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3rd International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2022

作者： Mao, DongRui Sui, Yuheng Wang, Sihan Internet of Things International College Beijing University of Posts and Telecommunications Beijing100876 China Software engineering School of applied technology Beijing University of information and science technology Beijing100101 China School of Management Huazhong University of Science and Technology Hubei Wuhan430074 China

ISBN: (纸本)9781510663473

In recent years, due to the development of Internet technology, more and more people begin to connect their daily life with Internet technology. Due to its intelligence, simple operation and daily characteristics, wearable devices have gradually entered the public's vision and been accepted by the public. More and more wearable devices appear with a variety of supporting software. This thesis explores how these software use different middleware for different functions, design the UI interface to make people easy to use and use different ways to protect user privacy. To analyse the advantages and disadvantages of the existing software products of wearable devices and propose changes and improvements for future software development, firstly find out some existing wearable device software, and analyse its data processing, functions, adopted protocols, user privacy management, and other aspects. Secondly, through searching corresponding thesis and questionnaires, finds users' dissatisfaction with existing wearable devices. Finally, based on the feedback of users, the data processing, privacy protection and other functions of the existing wearable device software are summarized, and the corresponding direction for the future improvement of wearable devices is put forward. © 2023 SPIE.

关键词： software design

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A Slice-Aware Network Embedding (SANE) Strategy for Network Service Requests in 6G

A Slice-Aware Network Embedding (SANE) Strategy for Network ...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Srinivasan, Thiruvenkadam Kowsalya, M. Kim, Hyung-Jin Ra, In-Ho School of Electrical Engineering Vellore Institute of Technology Vellore India Department of IT Applied System Engineering Chonbuk National University Jeonju Jeonju Korea Republic of School of Software Kunsan National University Gunsan Korea Republic of

ISBN: (纸本)9798350325188

6G networks are projected to provide more individualized end-to-end services and innovative cloud-edge applications than current 5G networks. Network slicing (NS) is required when embedding 6G mobile network resources and sharing them across devices with varying needs. Recent advancements in 6G NS have been propelled by Network Function Virtualization (NFV) and software Defined Networking (SDN) technologies, which virtualize network functions. Allocating resources in 6G is a pressing issue that needs additional study. Virtual network embedding focuses primarily on node mapping and link mapping, the two most important network properties. Resource allocation in a 6G network is addressed here by using the Slice-Aware Network Embedding (SANE) technique. The slice-defined node ranking method is shown for node mapping, and the k-shortest route algorithm is presented for link mapping, both of which are contributed to by this work. The performance of the proposed strategy is measured by looking at metrics like average acceptance rates, cost-to-revenue, and bandwidth utilization. The results of this study are compared to those of other researches done in the same area to demonstrate their validity. © 2023 IEEE.

关键词： 6G network slicing node ranking resource allocation shortest path algorithm

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Heterologous Image Matching Based on Salience Region Using Q-test and Kernel Density Estimation 4

Heterologous Image Matching Based on Salience Region Using Q...

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4th International Conference on Computer Vision and Pattern Analysis, ICCPA 2024

作者： Zhai, Jinlong Yan, Xiaohu Xu, Yang School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-MacaoGreater Bay Area Shenzhen Polytechnic University Shenzhen518055 China

ISBN: (纸本)9781510682528

To address the matching problem caused by the salience differences in spatial features, spectrum and contrast between heterologous images, a heterologous image matching method based on salience region using Q-test and kernel density estimation is proposed in this paper. Firstly, the center pixel point of each sub-region in the image is detected for salience difference with other points in its neighborhood by using the Q-test method. The detected points are defined as Q-points. Secondly, the point with the largest kernel density in all Q-points is calculated by using kernel density estimation function and defined as the M-point. Then, the region size is defined according to the coordinates of M-point, and the salience region is detected. Finally, to comprehensively evaluate the performance of the proposed method, five common types of heterologous images are selected as data sources, including point cloud depth maps, infrared images, electronic navigational maps, synthetic aperture radar images, and night-time light images. Based on the salience regions, comparative experiments are carried out using histogram of absolute phase consistency gradients (HAPCG) and histogram of the orientation of the weighted phase descriptor (HOWP). The results show that the matching performance of the proposed method is better than that of the original algorithm without salience region, especially on the type of electronic navigational map. © 2024 SPIE.

关键词： Radar imaging

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Deep Reinforcement Learning Based UAVs Trajectory Optimization for Maximum Communication Coverage of Users 4

Deep Reinforcement Learning Based UAVs Trajectory Optimizati...

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4th International Conference on Advances in Computer technology, Information Science and Communications, CTISC 2022

作者： Zhao, Yueyue Sun, Xuemei Liu, Yanjing Yang, Qingyong Tiangong University School of Computer Science and Technology Tianjin China Tianjin Sino-German University of Applied Acience School of Software and Communication Tianjin China

ISBN: (数字)9781665458726

ISBN: (纸本)9781665458726

With its flexibility and easy deployment, UAV can timely provide communication for areas with damaged infrastructure. However, due to some constraints of the UAV itself, such as limited energy and limited communication range, designing reasonable trajectory for UAV to improve the system performance has been studied. Considering UAV energy and user movement, we propose a deep reinforcement learning-based method of maximizing the number of users covered and saving energy (MUSE) to control UAVs. By observing system state, then the UAVs perform the optimal actions to maximize service users and prolong this network lifetime by maximizing the system reward. Simulation experiments show that the algorithm is superior to Q-learning in maximizing service users, moreover, with increasing number of UVAs, the system energy consumption is stable, which achieves the purpose of saving energy. © 2022 IEEE.

关键词： Reinforcement learning

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Light field depth estimation:A comprehensive survey from principles to future

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High-Confidence Computing 2024年第1期4卷 92-107页

作者： Tun Wang Hao Sheng Rongshan Chen Da Yang Zhenglong Cui Sizhe Wang Ruixuan Cong Mingyuan Zhao State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Zhongfa Aviation Institute Beihang UniversityHangzhou 311115China Faculty of Applied Sciences Macao Polytechnic UniversityMacao 999078China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Light Field(LF)depth estimation is an important research direction in the area of computer vision and computational photography,which aims to infer the depth information of different objects in threedimensional scenes by capturing LF *** this new era of significance,this article introduces a survey of the key concepts,methods,novel applications,and future trends in this *** summarize the LF depth estimation methods,which are usually based on the interaction of radiance from rays in all directions of the LF data,such as epipolar-plane,multi-view geometry,focal stack,and deep *** analyze the many challenges facing each of these approaches,including complex algorithms,large amounts of computation,and speed *** addition,this survey summarizes most of the currently available methods,conducts some comparative experiments,discusses the results,and investigates the novel directions in LF depth estimation.

关键词： Light field Depth estimation Deep learning Sub-aperture image Epipolar-plane image

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A Nature-Inspired AI Framework for Accurate Glaucoma Diagnosis

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Computer Modeling in Engineering & Sciences 2025年第4期143卷 539-567页

作者： Jahanzaib Latif Ahsan Wajahat Alishba Tahir Anas Bilal Mohammed Zakariah Abeer Alnuaim School of Software Northwestern Polytechnical UniversityXi’an710072China Shifa College of Medicine Shifa Tameer-e-millat UniversityIslamabad44000Pakistan College of Information Science and Technology Hainan Normal UniversityHaikou571158China Department of Computer Science and Engineering College of Applied Studies and Community ServiceKing Saud UniversityRiyadh11495Saudi Arabia

Glaucoma,a leading cause of blindness,demands early detection for effective *** AI-based diagnostic systems are gaining traction,their performance is often limited by challenges such as varying image backgrounds,pixel intensity inconsistencies,and object size *** address these limitations,we introduce an innovative,nature-inspired machine learning framework combining feature excitation-based dense segmentation networks(FEDS-Net)and an enhanced gray wolf optimization-supported support vectormachine(IGWO-SVM).This dual-stage approach begins with FEDS-Net,which utilizes a fuzzy integral(FI)technique to accurately segment the optic cup(OC)and optic disk(OD)from retinal images,even in the presence of uncertainty and *** the second stage,the IGWO-SVM model optimizes the SVM classification process,leveraging a gray wolf-inspired optimization strategy to fine-tune the kernel function for superior *** testing on three benchmark glaucoma image databases DRIONS-DB,Drishti-GS,and Rim-One-r3 demonstrates the efficacy of our method,achieving classification accuracies of 97.65%,94.88%,and 93.2%,*** results surpass existing state-of-the-art techniques,offering a promising solution for reliable and early glaucoma detection.

关键词： Glaucoma fundus images machine learning nature-inspired algorithm segmentation classification

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IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

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Digital Communications and Networks 2024年第1期10卷 190-204页

作者： Farhan Ullah Shamsher Ullah Gautam Srivastava Jerry Chun-Wei Lin School of Software Northwestern Polytechnical UniversityXian710072ShaanxiChina Knowledge Unit of Systems and Technology University of Management and TechnologySialkot 51040Pakistan Department of Math and Computer Science Brandon UniversityBrandonMB R7A 6A9Canada Department of Computer Science and Math Lebanese American UniversityBeirut1102Lebanon Western Norway University of Applied Sciences BergenNorway Research Centre for Interneural Computing China Medical UniversityTaichungTaiwanChina

A network intrusion detection system is critical for cyber security against llegitimate *** terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,*** terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal *** is challenging to identify a specific attack due to complex features and data imbalance *** address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced ***,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,***,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic ***,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority ***,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network ***,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep *** experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and *** explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

关键词： Network intrusion detection Transfer learning Features extraction Imbalance data Explainable AI Cybersecurity

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Multi-agent policy transfer via task relationship modeling

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Science China(Information Sciences) 2024年第8期67卷 102-114页

作者： Rongjun QIN Feng CHEN Tonghan WANG Lei YUAN Xiaoran WU Yipeng KANG Zongzhang ZHANG Chongjie ZHANG Yang YU National Key Laboratory for Novel Software Technology Nanjing University Polixir Technologies School of Engineering and Applied Sciences Harvard University Department of Computer Science and Technology Tsinghua University Institute for Interdisciplinary Information Sciences Tsinghua University

Team adaptation to new cooperative tasks is a hallmark of human intelligence, which has yet to be fully realized in learning agents. Previous studies on multi-agent transfer learning have accommodated teams of different sizes but heavily relied on the generalization ability of neural networks for adapting to unseen tasks. We posit that the relationship among tasks provides key information for policy *** utilize this relationship for efficient transfer by attempting to discover and exploit the knowledge among tasks from different teams, proposing to learn an effect-based task representation as a common latent space among tasks, and using it to build an alternatively fixed training scheme. Herein, we demonstrate that task representation can capture the relationship among teams and generalize to unseen tasks. Thus, the proposed method helps transfer the learned cooperation knowledge to new tasks after training on a few source ***, the learned transferred policies help solve tasks that are difficult to learn from scratch.

关键词： multi-agent reinforcement learning cooperative transfer learning task relationship modeling multi-agent policy reuse multi-agent multi-task learning

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Multi-Scale Electromagnetic Simulation Through Parallel Adaptive Mesh Refinement FDTD Method with Symmetric Positive Semi-Definite Subgridding Scheme

Multi-Scale Electromagnetic Simulation Through Parallel Adap...

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2023 International applied Computational Electromagnetics Society Symposium, ACES-China 2023

作者： Li, Hanyu Zhou, Haijing Bao, Xianfeng Chen, Xiaojie School of Physical Science and Technology Southwest Jiaotong University Chengdu China Institute of Applied Physics and Computational Mathematics Beijing China Software Center for High Performance Numerical Simulation Caep Beijing China

ISBN: (纸本)9781733509657

FDTD method is a common approach for electromagnetic propagation, scattering and coupling simulation. Conventional FDTD with universal mesh requires numerous computations to represent solve problems with multi-scale structures. In this paper, adaptive mesh refinement (AMR) strategy is performed to reduce the calculations along with symmetric positive semi-definite subgridding scheme to guarantee stability. Numerical example of a missile seeker coupling simulation shows that the computation time decrease down to 2% while preserving excellent precision. © 2023 applied Computational Electromagnetics Society (ACES).

关键词： Finite difference time domain method

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Strategies for Semi-Automated Registration of Historic Aerial Photographs Utilizing Street and Roof Segmentations as Durable Landmarks 34

Strategies for Semi-Automated Registration of Historic Aeria...

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34th European Modeling and Simulation Symposium, EMSS 2022

作者： Zwettler, Gerald A. Ono, Yuta Stradner, Michael Praschl, Christoph Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Graduate School of Software and Information Science Iwate Prefectural University Japan Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria

Historical and current aerial photographs are only of great value if the geolocation or address of the photographed areas is also available. In Western Europe, especially Austria, Germany and Czech Republic, there is a market for the sale of aerial photographs of one’s own private residential building. Automated geolocation is a feasible way to enable the sales agents to assign the addresses for the sale more quickly. In the course of this research work, a process chain is modeled that allows the assignment of aerial photographs to residential addresses using machine vision. After model-based rectifying the aerial images to compensate for perspective distortions, larger image blocks get assembled using image stitching. The assignment to a 2D reference map, such as satellite imagery via Google Maps, is done by applying a U-Net CNN after extracting durable image features such as roads or buildings. The mapping of aerial imagery to two-dimensional cartography is either automated via registration approaches or based on manually placed corresponding landmarks and homography. Test runs on imagery between the years 1969 and 2020 show that the labor-intensive process of geolocation of aerial imagery can be solved by the proposed process model in a hybrid way. © 2022 The Authors.

关键词： Antennas

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