725 research outputs found

    Appui aux ABC du Territoire du PNR de la Montagne de Reims

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    Stochastic discrete scale invariance: renormalization group operators and iterated function systems

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    Abstract We revisit here the notion of discrete scale invariance. Initially defined for signal indexed by the positive reals, we present a generalized version of discrete scale invariant signals relying on a renormalization group approach. In this view, the signals are seen as fixed point of a renormalization operator acting on a space of signal. We recall how to show that these fixed point present discrete scale invariance. As an illustration we use the random iterated function system as generators of random processes of the interval that are dicretely scale invariant

    Spectral Analysis of Multi-dimensional Self-similar Markov Processes

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    In this paper we consider a discrete scale invariant (DSI) process {X(t),t∈R+}\{X(t), t\in {\bf R^+}\} with scale l>1l>1. We consider to have some fix number of observations in every scale, say TT, and to get our samples at discrete points αk,k∈W\alpha^k, k\in {\bf W} where α\alpha is obtained by the equality l=αTl=\alpha^T and W={0,1,...}{\bf W}=\{0, 1,...\}. So we provide a discrete time scale invariant (DT-SI) process X(⋅)X(\cdot) with parameter space {αk,k∈W}\{\alpha^k, k\in {\bf W}\}. We find the spectral representation of the covariance function of such DT-SI process. By providing harmonic like representation of multi-dimensional self-similar processes, spectral density function of them are presented. We assume that the process {X(t),t∈R+}\{X(t), t\in {\bf R^+}\} is also Markov in the wide sense and provide a discrete time scale invariant Markov (DT-SIM) process with the above scheme of sampling. We present an example of DT-SIM process, simple Brownian motion, by the above sampling scheme and verify our results. Finally we find the spectral density matrix of such DT-SIM process and show that its associated TT-dimensional self-similar Markov process is fully specified by {RjH(1),RjH(0),j=0,1,...,T−1}\{R_{j}^H(1),R_{j}^H(0),j=0, 1,..., T-1\} where RjH(τ)R_j^H(\tau) is the covariance function of jjth and (j+τ)(j+\tau)th observations of the process.Comment: 16 page
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