@inproceedings{11747, abstract = {{In this paper, we present a neural network based classification algorithm for the discrimination of moving from stationary targets in the sight of an automotive radar sensor. Compared to existing algorithms, the proposed algorithm can take into account multiple local radar targets instead of performing classification inference on each target individually resulting in superior discrimination accuracy, especially suitable for non rigid objects, like pedestrians, which in general have a wide velocity spread when multiple targets are detected.}}, author = {{Grimm, Christopher and Breddermann, Tobias and Farhoud, Ridha and Fei, Tai and Warsitz, Ernst and Haeb-Umbach, Reinhold}}, booktitle = {{International Conference on Microwaves for Intelligent Mobility (ICMIM) 2018}}, title = {{{Discrimination of Stationary from Moving Targets with Recurrent Neural Networks in Automotive Radar}}}, year = {{2018}}, } @inproceedings{11907, abstract = {{The invention of the Variational Autoencoder enables the application of Neural Networks to a wide range of tasks in unsupervised learning, including the field of Acoustic Unit Discovery (AUD). The recently proposed Hidden Markov Model Variational Autoencoder (HMMVAE) allows a joint training of a neural network based feature extractor and a structured prior for the latent space given by a Hidden Markov Model. It has been shown that the HMMVAE significantly outperforms pure GMM-HMM based systems on the AUD task. However, the HMMVAE cannot autonomously infer the number of acoustic units and thus relies on the GMM-HMM system for initialization. This paper introduces the Bayesian Hidden Markov Model Variational Autoencoder (BHMMVAE) which solves these issues by embedding the HMMVAE in a Bayesian framework with a Dirichlet Process Prior for the distribution of the acoustic units, and diagonal or full-covariance Gaussians as emission distributions. Experiments on TIMIT and Xitsonga show that the BHMMVAE is able to autonomously infer a reasonable number of acoustic units, can be initialized without supervision by a GMM-HMM system, achieves computationally efficient stochastic variational inference by using natural gradient descent, and, additionally, improves the AUD performance over the HMMVAE.}}, author = {{Glarner, Thomas and Hanebrink, Patrick and Ebbers, Janek and Haeb-Umbach, Reinhold}}, booktitle = {{INTERSPEECH 2018, Hyderabad, India}}, title = {{{Full Bayesian Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery}}}, year = {{2018}}, } @inproceedings{11838, abstract = {{Distributed sensor data acquisition usually encompasses data sampling by the individual devices, where each of them has its own oscillator driving the local sampling process, resulting in slightly different sampling rates at the individual sensor nodes. Nevertheless, for certain downstream signal processing tasks it is important to compensate even for small sampling rate offsets. Aligning the sampling rates of oscillators which differ only by a few parts-per-million, is, however, challenging and quite different from traditional multirate signal processing tasks. In this paper we propose to transfer a precise but computationally demanding time domain approach, inspired by the Nyquist-Shannon sampling theorem, to an efficient frequency domain implementation. To this end a buffer control is employed which compensates for sampling offsets which are multiples of the sampling period, while a digital filter, realized by the wellknown Overlap-Save method, handles the fractional part of the sampling phase offset. With experiments on artificially misaligned data we investigate the parametrization, the efficiency, and the induced distortions of the proposed resampling method. It is shown that a favorable compromise between residual distortion and computational complexity is achieved, compared to other sampling rate offset compensation techniques.}}, author = {{Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}}, booktitle = {{26th European Signal Processing Conference (EUSIPCO 2018)}}, title = {{{Efficient Sampling Rate Offset Compensation - An Overlap-Save Based Approach}}}, year = {{2018}}, } @inproceedings{11876, abstract = {{This paper describes the systems for the single-array track and the multiple-array track of the 5th CHiME Challenge. The final system is a combination of multiple systems, using Confusion Network Combination (CNC). The different systems presented here are utilizing different front-ends and training sets for a Bidirectional Long Short-Term Memory (BLSTM) Acoustic Model (AM). The front-end was replaced by enhancements provided by Paderborn University [1]. The back-end has been implemented using RASR [2] and RETURNN [3]. Additionally, a system combination including the hypothesis word graphs from the system of the submission [1] has been performed, which results in the final best system.}}, author = {{Kitza, Markus and Michel, Wilfried and Boeddeker, Christoph and Heitkaemper, Jens and Menne, Tobias and Schlüter, Ralf and Ney, Hermann and Schmalenstroeer, Joerg and Drude, Lukas and Heymann, Jahn and Haeb-Umbach, Reinhold}}, booktitle = {{Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments, Hyderabad, India}}, title = {{{The RWTH/UPB System Combination for the CHiME 2018 Workshop}}}, year = {{2018}}, } @inproceedings{11836, abstract = {{Due to their distributed nature wireless acoustic sensor networks offer great potential for improved signal acquisition, processing and classification for applications such as monitoring and surveillance, home automation, or hands-free telecommunication. To reduce the communication demand with a central server and to raise the privacy level it is desirable to perform processing at node level. The limited processing and memory capabilities on a sensor node, however, stand in contrast to the compute and memory intensive deep learning algorithms used in modern speech and audio processing. In this work, we perform benchmarking of commonly used convolutional and recurrent neural network architectures on a Raspberry Pi based acoustic sensor node. We show that it is possible to run medium-sized neural network topologies used for speech enhancement and speech recognition in real time. For acoustic event recognition, where predictions in a lower temporal resolution are sufficient, it is even possible to run current state-of-the-art deep convolutional models with a real-time-factor of 0:11.}}, author = {{Ebbers, Janek and Heitkaemper, Jens and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}}, booktitle = {{ITG 2018, Oldenburg, Germany}}, title = {{{Benchmarking Neural Network Architectures for Acoustic Sensor Networks}}}, year = {{2018}}, } @inproceedings{11839, abstract = {{It has been experimentally verified that sampling rate offsets (SROs) between the input channels of an acoustic beamformer have a detrimental effect on the achievable SNR gains. In this paper we derive an analytic model to study the impact of SRO on the estimation of the spatial noise covariance matrix used in MVDR beamforming. It is shown that a perfect compensation of the SRO is impossible if the noise covariance matrix is estimated by time averaging, even if the SRO is perfectly known. The SRO should therefore be compensated for prior to beamformer coefficient estimation. We present a novel scheme where SRO compensation and beamforming closely interact, saving some computational effort compared to separate SRO adjustment followed by acoustic beamforming.}}, author = {{Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}}, booktitle = {{ITG 2018, Oldenburg, Germany}}, title = {{{Insights into the Interplay of Sampling Rate Offsets and MVDR Beamforming}}}, year = {{2018}}, } @inproceedings{6523, author = {{Weber, Daniel and Rafsan Jani, Mohammad Iffat and Grabo, Matti and Wallscheid, Oliver and Klaus, Tobias and Krauter, Stefan and Böcker, Joachim}}, booktitle = {{World Conference on Photovoltaic Energy Conversion (WCPEC-7), 45th IEEE PVSC, 28th PVSEC, 34th EU PVSEC.}}, location = {{Waikoloa Village, Big Island, Hawaii (USA)}}, title = {{{Lifetime Extension of Photovoltaic Modules by Influencing the Module Temperature Using Phase Change Material}}}, doi = {{ 10.1109/PVSC.2018.8548115}}, year = {{2018}}, } @inproceedings{53278, author = {{Soleymani, Mohammad and Lameiro, Christian and Schreier, Peter J. and Santamaria, Ignacio}}, booktitle = {{2018 IEEE Statistical Signal Processing Workshop (SSP)}}, publisher = {{IEEE}}, title = {{{Improper Signaling for OFDM Underlay Cognitive Radio Systems}}}, doi = {{10.1109/ssp.2018.8450843}}, year = {{2018}}, } @book{53595, editor = {{Bringmann, Oliver and Ecker, Wolfgang and Müller, Wolfgang and Müller-Gridschneder, Daniel}}, title = {{{Proceedings of the 1st International Workshop on Embedded Software for Industrial IoT - ESIIT}}}, year = {{2018}}, } @inproceedings{35648, author = {{Nofen, Barbara and Temmen, Katrin}}, booktitle = {{2018 IEEE Global Engineering Education Conference (EDUCON)}}, publisher = {{IEEE}}, title = {{{The lecture hall laboratory: Design of a field experiment for effectiveness analysis}}}, doi = {{10.1109/educon.2018.8363283}}, year = {{2018}}, } @article{37265, author = {{Friederici, D.-P.}}, journal = {{Die Hochschullehre}}, title = {{{Eine Untersuchung mit Studierenden über den Umgang mit ihrer Zeit}}}, year = {{2018}}, } @inproceedings{34130, abstract = {{There have been numerous studies so far highlighting the potential of Augmented Reality (AR) in different educational domains and its impact on learners regarding their increased motivation, improved learning, concentration on the topic etc. Ever since high-end AR applications could be used on smartphones, this technology has become suitable to be used in many formal and informal learning environments and educational institutions, beginning with Arts courses in preschool over Biology, History, Chemistry, Physics etc. in K-12 and universities as well as in vocational schools ([1], [2]), e.g. for assembly trainings [3]. However, less research has been done regarding proper educational design principles and guides identifying the learning-promoting characteristics as to their efficacy in an AR environment ([1], [4]). Particularly there is a big lack for design concepts in the field of preparation and accompanying tools for laboratory work, since current studies are only extending real papers or books with additional links, videos or static 3D models (e.g. [5], [6]). Hence, this paper investigates and focuses at a design concept for mobile device based AR application (App) to acquire and deepen practical skills in dealing with electro-technical laboratory equipment and components. In a previous paper, the potentials and limitation of AR technology regarding engineering education with a special focus on laboratory work have been investigated to avoid common mistakes in the design concept.}}, author = {{Alptekin, Mesut and Temmen, Katrin}}, booktitle = {{2018 IEEE Global Engineering Education Conference (EDUCON)}}, isbn = {{978-1-5386-2957-4}}, location = {{Santa Cruz de Tenerife, Spain}}, pages = {{963--968}}, publisher = {{IEEE}}, title = {{{Design concept and prototype for an augmented reality based virtual preparation laboratory training in electrical engineering}}}, doi = {{10.1109/educon.2018.8363334}}, year = {{2018}}, } @article{3427, abstract = {{We report on the coherent phase manipulation of quantum dot excitons by electric means. For our experiments, we use a low capacitance single quantum dot photodiode which is electrically controlled by a custom designed SiGe:C BiCMOS chip. The phase manipulation is performed and quantified in a Ramsey experiment, where ultrafast transient detuning of the exciton energy is performed synchronous to double pulse p/2 ps laser excitation. We are able to demonstrate electrically controlled phase manipulations with magnitudes up to 3p within 100 ps which is below the dephasing time of the quantum dot exciton.}}, author = {{Widhalm, Alex and Mukherjee, Amlan and Krehs, Sebastian and Sharma, Nandlal and Kölling, Peter and Thiede, Andreas and Reuter, Dirk and Förstner, Jens and Zrenner, Artur}}, issn = {{0003-6951}}, journal = {{Applied Physics Letters}}, keywords = {{tet_topic_qd}}, number = {{11}}, pages = {{111105}}, title = {{{Ultrafast electric phase control of a single exciton qubit}}}, doi = {{10.1063/1.5020364}}, volume = {{112}}, year = {{2018}}, } @inproceedings{39651, author = {{Meister, Tilo and Ellinger, Frank and Bartha, Johann W. and Berroth, Manfred and Burghartz, Joachim and Claus, Martin and Frey, Lothar and Gagliardi, Alessio and Grundmann, Marius and Hesselbarth, Jan and Klauk, Hagen and Leo, Karl and Lugli, Paolo and Mannsfeld, Stefan and Manoli, Yiannos and Negra, Renato and Neumaier, Daniel and Pfeiffer, Ullrich and Riedl, Thomas and Scheinert, Susanne and Scherf, Ullrich and Thiede, Andreas and Troster, Gerhard and Vossiek, Martin and Weigel, Robert and Wenger, Christian and Alavi, Golzar and Becherer, Markus and Chavarin, Carlos Alvarado and Darwish, Mohammed and Ellinger, Martin and Fan, Chun-Yu and Fritsch, Martin and Grotjahn, Frank and Gunia, Marco and Haase, Katherina and Hillger, Philipp and Ishida, Koichi and Jank, Michael and Knobelspies, Stefan and Kuhl, Matthias and Lupina, Grzegorz and Naghadeh, Shabnam Mohammadi and Munzenrieder, Niko and Ozbek, Sefa and Rasteh, Mahsa and Salvatore, Giovanni A. and Schrufer, Daniel and Strobel, Carsten and Theisen, Manuel and Tuckmantel, Christian and von Wenckstern, Holger and Wang, Zhenxing and Zhang, Zhipeng}}, booktitle = {{2017 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems (COMCAS)}}, publisher = {{IEEE}}, title = {{{Program FFlexCom — High frequency flexible bendable electronics for wireless communication systems}}}, doi = {{10.1109/comcas.2017.8244733}}, year = {{2018}}, } @inproceedings{38220, author = {{Noé, Reinhold and Koch, Benjamin}}, booktitle = {{Photonic Networks; 19th ITG-Symposium}}, pages = {{1--4}}, title = {{{Emulation of polarization fluctuations in glass fibers caused by lightning strikes}}}, year = {{2018}}, } @article{40700, abstract = {{We propose a technique that jointly detects the presence of almost-cyclostationary (ACS) signals in wide-sense stationary (WSS) noise and provides an estimate of their cycle period. Since the cycle period of an ACS process is not an integer, the approach is based on a combination of a resampling stage and a multiple hypothesis test, which deal separately with the fractional part and the integer part of the cycle period. The approach requires resampling the signal at many different rates, which is computationally expensive. For this reason we propose a filter bank structure that allows us to efficiently resample a signal at many different rates by identifying common interpolation stages among the set of resampling rates.}}, author = {{Horstmann, Stefanie and Ramírez, David and Schreier, Peter J.}}, journal = {{IEEE Signal Process. Lett.}}, number = {{11}}, pages = {{1695–1699}}, title = {{{Joint Detection of Almost-Cyclostationary Signals and Estimation of Their Cycle Period}}}, doi = {{10.1109/LSP.2018.2871961}}, volume = {{25}}, year = {{2018}}, } @article{40699, author = {{Xiao, Y-.H. and Huang, L. and Xie, J. and So, H. C.}}, journal = {{IEEE Transactions on Information Theory}}, number = {{3}}, pages = {{1784–1799}}, title = {{{Approximate Asymptotic Distribution of Locally Most Powerful Invariant Test for Independence Complex Case}}}, volume = {{64}}, year = {{2018}}, } @article{40701, author = {{Pries, Aaron and Ramírez, David and Schreier, Peter J.}}, journal = {{IEEE Trans. Wirel. Commun.}}, number = {{9}}, pages = {{6321–6334}}, title = {{{LMPIT-inspired Tests for Detecting a Cyclostationary Signal in Noise with Spatio-Temporal Structure}}}, volume = {{17}}, year = {{2018}}, } @article{40703, author = {{Rezaee, Mohsen and Schreier, Peter J.}}, journal = {{IEEE Trans. Wireless Comm.}}, number = {{8}}, pages = {{5397–5408}}, title = {{{A degrees-of-freedom-achieving scheme for the temporally correlated MIMO interference channel with delayed CSIT}}}, volume = {{17}}, year = {{2018}}, } @inproceedings{40702, author = {{Marrinan, T. and Hasija, Tanuj and Lameiro, C. and Schreier, P. J.}}, booktitle = {{Proc. European Signal Process. Conf. (EUSIPCO)}}, pages = {{1082–1086}}, title = {{{Complete Model Selection in Multiset Canonical Correlation Analysis}}}, year = {{2018}}, }