Institution
Vienna University of Technology
Education•Vienna, Austria•
About: Vienna University of Technology is a education organization based out in Vienna, Austria. It is known for research contribution in the topics: Laser & Cloud computing. The organization has 16723 authors who have published 49341 publications receiving 1302168 citations.
Topics: Laser, Cloud computing, Finite element method, Magnetization, Population
Papers published on a yearly basis
Papers
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TL;DR: The variety, domain architecture and subgroups of chitinases of filamentous fungi are shown, and how these data integrate with that from molecular biological studies on chit inases are discussed.
230 citations
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TL;DR: A new approach to constructing appearance models based on kernel canonical correlation analysis ( kernel-CCA), where a non-linear transformation of the input data is performed implicitly using kernel methods, which is especially well suited for relating two sets of measurements.
230 citations
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TL;DR: In this paper, the authors present examples of analyses by x-ray fluorescence spectrometry in art and archaeology, including pigments in paint layers and illuminated manusripts, of iridescent glasses and of medieval coins.
Abstract: This paper presents examples of analyses by x-ray fluorescence (XRF) spectrometry in art and archaeology, including pigments in paint layers and illuminated manusripts, of iridescent glasses and of medieval coins. Theoretical aspects of information depths and shielding effects in layered materials are discussed. Element maps were experimentally obtained by a specially designed x-ray spectrometer (1 × 1 mm pixel resolution) and by electron-excited XRF (electron microprobe). Copyright © 2000 John Wiley & Sons, Ltd.
229 citations
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TL;DR: In this paper, a novel technique to separate high and fluctuating amounts of hydrogen sulphide from raw biogas is presented that relies on a highly intensified method of chemical-oxidative scrubbing.
228 citations
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TL;DR: This paper presents a method to retrieve surface soil moisture (SSM) from the Sentinel-1 (S-1) satellites, which carry C-band Synthetic Aperture Radar (CSAR) sensors that provide the richest freely available SAR data source so far, unprecedented in accuracy and coverage.
Abstract: Soil moisture is a key environmental variable, important to, e.g., farmers, meteorologists, and disaster management units. Here, we present a method to retrieve surface soil moisture (SSM) from the Sentinel-1 (S-1) satellites, which carry C-band Synthetic Aperture Radar (CSAR) sensors that provide the richest freely available SAR data source so far, unprecedented in accuracy and coverage. Our SSM retrieval method, adapting well-established change detection algorithms, builds the first globally deployable soil moisture observation data set with 1-km resolution. This paper provides an algorithm formulation to be operated in data cube architectures and high-performance computing environments. It includes the novel dynamic Gaussian upscaling method for spatial upscaling of SAR imagery, harnessing its field-scale information and successfully mitigating effects from the SAR’s high signal complexity. Also, a new regression-based approach for estimating the radar slope is defined, coping with Sentinel-1’s inhomogeneity in spatial coverage. We employ the S-1 SSM algorithm on a 3-year S-1 data cube over Italy, obtaining a consistent set of model parameters and product masks, unperturbed by coverage discontinuities. An evaluation of therefrom generated S-1 SSM data, involving a 1-km soil water balance model over Umbria, yields high agreement over plains and agricultural areas, with low agreement over forests and strong topography. While positive biases during the growing season are detected, the excellent capability to capture small-scale soil moisture changes as from rainfall or irrigation is evident. The S-1 SSM is currently in preparation toward operational product dissemination in the Copernicus Global Land Service.
228 citations
Authors
Showing all 16934 results
Name | H-index | Papers | Citations |
---|---|---|---|
Krzysztof Matyjaszewski | 169 | 1431 | 128585 |
Wolfgang Wagner | 156 | 2342 | 123391 |
Marco Zanetti | 145 | 1439 | 104610 |
Sridhara Dasu | 140 | 1675 | 103185 |
Duncan Carlsmith | 138 | 1660 | 103642 |
Ulrich Heintz | 136 | 1688 | 99829 |
Matthew Herndon | 133 | 1732 | 97466 |
Frank Würthwein | 133 | 1584 | 94613 |
Alain Hervé | 132 | 1279 | 87763 |
Manfred Jeitler | 132 | 1278 | 89645 |
David Taylor | 131 | 2469 | 93220 |
Roberto Covarelli | 131 | 1516 | 89981 |
Patricia McBride | 129 | 1230 | 81787 |
David Smith | 129 | 2184 | 100917 |
Lindsey Gray | 129 | 1170 | 81317 |