国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

In the process of ultrasonic testing of polyethylene pipe defects, due to the low speed of sound propagation in polyethylene materials, loud scattering noise, extremely low signal-to-noise ratio, and the electrical signal interference of the instrument and equipment, the result of defect imaging can be affected. Therefore, it is particularly important to process the A-scan signal to improve the quality of the detected image. On the other hand, when ultrasonic phased array probes with more array elements are used to collect data of different types of polyethylene pipe defects, a large number of defect data will be obtained, which brings various difficulties to storage, transmission and processing. However, when using traditional compression sensing methods, if the signal has a low signal-to-noise ratio and a large reconstruction mean square error, it is difficult to retain important information in the signal, and it is more likely to cause the problem of loss of details at low bit-rate. Therefore, this paper proposes a sparse representation defect signal compression and reconstruction method based on K-SVD super complete dictionary learning. With the help of this learning algorithm, the over complete dictionary is trained, and the Gaussian random matrix is selected as the observation matrix and the orthogonal matching pursuit algorithm (OMP) is selected as the reconstruction algorithm to process the compressedSsensing of the polyethylene pipe defect echo signal. At the same time, the influence of the number of dictionary elements and the number of iterations on the reconstructed signal and imaging effect is analyzed.