cwtth: Cauchy's wavelet transform

In Rwave: Time-Frequency Analysis of 1-D Signals

Cauchy's wavelet transform

Description

Compute the continuous wavelet transform with (complex-valued) Cauchy's wavelet.

Usage

cwtTh(input, noctave, nvoice=1, moments, twoD=TRUE, plot=TRUE)

Arguments

input	input signal (possibly complex-valued).
noctave	number of powers of 2 for the scale variable.
nvoice	number of scales in each octave (i.e. between two consecutive powers of 2).
moments	number of vanishing moments.
twoD	logical variable set to T to organize the output as a 2D array (signal size x nb scales), otherwise, the output is a 3D array (signal size x noctave x nvoice).
plot	if set to T, display the modulus of the continuous wavelet transform on the graphic device.

Details

The output contains the (complex) values of the wavelet transform of the input signal. The format of the output can be

2D array (signal size x nb scales)

3D array (signal size x noctave x nvoice)

Value

tmp	continuous (complex) wavelet transform.

References

See discussions in the text of “Practical Time-Frequency Analysis”.

See Also

cwt, cwtp, DOG, gabor.

Examples

    x <- 1:512
    chirp <- sin(2*pi * (x + 0.002 * (x-256)^2 ) / 16)
    retChirp <- cwtTh(chirp, noctave=5, nvoice=12, moments=20)

Rwave documentation built on Oct. 22, 2022, 1:05 a.m.