In order to achieve high sensitivity of pressure magnitude and direction measurement, the elasto-optical effect of photonic crystal fiber is studied by the full vector finite element method. First of all, the birefringence characteristics of the fundamental mode of the edge-pass high birefringence photonic crystal fiber under lateral pressure and static liquid pressure are studied. The results show that the effective refractive index of the two polarization fundamental modes changes obviously under the static liquid pressure, the phase mode birefringence changes linearly with the pressure. The second, the influence of fiber structure on birefringence is discussed, the results show that the size of birefringence can be influenced by changing the shape, position and size of the air holes in the fiber. The structure of photonic crystal fiber with high birefringence is obtained by optimizing the structure. The result show that, when free space wavelength is 1.55μm and pressure is 120MPa, the sensitivity of its phase modal birefringence up to 1.498×10-5 MPa-1 and the maximum measurable hydrostatic pressure value is 353MPa. The research is meaningful to realize the pressure sensor with multi-angle and high sensitivity.
Human pose recognition based on bone node data collected by depth camera is a key problem in the field of human-computer interaction. To improve the accuracy of human pose recognition, a new algorithm based on multiple features and random forest model is proposed. Firstly, a 93-dimensional vector is defined, which contains the coordinate feature of the joint and the distance feature, and the distance feature is selected according to the spatial position of the joint. Then, in the process of body pose recognition, the random forest model is combined with Bagging algorithm to ensure the balance of samples, so as to improve the classification performance of the classifier for different samples. Finally, the performance test of the constructed classifier is carried out on the UTKinect-action3D Dataset. The experimental result shows that the algorithm can effectively identify a variety of human posture, and the recognition rate reaches more than 90%. The fusion of multiple features is of great significance to improve the accuracy of human posture recognition.
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