Asynchronous variational Lie group integration for geometrically exact beam dynamics - Demoures, Francois; Gay-Balmaz, Francois; Leitz, Thomas; Leyendecker, Sigrid; Ober-Blöbaum, Sina; Ratiu, Tudor S. (PAMM)

Discretized switching time optimization problems - Flasskamp, Kathrin; Murphey, Todd; Ober-Blobaum, Sina (2013 European Control Conference (ECC))

Optimization for discretized switched systems - Flaßkamp, Kathrin; Murphey, Todd; Ober-Blöbaum, Sina (PAMM)

Markov modelling for random dynamical systems - Froyland, Gary (Equadiff 99)

Computing time investigations for variational multirate integration - Gail, Tobias; Leyendecker, Sigrid; Ober-Blöbaum, Sina (PAMM)

A multiobjective optimization approach for optimal control problems of mechanical systems with uncertainties - Ober-Blöbaum, Sina; Seifried, Albert (2013 European Control Conference (ECC))

Set Oriented Methods for the Numerical Treatment of Multiobjective Optimization Problems - Schütze, Oliver; Witting, Katrin; Ober-Blöbaum, Sina; Dellnitz, Michael (EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation)

Discrete-time model of an IPMSM based on variational integrators - Specht, Andreas; Ober-Blobaum, Sina; Wallscheid, Oliver; Romaus, Christoph; Bocker, Joachim (2013 International Electric Machines & Drives Conference)

A variational approach to define robustness for parametric multiobjective optimization problems - Witting, Katrin; Ober-Blöbaum, Sina; Dellnitz, Michael (Journal of Global Optimization)

Analysis of team and player performance using recorded trajectory data - Timmermann, Robert; Dellnitz, Michael (Performance Analysis of Sport IX, Part 8, Routledge)

Running performance analysis in basketball using recorded trajectory data - Jakobsmeyer, R.; Schnittker, R.; Timmermann, R.; Zorn, R.; Rückert, U.; Baumeister, J. (Performance Analysis of Sport IX, Part 8, Routledge)

Blue-green emitting microdisks using low-temperature-grown ZnO on patterned silicon substrates - Ruth, Marcel; Zentgraf, Thomas; Meier, Cedrik (Optics Express)

Variational integrators for electric circuits - Ober-Blöbaum, Sina; Tao, Molei; Cheng, Mulin; Owhadi, Houman; Marsden, Jerrold E. (Journal of Computational Physics)

Interference-induced asymmetric transmission through a monolayer of anisotropic chiral metamolecules - Zhang, Shuang; Liu, Fu; Zentgraf, Thomas; Li, Jensen (Physical Review A)

Three-dimensional optical holography using a plasmonic metasurface - Huang, Lingling; Chen, Xianzhong; Mühlenbernd, Holger; Zhang, Hao; Chen, Shumei; Bai, Benfeng; Tan, Qiaofeng; Jin, Guofan; Cheah, Kok-Wai; Qiu, Cheng-Wei; Li, Jensen; Zentgraf, Thomas; et al. (Nature Communications)

Reversible Three-Dimensional Focusing of Visible Light with Ultrathin Plasmonic Flat Lens - Chen, Xianzhong; Huang, Lingling; Mühlenbernd, Holger; Li, Guixin; Bai, Benfeng; Tan, Qiaofeng; Jin, Guofan; Qiu, Cheng-Wei; Zentgraf, Thomas; Zhang, Shuang (Advanced Optical Materials)

Metalens with convex and concave functionality - Zhang, Shuang; Chen, Xianzhong; Huang, Lingling; Bai, Benfeng; Tan, Qiaofeng; Jin, Guofan; Mühlenbernd, Holger; Zentgraf, Thomas; Li, Guixin; Qiu, Cheng-Wei (SPIE Newsroom)

Helicity dependent directional surface plasmon polariton excitation using a metasurface with interfacial phase discontinuity - Huang, Lingling; Chen, Xianzhong; Bai, Benfeng; Tan, Qiaofeng; Jin, Guofan; Zentgraf, Thomas; Zhang, Shuang (Light: Science & Applications)

MUSE: Implementation of a Design Theory for Systems that Support Convergent and Divergent Thinking - Müller, Oliver; Debortoli, Stefan; Seidel, Stefan (International Conference on Design Science Research in Information Systems)

A Self-adaptive Heterogeneous Multi-core Architecture for Embedded Real-time Video Object Tracking - Happe, Markus; Lübbers, Enno; Platzner, Marco (International Journal of Real-time Image Processing)

Dynamic reliability management: Reconfiguring reliability-levels of hardware designs at runtime - Anwer, Jahanzeb; Meisner, Sebastian; Platzner, Marco (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

Beschleunigung von Tiefenberechnung aus Stereobildern durch FPGA-basierte Datenflussrechner - Bick, Christian

Improving transient state myoelectric signal recognition in hand movement classification using gyroscopes - Boschmann, Alexander; Nofen, Barbara; Platzner, Marco (Proc. IEEE Int. Conf. Eng. Med. Biolog. (EMBC))

Reducing the limb position effect in pattern recognition based myoelectric control using a high density electrode array - Boschmann, Alexander; Platzner, Marco (Proc. IEEE ISSNIP Biosignals and Biorobotics Conference (BRC))

Investigating Evolvable Hardware Classification for the BioSleeve Electromyographic Interface - Glette, Kyrre; Kaufmann, Paul; Assad, Christopher; Wolf, Michael (IEEE Intl. Conf. on Evolvable Systems (ICES))

Adapting Hardware Systems by Means of Multi-Objective Evolution - Kaufmann, Paul

Classification of Electromyographic Signals: Comparing Evolvable Hardware to Conventional Classifiers - Kaufmann, Paul; Glette, Kyrre; Gruber, Tiemo; Platzner, Marco; Torresen, Jim; Sick, Bernhard (IEEE Transactions on Evolutionary Computation)

Behavior Models for Electric Vehicles - Knoop, Michael

Verbesserung der Erkennungsrate eines Systems zur Klassifikation von EMG-Signalen durch den Einsatz eines hybriden Lagesensors - Nofen, Barbara

Überquerung der Styx - Betriebsparametervariation und Fehlerverhalten eines Platform FPGAs - Pudelko, Daniel

Identifikation und Wiederherstellung von kryptographischen Schlüsseln mit FPGAs - Riebler, Heinrich

MiBenchHybrid : Erweiterung eines Benchmarks um Hardwarebeschleunigung - Sprenger, Alexander

Beschleunigung von Einzelbild-Erkennungsverfahren auf Datenfluss basierenden HPC Systemen - Steppeler, Philipp

Real-Time Simulation of Distribution Grids with high Penetration of Regenerative and Distributed Generation - Toebermann, Christian; Geibel, Daniel; Hau, Manuel; Brandl, Ron; Kaufmann, Paul; Ma, Chenjie; Braun, Martin; Degner, Tobias (Real-Time Conference)

A fast TCAD-based methodology for Variation analysis of emerging nano-devices - Ghasemzadeh Mohammadi, Hassan; Gaillardon, Pierre-Emmanuel; Yazdani, Majid; De Micheli, Giovanni (2013 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFTS))

Vertically-stacked silicon nanowire transistors with controllable polarity: A robustness study - Gaillardon, Pierre-Emmanuel; Ghasemzadeh Mohammadi, Hassan; De Micheli, Giovanni (2013 14th Latin American Test Workshop-LATW)

Schema-based Parallel Compression and Decompression of XML Data - Böttcher, Stefan; Feldotto, Matthias; Hartel, Rita (WEBIST 2013 - Proceedings of the 9th International Conference on Web Information Systems and Technologies, Aachen, Germany, 8-10 May, 2013)
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.

GMM-based significance decoding - Abdelaziz, Ahmed H.; Zeiler, Steffen; Kolossa, Dorothea; Leutnant, Volker; Haeb-Umbach, Reinhold (Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on)
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.
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

MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations - Chinaev, Aleksej; Haeb-Umbach, Reinhold (38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013))
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.
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

Improved Single-Channel Nonstationary Noise Tracking by an Optimized MAP-based Postprocessor - Chinaev, Aleksej; Haeb-Umbach, Reinhold; Taghia, Jalal; Martin, Rainer (38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013))
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.

On the Acoustic Channel Identification in Multi-Microphone Systems via Adaptive Blind Signal Enhancement Techniques - Enzner, Gerald; Schmid, Dominic; Haeb-Umbach, Reinhold (21th European Signal Processing Conference (EUSIPCO 2013))
Among the different configurations of multi-microphone systems, e.g., in applications of speech dereverberation or denoising, we consider the case without a priori information of the microphone-array geometry. This naturally invokes explicit or implicit identification of source-receiver transfer functions as an indirect description of the microphone-array configuration. However, this blind channel identification (BCI) has been difficult due to the lack of unique identifiability in the presence of observation noise or near-common channel zeros. In this paper, we study the implicit BCI performance of blind signal enhancement techniques such as the adaptive principal component analysis (PCA) or the iterative blind equalization and channel identification (BENCH). To this end, we make use of a recently proposed metric, the normalized filter-projection misalignment (NFPM), which is tailored for BCI evaluation in ill-conditioned (e.g., noisy) scenarios. The resulting understanding of implicit BCI performance can help to judge the behavior of multi-microphone speech enhancement systems and the suitability of implicit BCI to serve channel-based (i.e., channel-informed) enhancement.

Unsupervised Word Segmentation from Noisy Input - Heymann, Jahn; Walter, Oliver; Haeb-Umbach, Reinhold; Raj, Bhiksha (Automatic Speech Recognition and Understanding Workshop (ASRU 2013))

Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning - Hoang, Manh Kha; Haeb-Umbach, Reinhold (38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013))
In this paper, we consider the Maximum Likelihood (ML) estimation of the parameters of a GAUSSIAN in the presence of censored, i.e., clipped data. We show that the resulting Expectation Maximization (EM) algorithm delivers virtually biasfree and efficient estimates, and we discuss its convergence properties. We also discuss optimal classification in the presence of censored data. Censored data are frequently encountered in wireless LAN positioning systems based on the fingerprinting method employing signal strength measurements, due to the limited sensitivity of the portable devices. Experiments both on simulated and real-world data demonstrate the effectiveness of the proposed algorithms.
Keywords: Gaussian processes; Global Positioning System; convergence; expectation-maximisation algorithm; fingerprint identification; indoor radio; signal classification; wireless LAN; EM algorithm; ML estimation; WiFi indoor positioning; censored Gaussian data classification; clipped data; convergence properties; expectation maximization algorithm; fingerprinting method; maximum likelihood estimation; optimal classification; parameters estimation; portable devices sensitivity; signal strength measurements; wireless LAN positioning systems; Convergence; IEEE 802.11 Standards; Maximum likelihood estimation; Parameter estimation; Position measurement; Training; Indoor positioning; censored data; expectation maximization; signal strength; wireless LAN

The reverb challenge: a common evaluation framework for dereverberation and recognition of reverberant speech - Kinoshita, Keisuke; Delcroix, Marc; Yoshioka, Takuya; Nakatani, Tomohiro; Habets, Emanuel; Haeb-Umbach, Reinhold; Leutnant, Volker; Sehr, Armin; Kellermann, Walter; Maas, Roland; Gannot, Sharon; Raj, Bhiksha (IEEE Workshop on Applications of Signal Processing to Audio and Acoustics)
Recently, substantial progress has been made in the field of reverberant speech signal processing, including both single- and multichannel de-reverberation techniques, and automatic speech recognition (ASR) techniques robust to reverberation. To evaluate state-of-the-art algorithms and obtain new insights regarding potential future research directions, we propose a common evaluation framework including datasets, tasks, and evaluation metrics for both speech enhancement and ASR techniques. The proposed framework will be used as a common basis for the REVERB (REverberant Voice Enhancement and Recognition Benchmark) challenge. This paper describes the rationale behind the challenge, and provides a detailed description of the evaluation framework and benchmark results.
Keywords: Reverberant speech; dereverberation; ASR; evaluation; challenge

Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition - Leutnant, Volker; Krueger, Alexander; Haeb-Umbach, Reinhold (IEEE Transactions on Audio, Speech, and Language Processing)
In this contribution we extend a previously proposed Bayesian approach for the enhancement of reverberant logarithmic mel power spectral coefficients for robust automatic speech recognition to the additional compensation of background noise. A recently proposed observation model is employed whose time-variant observation error statistics are obtained as a side product of the inference of the a posteriori probability density function of the clean speech feature vectors. Further a reduction of the computational effort and the memory requirements are achieved by using a recursive formulation of the observation model. The performance of the proposed algorithms is first experimentally studied on a connected digits recognition task with artificially created noisy reverberant data. It is shown that the use of the time-variant observation error model leads to a significant error rate reduction at low signal-to-noise ratios compared to a time-invariant model. Further experiments were conducted on a 5000 word task recorded in a reverberant and noisy environment. A significant word error rate reduction was obtained demonstrating the effectiveness of the approach on real-world data.
Keywords: Bayes methods; compensation; error statistics; reverberation; speech recognition; Bayesian feature enhancement; background noise; clean speech feature vectors; compensation; connected digits recognition task; error statistics; memory requirements; noisy reverberant data; posteriori probability density function; recursive formulation; reverberant logarithmic mel power spectral coefficients; robust automatic speech recognition; signal-to-noise ratios; time-variant observation; word error rate reduction; Robust automatic speech recognition; model-based Bayesian feature enhancement; observation model for reverberant and noisy speech; recursive observation model

Blind Speech Separation Exploiting Temporal and Spectral Correlations Using Turbo Decoding of 2D-HMMs - Tran Vu, Dang Hai; Haeb-Umbach, Reinhold (21th European Signal Processing Conference (EUSIPCO 2013))
We present a novel method to exploit correlations of adjacent time-frequency (TF)-slots for a sparseness-based blind speech separation (BSS) system. Usually, these correlations are exploited by some heuristic smoothing techniques in the post-processing of the estimated soft TF masks. We propose a different approach: Based on our previous work with one-dimensional (1D)-hidden Markov models (HMMs) along the time axis we extend the modeling to two-dimensional (2D)-HMMs to exploit both temporal and spectral correlations in the speech signal. Based on the principles of turbo decoding we solved the complex inference of 2D-HMMs by a modified forward-backward algorithm which operates alternatingly along the time and the frequency axis. Extrinsic information is exchanged between these steps such that increasingly better soft time-frequency masks are obtained, leading to improved speech separation performance in highly reverberant recording conditions.

Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation - Vu, Dang Hai Tran; Haeb-Umbach, Reinhold (38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013))
In this paper we present a speech presence probability (SPP) estimation algorithmwhich exploits both temporal and spectral correlations of speech. To this end, the SPP estimation is formulated as the posterior probability estimation of the states of a two-dimensional (2D) Hidden Markov Model (HMM). We derive an iterative algorithm to decode the 2D-HMM which is based on the turbo principle. The experimental results show that indeed the SPP estimates improve from iteration to iteration, and further clearly outperform another state-of-the-art SPP estimation algorithm.
Keywords: correlation methods; estimation theory; hidden Markov models; iterative methods; probability; spectral analysis; speech processing; 2D HMM; SPP estimates; iterative algorithm; posterior probability estimation; spectral correlation; speech presence probability estimation; state-of-the-art SPP estimation algorithm; temporal correlation; turbo principle; two-dimensional hidden Markov model; Correlation; Decoding; Estimation; Iterative decoding; Noise; Speech; Vectors

Unsupervised Word Discovery from Phonetic Input Using Nested Pitman-Yor Language Modeling - Walter, Oliver; Haeb-Umbach, Reinhold; Chaudhuri, Sourish; Raj, Bhiksha (IEEE International Conference on Robotics and Automation (ICRA 2013))
In this paper we consider the unsupervised word discovery from phonetic input. We employ a word segmentation algorithm which simultaneously develops a lexicon, i.e., the transcription of a word in terms of a phone sequence, learns a n-gram language model describing word and word sequence probabilities, and carries out the segmentation itself. The underlying statistical model is that of a Pitman-Yor process, a concept known from Bayesian non-parametrics, which allows for an a priori unknown and unlimited number of different words. Using a hierarchy of Pitman-Yor processes, language models of different order can be employed and nesting it with another hierarchy of Pitman-Yor processes on the phone level allows for backing off unknown word unigrams by phone m-grams. We present results on a large-vocabulary task, assuming an error-free phone sequence is given. We finish by discussing options how to cope with noisy phone sequences.

Hierarchical System for Word Discovery Exploiting DTW-Based Initialization - Walter, Oliver; Korthals, Timo; Haeb-Umbach, Reinhold; Raj, Bhiksha (Automatic Speech Recognition and Understanding Workshop (ASRU 2013))

A Novel Initialization Method for Unsupervised Learning of Acoustic Patterns in Speech (FGNT-2013-01) - Walter, Oliver; Schmalenstroeer, Joerg; Haeb-Umbach, Reinhold
In this paper we present a novel initialization method for unsupervised learning of acoustic patterns in recordings of continuous speech. The pattern discovery task is solved by dynamic time warping whose performance we improve by a smart starting point selection. This enables a more accurate discovery of patterns compared to conventional approaches. After graph-based clustering the patterns are employed for training hidden Markov models for an unsupervised speech acquisition. By iterating between model training and decoding in an EM-like framework the word accuracy is continuously improved. On the TIDIGITS corpus we achieve a word error rate of about 13 percent by the proposed unsupervised pattern discovery approach, which neither assumes knowledge of the acoustic units nor of the labels of the training data.