Green light devices with a top multilayer anode/EHI608/NPB/Alq3:ELL/ETL02/LiF/Mg:Ag/ out-capping layer configuration were experimentally prepared on silicon substrate.T he doping concentrations of emitting layer have strict effects on the driving voltages, luminances, efficiencies and electroluminescent(EL) spectra of OLED devices. Further, the microcavity characteristics of top-emitting green OLED devices were examined. Based on the transfer matrix theory and optical constants of materials, the transmission of multilayer thin films (ETL/EIL/Mg︰Ag/CPL) are numerically calculated with matlab program. The experimental results demonstrated that based on an increase in the dopant concentration of the guest materials, the electroluminescent (EL) efficiency, driving voltage and luminance of the organic light-emitting device increased. And the doped concentration of the guest is controlled at 3.5%, the efficiency of the device remained stable, the transmission of multilayer thin films (ETL/EIL/Mg︰Ag/ CPL) gradually increased, according to the increase in the CPL thickness of the cathode refractive index matching layers. When the CPL thickness is controlled at approximately 30 nm, the transmission in the entire visible range is maximized, and the transmission spectrum (CIEx, CIEy) value of multilayer thin films (ETL/EIL/Mg︰Ag/ CPL) are extremely near to the white equal energy (0.33, 0.33).项目、云南省技术创新人才培养项目（2017HB111）、云南省“万人计划”产业技术领军人才培养项目等资助。

To apply low-level detectors in high-reliability application environments, such as those in the aerospace field, it is important to test the environmental reliability of such detectors. In this paper, a thermal vacuum experiment method is proposed to test various types of space-borne detectors. By comparing the changes in the relative spectral response rate, dark current, driving current of the refrigerator, and refrigeration characteristics of each type of detector before and after the thermal vacuum environment test, their adaptabilities in the thermal vacuum environment are analyzed to enable early popularization. To avoid products with possible quality and other defects, only those products demonstrating the best performances are selected for aerospace applications. The results show that based on the performance indices of detectors that meet the design requirements after the thermal vacuum environment testing and screening, such devices have good reliability and stability, which can meet the requirements of aerospace loads.

Based on the open-circuit output function of a photovoltaic (PV) detector, the response model of a HgCdTe detector that is operable in the long-wave, medium-wave, and short-wave bands is established. Simulation results show that the temperature rise generated by laser irradiation reduces the output of the detector, and the detector produces a saturated output immediately upon direct irradiation by the laser. Based on practical applications, the threshold of soft damage to the detector is proposed.

Traditional scene-based non-uniformity correction algorithms generally suffer from non-uniformity residuals and ghosts. In view of this, we propose an infrared image adaptive algorithm based on the encoding and decoding residual network. The algorithm focuses on the characteristics of the adaptive correction problem. Following the UNet structure, the residual image is generated through multiscale sampling and learning residual mapping. Batch normalization and PReLU are used to improve the correction effect. Finally, the global skip connection is used to obtain the final correction result. The experimental results of correcting the simulated non-uniform infrared image sequence and the real infrared image sequence showed that this method improved the objective data of the peak signal to noise ratio (PSNR) and roughness, compared with existing non-uniformity correction algorithms. Moreover, the subjective visual effect was clearer, and the degree of detail retention was high.

Insulators are important equipment on the transmission line. if fault occurs, it will bring huge loss to power equipments. Locating and extracting insulator from infrared image of transmission line can basically reflect a variety of insulator faults, which is more practical in insulator identification and fault diagnosis.This paper proposes an insulator recognition method based on infrared images, which sequentially use the Speeded Up Robust Features (SURF) algorithm to extract the key feature points of the test infrared images, cluster the feature points based on the improved Fuzzy C-means (FCM) algorithm, identify and locate the insulators according to the shape feature values of the insulators and precise extraction of insulators based on improved image opening operation. This method makes full use of the advantages of infrared images and can accurately extract insulators, which lays a foundation for insulator fault diagnosis based on infrared images.

To solve the problem of low efficiency and accuracy of power equipment fault diagnosis using the traditional threshold segmentation method, an intelligent algorithm, the optimized Otsu algorithm was used for threshold segmentation of infrared images for fault diagnosis. According to the shortcomings of the basic moth-flame optimization, the improved moth-flame optimization algorithm is proposed. It was applied to the infrared image segmentation. By comparing its infrared image segmentation results with those of the particle swarm optimization, biogeography-based optimization, and moth–flame optimization algorithms, it was shown that the improved algorithm is successful. A multithreshold segmentation method for infrared images through the temperature region is proposed. It can accurately determine the temperature range of each part and ensure normal operation of the equipment.

To improve the classification accuracy of hyperspectral remote sensing images, a classification algorithm based on a multiscale convolutional neural network (CNN) is proposed. First, an isometric feature mapping algorithm was used to process hyperspectral data, to mine the nonlinear characteristics of the data and maintain the intrinsic geometric properties of data points. Second, training image blocks centered on labeled pixels were constructed, after which the multiscale CNNs were trained. Finally, the Softmax classifier was used to predict the label of the test pixel. The proposed method performed classification experiments on the Indian Pines, University of Pavia, and Salinas scene hyperspectral remote sensing datasets, and its performance was compared with a CNN, randomized principal component analysis (R-PCA CNN), a deep CNN with pixel-pair features (CNN-PPF), a cross-domain CNN (CD-CNN), and other algorithms. The experimental results showed that the overall recognition accuracy of the proposed method for the three datasets was 98.51%, 98.64%, and 99.39%, respectively, which was 8.35%, 6.37%, and 7.81% higher than that of the CNN algorithm, respectively. The proposed method performed better than the other four methods studied, in terms of both classification accuracy and Kappa coefficient, providing a superior method for hyperspectral remote sensing data classification.

The generation of infrared (IR) images of air vehicles in real time during flight is very important for research on the IR characteristics of air vehicles. The velocity, altitude, and attitude of air vehicles vary during flight. Moreover, the air vehicle body is affected by environmental and background radiation. Hence, it is very difficult to obtain an accurate temperature distribution of the air vehicle body. Because the temperature distribution is related to the state of motion, control models that determine the attitude and velocity are established, along with the surface heat balance equations. The adiabatic wall temperature distribution is solved by computational fluid dynamics, and simplified altitude algorithms are obtained. Finally, IR images of the air vehicle in different bands and at different altitudes and velocities are simulated.

Deep back-projection networks have excellent performance in the super-resolution reconstruction of visual images. This paper explores the application of deep back-projection networks to the super-resolution reconstruction of infrared images. In view of the characteristics of low infrared image contrast and low image quality, the following improvements were made in the framework of the deep back-projection network: adding a concatenation layer before the upsampling module, cascading the previous downsampling output and the original low-resolution preprocessed image as the input of the upsampling module. This was designed to improve the network's ability to obtain high-frequency information of the image and enhance the detail of the generated image. The experimental results proved that the proposed algorithm could create infrared super-resolution reconstructed images with richer details and improved visual effects.

To improve the enhancement of infrared thermal images, this paper proposes an algorithm based on an improved histogram clipping method. The algorithm determines the difference between the number of pixels in the histogram bins of the original thermal infrared image and the traditional histogram equalization image. Subsequently, based on a specific range criterion, the calculated difference of various histogram bins is divided into different blocks. Next, the histogram is redistributed, and a transform function is determined to obtain the enhanced thermal infrared image. The algorithm is based on an improved global histogram equalization method, which achieves a suitable balance between contrast enhancement, histogram shape, and detail information. The experimental results demonstrate that the average values of the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and mean-square error (MSE)—27.5, 0.923, and 590 respectively are better than those of other algorithms. Thus, this method effectively enhances the thermal infrared image.

Near-infrared radiation is an important component of night sky light. Making optimal use of near-infrared radiation is an effective method to improve the signal-to-noise ratio of true-color night vision imaging systems. However, green plants with high reflectance of near-infrared radiation introduce a strong color cast problem in true-color night vision images. In this study, we analyze the gray value distribution of green plant images in red, green, and blue channels and find that the depth of field of the green plant area is large. The difference between the all-pass channel and the red channel is the sum of the green and blue components, without the near-infrared radiation. In addition, the green and blue components of the green plant region are few in the difference image. Based on these two characteristics, in this study, we propose a color correction method for true-color night vision images. The method extracts green plants with differential depth of field, then uses the extracted green plant area to adjust the values of the red and blue channels, according to the color balance principle. The results show that the proposed method can effectively correct the influence of near-infrared radiation on image color and restore the image color of green plants in night vision images.

The Joule–Thomson (J–T) cryocooler is widely used in a variety of infrared devices. Maximizing the cooling capacity in finite structures is one of the foremost problems in J-T cryocooler miniaturization. In this study, a one-dimensional model based on the thermodynamic properties of real gas and heat leakage of components is established. The effects of three structural parameters (fin height, fin thickness, and fin pitch) used in a helical finned tube heat exchanger on the performance of a cryocooler are calculated and optimized using a genetic algorithm. The results show that the calculated data are in good agreement with the experimental data. In the specific working conditions and structural parameters employed, an increase in fin height and fin thickness would increase the entropy production and cooling capacity of the cold end of the heat exchanger, whereas an increase in fin pitch would have the opposite effect. Optimal parameters exist for maximizing the cooling capacity of the heat exchanger in this study. The analytical method established in this study could provide a simple and effective means of optimizing and designing a J–T in engineering applications.

To compute the operating range of the advanced infrared detector for the stealth aircraft, the combat simulation scenario of F-22 and F-35 is given in the paper. In the scenario, the infrared radiation intensity of F-22 is computed in middle wave-band and long wave-band, the fitting formula of the atmospheric transmittance is given under typical conditions, the characteristic parameters of the F-35 airborne IRST and AIM-9X infrared seeker are inferred and the operating ranges of F-35 airborne IRST and AIM-9X infrared seeker for F-22 are computed with the gradually approaching method. The computation shows that F-22 has good stealth capability and the operating ranges of F-35 airborne IRST is less than 62 km from the nose of F-22 while the infrared dog-fight missiles threat F-22 in short range strikes. The infrared radiation of F-22 is sensitive to the detection angle of the infrared sensors, thus in the aircraft fleet anti-stealth infrared detection, sparse formation is adopted to increase the detection probability.

This paper introduces a data acquisition system for a MEMS gyroscope using a PSoC development board, for use in infrared imaging guidance systems. Using a PSoC 5 as the control core, we proposed a design scheme with high quality, low cost, low power consumption, which satisfied the design requirements. We performed a detailed investigation regarding the realization process of the hardware and software of the PSoC 5. Static and dynamic experiments were designed for data analysis. It is shown that the data acquisition system has high measurement accuracy.

Far infrared (FIR) is a form of thermal radiation that has beneficial biological effects on human health. As a convenient and non-invasive important type of physiotherapy, FIR has been widely used in clinical fields such as diagnosis and treatment of diseases. The author collected many related works in the literature regarding the clinical application of FIR to elaborate on its molecular mechanism and to summarize its application in diagnosis and clinical treatments of diseases including dysmenorrhea, chronic pain, cough, pressure ulcer, tumor, and diarrhea. This review provides a reference for the disease diagnosis and evaluation of curative effect to promote the clinical application and development of FIR.