@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} }