@article{16674,
  author       = {{Thiere, B.}},
  issn         = {{0077-8923}},
  journal      = {{Annals of the New York Academy of Sciences}},
  pages        = {{44--54}},
  title        = {{{Return Time Dynamics as a Tool for Finding Almost Invariant Sets}}},
  doi          = {{10.1196/annals.1370.027}},
  year         = {{2006}},
}

@article{1671,
  author       = {{Krimphove, Dieter}},
  journal      = {{Finanz Betrieb }},
  number       = {{April}},
  pages        = {{255 ff.}},
  title        = {{{Zertifikathandel in Deutschland}}},
  year         = {{2006}},
}

@inproceedings{16878,
  author       = {{Domik, Gitta}},
  location     = {{Vienna}},
  title        = {{{A Computer Graphics Curriculum at the University of Paderborn }}},
  year         = {{2006}},
}

@inproceedings{16881,
  author       = {{Lindner, M. and Goetz, F. and Weise, R. and Fricke, H. and Burchert, W. and Domik, Gitta}},
  location     = {{Berlin}},
  title        = {{{Interaktive 3D-Visualisierung multidimensionaler Datensätze}}},
  year         = {{2006}},
}

@techreport{17011,
  author       = {{Dynia, Miroslaw and Kuhmlehn, Andreas and Kutylowski, Jaroslaw and Meyer auf der Heide, Friedhelm and Schindelhauer, Christian}},
  title        = {{{SmartS Simulator Design}}},
  year         = {{2006}},
}

@book{1716,
  author       = {{Krimphove, Dieter}},
  publisher    = {{Eul-Verlag}},
  title        = {{{Das Europäische Sachenrecht Eine rechtsvergleichende Analyse nach der Komparativen Institutionenökonomik}}},
  year         = {{2006}},
}

@inproceedings{10688,
  author       = {{Kaufmann, Paul and Platzner, Marco}},
  booktitle    = {{Intl. Conf. Military Applications of Programmable Logic Devices (MAPLD)}},
  title        = {{{Multi-objective Intrinsic Hardware Evolution}}},
  year         = {{2006}},
}

@misc{10716,
  author       = {{Mühlenbernd, Roland}},
  publisher    = {{Paderborn University}},
  title        = {{{FPGA-Implementierung eines server-basierten Schedulers für periodische Hardwaretasks}}},
  year         = {{2006}},
}

@inproceedings{11823,
  abstract     = {{In this study we evaluate transmission error compensation techniques for distributed speech recognition systems based on modification of the speech decoder. The candidates are marginalization, weighted Viterbi and our recently proposed soft-feature uncertainty decoding. For the latter, it is shown how the Bayesian speech recognition approach must be reformulated for recognition at the server side. The resulting predictive classifier is able to take account of the transmission errors by changing the contribution of the affected speech features to the acoustic score. The comparison of the experimental results has proven the superiority of our approach.}},
  author       = {{Ion, Valentin and Haeb-Umbach, Reinhold}},
  booktitle    = {{7. ITG-Fachtagung Sprachkommunikation}},
  title        = {{{Comparison of Decoder-based Transmission Error Compensation Techniques for Distributed Speech Recognition}}},
  year         = {{2006}},
}

@inproceedings{11824,
  abstract     = {{Soft-feature based speech recognition, which is an example of uncertainty decoding, has been proven to be a robust error mitigation method for distributed speech recognition over wireless channels exhibiting bit errors. In this paper we extend this concept to packet-oriented transmissions. The a posteriori probability density function of the lost feature vector, given the closest received neighbours, is computed. In the experiments, the nearest frame repetition, which is shown to be equivalent to the MAP estimate, outperforms the MMSE estimate for long bursts. Taking the variance into account at the speech recognition stage results in superior performance compared to classical schemes using point estimates. A computationally and memory efficient implementation of the proposed packet loss compensation scheme based on table lookup is presented}},
  author       = {{Ion, Valentin and Haeb-Umbach, Reinhold}},
  booktitle    = {{IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)}},
  keywords     = {{distributed speech recognition, least mean squares methods, MAP estimate, maximum likelihood estimation, MMSE estimate, packet loss compensation scheme, packet switched communication, posteriori probability density function, robust error mitigation method, soft-features, speech recognition, table lookup, voice communication, wireless channels}},
  pages        = {{I}},
  title        = {{{An Inexpensive Packet Loss Compensation Scheme for Distributed Speech Recognition Based on Soft-Features}}},
  doi          = {{10.1109/ICASSP.2006.1659984}},
  volume       = {{1}},
  year         = {{2006}},
}

@article{11825,
  abstract     = {{In this paper, we propose an enhanced error concealment strategy at the server side of a distributed speech recognition (DSR) system, which is fully compatible with the existing DSR standard. It is based on a Bayesian approach, where the a posteriori probability density of the error-free feature vector is computed, given all received feature vectors which are possibly corrupted by transmission errors. Rather than computing a point estimate, such as the MMSE estimate, and plugging it into the Bayesian decision rule, we employ uncertainty decoding, which results in an integration over the uncertainty in the feature domain. In a typical scenario the communication between the thin client, often a mobile device, and the recognition server spreads across heterogeneous networks. Both bit errors on circuit-switched links and lost data packets on IP connections are mitigated by our approach in a unified manner. The experiments reveal improved robustness both for small- and large-vocabulary recognition tasks.}},
  author       = {{Ion, Valentin and Haeb-Umbach, Reinhold}},
  journal      = {{Speech Communication}},
  keywords     = {{Channel error robustness, Distributed speech recognition, Soft features, Uncertainty decoding}},
  number       = {{11}},
  pages        = {{1435--1446}},
  title        = {{{Uncertainty decoding for distributed speech recognition over error-prone networks}}},
  doi          = {{10.1016/j.specom.2006.03.007}},
  volume       = {{48}},
  year         = {{2006}},
}

@inproceedings{11826,
  abstract     = {{The accuracy of distributed speech recognition has been shown to be very sensitive to errors occurring during transmission. One reason for this is that the classifier, usually trained under error free conditions, is unable to cope with the mismatch between an error free and error prone channel. In this paper we present a novel decision rule for classification which is able to account for channel errors. To achieve this, the classical Bayesian speech recognition approach has been reformulated for the server side, where the observation is known only to the extent, as is given by its a posteriori density function. We present a method to estimate the a posteriori density which is based on a Markov model of the source, which captures correlations of both static and dynamic features. A practical implementation is given, accompanied by experimental results for distributed speech recognition over an IP-network.}},
  author       = {{Ion, Valentin and Haeb-Umbach, Reinhold}},
  booktitle    = {{Interspeech 2006}},
  title        = {{{Improved Source Modeling and Predictive Classification for Channel Robust Speech Recognition}}},
  year         = {{2006}},
}

@inproceedings{11884,
  abstract     = {{In this paper we present the design of a particle filter for post filtering instantaneous positioning estimates of GSM mobile terminals. The instantaneous estimates are obtained by comparing signal power levels, which are reported by the mobile terminal to the base station, with a database of predictions using a novel statistically motivated similarity measure. Unlike a simple Euclidian distance measure, the proposed scheme incorporates inherent information about signal power level measurements requested by the serving base station but not reported by the mobile terminal. Furthermore, we show how the Monte Carlo method of particle filtering helps to obtain better position estimates and, surprisingly, also helps to reduce the computational complexity. Results are presented for real field data.}},
  author       = {{Peschke, Sven and Haeb-Umbach, Reinhold}},
  booktitle    = {{European Navigation Conference \& Exhibition (ENC 2006)}},
  title        = {{{A Probabilistic Similarity Measure and a Non-Linear Post-Filter for Mobile Phone Positioning using GSM Signal Power Measurements}}},
  year         = {{2006}},
}

@inproceedings{11885,
  abstract     = {{In this paper we present a novel and statistically motivated similarity measure for database assisted positioning of GSM mobile terminals by evaluating signal power level reports which are transmitted regulary. Unlike a simple Euclidian distance measure, the proposed scheme incorporates inherent information about signal power level measurements requested by the serving base station but not reported by the mobile terminal. Furthermore we show how the Monte Carlo method of nonlinear post filtering using particle filtering helps to obtain better position estimates and surprisingly also helps to reduce the computational complexity. Results are presented for real field data.}},
  author       = {{Peschke, Sven and Haeb-Umbach, Reinhold}},
  booktitle    = {{3rd Workshop on Positioning Navigation and Communication (WPNC 2006)}},
  title        = {{{Particle Filtering of Database assisted Positioning Estimates using a novel Similarity Measure for GSM Signal Power Level Measurements}}},
  year         = {{2006}},
}

@inproceedings{11928,
  abstract     = {{Broadband adaptive beamformers, which use a narrowband SNR-maximization optimization criterion for noise reduction, typically cause distortions of the desired speech signal at the beamformer output. In this paper two methods are investigated to control the speech distortion by comparing the eigenvector beamformer with a maximum likelihood beamformer: One is an analytic solution for the ideal case of absence of reverberation and the other one is a statistically motivated approach. We use the recently introduced gradient-ascent algorithm for adaptive principal eigenvector beamforming and then normalize the filter coefficients by the proposed distortion control methods. Experimental results in terms of the achievable SNR gain and a perceptual speech quality measure are given for the normalized eigenvector beamformer and are compared to standard beamforming methods.}},
  author       = {{Warsitz, Ernst and Haeb-Umbach, Reinhold}},
  booktitle    = {{32. Deutsche Jahrestagung fuer Akustik (DAGA 2006)}},
  title        = {{{Mehrkanalige Sprachsignalverarbeitung durch adaptives Eigenbeamforming fuer Freisprecheinrichtungen im Kraftfahrzeug}}},
  year         = {{2006}},
}

@inproceedings{11929,
  abstract     = {{Broadband adaptive beamformers, which use a narrowband SNR-maximization optimization criterion for noise reduction, typically cause distortions of the desired speech signal at the beamformer output. In this paper two methodsare investigated to control the speech distortion by comparing the eigenvector beamformer with a maximum likelihood beamformer: One is an analytic solution for the ideal case of absence of reverberation and the other one is a statistically motivated approach. We use the recently introduced gradient-ascent algorithm for adaptive principal eigenvector beamforming and then normalize the filter coefficient s by the proposed distortion control methods. Experimental results in terms of the achievable SNR gain and a perceptual speech quality measure are given for the normalized eigenvector beamformer and are compared to standard beamforming methods.}},
  author       = {{Warsitz, Ernst and Haeb-Umbach, Reinhold}},
  booktitle    = {{International Workshop on Acoustic Echo and Noise Control (IWAENC 2006)}},
  title        = {{{Controlling Speech Distortion in Adaptive Frequency-Domain Principal Eigenvector Beamforming}}},
  year         = {{2006}},
}

@inproceedings{11942,
  abstract     = {{Es wird ein marginalisiertes Partikelfilter beschrieben, das zur einkanaligen Sprachsignalverbesserung mit einem nichtlinearen dynamischen Zustandsmodell eingesetzt werden soll. Das System besteht aus einem Partikelfilter zum Tracking von LSP-Parametern und einem Kalman-Filter fuer jedes Partikel, das zur Sprachsignalverbesserung verwendet wird. In unserem Ansatz wird angenommen, dass die Parameter in kurzen Sprachsignalbloecken konstant sind, waehrend das Sprachsignal sich mit jedem Abtastwert aendert. Bei weissem Rauschen werden aehnliche SNR-Gewinne wie mit einem Kalman-EM-iterative Algorithmus erzielt, waehrend das Hintergrundrauschen und die Log-spektrale Distanz etwas geringer sind. Mit einem erweiterten Zustandsmodell wurden auch Untersuchungen fuer farbiges Rauschen durchgefuehrt.}},
  author       = {{Windmann, Stefan and Haeb-Umbach, Reinhold}},
  booktitle    = {{7. ITG-Fachtagung Sprachkommunikation}},
  title        = {{{Einkanalige Sprachsignalverbesserung mit Hilfe eines marginalisierten Partikelfilters}}},
  year         = {{2006}},
}

@inproceedings{11943,
  abstract     = {{A marginalized particle filter is proposed for performing single channel speech enhancement with a non-linear dynamic state model. The system consists of a particle filter for tracking line spectral pair (LSP) parameters and a Kalman filter per particle for speech enhancement. The state model for the LSPs has been learnt on clean speech training data. In our approach parameters and speech samples are processed at different time scales by assuming the parameters to be constant for small blocks of data. Further enhancement is obtained by an iteration which can be applied on these small blocks. The experiments show that similar SNR gains are obtained as with the Kalman-LM-iterative algorithm. However better values of the noise level and the log-spectral distance are achieved}},
  author       = {{Windmann, Stefan and Haeb-Umbach, Reinhold}},
  booktitle    = {{IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)}},
  keywords     = {{clean speech training data, iterative methods, iterative speech enhancement, Kalman filter, Kalman filters, Kalman-LM-iterative algorithm, line spectral pair parameters, log-spectral distance, marginalized particle filter, noise level, nonlinear dynamic state speech model, particle filtering (numerical methods), single channel speech enhancement, SNR gains, speech enhancement, speech samples}},
  pages        = {{I}},
  title        = {{{Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech and its Parameters}}},
  doi          = {{10.1109/ICASSP.2006.1660058}},
  volume       = {{1}},
  year         = {{2006}},
}

@inbook{12907,
  author       = {{Beutner, Marc and Schaumann, U and Twardy, M}},
  booktitle    = {{Berufs- und wirtschaftspädagogische Grundlagenforschung. Lehr-Lern-Prozesse und Kompetenzdiagnostik. Festschrift für Klaus Beck}},
  editor       = {{Minnameier, G and Wuttke, E}},
  pages        = {{289 -- 303}},
  publisher    = {{Peter Lang Verlag}},
  title        = {{{Neue Beruflichkeit und damit verbundene Anforderungen auf Basis des Kompetenzgedankens in der beruflichen Bildung}}},
  year         = {{2006}},
}

@inproceedings{15347,
  abstract     = {{Um das Schwingungsverhalten eines mit Flüssigkeit
gefüllten zylindrischen Stahltanks mit beliebiger Füllhöhe zu
modellieren, werden Ansätze aus der Schalentheorie genutzt.
Verschiedene analytische Modelle und Vereinfachungen
werden gegenübergestellt und für den leeren sowie komplett
gefüllten Zylinder gelöst. Zudem wird ein semianalytisches
Modell für den teilweise gefüllten Zylinder entworfen.
Simulationsergebnisse aus FEM-Simulationen sowie
Ergebnisse aus Testmessungen werden den Modellwerten
gegenübergestellt.}},
  author       = {{Kehl, Romina and Rautenberg, Jens and Henning, Bernd}},
  location     = {{Braunschweig}},
  pages        = {{407--408}},
  title        = {{{Schwingungsverhalten eines flüssigkeitsgefüllten Tanks}}},
  year         = {{2006}},
}