The complex thermal coupling between complementary metal-oxide semiconductor (CMOS) components and other modules of an in-orbit assembled telescope and significant changes in external heat flow make it difficult to determine the optimal parameters for the thermal design of CMOS components. In this study, a hybrid sensitivity analysis method was developed based on the mean impact value(MIV) algorithm and the traditional regression analysis (Pearson and Spearman) algorithms for comparison and validation. In addition, a sensitivity analysis of the thermal design parameters of the CMOS component was conducted, the optimal parameters were obtained, and thermal design optimization was achieved. Compared with the traditional traversal method for selecting thermal design parameters, the number of thermal design parameters decreases from the original 10 to two important and five minor parameters. This makes the selection of parameters purposeful and improves the thermal design efficiency. The simulation results show that the temperature fluctuation of 1.6℃–25.4 ℃ during the CMOS track cycle and the thermal design satisfy the operating temperature requirements, verifying the feasibility of the thermal design based on the hybrid sensitivity analysis method.

Conventional carbon dioxide sensors pose significant drawbacks such as large size and high power consumption. In recent years, with the development of semiconductor technology and materials, microelectromechanical systems (MEMS) technology has gradually become the focus of sensor research. In this study, a microelectromechanical system with an infrared light source chip structure for carbon dioxide sensors was designed and investigated. Silicon (Si) was used as the substrate, silicon dioxide (SiO2)–silicon nitride (Si3N4)–silicon dioxide (SiO2) was used as the support layer of the infrared light source, platinum (Pt), which has good ductility, was used as the heating resistor for the radiation area, and aluminum (Al) was used as the electrode. The support layer of the light source adopted a four-axis large rounded corner suspension structure, which significantly reduced the thermal conduction of heat from the radiation area to the substrate and provided better stability. Through finite element simulation analysis, the light source was characterized by low power consumption and a solid structure.

To achieve all-weather observations in the 15000 m stratosphere and meet the ground resolution of 470–1500 nm, a long-forward charge-coupled device (CCD) aerial camera was proposed. By correcting the secondary spectra generated by the long focal length and large aperture using H-ZK 7 with ZF6, the rate color difference control meets the design requirements. The temperature curve of the system at different heights was analyzed via thermal analysis of the optical system. The detector adopted a full-frame CCD detector with a single image element of 8 μm and a target surface size of 48 mm ??36 mm; the system reached the diffraction limit at 100 lp/mm, fulfilling the imaging requirements of the system.

The noise equivalent temperature difference (NETD) is an important parameter to characterize the sensitivity of infrared FPA detectors. In addition to the detectors, they are closely related to the application and test conditions. Starting from the NETD test method and test process, the NETD theoretical calculation formula for the photovoltaic infrared FPA detector was deduced, and the main factors affecting the NETD of the detectors were analyzed. The background-limit NETD of a typical infrared FPA detector under different integration times, storable charges, F numbers, and half-well fill output conditions was calculated and theoretically analyzed. Under a fixed background temperature, NETD with different temperature differences can be theoretically calculated from a certain temperature difference NETD. By selecting a typical detector for verification, a group of NETDs calculated from the normal NETD test results were found to be in good agreement with the test results under the corresponding temperature difference. The NETD calculation result for the 1 K temperature difference is significantly close to the actual situation, which can provide a reference for the application of thermal imager systems.

The reliability requirements for detector assembly have increased significantly with the development of infrared detector technology. Traditional methods of assessing detector reliability along with the entire system are insufficient to meet these requirements. To address this issue, this paper investigates the reliability verification of a 640×512 (25 ?m) mid-wave infrared detector module under environmental stress. Key components of the detector were subjected to high-temperature accelerated life tests and continuous operation tests. The overall reliability of the detector module was estimated based on the specialized reliability test data of critical components. The evaluation results, using selected reliability assessment methods, indicate that the developed module meets the reliability standards. The reliability verification and assessment methods used in this study provide an objective and accurate evaluation of the cooled infrared detector module.

This study addresses low accuracy in infrared object detection, which is often caused by low contrast and scale discrepancies. A parallel-pooling and self-distillation-YOLO (PPSD-YOLO) algorithm that incorporates several crucial features was proposed to solve this challenge. One of the main contributions of this study is the Fusion-P parallel pooling module, which smoothes the surrounding pixels and avoids overlooking important details. In addition, a small infrared object detection layer was added to enhance the detection accuracy of such objects. The initialization anchor frame for this layer was optimized using the K-means++ algorithm. The proposed algorithm includes a multiscale feature perception module (SA-RFE) in the neck layer, which fuses contextual information from various scales of the target for more accurate detection. During the training process, a modified self-distillation framework was used to rectify misdetected targets in the teacher model, leading to improved detection accuracy in the student model. Tests were conducted using the FLIR dataset to evaluate the proposed algorithm. The results show that PPSD-YOLO outperformed YOLOv7 by 2.7% in terms of mAP. This improvement can be attributed to the incorporation of a parallel pooling module, small-object detection layer, SA-RFE module, and a self-distillation framework. This study presents a comprehensive solution to the challenge of low detection accuracy in infrared object detection. The proposed PPSD-YOLO algorithm integrates advanced features that enhance accuracy and improve the overall performance of the detection system. These findings will be useful for researchers and practitioners in computer vision.

To address the problems of detail loss, inconspicuous targets, and low contrast in infrared and visible image fusion, a fusion method combining fast joint bilateral filtering (FJBF) and an improved pulse-coupled neural network (PCNN) was proposed. The operational efficiency can be effectively improved by ensuring the quality of the fused image. First, the source images were decomposed by fast joint bilateral filtering. Second, to extract significant structure and target information, a weighted average fusion rule based on a visual saliency graph (VSM) was adopted for the basic layer image, and an improved pulse-coupled neural network model was adopted for the detail layer image. All parameters of the PCNN can be adjusted according to the input bands, and the fusion image was reconstructed using the superimposed fusion map of the base layer and the fusion map of the detail layer. The experimental results show that this method can significantly improve the image fusion effect and effectively retain important information, such as targets, background details, and edges.

To improve the low clarity, low contrast, and insufficient texture details of infrared and visible image fusion, an image fusion algorithm based on a parameter-adaptive pulse-coupled neural network (PA-PCNN) was proposed. First, the source infrared image was dehazed by a dark channel to enhance the clarity of the image. Then, the source images were decomposed by non-subsampled shearlet transform (NSST), and the low-frequency coefficients were fused by the proposed global energy feature extraction algorithm combined with a modified spatial frequency adaptive weight. Texture energy was used as the external input of the PA-PCNN to fuse the high-frequency coefficients, and the fused gray image was obtained using the inverse NSST. To further enhance the perception of the human eye, a multiresolution color transfer algorithm was used to convert the grayscale image to a color image. The proposed method was compared with seven classical algorithms for two image pairs. The experimental results show that the proposed method is significantly better than the comparison algorithms in terms of evaluation indicators, and improves the clarity and detail information of the fused image, which verifies its effectiveness. The conversion of the fused grayscale images into pseudo-color images further enhances recognition and human eye perception.

This study investigates the problem of small-target detection in remote sensing and drone aerial images. These images have the characteristics of a small target scale, dense target distribution, and complex background, which makes feature extraction difficult. Most current algorithms for small-target detection ignore the impact of parameter quantity and inference speed on the practicality of the algorithm to improve accuracy. Therefore, this algorithm is impractical. To address these problems, this study proposes an improved YOLO v8 small target detection algorithm based on a lightweight multiscale fusion attention mechanism. The algorithm first adds the F operator to the FPN structure of YOLO v8, designs the weighted fusion of multiscale features, removes the P4 and P5 prediction layers in the network prediction layer, adds a P2 layer for small target prediction, improves the image input grid segmentation integration of the lightweight attention mechanism, and replaces the C2f module in the improved FPN with it, thereby improving the algorithm have better global perception ability and greatly reducing the parameter quantity. Compared to YOLO v8s, the mAP of this algorithm on the DOTA dataset increased by 4.4%, the network parameter quantity was reduced by 52%, and the FPS reached 46 frames. For the VisDrone dataset, this algorithm improved the accuracy by 8.3%.

Functional near-infrared spectroscopy (fNIRS) has attracted considerable attention in recent years in brain neuroscience as a brain imaging system with high temporal resolution, low cost, and high portability. However, motion artifacts in fNIRS signals interfere with the results of subsequent data analysis, and the denoising effect of some existing algorithms is insufficient. Therefore, a motion artifact correction algorithm for fNIRS signals based on a multilayer convolutional self-coding (MCAN) algorithm is proposed. The algorithm was used to correct three motion artifacts in the fNIRS signals. Next, the performance of the proposed algorithm was verified using simulation and experimental data and compared with several widely used algorithms. The results show that the MCAN algorithm performs satisfactorily in the remaining number of motion pseudo-traces, mean squared error, signal-to-noise ratio, square of Pearson correlation coefficient, and peak-to-peak error. Therefore, the proposed algorithm can be used as an efficient fNIRS signal preprocessing algorithm.

The super-resolution reconstruction method of UAV thermal infrared images based on anisotropic filtering was studied to improve UAV thermal infrared images resolution and provide efficient and accurate identification of UAV inspection images. The adaptive setting was based on the image edge area and smooth area gradient threshold, under the set by the anisotropic diffusion filter method. We aimed to remove the initial thermal infrared image noise smoothing area, maintain details, enhance the image edges, and obtain a thermal infrared image after denoising. To generate an image against the network input, the model and discriminant model were generated by game learning for super-resolution reconstruction of the thermal infrared image output. This method has high image edge detail protection, the ability to remove background noise, and an improved comprehensive denoising effect. Furthermore, it can obtain high-resolution and visual presentation of super-resolution reconstruction of thermal infrared images, as well as ensure the identification accuracy and efficiency of thermal infrared images in UAV inspection

With the vigorous development of the aviation industry in China, significant progress has been made in aeroengine technology. As key components of aeroengines, the development of aeroengine blades is crucial. Real-time and accurate monitoring of the blade temperature will help promote breakthroughs in related technologies for aeroengine blades. This study proposes a temperature calibration algorithm and three-dimensional reconstruction strategy for the temperature field based on infrared temperature measurements for aeroengine blade temperature monitoring. Based on the function fitting ability of the multilayer perceptron network, the functional relationship between the precise temperature of the target point and its surrounding temperature distribution is established, and the error of infrared temperature measurement is controlled within 1.24 °C. On this basis, through an innovative projection method and 3D point cloud normal vector estimation, the position mapping from 2D infrared image to 3D space has been achieved. We achieved a three-dimensional temperature field reconstruction via single-view infrared images through a simple process without dependence on multiview images.

To apply infrared imaging detection technology for gas leakage in petrochemical enterprises, experimental research has been conducted on common gases such as methane and ethylene. The influencing factors, such as gas infrared absorption characteristics, gas concentration, background temperature, and detector sensitivity, were systematically studied, and the characteristics of gas infrared thermal imaging technology and infrared spectral imaging technology were analyzed. Based on the experimental research and analysis, some suggestions have been put forward for the application of infrared imaging detection technology for gas leakage in petrochemical enterprises.

To study the adaptability of anti-reflection film in tropical marine environments and the protective effect of a waterproof layer on the film, two kinds of germanium-coated infrared antireflection film samples, with and without a waterproofing layer, were put into a tropical marine environment for natural exposure test under the shed. By observing and detecting the morphology, structure, composition, and transmittance of each cycle test sample, the failure mode, damage process, and change in transmittance were analyzed. The results show that the membrane was not damaged during one cycle under the shed. After two cycles, pitting occurred at the edge of the film layer, and the degree of corrosion became severe with an increase in test time, gradually extending from the edge to the center of the sample. After four test cycles, the edge corrosion of the samples without the waterproofing layer was more severe, and the corrosion of the samples with the waterproofing layer was lighter. The transmittance of the samples at 3.7–4.8 μm did not decrease, showing better adaptability to the tropical marine environment.

The optical constant (refractive index, extinction coefficient) and thickness of the film determine the optical properties of the coated part, so mastering the optical constant and thickness of the film according to the actual conditions is an important part of the film structure design and performance optimization. In this study, a Fourier transform infrared spectrometer was used to measure the reflectance spectrum curve of the sample. The target optimization function was constructed with the help of a different dispersion model, the simplex optimization algorithm was fitted to the reflectance spectrum curve, and the optical constant and thickness of the thin film were obtained using the target optimization function. The fitted optical constant and thickness of the thin film agree with the ellipsometer test results. When the inverted optical parameters and thickness of the thin film were incorporated into the theoretical reflectance calculation model and the reflectance curve obtained by the theoretical calculation model was in good agreement with the experimental test curve, the maximum relative error of the refractive index was less than 1.8%, the maximum relative error of thickness was less than 0.4%, and the maximum relative error of reflectance was less than 2%. This method only requires the measurement of the reflectance spectrum curve, and the optical parameters of the thin film can be obtained through calculations. This method has simple testing calculations, high accuracy, and a wide application range. This has important practical applications in structural design, optimization, and machining of optical thin films.