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International Conference on Advances in Biomedical engineering (ICABME)

作者： Roy Abi Zeid Daou Elio Geha Youssef Aoun Ali Hayek Josef Boercsoek Jose Javier Serrano Olmedo Biomedical Engineering department Furn El Chebbak Université La Sagesse Beirut Lebanon Education and Research Center MART Learning Chananir Lebanon Biomedical Technologies Department Faculty of Public Health Lebanese German University Jounieh Lebanon Department Lippstadt 2 Hardware Engineering Marker Allee 76-78 University of Applied Science Hamm-Lippstadt Hamm Department of Computer Architecture and System Programming Faculty of Electrical Engineering /Computer Science University of Kassel Germany Lab of Bioinstrumentation and Nanomedicine Centro de Tecnologia Biomedica Universidad Politecnica de Madrid Spain

Chronic back pain can present a serious health concern, with symptoms that can significantly affect an individual's well-being, mobility, and overall quality of life over an extended period. While chronic back pain may manifest suddenly in some cases, it often develops gradually and persists for weeks, and in untreated cases, it can linger for years. Hence, the utilization of assistive devices such as wearable posture-monitoring vests can offer valuable assistance and guidance to users. This research paper is dedicated to the development of a system for detecting, diagnosing, and correcting poor posture, specifically leaning posture. The vest is designed to provide users with visual, auditory, and tactile cues to help them address this issue, thereby reducing the risk associated with leaning. Additionally, an integrated electrical box has been designed to consolidate all components directly onto the main board in a secure enclosure. This box also displays the daily count of instances where the user has leaned. This system is characterized by its electrical safety, portability, compactness, comfort, and affordability. A comprehensive analysis of the system's performance has been conducted with a meticulous evaluation of accuracy. Each component of the system has undergone successful testing, and the system as a whole is currently in the testing phase. The results of these tests have indicated a lack of faulty errors and have demonstrated outstanding accuracy and detection rates. Over 100 individuals of varying ages, genders, and BMI categories were involved in testing, with each person wearing the device for an average of six hours. The accuracy rate achieved was 98.85%, with an average of 54.35 instances of poor posture detected per participant.

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Human Activity Recognition in Logistics Using Wearable Sensors and Deep Residual Network

Human Activity Recognition in Logistics Using Wearable Senso...

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IEEE Region 10 International Conference TENCON

作者： Sakorn Mekruksavanich Datchakorn Tancharoen Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Faculty of Engineering and Technology Panyapiwat Institute of Management Nonthaburi Thailand Department of Mathematics Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

Human action identification is a practical area of study with broad applicability in various domains, such as medical care, sport science, and manufacturing management. In logistics, it is essential to identify and examine individual actions, enabling machines to perceive and comprehend human motions for non-verbal interaction. This study specifically fo-cuses on efficiently classifying working activities in the logistics industry using wearable sensors, particularly in the context of human activity recognition. To achieve the research objective, a deep residual neural network was introduced, integrating convolutional layers, shortcut connections, and aggregated transformation for human activity recognition in logistics. The authors evaluated the effectiveness of their proposed deep learning model using the publicly accessible LARa dataset. The LARa dataset comprises a diverse range of human actions in the logistics domain, including standing, walking, cart handling, and synchronization. The details of activity were captured using wearable sensors affixed to different anatomical sites of the study participants. The experimental findings indicate that the model achieved a maximum F-measure of 85.30%.

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SEMI-IMPLICIT HYBRID FINITE VOLUME/FINITE ELEMENT METHOD FOR THE GPR MODEL OF CONTINUUM MECHANICS

arXiv

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arXiv 2024年

作者： Busto, Saray Río-Martín, Laura Department of Applied Mathematics Universidade de Santiago de Compostela Santiago de Compostela15782 Spain Galician Center for Mathematical Research and Technology CITMAga Santiago de Compostela15782 Spain Department of Information Engineering and Computer Science University of Trento via Sommarive 9 Povo Trento38123 Italy Laboratory of Applied Mathematics DICAM University of Trento Via Mesiano 77 Trento38123 Italy

We present a new hybrid semi-implicit finite volume / finite element numerical scheme for the solution of incompressible and weakly compressible media. From the continuum mechanics model proposed by Godunov, Peshkov and Romenski (GPR), we derive the incompressible GPR formulation as well as a weakly compressible GPR system. As for the original GPR model, the new formulations are able to describe different media, from elastoplastic solids to viscous fluids, depending on the values set for the model’s relaxation parameters. Then, we propose a new numerical method for the solution of both models based on the splitting of the original systems into three subsystems: one containing the convective part and non-conservative products, a second subsystem for the source terms of the distortion tensor and heat flux equations and, finally, a pressure subsystem. In the first stage of the algorithm, the transport subsystem is solved by employing an explicit finite volume method, while the source terms are solved implicitly. Next, the pressure subsystem is implicitly discretised using finite elements. Within this methodology, unstructured grids are employed, with the pressure defined in the primal grid and the rest of the variables computed in the dual grid. To evaluate the performance of the proposed scheme, a numerical convergence analysis is carried out, which confirms the second order of accuracy in space. A wide range of benchmarks is reproduced for the incompressible and weakly compressible cases, considering both solid and fluid media. These results demonstrate the good behaviour and robustness of the proposed scheme in a variety of scenarios and conditions. © 2024, CC BY-NC-ND.

关键词： Finite element method

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EQUILIBRIA AND STABILITY OF ONE MESSENGER RNA AND TWO MICRO RNA DYNAMICS

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2022年第4期84卷 3-8页

作者： Olteanu, Mircea Ştefan, Radu Department of Mathematical Methods and Models Faculty of Applied Sciences University "Politehnica" of Bucharest Romania Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computer Science University "Politehnica" of Bucharest Romania

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Effect of Oxidation of Aged Power Transformer Insulating Oil on UV-Vis Spectra

Effect of Oxidation of Aged Power Transformer Insulating Oil...

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International Conference on Conduction and Breakdown in Dielectric Liquids

作者： Abdellatif K. Rashed Diaa-Eldin A. Mansour A. Rezk Mohsen Ghali Department of Electrical Power and Machines Engineering Faculty of Engineering Tanta University Tanta Egypt Department of Physics Faculty of Sciences Cantabria University Santander Spain Department of Electrical Power Engineering School of Electronics Communication and Computer Engineering Egypt-Japan University of Science and Technology Alexandria Egypt Department of Physics Institute of Basic and Applied Science Egypt-Japan University of Science and Technology Alexandria Egypt Physics Department Faculty of Science Kafrelsheikh University Kafrelsheikh Egypt

Optical spectroscopy is a major rising condition monitoring tool used to assess the status of the insulating oil in power transformer with a non-destructive approach. Accelerated thermal ageing studies have been discussed considering the effect of temperature on the oil condition. These studies didn't perfectly reflect what is happening in a real in-service power transformer due to the absence of copper catalyst in the ageing process. This paper discusses the effect of oxidation for a more precise simulation of the in-service ageing process of insulating oil. The optical results when copper catalyst is included in the accelerated thermal ageing process are compared with previously obtained results of accelerated thermal ageing to deduce the change that took place in the UV-Vis spectra. Dielectric dissipation factor is assessed to correlate between the optical spectra and transformer oil characteristic parameters. Dissolved decay products were calculated for the samples containing copper catalyst with the same ageing days compared to only thermally accelerated aged samples without the addition of copper catalyst. Dissolved decay products were correlated to both ageing days and dielectric dissipation factor. It was found that the copper catalyst caused a great difference in the obtained UV-Vis spectra.

关键词： Integrated optics Oils Catalysts Accelerated aging Oil insulation Oxidation Dielectrics

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Sign Language Recognition System using Convolutional Neural Network

Sign Language Recognition System using Convolutional Neural ...

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Advanced Technologies in Intelligent Control, Environment, Computing & Communication engineering (ICATIECE), International Conference on

作者： T M Dudhane T. R. Chenthil Kanaga Priya P Jothibasu M Shri Chhatrapati Shivajiraje College of Engineering Pune Electronics and communication Engineering Jeppiaar Engineering college Computer Science and Engineering KPR Institute of Engineering and Technology Department of ECE PSG Institute of Technology and Applied Research Neelambur Coimbatore

ISBN: (纸本)9781665493970

Sign language is the common communication language for the hearing and speech-impaired community. It is hard for most people to communicate in sign language without an interpreter. Sign language refers to the tracking and identification of meaningful human expressions made with the hands, arms, fingers, heads, etc. The method used in this case converts the sign language movements into a spoken language that the listener may easily understand. The communication using sign language is useful for the peoples depend on gestural sign language but it is more complex for the other publics. The existing systems are not efficient since they are struggling with skin tone detection. But, adding a filter symbol can be recognized regardless of skin tone. In this work, primarily focused on analyzing convolutional neural networks (CNN). There are four kinds of layers: convolution layers, fully connected layers, pooling/subsampling layers and nonlinear layers for learning new characteristics.

关键词： Human computer interaction Program processors Tracking Convolution Computational modeling Symbols Gesture recognition

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The IDEAL framework for surgical robotics: development, comparative evaluation and long-term monitoring (vol 30, pg 61, 2024)

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NATURE MEDICINE 2024年第4期30卷 1213-1213页

作者： Marcus, Hani J. Ramirez, Pedro T. Khan, Danyal Z. Horsfall, Hugo Layard Hanrahan, John G. Williams, Simon C. Beard, David J. Bhat, Rani Catchpole, Ken Cook, Andrew Hutchison, Katrina Martin, Janet Melvin, Tom Stoyanov, Danail Rovers, Maroeska Raison, Nicholas Dasgupta, Prokar Noonan, David Stocken, Deborah Sturt, Georgia Vanhoestenberghe, Anne Vasey, Baptiste McCulloch, Peter Department of Neurosurgery National Hospital of Neurology and Neurosurgery London UK Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS) London UK Department of Obstetrics and Gynaecology Houston Methodist Hospital Neal Cancer Center Houston TX USA RCS Surgical Interventional Trials Unit (SITU) & Robotic and Digital Surgery Initiative (RADAR) Nuffield Dept Orthopaedics Rheumatology and Musculo-skeletal Sciences University of Oxford Oxford UK Department of Gynaecological Oncology Apollo Hospital Bengaluru India Department of Anaesthesia and Perioperative Medicine Medical University of South Carolina Charleston SC USA NIHR Coordinating Centre and Clinical Trials Unit University of Southampton Southampton UK Department of Philosophy Macquarie University Sydney Australia Department of Anesthesia & Perioperative Medicine University of Western Ontario Ontario Canada Department of Medical Gerontology School of Medicine Trinity College Dublin Dublin Republic of Ireland Department of Medical Imaging Radboudumc Nijmegen the Netherlands Department of Biomedical Engineering and Imaging Sciences King’s College London London UK King’s Health Partners Academic Surgery King’s College London London UK Moon Surgical Paris France RCSEng Surgical Trials Centre Leeds Institute of Clinical Trials Research University of Leeds Leeds UK Bowel Research UK London UK School of Biomedical Engineering & Imaging Sciences King’s College London London UK Department of Surgery Geneva University Hospital Geneva Switzerland Nuffield Department of Surgical Sciences University of Oxford John Radcliffe Hospital Oxford UK PRISMA Lab University of Naples Federico II Naples Italy Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich Switzerland School of Computer Science University of Birmingham Birmingham UK Division of Cardiac Surgery Brigham and Women’s Hospital Harvard Medical Schoo

The next generation of surgical robotics is poised to disrupt healthcare systems worldwide, requiring new frameworks for evaluation. However, evaluation during a surgical robot’s development is challenging due to their complex evolving nature, potential for wider system disruption and integration with complementary technologies like artificial intelligence. Comparative clinical studies require attention to intervention context, learning curves and standardized outcomes. Long-term monitoring needs to transition toward collaborative, transparent and inclusive consortiums for real-world data collection. Here, the Idea, Development, Exploration, Assessment and Long-term monitoring (IDEAL) Robotics Colloquium proposes recommendations for evaluation during development, comparative study and clinical monitoring of surgical robots—providing practical recommendations for developers, clinicians, patients and healthcare systems. Multiple perspectives are considered, including economics, surgical training, human factors, ethics, patient perspectives and sustainability. Further work is needed on standardized metrics, health economic assessment models and global applicability of recommendations.

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Rethinking domain generalization in medical image segmentation: One image as one domain

arXiv

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arXiv 2025年

作者： Hong, Jin Liu, Bo Long, Guoli Li, Siyue Muhammad, Khan School of Information Engineering Nanchang University Nanchang330031 China School of Mathematics and Computer Science Nanchang University Nanchang330031 China Department of Radiological Sciences University of California Los Angeles Los AngelesCA United States Department of Applied Artificial Intelligence School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

Medical image segmentation faces persistent challenges from domain discrepancies, particularly when integrating data across institutions. Notably, variations within single centers—driven by differences in scanner configurations or imaging protocols—often create shifts as substantial as those observed between facilities. To overcome these limitations, our work introduces the One Image as One Domain (OIOD) hypothesis, treating each image as an independent domain to enable adaptable generalization. Building on this principle, we present UniDDG, a disentanglement-driven framework that eliminates dependency on domain labels while remaining adaptable to both single- and multi-source scenarios. The architecture maintains consistent complexity regardless of data sources, streamlining implementation across clinical environments. UniDDG operates by isolating anatomical features from device-specific characteristics through cross-batch recombination of content and style components during segmentation and reconstruction. This process preserves structural consistency across imaging conditions while discarding scanner-induced artifacts. To further bolster resilience, two complementary innovations are integrated: Expansion Mask Attention (EMA) prioritizes anatomical boundaries in low-contrast regions, while Style Augmentation (SA) emulates diverse acquisition parameters by generating synthetic style variations. Validation across optic disc/cup and prostate segmentation tasks demonstrates the framework’s efficacy. In multi-to-single-center generalization, UniDDG achieved Dice scores of 84.43% (optic disc and optic cup) and 86.96% (prostate), and achieved that of 88.91% and 88.56% respectively for single-center generalization—surpassing existing state-of-the-art methods. These results highlight its capacity to mitigate domain discrepancies without architectural modifications, offering a scalable solution for real-world clinical heterogeneity. Copyright © 2025, The Authors. All rights

关键词： Image segmentation

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DATASET OF FLUORESCENCE SPECTRA AND CHEMICAL PARAMETERS OF OLIVE OILS

arXiv

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arXiv 2023年

作者： Venturini, Francesca Sperti, Michela Michelucci, Umberto Gucciardi, Arnaud Martos, Vanessa M. Deriu, Marco A. Institute of Applied Mathematics and Physics Zurich University of Applied Sciences Winterthur Switzerland Artificial Intelligence Research and Development TOELT LLC Switzerland PolitoBIOMed Lab Department of Mechanical Aerospace Engineering Politecnico di Torino Turin Italy Computer Science Department Lucerne University of Applied Sciences and Arts Lucerne Switzerland Artificial Intelligence Laboratory University of Ljubljana Ljubljana Slovenia Department of Plant Physiology Faculty of Sciences Biotechnology Institute University of Granada Spain

This dataset encompasses fluorescence spectra and chemical parameters of 24 olive oil samples from the 2019–2020 harvest provided by the producer Conde de Benalúa, Granada, Spain. The oils are characterized by different qualities: 10 extra virgin olive oil (EVOO), 8 virgin olive oil (VOO), and 6 lampante olive oil (LOO) samples. For each sample, the dataset includes fluorescence spectra obtained with two excitation wavelengths, oil quality, and five chemical parameters necessary for the quality assessment of olive oil. The fluorescence spectra were obtained by exciting the samples at 365 nm and 395 nm under identical conditions. The dataset includes the values of the following chemical parameters for each olive oil sample: acidity, peroxide value, K270, K232, ethyl esters, and the quality of the samples (EVOO, VOO, or LOO). The dataset offers a unique possibility for researchers in food technology to develop machine learning models based on fluorescence data for the quality assessment of olive oil due to the availability of both spectroscopic and chemical data. The dataset can be used, for example, to predict one or multiple chemical parameters or to classify samples based on their quality from fluorescence spectra. © 2023, CC BY-NC-ND.

关键词： Fluorescence

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Application of binary classification of magnetic resonance therapy images to detect the threat of the disease due to COVID-19 3

Application of binary classification of magnetic resonance t...

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3rd IEEE KhPI Week on Advanced Technology, KhPI Week 2022

作者： Zalevska, Olga Vanin, Volodymyr Sydogenko, Julia Finogenov, Oleksii Demydenko, Olga Yablonskyi, Petro Ladogubets, Tetyana Oliinyk, Yurii Miroshnichenko, Ivan National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Department of Automation of Power Processes and Systems Engineering Kyiv Ukraine National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Department of Descriptive Geometry Engineering and Computer Graphics Kyiv Ukraine National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Department of Computer-Aided Science and Data Processing Systems Kyiv Ukraine National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' The Department of Theory Practice and Translation of English Kyiv Ukraine National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Deprtament of Applied Mathematics Kyiv Ukraine

ISBN: (数字)9798350399202

ISBN: (纸本)9798350399202

The paper considers the use of a neural network for the binary classification of magnetic resonance imaging images to establish a possible disease of COVID-19. Processing of input data and their reduction to one format is provided, and neural network software for binary classification is developed and its training based on about 60,000 open patient images. The using of developed software is universal for medical doctors or ordinary Internet users and does not require additional training. © 2022 IEEE.

关键词： Magnetic resonance imaging

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