Fabry–Perot (F–P) tunable filter based on micro-electro-mechanical system (MEMS) was widely used in optical communication, laser, and optical imaging. At present, there is little research on F–P filters in the near-infrared band from 1 260 nm to 1 620 nm. Therefore, this paper designs a novel F–P filter based on MEMS. Three improved cantilevers beam circular bridge deck structures, including circular holes, V-shaped grooves, and square grooves, were analyzed through finite element simulation. The voltage-displacement, von Mises stress, and mirror flatness were obtained to select the optimal bridge deck structure. The results show that when different bridge decks reach the same displacement, the voltage required by the square grooves cantilever beam bridge deck is the smallest, and the von Mises stress and mirror flatness of the square grooves bridge deck structure can also meet the design requirements of the filter. Finally, the filtering performance of the optimized square grooves bridge deck structure filter is analyzed.

Fiber femtosecond optical frequency combs (OFCs) play a crucial role in achieving high-precision astronomical spectral calibration in the field of astronomy. However, OFCs generated by lasers are susceptible to disturbances from environmental factors and internal vibrations, leading to frequency drift and decreased stability. To address these, we develop a closed-loop servo control system utilizing error signals between the OFC and microwave frequency reference to stabilize the frequency. Then we design a remote-control component of the system, enabling real-time monitoring and precise control of the OFC. The results demonstrate that the system we designed not only achieves precise synchronization of the OFC’s carrier-envelope offset frequency with the microwave frequency reference, but also maintains long-term stability of the OFC, facilitating further advancements in high-precision astronomical spectral calibration light sources.

A temperature-insensitive micro-displacement sensor based on a spindle-shaped single mode fiber (SMF) is presented and demonstrated. The SMF is bent into a balloon-shaped SMF and burned into a spindle-shaped SMF by a flame. Due to the bending of the fiber, part of the incident light leaks from the fiber core into the fiber cladding, which excites higher order cladding modes. Therefore, modal interference occurs. The experimental results show that the maximum sensitivity of the sensors is −203 pm/µm when micro-displacement varies from 0 to 80 µm. The sensors are insensitive to temperature in the range of 20–70 °C. In addition, the sensors have the advantages of simple structure, low cost, small volume, high sensitivity and stability, which can be widely used in numerous high-precision industrial measurements and civil engineering structure monitoring fields.

Cesium lead iodide (CsPbI₃) is widely employed as the absorber material for perovskite solar cells (PSC) with its excellent photothermal stability. Here, an electron transport layer (ETL)-free CsPbI₃ PSC was modeled using the solar cell capacitance simulator (SCAPS) program. The simulation involves a series of parameters optimization, including the thickness, doping concentration, defect density and permittivity of the fluorine-doped tin oxide (FTO) electrode, the hole transport layer (HTL), and the perovskite (PVK) layer. Additionally, the defect density at the FTO/PVK interface and the PVK/HTL interface were considered. The study revealed that the power conversion efficiency (PCE) of the device is significantly affected by variations in the parameters of the PVK layer, especially the thickness and defect density. Moreover, the defect density at the contact interfaces also notably influences the device efficiency. After systematic computational optimization, the best device exhibited an open-circuit voltage (V_OC) of 1.16 V, a short-circuit current (J_SC) of 21.52 mA/cm², a fill factor (FF) of 87.83%, and a PCE of 21.9%, which is close to the full-structured device reported in experiment, demonstrating the potential of all inorganic PSCs with simplified structures.

The end tidal carbon dioxide (EtCO₂) is crucial for monitoring patients respiratory function, which reflects the status of lung ventilation and gas exchange. Therefore, achieving accurate measurements of EtCO₂ holds significant importance in clinical practice. The measurements of EtCO₂ based on wavelength modulation-direct absorption spectroscopy (WM-DAS) had great advantages and the noise reduction of spectrum was very important. An optimized variational mode decomposition (VMD) algorithm improved by the dung beetle optimization algorithm and wavelet packet denoising algorithm was proposed to enhance the measurement accuracy of EtCO₂ concentration. The dung beetle optimization algorithm was used to obtain the optimal number of decomposition mode layers K and secondary penalty factor α. The optimal parameters were used to decompose the original transmitted light intensity signal with noise, and a series of intrinsic mode functions (IMFs) were obtained. Pearson correlation coefficient (R) was used to select the pure signal and the noisy signal, and the noisy signal was denoised by wavelet packet denoising algorithm. The transmitted light intensity signal was reconstructed by the signal processed by wavelet packet denoising algorithm and the pure signal. The results showed that the proposed algorithm could effectively remove the noise of signal of transmitted light intensity and improve the accuracy of concentration measurements of EtCO₂.

Multimode fibers (MMFs) have great potential for endoscopic imaging due to the high number of modes and small core diameter. Deep learning based on neural networks has received increasing attention in the field of scattering image reconstruction. However, most of the research lies in designing complex network architectures to improve reconstruction, and these network models are not capable of reconstructing images in low ambient light. In this paper, a lightweight generative adversarial network (GAN) model combined with a histogram specification algorithm is designed to reconstruct dark-field speckles through MMF. Experimental results show that the reconstruction results of our algorithm have better metrics. Moreover, the model exhibits excellent cross-domain generalization ability with regards to the Fashion-MNIST dataset. It is worth mentioning that we probe and find that the speckles after inactivation still have the ability to be reconstructed, which increases the robustness of the model.

Industrial anomaly detection is dedicated to identifying and locating regions that deviate from the standard appearance. The prevailing approach achieves unsupervised anomaly detection through the reconstruction of images using autoencoders. Due to the simplistic structure of some abnormal regions, the autoencoder can effectively reconstruct these areas, consequently diminishing the model’s anomaly detection capabilities. To address this issue, this paper transforms the reconstruction task into the inpainting-filling-reconstruction task to increase the reconstruction error between abnormal samples and normal samples. The masked regions inpainted by the filling network are used to fill in the input image, thereby achieving an effect similar to masking. Unlike typical masking processes, this approach retains partial authentic information in the input image, rendering it partially visible. This is beneficial for the reconstruction network to repair the masked areas. Due to the consistent structure between the masked region inpainted by the filling network and the normal region, the filled abnormal regions display a complex structure that has not been learned, making it difficult for the reconstruction network to reconstruct the abnormal regions. Experimental results indicate that our method performs better than other methods on both the MVTec AD dataset and the MVTec LOCO AD dataset.

The rapid advancement of remote sensing technology has heightened concerns over the security of sensitive information. This paper presents an intelligent encryption scheme for remote sensing images using dimensionality variation. The scheme employs two high-dimensional chaotic systems to generate keys for simultaneous row-column scrambling and diffusion. By mapping a two-dimensional (2D) plain-image to a three-dimensional (3D) space, pixels are rearranged within a 3D cube using a chaotic key, followed by auto-correlation cyclic diffusion. Experimental results demonstrate that this approach significantly enhances encryption security, making it suitable for secure remote sensing image communication.

This paper firstly analyzes the characteristics of medical images, and then proposes a specialized compressive sensing algorithm called sparsity-precise iterative hard thresholding (SIHT), which is specifically designed to address their specific features such as low sparsity and low frequency. SIHT adaptively measures sparsity and step length which becomes more precise during the iteration process to achieve a certain quality improvement in medical image reconstruction. Experimental results demonstrate that as compared to other image compressive sensing (ICS) reconstruction algorithms across three different types of medical image datasets, SIHT can achieve the best subjective recovery quality particularly in terms of mitigating blocky artifacts and noise, where a notable improvement is obtained in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) of medical ICS reconstruction.

In the field of aircraft design and maintenance, with the innovation of cabin cable three-dimensional (3D) scanning and sensor technology, high-precision cabin point cloud data has become the key to improving the accuracy of cabin navigation and building a realistic virtual reality environment. In the face of largescale point cloud data, how to efficiently and uniformly construct a realistic virtual reality environment has become a challenge. In this paper, we propose a new low-parametric point cloud upsampling network (LPNet), which is based on the no-learn model to learn the complementary geometric knowledge between point clouds based on some simple data transformations, to efficiently retain the geometric properties of point clouds, and then input the results into the up-sampling module, and simply insert a few layers of multilayer perceptron (MLP) to efficiently generate high-resolution point clouds. It is able to efficiently generate high-resolution point clouds, showing great flexibility and realizing the efficient use of computational resources.

Detecting surface defects on steel, especially in complex loading environments, poses significant challenges. In response, we introduce EDFW-YOLO, an algorithm built upon you only look once version 8 (YOLOv8) specifically designed for detecting surface defects on hot-rolled steel strips. Our method enhances multi-scale feature fusion through the incorporation of the multi-scale conversion module (C2f-EMSC). Additionally, we elevate detection accuracy by integrating the dynamic head target detection head, the focal modulation module, and the WIoU_Loss bounding box regression function. Experimental results on the NEU-DET dataset demonstrate that our optimized YOLOv8 model achieves the mean average precision (mAP) of 77.7%, with a 5.2% increase in network constraint rate. To adapt to different operating environments, it further improved the mAP to 78.5% through data enhancement. Verification results on PCB defect data show that the algorithm has excellent generalization ability. This optimized algorithm significantly improves the extraction and fusion of surface defect features on hot-rolled strip steel and serves as a valuable reference for surface defect detection in alloy materials.