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Applied Visualization (AppVis)5 ECTS (englische Bezeichnung: Applied Visualization)
(Prüfungsordnungsmodul: Applied Visualization)
Modulverantwortliche/r: Tobias Günther Lehrende:
Tobias Günther
Startsemester: |
SS 2021 | Dauer: |
1 Semester | Turnus: |
jährlich (SS) |
Präsenzzeit: |
60 Std. | Eigenstudium: |
90 Std. | Sprache: |
Englisch |
Lehrveranstaltungen:
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Applied Visualization
(Vorlesung, 2 SWS, Tobias Günther, Do, 10:15 - 11:45, H4)
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Tutorials to Applied Visualization
(Übung, 2 SWS, Tobias Günther et al., Do, 14:15 - 15:45, 16:15 - 17:45, Zoom-Meeting)
Empfohlene Voraussetzungen:
Es wird empfohlen, folgende Module zu absolvieren, bevor dieses Modul belegt wird:
Algorithmen und Datenstrukturen (WS 2020/2021)
Inhalt:
The amount of data, generated in the pursuit of scientific discovery, keeps rapidly increasing across all major scientific disciplines. How can we make sense of large, time-dependent, high-dimensional and multi-variate data? This lecture provides an introduction into scientific visualization. Throughout the course, we cover the fundamental perception basics needed to convey information accurately. After categorizing different data types based on their dimensionality, we dive deeper into specific techniques for scalar and vector valued data. To facilitate the discovery of patterns and to support the communication of findings, we further elaborate on data reduction, feature extraction, and interactive exploration. This module covers the following topics:
a review of scalar and vector calculus
data structures and data acquisition techniques
direct and indirect scalar field visualization
geometry-based, feature-based and topology-based vector field visualization
multi-variate data visualization
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There are voluntary exercises. Theoretical exercises concentrate on feature extraction from scalar and vector data, while programming exercises demonstrate the use of frameworks, such as the Visualization Tool Kit, to implement interactive scientific data visualizations. The module is concluded with an electronic exam.
Lernziele und Kompetenzen:
Students are able to:
classify data and select appropriate visualization techniques
calculate differential properties of scalar and vector fields
identify features in scalar and vector-valued data
implement numerical extraction algorithms
learn the advantages and disadvantages of common visualization techniques
use perceptual basics to select appropriate visualization methods
explain and apply common interaction and data exploration paradigms
Literatur:
- M. Ward, G.G. Grinstein, D. Keim, Interactive Data Visualization: Foundations, Techniques, and Applications, Taylor & Francis, 2010
AC. Telea, Data Visualization: Principles and Practice, AK Peters, 2008
C.D. Hansen and C.R. Johnson, Visualization Handbook, Academic Press, 2004
G.M. Nielson, H. Hagen, H.Müller, Scientific Visualization, IEEE Computer Society Press, Los Alamitos, 1997
Verwendbarkeit des Moduls / Einpassung in den Musterstudienplan:
- Computational Engineering (Rechnergestütztes Ingenieurwesen) (Master of Science)
(Po-Vers. 2013 | TechFak | Computational Engineering (Rechnergestütztes Ingenieurwesen) (Master of Science) | Gesamtkonto | Wahlpflichtbereich Informatik | Wahlpflichtbereich Informatik | Applied Visualization)
Dieses Modul ist daneben auch in den Studienfächern "123#67#H", "Computational Engineering (Master of Science)", "Computational Engineering (Rechnergestütztes Ingenieurwesen) (Bachelor of Science)", "Informatik (Bachelor of Arts (2 Fächer))", "Informatik (Bachelor of Science)", "Informatik (Master of Science)", "Information and Communication Technology (Master of Science)", "Informations- und Kommunikationstechnik (Master of Science)", "International Information Systems (IIS) (Master of Science)", "Maschinenbau (Bachelor of Science)", "Maschinenbau (Master of Science)", "Mathematik (Bachelor of Science)", "Medizintechnik (Bachelor of Science)", "Medizintechnik (Master of Science)", "Physische Geographie (Bachelor of Science)" verwendbar. Details
Studien-/Prüfungsleistungen:
Applied Visualization (Prüfungsnummer: 37211)
(englischer Titel: Applied Visualisation)
zugeh. "mein campus"-Prüfung: | - 37211 Applied Visualization (Gewichtung: 100.0 %, Prüfung, Form: Klausur, Drittelnoten (mit 4,3), Dauer: -, 5 ECTS, Prüfung).
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- Prüfungsleistung, elektronische Prüfung, Dauer (in Minuten): 90, benotet, 5.0 ECTS
- Anteil an der Berechnung der Modulnote: 100.0 %
- weitere Erläuterungen:
Klausur in elektronischer Form mit einem Anteil im Antwort-Wahl-Verfahren
- Erstablegung: SS 2021, 1. Wdh.: WS 2021/2022
1. Prüfer: | Tobias Günther (100426) |
- Termin: 21.09.2021, 13:00 Uhr, Ort: Erlangen (91058), Gebbertstraße 123b, Ballspielhalle
Termin: 22.03.2022, 12:00 Uhr, Ort: Ballspielhalle, Gebbertstr. 123b, Erlangen
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UnivIS ist ein Produkt der Config eG, Buckenhof |
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