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A Unified Approach To Fractal Dimensions

A Unified Approach To Fractal Dimensions

Witold Kinsner
Copyright: © 2008 |Volume: 1 |Issue: 4 |Pages: 24
ISSN: 1938-7857|EISSN: 1938-7865|ISSN: 1938-7857|EISBN13: 9781615205455|EISSN: 1938-7865|DOI: 10.4018/jitr.2008100105
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MLA

Kinsner, Witold. "A Unified Approach To Fractal Dimensions." JITR vol.1, no.4 2008: pp.62-85. http://doi.org/10.4018/jitr.2008100105

APA

Kinsner, W. (2008). A Unified Approach To Fractal Dimensions. Journal of Information Technology Research (JITR), 1(4), 62-85. http://doi.org/10.4018/jitr.2008100105

Chicago

Kinsner, Witold. "A Unified Approach To Fractal Dimensions," Journal of Information Technology Research (JITR) 1, no.4: 62-85. http://doi.org/10.4018/jitr.2008100105

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Abstract

Many scientific papers treat the diversity of fractal dimensions as mere variations on either the same theme or a single definition. There is a need for a unified approach to fractal dimensions for there are fundamental differences between their definitions. This paper presents a new description of three essential classes of fractal dimensions based on: (1) morphology, (2) entropy, and (3) transforms, all unified through the generalized-entropy-based Rényi fractal dimension spectrum. It discusses practical algorithms for computing 15 different fractal dimensions representing the classes. Although the individual dimensions have already been described in the literature, the unified approach presented in this paper is unique in terms of (1) its progressive development of the fractal dimension concept, (2) similarity in the definitions and expressions, (3) analysis of the relation between the dimensions, and (4) their taxonomy. As a result, a number of new observations have been made, and new applications discovered. Of particular interest are behavioral processes (such as dishabituation), irreversible and birth-death growth phenomena (e.g., diffusion-limited aggregates (DLAs), dielectric discharges, and cellular automata), as well as dynamical non-stationary transient processes (such as speech and transients in radio transmitters), multi-fractal optimization of image compression using learned vector quantization with Kohonen’s self-organizing feature maps (SOFMs), and multi-fractal-based signal denoising.

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