@article{GebertHockZinneretal.2014, author = {Gebert, Steffen and Hock, David and Zinner, Thomas and Tran-Gia, Phuoc and Hoffmann, Marco and Jarschel, Michael and Schmidt, Ernst-Dieter and Braun, Ralf-Peter and Banse, Christian and K{\"o}psel, Andreas}, title = {Demonstrating the optimal placement of virtualized cellular network functions in case of large crowd events}, volume = {44}, journal = {ACM SIGCOMM Computer Communication Review: Proceedings of the SIGCOMM CHICAGO 2014 \& the best of the co-located workshops}, number = {4}, publisher = {ACM}, address = {New York}, issn = {0146-4833}, doi = {https://doi.org/10.1145/2740070.2631428}, pages = {359 -- 360}, year = {2014}, language = {en} } @article{NanDelSerWalshetal.2022, author = {Nan, Yang and Del Ser, Javier and Walsh, Simon and Sch{\"o}nlieb, Carola and Roberts, Michael and Selby, Ian and Howard, Kit and Owen, John and Neville, Jon and Guiot, Julien and Ernst, Benoit and Pastor, Ana and Alberich-Bayarri, Angel and Menzel, Marion Irene and Walsh, Sean and Vos, Wim and Flerin, Nina and Charbonnier, Jean-Paul and Rikxoort, Eva van and Chatterjee, Avishek and Woodruff, Henry and Lambin, Philippe and Cerd{\´a}-Alberich, Leonor and Mart{\´i}-Bonmat{\´i}, Luis and Herrera, Francisco and Yang, Guang}, title = {Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions}, volume = {2022}, journal = {Information Fusion}, number = {82}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1566-2535}, doi = {https://doi.org/10.1016/j.inffus.2022.01.001}, pages = {99 -- 122}, year = {2022}, abstract = {Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research}, language = {en} } @unpublished{NanDelSerWalshetal.2022, author = {Nan, Yang and Del Ser, Javier and Walsh, Simon and Sch{\"o}nlieb, Carola and Roberts, Michael and Selby, Ian and Howard, Kit and Owen, John and Neville, Jon and Guiot, Julien and Ernst, Benoit and Pastor, Ana and Alberich-Bayarri, Angel and Menzel, Marion Irene and Walsh, Sean and Vos, Wim and Flerin, Nina and Charbonnier, Jean-Paul and Rikxoort, Eva van and Chatterjee, Avishek and Woodruff, Henry and Lambin, Philippe and Cerd{\´a}-Alberich, Leonor and Mart{\´i}-Bonmat{\´i}, Luis and Herrera, Francisco and Yang, Guang}, title = {Data Harmonisation for Information Fusion in Digital Healthcare: A State-of-the-Art Systematic Review, Meta-Analysis and Future Research Directions}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2201.06505}, year = {2022}, abstract = {Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research.}, language = {en} } @article{HantiErnstFrey2013, author = {Hanti, Thomas and Ernst, Michael and Frey, Andreas}, title = {Higher Utilization of Multi-Core Processors in Dynamic Real-Time Software Systems}, volume = {1}, journal = {International Journal of Electrical Energy}, number = {4}, publisher = {Engineering and Technology Publishing}, address = {[s. l.]}, issn = {2301-3656}, pages = {249 -- 255}, year = {2013}, language = {en} } @inproceedings{HantiFreyErnst2014, author = {Hanti, Thomas and Frey, Andreas and Ernst, Michael}, title = {Phasenanalyse von Funktionen in einem hierarchischen asynchronen Multi-Core System}, booktitle = {Deutscher Luft- und Raumfahrtkongress 2014 - Netzpublikationen}, publisher = {DGLR}, address = {Bonn}, url = {https://publikationen.dglr.de/?tx_dglrpublications_pi1[document_id]=340044}, doi = {https://nbn-resolving.org/urn:nbn:de:101:1-2014121719126}, year = {2014}, language = {de} }