Abstract. In this work, a method to predict the rheological properties of ultrasonic gel, as a reference substance of cement paste, is presented. For this purpose, images are taken with a stereo camera system which show a mixing paddle moving through the ultrasonic gels of different consistency, thus setting them in motion. A digital elevation model (DEM) and a corresponding orthophoto are created from the image pairs using classical image matching and orthoprojection methods. These are used as inputs into a Convolutional Neural Network (CNN), which predicts the support points of a flow curve which classically have to be determined in a rheometer in the laboratory. A simple network architecture consisting of a small number of convolution layers is compared with a pre-trained ResNet-18, which is fine-tuned using gel images. In a second series of experiments, rheological parameters, which alternatively need to be deduced from the flow curve in a separate step, are determined directly from the images. In the third series of experiments, the influence of different factors is tested, such as the position of the cameras relative to the direction of paddle movement and the importance of the DEMs and orthophotos in the training. It is shown in this paper that it is possible to predict the rheological properties of the ultrasonic gels with a suitable setup with a satisfying accuracy.

Abstract. Providers for common navigation systems and mobile applications apply their route choice concepts for cars almost unmodified to cyclists. In contrast to motorists the latter are not significantly influenced by the traffic situation or speed limits, but notably by other factors like slopes and path’s surface type and quality. In a volunteered geographic information fashion this paper contributes a smartphone-based mobile sensing and evaluation approach for bicycle way’s roughness. It presents the complete process chain from data acquisition using the mobile app ”RideVibes” to a detailed data analysis on street segment level to finally enable a comfort sensitive route optimization and recommendation.