Conventional capacitive touchscreens in mobile devices often malfunction in the presence of moisture, dust, or gloved hands, leading to unreliable user interactions. To overcome these limitations, we present TouchAI, a multi-modal touch detection and localization system that leverages built-in inertial sensors and audio interfaces. Running as a background process, TouchAI constantly monitors inertial measurements for potential touch input and then activates acoustic sensing only upon detected events, thus reducing power consumption and privacy concerns associated with continuous audio recording. We introduce a novel event identification method that distinguishes touch-start and touch-end instances, providing precise timing for data sampling. For fine-grained touch localization, TouchAI employs a lightweight Transformer-based classification model to capture spatiotemporal features from combined acoustic-inertial signals. Experiments on commercial smartphones demonstrate true positive rates of 95.3% and 99.1% for touch detection and event identification, respectively, at false positive rates only less than 3%. Ultimately, TouchAI achieves up to 97.0% and 86.5% localization accuracy on 4×2 and 6×3 touch input grids, respectively, confirming its practicality and effectiveness as an alternative interface under touchscreen malfunction scenarios.

High accuracy and simultaneous positioning is an essential demand in future Intelligent Transportation Systems (ITS), while the mobility and dynamics of vehicles place great challenges. Single Base Station (BS) positioning has become popular for its fast speed, high convenience, and low cost. With the construction of 5G, the wide bandwidth and high separation capability of millimeter Wave (mmWave) bring more possibilities for vehicle positioning via single BS. However, mmWave signals have high distance attenuation and are easily blocked by obstacles. In urban scenarios, the prevalent None-Line-of-Sight (NLoS) situations have severe impacts on positioning accuracy. The multipath effects, Doppler effects, and tracking lags further degrade the performance. To address these issues, we introduce the Intelligent Reflecting Surface (IRS) to single BS vehicle positioning for beyond Line-of-Sight (LoS) communications. We study the advantages of IRS in urban ITS to alleviate the multipath effects, Doppler effects, and tracking delay. To realize the real-time target tracking for IRS, the Unscented Kalman Filter (UKF) is adopted, for which stable communications between the BS and moving vehicle can be maintained. Simulation results show that the utilization of IRS can significantly improve the positioning accuracy and the adoption of UKF further enhances the performance.

The Internet of Things (IoT) Network brings more possibilities and connections to indoor scenarios. With the growing number of mobile devices and online services, indoor wireless networks are required to achieve higher speed and capacity for carrying the exponentially increasing amount of data. The Tera-hertz (THz) communication, with its large spectrum resources, is considered to be the main technology to achieve ultra-high-speed wireless communications in the 6G era. However, THz signals are severely attenuated over distances and the transmission of narrow-beam signals in THz is severely affected by the None-Line-of-Sight (NLoS) environment. To tackle this indoor transmission issue, Intelligent Reflecting Surface (IRS) attracts attention with its beyond Line-of-Sight (LoS) ability and easy deployment. However, an IRS can only serve a limited number of users and requires proper allocation due to the constraints on hardware. The transmission performance is also severely affected by the interference among sub-channels. In this paper, we study IRS-aided data offloading for indoor THz networks. Considering the user interference, transmission conditions, and diversified tasks, we optimize the allocation of limited IRS resource as well as partition planning. A dynamic algorithm based on combina-torial optimization is proposed. Simulation results reveal that our proposed approach can significantly improve the offloading performance.