Polarimetric observations of comet Levy 1990c and of other comets: Some clues to the evolution of cometary dust

The evolution with the phase angle alpha of the polarization degree P of light scattered by comet Halley's dust is well documented. No significant discrepancy is found between Halley and Levy polarization curves near the inversion point. From all available cometary observations, we have derived polarimetric synthetic curves. Typically, a set of about 200 data points in the red wavelengths range exhibits a minimum for (alpha approximately equals 10.3 degrees, P approximately equals 1.8 percent) and an inversion point for (alpha approximately equals 22.4 degrees, P = 0 percent), with a slop of about 0.27 percent per degree. A significant spreading of some data (comets Austin 1982VI, Austin 1989c1, West 1976VI) is found at large phase angles. The analysis of our polarimetric maps of Levy reveals that the inner coma is heterogeneous. The increase of the inversion angle value with increasing distance from the photometric center is suspected to be due to the evolution with time of grains ejected from the nucleus. A fan like structure could be produced by a jet of grains freshly ejected

Registration using Graphics Processor Unit

Data point set registration is an important operation in coordinate metrology. Registration is the operation by which sampled point clouds are aligned with a CAD model by a 4X4 homogeneous transformation (e.g., rotation and translation). This alignment permits validation of the produced artifact\u27s geometry. State-of-the-art metrology systems are now capable of generating thousands, if not millions, of data points during an inspection operation, resulting in increased computational power to fully utilize these larger data sets. The registration process is an iterative nonlinear optimization operation having an execution time directly related to the number of points processed and CAD model complexity. The objective function to be minimized by this optimization is the sum of the square distances between each point in the point cloud and the closest surface in the CAD model. A brute force approach to registration, which is often used, is to compute the minimum distance between each point and each surface in the CAD model. As point cloud sizes and CAD model complexity increase, this approach becomes intractable and inefficient. Highly efficient numerical and analytical gradient based algorithms exist and their goal is to convergence to an optimal solution in minimum time. This thesis presents a new approach to efficiently perform the registration process by employing readily available computer hardware, the graphical processor unit (GPU). The data point set registration time for the GPU shows a significant improvement (around 15-20 times) over typical CPU performance. Efficient GPU programming decreases the complexity of the steps and improves the rate of convergence of the existing algorithms. The experimental setup reveals the exponential increasing nature of the CPU and the linear performance of the GPU in various aspects of an algorithm. The importance of CPU in the GPU programming is highlighted. The future implementations disclose the possible extensions of a GPU for higher order and complex coordinate metrology algorithms

Influence of mean distance between fibers on the effective gas thermal conductivity in highly porous fibrous materials

This work was supported by the Russian Goverment Grant No. 14.Z50.31.0036.Peer reviewedPostprin

Mining Heterogeneous Multivariate Time-Series for Learning Meaningful Patterns: Application to Home Health Telecare

For the last years, time-series mining has become a challenging issue for researchers. An important application lies in most monitoring purposes, which require analyzing large sets of time-series for learning usual patterns. Any deviation from this learned profile is then considered as an unexpected situation. Moreover, complex applications may involve the temporal study of several heterogeneous parameters. In that paper, we propose a method for mining heterogeneous multivariate time-series for learning meaningful patterns. The proposed approach allows for mixed time-series -- containing both pattern and non-pattern data -- such as for imprecise matches, outliers, stretching and global translating of patterns instances in time. We present the early results of our approach in the context of monitoring the health status of a person at home. The purpose is to build a behavioral profile of a person by analyzing the time variations of several quantitative or qualitative parameters recorded through a provision of sensors installed in the home