A popular method for nondestructive detection of underground targets is the remote sensing techniques. One of the most successful modalities for remote sensing of underground targets is the ground-penetrating radar (GPR), and it has been extensively used in many fields, such as military,^1,2 civil engineering,^3,4 and geological survey.⁵ GPR emits electromagnetic wave toward the ground, due to the different relative permittivity of the underground medium, the electromagnetic wave will be reflected back. When GPR antenna moves across the buried target, the time of arrival for responses from the target constitutes a hyperbola in the B-SCAN image. Therefore, the buried target detection can be viewed as mapping from the object space to an image space. Then, according to the characteristic curve of the target, the geometric parameters such as buried depth, width and height can be calculated.

Stroke is a high morbidity and mortality disease that poses a serious threat to people’s health. Early recognition of the various warning signs of stroke is necessary so that timely clinical intervention can help reduce the severity of stroke. Deep neural networks have powerful feature representation capabilities and can automatically learn discriminant features from large amounts of data. This paper uses a range of physiological characteristic parameters and collaborates with deep neural networks, such as the Wasserstein generative adversarial networks with gradient penalty and regression network, to construct a stroke prediction model. Firstly, to address the problem of imbalance between positive and negative samples in the stroke public data set, we performed positive sample data augmentation and utilized WGAN-GP to generate stroke data with high fidelity and used it for the training of the prediction network model. Then, the relationship between observable physiological characteristic parameters and the predicted risk of suffering a stroke was modeled as a nonlinear mapping transformation, and a stroke prediction model based on a deep regression network was designed. Finally, the proposed method is compared with commonly used machine learning-based classification algorithms such as decision tree, random forest, support vector machine, and artificial neural networks. The prediction results of the proposed method are optimal in the comprehensive measurement index F. Further ablation experiments also show that the designed prediction model has certain robustness and can effectively predict stroke diseases.

A new method for segmenting the target foreground from image frames is proposed by utilizing the theory of graph signal processing and the tensor decomposition model aiming at the problem that the segmentation results of the existing foreground segmentation methods in image frames under dynamic scenes are not high in accuracy. The intrinsic connection between image pixels in each frame of an image sequence is modeled as a graph, the image pixel intensities are modeled as graph signals, and the correlation between pixels is characterized by the graph model. According to the significant difference between the dynamic background and the target change in the moving foreground in the image sequence, the dynamic background region in each image frame is smoothed and suppressed, and the disturbing information of the dynamic background is transformed into the useful component information in the low-rank subspace. The connectivity between image pixels can be characterized by the graph Laplacian regularization term, and then the target foreground segmentation problem in the image sequence is equivalent to a constrained optimization problem with tensor decomposition and graph Laplacian regularization term. The alternating direction multiplier method is used to solve the optimization problem, and the simulation results on real scene data set verify the effectiveness of the algorithm.