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Recent advancements in image processing and machine-learning technologies have significantly improved vehicle monitoring and identification in road transportation systems. Vehicle classification (VC) is essential for effective monitoring and identification within large datasets. Detecting and classifying vehicles from surveillance videos into various categories is a complex challenge in current information acquisition and self-processing technology. In this paper, we implement a dual-phase procedure for vehicle selection by merging eXtreme Gradient Boosting (XGBoost) and the Multi-Objective Optimization Genetic Algorithm (Mob-GA) for VC in vehicle image datasets. In the initial phase, vehicle images are aligned using XGBoost to effectively eliminate insignificant images. In the final phase, the hybrid form of XGBoost and Mob-GA provides optimal vehicle classification with a pioneering attribute-selection technique applied by a prominent classifier on 10 publicly accessible vehicle datasets. Extensive experiments on publicly available large vehicle datasets have been conducted to demonstrate and compare the proposed approach. The experimental analysis was carried out using a myRIO FPGA board and HUSKY Lens for real-time measurements, achieving a faster execution time of 0.16 ns. The investigation results show that this hybrid algorithm offers improved evaluation measures compared to using XGBoost and Mob-GA individually for vehicle classification. [ABSTRACT FROM AUTHOR]

A limitation to the potential use of graphene as an electronic material is the lack of control over the 2D materials properties once it is deposited on a supporting substrate. Here, the use of Diamondlike Carbon (DLC) interlayers between the substrate and the graphene is shown to offer the prospect of overcoming this problem. The DLC films used here, more properly known as a-C:H with ~25% hydrogen content, have been terminated with N or F moieties prior to graphene deposition. It is found that nitrogen terminations lead to an optical band gap shrinkage in the DLC, whilst fluorine groups reduce the DLC's surface energy. CVD monolayer graphene subsequently transferred to DLC, N terminated DLC, and F terminated DLC has then been studied with AFM, Raman and XPS analysis, and correlated with Hall effect measurements that give an insight into the heterostructures electrical properties. The results show that different terminations strongly affect the electronic properties of the graphene heterostructures. G-F-DLC samples were p-type and displayed considerably higher mobility than the other heterostructures, whilst G-N-DLC samples supported higher carrier densities, being almost metallic in character. Since it would be possible to locally pattern the distribution of these differing surface terminations, this work offers the prospect for 2D lateral control of the electronic properties of graphene layers for device applications. [ABSTRACT FROM AUTHOR]