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  • Other Sources  (3)
  • Institute of Electrical and Electronics Engineers (IEEE)
  • 1
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    Institute of Electrical and Electronics Engineers (IEEE)
    In:  IEEE Geoscience and Remote Sensing Letters, 13 (3). pp. 334-338.
    Publication Date: 2020-07-29
    Description: Electromagnetic (EM) surveys are widely used in geophysical study. Reliable 3-D forward modeling is required for the inversion and interpretation of geophysical data. We have introduced a novel method for 3-D EM modeling using stretching grids, which uses a multigrid iterative solver for solving the formed linear system of equations based on the staggered finite-difference method. The developed algorithm is applied for the simulation of the magnetotelluric fields. We have tested this new algorithm using synthetic geoelectric models. The proposed multigrid method using stretching grids is also compared with the preconditioned Krylov-subspace solvers, the biconjugate gradients stabilized method, and the generalized minimum residual method. The developed multigrid method is proved to be more stable and requires less iterations to converge.
    Type: Article , PeerReviewed
    Format: text
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  • 2
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    Institute of Electrical and Electronics Engineers (IEEE)
    In:  In: OCEANS 2015. Institute of Electrical and Electronics Engineers (IEEE), Washington, pp. 1-5. ISBN 978-0-933957-43-5
    Publication Date: 2016-12-01
    Description: Computational underwater image analysis is developing into a mature field of research, with an increasing number of companies, academic groups and researchers showing interest in it. While on the one hand, the basic question is addressed by many groups, how algorithms can be applied to automatically detect and classify objects of interest (OOI) in underwater image footage, on the other hand the questions for efficiency and performance, i.e. the time a computer (or a compute cluster) needs to perform this task, has received much attention yet. In this paper we will show, how nowadays methods for high performance computing like parallelization and GPU computing via CUDA (Compute Unified Device Architecture) can be used to achieve both, image enhancement and segmentation in less than 0:2 sec per image (4224 x 2376 pixel) on average, which paves the way to real time online applications.
    Type: Book chapter , NonPeerReviewed
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  • 3
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    Institute of Electrical and Electronics Engineers (IEEE)
    In:  IEEE Transactions on Geoscience and Remote Sensing, 51 (6). pp. 3306-3318.
    Publication Date: 2020-07-29
    Description: Considering the sea ice decline in the Arctic during the last decades, polynyas are of high research interest since these features are core areas of new ice formation. The determination of ice formation requires accurate retrieval of polynya area and thin-ice thickness (TIT) distribution within the polynya. We use an established energy balance model to derive TITs with MODIS ice surface temperatures (Ts) and NCEP/DOE Reanalysis II in the Laptev Sea for two winter seasons. Improvements of the algorithm mainly concern the implementation of an iterative approach to calculate the atmospheric flux components taking the atmospheric stratification into account. Furthermore, a sensitivity study is performed to analyze the errors of the ice thickness. The results are the following: 1) 2-m air temperatures (Ta) and Ts have the highest impact on the retrieved ice thickness; 2) an overestimation of Ta yields smaller ice thickness errors as an underestimation of Ta; 3) NCEP Ta shows often a warm bias; and 4) the mean absolute error for ice thicknesses up to 20 cm is ±4.7 cm. Based on these results, we conclude that, despite the shortcomings of the NCEP data (coarse spatial resolution and no polynyas), this data set is appropriate in combination with MODIS Ts for the retrieval of TITs up to 20 cm in the Laptev Sea region. The TIT algorithm can be applied to other polynya regions and to past and future time periods. Our TIT product is a valuable data set for verification of other model and remote sensing ice thickness data.
    Type: Article , PeerReviewed
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