@inproceedings{18651,
  abstract     = {{We present an approach to deep neural network based (DNN-based) distance estimation in reverberant rooms for supporting geometry calibration tasks in wireless acoustic sensor networks. Signal diffuseness information from acoustic signals is aggregated via the coherent-to-diffuse power ratio to obtain a distance-related feature, which is mapped to a source-to-microphone distance estimate by means of a DNN. This information is then combined with direction-of-arrival estimates from compact microphone arrays to infer the geometry of the sensor network. Unlike many other approaches to geometry calibration, the proposed scheme does only require that the sampling clocks of the sensor nodes are roughly synchronized. In simulations we show that the proposed DNN-based distance estimator generalizes to unseen acoustic environments and that precise estimates of the sensor node positions are obtained. }},
  author       = {{Gburrek, Tobias and Schmalenstroeer, Joerg and Brendel, Andreas and Kellermann, Walter and Haeb-Umbach, Reinhold}},
  booktitle    = {{European Signal Processing Conference (EUSIPCO)}},
  title        = {{{Deep Neural Network based Distance Estimation for Geometry Calibration in Acoustic Sensor Network}}},
  year         = {{2020}},
}

@inproceedings{29940,
  abstract     = {{A full-bridge modular multilevel converter (MMC) is compared to a half-bridge-based MMC for high-current low-voltage DC-applications such as electrolysis, arc welding or datacenters with DC-power distribution. Usually, modular multilevel converters are used in high-voltage DC-applications (HVDC) in the multiple kV-range, but to meet the needs of a high-current demand at low output voltage levels, the modular converter concept requires adaptations. In the proposed concept, the MMC is used to step-down the three-phase medium-voltage of 10 kV. Therefore, each module is extended by an LLC resonant converter to adapt to the specific electrolyzers DC-voltage range of 142-220V and to provide galvanic isolation. The proposed MMC converter with full-bridge modules uses half the number of modules compared to a half-bridge-based MMC while reducing the voltage ripple by 78% and capacitor losses by 64% by rearranging the same components to ensure identical costs and volume. For additional reliability, a new robust algorithm for balancing conduction losses during the bypass phase is presented.}},
  author       = {{Unruh, Roland and Schafmeister, Frank and Fröhleke, Norbert and Böcker, Joachim}},
  booktitle    = {{PCIM Europe digital days 2020; International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management}},
  isbn         = {{978-3-8007-5245-4}},
  keywords     = {{Cascaded H-Bridge, Solid-State Transformer, Capacitor voltage ripple, Zero sequence voltage, Full-Bridge}},
  location     = {{Germany}},
  publisher    = {{VDE}},
  title        = {{{1-MW Full-Bridge MMC for High-Current Low-Voltage (100V-400V) DC-Applications}}},
  year         = {{2020}},
}

@inproceedings{30001,
  abstract     = {{Heat dissipation is a limiting factor in the performance of many power electronic components. Especially in the TO-263-7 package, which is used for several SiC-MOSFETs, the heat transfer must take place through the cross section of the printed circuit board (PCB) to the heatsink at the bottom side. Most commonly, thermal vias are used to form this path in a perpendicular direction through all PCB-layers. In a given soft- and hard switched example applications with the use of C3M0065090J SiC-MOSFETs, this conventional approach limited the component’s maximum heat dissipation to approx. 13 W. A recent alternative approach are massive copper blocks (”pedestals”) being integrated in PCBs and reaching from their top- to the bottom-side in relevant footprint areas under SMD-housed power semiconductors. Pedestals allowing to increase the heat dissipation in the given case to even 36 W. This step is achieved due to the clearly superior heat spreading capability of that massive thermal connection between SiC-MOSFET and heatsink. For the hard switched example application the number of switch-elements can be halved to one, by using the pedestal instead of thermal vias. Independently of optimizing the heat transfer path, the up-front avoidance of losses helps to stay within existing heat dissipation limits, of course. The dominant conduction losses of the mentioned soft-switched example application could be halved by changing to SiC-MOSFET types with significant lowered RDSon. By using pedestals and changing to SiC-MOSFETs with lowered RDSon, the number of switch-elements can also be halved for the soft switched application.}},
  author       = {{Strothmann, Benjamin and Piepenbrock, Till and Schafmeister, Frank and Böcker, Joachim}},
  booktitle    = {{PCIM Europe digital days 2020; International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management}},
  pages        = {{1--7}},
  title        = {{{Heat dissipation strategies for silicon carbide power SMDs and their use in different applications}}},
  year         = {{2020}},
}

@inproceedings{20766,
  abstract     = {{Recently, the source separation performance was greatly improved by time-domain audio source separation based on dual-path recurrent neural network (DPRNN). DPRNN is a simple but effective model for a long sequential data. While DPRNN is quite efficient in modeling a sequential data of the length of an utterance, i.e., about 5 to 10 second data, it is harder to apply it to longer sequences such as whole conversations consisting of multiple utterances. It is simply because, in such a case, the number of time steps consumed by its internal module called inter-chunk RNN becomes extremely large. To mitigate this problem, this paper proposes a multi-path RNN (MPRNN), a generalized version of DPRNN, that models the input data in a hierarchical manner. In the MPRNN framework, the input data is represented at several (>_ 3) time-resolutions, each of which is modeled by a specific RNN sub-module. For example, the RNN sub-module that deals with the finest resolution may model temporal relationship only within a phoneme, while the RNN sub-module handling the most coarse resolution may capture only the relationship between utterances such as speaker information. We perform experiments using simulated dialogue-like mixtures and show that MPRNN has greater model capacity, and it outperforms the current state-of-the-art DPRNN framework especially in online processing scenarios.}},
  author       = {{Kinoshita, Keisuke and von Neumann, Thilo and Delcroix, Marc and Nakatani, Tomohiro and Haeb-Umbach, Reinhold}},
  booktitle    = {{Proc. Interspeech 2020}},
  pages        = {{2652--2656}},
  title        = {{{Multi-Path RNN for Hierarchical Modeling of Long Sequential Data and its Application to Speaker Stream Separation}}},
  doi          = {{10.21437/Interspeech.2020-2388}},
  year         = {{2020}},
}

@inproceedings{20753,
  abstract     = {{In this paper we present our system for the detection and classification of acoustic scenes and events (DCASE) 2020 Challenge Task 4: Sound event detection and separation in domestic environments. We introduce two new models: the forward-backward convolutional recurrent neural network (FBCRNN) and the tag-conditioned convolutional neural network (CNN). The FBCRNN employs two recurrent neural network (RNN) classifiers sharing the same CNN for preprocessing. With one RNN processing a recording in forward direction and the other in backward direction, the two networks are trained to jointly predict audio tags, i.e., weak labels, at each time step within a recording, given that at each time step they have jointly processed the whole recording. The proposed training encourages the classifiers to tag events as soon as possible. Therefore, after training, the networks can be applied to shorter audio segments of, e.g., 200ms, allowing sound event detection (SED). Further, we propose a tag-conditioned CNN to complement SED. It is trained to predict strong labels while using (predicted) tags, i.e., weak labels, as additional input. For training pseudo strong labels from a FBCRNN ensemble are used. The presented system scored the fourth and third place in the systems and teams rankings, respectively. Subsequent improvements allow our system to even outperform the challenge baseline and winner systems in average by, respectively, 18.0% and 2.2% event-based F1-score on the validation set. Source code is publicly available at https://github.com/fgnt/pb_sed.}},
  author       = {{Ebbers, Janek and Haeb-Umbach, Reinhold}},
  booktitle    = {{Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020)}},
  title        = {{{Forward-Backward Convolutional Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled Semi-Supervised Sound Event Detection}}},
  year         = {{2020}},
}

@inproceedings{51879,
  author       = {{Poeplau, Michael and Ester, Stephan and Henning, Bernd and Wagner, Thorsten}},
  publisher    = {{AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf}},
  title        = {{{Zinkoxid als photostabiler Luminophor zur optischen Sauerstoffdetektion}}},
  doi          = {{10.5162/sensoren2019/5.2.3}},
  year         = {{2020}},
}

@article{53270,
  author       = {{Soleymani, Mohammad and Santamaria, Ignacio and Schreier, Peter J.}},
  issn         = {{0018-9545}},
  journal      = {{IEEE Transactions on Vehicular Technology}},
  keywords     = {{Electrical and Electronic Engineering, Computer Networks and Communications, Aerospace Engineering, Automotive Engineering}},
  number       = {{10}},
  pages        = {{11632--11645}},
  publisher    = {{Institute of Electrical and Electronics Engineers (IEEE)}},
  title        = {{{Improper Gaussian Signaling for the $K$-User MIMO Interference Channels With Hardware Impairments}}},
  doi          = {{10.1109/tvt.2020.3015558}},
  volume       = {{69}},
  year         = {{2020}},
}

@inproceedings{53269,
  author       = {{Soleymani, Mohammad and Santamaria, Ignacio and Maham, Behrouz and Schreier, Peter J.}},
  booktitle    = {{2020 28th European Signal Processing Conference (EUSIPCO)}},
  publisher    = {{IEEE}},
  title        = {{{Rate Region of the K-user MIMO Interference Channel with Imperfect Transmitters}}},
  doi          = {{10.23919/eusipco47968.2020.9287450}},
  year         = {{2020}},
}

@inbook{35639,
  author       = {{Alptekin, Mesut and Temmen, Katrin}},
  booktitle    = {{Advances in Intelligent Systems and Computing}},
  isbn         = {{9783030402730}},
  issn         = {{2194-5357}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Measuring Students’ Device Specific Competencies Using an Eye-Tracking Study on Oscilloscopes}}},
  doi          = {{10.1007/978-3-030-40274-7_53}},
  year         = {{2020}},
}

@article{37611,
  author       = {{Hasija, Tanuj and Marrinan, Timothy and Lameiro, Christian and Schreier, Peter J}},
  journal      = {{Signal Processing}},
  publisher    = {{Elsevier}},
  title        = {{{Determining the dimension and structure of the subspace correlated across multiple data sets}}},
  doi          = {{10.1016/j.sigpro.2020.107613}},
  volume       = {{176}},
  year         = {{2020}},
}

@article{17322,
  author       = {{Mukherjee, Amlan and Widhalm, Alex and Siebert, Dustin and Krehs, Sebastian and Sharma, Nandlal and Thiede, Andreas and Reuter, Dirk and Förstner, Jens and Zrenner, Artur}},
  issn         = {{0003-6951}},
  journal      = {{Applied Physics Letters}},
  keywords     = {{tet_topic_qd}},
  pages        = {{251103}},
  title        = {{{Electrically controlled rapid adiabatic passage in a single quantum dot}}},
  doi          = {{10.1063/5.0012257}},
  volume       = {{116}},
  year         = {{2020}},
}

@inproceedings{38213,
  author       = {{Koch, Benjamin and Noé, Reinhold}},
  booktitle    = {{Photonic Networks; 21th ITG-Symposium}},
  pages        = {{1--3}},
  title        = {{{PMD-Tolerant 20 krad/s Endless Polarization and Phase Control for BB84-Based QKD with TDM Pilot Signals}}},
  year         = {{2020}},
}

@inproceedings{38219,
  author       = {{Noé, Reinhold and Koch, Benjamin}},
  booktitle    = {{Photonic Networks; 21th ITG-Symposium}},
  pages        = {{1--4}},
  title        = {{{Comparison of Optical Polarization-Dependent Loss Measurement Methods}}},
  year         = {{2020}},
}

@inproceedings{40678,
  author       = {{Xiao, Yu-Hang and Ramírez, David and Schreier, Peter J}},
  booktitle    = {{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}},
  pages        = {{5365–5369}},
  title        = {{{A General Test for the Linear Structure of Covariance Matrices of Gaussian Populations}}},
  year         = {{2020}},
}

@inproceedings{13943,
  author       = {{Krumme, Matthias and Webersen, Manuel and Claes, Leander and Webersen, Yvonne}},
  booktitle    = {{Fortschritte der Akustik - DAGA 2020}},
  pages        = {{542--545}},
  title        = {{{Analoge Klangsynthese zur Vermittlung von Grundkenntnissen der Signalverarbeitung an Studierende nicht-technischer Fachrichtungen}}},
  year         = {{2020}},
}

@inproceedings{24023,
  abstract     = {{This paper presents an ultra-wideband and ultra-low noise frequency synthesizer using a mode-locked laser as its reference. The frequency synthesizer can lock in the frequency range from 2 GHz to 20 GHz on any harmonic of a mode-locked laser optical pulse train. The integrated rms-jitter (1 kHz-100 MHz) of the synthesizer is less than 5 fs in the frequency range from 4 GHz to 20 GHz with a typical value of 4 fs and a minimum of 3 fs. This is the first reported wideband phase locked loop achieving sub-10 fs rms-jitter for offset frequencies larger than 1 kHz.}},
  author       = {{Bahmanian, Meysam and Fard, Saeed and Koppelmann, Bastian and Scheytt, Christoph}},
  booktitle    = {{ 2020 IEEE/MTT-S International Microwave Symposium (IMS)}},
  publisher    = {{IEEE}},
  title        = {{{Wide-Band Frequency Synthesizer with Ultra-Low Phase Noise Using an Optical Clock Source}}},
  doi          = {{10.1109/IMS30576.2020.9224118}},
  year         = {{2020}},
}

@article{40675,
  author       = {{Soleymani, Mohammad and Santamaria, Ignacio and Schreier, Peter J}},
  journal      = {{IEEE Transactions on Vehicular Technology}},
  number       = {{10}},
  pages        = {{11632–11645}},
  publisher    = {{IEEE}},
  title        = {{{Improper Gaussian Signaling for the $K$-User MIMO Interference Channels With Hardware Impairments}}},
  volume       = {{69}},
  year         = {{2020}},
}

@article{40676,
  author       = {{Horstmann, Stefanie and Ramírez, David and Schreier, Peter J}},
  journal      = {{IEEE Transactions on Signal Processing}},
  number       = {{1}},
  pages        = {{2340–2355}},
  publisher    = {{IEEE}},
  title        = {{{Two-channel passive detection of cyclostationary signals}}},
  volume       = {{68}},
  year         = {{2020}},
}

@article{31710,
  author       = {{Vieluf, S and Scheer, V and Hasija, Tanuj and Schreier, PJ and Reinsberger, Claus}},
  issn         = {{1543-8627}},
  journal      = {{Res Sports Med}},
  number       = {{2}},
  pages        = {{231--240}},
  title        = {{{Multimodal approach towards understanding the changes in the autonomic nervous system induced by an ultramarathon.}}},
  volume       = {{28}},
  year         = {{2020}},
}

@inproceedings{42073,
  author       = {{Noroozi, Navid and Jackson, Roxanne and Quevedo, Daniel E. and Wirth, Fabian R. and Findeisen, Rolf}},
  booktitle    = {{2019 IEEE 58th Conference on Decision and Control (CDC)}},
  publisher    = {{IEEE}},
  title        = {{{On noise-to-state stability of stochastic discrete-time systems via finite-step Lyapunov functions}}},
  doi          = {{10.1109/cdc40024.2019.9030178}},
  year         = {{2020}},
}