Tests of heat shield materials in intense laser radiation

Heat shield materials were tested under intense radiation in a gas dynamic laser. The laser is described and test results are presented

On the Domain of Mixing Angles in Three Flavor Neutrino Oscillations

We clarify the domain needed for the mixing angles in three flavor neutrino oscillations. By comparing the ranges of the transition probabilities as functions of the domains of the mixing angles, we show that it is necessary and sufficient to let all mixing angles be in $[ 0, \pi/2 ]$. This holds irrespectively of any assumptions on the neutrino mass squared differences.Comment: 4 pages, 5 figure

Deep Network Uncertainty Maps for Indoor Navigation

Most mobile robots for indoor use rely on 2D laser scanners for localization, mapping and navigation. These sensors, however, cannot detect transparent surfaces or measure the full occupancy of complex objects such as tables. Deep Neural Networks have recently been proposed to overcome this limitation by learning to estimate object occupancy. These estimates are nevertheless subject to uncertainty, making the evaluation of their confidence an important issue for these measures to be useful for autonomous navigation and mapping. In this work we approach the problem from two sides. First we discuss uncertainty estimation in deep models, proposing a solution based on a fully convolutional neural network. The proposed architecture is not restricted by the assumption that the uncertainty follows a Gaussian model, as in the case of many popular solutions for deep model uncertainty estimation, such as Monte-Carlo Dropout. We present results showing that uncertainty over obstacle distances is actually better modeled with a Laplace distribution. Then, we propose a novel approach to build maps based on Deep Neural Network uncertainty models. In particular, we present an algorithm to build a map that includes information over obstacle distance estimates while taking into account the level of uncertainty in each estimate. We show how the constructed map can be used to increase global navigation safety by planning trajectories which avoid areas of high uncertainty, enabling higher autonomy for mobile robots in indoor settings.Comment: Accepted for publication in "2019 IEEE-RAS International Conference on Humanoid Robots (Humanoids)

Redovisning av medel erhållna från Myndigheten för nätverk och samarbete inom högre utbildning. Nätverks benämning: Biomedicin

Nätverksmötena har upplevts som mycket positiva och värdefulla och fler möten planeras. Sammantaget kan sägas att programmen vill verka för att samarbeta för att främja biomedicinarutbildningarnas popularitet och stärka studenternas yrkesidentitet och anställningsbarhet. De olika utbildningsorterna är inte benägna att konkurrera med varandra eller att tävla om att vara den bästa utbildningen eller populäraste utbildningsorten. Man föredrar att komplettera varandra och samverka för ett brett utbud av masterutbildningar