  We have presented a new and alternative algorithm for noise reduction using
the methods of discrete wavelet transform and numerical differentiation of the
data. In our method the threshold for reducing noise comes out automatically.
The algorithm has been applied to three model flow systems - Lorenz,
Autocatalator, and Rossler systems - all evolving chaotically. The method is
seen to work well for a wide range of noise strengths, even as large as 10% of
the signal level. We have also applied the method successfully to noisy time
series data obtained from the measurement of pressure fluctuations in a
fluidized bed, and also to that obtained by conductivity measurement in a
liquid surfactant experiment. In all the illustrations we have been able to
observe that there is a clean separation in the frequencies covered by the
differentiated signal and white noise.
