An efficient method of distinguishing chaos from noise

It is an important problem in chaos theory whether an observed irregular signal is deterministic chaotic or stochastic. We propose an efficient method for distinguishing deterministic chaotic from stochastic time series for short scalar time series. We first investigate, with the increase of the embedding dimension, the changing trend of the distance between two points which stay close in phase space. And then, we obtain the differences between Gaussian white noise and deterministic chaotic time series underlying this method. Finally, numerical experiments are presented to testify the validity and robustness of the method. Simulation results indicate that our method can distinguish deterministic chaotic from stochastic time series effectively even when the data are short and contaminated.     

二进小波变换的卷积定理

本文研究二进小波变换在信号处理中的应用.首先证明了两个满足容许性条件和规范性条件的二进小波的卷积和相关仍满足容许性和规范性条件,然后证明了二进小波变换的卷积定理和相关性定理,最后给出数值例子说明二进小波变换的卷积定理在加噪信号重构中的优越性.