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Innovative Swing Mechanism for Sustainable Energy Generation: Design, Performance, and IoT Integration

Procedia computer science 2025年 253卷 2753-2765页

作者： M.O. Okwu N. Abubakar B.A. Edward B.U. Oreko O.B. Otanocha O.F. Orikpete C. Maware J.O. Oyekale C. Ori L. Tartibu S. Khalid Department of Mechanical Engineering Federal University of Petroleum Resources Effurun Nigeria Computer Science Department Brunel University London Uxbridge UB8 3PH United Kingdom Centre for Occupational Health Safety and Environment (COHSE) University of Port Harcourt Choba Nigeria Fujio Cho Engineering Technology 210E Center for Robotics and Manufacturing Building University of Kentucky USA 814-131 Wentworth Drive Halifax Nova Scotia Canada Department of Mechanical and Industrial Engineering Technology University of Johannesburg South Africa Department of CSIT Mirpur University of Science and Technology (MUST) Mirpur AJK Pakistan

In this study, the choice of mild steel for the swing mechanism was essential due to its mechanical properties, strength, and environmental resistance. The swing system, incorporating a stand, swing, connecting rod, spur gear, bearings, freewheel, battery, dynamo, and sprocket, was designed for sustainable energy generation without compromising user safety. The design balanced ergonomic needs with performance characteristics, ensuring the structure could endure applied stresses. IoT integration allowed for advanced monitoring, enabling real-time data analysis, performance optimization, and issue detection. The study examined how swinging weight and oscillation frequency impacted the output voltage and current, findings showed that output voltage varied with the swinger’s weight. The swing mechanism, capable of generating 6-8 volts, successfully converted kinetic energy into electrical energy. This study serves as a renewable energy education model with IoT integration, demonstrating innovative energy solutions for parks, schools, playgrounds, and communities.

关键词： Swing mechanism energy generation IoT monitoring Renewable Energy Mechanical design

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Remote-controlled mechanical and directional motions of photoswitchable DNA condensates

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Nature Communications 2025年第1期16卷 1-16页

作者： Udono, Hirotake Nomura, Shin-ichiro M. Takinoue, Masahiro Department of Computer Science School of Computing Institute of Science Tokyo Kanagawa Yokohama 226-8501 Japan Department of Robotics Graduate School of Engineering Tohoku University Miyagi Sendai 980-8579 Japan Research Center for Autonomous Systems Materialogy (ASMat) Institute of Innovative Research (IIR) Institute of Science Tokyo Kanagawa Yokohama 226-8501 Japan

Membrane-free synthetic DNA-based condensates enable programmable control of dynamic behaviors as shown by phase-separated condensates in biological cells. We demonstrate remote-controlled microflow using photocontrollable state transitions of DNA condensates, assembled from multi-branched DNA nanostructures via sticky-end (SE) hybridization. Introducing azobenzene into SEs enables their photoswitchable binding affinity, which underlies photoreversible fluidity of the resulting condensates that transition between gel/liquid/dissociated states in a wavelength-dependent manner. Leveraging base-sequence programmability, spatially coupled orthogonal DNA condensates with divergent photoresponsive capabilities perform multi-modal mechanical actions that depend on azobenzene insertion sites in the SE, including switching flows radially expanding and converging under photoswitching. Localizing photoswitching within a DNA liquid condensate generates two distinct directional motions, whose contrasting morphology, direction, and lifetime are determined by switching frequency. Numerical simulations reveal its regulatory role in weight-adjusting energy-exchanging and energy-dissipative interactions between the photoirradiated and unirradiated domains. © The Author(s) 2025.

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An Overview on Economic Analysis of Internet of Everything

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IEEE Communications Surveys and Tutorials 2025年

作者： Ding, Ningning Ouyang, Xiaomin Gao, Lin Huang, Jianwei Xing, Guoliang Guangzhou511453 China Hong Kong University of Science and Technology Department of Computer Science and Engineering 999077 Hong Kong Harbin Institute of Technology School of Electronics and Information Engineering Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen China Chinese University of Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network CSIJRI Joint Research Centre on Smart Energy Storage Guangdong Shenzhen518172 China The Chinese University of Hong Kong Department of Information Engineering 999077 Hong Kong

The Internet of Everything (IoE) integrates people, processes, data, and things into a unified network, enabling highly interconnected systems with enhanced performance in terms of delay, reliability, and connection density. With the advent of next-generation networks, IoE has garnered significant attention for its transformative potential across various domains. However, while most existing studies focus on developing enabling technologies and deployment frameworks, the economic dimensions of IoE systems remain critically underexplored, despite being essential for ensuring their sustainability and scalability. This paper addresses this gap by providing a comprehensive review of the economic analysis of IoE systems. Specifically, we categorize existing research into four key aspects: information collection, information transmission, information fusion, and service application. We identify major economic challenges and propose modeling approaches, including game-theoretic and learning-based methods. Furthermore, we explore diverse IoE application scenarios, such as metaverse solutions and large-scale industrial systems, and summarize prevailing business patterns in the industry. By highlighting the economic dimension as a core component of IoE systems, this survey offers valuable insights into future research directions and outlines critical open challenges. © 1998-2012 IEEE.

关键词： Internet of Everything

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Learning dissection trajectories from expert surgical videos via imitation learning with equivariant diffusion

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Medical Image Analysis 2025年 103卷 103599页

作者： Wang, Hongyu Long, Yonghao Chen, Yueyao Yip, Hon-Chi Scheppach, Markus Chiu, Philip Wai-Yan Yam, Yeung Meng, Helen Mei-Ling Dou, Qi Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Surgery The Chinese University of Hong Kong Hong Kong Internal Medicine III - Gastroenterology University Hospital of Augsburg Augsburg Germany Multi-scale Medical Robotics Center and The Chinese University of Hong Kong Hong Kong Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Centre for Perceptual and Interactive Intelligence and The Chinese University of Hong Kong Hong Kong

Endoscopic Submucosal Dissection (ESD) constitutes a firmly well-established technique within endoscopic resection for the elimination of epithelial lesions. Dissection trajectory prediction in ESD videos has the potential to strengthen surgical skills training and simplify surgical skills training. However, this approach has been seldom explored in previous research. While imitation learning has proven effective in learning skills from expert demonstrations, it encounters difficulties in predicting uncertain future movements, learning geometric symmetries and generalizing to diverse surgical scenarios. This paper introduces imitation learning for the critical task of predicting dissection trajectories from expert video demonstrations. We propose a novel Implicit Diffusion Policy with Equivariant Representations for Imitation Learning (iDPOE) to address this variability. Our method implicitly models expert behaviors using a joint state–action distribution, capturing the inherent stochasticity of future dissection trajectories and enabling robust visual representation learning across various endoscopic views. By incorporating a diffusion model in policy learning, our approach facilitates efficient training and sampling, resulting in more accurate predictions and improved generalization. Additionally, we integrate equivariance into the learning process to enhance the model's ability to generalize to geometric symmetries in trajectory prediction. To enable conditional sampling from the implicit policy, we develop a forward-process guided action inference strategy to correct state mismatches. We evaluated our method using a collected ESD video dataset comprising nearly 2000 clips. Experimental results demonstrate that our approach outperforms both explicit and implicit state-of-the-art methods in trajectory prediction. As far as we know, this is the first endeavor to utilize imitation learning-based techniques for surgical skill learning in terms of dissection trajector

关键词： Endoscopy

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LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation

arXiv

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arXiv 2025年

作者： Zhang, Shengdong Jia, Fan Li, Xiang Zhang, Hao Shi, Jun Ma, Liyan Ying, Shihui Department of Mathematics School of Science Shanghai University Shanghai200444 China Institute University of Utah Salt Lake CityUT84102 United States School of Computer Science and Technology East China Normal University Shanghai China School of Communication and Information Engineering Shanghai University Shanghai200444 China School of Computer Engineering and Science Shanghai University Shanghai200444 China School of Mechatronic Engineering and Automation Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai200444 China Shanghai Institute of Applied Mathematics and Mechanics The School of Mechanics and Engineering Science Shanghai University Shanghai200072 China

Few-shot semantic segmentation (FSS) methods have shown great promise in handling data-scarce scenarios, particularly in medical image segmentation tasks. However, most existing FSS architectures lack sufficient interpretability and fail to fully incorporate the underlying physical structures of semantic regions. To address these issues, in this paper, we propose a novel deep unfolding network, called the Learned Mumford-Shah Network (LMSNet), for the FSS task. Specifically, motivated by the effectiveness of pixel-to-prototype comparison in prototypical FSS methods and the capability of deep priors to model complex spatial structures, we leverage our learned Mumford-Shah model (LMS model) as a mathematical foundation to integrate these insights into a unified framework. By reformulating the LMS model into prototype update and mask update tasks, we propose an alternating optimization algorithm to solve it efficiently. Further, the iterative steps of this algorithm are unfolded into corresponding network modules, resulting in LMS-Net with clear interpretability. Comprehensive experiments on three publicly available medical segmentation datasets verify the effectiveness of our method, demonstrating superior accuracy and robustness in handling complex structures and adapting to challenging segmentation scenarios. These results highlight the potential of LMS-Net to advance FSS in medical imaging applications. Our code will be available at: https:// ***/SDZhang01/LMSNet Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Single-Axis Solar Tracking Systems: A Comprehensive Design and Performance Study

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Procedia computer science 2025年 253卷 2740-2752页

作者： M.O. Okwu O.P. Eruero N. Abubakar B.A. Edward B.U. Oreko O.B. Otanocha O.F. Orikpete C. Maware K.C. Ezekiel C. Ori L. Tartibu Department of Mechanical Engineering Federal University of Petroleum Resources Effurun Nigeria Computer Science Department Brunel University London Uxbridge UB8 3PH United Kingdom Centre for Occupational Health Safety and Environment (COHSE) University of Port Harcourt Choba Nigeria Fujio Cho Engineering Technology 210E Center for Robotics and Manufacturing Building University of Kentucky USA Maintenance Engineering and Asset Management University of Manchester United Kingdom 814-131 Wentworth Drive Halifax Nova Scotia Canada Department of Mechanical and Industrial Engineering Technology University of Johannesburg South Africa

Abundant solar resources and increasing electricity demand make solar energy a promising renewable energy source in Sub-Saharan Africa. However, conventional stationary Photovoltaic (PV) systems face challenges in efficiently capturing solar irradiation. To address this limitation, the implementation of solar tracking systems becomes essential, as they optimize the energy output of PV systems by dynamically adjusting the orientation of the panels to align with the sun’s rays. This study presents a comprehensive design and performance evaluation of single-axis solar tracking systems in Delta State, Nigeria. The investigation focused on the energy provision efficiency of these systems, revealing that the single-axis tracker reached peak performance at year-end, providing 9.333 kWh of available solar energy and 9.296 kWh of user-available energy. Over the year, the system delivered a total of 100.625 kWh of available solar energy and 96.483 kWh of user-available energy. These findings highlight the significant role of solar tracking systems in maximizing energy harvest and underscore their potential for improving energy sustainability in Sub-Saharan Africa.

关键词： Solar Energy Photovoltaic Systems Solar Tracking Renewable Energy Energy Efficiency Photovoltaic System

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AR/VR Digital Twin for Simulation and Data Collection of Robotic Environments

AR/VR Digital Twin for Simulation and Data Collection of Rob...

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IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： João G. Martins Karle Nutonen Paulo Costa Vladimir Kuts Tauno Otto Armando Sousa Marcelo R. Petry CRIIS - Centre for Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal Faculty of Engineering University of Porto (FEUP) Porto Portugal ViruTech - Virumaa innovation center of digitalization and green technologies Taltech Virumaa College Kohtla-Jarve Estonia Department of Mechanical and Indusrial Taltech Tallinn Estonia

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

Digital twins enable real-time modeling, simulation, and monitoring of complex systems, driving advancements in automation, robotics, and industrial applications. This study presents a large-scale digital twin-testing facility for evaluating mobile robots and pilot robotic systems in a research laboratory environment. The platform integrates high-fidelity physical and environmental models, providing a controlled yet dynamic setting for analyzing robotic behavior. A key feature of the system is its comprehensive data collection framework, capturing critical parameters such as position, orientation, and velocity, which can be leveraged for machine learning, performance optimization, and decision-making. The facility also supports the simulation of discrete operational systems, using predictive modeling to bridge informational gaps when real-time data updates are unavailable. The digital twin was validated through a matrix manufacturing system simulation, with an Augmented Reality (AR) interface on the HoloLens 2 to overlay digital information onto mobile platform controllers, enhancing situational awareness. The main contributions include a digital twin framework for deploying data-driven robotic systems and three key AR/VR integration optimization methods. Demonstrated in a laboratory setting, the system is a versatile tool for research and industrial applications, fostering insights into robotic automation and digital twin scalability while reducing costs and risks associated with real-world testing.

关键词： Service robots Navigation Metaverse Databases Scalability Digital representation Real-time systems Digital twins Mobile robots Robots

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PAROS: Multi-Component Robotic System and an Image-Guided Patient Alignment for Robot-Assisted Ophthalmic Surgery

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IEEE Access 2025年 13卷 85056-85071页

作者： Alikhani, Alireza Nguyen, Van Dai Inagaki, Satoshi Busam, Benjamin Faridpooya, Koorosh Maier, Mathias Gehlbach, Peter Iordachita, Iulian Navab, Nassir Nasseri, M. Ali Zapp, Daniel Klinikum rechts der Isar der Technische Universität München Augenklinik und Poliklinik Munich 81675 Germany Technische Universität München Department of Computer Science Munich 85748 Germany NSK Ltd. Tokyo 141-8560 Japan Rotterdam Eye Hospital BH Rotterdam 3011 Netherlands Wilmer Eye Institute The Johns Hopkins Hospital Baltimore 21287 MD United States Johns Hopkins University Laboratory for Computational Sensing and Robotics Baltimore 21218 MD United States University of Alberta Department of Biomedical Engineering Edmonton T6G 2R3 AB Canada

Advancements in robot-assisted eye surgery have significantly enhanced precision in delicate procedures by enabling controlled movement of surgical instruments through a small entry point (trocar) placed on the outer layer of the eye (sclera). However, before these procedures can begin, the robot must first be prepared, draped, and calibrated. Then, it is positioned near the patient, where precise patient alignment is performed. Finally, the system ensures accurate alignment between the surgical instrument and the trocar, a critical step for safe and effective instrument insertion and surgical execution. Despite these advancements, integrating robotic systems into clinical practice remains challenging. The current manual preparation and alignment process is time-consuming and error-prone, emphasizing the need for a more automated and adaptive solution. Patient alignment time has been identified as a key factor differentiating robot-assisted and manual surgeries in overall procedure duration, with robot preparation and patient alignment extending surgery time by an average of 51 ± 6 minutes compared to manual methods. To address these challenges, this paper introduces a multi-component robotic system combined with an image-guided patient alignment methodology designed to reduce preparation and alignment time in robotic eye surgery while maintaining surgical efficiency. The robotic system consists of a 2D-Cart carrying a 4-degree-of-freedom (DOF) frame robot holding a 5-DOF micro-manipulator. It utilizes an image-guided, multi-stage alignment approach, enabling the system to efficiently be localized and stabilized in an optimal position beside the surgical bed and ensuring precise and adaptive positioning relative to the patient in the surgical draped condition. The proposed end-to-end robotic system preparation and patient alignment are validated through a user study involving eight medical experts in a simulated surgical environment using a draped phantom eye. The r

关键词： Clinical Integration in Medical robotics Medical Robots and Systems Surgical robotics: Planning

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Toward Lung Ultrasound Automation: Fully Autonomous Robotic Longitudinal and Transverse Scans Along Intercostal Spaces

IEEE Transactions on Medical Robotics and Bionics

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IEEE Transactions on Medical robotics and Bionics 2025年第2期7卷 768-781页

作者： Long Lei Yingbai Hu Zixing Jiang Juzheng Miao Xiao Luo Yu Zhang Qiong Wang Shujun Wang Zheng Li Pheng-Ann Heng Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Multi-Scale Medical Robotics Centre Ltd. The Chinese University of Hong Kong Hong Kong China Department of Surgery The Chinese University of Hong Kong Hong Kong China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Biomedical Engineering The Hong Kong Polytechnic University Hong Kong China Department of Surgery Chow Yuk Ho Technology Centre for Innovative Medicine Li Ka Shing Institute of Health Sciences and Multiscale Medical Robotics Centre Ltd. The Chinese University of Hong Kong Hong Kong China

Lung ultrasound scanning is essential for diagnosing lung diseases. The scan effectiveness critically depends on both longitudinal and transverse scans through intercostal spaces to reduce rib shadowing interference, as well as maintaining the probe perpendicular to pleura for pathological artifact generation. Achieving this level of scan quality often depends heavily on the experience of doctors. Robotic ultrasound scanning shows promise, but currently lacks a direct path planning method for intercostal scanning, and probe orientation does not consider imaging differences between lungs and solid organs. In this paper, we aim to fully automate two fundamental operations in lung ultrasound scanning: longitudinal and transverse scans. We propose pioneering path planning methods along intercostal spaces and innovative solutions for adaptive probe posture adjustment using real-time pleural line feedback, specifically addressing the unique characteristics of lung ultrasound scanning. This ensures the acquisition of high-quality, diagnostically meaningful ultrasound images. In addition, we develop a robotic lung ultrasound system to validate the proposed methods. Extensive experimental results on two volunteers and a chest phantom confirm the efficacy of our methods, and demonstrate the system’s feasibility in automated lung ultrasound examinations. Our work lays a solid foundation for automated robotic complete lung scanning.

关键词： Ultrasonic imaging Lungs Probes Robots Robot kinematics Medical robotics Ribs Medical services Surface morphology Computed tomography

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Brain Region Knowledge Based Dual-Stream Transformer for EEG Emotion Recognition

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IEEE Transactions on Consumer Electronics 2025年

作者： Lin, Yichuan Xu, Geng-Xin Liang, Haotian Wang, Yishan Wan, Feng Li, Ye Southern University of Science and Technology Shenzhen518055 China Research Center for Biomedical Information Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Hangzhou Institute of Advanced Technology Chinese Academy of Sciences China Department of Electrical and Computer Engineering The Faculty of Science and Technology The Centre for Cognitive and Brain Sciences The Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation University of Macau China

In consumer electronics, affective computing allows devices to recognize and respond to the emotional states of individuals, thereby enhancing personalization and satisfaction. Electroencephalogram (EEG)-based emotion recognition methods have garnered attention for their capability to provide real-time, objective insights into emotional and cognitive states. Existing research mainly focuses on extracting temporal and spectral features from EEG signals for emotion recognition. However, electrode shifts may occur across different experiments during data acquisition, resulting in models with limited robustness and constrained classification performance. To address this challenge, this paper introduces a Brain Region Knowledge based Dual-Stream network (BRKDSnet) for emotion recognition. It employs a dual-stream architecture to effectively fuse the temporal, spectral, and spatial features of EEG signals. By incorporating prior brain region partition knowledge, it efficiently aggregates channel features into distinct brain region features. Such a brain region based partition mitigates noise issues caused by electrode shifts during user wearing in consumer electronic applications, and leading to learn more robust feature representations. The experiments conducted on the widely used emotion recognition datasets SEED and SEED-IV shows that BRKDSnet achieves state-of-the-art results, with accuracies of 97.81% (improved by 0.64%-3.57% compared to existing works) and 90.76% (improved by 3.13%-11.39%) respectively. Finally, through analysis of the visualization results, it is found that EEG channels located in the temporal lobe contributed the most to emotion recognition classification, which also provides the potential for future applications in wearable devices. © 2025 IEEE.

关键词： Electroencephalography

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