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arXiv 2023年

作者： Pavanello, Fabio Marchand, Cedric O'Connor, Ian Orobtchouk, Régis Mandorlo, Fabien Letartre, Xavier Cueff, Sebastien Vatajelu, Elena Ioana Di Natale, Giorgio Cluzel, Benot Coillet, Aurelien Charbonnier, Benoit Noé, Pierre Kavan, Frantisek Zoldak, Martin Szaj, Michal Bienstman, Peter Van Vaerenbergh, Thomas Ruhrmair, Ulrich Flores, Paulo Guerra e Silva, Luis Chaves, Ricardo Silveira, Luis-Miguel Ceccato, Mariano Gizopoulos, Dimitris Papadimitriou, George Karakostas, Vasileios Brando, Axel Cazorla, Francisco J. Canal, Ramon Closas, Pau Gusi-Amigó, Adriá Crovetti, Paolo Carpegna, Alessio Carmona, Tzamn Melendez Di Carlo, Stefano Savino, Alessandro Univ. Lyon Ecole Centrale de Lyon INSA Lyon Université Claude Bernard Lyon 1 CPE Lyon CNRS INL France Univ. Grenoble Alpes CNRS Grenoble INP TIMA Grenoble38000 France ICB UMR CNRS 6303 Université de Bourgogne Franche-Comté Dijon France Univ. Grenoble Alpes CEA LETI Grenoble France ARGOTECH Náchod Czech Republic Ghent University - imec Gent Belgium Hewlett Packard Labs HPE Belgium DiegemB-1831 Belgium Physics Dept. LMU Munchen Munchen Germany INESC-ID Lisboa Lisbon Portugal Department of Computer Science University of Verona Verona Italy Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece Barcelona Supercomputing Center Barcelona Spain Universitat Politècnica de Catalunya Barcelona Spain Albora Technologies SL Barcelona Spain Italy Politecnico di Torino Control and Computer Eng. Department Italy

This special session paper introduces the Horizon Europe NEUROPULS project, which targets the development of secure and energy-efficient RISC-V interfaced neuromorphic accelerators using augmented silicon photonics technology. Our approach aims to develop an augmented silicon photonics platform, an FPGA-powered RISC-V-connected computing platform, and a complete simulation platform to demonstrate the neuromorphic accelerator capabilities. In particular, their main advantages and limitations will be addressed concerning the underpinning technology for each platform. Then, we will discuss three targeted use cases for edge-computing applications: Global National Satellite System (GNSS) anti-jamming, autonomous driving, and anomaly detection in edge devices. Finally, we will address the reliability and security aspects of the stand-alone accelerator implementation and the project use cases. Copyright © 2023, The Authors. All rights reserved.

关键词： Neural networks

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Removed: VitSeg: Weakly supervised vitiligo segmentation in skin image

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computerized medical imaging and graphics : the official journal of the computerized Medical Imaging Society 2020年 85卷 101779页

作者： Zhangxing Bian Siyu Xia Chao Xia Ming Shao Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing China Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA. Electronic address: jasonbian.zx@***. Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing China. Electronic address: xsy@***. School of Electronics Information and Electrical Engineering ShangHai Jiao Tong University Shanghai China. Electronic address: xiabc612@***. Dept. of Computer and Information Science University of Massachusetts Dartmouth Dartmouth MA USA. Electronic address: mshao@umassd.edu.

This article has been removed: please see Elsevier Policy on Article Withdrawal (https://***/about/our-business/policies/article-withdrawal). This article has been removed at the request of the Authors and Editor-in-Chief because complete consent was not obtained by the authors in accordance with journal policy prior to publication. The authors and the journal sincerely apologize for this oversight.

关键词： Convolutional neural network Saliency propagation Skin lesion Vitiligo Weakly supervised segmentation

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Royal Academy of Medicine in Ireland - Section of Bioengineering - Proceedings of meeting held in Dundalk, Co. Louth, in February 1998.

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IRISH JOURNAL OF MEDICAL science 1998年第4期167卷 256-276页

作者： [Anonymous] Orthopaedic Research Unit The Queen’s University of Belfast School of Biomedical Sciences The Queen’s University of Belfast Howmedica International Ltd. Raheen Industrial Estate Limerick Department of Mechanical Engineering Trinity College Dublin 2 Mater Hospital Dublin 7 Orthopaedic Research Laboratory Århus University Hospital Denmark University of Limerick Limerick Limerick Regional Hospital Raheen Limerick Boston Scientific Ireland Ltd. Galway Department of Mechanical Engineering University College Dublin Dublin Department of Electrical and Electronic Engineering University College Dublin Dublin Bioengineering Research Centre Department of Mechanical Engineering University College Dublin Dublin Northern Ireland Bio-Engineering Centre (NIBEC) School of Electrical and Mechanical Engineering University of Ulster Newtownabbey Department of Mechanical & Manufacturing Engineering The Ashby Building The Queen’s University of Belfast Johnson and Johnson Professional Orthopaedic Division Queensway England Department of Cardiology and Department of Medical Physics & Bioengineering St. James’s Hospital Dublin 8 Burke House Ellison Street Castlebar Royal College of Surgeons in Ireland Department of Surgery Beaumont Hospital Dublin Department of Electronic and Electrical Engineering Trinity College Dublin 2 Department of Aeronautical Engineering The Queen’s University of Belfast Vascular Unit Belfast City Hospital Department of Medical Physics and Bioengineering St. James’s Hospital Dublin 8 Centre for Biomedical Electronics Department of Electronic and Computer Engineering University of Limerick Limerick Department of Mechanical and Aeronautical Engineering University of Limerick Limerick School of Health Sciences University of Ulster Ireland School of Physiotherapy University of Ulster Newtownabbey Clinical Engineering NI Medical Physics Agency Belfast and Radiology Royal Victoria Hospital Musgrave Park Hospital University of Limerick Biomedical Institute (ULBMI) Uni

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Healthcare Informatics Society of Ireland

引用

Irish Journal of Medical science 1999年第4期168卷 276-282页

作者： Rigby, M. Sheridan, A. Draper, R. Stephens, G. Gaffney, P. Grimson, J. Avalos, G. Comerford, F. R. Kanagaratnam, B. Dineen, B. Hamilton, I. Jung, B. Felton, E. Berry, D. Grimson, W. Rice, D. H. Healy, M. Bruce, A. Kelly, J. Carroll, T. Grogan, A. Hurl, G. A. Hyland, D. Roberts, J. Brazil, J. Callanan, I. Devitt, A. O’Brien, T. O’Sullivan, U. M. Peyton, K. Wilson, M. J. Collins, R. Crawley, L. Foley, D. Strachan, H. McBrearty, P. Murnane, R. Murphy, E. Keane, V. Mulvany, F. Sweeney, P. D. Kenny, D. O’Connell, P. R. Balfe, P. Minogue, G. Centre for Health Planning & Management Keele University Keele Staffordshire Britain St. John of God Hospital Stillorgan Hospitaller Order of St. John of God Stillorgan Dept. of Computer Science Trinity College Dublin Clinical Chemistry Laboratory Tallaght Hospital Tallaght Dublin 24 Department of Experimental Medicine Faculty of Medicine National University of Ireland Galway Department of Health Promotion Faculty of Medicine National University of Ireland Galway School of Control Systems & Electrical Engineering Dublin Institute of Technology Ireland Shared Medical Systems Malvern USA Healy Services Dublin Universal Learning Systems Ltd. Dublin The Rotunda Hospital Dublin Mater Misericordiae Hospital Dublin Institute for Healthcare Informatics Dublin First Consulting Group Macclesfield Britain Virus Reference Laboratory UCD Belfield Dublin 4 Cappagh Orthopaedic Hospital Dublin Ireland Southern Health Board Cork Mater Misericordiae Hospital Dublin 7 DSS Dublin Ltd. Ireland University College Dublin Ireland Mary Aikenhead School of Nursing St. Vincent’s Hospital Dublin Blackrock Clinic Dublin Borders Community Health Services NHS Trust Britain James Connolly Memorial Hospital Blanchardstown Dublin 15 Kildare and West Wicklow Mental Health Service for People with A Learning Disability Eastern Health Board Dublin Health Research Board Dublin General Practice Research Unit The Medical Centre Dungloe Dept. of Clinical Biochemistry Our Lady’s Hospital for Sick Children Crumlin Dublin 12

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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LANDING AN UNMANNED AIR VEHICLE: VISION BASED MOTION ESTIMATION AND NONLINEAR control

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Asian Journal of control 2008年第3期1卷

作者： Omid Shakernia Yi Ma T. John Koo Shankar Sastry Dept. of Electrical Engineering & Computer Science University of California at Berkeley Berkeley CA94720-1774 U.S.A. Tak-Kuen John Koo received the B.Eng. degree in 1992 in Electronic Engineering and the M.Phil. in 1994 in Information Engineering both from the Chinese University of Hong Kong. From 1994 to 1995 he was a graduate student in Signal and Image Processing Institute at the University of Southern California. He is currently a Ph.D. Candidate in Electrical Engineering and Computer Sciences at the University of California at Berkeley. His research interests include nonlinear control theory hybrid systems inertial navigation systems with applications to unmanned aerial vehicles. He received the Distinguished M.Phil. Thesis Award of the Faculty of Engineering The Chinese University of Hong Kong in 1994. He was a consultant of SRI International in 1998. Currently he is the team leader of the Berkeley AeRobot Team and a delegate of The Graduate Assembly University of California at Berkeley. He is a student member of IEEE and SIAM. S. Shankar Sastry received his Ph.D. degree in 1981 from the University of California Berkeley. He was on the faculty of MIT from 1980-82 and Harvard University as a Gordon McKay professor in 1994. He is currently a Professor of Electrical Engineering and Computer Sciences and Bioengineering and Director of the Electronics Research Laboratory at Berkeley. He has held visiting appointments at the Australian National University Canberra the University of Rome Scuola Normale and University of Pisa the CNRS laboratory LAAS in Toulouse (poste rouge) and as a Vinton Hayes Visiting fellow at the Center for Intelligent Control Systems at MIT. His areas of research are nonlinear and adaptive control robotic telesurgery control of hybrid systems and biological motor control. He is a coauthor (with M. Bodson) of “Adaptive Control: Stability Convergence and Robustness Prentice Hall 1989.” and (with R. Murray and Z. Li) of “A Mathematical Introduction to Robotic Manipulati

In this paper, we use computer vision as a feedback sensor in a control loop for landing an unmanned air vehicle (UAV) on a landing pad. The vision problem we address here is then a special case of the classic ego-motion estimation problem since all feature points lie on a planar surface (the landing pad). We study together the discrete and differential versions of the ego-motion estimation, in order to obtain both position and velocity of the UAV relative to the landing pad. After briefly reviewing existing algorithm for the discrete case, we present, in a unified geometric framework, a new estimation scheme for solving the differential case. We further show how the obtained algorithms enable the vision sensor to be placed in the feedback loop as a state observer for landing control. These algorithms are linear, numerically robust, and computationally inexpensive hence suitable for real-time implementation. We present a thorough performance evaluation of the motion estimation algorithms under varying levels of image measurement noise, altitudes of the camera above the landing pad, and different camera motions relative to the landing pad. A landing controller is then designed for a full dynamic model of the UAV. Using geometric nonlinear control theory, the dynamics of the UAV are decoupled into an inner system and outer system. The proposed control scheme is then based on the differential flatness of the outer system. For the overall closed-loop system, conditions are provided under which exponential stability can be guaranteed. In the closed-loop system, the controller is tightly coupled with the vision based state estimation and the only auxiliary sensor are accelerometers for measuring acceleration of the UAV. Finally, we show through simulation results that the designed vision-in-the-loop controller generates stable landing maneuvers even for large levels of image measurement noise. Experiments on a real UAV will be presented in future work.

关键词： Motion estimation computer vision based control unmanned air vehicle differential flatness

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Intelligent multi-modal systems

British Telecom technology journal

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British Telecom technology journal 1998年第3期16卷 134-144页

作者： Tsui, K.C. Azvine, B. Djian, D. Voudouris, C. Xu, L.Q. Hong Kong Polytechnic University Hong Kong University of Essex King's College London United Kingdom BT Laboratories United Kingdom ATM Traffic Management United Kingdom Manchester University United Kingdom Dynamics and Control Research Group Engineering Department Manchester University United Kingdom Contr. and Instrum. Research Group Staffordshire University United Kingdom Intelligent Systems Research Unit BT Laboratories United Kingdom Dept. of Engineering Mathematics Bristol University United Kingdom Europ. Netwk. Excellence U. Institut d'Informatique d'Entreprise France Oxford University Robotics Research Group United Kingdom University of Nice France Ecole des Mines de Paris France Natl. Technical University of Athens Greece University of Essex United Kingdom Department of Computer Science University of Essex United Kingdom Intelligent Systems Research Group BT Laboratories United Kingdom Southeast University Nanjing China Institute of Acoustics Chinese Academy of Sciences Beijing China University of East Anglia United Kingdom University of Sussex King's College London United Kingdom University of Abertay Dundee United Kingdom

Research in human/computer interaction has mainly focused on natural language, text, speech and vision primarily in isolation. Recently there have been a number of research projects that have concentrated on the integration of such modalities using intelligent reasoners. The rationale is that many inherent ambiguities in single modes of communication can be resolved if extra information is available. This paper describes an intelligent multi-modal system called the Smart Work Manager. The main characteristics of the Smart Work Manager are that it can process speech, text, face images, gaze information and simulated gestures rising the mouse as input modalities, and its output is in the form of speech, text or graphics. The main components of the system are the reasoner, a speech system, a vision system, an integration platform and the application interface. The overall architecture of the system will be described together with the integration platform and the components of the system which include a non-intrusive neural network based gaze-tracking system. The paper concludes with a discussion on the applicability of such systems to intelligent human/computer interaction and lessons learnt in terms of reliability and efficiency.

关键词： Learning systems

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control SYSTEM DESIGN CONSIDERING A TRADEOFF BETWEEN EVALUATED UNCERTAINTY RANGES AND control PERFORMANCE

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Asian Journal of control 2008年第1期1卷

作者： Y. Wakasa Y. Yamamoto Dept. of Applied Analysis and Complex Dynamical Systems Graduate School of Informatics Kyoto University Kyoto Japan. Yuji Wakasa was born in Okayama Japan in 1968. He received the B.S. and M.S. degrees in engineering from Kyoto university Japan in 1992 and 1994 respectively. From 1994 to 1998 he was a Research Associate in the Department of Information Technology Okayama University. Since April 1998 he has been a Research Associate in the Graduate School of Informatics Kyoto University. His current research interests include robust control and control system design via mathematical programming. Yutaka Yamamoto received his B.S. and M.S. degrees in engineering from Kyoto University Kyoto Japan in 1972 and 1974 respectively and the M.S. and Ph.D. degree in mathematics from the University of Florida in 1976 and 1978 respectively. From 1978 to 1987 he was with Department of Applied Mathematics and Physics Kyoto University and from 1987 to 1997 with Department of Applied System Science. Since 1998 he is a professor at the current position. His current research interests include realization and robust control of distributed parameter systems learning control sampled-data systems and digital signal processing. Dr. Yamamoto is a receipient of the Sawaragi memorial paper award (1985) the Outstanding Paper Award of SICE (1987) Best Author Award of SICE (1990) the George Axelby Outstanding Paper Award of IEEE CSS in 1996 Takeda Paper Prize of SICE in 1997. He is a Fellow of IEEE. He was an associate editor of Automatica. He is currently an associate editor of IEEE Transactions on Automatic Control Systems and Control Letters and Mathematics of Control Signals and Systems. He is a member of the IEEE the Society of Instrument and Control Engineers (SICE) and the Institute of Systems Control and Information Engineers.

This paper presents a design method of control systems such that a designer can flexibly take account of tradeoffs between evaluated uncertainty ranges and the level of control performance. The problem is reduced to a BMI problem and approximately solved by LMIs. The convergence of the proposed approximation is proved in a modified sense. A numerical example shows the effectiveness of the proposed method in comparison with the standard robust control.

关键词： control system design LMI BMI tradeoff curves

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROscience 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Ε Lupi: measuring the heartbeat of a doubly-magnetic massive binary with BRITE-Constellation

arXiv

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arXiv 2019年

作者： Pablo, H. Shultz, M. Fuller, J. Wade, G.A. Paunzen, E. Mathis, S. Le Bouquin, J.-B. Pigulski, A. Handler, G. Alecian, E. Kuschnig, R. Moffat, A.F.J. Neiner, C. Popowicz, A. Rucinski, S. Smolec, R. Weiss, W. Zwintz, K. American Association of Variable Star Observers 49 Bay State Road CambridgeMA02138 United States Department of Physics and Astronomy University of Delaware 217 Sharp Lab NewarkDE19716 United States Department of Physics and Astronomy Uppsala University Box 516 Uppsala75120 TAPIR Walter Burke Institute for Theoretical Physics Mailcode 350-17 Caltech PasadenaCA91125 United States Kavli Institute for Theoretical Physics Kohn Hall University of California Santa BarbaraCA93106 United States Royal Military College of Canada Dept. of Physics and Space Science KingstonONK7K 7B4 Canada Department of Theoretical Physics and Astrophysics Masaryk University Kotlářská 2 BrnoCZ-611 37 Czech Republic IRFU CEA Université Paris-Saclay Gif-sur-YvetteF-91191 France Université Paris Diderot AIM Sorbonne Paris Cité CEA CNRS Gif-sur-YvetteF-91191 France LESIA Observatoire de Paris PSL University CNRS Sorbonne Université Université de Paris 5 place Jules Janssen MeudonF-92195 France Université Grenoble Alpes CNRS IPAG Grenoble38000 France Department of Astronomy University of Michigan Ann ArborMI48109 United States Instytut Astronomiczny Uniwersytet Wroclawski Kopernika 11 Wroclaw51-622 Poland Centrum Astronomiczne im. M. Kopernika Polska Akademia Nauk Bartycka 18 Warszawa00-716 Poland Graz University of Technology Institute of Communication Networks and Satellite Communications Inffeldgasse 12 Graz8010 Austria Université de Montréal C.P. 6128 Succ. Centre-Ville MontréalQCH3C 3J7 Canada Silesian University of Technology Institute of Automatic Control Gliwice Akademicka 16 Poland Dept. of Astronomy and Astrophysics University of Toronto 50 St George Street TorontoONM5S 3H4 Canada Nicolaus Copernicus Astronomical Center Bartycka 18 Warszawa00-716 Poland Institut für Astrophysik Universität Wien Türkenschanzstrasse 17 Wien1180 Austria Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstrasse 25/8 Innsbruck

Ε Lupi A is a binary system consisting of two main sequence early B-type stars Aa and Ab in a short period, moderately eccentric orbit. The close binary pair is the only doubly-magnetic massive binary currently known. Using photometric data from the BRITE-Constellation we identify a modest heartbeat variation. Combining the photometry with radial velocities of both components we determine a full orbital solution including empirical masses and radii. These results are compared with stellar evolution models as well as interferometry and the differences discussed. We also find additional photometric variability at several frequencies, finding it unlikely these frequencies can be caused by tidally excited oscillations. We do, however, determine that these signals are consistent with gravity mode pulsations typical for slowly pulsating B stars. Finally we discuss how the evolution of this system will be affected by magnetism, determining that tidal interactions will still be dominant. Copyright © 2019, The Authors. All rights reserved.

关键词： Photometry

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