IRF: Institutional Repository FHNW

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Das IRF ist das digitale Repositorium der FHNW. Es enthält Publikationen, studentische Arbeiten und Projekte.

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Publikation
Brain network integration dynamics are associated with loss and recovery of consciousness induced by sevoflurane
(Wiley, 2021) Luppi, Andrea I.; Golkowski, Daniel; Ranft, Andreas; Ilg, Rüdiger; Jordan, Denis; Menon, David K.; Stamatakis, Emmanuel A. [in: Human Brain Mapping]
The dynamic interplay of integration and segregation in the brain is at the core of leading theoretical accounts of consciousness. The human brain dynamically alternates between a sub‐state where integration predominates, and a predominantly segregated sub‐state, with different roles in supporting cognition and behaviour. Here, we combine graph theory and dynamic functional connectivity to compare resting‐state functional MRI data from healthy volunteers before, during, and after loss of responsiveness induced with different concentrations of the inhalational anaesthetic, sevoflurane. We show that dynamic states characterised by high brain integration are especially vulnerable to general anaesthesia, exhibiting attenuated complexity and diminished small‐world character. Crucially, these effects are reversed upon recovery, demonstrating their association with consciousness. Higher doses of sevoflurane (3% vol and burst‐suppression) also compromise the temporal balance of integration and segregation in the human brain. Additionally, we demonstrate that reduced anticorrelations between the brain's default mode and executive control networks dynamically reconfigure depending on the brain's state of integration or segregation. Taken together, our results demonstrate that the integrated sub‐state of brain connectivity is especially vulnerable to anaesthesia, in terms of both its complexity and information capacity, whose breakdown represents a generalisable biomarker of loss of consciousness and its recovery.
01A - Beitrag in wissenschaftlicher Zeitschrift
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Publikation
Visual feature engineering
(Institut Geomatik, Hochschule für Architektur, Bau und Geomatik FHNW, 2018) Bleisch, Susanne
Feature engineering is a key concept in machine learning describing the process of defining the characteristics of an observed phenomenon in a way that makes it usable by an algorithm (e.g., [3]). This process often includes domain knowledge to make the features, as well as the results of the algorithms, meaningful in the respective application area. In data analysis generally, including visual data analysis, the obtained results or insights are often dependent on the employed analysis method as well as the parameters and their imensions used. A simple but well-known example is the modifiable area unit problem [5]. Depending on the size and form of the spatial units chosen to aggregate the data, different visualizations and potentially interpretations of the information may result. In some cases, the chosen methods or algorithms and their parameters can be argued to be the right ones to support a specific analysis task, in other cases a sensitivity analysis may be helpful in determining the optimal values. Additionally, visual analytics, allowing tight integration of the interaction with the methods and parameters and the visualizations, has the potential to support the evaluation of the right or sensible analysis method and its parameters as well as to provide provenance information for the finally employed approach.
05 - Forschungs- oder Arbeitsbericht
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Publikation
Outdoor mobile mapping and AI-based 3D object detection with low-cost RGB-D cameras. The use case of on-street parking statistics
(MDPI, 2021) Nebiker, Stephan; Meyer, Jonas; Blaser, Stefan; Ammann, Manuela; Rhyner, Severin Eric [in: Remote sensing]
A successful application of low-cost 3D cameras in combination with artificial intelligence (AI)-based 3D object detection algorithms to outdoor mobile mapping would offer great potential for numerous mapping, asset inventory, and change detection tasks in the context of smart cities. This paper presents a mobile mapping system mounted on an electric tricycle and a procedure for creating on-street parking statistics, which allow government agencies and policy makers to verify and adjust parking policies in different city districts. Our method combines georeferenced red-green-blue-depth (RGB-D) imagery from two low-cost 3D cameras with state-of-the-art 3D object detection algorithms for extracting and mapping parked vehicles. Our investigations demonstrate the suitability of the latest generation of low-cost 3D cameras for real-world outdoor applications with respect to supported ranges, depth measurement accuracy, and robustness under varying lighting conditions. In an evaluation of suitable algorithms for detecting vehicles in the noisy and often incomplete 3D point clouds from RGB-D cameras, the 3D object detection network PointRCNN, which extends region-based convolutional neural networks (R-CNNs) to 3D point clouds, clearly outperformed all other candidates. The results of a mapping mission with 313 parking spaces show that our method is capable of reliably detecting parked cars with a precision of 100% and a recall of 97%. It can be applied to unslotted and slotted parking and different parking types including parallel, perpendicular, and angle parking.
01A - Beitrag in wissenschaftlicher Zeitschrift
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Publikation
Werden angehende Lehrpersonen durch das Studium kompetenter? Kompetenzaufbau und Standarderreichung in der berufswissenschaftlichen Ausbildung an drei Pädagogischen Hochschulen in der Schweiz und in Deutschland
(Springer, 2007) Baer, Matthias; Dörr, Günter; Fraefel, Urban; Kocher, Mirjam; Küster, Oliver; Larcher, Susanna; Müller, Peter; Sempert, Waltraud; Wyss, Corinne [in: Unterrichtswissenschaft]
Ziele, Methoden und erste Ergebnisse eines Forschungsprojektes werden dargestellt, an dem drei Pädagogische Hochschulen aus der Schweiz und Deutschland gemeinsam arbeiten. Im Zentrum dieser Studie steht die längsschnittliche Untersuchung der Kompetenzentwicklung von Studierenden (n=2161) des Lehramtes vom Beginn des Studiums bis zum Ende des Vorbereitungsdienstes (Deutschland) bzw. zum Ende des ersten Jahres der Berufseinführung (Schweiz). Methodisch basiert die Studie auf dem von Oser vorgelegten Standardkonzept, in dem handlungsorientierte Kompetenzprofile beschrieben werden, sowie auf den vier Dimensionen "Sachwissen", "diagnostische Kompetenz", "didaktische Kompetenz" und "Klassenführung". Es werden verschiedene Instrumente eingesetzt: (1) Fragebögen für Studierende und Schüler, (2) Vignetten, die berufspraktisches Wissen für die Unterrichtsplanung erheben, (3) ein sogenannter "Videotest", in dem Studierende Einschätzungen und Handlungsalternativen zu einer ihnen gezeigten, drehbuchbasierten Unterrichtsstunde darlegen sollen, sowie (4) Videostudien über durchgeführte Unterrichtsstunden. Neben den Selbsteinschätzungen der Studierenden liegen somit Daten von standardisierten Verfahren und Fremdeinschätzungen vor. Diese werden aufeinander bezogen, um ein empirisch abgestütztes Bild der Entwicklungsverläufe des Erwerbs komplexen berufspraktischen Wissens und didaktischer Handlungskompetenz in der Lehrerinnen- und Lehrerbildung sowie in der beruflichen Praxis zu erhalten. (DIPF/Orig.)
01A - Beitrag in wissenschaftlicher Zeitschrift
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Publikation
Robust and accurate image-based georeferencing exploiting relative orientation constraints
(Copernicus, 2018) Cavegn, Stefan; Blaser, S.; Nebiker, Stephan; Haala, N. [in: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences]
Urban environments with extended areas of poor GNSS coverage as well as indoor spaces that often rely on real-time SLAM algorithms for camera pose estimation require sophisticated georeferencing in order to fulfill our high requirements of a few centimeters for absolute 3D point measurement accuracies. Since we focus on image-based mobile mapping, we extended the structure-from-motion pipeline COLMAP with georeferencing capabilities by integrating exterior orientation parameters from direct sensor orientation or SLAM as well as ground control points into bundle adjustment. Furthermore, we exploit constraints for relative orientation parameters among all cameras in bundle adjustment, which leads to a significant robustness and accuracy increase especially by incorporating highly redundant multi-view image sequences. We evaluated our integrated georeferencing approach on two data sets, one captured outdoors by a vehicle-based multi-stereo mobile mapping system and the other captured indoors by a portable panoramic mobile mapping system. We obtained mean RMSE values for check point residuals between image-based georeferencing and tachymetry of 2 cm in an indoor area, and 3 cm in an urban environment where the measurement distances are a multiple compared to indoors. Moreover, in comparison to a solely image-based procedure, our integrated georeferencing approach showed a consistent accuracy increase by a factor of 2–3 at our outdoor test site. Due to pre-calibrated relative orientation parameters, images of all camera heads were oriented correctly in our challenging indoor environment. By performing self-calibration of relative orientation parameters among respective cameras of our vehicle-based mobile mapping system, remaining inaccuracies from suboptimal test field calibration were successfully compensated.
01A - Beitrag in wissenschaftlicher Zeitschrift