By Topic

Optimal wavelet expansion via sampled-data control theory

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Kashima, K. ; Dept. of Appl. Anal. & Complex Dynamical Syst., Kyoto Univ., Japan ; Yamamoto, Y. ; Nagahara, M.

Wavelet theory provides a new type of function expansion and and has found many applications in signal processing. The discrete wavelet transform of a signal x(t) in L2(R) is usually computed by the so-called pyramid algorithm. It however requires a proper initialization, i.e., expansion coefficients with respect to the basis of one of the desirable approximation subspaces. An interesting question is how we can obtain such coefficients when only sampled values of x(t) are available. The paper provides a design method for a digital filter that optimally gives such coefficients assuming certain a priori knowledge on the frequency characteristic of the target functions. We then extend the result to the case of non-orthogonal wavelets. Examples show the effectiveness of the proposed method

Published in:

Decision and Control, 2001. Proceedings of the 40th IEEE Conference on  (Volume:5 )

Date of Conference:

2001