Dynamic Compensation of Piezoresistive Pressure Sensor Based on Sparse Domain

In the process of transient test, due to the insufficient bandwidth of the pressure sensor, the test data is inaccurate. Firstly, based on the projection of the shock tube test signal in the sparse domain, the feature expression of the signal sample is obtained. Secondly, the problem of insufficient bandwidth is solved by inverse modeling of sensor dynamic compensation system based on swarm intelligence algorithm. In this paper, the method is used to compensate the shock tube test signals of the 85XX series pressure sensors made by the Endevco company of the United States, the working bandwidth of the sensor is widened obviously, the rise time of the pressure signal can be compensated to 12.5 μs, and the overshoot can be reduced to 8.96%. The repeatability of dynamic compensation is verified for the actual gun muzzle shock wave test data, the results show that the dynamic compensation can effectively recover the important indexes such as overpressure peak value and positive pressure action time, and the original shock wave signal is recovered from the high resonance data

Low Light Image Enhancement Based on Multi-Scale Network Fusion

At present, researchers have made great progress in the research of object detection, however, these studies mainly focus on the object detection of images under normal lighting, ignoring the target detection under low light. And images in the fields of automatic driving at night and surveillance are usually obtained in low-light environments. These images have problems such as poor brightness, low contrast, and obvious noise, which lead to a large amount of information loss in the image. And the performance of object detection in low light is reduced. In this paper, we propose a low-light image enhancement method based on multi-scale network fusion to solve the problems of images in low-light environments. Aiming at the problem that the effective information of low-light images is relatively small, we propose a preprocessing method for image nonlinear transformation and fusion, which improves the amount of available information in the light image. Then, in order to obtain a better enhancement effect, a multi-scale feature fusion method is proposed, which fuses features from different resolution levels in the network. The details of low-light areas in the image are improved, and the problem of feature loss caused by too deep network layers is solved. The experimental results show that our proposed method can achieve better enhancement effects on different datasets compared with the current mainstream methods. The average recall value of the object detection with our method is improved by 38.25%, which shows that our proposed method is effective and can promote the development of autonomous driving, monitoring, and other fields