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Data smoothing Raymond Cuijpers
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Index The moving average Convolution The difference operator Fourier transforms Gaussian smoothing Butterworth filters
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The moving average Let (S i ) be a data set S i,smooth = 1/3 (S i-1 + S i + S i+1 ) =
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Convolution Definition: The moving average is the same as convoluting the signal with a block function !
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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
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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
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The difference operator The difference operator amplifies noise Smoothing helps but at the cost of accuracy It becomes worse for higher order derivatives * =
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Differentiation and convolution Noisy = BAD Exact = GOOD Differentiation by convolution with derivative
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Fourier Transforms Definition: In the Fourier domain: convolution becomes multiplication differentiation becomes multiplication with iw
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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 –…
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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
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Gaussian smoothing The n-th order proper derivative of scale s is So in the discrete case we get
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Gaussian Smoothing Gaussian filters are the only 'natural' filters Together they form a linear Scale selective space of operators * =
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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
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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
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