Numerical differentiation is important in various applications. However, it should be taken with caution because it can greatly amplify the noise in the data, especially at high frequencies. When a wavelet function is the derivative of a smoothing function, the wavelet transform has the combined properties of smoothing and differentiation.
I don't have the Wavelet Toolbox, but the _dwt variant worked out-of-the-box for me, and it worked precisely as advertised. Many thanks to the author for publishing this!
really helpful, i was just looking into this and stumbled upon this file through google.
Your method worked to solve a problem that stumped me for years! I work with tracking data from ocean animals. These data can be very noisy, but I was able to extract good speed data.
Thank you, exactly what I needed.
Do you think the same can be applied also for integration?
Really very nice. Thanks.
Excellent. It may be perfect by adding more spline wavelet.
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