Recent Advances in Fractional Fourier Transforms and Applications, 3rd Edition

A special issue of Fractal and Fractional (ISSN 2504-3110). This special issue belongs to the section "General Mathematics, Analysis".

Deadline for manuscript submissions: 25 November 2026 | Viewed by 761

Special Issue Editors


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Guest Editor
Department of Computer Science, The University of Suwon, Hwaseong-si, Gyeonggi-do 18323, Republic of Korea
Interests: fractional Fourier transform; fractional integral operator and applications
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Mathematics and Statistics, Beijing Institute of Technology, Beijing 100081, China
Interests: signal and image processing; fractional Fourier transform and linear canonical transform theory and method; statistical data analysis and processing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Electronics and Information, Zhongyuan University of Technology, Zhengzhou 450007, China
Interests: signal and image processing; fractional Fourier transform
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of modern signal processing theory, processed signals have gradually developed from the early stationary signal to the non-stationary, non-Gaussian, non-single sampling complex signal. As one of the important branches of non-stationary signal processing theory, fractional Fourier transform (FRFT) is favored by many researchers due to its unique characteristics. In recent decades, an endless stream of new research results have been emerging. At present, FRFT is widely used in many fields of scientific research and engineering technology, such as swept filter, artificial neural network, wavelet transform, time–frequency analysis, time-varying filtering, complex transmission, partial differential equations, quantum mechanics, etc. In addition, FRFT can also be used to define fractional convolution, correlation, Hilbert transform, Riesz transform, and other operations, and can also be further generalized into the linear canonical transformation.

This Special Issue aims to continue to advance research on topics relating to the theory, algorithm development and application of fractional Fourier transform.

Topics that are invited for submission include (but are not limited to) the following:

  • Mathematical theory of FRFT;
  • Fractional integral transformation based on FRFT, such as Hilbert transform, Riesz transform, ;
  • Applications of FRFT in signal processing, PDE, information security and other fields;
  • Numerical algorithms of FRFT;
  • The generalization of FRFT (e.g., linear canonical transform (LCT), fractional wavelet transforms, and chirp Fourier transform) in theory and applications.

Please feel free to read and download all published articles in our previous editions:

https://www.mdpi.com/journal/fractalfract/special_issues/FRFT

and

https://www.mdpi.com/journal/fractalfract/special_issues/FRFT_II 

Prof. Dr. Zunwei Fu
Prof. Dr. Bingzhao Li
Dr. Xiangyang Lu
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 250 words) can be sent to the Editorial Office for assessment.

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. Fractal and Fractional 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 2700 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

  • fractional Fourier transform
  • linear canonical transform
  • digital signal processing
  • fractional integral operator
  • partial differential equations

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Published Papers (1 paper)

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Research

29 pages, 4560 KB  
Article
Graph Fractional Hilbert Transform: Theory and Application
by Daxiang Li and Zhichao Zhang
Fractal Fract. 2026, 10(2), 74; https://doi.org/10.3390/fractalfract10020074 - 23 Jan 2026
Viewed by 485
Abstract
The graph Hilbert transform (GHT) is a key tool in constructing analytic signals and extracting envelope and phase information in graph signal processing. However, its utility is limited by confinement to the graph Fourier domain, a fixed phase shift, information loss for real-valued [...] Read more.
The graph Hilbert transform (GHT) is a key tool in constructing analytic signals and extracting envelope and phase information in graph signal processing. However, its utility is limited by confinement to the graph Fourier domain, a fixed phase shift, information loss for real-valued spectral components, and the absence of tunable parameters. The graph fractional Fourier transform introduces domain flexibility through a fractional order parameter α but does not resolve the issues of phase rigidity and information loss. Inspired by the dual-parameter fractional Hilbert transform (FRHT) in classical signal processing, we propose the graph FRHT (GFRHT). The GFRHT incorporates a dual-parameter framework: the fractional order α enables analysis across arbitrary fractional domains, interpolating between vertex and spectral spaces, while the angle parameter β provides adjustable phase shifts and a non-zero real-valued response (cosβ) for real eigenvalues, thereby eliminating information loss. We formally define the GFRHT, establish its core properties, and design a method for graph analytic signal construction, enabling precise envelope extraction and demodulation. Experiments on anomaly identification, speech classification and edge detection demonstrate that GFRHT outperforms GHT, offering greater flexibility and superior performance in graph signal processing. Full article
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