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5 Publications
2024 | Preprint | LibreCat-ID: 53793 | 
Harder, H., & Peitz, S. (n.d.). Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines.
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2019 | Journal Article | LibreCat-ID: 10334
Schemmel, R., Hemsel, T., Dymel, C., Hunstig, M., Brökelmann, M., & Sextro, W. (2019). Using complex multi-dimensional vibration trajectories in ultrasonic bonding and welding. Sensors and Actuators A: Physical, 295, 653–662. https://doi.org/10.1016/j.sna.2019.04.025
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2018 | Conference Paper | LibreCat-ID: 9993
Dymel, C., Schemmel, R., Hemsel, T., Sextro, W., Brökelmann, M., & Hunstig, M. (2018). Experimental investigations on the impact of bond process parameters in two-dimensional ultrasonic copper bonding. In (Proceedings of 8th Electronics IEEE CPMT Symposium Japan (ICSJ 2018), Kyoto, Japan) (pp. 41–44).
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2018 | Conference Paper | LibreCat-ID: 4375
Benter, M., Knollmann, T., Meyer auf der Heide, F., Setzer, A., & Sundermeier, J. (2018). A Peer-to-Peer based Cloud Storage supporting orthogonal Range Queries of arbitrary Dimension. In Proceedings of the 4th International Symposium on Algorithmic Aspects of Cloud Computing (ALGOCLOUD). Helsinki. https://doi.org/10.1007/978-3-030-19759-9_4
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2013 | Conference Paper | LibreCat-ID: 11917
Vu, D. H. T., & Haeb-Umbach, R. (2013). Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation. In 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013) (pp. 863–867). https://doi.org/10.1109/ICASSP.2013.6637771
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