Abstract
In Chap. 3, we discussed the Fourier transform , which converts the stationary signal x(t) from the time domain to the frequency domain \( X(\omega ) \) and thus allows us to perform a frequency analysis. Thanks to this transform, we can determine the amplitudes and frequencies of the sine and cosine making up the signal x(t), but we cannot determine at what time the corresponding amplitude occurs. The STF transform used in the analysis of non-stationary signals allows us to obtain the distribution of frequency components in time, but we are faced with the problem of selecting the appropriate window width. Selection of the wrong width blurs the time –frequency data obtained as a result of applying the transform. In the wavelet transform, the problem of time–frequency resolution is solved by replacing the time window with a wavelet function.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsAuthor information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Layer, E., Tomczyk, K. (2015). Wavelet Transform. In: Signal Transforms in Dynamic Measurements. Studies in Systems, Decision and Control, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-319-13209-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-13209-9_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13208-2
Online ISBN: 978-3-319-13209-9
eBook Packages: EngineeringEngineering (R0)