Complex wavelets and shift invariance

Recently we have developed a new form of discrete wavelet transform, which generates complex coefficients by using a dual tree of wavelet filters to obtain their real and imaginary parts. This introduces limited redundancy (2 m:1 for m-dimensional signals) and allows the transform to provide approximate shift invariance and directionally selective filters (properties lacking in the traditional wavelet transform) while preserving the usual properties of perfect reconstruction and computational efficiency with good well-balanced frequency responses. In this paper we analyse why the new transform can be designed to be shift invariant, and describe how to estimate the accuracy of this approximation and design suitable filters to achieve this.

Power absorption invariance for brownian spring forcing

In [5] it was shown that, for a standard quarter-car vehicle model and a road disturbance whose velocity profile is white noise of intensity A, the mean power dissipated in the suspension is equal to kA/2 where k is the tyre vertical stiffness. It is remarkable that the power dissipation turns out to be independent of all masses and suspension parameters. The proof in [5] makes use of a spectral formulation of white noise and is specific to linear systems. This paper casts the result in a more general form and shows that it follows from a simple application of Ito calculus. © 2012 IEEE.