Detecció de prominència utilitzant el moviment de persones amb visió zenital

La detecció de prominència d’un espai visual és una tasca que tradicionalment ha estat abordada mitjançant l’anàlisi de l’espai amb tècniques de processat d’imatge, aquestes tècniques es basen a estudiar característiques com el color, la intensitat, la forma, l’orientació entre d’altres per acabar generant un mapa d’intensitats on cada punt representa un nivell d’interès. De forma alternativa, en aquest treball es pretén analitzar el moviment de persones dins un espai per tal de detectar la prominència generada per un grup d’objectes. La primera fase del treball ha consistit a captar vídeos de persones durant una exposició a un espai amb objectes d’interès i extreure informació dels mateixos obtenint diferents conjunts de dades. La segona part, ha estat basada en el desenvolupament i avaluació de mètodes que utilitzen els conjunts de dades anteriors per tal d’estimar el nivell d’interès aportat per cada objecte. Els resultats obtinguts mostren com un dels mètodes s’acosta força a unes estadístiques realitzades en la primera fase.La detección de prominencia de un espacio visual es una tasca que tradicionalmente ha sido abordada mediante el análisis del espacio con técnicas de procesado de imagen,estas técnicas están basadas en estudiar características como el color, la intensidad, la forma, la orientación entre otras para finalmente generar un mapa de intensidades donde cada punto representa un nivel de interés. De forma alternativa, en este trabajo se pretende analizar el movimiento de persones dentro de un espacio para poder detectar la prominencia generada por un grupo de objetos. La primera fase del trabajo ha consistido en captar vídeos de personas durante una exposición a un espacio con objetos de interés i extraer información de los mismos obteniendo diferentes conjuntos de datos. La segunda parte, ha sido basada en el desarrollo i avaluación de métodos que utilizan los conjuntos de datos anteriores con el fin de detectar el nivel de interés aportado por cada objeto. Los resultados obtenidos muestran como uno de los métodos se acerca bastante a unas estadísticas realizadas en la primera fase.The salience detection of a visual space is a task that traditionally has been solved by the analysis of the space with image processing techniques, these techniques are based on the study of specifications like color, intensity, orientation and so on to finally end with the generation of an intensity map where every point represents a level of interest. Alternatively, this project aims to analyze the movement of people in a room with the purpose of detecting the salience generated by a set of objects.The first phase of the project has consisted of recording people during an exposition in a space with objects of interest and extract information from those in order to get different data sets. The second phase, has been based on the development and evaluation of methods that use the previous data sets to estimate the interest level contributed by each object. The results show that one of the methods is similar to the statistics made in the first phase

SLAM-based 3D outdoor reconstructions from lidar data

The use of depth (RGBD) cameras to reconstruct large outdoor environments is not feasible due to lighting conditions and low depth range. LIDAR sensors can be used instead. Most state of the art SLAM methods are devoted to indoor environments and depth (RGBD) cameras. We have adapted two SLAM systems to work with LIDAR data. We have compared the systems for LIDAR and RGBD data by performing quantitative evaluations. Results show that the best method for LIDAR data is RTAB-Map with a clear difference. Additionally, RTAB-Map has been used to create 3D reconstructions with and without photometry from a visible color camera. This proves the potential of LIDAR sensors for the reconstruction of outdoor environments for immersion or audiovisual production applicationsPeer ReviewedPostprint (author's final draft

Experimental confirmation of efficient island divertor operation and successful neoclassical transport optimization in Wendelstein 7-X

We present recent highlights from the most recent operation phases of Wendelstein 7-X, the most advanced stellarator in the world. Stable detachment with good particle exhaust, low impurity content, and energy confinement times exceeding 100 ms, have been maintained for tens of seconds. Pellet fueling allows for plasma phases with reduced ion-temperature-gradient turbulence, and during such phases, the overall confinement is so good (energy confinement times often exceeding 200 ms) that the attained density and temperature profiles would not have been possible in less optimized devices, since they would have had neoclassical transport losses exceeding the heating applied in W7-X. This provides proof that the reduction of neoclassical transport through magnetic field optimization is successful. W7-X plasmas generally show good impurity screening and high plasma purity, but there is evidence of longer impurity confinement times during turbulence-suppressed phases.This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014-2018 and 2019-2020 under Grant Agreement No. 633053.Peer ReviewedArticle signat per 497 autors/es: Thomas Sunn Pedersen1,2,∗ , I. Abramovic3, P. Agostinetti4, M. Agredano Torres1, S. Äkäslompolo1, J. Alcuson Belloso1, P. Aleynikov1, K. Aleynikova1, M. Alhashimi1, A. Ali1, N. Allen5, A. Alonso6, G. Anda7, T. Andreeva1, C. Angioni8, A. Arkhipov8, A. Arnold1, W. Asad8, E. Ascasibar6, M.-H. Aumeunier9, K. Avramidis10, E. Aymerich11, S.-G. Baek3, J. Bähner1, A. Baillod12, M. Balden1, M. Balden8, J. Baldzuhn1, S. Ballinger3, M. Banduch1, S. Bannmann1, A. Banon Navarro8, A. Bañon Navarro ´ 1, T. Barbui13, C. Beidler1, C. Belafdil9, A. Bencze7, A. Benndorf1, M. Beurskens1, C. Biedermann1, O. Biletskyi14, B. Blackwell15, M. Blatzheim1, T. Bluhm1, D. Böckenhoff1, G. Bongiovi16, M. Borchardt1, D. Borodin17, J. Boscary8, H. Bosch1,18, T. Bosmann19, B. Böswirth8, L. Böttger1, A. Bottino8, S. Bozhenkov1, R. Brakel1, C. Brandt1, T. Bräuer1, H. Braune1, S. Brezinsek17, K. Brunner1, S. Buller1, R. Burhenn1, R. Bussiahn1, B. Buttenschön1, A. Buzás7, V. Bykov1, I. Calvo6, K. Camacho Mata1, I. Caminal20, B. Cannas11, A. Cappa6, A. Carls1, F. Carovani1, M. Carr21, D. Carralero6, B. Carvalho22, J. Casas20, D. Castano-Bardawil17, F. Castejon6, N. Chaudhary1, I. Chelis23, A. Chomiczewska24, J.W. Coenen13,17, M. Cole1, F. Cordella25, Y. Corre9, K. Crombe26, G. Cseh7, B. Csillag7, H. Damm1, C. Day10, M. de Baar27, E. De la Cal6, S. Degenkolbe1, A. Demby13, S. Denk3, C. Dhard1, A. Di Siena8,28, A. Dinklage12, T. Dittmar17, M. Dreval14, M. Drevlak1, P. Drewelow1, P. Drews17, D. Dunai7, E. Edlund3, F. Effenberg29, G. Ehrke1, M. Endler1, D.A. Ennis5, F.J. Escoto6, T. Estrada6, E. Fable8, N. Fahrenkamp1, A. Fanni11, J. Faustin1, J. Fellinger1, Y. Feng1, W. Figacz4, E. Flom13, O. Ford1, T. Fornal24, H. Frerichs13, S. Freundt1, G. Fuchert1, M. Fukuyama30, F. Füllenbach1, G. Gantenbein10, Y. Gao1, K. Garcia13, J.M. García Regaña6, I. García-Cortés6, J. Gaspar31, D.A. Gates29, J. Geiger1, B. Geiger13, L. Giudicotti32, A. González6, A. Goriaev26,33, D. Gradic1, M. Grahl1, J.P. Graves12, J. Green13, E. Grelier9, H. Greuner8, S. Groß1, H. Grote1, M. Groth34, M. Gruca24, O. Grulke1,35, M. Grün1, J. Guerrero Arnaiz1, S. Günter8, V. Haak1, M. Haas1, P. Hacker1, A. Hakola36, A. Hallenbert1, K. Hammond29, X. Han17,37, S.K. Hansen3, J.H. Harris38, H. Hartfuß1, D. Hartmann1, D. Hathiramani1, R. Hatzky8, J. Hawke39, S. Hegedus7, B. Hein8, B. Heinemann8, P. Helander12, S. Henneberg1, U. Hergenhahn8,40, C. Hidalgo6, F. Hindenlang8, M. Hirsch1, U. Höfel1, K.P. Hollfeld17, A. Holtz1, D. Hopf8, D. Höschen17, M. Houry9, J. Howard19, X. Huang41, M. Hubeny17, S. Hudson29, K. Ida9, Y. Igitkhanov10, V. Igochine8, S. Illy10, C. Ionita-Schrittwieser42, M. Isobe39, M. Jabłczynska ´ 24, S. Jablonski24, B. Jagielski1, M. Jakubowski1, A. Jansen van Vuuren1, J. Jelonnek10, F. Jenko8, F. Jenko8, T. Jensen35, H. Jenzsch1, P. Junghanns8, J. Kaczmarczyk24, J. Kallmeyer1, U. Kamionka1, M. Kandler8, S. Kasilov43, Y. Kazakov26, D. Kennedy1, A. Kharwandikar1, M. Khokhlov1, C. Kiefer8, C. Killer1, A. Kirschner17, R. Kleiber1, T. Klinger12, S. Klose1, J. Knauer1, A. Knieps17, F. Köchl44, G. Kocsis7, Ya.I. Kolesnichenko45, A. Könies1, R. König1, J. Kontula34, P. Kornejew1, J. Koschinsky, M.M. Kozulia14, A. Krämer-Flecken17, R. Krampitz1, M. Krause1, N. Krawczyk24, T. Kremeyer1, L. Krier10, D.M. Kriete5, M. Krychowiak1, I. Ksiazek46, M. Kubkowska24, M. Kuczynski1, G. Kühner1, A. Kumar15, T. Kurki-Suonio34, S. Kwak1, M. Landreman47, P.T. Lang8, A. Langenberg1, H.P. Laqua12, H. Laqua1, R. Laube1, S. Lazerson1, M. Lewerentz1, C. Li17, Y. Liang17, Ch. Linsmeier17, J. Lion1, A. Litnovsky17,48, S. Liu37, J. Lobsien1, J. Loizu12, J. Lore38, A. Lorenz1, U. Losada6, F. Louche26, R. Lunsford29, V. Lutsenko45, M. Machielsen12, F. Mackel8, J. Maisano-Brown3, O. Maj8, D. Makowski49, G. Manduchi50, E. Maragkoudakis6, O. Marchuk17, S. Marsen1, E. Martines4, J. Martinez-Fernandez6, M. Marushchenko1, S. Masuzaki41, D. Maurer5, M. Mayer8, K.J. McCarthy6, O. Mccormack4, P. McNeely1, H. Meister8, B. Mendelevitch8, S. Mendes1, A. Merlo1, A. Messian26, A. Mielczarek49, O. Mishchenko1, B. Missal1, R. Mitteau9, V.E. Moiseenko14, A. Mollen1, V. Moncada9, T. Mönnich1, T. Morisaki41, D. Moseev1, G. Motojima41, S. Mulas6, M. Mulsow1, M. Nagel1, D. Naujoks1, V. Naulin35, T. Neelis19, H. Neilson29, R. Neu8, O. Neubauer17, U. Neuner1, D. Nicolai17, S.K. Nielsen35, H. Niemann1, T. Nishiza1, T. Nishizawa1, T. Nishizawa8, C. Nührenberg1, R. Ochoukov8, J. Oelmann17, G. Offermanns17 K. Ogawa41, S. Okamura41, J. Ölmanns17, J. Ongena26, J. Oosterbeek1, M. Otte1, N. Pablant29, N. Panadero Alvarez6, N. Panadero Alvarez6, A. Pandey1, E. Pasch1, R. Pavlichenko14, A. Pavone1, E. Pawelec46, G. Pechstein1, G. Pelka24, V. Perseo1, B. Peterson41, D. Pilopp1, S. Pingel1, F. Pisano11, B. Plöckl8, G. Plunk1, P. Pölöskei1, B. Pompe2, A. Popov51, M. Porkolab3, J. Proll19, M.J. Pueschel19,27, M.-E. Puiatti52, A. Puig Sitjes1, F. Purps1, K. Rahbarnia1, M. Rasinski ´ 17, J. Rasmussen35, A. Reiman29, F. Reimold1, M. Reisner8, D. Reiter17, M. Richou9, R. Riedl8, J. Riemann1, K. Riße1, G. Roberg-Clark1, V. Rohde8, J. Romazanov17, D. Rondeshagen1, P. Rong1, L. Rudischhauser1, T. Rummel1, K. Rummel1, A. Runov1, N. Rust1, L. Ryc24, P. Salembier20, M. Salewski35, E. Sanchez6, S. Satake41, G. Satheeswaran17, J. Schacht1, E. Scharff1, F. Schauer8, J. Schilling1, G. Schlisio1, K. Schmid8, J. Schmitt5, O. Schmitz13, W. Schneider1, M. Schneider1, P. Schneider8, R. Schrittwieser42, T. Schröder1, M. Schröder1, R. Schroeder1, B. Schweer26, D. Schwörer1, E. Scott1, E. Scott8, B. Shanahan1, G. Sias11, P. Sichta29, M. Singer1, P. Sinha29, S. Sipliä34, C. Slaby1, M. Sleczka53, H. Smith1, J. Smoniewski54, E. Sonnendrücker8, M. Spolaore4, A. Spring1, R. Stadler8, D. Stanczak24, T. Stange1, I. Stepanov26, L. Stephey13, J. Stober8, U. Stroth8,55, E. Strumberger8, C. Suzuki41, Y. Suzuki41, J. Svensson1, T. Szabolics7, T. Szepesi7, M. Szücs7, F.L. Tabares6, N. Tamura41, A. Tancetti35, C. Tantos10, J. Terry3, H. Thienpondt6, H. Thomsen1, M. Thumm10, J.M. Travere9, P. Traverso5, J. Tretter8, E. Trier8, H. Trimino Mora1, T. Tsujimura41, Y. Turkin1, A. Tykhyi45, B. Unterberg17, P. van Eeten1, B.Ph. van Milligen6, M. van Schoor26, L. Vano1, S. Varoutis10, M. Vecsei7, L. Vela56, J.L. Velasco6, M. Vervier17, N. Vianello50, H. Viebke1, R. Vilbrandt1, G. Vogel8, N. Vogt1, C. Volkhausen1, A. von Stechow1, F. Wagner1, E. Wang17, H. Wang57, F. Warmer1, T. Wauters26, L. Wegener1, T. Wegner1, G. Weir1, U. Wenzel1, A. White3, F. Wilde1, F. Wilms1, T. Windisch1, M. Winkler1, A. Winter1, V. Winters1, R. Wolf118, A.M. Wright29, G.A. Wurden39, P. Xanthopoulos1, S. Xu17, H. Yamada58, H. Yamaguchi41, M. Yokoyama41, M. Yoshinuma41, Q. Yu8, M. Zamanov14, M. Zanini1, M. Zarnstorff29, D. Zhang1, S. Zhou17, J. Zhu1, C. Zhu29, M. Zilker8, A. Zocco1, H. Zohm8, S. Zoletnik7 and L. Zsuga7 // 1 Max Planck Institute for Plasma Physics, Garching and Greifswald, Germany: 2 University of Greifswald, Greifswald, Germany; 3 Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, United States of America; 4 Consorzio RFX, Corso Stati Uniti, 4-35127 Padova, Italy; 5 Auburn University, Auburn, AL 36849, United States of America; 6 CIEMAT, Avenida Complutense, 40, 28040 Madrid, Spain; 7 Center for Energy Research, Konkoly-Thegeut 29-33, 1121 Budapest, Hungary; 8 Max-Planck-Institute for Plasma Physics, Boltzmannstraße 2, 85748 Garching bei München, Germany; 9 CEA Cadarache, 13115 Saint-Paul-lez-Durance, France; 10 Karlsruhe Institute of Technology, Kaiserstr. 12, 76131 Karlsruhe, Germany; 11 University of Cagliari, Via Universita, 40, 09124 Cagliari, Italy; 12 École Polytechnique Fédérale de Lausanne, Swiss Plasma Center, CH-1015 Lausanne, Switzerland; 13 University of Wisconsin–Madison, Engineering Drive, Madison, WI 53706, United States of America; 14 Institute of Plasma Physics, National Science Center ‘Kharkiv Institute of Physics and Technology’, Kharkiv, Ukraine; 15 The Australian National University, Acton ACT 2601, Canberra, Australia; 16 Department of Engineering, University of Palermo, Viale delle Scienze, Edificio 6, Palermo, 90128, Italy; 17 Forschungszentrum Jülich GmbH, Institut für Energie-und Klimaforschung—Plasmaphysik, 52425 Jülich, Germany; 18 Technical University of Berlin, Strasse des 17. Juni 135, 10623 Berlin, Germany; 19 Eindhoven University of Technology, 5600 MB Eindhoven, Netherlands; 20 Universitat Politècnica de Catalunya. BarcelonaTech, C. Jordi Girona, 31, 08034 Barcelona, Spain; 21 Culham Center for Fusion Energy, Abingdon OX14 3EB, United Kingdom; 22 Instituto de Plasmas e Fusao Nuclear, Av. Rovisco Pais, 1049-001 Lisboa, Portugal; 23 Department of Physics, National and Kapodistrian University of Athens, 15784 Athens, Greece; 24 Institute of Plasma Physics and Laser Microfusion, 23 Hery Str., 01-497 Warsaw, Poland; 25 ENEA—Centro Ricerche Frascati, Via Enrico Fermi, 45, 00044 Frascati RM, Italy; 26 Laboratory for Plasma Physics, LPP-ERM/KMS, TEC Partner, B-1000 Brussels, Belgium; 27 Dutch Institute for Fundamental Energy Research, PO Box 6336, 5600 HH Eindhoven, Netherlands; 28 University of Texas, Austin, TX, United States of America; 29 Princeton Plasma Physics Laboratory, Princeton, NJ 08543, United States of America; 30 Kyushu University, 744 Motooka Nishi-ku, Fukuoka 819-0395, Japan; 31 Aix-Marseille University, Jardin du Pharo, 58 Boulevard Charles Livon, 13007, Marseille, France; 32 Department of Physics and Astronomy, Padova University, Via Marzolo 8, 35131 Padova, Italy; 33 Department of Applied Physics, Ghent University, Sint-Pietersnieuwstraat 41 B4, 9000 Ghent, Belgium; 34 Aalto University, 02150 Espoo, Finland; 35 Department of Physics, Technical University of Denmark, Anker Engelunds Vej, 2800 Kgs Lyngby, Denmark; 36 VTT Technical Research Center of Finland Ltd., PO Box 1000, FI-02044 VTT, Finland; 37 Institute of Plasma Physics, Chinese Academy of Sciences, 230031 Hefei, Anhui, China; 38 Oak Ridge National Laboratory, 1 Bethel Valley Rd, Oak Ridge, TN 37830, United States of America; 39 Los Alamos National Laboratory, NM 87545, United States of America; 40 Fritz-Haber-Institut der Max-Planck-Gesellschaft, 14195 Berlin, Germany; 41 National Institute for Fusion Science, National Institutes of Natural Sciences, 322-6 Oroshi-cho, Toki, Gifu Prefecture 509-5292, Japan; 42 Institute for Ion Physics and Applied Physics, University of Innsbruck, Innsbruck, Austria; 43 Graz University of Technology, Rechbauerstraße 12, 8010 GRAZ, Austria; 44 Austrian Academy of Science, Doktor-Ignaz-Seipel-Platz 2, 1010 Wien, Austria; 45 Institute for Nuclear Research, prospekt Nauky 47, Kyiv 03028, Ukraine; 46 University of Opole, plac Kopernika 11a, 45-001 Opole, Poland; 47 University of Maryland, Paint Branch Drive, College Park, MA 20742, United States of America; 48 National Research Nuclear University MEPhI, 115409 Moscow, Russian Federation; 49 Department of Microelectronics and Computer Science, Lodz University of Technology, Wolczanska 221/223, 90-924 Lodz, Poland; 50 Consiglio Nazionale delle Ricerche, Piazzale Aldo Moro, 7, 00185 Roma, Italy; 51 Ioffe Physical-Technical Institute of the Russian Academy of Sciences, 26 Politekhnicheskaya, St Petersburg 194021, Russian Federation; 52 Istituto di Fisica del Plasma Piero Caldirola, Via Roberto Cozzi, 53, 20125 Milano, Italy; 53 University of Szczecin, 70-453, aleja Papieza Jana Pawła II 22A, Szczecin, Poland; 54 Lawrence University, 711 E Boldt Way, Appleton, WI 54911, United States of America; 55 Physik-Department E28, Technische Universität München, 85747 Garching, Germany; 56 Universidad Carlos III de Madrid, Av. de la Universidad, 30 Madrid, Spain; 57 Yale University, New Haven, CT 06520, United States of America; 58 University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chhiab 277-0882, JapanObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.a - Per a 2030, augmentar la cooperació internacional per tal de facilitar l’accés a la investigació i a les tecnolo­gies energètiques no contaminants, incloses les fonts d’energia renovables, l’eficiència energètica i les tecnologies de combustibles fòssils avançades i menys contaminants, i promoure la inversió en infraestructures energètiques i tecnologies d’energia no contaminantPostprint (published version

Detecció de prominència utilitzant el moviment de persones amb visió zenital

La detecció de prominència d’un espai visual és una tasca que tradicionalment ha estat abordada mitjançant l’anàlisi de l’espai amb tècniques de processat d’imatge, aquestes tècniques es basen a estudiar característiques com el color, la intensitat, la forma, l’orientació entre d’altres per acabar generant un mapa d’intensitats on cada punt representa un nivell d’interès. De forma alternativa, en aquest treball es pretén analitzar el moviment de persones dins un espai per tal de detectar la prominència generada per un grup d’objectes. La primera fase del treball ha consistit a captar vídeos de persones durant una exposició a un espai amb objectes d’interès i extreure informació dels mateixos obtenint diferents conjunts de dades. La segona part, ha estat basada en el desenvolupament i avaluació de mètodes que utilitzen els conjunts de dades anteriors per tal d’estimar el nivell d’interès aportat per cada objecte. Els resultats obtinguts mostren com un dels mètodes s’acosta força a unes estadístiques realitzades en la primera fase.La detección de prominencia de un espacio visual es una tasca que tradicionalmente ha sido abordada mediante el análisis del espacio con técnicas de procesado de imagen,estas técnicas están basadas en estudiar características como el color, la intensidad, la forma, la orientación entre otras para finalmente generar un mapa de intensidades donde cada punto representa un nivel de interés. De forma alternativa, en este trabajo se pretende analizar el movimiento de persones dentro de un espacio para poder detectar la prominencia generada por un grupo de objetos. La primera fase del trabajo ha consistido en captar vídeos de personas durante una exposición a un espacio con objetos de interés i extraer información de los mismos obteniendo diferentes conjuntos de datos. La segunda parte, ha sido basada en el desarrollo i avaluación de métodos que utilizan los conjuntos de datos anteriores con el fin de detectar el nivel de interés aportado por cada objeto. Los resultados obtenidos muestran como uno de los métodos se acerca bastante a unas estadísticas realizadas en la primera fase.The salience detection of a visual space is a task that traditionally has been solved by the analysis of the space with image processing techniques, these techniques are based on the study of specifications like color, intensity, orientation and so on to finally end with the generation of an intensity map where every point represents a level of interest. Alternatively, this project aims to analyze the movement of people in a room with the purpose of detecting the salience generated by a set of objects.The first phase of the project has consisted of recording people during an exposition in a space with objects of interest and extract information from those in order to get different data sets. The second phase, has been based on the development and evaluation of methods that use the previous data sets to estimate the interest level contributed by each object. The results show that one of the methods is similar to the statistics made in the first phase

SLAM-based 3D outdoor reconstructions from lidar data

The use of depth (RGBD) cameras to reconstruct large outdoor environments is not feasible due to lighting conditions and low depth range. LIDAR sensors can be used instead. Most state of the art SLAM methods are devoted to indoor environments and depth (RGBD) cameras. We have adapted two SLAM systems to work with LIDAR data. We have compared the systems for LIDAR and RGBD data by performing quantitative evaluations. Results show that the best method for LIDAR data is RTAB-Map with a clear difference. Additionally, RTAB-Map has been used to create 3D reconstructions with and without photometry from a visible color camera. This proves the potential of LIDAR sensors for the reconstruction of outdoor environments for immersion or audiovisual production applicationsPeer Reviewe