Dosimetric effect by shallow air cavities in high energy electron beams

This study evaluates the dosimetric impact caused by an air cavity located at 2 mm depth from the top surface in a PMMA phantom irradiated by electron beams produced by a Siemens Primus linear accelerator. A systematic evaluation of the effect related to the cavity area and thickness as well as to the electron beam energy was performed by using Monte Carlo simulations (EGSnrc code), Pencil Beam algorithm and Gafchromic EBT2 films. A home-PMMA phantom with the same geometry as the simulated one was specifically constructed for the measurements. Our results indicate that the presence of the cavity causes an increase (up to 70%) of the dose maximum value as well as a shift forward of the position of the depthedose curve, compared to the homogeneous one. Pronounced dose discontinuities in the regions close to the lateral cavity edges are observed. The shape and magnitude of these discontinuities change with the dimension of the cavity. It is also found that the cavity effect is more pronounced (6%) for the 12 MeV electron beam and the presence of cavities with large thickness and small area introduces more significant variations (up to 70%) on the depthedose curves. Overall, the Gafchromic EBT2 film measurements were found in agreement within 3% with Monte Carlo calculations and predict well the fine details of the dosimetric change near the cavity interface. The Pencil Beam calculations underestimate the dose up to 40% compared to Monte Carlo simulations; in particular for the largest cavity thickness (2.8 cm).

Enhanced PCA-based localization using depth maps with missing data

In this paper a new method for self-localization of mobile robots, based on a PCA positioning sensor to operate in unstructured environments, is proposed and experimentally validated. The proposed PCA extension is able to perform the eigenvectors computation from a set of signals corrupted by missing data. The sensor package considered in this work contains a 2D depth sensor pointed upwards to the ceiling, providing depth images with missing data. The positioning sensor obtained is then integrated in a Linear Parameter Varying mobile robot model to obtain a self-localization system, based on linear Kalman filters, with globally stable position error estimates. A study consisting in adding synthetic random corrupted data to the captured depth images revealed that this extended PCA technique is able to reconstruct the signals, with improved accuracy. The self-localization system obtained is assessed in unstructured environments and the methodologies are validated even in the case of varying illumination conditions.