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Showing 25 to 26 of 26 entries
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Photonic crystal patterning of luminescent sol-gel films for light extraction.

Nanotechnology

Revaux A, Dantelle G, Decanini D, Guillemot F, Haghiri-Gosnet AM, Weisbuch C, Boilot JP, Gacoin T, Benisty H.
PMID: 21836324
Nanotechnology. 2011 Sep 07;22(36):365701. doi: 10.1088/0957-4484/22/36/365701. Epub 2011 Aug 11.

Structured luminescent thin films are investigated in the context of improved light extraction of phosphors for solid-state-lighting applications. Thin films composed of a sol-gel titania matrix doped with europium chelates are studied as a model system. These films, patterned...

Cite
Revaux A, Dantelle G, Decanini D, et al. Photonic crystal patterning of luminescent sol-gel films for light extraction. Nanotechnology. 2011;22(36):365701doi: 10.1088/0957-4484/22/36/365701.
Revaux, A., Dantelle, G., Decanini, D., Guillemot, F., Haghiri-Gosnet, A. M., Weisbuch, C., Boilot, J. P., Gacoin, T., & Benisty, H. (2011). Photonic crystal patterning of luminescent sol-gel films for light extraction. Nanotechnology, 22(36), 365701. https://doi.org/10.1088/0957-4484/22/36/365701
Revaux, Amelie, et al. "Photonic crystal patterning of luminescent sol-gel films for light extraction." Nanotechnology vol. 22,36 (2011): 365701. doi: https://doi.org/10.1088/0957-4484/22/36/365701
Revaux A, Dantelle G, Decanini D, Guillemot F, Haghiri-Gosnet AM, Weisbuch C, Boilot JP, Gacoin T, Benisty H. Photonic crystal patterning of luminescent sol-gel films for light extraction. Nanotechnology. 2011 Sep 07;22(36):365701. doi: 10.1088/0957-4484/22/36/365701. Epub 2011 Aug 11. PMID: 21836324.
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Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series.

IEEE journal of biomedical and health informatics

Wickstrom K, Mikalsen KO, Kampffmeyer M, Revhaug A, Jenssen R.
PMID: 33284756
IEEE J Biomed Health Inform. 2021 Jul;25(7):2435-2444. doi: 10.1109/JBHI.2020.3042637. Epub 2021 Jul 27.

Deep learning-based support systems have demonstrated encouraging results in numerous clinical applications involving the processing of time series data. While such systems often are very accurate, they have no inherent mechanism for explaining what influenced the predictions, which is...

Cite
Wickstrom K, Mikalsen KO, Kampffmeyer M, et al. Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series. IEEE J Biomed Health Inform. 2021;25(7):2435-2444doi: 10.1109/JBHI.2020.3042637.
Wickstrom, K., Mikalsen, K. O., Kampffmeyer, M., Revhaug, A., & Jenssen, R. (2021). Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series. IEEE journal of biomedical and health informatics, 25(7), 2435-2444. https://doi.org/10.1109/JBHI.2020.3042637
Wickstrom, Kristoffer, et al. "Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series." IEEE journal of biomedical and health informatics vol. 25,7 (2021): 2435-2444. doi: https://doi.org/10.1109/JBHI.2020.3042637
Wickstrom K, Mikalsen KO, Kampffmeyer M, Revhaug A, Jenssen R. Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series. IEEE J Biomed Health Inform. 2021 Jul;25(7):2435-2444. doi: 10.1109/JBHI.2020.3042637. Epub 2021 Jul 27. PMID: 33284756.
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Showing 25 to 26 of 26 entries