Non-line-of-sight (NLOS) imaging has been a hot research field recently. Time-of-flight-based (ToF-based) active algorithms are one of the bases for NLOS, which is also the focus of this paper. In the preliminary experiments, Filtered-back-projection (FBP), Light-cone-transformation (LCT), and F-K migration algorithms have shown some shortcomings. For instance, the performance of FBP is poor when it is applied to datasets with low spatial resolution. For objects dominated by specular reflections, LCT generates a significant amount of noise. Similarly, F-K migration produces noisy results when it is employed with low spatial resolution data. To overcome the limitations of these algorithms, we study windowed Fourier transform for NLOS imaging. Experiments are used to analyze the performance of different windowing techniques. From 2D to 3D, and from time to frequency domain, we apply Hanning windows with FBP, LCT, and F-K algorithms. The results demonstrate that, compared to time domain, the performance of an algorithm using windows in frequency domain is significantly enhanced. The reconstructions become significantly clearer. Previously unrecoverable contours are revealed. Image noise is greatly reduced. Then, we employ a set of 3D Kaiser windows with various coefficients in the frequency domain for reconstruction, as a comparison to Hanning windows. We find that the Hanning window function and Kaiser windows with β in the range from 4 to 9 best suits the NLOS imaging problem.
Geiger mode Avalanche Photo Diode (Gm-APD) array lidar is a lidar that can perform single-photon detection. It offers a wide range of applications due to its low power consumption, small size, and extended detecting distance. There haven't been many research on this detector's target classification because of its late development and small detector array. The classification technique based on the Gm-APD array lidar point cloud is the focus of this paper's research: Firstly, the Gm- APD array lidar is utilized to perform imaging tests on four targets from various angles in order to create a target classification dataset.Following that, several data preprocessing methods were chosen and implemented based on the characteristics of the obtained data, such as filling in missing values, performing range image and intensity image interpolation, using the principle of keyhole imaging to convert the range image to point cloud data, realizing the information fusion of distance image and intensity image, and using multiple point cloud data enhancement methods. Finally, the point cloud classification networks PointNet and PointNet++ are trained on point cloud data with varying levels of preprocessing, the results are compared and analyzed, and the impact of different preprocessing methods on the classification accuracy of the two networks is determined. Inferences were made and experiments were carried out to verify the inferences. The data set preprocessing method with the highest classification accuracy of the two networks is discovered, laying the groundwork for future Gm-APD lidar target classification and detection research.
The use environment of rubber shock absorber is changeable. The degree of maintaining stable working performance in different environments is an important reference factor for selecting shock absorber. Based on the temperature condition, this paper studies the damping effect of the same type of shock absorber on the same load at different temperatures. Keep the shock absorber at a certain temperature through the temperature box, and then conduct vibration test on the load to test the performance of the shock absorber. The results show that in the range of - 50 ~ 65 ℃, the performance of the shock absorber at low or high temperature changes little compared with that at normal temperature, which will not affect the shock absorption effect of the shock absorber at all.
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