Special Issue "Multidimensional Signal Processing and Its Applications"

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer and Engineer Science and Symmetry".

Deadline for manuscript submissions: 15 May 2021.

Special Issue Editors

Prof. Dr. Roumen Kountchev
Website
Guest Editor
Faculty of Telecommunications, Department Radio Communications and Video Technologies, Technical University of Sofia, 1000 Sofia, Bulgaria
Interests: 3D image representation; image compression; medical image enhancement; pattern recognition; 3D signal processing; image watermarking; deep learning
Prof. Dr. Rumen Mironov
Website
Guest Editor
Faculty of Telecommunications, Department Radio Communications and Video Technologies, Technical University of Sofia, 1000 Sofia, Bulgaria
Interests: image processing; multidimensional signal processing; pattern recognition; programming; digital signage systems

Special Issue Information

One of the main tendencies in signal processing is the creation of new approaches for intelligent processing and analysis of multidimensional (MD) signals in various application areas. The advance of the contemporary computer systems opens new abilities for synergic relation between theoretical approaches and their applications.  Symmetry plays an important role in signal processing as it can be used to reduce the complexity of the problems to be solved in various application areas of modern life such as telecommunications, computer vision, healthcare, bioinformatics, remote ecological monitoring, agriculture, forestry, etc.

This Special Issue is devoted to recent advances in MD signal processing related to the analysis and use of symmetries in different multidisciplinary areas. The aim of this Special Issue is to present investigations and achievements in the area of MD signal processing in various multidisciplinary areas: analysis and recognition of MD images, MD image representation, compression and super-resolution; MD images transmission; MD computer vision; learning-based MD image processing and recognition; neural networks for MD image processing; generic and fuzzy MD image object segmentation; MD image retrieval and mining; multi-spectral and multi-view intelligent image processing; web-based MD images search; forensic MD analysis; MD image interpolation; MD visualization, virtual and augmented reality; spatio-temporal filtering, and any other topics related to the concept of symmetry in MD signal processing.

Prof. Dr. Roumen Kountchev
Prof. Dr. Rumen Mironov
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 papers will be 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. Symmetry 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 1800 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

  • symmetry
  • multidimensional signal processing
  • tensor image decomposition
  • medical information systems
  • telecommunications
  • computer vision
  • healthcare
  • bioinformatics
  • remote ecological monitoring
  • agriculture
  • forestry

Published Papers (1 paper)

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Research

Open AccessArticle
The Generalized Bayes Method for High-Dimensional Data Recognition with Applications to Audio Signal Recognition
Symmetry 2021, 13(1), 19; https://doi.org/10.3390/sym13010019 - 24 Dec 2020
Abstract
High-dimensional data recognition problem based on the Gaussian Mixture model has useful applications in many area, such as audio signal recognition, image analysis, and biological evolution. The expectation-maximization algorithm is a popular approach to the derivation of the maximum likelihood estimators of the [...] Read more.
High-dimensional data recognition problem based on the Gaussian Mixture model has useful applications in many area, such as audio signal recognition, image analysis, and biological evolution. The expectation-maximization algorithm is a popular approach to the derivation of the maximum likelihood estimators of the Gaussian mixture model (GMM). An alternative solution is to adopt a generalized Bayes estimator for parameter estimation. In this study, an estimator based on the generalized Bayes approach is established. A simulation study shows that the proposed approach has a performance competitive to that of the conventional method in high-dimensional Gaussian mixture model recognition. We use a musical data example to illustrate this recognition problem. Suppose that we have audio data of a piece of music and know that the music is from one of four compositions, but we do not know exactly which composition it comes from. The generalized Bayes method shows a higher average recognition rate than the conventional method. This result shows that the generalized Bayes method is a competitor to the conventional method in this real application. Full article
(This article belongs to the Special Issue Multidimensional Signal Processing and Its Applications)
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