## 3 Methodology
### Feature Extraction Using Wavelet Transform  
In order to extract characteristics from ultrasonic signals, a number of effective signal-processing algorithms have been proposed. These include the Hilbert-Huang transform, the fast Fourier transform, and symbolic dynamic filtering. However, owing to the nonsta  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensors** & & **Advantages** & **Disadvantages** \\ \hline \multirow{8}{*}{Ultrasonic sensor} & \(\bullet\) & High sensitivity & \multirow{8}{*}{\(\bullet\)} \\  & \(\bullet\) & High frequency & \\  & \(\bullet\) & High penetrating power & \\  & \(\bullet\) & The ease of detecting external and deep & \\  & \(\bullet\) & objects & \\   Ultrasonic sensor & \(\bullet\) & High accuracy & \\  & \(\bullet\) & Simple interface with a microcontroller or & \\  & & any type of controller, & \\  & \(\bullet\) & Low power utilization & \\  & \(\bullet\) & Low cost & \\ \hline \multirow{8}{*}{Laser sensor} & \(\bullet\) & Sensitivity & \multirow{8}{*}{\(\bullet\)} \\  & \(\bullet\) & High resolution & \\  & \(\bullet\) & Reliability & \\  & \(\bullet\) & Wide measurement range & \(\bullet\) & Narrow range of operating \\ \hline \multirow{8}{*}{Radar sensor} & \(\bullet\) & Radar signal can penetrate through objects & \multirow{8}{*}{\(\bullet\)} \\  & \(\bullet\) & Can be used in any environmental & \\  & & conditions & \\  & \(\bullet\) & Can distinguish between moving and still & \\  & & objects & \\  & \(\bullet\) & Count passing people & \\  & \(\bullet\) & Accurate & \\  & \(\bullet\) & Reliable & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sensors advantages and disadvantages.  
Figure 3: Ultrasonic sensor process.  
tionary properties of ultrasonic defect signals, wavelet transform-based approaches have shown to be the most effective and commonly employed [3].  
When working with signals collected at discrete intervals in time, the DWT (discrete wavelet transform) technique is used to get the wavelet transform. In the frequency domain, the DWT performs an analysis by first applying a high-pass filter and then a low-pass filter, to the signal in order to separate it into its coarse approximation and detailed information, as illustrated in Figure 4[8, 9]. Figure 5 depicts DWT with different decomposition levels of the simulated ultrasonic signal. In our case, clustered DWT will be utilized to overcome the time variance and huge dimension of DWT coefficients. It employs the energy of several frequency bands to cluster coefficients [45, 46]. Clustering separates the discrete wavelet coefficient into disjoint clusters for which a single robust feature may be calculated. Finally, each component of the feature vector may be identified by calculating the energy of each cluster through its associated row vector. After employing a clustered discrete wavelet transform to extract features, the distance measurements are now complete.  
Here, we use the clustered wavelet transform as a black box, with the wavelet transform serving not only as a signal representation but also as a dimension reduction technique. In this study, we use the Haar wavelet transform, one of the fastest but also simplest wavelet transformations available. Since the calculation can be done locally without the need for a temporary array, it is a memory-efficient and computationally-efficient method for studying the regional features of a signal. As a result, signal energies are conserved and compressed. The next phase involves feeding these features into the neut  
Figure 4: (**A**) The decomposition process of wavelet transform; (**B**) the reconstruction process of wavelet transform; and (**C**) the theoretical spectral passing band of wavelet transform.