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Call for papers: Recent advances and applications of time-frequency signal analysis

EURASIP Journal on Advances in Signal Processing welcomes submissions to the new special issue on 'Recent advances and applications of time-frequency signal analysis'.

Most real-life signals are of nonstationary nature, and their separate analysis in the time or frequency domain is often limited, not offering a comprehensive insight into the signal’s time changing spectral properties. Also, standard frequency-domain methods perform spectral analysis without spatial/temporal localization of signal features. Hence, in order to efficiently extract the signal’s useful information (such as number of components, complexity, instantaneous frequencies, instantaneous amplitudes, etc.), joint time–frequency distributions should be applied. Extracting information from nonstationary signals in the time-frequency domain is a crucial step in designing computer-aid decision-support systems based on signal features used for data analysis, machine learning, automatic classification systems, etc.

However, time-frequency signal analysis faces numerous challenges, such as design of high-resolution time-frequency distributions, reduction of cross terms in quadratic time-frequency representations, development of methods robust to noise, challenges in hardware realizations of time-frequency representations, blind source separation, processing sparse signals acquired using compressive sensing, to name a few.
This special issue is an attempt to render a comprehensive venue for recent progress in solving these challenges both in terms of proposing novel methods with their fundamental mathematical background, as well as applying the existing time-frequency techniques to a wide range of practical applications (i.e. biomedical signal processing, audio, radar, sonar, seismology and many more).

Potential topics include, but are not limited to:

  • High resolution time-frequency representations
  • Reduced interference time-frequency distributions
  • Machine learning from signal time-frequency features 
  • Hardware realizations of time-frequency distributions 
  • Adaptive data-driven methods for instantaneous frequencies and amplitudes estimation
  • Denoising and noise robustness of time-frequency techniques
  • Sparse signal analysis (compressive sensing and time-frequency signal analysis)
  • Hardware for compressive sensing 
  • Blind source separation
  • Reducing computational load of time-frequency distributions
  • Applications of time-frequency signal analysis: biomedicine, radar, sonar, seismology, audio, speech, etc.

Submission instructions:

Before submitting your manuscript, please ensure you have carefully read the Instructions for Authors for EURASIP Journal on Advances in Signal Processing. The complete manuscript should be submitted through the EURASIP Journal on Advances in Signal Processing submission system. To ensure that you submit to the correct special issue please select the appropriate section in the drop-down menu upon submission. In addition, indicate within your cover letter that you wish your manuscript to be considered as part of the special issue on 'Recent advances and applications of time-frequency signal analysis'. All submissions will undergo rigorous peer review and accepted articles will be published within the journal as a collection.

Deadline for submissions: 15 October 2019

Lead guest editors:
Jonatan Lerga, University of Rijeka, Croatia

Guest editors:
Nicoletta Saulig, Juraj Dobrila University of Pula, Croatia
Milos Dakovic, University of Montenegro, Montenegro
Cornel Ioana, Grenoble Institute of Technology/GIPSA-lab, France
Danilo Orlando, Faculty of Engineering, Italy

Submissions will also benefit from the usual benefits of open access publication:

  • Rapid publication: Online submission, electronic peer review and production make the process of publishing your article simple and efficient
  • High visibility and international readership in your field: Open access publication ensures high visibility and maximum exposure for your work - anyone with online access can read your article
  • No space constraints: Publishing online means unlimited space for figures, extensive data and video footage
  • Authors retain copyright, licensing the article under a Creative Commons license: articles can be freely redistributed and reused as long as the article is correctly attributed

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