@phdthesis{44230,
  author       = {{Hagemeyer, Marc}},
  title        = {{{Untersuchung und Entwicklung eines modularen speicherbasierten Schweißstromgenerators mit geringster Stromschwankungsbreite für das Widerstandsschweißen}}},
  doi          = {{10.17619/UNIPB/1-1582}},
  year         = {{2022}},
}

@inproceedings{34238,
  abstract     = {{<jats:p>A monolithically integrated electronic-photonic Mach-Zehnder modulator is presented, incorporating electronic linear drivers along photonic components. An electro-optical 3 dB &amp; 6 dB bandwidth of 24 GHz and 34 GHz respectively was measured. The on-chip drivers decrease the V<jats:italic>
      <jats:sub>π</jats:sub>
    </jats:italic> by a factor of 10.</jats:p>}},
  author       = {{Kress, Christian and Schwabe, Tobias and Rhee, Hanjo and Kerman, Sarp and Scheytt, J. Christoph}},
  booktitle    = {{Optica Advanced Photonics Congress 2022}},
  publisher    = {{Optica Publishing Group}},
  title        = {{{Broadband Mach-Zehnder Modulator with Linear Driver in Electronic-Photonic Co-Integrated Platform}}},
  doi          = {{10.1364/iprsn.2022.im4c.1}},
  year         = {{2022}},
}

@inproceedings{45653,
  author       = {{Vernholz, Mats}},
  location     = {{Stuttgart}},
  title        = {{{Industrie 4.0 in der beruflichen Bildung – Automatisierter Maschinenbaulernbetrieb Paderborn }}},
  doi          = {{https://doi.org/10.48513/joted.v11i2.267 }},
  year         = {{2022}},
}

@article{47961,
  abstract     = {{<jats:p>Due to failures or even the absence of an electricity grid, microgrid systems are becoming popular solutions for electrifying African rural communities. However, they are heavily stressed and complex to control due to their intermittency and demand growth. Demand side management (DSM) serves as an option to increase the level of flexibility on the demand side by scheduling users’ consumption patterns profiles in response to supply. This paper proposes a demand-side management strategy based on load shifting and peak clipping. The proposed approach was modelled in a MATLAB/Simulink R2021a environment and was optimized using the artificial neural network (ANN) algorithm. Simulations were carried out to test the model’s efficacy in a stand-alone PV-battery microgrid in East Africa. The proposed algorithm reduces the peak demand, smoothing the load profile to the desired level, and improves the system’s peak to average ratio (PAR). The presence of deferrable loads has been considered to bring more flexible demand-side management. Results promise decreases in peak demand and peak to average ratio of about 31.2% and 7.5% through peak clipping. In addition, load shifting promises more flexibility to customers.</jats:p>}},
  author       = {{Philipo, Godiana Hagile and Kakande, Josephine Nakato and Krauter, Stefan}},
  issn         = {{1996-1073}},
  journal      = {{Energies}},
  keywords     = {{Energy (miscellaneous), Energy Engineering and Power Technology, Renewable Energy, Sustainability and the Environment, Electrical and Electronic Engineering, Control and Optimization, Engineering (miscellaneous), Building and Construction}},
  number       = {{14}},
  publisher    = {{MDPI AG}},
  title        = {{{Neural Network-Based Demand-Side Management in a Stand-Alone Solar PV-Battery Microgrid Using Load-Shifting and Peak-Clipping}}},
  doi          = {{10.3390/en15145215}},
  volume       = {{15}},
  year         = {{2022}},
}

@inproceedings{31331,
  author       = {{Hetkämper, Tim and Claes, Leander and Henning, Bernd}},
  booktitle    = {{Sensoren und Messsysteme - Beiträge der 21. ITG/GMA-Fachtagung}},
  isbn         = {{978-3-8007-5835-7}},
  location     = {{Nürnberg}},
  publisher    = {{VDE Verlag GmbH}},
  title        = {{{Schlieren imaging with fractional Fourier transform to visualise ultrasonic fields}}},
  year         = {{2022}},
}

@inproceedings{36112,
  author       = {{Pfeifer, Florian and Knorr, Lukas and Schlosser, Florian and Marten, Thorsten and Tröster, Thomas}},
  location     = {{Paphos, Zypern}},
  title        = {{{Ecological and Economical Feasibility of Inductive Heating for Sustainable Press Hardening Processes}}},
  year         = {{2022}},
}

@inproceedings{34136,
  author       = {{Grynko, Yevgen and Shkuratov, Yuriy and Alhaddad, Samer and Förstner, Jens}},
  keywords     = {{tet_topic_scattering}},
  location     = {{Granada, Spain}},
  publisher    = {{Copernicus GmbH}},
  title        = {{{Light backscattering from numerical analog of planetary regoliths}}},
  doi          = {{10.5194/epsc2022-151}},
  year         = {{2022}},
}

@article{59668,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Spin‐controlled lasers are highly interesting photonic devices and have been shown to provide ultrafast polarization dynamics in excess of 200 GHz. In contrast to conventional semiconductor lasers their temporal properties are not limited by the intensity dynamics, but are governed primarily by the interaction of the spin dynamics with the birefringent mode splitting that determines the polarization oscillation frequency. Another class of modern semiconductor lasers are high‐<jats:italic>β</jats:italic> emitters, which benefit from enhanced light–matter interaction due to strong mode confinement in low‐mode‐volume microcavities. In such structures, the emission properties can be tailored by the resonator geometry to realize for instance bimodal emission behavior in slightly elliptical micropillar cavities. This attractive feature is utilized to demonstrate and explore spin‐lasing effects in bimodal high‐<jats:italic>β</jats:italic> quantum dot micropillar lasers. The studied microlasers with a <jats:italic>β</jats:italic>‐factor of 4% show spin‐laser effects with experimental polarization oscillation frequencies up to 15 GHz and predicted frequencies up to about 100 GHz, which are controlled by the ellipticity of the resonator. These results reveal appealing prospects for very compact, ultrafast, and energy‐efficient spin‐lasers and can pave the way for future purely electrically injected spin‐lasers enabled by short injection path lengths.</jats:p>}},
  author       = {{Heermeier, Niels and Heuser, Tobias and Große, Jan and Jung, Natalie and Kaganskiy, Arsenty and Lindemann, Markus and Gerhardt, Nils Christopher and Hofmann, Martin R. and Reitzenstein, Stephan}},
  issn         = {{1863-8880}},
  journal      = {{Laser &amp; Photonics Reviews}},
  number       = {{4}},
  publisher    = {{Wiley}},
  title        = {{{Spin‐Lasing in Bimodal Quantum Dot Micropillar Cavities}}},
  doi          = {{10.1002/lpor.202100585}},
  volume       = {{16}},
  year         = {{2022}},
}

@inproceedings{59758,
  author       = {{Mwammenywa, Ibrahim and Kagarura, Geoffrey Mark and Petrov, Dmitry and Holle, Philip and Hilleringmann, Ulrich}},
  booktitle    = {{2021 International Conference on Electrical, Computer and Energy Technologies (ICECET)}},
  publisher    = {{IEEE}},
  title        = {{{LoRa-based Demand-side Load Monitoring and Management System for Microgrids in Africa}}},
  doi          = {{10.1109/icecet52533.2021.9698506}},
  year         = {{2022}},
}

@inproceedings{64306,
  author       = {{Heermeier, Niels and Jung, Natalie and Lindemann, Markus and Gerhardt, Nils Christopher and Hofmann, Martin R. and Heuser, Tobias and Große, Jan and Kaganskiy, Arsenty and Reitzenstein, Stephan}},
  booktitle    = {{Spintronics XV}},
  title        = {{{Spin lasing in high-beta bimodal quantum dot micropillar cavities }}},
  doi          = {{10.1117/12.2632687}},
  year         = {{2022}},
}

@article{64307,
  author       = {{Gurevich, Evgeny L. and Hofmann, Martin R. and Gerhardt, Nils Christopher and Neutsch, Krisztian}},
  journal      = {{Nanomaterials}},
  number       = {{3}},
  title        = {{{Investigation of laser-induced periodic surface structures using synthetic optical holography}}},
  doi          = {{10.3390/nano12030505}},
  volume       = {{13}},
  year         = {{2022}},
}

@techreport{49113,
  abstract     = {{In this report we present our system for the Detection and Classification of Acoustic Scenes and Events (DCASE) 2022 Challenge Task 4: Sound Event Detection in Domestic Environments 1 . As in previous editions of the Challenge, we use forward-backward convolutional recurrent neural networks (FBCRNNs) [1, 2] for weakly labeled and semi-supervised sound event detection (SED) and eventually generate strong pseudo labels for weakly labeled and unlabeled data. Then, (tag-conditioned) bidirectional CRNNs (Bi-CRNNs) [1, 2] are trained in a strongly supervised manner as our final SED models. In each of the training stages we use multiple iterations of self-training. Compared to previous editions, we improved our system performance by 1) some tweaks regarding data augmentation, pseudo labeling and inference 2) using weakly labeled AudioSet data [3] for pretraining larger networks and 3) augmenting the DESED data [4] with strongly labeled AudioSet data [5] for finetuning of the networks. Source code is publicly available at https://github.com/fgnt/pb_sed.}},
  author       = {{Ebbers, Janek and Haeb-Umbach, Reinhold}},
  title        = {{{Pre-Training And Self-Training For Sound Event Detection In Domestic Environments}}},
  year         = {{2022}},
}

@misc{48628,
  author       = {{Kruse, Stephan and Scheytt, J. Christoph}},
  title        = {{{Elektrooptischer Mischer}}},
  year         = {{2022}},
}

@inproceedings{33509,
  abstract     = {{In this publication a novel method for far-field prediction from magnetic Huygens box data based on the boundary element method (BEM) is presented. Two examples are considered for the validation of this method. The first example represents an electric dipole so that the obtained calculations can be compared to an analytical solution. As a second example, a printed circuit board is considered and the calculated far-field is compared to a fullwave simulation. In both cases, the calculations for different field integral equations are under comparison, and the results indicate that the presented method performs very well with a combined field integral equation, for the specified problem, when only magnetic Huygens box data is given.}},
  author       = {{Marschalt, Christoph and Schroder, Dominik and Lange, Sven and Hilleringmann, Ulrich and Hedayat, Christian and Kuhn, Harald and Sievers, Denis and Förstner, Jens}},
  booktitle    = {{2022 Smart Systems Integration (SSI)}},
  keywords     = {{Near-Field Scanning, Huygens Box, Boundary Element Method, Method of Moments, tet_topic_hf, tet_enas}},
  location     = {{Grenoble, France}},
  publisher    = {{IEEE}},
  title        = {{{Far-field Calculation from magnetic Huygens Box Data using the Boundary Element Method}}},
  doi          = {{10.1109/ssi56489.2022.9901431}},
  year         = {{2022}},
}

@inproceedings{33848,
  abstract     = {{Impressive progress in neural network-based single-channel speech source
separation has been made in recent years. But those improvements have been
mostly reported on anechoic data, a situation that is hardly met in practice.
Taking the SepFormer as a starting point, which achieves state-of-the-art
performance on anechoic mixtures, we gradually modify it to optimize its
performance on reverberant mixtures. Although this leads to a word error rate
improvement by 7 percentage points compared to the standard SepFormer
implementation, the system ends up with only marginally better performance than
a PIT-BLSTM separation system, that is optimized with rather straightforward
means. This is surprising and at the same time sobering, challenging the
practical usefulness of many improvements reported in recent years for monaural
source separation on nonreverberant data.}},
  author       = {{Cord-Landwehr, Tobias and Boeddeker, Christoph and von Neumann, Thilo and Zorila, Catalin and Doddipatla, Rama and Haeb-Umbach, Reinhold}},
  booktitle    = {{2022 International Workshop on Acoustic Signal Enhancement (IWAENC)}},
  publisher    = {{IEEE}},
  title        = {{{Monaural source separation: From anechoic to reverberant environments}}},
  year         = {{2022}},
}

@inproceedings{33819,
  author       = {{von Neumann, Thilo and Kinoshita, Keisuke and Boeddeker, Christoph and Delcroix, Marc and Haeb-Umbach, Reinhold}},
  booktitle    = {{ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}},
  publisher    = {{IEEE}},
  title        = {{{SA-SDR: A Novel Loss Function for Separation of Meeting Style Data}}},
  doi          = {{10.1109/icassp43922.2022.9746757}},
  year         = {{2022}},
}

@misc{33816,
  author       = {{Gburrek, Tobias and Boeddeker, Christoph and von Neumann, Thilo and Cord-Landwehr, Tobias and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}},
  publisher    = {{arXiv}},
  title        = {{{A Meeting Transcription System for an Ad-Hoc Acoustic Sensor Network}}},
  doi          = {{10.48550/ARXIV.2205.00944}},
  year         = {{2022}},
}

@inproceedings{33954,
  author       = {{Boeddeker, Christoph and Cord-Landwehr, Tobias and von Neumann, Thilo and Haeb-Umbach, Reinhold}},
  booktitle    = {{Interspeech 2022}},
  publisher    = {{ISCA}},
  title        = {{{An Initialization Scheme for Meeting Separation with Spatial Mixture Models}}},
  doi          = {{10.21437/interspeech.2022-10929}},
  year         = {{2022}},
}

@inproceedings{33958,
  abstract     = {{Recent speaker diarization studies showed that integration of end-to-end neural diarization (EEND) and clustering-based diarization is a promising approach for achieving state-of-the-art performance on various tasks. Such an approach first divides an observed signal into fixed-length segments, then performs {\it segment-level} local diarization based on an EEND module, and merges the segment-level results via clustering to form a final global diarization result. The segmentation is done to limit the number of speakers in each segment since the current EEND cannot handle a large number of speakers. In this paper, we argue that such an approach involving the segmentation has several issues; for example, it inevitably faces a dilemma that larger segment sizes increase both the context available for enhancing the performance and the number of speakers for the local EEND module to handle. To resolve such a problem, this paper proposes a novel framework that performs diarization without segmentation. However, it can still handle challenging data containing many speakers and a significant amount of overlapping speech. The proposed method can take an entire meeting for inference and perform {\it utterance-by-utterance} diarization that clusters utterance activities in terms of speakers. To this end, we leverage a neural network training scheme called Graph-PIT proposed recently for neural source separation. Experiments with simulated active-meeting-like data and CALLHOME data show the superiority of the proposed approach over the conventional methods.}},
  author       = {{Kinoshita, Keisuke and von Neumann, Thilo and Delcroix, Marc and Boeddeker, Christoph and Haeb-Umbach, Reinhold}},
  booktitle    = {{Proc. Interspeech 2022}},
  pages        = {{1486--1490}},
  publisher    = {{ISCA}},
  title        = {{{Utterance-by-utterance overlap-aware neural diarization with Graph-PIT}}},
  doi          = {{10.21437/Interspeech.2022-11408}},
  year         = {{2022}},
}

@inproceedings{31805,
  author       = {{Kruse, Stephan and Bahmanian, Meysam and Fard, Saeed and Meinecke, Marc-Michael and Kurz, Heiko G. and Scheytt, Christoph}},
  booktitle    = {{European Radar Conference (EuRAD)}},
  title        = {{{A Low Phase Noise 77 GHz Frequency Synthesizer for Long Range Radar}}},
  doi          = {{10.23919/EuRAD54643.2022.9924677}},
  year         = {{2022}},
}