Hydrogen-terminated diamond surfaces, emerging as a promising two-dimensional (2D) electron platform with great thermal and electronic properties, hold great potential for the next-generation high power and high frequency field effect transistor (FET). However, ideal gate dielectrics with high crystallinity and defect-free surfaces are still largely elusive. In this work, using the contamination-free pickup transfer method, hexagonal boron nitride (h-BN) flakes were fabricated on top of the hydrogen-terminated diamond surface to serve as a gate material and the passivation layer. The morphological and optical characterizations revealed the formation of homogeneous and intimate interface between h-BN and diamond. Benefiting from the h-BN gate dielectric layer, the maximum drain current density, subthreshold swing, and on/off ratio of diamond FET are measured to be −210.3 mA mm−1, 161 mV/dec, and 106, respectively. Especially, the transport measurement shows an almost constant Hall mobility of around 260 cm2 V−1 s−1 in the hole density range of 2 − 6 × 1012 cm−2, suggesting the excellent gate controllability of h-BN. Our results indicate that h-BN could form high-quality interface with hydrogen-terminated diamond, paving the way for the development of diamond-based electronic applications.

With the advances of deep learning research in the past few years, healthcare and smart medicines have been significantly developed. Inspired by the wide application of deep learning in medical image classification and disease diagnosis, this paper further proposes a variant of the Residual Network framework to classify the oral ulcer images in real‐time. In particular, image pre‐processing and enhancement techniques are used to enrich the datasets and reduce model overfitting. Besides, the transfer learning is further introduced into the residual blocks to improve the classification accuracy, with the later layers trained from the labeled datasets. To validate the performance of authors' proposal, it is compared with other classic deep learning models with respect to the classification sensitivity, specificity, and accuracy. The experimental results show that authors' approach outperforms those classic classification networks when the oral ulcers are classified and diagnosed in real‐time.

Toll stations are bottlenecks in the traffic flow of expressways, and the evaluation of their capacity is essential for the operation of the expressway. Traditional capacity studies are mostly based on theoretical modelling of traffic engineering or simulation, with a focus on parameter tuning and idealized hypotheses, resulting in poor reliability. In view of the coexistence of electronic toll collection lanes and compound toll collection lanes at toll stations of expressways in China, the present study analyses the capacity of entrance and exit lanes of toll stations under mixed traffic conditions using a real toll data-driven approach. Firstly, the service time of a single vehicle during the saturation period was taken as the starting point for the capacity estimates. Secondly, the variation in service time for multiple categories of vehicles is modelled using lognormal distribution. Finally, the capacity of the two types of toll lanes at the designated toll station is determined. The important outcome of this study is the calculation of authentic capacity at the toll stations and the discussion of individual special toll lanes. Accordingly, it contributes to the development of appropriate policies to manage the operation of the toll plaza effectively.

Sensor ontologies formally model the core concepts in the sensor domain and their relationships, which facilitates the trusted communication and collaboration of Artificial Intelligence of Things (AIoT). However, due to the subjectivity of the ontology building process, sensor ontologies might be defined by different terms, leading to the problem of heterogeneity. In order to integrate the knowledge of two heterogeneous sensor ontologies, it is necessary to determine the correspondence between two heterogeneous concepts, which is the so-called ontology matching. Recently, more and more neural networks have been considered as an effective approach to address the ontology heterogeneity problem, but they require a large number of manually labelled training samples to train the network, which poses an open challenge. In order to improve the quality of the sensor ontology alignment, an unsupervised neural network model is proposed in this work. It first models the ontology matching problem as a binary classification problem, and then uses a competitive learning strategy to efficiently cluster the ontologies to be matched, which does not require the labelled training samples. The experiment utilizes the benchmark track provided by the Ontology Alignment Evaluation Initiative (OAEI) and multiple real sensor ontology alignment tasks to test our proposal’s performance. The experimental results show that the proposed approach is able to determine higher quality alignment results compared to other matching strategies under different domain knowledge such as bibliographic and real sensor ontologies.

Cycling is a popular sport, however, many enthusiasts can't go outside for the weather and traffic accidents. In order to solve this problem, we propose a virtual reality cycling system with multi-sensor fusion. Hall sensors, gyroscopes and other sensors are used to capture the cycling speed and rotation angle. Using Bluetooth to exchange realtime data with the mobile virtual reality scene, we can monitor the movement consumption and the speed and distance of the bicycle in real time. In order to simulate the natural wind effect during cycling and enhance the cycling experience, the real time speed of the bicycle is used to drive the motor to generate the natural wind from the fan. Finally, the virtual simulation scene of bicycle riding is built through the virtual reality environment of mobile phone, and the indoor VR cycling effect is realized.