Vehicle Communication using Secrecy Capacity

We address secure vehicle communication using secrecy capacity. In particular, we research the relationship between secrecy capacity and various types of parameters that determine secrecy capacity in the vehicular wireless network. For example, we examine the relationship between vehicle speed and secrecy capacity, the relationship between the response time and secrecy capacity of an autonomous vehicle, and the relationship between transmission power and secrecy capacity. In particular, the autonomous vehicle has set the system modeling on the assumption that the speed of the vehicle is related to the safety distance. We propose new vehicle communication to maintain a certain level of secrecy capacity according to various parameters. As a result, we can expect safer communication security of autonomous vehicles in 5G communications.Comment: 17 Pages, 12 Figure

Self-Selective Correlation Ship Tracking Method for Smart Ocean System

In recent years, with the development of the marine industry, navigation environment becomes more complicated. Some artificial intelligence technologies, such as computer vision, can recognize, track and count the sailing ships to ensure the maritime security and facilitates the management for Smart Ocean System. Aiming at the scaling problem and boundary effect problem of traditional correlation filtering methods, we propose a self-selective correlation filtering method based on box regression (BRCF). The proposed method mainly include: 1) A self-selective model with negative samples mining method which effectively reduces the boundary effect in strengthening the classification ability of classifier at the same time; 2) A bounding box regression method combined with a key points matching method for the scale prediction, leading to a fast and efficient calculation. The experimental results show that the proposed method can effectively deal with the problem of ship size changes and background interference. The success rates and precisions were higher than Discriminative Scale Space Tracking (DSST) by over 8 percentage points on the marine traffic dataset of our laboratory. In terms of processing speed, the proposed method is higher than DSST by nearly 22 Frames Per Second (FPS)

Advancement in infotainment system in automotive sector with vehicular cloud network and current state of art

The automotive industry has been incorporating various technological advancement on top-end versions of the vehicle order to improvise the degree of comfortability as well as enhancing the safer driving system. Infotainment system is one such pivotal system which not only makes the vehicle smart but also offers abundance of information as well as entertainment to the driver and passenger. The capability to offer extensive relay of service through infotainment system is highly dependent on vehicular adhoc network as well as back end support of cloud environment. However, it is know that such legacy system of vehicular adhoc network is also characterized by various problems associated with channel capacity, latency, heterogeneous network processing, and many more. Therefore, this paper offers a comprehensive insight to the research work being carried out towards leveraging the infotainment system in order to obtain the true picture of strength, limitation, and open end problems associated with infotainment system

A self-selective correlation ship tracking method for smart ocean systems

In recent years, with the development of the marine industry, the ship navigation environment has become more complicated. Some artificial intelligence technologies, such as computer vision, can recognize, track and count sailing ships to ensure maritime security and facilitate management for Smart Ocean systems. Aiming at the scaling problem and boundary effect problem of traditional correlation filtering methods, we propose a self-selective correlation filtering method based on box regression (BRCF). The proposed method mainly includes: (1) A self-selective model with a negative samples mining method which effectively reduces the boundary effect in strengthening the classification ability of the classifier at the same time; (2) a bounding box regression method combined with a key points matching method for the scale prediction, leading to a fast and efficient calculation. The experimental results show that the proposed method can effectively deal with the problem of ship size changes and background interference. The success rates and precisions were over 8 % higher than Discriminative Scale Space Tracking (DSST) on the marine traffic dataset of our laboratory. In terms of processing speed, the proposed method is higher than DSST by nearly 22 frames per second (FPS).This research was supported by the National Natural Science Foundation of China under Grant (No. 61772387 and No. 61802296), the Fundamental Research Funds of Ministry of Education and China Mobile (MCM20170202), the Fundamental Research Funds for the Central Universities (JB180101), China Postdoctoral Science Foundation Grant (No. 2017M620438), and supported by ISN State Key Laboratory