## 6 Extended Evaluation: Mobile Scenario
### Results  
We set the robot to move from one end of the table to the other along a straight line trajectory, traversing positions A, B and two marked out-of-FOV points (see Figure 6 for an illustration). In each experiment trial we chose a specific speed of the robot and shutter switching time and move the robot along the calibrated trajectory. The receiver selects the pixel status as OPEN/CLOSE based on, first computing the SNR on each pixel and flagging the pixels which have SNR above an empirical threshold (Table 5) for each switching time selection, and then the receiver making a recommendation on which pixels to OPEN/CLOSE based on whether it has detected the ID sequence (decode bits, correlate with sequence and flag success if correlation above 90%). We set our experimentation to record the system's pixel shutter recommendations in the form of a 2 bit binary representation as listed in Table 6. For example, a 01 implies that the system correctly identified that Pixel 1 should be open and identified the signal on Pixel 1 using the 5 bit Barker ID sequence transmitted as payload. In Figure 7, we report our system recommendation output for three speeds of the robot (20 dps or 0.53 cm/s, 15 dps or 0.4 cm/s, 10 dps or 0.26 cm/s) and 1000 ms shutter switching time. There is an ERROR case of recommendation where both pixels are open (binary representation: 11) in both 15 and 10 dps robot's speed experimentation. During this time, the robot's position is at the intersection of A & B, where both pixels are illuminated at the same time and the measured SNR values in both pixels are higher than the threshold value. However, we observed over 5 trials, even in the reported least accurate case, the ERROR cases occur largely in the transition phase from one pixel to another. This can be resolved by synchronizing the transition time with the speed of the robot or by speeding up shutter sampling (smaller intervals) so that even if the error occurs during a transition, there are enough samples to identify the behavior and a suitable action can be taken on the receiver. Further, more receiver samples imply possibility of using probabilistic estimation of whether the LED has changed position or not. \begin{table}
\begin{tabular}{c c} \hline \hline
**Configurations** & **Binary Representation** \\ \hline Pixel 2 Closed, Pixel 1 Closed & 00 \\ Pixel 2 Closed, Pixel 1 Open & 01 \\ Pixel 2 Open, Pixel 1 Closed & 10 \\ Pixel 2 Open, Pixel 1 Open & 11 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Binary mapping of the output or shutter pixels status recommendations. Figure 6: Mobility experiments with the variation of LED Transmitter’s position: A (**left**), B (**center**) and Out of Field of Views (OFOV) (**right**). As an extended experiment we evaluate how the system performs for much smaller sampling time intervals such as 100 ms and 50 ms. These sampling intervals can cater to real-time human-computer interaction applications and other applications using real-time feedback. While the focus of this work is not necessarily to emphasize only one category of application, we do explore on the success and limitations of our system under such fast sampling time settings. We report the results from our robot movement based experiment results, discussed previously, for these cases and report them in Figures 8 and 9, respectively. We observe that in most sampled points the system recommends correctly, however, some cases of error when the LEDs are not in FOV or transitioning. These errors can be resolved by employing statistical filtering to the recommendation/identification results in each sample over the duration of a reasonable and practical time window. For example, even if the sampling duration is 100 ms, the samples across a 1 s time window can be considered for statistical estimation and filtering noise to ensure the correct receiver sampling area is accounted. Another issue, the leakage of LED signals through the shutter even if it is CLOSED.