Device-free passive intrusion detection is a promising technology to determine whether moving subjects are present without deploying any specific sensors or devices in the area of interest. With the rapid development of wireless technology, multi-input multi-output (MIMO) and orthogonal frequency-division multiplexing (OFDM) which were originally exploited to improve the stability and bandwidth of Wi-Fi communication, can now support extensive applications such as indoor intrusion detection, patient monitoring, and healthcare monitoring for the elderly. At present, most research works use channel state information (CSI) in the IEEE 802.11n standard to analyze signals and select features. However, there are very limited studies on intrusion detection in real home environments that consider scenarios that include different motion speeds, different numbers of intruders, varying locations of devices, and whether people are present sleeping at home. In this paper, we propose an adaptive real-time indoor intrusion detection system using subcarrier correlation-based features based on the characteristics of narrow frequency spacing of adjacent subcarriers. We propose a link-pair selection algorithm for choosing an optimal link pair as a baseline for subsequent CSI processing. We prototype our system on commercial Wi-Fi devices and compare the overall performance with those of state-of-the-art approaches. The experimental results demonstrate that our system achieves impressive performance regardless of intruder’s motion speeds, number of intruders, non-line-of-sight conditions, and sleeping occupant conditions.

During on‐site teaching for university students, the level of concentration of every student is an important indicator for the evaluation of teaching quality. Traditionally, teachers rely on subjective methods for observing students’ learning status. Due to the volume of on‐site crowds, teachers are unable to stay on top of the learning status of each student. Meanwhile, because of the subjective evaluation, the results would not be precise. With the fast development of artificial intelligence and machine learning, it is possible to adopt deep learning technology to achieve scientific evaluation of the classroom teaching quality. This paper proposes an integrated evaluation model based on deep learning technology, incorporating YOLOX model, Retinaface model, and SCN model. Among which, YOLOX model is used to detect the area of the students’ upper body, Retinaface model is adopted to assess the head‐up rate, and SCN model is used to recognize the facial expression. The experimental results have shown that our model can achieve 93.1% object detection accuracy, more than 85% face recognition accuracy, and 87.39% expression recognition accuracy. We further develop a model to use the combination of head‐up rate and facial expression scores to jointly evaluate classroom teaching quality. Five teaching professors’ evaluations of our classroom video images confirmed that our proposed model is effective in objectively evaluating the on‐site teaching quality.

This study aims to investigate innovative mobile virtual reality (VR) interaction designs, using affordable VR devices, for enhancing the learning of computer science concepts and theories. Smartphones have been instrumental in facilitating both augmented reality (AR) and fully immersive VR experiences with low-cost platforms. A diverse range of mobile apps empowers users to engage in AR experiences, allowing them to view virtual elements on their smartphone displays and interact with them within their real-world surroundings. On the other hand, fully immersive mobile VR experiences necessitate a VR viewer device, like Google Cardboard, equipped with a smartphone slot for enhanced immersion. This cost-effective VR platform can be operated with a single button press for interaction controls, which enable users to interact with its virtual objects presented in the immersive 3D view. However, to facilitate more complex interactions, a challenge we must address in the development of smartphone-based VR applications is the limited control options they provide. Without additional controllers, mobile VR applications rely on the movements of its head-mounted device and a single available button for user input. To overcome these control limitations, we proposed implementing a combination of head pointing, dwell selection, and various button operations, including button hold, double-button click, and triple-button click. We implemented and showcased these functions within an educational VR game focused on practicing the minimum spanning tree algorithm. We aim to design innovative mobile VR interactions to facilitate effective learning of abstract computer science concepts and theories.