Self-similar set identification in the time-scale domain

We study the wavelet transform of deterministic self-similar signals and derive their properties, as well as a new algorithm for identification of the self-similarity parameter. We also include such applications as characterization and analysis of real chaotic signals in the presence of additive noise.