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arXiv 2021年

作者： Scott, Ryan T. Antonsen, Erik L. Sanders, Lauren M. Hastings, Jaden J.A. Park, Seung-Min Mackintosh, Graham Reynolds, Robert J. Hoarfrost, Adrienne L. Sawyer, Aenor Greene, Casey S. Glicksberg, Benjamin S. Theriot, Corey A. Berrios, Daniel C. Miller, Jack Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Chalk, Stuart Delgado-Aparicio, Guillermo M. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Kalantari, John Khezeli, Kia Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Martin, Hector Garcia Mason, Christopher E. Matar, Mona Mias, George I. Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Parsons-Wingerter, Patricia Prabhu, R.K. Qutub, Amina Ann Rask, Jon Saravia-Butler, Amanda Saria, Suchi Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Yang, Jason H. Zitnik, Marinka Costes, Sylvain V. KBR Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Department of Urology Department of Radiology Stanford University School of Medicine StanfordCA94305 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett FieldCA94035 United States Mortality Research & Consulting Inc. HoustonTX77058 United States Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States UC Space Health Department of Orthopaedic Surgery University of California San Francisco San FranciscoCA94143 United States Center for Health AI Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine Anschutz Medical Campus AuroraCO80045 United States Hasso Plattner Institute for Digital Health at Mount Sinai Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New YorkNY10065 United States Department of Preventive Medicine and Community Health UTMB GalvestonTX77551 United States Human Health and Performance Directorate NASA Johnson Space Center HoustonTX77058 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Data Scienc

Human space exploration beyond low Earth orbit will involve missions of significant distance and duration. To effectively mitigate myriad space health hazards, paradigm shifts in data and space health systems are necessary to enable Earth-independence, rather than Earth-reliance. Promising developments in the fields of artificial intelligence and machine learning for biology and health can address these needs. We propose an appropriately autonomous and intelligent Precision Space Health system that will monitor, aggregate, and assess biomedical statuses;analyze and predict personalized adverse health outcomes;adapt and respond to newly accumulated data;and provide preventive, actionable, and timely insights to individual deep space crew members and iterative decision support to their crew medical officer. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration, on future applications of artificial intelligence in space biology and health. In the next decade, biomonitoring technology, biomarker science, spacecraft hardware, intelligent software, and streamlined data management must mature and be woven together into a Precision Space Health system to enable humanity to thrive in deep space. © 2021, CC BY.

关键词： Earth (planet)

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Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs

arXiv

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arXiv 2021年

作者： Sanders, Lauren M. Yang, Jason H. Scott, Ryan T. Qutub, Amina Ann Martin, Hector Garcia Berrios, Daniel C. Hastings, Jaden J.A. Rask, Jon Mackintosh, Graham Hoarfrost, Adrienne L. Chalk, Stuart Kalantari, John Khezeli, Kia Antonsen, Erik L. Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Delgado-Aparicio, Guillermo M. Glicksberg, Benjamin S. Greene, Casey S. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Mason, Christopher E. Matar, Mona Mias, George I. Miller, Jack Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Park, Seung-Min Parsons-Wingerter, Patricia Prabhu, R.K. Reynolds, Robert J. Saravia-Butler, Amanda Saria, Suchi Sawyer, Aenor Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik Theriot, Corey A. van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Zitnik, Marinka Costes, Sylvain V. Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Center for Emerging and Re-Emerging Pathogens Department of Microbiology Biochemistry and Molecular Genetics Rutgers New Jersey Medical School NewarkNJ07103 United States KBR Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States AI MATRIX Consortium Department of Biomedical Engineering University of Texas San Antonio and UT Health Sciences San AntonioTX78249 United States Biological Systems and Engineering Division Lawrence Berkeley National Lab. BerkeleyCA94608 United States DOE Agile BioFoundry EmeryvilleCA94608 United States Joint BioEnergy Institute EmeryvilleCA94608 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Office of the Center Director NASA Ames Research Center Moffett Field CA94035 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett Field CA94035 United States Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Center for Individualized Medicine Department of Surgery Department of Quantitative Health Sciences Mayo Clinic RochesterMN55905 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin - Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Data Science Analytics Georgia Institute of Technology Lima15024

Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data, and model organisms from both spaceborne and ground-analog studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally autonomous, light, agile, and intelligent to expedite knowledge discovery. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning, and modeling applications which offer key solutions toward these space biology challenges. In the next decade, the synthesis of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modeling and analytics, support maximally autonomous and reproducible experiments, and efficiently manage spaceborne data and metadata, all with the goal to enable life to thrive in deep space. © 2021, CC BY.

关键词： Predictive analytics

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Augmentation of wastewater-based epidemiology with machine learning to support global health surveillance

Nature Water

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Nature Water 2025年 1-11页

作者： Eva Aßmann Christopher Irrgang Hugues Richard Martin Hölzer Timo Greiner Alexander Ullrich Matthew Wade Shelesh Agrawal Susanne Lackner Fabian Amman Sindy Böttcher Markus Landthaler Emanuel Wyler Serghei Mangul Viorel Munteanu Fotis Psomopoulos Maureen Smith Maria Trofimova Max von Kleist Centre for Artificial Intelligence in Public Health Research (ZKI-PH) Robert Koch Institute Berlin Germany Genome Competence Center (MF1) Robert Koch Institute Berlin Germany Department of Infectious Disease Epidemiology Robert Koch Institute Berlin Germany Chief Data Officer Group UK Health Security Agency London UK Chair of Water and Environmental Biotechnology Institute IWAR Department of Civil and Environmental Engineering Sciences Technical University of Darmstadt Darmstadt Germany CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences Vienna Austria Institute for Hygiene and Applied Immunology Medical University of Vienna Vienna Austria Gastroenteritis and Hepatitis Pathogens and Enteroviruses (FG15) Robert Koch Institute Berlin Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) Berlin Germany Institute for Biology Humboldt-Universität zu Berlin Berlin Germany Titus Family Department of Clinical Pharmacy USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences University of Southern California Los Angeles CA USA Department of Biological and Morphofunctional Sciences College of Medicine and Biological Sciences Stefan cel Mare University of Suceava Suceava Romania Department of Computers Informatics and Microelectronics Technical University of Moldova Chisinau Moldova Institute of Applied Biosciences Center for Research and Technology Hellas Thessaloniki Greece Systems Medicine of Infectious Disease (P5) Robert Koch Institute Berlin Germany Department of Mathematics and Computer Science Freie Universität Berlin Berlin Germany

Wastewater-based epidemiology (WBE) has proven to be a valuable tool for monitoring the evolution and spread of global health threats, from pathogens to antimicrobial resistances. Throughout the COVID-19 pandemic, multiple wastewater surveillance programmes have advanced statistical and machine learning methods for detecting pathogens from wastewater sequencing data and correlating measured targets with the represented population to infer meaningful conclusions for public health. Integrating contextual data can account for measurement uncertainties across the WBE workflow that affect the reliability of analyses. However, the broader availability and harmonization of data are major obstacles to method development. Here we review the benefits and limitations of wastewater-related data streams, highlighting the potential of machine learning to leverage these streams for normalization and other WBE applications. We emphasize the relevance of developing global frameworks for integrating WBE with other health surveillance systems and discuss next steps to address current and foreseeable challenges for robust and interpretable machine learning-enhanced WBE.

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SEED: Entity oriented information search and exploration 22

SEED: Entity oriented information search and exploration

引用

22nd International Conference on Intelligent User Interfaces, IUI 2017

作者： Chen, Jun Jacucci, Giulio Chen, Yueguo Ruotsalo, Tuukka School of Information Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Helsinki Institute for Information Technology HIIT Department of Computer Science University of Helsinki Finland

ISBN: (纸本)9781450348935

Entity search and exploration can enrich search user interfaces by presenting relevant information instantly and offering relevant exploration pointers to users. Previous research has demonstrated that large knowledge Graphs allow exploitation and recommendation of explicit links between the entities and other information to improve information access and ranking. However, less attention has been devoted to user interfaces for effectively presenting results, recommending related entities and explaining relations between entities. We introduce a system called SEED which is designed to support entity search and exploration in large knowledge Graphs. We demonstrate SEED using a dataset of hundreds of thousands of movie related entities from the DBpedia knowledge Graph. The system utilizes a graph embedding model for ranking entities and their relations, recommending related entities, and explaining their interrelations. Copyright is held by the author/owner(s).

关键词： knowledge graph

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RDF spreadsheet editor: Get (G)rid of your RDF data entry problems

RDF spreadsheet editor: Get (G)rid of your RDF data entry pr...

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2017 ISWC Posters and Demonstrations and Industry Tracks, ISWC-P and D-Industry 2017

作者： Schröder, Markus Jilek, Christian Hees, Jörn Hertling, Sven Dengel, Andreas GmbH Trippstadter Straße 122 Kaiserslautern67663 Germany Data and Web Science Group University of Mannheim Germany Knowledge-Based Systems Group Department of Computer Science University of Kaiserslautern P.O. Box 3049 Kaiserslautern67653 Germany

Spreadsheets are widely used by knowledge workers, especially in the industrial sector. Their methodology enables a well understood, easy and fast possibility to enter data. As filling out a spreadsheet is more accessible to common knowledge workers than defining RDF statements, in this paper, we present a tool which uses the spreadsheet metaphor to enable various kinds of users to easily create RDF data whether they are RDF experts or novices. Our approach aims at supporting users in manually filling a knowledge base with their expertise. This is usually a collaborative process involving a team of domain experts and knowledge engineers. To immediately see modelling consequences and updates by others, each entry is simultaneously transferred into triple statements.

关键词： Spreadsheets

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BPRIM: An integrated framework for business process management and risk management

引用

computers in Industry 2019年 113卷

作者： Lamine, Elyes Thabet, Rafika Sienou, Amadou Bork, Dominik Fontanili, Franck Pingaud, Herve Toulouse University ISIS Institut National Universitaire Champollion Rue Firmin-OulèsCastres81104 France Toulouse University IMT Mines Albi Department of Industrial Engineering Route de Teillet Albi Cedex 981013 France abamix Consulting – Business Information Systems Unit Stuttgart Germany MARS Laboratory University of Sousse ISITCom Hammam Sousse Tunisia University of Vienna Faculty of Computer Science Research Group Knowledge Engineering Währingerstrasse 29 Vienna1090 Austria Toulouse University CNRS LGC Toulouse Cedex 04F-31432 France

Enterprise engineering deals with the design of processes which aim to improve the structure and efficiency of business organizations. It develops approaches based on modeling techniques, particularly on business process modeling, to ensure the quality and the global consistency of enterprise strategies and expectations. Nowadays, risk consideration in enterprise engineering is a growing concern since the business environment is becoming more and more competitive, complex, and unpredictable. To face this concern, a paradigm named risk-aware business process management (R-BPM) has recently emerged. It seeks to integrate the two traditionally isolated fields of risk management and business process management. Despite the significant benefits that can arise from the use of R-BPM, it suffers from a lack of solid scientific foundations and dedicated tooling. This present research work contributes to bridging that gap in a twofold way: (i) by establishing the BPRIM business process-risk integrated method framework, and (ii) by designing a dedicated tool, named ADOBPRIM which supports the efficient application of the BPRIM framework. This paper first comprehensively presents the foundation of BPRIM which is based on three main components and, secondly, its dedicated tool ADOBPRIM which was designed using the ADOxx meta-modeling platform. An evaluation with a real case study in the health care domain shows the relevance of the methodological framework. © 2019 Elsevier B.V.

关键词： Risk management

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It’s Time to Consider ‘Time’ when Evaluating Recommender-System Algorithms [Proposal]

arXiv

引用

arXiv 2017年

作者： Beel, Joeran Trinity College Dublin Department of Computer Science and Statistics Knowledge and Data Engineering Group ADAPT Centre Ireland

In this position paper, we question the current practice of calculating evaluation metrics for recommender systems as single numbers (e.g. precision p=.28 or mean absolute error MAE = 1.21). We argue that single numbers express only average effectiveness over usually a rather long period (e.g. a year or even longer). This practice provides only a vague and static view of the data. We propose that recommender-system researchers should instead calculate metrics for time-series such as weeks or months, and plot the results in e.g. a line chart. This way, results show how algorithms’ effectiveness develops over time, and hence the results allow drawing more meaningful conclusions about how an algorithm will perform in the future. In this paper, we explain our reasoning, provide an example to illustrate our reasoning and present suggestions for what the community should do next. Copyright © 2017, The Authors. All rights reserved.

关键词： Recommender systems

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Global, regional, and national burden of neck pain, 1990–2020, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021

引用

The Lancet Rheumatology 2024年第3期6卷 e142-e155页

作者： Wu, Ai-Min Cross, Marita Elliott, James M. Culbreth, Garland T. Cousin, Ewerton Haile, Lydia M. Steinmetz, Jaimie D. Hagins, Hailey Kopec, Jacek A. Brooks, Peter M. Woolf, Anthony D. Kopansky-Giles, Deborah R. Walton, David M. Treleaven, Julia M. Dreinhoefer, Karsten E. Betteridge, Neil Abbasifard, Mitra Abbasi-Kangevari, Zeinab Addo, Isaac Yeboah Adesina, Miracle Ayomikun Adnani, Qorinah Estiningtyas Sakilah Aithala, Janardhana P. Alhalaiqa, Fadwa Alhalaiqa Naji Alimohamadi, Yousef Amiri, Sohrab Amu, Hubert Antony, Benny Arabloo, Jalal Aravkin, Aleksandr Y. Asghari-Jafarabadi, Mohammad Atomsa, Gamechu Hunde Azadnajafabad, Sina Azzam, Ahmed Y. Baghdadi, Soroush Balogun, Saliu A. Balta, Asaminew Birhanu Banach, Maciej Banakar, Morteza Barrow, Amadou Bashiri, Azadeh Bekele, Alehegn Bensenor, Isabela M. Bhardwaj, Pankaj Bhat, Ajay Nagesh Bilchut, Awraris Hailu Briggs, Andrew M. Buchbinder, Rachelle Cao, Chao Chaurasia, Akhilanand Chirinos-Caceres, Jesus Lorenzo Christensen, Steffan Wittrup McPhee Coberly, Kaleb Dadras, Omid Dai, Xiaochen de Luca, Katie Dehghan, Azizallah Dong, Huan-Ji Ekholuenetale, Michael Elhadi, Muhammed Eshetu, Habitu Birhan Eskandarieh, Sharareh Etaee, Farshid Fagbamigbe, Adeniyi Francis Fares, Jawad Fatehizadeh, Ali Feizkhah, Alireza Ferreira, Manuela L. Ferreira, Nuno Fischer, Florian Franklin, Richard Charles Ganesan, Balasankar Gebremichael, Mathewos Alemu Gerema, Urge Gholami, Ali Ghozy, Sherief Gill, Tiffany K. Golechha, Mahaveer Goleij, Pouya Golinelli, Davide Graham, Simon Matthew Haj-Mirzaian, Arvin Harlianto, Netanja I. Hartvigsen, Jan Hasanian, Mohammad Hassen, Mohammed Bheser Hay, Simon I. Hebert, Jeffrey J. Heidari, Golnaz Hoveidaei, Amir Human Hsiao, Alexander Kevin Ibitoye, Segun Emmanuel Iwu, Chidozie C.D. Jacob, Louis Janodia, Manthan Dilipkumar Jin, Yingzhao Jonas, Jost B. Joshua, Charity Ehimwenma Kandel, Himal Khader, Yousef Saleh Khajuria, Himanshu Khan, Ejaz Ahmad Khan, Moien A.B. Khatatbeh, Moawiah Mohammad Khateri, Sorour Khayat Kashani, Hamid Reza Khonji, Mohammad Saeid Department of Orthopaedics The Second Affiliated Hospital of Wenzhou Medical University Wenzhou China Faculty of Medicine and Health University of Sydney Sydney NSW Australia Global Alliance for Musculoskeletal Health Sydney NSW Australia Kolling Institute of Medical Research University of Sydney Northern Sydney Local Health District St Leonards NSW Australia Department of Physical Therapy and Human Movement Sciences Northwestern University Chicago IL United States Institute for Health Metrics and Evaluation University of Washington Seattle WA United States School of Population and Public Health University of British Columbia Vancouver BC Canada Arthritis Research Canada Richmond BC Canada US Department of Veterans Affairs (VA) Birmingham AL United States Centre for Health Policy University of Melbourne Melbourne VIC Australia Bone and Joint Research Group Royal Cornwall Hospital Truro United Kingdom Department of Family and Community Medicine University of Toronto Toronto ON Canada Department of Research and Innovation Canadian Memorial Chiropractic College Toronto ON Canada Department of Physical Therapy University of Sydney Sydney NSW Australia School of Physical Therapy Western University London ON Canada School of Health and Rehabilitation Sciences The University of Queensland Brisbane Australia Center of Musculoskeletal Surgery Charité Universitätsmedizin Berlin (Charité Medical University Berlin) Berlin Germany London United Kingdom Department of Internal Medicine Rafsanjan University of Medical Sciences Rafsanjan Iran Clinical Research Development Unit Rafsanjan University of Medical Sciences Rafsanjan Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Tehran University of Medical Sciences Tehran Iran Centre for Social Research in Health University of New South Wales Sydney NSW

Background: Neck pain is a highly prevalent condition that leads to considerable pain, disability, and economic cost. We present the most current estimates of neck pain prevalence and years lived with disability (YLDs) from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) by age, sex, and location, with forecasted prevalence to 2050. Methods: Systematic reviews identified population-representative surveys used to estimate the prevalence of and YLDs from neck pain in 204 countries and territories, spanning from 1990 to 2020, with additional data from opportunistic review. Medical claims data from Taiwan (province of China) were also included. Input data were pooled using DisMod-MR 2.1, a Bayesian meta-regression tool. Prevalence was forecast to 2050 using a mixed-effects model using Socio-demographic Index as a predictor and multiplying by projected population estimates. We present 95% UIs for every metric based on the 2·5th and 97·5th percentiles of 100 draws of the posterior distribution. Findings: Globally, in 2020, neck pain affected 203 million (95% uncertainty interval [UI] 163–253) people. The global age-standardised prevalence rate of neck pain was estimated to be 2450 (1960–3040) per 100 000 population and global age-standardised YLD rate was estimated to be 244 (165–346) per 100 000. The age-standardised prevalence rate remained stable between 1990 and 2020 (percentage change 0·2% [–1·3 to 1·7]). Globally, females had a higher age-standardised prevalence rate (2890 [2330–3620] per 100 000) than males (2000 [1600–2480] per 100 000), with the prevalence peaking between 45 years and 74 years in male and female sexes. By 2050, the estimated global number of neck pain cases is projected to be 269 million (219–322), with an increase of 32·5% (23·9–42·3) from 2020 to 2050. Decomposition analysis of the projections showed population growth was the primary contributing factor, followed by population ageing. Interpretation: Although age-standardi

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A study of complex deep learning networks on high-performance, neuromorphic, and quantum computers

A study of complex deep learning networks on high-performanc...

引用

作者： Potok, Thomas E. Schuman, Catherine Young, Steven Patton, Robert Spedalieri, Federico Liu, Jeremy Yao, Ke-Thia Rose, Garrett Chakma, Gangotree Computational Data Analytics Group Oak Ridge National Laboratory P.O. Box 2008 Oak RidgeTN37831 United States University of Southern California Information Sciences Institute 4676 Admiralty Way Marina del ReyCA90292 United States Department of Electrical Engineering and Computer Science University of Tennessee 1520 Middle Dr KnoxvilleTN37996 United States

Current deep learning approaches have been very successful using convolutional neural networks trained on large graphical-processing-unit-based computers. Three limitations of this approach are that (1) they are based on a simple layered network topology, i.e., highly connected layers, without intra-layer connections;(2) the networks are manually configured to achieve optimal results, and (3) the implementation of the network model is expensive in both cost and power. In this article, we evaluate deep learning models using three different computing architectures to address these problems: quantum computing to train complex topologies, high performance computing to automatically determine network topology, and neuromorphic computing for a low-power hardware implementation. We use the MNIST dataset for our experiment, due to input size limitations of current quantum computers. Our results show the feasibility of using the three architectures in tandem to address the above deep learning *** show that a quantum computer can find high quality values of intra-layer connection weights in a tractable time as the complexity of the network increases, a high performance computer can find optimal layer-based topologies, and a neuromorphic computer can represent the complex topology and weights derived from the other architectures in low power memristive hardware. © 2018 ACM.

关键词： Deep learning

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Flexible Co-Planar Waveguide (CPW)-Fed Y-Shaped Patch UWB Antenna for Off-Body Communication

引用

Journal of Physics: Conference Series 2020年第1期1464卷

作者： S. Kassim H. A. Rahim P. J. Soh M. Abdulmalek M. Jusoh M. H. Jamaluddin N. S. Sabli M. N. Yassin T. Sabapathy F. H. Wee M. N. Osman N. Ismail Bioelectromagnetics Research Group (BioEM) School of Computer and Communication Engineering Universiti Malaysia Perlis (UniMAP) Kampus Pauh Putra Perlis 02600 Arau Perlis Malaysia Advanced Communication Engineering Centre COE School of Computer and Communication Engineering Universiti Malaysia Perlis Kampus Pauh Putra Perlis 02600 Malaysia Department of Engineering and Information Science University of Wollongong in Dubai Block 15 Dubai Knowledge Village Dubai UAE Universiti Teknologi Malaysia Faculty of Electrical Engineering Universiti Teknologi Malaysia (UTM) Johor UTM Skudai MY 81310 Faculty of Mechanical Engineering Universiti Malaysia Pahang (UMP) Pekan Pahang Malaysia School of Computer and Communication Engineering Universiti Malaysia Perlis (UniMAP) Kampus Pauh Putra Perlis 02600 Arau Perlis Malaysia

This paper intends to design an Ultra-Wideband (UWB) antenna for future Internet of Things (IoT) applications for off-body Wireless Body Area Networks (WBAN) communication. An antenna based on the Y-shaped patch fed using co-planar waveguide (CPW) line, with a full ground plane is designed. It is implemented on two different substrates, namely a 5mm thick Rogers RO4350B and a 5-mm-thick felt textile. Parametric analysis of antenna is performed by changing its critical dimensions and monitoring parameters such as gain, bandwidth, efficiency, radiation pattern when using both substrates. Besides that, the bending effects towards reflection coefficient and radiation patterns are also studied. The final patch size with the Y-shaped slot is 36 × 40 mm2 for both substrates. The antenna is capable of providing coverage for the bands from 8 to 10 GHz. Finally, the antenna designed on RO4350B substrate outperforms the antenna designed on felt by about four times in terms of bandwidth, with 3.3 GHz (7.7–11 GHz).

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