# Preprocessing for Unintended Conducted Emissions Classification with ResNet
## 4 Discussion  
For the particular data set studied, these results show that a ResNet classifier works best in the frequency domain with a 128K wide Kaiser \(\beta=8\) window. However, these results are highly dependent on the characteristics of the signals being classified, noise characteristics present in the collections, the quality and amount of data for training, and the type of deep network. One school of thought is that there is no need to preprocess data for a deep network as it will learn the features of interest. A deep neural network could learn an FFT or specific preprocessing technique given enough data, however, the time versus frequency analysis in this effort shows that FFT preprocessing led to a higher accuracy for the amount of data provided for training. If there are limits in the data collection to short time periods or if the signals being classified change in time such that higher time resolution is required, then a shorter window may be needed. If the spectral bandwidth of UCE features from items to be classified varies significantly from the items in this data set, the optimal window type varies. The results presented in this paper show the value that signal processing techniques provide to preprocessed data for classificaton using a deep neural network. A great difference exists between helpful preprocessing and optimal preprocessing. This research has many possible directions of future work to improve classification performance, as can be seen in the window type and window length test results. A longer FFT analysis window would have likely produced a better result. However, we understand the trend of diminishing returns and know that we lose time-domain resolution as we increase the FFT window length. Therefore, we simply noted the improvements and trend. Additionally, the best of the window types (Kaiser \(\beta=8\)) were not exactly in the optimal place, as can be seen from the other results (Kaiser \(\beta=4\) and \(\beta=12\)). The different levels of improvement made for the various chosen techniques were relatively large. Tests showed that the deep learning architecture of the ResNet accurately classified the set of 18 relatively low-power devices, based upon the UCE even in low FNR situations. Even though complete explainability of reasons for results is currently relatively limited in such deep networks, the authors demonstrated that methodical data augmentation techniques can be used with a spectrum of preprocessing techniques to hypothesize what features and noise are of most significance to the deep learning network. This ability to hypothesize features used by the deep classifier can provide some measure of predictability of performance in a variety of untested scenarios via extrapolation of results and expert-based analysis. The results here demonstrated that the spectral content was of most importance to the ResNet and that a transformation to the frequency domain combined with good window choices resulted in improved performance in the presence of strong AWGN. The application of a window acted as a filter and provided a certain amount of processing gain and increase of the FNR. The amount of processing gain varied from window to window and was seen directly in a proportional manner as the window properties changed in the ResNet classification results. Training time decreased when better preprocessing was performed. Additionally, based on results, the authors conclude that the preprocessing gains produced diminishing returns (measured in terms of the ResNet classification accuracy) as the FFT window size increased. The authors' conclusion regarding this effect is as follows:  
1. The bin size started becoming as narrow as the features being used by the ResNet, and more narrow than is necessary to distinguish any of the spectral features when compared from device to device. 2. The FFT processing gain lowered the noise floor beyond all of the features easier to find near the floor.