Owing to its advantages of low vibration, long lifespan, and high reliability, the pulse tube refrigerator has demonstrated significant potential in applications such as high-temperature superconducting systems and satellite-borne infrared devices. As a result, it has become one of the prominent research areas in recent years. However, the inherent refrigeration efficiency of a pulse tube refrigerator using traditional phase modulation methods is generally lower than that of a Stirling refrigerator. This is primarily because the acoustic power at the hot end is dissipated as heat. Recovering this acoustic power can enhance refrigeration efficiency and reduce overall system weight, which is especially beneficial for refrigerators with high cooling capacities operating in high-temperature environments. This paper reviews the current state of research on acoustic power recovery methods for pulse tube refrigerators, both domestically and internationally, and explores their application prospects and development trends. Finally, a pulse tube refrigerator incorporating acoustic power recovery suitable for use in a KIP unit is presented.

In view of the problems of the short pupil distance and poor imaging quality of traditional eyepiece systems, an aspheric long interpupillary eyepiece with an interpupillary distance of 90 mm and a relative lens distance of 3.3 is designed to adapt military sighting devices for combat needs. First, based on a refractive eyepiece optical system composed of a spherical surface, an aspheric design is introduced to further improve the imaging quality of the eyepiece system and simplify the system structure, and a refractive long interpupillary eyepiece containing an aspheric surface is obtained, which consists of five lenses. The results show that at a spatial frequency of 50 lp/mm, the Modulation Transfer Function (MTF) of the entire field of view exceeds 0.4 for long interpupillary eyepieces with an aspheric design. The tolerances and imaging quality of the eyepiece were analyzed; the tolerances are reasonable, and the image quality is excellent. The total length of the optical system is 48.3 mm and the weight is 129.2 g, whereby the length was shortened by 24.2% and the weight reduced by 35.7% compared with a traditional refractive eyepiece. Therefore, being lightweight, it has the characteristics of a compact structure, and exhibits a high image quality.

To meet the demand for compactness in long-wave infrared zoom optical systems, a linear dual-group linkage continuous zoom structure was utilized to simplify the system design. A compact long-wave infrared zoom optical system was developed using an uncooled infrared detector with a 384×288 array and a 25 m pixel size, incorporating four single-crystal silicon (Si) lenses. The system has a total length of 160 mm, operates in the 8-12 m wavelength band, offers a zoom range of 100-200 mm, a field of view from 3.42° to 6.80°, and maintains a constant F-number of 1.4. The design incorporates a diffractive optical element to achieve athermalization and eliminate secondary spectrum aberrations. Additionally, a conical surface and an even-order aspherical surface are introduced to balance spherical and chromatic aberrations caused by the long focal length and large aperture. The design results indicate that the system is compact and can achieve high-quality infrared thermal imaging within a temperature range of -40℃ to 60℃. The modulation transfer function (MTF) across the entire field of view exceeds 0.3 at the Nyquist frequency of 20 lp/mm. The zoom cam curve is smooth and free of inflection points, and the tolerance analysis confirms good manufacturability.

Dual-field scanning optical systems can search for and track targets across large airspaces and over long distances, maintain good image quality, and achieve registration and splicing of sequential images. They have broad application prospects in security, national defense, and other fields. To meet the requirements of high resolution, low cost, miniaturization, and lightweight design in infrared systems, a dual field fast scanning optical system was designed using CODE-V software based on a long wave, uncooled, large format focal plane array detector with a resolution of 1024×768 pixels. The system consists of a Kepler telescope group and a focusing lens group. It achieves dual-FOV zoom functionality through the axial movement of the zoom group. A scanning galvanometer in the telecentric optical path moves rapidly and reciprocally to compensate for target motion and expand the field-of-view angle. The influence of spatial vibration on imaging quality is analyzed by using the dynamic modulation transfer function method to ensure that there are no problems such as image blurring and trailing during the motion process. The optical system distortion is less than 0.5%, achieving high-precision image registration throughout the entire field of view and maintaining stable and clear imaging during scanning.

An electron bombardment active pixel sensor (EBAPS) is a hybrid optoelectronic imaging device that combines vacuum and solid-state devices. Its vacuum sealing requires high-temperature baking and degassing processes. During development, a key challenge is addressing the issue of the epoxy adhesive used to bond the silicon-based chip in the complementary metal oxide semiconductor (CMOS) image sensor to the ceramic base, which may decompose at high temperatures, causing the CMOS chip to detach and fail to capture images. To address this, the project team used three types of solder alloys with similar melting points: Au88Ge12, Pb92.5In5Ag2.5, and Zn-Al-Ag-Cu to bond the metallized CMOS silicon-based chip to the ceramic base. The cross-sections of the CMOS image sensors soldered with these three types of solders were tested and characterized using scanning electron microscopy (SEM) and energy dispersive spectrometry (EDS) to analyze the performance of the solder joints. The results indicated that the CMOS image sensor soldered with Au88Ge12 achieved stable and reliable connection performance.

To realize rapid calculation of the quantum efficiency of different infrared detectors and evaluate their performance advantages and disadvantages, this paper designed quantum efficiency calculation software for infrared detectors based on MATLAB. The software was developed according to the principles of quantum efficiency calculation for infrared focal plane detectors. It loads the voltage signals collected by the test system and implements quantum efficiency calculation and histogram display functions through callback functions. The results show that, in engineering applications, the calculation software demonstrates good scalability and versatility, enabling rapid evaluation of infrared detector performance.

To solve the problems of limited infrared features and poor feature matching in the process of infrared image stitching, this paper proposes an algorithm called sparse depth feature infrared image stitching (SDFS). The algorithm first extracts a dense depth feature map using a convolutional neural network, then calculates and describes sparse feature points from the feature map to enhance the quality of feature point extraction. Next, the K-nearest neighbor search method is used to perform coarse matching of sparse feature points, followed by the application of a dynamic distance ratio strategy to refine the matching results and improve matching accuracy. Finally, based on the matching results, a homography matrix is calculated for image projection transformation, and adaptive factor-weighted fusion is used to achieve seamless fusion and splicing of the image. Experimental results show that the algorithm exhibits high robustness and can effectively adapt to infrared image stitching in different scenes. The stitching accuracy and display effect outperform commonly used stitching algorithms based on SIFT or SURF feature extraction.

Aiming at the problem of target misdetection and missed detection in infrared images under complex street backgrounds due to factors such as occlusion and lack of texture details, this paper proposes an infrared target detection algorithm for complex street scenes. Using YOLOv8n as the baseline model, firstly, a multi branch convolutional structure is designed to enhance feature extraction and expression. Structural reparameterization is used to decouple the training and inference stages, improve the inference speed of the model, and global self attention estimation is introduced to accelerate the calculation of attention. The time complexity is reduced to O(n), enabling the convolutional kernel attention to achieve dynamic identity. Secondly, combining the advantages of depthwise separable convolution and deformable convolution, after feature fusion between the upsampling results and the output features of the backbone network, a salient information aware deformable convolution attention gating mechanism is introduced to improve the semantic information richness of the fused features. Finally, An efficient intersection and union ratio replace the localization loss function, calculate the length and width influence factors of the predicted box and the true box separately, and accelerate the convergence speed. Validation experiments were conducted on the Flir dataset, and the average accuracy of the improved algorithm reached 79.5%, which is 3.9% higher than the YOLOv8n algorithm. This validates the superiority of the proposed algorithm in infrared target detection under complex street backgrounds.

To fully utilize the spatial and spectral information of hyperspectral images and suppress image noise, a hyperspectral anomaly detection method based on local contrast and multidirectional gradient analysis is proposed. First, to leverage local spectral information, a local contrast strategy is introduced, generating a spectral detection score map based on the brightness difference between the target and the background. Then, to reduce computational complexity, a spectral fusion-based dimensionality reduction technique is proposed to process hyperspectral images. In addition, a local multidirectional gradient feature method is proposed to reduce image noise, retain local detail features, and generate a multidirectional gradient detection score map. Finally, the anomaly result map is obtained by fusing the spectral and gradient-based score graphs. Experimental results on four classical datasets demonstrate that the proposed method can successfully display abnormal targets in the result graph, achieving higher detection accuracy and lower false alarm rates compared to seven existing methods.

The infrared images captured by the uncooled detector often exhibit interference issues, such as blurred edge details and uneven grayscale distribution, which can significantly impact the accuracy of object segmentation. To address this, we propose an enhanced implicit shape representation framework based on a sparse representation model. This framework guides the evolution of implicit shapes using sparse linear combinations of probabilistic shapes drawn from a predefined dictionary. First, representative shape components are selected from the dictionary to form sparse combinations that effectively model the target shape. The object contour prior is implicitly incorporated into the sparse representation, facilitating more accurate contour alignment. A new energy function is then constructed, integrating region-based segmentation with sparse representation. The optimal level-set function is obtained through iterative optimization, ultimately yielding precise object segmentation results. Experimental evaluations demonstrate that the proposed model delivers robust segmentation performance, especially for typical objects in complex backgrounds.

Existing Transformer-based fusion methods employ a self-attention mechanism to model the global dependency of the image context, which can generate superior fusion performance. However, due to the high complexity of the models related to attention mechanisms, the training efficiency is low, which limits the practical application of image fusion. Therefore, a multilayer perceptron interactive fusion method for Infrared and visible images, called MLPFuse, is proposed. First, a lightweight multilayer perceptron network architecture is constructed that uses a fully connected layer to establish global dependencies. This framework can achieve high computational efficiency while retaining strong feature representation capabilities. Second, a cascaded token- and channel-wise interaction model is designed to realize feature interaction between different tokens and independent channels to focus on the inherent features of the source images and enhance the feature complementarity of different modalities. Compared to seven typical fusion methods, the experimental results on the TNO and MSRS datasets and object detection tasks show that the proposed MLPFuse outperforms other methods in terms of subjective visual descriptions and objective metric evaluations. This method utilizes a multilayer perceptron to model the long-distance dependency of images and constructs a cascaded token-wise and channel-wise interaction model to extract the global features of images from spatial and channel dimensions. Compared with other typical fusion methods, our MLPFuse achieves remarkable fusion performance and competitive computational efficiency.

Unsupervised visible-infrared person re-identification (USVI-ReID) is a highly important and challenging task. The key difficulty lies in effectively generating pseudo-labels and establishing cross-modality correspondences without relying on any annotations. Recently, generating pseudo-labels using clustering algorithms has attracted increasing attention in USVI-ReID. However, previous methods typically selected a single centroid prototype to represent an individual or randomly selected prototypes based on a fixed strategy for cross-modal correspondence. This approach not only overlooks the diversity of individual characteristics but also fails to account for the negative impact of incorrect samples on model training during clustering. To address these issues, we propose soft-weight prototype contrastive learning (SWPCL). This method first introduces a soft prototype (SP) selection strategy, which selects the nearest neighbor samples of the centroid prototype as the soft prototype based on the similarity between individual features, providing rich positive supervised information to the model. To further eliminate the interference of erroneous prototypes on model training, a soft-weight (SW) strategy is proposed to quantitatively measure the correlation between each selected soft prototype and the corresponding centroid prototype. These prototypes are then integrated into contrastive learning through a soft-weighting mechanism. Finally, a progressive learning strategy is introduced to gradually shift the model's focus toward reliable soft prototypes, thereby avoiding clustering degradation. Extensive experiments on the public SYSU-MM01 and RegDB datasets demonstrate the effectiveness of the proposed SWPCL method.

Given the material characteristics and manufacturing process of refrigerated infrared detectors, pixel inconsistency is common in infrared images. This non-uniformity of infrared images can significantly degrade the quality of on-orbit imaging. In particular, detecting point targets against a uniform background becomes more challenging, often increasing the false alarm rate during on-orbit detection. The two-point correction method is a typical processing method for addressing image in-homogeneity in infrared remote senor data in orbit. This method can effectively restrain image in-homogeneity to a certain extent. However, the correction effect of this image processing method only depends on imaging parameters of the camera but also on the working temperature of the detector in the time domain. Therefore, even if the imaging parameters of the optical remote sensor of the refrigerated infrared detector are fixed in each imaging mission, the correction coefficients must still be calculated in real time. This is achieved by obtaining internal calibration images of the on-board calibrator, thereby ensuring that the heterogeneity of the infrared image is suppressed. Based on a linear push-sweep mid-wave infrared remote sensing camera, an adaptive time-domain correction algorithm in is proposed. Currently, the algorithm has already been deployed in on-orbit optical remote sensors, where it effectively reduces the frequency of in-orbit calibrations and enhances the processing capability of emergency imaging in the infrared exterior passage way.

This study proposes an improved detection algorithm, YOLOv7-FSE (YOLOv7 with FReLU-SiLU-EIOU enhancements), to address the challenges of low resolution and poor detection accuracy in infrared images of composite material defects in aircraft. These limitations make it difficult to accurately characterize defect features. The proposed algorithm introduces several key modifications to the original YOLOv7 architecture. First, the SiLU activation function is replaced with the funnel activation function FReLU to improve spatial sensitivity to defect features. Subsequently, space-to-depth convolution (SPD Convolution) is employed to improve the feature extraction process, thereby enhancing the algorithm's ability to characterize complex defect features in low resolution infrared images. Finally, the EIOU loss function is replaced by the CIOU loss function, and the boundary box recognition weights are optimized to generate higher quality anchor boxes, further improving overall detection performance. Comparison results demonstrate that YOLOv7-FSE outperforms traditional detection methods such as Faster RCNN and YOLOv3. Specifically, it achieves a mean average precision (mAP) improvement of 10.8% over Faster R-CNN and 10.1% over YOLOv3. Compared to the original YOLOv7, the precision (P) increases from 88.3% to 94.9%, while the mAP rises from 90.1% to 97.7%. The YOLOv7-FSE algorithm is well-suited for infrared detection of composite material defects on aircraft surfaces and holds significant potential for integration with embedded devices for rapid, on-site defect detection.

To address the limitations of traditional insulator fault detection methods: specifically the inadequate capture of details and poor performance in identifying small targets, a novel dual-stream attention-based approach is proposed. This method combines a deep supervised attention generative adversarial network (DSAGAN) with the YOLOv8 object detection algorithm. In the proposed DSAGAN, infrared and visible light images of insulators are fused using an attention mechanism embedded within the generator of the generative adversarial network (GAN) to enhance fusion quality. The generator and discriminator form an adversarial network, where the least squares (LS) loss function is employed instead of the conventional cross-entropy loss. This substitution helps preserve finer image details and improves the stability of the DSAGAN. The fused images are then subjected to fault detection using the YOLOv8 object detection algorithm. Experimental results demonstrate that the five evaluation indexes of the insulator images fused by DSAGAN outperform those of seven other fusion methods. YOLOv8 object detection algorithm achieves a mean average precision (mAP) of 0.917 and 0.639 for detecting insulator damages, flashovers, glass losses, and polymer contaminations at thresholds of 0.5 and 0.5 to 0.95, respectively, representing improvements of 0.026 and 0.08 compared to YOLOv5. Furthermore, the fault recognition rates of fused images for different types of insulator faults surpass those of single infrared or visible light images. The average recognition rate reaches 93%, marking improvements of 6.25% and 4.5% over infrared and visible light images, respectively.

Terahertz time-domain spectroscopy (THz-TDS) is a type of far-infrared spectroscopy that reflects the internal characteristics of substances and provides rich physical and chemical information. Therefore, terahertz waves can be used to qualitatively identify food additives containing nitrogen. Hierarchical analysis, originally developed for solving evaluation-type problems, is introduced in this study to the field of qualitative analysis of terahertz spectra. This paper proposes and evaluates a qualitative identification method that combines THz-TDS with hierarchical analysis. In this study, six nitrogen-containing food additives were selected as experimental samples. First, the acquired terahertz time-domain spectral data were preprocessed and transformed into four datasets: peak values, peak positions, peak numbers, and overall spectral trends. Next, the data were divided into comparison and test sets to construct a qualitative identification model incorporating hierarchical analysis, followed by parameter optimization. The results indicated that the qualitative identification accuracy of additives based on single factors: overall trend, peak value, peak position, and peak number were 80.23%, 70.93%, 67.44%, and 40.70%, respectively. The multi-factor hierarchical analysis-based method improved the identification accuracy to 92.44%. In addition, by binarizing the fuzzy characterization of the absorption spectrum data during preprocessing and using it as the basis for assessing overall trends, the recognition accuracy increased to 94.19% when combined with the hierarchical analysis model. These results demonstrate the effectiveness of the proposed qualitative identification algorithm. The method is straightforward, does not require training, and is well-suited for rapid qualitative detection of small sample sets.