This paper introduces the infrared crystal phase, and provides unconventional mechanistic insights into the commonly thought “crystal phase”. The critical challenge with the obtained fidelity phase is the unwrapping process, which was addressed using self-attention mechanism deep learning infrared-band holography. This method strikes a balance between the theoretical rigor of physical models and the flexibility of data-driven approaches. Specifically, utilizing a short wavelength infrared digital holographic system and algorithm resulted in the acquisition of high-quality wrapped phases. Then, the network architecture was applied for phase unwrapping. Through demonstrative applications, static phase-type thickness variation was measured in samples. During moments of intense phase transitions, the microstructural evolution of Na₂CO₃ crystals was monitored, and the process of perovskite material film formation was observed. The results demonstrated that environmental detection noise and twin images were effectively suppressed, and phase values were also dramatically varied after stabilization of the traditional amplitude signal. These discoveries guide the characterization of novel materials and also provide insights into alterations of properties during crystal preparation and growth, which is crucial for the final outcome.

Numerous studies employ multi-scale decomposition to improve the prediction performance of neural networks, but the grounds for selecting the decomposition algorithm are not explained, and the effects of decomposition algorithms on other performance of neural networks are also lacking further study. This paper studies the influence of commonly used multi-scale decomposition algorithms including EMD (Empirical Mode Decomposition), EEMD(Ensemble Empirical Mode Decomposition), CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise), VMD (Variational Mode Decomposition), WD (Wavelet Decomposition), and WPD (Wavelet Packet Decomposition) on the performance of Neural Networks. Decomposition algorithms are adopted to decompose traffic flow data into component signals, and then K-means is used to cluster component signals into volatility components, periodic components, and residual components. A Bi-directional LSTM (BiLSTM) neural network is adopted as the standard model for training and forecasting. Finally, three metrics, including prediction performance, robustness, and generalization performance are proposed to evaluate the influence of the multi-scale decomposition algorithm for neural networks comprehensively. By comparing the evaluation results of different hybrid models, this study provides some useful suggestions on proper multi-scale decomposition algorithm selection in short-time traffic flow prediction.