Positron annihilation lifetime measurements have been carried out for glassy poly(ethylene terephthalate) (PET) as a function of temperature in the temperature range from 15 to 300 K. The structural relaxation and transition were studied by positron annihilation lifetime spectroscopy (PALS). According to the variation of orthopositronium(o-Ps) lifetime with temperature, two intercept temperatures were observed and were designated as the secondary transition temperatures T γ (γ transition) and Tβ (β transition). The coefficient of thermal expansion of the free volume hole was estimated from the temperature dependence of the free volume. We found that the size of free volume holes does not change significantly with temperature when T < Tγ, and that it increases evidently with temperature above Tγ. On the other hand, the distribution of the free volume was examined by developed maximum entropy lifetime method (MELT). Continuous analysis clearly indicated that the free volume distributions were broadened and that the value of peak was shifted toward high value with increasing temperature.

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Abstract. Extracting roads from aerial images is a challenging task in the field of remote sensing. Most approaches formulate road extraction as a segmentation problem and use thinning and edge detection to obtain road centerlines and edge lines, which could produce spurs around the extracted centerlines/edge lines. In this study, a novel regression-based method is proposed to extract road centerlines and edge lines directly from aerial images. The method consists of three major steps. First, an end-to-end regression network based on CNN is trained to predict confidence maps for road centerlines and estimate road width. Then, after the CNN predicts the confidence map, non-maximum suppression and road tracking are applied to extract accurate road centerlines and construct road topology. Meanwhile, Road edge lines are generated based on the road width estimated by the CNN. Finally, in order to improve the connectivity of extracted road network, tensor voting is applied to detect road intersections and the detected intersections are used as guidance for the overcome of discontinuities. The experiments conducted on the SpaceNet and DeepGlobe datasets show that our approach achieves better performance than other methods.