## 3 Own Errors of the Wavelet Transform Algorithm
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Quantity**} & \multicolumn{5}{c}{**WT Algorithm Input Values Range**} \\  & \([-1;1]\) & \([-2;2]\) & \([-3;3]\) & \([0;2]\) & \([0;4]\) & \([3;9]\) \\ \hline \(S_{2,0}\) & \(1.03\times 10^{-7}\) & \(4.12\times 10^{-7}\) & \(9.74\times 10^{-7}\) & \(7.08\times 10^{-7}\) & \(2.83\times 10^{-6}\) & \(2.16\times 10^{-5}\) \\ \(S_{2,1}\) & \(1.11\times 10^{-7}\) & \(4.43\times 10^{-7}\) & \(1.01\times 10^{-6}\) & \(9.55\times 10^{-7}\) & \(3.82\times 10^{-6}\) & \(2.75\times 10^{-5}\) \\ \(T_{2,0}\) & \(1.31\times 10^{-7}\) & \(5.24\times 10^{-7}\) & \(1.21\times 10^{-6}\) & \(2.87\times 10^{-7}\) & \(1.15\times 10^{-6}\) & \(7.06\times 10^{-6}\) \\ \(T_{2,1}\) & \(1.07\times 10^{-7}\) & \(4.29\times 10^{-7}\) & \(9.73\times 10^{-7}\) & \(3.36\times 10^{-7}\) & \(1.34\times 10^{-6}\) & \(9.30\times 10^{-6}\) \\ \(T_{1,0}\) & \(7.09\times 10^{-8}\) & \(2.85\times 10^{-7}\) & \(6.99\times 10^{-7}\) & \(1.65\times 10^{-7}\) & \(3.56\times 10^{-7}\) & \(4.29\times 10^{-6}\) \\ \(T_{1,1}\) & \(5.83\times 10^{-8}\) & \(2.33\times 10^{-7}\) & \(5.87\times 10^{-7}\) & \(1.49\times 10^{-7}\) & \(5.95\times 10^{-7}\) & \(4.05\times 10^{-6}\) \\ \(T_{1,2}\) & \(5.84\times 10^{-8}\) & \(2.34\times 10^{-7}\) & \(5.85\times 10^{-7}\) & \(1.49\times 10^{-7}\) & \(5.97\times 10^{-7}\) & \(4.05\times 10^{-6}\) \\ \(T_{1,3}\) & \(5.53\times 10^{-8}\) & \(2.21\times 10^{-7}\) & \(5.41\times 10^{-7}\) & \(1.78\times 10^{-7}\) & \(7.08\times 10^{-7}\) & \(5.26\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the simulation-obtained values of the rounding error signal variance of subsequent output quantities of the discrete wavelet transform algorithm for the wavelet “db2” with two iterations of the decomposition process, for numbers with a length of 16 bits, depending on the range of possible values of the implementation of the input quantitiesIn analyzing the results presented in Tables 1 and 2, several of the most important relationships can be noticed. The value of the variance of the self-error signal has the following characteristics:  
* It depends on the number of arithmetic operations performed on non-zero coefficients of the transformation matrix and increases with the number of these operations;
* It depends on the range of possible implementation values of the algorithm's input quantities and increases as this range is extended;
* It depends on the length of the word used by the algorithm and decreases as this length increases. Therefore, from the perspective of the measuring chain design, this value will rely on the number of the input quantities of the WT algorithm, the iterations of the signal decomposition process, and the order and type of the mother wavelet used. It is also noticeable that for the same stage of signal decomposition, the error signal parameters are nearly identical for each output quantity. Hence, for each algorithm implementation, the parameters of the self-error signals should be determined for the subsequent decomposition stages. The experiment conditions should closely resemble the actual operational conditions of the analyzed algorithm.