Mt department Nazirova E. Sh

Figure 2.12 The noise isolated in the first stage.



Figure 2.13 The noise isolated in the second stage.

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Figure 2.14 The noise isolated in the third stage.



Figure 2.15 The noise isolated in the third stage.



Figure 2.16 The noise isolated in the last stage.

It can be seen that as the phase increases, the isolated noise also decreases. Our task is to visualize the result of the actions that take place in the process when we clear the signal using the wavelet function, so we consider the histograms and graphs of all the statistics in the process of clearing the signal from noise.

Figure 2.17 Original signal statistics.

We can view the original signal through two histograms. In the first histogram, the values received by the signal are shown in the section of their reception. It can be seen that the values between 4 and 6 are more repetitive. The second histogram shows the signal interference. It can be seen that at the end of the signal the noise increased.

Figure 2.18 Synthesized signal statistics.

The main parameter of noise clearance using the wavelet functions is that when the cleared signal and the isolated noise are added through the reverse phase, the signal will have to return to its original state again. This graph is based on a signal that has been cleared of noise and re-collected its noise. As we can see, the result is very similar to the original signal.

Figure 2.19 Result signal approximation.

Noise values are removed during alarm cleaning. Once the noise values are removed, a gap is created in place. It will be necessary to approximate the signal values to fill these gaps. In the image above, you can see the approximation of the signal that passed the final cleaning. The cleared signal showed that the signal received more values between 0 and 2. The approximation process is performed at each stage. Below I give an approximation of each stage.