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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Luiz H. N. Oliveira Wellington A. Amaral Sandro A. P. Haddad Daniel M. Muñoz Eduardo A. Barcelos Vinícius V. M. Sotero Emmanuel K. M. Jonhson Pâmela R. A. N. Campagnucci Luis V. N. Lima Department of Mechanical Engineering Faculty of Technology Mechatronics Graduate Program University of Brasilia Brasilia DF Brazil Faculty of Gama Electronics Engineering Undergraduate Program

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

The demand for efficient power management solutions in electronic devices has increased significantly, especially for microcontrollers operating in low-power modes. This article presents the design and implementation of a Pulse Frequency Modulation (PFM)-based buck converter for applications in 32-bit microcontrollers using 22nm CMOS technology. The methodology involves the development of a Constant On Time (COT) architecture that maintains a fixed duration for the activation of the high-side MOSFET, using a monostable multivibrator to generate the necessary timing signals. The converter's performance was simulated under various load conditions and process corners to evaluate its efficiency and output characteristics. The results show that the proposed buck converter achieves high efficiency, with peak values above 95%, while maintaining a low output ripple. Specifically, the converter exhibits an efficiency of 93.4% at a load of 1mA, with output ripple voltages as low as 2.20 mV under ideal conditions. The contributions of this work lie in the advancement of PFM converter technology for modern microcontroller applications, demonstrating the potential of 22nm CMOS processes to improve power efficiency and performance.

关键词： Semiconductor device modeling Performance evaluation MOSFET Buck converters Microcontrollers Power system management Multivibrators Timing Internet of Things Load modeling

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Corrigendum to "Effect of cell imprinting on viability and drug susceptibility of breast cancer cells to doxorubicin" [Acta Biomaterialia, 2020, 113, 119-129] (Acta Biomaterialia (2020) 113 (119–129), (S1742706120303287), (10.1016/***.2020.06.007))

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Acta Biomaterialia 2025年 198卷 571-572页

作者： Shahriyari, Fatemeh Janmaleki, Mohsen Sharifi, Shahriar Hesar, Milad Eyvazi Hoshian, Sasha Taghiabadi, Reza Razaghian, Ahmad Ghadiri, Majid Peirovi, Afshin Mahmoudi, Morteza Nezhad, Amir Sanati Khademhosseini, Ali Division of Engineering in Medicine Department of Medicine Brigham and Women's Hospital Harvard Medical School CambridgeMA United States Division of Health Sciences & Technology Harvard-Massachusetts Institute of Technology Massachusetts Institute of Technology CambridgeMA United States Department of Materials Engineering Faculty of Engineering Imam Khomeini International University Qazvin Iran Biomedical Engineering Graduate Program University of Calgary Canada Precision Health Program and Department of Radiology Michigan State University East LansingMI48824 United States RWTH Aachen University Templergraben 55 Aachen52062 Germany Department of Chemistry and Materials Science Aalto University School of Chemical Engineering Espoo Finland Department of Mechanical Engineering Faculty of Engineering Imam Khomeini International University Qazvin Iran Nano-Biotechnology Department Pasteur Institute of Iran Tehran Iran Departments of Bioengineering Chemical and Biomolecular Engineering and Radiological Sciences Center for Minimally Invasive Therapeutics California NanoSystems Institute University of California – Los Angeles Los AngelesCA United States Terasaki Institute for Biomedical Innovation Los AngelesCA United States

The authors regret to report that the images for panels h (Plain PDMS) and I (MCF7-imprinted PDMS) in Fig. 3 were mistakenly taken from the same dataset, resulting in the omission of the correct image in panel h for the Plain PDMS day 7 image. We sincerely apologize for this error. For an accurate representation, please see the corrected Fig. 3 provided here along with its original figure caption. This does not affect the validity of the statistical analyses or the overall conclusions drawn from the study. © 2025

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Effect of TiO2 nanoparticles and hydrogen on the combustion, performance, and emissions of madhuca biodiesel in a diesel engine

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International Journal of Hydrogen Energy 2025年

作者： Madhan Kumar, A. Senthil Nathan, K. Manickam, Selvaraj Gupta, Deepak Vikneswaran, M. Dhamodaran, Gopinath Seetharaman, Sathyanarayanan Thiagarajan, S. Al-Ansari, Mysoon M. Department of Mechanical Engineering Saveetha Engineering College Saveetha Nagar Thandalam Chennai India Department of Mechanical Engineering E.G.S. Pillay Engineering College Tamilnadu Nagapattinam611002 India Department of Automation and Robotics IMPACT College of Engineering and Applied Sciences Bangalore India Department of Mechanical Engineering Graphic Era Hill University Uttarakhand Dehradun248002 India Department of Mechanical Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun248002 India Department of Mechanical Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Ramapuram Tamilnadu Chennai600089 India Department of Mechanical Engineering Easwari Engineering College Tamilnadu Chennai India Department of Chemistry College of Science King Saud University P.O. Box. 2455 Riyadh11451 Saudi Arabia Centre for Sustainable Energy Technologies Easwari Engineering College Tamilnadu Chennai India Centre of Research SRM TRP Engineering College TiruchirapalliTamilnadu India

This study investigates the combustion, performance, and emission characteristics of a diesel engine fueled with neat diesel, M20 (a blend of 20 % Madhuca oil and 80 % diesel), and M20 augmented with Titanium dioxide (TiO2) nanoparticles (50 ppm and 100 ppm), under dual-fuel conditions with a constant hydrogen flow rate of 10 LPM. The research aims to address the challenges of biodiesel adoption by leveraging TiO2 nanoparticles and hydrogen to optimize fuel efficiency and emissions. The results reveal that the M20 blend enhanced with 100 ppm TiO2 nanoparticles achieved a peak improvement in brake thermal efficiency (BTE) by 6.76 %, while simultaneously reducing brake-specific fuel consumption (BSFC) by 4.46 %, compared to the M20 blend. Also, emission analysis demonstrated significant reductions in hydrocarbon (HC) and carbon monoxide (CO) emissions, achieving decreases of 2.63 % and 8.53 %, respectively, with the same blend. The inclusion of hydrogen as a secondary fuel with M20 and 100 ppm TiO2 showed 24.23 % improved BTE and 8.32 % reduced BSFC, along with significant emissions reductions: 13.16 % HC, 19.02 % CO, and 4.04 % smoke opacity. However, nitrogen oxide (NOx) emissions increased by 12.67 % due to elevated in-cylinder temperatures with the M20 blend. These findings underscore the potential of Madhuca oil-based biodiesel, when enhanced with nanoparticles and hydrogen, to serve as a sustainable alternative to conventional diesel. By balancing improved performance and reduced environmental impact, this study offers valuable insights into achieving cleaner and more efficient combustion in diesel engines, contributing to the advancement of biofuel applications and emission compliance strategies. © 2025

关键词： Diesel engines

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Automated Parts Segmentation of Sperm via a Contrastive Learning-Based Part Matching Network

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IEEE Transactions on Automation Science and engineering 2025年 22卷 15314-15325页

作者： Chen, Wenyuan Song, Haocong Shan, Guanqiao Dai, Changsheng Liu, Hang Jiang, Aojun Sun, Chen Ru, Changhai Librach, Clifford Zhang, Zhuoran Sun, Yu University of Toronto Department of Computer Science TorontoONM5S 2E4 Canada University of Toronto Department of Mechanical and Industrial Engineering TorontoONM5S 3G8 Canada Dalian University of Technology Institute of Robotics and Intelligent Systems Liaoning Dalian China Suzhou University of Science and Technology School of Electronic and Information Engineering Suzhou215009 China CReATe Fertility Centre TorontoONM5G 1N8 Canada

Sperm morphology measurement is vital for diagnosing male infertility, which involves quantification of multiple subcellular parts for each sperm. Instance-aware part segmentation networks have been introduced to address this task by automatically identifying individual sperm and segmenting their subcellular parts. However, major limitations of state-of-the-art instance-aware part segmentation networks include: 1) they are time-consuming and computational expensive due to sequential processing and multi-stage frameworks;2) they perform poorly for densely packed sperm that overlap or cross over one another. To overcome these challenges, this paper proposes 1) integrating instance identification and subcellular part segmentation within a single-stage framework to save inference time and memory usage;2) dividing a sperm target into simpler components (head and tail) to improve prediction accuracy, followed by a contrastive learning-based matching method to pair the head and tail. Experimental results on our clinically collected human sperm dataset demonstrated that the proposed network not only outperformed state-of-the-art CP-Net (by 3.5% APpvol) but also achieved realtime inference (48.0 frames per second), effectively meeting the clinical requirements for automated parts segmentation of sperm. © 2004-2012 IEEE.

关键词： Image segmentation

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Towards Wearable Interfaces for Robotic Caregiving

arXiv

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arXiv 2025年

作者： Padmanabha, Akhil Majidi, Carmel Erickson, Zackory Robotics Institute Carnegie Mellon University United States Department of Mechanical Engineering Carnegie Mellon University United States

Physically assistive robots in home environments can enhance the autonomy of individuals with impairments, allowing them to regain the ability to conduct self-care and household tasks. Individuals with physical limitations may find existing interfaces challenging to use, highlighting the need for novel interfaces that can effectively support them. In this work, we present insights on the design and evaluation of an active control wearable interface named HAT, Head-Worn Assistive Teleoperation. To tackle challenges in user workload while using such interfaces, we propose and evaluate a shared control algorithm named Driver Assistance. Finally, we introduce the concept of passive control, in which wearable interfaces detect implicit human signals to inform and guide robotic actions during caregiving tasks, with the aim of reducing user workload while potentially preserving the feeling of control. Copyright © 2025, The Authors. All rights reserved.

关键词： Remote control

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BIRD: Battlefield-Integrated Reconnaissance Drone with VQA

BIRD: Battlefield-Integrated Reconnaissance Drone with VQA

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Rayon, Nathan Menser, Kobi Catalano, Louis Vega, Jonathan Najarro, Julio Sevil, Hakki Erhan Department of Computer Science University of West Florida FL32514 United States Department of Mechanical Engineering University of West Florida FL32514 United States Catalano Aerospace LLC FL32502 United States Department of Intelligent Systems & Robotics University of West Florida FL32514 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper introduces a new unmanned aerial system (UAS) design featuring coaxial rotors and tethered operation with a base station. The coaxial rotor configuration enhances stability and maneuverability, particularly in challenging environments. The tethered connection to a base station ensures prolonged flight duration and enables continuous power and data transmission. Moreover, the UAS incorporates a visual question answering (VQA) based target identification system. This system leverages advanced computer vision algorithms to analyze visual input from onboard cameras, coupled with natural language processing techniques to interpret and respond to queries regarding identified targets. The integration of VQA technology enables critical decision-making decisions through providing crucial information identify about targets in real-time scenarios. The new design has a significant potential for applications in surveillance, reconnaissance, search and rescue, and disaster response, as this design was conceptualized to provide persistent Intelligence, Reconnaissance & Surveillance (ISR) capabilities. Further analysis of the concept design was conducted to investigate the feasibility of novel design characteristics. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Reconnaissance aircraft

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Knee osteoarthritis severity detection using deep inception transfer learning

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Computers in Biology and Medicine 2025年 186卷 109641-109641页

作者： Sohail, Muhammad Azad, Muhammad Muzammil Kim, Heung Soo Department of Mechanical Robotics and Energy Engineering Dongguk University-Seoul 30 Pildong-ro 1-gil Jung-gu Seoul04620 Korea Republic of

Osteoarthritis (OA) is a prevalent condition resulting in physical limitations. Early detection of OA is critical to effectively manage this condition. However, the diagnosis of early-stage arthritis remains challenging. The Kellgren and Lawrence (KL) grading system is a common method that is accepted worldwide, uses five grades to classify the severity of OA, and relies on the ability of the orthopedist to accurately interpret radiograph images. To improve the accuracy of radiograph image interpretation, artificial intelligence-assisted models have been developed that include shallow or deep learning approaches and multi-step techniques;however, their accuracy remains variable. This work proposes a transfer learning approach using an InceptionV3-based model fine-tuned on the Osteoarthritis Initiative dataset, and aims to enhance the identification of OA severity levels through dual-stage preprocessing and convolutional neural networks for feature extraction. The fine-tuned IV3 (FT−IV3) model outperformed the IV3 model with training, validation, and testing accuracies of (96.33, 93.82, and 92.25) %, compared to IV3 accuracies of (91.64, 82.04, and 86.20) %, respectively. Additionally, Cohen's Kappa value for the FT−IV3 model (90.69 %) exceeds that of the IV3 model (83.15 %), indicating a better diagnosis of OA severity. This improvement allows the FT−IV3 model to effectively classify moderate and severe-grade OA. © 2024 Elsevier Ltd

关键词： Joints (anatomy)

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Human-in-the-loop Optimization of Hip Exoskeleton Assistance During Stair Climbing

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IEEE Transactions on Biomedical engineering 2025年 PP卷 PP页

作者： Park, Dongho An, Jimin Lee, Dawit Kang, Inseung Young, Aaron J. Woodruff School of Mechanical Engineering the Institute for Robotics and Intelligent Machines Georgia Institute of Technology AtlantaGA30332-0405 United States the Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGA30332-0405 United States the Department of Mechanical Engineering Carnegie Mellon University PittsburghPA15213 United States the Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGA30332 United States the Department of Bioengineering Stanford University StanfordCA94305 United States the Woodruff School of Mechanical Engineering the Institute for Robotics and Intelligent Machines Georgia Institute of Technology AtlantaGA30332-0405 United States

—This study applies human-in-the-loop optimization to identify optimal hip exoskeleton assistance patterns for stair climbing. Ten participants underwent optimization to individualize hip flexion and extension assistance, followed by a validation comparing optimized assistance (OPT) to biological hip moment-based assistance (BIO), no assistance (No-Assist), and no exoskeleton (No-Exo) conditions. OPT reduced metabolic cost by 4.5% compared to No-Exo, 11.44% compared to No-Assist, and 5.02% compared to BIO, demonstrating the effectiveness of the optimization approach. Statistical analysis revealed distinct characteristics in optimal assistance timing and magnitude that deviated systematically from biological hip moment patterns. Compared to BIO, OPT exhibited later peak flexion timing (76.4 ± 3.7% vs 65.0%), shorter flexion duration (29.2 ± 3.6% vs 40.0%), later peak extension timing (26.7 ± 3.8% vs 20.0% of gait cycle), and higher peak flexion magnitude (11.1 ± 1.5 Nm vs 10.0 Nm). While individual optimal assistance profiles varied across participants, comparison between individually optimized parameters and the best subject-independent parameters identified through post-hoc analysis showed consistency. On average, metabolic rate convergence was achieved after 18 iterations, while most exoskeleton control parameters did not reach our convergence criteria within 20 iterations. These findings demonstrate that human-in-the-loop optimization can effectively identify task-specific assistance patterns for stair climbing, while the consistency between individual and subject-independent parameters suggests the potential for developing generalized assistance strategies. The systematic differences between optimized and biological moment-based assistance underscore the fundamental distinctions between biological torque-based control and optimal control for exoskeleton assistance. © 2025 IEEE Computer Society. All rights reserved.

关键词： Exoskeleton (robotics)

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Improving mechanical Durability of SLA-Printed Components for Load-Bearing 154th

Improving Mechanical Durability of SLA-Printed Components fo...

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154th Annual Meeting and Exhibition of The Minerals, Metals and Materials Society, TMS 2025

作者： Fani, Niloofar Monfared, Armaghan Hashemi Sadeghzade, Sorour Tavangarian, Fariborz Mechanical Engineering Program School of Science Engineering and Technology Pennsylvania State University Harrisburg MiddletownPA17057 United States Department of Biomedical Engineering Pennsylvania State University State College University ParkPA16802 United States School of Engineering Westlake University Hangzhou310024 China

ISBN: (纸本)9783031807473

This study explored the impact of post-printing parameters in stereolithography (SLA) technology, particularly focusing on how photopolymerization affected the mechanical properties of samples. Building on previous research, the investigation revealed that different curing temperatures significantly influenced the rigidity and stiffness of the printed samples. The mechanical properties of the samples were evaluated through three-point bending tests. Curing at 60 °C left some polymer chains uncured, resulting in less rigid samples but increased load capacity (up to 30 ± 2.4 N) and deflection (2.9 ± 0.6 mm). On the other hand, curing at 70 °C enhanced the rigidity and stiffness, especially for larger samples. The results demonstrated that photopolymerization and the resulting polymer chain cross-linking are crucial for achieving the desired mechanical properties. Notably, as the diameter of the samples decreased, the difference in stiffness between the 60 and 70 °C cured samples also reduced. The findings highlight the importance of optimizing curing temperatures to tailor the mechanical performance of SLA-printed components for specific applications. © The Minerals, Metals & Materials Society 2025.

关键词： Bending tests

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Recent Advances in Artificial Intelligence-Driven Prognostics and Health Management of Mobility Batteries

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Multiscale Science and engineering 2025年 1-16页

作者： Kumar, Prashant Kim, Heung Soo Kim, Sungjun Kim, Hyun-Seok Moon, Janghyuk Department of AI and Big Data Woosong University Daejeon Republic of Korea Department of Mechanical Robotics and Energy Engineering Dongguk University Seoul Republic of Korea Division of Electronics and Electrical Engineering Dongguk University Seoul Republic of Korea Department of Energy Systems Engineering Chung-Ang University Seoul Republic of Korea

The rise in demand for sustainable transportation solutions in recent years has resulted in Electric vehicles (EVs) attracting substantial interest. The rapid rise in EV adoption has led to the need for efficient prognostics and health management (PHM) solutions. To ensure reliable and safe operation, the batteries that power EVs require maintenance. This paper reviews the detailed artificial intelligence (AI)-driven PHM framework for mobility batteries. Different mobility batteries have different chemistries owing to the different battery materials used for their development. AI-driven PHM uses machine learning, deep learning, and data-driven techniques to accurately estimate key battery parameters that include state of health and remaining useful life. We review data collection strategies, data processing approaches, feature development, and battery health assessment techniques based on AI, reviewing various AI methodologies that include artificial neural networks, Gaussian process regression, convolutional neural networks, and many more. This inclusive review of the recent trends and methodologies of AI-based mobility battery PHM provides a framework to develop a future efficient PHM solution for safer, efficient, and reliable battery systems.

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