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Temporal information is critical for routing computation in the vehicular network. It plays a vital role in the vehicular network. Till now, most existing routing schemes in vehicular networks consider the networks as a sequence of static graphs. We need to find an appropriate method to process temporal information into routing computation. Thus, in this paper, we propose a routing algorithm based on the Hidden Markov Model (HMM) and temporal graph, namely, Prediction-Based Temporal Graph Routing Algorithm (PT-GROUT). This new algorithm considers the vehicular network as a temporal graph, in which each data transmission as an edge has its specific temporal information. To better capture the temporal information, we select Software-Defined Vehicular Network (SDVN) as our network architecture, which is a preferred architecture for processing the temporal graph regarding the vehicular network since all vehicle statuses can be easily managed. To compute the future routing path accurately and efficiently, the future temporal graph is predicted by applying HMM, in which we model the current vehicular network with dynamic programming and greedy strategies. With the temporal information and reasonable setting of HMM, PT-GROUT can better evaluate the vehicular network and discover the evolution of the internal structure of the network. The optimal routing path can be achieved more efficiently. The simulation results demonstrate that PT-GROUT can substantially improve the computation efficiency and reduce packet loss and delivery delay compared with its counterparts. [ABSTRACT FROM AUTHOR]

SDVN is a promising architecture to extend the computation resources which break through the limitations of current vehicular networks. It is possible to learn new networking schemes by observing the surrounding environment in SDVN. However, within SDVN, the construction and application of such schemes still lack proper consideration in data collection, prediction, verification, and validation before applying these schemes in the real network, which is due to the limited knowledge of the physical environment. Intelligent Digital Twin (IDT) was initially designed for realizing intelligent manufacturing by virtualizing and learning the data of the physical space in cyberspace. Hence, bringing IDT to networking can provide additional valuable functionalities to meet the above considerations by constructing a virtual intelligent network space, aiming to realize the iterative update of the networking schemes in an adaptive way. In this article, we introduce a new network architecture, IDT-SDVN, by maximizing the advantages of SDVNs. We present the challenges and open issues of IDT-SDVNs. A case study is presented to demonstrate the effectiveness of SDVNs. The experimental results show that significant improvement of performance is achieved for vehicular networking with the proposed IDT-SDVNs. [ABSTRACT FROM AUTHOR]

We successfully realized 14 inch flexible LCD display with FHD resolution. The LCD panel has the thickness less than 0.3 mm, which using new type of colorless PI as substrate. The flexibel LCD is IPS mode. The key technology includes the conbination of indium gallium zinc oxide (IGZO) TFT, Color filter on Array substrate(COA)and Black Photo Spacer(BPS). [ABSTRACT FROM AUTHOR]