Advances in Fractional Order Signal Processing: Theory and Methods

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

Deadline for manuscript submissions: 31 March 2026 | Viewed by 7

Special Issue Editors


E-Mail Website
Guest Editor
School of Mathematics and Statistics, Xidian University, Xi'an 710071, China
Interests: fractional Fourier transform; sparse optimization; cone and stochastic optimization
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Mathematics and Statistics, Xidian University, Xi’an 710071, China
Interests: fractional order signal processing theory and method; time-frequency analysis; sampling theory; sparse discrete algorithm; graph signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of information technology, the research object of signal processing gradually shifts from relatively simple and stable signals to more complex signals such as non-stationary, non-Gaussian, and time-varying. Traditional signal processing theories and methods can no longer meet practical needs. Fractional Fourier transform uses a set of linear frequency-modulated orthogonal bases to decompose signals, making it suitable for processing non-stationary signals. The theory and methods of fractional order signal processing are favored by many researchers due to their unique characteristics. With the demand for big data and real-time signal processing, sparse fractional order transformations and extensions, as well as fast algorithms, have been developed and widely applied in spectral sensing, image recognition and fusion, compressed sampling, and sparse representation. With the continuous emergence of large-scale irregular high-dimensional signals, fractional order graph signal processing has been developed.

This Special Issue aims to continue advancing research on topics related to fractional order signal processing theory, methods, and applications. The topics for invitation submission include (but are not limited to) the following:

  • Fractional order signal processing;
  • Sparse fractional order signal processing;
  • Fractional order graph signal processing;
  • Fractional order image processing;
  • Fractional order machine learning.

Dr. Yuan-Min Li
Prof. Dr. Deyun Wei
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. 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 integral transform
  • sparse fractional order transform
  • sparse representation and fast algorithm
  • graph transform
  • sampling and filtering
  • convolutional neural network
  • digital signal processing

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Published Papers

This special issue is now open for submission.
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