The extinction coefficient, depolarization ratio, and haze height of atmospheric aerosols during hazy weather are used to realize real-time atmospheric monitoring and study the optical characteristics of aerosols in hazy weather conditions. These characteristics are retrieved based on the observation data obtained from a multi-wavelength aerosol lidar at the Haidian Meteorological Bureau in Beijing, China, from November 4 to 7, 2017. Combining these characteristics with the meteorological parameters obtained from the sounding data, the air mass source and direction during hazy weather conditions are analyzed using the hybrid single particle Lagrangian integrated trajectory (HYSPLIT) model. The analysis concludes that increased relative humidity, low wind speeds, and the presence of an inversion layer are important factors for the formation of hazy weather conditions. The transportation of pollutants through the Hebei Province influences the formation of the haze in Beijing's Haidian District, and haze dissipation is affected by the northwest wind, which provides main conditions for the upward diffusion and disappearance of pollutants.

During the inversion of atmospheric extinction coefficient, choosing the boundary value of the extinction coefficient is critical. In this study, a new method based on lateral Steffensen-like third-order method is proposed for determining the boundary value of atmospheric aerosol extinction coefficient. To confirm the method reliability, the proposed method is applied to both simulated and actual measured echo signals. Results show that the proposed method has high iterative speed and converges to the boundary value of extinction coefficient after a few iterations, which can invert atmospheric extinction coefficient more accurately.

This study presents a stochastic parallel gradient descent (SPGD) algorithm based on the optimization of square of wavefront aberration gradient to improve the convergence speed of the SPGD algorithm for correcting the wavefront aberrations. Based on the principle of the SPGD algorithm, this study analyzes the constrained factors of the convergence speed in the algorithm iteration. Further, the linear relation between the square of wavefront aberration gradient and disturbances from random aberration is analyzed by deriving the theoretical formula; subsequently, the square of wavefront aberration gradient is approximately calculated by using the far-field spot's normalized second moment, and the optical correction of wavefront aberrations is finally realized. The convergence speed and correction effect of the proposed SPGD algorithm are then analyzed using numeric simulations and compared with those of the previous SPGD algorithms. Finally, a wavefront sensorless adaptive optics correction experiment for Fresnel zone plate wavefront aberrations is performed to validate the performance of the proposed SPGD algorithm. The numeric simulation and results of correction experiments consistently denote that the proposed method has a high convergence speed and a robust corresponding adaptability.

The single-light-source dual-optical path visibility meter is a kind of camera type visibility meters, and its light source and optical path system directly affect the accuracy of visibility inversion. In view of the error caused by the instability of the light source and the split light path in the original system, the integrating sphere is used as the improved source of the single-light-source dual-optical path visibility meter, and the near and far reflection end is used to split an integrating sphere light source. The near and far light spots captured by the camera are extracted and brought into Koschmieder's law. The experimental results show that compared with the LED light source, the integrating sphere light source proposed in this paper reduces the uniformity error by 5%. Compared with the transmission type visibility meter LT-31 and the scattering type visibility meter VPF-730 under different visibility conditions, the root-mean-square (RMS) relative error and relative average error of this scheme are much less than 20%, which is in line with the WMO's error requirement of the visibility meters.

An adaptive digital predistortion (DPD) technique is proposed to eliminate the nonlinear distortion in visible light communication systems based on orthogonal frequency division multiplexing (OFDM). In particular, the nonlinear distortion in the LED channel is eliminated by using the proposed adaptive DPD technique with two feedback signals, whereas the linear distortion in the LED channel is eliminated by preequalization in the frequency domain. The proposed scheme can effectively improve the performance of visible light communication systems. In particular, the experimental results denote that the proposed adaptive DPD scheme can effectively eliminate the out-of-band and in-band noise of OFDM.

Herein, for the propagation and interaction of finite energy Airy-Hermite-Gaussian (FEAHG) beams in a medium with a potential well, a linear parabolic (Schr dinger-like) equation was applied as the theoretical model in a split-step Fourier numerical simulation. Results show that the optical field structure of FEAHG beams rotates in the medium with a potential well. The greater the intensity of the potential well is, the shorter the propagation distance for the optical field structure completing rotation is. In addition, further investigation of the interaction between two FEAHG beams with different initial intervals in the medium with a potential well reveals that the number of light spots during propagation changes.

The object tracking method based on least soft-threshold squares deals with the appearance change and outlier of video well. However, when the object subspace is influenced by interference such as posture change or occlusion, the tracking robustness is not completely effective. To solve this problem, this study proposes an online object tracking algorithm which combines least soft-threshold squares with compressed Haar-like feature matching in the framework of Bayes lemma. First, we employ the quantitative occlusion for the least soft-threshold squares based tracker to measure the extent of interference of outlier of observed samples. Then, we sieve the observed object again with the compressed Haar-like feature matching when the single-frame matching response of the tracker is very low. Meanwhile, by reducing the number of independent observed samples through the observed confidence coefficient, the computation complexity can be reduced. The experimental results show that the proposed method can be more effective than other methods.

To overcome the problems that captured image contains a considerable noise and affects the processing result, and beetle swarm optimization (BSO) algorithm is easy to fall into the local optimal solution in texture image processing, an improved sine cosine strategy based beetle swarm optimization (SCBSO) algorithm is proposed and applied to low-illuminance texture image enhancement. First, a logistic model is introduced to increase the diversity of the initial solution group. Then, combined with the SCBSO, the search strategy of the algorithm is improved and time-varying acceleration factor is added to realize the automatic updating of the parameters, thereby improving the convergence speed and search accuracy. Finally, the improved SCBSO algorithm is combined with the chromosome structure to achieve an accurate search for the optimal grayscale distribution of the image. In a standard function test, the SCBSO algorithm shortens the performance time by 16.56% and 14.78% compared to the original algorithm under two categories of functions. The image contrast is enhanced and the natural characteristics are better preserved. As compared with the comparison algorithm, the lightness order error (LOE) of the SCBSO algorithm is reduced by 37.8%, the visual information fidelity (VIF) is increased by 15.3%, and the peak signal-to-noise ratio (PSNR) is increased by 12.9%. The textural features of the image are well preserved during denoising.

In this study, a classification method is proposed based on a circular convolutional neural network (CNN) to deal with the issues of over-fitting and fine-grained classification of the malignant and highly deteriorated thyroid nodules. First, the Xception network and long short-term memory network (LSTM) are used as two non-interference parts. Next, the features of thyroid nodule samples are separately extracted to obtain two weight matrices, which are subsequently merged into a single weight matrix using the Merge algorithm. Further, the single weight matrix is imported into the CNN for feature extraction and pooling. Finally, the Softmax function of L2 regularization is used as a classifier to complete the training and testing of the circular CNN. Our experimental results denote that the accuracy of the fine-grained classification of malignant thyroid nodules is 87.00%, denoting a good feature-extraction capability.

In this study, a text interference elimination algorithm is proposed based on the total variation (TV) model with respect to the visual influence of the text in an image. Roberts operator and binary morphology are applied to detect and accurately locate the interferential text area in an image. Subsequently, the interferential text can be effectively eliminated by considering the determined interferential text area as the constraint condition of the TV model and solving it. The simulation experiments denote that the proposed method is efficient and practical for the detection and elimination of interferential text from images.

The wide use of deep learning and convolutional neural networks in recent years has been one of the main reasons for performance improvement in image semantic segmentation. However, the current image semantic segmentation algorithms have certain drawbacks. For example, the semantic information is not fully used, and the discrimination between different semantic categories is not large enough. Therefore, we propose a hierarchical context information mechanism to achieve better semantic segmentation performance. The long-range dependency information and local context information (extracted from the hierarchical features) are conducive to enriching information and discriminating among different types of semantic categories. Our experiments demonstrate the effectiveness of the proposed method. The proposed method achieves a segmentation accuracy of 77.2% on Cityscapes val dataset.

An algorithm is proposed to enhance the optical interferometry fringe patterns based on the multiscale Retinex algorithm, and its principle and implementation are described. Three different scales, i.e., large, medium, and small, are selected, and the advantages of each scale are combined. Further, different weights are considered for different scales according to the characteristics of the optical interferometry fringe patterns. Subsequently, the proposed method is compared with two commonly used optical interferometry fringe pattern enhancement methods by the experiment, and the effectiveness of the proposed method is demonstrated through subjective and objective evaluations. The experimental results denote that the algorithm proposed in this study can significantly improve the visibility of the fringe patterns exhibiting low contrast and overcome the difficulty associated with the subsequent processing of fringe patterns owing to uneven illumination.

Herein, a method to represent a mitosis event is proposed by using the feature of cell evolution in the time domain. First, three kinds of features are extracted for each frame of the mitotic sequence, i.e., the generalized search tree, scale invariant feature transformation, and convolutional neural network. Each series of extracted features is handled using the pooling method in spatial and temporal dimensions. Subsequently, the processed series of pooling features are combined into a vector to represent the final mitotic event characteristics. Finally, the combined feature vector is used as the classifier input, and the traditional machine learning method of support vector machine is used to address the mitotic recognition problem. The experimental results denote that the proposed method is superior to the traditional method with respect to the precision and recall rate and is more appropriate for mitosis detection applications.

The theoretically accurate prediction for cracks in concrete structures is difficult to realize. Applications of the optical frequency domain reflectometry (OFDR) technique in the identification and monitoring of cracks of concrete structures are studied by using the model test of concrete beams. Test results show that the OFDR technique can realize the warning of 0.002 mm micro-crack in concrete structures. The crack position can be located with a spatial resolution of 1 cm. The development process of cracks can also be monitored. Compared with other distributed monitoring techniques, the OFDR technique is more accurate in crack location and development monitoring and has a broad application prospect.

The effects of two polarization components of different polarization states on the interference fringe contrast are investigated based on the principle of Michelson interferometer. Simultaneously, a Matlab-based interference fringe contrast calculation method is used to quickly evaluate the fringe image contrast. Finally, the relationship between the polarization components of different polarization states and the interference fringe contrast can be obtained by analyzing the experimental data. The results demonstrate that this research can be applied to either interference instruments or interference experiments. Introducing polarization modulation at the light source to obtain high-contrast interference fringe images will improve the measurement accuracy.

Herein, V8C7 nanoparticles were mixed with 316L stainless steel powder via ball-milling for selective laser melting (SLM)-assisted three-dimensional (3D) printing. Then, a lattice structural V8C7/316L composite was printed via SLM. Using optimized SLM process parameters, the density of composite solid parts as high as 99.4% was achieved. In addition, during the laser melting-solidification process, VCx reinforcements were generated via the decomposition-precipitation mechanism of V8C7 as the nanoscale nucleation sites. Consequently, the dispersed VCx nanoparticles were preferentially distributed along the austenite grain boundaries, thereby further inhibiting the grain-coarsening of austenite during rapid solidification. The nano-VCx reinforcements and the ultrafine metal-matrix grains that were nearly equiaxed, contributed to a significant increase in the specific strength of V8C7/316L lattice structures.

Herein, a novel process of direct deposition (cladding) of wires with multi-beam laser and on-centerline heat is proposed and tested. In the single-pass deposition experiment, the efficiency of laser deposition of hot wire is higher than that of cold wire, and the clad beads and the substrate easily bond together. Hot wire is used in tests for deposition with multidirectional laser scanning, tests for single-layer multi-pass lapping deposition, and tests for deposition of thin walls and solids. Results show that stable and continuous wire-feeding deposition can be finished in the proposed process. The surface of the finished clad bead is smooth with uniform sections, and lacks burrs or spheroidization defects. Moreover, the deposited surface of the sample is substantially planar in any scanning direction. The deposited cuboid solid produced by this process demonstrates that the new process can be used to form three-dimensional complex structures.

To improve the corrosion resistance of the 42CrMo alloy, a Stellite-6 coating was prepared on the surface of the 42CrMo alloy by laser cladding. The average corrosion rate and electrochemical characteristics of the Stellite-6 coating in a 3.5% NaCl solution were studied through an immersion experiment and electrochemical workstation. After the immersion experiment, the samples were analyzed via scanning electron microscopy (SEM). The test results show that the average corrosion rate of the laser cladding Stellite-6 coating is considerably lower than that of the matrix. In addition, the corrosion behavior of the coating in a NaCl solution is mainly pitting corrosion. Laser power considerably affects the quality of the cladding layer. When the laser power is 2500 W, the open circuit potential is -0.15 V, self-corrosion current is 3.294×10 -3 A/cm 2, and impedance is 6742.5 Ω·cm 2. Furthermore, the nucleation and grain growth rates of the molten pool increase, the grain is refined, the microstructure of the molten pool primarily contains fine equiaxed crystals, and the structure is more dense and uniform. The Cr3C2 passivation film formed on the corrosion surface inhibits the anode activity. In addition, the passivation film is clearly protective, which improves the corrosion resistance of the coating.

The LD pumped Nd∶YAG laser texturing equipment exhibiting a short pulse is established to explore the evolution process of the crater pit morphology generated by laser texturing. The effects of the process parameters, including the single pulse energy, pulse width, number of pulses, and defocusing amount, are investigated using the single factor method. Further, the evolution law of the texturing pit morphology on the 3A21 aluminum alloy sample surface is analyzed using an almost flat-top laser beam. The results denote that the pit morphology changes from an inverted trapezoid to an inverted triangle with the increasing single pulse energy. The competing metal slag and meteorite crater are formed around the pit as the pulse width narrows; further, the pit morphology and its characteristic parameters are observed to become stable under the action of sufficient pulses, and the micro-protrusion gradually decreases until it vanishes with an increase in the defocusing amount. These results establish a solid technological foundation for the 3A21 aluminum alloy with high-quality and morphology-controllable laser texturing.

Arc additive manufacturing technology with high efficiency, low cost, short manufacturing cycle, and no molds represents a new method for the production of large and complex metal structures. In this study, based on the ANSYS parametric design language APDL, the thermophysical parameters of ER50-6 carbon steel-welding wire are obtained using Jmatpro and the dynamic simulation of arc additive manufacturing process is realized by the birth-death-element method. The feasibility and correctness of the simulation of the temperature variation in welding and post-weld cooling are verified via comparison with the experimental results. The variation rule of the temperature field in arc-additive manufacturing under different substrate thicknesses is analyzed to obtain optimal substrate thickness. Furthermore, the study explores the variation of the temperature field during the multi-layer additive manufacturing process of straight, single wall parts. This provides an important theoretical basis for forging modification process of the formed parts based on additive manufacturing residual temperature.

It is difficult to recognize a certain text of interest in the image using the optical character recognition (OCR) method; particularly in natural scenes, the recognition results usually contain a large number of noisy texts. To address this problem, a model termed bidirectional long short term memory-condition random field (BLSTM-CRF) based on a recurrent neural network for extracting texts of interest is proposed in this study. First, a BLSTM network is implemented to capture the context information of the sequence obtained by the OCR method, thereby obtaining feature sequences. Second, the relationships between the model features and tags are established by introducing the CRF. Then the text of interest can be obtained through the tags. Experimental results indicate that the proposed method can achieve an accuracy of 88.52% on YNIDREAL dataset. Compared with the CRF model, the accuracy of the proposed method is improved by 16.39 percentage points, which proves the feasibility and robustness of the proposed method.

This study proposes a new cosine distance loss function based on the traditional Softmax loss function and Island loss function to guide the learning of deep convolution neural networks and solve the problem of large difference in intra-class expressions and high similarity in inter-class expressions in the facial expression recognition tasks. The proposed method not only reduces the difference of intra-class features in the feature space, but also increases the distribution of inter-class features, thereby improving the effect of feature discrimination. After conducting several experiments and analyses, the accuracy of the facial expression recognition algorithm is observed to be 83.196% based on the RAF-DB facial expression dataset, and the effect is better than those obtained using the Softmax loss function and the Island loss function. Furthermore, the proposed algorithm is highly superior with respect to the facial expression recognition tasks.

This paper proposes a point cloud registration algorithm based on extended point feature histogram (EPFH) feature. In the proposed algorithm, the strategy of rough registration prior to fine registration is adopted. The purpose of this paper is to overcome the problems of low registration accuracy and slow speed which are encountered in traditional registration methods. Initially, the intrinsic shape signature (ISS) feature-detection algorithm is used to obtain the salient feature-point set on the point clouds. Then, the EPFH feature description is applied on these feature points. Subsequently, the rigid body transformation matrix is estimated using the sampling consistency algorithm to complete the initial registration of the point clouds and the target point clouds. The k-d tree-based iterative nearest-point algorithm is used to implement the fine registration of the two-point clouds. Finally, experimental verification is performed by applying the proposed algorithm to the public data set and the terracotta warrior data set. The experimental results show that the proposed algorithm exhibits higher registration accuracy and higher speed than traditional methods.

This study proposes a hybrid tracking registration method based on saliency detection for solving the insufficient robustness of tracking registration in a complex environment and the huge feature-searching space of the augmented reality system. Based on this method, the mean-shift iteration is initially employed to predict the candidate target position. Subsequently, a two-dimensional Gaussian function is constructed with a peak at the target center, and a visual saliency map of the fusion center prior is generated. Next, the target salient feature is extracted, and the mean-shift algorithm is applied to tracking. Furthermore, the similarity measurement coefficient is used to determine whether to utilize the deformation diversity similarity-matching algorithm for relocating the target. Finally, we construct a multiscale-space fast directional binary description algorithm that performs the feature detection and matching calculation with respect to the local target area to obtain the registration parameters, and the virtual-real fusion is completed. The experimental results demonstrate that the proposed method effectively solves the problems of tracking instability and low accuracy of target detection by using the target-tracking algorithm in the cases of background clutter, target occlusion, and target loss, improving the stability and robustness of the augmented reality system.

To make the face recognition features learned from the convolutional neural network easier to identify, this paper improves the angular distance loss function A-Softmax by incorporating the facial attributes, such as gender, age, and race, into the training process. By using an attribute-driven loss function and regularizing the feature mapping with attribute proximity, the experimental result shows that more attribute-related discriminating features are learned by the proposed method. The improved algorithm has achieved good results in the face verification datasets, such as LFW, CFP, AgeDB, and MegaFace, verifying the effectiveness of the improved algorithm.

Herein, we study the Heisenberg two-qubit XYZ chain in a z -directional non-uniform magnetic field by considering concurrence to be the entanglement metric. Further, we explore the properties of ground-state entanglement in different parameter ranges, calculate the critical magnetic field value, and discuss the relations between the average/critical magnetic fields and the quantum phase transition. We also analyze the interaction of the two adjacent qubits of the z -component J z of the spin and investigate the influences of the anisotropy parameters γ B and γ J on the thermal entanglement of the Heisenberg model. The relations among the anisotropy, coupling parameter J z , and thermal entanglement can be better illustrated by drawing images. The results denote that in a two-qubit system at an effective temperature T , the critical magnetic field Bc decreases and the entanglement gradually disappears as γ J increases when the coupling parameter J z is equal to 0. However, when the coupling parameter J z is greater than 0, the ranges of magnetic field and temperature with the maximum degree of entanglement increase with increasing J z for appropriate values of the anisotropy parameters γ B and γ J , effectively enhancing the entanglement.

To investigate the influence of oceanic turbulence on single photon capture probability, this paper derives the single photon capture probability model in the turbulent ocean environment. The entire research is based on Hermite Gaussian beam and Nikishov oceanic turbulence model and follows the rules of Rytov approximation and Kolmogorov spectrum. The influence of different oceanic turbulence parameters on single photon capture probability is also analyzed. The results show that the single photon capture probability decreases significantly after one hundred meters of transmission in the turbulent ocean environment. Besides, as the temperature variance dissipation rate increases, the single photon capture probability decreases. When the salinity factor dominates the oceanic turbulence, the single photon capture probability also decreases and the salinity change has a significant impact on the single photon capture probability of the turbulent ocean environment. Increasing the receiver aperture value and shortening the pulse interval can effectively improve the single photon capture probability. However, an increase in the temperature variance dissipation rate and a decrease in the transmission distance can reduce the influence of the pulse interval on the single photon capture probability.

In this study, we propose a theoretical scheme for generating and stabilizing a maximally entangled state of two atoms trapped in two directly coupled cavities. Herein, two-level atoms are coupled to the two quantized cavity fields with large detuning and that each atom is subjected to non-resonant driving by two weak classical fields. By introducing nonlocal bosonic modes, we find that the two identical atoms are resonantly coupled to one of the collective optical cavity modes, whereas they are coupled to the other collective optical cavity mode with large detuning. The target steady state can be prepared using the combined effect between the unitary dynamics of the classical fields and the dissipative process of the collective optical cavity modes in the dressed-state subspaces. The steady state can be prepared without requiring any special initial state. The numerical simulation shows that the Bell state can be obtained with high fidelity and that our scheme is robust to fluctuations of the system parameters.

Resultsof obstacle detection and passable area extraction are fused to detect the passing ability of the passable area. Multi-road real-time vehicle experiments show that the proposed algorithm can accurately detect obstacles and passable road areas, with an average detection accuracy of 94.13% and an average processing time of 69 ms, meeting the real-time requirements of intelligent vehicles.

The application of holographic technology has enabled three-dimensional (3D) displays to develop rapidly, providing users an unparalleled visual experience. The calculation and generation processes of holographic images and video generate a considerable amount of data, causing great difficulties for transmission and storage. To meet the high-definition and real-time requirements of 3D displays, the compression of holographic data is critical. Since holographic images have distinct characteristics than ordinary images, it is difficult to achieve optimal results using existing compression techniques. This study introduces the main technical challenges of holographic compression and summarizes the commonly used metrics for holographic data compression. The cutting-edge technologies proposed by national research groups related to holographic-image-coding-quantization processing, transform-coefficient simplification, optimization of the international standard hologram format, and development of a new standard framework are discussed in detail, and their respective advantages and disadvantages are detailed. Finally, a future direction for research on holographic-compression technology is proposed.

Ultra-resolution optical microscopy, which breaks through the diffraction limit, is typically used to observe structural characteristics and interactions of subcells. This method has great significance for the study of genomes and tackling major diseases. This paper begins by introducing the working principles of four typical super-resolution microscopic imaging techniques. Subsequently, research progress in the areas of multi-color fluorescence imaging and three-dimensional imaging is emphasized. Finally, recent applications of super-resolution optical imaging for cell activity observation, bacterial cell research, and cytoskeleton observation are reviewed both domestically and abroad. The main factors reportedly affecting the imaging quality are poor light stability of the fluorescent protein, low light activation rate, and weak fluorescence intensity. Solution of the above problems will lead to the widespread use of super-resolution optical imaging for the three-dimensional imaging of thick samples, multi-color fluorescence imaging, and fast imaging of living cells, ultimately furthering the development of life science and materials science.

The discovery of multi-principal high-entropy alloys breaks the shackles of complex intermetallic compounds produced by the traditional multi-component alloys. Based on its unique high-entropy effect, a simple phase structure that exhibits excellent comprehensive performance can be generated. The high-entropy alloys exhibit many unique properties, including mechanical properties, high-temperature properties, and corrosion resistance. This study introduces the composition control of high-entropy alloys. Further, the research status of high-entropy alloy coatings, which are prepared using the laser cladding technology, with high hardness, thermal stability, high-temperature oxidation resistance, corrosion resistance, and wear resistance is reviewed. The development foreground of the laser cladding high-entropy alloy coatings is prospected.

In this study, eight LED lighting sources with a large color-temperature range (2678-7258 K), especially the ultralow-color-temperature LED, are selected to quantitatively analyze the variation in the non-visual biological effect of LED lighting with the color temperature. The spectral distribution data for the visible band at 380-800 nm are collected. The color-coordinate Z value of the 1931CIE-XYZ standard chromaticity system, the blue-light proportion RC of the 415-508-nm band, and the rhythm factor KC denote that the non-visual biological effect of the LED light source increases along with an increase in the color temperature from 2678 K to 7258 K. Furthermore, the color-coordinate Z value of the 1931 CIE-XYZ standard colorimetric system and the blue-light proportion at 415-508 nm can be used for quantitatively characterizing the strength of the non-visual biological effect. The functional expressions with respect to the changes in the color-coordinate Z value, the blue-light proportion RC in 415-508 nm, and the rhythm factor KC with the color temperature are Z=0.01499+5.24009×10 -5×TC, RC=1.41985+0.00508×TC, and KC=0.11895+6.06953×10 -4×TC, respectively. Further, the corresponding squares of the correlation coefficient R2 are 0.94427, 0.93589, and 0.9598. The functional expressions of the color-coordinate Z value and the blue-light proportion RC at 415-508 nm with respect to the rhythm factor KC are KC=11.40331×Z-0.01209 and KC=0.11698×RC + 0.00618, respectively; the squares of the correlation coefficient R2 are 0.97991 and 0.97644, respectively. These observations can be used for the quantitative analysis of the non-visual biological effect of the LED lighting sources and provide guidance for the development of high-quality-health LED illumination products.