The denoising, feature extraction, and prediction of nonlinear and nonstationary signals are of great significance as they can provide convenience and a basis for signal detection and tracking. The traditional signal processing methods have some limitations for nonlinear and nonstationary signal processing, but mode decomposition algorithms are suitable for dealing with nonlinear and nonstationary signals. With the development of mode decomposition algorithms, many new and improved methods have been proposed, such as ensemble empirical mode decomposition (EEMD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and variational mode decomposition (VMD). These new mode decomposition algorithms can be applied to nonlinear and nonstationary signals, which promotes the development of nonlinear and nonstationary signal processing.

Due to the problems of mode mixing, end effect, and lack of adaptability, the mode decomposition algorithm is a key challenge in nonlinear and nonstationary signal processing. This directly determines the performance of denoising, feature extraction, and prediction, and is vital for the further processing of nonlinear and nonstationary signals.

This Special Issue welcomes manuscripts that address any aspects associated with mode decomposition in nonlinear and nonstationary signal processing, such as fault diagnosis, acoustic signal processing, and medical signal processing. Manuscripts attempting the integration of mode decomposition with other concepts and addressing the role of mode decomposition in interdisciplinary research are particularly encouraged. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

• Nonlinear and nonstationary signal processing based on mode decomposition
• Fault diagnosis based on mode decomposition
• Acoustic signal processing based on mode decomposition
• Vibration signal denoising based on mode decomposition
• Fault feature extraction based on mode decomposition
• Financial prediction based on mode decomposition
• ECG cardiac detection based on mode decomposition
• Speech recognition based on mode decomposition
• Epileptic signal classification with deep EEG features