Coherent illumination of an optically rough surface creates random phase variations in the reflected electric field. Free-space propagation converts these phase variations into irradiance variations in both the pupil and image planes, known as pupil- and image-plane speckle. Infrared imaging systems are often parameterized by the quantity Fλ/d, which relates the cutoff frequencies passed by the optical diffraction MTF to the frequencies passed by the detector MTF. We present both analytical expressions and Monte-Carlo wave-optics simulations to determine the relationship between image-plane speckle contrast and the first-order system parameters utilized in Fλ/d (focal length, aperture size, wavelength, and detector size). For designers of active imaging systems, we provide input on speckle mitigation using Fλ/d to consider in system design.

Digital image correlation (DIC) measurement methods are widely accepted in the field of aeroengine blade measurements. However, there are many factors affecting the accuracy of DIC in engine blade high-temperature measurements, such as thermal radiation and thermal disturbance. To solve these problems, we propose an image preprocessing method to improve the accuracy of high-temperature DIC measurements. A series of high-temperature experiments are performed. The experimental results show that the proposed method can effectively eliminate the influence of thermal radiation and thermal disturbance caused by the high-temperature environment. The method reduces the displacement error, which can be eliminated by ∼70%. Experimental results verified the effectiveness of the proposed method.

The fastener plays a critical role in ensuring the stability and safety of modern engineering structures. However, factors such as vibration, shock, temperature changes, and others can cause fasteners to become loose, thereby compromising the stability and safety of the overall structure. Currently, fastener inspection is primarily done manually, and there is no widely adopted automatic method for real-time detection of fastener looseness. We proposed a fastener looseness inspection method based on digital shearing speckle pattern interferometry (DSSPI) and convolutional neural network (CNN). The key distinction between our proposed method and traditional vision-based methods is that our approach can identify loose fasteners without relying on direct visual observation of the fasteners themselves. Our approach involved setting up a DSSPI system to capture shearing speckle patterns, and the CNN model automatically extracted features from these images and classified them to detect loose fasteners. Principle-validation experiments were conducted to verify the feasibility of the methodology. The trained model achieved a classification accuracy of 92.57%, with a resolution of 0.04 mm and a completion time of only 2.03 ms for a single judgment. That is quite accurate, more sensitive, and much faster than the three-dimensional vision-based method. Furthermore, our proposed method has strong robustness to ambient light, while the incorporation of Random Erasing technology enhances robustness to part occlusion or damage of the measured surface. Most notably, our method can detect loose fasteners before deformation, a capability that conventional vision-based methods lack. These findings demonstrate the tremendous potential of our method for real-time inspection of fastener looseness.

We report the development and working of a compact rubidium atom magneto-optical trap (MOT) operated with a hollow pyramidal mirror and a single laser beam. This type of compact MOT is suitable for developing portable atom-optic devices as it works with a smaller number of optical components compared with the conventional MOT setup. The application of this compact MOT setup for pressure sensing has been demonstrated.

In millimeter-wave imaging systems, the inversion efficiency of both the narrowband and broadband systems is affected by the target’s distribution distance. The mismatched distances cause inefficiency of the system, and the recovered images suffer from displacement blur. We analyze in detail the reasons for this phenomenon and propose a simple method to overcome it. Minimizing the sharpness metric of images is a key strategy to solve this problem. Using this method, the exact range information of the target can be obtained from the minimum sharpness of the unknown object, and then, the region of interest of the inversion can be located. Both synthetic and full-wave simulation data demonstrate the superiority of the method. Finally, the efficiency of the method is verified by the in-house developed test system.

For a fringe projection profilometry, the projector calibration is the key step to ensure the measurement accuracy of the system. However, conventional methods for the projector calibration are usually involved a complex system model and a calibration process. Therefore, an approach for accurate projector calibration based on the Grey Wolf Optimization (GWO) algorithm and the multi-layer perceptron (MLP) is proposed to simplify the process and improve the generalization. In this method, in order to compensate the errors of projected image coordinates of the calibrated corners obtained by using the projective transformation matrix, the parameters of the MLP network are optimized by using the GWO to construct the GWO-MLP network. Then, it is trained to compensate the errors of the coordinates of characteristic corners and is available to calibrate the projector. The experiments verify the feasibility and effectiveness of the proposed method.

In intelligent transportation systems, distributed acoustic sensing offers unparalleled advantages in monitoring and analyzing vehicle characteristics and behaviors in real time over the entire optical fiber. In this work, an accurate and efficient φ-optical time domain reflectometer-based load recognition method for light and heavy loaded vehicles is proposed. Before load recognition, wavelet denoising and 1D-mean filtering methods are used to denoise the signals; then the Mel spectrograms of the signals are extracted as the features input to the load recognition model with a backbone of EfficientNet convolutional neural network. The validation results show that, using an ∼47 km sensing optical fiber, the recognition of light and heavy loaded vehicles can well meet the needs of real-time data analysis and decision making of intelligent transportation, with an average recognition accuracy of 97.81% within 14 ms for each recognition.

Extended Kalman filter (EKF) is a popular method to address the fast rotation of the state of polarization and large polarization mode dispersion in coherent optical communication. But it is usually underperforming in high-order modulation. Radius-directed idea is introduced to enhance the performance of EKF-based method. We call the proposed scheme radius-directed extended Kalman filter (RD-EKF). The performance of the proposed Kalman scheme is verified both in the polarization-division multiplexed 16QAM and 64QAM system. By the results, the RD-EKF scheme has better performance and less complexity than conventional EKF method in the high modulation format system.