Abstract:With the advent of various new types of detection radars, advanced infrared detectors and precision guided weapons, infrared & radar compatible stealth materials have become the focus of current stealth technology research. The stealth principle and research status of traditional and new infrared & radar compatible stealth materials are reviewed, and the development direction of future infrared & radar compatible stealth materials is summarized and forecasted.

Abstract:The morphology, composition and section of surface defects in HgCdTe films by liquid phase epitaxial were analyzed by means of scanning electron microscopy, energy spectrum analysis, optical profiler and metallographic microscope. The characteristics and sources of different types of surface defects are studied. The results show that the crystalline defects existing on the surface of HgCdTe films by liquid phase epitaxial are usually large or in sheet distribution, which have a significant impact on subsequent devices. By analyzing its causes, it can be found that the improvement of homogeneity of HgCdTe solution is the key to reducing such defects and improving the quality of HgCdTe films.

Abstract:The theoretical model of infrared point target detection system is systematically analyzed. The transmittance of medium-wave and long-wave infrared light under typical atmospheric conditions is calculated by establishing the atmospheric stratification model and combining the MODTRAN model. The comparison data of the two calculation results can provide reference for the band selection in the design of infrared detection system. By analyzing the theoretical model of infrared radiation intensity of targets, the infrared radiation intensity of several typical targets in different wavebands is calculated, which provides support for the demonstration of operation range for IR system. Based on the probability density distribution function of infrared image noise, the theoretical formulas of image signal-to-noise ratio, system detection probability and false alarm probability are derived, which provide reference for setting detection threshold. The theoretical model analysis results of infrared point target detection system''s operation range can offer an important theoretical support for improving the credibility of infrared detection system''s operation range demonstration and the rationality of system parameter design.

Abstract:In order to solving the problem of multi-target detection of infrared image in urban background by traditional methods, migration learning technology is used to migrate the target detection framework of visible light in deep learning to the infrared domain. A model is built by the method. The model''s small target detection performance is very good, and the average precision mAP(IoU=0.50)of the test set is 0.858 on the produced test set. A preliminary study of the relationship between training data and model detection performance was also conducted. Two training sets of large data volume and small data volume were produced, the model was trained, and then tested on the same test set. The average precision mAP(IoU=0.50)of the small data set is 0.615. The experimental results show that the diversity, quantity and quality of the data will affect the quality of the model.

Abstract:The noise sources and denoising methods of functional near-infrared spectroscopy (fNIRS) brain imaging are reviewed. The method and operation of suppressing noise are analyzed and given from the aspects of imaging principle, noise source and occurrence mechanism. The composition and characteristics of the interference are analyzed in detail, the effective removal method is given, and the signal quality algorithm in the process of brain imaging analysis and modeling of near-infrared spectroscopy is improved. These methods can provide guidance for the analysis and processing of near-infrared spectroscopy brain imaging data. Three noise sources that affect near-infrared spectroscopy brain imaging signals are summarized: instrumental noise, experimental error and physiological interference from the body. Two practical denoising algorithms are given and the development trend of imaging technology is expounded.

Abstract:Hyperspectral remote sensing data provides the possibility for fine identification of tree species. In order to explore the ability of hyperspectral data in tree species identification, this study is based on the leaf hyperspectral data of eight major tree species in the heritiera littoralis community of Baguang, Shenzhen, and compared the performance of six spectral preprocessing methods and two classification methods to classify tree species. Then based on the random forest algorithm, the importance of the each band was evaluated. The results showed that the first derivative preprocessing method had the best performance in classification and identification, and the average classification accuracy was 76.65%. The random forest regression method had better performance than the support vector regression algorithm, and the model average classification recognition accuracy was 73.07%. It can be seen from the confusion matrix that Aidia pycnantha, Aporosa dioica, Cinnamomum burmanni were recoginized as Sterculia lanceolato. There were the misclassification between Scheffero octorphylla and aporosa diocia. And Heritiera littoralis was also misclassified as Ficus microcarpa. Spectral data near 400 nm, 495 nm, 615-675 nm, 835 nm, 915-975 nm, 1035-1065 nm, 1085-1135 nm, 1265-1275 nm, 1425-1535 nm, 2040 nm, 2100-2270 nm, and 2430 nm are identified as the spectral features, which are most important for the classification of eight tree species.