This article is concerned with the problem of adaptive event-triggered tracking control for a class of uncertain stochastic nonlinear systems in strict-feedback form. First, with the help of fuzzy logic systems to approximate the unknown nonlinear functions, a robust fuzzy state observer is constructed to estimate all the unmeasurable states. Next, an adaptive output feedback controller, which can adjust the variables online is designed by using the backstepping scheme. Concomitantly, in order to reduce the computation of communication process, a new event triggering condition involving the decreasing function of tracking errors is introduced. Moreover, the desired closed-loop stability of the resulting systems can be achieved by exploiting Lyapunov function analysis. Finally, simulation results verify the effectiveness of the proposed method.

In complex field environments, traffic sign detection faces many challenges, such as the effects of light variations, occlusion and sensor resolution, which can lead to a decrease in detection accuracy. To cope with these problems, a multi-information attention fusion traffic sign detection network MIAF-Net is proposed. Firstly, a backbone network with linear transformations was designed to improve the efficiency and accuracy of feature extraction. Secondly, an attention balance feature pyramid network was designed to enhance the correlation between foreground features and surrounding semantics, refine and balance semantic features, and improve the expressive ability of feature maps by fusing and learning multi-scale information. Finally, a detection head with multi-scale information fusion is designed to provide different features for category prediction and boundary regression, increasing the reliability of traffic sign detection and classification. In the experimental part, three traffic sign datasets (TT100K, CCTSDB and DFG) were used to fully evaluate MIAF-Net and compare it with existing state-of-the-art traffic sign detection methods, the results show that MIAF-Net exhibits a very superior performance in the traffic sign detection task. In addition, in the edge device deployment experiments, MIAF-Net demonstrates its real-time performance and low memory access, which shows that the proposed method is not only superior in accuracy, but also has good deployment utility.