@inproceedings{10620,
  author       = {{Anwer, Jahanzeb and Meisner, Sebastian and Platzner, Marco}},
  booktitle    = {{Reconfigurable Computing and FPGAs (ReConFig), 2013 International Conference on}},
  keywords     = {{fault tolerant computing, field programmable gate arrays, logic design, reliability, BYU-LANL tool, DRM tool flow, FPGA based hardware designs, avionic application, device technologies, dynamic reliability management, fault-tolerant operation, hardware designs, reconfiguring reliability levels, space applications, Field programmable gate arrays, Hardware, Redundancy, Reliability engineering, Runtime, Tunneling magnetoresistance}},
  pages        = {{1--6}},
  title        = {{{Dynamic reliability management: Reconfiguring reliability-levels of hardware designs at runtime}}},
  doi          = {{10.1109/ReConFig.2013.6732280}},
  year         = {{2013}},
}

@misc{10626,
  author       = {{Bick, Christian}},
  publisher    = {{Paderborn University}},
  title        = {{{Beschleunigung von Tiefenberechnung aus Stereobildern durch FPGA-basierte Datenflussrechner}}},
  year         = {{2013}},
}

@inproceedings{10634,
  author       = {{Boschmann, Alexander and Nofen, Barbara and Platzner, Marco}},
  booktitle    = {{Proc. IEEE Int. Conf. Eng. Med. Biolog. (EMBC)}},
  title        = {{{Improving transient state myoelectric signal recognition in hand movement classification using gyroscopes}}},
  year         = {{2013}},
}

@inproceedings{10635,
  author       = {{Boschmann, Alexander and Platzner, Marco}},
  booktitle    = {{Proc. IEEE ISSNIP Biosignals and Biorobotics Conference (BRC)}},
  title        = {{{Reducing the limb position effect in pattern recognition based myoelectric control using a high density electrode array}}},
  year         = {{2013}},
}

@inproceedings{10655,
  author       = {{Glette, Kyrre and Kaufmann, Paul and Assad, Christopher and Wolf, Michael}},
  booktitle    = {{IEEE Intl. Conf. on Evolvable Systems (ICES)}},
  pages        = {{1--1}},
  publisher    = {{Springer}},
  title        = {{{Investigating Evolvable Hardware Classification for the BioSleeve Electromyographic Interface}}},
  volume       = {{1}},
  year         = {{2013}},
}

@book{10681,
  author       = {{Kaufmann, Paul}},
  publisher    = {{Logos Verlag}},
  title        = {{{Adapting Hardware Systems by Means of Multi-Objective Evolution}}},
  year         = {{2013}},
}

@article{10684,
  author       = {{Kaufmann, Paul and Glette, Kyrre and Gruber, Tiemo and Platzner, Marco and Torresen, Jim and Sick, Bernhard}},
  journal      = {{IEEE Transactions on Evolutionary Computation}},
  number       = {{1}},
  pages        = {{46--63}},
  title        = {{{Classification of Electromyographic Signals: Comparing Evolvable Hardware to Conventional Classifiers}}},
  doi          = {{10.1109/TEVC.2012.2185845}},
  volume       = {{17}},
  year         = {{2013}},
}

@misc{10700,
  author       = {{Knoop, Michael}},
  publisher    = {{IWES Kassel}},
  title        = {{{Behavior Models for Electric Vehicles}}},
  year         = {{2013}},
}

@misc{10720,
  author       = {{Nofen, Barbara}},
  publisher    = {{Paderborn University}},
  title        = {{{Verbesserung der Erkennungsrate eines Systems zur Klassifikation von EMG-Signalen durch den Einsatz eines hybriden Lagesensors}}},
  year         = {{2013}},
}

@misc{10727,
  author       = {{Pudelko, Daniel}},
  publisher    = {{Paderborn University}},
  title        = {{{Überquerung der Styx - Betriebsparametervariation und Fehlerverhalten eines Platform FPGAs}}},
  year         = {{2013}},
}

@misc{10730,
  author       = {{Riebler, Heinrich}},
  publisher    = {{Paderborn University}},
  title        = {{{Identifikation und Wiederherstellung von kryptographischen Schlüsseln mit FPGAs}}},
  year         = {{2013}},
}

@misc{10741,
  author       = {{Sprenger, Alexander}},
  publisher    = {{Paderborn University}},
  title        = {{{MiBenchHybrid : Erweiterung eines Benchmarks um Hardwarebeschleunigung}}},
  year         = {{2013}},
}

@misc{10743,
  author       = {{Steppeler, Philipp}},
  publisher    = {{Paderborn University}},
  title        = {{{Beschleunigung von Einzelbild-Erkennungsverfahren auf Datenfluss basierenden HPC Systemen}}},
  year         = {{2013}},
}

@inproceedings{10745,
  author       = {{Toebermann, Christian and Geibel, Daniel and Hau, Manuel and Brandl, Ron and Kaufmann, Paul and Ma, Chenjie and Braun, Martin and Degner, Tobias}},
  booktitle    = {{Real-Time Conference}},
  publisher    = {{OPAL RT Paris}},
  title        = {{{Real-Time Simulation of Distribution Grids with high Penetration of Regenerative and Distributed Generation}}},
  year         = {{2013}},
}

@inproceedings{10774,
  author       = {{Ghasemzadeh Mohammadi, Hassan and Gaillardon, Pierre-Emmanuel and Yazdani, Majid and De Micheli, Giovanni}},
  booktitle    = {{2013 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFTS)}},
  pages        = {{83--88}},
  publisher    = {{IEEE}},
  title        = {{{A fast TCAD-based methodology for Variation analysis of emerging nano-devices}}},
  doi          = {{10.1109/DFT.2013.6653587}},
  year         = {{2013}},
}

@inproceedings{10775,
  author       = {{Gaillardon, Pierre-Emmanuel and Ghasemzadeh Mohammadi, Hassan and De Micheli, Giovanni}},
  booktitle    = {{2013 14th Latin American Test Workshop-LATW}},
  pages        = {{1--6}},
  publisher    = {{IEEE}},
  title        = {{{Vertically-stacked silicon nanowire transistors with controllable polarity: A robustness study}}},
  doi          = {{10.1109/LATW.2013.6562673}},
  year         = {{2013}},
}

@inproceedings{1093,
  abstract     = {{Whenever huge amounts of XML data have to be transferred from a web server to multiple clients, the transferred data volumes can be reduced significantly by sending compressed XML instead of plain XML. Whenever applications require querying a compressed XML format and XML compression or decompression time is a bottleneck, parallel XML compression and parallel decompression may be of significant advantage. We choose the XML compressor XSDS as starting point for our new approach to parallel compression and parallel decompression of XML documents for the following reasons. First, XSDS generally reaches stronger compression ratios than other compressors like gzip, bzip2, and XMill. Second, in contrast to these compressors, XSDS not only supports XPath queries on compressed XML data, but also XPath queries can be evaluated on XSDS compressed data even faster than on uncompressed XML. We propose a String-search-based parsing approach to parallelize XML compression with XSDS, and we show that we can speed-up the compression of XML documents by a factor of 1.4 and that we can speed-up the decompression time even by a factor of up to 7 on a quad-core processor.}},
  author       = {{Böttcher, Stefan and Feldotto, Matthias and Hartel, Rita}},
  booktitle    = {{WEBIST 2013 - Proceedings of the 9th International Conference on Web Information Systems and Technologies, Aachen, Germany, 8-10 May, 2013}},
  pages        = {{77--86}},
  title        = {{{Schema-based Parallel Compression and Decompression of XML Data}}},
  doi          = {{10.5220/0004366300770086}},
  year         = {{2013}},
}

@inproceedings{11716,
  abstract     = {{The accuracy of automatic speech recognition systems in noisy and reverberant environments can be improved notably by exploiting the uncertainty of the estimated speech features using so-called uncertainty-of-observation techniques. In this paper, we introduce a new Bayesian decision rule that can serve as a mathematical framework from which both known and new uncertainty-of-observation techniques can be either derived or approximated. The new decision rule in its direct form leads to the new significance decoding approach for Gaussian mixture models, which results in better performance compared to standard uncertainty-of-observation techniques in different additive and convolutive noise scenarios.}},
  author       = {{Abdelaziz, Ahmed H. and Zeiler, Steffen and Kolossa, Dorothea and Leutnant, Volker and Haeb-Umbach, Reinhold}},
  booktitle    = {{Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on}},
  issn         = {{1520-6149}},
  keywords     = {{Bayes methods, Gaussian processes, convolution, decision theory, decoding, noise, reverberation, speech coding, speech recognition, Bayesian decision rule, GMM, Gaussian mixture models, additive noise scenarios, automatic speech recognition systems, convolutive noise scenarios, decoding approach, mathematical framework, reverberant environments, significance decoding, speech feature estimation, uncertainty-of-observation techniques, Hidden Markov models, Maximum likelihood decoding, Noise, Speech, Speech recognition, Uncertainty, Uncertainty-of-observation, modified imputation, noise robust speech recognition, significance decoding, uncertainty decoding}},
  pages        = {{6827--6831}},
  title        = {{{GMM-based significance decoding}}},
  doi          = {{10.1109/ICASSP.2013.6638984}},
  year         = {{2013}},
}

@inproceedings{11740,
  abstract     = {{In this contribution we derive the Maximum A-Posteriori (MAP) estimates of the parameters of a Gaussian Mixture Model (GMM) in the presence of noisy observations. We assume the distortion to be white Gaussian noise of known mean and variance. An approximate conjugate prior of the GMM parameters is derived allowing for a computationally efficient implementation in a sequential estimation framework. Simulations on artificially generated data demonstrate the superiority of the proposed method compared to the Maximum Likelihood technique and to the ordinary MAP approach, whose estimates are corrected by the known statistics of the distortion in a straightforward manner.}},
  author       = {{Chinaev, Aleksej and Haeb-Umbach, Reinhold}},
  booktitle    = {{38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)}},
  issn         = {{1520-6149}},
  keywords     = {{Gaussian noise, maximum likelihood estimation, parameter estimation, GMM parameter, Gaussian mixture model, MAP estimation, Map-based estimation, maximum a-posteriori estimation, maximum likelihood technique, noisy observation, sequential estimation framework, white Gaussian noise, Additive noise, Gaussian mixture model, Maximum likelihood estimation, Noise measurement, Gaussian mixture model, Maximum a posteriori estimation, Maximum likelihood estimation}},
  pages        = {{3352--3356}},
  title        = {{{MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations}}},
  doi          = {{10.1109/ICASSP.2013.6638279}},
  year         = {{2013}},
}

@inproceedings{11742,
  abstract     = {{In this paper we present an improved version of the recently proposed Maximum A-Posteriori (MAP) based noise power spectral density estimator. An empirical bias compensation and bandwidth adjustment reduce bias and variance of the noise variance estimates. The main advantage of the MAP-based postprocessor is its low estimation variance. The estimator is employed in the second stage of a two-stage single-channel speech enhancement system, where eight different state-of-the-art noise tracking algorithms were tested in the first stage. While the postprocessor hardly affects the results in stationary noise scenarios, it becomes the more effective the more nonstationary the noise is. The proposed postprocessor was able to improve all systems in babble noise w.r.t. the perceptual evaluation of speech quality performance.}},
  author       = {{Chinaev, Aleksej and Haeb-Umbach, Reinhold and Taghia, Jalal and Martin, Rainer}},
  booktitle    = {{38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)}},
  issn         = {{1520-6149}},
  pages        = {{7477--7481}},
  title        = {{{Improved Single-Channel Nonstationary Noise Tracking by an Optimized MAP-based Postprocessor}}},
  doi          = {{10.1109/ICASSP.2013.6639116}},
  year         = {{2013}},
}