Refine
Document Type
- Article (8)
- conference proceeding (article) (3)
- conference proceeding (presentation, abstract) (3)
- Preprint (3)
- Part of a Book (1)
- Edited collection (1)
- Part of Periodical (1)
- Report (1)
- Working Paper (1)
Is part of the Bibliography
- no (22)
Keywords
- Kernspintomografie (4)
- Photoacoustic spectroscopy (4)
- Positronen-Emissions-Tomografie (4)
- Schwächung (4)
- Bildgebendes Verfahren (3)
- PET/CT (2)
- Registrierung <Bildverarbeitung> (2)
- Acetone (1)
- Acetylene (1)
- Acoustic resonance monitoring (1)
Institute
- Fakultät Angewandte Natur- und Kulturwissenschaften (10)
- Sensorik-Applikationszentrum (SappZ) (8)
- Fakultät Informatik und Mathematik (6)
- Regensburg Medical Image Computing (ReMIC) (5)
- Fakultät Maschinenbau (2)
- Labor Biofluidmechanik (2)
- Regensburg Center of Biomedical Engineering - RCBE (2)
- Fakultät Architektur (1)
- Fakultät Betriebswirtschaft (1)
- Fakultät Elektro- und Informationstechnik (1)
Begutachtungsstatus
- peer-reviewed (6)
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 five day period at Hohenpeißenberg, Germany. As a reference device a calibrated cavity ringdown spectrometer Picarro G2301 from the meteorological observatory 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 environmental influences. The obtained results show that relaxation effects due to fluctuating environmental conditions, e.g. ambient humidity, are a non-negligible factor in PA sensor systems. Applying algorithm compensation techniques, which are capable of calculating the influence of relaxational 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.
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.
The Exoplanet Imaging Data Challenge is a community-wide effort meant to offer a platform for a fair and common comparison of image processing methods designed for exoplanet direct detection. For this purpose, it gathers on a dedicated repository (Zenodo), data from several high-contrast ground-based instruments worldwide in which we injected synthetic planetary signals. The data challenge is hosted on the CodaLab competition platform, where participants can upload their results. The specifications of the data challenge are published on our website https://exoplanet-imaging-challenge.github.io/. The first phase, launched on the 1st of September 2019 and closed on the 1st of October 2020, consisted in detecting point sources in two types of common data-set in the field of high-contrast imaging: data taken in pupil-tracking mode at one wavelength (subchallenge 1, also referred to as ADI) and multispectral data taken in pupil-tracking mode (subchallenge 2, also referred to as ADI+mSDI). In this paper, we describe the approach, organisational lessons-learnt and current limitations of the data challenge, as well as preliminary results of the participants’ submissions for this first phase. In the future, we plan to provide permanent access to the standard library of data sets and metrics, in order to guide the validation and support the publications of innovative image processing algorithms dedicated to high-contrast imaging of planetary systems.
Während KPIs und Dashboards in großer Zahl für den Betrieb eines Unternehmens im eingeschwungenen Zustand zur Verfügung stehen, scheint es insbesondere in der digitalen Transformation besonders schwierig zu sein, Orientierung zu behalten und zu vermitteln. Welche Aspekte sind dabei wichtig? Was ist der Einfluss der Unternehmenskultur? In welcher Reihenfolge sollen wir vorgehen? Wie können wir die wichtigen Parameter messen und visualisieren? Auf diese Fragen geben wir unter Berücksichtigung unterschiedlicher Modelle und Perspektiven Antworten und zeigen am hypothetischen Beispiel eines Autohauses mögliche konkrete Aktionen exemplarisch auf.
Der Einsatz von künstlicher Intelligenz im Gesundheitsbereich verspricht besonders großen Nutzen durch eine bessere Versorgung sowie effizientere Abläufe und bietet damit letztlich auch ökonomische Vorteile. Dem stehen unter anderem Befürchtungen entgegen, dass sich durch den Einsatz von künstlicher Intelligenz das Arzt-Patienten-Verhältnis verändern könnte, Arbeitsplätze gefährdet seien oder die Ökonomisierung des Gesundheitswesens einen weiteren Schub erfahren könnte. Zuweilen wird die Debatte um diese Technologie, zumal in der Öffentlichkeit, emotional und fern sachlicher Argumente geführt. Die Autorinnen und Autoren untersuchen die Geschichte des KI-Einsatzes in der Medizin, deren öffentliche Wahrnehmung, Governance der KI, die Möglichkeiten und Grenzen der Technik sowie Einsatzgebiete, die bisher noch nicht oder nur wenig im Fokus der Aufmerksamkeit waren. Dabei erweist sich die KI als leistungsfähiges Werkzeug, das zahlreiche ethische und soziale Fragen aufwirft, die bei der Einführung anderer Technologien bereits gestellt wurden; allerdings gibt es auch neue Herausforderungen, denen sich Professionen, Politik und Gesellschaft stellen müssen.
We present a sensitive and compact interband cascade laser (ICL) based photoacoustic setup for the detection of gaseous hydrocarbons and discuss its applicability towards trace gas analysis. We investigated the performance of the sensor for trace concentrations of methane, ethane and acetylene diluted in nitrogen. The excitation of methane and ethane was accomplished using one tunable diode laser, covering a range from 3360 to 3372 nm, which was replaced by a separate ICL at 3025 nm for acetylene detection. The influence of ambient parameters such as temperature, flow rate and pressure as well as potential cross-sensitivities towards O2 and H2O have been examined in terms of methane and acetylene detection. A series of simulations proved several of these influences to be attributed to relaxation effects. With a 3σ limit of detection (LoD) of 6.8 parts per billion (ppbV) in case of methane, 2.3 ppbV regarding ethane and 3.6 ppbV in terms of acetylene, the sensor demonstrates a great potential for applications in the field of trace gas analysis.
Successful transfer of photoacoustic gas sensors from laboratory to real-life applications requires knowledge about potential cross-sensitivities towards environmental and gas matrix changes. Multi-dimensional calibration in case of cross-sensitivities can become very complex or even unfeasible. To address this challenge, we present a novel algorithm to compute the collision based non-radiative efficiency and phase lag of energy relaxation on a molecular level (CoNRad) for photoacoustic signal calculation. This algorithmic approach allows to calculate the entire elaxation cascade of arbitrarily complex systems, yielding a theoretical photoacoustic signal. In this work the influence of varying bulk compositions, i.e. nitrogen (N2), oxygen (O2) and water (H2O) on the photoacoustic signal during methane (CH4) detection is demonstrated. The applicability of the algorithm to other photoacoustic setups is shown exemplary by applying it to the relaxational system investigated in [1]. Hayden et al. examined the effect of water on photoacoustic carbon monoxide (CO) detection.
Forschungsbericht 2012
(2012)