## 5 Case Study B: Underwater OCC under MIMO Diversity  
In regards to future exploration, another avenue that this research study is beginning to explore is in the use case of a camera-based VLC system in an underwater setting. The practical application would be related to network communication between different submarines and boat to a submarine, etc. The following is an evaluation of the underwater LED data with a DNN. The data that were obtained was of an LED, blue in this case, operating at 50 Hz for about 17 min while a stereo camera operated at 100 FPS to capture the LED signal. The transmission was not meant to carry any information but rather only conducted to capture the LED blinking to create a repository of data for DNN training and analysis. The LED used was an LED bar that consisted of six individual LEDs rather than a single or grid of LEDs. The camera in this setup is Stereolab's ZED-2 stereo camera which achieves 100 FPS at VGA (640 \(\times\) 480) pixels resolution. The distance between the camera and LED was 4.5 m.  
For purposes of analyzing and using the data, the first step was to extract all the frames from the video and then crop the frames so that only the LED was within the frame. This is because the region of interest in this study has been identifying the LED state, i.e., ON or OFF. Since the camera is a stereo camera, within each frame are two images of the LED, as shown in Figure 7. Basically, each image is 1280 \(\times\) 480 pixels, but there are two frames within each image of size 640 \(\times\) 480 pixels. Therefore, in order to increase the number of samples available to train and validate later, the LED in each subframe of the raw image was cropped and stored separately. After cropping the LEDs from each frame, it was resized to 32 \(\times\) 32 \(\times\) 3 because that was the input dimension size set for the DNNs used in this research. Figure 8 shows an example of the LED cropped and resized. In total, there were 199,716 raw frames available for analysis, where half were from the left LED in the image and the other half were from the right LED in the image. Next, the images of the cropped LEDs were labeled, however, due to the time constraint, instead of manually labeling the images by hand, the maximum pixel intensity of the image was used as a measure to determine if the LED was ON or OFF. After the raw frames were both extracted, cropped, resized and labeled, 10% of the dataset was randomly chosen and upsampled via augmentation. In total, 219,516 frames were valuable to validate and train with. After all the frames were preprocessed, two  
Figure 8: Cropped LED From Zed Frame. Figure 7: Zed Camera Frame. evaluations were conducted: (1) performance of the empirical study model against this new dataset and (2) a newly trained model on this dataset, which is validated against data from the underwater dataset. To evaluate the performance of the MIMO model against this new dataset, all the processed frames were given to the model, predicted by the model and then cross-checked with the ground truth to observe how well the model performed. This analysis was conducted to observe how transferable the model was from one application/environment to another. Figure 9 shows the confusion matrix of the MIMO model validation results against the underwater LED dataset. Overall the model did no better than a flip of a coin with an overall accuracy of approximately 58.31%, which illustrates that the model is data specific. Next, the same model, i.e., the same architecture and same parameters/hyperparameters, that was used in the empirical study was trained on just the underwater LED dataset and then validated. This was performed to investigate if a model trained on the specific data performed better with the new data. Therefore, 60% of the underwater LED dataset was used for training the model, and the other 40% was used for validating the dataset.