@inproceedings{10674, author = {{Ho, Nam and Kaufmann, Paul and Platzner, Marco}}, booktitle = {{24th Intl. Conf. on Field Programmable Logic and Applications (FPL)}}, keywords = {{Linux, hardware-software codesign, multiprocessing systems, parallel processing, LEON3 multicore platform, Linux kernel, PMU, hardware counters, hardware-software infrastructure, high performance embedded computing, perf_event, performance monitoring unit, Computer architecture, Hardware, Monitoring, Phasor measurement units, Radiation detectors, Registers, Software}}, pages = {{1--4}}, title = {{{A hardware/software infrastructure for performance monitoring on LEON3 multicore platforms}}}, doi = {{10.1109/FPL.2014.6927437}}, year = {{2014}}, } @inproceedings{10677, author = {{Ho, Nam and Kaufmann, Paul and Platzner, Marco}}, booktitle = {{2014 {IEEE} Intl. Conf. on Evolvable Systems (ICES)}}, keywords = {{Linux, cache storage, embedded systems, granular computing, multiprocessing systems, reconfigurable architectures, Leon3 SPARe processor, custom logic events, evolvable-self-adaptable processor cache, fine granular profiling, integer unit events, measurement infrastructure, microarchitectural events, multicore embedded system, perf_event standard Linux performance measurement interface, processor properties, run-time reconfigurable memory-to-cache address mapping engine, run-time reconfigurable multicore infrastructure, split-level caching, Field programmable gate arrays, Frequency locked loops, Irrigation, Phasor measurement units, Registers, Weaving}}, pages = {{31--37}}, title = {{{Towards self-adaptive caches: A run-time reconfigurable multi-core infrastructure}}}, doi = {{10.1109/ICES.2014.7008719}}, year = {{2014}}, } @misc{10679, author = {{König, Fabian}}, publisher = {{Paderborn University}}, title = {{{EMG-basierte simultane und proportionale Online-Steuerung einer virtuellen Prothese}}}, year = {{2014}}, } @misc{10701, author = {{Koch, Benjamin}}, publisher = {{Paderborn University}}, title = {{{Hardware Acceleration of Mechatronic Controllers on a Zynq Platform FPGA}}}, year = {{2014}}, } @misc{10715, author = {{Mittendorf, Robert}}, publisher = {{Paderborn University}}, title = {{{Advanced AES-key recovery from decayed RAM using multi-threading and FPGAs}}}, year = {{2014}}, } @misc{10732, author = {{Rüthing, Christoph}}, publisher = {{Paderborn University}}, title = {{{The Xilinx Zynq Architecture as a Platform for Reconfigurable Heterogeneous Multi-Cores}}}, year = {{2014}}, } @phdthesis{10733, abstract = {{Monte-Carlo Tree Search (MCTS) is a class of simulation-based search algorithms. It brought about great success in the past few years regarding the evaluation of deterministic two-player games such as the Asian board game Go. In this thesis, we present a parallelization of the most popular MCTS variant for large HPC compute clusters that efficiently shares a single game tree representation in a distributed memory environment and scales up to 128 compute nodes and 2048 cores. It is hereby one of the most powerful MCTS parallelizations to date. In order to measure the impact of our parallelization on the search quality and remain comparable to the most advanced MCTS implementations to date, we implemented it in a state-of-the-art Go engine Gomorra, making it competitive with the strongest Go programs in the world. We further present an empirical comparison of different Bayesian ranking systems when being used for predicting expert moves for the game of Go and introduce a novel technique for automated detection and analysis of evaluation uncertainties that show up during MCTS searches.}}, author = {{Schäfers, Lars}}, isbn = {{978-3-8325-3748-7}}, pages = {{133}}, publisher = {{Logos Verlag Berlin GmbH}}, title = {{{Parallel Monte-Carlo Tree Search for HPC Systems and its Application to Computer Go}}}, year = {{2014}}, } @inproceedings{10738, author = {{Shen, Cong and Kaufmann, Paul and Braun, Martin}}, booktitle = {{IEEE Power and Energy Society General Meeting (IEEE GM)}}, title = {{{Optimizing the Generator Start-up Sequence After a Power System Blackout}}}, year = {{2014}}, } @inproceedings{10739, author = {{Shen, Cong and Kaufmann, Paul and Braun, Martin}}, booktitle = {{Power Systems Computation Conference (PSCC)}}, publisher = {{IEEE}}, title = {{{A New Distribution Network Reconfiguration and Restoration Path Selection Algorithm}}}, year = {{2014}}, } @misc{10744, author = {{Surmund, Sebastian}}, publisher = {{Paderborn University}}, title = {{{Multithreaded Parallelization of Mechatronic Controllers on a Zynq Platform FPGA}}}, year = {{2014}}, } @book{10756, author = {{I. Esparcia-Alc{\'a}zar, Anna and Eiben, A.E. and Agapitos, Alexandros and Sim{\~o}es, Anabela and G.B. Tettamanzi, Andrea and Della Cioppa, Antonio and M. Mora, Antonio and Cotta, Carlos and Tarantino, Ernesto and Haasdijk, Evert and Divina, Federico and Fern{\'a}ndez de Vega, Francisco and Squillero, Giovanni and De Falco, Ivanoe and Ignacio Hidalgo, J. and Sim, Kevin and Glette, Kyrre and Zhang, Mengjie and Urquhart, Neil and Burelli, Paolo and Kaufmann, Paul and Po{\v s}{\'\i}k, Petr and Schaefer, Robert and Drechsler, Rolf and Antipolis, Sophia and Cagnoni, Stefano and Thanh Nguyen, Trung and S. Bush (editors), William}}, publisher = {{Springer}}, title = {{{Applications of Evolutionary Computation - 17th European Conference, EvoApplications}}}, volume = {{8602}}, year = {{2014}}, } @inproceedings{10764, author = {{Anwer, Jahanzeb and Platzner, Marco}}, booktitle = {{IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT)}}, pages = {{177--184}}, publisher = {{IEEE}}, title = {{{Analytic reliability evaluation for fault-tolerant circuit structures on FPGAs}}}, doi = {{10.1109/DFT.2014.6962108}}, year = {{2014}}, } @inproceedings{10773, author = {{Ghasemzadeh Mohammadi, Hassan and Gaillardon, Pierre-Emmanuel and Yazdani, Majid and De Micheli, Giovanni}}, booktitle = {{2014 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH)}}, pages = {{163--168}}, publisher = {{IEEE}}, title = {{{Fast process variation analysis in nano-scaled technologies using column-wise sparse parameter selection}}}, doi = {{10.1109/NANOARCH.2014.6880479}}, year = {{2014}}, } @article{10791, author = {{Trier, Matthias and Richter, Alexander}}, issn = {{1350-1917}}, journal = {{Information Systems Journal}}, pages = {{465--488}}, title = {{{The deep structure of organizational online networking - an actor-oriented case study}}}, doi = {{10.1111/isj.12047}}, year = {{2014}}, } @inproceedings{11746, abstract = {{ "A method for nonstationary noise robust automatic speech recognition (ASR) is to first estimate the changing noise statistics and second clean up the features prior to recognition accordingly. Here, the first is accomplished by noise tracking in the spectral domain, while the second relies on Bayesian enhancement in the feature domain. In this way we take advantage of our recently proposed maximum a-posteriori based (MAP-B) noise power spectral density estimation algorithm, which is able to estimate the noise statistics even in time-frequency bins dominated by speech. We show that MAP-B noise tracking leads to an improved noise model estimate in the feature domain compared to estimating noise in speech absence periods only, if the bias resulting from the nonlinear transformation from the spectral to the feature domain is accounted for. Consequently, ASR results are improved, as is shown by experiments conducted on the Aurora IV database." }}, author = {{Chinaev, Aleksej and Puels, Marc and Haeb-Umbach, Reinhold}}, booktitle = {{11. ITG Fachtagung Sprachkommunikation (ITG 2014)}}, title = {{{Spectral Noise Tracking for Improved Nonstationary Noise Robust ASR}}}, year = {{2014}}, } @inproceedings{11752, abstract = {{ "In this contribution we derive a variational EM (VEM) algorithm for model selection in complex Watson mixture models, which have been recently proposed as a model of the distribution of normalized microphone array signals in the short-time Fourier transform domain. The VEM algorithm is applied to count the number of active sources in a speech mixture by iteratively estimating the mode vectors of the Watson distributions and suppressing the signals from the corresponding directions. A key theoretical contribution is the derivation of the MMSE estimate of a quadratic form involving the mode vector of the Watson distribution. The experimental results demonstrate the effectiveness of the source counting approach at moderately low SNR. It is further shown that the VEM algorithm is more robust w.r.t. used threshold values." }}, author = {{Drude, Lukas and Chinaev, Aleksej and Tran Vu, Dang Hai and Haeb-Umbach, Reinhold}}, booktitle = {{39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)}}, title = {{{Source Counting in Speech Mixtures Using a Variational EM Approach for Complexwatson Mixture Models}}}, year = {{2014}}, } @inproceedings{11753, abstract = {{This contribution describes a step-wise source counting algorithm to determine the number of speakers in an offline scenario. Each speaker is identified by a variational expectation maximization (VEM) algorithm for complex Watson mixture models and therefore directly yields beamforming vectors for a subsequent speech separation process. An observation selection criterion is proposed which improves the robustness of the source counting in noise. The algorithm is compared to an alternative VEM approach with Gaussian mixture models based on directions of arrival and shown to deliver improved source counting accuracy. The article concludes by extending the offline algorithm towards a low-latency online estimation of the number of active sources from the streaming input data.}}, author = {{Drude, Lukas and Chinaev, Aleksej and Tran Vu, Dang Hai and Haeb-Umbach, Reinhold}}, booktitle = {{14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014)}}, keywords = {{Accuracy, Acoustics, Estimation, Mathematical model, Soruce separation, Speech, Vectors, Bayes methods, Blind source separation, Directional statistics, Number of speakers, Speaker diarization}}, pages = {{213--217}}, title = {{{Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models}}}, year = {{2014}}, } @inproceedings{11814, abstract = {{ "In this paper we present an algorithm for the unsupervised segmentation of a lattice produced by a phoneme recognizer into words. Using a lattice rather than a single phoneme string accounts for the uncertainty of the recognizer about the true label sequence. An example application is the discovery of lexical units from the output of an error-prone phoneme recognizer in a zero-resource setting, where neither the lexicon nor the language model (LM) is known. We propose a computationally efficient iterative approach, which alternates between the following two steps: First, the most probable string is extracted from the lattice using a phoneme LM learned on the segmentation result of the previous iteration. Second, word segmentation is performed on the extracted string using a word and phoneme LM which is learned alongside the new segmentation. We present results on lattices produced by a phoneme recognizer on the WSJCAM0 dataset. We show that our approach delivers superior segmentation performance than an earlier approach found in the literature, in particular for higher-order language models. " }}, author = {{Heymann, Jahn and Walter, Oliver and Haeb-Umbach, Reinhold and Raj, Bhiksha}}, booktitle = {{39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)}}, title = {{{Iterative Bayesian Word Segmentation for Unspuervised Vocabulary Discovery from Phoneme Lattices}}}, year = {{2014}}, } @inproceedings{11831, abstract = {{ "Several self-localization algorithms have been proposed, that determine the positions of either acoustic or visual sensors autonomously. Usually these positions are given in a modality specific coordinate system, with an unknown rotation, translation and scale between the different systems. For a joint audiovisual tracking, where the different modalities support each other, the two modalities need to be mapped into a common coordinate system. In this paper we propose to estimate this mapping based on audiovisual correlates, i.e., a speaker that can be localized by both, a microphone and a camera network separately. The voice is tracked by a microphone network, which had to be calibrated by a self-localization algorithm at first, and the head is tracked by a calibrated camera network. Unlike existing Singular Value Decomposition based approaches to estimate the coordinate system mapping, we propose to perform an estimation in the shape domain, which turns out to be computationally more efficient. Simulations of the self-localization of an acoustic sensor network and a following coordinate mapping for a joint speaker localization showed a significant improvement of the localization performance, since the modalities were able to support each other." }}, author = {{Jacob, Florian and Haeb-Umbach, Reinhold}}, booktitle = {{11. ITG Fachtagung Sprachkommunikation (ITG 2014)}}, title = {{{Coordinate Mapping Between an Acoustic and Visual Sensor Network in the Shape Domain for a Joint Self-Calibrating Speaker Tracking}}}, year = {{2014}}, } @article{11861, abstract = {{In this contribution we present a theoretical and experimental investigation into the effects of reverberation and noise on features in the logarithmic mel power spectral domain, an intermediate stage in the computation of the mel frequency cepstral coefficients, prevalent in automatic speech recognition (ASR). Gaining insight into the complex interaction between clean speech, noise, and noisy reverberant speech features is essential for any ASR system to be robust against noise and reverberation present in distant microphone input signals. The findings are gathered in a probabilistic formulation of an observation model which may be used in model-based feature compensation schemes. The proposed observation model extends previous models in three major directions: First, the contribution of additive background noise to the observation error is explicitly taken into account. Second, an energy compensation constant is introduced which ensures an unbiased estimate of the reverberant speech features, and, third, a recursive variant of the observation model is developed resulting in reduced computational complexity when used in model-based feature compensation. The experimental section is used to evaluate the accuracy of the model and to describe how its parameters can be determined from test data.}}, author = {{Leutnant, Volker and Krueger, Alexander and Haeb-Umbach, Reinhold}}, issn = {{2329-9290}}, journal = {{IEEE/ACM Transactions on Audio, Speech, and Language Processing}}, keywords = {{computational complexity, reverberation, speech recognition, automatic speech recognition, background noise, clean speech, computational complexity, energy compensation, logarithmic mel power spectral domain, mel frequency cepstral coefficients, microphone input signals, model-based feature compensation schemes, noisy reverberant speech automatic recognition, noisy reverberant speech features, reverberation, Atmospheric modeling, Computational modeling, Noise, Noise measurement, Reverberation, Speech, Vectors, Model-based feature compensation, observation model for reverberant and noisy speech, recursive observation model, robust automatic speech recognition}}, number = {{1}}, pages = {{95--109}}, title = {{{A New Observation Model in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech}}}, doi = {{10.1109/TASLP.2013.2285480}}, volume = {{22}}, year = {{2014}}, }