Aiming at the problems of large number of model parameters, large computational cost and low accuracy when using deep learning algorithms for pointer instrument recognition task, an intelligent detection and recognition system of circular pointer instrument based on the combination of improved pre-trained MobileNetV2 network model and circular Hough transform was proposed. Firstly, the Hough transform was used to solve the interference problem of non-circular areas in complex scene. Then, the circular areas were extracted to construct datasets. Finally, the circular pointer instrument recognition was realized by using the improved pre-trained MobileNetV2 network model. The average confusion matrix was used to measure the performance of the proposed model. Experimental results show that, the recognition rate of the proposed system in the recognition task of circular pointer instruments reaches 99.76%. At the same time, the results of comparing the proposed model with other five different network models show that the proposed model and ResNet50 both have the highest accuracy, but compared with ResNet50, the proposed network model has the model parameter number and model computational cost reduced by 90.51% and 92.40% respectively, verifying that the proposed model is helpful for the further deployment and implementation of industrial grade real-time circular pointer instrument detection and recognition in mobile terminals or embedded devices.

In order to solve the real-time issue of burst data in traffic monitoring, an Medium Access Control (MAC) protocol based on fuzzy clustering called FC-MAC was proposed. This protocol works by alternating between Time Division Multiple Access (TDMA) and improved Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA), which not only ensures periodic data transmission, but also enhances the real-time property of burst data. The method of fuzzy clustering was introduced to the phase of CSMA/CA, the nodes in a cluster were clustered according to factor vectors and assigned with different levels of priority according to their vectors, and burst data with higher priority was transmitted earlier. In addition, according to the timing slot allocation strategy of FC-MAC, a method of hierarchical random delay was presented to reduce the number of nodes accessing to sink node simultaneously and decrease the data delay caused by backoff mechanism. Simulation results show that the energy consumption of FC-MAC is between Z-MAC and S-LMAC. In the case of reducing delay of burst data, FC-MAC increases the network throughput by 11.2% and 21.3% respectively compared to Z-MAC and S-LMAC, and it is more adaptive to the change of network traffic.