High-speed surface-property recognition by 140-GHz frequency

In the field of integrated sensing and communication, there's a growing need for advanced environmental perception. The terahertz (THz) frequency band, significant for ultra-high-speed data connections, shows promise in environmental sensing, particularly in detecting surface textures crucial for autonomous system's decision-making. However, traditional numerical methods for parameter estimation in these environments struggle with accuracy, speed, and stability, especially in high-speed scenarios like vehicle-to-everything communications. This study introduces a deep learning approach for identifying surface roughness using a 140-GHz setup tailored for high-speed conditions. A high-speed data acquisition system was developed to mimic real-world scenarios, and a diverse set of rough surface samples was collected for realistic high-speed datasets to train the models. The model was trained and validated in three challenging scenarios: random occlusions, sparse data, and narrow-angle observations. The results demonstrate the method's effectiveness in high-speed conditions, suggesting terahertz frequencies' potential in future sensing and communication applications.