A dimensional result for random self-similar sets

A very important property of a deterministic self-similar set is that its Hausdorff dimension and upper box-counting dimension coincide. This paper considers the random case. We show that for a random self-similar set, its Hausdorff dimension and upper box-counting dimension are equal

     

DIMENSIONS FOR RANDOM SELF-CONFORMAL SETS

1　IntroductionTherehasbeenconsiderableinterestinfractals,bothintheiroccurrenceinthesciences,andintheirmathematicaltheory .Awideclassoffractalsetsaregeneratedbyiteratedfunc tionsystem .Aself similarsetinRdisacompactsetKfulfillingtheinvarianceK =∪Ni=1 SiK ,whereS1,S2 ,… ,SNarecontractivesimilarities.IfS1,S2 ,… ,SNarecontractiveconfor malmappings,weobtainself conformalset.Itiswell known(seeHutchinson [1 2 ] )that,givenafamilyofsuchmappings,thereisauniquecompactsetwiththisproperty .Ifth…     

DIMENSIONS FORRANDOM SELF——CONFORMAL SETS

A set is called regular if its Hausdorff dimension and upper box-counting dimension coincide. In this paper, we prove that the random self-con formal set is regular almost surely. Also we determine the dimen-sions for a class of random self-con formal sets.     

Rd中一类齐次Moran集的盒维数

给定Rd 中的Moran集类 ,本文证明了对介于该集类中元素的上盒维数的最大值和最小值之间的任何一个数值s,总存在该集类中的一个元素 ,其上盒维数等于s,对下盒维数、修正的下盒维数也有类似的性质成立 ,从而给文 [1 ]中的猜想 1一个肯定的回答 .此外 ,还讨论了齐次Cantor集和偏次Cantor集盒维数存在性之间的关系 .     

Surreal dimensions

In this paper we use Conway's surreal numbers to define a refinement of the box-counting dimension of a subset of a metric space. The surreal dimension of such a subset is well-defined in many cases in which the box-counting dimension is not. Surreal dimensions refine box-counting dimensions due to the fact that the class of surreal numbers contains infinitesimal elements as well as every real number. We compute the surreal dimensions of generalized Cantor sets, and we state some open problems.     

Box-counting by Hölder’s traveling salesman

We provide a sufficient Dini-type condition for a subset of a complete, quasiconvex metric space to be covered by a Hölder curve. This implies in particular that if the upper box-counting dimension is less than $$d \ge 1$$, then it can be covered by an $$\frac{1}{d}$$-Hölder curve. On the other hand, for each $$1\le d <2$$ we give an example of a compact set in the plane with lower box-counting dimension equal to zero and upper box-counting dimension equal to d, just failing the above Dini-type condition, that can not be covered by a countable collection of $$\frac{1}{d}$$-Hölder curves.     

同心圆靶的计盒维数

本文讨论了以严格递减的正数列{rn}为半径的同心圆靶E和将E的间隔按从大到小的顺序重排后得到新的同心圆靶F的计盒维数的关系，证明了E的计盒维数不小于F的计盒维数，且举例说明了不等式可严格成立。     

Construction of fractal surfaces by recurrent fractal interpolation curves

A method to construct fractal surfaces by recurrent fractal curves is provided. First we construct fractal interpolation curves using a recurrent iterated functions system (RIFS) with function scaling factors and estimate their box-counting dimension. Then we present a method of construction of wider class of fractal surfaces by fractal curves and Lipschitz functions and calculate the box-counting dimension of the constructed surfaces. Finally, we combine both methods to have more flexible constructions of fractal surfaces.     

FRACTIONAL INTEGRAL AND FRACTAL FUNCTION

In this paper, the relationship between Riemann-Liouville fractional integral and the box-counting dimension of graphs of fractal functions is discussed.     

Fractal structure and fractal dimension determination at nanometer scale

Three-dimensional fractures of different fractal dimensions have been constructed with successive random addition algorithm, the applicability of various dimension determination methods at nanometer scale has been studied. As to the metallic fractures, owing to the limited number of slit islands in a slit plane or limited datum number at nanometer scale, it is difficult to use the area-perimeter method or power spectrum method to determine the fractal dimension. Simulation indicates that box-counting method can be used to determine the fractal dimension at nanometer scale. The dimensions of fractures of valve steel 5Cr21Mn9Ni4N have been determined with STM. Results confirmed that fractal dimension varies with direction at nanometer scale. Our study revealed that, as to theoretical profiles, the dependence of frsctal dimension with direction is simply owing to the limited data set number, i.e. the effect of boundaries. However, the dependence of fractal dimension with direction at nanometer scale in real metallic fractures is correlated to the intrinsic characteristics of the materials in addition to the effect of boundaries. The relationship of fractal dimensions with the mechanical properties of materials at macrometer scale also exists at nanometer scale. Project supported by the National Natural Science Foundation of China (Grant Nos. 59771050 and 59872004) and the Foundation Fund of Ministry of Metallurgical Industry.     

Closed contour fractal dimension estimation by the Fourier transform

This work proposes a novel technique for the numerical calculus of the fractal dimension of fractal objects which can be represented as a closed contour. The proposed method maps the fractal contour onto a complex signal and calculates its fractal dimension using the Fourier transform. The Fourier power spectrum is obtained and an exponential relation is verified between the power and the frequency. From the parameter (exponent) of the relation, is obtained the fractal dimension. The method is compared to other classical fractal dimension estimation methods in the literature, e.g., Bouligand–Minkowski, box-counting and classical Fourier. The comparison is achieved by the calculus of the fractal dimension of fractal contours whose dimensions are well-known analytically. The results showed the high precision and robustness of the proposed technique.     

The Hausdorff dimension of the level sets of Takagi’s function

Recently, Maddock (2006) [12] has conjectured that the Hausdorff dimension of each level set of Takagi’s function is at most 1/2. We prove this conjecture using the self-affinity of the function of Takagi and the existing relationship between the Hausdorff and box-counting dimensions.     

低复杂度序列的维数

本文研究符号空间中由零拓扑熵序列组成的集合.通过构造适当的自相似集,证明了该集合的盒维数为1,而Hausdorff维数为0.     

Hausdorff Dimension of the Range and the Graph of Stable-Like Processes

We determine the Hausdorff dimension for the range of a class of pure jump Markov processes in \(\mathbb {R}^d\), which turns out to be random and depends on the trajectories of these processes. The key argument is carried out through the SDE representation of these processes. The method developed here also allows to compute the Hausdorff dimension for the graph.     

Properties of Some Overlapping Self-Similar and Some Self-Affine Measures

We generalize theorems of Peres and Solomyak about the abso- lute continuity resp. singularity of Bernoulli convolutions ([19], [16], [17]) to a broader class of self-similar measures on the real line. Using the dimension the- ory of ergodic measures (see [11] and [2]) we find a formula for the dimension of certain self-affine measures in terms of the dimension of the above mentioned self- similar measures. Combining these results we show the identity of Hausdorff and box-counting dimension of a special class of self-affine sets.     

Limit theorems for projections of random walk on a hypersphere

We show that almost any one-dimensional projection of a suitably scaled random walk on a hypercube, inscribed in a hypersphere, converges weakly to an Ornstein–Uhlenbeck process as the dimension of the sphere tends to infinity. We also observe that the same result holds when the random walk is replaced with spherical Brownian motion. This latter result can be viewed as a “functional” generalisation of Poincaré’s observation for projections of uniform measure on high dimensional spheres; the former result is an analogous generalisation of the Bernoulli–Laplace central limit theorem. Given the relation of these two classic results to the central limit theorem for convex bodies, the modest results provided here would appear to motivate a functional generalisation.     

Dimensions of the coordinate functions of space-filling curves

The graphs of coordinate functions of space-filling curves such as those described by Peano, Hilbert, Pólya and others, are typical examples of self-affine sets, and their Hausdorff dimensions have been the subject of several articles in the mathematical literature. In the first half of this paper, we describe how the study of dimensions of self-affine sets was motivated, at least in part, by these coordinate functions and their natural generalizations, and review the relevant literature. In the second part, we present new results on the coordinate functions of Pólya's one-parameter family of space-filling curves. We give a lower bound for the Hausdorff dimension of their graphs which is fairly close to the box-counting dimension. Our techniques are largely probabilistic. The fact that the exact dimension remains elusive seems to indicate the need for further work in the area of self-affine sets.     

Dimension estimation in sufficient dimension reduction: A unifying approach

Sufficient Dimension Reduction (SDR) in regression comprises the estimation of the dimension of the smallest (central) dimension reduction subspace and its basis elements. For SDR methods based on a kernel matrix, such as SIR and SAVE, the dimension estimation is equivalent to the estimation of the rank of a random matrix which is the sample based estimate of the kernel. A test for the rank of a random matrix amounts to testing how many of its eigen or singular values are equal to zero. We propose two tests based on the smallest eigen or singular values of the estimated matrix: an asymptotic weighted chi-square test and a Wald-type asymptotic chi-square test. We also provide an asymptotic chi-square test for assessing whether elements of the left singular vectors of the random matrix are zero. These methods together constitute a unified approach for all SDR methods based on a kernel matrix that covers estimation of the central subspace and its dimension, as well as assessment of variable contribution to the lower-dimensional predictor projections with variable selection, a special case. A small power simulation study shows that the proposed and existing tests, specific to each SDR method, perform similarly with respect to power and achievement of the nominal level. Also, the importance of the choice of the number of slices as a tuning parameter is further exhibited.     

Parameter Estimation for Stochastic Parabolic Equations: Asymptotic Properties of a Two-Dimensional Projection-Based Estimator

A two-dimensional parameter is estimated from the observations of a random field defined on a compact manifold by a stochastic parabolic equation. Unlike the previous works on the subject, the equation is not necessarily diagonalizable, and no assumptions are made about the eigenfunctions of the operators in the equation. The estimator is based on certain finite-dimensional projections of the observed random field, and the asymptotic properties of the estimator are studied as the dimension of the projection is increased while the observation time is fixed. Simple conditions are found for the consistency and asymptotic normality of the estimator. An application to a problem in oceanography is discussed.     

Bivariate occupation measure dimension of multidimensional processes

Bivariate occupation measure dimension is a new dimension for multidimensional random processes. This dimension is given by the asymptotic behavior of its bivariate occupation measure. Firstly, we compare this dimension with the Hausdorff dimension. Secondly, we study relations between these dimensions and the existence of local time or self-intersection local time of the process. Finally, we compute the local correlation dimension of multidimensional Gaussian and stable processes with local Hölder properties and show it has the same value that the Hausdorff dimension of its image have. By the way, we give a new a.s. convergence of the bivariate occupation measure of a multidimensional fractional Brownian or particular stable motion (and thus of a spatial Brownian or Lévy stable motion).