# Design and Development of an SVM-Powered Underwater Acoustic Modem
## 4 Discussion  
In the preliminary phases of this research, the fast Fourier transform (FFT) was employed for transforming time-domain signals into their frequency-domain representations. However, significant challenges were encountered due to the non-periodic nature of the underwater acoustic signals and their non-zero starting points. This resulted in frequency (energy) leakage, thereby complicating the accurate analysis of these signals. The short-time Fourier transform (STFT) presented an alternative with its time-frequency representation capabilities. Despite its promise, the STFT was confronted with limitations related to the dimensionality and resolution. It generated a considerably larger set of features (52 bins) compared with the more efficient CWT, which produced only 11 features. This efficiency made the CWT a more suitable choice for feature extraction within the constraints and goals of the underwater signal analysis. The main advantages of the CWT can be summarized as follows:  
* Time-frequency localization: Contrary to the FFT/DFT, which provide purely frequency-domain information, and the STFT, which offers a rudimentary time-frequency analysis, the CWT yields a nuanced and continuous time-frequency representation of signals. This is especially beneficial for analyzing non-stationary signals or those with time-varying characteristics prevalent in underwater communication. * Flexibility in feature extraction: The ability of the CWT to analyze signals across all possible scales affords unmatched flexibility in capturing transient or rapidly evolving features within signals. This adaptability was crucial for identifying nuanced features in modulated underwater signals, which could not be adequately captured by the fixed and dyadic scales of the DWT or the limited resolution of the STFT. * Dimensionality and computational efficiency: By extracting a reduced number of features (11 features with the CWT) compared with the STFT approach (52 bins), the classification process was significantly streamlined, enhancing computational efficiency without sacrificing the depth of the signal representation. * Overcoming limitations of Fourier transform methods: The CWT approach to signal analysis effectively addressed the challenges of frequency leakage and the analysis of non-periodic underwater acoustic signals, which greatly complicated the use of traditional FFT/DFT methods. This comparative analysis underscores the transformative potential of the CWT in overcoming the intrinsic limitations of Fourier transform methods, particularly in the complex realm of underwater acoustic signal processing. In the development of the classification model, a remarkable synergy emerged from the combination of the SVM, CWT, and strategic inclusion of the \"C\" symbol as a delimiter between characters. This innovative approach was shown to be instrumental in achieving exceptional robustness and reliability in signal classification, eliminating the immediate need for the Reed-Solomon error correction techniques typically employed to enhance signal integrity. Notably, misclassifications involving the \"1\" or \"0\" symbols consistently occurred with an adjacent \"C\" symbol, primarily at the boundaries of signal sequences. This observation is crucial, as it underscores the absence of direct misclassifications between \"1\" and \"0\", effectively minimizing the potential for error in critical signal interpretation. The \"C\" symbol serves as a robust buffer, significantly reducing ambiguity and ensuring high precision in the classification of \"1\" and \"0\". Thus, the strength of the model lies not merely in its high precision and recall metrics but in its fundamental design, which leverages the \"C\" symbol to navigate the complexities of underwater acoustic signal processing. This strategic choice highlights the model's innovative edge, offering a powerful testament to its capacity to deliver reliable classifications without the immediate recourse to traditional error correction strategies.