@inproceedings{HauslerFischerWunderlichetal., author = {Hausler, Peter and Fischer, Johannes and Wunderlich, Lukas and Recum, Patrick and Peller, Sebastian and Hirsch, Thomas and Bierl, Rudolf}, title = {Miniaturisierte Sensoren basierend auf Oberfl{\"a}chenplasmonenresonanz, Chancen und Herausforderungen}, series = {DGaO-Proceedings 2021}, booktitle = {DGaO-Proceedings 2021}, publisher = {Dt. Gesellschaft f{\"u}r angewandte Optik}, address = {Erlangen-N{\"u}rnberg}, abstract = {Derzeit gibt es zahlreiche Bereiche, wie Umwelt Monitoring und zivile Infrastruktur in denen geeignete Sensoren f{\"u}r die {\"U}berwachung der Systeme fehlen. SPR-basierte Sensoren haben das Potential diese L{\"u}cke zu schließen. Um f{\"u}r den Einsatz in der Umwelt tauglich zu werden, m{\"u}ssen die Sensoren noch robuster werden. Hier wird eine m{\"o}gliche L{\"o}sung gezeigt.}, language = {de} } @article{MoserJobstBierletal., author = {Moser, Elisabeth and Jobst, Simon and Bierl, Rudolf and Jenko, Frank}, title = {A Deep Learning System to Transform Cross-Section Spectra to Varying Environmental Conditions}, series = {Vibrational Spectroscopy}, volume = {122}, journal = {Vibrational Spectroscopy}, number = {September}, publisher = {Elsevier}, issn = {0924-2031}, doi = {10.1016/j.vibspec.2022.103410}, abstract = {Absorption cross-sections provide a basis for many gas sensing applications. Therefore, any error in molecular cross-sections caused by varying environmental conditions propagates to spectroscopic applications. Original molecular cross-sections in varying environmental conditions can only be simulated for some molecules, whereas for most multi-atom molecules, one must rely on high-precision measurements at certain environmental configurations. In this study, a deep learning system trained with simulated absorption cross-sections for predicting cross-sections at a different pressure configuration is presented. The system's capability to transfer to measured, multi-atom cross-sections is demonstrated. Thus, it provides an alternative to (pseudo-) line lists whenever the required information for simulation is unavailable. The predictive performance of the system was evaluated on validation data via simulation, and its transfer learning capabilities were demonstrated on actual measurement chlorine nitrate data. From the comparison between the system and line lists, the system shows slightly worse performance than pseudo-line lists but its predictive quality is still deemed acceptable with less than 5\% relative integral change with a highly localized error around the peak center. This opens a promising way for further research to use deep learning to simulate the effect of varying environmental conditions on absorption cross-sections.}, language = {en} } @inproceedings{MoserPangerlJobstetal., author = {Moser, Elisabeth and Pangerl, Jonas and Jobst, Simon and Weigl, Stefan and Bierl, Rudolf}, title = {Modeling the Photoacoustic Spectrum of a Quantum Cascade Laser for Human Breath}, series = {Optical Sensors and Sensing Congress 2022 (AIS, LACSEA, Sensors, ES): July 11 - 15, 2022, Vancouver, British Columbia Canada}, booktitle = {Optical Sensors and Sensing Congress 2022 (AIS, LACSEA, Sensors, ES): July 11 - 15, 2022, Vancouver, British Columbia Canada}, publisher = {Optica Publishing Group}, isbn = {978-1-957171-10-4}, doi = {10.1364/AIS.2022.ATu3G.2}, abstract = {A modeling approach to create a photoacoustic spectrum from synthetic data is presented and evaluated. The resulting model reaches a MAPE score of 2.7\% and can be used to enable data-driven development in future work.}, subject = {Quantenkaskadenlaser}, language = {en} } @article{GoldschmidtMoserNitzscheetal., author = {Goldschmidt, Jens and Moser, Elisabeth and Nitzsche, Leonard and Bierl, Rudolf and W{\"o}llenstein, J{\"u}rgen}, title = {Improving the performance of artificial neural networks trained on synthetic data in gas spectroscopy - a study on two sensing approaches}, series = {tm - Technisches Messen}, journal = {tm - Technisches Messen}, edition = {Online ver{\"o}ffentlicht}, publisher = {Oldenbourg Wissenschaftsverlag}, doi = {10.1515/teme-2023-0051}, abstract = {Artificial neural networks (ANNs) are used in quantitative infrared gas spectroscopy to predict concentrations on multi-component absorption spectra. Training of ANNs requires vast amounts of labelled training data which may be elaborate and time consuming to obtain. Additional data can be gained by the utilization of synthetically generated spectra, but at the cost of systematic deviations to measured data. Here, we present two approaches to train ANNs with a combination of comparatively small, measured data sets and synthetically generated data. For the first approach a neural network is trained hybridly with synthetically generated infrared absorption spectra of mixtures of N2O and CO and measured zero-gas spectra, taken with a mid-infrared dual comb spectrometer. This improves the mean absolute error (MAE) of the network predictions from 0.46 to 0.01 ppmV and 0.24 to 0.01 ppmV for the concentration predictions of N2O and CO respectively for zero-gas measurements which was previously observed for training with purely synthetic data. At the same time a similar performance on spectra from gas mixtures of 0-100 ppmV N2O and 0 to 60 ppmV CO was achieved. For the second approach an ANN pre-trained on synthetic infrared spectra of mixtures of acetone and ethanol is retrained on a small dataset consisting of 26 spectra taken with a mid-infrared photoacoustic spectrometer. In this case the MAE for the concentration predictions of ethanol and acetone are improved by 45 \% and 20 \% in comparison to purely synthetic training. This shows the capability of using synthetically generated data to train ANNs in combination with small amounts of measured data to further improve neural networks for gas sensing and the transferability between different sensing approaches.}, language = {en} } @inproceedings{UlreichMoserOlbrichetal., author = {Ulreich, Fabian and Moser, Elisabeth and Olbrich, Florian and Ebert, Martin and Bierl, Rudolf and Kaup, Andr{\´e}}, title = {Luminance Simulation in CARLA under Cloud Coverage - Model Validation and Implications}, series = {2023 IEEE International Workshop on Metrology for Automotive (MetroAutomotive), -30 June 2023, Modena, Italy}, booktitle = {2023 IEEE International Workshop on Metrology for Automotive (MetroAutomotive), -30 June 2023, Modena, Italy}, editor = {Keil, Rudolf and Tschorn, Jan Alexander and T{\"u}mler, Johannes and Altinsoy, Mehmet Ercan}, publisher = {IEEE}, isbn = {979-8-3503-2187-6}, doi = {10.1109/MetroAutomotive57488.2023.10219098}, pages = {228 -- 233}, abstract = {To decrease the number of kilometers driven during the development of autonomous cars or driving assistance systems, performant simulation tools are necessary. Currently, domain distance effects between simulation and reality are limiting the successful application of rendering engines in data-driven perception tasks. In order to mitigate those domain distance effects, simulation tools have to be as close to reality as possible for the given task. For optical sensors like cameras, the luminance of the scene is essential. We provide within this paper a method to measure the luminance of rendered scenes within CARLA, an often used open-source simulation environment. Thereby, it is possible to validate the environment and weather models by taking real-world measurements with photometric sensors or with the help of open-source weather data, published e.g. by the German federal service for weather data (DWD - "Deutscher Wetterdienst"). Employing our proposed luminance measurement, the domain gap resulting from the simulation can be specified, which makes it possible to evaluate the statements about the safety of the automated driving system determined within the simulation. We show that the ratio between global and diffuse radiation modeled by the default atmosphere models within CARLA are under limited conditions similar to real-world measurements taken by the DWD. Nevertheless, we show, that the ratio's temporal variability in real-world situations is not modeled by CARLA.}, language = {en} } @article{MuellerWeiglMuellerWilliamsetal., author = {M{\"u}ller, Max and Weigl, Stefan and M{\"u}ller-Williams, Jennifer and Lindauer, Matthias and R{\"u}ck, Thomas and Jobst, Simon and Bierl, Rudolf and Matysik, Frank-Michael}, title = {Comparison of photoacoustic spectroscopy and cavity ring-down spectroscopy for ambient methane monitoring at Hohenpeißenberg}, series = {Atmospheric Measurement Techniques}, volume = {16}, journal = {Atmospheric Measurement Techniques}, number = {18}, publisher = {Copernicus Publications}, issn = {1867-8548}, doi = {10.5194/amt-16-4263-2023}, pages = {4263 -- 4270}, abstract = {With an atmospheric concentration of approximately 2000 parts per billion (ppbV, 10-9), methane (CH4) is the second most abundant greenhouse gas (GHG) in the atmosphere after carbon dioxide (CO2). The task of long-term and spatially resolved GHG monitoring to verify whether climate policy actions are effective is becoming more crucial as climate change progresses. In this paper we report the CH4 concentration readings of our photoacoustic (PA) sensor over a 5 d period at Hohenpeißenberg, Germany. As a reference device, a calibrated cavity ring-down spectrometer, Picarro G2301, from the meteorological observatory of the German Weather Service (DWD) was employed. Trace gas measurements with photoacoustic instruments promise to provide low detection limits at comparably low costs. However, PA devices are often susceptible to cross-sensitivities related to fluctuating environmental conditions, e.g. ambient humidity. The obtained results show that for PA sensor systems non-radiative relaxation effects induced by varying humidity are a non-negligible factor. Applying algorithm compensation techniques, which are capable of calculating the influence of non-radiative relaxation effects on the photoacoustic signal, increase the accuracy of the photoacoustic sensor significantly. With an average relative deviation of 1.11 \% from the G2301, the photoacoustic sensor shows good agreement with the reference instrument.}, language = {en} } @unpublished{PangerlMoserMuelleretal., author = {Pangerl, Jonas and Moser, Elisabeth and M{\"u}ller, Max and Weigl, Stefan and Jobst, Simon and R{\"u}ck, Thomas and Bierl, Rudolf and Matysik, Frank-Michael}, title = {A Highly Sensitive Acetone and Ethanol Quantum Cascade Laser Based Photoacoustic Sensor: Characterization and Multi-Component Spectra Recording in Synthetic Breath}, series = {SSRN Electronic Journal}, journal = {SSRN Electronic Journal}, publisher = {Elsevier}, doi = {10.2139/ssrn.4305376}, abstract = {Trace gas analysis in breath is challenging due to the vast number of different components. We present a highly sensitive quantum cascade laser based photoacoustic setup for breath analysis. Scanning the range between 8260 and 8270 nm with a spectral resolution of 48 pm, we are able to quantify acetone and ethanol within a typical breath matrix containing water and CO2. We photoacoustically acquired spectra within this region of mid-infra-red light and prove that those spectra do not suffer from non-spectral interferences. The purely additive behavior of a breath sample spectrum was verified by comparing it with the independently acquired single component spectra using Pearson and Spearman correlation coefficients. A previously presented simulation approach is improved and an error attribution study is presented. With a 3σ detection limit of 6.5 ppbV in terms of ethanol and 250 pptV regarding acetone, our system is among the best performing presented so far.}, language = {en} } @misc{OlbrichPongratzBierletal., author = {Olbrich, Florian and Pongratz, Christian and Bierl, Rudolf and Ehrlich, Ingo}, title = {Method and System for Evaluating a Structural Integrity of an Aerial Vehicle}, language = {en} } @inproceedings{PellerZanklFischeretal., author = {Peller, Sebastian and Zankl, Tobias and Fischer, Christoph and Bierl, Rudolf}, title = {Fast sound field characterization of beamforming capable capacitive micromachined ultrasonic transducer (CMUT) arrays by refracto-vibrometry}, series = {IEEE IUS 2023, International Ultrasonics Symposium, Montr{\´e}al, September 3-8, 2023}, booktitle = {IEEE IUS 2023, International Ultrasonics Symposium, Montr{\´e}al, September 3-8, 2023}, publisher = {IEEE}, isbn = {979-8-3503-4645-9}, issn = {1948-5727}, doi = {10.1109/IUS51837.2023.10308126}, pages = {1 -- 3}, abstract = {We introduce a time-optimized setup based on refracto-vibrometry for the purpose of scanning the sound field of capacitive micromachined ultrasonic transducer (CMUT) arrays primarily for the qualification of their beamforming capability. In comparison to commonly used microphones on a traversing stage the proposed method is substantially faster and consumes only a few minutes of time for a complete two-dimensional sound field containing about 100.000 scan points.}, language = {en} } @misc{RueckBierlLechneretal., author = {R{\"u}ck, Thomas and Bierl, Rudolf and Lechner, Alfred and Graf, Antonia and Dams, Florian and Schreiner, Rupert and Auchter, Eberhard and Kriz, Willy and Deubzer, MIchael and Schiller, Frank and Mottok, J{\"u}rgen and Niemetz, Michael and Margull, Ulrich and Hagel, Georg and Utesch, Matthias and Waldherr, Franz and B{\"o}hm, Matthias and Fraunhoffer, Judith and Gardeia, Armin and Schneider, Ralph and Streubel, Janet and Landes, Dieter and Studt, Reimer and Peuker, Dominik and Scharfenberg, Georg and Hook, Christian and Schuster, Dietwald and Ehrlich, Ingo and Dinnebier, Heinrich and Briem, Ulrich and L{\"a}mmlein, Stephan and Koder, Alexander and Bialek, Adam and Genewsky, Axel and Neumeier, Michael and Schlosser, Philipp and Rabl, Hans-Peter and Paule, Matthias and Galster, Christoph and Schiedermeier, Michael and Zwickel, Andreas and Hobmeier, Christoph and Bischoff, Tobias and Rill, Georg and Schaeffer, Thomas and Arbesmeier, Martin and Groß, Andreas and Schlegl, Thomas and Becker, Mark and Senn, Konrad and Schliekmann, Claus and Scholz, Peter and Sippl, Christian and Grill, Martin}, title = {Forschungsbericht 2011 / Hochschule f{\"u}r Angewandte Wissenschaften - Fachhochschule Regensburg}, editor = {Eckstein, Josef}, address = {Regensburg}, organization = {Hochschule f{\"u}r Angewandte Wissenschaften Regensburg}, issn = {1868-3533}, doi = {10.35096/othr/pub-732}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-7321}, language = {de} } @inproceedings{RothHauslerBierl, author = {Roth, Carina and Hausler, Peter and Bierl, Rudolf}, title = {Einbindung eines 2D-Photodetektors in einen hochintegrierten SPR-Imaging-Sensor}, series = {7. MikroSystemTechnik Kongress "MEMS, Mikroelektronik, Systeme", 23.-25. Oktober 2017, M{\"u}nchen}, booktitle = {7. MikroSystemTechnik Kongress "MEMS, Mikroelektronik, Systeme", 23.-25. Oktober 2017, M{\"u}nchen}, publisher = {VDE-Verlag}, address = {M{\"u}nchen}, isbn = {978-3-8007-4491-6}, pages = {848 -- 850}, abstract = {Die Oberfl{\"a}chenplasmonenresonanzspektroskopie (SPR) ist eine hochempfindliche Messmethode, die es erlaubt, Gase und Fl{\"u}ssigkeiten zerst{\"o}rungs- und markierungsfrei in Echtzeit zu analysieren. Bisher vornehmlich im Labormaßstab in der Bioanalytik und dem Wirkstoffscreening eingesetzt, soll diese Technologie nun miniaturisiert und f{\"u}r weitere Anwendungsgebiete zug{\"a}nglich gemacht werden. Dazu wird ein kompakter Micro-Opto-Electro-Mechanical Systems Sensor (MOEMS) entwickelt, der mit Hilfe des SPR Imaging {\"A}nderungen der chemischen Zusammensetzung verschiedener Fl{\"u}ssigkeiten inline messen kann.}, language = {de} } @inproceedings{BauerVitzthumeckerBierletal., author = {Bauer, Lukas and Vitzthumecker, Thomas and Bierl, Rudolf and Ehrnsperger, Matthias}, title = {Machine-learning-based detection and severity estimation of drought stress in plants using hyperspectral imaging data}, series = {Remote Sensing for Agriculture, Ecosystems, and Hydrology XXVII}, booktitle = {Remote Sensing for Agriculture, Ecosystems, and Hydrology XXVII}, publisher = {SPIE}, doi = {10.1117/12.3072011}, pages = {7}, abstract = {Growing food demand due to population growth, coupled with increasingly frequent and severe droughts caused by climate change make water increasingly scarce. To address this, accurate assessment of plant water demand is essential for precise drought treatment and water conservation. Hyperspectral imaging (HSI) captures hypercubes, a combination of spectral and spatial data and offers promising capabilities for detection of plant stresses. However, most reported approaches only use selected spectral bands or indices, neglecting the full hypercube information. This is assumed to limit the detection accuracy. To overcome these limitations, we aim to develop a measurement pipeline to generate a comprehensive dataset comprising hypercubes of plants under varying drought stress levels along with selected physiological, environmental, and illumination data. This dataset will be used to train suitable data-driven models that enable improved drought stress detection as well as the non-invasive determination of physiological parameters based on HSI data.}, language = {en} } @unpublished{FischerHirschReitmeieretal., author = {Fischer, Johannes and Hirsch, Thomas and Reitmeier, Torsten and Bierl, Rudolf}, title = {Real-time hardware-based processing of high-precision detector signals for surface plasmon resonance spectroscopy}, doi = {10.2139/ssrn.5971170}, pages = {16}, abstract = {Surface plasmon resonance (SPR) is limited by small-signal detectability and drift when subtraction occurs in software after digitization. We introduce an SPR detector that performs on-detector amplification and analog differential readout, eliminating moving parts and software-heavy correction. The hardware-native subtraction boosts the usable ADC range and suppresses illumination and environmental noise. In fixed-angle refractive-index steps (NaCl), the platform resolves Δn_min ≈ 1.8 × 10⁻⁷ RIU compared to 4.6-7.2 × 10⁻⁶ RIU on a commercial comparator and improves small-signal SNR by up to ∼5,000-fold, while remaining competitive at high signal levels. In a model IgG-BSA assay, the detector's low noise floor clarifies early binding and equilibrium transitions. By generating inherently clean raw signals, this hardware-native approach dramatically enhances sensitivity and long-term stability for label-free biosensing and inline process analytics while rendering AI-based or complex post-processing entirely unnecessary. The concept generalizes across platforms and opens a compact route to robust, high-fidelity SPR in complex environments, with a clear path toward multi-wavelength and arrayed detectors for high-throughput chemical monitoring.}, language = {en} }