Bauhaus University, Weimar

About: Bauhaus University, Weimar is a education organization based out in Weimar, Thüringen, Germany. It is known for research contribution in the topics: Finite element method & Isogeometric analysis. The organization has 1421 authors who have published 2998 publications receiving 104454 citations. The organization is also known as: Bauhaus-Universität Weimar & Hochschule für Architektur und Bauwesen.

Scene consistency and spatial presence increase the sensation of self-motion in virtual reality

Abstract: The illusion of self-motion induced by moving visual stimuli ("vection") has typically been attributed to low-level, bottom-up perceptual processes. Therefore, past research has focused primarily on examining how physical parameters of the visual stimulus (contrast, number of vertical edges etc.) affect vection. Here, we investigated whether higher-level cognitive and top-down processes - namely global scene consistency and spatial presence - also contribute to the illusion. These factors were indirectly manipulated by presenting either a natural scene (the Tubingen market place) or various scrambled and thus globally inconsistent versions of the same stimulus. Due to the scene scrambling, the stimulus could no longer be perceived as a consistent 3D scene, which was expected to decrease spatial presence and thus impair vection. Twelve naive observers were asked to indicate the onset, intensity, and convincingness of circular vection induced by rotating visual stimuli presented on a curved projection screen (FOV: 54°x45°). Spatial presence was assessed using presence questionnaires. As predicted, scene scrambling impaired both vection and presence ratings for all dependent measures. Neither type nor severity of scrambling, however, showed any clear effect. The data suggest that higher-level information (the interpretation of the globally consistent stimulus as a 3D scene and stable reference frame) dominated over the low-level (bottom-up) information (more contrast edges in the scrambled stimuli, which are known to facilitate vection). Results suggest a direct relation between spatial presence and self-motion perception. We posit that stimuli depicting globally consistent, naturalistic scenes provide observers with a convincing spatial reference frame for the simulated environment which allows them to feel "spatially present" therein. We propose that this, in turn, increases the believability of the visual stimuli as a stable "scene" with respect to which visual motion is more likely to be judged as self-motion. We propose that not only low-level, bottom-up factors, but also higher-level factors such as the meaning of the stimulus are relevant for self-motion perception and should thus receive more attention. This work has important implications for both our understanding of selfmotion perception and motion simulator design and applications.

Artificial Intelligence Techniques for Smart City Applications

Abstract: Recent developments in artificial intelligence (AI), in particular machine learning (ML), have been significantly advancing smart city applications. Smart infrastructure, which is an essential component of smart cities, is equipped with wireless sensor networks that autonomously collect, analyze, and communicate structural data, referred to as “smart monitoring”. AI algorithms provide abilities to process large amounts of data and to detect patterns and features that would remain undetected using traditional approaches. Despite these capabilities, the application of AI algorithms to smart monitoring is still limited due to mistrust expressed by engineers towards the generally opaque AI inner processes. To enhance confidence in AI, the “black-box” nature of AI algorithms for smart monitoring needs to be explained to the engineers, resulting in so-called “explainable artificial intelligence” (XAI). However, when aiming at improving the explainability of AI algorithms through XAI for smart monitoring, the variety of AI algorithms requires proper categorization. Therefore, this review paper first identifies objectives of smart monitoring, serving as a basis to categorize AI algorithms or, more precisely, ML algorithms for smart monitoring. ML algorithms for smart monitoring are then reviewed and categorized. As a result, an overview of ML algorithms used for smart monitoring is presented, providing an overview of categories of ML algorithms for smart monitoring that may be modified to achieve explainable artificial intelligence in civil engineering.

An Image-Warping Architecture for VR: Low Latency versus Image Quality

Abstract: Designing low end-to-end latency system architectures for virtual reality is still an open and challenging problem. We describe the design, implementation and evaluation of a client-server depth-image warping architecture that updates and displays the scene graph at the refresh rate of the display. Our approach works for scenes consisting of dynamic and interactive objects. The end-to-end latency is minimized as well as smooth object motion generated. However, this comes at the expense of image quality inherent to warping techniques. We evaluate the architecture and its design trade-offs by comparing latency and image quality to a conventional rendering system. Our experience with the system confirms that the approach facilitates common interaction tasks such as navigation and object manipulation.

Record Low Thermal Conductivity of Polycrystalline MoS2 Films: Tuning the Thermal Conductivity by Grain Orientation

Abstract: We report a record low thermal conductivity in polycrystalline MoS2 obtained for ultrathin films with varying grain sizes and orientations. By optimizing the sulfurization parameters of nanometer-thick Mo layers, five MoS2 films containing a combination of horizontally and vertically oriented grains, with respect to the bulk (001) monocrystal, were grown. From transmission electron microscopy, the average grain size, typically below 10 nm, and proportion of differently oriented grains were extracted. The thermal conductivity of the suspended samples was extracted from a Raman laser-power-dependent study, and the lowest value of thermal conductivity of 0.27 W m–1 K–1, which reaches a similar value as that of Teflon, is obtained in a polycrystalline sample formed by a combination of horizontally and vertically oriented grains in similar proportion. Analysis by means of molecular dynamics and finite element method simulations confirm that such a grain arrangement leads to lower grain boundary conductance. We...