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International Journal of Interactive Mobile Technologies 2023年第23期17卷 4-19页

作者： Chi, Jing On, Chin Kim Zhang, Haopeng Chai, Soo See Faculty of Computing and Informatics University Malaysia Sabah Sabah Malaysia School Information and Electrical Engineering Hebei University of Engineering Hebei China Faculty of Computer Science and Information Technology Universiti Malaysia Sarawak Sarawak Malaysia

With the emergence of deep learning, Convolutional Neural Network (CNN) models have been proposed to advance the progress of various applications, including face recognition, object detection, pattern recognition, and number plate recognition. The utilization of CNNs in these areas has considerably improved security and surveillance capabilities by providing automated recognition solutions, such as traffic surveillance, access control devices, biometric security systems, and attendance systems. However, there is still room for improvement in this field. This paper discusses several classic CNN models, such as LeNet-5, AlexNet, VGGNet, GoogLeNet, and ResNet, as well as lightweight models for mobile-based applications, such as MobileNet, ShuffleNet, and EfficientNet. Additionally, deep CNN-based face recognition models, such as DeepFace, DeepID, FaceNet, and SphereFace, are explored, along with their architectural characteristics, advantages, disadvantages, and recognition accuracy. The results indicate that many scholars are researching lightweight face recognition, but applying it to mobile devices is impractical due to high computational costs. Furthermore, noise label learning is not robust in actual scenarios, and unlabeled face learning is expensive in manual labeling. Finally, this paper concludes with a discussion of the current problems faced by face recognition technology and its potential future directions for development. © 2023 by the authors of this article. Published under CC-BY. All Rights Reserved.

关键词： Neural network models

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RecStitchNet:Learning to stitch images with rectangular boundaries

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Computational Visual Media 2024年第4期10卷 687-703页

作者： Yun Zhang Yu-Kun Lai Lang Nie Fang-Lue Zhang Lin Xu Key Lab of Film and TV Media Technology of Zhejiang Province College of Media EngineeringCommunication University of ZhejiangHangzhou 310018China School of Computer Science and Informatics Cardiff UniversityCardiff CF244AGUK Institute of Information Science Beijing Jiaotong UniversityBeijing 100091China School of Engineering and Computer Science Victoria University of WellingtonWellington 6012New Zealand STEM University of South AustraliaAdelaide 5095Australia

Irregular boundaries in image stitching naturally occur due to freely moving *** deal with this problem,existing methods focus on optimizing mesh warping to make boundaries regular using the traditional explicit ***,previous methods always depend on hand-crafted features(e.g.,keypoints and line segments).Thus,failures often happen in overlapping regions without distinctive *** this paper,we address this problem by proposing RecStitchNet,a reasonable and effective network for image stitching with rectangular *** that both stitching and imposing rectangularity are non-trivial tasks in the learning-based framework,we propose a three-step progressive learning based strategy,which not only simplifies this task,but gradually achieves a good balance between stitching and imposing *** the first step,we perform initial stitching by a pre-trained state-of-the-art image stitching model,to produce initially warped stitching results without considering the boundary ***,we use a regression network with a comprehensive objective regarding mesh,perception,and shape to further encourage the stitched meshes to have rectangular boundaries with high content ***,we propose an unsupervised instance-wise optimization strategy to refine the stitched meshes iteratively,which can effectively improve the stitching results in terms of feature alignment,as well as boundary and structure *** to the lack of stitching datasets and the difficulty of label generation,we propose to generate a stitching dataset with rectangular stitched images as pseudo-ground-truth labels,and the performance upper bound induced from the it can be broken by our unsupervised *** and quantitative results and evaluations demonstrate the advantages of our method over the state-of-the-art.

关键词： image stitching boundaries convolutional neural network

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Cyber Attacks Detection using Deep Learning Methods for Resilient Operation in DC Shipboard Microgrids

Cyber Attacks Detection using Deep Learning Methods for Resi...

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2024 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2024

作者： Ali, Zulfiqar Hussain, Tahir Su, Chun-Lien Jurcut, Anca Delia Baloch, Shazia Sadiq, Muhammad National Kaohsiung University of Science and Technology Department of Electrical Engineering Kaohsiung city Taiwan Graduate School of Informatics and Engineering The University of Electro-Communication Department of Informatics Tokyo Japan School of Computer Science University College Dublin BelfieldDublin 4 Ireland Balochistan University of Engineering and Technology Department of Electrical Engineering Khuzdar Pakistan

ISBN: (纸本)9798350352290

Cyber resilience has become paramount as a transition of maritime systems towards digitization, particularly within DC shipboard microgrids (SMGs). Adopting innovative communication technologies can enhance the resilience of SMGs for stable operation. However, challenges like false data injection and Man-in-The-Middle attacks pose significant threats to SMG operations when integrating these technologies into ship intelligent grids. In this regard, this paper proposes a reliable deep learning (DL) method, especially an Artificial neural network (ANN) with a deep encoder-decoder architecture, for the detection of cyber-intrusions and mitigating their effects, ensuring system control and stability for resilient operation of SMG. Detecting malicious data intrusions is crucial for maintaining optimal grid conditions and preventing disruptions in load dispatch. The proposed method utilizes a fusion of current and voltage data features for comprehensive DL model training, resulting in an adequate level of detection accuracy and providing cybersecurity analysis for SMGs, addressing component-level attacks, and devising defense strategies from aspects of detection, mitigation, and prevention. Furthermore, the deep ANN is fine-tuned with optimal hyperparameters to effectively counter cyberattacks, achieving an enhanced accuracy rate of 97.51% and minimal loss of 0.101%, surpassing conventional machine learning approaches. Rigorous test scenarios are performed to validate the robustness of the proposed method, emphasizing the cyber resilience of DC SMGs for enhanced security and operational integrity. © 2024 IEEE.

关键词： Network intrusion

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Ionospheric Response to the X2.8 Solar Flare on 14 December 2023

Ionospheric Response to the X2.8 Solar Flare on 14 December ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Krishnendu Sekhar Paul Haris Haralambous Frederick Research Center Nicosia Cyprus Department of Electrical Engineering Computer Engineering and Informatics Frederick University Nicosia Cyprus

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Solar flares are violent eruptions on the Sun that affect the Earth’s sunlit ionosphere almost instantly. Understanding how corresponding ionospheric characteristics vary is crucial for evaluating such effects. This study aims to highlight the importance of ionosondes in monitoring such effects by utilising foF2, fminF and hmF2 data from nine Digisonde stations to demonstrate their response to the X-2.8 class flare that occurred on December 14, 2023. According to this event the solar flare is shown to cause a decrease in the foF2 profile and an increase in fminF and hmF2. We suggest that these digisonde data, along with high-resolution observations of solar EUV flux emissions and ionospheric models, might help us gain a better understanding of the ionospheric dynamics and photochemistry that support the capacity to evaluate these effects on relevant communication systems such as HF.

关键词： Analytical models Communication systems Geoscience and remote sensing Data models Continents Sun Monitoring

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Atherosclerotic Carotid Plaque AM-FM Texture Feature Variability in Ultrasound Video

Atherosclerotic Carotid Plaque AM-FM Texture Feature Variabi...

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IEEE International Symposium on Biomedical Imaging

作者： Kyriacos P. Constantinou Ioannis P. Constantinou Christos P. Loizou Efthyvoulos Kyriacou Andreas Panayides Andrew N. Nicolaides Constantinos S. Pattichis Marios S. Pattichis Department of Computer Science University of Cyprus Nicosia Cyprus Istognosis Ltd Nicosia Cyprus Dept. of Electrical Computer Engineering and Informatics Cyprus University of Technology Cyprus CYENS Centre of Excellence Nicosia Cyprus Cyprus Cardiovascular Disease Educational Research Trust Nicosia Cyprus Dept. of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

The objective of this work was to investigate the Amplitude Modulation - Frequency Modulation (AM-FM) texture feature variability in carotid ultrasound video during the cardiac cycle at systole and diastole. The goal here was to identify AM-FM features that are associated with increased risk of stroke. We computed the instantaneous amplitude, instantaneous phase and the magnitude of instantaneous frequency to extract plaque histogram features. A small dataset of 5 asymptomatic and 3 symptomatic videos were analyzed. Selected AM-FM plaque histogram texture features extracted during the cardiac cycle at the systolic and diastolic states were statistically significantly different between asymptomatic and symptomatic videos. However, further evaluation with more subjects needs to be carried out to exploit the usefulness of the proposed analysis in the clinical context.

关键词： Histograms Ultrasonic imaging Frequency modulation Stability criteria Atherosclerosis Feature extraction Object recognition

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Assessing the Viability of AC to DC Microgrid Conversion: A Comprehensive Analysis of Technical and Economic Factors

Assessing the Viability of AC to DC Microgrid Conversion: A ...

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International Universities Power engineering Conference (UPEC)

作者： Chrysanthos Charalambous Alexis Polycarpou Venizelos Efthymiou George E. Georghiou Department of Electrical and Computer Engineering PV Technology Laboratory University of Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus Department of Electrical Computer Engineering and Informatics Frederick University Nicosia Cyprus

ISBN: (数字)9798350379730

ISBN: (纸本)9798350379747

In the face of global warming and the acceleration of climate changes, urgent and decisive measures must be taken to mitigate these phenomena. This study addresses this urgent need by exploring the transition from traditional AC distribution systems to more efficient DC and hybrid AC/DC systems, recognizing the utmost importance of both technical developments and economic considerations. Through a thorough overview of existing literature and empirical knowledge gathered from the nanogrid of FOSS Research Centre for Sustainable Energy of university of cyprus, this research clarifies the inherent advantages of DC and AC/DC hybrid systems. In addition, it presents a detailed case study for the comprehensive assessment of the feasibility and effectiveness of these systems. The preliminary economic analysis carried out in this study demonstrates the cost-effectiveness and performance improvements offered by hybrid AC/DC microgrids compared to conventional AC configurations. According to the findings of this work it is obvious that the ROI increases with the increase of DC loads over AC loads until the case where DC loads reach the 75% of the total load (ROI=13.9%). However, even in the case of Full DC microgrid, the ROI is higher by 1% compared to the case of Full AC microgrid thus significantly reducing the payback period. These findings not only underline the urgency of the transition to more efficient and sustainable energy distribution systems, but also provide valuable information for policymakers, industry stakeholders and researchers. By embracing these innovative technologies, a more resilient and environmentally responsive energy infrastructure can be promoted that is in line with the demands of a rapidly changing global climate.

关键词： Power engineering Scalability Systems architecture Microgrids Hybrid power systems Software Production facilities Stakeholders Sustainable development Meteorology

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Gaussian Backbone-Based Spherical Evolutionary Algorithm with Cross-search for engineering Problems

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Journal of Bionic engineering 2024年第2期21卷 1055-1091页

作者： Yupeng Li Dong Zhao Ali Asghar Heidari Shuihua Wang Huiling Chen Yudong Zhang College of Computer Science and Technology Changchun Normal UniversityChangchun130032JilinChina School of Surveying and Geospatial Engineering College of EngineeringUniversity of TehranTehranIran School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Biological Sciences Xi’an Jiaotong-Liverpool UniversitySuzhou215123JiangsuChina Key Laboratory of Intelligent Informatics for Safety and Emergency of Zhejiang Province Wenzhou UniversityWenzhou325035China School of Computer Science and Engineering Southeast UniversityNanjing210096China

In recent years,with the increasing demand for social production,engineering design problems have gradually become more and more *** novel and well-performing meta-heuristic algorithms have been studied and developed to cope with this *** them,the Spherical Evolutionary Algorithm(SE)is one of the classical representative methods that proposed in recent years with admirable optimization ***,it tends to stagnate prematurely to local optima in solving some specific ***,this paper proposes an SE variant integrating the Cross-search Mutation(CSM)and Gaussian Backbone Strategy(GBS),called *** this study,the CSM can enhance its social learning ability,which strengthens the utilization rate of SE on effective information;the GBS cooperates with the original rules of SE to further improve the convergence effect of *** objectively demonstrate the core advantages of CGSE,this paper designs a series of global optimization experiments based on IEEE CEC2017,and CGSE is used to solve six engineering design problems with *** final experimental results fully showcase that,compared with the existing well-known methods,CGSE has a very significant competitive advantage in global tasks and has certain practical value in real ***,the proposed CGSE is a promising and first-rate algorithm with good potential strength in the field of engineering design.

关键词： Meta-heuristic algorithms engineering optimization Spherical evolution algorithm Global optimization

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Maritime radar odometry inspired by visual odometry 26

Maritime radar odometry inspired by visual odometry

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26th International Conference on Information Fusion, FUSION 2023

作者： Flemmen, Henrik D. Mester, Rudolf Stahl, Annette Bryne, Torleiv H. Brekke, Edmund Førland Norwegian University of Science and Technology Department of Engineering Cybernetics Norway Norwegian University of Science and Technology Department of Informatics and Computer Science Norway

ISBN: (纸本)9798890344854

Future autonomous ships will need several redundant positioning systems to navigate reliably. Global Navigation Satellite Systems are highly accurate but they are susceptible to disruptions and intentional jamming. Maritime radars have long range and are robust against bad weather and darkness, but the use for ownship motion estimation has received relatively little attention in the research field. In this work, we present a radar odometry estimation method inspired by advances in visual odometry and simultaneous localization and mapping. The method works on raw radar data in a coastal environment and combines the Kanade-Lucas-Tomashi tracker with a factor graph back-end. We test it on data from a large ship with a maritime radar with a range of 19 km. We find that it is robust with only a small drift and no erroneous jumps in the estimate. © 2023 International Society of Information Fusion.

关键词： Motion estimation

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Advancing Color Perception: An IoT-driven Pedagogical System for Visually Impaired Children 1

Advancing Color Perception: An IoT-driven Pedagogical System...

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1st IEEE International Conference on Advances in Signal Processing, Power, Communication, and Computing, ASPCC 2024

作者： Balage, Prasandika Jayawardene, Nuwan University of Westminster School of Computer Science and Engineering London United Kingdom Informatics Institute of Technology Department of Computing Colombo Sri Lanka

ISBN: (纸本)9798350355093

Visual impairment is characterized by the loss of vision, encompassing both complete blindness and partial vision loss. Studies reveal a notable prevalence of visual impairment among school-aged children. The significance of colors in acquiring and discerning visual information is crucial, aiding children in effectively perceiving and comprehending their surroundings. This study presents an Internet of Things (IoT) system designed for teaching colors to visually impaired children. The system achieved an accuracy rate surpassing 77.8%, and it is operated effectively regardless of surrounding lighting conditions. Given that the target users of the system are visually impaired children, this research was conducted in close alignment with Human-computer Interaction (HCI) principles. © 2024 IEEE.

关键词： Color vision

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ML-Aided Dynamic Clustering and Classification of UEs as VBs in D2D Communication Networks

ML-Aided Dynamic Clustering and Classification of UEs as VBs...

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2023 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2023

作者： Ioannou, Iacovos Nagaradjane, Prabagarane Khalifeh, Ala' Christophorou, Christophoros Vassiliou, Vasos Sashank, Gundepudi V. Surya Jain, Charu Pitsillides, Andreas University of Cyprus Department of Computer Science Cyprus Cyens - Centre of Excellence Cyprus Sri Sivasubramaniya Nadar College of Engineering Department of Ece Chennai India German Jordanian University Department of Electrical Engineering and Information Technology Jordan Department of Electrical & Electronic Engineering South Africa

ISBN: (纸本)9798350300451

With the next generation of mobile devices and streaming services such as Virtual Reality, Augmented Reality, and Meta being available worldwide, the network's data rate, latency, and connectivity must be improved. Even though 5G provides the user with the required Quality of Service (QoS) in terms of data rate and reduced delays by transmitting signals in the higher frequencies (called New Radio (NR)), it faces a lot of attenuation, leading to a short communication range. However, to meet goals set by utilising 6G requirements, many technological advancements and components must be incorporated into the network. The dense disposition of small cells will help reduce the network traffic in hotspot areas and increase the coverage and spectral efficiency. Nonetheless, the current deployment of 5G Base Stations (BSs) and small cells is static and cannot move around even though they are deployed in hot spot areas, leading to high operational costs. Furthermore, more than these static deployments of base stations can be required in an unpredictable scenario of extreme crowd movement. To overcome these issues, Device to Device (D2D) Communication with the dynamic deployment of Virtual Base stations (VBSs) can be called upon, which can be achieved by using User Equipment (UE) such as phones or laptops to mimic the functions of a Base Station (BS). Therefore, in this paper, a User Equipment based Virtual Base Station (UE-VBS) is studied, which will act as a secondary base station and, in turn, help alleviate the traffic load in the network. Specifically, as one UE cannot relieve the entire network traffic load, the network area is split into different clusters by using an unsupervised Machine Learning (ML) clustering technique(i.e., K-Means with Mean Shift Clustering), and a single UE is selected to act as a VBS for that cluster with the utilisation of supervised ML classification techniques (i.e., Decision Trees, Logistic Regression, Linear Discriminant Analysis And Quadrat

关键词： Decision trees

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