![]() In, a biorthogonal wavelet tree is applied to perform intelligent sensor embedded signal classification. In, wavelet packet to scan probe microscope is applied to improve the quality of scanning images and the control system strategy. ![]() In, the wavelet packet transformation method can be applied to the structural characteristics of signal location by mathematical induction. It is more suitable to analyze and process nonstationary signals and can better distinguish the abrupt parts of the signal and noise to perform the signal denoising. Wavelet analysis with the characteristics of multiresolution analysis is a time-frequency analysis method and can analyze the time-frequency and frequency domain of the signal at the same time, which has been widely used in many fields. ![]() The short-time Fourier transform overcomes the shortcomings of the worst local analysis on signal by the traditional Fourier transform, but it cannot achieve fast analysis on signal. The concrete details of the signals cannot be obtained by the traditional Fourier transform, but the frequency components of known signals can only be obtained and the time of each component is unknown, which causes the time-frequency description and noise reduction effect of the nonstationary signal to be worse. Therefore, it is necessary to denoise the signal to better understand the target signal and to apply the signal in a wider range, such as signal positioning, fault diagnosis, and analysis. The received signals from the MEMS hydrophone are that the target signals are inevitably mixed with different noises, such as biological noise, background noise, and tugboat noise. However, there exists the complex environment in the ocean, which leads to the complex acoustic wave. The state parameters such as the target category, the relative angle, and the position of the sound source are obtained by processing the received signal. A MEMS hydrophone is an important tool to be applied to receive the underwater signal. More and more people have devoted to detecting the ocean and continually seeking the acoustic wave detection technology with long propagation distance, fast propagation speed, and small energy loss. There are rich resources in the marine environment, which are an important treasure trove to have influences on human development in the future. Therefore, the proposed MVO-PSO-VMD-CC-WT is suitable for the signal denoising and can be applied into the actual experiments in signal processing. And the proposed MVO-PSO-VMD-CC-WT algorithm is effectively applied to perform the signal denoising of the actual lake experiments. Two simulated experimental results show that the MVO-PSO-VMD-CC-WT algorithm which has the highest signal-to-noise ratio and the least RMSE is superior to the other compared algorithms. ![]() Then, the obtained optimal individual is utilized to perform the signal denoising by MVO-PSO-VMD-CC-WT by the use of the above repeated signal processing. For every individual, the original signal is decomposed by VMD into pure components, noisy components, and noise components in terms of CC judgment, where the pure components are directly retained, the noisy components are denoised by WT denoising, and the noise components are discarded, and then, the denoised noisy components and the pure components are reconstructed to be the denoised signal of the original signal. To eliminate the noise from the signals received by MEMS vector hydrophone, a joint algorithm is proposed in this paper based on wavelet threshold (WT) denoising, variational mode decomposition (VMD) optimized by a hybrid algorithm of Multiverse Optimizer (MVO) and Particle Swarm Optimization (PSO), and correlation coefficient (CC) judgment to perform the signal denoising of MEMS vector hydrophone, named as MVO-PSO-VMD-CC-WT, whose fitness function is the root mean square error (RMSE) and whose individual is the parameters of VMD. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |