@misc{10642, author = {{Breitlauch, Daniel}}, publisher = {{Paderborn University}}, title = {{{Evolvable Cache Controller}}}, year = {{2010}}, } @misc{10649, author = {{Dridger, Denis}}, publisher = {{Paderborn University}}, title = {{{Soft Microprocessors with tightly coupled Application-Specific Coprocessors}}}, year = {{2010}}, } @misc{10657, author = {{Graf, Tobias}}, publisher = {{Paderborn University}}, title = {{{Parallelization of the UCT Algorithm on HPC-Clusters}}}, year = {{2010}}, } @misc{10670, author = {{Fröse, Viktor and Ibers, Rüdiger and Hellebrand, Sybille}}, keywords = {{WORKSHOP}}, title = {{{Testdatenkompression mit Hilfe der Netzwerkinfrastruktur}}}, year = {{2010}}, } @inproceedings{10683, author = {{Kaufmann, Paul and Englehart, Kevin and Platzner, Marco}}, booktitle = {{International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}}, pages = {{6357--6360}}, publisher = {{IEEE}}, title = {{{Fluctuating EMG Signals: Investigating Long-term Effects of Pattern Matching Algorithms}}}, year = {{2010}}, } @inproceedings{10686, author = {{Kaufmann, Paul and Knieper, Tobias and Platzner, Marco}}, booktitle = {{IEEE World Congress on Computational Intelligence (WCCI), Congress on Evolutionary Computation (CEC)}}, pages = {{541--548}}, publisher = {{IEEE}}, title = {{{A Novel Hybrid Evolutionary Strategy and its Periodization with Multi-objective Genetic Optimizers}}}, year = {{2010}}, } @article{10694, author = {{Kebschull, Udo and Platzner, Marco and Teich, Jürgen}}, issn = {{1751-8601}}, journal = {{IET Computers Digital Techniques}}, number = {{3}}, pages = {{157--158}}, title = {{{Selected papers from the 18th International Conference on Field Programmable Logic and Applications, FPL 2008 (editorial)}}}, doi = {{10.1049/iet-cdt.2010.9044}}, volume = {{4}}, year = {{2010}}, } @misc{10697, author = {{Knieper, Tobias}}, publisher = {{Paderborn University}}, title = {{{Hybridization of Global Multi-Objective and Local Search Techniques}}}, year = {{2010}}, } @inproceedings{10699, author = {{Knieper, Tobias and Kaufmann, Paul and Glette, Kyrre and Platzner, Marco and Torresen, Jim}}, booktitle = {{IEEE Intl. Conf. on Evolvable Systems (ICES)}}, pages = {{250--261}}, publisher = {{Springer}}, title = {{{Coping with Resource Fluctuations: The Run-time Reconfigurable Functional Unit Row Classifier Architecture}}}, volume = {{6274}}, year = {{2010}}, } @inbook{10704, author = {{Lübbers, Enno and Platzner, Marco}}, booktitle = {{Dynamically Reconfigurable Systems: Architectures, Design Methods and Applications}}, editor = {{Platzner, Marco and Teich, Jürgen and Wehn, Norbert}}, pages = {{269--290}}, publisher = {{Springer-Verlag GmbH}}, title = {{{ReconOS: An Operating System for Dynamically Reconfigurable Hardware}}}, doi = {{10.1007/978-90-481-3485-4_13}}, year = {{2010}}, } @misc{10710, author = {{Meiche, Robert}}, publisher = {{Paderborn University}}, title = {{{FPGA/CPU Multicore-Plattform für ReconOS/eCos}}}, year = {{2010}}, } @misc{10717, author = {{Niekamp, Manuel}}, publisher = {{Paderborn University}}, title = {{{Transparente Hardwarebeschleunigung durch Shared Library Interposing}}}, year = {{2010}}, } @misc{10731, author = {{Runde, Bodo}}, publisher = {{Paderborn University}}, title = {{{A Token-Ring Network-On-Chip for Message Passing in ReconOS}}}, year = {{2010}}, } @misc{10752, author = {{Wiersema, Tobias}}, publisher = {{Paderborn University}}, title = {{{Scheduling Support for Heterogeneous Hardware Accelerators under Linux}}}, year = {{2010}}, } @book{10763, editor = {{Platzner, Marco and Teich, Jürgen and Wehn, Norbert}}, isbn = {{9048134846}}, publisher = {{Springer-Verlag GmbH}}, title = {{{Dynamically Reconfigurable Systems: Architectures, Design Methods and Applications}}}, doi = {{10.1007/978-90-481-3485-4}}, year = {{2010}}, } @inproceedings{10776, author = {{Khatir, Mehrdad and Ghasemzadeh Mohammadi, Hassan and Ejlali, Alireza}}, booktitle = {{Computer Design (ICCD), 2010 IEEE International Conference on}}, pages = {{138--144}}, publisher = {{IEEE}}, title = {{{Sub-threshold charge recovery circuits}}}, doi = {{10.1109/ICCD.2010.5647815}}, year = {{2010}}, } @inproceedings{11726, abstract = {{In this paper we present a robust location estimation algorithm especially focused on the accuracy in vertical position. A loosely-coupled error state space Kalman filter, which fuses sensor data of an Inertial Measurement Unit and the output of a Global Positioning System device, is augmented by height information from an altitude measurement unit. This unit consists of a barometric altimeter whose output is fused with topographic map information by a Kalman filter to provide robust information about the current vertical user position. These data replace the less reliable vertical position information provided the GPS device. It is shown that typical barometric errors like thermal divergences and fluctuations in the pressure due to changing weather conditions can be compensated by the topographic map information and the barometric error Kalman filter. The resulting height information is shown not only to be more reliable than height information provided by GPS. It also turns out that it leads to better attitude and thus better overall localization estimation accuracy due to the coupling of spatial orientations via the Direct Cosine Matrix. Results are presented both for artificially generated and field test data, where the user is moving by car.}}, author = {{Bevermeier, Maik and Walter, Oliver and Peschke, Sven and Haeb-Umbach, Reinhold}}, booktitle = {{7th Workshop on Positioning Navigation and Communication (WPNC 2010)}}, keywords = {{altitude measurement unit, barometers, barometric altimeter, barometric error Kalman filter, barometric height estimation, direct cosine matrix, global positioning system, Global Positioning System, GPS device, height information, height measurement, inertial measurement unit, Kalman filters, loosely-coupled error state space Kalman filter, loosely-coupled Kalman-filter, map matching, robust information, robust location estimation, sensor fusion, topographic map information, vertical user position}}, pages = {{128--134}}, title = {{{Barometric height estimation combined with map-matching in a loosely-coupled Kalman-filter}}}, doi = {{10.1109/WPNC.2010.5650745}}, year = {{2010}}, } @article{11846, abstract = {{In this paper, we present a new technique for automatic speech recognition (ASR) in reverberant environments. Our approach is aimed at the enhancement of the logarithmic Mel power spectrum, which is computed at an intermediate stage to obtain the widely used Mel frequency cepstral coefficients (MFCCs). Given the reverberant logarithmic Mel power spectral coefficients (LMPSCs), a minimum mean square error estimate of the clean LMPSCs is computed by carrying out Bayesian inference. We employ switching linear dynamical models as an a priori model for the dynamics of the clean LMPSCs. Further, we derive a stochastic observation model which relates the clean to the reverberant LMPSCs through a simplified model of the room impulse response (RIR). This model requires only two parameters, namely RIR energy and reverberation time, which can be estimated from the captured microphone signal. The performance of the proposed enhancement technique is studied on the AURORA5 database and compared to that of constrained maximum-likelihood linear regression (CMLLR). It is shown by experimental results that our approach significantly outperforms CMLLR and that up to 80\% of the errors caused by the reverberation are recovered. In addition to the fact that the approach is compatible with the standard MFCC feature vectors, it leaves the ASR back-end unchanged. It is of moderate computational complexity and suitable for real time applications.}}, author = {{Krueger, Alexander and Haeb-Umbach, Reinhold}}, journal = {{IEEE Transactions on Audio, Speech, and Language Processing}}, keywords = {{ASR, AURORA5 database, automatic speech recognition, Bayesian inference, belief networks, CMLLR, computational complexity, constrained maximum likelihood linear regression, least mean squares methods, LMPSC computation, logarithmic Mel power spectrum, maximum likelihood estimation, Mel frequency cepstral coefficients, MFCC feature vectors, microphone signal, minimum mean square error estimation, model-based feature enhancement, regression analysis, reverberant speech recognition, reverberation, RIR energy, room impulse response, speech recognition, stochastic observation model, stochastic processes}}, number = {{7}}, pages = {{1692--1707}}, title = {{{Model-Based Feature Enhancement for Reverberant Speech Recognition}}}, doi = {{10.1109/TASL.2010.2049684}}, volume = {{18}}, year = {{2010}}, } @inproceedings{11857, abstract = {{Traditionally, ASR systems are based on hidden Markov models with Gaussian mixtures modelling the state-conditioned feature distribution. The inherent assumption of conditional independence, stating that a feature's likelihood solely depends on the current HMM state, makes the search computationally tractable, nevertheless has also been identified to be a major reason for the lack of robustness of such systems. Linear dynamic models have been proposed to overcome this weakness by employing a hidden dynamic state process underlying the observed features. Though performance of linear dynamic models on continuous speech/phone recognition tasks has been shown to be superior to that of equivalent static models, this approach still cannot compete with the established acoustic models. In this paper we consider the combination of hidden Markov models based on Gaussian mixture densities (GMM-HMMs) and linear dynamic models (LDMs) as the acoustic model for automatic speech recognition systems. In doing so, the individual strengths of both models, i.e. the modelling of long-term temporal dependencies by the GMM-HMM and the direct modelling of statistical dependencies between consecutive feature vectors by the LDM, are exploited. Phone classification experiments conducted on the TIMIT database indicate the prospective use of this approach for the application to continuous speech recognition.}}, author = {{Leutnant, Volker and Haeb-Umbach, Reinhold}}, booktitle = {{36. Deutsche Jahrestagung fuer Akustik (DAGA 2010)}}, title = {{{Options for Modelling Temporal Statistical Dependencies in an Acoustic Model for ASR}}}, year = {{2010}}, } @inproceedings{11858, abstract = {{Linear dynamic models (LDMs) have been shown to be a viable alternative to hidden Markov models (HMMs) on small-vocabulary recognition tasks, such as phone classification. In this paper we investigate various statistical model combination approaches for a hybrid HMM-LDM recognizer, resulting in a phone classification performance that outperforms the best individual classifier. Further, we report on continuous speech recognition experiments on the AURORA4 corpus, where the model combination is carried out on wordgraph rescoring. While the hybrid system improves the HMM system in the case of monophone HMMs, the performance of the triphone HMM model could not be improved by monophone LDMs, asking for the need to introduce context-dependency also in the LDM model inventory.}}, author = {{Leutnant, Volker and Haeb-Umbach, Reinhold}}, booktitle = {{Interspeech 2010}}, title = {{{On the Exploitation of Hidden Markov Models and Linear Dynamic Models in a Hybrid Decoder Architecture for Continuous Speech Recognition}}}, year = {{2010}}, }