# Design and Development of an SVM-Powered Underwater Acoustic Modem
## 3 Results
### Signal Classification Accuracy  
The SVM model exhibited high classification accuracy across a range of SNRs, showcasing its robustness in noisy underwater environments. The performance metrics employed were as follows:  
* Accuracy across different SNRs.
* Precision and recall metrics.
* Overall system robustness.  
The precision, recall, and accuracy of the signal classification model are calculated as follows:  
* Precision: True Positives; which measures the proportion of correctly identified positive instances among all instances that were labeled as positive by the model.
* Recall: True Positives + False Negatives, which assesses the proportion of actual positive instances that were correctly identified by the model.
* Accuracy: True Positives+ True Negatives + Total Population, reflecting the overall proportion of correct predictions made by the model.  
These metrics are crucial for evaluating the performance of the model, especially under varying SNR conditions in underwater communication scenarios.  
The confusion matrix of the cross-validation partition of the data (remaining 20% of the data that were not used for training) is shown in Figure 4(a). In addition, the precision, recall, and accuracy metrics are summarized in the aggregated confusion matrix across all SNR levels shown in Figure 4(b).  
The model accuracy obtained across all different SNR levels is listed in Table 1.  
The precision and recall for each class are shown in Table 2 for the best case of SNR = 25 dB. It is important to notice the high quality and precision achieved in the classification of the signals.  
In order to highlight the good performance of the system and the model developed, the precision and recall were analyzed in deeper detail for the worst case scenario of an SNR level of 5 dB and the best case scenario of 25 dB. For this purpose, the scalogram of each case was computed due to its advantages for analyzing real-world signals with features occurring at different scales. These scalograms show the CWT of the signals as a function of the frequency for the worst case of 5 dB and best case of 25 dB in Figure 6, together with  
\begin{table}
\begin{tabular}{c c} \hline
**SNR (dB)** & **Accuracy** \\ \hline  
\hline \end{tabular}
\end{table}
Table 1: Model accuracy across different SNR levels.  
Figure 5: Confusion matrices for cross-validation and training-test sets. (**a**) Confusion matrix using CV partition. (**b**) Summed confusion matrix.  
the real signals transmitted for each case. In both cases, the features of the signals are clearly identified and are very similar despite the difference of the value of the SNR. Note that the temporal signals in Figure 6a,b are presented in milliseconds to offer a real-world temporal context to these signals, and to give a sense of the duration and timing of the signals as they occur in the physical world. The scalograms in Figure 6c,d, on the other hand, operate within the sample number domain, as this is the standard practice in signal processing for feature extraction, especially when preparing data for SVM classification. Then, these scalograms offer a visual depiction of the signal's frequency content over sample numbers, demonstrating the SVM input following the feature extraction process. The transition from milliseconds in Figure 6a,b to sample numbers in Figure 6c,d is a standard step in signal processing for feature extraction, providing the groundwork for the accurate classification by an SVM.  
optimizing the CWT for the specific application in underwater acoustic signal analysis. This subsection delves into the nuances of this optimization process.