@article{11955, author = {{Li, Tianyou and Wei, Qunshuo and Reineke, Bernhard and Walter, Felicitas and Wang, Yongtian and Zentgraf, Thomas and Huang, Lingling}}, issn = {{1094-4087}}, journal = {{Optics Express}}, number = {{15}}, pages = {{21153--21162}}, title = {{{Reconfigurable metasurface hologram by utilizing addressable dynamic pixels}}}, doi = {{10.1364/oe.27.021153}}, volume = {{27}}, year = {{2019}}, } @inproceedings{11965, abstract = {{We present an unsupervised training approach for a neural network-based mask estimator in an acoustic beamforming application. The network is trained to maximize a likelihood criterion derived from a spatial mixture model of the observations. It is trained from scratch without requiring any parallel data consisting of degraded input and clean training targets. Thus, training can be carried out on real recordings of noisy speech rather than simulated ones. In contrast to previous work on unsupervised training of neural mask estimators, our approach avoids the need for a possibly pre-trained teacher model entirely. We demonstrate the effectiveness of our approach by speech recognition experiments on two different datasets: one mainly deteriorated by noise (CHiME 4) and one by reverberation (REVERB). The results show that the performance of the proposed system is on par with a supervised system using oracle target masks for training and with a system trained using a model-based teacher.}}, author = {{Drude, Lukas and Heymann, Jahn and Haeb-Umbach, Reinhold}}, booktitle = {{INTERSPEECH 2019, Graz, Austria}}, title = {{{Unsupervised training of neural mask-based beamforming}}}, year = {{2019}}, } @inproceedings{11985, author = {{Bronner, Fabian and Sommer, Christoph}}, booktitle = {{2018 IEEE Vehicular Networking Conference (VNC)}}, isbn = {{9781538694282}}, title = {{{Efficient Multi-Channel Simulation of Wireless Communications}}}, doi = {{10.1109/vnc.2018.8628350}}, year = {{2019}}, } @inbook{12043, author = {{Reinold, Peter and Meyer, Norbert and Buse, Dominik and Klingler, Florian and Sommer, Christoph and Dressler, Falko and Eisenbarth, Markus and Andert, Jakob}}, booktitle = {{Proceedings}}, isbn = {{9783658252939}}, issn = {{2198-7432}}, title = {{{Verkehrssimulation im Hardware-in-the-Loop-Steuergerätetest}}}, doi = {{10.1007/978-3-658-25294-6_15}}, year = {{2019}}, } @inbook{12072, author = {{Sommer, Christoph and Eckhoff, David and Brummer, Alexander and Buse, Dominik S. and Hagenauer, Florian and Joerer, Stefan and Segata, Michele}}, booktitle = {{Recent Advances in Network Simulation}}, isbn = {{9783030128418}}, issn = {{2522-8595}}, title = {{{Veins: The Open Source Vehicular Network Simulation Framework}}}, doi = {{10.1007/978-3-030-12842-5_6}}, year = {{2019}}, } @inproceedings{12076, author = {{Yigitbas, Enes and Heindörfer, Joshua and Engels, Gregor}}, booktitle = {{Proceedings of the Mensch und Computer 2019 (MuC ’19)}}, pages = {{885----888}}, publisher = {{ACM}}, title = {{{A Context-aware Virtual Reality First Aid Training Application}}}, year = {{2019}}, } @techreport{12077, abstract = {{Die Komplexität von Steuersystemen gewinnt in der Debatte um den internationalen Steuerwettbewerb zunehmend an Bedeutung. Im vorliegenden Beitrag erfolgt, basierend auf den Daten, die dem Tax Complexity Index (www.taxcomplexity.org) zugrunde liegen, eine umfassende Gegenüberstellung der Komplexität der Steuersysteme von Deutschland und Öster-reich unter Berücksichtigung der Mittelwerte aller Länder. Die Steuergesetze weisen sowohl in Deutschland als auch in Österreich einen verhältnismäßig hohen Grad an Komplexität auf. Bei den steuerlichen Rahmenbedingungen fällt der Grad an Komplexität in beiden Ländern dagegen niedrig aus, wobei Österreich im Durchschnitt weniger komplex ist als Deutschland.}}, author = {{Hoppe, Thomas and Rechbauer, Martina and Sturm, Susann}}, title = {{{Steuerkomplexität im Vergleich zwischen Deutschland und Österreich – Eine Analyse des Status quo}}}, year = {{2019}}, } @inproceedings{12870, author = {{Feldkord, Björn and Knollmann, Till and Malatyali, Manuel and Meyer auf der Heide, Friedhelm}}, booktitle = {{Proceedings of the 17th Workshop on Approximation and Online Algorithms (WAOA)}}, pages = {{120 -- 137}}, publisher = {{Springer}}, title = {{{Managing Multiple Mobile Resources}}}, doi = {{10.1007/978-3-030-39479-0_9}}, year = {{2019}}, } @inproceedings{12874, abstract = {{We propose a training scheme to train neural network-based source separation algorithms from scratch when parallel clean data is unavailable. In particular, we demonstrate that an unsupervised spatial clustering algorithm is sufficient to guide the training of a deep clustering system. We argue that previous work on deep clustering requires strong supervision and elaborate on why this is a limitation. We demonstrate that (a) the single-channel deep clustering system trained according to the proposed scheme alone is able to achieve a similar performance as the multi-channel teacher in terms of word error rates and (b) initializing the spatial clustering approach with the deep clustering result yields a relative word error rate reduction of 26% over the unsupervised teacher.}}, author = {{Drude, Lukas and Hasenklever, Daniel and Haeb-Umbach, Reinhold}}, booktitle = {{ICASSP 2019, Brighton, UK}}, title = {{{Unsupervised Training of a Deep Clustering Model for Multichannel Blind Source Separation}}}, year = {{2019}}, } @inproceedings{12875, abstract = {{Signal dereverberation using the Weighted Prediction Error (WPE) method has been proven to be an effective means to raise the accuracy of far-field speech recognition. First proposed as an iterative algorithm, follow-up works have reformulated it as a recursive least squares algorithm and therefore enabled its use in online applications. For this algorithm, the estimation of the power spectral density (PSD) of the anechoic signal plays an important role and strongly influences its performance. Recently, we showed that using a neural network PSD estimator leads to improved performance for online automatic speech recognition. This, however, comes at a price. To train the network, we require parallel data, i.e., utterances simultaneously available in clean and reverberated form. Here we propose to overcome this limitation by training the network jointly with the acoustic model of the speech recognizer. To be specific, the gradients computed from the cross-entropy loss between the target senone sequence and the acoustic model network output is backpropagated through the complex-valued dereverberation filter estimation to the neural network for PSD estimation. Evaluation on two databases demonstrates improved performance for on-line processing scenarios while imposing fewer requirements on the available training data and thus widening the range of applications.}}, author = {{Heymann, Jahn and Drude, Lukas and Haeb-Umbach, Reinhold and Kinoshita, Keisuke and Nakatani, Tomohiro}}, booktitle = {{ICASSP 2019, Brighton, UK}}, title = {{{Joint Optimization of Neural Network-based WPE Dereverberation and Acoustic Model for Robust Online ASR}}}, year = {{2019}}, } @inproceedings{12876, abstract = {{In this paper, we present libDirectional, a MATLAB library for directional statistics and directional estimation. It supports a variety of commonly used distributions on the unit circle, such as the von Mises, wrapped normal, and wrapped Cauchy distributions. Furthermore, various distributions on higher-dimensional manifolds such as the unit hypersphere and the hypertorus are available. Based on these distributions, several recursive filtering algorithms in libDirectional allow estimation on these manifolds. The functionality is implemented in a clear, well-documented, and object-oriented structure that is both easy to use and easy to extend.}}, author = {{Kurz, Gerhard and Gilitschenski, Igor and Pfaff, Florian and Drude, Lukas and Hanebeck, Uwe D. and Haeb-Umbach, Reinhold and Siegwart, Roland Y.}}, booktitle = {{Journal of Statistical Software 89(4)}}, title = {{{Directional Statistics and Filtering Using libDirectional}}}, year = {{2019}}, } @inproceedings{12880, abstract = {{By distributing the computational load over the nodes of a Wireless Acoustic Sensor Network (WASN), the real-time capability of the TRINICON (TRIple-N-Independent component analysis for CONvolutive mixtures) framework for Blind Source Separation (BSS) can be ensured, even if the individual network nodes are not powerful enough to run TRINICON in real-time by themselves. To optimally utilize the limited computing power and data rate in WASNs, the MARVELO (Multicast-Aware Routing for Virtual network Embedding with Loops in Overlays) framework is expanded for use with TRINICON, while a feature-based selection scheme is proposed to exploit the most beneficial parts of the input signal for adapting the demixing system. The simulation results of realistic scenarios show only a minor degradation of the separation performance even in heavily resource-limited situations.}}, author = {{Guenther, Michael and Afifi, Haitham and Brendel, Andreas and Karl, Holger and Kellermann, Walter}}, booktitle = {{2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) (WASPAA 2019)}}, title = {{{Sparse Adaptation of Distributed Blind Source Separation in Acoustic Sensor Networks}}}, year = {{2019}}, } @inproceedings{12881, abstract = {{Internet of Things (IoT) applications witness an exceptional evolution of traffic demands, while existing protocols, as seen in wireless sensor networks (WSNs), struggle to cope with these demands. Traditional protocols rely on finding a routing path between sensors generating data and sinks acting as gateway or databases. Meanwhile, the network will suffer from high collisions in case of high data rates. In this context, in-network processing solutions are used to leverage the wireless nodes' computations, by distributing processing tasks on the nodes along the routing path. Although in-network processing solutions are very popular in wired networks (e.g., data centers and wide area networks), there are many challenges to adopt these solutions in wireless networks, due to the interference problem. In this paper, we solve the problem of routing and task distribution jointly using a greedy Virtual Network Embedding (VNE) algorithm, and consider power control as well. Through simulations, we compare the proposed algorithm to optimal solutions and show that it achieves good results in terms of delay. Moreover, we discuss its sub-optimality by driving tight lower bounds and loose upper bounds. We also compare our solution with another wireless VNE solution to show the trade-off between delay and symbol error rate.}}, author = {{Afifi, Haitham and Karl, Holger}}, booktitle = {{2019 12th IFIP Wireless and Mobile Networking Conference (WMNC) (WMNC'19)}}, title = {{{An Approximate Power Control Algorithm for a Multi-Cast Wireless Virtual Network Embedding}}}, year = {{2019}}, } @inproceedings{12882, abstract = {{One of the major challenges in implementing wireless virtualization is the resource discovery. This is particularly important for the embedding-algorithms that are used to distribute the tasks to nodes. MARVELO is a prototype framework for executing different distributed algorithms on the top of a wireless (802.11) ad-hoc network. The aim of MARVELO is to select the nodes for running the algorithms and to define the routing between the nodes. Hence, it also supports monitoring functionalities to collect information about the available resources and to assist in profiling the algorithms. The objective of this demo is to show how MAVRLEO distributes tasks in an ad-hoc network, based on a feedback from our monitoring tool. Additionally, we explain the work-flow, composition and execution of the framework.}}, author = {{Afifi, Haitham and Karl, Holger and Eikenberg, Sebastian and Mueller, Arnold and Gansel, Lars and Makejkin, Alexander and Hannemann, Kai and Schellenberg, Rafael}}, booktitle = {{2019 IEEE Wireless Communications and Networking Conference (WCNC) (IEEE WCNC 2019) (Demo)}}, keywords = {{WSN, virtualization, VNE}}, title = {{{A Rapid Prototyping for Wireless Virtual Network Embedding using MARVELO}}}, year = {{2019}}, } @misc{12885, author = {{Haltermann, Jan Frederik}}, title = {{{Analyzing Data Usage in Array Programs}}}, year = {{2019}}, } @inproceedings{12889, author = {{Yigitbas, Enes and Jovanovikj, Ivan and Sauer, Stefan and Engels, Gregor}}, booktitle = {{Handling Security, Usability, User Experience and Reliability in User-Centered Development Processes (IFIP WG 13.2 & WG 13.5 International Workshop @ INTERACT2019)}}, title = {{{A Model-based Framework for Context-aware Augmented Reality Applications }}}, year = {{2019}}, } @article{12890, abstract = {{We formulate a generic framework for blind source separation (BSS), which allows integrating data-driven spectro-temporal methods, such as deep clustering and deep attractor networks, with physically motivated probabilistic spatial methods, such as complex angular central Gaussian mixture models. The integrated model exploits the complementary strengths of the two approaches to BSS: the strong modeling power of neural networks, which, however, is based on supervised learning, and the ease of unsupervised learning of the spatial mixture models whose few parameters can be estimated on as little as a single segment of a real mixture of speech. Experiments are carried out on both artificially mixed speech and true recordings of speech mixtures. The experiments verify that the integrated models consistently outperform the individual components. We further extend the models to cope with noisy, reverberant speech and introduce a cross-domain teacher–student training where the mixture model serves as the teacher to provide training targets for the student neural network.}}, author = {{Drude, Lukas and Haeb-Umbach, Reinhold}}, issn = {{1941-0484}}, journal = {{IEEE Journal of Selected Topics in Signal Processing}}, title = {{{Integration of Neural Networks and Probabilistic Spatial Models for Acoustic Blind Source Separation}}}, doi = {{10.1109/JSTSP.2019.2912565}}, year = {{2019}}, } @inproceedings{12894, author = {{Augstein, Mirjam and Herder, Eelco and Wörndl, Wolfgang and Yigitbas, Enes}}, booktitle = {{30th ACM Conference on Hypertext and Social Media (HT ’19), September 17–20, 2019, Hof, Germany}}, publisher = {{ACM}}, title = {{{ABIS 2019 – 23rd International Workshop on Personalization and Recommendation on the Web and Beyond}}}, year = {{2019}}, } @article{12908, author = {{Hammer, Manfred and Ebers, Lena and Förstner, Jens}}, issn = {{0740-3224}}, journal = {{Journal of the Optical Society of America B}}, keywords = {{tet_topic_waveguides}}, pages = {{2395}}, title = {{{Oblique quasi-lossless excitation of a thin silicon slab waveguide: a guided-wave variant of an anti-reflection coating}}}, doi = {{10.1364/josab.36.002395}}, volume = {{36}}, year = {{2019}}, } @inproceedings{12912, author = {{Razzaghi Kouchaksaraei, Hadi and Karl, Holger}}, booktitle = {{15th International Conference on Network and Service Management (CNSM)}}, location = {{Halifax, Canada}}, title = {{{Quantitative Analysis of Dynamically Provisioned Heterogeneous Network Services}}}, year = {{2019}}, }