Presentation is loading. Please wait.

Presentation is loading. Please wait.

Data smoothing Raymond Cuijpers. Index The moving average Convolution The difference operator Fourier transforms Gaussian smoothing Butterworth filters.

Similar presentations


Presentation on theme: "Data smoothing Raymond Cuijpers. Index The moving average Convolution The difference operator Fourier transforms Gaussian smoothing Butterworth filters."— Presentation transcript:

1 Data smoothing Raymond Cuijpers

2 Index The moving average Convolution The difference operator Fourier transforms Gaussian smoothing Butterworth filters

3 The moving average Let (S i ) be a data set S i,smooth = 1/3 (S i-1 + S i + S i+1 ) =

4 Convolution Definition: The moving average is the same as convoluting the signal with a block function !

5 Convolution f * g = g * f f * (g + h) = f * g + f * h (f * g) * h = f * (g * h) f * 0 = 0 But 1 * g ≠ g

6 The difference operator The velocity is the derivative of the displace- ment, for discrete signals this becomes the difference operator. Discrete differentiation = convolution with difference operator Velocity estimated at i+1/2

7 The difference operator The difference operator amplifies noise Smoothing helps but at the cost of accuracy It becomes worse for higher order derivatives * =

8 Differentiation and convolution Noisy = BAD Exact = GOOD Differentiation by convolution with derivative

9 Fourier Transforms Definition: In the Fourier domain: convolution becomes multiplication differentiation becomes multiplication with iw

10 Fourier Transforms Calculating convolutions using Fourier transforms is much faster for large data sets than direct computation: There are many other transforms/expansions –Sine and Cosine transforms –Laplace transform –Legendre polynomials –Hermite polynomials (=Gaussian) –Bessel Functions –…

11 Gaussian smoothing Let S(t) be a signal then the blurred signal is Where is the Gaussian kernel The derivative of a noisy S(t) is ill-posed, but

12 Gaussian smoothing The n-th order proper derivative of scale s is So in the discrete case we get

13 Gaussian Smoothing Gaussian filters are the only 'natural' filters Together they form a linear Scale selective space of operators * =

14 Butterworth Filters Noise is usually high frequent and not the signal Butterworth filters work by throwing away high frequencies in the Fourier transform A good choice of cut-off frequency is paramount

15 Butterworth filters Advantage: Easy to implement in electronic circuit Disadvantage: Introduces a phase shift. Solution is to apply it twice in opposite directions 'Ringing': jumps in the signal produces oscillations Depends strongly on the nature of the noise and the choice of cut-off frequency


Download ppt "Data smoothing Raymond Cuijpers. Index The moving average Convolution The difference operator Fourier transforms Gaussian smoothing Butterworth filters."

Similar presentations


Ads by Google