FindGraph: Convolution describes relationship between input signal, impulse response or output

Convolution

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

• the input signal Y[i],
• the impulse response F[i] (filter kernel),
• the output signal W[i].

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

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

• the input signal Y[i],
• the impulse response F[i],
• the output signal W[i].