@article{32183, author = {{Hou, W and Yao, Y and Li, Y and Peng, B and Shi, K and Zhou, Z and Pan, J and Liu, M and Hu, J}}, issn = {{2095-025x}}, journal = {{Frontiers of materials science}}, number = {{1}}, title = {{{Linearly shifting ferromagnetic resonance response of La0.7Sr0.3MnO3 thin film for body temperature sensors}}}, volume = {{16}}, year = {{2022}}, } @article{32234, author = {{Wojciechowski, M}}, issn = {{2352-3409}}, journal = {{Data Brief}}, pages = {{108318}}, title = {{{Dataset for random uniform distributions of 2D circles and 3D spheres.}}}, volume = {{43}}, year = {{2022}}, } @inproceedings{34140, abstract = {{In this paper, machine learning techniques will be used to classify different PCB layouts given their electromagnetic frequency spectra. These spectra result from a simulated near-field measurement of electric field strengths at different locations. Measured values consist of real and imaginary parts (amplitude and phase) in X, Y and Z directions. Training data was obtained in the time domain by varying transmission line geometries (size, distance and signaling). It was then transformed into the frequency domain and used as deep neural network input. Principal component analysis was applied to reduce the sample dimension. The results show that classifying different designs is possible with high accuracy based on synthetic data. Future work comprises measurements of real, custom-made PCB with varying parameters to adapt the simulation model and also test the neural network. Finally, the trained model could be used to give hints about the error’s cause when overshooting EMC limits.}}, author = {{Maalouly, Jad and Hemker, Dennis and Hedayat, Christian and Rückert, Christian and Kaufmann, Ivan and Olbrich, Marcel and Lange, Sven and Mathis, Harald}}, booktitle = {{2022 Kleinheubach Conference}}, keywords = {{emc, pcb, electronic system development, machine learning, neural network}}, location = {{Miltenberg, Germany}}, publisher = {{IEEE}}, title = {{{AI Assisted Interference Classification to Improve EMC Troubleshooting in Electronic System Development}}}, year = {{2022}}, } @inproceedings{31171, author = {{Bernemann, Sören Antonius and Maćkowiak, Jan and Maćkowiak, Jerzy and Bertling, René and Lutters, Nicole and Kenig, Eugeny}}, location = {{Frankfurt am Main}}, title = {{{Entwicklung eines innovativen Trennapparates zur Stickstoffrückgewinnung aus landwirtschaftlichen Abfällen}}}, year = {{2022}}, } @article{31574, abstract = {{We model negative polarization, which is observed for planetary regoliths at backscattering, solving a full wave problem of light scattering with a numerically exact Discontinuous Galerkin Time Domain (DGTD) method. Pieces of layers with the bulk packing density of particles close to 0.5 are used. The model particles are highly absorbing and have irregular shapes and sizes larger than the wavelength of light. This represents a realistic analog of low-albedo planetary regoliths. Our simulations confirm coherent backscattering mechanism of the origin of negative polarization. We show that angular profiles of polarization are stabilized if the number of particles in a layer piece becomes larger than ten. This allows application of our approach to the negative polarization modeling for planetary regoliths.}}, author = {{Grynko, Yevgen and Shkuratov, Yuriy and Alhaddad, Samer and Förstner, Jens}}, issn = {{0019-1035}}, journal = {{Icarus}}, keywords = {{tet_topic_scattering}}, pages = {{115099}}, publisher = {{Elsevier BV}}, title = {{{Negative polarization of light at backscattering from a numerical analog of planetary regoliths}}}, doi = {{10.1016/j.icarus.2022.115099}}, volume = {{384}}, year = {{2022}}, } @inproceedings{33508, abstract = {{In this work, methods will be evaluated to numerically calculate the passive electrical parameters of planar coils. These parameters can then be used to optimize inductive applications such as wireless power transmission. The focus here will be on inductive localization, which uses high-frequency magnetic fields and the resulting induced voltage to provide localization through the coupling parameter mutual inductance. To achieve localization with high accuracy and best possible operation (resonance, signal strength, etc.), the coil parameters need to be well known. For this reason, some numerical methods for the calculation of these quantities are presented and validated. In addition, the physical effects are thereby considered in more detail, allowing the localization procedure to be better optimized compared to simulative black-box methods. The goal should be a dedicated simulation platform for planar coils to be able to develop training data for neural networks and to test and optimize localization algorithms.}}, author = {{Lange, Sven and Hedayat, Christian and Kuhn, Harald and Hilleringmann, Ulrich}}, booktitle = {{2022 Smart Systems Integration (SSI)}}, keywords = {{Simulation Environment, Inductive Localization, Coil Parameters, Inductive Applications, Near-Field}}, location = {{Grenoble, France}}, publisher = {{IEEE}}, title = {{{Modeling and Characterization of a 3D Environment for the Design of an Inductively Based Locating Method by Coil Couplings}}}, doi = {{10.1109/ssi56489.2022.9901416}}, year = {{2022}}, } @inproceedings{33510, abstract = {{In the manufacture of real wood products, defects can quickly occur during the production process. To quickly sort out these defects, a system is needed that finds damage in the irregularly structured surfaces of the product. The difficulty in this task is that each surface is visually different and no standard defects can be defined. Thus, damage detection using correlation does not work, so this paper will test different machine learning methods. To evaluate different machine learning methods, a data set is needed. For this reason, the available samples were recorded manually using a static fixed camera. Subsequently, the images were divided into sub-images, which resulted in a relatively small data set. Next, a convolutional neural network (CNN) was constructed to classify the images. However, this approach did not lead to a generalized solution, so the dataset was hashed using the a- and pHash. These hash values were then trained with a fully supervised system that will later serve as a reference model, in the semi-supervised learning procedures. To improve the supervised model and not have to label every data point, semi-supervised learning methods are used in the following. For this purpose, the CEAL method (wrapper method) is considered in the first and then the Π-Model (intrinsically semi-supervised).}}, author = {{Sander, Tom and Lange, Sven and Hilleringmann, Ulrich and Geneiß, Volker and Hedayat, Christian and Kuhn, Harald}}, booktitle = {{2022 Smart Systems Integration (SSI)}}, keywords = {{Machine Learning, CNN, Hashing, semi-supervised learning}}, location = {{Grenoble, France}}, publisher = {{IEEE}}, title = {{{Detection of Defects on Irregularly Structured Surfaces using Supervised and Semi-Supervised Learning Methods}}}, doi = {{10.1109/ssi56489.2022.9901433}}, year = {{2022}}, } @inproceedings{33522, author = {{Bernemann, Sören Antonius and Maćkowiak, Jan and Maćkowiak, Jerzy and Bertling, René and Lutters, Nicole and Kenig, Eugeny}}, keywords = {{CFD, simulation, agricultural waste, multiphase}}, location = {{Frankfurt am Main}}, title = {{{Development of an innovative separation unit for nitrogen recovery from agricultural waste}}}, year = {{2022}}, } @inbook{33466, abstract = {{We review our results of numerical simulations of light scattering from different systems of densely packed irregular particles. We consider spherical clusters, thick layers and monolayers with realistic topologies and dimensions much larger than the wavelength of light. The maximum bulk packing density of clusters is 0.5. A numerically exact solution of the electromagnetic problem is obtained using the Discontinuous Galerkin Time Domain method and with application of high- performance computing. We show that high packing density causes light localization in such structures which makes an impact on the opposition phenomena: backscattering intensity surge and negative linear polarization feature. Diffuse multiple scattering is significantly reduced in the case of non-absorbing particles and near-field interaction results in a percolation-like light transport determined by the topology of the medium. With this the negative polarization feature caused by single scattering gets enhanced if compared to lower density samples. We also confirm coherent double scattering mechanism of negative polarization for light scattered from dense absorbing slabs. In this case convergent result for the scattering angle polarization dependency at backscattering can be obtained for a layer of just a few tens of particles if they are larger than the wavelength.}}, author = {{Grynko, Yevgen and Shkuratov, Yuriy and Alhaddad, Samer and Förstner, Jens}}, booktitle = {{Springer Series in Light Scattering - Volume 8: Light Polarization and Multiple Scattering in Turbid Media}}, editor = {{Kokhanovsky, Alexander}}, isbn = {{9783031102974}}, issn = {{2509-2790}}, keywords = {{tet_topic_scattering}}, publisher = {{Springer International Publishing}}, title = {{{Light Scattering by Large Densely Packed Clusters of Particles}}}, doi = {{10.1007/978-3-031-10298-1_4}}, volume = {{8}}, year = {{2022}}, } @inproceedings{33471, abstract = {{The intelligibility of demodulated audio signals from analog high frequency transmissions, e.g., using single-sideband (SSB) modulation, can be severely degraded by channel distortions and/or a mismatch between modulation and demodulation carrier frequency. In this work a neural network (NN)-based approach for carrier frequency offset (CFO) estimation from demodulated SSB signals is proposed, whereby a task specific architecture is presented. Additionally, a simulation framework for SSB signals is introduced and utilized for training the NNs. The CFO estimator is combined with a speech enhancement network to investigate its influence on the enhancement performance. The NN-based system is compared to a recently proposed pitch tracking based approach on publicly available data from real high frequency transmissions. Experiments show that the NN exhibits good CFO estimation properties and results in significant improvements in speech intelligibility, especially when combined with a noise reduction network.}}, author = {{Heitkämper, Jens and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}}, booktitle = {{Proceedings of the 30th European Signal Processing Conference (EUSIPCO)}}, location = {{Belgrad}}, title = {{{Neural Network Based Carrier Frequency Offset Estimation From Speech Transmitted Over High Frequency Channels}}}, year = {{2022}}, } @inproceedings{33847, abstract = {{The scope of speech enhancement has changed from a monolithic view of single, independent tasks, to a joint processing of complex conversational speech recordings. Training and evaluation of these single tasks requires synthetic data with access to intermediate signals that is as close as possible to the evaluation scenario. As such data often is not available, many works instead use specialized databases for the training of each system component, e.g WSJ0-mix for source separation. We present a Multi-purpose Multi-Speaker Mixture Signal Generator (MMS-MSG) for generating a variety of speech mixture signals based on any speech corpus, ranging from classical anechoic mixtures (e.g., WSJ0-mix) over reverberant mixtures (e.g., SMS-WSJ) to meeting-style data. Its highly modular and flexible structure allows for the simulation of diverse environments and dynamic mixing, while simultaneously enabling an easy extension and modification to generate new scenarios and mixture types. These meetings can be used for prototyping, evaluation, or training purposes. We provide example evaluation data and baseline results for meetings based on the WSJ corpus. Further, we demonstrate the usefulness for realistic scenarios by using MMS-MSG to provide training data for the LibriCSS database.}}, author = {{Cord-Landwehr, Tobias and von Neumann, Thilo and Boeddeker, Christoph and Haeb-Umbach, Reinhold}}, booktitle = {{2022 International Workshop on Acoustic Signal Enhancement (IWAENC)}}, location = {{Bamberg}}, title = {{{MMS-MSG: A Multi-purpose Multi-Speaker Mixture Signal Generator}}}, year = {{2022}}, } @article{30863, abstract = {{Abstract In this paper a measurement procedure to identify viscoelastic material parameters of plate-like samples using broadband ultrasonic waves is presented. Ultrasonic Lamb waves are excited via the thermoelastic effect using laser radiation and detected by a piezoelectric transducer. The resulting measurement data is transformed to yield information about multiple propagating Lamb waves as well as their attenuation. These results are compared to simulation results in an inverse procedure to identify the parameters of an elastic and a viscoelastic material model.}}, author = {{Johannesmann, Sarah and Claes, Leander and Feldmann, Nadine and Zeipert, Henning and Henning, Bernd}}, issn = {{2196-7113}}, journal = {{tm - Technisches Messen}}, keywords = {{Electrical and Electronic Engineering, Instrumentation}}, number = {{7 - 8}}, pages = {{493 -- 506}}, publisher = {{Walter de Gruyter GmbH}}, title = {{{Lamb wave based approach to the determination of acoustic material parameters}}}, doi = {{10.1515/teme-2021-0134}}, volume = {{89}}, year = {{2022}}, } @misc{6560, author = {{Johannesmann, Sarah}}, title = {{{Inverses Verfahren zur Bestimmung viskoelastischer Materialparameter}}}, year = {{2022}}, } @inproceedings{33857, author = {{Kuhlmann, Michael and Seebauer, Fritz and Ebbers, Janek and Wagner, Petra and Haeb-Umbach, Reinhold}}, booktitle = {{Interspeech 2022}}, publisher = {{ISCA}}, title = {{{Investigation into Target Speaking Rate Adaptation for Voice Conversion}}}, doi = {{10.21437/interspeech.2022-10740}}, year = {{2022}}, } @inproceedings{33808, author = {{Gburrek, Tobias and Schmalenstroeer, Joerg and Heitkaemper, Jens and Haeb-Umbach, Reinhold}}, booktitle = {{2022 International Workshop on Acoustic Signal Enhancement (IWAENC)}}, location = {{ Bamberg, Germany }}, publisher = {{IEEE}}, title = {{{Informed vs. Blind Beamforming in Ad-Hoc Acoustic Sensor Networks for Meeting Transcription}}}, doi = {{10.1109/IWAENC53105.2022.9914772}}, year = {{2022}}, } @article{50146, abstract = {{Recent advances in numerical methods significantly pushed forward the understanding of electrons coupled to quantized lattice vibrations. At this stage, it becomes increasingly important to also account for the effects of physically inevitable environments. In particular, we study the transport properties of the Hubbard-Holstein Hamiltonian that models a large class of materials characterized by strong electron-phonon coupling, in contact with a dissipative environment. Even in the one-dimensional and isolated case, simulating the quantum dynamics of such a system with high accuracy is very challenging due to the infinite dimensionality of the phononic Hilbert spaces. For this reason, the effects of dissipation on the conductance properties of such systems have not been investigated systematically so far. We combine the non-Markovian hierarchy of pure states method and the Markovian quantum jumps method with the newly introduced projected purified density-matrix renormalization group, creating powerful tensor-network methods for dissipative quantum many-body systems. Investigating their numerical properties, we find a significant speedup up to a factor $\sim 30$ compared to conventional tensor-network techniques. We apply these methods to study dissipative quenches, aiming for an in-depth understanding of the formation, stability, and quasi-particle properties of bipolarons. Surprisingly, our results show that in the metallic phase dissipation localizes the bipolarons, which is reminiscent of an indirect quantum Zeno effect. However, the bipolaronic binding energy remains mainly unaffected, even in the presence of strong dissipation, exhibiting remarkable bipolaron stability. These findings shed light on the problem of designing real materials exhibiting phonon-mediated high-$T_\mathrm{C}$ superconductivity.}}, author = {{Moroder, Mattia and Grundner, Martin and Damanet, François and Schollwöck, Ulrich and Mardazad, Sam and Flannigan, Stuart and Köhler, Thomas and Paeckel, Sebastian}}, journal = {{Physical Review B 107, 214310 (2023)}}, title = {{{Stable bipolarons in open quantum systems}}}, doi = {{10.1103/PhysRevB.107.214310}}, year = {{2022}}, } @article{50148, abstract = {{We develop a general decomposition of an ensemble of initial density profiles in terms of an average state and a basis of modes that represent the event-by-event fluctuations of the initial state. The basis is determined such that the probability distributions of the amplitudes of different modes are uncorrelated. Based on this decomposition, we quantify the different types and probabilities of event-by-event fluctuations in Glauber and Saturation models and investigate how the various modes affect different characteristics of the initial state. We perform simulations of the dynamical evolution with KoMPoST and MUSIC to investigate the impact of the modes on final-state observables and their correlations.}}, author = {{Borghini, Nicolas and Borrell, Marc and Feld, Nina and Roch, Hendrik and Schlichting, Sören and Werthmann, Clemens}}, journal = {{Phys. Rev. C 107 (2023) 034905}}, title = {{{Statistical analysis of initial state and final state response in heavy-ion collisions}}}, doi = {{10.1103/PhysRevC.107.034905}}, year = {{2022}}, } @article{50149, abstract = {{Abstract RNA editing processes are strikingly different in animals and plants. Up to thousands of specific cytidines are converted into uridines in plant chloroplasts and mitochondria whereas up to millions of adenosines are converted into inosines in animal nucleo-cytosolic RNAs. It is unknown whether these two different RNA editing machineries are mutually incompatible. RNA-binding pentatricopeptide repeat (PPR) proteins are the key factors of plant organelle cytidine-to-uridine RNA editing. The complete absence of PPR mediated editing of cytosolic RNAs might be due to a yet unknown barrier that prevents its activity in the cytosol. Here, we transferred two plant mitochondrial PPR-type editing factors into human cell lines to explore whether they could operate in the nucleo-cytosolic environment. PPR56 and PPR65 not only faithfully edited their native, co-transcribed targets but also different sets of off-targets in the human background transcriptome. More than 900 of such off-targets with editing efficiencies up to 91%, largely explained by known PPR-RNA binding properties, were identified for PPR56. Engineering two crucial amino acid positions in its PPR array led to predictable shifts in target recognition. We conclude that plant PPR editing factors can operate in the entirely different genetic environment of the human nucleo-cytosol and can be intentionally re-engineered towards new targets.}}, author = {{Lesch, Elena and Schilling, Maximilian T and Brenner, Sarah and Yang, Yingying and Gruss, Oliver J and Knoop, Volker and Schallenberg-Rüdinger, Mareike}}, issn = {{0305-1048}}, journal = {{Nucleic Acids Research}}, keywords = {{Genetics}}, number = {{17}}, pages = {{9966--9983}}, publisher = {{Oxford University Press (OUP)}}, title = {{{Plant mitochondrial RNA editing factors can perform targeted C-to-U editing of nuclear transcripts in human cells}}}, doi = {{10.1093/nar/gkac752}}, volume = {{50}}, year = {{2022}}, } @unpublished{50224, abstract = {{We study the influence of fringing magnetic fields on turbulent thermal convection in a horizontally extended rectangular domain. The magnetic field is created in the gap between two semi-infinite planar magnetic poles, with the convection layer located near the edge of the gap. We employ direct numerical simulations in this setup for fixed Rayleigh and small Prandtl numbers, but vary the fringe-width by controlling the gap between the magnetic poles and the convection cell. The magnetic field generated by the magnets is strong enough to cease the flow in high magnetic flux region of the convection cell. We observe that as the local vertical magnetic field strength increases, the large scale structures become thinner and align themselves perpendicular to the longitudinal sidewalls. We determine the local Nusselt and Reynolds numbers as functions of the local Hartmann number (based on the vertical component of the magnetic field) and estimate the global heat and momentum transport. We show that the global heat transport decreases with increasing fringe-width for strong magnetic fields but increases with increasing fringe-width for weak magnetic fields. In the regions of large vertical magnetic fields, the convective motion becomes confined to the vicinity of the sidewalls. The amplitudes of these wall modes show a non-monotonic dependence on the fringe-width.}}, author = {{Bhattacharya, Shashwat and Boeck, Thomas and Krasnov, Dmitry and Schumacher, Jörg}}, booktitle = {{arXiv:2211.00559}}, title = {{{Effects of strong fringing magnetic fields on turbulent thermal convection}}}, year = {{2022}}, } @article{54849, abstract = {{The third‐order susceptibility of lithium niobate (LiNbO3) is calculated within a Berry‐phase formulation of the dynamical polarization based on the electronic structure obtained within density‐functional theory (DFT). Maximum values of the order of  m V are calculated for photon energies between 1.2 and 2 eV, i.e., in the lower half of the optical bandgap of lithium niobate. Both free and bound electron (bi)polarons are found to lead to a remarkable enhancement of the third‐order susceptibility for photon energies below 1 eV.}}, author = {{Kozub, Agnieszka L. and Gerstmann, Uwe and Schmidt, Wolf Gero}}, issn = {{0370-1972}}, journal = {{physica status solidi (b)}}, number = {{2}}, publisher = {{Wiley}}, title = {{{Third‐Order Susceptibility of Lithium Niobate: Influence of Polarons and Bipolarons}}}, doi = {{10.1002/pssb.202200453}}, volume = {{260}}, year = {{2022}}, }