The use of gait micro-Doppler signatures to identify a person is a hot topic of research. In this paper, we present a new CNN-based method called Multi-Scale CNN (MS-CNN) to obtain features at multiple scales. It extracts shallow features at low-level multi-scale blocks by using multiple kernels at the same time, then extracts deep features and fuses multi-branch embedding features at high-level multi-branch blocks. Experimental results reveal that our method outperforms other commonly used CNN algorithms in terms of accuracy, allowing it to be used for personal identification.
The geometry of airfoils, crucial in predicting aerodynamic coefficients, can be perceived by three existing methods: manual definition of geometry parameters, polynomial and deep learning. The first two methods are capable of directly obtaining geometry-features or polynomial coefficients from airfoil coordinates, but they are insufficient to extract latent features. Current deep learning techniques are capable of extracting latent features from only Euclidean space. However, it has been proven that curves of airfoils are exists in Riemannian space. Inspired by deep learning and polynomial (especially the Bézier polynomial) and deep learning, we propose a Geometry-based Feature Extraction method (GFE) to extract airfoil geometry-features from both Euclidean space and Riemannian space. By utilizing Bézier curve, GFE can extract geometry-features (i.e., manifold metrics) of airfoils in Riemannian space. Through an internal auto-encoder, GFE encodes airfoil coordinates and manifold metrics to generate a novel feature representation. The UIUC airfoil dataset is employed to test the feasibility of GFE. Experimental results show that smooth and realistic airfoils can be rebuilt based on the geometry-features extracted by GFE, and the average MSE of rebuilt airfoils is 38.66% lower than those rebuilt by existing auto-encoders.
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