Magic wavelengths for the 6s^2\,^1S_0-6s6p\,^3P_1^o transition in ytterbium atom

The static and dynamic electric-dipole polarizabilities of the 6s^2\,^1S_0 and 6s6p\,^3P_1^o states of Yb are calculated by using the relativistic ab initio method. Focusing on the red detuning region to the 6s^2\,^1S_0-6s6p\,^3P_1^o transition, we find two magic wavelengths at 1035.7(2) nm and 612.9(2) nm for the 6s^2\,^1S_0-6s6p\,^3P_1^o, M_J=0 transition and three magic wavelengthes at 1517.68(6) nm, 1036.0(3) nm and 858(12) nm for the 6s^2\,^1S_0-6s6p\,^3P_1^o, M_J=\pm1 transitions. Such magic wavelengths are of particular interest for attaining the state-insensitive cooling, trapping, and quantum manipulation of neutral Yb atom.Comment: 13 pages, 3 figure

Container Number Recognition Method Based on SSD_MobileNet and SVM

Aiming at how to realize the recognition of the container number on the container surface at the entrance and exit of the port, a method based on image affine transformation and SVM classifier is proposed. The main process includes truck target detection, box number area detection, text correction stage, image preprocessing stage and segmentation detection and recognition stage. Firstly, a kind of container truck detection program based on frame difference method and decreasing sequence of connected domain is proposed; secondly, a method of container number area detection based on SSD_MobileNet is proposed; in the case number recognition stage, a text correction method based on image affine transformation is proposed, and different processing methods are proposed for vertical sequence box number and horizontal sequence box number in image preprocessing stage In the stage of segmentation detection and recognition, a character segmentation algorithm based on connected domain segmentation and a segmentation detection and recognition algorithm based on SVM classifier are proposed. Through the detection and recognition of container images in the field monitoring video, the accuracy rate of regional detection can reach 97%, and the accuracy rate of character recognition can reach 95%, and it can achieve good real-time performance