In this paper, we focus on ensuring encrypted vehicular communication using wireless controller area network performance at high node densities, by means of Dedicated Short-Range Communication (DSRC) algorithms. We analyses the effect of the vehicular communication parameters, message-rate, data-rate, transmission power and carrier sensing threshold, on the application performance. After a state-of-the-art analysis, we propose a data-rate DSRC algorithm. Simulation studies show that DSRC performs better than other decentralized vehicular communication algorithms for a wide range of application requirements and densities. Vehicular communication plays one of the most important roles for future autonomous vehicle. We have systematically investigated the impact of vehicular communication using the MATLAB application platform and achieved an accuracy of 93.74% after encrypting all the communications between the vehicles and securing them by applying the encryption on V2V communication in comparison with the existing system of Sensor Networks which stands at 92.97%. The transmission time for the encryption is 165 seconds while the rate of encryption is as low as 120 Mbps for the proposed awareness range of vehicles to vehicle using DSRC algorithm in Wireless-Controller Area Network for communication. Experimental results show that our proposed method performs 3% better than the recently developed algorithms.
This research aims to understand the enhancing reading advancement using eye gaze tracking in regards to pull the increase of time interacting with such devices along. In order to realize that, user should have a good understanding of the reading process and of the eye gaze tracking systems; as well as a good understanding of the issues existing while using eye gaze tracking system for reading process. Some issues are very common, so our proposed implementation algorithm compensate these issues. To obtain the best results possible, two mains algorithm have been implemented: the baseline algorithm and the algorithm to smooth the data. The tracking error rate is calculated based on changing points and missed changing points. In [21], a previous implementation on the same data was done and the final tracking error rate value was of 126%. The tracking error rate value seems to be abnormally high but this value is actually useful as described in [21]. For this system, all the algorithms used give a final tracking error rate value of 114.6%. Three main origins of the accuracy of the eye gaze reading were normal fixation, regression, skip fixation; and accuracies are displayed by the tracking rate value obtained. The three main sources of errors are the calibration drift, the quality of the setup and the physical characteristics of the eyes. For the tests, the graphical interface uses characters with an average height of 24 pixels for the text. By considering that the subject was approximately at 60 centimeters of the tracker. The character on the screen represents an angle of ±0.88◦; which is just above the threshold of ±0.5◦ imposed by the physical characteristics of the eyeball for the advancement of reading using eye gaze tracking.