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International Conference on ent Computing and Control Systems (ICICCS)

作者： C. Kalaivanan Ayman Amer Maria Rubiston. M M. Vennila Jayant Giri M. Dinesh Department of EEE Sona College of Technology Salem Department of Energy Engineer Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of ECE S.A. Engineering College Chennai Department of of Robotics and Automation Karpaga Vinayaga College of Engineering and Technology Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur Division of Research and Development Lovely Professional University Phagwara India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

IoT technologies to bolster residential security, safety, and sustainability. By leveraging facial recognition for door access, enhancing fire safety monitoring, and integrating energy harvesting, it aims to create more resilient and eco-friendly residential environments. These innovations offer comprehensive solutions to address evolving challenges and optimize residential living. An ED Model coupled with Crow Search Optimization (CSO) for enhancing the efficiency and reliability of IoT applications. The ED model integrates static routing mechanisms with efficient protocols like AOMDV, ensuring robust connectivity and energy-efficient operation in diverse environments. Leveraging CSO, inspired by collective crow intelligence, enhances network performance by dynamically adapting to changing conditions. Through simulations and comprehensive analysis, the proposed approach demonstrates superior performance compared to existing models, achieving high data delivery ratio, network throughput, and energy efficiency. This integrated framework offers a sustainable solution for resilient IoT deployments, addressing evolving challenges and optimizing residential living environments.

关键词： Adaptation models Analytical models Routing Throughput Energy efficiency Data models Internet of Things Reliability Sustainable development Optimization

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Medical waste sorting machine development with IoT and YOLO model utilization

Journal of Engineering and Applied Science

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Journal of engineering and Applied Science 2025年第1期72卷 1-25页

作者： Moktar, Muhammad Hafizuddin Mohamed, Hassan Hajjaj, Sami Salama Hussen Baharuddin, Mohd Zafri Centre for Advanced Mechatronics and Robotics (CaMaRo) Universiti Tenaga Nasional (UNITEN) Kajang Malaysia Institute of Sustainable Energy (ISE) Universiti Tenaga Nasional (UNITEN) Kajang Malaysia School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University Bandar Sunway Malaysia Research Centre For Human-Machine Collaboration (HUMAC) Faculty of Engineering and Technology Sunway University Bandar Sunway Malaysia Institute of Engineering Infrastructure (IEI) Department of Electrical & Electronics Engineering Universiti Tenaga Nasional (UNITEN) Kajang Malaysia

The Internet of Things (IoT) application has been seen across various sectors to improve management practices. In medical waste management post-pandemic, the integration of IoT and artificial intelligence (AI) has notably enhanced sorting methods. The increased medical waste has brought about significant environmental challenges, impacting community health, land, and ocean ecosystems. We aim to develop a medical waste sorting system integrated with the You Only Look Once model (YOLO) and IoT for monitoring purposes. We can observe how effective the YOLO model is in sorting applications. This work trained and compared YOLO models from YOLO v5 to YOLO v9. YOLO v8 outperformed, and we implemented it into the sorting system. The mechanical, software, and programming elements were combined to develop the sorting prototype. From the training results, YOLO v8 achieved 98% mean average precision (mAP), 0.958 and 0.963 precision and recall, respectively. Other than that, the sorting evaluation was done during the final testing, with 93.75% accuracy. Further results were explained in the paper. The developed sorting prototype detected, classified, and sorted the medical waste, including facemasks, gloves, syringes, and sharp waste, based on the YOLO model. Further development is essential to improve the system in many aspects. Therefore, this system can be implemented practically in actual medical waste management. This work is a move that aligns with the third Sustainable Development Goal, where we focused on an automated medical waste system, ensuring sustainable health and Good Health and Well-being (SDG3).

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A research on Submarine Cable Path Planning 8

A Research on Submarine Cable Path Planning

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8th Symposium on Novel Photoelectronic Detection Technology and Applications

作者： Wang, Xinyu Cheng, Ge Wang, Zengfu Zukerman, Moshe Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong China Railway Major Bridge Reconnaissance & Design Institute Co. Ltd. Wuhan China Research & Development Institute Northwestern Polytechnical University in Shenzhen Shenzhen China School of Automation Northwestern Polytechnical University Xi'an China

ISBN: (数字)9781510653122

ISBN: (纸本)9781510653115

Submarine optical fiber cables are essential carriers of international data transmission, and their laying and maintenance require significant investments. However, the failure of the submarine cable may cause severe socioeconomic damages, costs and disruptions. Therefore, it is important to build a cost-effective and resilient submarine cable network that can withstand potential natural or human-made disasters. This paper first discusses the current research progress on submarine cable path planning and cable network design. Next, it focuses on our approach based on a triangulated manifold is used to model the earth's surface based on real-world geographic data. Then, for submarine cable path planning between two given end-points, the fast marching method (FMM) is used to solve a multi-objective path planning problem that takes into account a variety of design considerations aiming to optimize the survivability and cost of submarine cables. This multi-objective problem is then transformed into a single-objective problem by weighted sum method for solution. Finally, to overcome the difficulties associated with the massive data size that are prevalent in practical scalability and high-precision cable path planning design, two methods named multi-resolution and parallel FMM are proposed. Experimental results show the cable paths obtained by the multi-resolution and parallel FMM have lower cost, shorter length, and higher resolution than the results obtained from the sequential FMM method, which help to achieve scalability and high-quality cable path planning in the future. © 2022 SPIE

关键词： Submarines

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Prediction about residual stress and microhardness of material subjected to multiple overlap laser shock processing using artificial neural network

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Journal of Central South University 2022年第10期29卷 3346-3360页

作者： WU Jia-jun HUANG Zheng QIAO Hong-chao WEI Bo-xin ZHAO Yong-jie LI Jing-feng ZHAO Ji-bin State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China Institute of Metal Research Chinese Academy of SciencesShenyang 110016China School of Materials Science and Engineering University of Science and Technology of ChinaShenyang 110016China Faculty of Science and Engineering University of HullHull HU67RXUnited Kingdom Department of Chemistry Tsinghua UniversityBeijing 100084China

In this work,the nickel-based powder metallurgy superalloy FGH95 was selected as experimental material,and the experimental parameters in multiple overlap laser shock processing(LSP)treatment were selected based on orthogonal experimental *** experimental data of residual stress and microhardness were measured in the same *** residual stress and microhardness laws were investigated and *** neural network(ANN)with four layers(4-N-(N-1)-2)was applied to predict the residual stress and microhardness of FGH95 subjected to multiple overlap *** experimental data were divided as training-testing sets in *** energy,overlap rate,shocked times and depth were set as inputs,while residual stress and microhardness were set as *** prediction performances with different network configuration of developed ANN models were compared and *** developed ANN model with network configuration of 4-7-6-2 showed the best predict *** predicted values showed a good agreement with the experimental *** addition,the correlation coefficients among all the parameters and the effect of LSP parameters on materials response were *** can be concluded that ANN is a useful method to predict residual stress and microhardness of material subjected to LSP when with limited experimental data.

关键词： laser shock processing residual stress microhardness artificial neural network

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A Multi-stage Precision Landing Method for Autonomous eVTOL Based on Multi-marker Joint Localization

A Multi-stage Precision Landing Method for Autonomous eVTOL ...

引用

IEEE International Conference on robotics and Biomimetics

作者： Senwei Xiang Minxiang Ye Shiqiang Zhu Jason Gu Anhuan Xie Zehua Men Zhejiang Lab Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

ISBN: (纸本)9781665481106

Electric vertical takeoff and landing aircraft (eVTOL) has drawn more and more attention from home and abroad in recent years. It is believed autonomous eVTOL will create a new era of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM). To autonomous eVTOL, precision landing is an extremely critical operation for it directly affects flight safety. In this paper, we analyze the special issues an eVTOL will encounter when it lands in the UAM applications. A multi-stage precision landing method based on multi-marker joint localization is proposed accordingly. Our method contains three key elements: a compatible vertiport with multiple visual markers; an accurate, fast detection and localization algorithm for the vertiport and a multi-stage landing strategy. We implement our method on an eVTOL prototype named ZJ-Copter developed by Zhejiang Lab. A series of real-world experiments have been conducted to validate the effectiveness and accuracy of the proposed method. Experiment results show that our method works well in real-world scenarios for autonomous eVTOL without GPS signal during landing process. The positioning accuracy is less than 0.1m (altitude $< \mathbf{10}\mathbf{m}$ ), while the landing accuracy is less than 0.5m.

关键词： Location awareness Visualization Robot kinematics Helicopters Prototypes Feature extraction Safety

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Graph Neural Network Based Method for Path Planning Problem

arXiv

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arXiv 2023年

作者： Diao, Xingrong Chi, Wenzheng Wang, Jiankun Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China The Robotics and Microsystems Center School of Mechanical and Electric Engineering Soochow University Suzhou215021 China

Sampling-based path planning is a widely used method in robotics, particularly in high-dimensional state space. Among the whole process of path planning, collision detection is the most time-consuming operation. In this paper, we propose a learning-based path planning method that aims to reduce the number of collision detections. We develop an efficient neural network model based on Graph Neural Networks (GNN). The model outputs weights for each neighbor based on the obstacle, searched path, and random geometric graph, which are used to guide the planner in avoiding obstacles. We evaluate the proposed method's efficiency through simulated random worlds and real-world experiments, respectively. The results demonstrate that the proposed method significantly reduces the number of collision detections and improves the path planning speed in high-dimensional environments. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

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Study on spiral winding swimming motion control of a slender legless creature model

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High Technology Letters 2021年第4期27卷 415-421页

作者： Lu Zhenli Ma Zhipeng Marko Pencic Maja Cavic Borovac Branislav Roumiana Ilieva Bojan Nemec Marjan Mernik School of Electrical Engineering and Automation Changshu Institute of TechnologyChangshu 215500P.R.China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116P.R.China Faculty of Technical Sciences University of Novi SadNovi Sad 621000Serbia Faculty of Management Technical University of SofiaSofia 1000Bulgaria Department of Automatics Biocybernetics and RoboticsJožef Stefan InstituteLjubljana 1000Slovenia Faculty of Electrical Engineering and Computer Science University of MariborMaribor 462000Slovenia

Through the observation and analysis of the motion trajectory of spiral winding motion for slender legless biological creatures in water,V-REP software is adopted to build a dynamic simulator to study on the mechanism of spiral winding swimming(SWS)*** using the output of spiral function(SF),the dynamic simulation model of slender legless creature(SLC)realizes the SWS motion in *** corresponding experiments under the control of different bending angle of SF to control the dynamic model are also carried out to analyze the water performance of SWS in still *** with the output of two different bending angles of the spiral function,the dynamic model can be used to realize SWS and up/down *** work provides technical reserve and experimental platform for the corresponding study in related fields.

关键词： V-REP spiral winding swimming(SWS) motion control slender legless creature(SLC)

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Experimental implementation of chaos-based video encryption using visible light communication technology

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Applied Optics 2025年第9期64卷 C101-C118页

作者： Fotso, Sorel Bagio Nono Mbé, Jimmi Hervé Talla Takotue, Jean Ernest Takougang, Armand Cyrille Nzeukou Ndjiongue, Alain Richard Research Unit of Condensed Matter Electronics and Signal Processing Department of Physics University of Dschang P.O. Box 67 Dschang Cameroon Research Unit of Automation and Applied Computer Department of Electrical Engineering IUT-FV Bandjoun University of Dschang P.O. Box: 134 Bandjoun Cameroon Laboratory of Industrial Systems and Environmental Engineering Fotso Victor University Institute of Technology University of Dschang P.O. Box 134 Bandjoun Cameroon School of Electrical and Information Engineering Faculty of Engineering and the Built Environment University of the Witwatersrand Johannesburg South Africa

Simulations are commonly used to develop and evaluate video encryption algorithms. Although these approaches are useful for demonstrating theoretical feasibility and algorithm performance, they neglect practical challenges and real-world communication conditions. This oversight creates a critical gap in evaluating their effectiveness and security in practical applications. In this paper, we describe a novel, to the best of our knowledge, video encryption scheme that employs a modified chaotic Colpitts oscillator, a customized Josephus problem, and ribonucleic acid (RNA) operations, and is implemented in visible light communication (VLC) environment. The modified Colpitts oscillator reveals significant phenomena, the most prominent for this study being chaos. The encryption scheme involves block scrambling and pixel-level permutation employing a customized Josephus problem and a column and row shifting technique. Diffusion is then achieved through a custom Josephus problem-oriented RNA operation combined with basic RNA operations and cipher block chaining (CBC). Based on the results, we found that the customized Josephus problem-based permutation performed more efficiently than traditional methods, whereas the column and row shifting permutation was more robust. By eliminating key sequence coding and item-to-item operations, the proposed RNA operation reduces computational overhead. This video encryption scheme is proven to be effective and resilient at securing video data transmitted over the VLC channel, and it constitutes a significant advancement in areas such as surveillance, medical imaging, and military communication requiring robust secured video data. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Medical imaging

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Deep learning for brain age estimation: A systematic review

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Information Fusion 2023年 96卷 130-143页

作者： Tanveer, M. Ganaie, M.A. Beheshti, Iman Goel, Tripti Ahmad, Nehal Lai, Kuan-Ting Huang, Kaizhu Zhang, Yu-Dong Del Ser, Javier Lin, Chin-Teng Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Robotics University of Michigan Ann ArborMI48109 United States Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Biomedical Imaging Lab National Institute of Technology Silchar Assam 788010 India Department of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Data Science Research Center Duke Kunshan University China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom Derio48160 Spain Bilbao48013 Spain School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated with the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required to elicit accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models. © 2023 Elsevier B.V.

关键词： Deep neural networks

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IEEE T-BIOM Editorial Board Changes

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity Science 2024年第1期7卷 1-2页

作者： Naser Damer Weihong Deng Jianjiang Feng Vishal M. Patel Ajita Rattani Mark Nixon Smart Living & Biometric Technologies Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing China Department of Automation Tsinghua University Beijing China Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD USA Department of Computer Science and Engineering College of Engineering University of North Texas Denton TX USA

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