摘要：
We propose a simple quasi-continuous monolayer graphene structure and achieve a dynamically tunable triple plasmon-induced transparency (PIT) effect in the proposed structure. Additional analyses indicate that the proposed structure contains a self-constructed bright-dark-dark mode system. A uniform theoretical model is introduced to investigate the spectral response characteristics and slow light-effects in the proposed system, and the theoretical and the simulated results exhibit high consistency. In addition, the influences of the Fermi level and the carrier mobility of graphene on transmission spectra are discussed. It is found that each PIT window exhibits an independent dynamical adjustability owing to the quasi-continuity of the proposed structure. Finally, the slow-light effects are investigated based on the calculation of the group refractive index and phase shift. It is found that the structure displays excellent slow-light effects near the PIT windows with high-group indices, and the maximum group index of each PIT window exceeds 1000 when the carrier mobility of graphene increases to 3.5 m2 V−1 s−1. The proposed structure has potential to be used in multichannel filters, optical switches, modulators, and slow light devices. Additionally, the established theoretical model lays a theoretical basis for research on multimode coupling effects.
作者机构：
Joint Laboratory for Extreme Conditions Matter Properties, Southwest University of Science and Technology, Mianyang, China;[Tang, Yongjian] Sichuan Civil-Military Integration Institute, Mianyang, China;[Wu, Xuanguang; Huang, Zhen] School of National Defense Science and Technology, Southwest University of Science and Technology, Mianyang, China;[Yi, Yougen] College of Physics and Electronics, Central South University, Changsha, China;[Zhang, Guangfu] School of Communication and Electronic Engineering, Hunan City University, Yiyang, China
通讯机构：
[Zao Yi] J;[Guangfu Zhang] S;Joint Laboratory for Extreme Conditions Matter Properties, Southwest University of Science and Technology, Mianyang, China<&wdkj&>Sichuan Civil-Military Integration Institute, Mianyang, China<&wdkj&>School of Communication and Electronic Engineering, Hunan City University, Yiyang, China
摘要：
Road traffic is an important component of the national economy and social life. Promoting intelligent and Informa ionization construction in the field of road traffic is conducive to the construction of smart cities and the formulation of macro strategies and construction plans for urban traffic development. Aiming at the shortcomings of the current road traffic system, this article, on the basis of combining convolution neural network, situational awareness technology, database and other technologies, takes the road traffic situational awareness system as the research object, and analyzes the information collection, processing, and analysis process of road traffic situational awareness system. Convolutional neural networks (CNN), region-CNN (R-CNN), fast R-CNN, and faster R-CNN are used for vehicle class classification and location identification in road image big data. The deep convolutional neural network model based on road traffic image big data was further established, and the system requirements analysis and system framework design and implementation were carried out. Through the analysis and trial of actual cases, the results show the application effect of the realized road traffic situational awareness system, which provides a scientific reference and basis for the establishment of modern intelligent transportation system.
