Simulation of gradient period polarization volume gratings for augmented reality displays.

Augmented reality (AR) displays are gaining attention as next-generation intelligent display technologies. Diffractive waveguide technologies are progressively becoming the AR display industry's preferred option. Gradient period polarization volume holographic gratings (PVGs), which are considered to have the potential to expand the field of view (FOV) of waveguide display systems due to their wide bandwidth diffraction characteristics, have been proposed as coupling elements for diffraction waveguide systems in recent years. Here, what we believe to be a novel modeling method for gradient period PVGs is proposed by incorporating grating stacking and scattering analysis utilizing rigorous coupled-wave analysis (RCWA) theory. The diffraction efficiency and polarization response were extensively explored using this simulation model. In addition, a dual-layer full-color diffractive waveguide imaging simulation using proposed gradient period PVGs is accomplished in Zemax software using a self-compiled dynamic link library (DLL), achieving a 53° diagonal FOV at a 16:9 aspect ratio. This work furthers the development of PVGs by providing unique ideas for the field of view design of AR display.

Construction of classification models for pathogenic bacteria based on LIBS combined with different machine learning algorithms.

Bacteria, especially foodborne pathogens, seriously threaten human life and health. Rapid discrimination techniques for foodborne pathogens are still urgently needed. At present, laser-induced breakdown spectroscopy (LIBS), combined with machine learning algorithms, is seen as fast recognition technology for pathogenic bacteria. However, there is still a lack of research on evaluating the differences between different bacterial classification models. In this work, five species of foodborne pathogens were analyzed via LIBS; then, the preprocessing effect of five filtering methods was compared to improve accuracy. The preprocessed spectral data were further analyzed with a support vector machine (SVM), a backpropagation neural network (BP), and k-nearest neighbor (KNN). Upon comparing the capacity of the three algorithms to classify pathogenic bacteria, the most suitable one was selected. The signal-to-noise ratio and mean square error of the spectral data after applying a Savitzky-Golay filter reached 17.4540 and 0.0020, respectively. The SVM algorithm, BP algorithm, and KNN algorithm attained the highest classification accuracy for pathogenic bacteria, reaching 98%, 97%, and 96%, respectively. The results indicate that, with the support of a machine learning algorithm, LIBS technology demonstrates superior performance, and the combination of the two is expected to be a powerful tool for pathogen classification.