The photoplethysmography (PPG) is inevitably corrupted by many kinds of noise no matter whether its acquisition mode is transmittance or reflectance. To enhance the quality of PPG signals, many studies have made great progress in PPG denoising by adding extra sensors and developing complex algorithms. Considering the reasonable cost, compact size, and real-time and easy implementation, this study proposed a simple real-time denoising method based on double median filters which can be integrated in microcontroller of commercial or portable pulse oximeters without adding extra hardware. First, we used the boundary extension to preserve the signal boundary distortion and designed a first median filter with the time window at approximately 78 ms to eliminate the high-frequency components of the signal. Then, through the second median filter with a time window which was about 780 ms, we estimated the low-frequency components. Finally, we removed the estimated low-frequency components from the signal to obtain the denoised signal. Through comparing the multiple sets of signals under calmly sitting and slightly moving postures, the PPG signals contained noises no matter whether collected by the transmittance-mode or the reflectance-mode. To evaluate the proposed method, we conducted measured, simulated experiments and a strong noisy environment experiment. Through comparing the morphology distortions, frequency spectra, and the signal-to-noise ratios (SNRs), the results showed that the proposed method can suppress noise effectively and preserve the essential morphological features from PPG signals. As a result, the proposed method can enhance the quality of PPG signals and, thus, can contribute to the improvement of the calculation accuracy of the subsequent physiological parameters. In addition, the proposed method could be a good choice to address the real-time noise reduction of portable PPG measuring instruments.

To solve the problem of electromagnetic reflections caused by the termination of finite-difference time-domain (FDTD) grids, we apply the complex frequency-shifted perfectly matched layer (CFS-PML) to airborne transient electromagnetic (ATEM) modeling in a source-free medium. To implement the CFS-PML, two important aspects are improved. First, our method adopts the source-free Maxwell’s equations as the governing equations and introduces the divergence condition, consequently, the discrete form of Maxwell’s third equation is derived with regard to the CFS-PML form. Second, because our method adopts an inhomogeneous time-step, a recursive formula composed of convolution items based on a nonuniform time-step is proposed. The proposed approach is verified via a calculation of the electromagnetic response using homogeneous half-space models with different conductivities. The results show that the CFS-PML can reduce a 60 dB relative errors in late times. Moreover, this approach is also applied to 3D anomalous models; the results indicate that the proposed method can reduce reflections and substantially improve the identification of anomalous bodies. Consequently, the CFS-PML has good implications for ATEM modeling in a source-free medium.

The ground-source airborne time-domain electromagnetic (GATEM) system is an efficient geophysical survey system for geological surveys and mineral surveys. The geological resistivity structure is obtained by inversion methods, however, the deterministic inversion methods can only provide an optimal resistivity model. The Bayesian inversion method can provide the posterior probability distribution, however, it requires a large amount of calculation. In this article, to improve the efficiency, a surrogate modeling based on neural network (NN) is applied to replace forward simulation calculation in the Bayesian inversion method. The accuracy of the NN-based surrogate modeling is related to the training sample set. To obtain high precision inversion results, the surrogate modeling based on NN will be update adaptively online. Above all, an initial NN-based surrogate modeling is trained on a sample set of prior information. The high-fidelity surrogate modeling based on NN is obtained through new sample sets of GATEM data which are generated to update the surrogate modeling, if the NN-based surrogate modeling is inaccurate when the Bayesian inversion method is running. An optimal solution model and the posterior probability distribution of model parameters for GATEM inversion results are calculated through the Bayesian method. The effectiveness of the Bayesian inversion method with a surrogate modeling based on NN is verified by the GATEM responses for typical geological models.

With the increasing penetration of renewable energy sources, variations in grid impedance and background voltage harmonics have become major challenges to the operational stability and power quality of grid-connected inverters. Although conventional grid-voltage proportional feedforward (GVPF) effectively suppresses grid-voltage disturbances, its frequency-domain phase characteristics are highly sensitive to grid impedance variations, leading to reduced stability margins and even system instability under weak-grid conditions. To address this issue, this paper proposes a Frequency-Domain Phase-Shaping-Based Multi-Quasi-Proportional Resonant (FDPS-MQPR-PGVF) Control Strategy. Based on the established system impedance model, the phase characteristics at critical harmonic frequencies are systematically reshaped according to the impedance-based stability criterion (IBSC), enabling a systematic controller parameter design that simultaneously enhances weak-grid stability and background harmonic suppression. Simulation and experimental results demonstrate that the proposed strategy maintains stable operation under weak-grid conditions while significantly improving harmonic suppression performance. The proposed method provides an effective frequency-domain phase-shaping framework for enhancing the stability and harmonic performance of grid-connected inverters operating in weak grids.