Derivatives of the noisy signal based on Gaussian wavelet

Versión 4.0 (1,97 KB) por Zhaoyi Yan
This code achieves n-th order derivatives of a noisy signal sampled at discrete time points.
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Actualizado 17 may 2022

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Calculating noisy signal derivatives is a highly ill problem. According to the algorithm proposed in this paper (ref: https://doi.org/10.1016/j.chemolab.2003.08.001 ), a wavelet-based method can be used to suppress the noise, which is the basis of the code. The requirement for the input data is time vector is monotonic;

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Zhaoyi Yan (2024). Derivatives of the noisy signal based on Gaussian wavelet (https://www.mathworks.com/matlabcentral/fileexchange/102549-derivatives-of-the-noisy-signal-based-on-gaussian-wavelet), MATLAB Central File Exchange. Recuperado .

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Se creó con R2021b
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Versión Publicado Notas de la versión
4.0

input f vector can be aperiodic.

3.0.4

update: tinq input can be scalar or vector.

Note that : faket and pvec should keep the same shape (column or row vector).

2.0.4

Non-periodic signals are acceptable.

1.0.4

fix some error

1.0.3

modify some expressions.

1.0.2

modify some expressions.

1.0.1

Correct some error

1.0.0