Fractals

In this paper, we present a method to estimate the fractal dimension of plant foliage in three dimensions (3D). This method is derived from the two-surface method introduced in the 90s to estimate the fractal dimension of tree species from field measurements on collections of trees. Here we adapted the method to individual plants. The multiscale topology and geometry of the plant must first be digitized in 3D. Then leafy branching systems of different sizes are constructed from the plant database, using the topological information. 3D convex envelops are then computed for each leafy branching system. The fractal dimension of the plant is finally estimated by comparing the total leaf area and the convex envelop area of these leafy modules. The method was assessed on a set of four peach trees entirely digitized at shoot scale. Results show that the peach trees have a marked self-similar foliage with fractal dimension close to 2.4.

In this paper, we attempt musical analysis by measuring fractal dimension (D) of musical pieces played by several musical instruments. We collected solo performances of popular instruments of Western and Eastern origin as samples. We attempted usual spectral analysis of the selected clips to observe peaks of fundamental and harmonics in frequency regime. After appropriate processing, we converted them into time series data sets and computed their fractal dimension. Based on our results, we conclude that instrumental musical sounds may have higher Ds than those computed from vocal performances of different types of Indian songs.

A fractal analysis of permeability for power-law fluids in porous media is presented based on the fractal characters of pore size distributions and tortuous flow paths/streamlines in the media. The proposed permeability model for power-law fluids in porous media is expressed as a function of the fractal dimensions of pore size distributions and tortuous flow paths/streamlines, porosity and microstructural parameters, as well as power exponent, and there is no empirical constant in the proposed model and every parameter in the model has clear physical meaning. The results predicted by the present fractal permeability model show that the model predictions (as the power exponent is 1) are in agreement with the available experimental data, and the predicted permeabilities (as the power exponent is not equal to 1) increase with the power exponent, which is also consistent with the physical situation.

We present a multifractal analysis of Mount St. Helens seismic activity during 1980–2002. The seismic time distribution is studied in relation to the eruptive activity, mainly marked by the 1980 major explosive eruptions and by the 1980–1986 dome building eruptions. The spectrum of the generalized fractal dimensions, i.e. D_q versus q, extracted from the data, allows us to identify two main earthquake time distribution patterns. The first one exhibits a multifractal clustering correlated to the intense seismic swarms of the dome building activity. The second one is characterized by an almost constant value of D_q ≈ 1, as for a random uniform distribution. The time evolution of D_q (for q = 0.2), calculated on a fixed number of events window and at different depths, shows that the brittle mechanical response of the shallow layers to rapid magma intrusions, during the eruptive periods, is revealed by sharp changes, acting at a short time scale (order of days), and by the lowest values of D_q (≈ 0.3). Conversely, for deeper earthquakes, characterized by intense seismic swarms, D_q do not show obvious changes during the whole analyzed period, suggesting that the earthquakes, related to the deep magma supply system, are characterized by a minor degree of clustering, which is independent of the eruptive activity.

Sea clutter refers to the radar returns from a patch of ocean surface. Accurate modeling of sea clutter and robust detection of low observable targets within sea clutter are important problems in remote sensing and radar signal processing applications. Due to lack of fundamental understanding of the nature of sea clutter, however, no simple and effective methods for detecting targets within sea clutter have been proposed. To help solve this important problem, we apply three types of fractal scaling analyses, fluctuation analysis (FA), detrended fluctuation analysis (DFA), and the wavelet-based fractal scaling analysis to study sea clutter. Our analyses show that sea clutter data exhibit fractal behaviors in the time scale range of about 0.01 seconds to a few seconds. The physical significance of these time scales is discussed. We emphasize that time scales characterizing fractal scaling break are among the most important features for detecting patterns using fractal theory. By systematically studying 392 sea clutter time series measured under various sea and weather conditions, we find very effective methods for detecting targets within sea clutter. Based on the data available to us, the accuracy of these methods is close to 100%.

A stationary time series is said to be long-range dependent (LRD) if its autocovariance function decays as a power of the lag, in such a way that the sum (over all lags) of the autocovariances diverges. The asymptotic rate of decay is determined by a parameter H, called the Hurst parameter. The time series is said to be short-range dependent (H = 1/2) if the sum converges. It is commonly believed that a random permutation of a sequence maintains the marginal distribution of each element but destroys the dependence, and in particular, that a random permutation of an LRD sequence creates a new sequence whose estimate of Hurst parameter H is close to 1/2. This paper provides a theoretical basis for investigating these claims. In reality, a complete random permutation does not destroy the covariances, but merely equalizes them. The common value of the equalized covariances depends on the length N of the original sequence and it decreases to 0 as N → ∞. Using the periodogram method, we explain why one is led to think, mistakenly, that the randomized sequence yields an "estimated H close to 1/2.".

In this paper, we consider a wide class of iterated function systems in R³, and show that their attractors are a class of fractal interpolation surfaces. Based on a refinement equation, we investigate the properties of smoothness of the fractal interpolation functions, and give the results of the smoothness in several cases.

A family of the spread harmonic measures is naturally generated by partially reflected Brownian motion. Their relation to the mixed boundary value problem makes them important to characterize the transfer capacity of irregular interfaces in Laplacian transport processes. This family presents a continuous transition between the harmonic measure (Dirichlet condition) and the Hausdorff measure (Neumann condition). It is found that the scaling properties of the spread harmonic measures on prefractal boundaries are characterized by a set of multifractal exponent functions depending on the only scaling parameter. A conjectural extension of the spread harmonic measures to fractal boundaries is proposed. The developed concepts are applied to give a new explanation of the anomalous constant phase angle frequency behavior in electrochemistry.

We prove that the modified von Koch snowflake curve, which we get as a limit by starting from an equilateral triangle (or from a segment) and repeatedly replacing the middle portion c of each interval by the other two sides of an equilateral triangle (and the corresponding von Koch snowflake domain), is non-self-intersecting if and only if c < ½. This answers a question of M. van den Berg.