockchain, AI and IoT for Effective Monitoring and Securing Smart City Infrastructure 2

Convergence of Blockchain, AI and IoT for Effective Monitori...

引用

2nd International Conference on Multidisciplinary engineering and Applied Science, ICMEAS 2023

作者： Agajo, James Imoru, Odunayo Aigbavboa, Clinton Mishra, Amit Onah, Fidelis Ijala, Abdullahi Danjuma Federal University of Technology Department of Computer Engineering Niger State Minna Nigeria University of Johannesburg Department of Electrical and Computer Engineering South Africa University of Johannesburg Faculty of Engineering and the Built Environment South Africa Department of Computer Science School of Computing and Applied Science Baze University Abuja Nigeria Veritas University Faculty of Engineering Abuja Nigeria Nigeria Communication Commission Abuja Nigeria

ISBN: (纸本)9798350358834

The convergence of block chain, artificial intelligence, and the internet of things (IoT) for effective monitoring and securing smart city infrastructure combines these three hot trends in technology and promises to advance the development of smart cities. Blockchain, AI, and IoT represent one of the most notable technological revolutions since the combination of computing and transaction processing systems, which is now having a positive impact. Due to the actions of cyber assault, the applications of these technologies will soon have such a significant impact on our society that it will be difficult to picture life without them. The Internet of Things Infrastructure is vulnerable to assaults since it is hosted on the cloud. Modern methods are thoroughly examined to talk about the advantages and difficulties in each domain. The combination of these three will change technology and advance a number of fields that will benefit academics and experts in industry, fostering improved service delivery and enhancing corporate structures. The convergence will change the game since it will address security and data privacy concerns. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Deep Reinforcement Learning Based Bus Stop-Skipping Strategy

Deep Reinforcement Learning Based Bus Stop-Skipping Strategy

引用

2023 International Technical Conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Tham, Mau-Luen Tay, Bee-Sim Khor, Kok-Chin Phon-Amnuaisuk, Somnuk Universiti Tunku Abdul Rahman Lee Kong Chian Faculty of Engineering and Science Department of Electrical and Electronic Engineering Malaysia Department of Internet Engineering and Computer Science Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Malaysia School of Computing and Informatics Universiti Teknologi Brunei Brunei

ISBN: (纸本)9798350326413

Stop-skipping strategy can benefit both bus operators and passengers if the control is intelligent enough to adapt to the changes in passenger demands and traffic conditions. This is possible via deep reinforcement learning (DRL), where an agent can learn the optimal policy by continuously interacting with the dynamic bus operating environment. In this paper, one express bus lane followed by one no-skip flow is treated as one episode for bus route optimization. The objective is to maximize the passenger satisfaction level while minimizing the bus operator expenditures. To this end, a reward function is formulated as a function of passenger waiting time, passenger in-vehicle time, and total bus travel time. By training an agent of a double deep Q-network (DDQN), simulation results show that the agent can intelligently skip the stations and outperform the noskip method, under different passenger distribution patterns. © 2023 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote Sensing Images

引用

computers, Materials & Continua 2023年第2期74卷 3117-3131页

作者： Saeed Masoud Alshahrani Saud S.Alotaibi Shaha Al-Otaibi Mohamed Mousa Anwer Mustafa Hilal Amgad Atta Abdelmageed Abdelwahed Motwakel Mohamed I.Eldesouki Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Object detection(OD)in remote sensing images(RSI)acts as a vital part in numerous civilian and military application areas,like urban planning,geographic information system(GIS),and search and rescue *** recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition *** latest advancements in deep learning(DL)approaches permit the design of effectual OD *** study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection(AEODCNN-VD)model on Remote Sensing *** proposed AEODCNN-VD model focuses on the identification of vehicles accurately and *** detect vehicles,the presented AEODCNN-VD model employs single shot detector(SSD)with Inception network as a baseline *** addition,Multiway Feature Pyramid Network(MFPN)is used for handling objects of varying sizes in *** features from the Inception model are passed into theMFPNformultiway andmultiscale feature ***,the fused features are passed into bounding box and class prediction *** enhancing the detection efficiency of the AEODCNN-VD approach,AEO based hyperparameter optimizer is used,which is stimulated by the energy transfer strategies such as production,consumption,and decomposition in an *** performance validation of the presentedmethod on benchmark datasets showed promising performance over recent DL models.

关键词： Object detection remote sensing vehicle detection artificial ecosystem optimizer convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

Automated 3D Urban Landscapes Visualization Using Open Data Sources on the Example of the City of Zagreb

引用

KN - Journal of Cartography and Geographic Information 2022年第2期72卷 139-152页

作者： Komadina, Adrian Mihajlović, Željka Department of Electronics Microelectronics Computer and Intelligent Systems Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 Zagreb 10000 Croatia

Geographical location visualization is important to create virtual environments when performing simulations. This study uses publicly available data sources to create three-dimensional (3D) views of geographical locations, focusing on the city of Zagreb as a case study, and presents an automated solution that integrates data from four different sources to achieve this. Publicly available data sources (OpenStreetMap, Google Maps Elevation API, MapBox Static Images API) were used, and GIS Zrinjevac which is unique to Zagreb. Based on these sources, individual 3D objects were generated and displayed virtually in the form of an interactive 3D map. The proposed solution attempts to balance the differing levels of detail achieved using other techniques. The solution was implemented in the form of an application created using the Unity game engine. The results were analyzed to evaluate the solution’s validity and the created application’s performance. The analysis showed that the total execution time dependence is quadratically polynomial to the amount of retrieved data, and linear to the number of height map points. Examining the application’s update rate showed that the buildings and individual models used for polygon and point data had the greatest impact. Finally, possible improvements, alternative approaches, and advantages and disadvantages of the proposed solution are compared with other techniques used in this research area. © 2022, Deutsche Gesellschaft für Kartographie e.V.

关键词： Cartographic data Data visualization GIS Zrinjevac Google maps elevation API OpenStreetMap Virtual environment

来源：评论

学校读者我要写书评

暂无评论

Towards new realities: Implications of personalized online layers in our daily lives

引用

i-com 2024年第2期23卷 221-229页

作者： Herder, Eelco Stojko, Laura Strecker, Jannis Neumayr, Thomas Yigitbas, Enes Augstein, Mirjam Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Department of Computer Science University of the Bundeswehr Munich Neubiberg Germany Institute of Computer Science University of St. Gallen St. Gallen Switzerland Research Group Personalized Environments and Collaborative Systems University of Applied Sciences Upper Austria Hagenberg Austria Faculty of Electrical Engineering Computer Science and Mathematics Paderborn University Paderborn Germany

We are currently in a period of upheaval, as many new technologies are emerging that open up new possibilities to shape our everyday lives. Particularly, within the field of Personalized Human-computer Interaction we observe high potential, but also challenges. In this article, we explore how an increasing amount of online services and tools not only further facilitates our lives, but also shapes our lives and how we perceive our environments. For this purpose, we adopt the metaphor of personalized 'online layers' and show how these layers are and will be interwoven with the lives that we live in the 'human layer' of the real world. © 2024 the author(s), published by De Gruyter, Berlin/Boston.

关键词： Human computer interaction

来源：评论

学校读者我要写书评

暂无评论

On the System Performance of Mobile Edge computing in an Uplink NOMA WSN With a Multiantenna Access Point Over Nakagami-m Fading

引用

IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 668-685页

作者： Van-Truong Truong Van Nhan Vo Dac-Binh Ha Chakchai So-In the Faculty of Electrical-Electronic Engineering Duy Tan University the Institute of Research and Development Duy Tan University the Faculty of Information Technology Duy Tan University IEEE the Applied Network Technology(ANT)Laboratory Department of Computer ScienceCollege of ComputingKhon Kaen University

In this paper,we study the system performance of mobile edge computing(MEC)wireless sensor networks(WSNs)using a multiantenna access point(AP)and two sensor clusters based on uplink nonorthogonal multiple access(NOMA).Due to limited computation and energy resources,the cluster heads(CHs)offload their tasks to a multiantenna AP over Nakagami-m *** proposed a combination protocol for NOMA-MEC-WSNs in which the AP selects either selection combining(SC)or maximal ratio combining(MRC)and each cluster selects a CH to participate in the communication process by employing the sensor node(SN)*** derive the closed-form exact expressions of the successful computation probability(SCP)to evaluate the system performance with the latency and energy consumption constraints of the considered *** results are provided to gain insight into the system performance in terms of the SCP based on system parameters such as the number of AP antennas,number of SNs in each cluster,task length,working frequency,offloading ratio,and transmit power ***,to determine the optimal resource parameters,i.e.,the offloading ratio,power allocation of the two CHs,and MEC AP resources,we proposed two algorithms to achieve the best system *** approach reveals that the optimal parameters with different schemes significantly improve SCP compared to other similar *** use Monte Carlo simulations to confirm the validity of our analysis.

关键词： Mobile edge computing(MEC) Nakagami-m fading offloading selection combining(SC)/maximal ratio combining(MRC) successful computation probability(SCP) uplink nonortho-gonal multiple access(NOMA) wireless sensor networks(WSNs)

来源：评论

学校读者我要写书评

暂无评论

Entropy Based Feature Fusion Using Deep Learning for Waste Object Detection and Classification Model

引用

computer Systems Science & engineering 2023年第12期47卷 2953-2969页

作者： Ehab Bahaudien Ashary Sahar Jambi Rehab B.Ashari Mahmoud Ragab Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser CityCairo11884Egypt

Object Detection is the task of localization and classification of objects in a video or *** recent times,because of its widespread applications,it has obtained more *** the modern world,waste pollution is one significant environmental *** prominence of recycling is known very well for both ecological and economic reasons,and the industry needs higher *** object detection utilizing deep learning(DL)involves training a machine-learning method to classify and detect various types of waste in videos or *** technology is utilized for several purposes recycling and sorting waste,enhancing waste management and reducing environmental *** studies of automatic waste detection are difficult to compare because of the need for benchmarks and broadly accepted standards concerning the employed data ***,this study designs an Entropy-based Feature Fusion using Deep Learning forWasteObject Detection and Classification(EFFDL-WODC)*** presented EFFDL-WODC system inherits the concepts of feature fusion and DL techniques for the effectual recognition and classification of various kinds of waste *** the presented EFFDL-WODC system,two major procedures can be contained,such as waste object detection and waste object *** object detection,the EFFDL-WODC technique uses a YOLOv7 object detector with a fusionbased backbone *** addition,entropy feature fusion-based models such as VGG-16,SqueezeNet,and NASNetmodels are ***,the EFFDL-WODC technique uses a graph convolutional network(GCN)model performed for the classification of detected waste *** performance validation of the EFFDL-WODC approach was validated on the benchmark *** comprehensive comparative results demonstrated the improved performance of the EFFDL-WODC technique over recent approaches.

关键词： Object detection object classification waste management deep learning feature fusion

来源：评论

学校读者我要写书评

暂无评论

An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

引用

computers, Materials & Continua 2023年第2期74卷 2905-2925页

作者： Walid El-Shafai Noha A.El-Hag Ahmed Sedik Ghada Elbanby Fathi E.Abd El-Samie Naglaa F.Soliman Hussah Nasser AlEisa Mohammed E.Abdel Samea Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Medical Imaging and Interventional Radiology National Liver InstituteMenoufia UniversityEgypt

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper proposes a deep learning model for the medical image fusion *** model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR ***,an additional process is executed on the extracted *** that,the fused feature map is reconstructed to obtain the resulting fused ***,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement *** realistic datasets of different modalities and diseases are tested and ***,real datasets are tested in the simulation analysis.

关键词： Image fusion CNN deep learning feature extraction evaluation metrics medical diagnosis

来源：评论

学校读者我要写书评

暂无评论

Reversible Watermarking Method with Low Distortion for the Secure Transmission of Medical Images

引用

computer Modeling in engineering & Sciences 2022年第3期130卷 1309-1324页

作者： Rizwan Taj Feng Tao Shahzada Khurram Ateeq Ur Rehman Syed Kamran Haider Akber Abid Gardezi Saima Kanwal School of Computer and Communication Lanzhou University of TechnologyLanzhouChina Faculty of Computing The Islamia University of BahawalpurBahawalpurPakistan Department of Electrical Engineering Government College UniversityLahorePakistan College of Internet of Things Engineering Hohai UniversityChangzhouChina Department of Computer Science COMSATS University IslamabadIslamabadPakistan

In telemedicine,the realization of reversible watermarking through information security is an emerging research ***,adding watermarks hinders the distribution of pixels in the cover image because it creates distortions(which lead to an increase in the detection probability).In this article,we introduce a reversible watermarking method that can transmit medical images with minimal distortion and high *** proposed method selects two adjacent gray pixels whose least significant bit(LSB)is different from the relevant message bit and then calculates the distortion *** use the LSB pairing method to embed the secret matrix of patient record into the cover image and exchange pixel *** results show that the designed method is robust to different attacks and has a high PSNR(peak signal-to-noise ratio)*** MRI image quality and imperceptibility are verified by embedding a secret matrix of up to 262,688 bits to achieve an average PSNR of 51.657 *** addition,the proposed algorithm is tested against the latest technology on standard images,and it is found that the average PSNR of our proposed reversible watermarking technology is higher(i.e.,51.71 dB).Numerical results show that the algorithm can be extended to normal images and medical images.

关键词： LSB reversible watermarking medical images security data distortion

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：