Time-Frequency Analysis, AM-FM Models, and Mode Decompositions

Dear Colleagues,

Time-frequency analysis (TFA) describes signal processing techniques which represent the information content of a signal simultaneously in both time and frequency domains. The nonuniqueness associated with the expansion of a one-dimensional time-series into a two-dimensional time-frequency representation has resulted in a variety of approaches, each with strengths and weaknesses. Traditional methodologies began with investigations into integral transforms that generalize the Fourier transform, such as the short-time Fourier transform (STFT) and other time-frequency distributions. In recent years, AM-FM models and adaptive mode decompositions have made further advances in modeling signals that have nonstationary frequency components, and several new developments in time-frequency reassignment and synchrosqueezing methods have been made.

This Special Issue aims to provide a forum for the presentation of new and improved techniques and theory related to the field of time-frequency analysis. This includes both traditional methodologies such as time-frequency distributions as well as more modern approaches such as AM-FM models and adaptive mode decompositions. Works that utilize methodologies based on entropy or other topics from information theory are especially encouraged. This Special Issue will accept unpublished original papers and comprehensive reviews focused on (but not restricted to) the following research areas:

• TFA theory;
• TFA-based mathematical modeling of natural phenomena, artificial systems, and engineering problems;
• Analysis of nonstationary signals using TFA;
• Analysis of chaotic or nonlinear systems using TFA;
• Signal denoising using TFA;
• Image processing using TFA;
• Speech processing using TFA;
• Sonar and radar processing using TFA;
• Biomedical signal (ECG, EEG, EMG, etc.) processing using TFA;
• Instantaneous frequency estimation algorithms;
• New and improved AM-FM signal models;
• Novel numerical methods for signal decomposition, especially those based on entropy or information theory;
• Improved mode decompositions (empirical mode decomposition, variational mode decomposition, etc.);
• Robust TF signal representations;
• Multidimensional extensions of TFa concepts;
• Reassignment, synchrosqueezing and phase-based methods;
• Time-frequency representations obtained using artificial intelligence and machine learning.

Dr. Steven Sandoval
Prof. Dr. Abdel Ouahab Boudraa
Dr. Marcelo Alejandro Colominas
Dr. Víctor Murray
Guest Editors

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