Manufacturing of Porous Glass by Femtosecond Laser Welding.

Based on femtosecond laser glass welding, four different porous structures of welding spots were formed by the manufacturing processes of spatiotemporal beam shaping and alternating high repetition rate transformation. Compared with an ordinary Gaussian beam, the welding spot fabricated by the flattened Gaussian beam had smoother welding edges with little debris, and the bottom of the welding spot pore was flat. Instead of a fixed high repetition rate, periodically alternating high repetition rates were adopted, which induced multiple refractive indices in the welding spot pore. The welding spot pores manufactured by spatiotemporal beam shaping and alternating high repetition rate transformation have a special structure and excellent properties, which correspond to superior functions of porous glass.

Video-Based Person Re-Identification by an End-To-End Learning Architecture with Hybrid Deep Appearance-Temporal Feature.

Video-based person re-identification is an important task with the challenges of lighting variation, low-resolution images, background clutter, occlusion, and human appearance similarity in the multi-camera visual sensor networks. In this paper, we propose a video-based person re-identification method called the end-to-end learning architecture with hybrid deep appearance-temporal feature. It can learn the appearance features of pivotal frames, the temporal features, and the independent distance metric of different features. This architecture consists of two-stream deep feature structure and two Siamese networks. For the first-stream structure, we propose the Two-branch Appearance Feature (TAF) sub-structure to obtain the appearance information of persons, and used one of the two Siamese networks to learn the similarity of appearance features of a pairwise person. To utilize the temporal information, we designed the second-stream structure that consisting of the Optical flow Temporal Feature (OTF) sub-structure and another Siamese network, to learn the person's temporal features and the distances of pairwise features. In addition, we select the pivotal frames of video as inputs to the Inception-V3 network on the Two-branch Appearance Feature sub-structure, and employ the salience-learning fusion layer to fuse the learned global and local appearance features. Extensive experimental results on the PRID2011, iLIDS-VID, and Motion Analysis and Re-identification Set (MARS) datasets showed that the respective proposed architectures reached 79%, 59% and 72% at Rank-1 and had advantages over state-of-the-art algorithms. Meanwhile, it also improved the feature representation ability of persons.