# Preprocessing for Unintended Conducted Emissions Classification with ResNet
## 2 Materials and Methods
### Description of Data Used
#### 2.1.2 Data Set Augmentation  
The authors added AWGN to the data set at various levels and at various points in the experiment because AWGN is well-characterized and can be easily and accurately generated and measured. Therefore, the choice of AWGN allows this effort to focus on preprocessing methods for classification of UCE signals.  
For data acquisition at the end of a channel through an uncontrolled power system, the authors received instantiations of a random process (features of interest and noise),  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Device Type** & **Device Name** \\ \hline \hline \
\begin{tabular}{c} corded phone \\ VoIP (voice over internet protocol) phone \\ Uninterruptible Power Supply \\ single board computer \\ monitor \\ media streaming device \\ desktop computer \\ software defined radio \\ desktop computer \\ monitor \\ light \\ BluRay player \\ phone charger \\ electronic learning system \\ router \\ gaming system \\ single board computer \\ gaming system \\ \end{tabular} &
\begin{tabular}{c} cortelophone \\ polycomvop \\ cyberpowerups \\ raspberrypi \\ dellmonitor \\ roku2 xs \\ delloptiplex \\ usrpe310 \\ dellyps \\ viewwincomitor \\ fluorescents \\ viziobluray \\ lgphone \\ vtechvsmile \\ linksysrouter \\ wiiu \\ orotidxu4 \\ xboxone \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: COTS devices collected and classified via conducted UCE.  
and the mechanisms (time-varying linear and nonlinear) that produce this data are a part of the random process. Obtaining a statistically significant variety, and number of instantiations, is important to properly train the ResNet classifier. The anticipated stationarity and ergodicity of the random process is something to consider in this collection process and should influence the number of instantiations collected, as well as the collection interval, as noted in Section 3. Waiting for steady-state operation of the tested equipment before collection, controlling the environment during the collection [3], and adding noise generated by a statistically stationary process were all used to increase the ergodicity of the data set. In this work, the standard deviation of the AWGN added to the signals is a function of the standard deviation of first collections for of the components, as shown in Equation (1).  
\[\sigma_{G}=M\sigma_{d}, \tag{1}\]  
where \(M\) in the case of \"15\(\times\) noise\" is 15 and the \(\sigma_{d}\) is the standard deviation of all of the first-round collections for all devices. \(\sigma_{d}\) is used as a reference point for the remainder of the experiments and across all noise instantiations that are generated. \(\sigma_{G}\) is the resulting standard deviation of the white Gaussian noise, which is applied as described by Equation (2).  
\[\tilde{x}(n)=x(n)+G(n), \tag{2}\]  
where \(G(n)\) is a white Gaussian noise sample with a mean of zero and standard deviation of \(\sigma_{G}\), \(x(n)\) is the original data sample, \(\tilde{x}(n)\) is the resulting noise-degraded sample, and \(n\) is the sample number. The summation was performed over a segment length of \(N\), which was also the window length of the fast Fourier transform (FFT) computations that were performed in this experimentation.