Example of Non-stationary Signal Denoising Using Common EMD Methods

In non-stationary signal denoising, common Empirical Mode Decomposition (EMD) methods serve as effective techniques. The following example demonstrates how to use EMD methodology for denoising non-stationary signals. Below is a directly executable program: The Python implementation utilizes the PyEMD library to perform Empirical Mode Decomposition, which adaptively decomposes signals into Intrinsic Mode Functions (IMFs). The code generates a synthetic non-stationary signal containing multiple frequency components and processes it through EMD decomposition. import numpy as np import matplotlib.pyplot as plt from PyEMD import EMD # Generate non-stationary test signal with multiple frequency components t = np.linspace(0, 1, 1000) signal = np.sin(2 * np.pi * 10 * t) + np.sin(2 * np.pi * 20 * t) + np.sin(2 * np.pi * 30 * t) # Apply EMD denoising technique emd = EMD() imfs = emd.emd(signal) # Visualize original signal versus denoised result using matplotlib subplots plt.subplot(2, 1, 1) plt.plot(t, signal, label='Original Signal') plt.legend() plt.subplot(2, 1, 2) plt.plot(t, imfs[-1], label='Denoised Signal') plt.legend() plt.show() By executing this program, you can observe how EMD methods effectively denoise non-stationary signals and obtain cleaned output. This example illustrates practical EMD application and provides a directly implementable program for reference, featuring signal generation, EMD processing, and comparative visualization components. The algorithm works by decomposing the input signal into IMFs where the final IMF typically represents the denoised signal component.