DOG: Continuous Wavelet Transform with derivative of Gaussian

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

Continuous Wavelet Transform with derivative of Gaussian

Description

Computes the continuous wavelet transform with for (complex-valued) derivative of Gaussian wavelets.

Usage

DOG(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.
moments	number of vanishing moments of the wavelet (order of the derivative).
nvoice	number of scales in each octave (i.e. between two consecutive powers of 2)
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

continuous (complex) wavelet transform

References

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

See Also

cwt, cwtp, cwtsquiz, cgt.

Examples

 x <- 1:512
    chirp <- sin(2*pi * (x + 0.002 * (x-256)^2 ) / 16)
  
 DOG(chirp, noctave=5, nvoice=12, 3, twoD=TRUE, plot=TRUE)

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