@phdthesis{Hock2014,
  author    = {Hock, David Rog{\´e}r},
  title     = {Analysis and Optimization of Resilient Routing in Core Communication Networks},
  issn      = {1432-8801},
  doi       = {10.25972/OPUS-10168},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-101681},
  school      = {Universit{\"a}t W{\"u}rzburg},
  pages     = {175},
  year      = {2014},
  abstract  = {Routing is one of the most important issues in any communication network. It defines on which path packets are transmitted from the source of a connection to the destination. It allows to control the distribution of flows between different locations in the network and thereby is a means to influence the load distribution or to reach certain constraints imposed by particular applications. As failures in communication networks appear regularly and cannot be completely avoided, routing is required to be resilient against such outages, i.e., routing still has to be able to forward packets on backup paths even if primary paths are not working any more. Throughout the years, various routing technologies have been introduced that are very different in their control structure, in their way of working, and in their ability to handle certain failure cases. Each of the different routing approaches opens up their own specific questions regarding configuration, optimization, and inclusion of resilience issues. This monograph investigates, with the example of three particular routing technologies, some concrete issues regarding the analysis and optimization of resilience. It thereby contributes to a better general, technology-independent understanding of these approaches and of their diverse potential for the use in future network architectures. The first considered routing type, is decentralized intra-domain routing based on administrative IP link costs and the shortest path principle. Typical examples are common today's intra-domain routing protocols OSPF and IS-IS. This type of routing includes automatic restoration abilities in case of failures what makes it in general very robust even in the case of severe network outages including several failed components. Furthermore, special IP-Fast Reroute mechanisms allow for a faster reaction on outages. For routing based on link costs, traffic engineering, e.g. the optimization of the maximum relative link load in the network, can be done indirectly by changing the administrative link costs to adequate values. The second considered routing type, MPLS-based routing, is based on the a priori configuration of primary and backup paths, so-called Label Switched Paths. The routing layout of MPLS paths offers more freedom compared to IP-based routing as it is not restricted by any shortest path constraints but any paths can be setup. However, this in general involves a higher configuration effort. Finally, in the third considered routing type, typically centralized routing using a Software Defined Networking (SDN) architecture, simple switches only forward packets according to routing decisions made by centralized controller units. SDN-based routing layouts offer the same freedom as for explicit paths configured using MPLS. In case of a failure, new rules can be setup by the controllers to continue the routing in the reduced topology. However, new resilience issues arise caused by the centralized architecture. If controllers are not reachable anymore, the forwarding rules in the single nodes cannot be adapted anymore. This might render a rerouting in case of connection problems in severe failure scenarios infeasible.},
  subject      = {Leistungsbewertung},
  language  = {en}
}
@article{HockTerekhovStefanescuetal.2021,
  author    = {Hock, Michael and Terekhov, Maxim and Stefanescu, Maria Roxana and Lohr, David and Herz, Stefan and Reiter, Theresa and Ankenbrand, Markus and Kosmala, Aleksander and Gassenmaier, Tobias and Juchem, Christoph and Schreiber, Laura Maria},
  title     = {B\(_{0}\) shimming of the human heart at 7T},
  series = {Magnetic Resonance in Medicine},
  volume    = {85},
  journal   = {Magnetic Resonance in Medicine},
  number    = {1},
  doi       = {10.1002/mrm.28423},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-218096},
  pages     = {182 -- 196},
  year      = {2021},
  abstract  = {Purpose Inhomogeneities of the static magnetic B\(_{0}\) field are a major limiting factor in cardiac MRI at ultrahigh field (≥ 7T), as they result in signal loss and image distortions. Different magnetic susceptibilities of the myocardium and surrounding tissue in combination with cardiac motion lead to strong spatio-temporal B\(_{0}\)-field inhomogeneities, and their homogenization (B0 shimming) is a prerequisite. Limitations of state-of-the-art shimming are described, regional B\(_{0}\) variations are measured, and a methodology for spherical harmonics shimming of the B\(_{0}\) field within the human myocardium is proposed. Methods The spatial B\(_{0}\)-field distribution in the heart was analyzed as well as temporal B\(_{0}\)-field variations in the myocardium over the cardiac cycle. Different shim region-of-interest selections were compared, and hardware limitations of spherical harmonics B\(_{0}\) shimming were evaluated by calibration-based B0-field modeling. The role of third-order spherical harmonics terms was analyzed as well as potential benefits from cardiac phase-specific shimming. Results The strongest B\(_{0}\)-field inhomogeneities were observed in localized spots within the left-ventricular and right-ventricular myocardium and varied between systolic and diastolic cardiac phases. An anatomy-driven shim region-of-interest selection allowed for improved B\(_{0}\)-field homogeneity compared with a standard shim region-of-interest cuboid. Third-order spherical harmonics terms were demonstrated to be beneficial for shimming of these myocardial B\(_{0}\)-field inhomogeneities. Initial results from the in vivo implementation of a potential shim strategy were obtained. Simulated cardiac phase-specific shimming was performed, and a shim term-by-term analysis revealed periodic variations of required currents. Conclusion Challenges in state-of-the-art B\(_{0}\) shimming of the human heart at 7 T were described. Cardiac phase-specific shimming strategies were found to be superior to vendor-supplied shimming.},
  language  = {en}
}
@article{AnkenbrandShainbergHocketal.2021,
  author    = {Ankenbrand, Markus J. and Shainberg, Liliia and Hock, Michael and Lohr, David and Schreiber, Laura M.},
  title     = {Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI},
  series = {BMC Medical Imaging},
  volume    = {21},
  journal   = {BMC Medical Imaging},
  number    = {1},
  doi       = {10.1186/s12880-021-00551-1},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-259169},
  pages     = {27},
  year      = {2021},
  abstract  = {Background Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance. Results We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model. Conclusions Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.},
  language  = {en}
}
@techreport{BaumgartBredebachHermetal.2022,
  author    = {Baumgart, Michael and Bredebach, Patrick and Herm, Lukas-Valentin and Hock, David and Hofmann, Adrian and Janiesch, Christian and Jankowski, Leif Ole and Kampik, Timotheus and Keil, Matthias and Kolb, Julian and Kr{\"o}hn, Michael and Pytel, Norman and Schaschek, Myriam and St{\"u}bs, Oliver and Winkelmann, Axel and Zeiß, Christian},
  title     = {Plattform f{\"u}r das integrierte Management von Kollaborationen in Wertsch{\"o}pfungsnetzwerken (PIMKoWe)},
  editor    = {Winkelmann, Axel and Janiesch, Christian},
  issn      = {2199-0328},
  doi       = {10.25972/OPUS-29335},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-293354},
  pages     = {248},
  year      = {2022},
  abstract  = {Das Verbundprojekt „Plattform f{\"u}r das integrierte Management von Kollaborationen in Wertsch{\"o}pfungsnetzwerken" (PIMKoWe - F{\"o}rderkennzeichen „02P17D160") ist ein Forschungsvorhaben im Rahmen des Forschungsprogramms „Innovationen f{\"u}r die Produktion, Dienstleistung und Arbeit von morgen" der Bekanntmachung „Industrie 4.0 - Intelligente Kollaborationen in dynamischen Wertsch{\"o}pfungs-netzwerken" (InKoWe). Das Forschungsvorhaben wurde mit Mitteln des Bundesministeriums f{\"u}r Bildung und Forschung (BMBF) gef{\"o}rdert und durch den Projekttr{\"a}ger des Karlsruher Instituts f{\"u}r Technologie (PTKA) betreut. Ziel des Forschungsprojekts PIMKoWe ist die Entwicklung und Bereitstellung einer Plattforml{\"o}sung zur Flexibilisierung, Automatisierung und Absicherung von Kooperationen in Wertsch{\"o}pfungsnetzwerken des industriellen Sektors.},
  subject      = {Blockchain},
  language  = {de}
}