Wi-Fi-based person identification (PI) tasks are performed by analyzing the fluctuating characteristics of the Channel State Information (CSI) data to determine whether the person's identity is legitimate. This technology can be used for intrusion detection and keyless access to restricted areas. However, the related research rarely considers the restricted computing resources and the complexity of real-world environments, resulting in lacking practicality in some scenarios, such as intrusion detection tasks in remote substations without public network coverage. In this paper, we propose a novel neural network model named SimpleViTFi, a lightweight classification model based on Vision Transformer (ViT), which adds a downsampling mechanism, a distinctive patch embedding method and learnable positional embedding to the cropped ViT architecture. We employ the latest IEEE 802.11ac 80MHz CSI dataset provided by [1]. The CSI matrix is abstracted into a special “image” after pre-processing and fed into the trained SimpleViTFi for classification. The experimental results demonstrate that the proposed SimpleViTFi has lower computational resource overhead and better accuracy than traditional classification models, reflecting the robustness on LOS or NLOS CSI data generated by different Tx-Rx devices and acquired by different monitors.

This paper describes the evaluation of a fiber-optic reflective displacement sensor that is compensated for variations in light source intensity, pressure, temperature and opacity of ambient medium. Additionally, the distance information is averaged over several points on the target surface, which reduces signal fluctuations due to inhomogeneities. Furthermore, a practical optical fiber reflective sensor model of measuring oil film thickness for thrust bearing is set up in this paper. Actual measurements were made with HEC 3000 tons' thrust bearing and the results were in good agreement with theoretical calculations.

At work, at home, and in some public places, a desktop PC is usually available nowadays. Therefore, it is important for users to be able to play various videos on different PCs smoothly, but the diversity of codec types complicates the situation. Although some mainstream media players can try to download the needed codec automatically, this may fail for average users because installing the codec usually requires administrator privileges to complete, while the user may not be the owner of the PC. We believe an ideal solution should work without users' intervention, and need no special privileges. This paper proposes such a user-friendly, program-transparent solution for Windows-based media players. It runs the media player in a user-mode virtualization environment, and then downloads the needed codec on-the-fly. Because of API (Application Programming Interface) interception, some resource-accessing API calls from the player will be redirected to the downloaded codec resources. Then from the viewpoint of the player, the necessary codec exists locally and it can handle the video smoothly, although neither system registry nor system folders was modified during this process. Besides convenience, the principle of least privilege is maintained and the host system is left clean. This paper completely analyzes the technical issues and presents such a prototype which can work with DirectShow-compatible players. Performance tests show that the overhead is negligible. Moreover, our solution conforms to the Software-As-A-Service (SaaS) mode, which is very promising in the Internet era.