Photodynamic therapy (PDT) is considered a promising noninvasive therapeutic strategy in biomedicine, especially by utilizing low-level laser therapy (LLLT) in visible and near-infrared spectra to trigger biological responses. The major challenge of PDT in applications is the complicated and time-consuming biological methodological measurements in identification of light formulas for different diseases. Here, we demonstrate a rapid and label-free identification method based on artificial intelligence (AI)-assisted terahertz imaging for efficient light formulas in LLLT of acute lung injury (ALI). The gray histogram of terahertz images is developed as the biophysical characteristics to identify the therapeutic effect. Label-free terahertz imaging is sequentially performed using rapid super-resolution imaging reconstruction and automatic identification algorithm based on a voting classifier. The results indicate that the therapeutic effect of LLLT with different light wavelengths and irradiation times for ALI can be identified using this method with a high accuracy of 91.22% in 33 s, which is more than 400 times faster than the biological methodology and more than 200 times faster than the scanning terahertz imaging technology. It may serve as a new tool for the development and application of PDT.

Electrical impedance tomography (EIT) has been applied in the field of human-computer interaction due to its advantages including the fact that it is non-invasive and has both low power consumption and a low cost. Previous work has focused on static gesture recognition based on EIT. Compared with static gestures, dynamic gestures are more informative and can achieve more functions in human-machine collaboration. In order to verify the feasibility of dynamic gesture recognition based on EIT, a traditional excitation drive pattern is optimized in this paper. The drive pattern of the fixed excitation electrode is tested for the first time to simplify the measurement process of the dynamic gesture. To improve the recognition accuracy of the dynamic gestures, a dual-channel feature extraction network combining a convolutional neural network (CNN) and gated recurrent unit (GRU), namely CG-SVM, is proposed. The new center distance loss is designed in order to simultaneously supervise the intra-class distance and inter-class distance. As a result, the discriminability of the confusing data is improved. With the new excitation drive pattern and classification network, the recognition accuracy of different interference data has increased by 2.7~14.2%. The new method has stronger robustness, and realizes the dynamic gesture recognition based on EIT for the first time.

Terahertz (THz) photonic crystal (PC) waveguides show promise as an efficient and versatile waveguiding platform for communication, sensing, and imaging. However, low-frequency THz PC waveguides with a low-cost and easy fabrication remain challenging. To address this issue, a THz PC waveguide with a lilac-shaped defect has been designed and fabricated by 3D printing based on stereolithography (SLA). The reflection and transmission characteristics of the proposed waveguide have been analyzed using the finite difference frequency domain (FDFD) method. The waveguide spectral response is further optimized by changing the distance of the lilac-shaped resonant cavities. Consistent with the results of numerical modeling, the measured results show that the waveguide performs a resonant reflection in the region of 0.2 to 0.3 THz and low-pass transmission in the 6G mobile communication window. Furthermore, in order to characterize the performance of the proposed waveguide, parameters have been analyzed, including the Q factor, resonant frequency, and bandwidth. This work supplies a novel pathway for the design and fabrication of a low-frequency THz PC waveguide with potential applications in communication, sensing, and imaging.