Local sine and cosine bases of Coifman and Meyer and the construction of smooth wavelets
Wavelets: a tutorial in theory and applications
Wavelet Analysis of Bifurcation in a Competition Model
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part II
Wavelet Analysis of Pulses in the Fitzhugh Model
ICCSA '08 Proceeding sof the international conference on Computational Science and Its Applications, Part I
Analysis of singularities by short haar wavelet transform
ICCSA'06 Proceedings of the 6th international conference on Computational Science and Its Applications - Volume Part I
Wavelet analysis of spike train in the fitzhugh model
Transactions on Computational Science VI
Hi-index | 0.98 |
A wavelet-based technique is proposed for analysing localized significant changes in observed data, in the presence of noise. The main tasks of the proposed technique are 1.(a) denoising the observed data without removing localized significant changes, 2.(b) classifying the detected sharp jumps (spikes), and 3.(c) obtaining a smooth trend (deterministic function) to represent the time-series evolution. By using the Haar discrete wavelet transform, the sequence of data is transformed into a sequence of wavelet coefficients. The Haar wavelet coefficients together with their rate of change, represent local changes and local correlation of data, therefore, their analysis gives rise to multi-dimensional thresholds and constraints which allow both the denoising and the sorting of data in a suitable space.