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72 The PCB Magazine • July 2016 the IR sensor, and the appropriate digital signal analysis to output automatically the XY ther- mal diffusivity. Sample shape and size can be easily standardized. This method, combined with the existing ones to measure specific heat, density and ther- mal diffusivity in the Z-axis, allows getting the often missing data about thermal conductivity in the XY plane, along the glass fibers. It also gives the possibility of discriminate more pre- cisely X and Y thermal conductivities by ro- tating the sample, in case base materials have also an anisotropy between yarn and chain of the woven glass mesh. It will be continued, im- proved, and deployed on a large range of print- ed circuit board dielectric base materials to help manufacturers and designers to optimize ther- mal management of printed circuit board and more generally electronic system performances and efficiency. PCB References 1. Hadisaroyo D., Batsale J. C.,Degiovanni A., « Un appareillage simple pour la mesure de la diffusivité thermique de plaques minces », Journal de Physique III, vol. 2 No. 1, 1992. 2. Guiles C., "Beating the Heat – A non- mathematical introduction to thermal proper- ties (Part I)," Arlon, 2009. 3. Mayoh I., "Thermally conductive substrates & thermal management," Ventec Europe Limit- ed, 2014 IPC-TM-650-2-4-50 ASTM standards. This paper was originally presented at IPC APEX EXPO 2016 and published in the proceedings. François Lechleiter is R&D manager for Cimulec Group. Yves Jannot is a research engineer at LEMTA CNRS. An automated camera system was able to learn how to better film basketball and soccer games—and smoothly recover from mistakes— by watching human camera opera- tors, scientists at Disney Research report. The result was footage without much of the jerkiness that plagues automated cam- eras, said Peter Carr, senior research engineer at Dis- ney Research. Carr, along with colleagues at Disney Research and the California Institute of Technology, will de- scribe the theoretical underpinnings of their new approach June 19 at the International Conference on Machine Learning (ICML) in New York City. The Disney Research and Caltech researchers, joined by colleagues at the University of British Columbia, will describe their field experience at the IEEE Conference on Computer Vision Pattern Recognition (CVPR) 2016 in Las Vegas. "This research demonstrates a significant advance in the use of im- itation learning to improve camera planning and control during game conditions," said Jessica Hodgins, vice president at Disney Research. Current optical tracking tech- nology can't reliably follow the ball automatically for the duration of a match, but the au- tomated camera system can follow the general flow of the game by studying detected player positions. Because of imperfect sensing, automated cameras generate jittery footage—especially when incorrectly anticipating how the game is about to unfold. Researchers developed new machine-learning al- gorithms to ensure automated cameras could strike the right balance between smoothness and closely following the action. Unlike established learning al- gorithms, the proposed approach repeats multiple times, and learns by analyzing the deviations it makes from the human operator at each iteration. Computer Improves Automated Sports Broadcasts A THERMAL CONDUCTIVITY MEASUREMENT METHOD, ADAPTED TO COMPOSITE MATERIALS

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