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Entropy
Special Issues
Time-Frequency Analysis, AM-FM Models, and Mode Decompositions
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Time-Frequency Analysis, AM-FM Models, and Mode Decompositions

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".

Deadline for manuscript submissions: closed (15 October 2022) | Viewed by 4467

Special Issue Editors

Dr. Steven Sandoval

E-Mail Website
Guest Editor
Klipsch School of Electrical and Computer Engineering, New Mexico State University, Las Cruces, NM 88003, USA
Interests: signal processing; time-frequency analysis; geometric algebra; machine learning
Prof. Dr. Abdel Ouahab Boudraa

E-Mail Website
Co-Guest Editor
Ecole Navale & Arts et Metiers Institute of Technology, 75013 Paris, France
Interests: non-stationary signal analysis; AM-FM modeling; Teager-Kaiser energy operators for time and frequency analysis; data driven methods (EMD, MEMD,...)
Dr. Marcelo Alejandro Colominas

E-Mail Website
Co-Guest Editor
CONICET (National Science Council), Godoy Cruz 2290, Buenos Aires, Argentina
Interests: biomedical signal processing; time-frequency analysis; image processing
Dr. Víctor Murray

E-Mail Website
Co-Guest Editor
Department of Electrical Engineering, Universidad de Ingeniería y Tecnología, Barranco 15063, Peru
Interests: image processing; biomedical signal processing; time-frequency analysis; AM-FM models

Special Issue Information

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • time-frequency analysis
  • AM-FM models
  • adaptive mode decompositions and data-driven methods
  • instantaneous frequency estimation
  • nonstationary signal analysis
  • reassignment and synchrosqueezing
  • entropy-based signal decompositions
  • entropy-based time-frequency distributions

Published Papers (2 papers)

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Research

18 pages, 12693 KiB  
Article
Recasting the (Synchrosqueezed) Short-Time Fourier Transform as an Instantaneous Spectrum
by Steven Sandoval and Phillip L. De Leon
Entropy 2022, 24(4), 518; https://0-doi-org.brum.beds.ac.uk/10.3390/e24040518 - 06 Apr 2022
Cited by 7 | Viewed by 1797
Abstract
In a previous work, we proposed a time-frequency analysis called instantaneous spectral analysis (ISA), which generalizes the notion of the Fourier spectrum and in which instantaneous frequency is utilized to the fullest extent. In this paper, we recast both the Fourier transform (FT) [...] Read more.
In a previous work, we proposed a time-frequency analysis called instantaneous spectral analysis (ISA), which generalizes the notion of the Fourier spectrum and in which instantaneous frequency is utilized to the fullest extent. In this paper, we recast both the Fourier transform (FT) and filterbank (FB) interpretations of the short-time Fourier transform (STFT) as instantaneous spectra. We show that to recast the FB interpretation of STFT as an instantaneous spectrum with valid structure, frequency reassignment is a fundamental necessity, thus demonstrating that this IS is closely related to the synchrosqueezed STFT. This result provides a new theoretical motivation for the synchrosqueezed STFT. Finally, we illustrate through example the instantaneous spectra corresponding to the FT and FB interpretations of STFT using two closed-form examples. Full article
(This article belongs to the Special Issue Time-Frequency Analysis, AM-FM Models, and Mode Decompositions)
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12 pages, 469 KiB  
Article
An Efficient and Accurate Multi-Sensor IF Estimator Based on DOA Information and Order of Fractional Fourier Transform
by Nabeel Ali Khan, Sadiq Ali and Kwonhue Choi
Entropy 2022, 24(4), 452; https://0-doi-org.brum.beds.ac.uk/10.3390/e24040452 - 25 Mar 2022
Cited by 5 | Viewed by 1587
Abstract
Instantaneous frequency in multi-sensor recordings is an important parameter for estimation of direction of arrival estimation, source separation, and sparse reconstruction. The instantaneous frequency estimation problem becomes challenging when signal components have close or overlapping signatures and the number of sensors is less [...] Read more.
Instantaneous frequency in multi-sensor recordings is an important parameter for estimation of direction of arrival estimation, source separation, and sparse reconstruction. The instantaneous frequency estimation problem becomes challenging when signal components have close or overlapping signatures and the number of sensors is less than the number of sources. In this study, we develop a computationally efficient method that exploits the direction of the IF curve in addition to the angle of arrival as additional features for the accurate tracking of IF curves. Experimental results show that the proposed scheme achieves better accuracy compared to the-state-of-art method in terms of mean square error (MSE) with a slight increase in the computational cost, i.e., the proposed method achieves MSE of −50 dB at the signal to noise ratio of 0 dB whereas the existing method achieves the MSE of −38 dB. Full article
(This article belongs to the Special Issue Time-Frequency Analysis, AM-FM Models, and Mode Decompositions)
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