Astroinformatics: a Synthesis Between Astronomical Imaging and Information & Communication Technologies
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												  Applications of Digital Image Processing in Real Time WorldINTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 8, ISSUE 12, DECEMBER 2019 ISSN 2277-8616 Applications Of Digital Image Processing In Real Time World B.Sridhar Abstract :-- Digital contents are the essential kind of analyzing, information perceived and which are explained by the human brain. In our brain, one third of the cortical area is focused only to visual information processing. Digital image processing permits the expandable values of different algorithms to be given to the input section and prevent the problems of noise and distortion during the image processing. Hence it deserves more advantages than analog based image processing. Index Terms:-- Agriculture, Biomedical imaging, Face recognition, image enhancement, Multimedia Security, Authentication —————————— —————————— 2 REAL TIME APPLICATIONS OF IMAGE PROCESSING 1 INTRODUCTION This chapter reviews the recent advances in image Digital image processing is dependably a catching the processing techniques for various applications, which more attention field and it freely transfer the upgraded include agriculture, multimedia security, Remote sensing, multimedia data for human understanding and analyzing Computer vision, Medical applications, Biometric of image information for capacity, transmission, and verification, etc,. representation for machine perception[1]. Generally, the stages of investigation of digital image can be followed 2.1 Agriculture and the workflow statement of the digital image In the present situation, due to the huge density of population, gives the horrible results of demand of food, processing (DIP) is displayed in Figure 1. diminishments in agricultural land, environmental variation and the political instability, the agriculture industries are trying to find the new solution for enhancing the essence of the productivity and sustainability.―In order to support and satifisfied the needs of the farmers Precision agriculture is employed [2].
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												  Scientific Data Mining in AstronomySCIENTIFIC DATA MINING IN ASTRONOMY Kirk D. Borne Department of Computational and Data Sciences, George Mason University, Fairfax, VA 22030, USA [email protected] Abstract We describe the application of data mining algorithms to research prob- lems in astronomy. We posit that data mining has always been fundamen- tal to astronomical research, since data mining is the basis of evidence- based discovery, including classification, clustering, and novelty discov- ery. These algorithms represent a major set of computational tools for discovery in large databases, which will be increasingly essential in the era of data-intensive astronomy. Historical examples of data mining in astronomy are reviewed, followed by a discussion of one of the largest data-producing projects anticipated for the coming decade: the Large Synoptic Survey Telescope (LSST). To facilitate data-driven discoveries in astronomy, we envision a new data-oriented research paradigm for astron- omy and astrophysics – astroinformatics. Astroinformatics is described as both a research approach and an educational imperative for modern data-intensive astronomy. An important application area for large time- domain sky surveys (such as LSST) is the rapid identification, charac- terization, and classification of real-time sky events (including moving objects, photometrically variable objects, and the appearance of tran- sients). We describe one possible implementation of a classification broker for such events, which incorporates several astroinformatics techniques: user annotation, semantic tagging, metadata markup, heterogeneous data integration, and distributed data mining. Examples of these types of collaborative classification and discovery approaches within other science disciplines are presented. arXiv:0911.0505v1 [astro-ph.IM] 3 Nov 2009 1 Introduction It has been said that astronomers have been doing data mining for centuries: “the data are mine, and you cannot have them!”.
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												  Health Informatics PrinciplesHealth Informatics Principles Foundational Curriculum: Cluster 4: Informatics Module 7: The Informatics Process and Principles of Health Informatics Unit 2: Health Informatics Principles FC-C4M7U2 Curriculum Developers: Angelique Blake, Rachelle Blake, Pauliina Hulkkonen, Sonja Huotari, Milla Jauhiainen, Johanna Tolonen, and Alpo Vӓrri This work is produced by the EU*US eHealth Work Project. This project has received funding from the European Union’s Horizon 2020 research and 21/60 innovation programme under Grant Agreement No. 727552 1 EUUSEHEALTHWORK Unit Objectives • Describe the evolution of informatics • Explain the benefits and challenges of informatics • Differentiate between information technology and informatics • Identify the three dimensions of health informatics • State the main principles of health informatics in each dimension This work is produced by the EU*US eHealth Work Project. This project has received funding from the European Union’s Horizon 2020 research and FC-C4M7U2 innovation programme under Grant Agreement No. 727552 2 EUUSEHEALTHWORK The Evolution of Health Informatics (1940s-1950s) • In 1940, the first modern computer was built called the ENIAC. It was 24.5 metric tonnes (27 tons) in volume and took up 63 m2 (680 sq. ft.) of space • In 1950 health informatics began to take off with the rise of computers and microchips. The earliest use was in dental projects during late 50s in the US. • Worldwide use of computer technology in healthcare began in the early 1950s with the rise of mainframe computers This work is produced by the EU*US eHealth Work Project. This project has received funding from the European Union’s Horizon 2020 research and FC-C4M7U2 innovation programme under Grant Agreement No.
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												  Draft Common Framework for Earth-Observation DataTHE U.S. GROUP ON EARTH OBSERVATIONS DRAFT COMMON FRAMEWORK FOR EARTH-OBSERVATION DATA Table of Contents Background ..................................................................................................................................... 2 Purpose of the Common Framework ........................................................................................... 2 Target User of the Common Framework ................................................................................. 4 Scope of the Common Framework........................................................................................... 5 Structure of the Common Framework ...................................................................................... 6 Data Search and Discovery Services .............................................................................................. 7 Introduction ................................................................................................................................. 7 Standards and Protocols............................................................................................................... 8 Methods and Practices ............................................................................................................... 10 Implementations ........................................................................................................................ 11 Software ................................................................................................................................
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												  Information- Processing Conceptualizations of Human Cognition: Past, Present, and Future13 Information- Processing Conceptualizations of Human Cognition: Past, Present, and Future Elizabeth E Loftus and Jonathan w: Schooler Historically, scholars have used contemporary machines as models of the mind. Today, much of what we know about human cognition is guided by a three-stage computer model. Sensory memory is attributed with the qualities of a computer buffer store in which individual inputs are briefly maintained until a meaningful entry is recognized. Short-term memory is equivalent to the working memory of a computer, being highly flexible yet having only a limited capacity. Finally, long-term memory resembles the auxiliary store of a computer, with a virtually unlimited capacity for a variety of information. Although the computer analogy provides a useful framework for describing much of the present research on human cogni- tion, it is insufficient in several respects. For example, it does not ade- quately represent the distinction between conscious and non-conscious thought. As an alternative. a corporate metaphor of the mind is suggested as a possible vehiclefor guiding future research. Depicting the mind as a corporation accommodates many aspects of cognition including con- sciousness. In addition, by offering a more familiar framework, a corpo- rate model is easily applied to subjective psychological experience as well as other real world phenomena. Currently, the most influential approach in cognitive psychology is based on analogies derived from the digital computer. The information processing approach has been an important source of models and ideas but the fate of its predecessors should serve to keep us humble concerning its eventual success. In 30 years, the computer-based information processing approach that cur- rently reigns may seem as invalid to the humlln mind liSthe wax-tablet or tl'kphOlIl' switchhoard mmkls do todoy.
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												  Distributed Cognition: Understanding Complex Sociotechnical InformaticsApplied Interdisciplinary Theory in Health Informatics 75 P. Scott et al. (Eds.) © 2019 The authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). doi:10.3233/SHTI190113 Distributed Cognition: Understanding Complex Sociotechnical Informatics Dominic FURNISSa,1, Sara GARFIELD b, c, Fran HUSSON b, Ann BLANDFORD a and Bryony Dean FRANKLIN b, c a University College London, Gower Street, London; UK b Imperial College Healthcare NHS Trust, London; UK c UCL School of Pharmacy, London; UK Abstract. Distributed cognition theory posits that our cognitive tasks are so tightly coupled to the environment that cognition extends into the environment, beyond the skin and the skull. It uses cognitive concepts to describe information processing across external representations, social networks and across different periods of time. Distributed cognition lends itself to exploring how people interact with technology in the workplace, issues to do with communication and coordination, how people’s thinking extends into the environment and sociotechnical system architecture and performance more broadly. We provide an overview of early work that established distributed cognition theory, describe more recent work that facilitates its application, and outline how this theory has been used in health informatics. We present two use cases to show how distributed cognition can be used at the formative and summative stages of a project life cycle. In both cases, key determinants that influence performance of the sociotechnical system and/or the technology are identified. We argue that distributed cognition theory can have descriptive, rhetorical, inferential and application power.
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												  Astroinformatics: Image Processing and Analysis of Digitized Astronomical Data with Web-Based ImplementationASTROINFORMATICS: IMAGE PROCESSING AND ANALYSIS OF DIGITIZED ASTRONOMICAL DATA WITH WEB-BASED IMPLEMENTATION N. Kirov(1;2), O. Kounchev(2), M. Tsvetkov(3) (1)Computer Science Department, New Bulgarian University (2)Institute of Mathematics and Informatics, Bulgarian Academy of Sciences (3)Institute of Astronomy, Bulgarian Academy of Sciences [email protected], [email protected], [email protected] ABSTRACT The newly born area of Astroinformatics has arisen as an interdisciplinary domain from Astronomy and Information and Communication Technologies (ICT). Recently, four institutes of the Bulgarian Academy of Sciences launched a joint project called “Astroinformatics”. The main goal of the project is the development of methods and techniques for preservation and exploitation of the scientific, cultural and historic heritage of the astronomical observations. The Wide-Field Plate Data Base is an ICT project of the Institute of Astronomy, which has been launched in 1991, supported by International Astronomic Union [1]. Now over two million photographic plates, obtained with professional telescopes at 125 observatories worldwide, are identified and collected in this database. Digitization of photographic plates is an actual task for astronomical society. So far 150 000 plates have been digitized through several European research programs. As a result, a huge amount of data is collected (with about 2TB in size). The access, manipulation and data mining of this data is a serious challenge for the ICT community. In this article we discuss goals, tasks and achieve- ments in the area of Astroinformatics. 1 THE WIDE-FIELD PLATE DATA BASE The basic information for all wide-field (> 1o) photographic astronomical plate archives and for their content, stored in many observatories is collected in the Wide-Field Plate Database (WFPDB, skyarchive.org), established in 1991 and developed in the Institute of Astronomy, Bulgar- ian Academy of Sciences.
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												  Astro2020 State of the Profession Consideration White PaperAstro2020 State of the Profession Consideration White Paper Realizing the potential of astrostatistics and astroinformatics September 27, 2019 Principal Author: Name: Gwendolyn Eadie4;5;6;15;17 Email: [email protected] Co-authors: Thomas Loredo1;19, Ashish A. Mahabal2;15;16;18, Aneta Siemiginowska3;15, Eric Feigelson7;15, Eric B. Ford7;15, S.G. Djorgovski2;20, Matthew Graham2;15;16, Zˇeljko Ivezic´6;16, Kirk Borne8;15, Jessi Cisewski-Kehe9;15;17, J. E. G. Peek10;11, Chad Schafer12;19, Padma A. Yanamandra-Fisher13;15, C. Alex Young14;15 1Cornell University, Cornell Center for Astrophysics and Planetary Science (CCAPS) & Department of Statistical Sciences, Ithaca, NY 14853, USA 2Division of Physics, Mathematics, & Astronomy, California Institute of Technology, Pasadena, CA 91125, USA 3Center for Astrophysics j Harvard & Smithsonian, Cambridge, MA 02138, USA 4eScience Institute, University of Washington, Seattle, WA 98195, USA 5DIRAC Institute, Department of Astronomy, University of Washington, Seattle, WA 98195, USA 6Department of Astronomy, University of Washington, Seattle, WA 98195, USA 7Penn State University, University Park, PA 16802, USA 8Booz Allen Hamilton, Annapolis Junction, MD, USA 9Department of Statistics & Data Science, Yale University, New Haven, CT 06511, USA 10Department of Physics & Astronomy, Johns Hopkins University, Baltimore, MD 21218, USA arXiv:1909.11714v1 [astro-ph.IM] 25 Sep 2019 11Space Telescope Science Institute, Baltimore, MD 21218, USA 12Department of Statistics & Data Science Carnegie Mellon University, Pittsburgh,
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												  Ontology Architectures for the Orbital Space Environment and Space Situational Awareness DomainOntology Architectures for the Orbital Space Environment and Space Situational Awareness Domain Robert John ROVETTOa,1 a University of Maryland, College Park Alumnus (2007), The State University of New York at Buffalo Alumnus (2011) American Public University System/AMU, space studies Abstract. This paper applies some ontology architectures to the space domain, specifically the orbital and near-earth space environment and the space situational awareness domain. I briefly summarize local, single and hybrid ontology architectures, and offer potential space ontology architectures for each by showing how actual space data sources and space organizations would be involved. Keywords. Ontology, formal ontology, ontology engineering, ontological architecture, space ontology, space environment ontology, space object ontology, space domain ontology, space situational awareness ontology, data-sharing, astroinformatics. 1. Introduction This paper applies some general ontology architectures to the space domain, specifically the orbital space environment and the space situational awareness (SSA) domain. As the number of artificial satellites in orbit increases, the potential for orbital debris and orbital collisions increases. This spotlights the need for more accurate and complete situational awareness of the space environment. This, in turn, requires gathering more data, sharing it, and analyzing it to generate knowledge that should be actionable in order to safeguard lives and infrastructure in space and on the surface of Earth. Toward this, ontologies may provide one avenue. This paper serves to introduce the space community to some ontology architecture options when considering computational ontology for their space data and space domain-modeling needs. I briefly summarize local, single and hybrid ontology architectures [1][2], and offer potential space domain architectures for each by showing how actual space data sources and space organizations would be involved.
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												  Augmented Cognition for Bioinformatics Problem SolvingAugmented Cognition for Bioinformatics Problem Solving Olga Anna Kuchar and Jorge Reyes-Spindola Michel Benaroch Pacific Northwest National Laboratory Center for Creation and Management of Digital P.O. Box 999, 902 Battelle Blvd. Ventures Richland, WA, 99352 Martin J. Whitman School of Management Syracuse {olga.kuchar, jorge.reyes.spindola}@pnl.gov University, Syracuse, NY, 13244 [email protected] Abstract We describe a new computational cognitive model that has been developed for solving complex problems in bioinformatics. This model addresses bottlenecks in the information processing stages inherent in the bioinformatics domain due to the complex nature of both the volume and type of data and the knowledge required in solving these problems. There is a restriction on the amount of mental tasks a bioinformatician can handle when solving biology problems. Bioinformaticians are overwhelmed at the amount of fluctuating knowledge, data, and tools available in solving problems, but to create an intelligent system to aid in this problem-solving task is difficult because of the constant flux of data and knowledge; thus, bioinformatics poses challenges to intelligent systems and a new model needs to be created to handle such problem-solving issues. To create a problem-solving system for such an environment, one needs to consider the scientists and their environment, in order to determine how humans can function in such conditions. This paper describes our experiences in developing a complex cognitive system to aid biologists in knowledge discovery. We describe the problem domain, evolution of our cognitive model, the model itself, how this model relates to current literature, and summarize our ongoing research efforts.
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												  (IMIA) on Education in Health and Medical InformaticsThis document has been published in Methods of Information in Medicine 39(2000), 267-277. Updated version October 2000, with corrections in footnote of section 4.2 Recommendations of the International Medical Informatics Association (IMIA) on Education in Health and Medical Informatics International Medical Informatics Association, Working Group 1: Health and Medical Informatics Education Abstract: The International Medical Informatics Association (IMIA) agreed on international recommendations in health informatics / medical informatics education. These should help to establish courses, course tracks or even complete programs in this field, to further develop existing educational activities in the various nations and to support international initiatives concerning education in health and medical informatics (HMI), particularly international activities in educating HMI specialists and the sharing of courseware. The IMIA recommendations centre on educational needs for health care professionals to acquire knowledge and skills in information processing and information and communication technology. The educational needs are described as a three-dimensional framework. The dimensions are: 1) professionals in health care (physicians, nurses, HMI professionals, ...), 2) type of specialisation in health and medical informatics (IT users, HMI specialists) and 3) stage of career progression (bachelor, master, ...). Learning outcomes are defined in terms of knowledge and practical skills for health care professionals in their role (a) as IT user and (b) as HMI specialist. Recommendations are given for courses/course tracks in HMI as part of educational programs in medicine, nursing, health care management, dentistry, pharmacy, public health, health record administration, and informatics/computer science as well as for dedicated programs in HMI (with bachelor, master or doctor degree).
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												  Is It Time for Cognitive Bioinformatics?g in Geno nin m i ic M s ta & a P Lisitsa et al., J Data Mining Genomics Proteomics 2015, 6:2 D r f o Journal of o t e l DOI: 10.4172/2153-0602.1000173 o a m n r i c u s o J ISSN: 2153-0602 Data Mining in Genomics & Proteomics Review Article Open Access Is it Time for Cognitive Bioinformatics? Andrey Lisitsa1,2*, Elizabeth Stewart2,3, Eugene Kolker2-5 1The Russian Human Proteome Organization (RHUPO), Institute of Biomedical Chemistry, Moscow, Russian Federation 2Data Enabled Life Sciences Alliance (DELSA Global), Moscow, Russian Federation 3Bioinformatics and High-Throughput Data Analysis Laboratory, Seattle Children’s Research Institute, Seattle, WA, USA 4Predictive Analytics, Seattle Children’s Hospital, Seattle, WA, USA 5Departments of Biomedical Informatics & Medical Education and Pediatrics, University of Washington, Seattle, WA, USA Abstract The concept of cognitive bioinformatics has been proposed for structuring of knowledge in the field of molecular biology. While cognitive science is considered as “thinking about the process of thinking”, cognitive bioinformatics strives to capture the process of thought and analysis as applied to the challenging intersection of diverse fields such as biology, informatics, and computer science collectively known as bioinformatics. Ten years ago cognitive bioinformatics was introduced as a model of the analysis performed by scientists working with molecular biology and biomedical web resources. At present, the concept of cognitive bioinformatics can be examined in the context of the opportunities represented by the information “data deluge” of life sciences technologies. The unbalanced nature of accumulating information along with some challenges poses currently intractable problems for researchers.