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The interpretation of spectral line shapes for plasma characterization is well established as a diagnostic technique for determining number density and electron temperature, essential parameters for predicting radiation dynamics, local thermodynamic equilibrium, and atomic kinetics in laboratory and astrophysical environments. Specifically, Stark broadening is often used to diagnose a plasma's electron density via a tracer element. Weare interested in testing whether Stark broadening models can predict the plasma density from multiple elements within the same plasma, and whether this is done self-consistently. In order to do so, we diagnose transmission spectra through ~0.4-um-thick Mg-NaF foil that is heated and backlit by the Z Pinch Dynamic Hohlraum (ZPDH) at Sandia's Z machine. This foil is tamped on the front and back by incremental amounts of CH, allowing for electron densities between 1x1021 and 1x1022 to be reached. The foil was heated such that He-like charge states were reached for all three elements, allowing for investigation of Stark broadening of those lines. The amount of broadening found from different elements, and different tamper thicknesses will be presented.

This paper focuses on the problem of using artificial, deep neural networks in terrain recognition task based on data from vision sensor. Information about a terrain class is valuable for mobile robots, as it can improve their motion control algorithm performance through the use of information about surface properties. In this work RGB-D sensor was used for providing vision data, which comprise a depth map and infrared image in addition to the standard RGB data. Our own model of the artificial neural network is presented in this work. It was trained using the latest machine learning libraries. The results of this work demonstrate the performance of artificial neural networks in the terrain recognition task and give some hints how to improve classification in the future.

An increasing number of minimally invasive cardiac surgeries are guided by real-time 3D ultrasound. We developed a multi-view autostereoscopic system for real-time image guidance with 3D ultrasound. Furthermore, we performed a user study to test the effect of the system on the completion times and a number of errors made during specially designed tasks. The system maintained rendering frame rate at 20 fps and satisfactory added latency of 45–70 ms. In the study, the test subjects controlled a MitraClip device, while being guided by 3D ultrasound presented on either a standard 2D display or a multi-view autostereoscopic display. No significant difference in the completion times between the two visualization systems was measured. However, the test subjects performed 2 errors while using a standard 2D display.