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Durch den digitalen Wandel haben Kommunen neue Entwicklungsmöglichkeiten im Bereich der Smart City. Diese Arbeit stellt eine Übersicht darüber dar, wie mithilfe von IoT, Big Data, Datenbanken, Digitalen Zwillingen und weiteren Technologien, eine Mikroklima-Analyse und Steuerung ermöglicht werden kann.
In this paper, we present a method for detecting objects of interest, including cars, humans, and fire, in aerial images captured by unmanned aerial vehicles (UAVs) usually during vegetation fires. To achieve this, we use artificial neural networks and create a dataset for supervised learning. We accomplish the assisted labeling of the dataset through the implementation of an object detection pipeline that combines classic image processing techniques with pretrained neural networks. In addition, we develop a data augmentation pipeline to augment the dataset with utomatically labeled images. Finally, we evaluate the performance of different neural networks.
In the realm of digital situational awareness during disaster situations, accurate digital representations,
like 3D models, play an indispensable role. To ensure the
safety of rescue teams, robotic platforms are often deployed
to generate these models. In this paper, we introduce an
innovative approach that synergizes the capabilities of compact Unmaned Arial Vehicles (UAVs), smaller than 30 cm, equipped with 360° cameras and the advances of Neural Radiance Fields (NeRFs). A NeRF, a specialized neural network, can deduce a 3D representation of any scene using 2D images and then synthesize it from various angles upon request. This method is especially tailored for urban environments which have experienced significant destruction, where the structural integrity of buildings is compromised to the point of barring entry—commonly observed post-earthquakes and after severe fires. We have tested our approach through recent post-fire scenario, underlining the efficacy of NeRFs even in challenging outdoor environments characterized by water, snow, varying light conditions, and reflective surfaces.
Desert ants Cataglyphis spec. monitor inclination and distance covered through force-based sensing in their legs. To transfer this mechanism to legged robots, artificial neural networks are used to determine the inclination angle of an experimental ramp from the motor data of the legs of a commercial hexapod walking robot. It is possible to determine the inclination angle of the ramp based on the motor data of the robot legs read out during a run. The result is independent of the weight and orientation of the robot on the ramp and hence robust enough to serve as an independent odometer.
Biomechanische Untersuchungen zum Öffnungsmechanismus von verholzten Früchten der Gattung Hakea
(2023)
Die Arten H. sericea und H. salicifolia (Proteaceae) sind in Australien heimisch. Ihr natürlicher Lebensraum ist trocken und nährstoffarm, und sie sind regelmäßig Buschbränden ausgesetzt. Durch den Feuchtigkeitsverlust “schrumpft“ die Frucht und öffnet sich, wobei zwei Samen freigesetzt werden. Diese Arbeit vergleicht das Öffnungsverhalten von manipulierten Früchten, das Schwindmaß, die Öffnungskraft, den Elastizitätsmodul und die Druckfestigkeit der beiden Arten und untersucht den Einfluss verschiedener Gewebe auf die Öffnung. Es wird festgestellt, dass das Mesokarp hauptsächlich für das anisotrope Schwindverhalten verantwortlich ist.
This paper reveals various approaches undertaken over more than two decades of teaching undergraduate programming classes at different Higher Education Institutions, in order to improve student activation and participation in class and consequently teaching and learning effectiveness.
While new technologies and the ubiquity of smartphones and internet access has brought new tools to the classroom and opened new didactic approaches, lessons learned from this personal long-term study show that neither technology itself nor any single new and often hyped didactic approach ensured sustained improvement of student activation. Rather it needs an integrated yet open approach towards a participative learning space supported but not created by new tools, technology and innovative teaching methods.
This paper presents a pragmatic approach for stepwise introduction of peer assessment elements in undergraduate programming classes, discusses some lessons learned so far and directions for further work. Students are invited to challenge their peers with their own programming exercises to be submitted through Moodle and evaluated by other students according to a predefined rubric and supervised by teaching assistants. Preliminary results show an increased activation and motivation of students leading to a better performance in the final programming exams.