# DOG: Continuous Wavelet Transform with derivative of Gaussian In Rwave: Time-Frequency Analysis of 1-D Signals

## Description

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

## Usage

 `1` ```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

 ```1 2 3 4``` ``` 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 May 2, 2019, 9:15 a.m.