The convolution and the correlation

Select menu item <Analysis><Convolution> to apply the convolution for digitally filtering data.

In linear systems convolution is used to describe the relationship between three signals of interest:

Convolution is defined by the convolution sum:

FindGraph includes several filter kernels F(u):

Common filter kernels (Delta function, Amplification, Attenuation, Inverting, Shift, Echo, First derivative, Integral).
Low-pass filter kernels to provide an averaging (smoothing) of the signal (exponential, rectangular, Gauss function, sinc function with Hamming or with Blackman windows).
High-pass filter kernels.

You can draw your own signal or to modify a selected signal. Simple click on left chart and use the mouse to draw your own signal.

You can use series of points or any function (formula), you created, as filter F[u]

Correlation is a mathematical operation that is very similar to convolution. Convolution is the relationship between:

This output signal is called the cross-correlation of the two input signals: