We present analysis of spatial patterns of generic disease spread simulated by a stochastic long-range correlation SIR model, where individuals can be infected at long distance in a power law distribution. We integrated various tools, namely perimeter, circularity, fractal dimension, and aggregation index to characterize and investigate spatial pattern formations. Our primary goal was to unders...

The fractal doubly stochastic Poisson process (FDSPP) model of molecular evolution, like other doubly stochastic Poisson models, agrees with the high estimates for the index of dispersion found from sequence comparisons. Unlike certain previous models, the FDSPP also predicts a positive geometric correlation between the index of dispersion and the mean number of substitutions. Such a relationsh...

Fractal objects like Sierpinski triangle and Fern have very high visual complexity and low storage-information content. For generating computer graphic images and compression of such objects, Iterated Function Systems (IFS) {[3] , [1]} are recently being used. The main problem in fractal encoding using IFS is large amount of time taken for the compression of the fractal object. Our endeavor in ...

Keywords: Fractal structure Fractal dimension Box-counting dimension Domain of words Language Regular expression Binary-coded decimal Search tree a b s t r a c t A fractal structure is a tool that is used to study the fractal behavior of a space. In this paper, we show how to apply a new concept of fractal dimension for fractal structures, extending the use of the box-counting dimension to new ...

Stochastic fractal signals can be characterized by the Hurst coefficient H, which is related to the exponents of various power-law statistics characteristic of these processes. Two techniques widely used to estimate H are spectral analysis and detrended fluctuation analysis (DFA). This paper examines the analytical link between these two measures and shows that they are related through an integ...

A global optimization algorithm i s introduced which generalizes Kushner’s univariate search [1]. It aims to minimize the number o f probes (function evaluations) required for a g i v e n confidence in the results. All known p r o b e s contribute to a stochastic model of the underly ing “score surface”; this model is interrogated for the location most likely to exceed the current result goal. ...