In this work, a surface plasmon resonance (SPR) sensor based on a D-shaped germanium-doped photonic crystal fiber (PCF) is proposed. The finite element method (FEM) is introduced to analyze the structure parameters, such as germanium-doped concentration, lattice pitch, and air hole size. In addition, the coupling properties and birefringence properties of PCF are also studied. The computer simulation results indicate that two different surface plasmon polariton (SPP) coupling modes are produced on the polished surface, covered with metal film, when the analyte refractive index (RI) is $1.34$. Then, with the increase of the RI, the incompleteness of one of the coupling modes will be transformed into the complete coupling. The effect of germanium concentration on the birefringence is also analyzed. It has an optimal wavelength sensitivity of 5600 nm/RIU when the RI is 1.37. This sensor exhibits a maximum birefringence of 1.06 × 10⁻² and a resolution of 1.78 × 10⁻⁵ RIU with high linearity.

Skeleton-based action recognition is a widely used task in action related research because of its clear features and the invariance of human appearances and illumination. Furthermore, it can also effectively improve the robustness of the action recognition. Graph convolutional networks have been implemented on those skeletal data to recognize actions. Recent studies have shown that the graph convolutional neural network works well in the action recognition task using spatial and temporal features of skeleton data. The prevalent methods to extract the spatial and temporal features purely rely on a deep network to learn from primitive 3D position. In this paper, we propose a novel action recognition method applying high-order spatial and temporal features from skeleton data, such as velocity features, acceleration features, and relative distance between 3D joints. Meanwhile, a method of multi-stream feature fusion is adopted to fuse these high-order features we proposed. Extensive experiments on Two large and challenging datasets, NTU-RGBD and NTU-RGBD-120, indicate that our model achieves the state-of-the-art performance.