Recent Applications of Fractal-Wavelet Analysis in Applied Science and Engineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 2395

Special Issue Editors


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1. Institute of Biosciences, Letters and Exact Sciences, São Paulo State University (UNESP), São José do Rio Preto, SP 15054-000, Brazil
2. Department of Basic and Applied Sciences for Engineering (SBAI), University of Rome La Sapienza, 00161 Rome, Italy
3. Department of Economics and Statistics (DISES), University of Salerno, 84084 Fisciano, Italy
Interests: invariance; transformation; complex analysis; fractional calculus; wavelet analysis; fractal geometry; applied functional analysis; dynamical systems; image compression; data compression; pattern recognition; similarity; information theory; Shannon theory; antenna theory; image processing
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Guest Editor
São Paulo State University, Rua Cristóvão Colombo 2265, São José do Rio Preto, SP 15054-000, Brazil
Interests: digital signal processing; speech processing; speech and language processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last four decades, there has been a growing interest in both fractal geometry and wavelet analysis. Indeed, these two theories have been widely applied both in pure and applied science. In particular, fractal sets have become extremely popular thanks to their flexibility in modelling for real-world applications. Image processing, electromagnetism, economy, finance, and medicine are just a few of many research fields where fractal geometry is widely used. Likewise, wavelet analysis has become popular and important due mainly to several applications in numerous and widespread fields (signal processing, electromagnetism, information theory, etc.). In particular, both image processing and electromagnetism have shown that the fractal-wavelet approach can shed some new light on several unsolved problems. In fact, nonlinear models are often described and approximated by pre-fractal sets and wavelet expansions, respectively. Both theories show rather strikingly that contemporary mathematics is capable of providing ever more refined models for real-world applications. The choice of pre-fractal set and wavelet basis relies on the technical requirements and complexity of the physics problem. In the last fifteen years, several publications have appeared documenting the interest of many researchers in this topic.

In this Special Issue, we invite and welcome review, expository, and original papers dealing with recent advances in fractal-wavelet analysis and from a more general point of view, all theoretical and practical investigations in physics and engineering focused on this topic.

The main topics of this Special Issue include (but are not limited to):

  1. Fractality of discrete sets.
  2. Fractal sets and number theory.
  3. Iterated function systems random fractals.
  4. Fractal markets.
  5. Fractal-wavelet models and electromagnetic radiation.
  6. Wavelet theory and image processing.
  7. Wavelet models in probability.
  8. Biomedical engineering and wavelet modelling.
  9. Wavelet finance.

Dr. Emanuel Guariglia
Prof. Dr. Rodrigo Capobianco Guido
Guest Editors

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Keywords

  • Fractal Set
  • Fractal Market
  • Lyapunov Exponent
  • Chaoticity
  • Wavelet Expansion
  • Multiresolution Analysis
  • Wavelet Packet
  • Dynamical System

Published Papers (1 paper)

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Research

16 pages, 4493 KiB  
Article
Parallel fuzzy minimals on GPU
by Aleardo Manacero, Emanuel Guariglia, Thiago Alexandre de Souza, Renata Spolon Lobato and Roberta Spolon
Appl. Sci. 2022, 12(5), 2385; https://doi.org/10.3390/app12052385 - 25 Feb 2022
Cited by 1 | Viewed by 1344
Abstract
Clustering is a classification method that organizes objects into groups based on their similarity. Data clustering can extract valuable information, such as human behavior, trends, and so on, from large datasets by using either hard or fuzzy approaches. However, this is a time-consuming [...] Read more.
Clustering is a classification method that organizes objects into groups based on their similarity. Data clustering can extract valuable information, such as human behavior, trends, and so on, from large datasets by using either hard or fuzzy approaches. However, this is a time-consuming problem due to the increasing volumes of data collected. In this context, sequential executions are not feasible and their parallelization is mandatory to complete the process in an acceptable time. Parallelization requires redesigning algorithms to take advantage of massively parallel platforms. In this paper we propose a novel parallel implementation of the fuzzy minimals algorithm on graphics processing unit as a high-performance low-cost solution for common clustering issues. The performance of this implementation is compared with an equivalent algorithm based on the message passing interface. Numerical simulations show that the proposed solution on graphics processing unit can achieve high performances with regards to the cost-accuracy ratio. Full article
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