Automated progress monitoring becomes more and more common in the construction industry. Recent approaches often use new methods like Unmanned aerial vehicles (UAVs) for a capturing large construction sites. However, the used methods often lack accuracy due to occluded elements and/or reconstruction inaccuracies from using photogrammetric methods. This paper presents a novel approach for further improvement of element detection rates. 4D BIM semantic information is used, to generate precise "as-planned" geometric models. These models are used to render a building from all points of view during the monitoring phase. Based on this information, a more accurate and reliable estimation of all detected elements can be achieved.

A circuit-simulation-based method is used to determine the thermally-induced bit error rate of superconducting Single Flux Quantum logic circuits. Simulations are used to evaluate the multidimensional Gaussian integral across noise current sources attached to the active devices. The method is data-assisted and has predictive power. Measurement determines the value of a single parameter, effective noise bandwidth, for each error mechanism. The errors in the distributed networks of comparator-free Reciprocal Quantum Logic nucleate across multiple Josephson junctions, so the effective critical current is about three times that of the individual devices. The effective noise bandwidth is only 6%–23% of the junction plasma frequency at a modest clock rate of 3.4 GHz, which is 1% of the plasma frequency. This analysis shows the ways measured bit error rate comes out so much lower than simplistic estimates based on isolated devices.