Vienna University of Technology

About: Vienna University of Technology is a education organization based out in Vienna, Austria. It is known for research contribution in the topics: Laser & Context (language use). The organization has 16723 authors who have published 49341 publications receiving 1302168 citations.

Urban vegetation detection using radiometrically calibrated small-footprint full-waveform airborne LiDAR data

Abstract: This paper introduces a new GIS workflow for urban vegetation mapping from high-density (50 pts./m2) full-waveform airborne LiDAR data, combining the advantages of both raster and point cloud based analysis. Polygon segments derived by edge-based segmentation of the normalized digital surface model are used for classification. A rich set of segment features based on the point cloud and derived from full-waveform attributes is built, serving as input for a decision tree and artificial neural network (ANN) classifier. Exploratory data analysis and detailed investigation of the discriminative power of selected point cloud and full-waveform LiDAR observables indicate a high value of the occurrence of multiple distinct targets in a laser beam (i.e. ‘echo ratio’) for vegetation classification (98% correctness). The radiometric full-waveform observables (e.g. backscattering coefficient) do not suffice as single discriminators with low correctness values using a decision tree classifier (⩽72% correctness) but higher values with ANN (⩽95% correctness). Tests using reduced point densities indicate that the derived segment features and classification accuracies remain relatively stable even up to a reduction factor of 10 (5 pts./m2). In a representative study area in the City of Vienna/Austria the applicability of the developed object-based GIS workflow is demonstrated. The unique high density full-waveform LiDAR data open a new scale in 3D object characterization but demands for novel joint strategies in object-based raster and 3D point cloud analysis.

Equilibrium selection in a nonlinear duopoly game with adaptive expectations

Abstract: We analyze a nonlinear discrete time Cournot duopoly game, where players have adaptive expectations. The evolution of expected outputs over time is generated by the iteration of a noninvertible two-dimensional map. The long-run behavior is characterized by multistability, that is, the presence of coexisting stable consistent beliefs, which correspond to Nash equilibria in the quantity space. Hence, a problem of equilibrium selection arises and the long run outcome strongly depends on the choice of the players’ initial beliefs. We analyze the basins of attraction and their qualitative changes as the model parameters vary. We illustrate that the basins might be nonconnected sets and reveal the mechanism which is responsible for this often-neglected kind of complexity. The analysis of the global bifurcations which cause qualitative changes in the topological structure of the basins is carried out by the method of critical curves.

Characterizing Guidance in Visual Analytics

Abstract: Visual analytics (VA) is typically applied in scenarios where complex data has to be analyzed. Unfortunately, there is a natural correlation between the complexity of the data and the complexity of the tools to study them. An adverse effect of complicated tools is that analytical goals are more difficult to reach. Therefore, it makes sense to consider methods that guide or assist users in the visual analysis process. Several such methods already exist in the literature, yet we are lacking a general model that facilitates in-depth reasoning about guidance. We establish such a model by extending van Wijk's model of visualization with the fundamental components of guidance. Guidance is defined as a process that gradually narrows the gap that hinders effective continuation of the data analysis. We describe diverse inputs based on which guidance can be generated and discuss different degrees of guidance and means to incorporate guidance into VA tools. We use existing guidance approaches from the literature to illustrate the various aspects of our model. As a conclusion, we identify research challenges and suggest directions for future studies. With our work we take a necessary step to pave the way to a systematic development of guidance techniques that effectively support users in the context of VA.

The role of charge trapping in MoS 2 /SiO 2 and MoS 2 /hBN field-effect transistors

Abstract: The commonly observed hysteresis in the transfer characteristics of MoS2 transistors is typically associated with charge traps in the gate insulator. Since in Si technologies such traps can lead to severe reliability issues, we perform a combined study of both the hysteresis as well as the arguably most important reliability issue, the bias-temperature instability. We use single-layer MoS2 FETs with SiO2 and hBN insulators and demonstrate that both phenomena are indeed due to traps in the gate insulator with time constants distributed over wide timescales, where the faster ones lead to hysteresis and the slower ones to bias-temperature instabilities. Our data show that the use of hBN as a gate insulator considerably reduces the number of accessible slow traps and thus improves the reliability. However, the reliability of hBN insulators deteriorates with increasing temperature due to the thermally activated nature of charge trapping.