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Symbolic Entropy Analysis and Its Applications III

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 3025

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Special Issue Information

Dear Colleagues,

Symbolic data analysis has received a great deal of attention over the last few years and has been applied to many research areas, including astrophysics and geophysics, biology and medicine, fluid flow, chemistry, mechanical systems, artificial intelligence, communication systems, and, recently, data mining and big data. A fundamental step in this methodology is the quantization of original data into a corresponding sequence of symbols. The resulting time series is then considered a transformed version of the original data, allowing to highlight its temporal information. Indeed, it has been proven that this symbolization procedure can notably improve signal-to-noise ratios in some noisy time series. Moreover, symbolic data analysis also makes communication and numerical computation more efficient and effective, compared with the processing of continuous-valued time series.

However, symbolization of a time series always involves information loss; hence, this process deserves special attention. This challenge, along with other problems associated with symbolic entropy analysis, has been addressed in the first and second volume of the Special Issue on “Symbolic Entropy Analysis and Its Applications”. Given its success, this third volume aims to compile key current research on novel symbolization approaches, as well as applications of this kind of analysis to reveal pioneering information from different types of time series. Hence, manuscripts dealing with these topics will be welcome.

Volume I: https://www.mdpi.com/journal/entropy/special_issues/symbolic_entropy_analysis

Volume II: https://www.mdpi.com/journal/entropy/special_issues/Symbolic_Entropy_II

Prof. Dr. Raúl Alcaraz
Guest Editor

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. Entropy 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 2600 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.

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15 pages, 3509 KiB  
Article
A Multigraph-Defined Distribution Function in a Simulation Model of a Communication Network
by Slobodan Miletic, Ivan Pokrajac, Karelia Pena-Pena, Gonzalo R. Arce and Vladimir Mladenovic
Entropy 2022, 24(9), 1294; https://doi.org/10.3390/e24091294 - 14 Sep 2022
Cited by 1 | Viewed by 1064
Abstract
We presented a method based on multigraphs to mathematically define a distribution function in time for the generation of data exchange in a special-purpose communication network. This is needed for the modeling and design of communication networks (CNs) consisting of integrated telecommunications and [...] Read more.
We presented a method based on multigraphs to mathematically define a distribution function in time for the generation of data exchange in a special-purpose communication network. This is needed for the modeling and design of communication networks (CNs) consisting of integrated telecommunications and computer networks (ITCN). Simulation models require a precise definition of network traffic communication. An additional problem for describing the network traffic in simulation models is the mathematical model of data distribution, according to which the generation and exchange of certain types and quantities of data are realized. The application of multigraphs enabled the time and quantity of the data distribution to be displayed as operational procedures for a special-purpose communication unit. A multigraph was formed for each data-exchange time and allowed its associated adjacency matrix to be defined. Using the matrix estimation method allowed the mathematical definition of the distribution function values. The application of the described method for the use of multigraphs enabled a more accurate mathematical description of real traffic in communication networks. Full article
(This article belongs to the Special Issue Symbolic Entropy Analysis and Its Applications III)
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20 pages, 362 KiB  
Article
Spectra of Self-Similar Measures
by Yong-Shen Cao, Qi-Rong Deng and Ming-Tian Li
Entropy 2022, 24(8), 1142; https://doi.org/10.3390/e24081142 - 17 Aug 2022
Viewed by 1302
Abstract
This paper is devoted to the characterization of spectrum candidates with a new tree structure to be the spectra of a spectral self-similar measure μN,D generated by the finite integer digit set D and the compression ratio N1 [...] Read more.
This paper is devoted to the characterization of spectrum candidates with a new tree structure to be the spectra of a spectral self-similar measure μN,D generated by the finite integer digit set D and the compression ratio N1. The tree structure is introduced with the language of symbolic space and widens the field of spectrum candidates. The spectrum candidate considered by Łaba and Wang is a set with a special tree structure. After showing a new criterion for the spectrum candidate with a tree structure to be a spectrum of μN,D, three sufficient and necessary conditions for the spectrum candidate with a tree structure to be a spectrum of μN,D were obtained. This result extends the conclusion of Łaba and Wang. As an application, an example of spectrum candidate Λ(N,B) with the tree structure associated with a self-similar measure is given. By our results, we obtain that Λ(N,B) is a spectrum of the self-similar measure. However, neither the method of Łaba and Wang nor that of Strichartz is applicable to the set Λ(N,B). Full article
(This article belongs to the Special Issue Symbolic Entropy Analysis and Its Applications III)
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