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arXiv 2024年

作者： Raducan, S.D. Jutzi, M. Cheng, A.F. Zhang, Y. Barnouin, O. Collins, G.S. Daly, R.T. Davison, T.M. Ernst, C.M. Farnham, T.L. Ferrari, F. Hirabayashi, M. Kumamoto, K.M. Michel, P. Murdoch, N. Nakano, R. Pajola, M. Rossi, A. Agrusa, H.F. Barbee, B.W. Syal, M. Bruck Chabot, N.L. Dotto, E. Fahnestock, E.G. Hasselmann, P.H. Herreros, I. Ivanovski, S. Li, J.-Y. Lucchetti, A. Luther, R. Ormö, J. Owen, M. Pravec, P. Rivkin, A.S. Robin, C.Q. Sánchez, P. Tusberti, F. Wünnemann, K. Zinzi, A. Epifani, E. Mazzotta Manzoni, C. May, B.H. Physics Institute University of Bern Switzerland Johns Hopkins University Applied Physics Laboratory LaurelMD United States Department of Aerospace Engineering University of Maryland College ParkMD United States Department of Earth Science and Engineering Imperial College London United Kingdom Department of Aerospace Science and Technology Politecnico di Milano Italy Guggenheim School of Aerospace Engineering Georgia Institute of Technology AtlantaGA United States Department of Aerospace Engineering Auburn University AuburnAL United States Lawrence Livermore National Laboratory LivermoreCA United States Université Côte dAzur Observatoire de la Côte dAzur CNRS Laboratoire Lagrange Nice France University of Tokyo Department of Systems Innovation School of Engineering Tokyo Japan Université de Toulouse Toulouse France INAF OAPD Astronomical Observatory of Padova Italy IFAC CNR FI Sesto Fiorentino Italy NASA Goddard Space Flight Center GreenbeltMD United States INAF Osservatorio Astronomico di Roma Italy Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States CSIC INTA Carretera de Ajalvir km4 Torrejón de Ardoz28850 Spain INAF Osservatorio Astronomico di Trieste Trieste Italy Planetary Science Institute TucsonAZ United States Museum für Naturkunde Leibniz Institute for Evolution and Biodiversity Science Berlin Germany Astronomical Institute The Czech Academy of Sciences Czech Republic Colorado Center for Astrodynamics Research University of Colorado Boulder BoulderCO United States Agenzia Spaziale Italiana Space Science Data Center Italy London Stereoscopic Company London United Kingdom

On September 26, 2022, NASA’s Double Asteroid Redirection Test (DART) mission successfully impacted Dimorphos, the natural satellite of the binary near-Earth asteroid (65803) Didymos. Numerical simulations of the impact provide a means to explore target surface material properties and structures, consistent with the observed momentum deflection efficiency, ejecta cone geometry, and ejected mass. Our simulation, which best matches observations, indicates that Dimorphos is weak, with a cohesive strength of less than a few pascals (Pa), similar to asteroids (162173) Ryugu and (101955) Bennu. We find that a bulk density of Dimorphos, ρB, lower than ≈ 2400 kg/m3, and a low volume fraction of boulders ( 40 vol%) on the surface and in the shallow subsurface, are consistent with measured data from the DART experiment. These findings suggest Dimorphos is a rubble pile that might have formed through rotational mass shedding and re-accumulation from Didymos. Our simulations indicate that the DART impact caused global deformation and resurfacing of Dimorphos. ESA’s upcoming Hera mission may find a re-shaped asteroid, rather than a well-defined crater. © 2024, CC BY.

关键词： Asteroids

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State Estimation for Non-linear Fully-implicit, Index-1 Differential Algebraic Equation systems

State Estimation for Non-linear Fully-implicit, Index-1 Diff...

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American Control Conference

作者： Neeraja Srinivasan Nirav Bhatt Sridharakumar Narasimhan Systems and Control group Indian Institute of Technology Madras Chennai India Department of Biotechnology and member Robert Bosch Centre for Data Science and Artificial Intelligence and Initiative for Biological Systems Engineering Indian Institute of Technology Madras Chennai India Department of Chemical Engineering and member of the Robert Bosch Centre for Data Science and Artificial Intelligence Indian Institute of Technology Madras Chennai India

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

The Kalman filter and its variants have been developed for state estimation in semi-explicit, index-1 DAE systems in current literature. In this work, we develop a method for state estimation in non-linear fully-implicit, index-1 differential algebraic equation (DAEs) systems. In order to extend the Kalman filtering techniques for fully-implicit index-1 DAE systems, in the correction step we convert the fully-implicit DAE into a system of ordinary differential equations (ODEs). This is achieved by the index reduction of DAE using the method of successive differentiation of algebraic equations. This is a challenging problem as the fully-implicit DAE system does not contain explicit algebraic states unlike in the semi-explicit case. In this work, we propose a linear transformation of the mass matrix which enables us to find candidate algebraic states for the system. This transformation on the mass matrix is relatively simpler than constructing the transformation matrices in the Weierstrass-Kronecker canonical form. We illustrate our proposed method with two examples, a linear and a non-linear fully-implicit index-1 DAE system.

关键词： Mathematical model Kalman filters Indexes Differential equations State estimation Computational modeling

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-Neural network-based optimization of hydrogen fuel production energy system with proton exchange electrolyzer supported nanomaterial

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Fuel 2023年第Part1期332卷

作者： Hai, Tao Hikmat Hama Aziz, Kosar Zhou, Jincheng Dhahad, Hayder A. Sharma, Kamal Fahad Almojil, Sattam Ibrahim Almohana, Abdulaziz Fahmi Alali, Abdulrhman Ismail Kh, Teeba Mehrez, Sadok Abdelrahman, Anas School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun 558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Guizhou Duyun 558000 China nstitute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Selangor Shah Alam 40450 Malaysia Department of Chemistry College of Science University of Sulaimani Qlyasan Street Kurdistan Region 46001 Iraq Department of Medical Laboratory of Science College of Health Sciences University of Human Development Sulaimaniyah Iraq Mechanical Engineering Department University of Technology Baghdad Iraq Institute of Engineering and Technology GLA University Uttar Pradesh Mathura 281406 India Department of Civil Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Computer Engineering College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq Department of Mechanical Engineering College of Engineering at Al Kharj Prince Sattam bin Abdulaziz University 16273 Saudi Arabia Department of Mechanical Engineering University of Tunis El Manar ENIT BP 37 Le Belvédère Tunis 1002 Tunisia Department of Chemical Engineering Faculty of Engineering & Technology Future University in Egypt New Cairo 11500 Tunisia Key Laboratory of Complex Systems and Intelligent Optimization of Qiannan Duyun 558000 China

In this research, we try to investigate a solar-geothermal energy system. This system includes three turbines for power production, a PEM electrolyzer for hydrogen production, and a thermoelectric for generating electricity from excess heat. In addition, the seawater will be passed through the osmotic cycle to gain fresh water. The required power for this osmotic cycle will be obtained through the energy produced by the main turbines. The generated load, hydrogen production flow rate, purified water flow rate, and heating consumption are assessed in this study. The results showed that this system can produce 3.8 megawatts of electricity as well as 8 g per second of hydrogen fuel at the operating point. Also, the energy efficiency of this system is estimated to be 19%. Afterward, machine learning methods are used to optimize designing parameters, and the optimum operating point in terms of useful power and stored fuel flow rate is obtained by a genetic algorithm. The optimum operating point of this energy system has a useful power output of 4.099 megawatts and a hydrogen flow rate of 29 g per second. In the end, the distribution of the design parameters is displayed for points of the beam curve. © 2022

关键词： Biofuel Electrolyzer Fuel production Hydrogen fuel Optimization Solar energy

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Leveraging Artificial Intelligence for Digital Symptom Management in Oncology: The Development of CRCWeb

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JMIR cancer 2025年 11卷 2025 May 5页

作者： Sizuo Liu Yufen Lin Runze Yan Zhiyuan Wang Delgersuren Bold Xiao Hu Department of Computer Science Nell Hodgson Woodruff School of Nursing Emory University Atlanta US. Nell Hodgson Woodruff School of Nursing Winship Cancer Institute Emory University Atlanta US. Center for Data Science Nell Hodgson Woodruff School of Nursing Emory University Atlanta GE. Department of Systems and Information Engineering University of Virginia Charlottesville US. Center for Data Science Nell Hodgson Woodruff School of Nursing Emory University 1520 Clifton Rd Atlanta US.

UNSTRUCTURED:Digital health interventions offer promise for scalable and accessible healthcare, but access is still limited by some participatory challenges, especially for disadvantaged families facing limited health literacy, language barriers, low income, or living in marginalized areas. These issues are particularly pronounced for colorectal cancer (CRC) patients, who often experience distressing symptoms and struggle with educational materials due to complex jargon, fatigue, or reading level mismatches. To address these issues, we developed and assessed the feasibility of a digital health platform, CRCWeb, to improve the accessibility of educational resources on symptom management for disadvantaged CRC patients and their caregivers facing limited health literacy or low income. CRCWeb was developed through a stakeholder-centered participatory design approach. Two-phase semi-structured interviews with patients, caregivers, and oncology experts informed the iterative design process. From the interviews, we developed the following five key design principles: user-friendly navigation, multimedia integration, concise and clear content, enhanced accessibility for individuals with vision and reading disabilities, and scalability for future content expansion. Initial feedback from iterative stakeholder engagements confirmed high user satisfaction, with participants rating CRCWeb an average of 3.98 out of 5 on the post-intervention survey. Additionally, using GenAI tools, including large language models (LLMs) like ChatGPT and multimedia generation tools such as Pictory, complex healthcare guidelines were transformed into concise, easily comprehensible multimedia content, and made accessible through CRCWeb. User engagement was notably higher among disadvantaged participants with limited health literacy or low income, who logged into the platform 2.52 times more frequently than non-disadvantaged participants. The structured development approach of CRCWeb demonstrates that

关键词： artificial intelligence colorectal cancer generative artificial intelligence health disparity health equity large language model software engineering

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Crowdsourcing Geospatial data for Earth and Human Observations:A Review

Journal of Remote Sensing

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Journal of Remote Sensing 2024年第1期4卷 766-790页

作者： Xiao Huang Siqin Wang Di Yang Tao Hu Meixu Chen Mengxi Zhang Guiming Zhang Filip Biljecki Tianjun Lu Lei Zou Connor Y.H.Wu Yoo Min Park Xiao Li Yunzhe Liu Hongchao Fan Jessica Mitchell Zhenlong Li Alexer Hohl Department of Environmental Sciences Emory UniversityAtlantaGAUSA Spatial Sciences Institute University of Southern CaliforniaLos AngelesCAUSA Wyoming Geographic Information Science Center University of WyomingLaramieWYUSA Department of Geography Oklahoma State UniversityStillwaterOKUSA Department of Geography and Planning University of LiverpoolLiverpoolUK Carilion School of Medicine Virginia TechBlacksburgVAUSA Department of Geography&the Environment University of DenverDenverCOUSA Department of Architecture National University of SingaporeSingaporeSingapore Department of Epidemiology and Environmental Health College of Public HealthUniversity of KentuckyLexingtonKYUSA Department of Geography Texas A&M UniversityCollege StationTXUSA Department of Management Science and Information Systems Oklahoma State UniversityStillwaterOKUSA Department of Geography University of ConnecticutStorrsCTUSA Transport Studies Unit University of OxfordOxfordUK The MRC Centre for Environment and Health Imperial College LondonLondonUK Department of Civil and Environmental Engineering Norwegian University of Science and TechnologyTrondheimNorway Spatial Analysis Lab University of MontanaMissoulaMTUSA Geoinformation and Big Data Research Laboratory Department of GeographyThe Pennsylvania State UniversityUniversity ParkPAUSA Department of Geography The University of UtahSalt Lake CityUTUSA.

The transformation from authoritative to user-generated data landscapes has garnered considerable attention,notably with the proliferation of crowdsourced geospatial *** by advancements in digital technology and high-speed communication,this paradigm shift has democratized data collection,obliterating traditional barriers between data producers and *** previous literature has compartmentalized this subject into distinct platforms and application domains,this review offers a holistic examination of crowdsourced geospatial *** a narrative review approach due to the interdisciplinary nature of the topic,we investigate both human and Earth observations through crowdsourced *** review categorizes the diverse applications of these data and rigorously examines specific platforms and paradigms pertinent to data ***,it addresses salient challenges,encompassing data quality,inherent biases,and ethical *** contend that this thorough analysis will serve as an invaluable scholarly resource,encapsulating the current state-of-the-art in crowdsourced geospatial data,and offering strategic directions for future interdisciplinary research and applications across various sectors.

关键词： crowdsourced geospatial datafacilitated crowdsourcing human observations geospatial data high speed communication paradigm shift earth observations digital technology

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Investigation of the hyperchaos and control in the fractional order financial system with profit margin

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Partial Differential Equations in Applied Mathematics 2024年 9卷

作者： Johansyah, Muhamad Deni Sambas, Aceng Qureshi, Sania Zheng, Song Abed-Elhameed, Tarek M. Vaidyanathan, Sundarapandian Sulaiman, Ibrahim Mohammed Department of Mathematics Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Campus Besut Terengganu 22200 Malaysia Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Tamansari Gobras Tasikmalaya 46196 Indonesia Department of Computer Science and Mathematics Lebanese American University Beirut 13-5053 Lebanon Department of Mathematics Near East University Mersin 99138 Turkey Department of Basic Sciences and Related Studies Mehran University of Engineering and Technology Jamshoro 72060 Pakistan School of Data Science Zhejiang University of Finance and Economics Hangzhou 310018 China Institute of Mathematics and Interdisciplinary Sciences Zhejiang University of Finance and Economics 310018 China Department of Mathematics Faculty of Science Assiut University Assiut 71516 Egypt Centre for Control Systems Vel Tech University Avadi Tamil Nadu Chennai 600062 India Institute of Strategic Industrial Decision Modelling School of Quantitative Sciences Universiti Utara Malaysia Sintok Bukit Kayu Hitam Kedah 06010 Malaysia Faculty of Education and Arts Sohar University Sohar 311 Oman

This research study proposes a novel hyperchaotic finance system with profit margin and then utilizes the Adomian Decomposition Method (ADM) to tackle the solution of the associated Caputo-derivative based fractional-order hyperchaotic finance system with profit margin. Numerical simulations and analyses, such as the evaluation of Lyapunov exponents (LE), the creation of bifurcation diagrams (BD), the complexity analysis (CA), and the 0-1 test, are used to get a full picture of the system. The results of our study demonstrate the occurrence of periodic, chaotic, and hyperchaotic dynamics in the system. Furthermore, the overall criteria for the existence and the uniqueness of the exact solutions for Caputo fractional-order models are presented. In addition, we present a control methodology for the fractional-order hyperchaotic financial system utilizing the Laplace transform and linear feedback control. Significantly, our research showcases a noteworthy correlation between the analytical results and numerical simulations, emphasizing the soundness and effectiveness of our methodology. © 2024 The Author(s)

关键词： Chaotic system Financial system Fractional order Hyperchaos

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Infrequent activities predict economic outcomes in major American cities

Nature Cities

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Nature Cities 2024年 305-314页

作者： Shenhao Wang Esteban Moro Alex ‘Sandy’ Pentland Yunhan Zheng Guang Wang Takahiro Yabe Department of Urban and Regional Planning University of Florida Gainesville Florida USA Media Lab Massachusetts Institute of Technology Cambridge MA USA Institute for Data Systems and Society Massachusetts Institute of Technology Cambridge MA USA Network Science Institute Northeastern University Boston MA USA Department of Civil and Environmental Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Computer Science Florida State University Tallahassee USA Tandon School of Engineering New York University Brooklyn NY USA

Many studies have revealed the predictive power of the most frequent, regular and habitual mobility patterns. However, it remains unclear which components of the mobility patterns contain the most informative signals for predicting disparate economic development across urban areas. Here we use machine learning to predict economic outcomes by analyzing the heterogeneous mobility networks of 687 activities from more than 560,000 anonymized users in Boston, Chicago and Miami. We find that mobility patterns are highly predictive of the current and future economic development in major American cities but, surprisingly, the high predictive power is concentrated on infrequent, irregular and exploratory activities. These predictive activities account for only less than 2% of total visits but successfully explain more than 50% of variation in economic outcomes. Future research should shift more attention from regular visits to irregular activities, and policymakers could leverage these infrequent yet informative activities to manage urban economic development.

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MOST DISCRIMINATIVE STIMULI FOR FUNCTIONAL CELL TYPE CLUSTERING

arXiv

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arXiv 2023年

作者： Burg, Max F. Zenkel, Thomas Vystrčilová, Michaela Oesterle, Jonathan Höfling, Larissa Willeke, Konstantin F. Lause, Jan Müller, Sarah Fahey, Paul G. Ding, Zhiwei Restivo, Kelli Sridhar, Shashwat Gollisch, Tim Berens, Philipp Tolias, Andreas S. Euler, Thomas Bethge, Matthias Ecker, Alexander S. International Max Planck Research School for Intelligent Systems Tübingen Germany Institute of Computer Science University of Göttingen Campus Institute Data Science Germany Tübingen AI Center University of Tübingen Germany Institute of Ophthalmic Research University of Tübingen Germany Centre for Integrative Neuroscience University of Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Germany Hertie Institute for AI in Brain Health University of Tübingen Germany Department of Neuroscience Baylor College of Medicine HoustonTX United States Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States University Medical Center Göttingen Department of Ophthalmology Germany Bernstein Center for Computational Neuroscience Göttingen Germany University of Göttingen Germany Department of Electrical and Computer Engineering Rice University HoustonTX United States Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types can be identified by carefully selected stimuli, but this requires expert domain knowledge and biases the procedure towards previously known cell types. In the visual cortex, it is still unknown what functional types exist and how to identify them. Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed. Here we propose an optimization-based clustering approach using deep predictive models to obtain functional clusters of neurons using Most Discriminative Stimuli (MDS). Our approach alternates between stimulus optimization with cluster reassignment akin to an expectation-maximization algorithm. The algorithm recovers functional clusters in mouse retina, marmoset retina and macaque visual area V4. This demonstrates that our approach can successfully find discriminative stimuli across species, stages of the visual system and recording techniques. The resulting most discriminative stimuli can be used to assign functional cell types fast and on the fly, without the need to train complex predictive models or show a large natural scene dataset, paving the way for experiments that were previously limited by experimental time. Crucially, MDS are interpretable: they visualize the distinctive stimulus patterns that most unambiguously identify a specific type of neuron. © 2023, CC BY-NC-SA.

关键词： Maximum principle

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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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A Mobile Cloud-Based eHealth Scheme

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Computers, Materials & Continua 2020年第4期63卷 31-39页

作者： Yihe Liu Aaqif Afzaal Abbasi Atefeh Aghaei Almas Abbasi Amir Mosavi Shahaboddin Shamshirband Mohammed A.A.Al-qaness Data Recovery Key Laboratory of Sichuan Province College of Computer Science and AINeijiang Normal UniversityNeijiang641100China Department of Software Engineering Foundation UniversityIslamabad44000Pakistan Department of Communication Faculty of Social SciencesUniversity of TehranTehran***Iran Department of Computer Science International Islamic UniversityIslamabad46000Pakistan Institute of Research and Development Duy Tan UniversityDa Nang550000Vietnam Kalman Kando Faculty of Electrical Engineering Obuda UniversityBudapest1034Hungary School of the Built Environment Oxford Brookes UniversityOxfordOX30BPUK Department for Management of Science and Technology Development Ton Duc Thang UniversityHo Chi Minh CityVietnam Faculty of Information Technology Ton Duc Thang UniversityHo Chi Minh CityVietnam School of Computer Science Wuhan UniversityWuhan430072China

Mobile cloud computing is an emerging field that is gaining popularity across borders at a rapid ***,the field of health informatics is also considered as an extremely important *** work observes the collaboration between these two fields to solve the traditional problem of extracting Electrocardiogram signals from trace reports and then performing *** developed system has two front ends,the first dedicated for the user to perform the photographing of the trace *** the photographing is complete,mobile computing is used to extract the *** the signal is extracted,it is uploaded into the server and further analysis is performed on the signal in the *** this is done,the second interface,intended for the use of the physician,can download and view the trace from the *** data is securely held using a password-based authentication *** system presented here is one of the first attempts at delivering the total solution,and after further upgrades,it will be possible to deploy the system in a commercial setting.

关键词： Cloud computing electrocardiograms health-care signal analysis signal processing

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