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003			ES-MaUEC
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007			cr nn 008mamaa
008			230325s2017 sz | s |||| 0|eng d
020			_a9783031026041
024	7		_a10.1007/978-3-031-02604-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.I52 _b2017 EB
100	1		_aFalk, Martin _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687557 _q(Martin Samuel)
245	1	0	_aInteractive GPU-based Visualization of Large Dynamic Particle Data _cby Martin Falk, Sebastian Grottel, Michael Krone, Guido Reina
250			_a1st edition 2017
264		1	_aCham _bSpringer International Publishing _c2017
300			_a1 recurso en línea (XII, 109 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Visualization _x2159-5178
505	0		_aAcknowledgments -- Figure Credits -- Introduction -- History -- GPU-based Glyph Ray Casting -- Acceleration Strategies -- Data Structures -- Efficient Nearest Neighbor Search on the GPU -- Improved Visual Quality -- Application-driven Abstractions -- Summary and Outlook -- Bibliography -- Authors' Biographies.
520			_aPrevalent types of data in scientific visualization are volumetric data, vector field data, and particle-based data. Particle data typically originates from measurements and simulations in various fields, such as life sciences or physics. The particles are often visualized directly, that is, by simple representants like spheres. Interactive rendering facilitates the exploration and visual analysis of the data. With increasing data set sizes in terms of particle numbers, interactive high-quality visualization is a challenging task. This is especially true for dynamic data or abstract representations that are based on the raw particle data. This book covers direct particle visualization using simple glyphs as well as abstractions that are application-driven such as clustering and aggregation. It targets visualization researchers and developers who are interested in visualization techniques for large, dynamic particle-based data. Its explanations focus on GPU-accelerated algorithms for high-performance rendering and data processing that run in real-time on modern desktop hardware. Consequently, the implementation of said algorithms and the required data structures to make use of the capabilities of modern graphics APIs are discussed in detail. Furthermore, it covers GPU-accelerated methods for the generation of application-dependent abstract representations. This includes various representations commonly used in application areas such as structural biology, systems biology, thermodynamics, and astrophysics.
988			_aSynthesis Collection of Technology_2017
650		7	_2embne _9145622 _aSistemas de visualización de información
650		7	_2embne _9141143 _aGráficos de ordenador
650		7	_2embne _9495511 _aDatos masivos
700	1		_aGrottel, Sebastian _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687558
700	1		_aKrone, Michael _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687559 _c(Computer scientist)
700	1		_aReina, Guido _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687560 _c(Computer scientist)
776	0	8	_iPrinted edition: _z9783031014765
776	0	8	_iPrinted edition: _z9783031037320
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02604-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _eIG _zSI