Signal Processing and Time-Frequency Analysis

A special issue of Signals (ISSN 2624-6120).

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 5916

Special Issue Editor

Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland
Interests: nonlinear signal processing; time-frequency signal representation; sparsity techniques; system identification; multisensor fusion; biomedical signal processing

Special Issue Information

Dear colleagues,

Time-frequency analysis (TFA) is a set of signal processing methods, techniques, and algorithms based on two types of variables, i.e., time and frequency. It is an alternative to traditional approaches in which time or frequency is used independently.

TFA is an approach that works well with non-stationary signals. The Nonstationarity of the signal means that there is a time-dependency of the signal frequency spectrum. In time-frequency algorithms, the variables of time and frequency are not mutually exclusive but present together. It is an important feature of the TFA that helps analyze non-stationary signals.

One of the most frequently used methods of time-frequency analysis is a short-time Fourier transform. The idea behind this method is to apply the Fourier transform to a portion of the signal.

Over recent years, the researcher proposed many other TFA methods, i.e., wavelet transform, Gabor transform, Wigner-Ville distribution, and Hilbert-Huang transform to name a few.

TFA methods can be applied to solve classical signal processing problems as denoising or detrending, can be a part of the recognition system to generate features in time-frequency domain (machine condition monitoring, speech recognition, etc.), image enhancement (from radars and sonars, etc.) and signal detection and image segmentation.

Therefore, the following contributions regarding Signal Processing and Time-Frequency Analysis are welcome:

TFA-based signal denoising and detrending;

TFA-based image enhancement;

TFA-based machine condition monitoring;

TFA-based speech processing and recognition;

TFA-based biomedical signal processing and feature extraction;

TFA-based seismological signal processing;

TFA-based signal change detection;

TFA-based image processing and segmentation.

Dr. Krzysztof Brzostowski
Guest Editor

Manuscript Submission Information

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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. Signals is an international peer-reviewed open access quarterly journal published by MDPI.

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Published Papers (2 papers)

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Research

13 pages, 915 KiB  
Article
Dynamic Functional Principal Components for Testing Causality
by Matthieu Saumard and Bilal Hadjadji
Signals 2021, 2(2), 353-365; https://doi.org/10.3390/signals2020022 - 08 Jun 2021
Viewed by 2228
Abstract
In this paper, we investigate the causality in the sense of Granger for functional time series. The concept of causality for functional time series is defined, and a statistical procedure of testing the hypothesis of non-causality is proposed. The procedure is based on [...] Read more.
In this paper, we investigate the causality in the sense of Granger for functional time series. The concept of causality for functional time series is defined, and a statistical procedure of testing the hypothesis of non-causality is proposed. The procedure is based on projections on dynamic functional principal components and the use of a multivariate Granger test. A comparative study with existing procedures shows the good results of our test. An illustration on a real dataset is provided to attest the performance of the proposed procedure. Full article
(This article belongs to the Special Issue Signal Processing and Time-Frequency Analysis)
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12 pages, 1452 KiB  
Article
Adaptive Sparse Cyclic Coordinate Descent for Sparse Frequency Estimation
by Yuneisy E. Garcia Guzman and Michael Lunglmayr
Signals 2021, 2(2), 189-200; https://doi.org/10.3390/signals2020015 - 15 Apr 2021
Cited by 1 | Viewed by 1877
Abstract
The frequency estimation of multiple complex sinusoids in the presence of noise is important for many signal processing applications. As already discussed in the literature, this problem can be reformulated as a sparse representation problem. In this letter, such a formulation is derived [...] Read more.
The frequency estimation of multiple complex sinusoids in the presence of noise is important for many signal processing applications. As already discussed in the literature, this problem can be reformulated as a sparse representation problem. In this letter, such a formulation is derived and an algorithm based on sparse cyclic coordinate descent (SCCD) for estimating the frequency parameters is proposed. The algorithm adaptively reduces the size of the used frequency grid, which eases the computational burden. Simulation results revealed that the proposed algorithm achieves similar performance to the original formulation and the Root-multiple signal classification (MUSIC) algorithm in terms of the mean square error (MSE), with significantly less complexity. Full article
(This article belongs to the Special Issue Signal Processing and Time-Frequency Analysis)
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