## 3 DNN-Based Demodulation Evaluation
### Supervised Model Analysis Summary  
As for all the frame results as a collective, 2 Level data performed the best, followed by 3 Level data and followed by 4 Level data. There are several reasons for this but the one that is the most compelling is that the intensity level between some class labels might have been too minute that it was difficult for the model to distinguish between them. As for Resnet50 and Inception V3 models, these two models performed pretty poorly across all three level types. One thing to note which is not described in the tables above is that the training accuracy for these two models would consistently reach an accuracy of 90+% and validation/test set accuracy of less than 40%. Basically, these models were overfitting more often than not. As mentioned earlier, these models all had their dropout layers set to 0.2, i.e., \"drop\" 20% of the neurons in the layer. However, after collecting the results above, a sample of dropout values were used for both Inception V3 and Resnet50, which showed that the model stopped overfitting as dropout increased, but the validation/test set accuracy remained the same, which is why those results are not presented here. Another intuition/idea that explains the results for the Inception V3 model and Resnet50 model's \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Softmax Full** & **Softmax** & **Sigmoid Full** & **Sigmoid** \\  & **Image** & **Cropped Image** & **Image** & **Cropped Image** \\ \hline VGG16 & 93.50\% & 95.99\% & 75.59\% & 73.79\% \\ \hline Resnet50 & 69.99\% & 35.60\% & 66.00\% & 34.40\% \\ Inception V3 & 37.59\% & 35.19\% & 28.00\% & 23.00\% \\ \(3\) Layer & 50.59\% & 75.00\% & 70.99\% & 74.59\% \\ Convolution & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: 4 Level Full Color Model Evaluations. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Softmax Full** & **Softmax** & **Sigmoid Full** & **Sigmoid** \\  & **Image** & **Cropped Image** & **Image** & **Cropped Image** \\ \hline VGG16 & 98.00\% & 96.79\% & 95.99\% & 96.60\% \\ Resnet50 & 77.60\% & 53.20\% & 79.00\% & 50.00\% \\ Inception V3 & 42.80\% & 32.23\% & 31.60\% & 32.82\% \\
3 Layer & & & & \\ Convolution & 94.99\% & 94.40\% & 91.20\% & 93.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: 3 Level Full Color Model Evaluations. poor classification results is that the size of the LED dataset was too small as these models require a lot of data to learn the granularities in the data distribution, especially in the case of Inception V3 which uses adaptive learning via filter sizes. Perhaps having a larger a dataset might improve these model's performance. In an ideal case, the three major DNNs should have performed well above 90% on the validation/test set. In addition, the least accurate model should have been the simple three layer convolution as it did not have any special techniques nor was it a deep model. In any case, if the Resnet50 and Inception V3 model are disregarded, which were hypothesized to have failed because of lack of data, the ideal case holds for VGG16 and the simple three layer convolution. With that in mind, it can also be said that in terms of neural network theory, the Resnet50 model should have performed the best as it is the deepest. This would be followed by Inception V3 and then the VGG16 model and lastly the three layer model. This scenario occurs only if the criterion for success is based on how deep the network is. Another point to make is that perhaps the VGG16 model performed well because of its unique architecture wherein only 3 \(\times\) 3 filters are used and the fact that more non-lienar activations are used per step in the model. This would actually be pivotal because the data represents concentrated areas within the image. Thus, extracting the most relevant information would mean that a small receptive field would be best to employ. In terms of how well the DNN supervised models performed compared to the basic thresholding based approach, it is somewhat unclear.