@inproceedings{10777,
  author       = {{Ghasemzadeh Mohammadi, Hassan and Miremadi, Seyed Ghassem and Ejlali, Alireza}},
  booktitle    = {{Dependable Computing (PRDC), 2009 IEEE Pacific Rim International Symposium on}},
  pages        = {{252--255}},
  publisher    = {{IEEE}},
  title        = {{{Signature Self Checking (SSC): A Low-Cost Reliable Control Logic for Pipelined Microprocessors}}},
  doi          = {{10.1109/PRDC.2009.69}},
  year         = {{2009}},
}

@inproceedings{11723,
  abstract     = {{In this paper we present a novel vehicle tracking algorithm, which is based on multi-level sensor fusion of GPS (global positioning system) with Inertial Measurement Unit sensor data. It is shown that the robustness of the system to temporary dropouts of the GPS signal, which may occur due to limited visibility of satellites in narrow street canyons or tunnels, is greatly improved by sensor fusion. We further demonstrate how the observation and state noise covariances of the employed Kalman filters can be estimated alongside the filtering by an application of the Expectation-Maximization algorithm. The proposed time-variant multi-level Kalman filter is shown to outperform an Interacting Multiple Model approach while at the same time being computationally less demanding.}},
  author       = {{Bevermeier, Maik and Peschke, Sven and Haeb-Umbach, Reinhold}},
  booktitle    = {{6th Workshop on Positioning Navigation and Communication (WPNC 2009)}},
  keywords     = {{covariance matrices, expectation-maximisation algorithm, expectation-maximization algorithm, global positioning system, Global Positioning System, GPS, inertial measurement unit, interacting multiple model approach, Kalman filters, multilevel sensor fusion, narrow street canyons, narrow tunnels, online parameter estimation, parameter estimation, road vehicles, robust vehicle localization, sensor fusion, state noise covariances, time-variant multilevel Kalman filter, vehicle tracking algorithm}},
  pages        = {{235--242}},
  title        = {{{Robust vehicle localization based on multi-level sensor fusion and online parameter estimation}}},
  doi          = {{10.1109/WPNC.2009.4907833}},
  year         = {{2009}},
}

@inproceedings{11724,
  abstract     = {{In this paper we present a novel vehicle tracking method which is based on multi-stage Kalman filtering of GPS and IMU sensor data. After individual Kalman filtering of GPS and IMU measurements the estimates of the orientation of the vehicle are combined in an optimal manner to improve the robustness towards drift errors. The tracking algorithm incorporates the estimation of time-variant covariance parameters by using an iterative block Expectation-Maximization algorithm to account for time-variant driving conditions and measurement quality. The proposed system is compared to an interacting multiple model approach (IMM) and achieves improved localization accuracy at lower computational complexity. Furthermore we show how the joint parameter estimation and localizaiton can be conducted with streaming input data to be able to track vehicles in a real driving environment.}},
  author       = {{Bevermeier, Maik and Peschke, Sven and Haeb-Umbach, Reinhold}},
  booktitle    = {{IEEE 69th Vehicular Technology Conference (VTC 2009 Spring)}},
  keywords     = {{computational complexity, expectation-maximisation algorithm, Global Positioning System, inertial measurement unit, inertial navigation, interacting multiple model, iterative block expectation-maximization algorithm, Kalman filters, multi-stage Kalman filter, parameter estimation, road vehicles, vehicle positioning, vehicle tracking}},
  pages        = {{1--5}},
  title        = {{{Joint Parameter Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning}}},
  doi          = {{10.1109/VETECS.2009.5073634}},
  year         = {{2009}},
}

@inproceedings{11725,
  author       = {{Bevermeier, Maik and Peschke, Sven and Haeb-Umbach, Reinhold}},
  booktitle    = {{DGON Navigationskonvent 2009}},
  title        = {{{Eine Plattform fuer Mehrwertdienste im Bereich Logistik - Drahtlose Fahrzeug- und Laderaumueberwachung fuer LKW mit Hilfe einer Maut-On-Board Unit}}},
  year         = {{2009}},
}

@inproceedings{11847,
  abstract     = {{In this paper we present a new feature space dereverberation technique for automatic speech recognition. We derive an expression for the dependence of the reverberant speech features in the log-mel spectral domain on the non-reverberant speech features and the room impulse response. The obtained observation model is used for a model based speech enhancement based on Kalman filtering. The performance of the proposed enhancement technique is studied on the AURORA5 database. In our currently best configuration, which includes uncertainty decoding, the number of recognition errors is approximately halved compared to the recognition of unprocessed speech.}},
  author       = {{Krueger, Alexander and Haeb-Umbach, Reinhold}},
  booktitle    = {{Interspeech 2009}},
  title        = {{{Model based feature enhancement for automatic speech recognition in reverberant environments}}},
  year         = {{2009}},
}

@inproceedings{11859,
  abstract     = {{In this paper we present an Uncertainty Decoding rule which exploits feature reliability information and interframe correlation for noise robust speech recognition. The reliability information can be obtained either from conditional Bayesian estimation, where speech and noise feature vectors are tracked jointly, or by augmenting conventional point estimation methods with heuristics about the estimator's reliability. Experimental results on the AURORA2 database demonstrate on the one hand that Uncertainty Decoding improves recognition performance, while on the other hand it is seen that the severe approximations needed to arrive at computationally tractable solutions have their noticable impact on recognition performance.}},
  author       = {{Leutnant, Volker and Haeb-Umbach, Reinhold}},
  booktitle    = {{International Conference on Acoustics (NAG/DAGA 2009)}},
  title        = {{{On the Estimation and Use of Feature Reliability Information for Noise Robust Speech Recognition}}},
  year         = {{2009}},
}

@inproceedings{11860,
  abstract     = {{In this paper we present an analytic derivation of the moments of the phase factor between clean speech and noise cepstral or log-mel-spectral feature vectors. The development shows, among others, that the probability density of the phase factor is of sub-Gaussian nature and that it is independent of the noise type and the signal-to-noise ratio, however dependent on the mel filter bank index. Further we show how to compute the contribution of the phase factor to both the mean and the vari- ance of the noisy speech observation likelihood, which relates the speech and noise feature vectors to those of noisy speech. The resulting phase-sensitive observation model is then used in model-based speech feature enhancement, leading to significant improvements in word accuracy on the AURORA2 database.}},
  author       = {{Leutnant, Volker and Haeb-Umbach, Reinhold}},
  booktitle    = {{Interspeech 2009}},
  title        = {{{An analytic derivation of a phase-sensitive observation model for noise robust speech recognition}}},
  year         = {{2009}},
}

@inproceedings{11881,
  abstract     = {{A combination of GPS (global positioning system) and INS (inertial navigation system) is known to provide high precision and highly robust vehicle localization. Notably during times when the GPS signal has a poor quality, e.g. due to the lack of a sufficiently large number of visible satellites, the INS, which may consist of a gyroscope and an odometer, will lead to improved positioning accuracy. In this paper we show how velocity information obtained from GSM (global system for mobile communications) signalling, rather than from a tachometer, can be used together with a gyroscope sensor to support localization in the presence of temporarily unavailable GPS data. We propose a sensor fusion system architecture and present simulation results that show the effectiveness of this approach.}},
  author       = {{Peschke, Sven and Bevermeier, Maik and Haeb-Umbach, Reinhold}},
  booktitle    = {{6th Workshop on Positioning Navigation and Communication (WPNC 2009)}},
  keywords     = {{cellular radio, distance measurement, global positioning system, Global Positioning System, global system for mobile communications, GPS positioning approach, GSM velocity, gyroscopes, gyroscope sensor, inertial navigation, inertial navigation system, odometer, sensor fusion system architecture, sensors}},
  pages        = {{195--202}},
  title        = {{{A GPS positioning approach exploiting GSM velocity estimates}}},
  doi          = {{10.1109/WPNC.2009.4907827}},
  year         = {{2009}},
}

@inproceedings{11882,
  author       = {{Peschke, Sven and Bevermeier, Maik and Haeb-Umbach, Reinhold}},
  booktitle    = {{DGON Navigationskonvent 2009}},
  title        = {{{Verbesserung von GPS-basierter Ortung durch GSM-Geschwindigkeitsschaetzungen}}},
  year         = {{2009}},
}

@article{11937,
  abstract     = {{In automatic speech recognition, hidden Markov models (HMMs) are commonly used for speech decoding, while switching linear dynamic models (SLDMs) can be employed for a preceding model-based speech feature enhancement. In this paper, these model types are combined in order to obtain a novel iterative speech feature enhancement and recognition architecture. It is shown that speech feature enhancement with SLDMs can be improved by feeding back information from the HMM to the enhancement stage. Two different feedback structures are derived. In the first, the posteriors of the HMM states are used to control the model probabilities of the SLDMs, while in the second they are employed to directly influence the estimate of the speech feature distribution. Both approaches lead to improvements in recognition accuracy both on the AURORA2 and AURORA4 databases compared to non-iterative speech feature enhancement with SLDMs. It is also shown that a combination with uncertainty decoding further enhances performance.}},
  author       = {{Windmann, Stefan and Haeb-Umbach, Reinhold}},
  journal      = {{IEEE Transactions on Audio, Speech, and Language Processing}},
  keywords     = {{AURORA2 databases, AURORA4 databases, automatic speech recognition, feedback structures, hidden Markov models, HMM, iterative methods, iterative speech feature enhancement, model probabilities, speech decoding, speech enhancement, speech feature distribution, speech recognition, switching linear dynamic models}},
  number       = {{5}},
  pages        = {{974--984}},
  title        = {{{Approaches to Iterative Speech Feature Enhancement and Recognition}}},
  doi          = {{10.1109/TASL.2009.2014894}},
  volume       = {{17}},
  year         = {{2009}},
}

@article{11938,
  abstract     = {{In this paper, parameter estimation of a state-space model of noise or noisy speech cepstra is investigated. A blockwise EM algorithm is derived for the estimation of the state and observation noise covariance from noise-only input data. It is supposed to be used during the offline training mode of a speech recognizer. Further a sequential online EM algorithm is developed to adapt the observation noise covariance on noisy speech cepstra at its input. The estimated parameters are then used in model-based speech feature enhancement for noise-robust automatic speech recognition. Experiments on the AURORA4 database lead to improved recognition results with a linear state model compared to the assumption of stationary noise.}},
  author       = {{Windmann, Stefan and Haeb-Umbach, Reinhold}},
  journal      = {{IEEE Transactions on Audio, Speech, and Language Processing}},
  keywords     = {{AURORA4 database, blockwise EM algorithm, covariance analysis, linear state model, noise covariance, noise-robust automatic speech recognition, noisy speech cepstra, offline training mode, parameter estimation, speech recognition, speech recognition equipment, speech recognizer, state-space methods, state-space model}},
  number       = {{8}},
  pages        = {{1577--1590}},
  title        = {{{Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition}}},
  doi          = {{10.1109/TASL.2009.2023172}},
  volume       = {{17}},
  year         = {{2009}},
}

@article{12060,
  author       = {{Sommer, Christoph and Dietrich, Isabel and Dressler, Falko}},
  issn         = {{1383-469X}},
  journal      = {{Mobile Networks and Applications}},
  pages        = {{786--801}},
  title        = {{{Simulation of Ad Hoc Routing Protocols using OMNeT++}}},
  doi          = {{10.1007/s11036-009-0174-5}},
  year         = {{2009}},
}

@article{15680,
  author       = {{Börstler, Jürgen and S. Hall, Mark and Nordström, Marie and H. Paterson, James and Sanders, Kate and Schulte, Carsten and Thomas, Lynda}},
  journal      = {{SIGCSE Bulletin}},
  number       = {{4}},
  pages        = {{126--143}},
  title        = {{{An evaluation of object oriented example programs in introductory programming textbooks}}},
  volume       = {{41}},
  year         = {{2009}},
}

@inproceedings{15681,
  author       = {{Schulte, Carsten and Tolksdorf, Robert}},
  booktitle    = {{DeLFI Workshops}},
  pages        = {{219--225}},
  publisher    = {{Logos Verlag}},
  title        = {{{Qualitätssicherung in einer interaktiven und lerneraktivierenden E-Learning-Umgebung}}},
  year         = {{2009}},
}

@inproceedings{15682,
  author       = {{Ehlert, Albrecht and Schulte, Carsten}},
  booktitle    = {{ICER}},
  pages        = {{15--26}},
  publisher    = {{ACM}},
  title        = {{{Empirical comparison of objects-first and objects-later}}},
  year         = {{2009}},
}

@inproceedings{15683,
  author       = {{Brinda, Torsten and Puhlmann, Hermann and Schulte, Carsten}},
  booktitle    = {{ITiCSE}},
  pages        = {{288--292}},
  publisher    = {{ACM}},
  title        = {{{Bridging ICT and CS: educational standards for computer science in lower secondary education}}},
  year         = {{2009}},
}

@inproceedings{15684,
  author       = {{Ehlert, Albrecht and Schulte, Carsten}},
  booktitle    = {{INFOS}},
  pages        = {{121--132}},
  publisher    = {{GI}},
  title        = {{{Unterschiede im Lernerfolg von Schülerinnen und Sch\üern in Abhängigkeit von der zeitlichen Reihenfolge der Themen (OOP-First bzw. OOP-Later)}}},
  volume       = {{P-156}},
  year         = {{2009}},
}

@inproceedings{15685,
  author       = {{Koubek, Jochen and Schulte, Carsten and Schulze, Peter and Witten, Helmut}},
  booktitle    = {{INFOS}},
  pages        = {{268--279}},
  publisher    = {{GI}},
  title        = {{{Informatik im Kontext (IniK) - Ein integratives Unterrichtskonzept für den Informatikunterricht}}},
  volume       = {{P-156}},
  year         = {{2009}},
}

@inproceedings{15686,
  author       = {{Schulte, Carsten}},
  booktitle    = {{INFOS}},
  pages        = {{355--366}},
  publisher    = {{GI}},
  title        = {{{Dualitätsrekonstruktion als Hilfsmittel zur Entwicklung und Planung von Informatikunterricht}}},
  volume       = {{P-156}},
  year         = {{2009}},
}

@inproceedings{15773,
  author       = {{Cheng, W. and Hüllermeier, Eyke}},
  booktitle    = {{In Proceedings MLD-2009 1st  International Workshop on Learning from Multi-Label Data, Bled, Slovenia}},
  pages        = {{28--38}},
  title        = {{{A simple instance-based approach to multilabel classification using the Mallows model}}},
  year         = {{2009}},
}