## 3 Results and Discussion
### Real-Time Study  
Further, the performance of the proposed MIMO system is evaluated in real-time. A hardware model of the QR inspired LEA is developed using the Version 5 QR code. In this setup, 24 LEDs are placed with the finder and alignment pattern by replacing all the data blocks of the QR code. The LEDs in the LEA are orderly placed and a unique background is chosen for minimizing the interference. Finder patterns and the alignment pattern are represented in dark color to isolate from the LEA and background as shown in Figure 24. The parameters used for this real-time study are given in Table 4. The LEA is controlled by a computer using Arduino Mega. The data stream that needs to be transmitted are encrypted in the computer and sent serially to the Arduino Mega. The Arduino Mega sets the corresponding LED status in accordance with the ciphertext. A mobile phone camera is used as a receiver to acquire the image of this hardware setup. This image is used to detect the QR code and subsequently the LED status is detected using the adaptive threshold technique. This adaptive threshold technique is capable of providing compensation to the interference due to the ambient and adjacent light intensity. The LED status is used to obtain the ciphertext and decrypted using the stream cipher technique with the proposed key. \begin{table}
\begin{tabular}{l l c} \hline \hline  & **Parameters** & **Values** \\ \hline \multirow{7}{*}{Transmitter} & QR code version & 5 \\  & Size of QR-LEA (\(l_{LEA}\)) & 20 cm \(\times\) 20 cm \\  & Total No. of LEDs & 24 \\  & LED type & Ultra-Bright Red \\  & Wavelength & 660 nm \\  & LED lens type & Dome Water clear \\  & Viewing angle & 20 Degree \\  & Forward current of an LED & 20 mA \\  & Development Board & Arduino Mega \\ \hline \multirow{3}{*}{Receiver} & Sensor & OPPO F3 rear camera \\  & Camera Resolution & 13 Mega Pixel \\ \cline{1-1}  & Total Frames & 1000 \\ \cline{1-1}  & Total number of bits transmitted & 24,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Real-time parameters. Figure 23: Real-time. (**a**) Translated, (**b**) rotated, (**c**) scaled image. The real-time setup is tested over distortions in the translation, rotation, and scaling motion as follows. The camera is mounted in the distance to cover the entire region of the QR-LEA pattern. The QR-LEA is kept stationary, and the receiver camera is distorted to emulate the inter-vehicle communication. The camera is moved randomly in both the x and y plane to create the translation distortion. It is ensured that the QR-LEA is visible within the FOV at each frame. The camera is rotated about \(\pm 15\)-degrees, and the QR-LEA images are acquired. The orthogonal distance between the camera and the LEA is adjusted to incorporate scaling. It is observed that a minimum of \(225\times 225\) pixel occupancy of LEA (\(N_{LEA}\)) is sufficient for detection and tracking. The image size is about \(3120\times 4160\) pixel (\(N_{I}\)), which makes the allowable distance between the camera and LEA (\(D_{m}\)) to be around 2.77 m as in (2). Thus, the physical dimension of LEA and the resolution of the camera determines the allowable distance between the camera and LEA (Scale factor). The BER performance of the proposed system is studied with the dynamic data stream. A pre-defined set of data stream is parallelly fed to the LEDs of the LEA. This dynamic varying LEA is captured by the camera and tracked by the proposed particle filter algorithm. Finally, the observed LEA pattern is decoded to recover the received data stream. This data stream is compared with the transmitted data, and the BER is evaluated. \[D_{m}=\frac{N_{I}}{N_{LEA}}I_{LEA} \tag{2}\]  
About 1000 frames used to transmit 24,000 bits are considered in this study. The impact of an adaptive threshold technique in the BER performance is evaluated with and without the particle filter.