Self-sustaining Ultra-wideband Positioning System for Event-driven Indoor Localization

Smart and unobtrusive mobile sensor nodes that accurately track their own position have the potential to augment data collection with location-based functions. To attain this vision of unobtrusiveness, the sensor nodes must have a compact form factor and operate over long periods without battery recharging or replacement. This paper presents a self-sustaining and accurate ultra-wideband-based indoor location system with conservative infrastructure overhead. An event-driven sensing approach allows for balancing the limited energy harvested in indoor conditions with the power consumption of ultra-wideband transceivers. The presented tag-centralized concept, which combines heterogeneous system design with embedded processing, minimizes idle consumption without sacrificing functionality. Despite modest infrastructure requirements, high localization accuracy is achieved with error-correcting double-sided two-way ranging and embedded optimal multilateration. Experimental results demonstrate the benefits of the proposed system: the node achieves a quiescent current of $47~nA$ and operates at $1.2~\mu A$ while performing energy harvesting and motion detection. The energy consumption for position updates, with an accuracy of $40~cm$ (2D) in realistic non-line-of-sight conditions, is $10.84~mJ$. In an asset tracking case study within a $200~m^2$ multi-room office space, the achieved accuracy level allows for identifying 36 different desk and storage locations with an accuracy of over $95~{\%}$. The system`s long-time self-sustainability has been analyzed over $700~days$ in multiple indoor lighting situations

A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation

Recent work has shown that optical flow estimation can be formulated as a supervised learning task and can be successfully solved with convolutional networks. Training of the so-called FlowNet was enabled by a large synthetically generated dataset. The present paper extends the concept of optical flow estimation via convolutional networks to disparity and scene flow estimation. To this end, we propose three synthetic stereo video datasets with sufficient realism, variation, and size to successfully train large networks. Our datasets are the first large-scale datasets to enable training and evaluating scene flow methods. Besides the datasets, we present a convolutional network for real-time disparity estimation that provides state-of-the-art results. By combining a flow and disparity estimation network and training it jointly, we demonstrate the first scene flow estimation with a convolutional network.Comment: Includes supplementary materia

Bäuerliche Experimente in Österreich – Beurteilung von Video als möglicher Auslöser der Experimentiertätigkeit von Biobäuerinnen und Biobauern

Farmers’ experiments are an integral element of agricultural practice, contribute to the development of local knowledge and form the precondition for local innovations. This study addresses organic farmers’ experiments in Austria, and specifically video as tool for capturing and sharing lessons learned from farmers’ experimentation, as well as the potential of video to trigger farmers’ experiments. For 85 % of the surveyed organic farmers (n=34) farmers’ experiments were considered to have high relevance in the course of their farming activities. The elaborated videos stimulated 71 % of the farmers to conduct experiments. The videos were successfully applicable in adult and student agricultural education. After watching them, 12 of 16 students (75 %) came up with ideas for experiments they would like to try at their parents’ farms

Energy Inflation and House Price Corrections

We analyze empirically the role played by energy inflation as a determinant of downward corrections in house prices. Using a dataset for 18 OECD economies spanning the last four decades, we identify periods of downward house price adjustment and estimate conditional logit models to measure the effect of energy inflation on the probability of these house price corrections after controlling for other relevant macroeconomic variables. Our results give strong evidence that increases in energy price inflation raise the probability of such corrective periods taking place. This phenomenon could be explained by various channels: through the adverse effects of energy prices on economic activity and income reducing the demand for housing; through the particular impact on construction and operation costs and their effects on the supply and demand of housing; through the reaction of monetary policy on inflation withdrawing liquidity and further reducing demand; through improving attractiveness of commodity versus housing investment on asset markets; or through a lagging impact of common factors on both variables, such as economic growth. Our results contribute to the understanding of the pass-through of oil price shocks to financial markets and imply that energy price inflation should serve as a leading indicator for the analysis of macro-financial risks. (authors' abstract