## 5 Discussion  
An accurate visualization of graphical representation of peripheral state needs to consider two issues. The first one is a proper visualizing algorithm and the second is the method to assess achieved results. Determining the brightness of objects based on their graphical representation is a common task [26]. For the purposes of analyzing acquired graphical data, the authors adopts a simplified method based on two features of the light emitting element normalized graphic representation, namely its size expressed in non-zero pixels [27] and the basic histogram statistics [28]. The analysis of the obtained results leads to the following considerations:  
1. [label=0., ref=1]
2. Visualizing multiplexing seven-segment LED displays is satisfyingly accurate without any additional modification to proposed visualization algorithm. After image normalization a W2 LED module brightness is almost the same, in a statistic manner, as for real hardware. The difference of statistics is at level 0.02 for the mean value and 0.01 for the median. The conclusion can be extended to results analysis without an image normalization as a comparable relation between any two LED digits brightness which are almost identical for a virtual and a real peripheral;
3. Comparison of real and virtual LED bar visualization accuracy cannot be carried out reliable using only histogram statistics. This is caused by size changing of real image representation of LEDs along with their brightness. An additional feature is needed, which was taken into account;
4. More accurate result analysis can introduce the resulting compound brightness factor as a multiplication of relative image size, expressed as a relation of non-zero pixels counts of two LEDs (analyzed and fully driven), and chosen histogram statistics. Based on this, the set of factors can be achieved that are more suitable for direct comparison (Figures 8 and 9).  
Figure 8: Comparison of LED normalized compound brightness factors with (W) and without (N) brightness compensation for 0%, 10%, 30%, and 100% duty cycle of PWM scheme.  
Figure 9: Comparison of LED normalized compound brightness factors with (W) and without (N) brightness compensation for 0%, 30%, 60%, and 100% duty cycle of PWM scheme for different photo exposure of the real MKD-51.  
* As shown in Figures 8 and 9, the proposed algorithm of LEDs visualization is following changes in controlled LED perceived brightness. In the case of implemented algorithm without additional brightness compensation, its perceived brightness was considered to be far insufficient in receipt. Using additional \(f_{map}\) function, correcting visualized LED brightness was carried out. Although analysis of relative factors indicate that the compensation could be more intensive, final visualization results perceived by the human eye deemed sufficient. The \(a\) and \(b\) correction factors, chosen in the \(f_{map}\) function, have been selected empirically in order to obtain more appropriate visual representation of simulated peripheral behavior. They were not selected analytically or optimally, which may be the subject of potential further research, as well as the class of \(f_{map}\) functions modifying brightness, which may be more closely related to the visualized type of LED or another peripheral object.  
The photos of the actual hardware being analyzed in the article were not taken in identical conditions. Moreover, for a more reliable, comprehensive, and robust [29] assessment of proposed solutions, they should not be taken in stationary manner and should even emphasize various features of the object [30]. This means that the chosen methods should not rely on the stationary nature of the image acquiring parameters. One solution to this issue is an image normalization [31], which was performed according to instructions given in the article criteria. The data prepared in this way can be compared in more reliable manner, and this has been carried out in this article.