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Encode Barcode In NET Framework Using Barcode generator NET Control to generate, create barcode image in VS NET applications. resides in the signal irregularity rather than, for example, in its amplitude or in its Fourier transform (this is notably the case for edge detection in image processing) The rst part of this chapter is thus devoted to studying the properties of the H lder function of signals The question that naturally arises is the following: given a continuous function f on [0, 1], what is the most general form that can be taken by f By generalizing the notion of iterated function systems (IFS), we answer this question by characterizing the class of functions f and by giving an explicit method to construct a function whose H lder function is prescribed in this class This generalization enables us to de ne a new class of functions, that of generalized iterated function systems (GIFS) This will allow the development of a new approach to estimate the H lder function of a given signal An interesting feature of the H lder function is that it can be very simple while the signal is irregular For example, although they are nowhere differentiable, Weierstrass function [WEI 95] and fractional Brownian motion (FBM) [MAND 68] have a constant H lder function However, there are signals with very irregular appearance for which the H lder function is even more irregular; eg continuous signals f such that f is discontinuous everywhere While the canonical example is that of IFS, it turns out to be more interesting to use another description for the signal: the multifractal spectrum Instead of attributing to each t the value of the H lder function, all the points with same exponent in a sub-set E are aggregated and the irregularity is characterized in a global manner by calculating, for each value of , the Hausdorff dimension of the set E This yields a geometric estimation of the size of the subparts of the support of f where a given singularity appears This type of analysis, rst referenced in [MAND 72, MAND 74] and in the context of turbulence [FRI 95], has since been used often It has been studied at a theoretical level (analysis of self-similar measures or functions in a deterministic [BAC 93, OLS 02, RIE 94, RIE 95] and random [ARB 02, FAL 94, GRA 87, MAND 89, MAU 86, OLS 94] context, extension to capacities, higher order of spectra [VOJ 95]) and directly applied (study of DLA sequences [EVE 92, MAND 91], analysis of earthquake distribution [HIR 02], signal processing [LEV 95] and traf c analysis) The second part of this chapter deals with multifractal analysis Self-similar functions constitutes the paradigm of multifractal signals , as most of the quantities of interest can be explicitly calculated In particular, it has been demonstrated that the multifractal formalism, which connects the Hausdorff multifractal spectrum to the Legendre transform of a partition function, holds for elf-similar measures and functions, and various extensions of the latter were considered [RIE 95] The multifractal formalism enables us to reduce the calculation of the Hausdorff spectrum. itextsharp edit existing pdf c# c# 4.0 - creating a pdf editor like application in c# - Stack Overflow
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