@article{HartmannHelbigBiernackaetal.2019,
  author    = {Hartmann, Niklas K. and Helbig, Kerstin and Biernacka, Katarzyna and Buchholz, Petra and Dolzycka, Dominika and Hartmann, Thomas and Hiemenz, Bea and Jacob, Boris and Kuberek, Monika and Weiß, Nadin and Dreyer, Malte},
  title     = {L{\"o}sungen und Leitf{\"a}den f{\"u}r das institutionelle Forschungsdatenmanagement},
  series = {o-bib Das offene Bibliotheksjournal},
  volume    = {6},
  journal   = {o-bib Das offene Bibliotheksjournal},
  number    = {3},
  publisher = {VDB - Verein Deutscher Bibliothekarinnen und Bibliothekare e.V.},
  address   = {Erlangen},
  issn      = {2363-9814},
  doi       = {10.5282/o-bib/2019H3S21-39},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-472370},
  pages     = {21 -- 39},
  year      = {2019},
  abstract  = {Hochschulen und deren Zentraleinrichtungen besch{\"a}ftigen sich zunehmend mit dem Thema Forschungsdatenmanagement (FDM), um ihre Forschenden ad{\"a}quat zu unterst{\"u}tzen. Nicht zuletzt aufgrund neuer Verlags- und F{\"o}rderanforderungen w{\"u}nschen sich Forschende Beratung und Orientierung, wie sie mit ihren Forschungsdaten umgehen sollen. Damit Hochschulen schnell und nachhaltig L{\"o}sungen zum institutionellen FDM etablieren k{\"o}nnen, haben f{\"u}nf Berliner und Brandenburger Universit{\"a}ten im gemeinsamen Verbundvorhaben FDMentor mit F{\"o}rderung des Bundesministeriums f{\"u}r Bildung und Forschung (BMBF) entsprechende Leitf{\"a}den und Werkzeuge erarbeitet. Die innerhalb von zwei Jahren (2017-2019) entstandenen Ergebnisse in den Bereichen Strategieentwicklung, Forschungsdaten-Policy, rechtliche Aspekte und Kompetenzausbau finden {\"u}ber das Verbundprojekt hinaus ihre Anwendung.},
  language  = {de}
}
@article{DamnikGierlProskeetal.2018,
  author    = {Damnik, Gregor and Gierl, Mark and Proske, Antje and K{\"o}rndle, Hermann and Narciss, Susanne},
  title     = {Automatische Erzeugung von Aufgaben als Mittel zur Erh{\"o}hung von Interaktivit{\"a}t und Adaptivit{\"a}t in digitalen Lernressourcen},
  series = {E-Learning Symposium 2018},
  journal   = {E-Learning Symposium 2018},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  doi       = {10.25932/publishup-42184},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-421842},
  pages     = {5 -- 16},
  year      = {2018},
  abstract  = {Digitale Medien enthalten bislang vor allem Inhalte in verschiedenen Darstellungsformen. Dies allein erzeugt jedoch nur einen geringen Mehrwert zu klassischen Lernressourcen, da die Kriterien der Interaktivit{\"a}t und Adaptivit{\"a}t nicht mit einbezogen werden. Dies scheitert jedoch oft an dem damit verbundenen Erstellungsaufwand. Der folgende Beitrag zeigt, wie durch die automatische Erzeugung von Aufgaben ein hochwertiger Wissenserwerb mit digitalen Medien erm{\"o}glicht wird. Ferner werden Vor- und Nachteile der automatischen Erstellung von Aufgaben er{\"o}rtert.},
  language  = {de}
}
@article{RezaeiNaeppiLippertetal.2020,
  author    = {Rezaei, Mina and N{\"a}ppi, Janne J. and Lippert, Christoph and Meinel, Christoph and Yoshida, Hiroyuki},
  title     = {Generative multi-adversarial network for striking the right balance in abdominal image segmentation},
  series = {International journal of computer assisted radiology and surgery},
  volume    = {15},
  journal   = {International journal of computer assisted radiology and surgery},
  number    = {11},
  publisher = {Springer},
  address   = {Berlin},
  issn      = {1861-6410},
  doi       = {10.1007/s11548-020-02254-4},
  pages     = {1847 -- 1858},
  year      = {2020},
  abstract  = {Purpose: The identification of abnormalities that are relatively rare within otherwise normal anatomy is a major challenge for deep learning in the semantic segmentation of medical images. The small number of samples of the minority classes in the training data makes the learning of optimal classification challenging, while the more frequently occurring samples of the majority class hamper the generalization of the classification boundary between infrequently occurring target objects and classes. In this paper, we developed a novel generative multi-adversarial network, called Ensemble-GAN, for mitigating this class imbalance problem in the semantic segmentation of abdominal images. Method: The Ensemble-GAN framework is composed of a single-generator and a multi-discriminator variant for handling the class imbalance problem to provide a better generalization than existing approaches. The ensemble model aggregates the estimates of multiple models by training from different initializations and losses from various subsets of the training data. The single generator network analyzes the input image as a condition to predict a corresponding semantic segmentation image by use of feedback from the ensemble of discriminator networks. To evaluate the framework, we trained our framework on two public datasets, with different imbalance ratios and imaging modalities: the Chaos 2019 and the LiTS 2017. Result: In terms of the F1 score, the accuracies of the semantic segmentation of healthy spleen, liver, and left and right kidneys were 0.93, 0.96, 0.90 and 0.94, respectively. The overall F1 scores for simultaneous segmentation of the lesions and liver were 0.83 and 0.94, respectively. Conclusion: The proposed Ensemble-GAN framework demonstrated outstanding performance in the semantic segmentation of medical images in comparison with other approaches on popular abdominal imaging benchmarks. The Ensemble-GAN has the potential to segment abdominal images more accurately than human experts.},
  language  = {en}
}
@article{Theuer2018,
  author    = {Theuer, Hanna Katharina},
  title     = {Automatisierungstechnik II},
  series = {Industrial Internet of Things - Grundlagen. Band 1},
  journal   = {Industrial Internet of Things - Grundlagen. Band 1},
  publisher = {Gito},
  address   = {Berlin},
  isbn      = {978-3-95545-247-6},
  pages     = {203 -- 236},
  year      = {2018},
  language  = {de}
}
@article{FanStegmannSchrappeetal.,
  author    = {Fan, Xin and Stegmann, Mikkel B. and Schrappe, Oliver and Zeidler, Steffen and Jensen, Isac G. and Thorsen, Jannich and Bjerregaard, Tobias and Krstić, Miloš},
  title     = {Frequency-domain optimization of digital switching noise based on clock scheduling},
  series = {IEEE Transactions on Circuits and Systems I},
  volume    = {63},
  journal   = {IEEE Transactions on Circuits and Systems I},
  number    = {7},
  issn      = {1549-8328},
  doi       = {10.1109/TCSI.2016.2546118},
  pages     = {982 -- 993},
  abstract  = {The simultaneous switching activity in digital circuits challenges the design of mixed-signal SoCs. Rather than focusing on time-domain noise voltage minimization, this work optimizes switching noise in the frequency domain. A two-tier solution based on the on-chip clock scheduling is proposed. First, to cope with the switching noise at the fundamental clock frequency, which usually dominates in terms of noise power, a two-phase clocking scheme is employed for system timing. Second, on-chip clock latencies are manipulated to target harmonic peaks in specific frequency bands for the spectral noise optimization. An automated design flow, which allows for noise optimization in user-defined application-specific frequency bands, is developed. The effectiveness of our design solution is validated by measurements of substrate noise and conductive EMI (electromagnetic interference) noise on a test chip, which consists of four wireless sensor node baseband processors each addressing a distinct clock-tree-synthesis strategy. Compared to the reference synchronous design, the proposed clock scheduling solution substantially reduces noise in the target GSM-850 band, i.e., by 11.1 dB on the substrate noise and 12.9 dB on the EMI noise, along with dramatic noise peak drops measured at the 50-MHz clock frequency.},
  language  = {en}
}