This paper compared noise removal using the Fourier series and the Haar wavelet transformations. The results showed
that noise from the measured data can be filtered by neglecting high-order terms of Fourier coefficients. It also showed
that signal denoising can be achieved by Haar wavelet transformation by filtering the noise before inverting the
transformed data back to time domain. A further comparison using a set of data with variation 6.3mV from five
measurements of a sample showed that the variations after denoising can be reduced to 3.8mV by the Fourier series and
to 2.3mV by 3-level Haar wavelet. Both methods can filter noise in signal and keep the predicted curve consistent with
the measured data. The signal becomes smooth if denoised by the Fourier series but the variation of signal, however, can
be reduced more if denoised by the Haar wavelet. Moreover, from the computation complexity viewpoint, signal
denoising by Haar wavelet is much better than that by Fourier series.
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