## 5 Simulation and Discussion
### Sensory Filtration for Aquatic Monitoring
We might plot \(e(n)\) vs. \(v(n)\) alongside the residual signal \(e(n)-v(n)\) in the same plot, shown in Figure 2. It is worth noting that the system converged the signal after approximately 3 s of adaptation with this step size by subtracting a pure sinusoid from the original signal's sinusoid, as shown in Table 4. \begin{table}
\begin{tabular}{l l l l} \hline
**X(K)** & **D(K)** & **Y(K)** & **E(K)** \\ \hline
5.9733440000000 & 5.0980800812507553 & 5.0000000000000 & 0.0980800812507553 \\
5.84471900000000 & 5.711524107253188 & 2.73937801658796 & \(-\)2.02785390933477 \\
5.9530000000000 & 5.322718586092638 & 4.292949022658 & \(-\)0.110615667608750 \\
6.92412500000000 & 6.74139579845585 & 5.00895230402047 & 0.895230402048 \(\times\) 10\({}^{-15}\) \\
6.9050940000000 & 6.20854989838715 & 6.518660090901947 & 0.51866009090194 \(\times\) 10\({}^{-17}\) \\
5.94250000000000 & 5.81489146868828 & 5.994407024078749 & 0.99440702407874 \(\times\) 10\({}^{-19}\) \\
6.97990600000000 & 6.41286461886760 & 5.14120845848566 \(\times\) 10\({}^{22}\) & \(-\)0.14120845848566 \(\times\) 10\({}^{-22}\) \\
6.8749060000000 & 6.48997667476100 & 5.15674236735994 \(\times\) 10\({}^{24}\) & 0.15674236735994 \(\times\) 10\({}^{-24}\) \\ \hline \end{tabular}
\end{table} 
Table 4: An overview of the pH-adapted results from the sensors in each utilized pond. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Water** & & & & **Water** & & & **Water** & & \\
**Surface** & **Air Temp.** & **pH** & **NH3** & **Bottom** & **Turbidity** & **Fish** & **Stamp** & **Surface** & **Air Temp.** \\
**Temp.** & & & & **Temp.** & & & & **Temp.** & \\ \hline
25.63 & 22.13 & 5.95 & 7.5 & 31 & 7.2 & Sing & 19:11:53 & 25.63 & 22.13 \\
25.63 & 22.13 & 5.95 & 7.9 & 25 & 5.6 & Rui & 19:01:33 & 25.63 & 22.13 \\
25.56 & 22.13 & 5.96 & 8.5 & 32 & 6.0 & Koi & 19:02:48 & 25.56 & 22.13 \\
25.56 & 22.13 & 5.95 & 8.1 & 29 & 7.2 & Prawn & 19:01:13 & 25.56 & 22.13 \\
25.56 & 22.13 & 5.92 & 7.9 & 28 & 6.1 & Sing & 19:00:03 & 25.56 & 22.13 \\
25.56 & 22.13 & 5.94 & 8.5 & 30 & 3.0 & Tilapia & 19:03:08 & 25.56 & 22.13 \\
25.56 & 22.13 & 5.94 & 7.4 & 29 & 3.7 & Pangas & 19:06:08 & 25.56 & 22.13 \\ \hline \end{tabular}
\end{table} 
Table 3: Gathered sample of readings observed from the deployed sensors for the designated fishing ponds in the proposed UWSN. Table 4 summarizes the yields of an integrated filter in the sensory mode via a treated pH sample. It was demonstrated that the size of the noise was finally eliminated using the two processes of FIR filtration and ALE adaptive refining. This was accomplished by first establishing a DSP.FIR filter object that represented the filtration system to be identified. The filter coefficients were then designed using the \"firchand\" function. The developed filter is a low-pass filter with a stopband ripple of 0.2. After that, the signal \(x(k)\) was sent to the FIR filter. The desired signal \(d(k)\) is generated by the FIR filter as \(d\) = step (DSP.FIR filter, \(x\)). Following that, preparing the adaptive filter object necessitates the use of starting values for estimating the filter coefficients as well as the ALE step size for estimating the filter coefficients. Then, set the dsp.ALE filter's \"Initial Conditions\" property to the desired initial filter weight values. For the step size, 0.8 is a fair compromise between being large enough to converge well within 250 iterations (1000 input sample points) and being tiny enough to get an accurate estimate of the FIR filter's outputs. As a result, we discovered that the error ratio in the shown pH sample is hidden and close to zero in many samples, such as e (5.9733440000000).