The interoperations of endpoint devices are generally achieved by gateways in Internet of Things (IoT) systems. However, the gateways mainly focus on networking communication, which is lack of data logic control capabilities. The microcontrollers with embedded intelligence could work as an intermediate device to help the interconnections of the endpoint devices. Moreover, they could help control the endpoint devices. In this paper, a microcontroller architecture with intelligent and scalable characteristics is proposed. The intelligence means that the microcontroller could control the target endpoint devices by its logical circuits, and the scalability means that the microcontroller architecture could be easily extended to deal with more complex problems. Two real world industrial implementations of the proposed architecture are introduced. The implementations show that the microcontroller is important to provide the intelligent services to users in IoT systems. Furthermore, a simulation experiment based on the cloud model is designed to evaluate the proposed method. The experimental results demonstrate the effectiveness of the proposed architecture.

Existing studies on autonomous driving methods focus on the fusion of onboard sensor data. However, the driving behavior might be unsteady because of the uncertainties of environments. In this article, an expectation line is proposed to quantify the driving behavior motivated by the driving continuity of human drivers. Furthermore, the smooth driving could be achieved by predicting the future trajectory of the expectation line. First, a convolutional neural network-based method is applied to detect lanes in images sampled from driving video. Second, the expectation line is defined to model driving behavior of an autonomous vehicle. Finally, the long short-term memory-based method is applied to the expectation line so that the future trajectory of the vehicle could be predicted. By incorporating convolutional neural network- and long short-term memory-based methods, the autonomous vehicles could smoothly drive because of the prior information. The proposed method is evaluated using driving video data, and the experimental results demonstrate that the proposed method outperforms methods without trajectory predictions.

Learning a language poses a significant challenge for learners, who must comprehend the intricacies of language and analyze the relationships among its components. While computer technologies have been developed to assist language learning, they fail to mirror the human cognitive process. This paper examines the application of a multimodal dialogue system to enhance language learning outcomes. The system boasts several advantages. Firstly, smart devices can collect multimodal data in learning environments to monitor the learner’s status in real-time, thus enhancing the accuracy of intention recognition. Secondly, the system can interact with learners naturally by analyzing their multimodal data, resulting in improved language skills. Finally, application scenarios are designed based on the defined multimodal dialogue system, which effectively demonstrates the system’s ability to enhance language learning performance.