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Science data with hidden periodic structure - new perspectives

Edited by:
Agnieszka Wyłomańska, Wroclaw University of Science and Technology, Poland
Antonio Napolitano, University of Napoli "Parthenope", Italy
Ran Tao, Beijing Institute of Technology, China

Submission Status: Open   |   Submission Deadline: 31 December 2024 


EURASIP Journal on Advances in Signal Processing is calling for submissions to our Collection on 'Science data with hidden periodic structure - new perspectives.' This collection welcomes original research articles in the field of periodic (or quasi periodic) phenomena focusing at modeling, analysis, and exploitation of these hidden periodicities. This provides deep knowledge on the observed phenomenon and better performance in signal processing algorithms aimed at extracting information from the available data.

About the Collection

EURASIP Journal on Advances in Signal Processing is calling for submissions to our Collection on 'Science data with hidden periodic structure - new perspectives.' 

Many physical phenomena are originated by the interaction of periodic (or quasi periodic) phenomena with random ones. The results are processes that are not periodic but whose statistical functions are periodic functions of time. Such kinds of processes are ubiquitous in science data and their hidden periodic structure can be recovered by estimating their statistical functions. In communications, radar, sonar, and telemetry, periodicities in the statistical functions are due to the modulation by random data of carriers or pulse trains. In the vibroacoustic signals of mechanical machinery, periodicities are due to rotations of gears, belts, and bearings. In radio astronomy data, periodicities are due to the revolution and rotation of planets and pulsation of stars. In human biological signals, periodicities in statistical functions are due to heart pulsation or alternation of day and night. Hidden periodicities are present in genome sequences, diffusion processes of molecular dynamics, and signals encountered in neuroscience. The modeling, analysis, and exploitation of these hidden periodicities provides deep knowledge on the observed phenomenon and better performance in signal processing algorithms aimed at extracting information from the available data.

  1. We address the issue of detecting hidden periodicity when the signal exhibits periodic correlation, but is additionally affected by non-Gaussian noise with unknown characteristics. This scenario is common in v...

    Authors: Wojciech Żuławiński, Jerome Antoni, Radosław Zimroz and Agnieszka Wyłomańska
    Citation: EURASIP Journal on Advances in Signal Processing 2024 2024:71
    Content type: Research Published on:

Submission Guidelines

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This Collection welcomes submission of Research articles and Reviews. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal. 

Articles for this Collection should be submitted via our submission system, Snapp. Please, select the appropriate Collection title “Science data with hidden periodic structure - new perspectives" under the “Details” tab during the submission stage. Articles will undergo the journal’s standard peer-review process and are subject to all the journal’s standard policies. Articles will be added to the Collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer-review process. The peer-review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.