Carlo Cattani

Dimension-independent modeling with simplicial complexes

Dealing with simplicial decompositions which are dimension independent allows for the convergence of disparate viewpoints from computer graphics, solid and geometric modeling. In this framework it is possible to treat in a unified manner several geometric problems, such as solid modeling of articulated objects, simplicial approximation of curved manifolds, motion encoding and interference detection, free configuration space computation, and graphical representation of multidimensional data. In the paper the authora describe the winged scheme, a simple representation based on simplicial decompositions, which can be used for linear polyhedra of any dimension and which allows for “solid” approximation of curved manifolds when combined with curved maps (e.g., NURBS). Various operators and algorithms are discussed, including boundary evaluation, linear and screw extrusion, grid generation, simplicial maps, and set operations. A simple manipulation language is also introduced, and some nontrivial examples are discussed.

Wavelet and wave analysis as applied to materials with micro or nanostructure

Wavelets and Wavelet Analysis Materials with Internal Structure Analysis of Waves in Materials Analysis of Simple and Solitary Waves in Materials Computer Analysis of Solitary Elastic Waves.

Markov Models for Image Labeling

Markov random field (MRF) is a widely used probabilistic model for expressing interaction of different events. One of the most successful applications is to solve image labeling problems in computer vision. This paper provides a survey of recent advances in this field. We give the background, basic concepts, and fundamental formulation of MRF. Two distinct kinds of discrete optimization methods, that is, belief propagation and graph cut, are discussed. We further focus on the solutions of two classical vision problems, that is, stereo and binary image segmentation using MRF model.

Shannon wavelets theory.

Shannon wavelets are studied together with their differential properties (known as connection coefficients). It is shown that the Shannon sampling theorem can be considered in a more general approach suitable for analyzing functions ranging in multifrequency bands. This generalization coincides with the Shannon wavelet reconstruction of functions. The differential properties of Shannon wavelets are also studied through the connection coefficients. It is shown that Shannon wavelets are -functions and their any order derivatives can be analytically defined by some kind of a finite hypergeometric series. These coefficients make it possible to define the wavelet reconstruction of the derivatives of the -functions.

Traffic Dynamics on Complex Networks: A Survey

Traffic dynamics on complex networks are intriguing in recent years due to their practical implications in real communication networks. In this survey, we give a brief review of studies on traffic routing dynamics on complex networks. Strategies for improving transport efficiency, including designing efficient routing strategies and making appropriate adjustments to the underlying network structure, are introduced in this survey. Finally, a few open problems are discussed in this survey.

Harmonic wavelet approximation of random, fractal and high frequency signals

The analysis of a periodic signal with localized random (or high frequency) noise is given by using harmonic wavelets. Since they are orthogonal to the Fourier basis, by defining a projection wavelet operator the signal is automatically decomposed into the localized pulse and the periodic function. An application to the analysis of a self-similar non-stationary noise is also given.

Fractals and Hidden Symmetries in DNA

This paper deals with the digital complex representation of a DNA sequence and the analysis of existing correlations by wavelets. The symbolic DNA sequence is mapped into a nonlinear time series. By studying this time series the existence of fractal shapes and symmetries will be shown. At first step, the indicator matrix enables us to recognize some typical patterns of nucleotide distribution. The DNA sequence, of the influenza virus A (H1N1), is investigated by using the complex representation, together with the corresponding walks on DNA; in particular, it is shown that DNA walks are fractals. Finally, by using the wavelet analysis, the existence of symmetries is proven.

On exact traveling-wave solutions for local fractional Korteweg-de Vries equation

This paper investigates the Korteweg-de Vries equation within the scope of the local fractional derivative formulation. The exact traveling wave solutions of non-differentiable type with the generalized functions defined on Cantor sets are analyzed. The results for the non-differentiable solutions when fractal dimension is 1 are also discussed. It is shown that the exact solutions for the local fractional Korteweg-de Vries equation characterize the fractal wave on shallow water surfaces.

Harmonic wavelet method towards solution of the Fredholm type integral equations of the second kind

Periodic harmonic wavelets (PHW) were applied as basis functions in solution of the Fredholm integral equations of the second kind. Two equations were solved in order to find out advantages and disadvantages of such choice of the basis functions. It is proved that PHW satisfy the properties of the multiresolution analysis.

Viewing Sea Level by a One-Dimensional Random Function with Long Memory

Sea level fluctuation gains increasing interests in several fields, such as geoscience and ocean dynamics. Recently, the long-range dependence (LRD) or long memory, which is measured by the Hurst parameter, denoted by H, of sea level was reported by Barbosa et al. (2006). However, reports regarding the local roughness of sea level, which is characterized by fractal dimension, denoted by D, of sea level, are rarely seen. Note that a common model describing a random function with LRD is fractional Gaussian noise (fGn), which is the increment process of fractional Brownian motion (fBm) (Beran (1994)). If using the model of fGn, D of a random function is greater than 1 and less than 2 because D is restricted by H with the restriction . In this paper, we introduce the concept of one-dimensional random functions with LRD based on a specific class of processes called the Cauchy-class (CC) process, towards separately characterizing the local roughness and the long-range persistence of sea level. In order to achieve this goal, we present the power spectrum density (PSD) function of the CC process in the closed form. The case study for modeling real data of sea level collected by the National Data Buoy Center (NDBC) at six stations in the Florida and Eastern Gulf of Mexico demonstrates that the sea level may be one-dimensional but LRD. The case study also implies that the CC process might be a possible model of sea level. In addition to these, this paper also exhibits the yearly multiscale phenomenon of sea level.