Mt department Nazirova E. Sh

Figure 2.20 The first stage is the resultant signal approximation.



Figure 2.21 The second stage is the resultant signal approximation.



Figure 2.22 The third stage is the resultant signal approximation.



Figure 2.23 The fourth stage is the resultant signal approximation.

We have considered the isolated interactions above. But we were not familiar with their histograms. Therefore, we only see the noise statistics obtained in the first and last stage.

Figure 2.24 Isolated noise statistics.



Figure 2.25 Isolated noise statistics.

We can see that as the noise decreases, so does the range of values it accepts.

Figure 2.26 Compression using a globe value.

It is also possible to compress signals using the wavelet functions. Compression is performed on the basis of clearing the signal from excessive noise. But compression is done using the parameter. Depending on the type and importance of the alarm being cleaned, it will be possible to change the alarm cleaning method using compression. We first see the compression result based on the global value.

Figure 2.27 Compression using a globe value.

We observe the result by placing the original signal and the cleared signal on top of each other. In this case, red means the original signal, pink means the compressed signal.

Figure 2.28 Compression using a globe value.

We try to put the resulting signal from compression and the original signal on top of each other by means of a balanced value.

Figure 2.29 Comparison of compressed signals in two ways.

The difference between the global value and the balanced value compression signals can be easily seen. To do this, we magnify and compare the same area of the signal. By clicking the Denois button we will be able to see the noise removal settings and perform the cleaning. The following window will open.

Figure 2.30 Signal clearance dialect.

We almost don’t see anything new in this window because we got to know them by pressing all the buttons. But these graphs and histograms will be needed when clearing the signal. This is why it is shown in the Noise Clear dialog.

What function to use plays an important role in clearing interference with wavelet functions. For example, let's clear the same signal from interference with a biortogonal wavelet. The result is as follows:



Figure 2.31 A signal cleared using a biortogonal wavelet.

The signal cleared using the Meyer function is as follows:

Figure 2.32 A signal cleared using a Meyer wavelet