TY - JOUR A1 - Frikel, Jürgen T1 - A measurement-based model for image reconstruction in MPI using a FFL JF - Proceedings of the 20th International Conference of Numerical Analysis and Applied Mathematics (ICNAAM-2022), 2022, Heraklion (Crete, Greece) N2 - Most of the common model-based reconstruction schemes in magnetic particle imaging (MPI) use idealized assumptions, e.g., of an ideal field-free-line (FFL) topology. However, the magnetic fields that are generated in real MPI scanners have distortions and, therefore, model-based approaches often lead to inaccurate reconstructions and may contain artifacts. In order to improve the reconstruction quality in MPI, more realistic MPI models need to be derived. In the present work, we address this problem and present a hybrid model for MPI that allows us to incorporates real measurements of the applied magnetic fields. We will explain that the measurements, that are needed to setup a model for the magnetic fields, can be obtained in a novel calibration procedure that is independent of the resolution and which is much less time-consuming than the one employed in measurement-based MPI reconstructions.We will also present a discretization strategy for this model, that can be used in context of algebraic reconstructions. The presented approach was validated on simulated data in [1], however, its evaluation on real data is a topic for future research. Y1 - 2023 PB - AIP Publishing ER - TY - JOUR A1 - Göppel, Simon A1 - Frikel, Jürgen A1 - Haltmeier, Markus T1 - Regularization of Inverse Problems with Translation Invariant Frames JF - Proceedings of the 20th International Conference of Numerical Analysis and Applied Mathematics (ICNAAM-2022), 2022, Heraklion (Crete, Greece) N2 - In various fields of applications, inverse problems are characterized by their sensitivity to data perturbations which can cause severe reconstruction errors. Hence, regularization procedures are employed in order to ensure stability and reconstruction quality. To overcome limitations of classical approaches such as the filtered singular value decomposition (SVD), frame based diagonalization methods have been studied in the recent years, e.g., wavelet-vagulette (WVD) decomposition. While these methods can be well adapted to the problem at hand, it is well-known, that the lack of translation invariance in multiscale systems can cause specific artifacts in the recovered object. Thus, to overcome these drawbacks we use the translation invariant diagonal frame decomposition (TI-DFD) of linear operators. For illustration, we construct a TI-WVD for one-dimensional integration operator, and confirm our theoretical findings by numerical simulations. Y1 - 2023 PB - AIP Publishing ER - TY - JOUR A1 - Lukyanets, S. A1 - Snarskii, Andrei A. A1 - Shamonin (Chamonine), Mikhail A1 - Bakaev, V. V. T1 - Calculation of magnetic leakage field from a surface defect in a linear ferromagnetic material: an analytical approach JF - NDT & E International N2 - A novel analytical approach for calculating the magnetic leakage field from surface defects is proposed and demonstrated for the case of a linear ferromagnetic material. The novelty of the theory is that it relates the distribution of induced magnetic charges to the surface shape. An excellent agreement between the analytical and numerical results is shown. The functional relations between different magnetic field components are discussed. KW - Magnetic methods KW - Flux leakage KW - Mathematical model KW - Defects Y1 - 2003 U6 - https://doi.org/10.1016/s0963-8695(02)00071-3 VL - 36 IS - 1 SP - 51 EP - 55 PB - Elsevier ER - TY - CHAP A1 - Rasp, Franziska A1 - Sahinbas, Burak A1 - Reisinger, Thomas A1 - Hipp, Susanne A1 - Leitgeb, Erich T1 - From Ray Tracing to Channel Impulse Responses: A Review on the Description of Polarimetric Time-Invariant SISO Channels T2 - 2024 International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications (CoBCom), 09-11 July 2024, Graz, Austria N2 - One key enabler of autonomous driving is vehicle-to-everything (V2X) communication and positioning using cellular technologies. To design a vehicular localization system, the understanding and modeling of the physical radio channel (RC) is crucial. Polarization is an important yet often overlooked parameter of the RC. The combined polarization of the transmitting and receiving antennas, along with the environment, significantly affects the Channel Impulse Response (CIR) and localization performance. This paper aims to provide a deterministic description of the time-invariant Single-Input Single-Output (SISO) RC with focus on its polarimetric properties. The aim is to link the understanding of physical wave propagation directly to the conventional CIR descriptions used in localization and signal processing. The focus is on SISO RC modeling for arbitrary antennas, including Line-of-Sight (LoS) and multiple reflections. This study intentionally excludes diffraction, diffuse scattering, and bandwidth limitations, yet establishes a foundational frame-work for future multi-antenna systems with full polarimetric capabilities. The paper describes wave propagation using the Jones Calculus to separate polarimetric wave components, and models the polarimetric reception typical of RC modeling methods in positioning. This approach allows for the calculation of received signal at the antenna feed point. The application context of this research is in ranging or positioning within the V2X framework. KW - Channel Modeling KW - Ray Tracing KW - Wave Propagation KW - Friis KW - Jones Calculus KW - Polarization Diversity Y1 - 2024 SN - 979-8-3503-8845-9 U6 - https://doi.org/10.1109/CoBCom62281.2024.10631174 SN - 2834-8265 SP - 1 EP - 8 PB - IEEE ER - TY - CHAP A1 - Rasp, Franziska A1 - Eberlein, Ernst A1 - Perner, Bastian A1 - Roth-Mandutz, Elke A1 - Hipp, Susanne T1 - Enhanced 5G Sidelink Ranging Based on Carrier Aggregation T2 - 2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN), Nuremberg, Germany, 25-28 September 2023 N2 - Radio access network based positioning and ranging technologies become more and more important for autonomous driving. Positioning on the 5G Sidelink (SL) is discussed in Release 18 (2022-2024) of the Third Generation Partnership Project. For accurate timing-based ranging, a high temporal and spatial resolution is fundamental. The bandwidth for 5G SL positioning is limited due to sharing the existing SL resources with communication. Therefore, we send the positioning reference signal on two separate Component Carriers, i.e. we use a fragmented spectrum. We introduce a two step algorithm for estimating the time of arrival using the inflection point method. We proved a fragmented spectrum can achieve the same accuracy as a continuous spectrum. KW - Sidelink KW - 5G mobile communications KW - Distance measurement KW - Ranging KW - Positioning Y1 - 2023 SN - 9798350320114 U6 - https://doi.org/10.1109/IPIN57070.2023.10332548 SP - 1 EP - 6 PB - IEEE ER - TY - JOUR A1 - Reschke, Johannes A1 - Neumann, Cornelius A1 - Berlitz, Stephan T1 - Personalised neural networks for a driver intention prediction: communication as enabler for automated driving JF - Advanced Optical Technologies N2 - In everyday traffic, pedestrians rely on informal communication with other road users. In case of auto� mated vehicles, this communication can be replaced by light signals, which need to be learned beforehand. Prior to an extensive introduction of automated vehicles, a learning phase for these light signals can be set up in manual driving with help of a driver intention prediction. Therefore, a three-staged algorithm consisting of a neural network, a random forest and a conditional stage, is implemented. Using this algorithm, a true-positive rate (TPR) of 94.0% for a 5.0% false-positive rate (FPR) can be achieved. To improve this process, a personalization pro� cedure is implemented, using driver-specific behaviours, resulting in TPRs ranging from 91.5 to 96.6% for a FPR of 5.0%. Transfer learning of neural networks improves the prediction accuracy of almost all drivers. In order to introduce the implemented algorithm in today’s traffic, especially the FPR has to be improved considerably. KW - automotive lighting KW - learning signals KW - recurrent neural networks KW - time sequence processing KW - vehicle-pedestrian-communication Y1 - 2020 U6 - https://doi.org/10.1515/aot-2020-0035 VL - 9 IS - 6 SP - 357 EP - 364 PB - De Gruyter ER - TY - CHAP A1 - Schenke, Diana Marie A1 - Baumann, Timo ED - Möller, Sebastian ED - Knoeferle, Pia ED - Schulte, Britta ED - Feldhus, Nils T1 - Controlled Diversity: Length-optimized Natural Language Generation T2 - Proceedings of the ISCA/ITG Workshop on Diversity in Large Speech and Language Models, February 20, 2025 in Berlin, Germany N2 - LLMs are not generally able to adjust the length of their outputs based on strict length requirements, a capability that would improve their usefulness in applications that require adherence to diverse user and system requirements. We present an approach to train LLMs to acquire this capability by augmenting existing data and applying existing fine-tuning techniques, which we compare based on the trained models’ adherence to the length requirement and overall response quality relative to the baseline model. Our results demonstrate that these techniques can be successfully applied to train LLMs to adhere to length requirements, with the trained models generating texts which better align to the length requirements. Our results indicate that our method may change the response quality when using training data that was not generated by the baseline model. This allows simultaneous alignment to another training objective in certain scenarios, but is undesirable otherwise. Training on a dataset containing the model’s own responses eliminates this issue. Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-80534 ER - TY - INPR A1 - Amanpour, Ali A1 - Baumann, Timo A1 - Hofmann, Ulrich G. T1 - Optimizing Neural Data Analysis: Determining Minimum Recording Length for Unambigous Signal Processing N2 - Advanced silicon electrode arrays facilitate the recording of thousands of neurons, generating extensive neural data that imposes a significant burden on researchers and processing algorithms. Thus, real-time analysis pipelines are gaining increasing value, while at the same time having to deal with non-stationary and noisy data. We intend to apply Machine Learning (ML) algorithms to a dense set of recordings from rat brains in order to prepare a functional atlas, correlating neuronal signals with anatomical position. While doing so, we needed to decide on a rational way which recording snippet length would best represent the original, longer source sequence and thus suffices to be further processed for anatomical correlation. We implemented an algorithm to evaluate the spectral information of systematically length varied records based on similarity to the original record. For our dataset a recording duration of 3 seconds satisfied moderate requirements across all channels, thus allowing us to reduce computational load for ongoing ML classification of microprobe sourced electrophysiologic signals. Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-80606 ER - TY - JOUR A1 - Nigel G. Ward, A1 - Vega, Alejandro A1 - Baumann, Timo T1 - Prosodic and Temporal Features for Language Modeling for Dialog JF - Speech Communication N2 - If we can model the cognitive and communicative processes underlying speech, we should be able to better predict what a speaker will do. With this idea as inspiration, we examine a number of prosodic and timing features as potential sources of information on what words the speaker is likely to say next. In spontaneous dialog we find that word probabilities do vary with such features. Using perplexity as the metric, the most informative of these included recent speaking rate, volume, and pitch, and time until end of utterance. Using simple combinations of such features to augment trigram language models gave up to a 8.4% perplexity benefit on the Switchboard corpus, and up to a 1.0% relative reduction in word error rate (0.3% absolute) on the Verbmobil II corpus. KW - Dialog dynamics KW - Dialog state KW - Prosody KW - Interlocutor behavior KW - Word probabilities KW - Prediction KW - Perplexity KW - Speech recognition KW - Switchboard corpus KW - Verbmobil corpus Y1 - 2012 U6 - https://doi.org/10.1016/j.specom.2011.07.009 VL - 54 IS - 2 SP - 161 EP - 174 PB - ELSEVIER ER - TY - JOUR A1 - Vogl, Stefanie A1 - Laux, Patrick A1 - Bialas, Joachim A1 - Reifenberger, Christian T1 - Modelling Precipitation Intensities from X-Band Radar Measurements Using Artificial Neural Networks—A Feasibility Study for the Bavarian Oberland Region JF - water N2 - Radar data may potentially provide valuable information for precipitation quantification, especially in regions with a sparse network of in situ observations or in regions with complex topography. Therefore, our aim is to conduct a feasibility study to quantify precipitation intensities based on radar measurements and additional meteorological variables. Beyond the well-established Z–R relationship for the quantification, this study employs Artificial Neural Networks (ANNs) in different settings and analyses their performance. For this purpose, the radar data of a station in Upper Bavaria (Germany) is used and analysed for its performance in quantifying in situ observations. More specifically, the effects of time resolution, time offsets in the input data, and meteorological factors on the performance of the ANNs are investigated. It is found that ANNs that use actual reflectivity as only input are outperforming the standard Z–R relationship in reproducing ground precipitation. This is reflected by an increase in correlation between modelled and observed data from 0.67 (Z–R) to 0.78 (ANN) for hourly and 0.61 to 0.86, respectively, for 10 min time resolution. However, the focus of this study was to investigate if model accuracy benefits from additional input features. It is shown that an expansion of the input feature space by using time-lagged reflectivity with lags up to two and additional meteorological variables such as temperature, relative humidity, and sunshine duration significantly increases model performance. Thus, overall, it is shown that a systematic predictor screening and the correspondent extension of the input feature space substantially improves the performance of a simple Neural Network model. For instance, air temperature and relative humidity provide valuable additional input information. It is concluded that model performance is dependent on all three ingredients: time resolution, time lagged information, and additional meteorological input features. Taking all of these into account, the model performance can be optimized to a correlation of 0.9 and minimum model bias of 0.002 between observed and modelled precipitation data even with a simple ANN architecture. Y1 - 2022 U6 - https://doi.org/10.3390/w14030276 VL - 14 IS - 3 SP - 1 EP - 17 PB - MDPI ER -