Measurement of Cosmic-ray Muons and Muon-induced Neutrons in the Aberdeen Tunnel Underground Laboratory

We have measured the muon flux and production rate of muon-induced neutrons at a depth of 611 m water equivalent. Our apparatus comprises three layers of crossed plastic scintillator hodoscopes for tracking the incident cosmic-ray muons and 760 L of gadolinium-doped liquid scintillator for producing and detecting neutrons. The vertical muon intensity was measured to be $I_{\mu} = (5.7 \pm 0.6) \times 10^{-6}$ cm$^{-2}$s$^{-1}$sr$^{-1}$. The yield of muon-induced neutrons in the liquid scintillator was determined to be $Y_{n} = (1.19 \pm 0.08 (stat) \pm 0.21 (syst)) \times 10^{-4}$ neutrons/($\mu\cdot$g$\cdot$cm$^{-2}$). A fit to the recently measured neutron yields at different depths gave a mean muon energy dependence of $\left\langle E_{\mu} \right\rangle^{0.76 \pm 0.03}$ for liquid-scintillator targets.Comment: 14 pages, 17 figures, 3 table

Doping and Irradiation Controlled Vortex Pinning Behavior in BaFe2(As1-xPx)2 Single Crystals

We report on the systematic evolution of vortex pinning behavior in isovalent doped single crystals of BaFe2(As1-xPx)2. Proceeding from optimal doped to ovedoped samples, we find a clear transfor- mation of the magnetization hysteresis from a fishtail behavior to a distinct peak effect followed by a reversible magnetization and Bean Livingston surface barriers. Strong point pinning dominates the vortex behavior at low fields whereas weak collective pinning determines the behavior at higher fields. In addition to doping effects, we show that particle irradiation by energetic protons can tune vortex pinning in these materials.Comment: 4 pages, 4 figures,significant change of eraly version, accepted by PRB rapid communication

Computationally efficient solutions for tracking people with a mobile robot: an experimental evaluation of Bayesian filters

Modern service robots will soon become an essential part of modern society. As they have to move and act in human environments, it is essential for them to be provided with a fast and reliable tracking system that localizes people in the neighbourhood. It is therefore important to select the most appropriate filter to estimate the position of these persons. This paper presents three efficient implementations of multisensor-human tracking based on different Bayesian estimators: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Sampling Importance Resampling (SIR) particle filter. The system implemented on a mobile robot is explained, introducing the methods used to detect and estimate the position of multiple people. Then, the solutions based on the three filters are discussed in detail. Several real experiments are conducted to evaluate their performance, which is compared in terms of accuracy, robustness and execution time of the estimation. The results show that a solution based on the UKF can perform as good as particle filters and can be often a better choice when computational efficiency is a key issue