Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Stefan Thumfart"'
Autor:
Philipp Moser, Gernot Reishofer, Robert Prückl, Stefan Schaffelhofer, Sascha Freigang, Stefan Thumfart, Kariem Mahdy Ali
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract Transcranial magnetic stimulation (TMS) has emerged as a promising neuromodulation technique with both therapeutic and diagnostic applications. As accurate coil placement is known to be essential for focal stimulation, computational models h
Externí odkaz:
https://doaj.org/article/f4db632568b84ee6becd63c830b33291
Autor:
Nico Stroh, Harald Stefanits, Alexander Maletzky, Sophie Kaltenleithner, Stefan Thumfart, Michael Giretzlehner, Richard Drexler, Franz L. Ricklefs, Lasse Dührsen, Stefan Aspalter, Philip Rauch, Andreas Gruber, Matthias Gmeiner
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-14 (2023)
Abstract Machine learning (ML) has revolutionized data processing in recent years. This study presents the results of the first prediction models based on a long-term monocentric data registry of patients with microsurgically treated unruptured intra
Externí odkaz:
https://doaj.org/article/d994ca0583cb4cf7b76fcab6b629caf6
Autor:
Alexander Maletzky, Carl Böck, Thomas Tschoellitsch, Theresa Roland, Helga Ludwig, Stefan Thumfart, Michael Giretzlehner, Sepp Hochreiter, Jens Meier
Publikováno v:
JMIR Medical Informatics, Vol 10, Iss 10, p e38557 (2022)
Electronic health records (EHRs) have been successfully used in data science and machine learning projects. However, most of these data are collected for clinical use rather than for retrospective analysis. This means that researchers typically face
Externí odkaz:
https://doaj.org/article/712e200a996542efa54f6f29f66c27fb
Publikováno v:
Fluids, Vol 8, Iss 2, p 46 (2023)
Machine learning-based modeling of physical systems has attracted significant interest in recent years. Based solely on the underlying physical equations and initial and boundary conditions, these new approaches allow to approximate, for example, the
Externí odkaz:
https://doaj.org/article/af557815319c41c7bf32fdb1b62e5678
Publikováno v:
Data, Vol 8, Iss 1, p 16 (2023)
Advanced Driver Assistance Systems rely on automated traffic sign recognition. Today, Deep Learning methods outperform other approaches in terms of accuracy and processing time; however, they require vast and well-curated data sets for training. In t
Externí odkaz:
https://doaj.org/article/a087e5ab977d4f1fb8c910ea747fb8f3
Autor:
Richard Henrikus Augustinus Hubertus Jacobs, Koen V Haak, Stefan Thumfart, Remco J Renken, Brian Henson, Frans W. Cornelissen
Publikováno v:
Frontiers in Human Neuroscience, Vol 10 (2016)
Our world is filled with texture. For the human visual system, this is an important source of information for assessing environmental and material properties. Indeed – and presumably for this reason– the human visual system has regions dedicated
Externí odkaz:
https://doaj.org/article/538df91abfd541db9411bf06f0eddb48
Autor:
Marije van Beilen, Harold Bult, Remco Renken, Markus Stieger, Stefan Thumfart, Frans Cornelissen, Valesca Kooijman
Publikováno v:
PLoS ONE, Vol 6, Iss 9, p e23857 (2011)
Little is known about the influence of visual characteristics other than colour on flavor perception, and the complex interactions between more than two sensory modalities. This study focused on the effects of recognizability of visual (texture) info
Externí odkaz:
https://doaj.org/article/0114cc10621a450dba23869742159e8d
Publikováno v:
Journal of Eye Movement Research, Vol 3, Iss 4 (2010)
Top-down influences on the guidance of the eyes are generally modeled as modulating influences on bottom-up salience maps. Interested in task-driven influences on how, rather than where, the eyes are guided, we expected differences in eye movement pa
Externí odkaz:
https://doaj.org/article/45a066350c314d47b8dab9dd81321287
Autor:
Ahmed Alshenoudy, Bertram Sabrowsky-Hirsch, Stefan Thumfart, Michael Giretzlehner, Erich Kobler
Publikováno v:
IFIP Advances in Information and Communication Technology ISBN: 9783031341106
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::62ae3b3b5a97057081dd0f1e4d12a2a4
https://doi.org/10.1007/978-3-031-34111-3_27
https://doi.org/10.1007/978-3-031-34111-3_27
Publikováno v:
IEEE Journal of Photovoltaics. 10:676-684
As photovoltaic systems grow in size, there has been an increasing desire to automate the detection of arc faults. Any automated system for arc detection must be as fast and accurate as possible: delayed detection of electrical arcs can lead to fire