In this study, a numerical simulation is conducted using the improved delayed detached eddy simulation (IDDES) method, the dual time-stepping LU-SGS method, and the proper orthogonal decomposition (POD) method, and the unsteady characteristics of aero-optical effects of the turret wake are analyzed. Although the simulated streamlines generally agree with oil flow test, some differences can be observed with respect to the flowfield structure of the turret wake. The flow around a turret can be easily separated. Further, the instability of the separated shear layer can be attributed to the K-H instability wave, resulting in the emergence of the shedding vortex streets. The unsteady complex flow in the turret wake area can be attributed to the interaction between the vortex streets and neck vortexes. The aero-optical effects of the turret wake are dominated by the steady-state aberration that accounts for 70% and can weaken the unsteady characteristics of the aero-optical effects. The wavefront is dominated by low-order modals, and the number of small-scale structures included in the modals will increase with the increasing order. According to the scaling law, the time mean value of the root-mean-square optical path difference is approximately 0.48λ under an altitude of 10 km and a turret diameter of 2 m; thus, the aero-optical effect will be further intensified, which can negatively affect the ABL performance.

Quantum-noise randomized cipher (QNRC) is an information anti-interception communication method that combines the principles of quantum mechanics with classical stream cipher. In this study, the characteristics of a phase-shift keying (PSK) QNRC system are simulated and verified through a simulation system based on commercial components using VPI simulation software. In particular, the ciphertext electrical signals and the key electrical signals are generated based on an arbitrary waveform generator. At the receiving end, the binary signal is recovered from the multilevel ciphertext signal using a phase modulator and a differential phase-shift keying (DPSK) receiver. Finally, error-free communication is achieved at a data transmission rate and transmission distance of 2.5 Gbit/s and 500 km, respectively, in which the mechanism of the ciphertext is 256. This PSK scheme overcomes the problem of the intensity-shift keying (ISK) scheme, in which the powers of the transmitter and receiver must be matched during demodulation. The increase of mechanisms does not degrade the transmission performance of the PSK scheme and will help improve system security. Thus, the PSK scheme has potential security advantages.

We analyze the channel mismatches in a multi-channel demultiplexing photonic analog-to-digital converter (PADC) applied in wideband radar receiving. We demonstrate a model of the channel mismatches and a method for extracting the mismatch information, and develop a channel-mismatch compensation algorithm. We analyze and compare the short-time Fourier transform spectra of the echo signals from the corner reflectors and the drone before and after the channel-mismatch compensation. The results confirm that the compensation algorithm can effectively suppress the intrinsic mismatches in the amplitude and delay mismatch caused by the multi-channel demultiplexing architecture of the PADC. The radar achieves wideband detection, covering a 4 GHz-bandwidth segment of the X-band and Ka-band signals. By comparison between the target detections of static and long-distance flying corner reflectors, we find that the compensated PADC effectively performs one-dimensional imaging of the corner reflectors installed 10 cm apart.

Herein, for low-illumination application scenes, an end-to-end convolutional neural network (CNN) is proposed for the fusion of near-infrared (NIR) and visible images. The fused image can combine the signal-to-noise ratio of an NIR image and the color of visible images. To verify the capability of CNN for practical fusion tasks, a real dataset with accurate registration was collected. Moreover, the training set was preprocessed via information fusion, thereby enabling the network to extract additional information from NIR images. Experimental results reveal that the proposed method is superior to existing fusion methods in terms of visual quality and quantitative measurements.

This paper presents a new skull point cloud registration method based on curvature maps to improve the registration accuracy and convergence speed of the skull point cloud model. First, a three-dimensional shape block centered on the feature points and containing its adjacent points is extracted from the skull point cloud, and all the points are projected onto the two-dimensional plane. Furthermore, the projection points are quantized into the corresponding units in the two-dimensional supporting area, and the weighted curvature is encoded as curvature distribution images to construct the region curvature map descriptors of the feature points. Then, matching point pairs are established by matching points with similar local shapes based on regional curvature map descriptors, and the rigid body transformation relationship between skull point clouds is calculated using the singular value decomposition method to realize skull coarse registration. Finally, the iterative closest point (ICP) algorithm is improved by introducing dynamic iteration coefficients and used to achieve fine skull registration. The experiment results demonstrate that the proposed rough registration method is an effective initial registration method. Compared with the original ICP algorithm, the improved ICP algorithm increases the registration accuracy and convergence speed by approximately 11% and 37%, respectively, and reduces the time-consumption by approximately 34%. The bunny point cloud model is used to verify the generalization ability of the proposed method. The results demonstrate that the registration effects of the improved ICP algorithm are better than those of the original ICP algorithm.

Visible and infrared images are important ways for power inspection robot to detect the health status of power equipment. Image registration can combine the advantages of two types of images and provide a better basis for subsequent status monitoring. To improve the registration accuracy due to the blur of infrared image, this paper proposes a normalized mutual information algorithm based on saliency gradient. First, based on the visual saliency detection of the infrared image, the edge gradient information of saliency area is enhanced. Second, the saliency gradient information and normalized mutual information are combined as a measurement function of registration. Third, to improve the convergence of the image registration algorithm, a cultural wolf pack algorithm is proposed. This algorithm introduces the hierarchical evolutionary characteristics of cultural algorithm into the wolf pack algorithm to establish the belief space and population space. In the iterative process, the evolution of population space is guided by the knowledge of belief space. Finally, the substation inspection image, standard registration test image set, and standard test functions are selected for comparative experiments. The results show that the proposed algorithm has better performance in registration rate and registration speed.

Generally, the objects in an image have complex shapes and sizes. Therefore, it is difficult for the existing morphological features to completely describe the significant spatial information of the image. Hence, a morphological saliency profile is developed in this study based on the saliency measure. The grayscale and contour information of a particular area can be used to estimate the value of the saliency measure. This measure is used to describe the importance of a target in a scene. Thus, the important area of an image can be extracted based on the local maximum value of the saliency measure, and its spatial information can be obtained based on the multi-level features. When extracting the morphological saliency profile, attribute filtering based on the saliency measure is performed to eliminate redundant image details and retain the saliency profile of the image. Subsequently, the hierarchical spatial features are generated according to the saliency of the organization structure in the image. Two hyperspectral datasets are used in this experiment for verification. The experimental results demonstrate that the classification performance of the proposed algorithm is superior to those of the existing morphological feature extraction algorithms.

Imaging based on speckle autocorrelation is a research hotspot in the field of scattering imaging, but the statistical noise in the imaging process will affect the imaging results. In this paper, by utilizing the response of point spread function (PSF) to wavelength, we propose a method to improve the imaging quality of binary target. Since the PSF is sensitive to the wavelength, and the PSF corresponding to different wavelengths is different or even completely uncorrelated with each other. The autocorrelation superposition of different speckle patterns with uncorrelated PSFs can effectively suppress the statistical noise in the autocorrelation, and thus improve the quality of reconstructions. Simulation and experimental results show that, at the same detection distance and sampling area, compared to the single wavelength sampling failed in reconstruction, the proposed method can reconstruct the target completely and accurately.

Lens-free inline holography contains zero-order image noise and twin image noise. Methods based on the Fresnel diffraction model can suppress these noise, but require many lens-free images. To resolve this problem, this paper proposes a lens-free imaging method based on generative adversarial networks (GAN). First, the defocusing distance of a lens-free image is calculated under partially coherent illumination, and the object plane image with zero-order image and twin image is reconstructed through back diffraction propagation according to the defocusing distance. Next, the object plane image is registered with commercial microscope images which are the gold standard. The registered images are taken as the training inputs of the GAN. Finally, the trained kernel function of the GAN is used for reconstructing the lens-free images, thus obtaining clear target images. The experimental results show that the proposed method can effectively suppress the zero-order image and twin image and significantly improve (up to 4×commercial microscope objective) the contrast and clarity of the image. Because the proposed method requires only a single lens-free image and omits Fourier transforms and other complex operations in the image reconstruction stage, it greatly shortens the imaging time. The proposed method requires fewer training data, better converges the loss function, and has higher processing efficiency than the method based on convolutional neural networks (CNN).

The Shack-Hartmann wavefront sensor (SHWS) generally requires that the focal spot of each microlens must remain in its corresponding sub-aperture area so that it is easy to decide the mapping relationship of spots and subapertures. We present a software-based recognition method, i.e., an iterative extrapolation method, which can still find the corresponding spot of each subaperture when the spots get out of their subaperture areas, so that the SHWS obtains a much larger dynamic range. First, this method finds a 3×3 sortable spot-array and establishes a polynomial function for the relationship between the centroid coordinate of the spot and its row and column numbers to predict and find the adjacent spots. Then it uses a new 3×3 spot-array on the edge of the already-found spots to continue searching outwards until all the spots are found. Finally, it decides the corresponding spot of each subaperture by adjusting all the row and column numbers by an offset. The performance of this iterative extrapolation method to expand the dynamic range of the SHWS under various types of wavefront aberrations is studied by simulation, and the experiments are carried out to further verify the practicability of this method.

When multi-view structured light is used for three-dimensional measurement of metallic objects, the structured light images obtained by each camera exhibit obvious differences because the reflected light intensity significantly changes with the angle; thus, the degree of matching of images is reduced. At some locations with strong reflections, stray light can even render the measurement of line structured light impossible. Moreover, when the same parameter is used to match or process the image, the accuracy of the measurement is poor if materials with large differences in reflectivity and reflection performance are simultaneously encountered. To mitigate these problems, a three-dimensional structured light measurement method and an instrument calibration method based on the formation of multiple reflected images are proposed. The three-dimensional structured light measurement method solves the spatial coordinates of structured light image points by establishing a relation between the image and spatial coordinates of the corresponding points of the multiple reflected images. Experimental results indicate that to a certain extent, this method overcomes the problems of large brightness contrast and high reflectance encountered in structured light measurement of high-reflection surfaces. Moreover, the measurement accuracy in our experimental platform reaches the engineering requirement of ±0.1 mm.

An optically pumped mid-infrared HBr gas laser based on hollow-core fiber (HCF) is reported. A tunable narrow linewidth 2 μm continuous-wave thulium-doped fiber amplifier is used to pump a 4.4 m anti-resonance hollow-core fiber filled with low-pressure HBr gas. When the seed wavelength is precisely tuned to the R(2) absorption line near 1971.7 nm of hydrogen bromide isotope H 79Br, the H 79Br molecules in the vibrational ground state v0 will transit to the vibrational excited-state v2, then a population inversion will be formed between the vibrational excited states v2 and v1. Due to the transition selection rules, two transitions will simultaneously occur by the spectral lines R(2) and P(4), whose wavelengths are 3977.2 nm and 4165.3 nm, respectively. When the HBr pressure is 6.2 mbar, the maximum output power of 4 μm laser is 125 mW, and the optic-to-optic conversion efficiency is about 10% in terms of the pump light coupled into the HCF. By further improving the transmission loss spectrum of the HCF and increasing the coupling efficiency of the pump light, the laser efficiency and output power can be greatly improved, and a wide-range tunable mid-infrared laser emission could be obtained owing to the energy level characteristics of HBr molecules in the future.

In this paper, we propose a narrow-linewidth random fiber laser based on random distributed grating array to achieve a short cavity length and narrow-linewidth random laser with low pump threshold and high slope efficiency. The random grating based on randomly distributed grating array can effectively improve the pump threshold and pump power of the random fiber laser. Based on the localization theory of light, a narrow-linewidth random fiber laser is constructed with the ultra-narrow relfection peak of the random fiber grating. The laser can generate a stable narrow linewidth random laser output of 1.33 kHz, the pump threshold is approximately 24.5 mW, and the slope efficiency is approximately 10%. Moreover, the central wavelength and laser output power remain stable. Compared with other lasers, the proposed laser has a lower pump threshold, higher slope efficiency, shorter cavity length, and relatively simpler structure. Accordingly, the proposed laser can potentially be applied in several applications, including optical communication, narrow-linewidth high power fiber lasers, and fiber sensors.

To address the problem of bulk damage of K9 optical components induced by laser irradiation, the on-line imaging technology is used to obtain front- and side-view images of laser-induced bulk damage. The characteristics of filament damage in bulk damage, influence of nucleation damage in filament damage, and effect of crack on damage growth are investigated. Results indicate that when the irradiation energy is larger than the bulk damage threshold, the bulk damage appear in the forms of filament damage, nucleation damage, and damage growth. However, only the nucleation damage site closest to the direction of the incident laser beam continues to grow under subsequent laser irradiation. Moreover, in the damage-growth process, the cracks of nucleation damage propagate along the longitudinal and reverse directions of propagation of the laser beam. Further, under the same laser irradiation, the growth of damage area strongly depends on the length of the filament damage site. Moreover, the damage-growth coefficient of the damage site observed in the side direction is larger than that observed in the front direction. These results can provide a reference for investigating bulk damage and damage growth mechanisms under subsequent laser irradiation of K9 optical components.

In order to attain the successful implantation of ZnO nanorod arrays in advanced optoelectronic devices, it is necessary to achieve the fast growth of nanorods and the morphology and photoelectric property control of nanorods. Electrodeposition is used to fabricate the ZnO nanorod arrays. Hexamethylenetetramine is added into the basic electrolytes and the nanorods are characterized to analyze their morphological, optical and electrical properties. Hexamethylenetetramine results in a boost in the growth rate of ZnO nanorods. Compared with those of samples without using hexamethylenetetramine, the growth rate of nanorods using hexamethylenetetramine is increased by 356%. Hexamethylenetetramine leads to the decrease in the diameter and density of nanorods. As a result, the distance between nanorods is enlarged to 58 nm. Hexamethylenetetramine results in the redshift of the optical band gap energy of ZnO nanorods by ~0.12 eV. Hexamethylenetetramine leads to the decrease in the Stokes shift by 0.15 eV, which suppresses the non-radiative recombination in ZnO nanorods. The use of hexamethylenetetramine achieves the fast growth of ZnO nanorods as well as their tailored physical properties, such as optical band energy, near band edge emission, Stokes shift, and non-radiative recombination.

To utilize GF-6 WFV data more efficiently, the cloud detection algorithm, which is based on cloud detection algorithm-generating (CDAG) algorithm, is investigated in this study. The proposed method can effectively realize high-precision cloud detection of multi-spectral satellite sensors by completely mining the spectral difference information of the cloud and the typical surface in visible and near-infrared bands. Considering that the spectral range of GF-6 WFV is relatively narrow, and the recognition ability of the cloud and the bright surface is relatively weak, we add the dispersion index and bright surface index, and use more band combinations to further analyze the differences between cloud and clear pixels so as to improve the recognition accuracy of typical surface and cloud. Cloud detection results from different sub-regions are varified through remote visual interpretation, which suggests that the overall accuracy reaches 85.16%, 14.84% of clouds are not identified, and 2.39% of the surface is incorrectly identified as clouds, thereby demonstrating the proposed method can achieve high recognition accuracy.

Hyperspectral image is typical high-dimensional data which contains abundant information in both spectral and spatial dimensions. In this paper, a hyperspectral image classification method based on three-dimensional dilated convolutional residual neural network is proposed for characterizing large amounts of data in the spectral dimension during hyperspectral image classification. In this method, hyperspectral pixel cubes were applied as input data. Further, a three-dimensional convolutional kernel was used to simultaneously extract the spectral and spatial characteristics of hyperspectral data. Then the receptive field of the convolutional kernel was enhanced without adding network parameters or reducing data features by introducing a dilated structure in the convolutional kernel. Thus, the classification accuracy of the neural network was improved, avoiding the problem of gradient disappearance caused by the deepening of network layers using a residual structure. Finally, the Softmax classifier was used to complete the classification of hyperspectral pixels. Results show that this method obtained an overall classification accuracy of 97.303% and 97.236% on the Indian Pines and Salinas datasets, respectively, exhibiting a better classification effect than other control groups. Thus, the proposed method can improve the classification performance of hyperspectral images.

The semi-global stereo matching (SGM) for remote sensing images is sensitive to noise and produces fringes in the areas with discontinuous disparity and weak texture, resulting in a low matching rate. An SGM algorithm for remote sensing images combined with speeded up robust features (SURF) is proposed herein. First, SURF is used to calculate feature-matching points and the main directions of the feature points in remote sensing images, and a fast nearest neighbor search algorithm is applied to eliminate the inaccurate matching points. Then, the Census transformation is used to calculate the matching cost of the two remote sensing images, and the path weight of the SGM algorithm in a different convergence path direction is adjusted by the main direction of the feature points. Finally, improved weighted joint bilateral filtering (WJBF) method is applied to refine the disparity to remove noise and fringes in the disparity maps. Experiments are performed on WorldView, IKONOS, and SuperView-1 remote sensing image datasets. Results show that the proposed algorithm is superior to the contrast algorithms in both subjective and objective evaluation indexes, effectively eliminating the fringes and noise in weak texture and disparity discontinuity area and improves the stereo-matching accuracy.

Aiming at the problem that the maximum measurement speed of conventional laser coherent detection of high-speed moving target is limited, in this paper, a Doppler measurement method with large dynamic range of four-light coherent mixing is proposed. Compared with traditional laser coherent detection, this method can double the maximum measurement speed and realize the speed measurement of high-speed and ultra-high-speed targets. Since the four-light coherent mixing detection system has one more local oscillator light than the heterodyne laser coherent detection system, the local shot noise increases. Therefore, according to the definition of signal-to-noise ratio, the signal-to-noise ratio formula of four-light coherent mixing is derived. By using the formula, theoretical and numerical analyses are carried out from three aspects of coherence length, receiving aperture, and mismatch angle. The optimal relationship between coherent length and receiving aperture is obtained.

In the existing high-resolution and large-scale target remote sensing image object detection, the traditional method cannot achieve airport target recognition from optical remote sensing images quickly and accurately due to the single feature extraction and slow speed. Inspired by the hierarchical cognition of the human visual system, the continuous learning of residual-based convolution neural network (CLRNet) suitable for medium and high resolution optical remote sensing images is proposed. Firstly, the depth residual block is constructed as the feature extraction network. Secondly, the continuous learning method is used to fine tune the airport detection model from the massive remote sensing data. After continuous learning process, the model with strong robustness and low forgetting degree is obtained. The model can accurately and quickly identify airport from optical remote sensing images under massive and complex backgrounds. Our model has a better recognition effect for airports covered by thin clouds or incompletely captured by satellites. The domestic Jilin-1 satellite image dataset is selected for testing. Experiments show that the accuracy of the detection method mAP (IoU is not less than 0.5) can reach 0.9613, and the detection speed can reach 0.23 s per scene.

In the harsh measurement environment, the high-frequency interference has a great impact on the harmonic signal and reduces the measurement accuracy of gas parameters when using absorption spectroscopy method. To improve the target detection performance, frequency multiplexing based on high-frequency reference signals is proposed. The method can extract interference signals and correct the transmitted intensity of reference signal by using a high-frequency reference signal, thereby accurately extracting the harmonic sighanl of detected signal, improving the accuracy of gas parameter measurement and expanding the application range of absorption spectroscopy method. The numerical simulation and the establishment of experimental measurement system to measure the molar fraction of methane verify that the method has the advantages of excellent high-frequency interference suppression and high measurement accuracy of gas parameters.

Aiming at the fringe count error (FCE) in the spaceborne infrared Fourier spectrometer, the reason for its formation and its influence on the recovered spectrum are systematically introduced. Further, detection and correction methods are proposed. The number of FCEs can be accurately obtained by linearly fitting the shifted phase of the interferogram. The accuracy of the method is verified via a theoretical simulation, resulting in an error as low as 1.25%. Actual data are used to verify the proposed method.The verification results show that the method can achieve better detection and correction.

To obtain the degradation ruler and mechanism of the solar cell containing distributed Bragg reflectors (DBR) at electron irradiation, Macleod software was used to design suitable DBR for upright metamorphic GaInP/Ga(In)As/Ge triple-junction solar cell, and 1 MeV electron irradiation experiment was carried out on the solar cell. Al last, the electrical properties degradation was analyzed by the method of mathematically fitting. The results show that the theoretical highest reflectance is basically consistent with the experimental value. With the increase of irradiation fluence, the electrical parameters deteriorate is seriously and the degradation rate of short-circuit current is larger than that of open-circuit voltage, the external quantum efficiency degradation of the solar cell in long wavelength is gradually serious, and the degradation rate of short-circuit current in the Ge sub-cell is larger than that of other sub-cells. With the increase of irradiation influnce,the maximum reflectance of the DBR gradually degrades in bandwidth area under the same irradiation condition, but the short-circuit current can still be improved when the irradiation fluence is less than 2×10 15 e/cm 2, showing that the DBR has positive effects on anti-irradiation.

In order to investigate the influence of different primary colors on observers’ color matching accuracy and observer variability, LED-Panel with 6 channels (R1R2G1G2B1B2) was employed and 4 corresponding combinations with different RGB primary colors were generated. 24 observers (aged from 21 to 25) with color normal vision were organized to carry out the color matching experiment. There are 6 colors, including white and 5 CIE recommended colors were displayed on NEC-PA242 professional display as target colors, and in total, 720 sets of data were collected. CIE 1976 u'v' chromaticity values were used to analyze the color matching accuracy. The results indicated that higher accuracy was occurred in the combination of 636 nm-534 nm-452 nm,with Δ(u', v') value of 0.0062, while the accuracy was poor based on 636 nm-524 nm-472 nm, with Δ(u', v') value of 0.0084. The observer's covariance chromaticity ellipses were constructed using the inverse covariance matrix parameters of u'v' value of the observers’ matching data. The results showed that based on the combination of 636 nm-524 nm-452 nm, the average size of the ellipse was the smallest (S=0.0055) and the largest size was occurred in the combination of 636 nm-508 nm-452 nm (S=0.0094). The color matching accuracy and the observer variability were less affected by the red channel, and more affected by the peak wavelength positions of the blue and green channels.