Recognition of biological tissue denaturation is a vital work in high-intensity focused ultrasound (HIFU) therapy. Multiscale permutation entropy (MPE) is a nonlinear signal processing method for feature extraction, widely applied to the recognition of biological tissue denaturation. However, the typical MPE cannot derive a stable entropy due to intensity information loss during the coarse-graining process. For this problem, an improved multiscale permutation entropy (IMPE) is proposed in this work. IMPE is obtained through refining and reconstructing MPE. Compared with MPE, the IMPE overcomes the deficiency of amplitude information loss due to the coarse-graining process when computing signal complexity. Through the simulation of calculating MPE and IMPE from white Gaussian noise, it is found that the entropy derived by IMPE is more stable than that derived by MPE. The processing method based on IMPE feature extraction is applied to the experimental ultrasonic scattered echo signals in HIFU treatment. Support vector machine and Gustafson–Kessel fuzzy clustering based on MPE and IMPE feature extraction are also used for biological tissue denaturation classification and recognition. The results calculated from the different combination algorithms show that the recognition of biological tissue denaturation based on IMPE-GK clustering is more reliable with the accuracy of 95.5%.

Biological tissue damage monitoring is an indispensable part of high-intensity focused ultrasound (HIFU) treatment. As a nonlinear method, multi-scale permutation entropy (MPE) is widely used in the monitoring of biological tissue. However, the traditional MPE method neglects the amplitude information when calculating the time series complexity, and the stability of MPE is poor due to the defects in the coarse-grained process. In order to solve the above problems, the method of improved coarse-grained multi-scale weighted permutation entropy (IMWPE) is proposed in this paper. Compared with the MPE, the IMWPE method not only includes the amplitude of signal when calculating the signal complexity, but also improves the stability of entropy value. The IMWPE method is applied to the HIFU echo signals during HIFU treatment, and the probabilistic neural network (PNN) is used for monitoring the biological tissue damage. The results show that compared with multi-scale sample entropy (MSE)-PNN and MPE-PNN methods, the proposed IMWPE-PNN method can correctly identify all the normal tissues, and can more effectively identify damaged tissues and denatured tissues. The recognition rate for the three kinds of biological tissues is higher, up to 96.7%. This means that the IMWPE-PNN method can better monitor the status of biological tissue damage during HIFU treatment.

LED (light-emitting diode)-lidar (light detection and ranging) has gradually been focused on by researchers because of its characteristics of low power, high stability, and safety to human eyes. However, LED-lidar systems are easily disturbed by background light noise. Echo signal denoising is an essential work that directly affects the measurement accuracy of the LED-lidar system. The traditional variational modal decomposition (VMD) method in lidar signal denoising relies on practical experience to optimize the critical parameters of quadratic penalty factor α and the number of intrinsic mode function (IMF) components K globally, which is hard to denoise effectively. For this problem, a denoising method based on VMD with the adaptive weighted particle swarm optimization (PSO) is proposed in this work. The PSO-VMD method adaptively adjusts the weight value ω for different lidar echo signals and optimizes of the parameters α and K globally. The LED-lidar echo signals are denoised by moving average, VMD, and PSO-VMD. Using the denoised echo signals, the range compensation waveforms and the extinction coefficients are derived. The results show that the PSO-VMD denoised echo signal has the highest R-square value of 0.9972 and the minimum standard deviation value of 5.7369, while the values of r-square and standard deviation of the echo signal denoised by moving average and VMD method are 0.9902, 9.7450, 0.9945, and 7.3588, respectively. The derived distance compensation waveforms and extinction coefficients based on the PSO-VMD denoising have better stability than those based on the moving average and VMD denoising.

Magnetotelluric (MT) data processing can increase the reliability of measured data. Traditional MT de-noising methods are usually filtered in entire MT time-series sequence, which result in losing of useful MT signals and the decrease of imaging accuracy of electromagnetic inversion. However, targeted MT noise separation can retain the part of data not affected by strong noise, and enhance the quality of MT data. Thus, we proposed a novel method for MT noise separation, which using refined composite multiscale dispersion entropy (RCMDE) and orthogonal matching pursuit (OMP). Firstly, the RCMDE characteristic parameters are extracted from each segment of the MT time-series. Then, the characteristic parameters are input to the fuzzy c-mean (FCM) clustering for automatic identification of MT signal and noise. Next, OMP method is utilized to remove the identified noise segments independently. Finally, the reconstructed signal consists of the denoised data segments and the identified useful signal segments. We conducted the simulation experiments and algorithm evaluation on the EMTF data, simulated data and measured sites. The results indicate that the RCMDE can improve the stability of multiscale dispersion entropy (MDE) and multiscale entropy (MSE) by analyzing the characteristics of the signal samples library, effectively dividing MT signals and noise. Compared with the existing techniques of the entire time domain de-noising and signal-noise identification, the proposed method used RCMDE and OMP as characteristic parameter and noise separation, simplified the multi-features fusion, and improved the accuracy of signal-noise identification. Moreover, the de-noising efficiency has accelerated, and the MT data quality of low-frequency band has improved greatly.