Dam surface displacement measurement can monitor the safety of dam operation and provide early warning for dam accidents. This paper proposes a method for measuring the displacement of the dam surface based on double vision sensors. Mathematical morphology is combined with the Roberts edge detection operator to complete the sub-pixel extraction of the marker edge. The method makes the edge of the marker in the image smoother compared with the Zernike moment sub-pixel edge detection algorithm. The edge gray center of gravity method is proposed to realize the positioning of the marker, which improves the precision of the vision measurement method. Additionally, this paper proposes the design of a test system and describes the experiments conducted in this study. The results revealed that the proposed method can achieve millimeter-level measurement accuracy and has the advantages of simple operation, short measurement period, online monitoring, and low cost, which satisfies the requirements for measuring the dam surface displacement.

Ground-penetrating radar (GPR) is an effective tool for subsurface detection. Due to the influence of the environment and equipment, the echoes of GPR contain significant noise. In order to suppress noise for GPR data, a method based on singular value decomposition (SVD) of a window-length-optimized Hankel matrix is proposed in this paper. First, SVD is applied to decompose the Hankel matrix of the original data, and the fourth root of the fourth central moment of singular values is used to optimize the window length of the Hankel matrix. Then, the difference spectrum of singular values is used to construct a threshold, which is used to distinguish between components of effective signals and components of noise. Finally, the Hankel matrix is reconstructed with singular values corresponding to effective signals to suppress noise, and the denoised data are recovered from the reconstructed Hankel matrix. The effectiveness of the proposed method is verified with both synthetic and field measurements. The experimental results show that the proposed method can effectively improve noise removal performance under different detection scenarios.

The performance of IR small object detection algorithms determines the detection capability and reliability of electrooptic tracking devices in complex environments. For IR images with sea–air backgrounds, reflections on surface waves increase background thermal noise that might occlude or alter small objects, complicating their detection. Additionally, clouds and waves complicate background texture, also reducing object detectability. In this study, we propose a new sea–air background IR (SAIR) detection model on the basis of the YOLOv8 network, called SAIR-YOLO, with three major improvements. First, an asymptotic multiscale feature fusion network gradually integrates different-scale features to mitigate the semantic gap between non-adjacent features while reducing the influence of sea–air background noise on feature representation. Second, a strengthened detection head discriminates irrelevant background features and focuses network attention on the objects. Third, a hybrid intersection-over-union (IoU) loss function improves detection performance, by focusing on shape similarities, and expands the effective regression range. Experimental results yield average SAIR-YOLO precisions of 80.2%, 84.4%, and 96.4% for three distinct datasets: a custom dataset and the SIRST-V2 and NUDT-SIRST publicly available datasets. This represents improvements of 7.0%, 4.9%, and 0.7%, respectively, on the YOLOv8 model.

We propose and experimentally demonstrate on-chip energy-efficient Gray code generation based on a scalable and compact optical encoder. The operation principle of the integrated encoder is based on the nonlinear effects in silicon opto-mechanical microring resonators (MRRs), which could be excited by ultra-low input powers. By adjusting the input powers with hierarchical power levels, the red-shifts of the MRR transmission spectrum could be flexibly manipulated. In this case, the output powers of the MRR drop-port and through-port could be controllable and switchable to generate different codes. Through tuning the optical encoding powers in a specific rule, 4-bit and 2-bit Gray codes have been successfully demonstrated. The required power of the proposed 4-bit encoder is as low as -6 dBm, which is favorable to build energy-efficient optical encoding systems. To the best of our knowledge, we have realized a nano-mechanical optical encoder with the lowest required power. Moreover, the encoder structure has great scalability, thus 2N-bit Gray code could be generated by utilizing 2N-1 add-drop MRRs. With the dominant advantages of ultra-low power consumption (-6 dBm), compact size (0.08 mm2) and great scalability, the proposed optical encoder has many significant applications in optical encoding and communication chips.

Images acquired using vision sensors are easily affected by environmental limitations, especially rain streaks. These streaks will seriously reduce image quality, which, in turn, reduces the accuracy of the algorithms that use the resulting images in vision sensor systems. In this paper, we proposed a method that combined the bilateral filter with the generative adversarial network to eliminate the interference of rain streaks. Unlike other methods that use all the information in an image as the input to the generative adversarial network, we used a bilateral filter to preprocess and separate the high frequency part of the original image. The generator for the high-frequency layer of the image was designed to generate an image with no rain streaks. The high-frequency information of the image was used in a high-frequency global discriminator designed to measure the authenticity of the generated image from multiple perspectives. We also designed a loss function based on the structural similarity index to further improve the effect of removal of the rain streaks. An ablation experiment proved the validity of the method. We also compared images in synthetic and real-world datasets. Our method could retain more image information, and the generated image was clearer.