In response to challenges in traditional offline experiments in optical physics, such as high trial-and-error costs, expensive equipment, limited design parameters, and restricted explanations of instrument structures and principles, this paper proposes a design method for an optical physics virtual simulation system based on Unreal Engine 5. The method employs a generic design architecture to partition system units and utilizes technologies like pixel streaming, rendering engines, and physics engines to implement the design of the virtual simulation system. This system covers the entire experimental process, including video learning, instrument adjustment, phenomenon observation, data measurement and recording, and grade inquiry. The proposed design method enhances the immersive experience of experiments, reduces the hardware performance requirements for users, and allows access and utilization across multiple platforms. It efficiently facilitates physics teaching through simulated experiments, thereby effectively improving the quality of experimental teaching. Finally, through a comparison with existing virtual simulation systems, it is demonstrated that the system can save approximately 94.76% of the average CPU usage and provide better immersion and user experience advantages.

In this paper, we propose an advanced three-dimensional visualization method for an integral imaging microscope system to simultaneously improve the resolution and quality of the reconstructed image. The main advance of the proposed method is that it generates a high-quality three-dimensional model without limitation of resolution by combining the high-resolution two-dimensional color image with depth data obtained through a fully convolutional neural network. First, the high-resolution two-dimensional image and an elemental image array for a specimen are captured, and the orthographic-view image is reconstructed from the elemental image array. Then, via a convolutional neural network-based depth estimation after the brightness of input images are uniformed, a more accurate and improved depth image is generated; and the noise of result depth image is filtered. Subsequently, the estimated depth data is combined with the high-resolution two-dimensional image and transformed into a high-quality three-dimensional model. In the experiment, it was confirmed that the displayed high-quality three-dimensional model could be visualized very similarly to the original image.

Real objects-based full-color holographic display systems usually collect data with a depth camera and then modulate the input light source to reconstruct the color three-dimensional scene of the real object. However, at present, the main problems of the real-time high quality full-color 3D display are slow speed, low reconstruction quality, and high consumption of hardware resources caused by excessive computing. Based on the hybrid Taylor Rayleigh–Sommerfeld diffraction algorithm and previous studies on full-color holographic systems, our paper proposes Taylor Rayleigh–Sommerfeld diffraction point cloud grid algorithm (TR-PCG), which is to perform Taylor expansion on the radial value of Rayleigh–Sommerfeld diffraction in the hologram generation stage and modify the data type to effectively accelerate the calculation speed and ensure the reconstruction quality. Compared with the wave-front recording plane, traditional point cloud gridding (PCG), C-PCG, and Rayleigh–Sommerfeld PCG without Taylor expansion, the computational complexity is significantly reduced. We demonstrate the feasibility of the proposed method through experiments.

Diurnal Variation of Seasonal Precipitation over the CONUS: A Comparison of Gauge Observations with TRMM Data: Diurnal variation of precipitation is a fundamental periodic signal of local climate. Comprehensive study of diurnal variation of precipitation is helpful in studying the formation of local climate and validating satellite precipitation products. In this study, a comparison is drawn between precipitation gauge observations and Tropical Rainfall Measuring Mission (TRMM) 3B42 data on diurnal variation of precipitation. First, using the K-means clustering algorithm, stations with gauge observations and pixels with TRMM data are divided into different groups according to the diurnal variation of precipitation, respectively. In each group, the stations have similar diurnal variation of precipitation. Then maps of diurnal variation of precipitation for gauge observations and TRMM data are obtained. According to these maps, the diurnal variation of precipitation over the contiguous United States (CONUS) presents seasonal variability in both gauge observations and TRMM data. In addition, the diurnal variation of precipitation shows clustered features in space. However, the spatial patterns of the obtained maps do not match, and the TRMM satellite data perform poorly in capturing the hourly precipitation event. Finally, the possible mechanism behind the prevailing nocturnal precipitation over the middle of the CONUS is discussed, with the prevailing nocturnal precipitation judged likely to be strongly related to the mountain-plains solenoid (MPS) circulation.

In this study, we proposed a holographic identity verification encryption system that integrates face recognition, air-writing, and the multiple point cloud gridding encryption (M-PCGE) method to ensure multi-level security for objects. The experimental results show that the M-PCGE algorithm proposed in this paper achieves image encryption and decryption quickly with a high degree of restoration, and the security is verified.