@inproceedings{10622,
  author       = {{Anwer, Jahanzeb and Platzner, Marco}},
  booktitle    = {{Euromicro Conference on Digital System Design (DSD)}},
  title        = {{{Boolean Difference Based Reliability Evaluation of Fault Tolerant Circuit Structures on FPGAs}}},
  doi          = {{10.1109/DSD.2016.35}},
  year         = {{2016}},
}

@inproceedings{10631,
  author       = {{Boschmann, Alexander and Dosen, Strahinja and Werner, Andreas and Raies, Ali and Farina, Dario}},
  booktitle    = {{Proc. IEEE Int. Conf. Biomed. Health Informatics (BHI)}},
  title        = {{{A novel immersive augmented reality system for prosthesis training and assessment}}},
  year         = {{2016}},
}

@article{10661,
  author       = {{Graf, Tobias and Platzner, Marco}},
  journal      = {{Journal Theoretical Computer Science}},
  pages        = {{53--62}},
  publisher    = {{Elsevier}},
  title        = {{{Adaptive playouts for online learning of policies during Monte Carlo Tree Search}}},
  doi          = {{10.1016/j.tcs.2016.06.029}},
  volume       = {{644}},
  year         = {{2016}},
}

@misc{10695,
  author       = {{Horstmann, Jens}},
  publisher    = {{Paderborn University}},
  title        = {{{Beschleunigte Simulation elektrischer Stromnetze mit GPUs}}},
  year         = {{2016}},
}

@article{10705,
  author       = {{Ma, Chenjie and Kaufmann, Paul and Töbermann, J.-Christian and Braun, Martin}},
  journal      = {{Renewable Energy}},
  number       = {{(part 2)}},
  pages        = {{946--953}},
  publisher    = {{Elsevier}},
  title        = {{{Optimal Generation Dispatch of Distributed Generators Considering Fair Contribution to Grid Voltage Control}}},
  doi          = {{10.1016/j.renene.2015.07.083}},
  volume       = {{87}},
  year         = {{2016}},
}

@misc{10706,
  author       = {{Makeswaran, Vignesh}},
  publisher    = {{Paderborn University}},
  title        = {{{Operating System Support for Reconfigurable Cache}}},
  year         = {{2016}},
}

@misc{10707,
  author       = {{Ibne Ashraf, Ishraq}},
  publisher    = {{Paderborn University}},
  title        = {{{Private/Shared Data Classification and Implementation for a Multi-Softcore Platform}}},
  year         = {{2016}},
}

@inproceedings{10712,
  author       = {{Meisner, Sebastian and Platzner, Marco}},
  booktitle    = {{Reconfigurable Computing and FPGAs (ReConFig), 2016 International Conference on}},
  pages        = {{1--8}},
  title        = {{{Thread Shadowing: On the Effectiveness of Error Detection at the Hardware Thread Level}}},
  doi          = {{10.1109/ReConFig.2016.7857193}},
  year         = {{2016}},
}

@misc{10755,
  author       = {{Schmidt, Marco}},
  publisher    = {{Paderborn University}},
  title        = {{{Konzeption und Implementierung einer digitalen Ansteuerung für den Betrieb einer elektrischen Sendereinheit für induktive Energieübertragung}}},
  year         = {{2016}},
}

@book{10758,
  author       = {{Squillero, Giovanni and Burelli, Paolo and M. Mora, Antonio and Agapitos, Alexandros and S. Bush, William and Cagnoni, Stefano and Cotta, Carlos and De Falco, Ivanoe and Della Cioppa, Antonio and Divina, Federico and Eiben, A.E. and I. Esparcia-Alc{\'a}zar, Anna and Fern{\'a}ndez de Vega, Francisco and Glette, Kyrre and Haasdijk, Evert and Ignacio Hidalgo, J. and Kampouridis, Michael and Kaufmann, Paul and Mavrovouniotis, Michalis and Thanh Nguyen, Trung and Schaefer, Robert and Sim, Kevin and Tarantino, Ernesto and Urquhart, Neil and Zhang (editors), Mengjie}},
  publisher    = {{Springer}},
  title        = {{{Applications of Evolutionary Computation - 19th European Conference, EvoApplications}}},
  volume       = {{9597}},
  year         = {{2016}},
}

@inproceedings{10766,
  author       = {{Ghribi, Ines and Ben Abdallah, Riadh and Khalgui, Mohamed and Platzner, Marco}},
  booktitle    = {{Proceedings of the 30th European Simulation and Modelling Conference (ESM)}},
  title        = {{{RCo-Design: New Visual Environment for Reconfigurable Embedded Systems}}},
  year         = {{2016}},
}

@inproceedings{10768,
  author       = {{Ghribi, Ines and Ben Abdallah, Riadh and Khalgui, Mohamed and Platzner, Marco}},
  booktitle    = {{Proceedings of the 11th International Conference on Software Engineering and Applications (ICSOFT-EA)}},
  pages        = {{185--195}},
  title        = {{{New Co-design Methodology for Real-time Embedded Systems}}},
  year         = {{2016}},
}

@article{10769,
  author       = {{Ghasemzadeh Mohammadi, Hassan and Gaillardon, Pierre-Emmanuel and De Micheli, Giovanni}},
  journal      = {{IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems}},
  number       = {{99}},
  pages        = {{1--1}},
  publisher    = {{IEEE}},
  title        = {{{Efficient Statistical Parameter Selection for Nonlinear Modeling of Process/Performance Variation}}},
  doi          = {{10.1109/TCAD.2016.2547908}},
  volume       = {{PP}},
  year         = {{2016}},
}

@misc{10781,
  author       = {{Hermansen, Sven}},
  publisher    = {{Paderborn University}},
  title        = {{{Custom Memory Controller for ReconOS}}},
  year         = {{2016}},
}

@misc{10785,
  author       = {{Fürnkranz, J. and Hüllermeier, Eyke}},
  booktitle    = {{Encyclopedia of Machine Learning and Data Mining}},
  editor       = {{Sammut, C. and Webb, G.I.}},
  publisher    = {{Springer}},
  title        = {{{Preference Learning}}},
  year         = {{2016}},
}

@misc{1082,
  author       = {{Handirk, Tobias}},
  publisher    = {{Universität Paderborn}},
  title        = {{{Über die Rolle von Informationen in Verkehrsnetzwerken}}},
  year         = {{2016}},
}

@inproceedings{11738,
  abstract     = {{In this contribution we investigate a priori signal-to-noise ratio (SNR) estimation, a crucial component of a single-channel speech enhancement system based on spectral subtraction. The majority of the state-of-the art a priori SNR estimators work in the power spectral domain, which is, however, not confirmed to be the optimal domain for the estimation. Motivated by the generalized spectral subtraction rule, we show how the estimation of the a priori SNR can be formulated in the so called generalized SNR domain. This formulation allows to generalize the widely used decision directed (DD) approach. An experimental investigation with different noise types reveals the superiority of the generalized DD approach over the conventional DD approach in terms of both the mean opinion score - listening quality objective measure and the output global SNR in the medium to high input SNR regime, while we show that the power spectrum is the optimal domain for low SNR. We further develop a parameterization which adjusts the domain of estimation automatically according to the estimated input global SNR. Index Terms: single-channel speech enhancement, a priori SNR estimation, generalized spectral subtraction}},
  author       = {{Chinaev, Aleksej and Haeb-Umbach, Reinhold}},
  booktitle    = {{INTERSPEECH 2016, San Francisco, USA}},
  title        = {{{A Priori SNR Estimation Using a Generalized Decision Directed Approach}}},
  year         = {{2016}},
}

@inproceedings{11743,
  abstract     = {{This contribution introduces a novel causal a priori signal-to-noise ratio (SNR) estimator for single-channel speech enhancement. To exploit the advantages of the generalized spectral subtraction, a normalized ?-order magnitude (NAOM) domain is introduced where an a priori SNR estimation is carried out. In this domain, the NAOM coefficients of noise and clean speech signals are modeled by a Weibull distribution and aWeibullmixturemodel (WMM), respectively. While the parameters of the noise model are calculated from the noise power spectral density estimates, the speechWMM parameters are estimated from the noisy signal by applying a causal Expectation-Maximization algorithm. Further a maximum a posteriori estimate of the a priori SNR is developed. The experiments in different noisy environments show the superiority of the proposed estimator compared to the well-known decision-directed approach in terms of estimation error, estimator variance and speech quality of the enhanced signals when used for speech enhancement.}},
  author       = {{Chinaev, Aleksej and Heitkaemper, Jens and Haeb-Umbach, Reinhold}},
  booktitle    = {{12. ITG Fachtagung Sprachkommunikation (ITG 2016)}},
  title        = {{{A Priori SNR Estimation Using Weibull Mixture Model}}},
  year         = {{2016}},
}

@inproceedings{11744,
  abstract     = {{A noise power spectral density (PSD) estimation is an indispensable component of speech spectral enhancement systems. In this paper we present a noise PSD tracking algorithm, which employs a noise presence probability estimate delivered by a deep neural network (DNN). The algorithm provides a causal noise PSD estimate and can thus be used in speech enhancement systems for communication purposes. An extensive performance comparison has been carried out with ten causal state-of-the-art noise tracking algorithms taken from the literature and categorized acc. to applied techniques. The experiments showed that the proposed DNN-based noise PSD tracker outperforms all competing methods with respect to all tested performance measures, which include the noise tracking performance and the performance of a speech enhancement system employing the noise tracking component.}},
  author       = {{Chinaev, Aleksej and Heymann, Jahn and Drude, Lukas and Haeb-Umbach, Reinhold}},
  booktitle    = {{12. ITG Fachtagung Sprachkommunikation (ITG 2016)}},
  title        = {{{Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs}}},
  year         = {{2016}},
}

@inproceedings{11751,
  author       = {{Drude, Lukas and Boeddeker, Christoph and Haeb-Umbach, Reinhold}},
  booktitle    = {{Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)}},
  title        = {{{Blind Speech Separation based on Complex Spherical k-Mode Clustering}}},
  year         = {{2016}},
}