In this study, the intensity and phase distributions of a ring Airy-Gaussian vortex beam propagating in atmospheric turbulence are numerically studied based on the split-step Fourier method. The propagation characteristics of the ring Airy-Gaussian vortex beam in atmospheric turbulence are discussed and compared to those in a free space. The impacts of propagation distance and distribution factor on the average intensity of ring Airy-Gaussian vortex beam in atmospheric turbulence are revealed. Results demonstrate that the phase wavefronts of the beam during the propagation process are distorted due to the random disturbance of the atmospheric refractive index. The equiphase line of the beam turns into an arc due to the orbital angular momentum, and the arc becomes non-smooth gradually with the increase of the propagation distance. In addition, the variation of the average intensity of ring Airy-Gaussian vortex beam with the propagation distance in atmospheric turbulence follows the Airy function distribution. The smaller the distribution factor is, the more obvious the variation of the phase wavefronts with the propagation distance is. The stability and propagation quality of the beam are affected by the distribution factor.

Measurement of topological charges (TC) for vortex beams is a key technology for the practical application of vortex beams. Herein, combining the gradual change of the gradually-changing-period grating with the complete symmetry of the annular grating, a new type of gradually-changing-period annular grating for measuring higher-order TC of vortex beams is developed by optimizing the structure of the ring grating with an additional periodic gradient. Numerical and experimental results demonstrate that a far-field diffraction pattern is formed by collimating the vortex beam onto the ring grating. The results demonstrate that the variation of the patterns accurately corresponds to the orbital angular momentum states of the vortex beams. Consequently, the number of TCs measured in the experiment is ±30. It provides a new measurement method for vortex beams.

This study proposes a new method to detect the orbital angular momentum of vortex beams using interferometry. Experimentally, when a spatial light modulator is used to generate a vortex beam, coaxial interference directly occurs only by a modulated vortex beam and an incident Gaussian beam which is not fully modulated, producing a perfect petal-like interference pattern. The number of interfering petals coincides with the value of the orbital angular momentum carried by the vortex beam; thus, a separate reference beam is not required when we detect the orbital angular momentum of vortex beams produced by the spatial light modulator. This study analyzes the interference pattern of Bessel-Gaussian and Laguerre-Gaussian beams, which can reflect the orbital angular momentum of the beam, i.e., the azimuth factor and the positive and negative values of the orbital angular momentum together with the radial factor of the Laguerre-Gaussian beam. Notably, the interference results are clear, and the stability is high.

This study investigates the application of a pilot signal in an orthogonal frequency division multiplexing (OFDM)-intensity modulated optical wireless communication system to reduce the peak-to-average power ratio (PAPR). The pilot-signal-phase selection is based on a selected mapping (SLM) algorithm. The maximum-likelihood criterion is used to estimate the pilot signal at the receiver. In a reliable communication link with a bit error rate (BER) of less than 10-3, the BER of the pilot-assisted optical OFDM system is basically the same as that of the basic optical OFDM system (without pilot and PAPR reduction technology). Under the condition of a high-order (magnitude M>4) constellation, the pilot-assisted PAPR reduction technology is superior to the traditional SLM technology. Compared with the basic optical OFDM system, the pilot-assisted optical OFDM system can reduce the PAPR by approximate 2.5 dB under suitable conditions. Furthermore, the pilot-assisted optical OFDM system can reduce PAPR more effectively at a moderate point of M=64 and a complementary cumulative distribution function of 10 -4, without reducing the system's error performance.

A stochastic configuration network (SCN) introduces inequality constraints to limit the assignment of input weights and biases. The network can approximate arbitrary mathematical function and data model as the number of hidden nodes gradually increases. In the process of SCN construction, the properties of the network itself and the ill-posed and ill-conditioned problems of the sample data may cause over-fitting of the network model. This study proposes an improved SCN model based on the Dropout technology, called Dropout-SCN, to improve the recognition accuracy of the network model by adaptively constraining the output weight distribution. We then perform a verification using optical fiber data. Compared with the traditional SCN and L2 norm regularized SCN models, the Dropout-SCN model has a lower test error, which effectively prevents the network over-fitting problem and improves the recognition accuracy of the intrusion signals in the optical fiber pre-warning system.

Vehicle recognition has a large amount of computation and complex extracted features, while the traditional neural network has incomplete features defined by end-layer features. Therefore, we propose a new vehicle recognition method based on multi-layer features of the convolutional neural network (CNN) and support vector machine (SVM). Firstly, the CNN model is constructed based on the traditional AlexNet model, while the optimal vehicle recognition model is obtained by analyzing the effect of parameter change on the accuracy. Further, the multi-layer vehicle feature map is extracted, and a multi-attribute vehicle feature vector is formed by the serial fusion method and the principal component analysis to enhance the comprehensiveness of the feature and reduce the computational complexity. Finally, vehicle recognition is realized by using the SVM classifier instead of the output layer of CNN, which improves the generalization and error-correction abilities of the model. The experimental results reveal that the proposed method has remarkable performance in classification accuracy and recognition speed; additionally, it has better robustness, compared with the traditional methods.

Owing to complex changes in face pose and the obvious influence on face recognition performance, a new approach is proposed for multi-pose face recognition based on the fusion of the LSTM (long short term memory network) and convolutional neural network-based cascade deep network (LCCDN) and incremental clustering. First, a LCCDN is designed to locate facial landmarks, and the memory function of the LSTM in LCCDN is used to explore the spatial contextual information between facial landmarks; then, facial landmarks are initialized. A CNN network model is used to fine facial landmarks by employing a coarse-to-fine strategy. Next, we consider the facial landmarks as face orientation descriptors. Simultaneously, to adapt to the dynamic updating of the face pose, an entropy-induced metric-based incremental clustering method is used to construct a face-pose pool by dynamically clustering head poses. In this manner, multi-pose face recognition is realized by establishing various face classification models with different poses. The recognition accuracies using the CAS-PEAL-R1, CFP, and Multi-PIE datasets are 96.75%, 96.50%, and 97.82%, respectively. In addition, comparisons with existing multi-pose face recognition methods highlight the superior performance of the proposed method.

This paper proposes a method to monitor video-based pedestrian re-identification based on adaptive weight assignment using deep learning. The contribution rate of the pedestrian attribute to the classification is calculated based on the training difficulty of the pedestrian attribute reflected by verification loss along with the correspondence information entropy of the pedestrian attribute and pedestrian category. The training loss weight of the pedestrian attribute multi-task classification is adaptively solved. The negative transfer problem caused by the same loss weight is assigned to improve the generalization abilities of each task learner and pedestrian re-identification. The trained model solves the attribute probability and combines the conditional probability to discriminate the pedestrian category using the mapping relationship between the pedestrian attribute and the pedestrian category in the existing data set, which overcomes the problem that cannot identify pedestrian category because of the dramatic change of the pedestrian appearance. Based on objective and quantitative comparison with similar methods on different public data test sets, the results show that the method is effective and feasible.

Orientation detection is not considerably performed for describing the microstructures of three-dimensional (3D) fibers. Therefore, a novel 3D fiber orientation detection method is proposed based on a facet model for converting the 3D fiber orientation detection into two-dimensional (2D) plane detection, reducing the computational complexity and realizing the accurate detection of different diameter fiber orientations. The size of the model window should be slightly larger than the fiber diameter for obtaining the optimal results. The experimental results denote that the proposed method is more accurate and fast when compared with the existing typical algorithms at obtaining the 3D fiber orientation data in case of different diameters.

This study proposes a Poisson surface reconstruction algorithm based on the improved normal orientation to verify the inaccurate reconstruction results obtained because of the inconsistent normal orientation of the three-dimensional point cloud in the Poisson surface reconstruction process. First, the original point cloud is segmented into a number of cubic voxels using the bounding box and each valid voxel is marked as “position known” or “position unknown”. Then, a k-dimensional tree is used to reorder the point cloud in each valid voxel; the principal component analysis method is used for normal estimation. The new order is used as the normal orientation of propagation to realize the local orientation of the point cloud normal. For voxels marked as “position known”, the global orientation of the normal is completed according to the physical position of the voxel centroid. For voxels marked as “position unknown”, the global orientation of the normal is implemented according to the normal of the adjacent voxels. Finally, Poisson surface reconstruction is performed on the point cloud in combination with the normal orientation results. Experimental results demonstrate that the proposed algorithm can orient the normal of the complex surface point cloud to the same side and that the deviation of the normal estimation is approximately 0. Thus, the proposed algorithm can properly realize Poisson surface reconstruction and improve the accuracy of reconstructed surface.

The latent low-rank representation (LatLRR) is applied in the field of pattern recognition as a classical unsupervised feature extraction algorithm. However, the dimensions of the features obtained using the algorithm cannot be reduced. Two low-rank matrices are separately learned by the algorithm such that the overall optimality cannot be guaranteed. Furthermore, the algorithm ignores the samples' residuals in the learning process. This study proposes a joint projection learning algorithm based on the LatLRR to address these problems. First, projection and reconstruction matrices are used to approximate the low-rank projection matrix in the LatLRR such that the algorithm can extract discriminative features while reducing the samples' dimensions. Second, the projection, reconstruction, and low-rank matrices are jointly learned by the algorithm such that they can be mutually boosted. The obtained projection can extract more discriminative features. Simultaneously, the samples' residuals in the process of learning are constrained in the algorithm model. Third, the alternating iterative method is used to solve the model. Experiments on multiple datasets show that the algorithm can effectively reduce the samples' dimensions while further improving the discriminative ability.

We systematically analyze the parameters of rolling guidance filtering and propose a hybrid multi-scale decomposition method based on rolling guidance filtering, utilizing the law of parameter variation. First, the infrared and visible images are decomposed into base, small-scale, and large-scale layers using this method. Second, a fusion rule that combines the pixel and gradient energies is applied to the base layer, and another fusion rule based on an adaptive dual-channel pulse-coupled neural network (DAPCNN) is used to combine the large- and small-scale layers. Finally, the fused image is obtained via inverse hybrid multi-scale decomposition. Compared with other common image decomposition methods, the proposed method can not only extract the image's texture details and preserve its edge features but also prevent the halo phenomenon at the edges. The experimental results show that the proposed method can extract the target information from the infrared image and fuse it into the visible image. Compared with existing fusion methods, the proposed method shows clear advantages not only in a subjective human visual evaluation, but also in several objective evaluation metrics, namely the mutual information, information entropy, standard deviation, non-linear correlation information entropy, and Chen-Varshney indexes.

Since local feature descriptors extracted from an image using the traditional scale-invariant feature transform (SIFT) method are 128-dimensional vectors, the matching time is too long, which limits their applicability in some cases such as feature point matching based on the three-dimensional reconstruction. To tackle this problem, a SIFT feature descriptor dimensionality reduction method based on a convolutional neural network is proposed. The powerful learning ability of the convolutional neural network is used to realize the dimensionality reduction of SIFT feature descriptors while maintaining their good affine transformation invariance. The experimental results demonstrate that the new feature descriptors obtained using the proposed method generalize well against affine transformations, such as rotation, scale, viewpoint, and illumination, after reducing their dimensionality to 32. Furthermore, the matching speed of the feature descriptors obtained using the proposed method is nearly five times faster than that of the SIFT feature descriptors.

Human head detection is an important research subject for counting people and is often considered to be a useful approach for video monitoring. The challenges associated with human head detection include instance occlusion, background interference, and uneven illumination; this study aims to address these challenges through a method based on the regional fully convolutional neural network. Initially, in the feature learning stage, features are acquired using a residual network (ResNet), and the region of interest is obtained through regional proposal networks. Subsequently, a deformable convolution layer is added into ResNet, and the region of interest is provided as input into the pooling layer for deformable position-sensitive mean pooling. Finally, the target location is classified and refined along with the alignment of the proposed position-sensitive region of interest to complete the pooling operation. Further, the anchor generation rules in regional proposal networks are updated to improve the detection effect of the network based on multi-scale head. The detection ability of head targets under complex tasks is improved using an online hard sample mining algorithm; subsequently, the mutual interference between the bounding boxes is reduced by the soft non-maximum suppression. After applying the proposed method to the HollywoodHeads dataset, the average recognition accuracy is confirmed to become 83.24%, which is better than those of other methods in the current literature.

The synthetic aperture algorithm can achieve bidirectional dynamic focusing of ultrasound transmission and reception, thus improving imaging resolution; however, some problems still exist such as low image signal-to-noise ratios (SNRs) and obvious side lobes. To further improve the quality of ultrasound imaging, this study proposes a synthetic aperture ultrasound imaging algorithm called coherence factor and complementary sequences (CFCS) based on coded excitation and coherence factor. First, a pair of Golay complementary sequences are adopted as the excitation signal, and the SNR of the image is increased by increasing the coding length of the transmitted pulse. Simultaneously, the good autocorrelation characteristics of complementary sequences are exploited to eliminate the distance side lobes; thus, the axial resolution of the image is improved. In addition, a coherence factor is introduced to suppress the lateral side lobes in the image. The simulations indicate that, compared with the synthetic aperture algorithm based on Barker coded excitation, the CFCS algorithm can effectively eliminate the distance side lobes; the axial resolution is also improved by 47.4% using the CFCS algorithm. Furthermore, compared with the synthetic aperture algorithm based on coherence factor, the CFCS algorithm's axial resolution and SNR increase by 31% and ~18 dB, respectively.

To achieve a simple and fast calibration of parameters for three-dimensional imaging system with structured light, a method of obtaining a large number of calibration data using a homography matrix is presented. First, the image coordinates and the corresponding world coordinates of the rectangular corner points on the left and right sides of a stereo target are obtained, and the homography matrices of the left and right sides of the target surface from the image coordinates to the world coordinates are then calculated respectively. Next, all the pixels in the rectangular frame of the stereo target in the image are applied to the calculated homography matrix to obtain the corresponding world coordinates. Finally, we calibrate the three-dimensional imaging system using the image coordinates of nearly all the pixels on the target surface of the stereo target in the image and the corresponding world coordinates. A stereo target corresponding to the proposed calibration method is designed. Based on the stereo target, the three-dimensional imaging system with structured light is calibrated and experiments are carried out using the calibrated system. The experimental results show that the calibration method has high calibration accuracy, the reconstructed three-dimensional point cloud image has a good visual effect, and the calibration process is simple, which reduces the cost of calibration and is valuable for practical applications.

We propose a fast polarimetric dehazing method based on the hue, saturation, and intensity color space. Because the intensity is unrelated to color in the HSI color space, the proposed method is used to dehaze the intensity channel of an image, while its color distortion is corrected using a color-constancy method. This method not only shows good image-information restoration capacity but also increases the dehazing efficiency. Compared with the existing dehazing methods, the proposed method can obtain the same or better experimental results and has higher execution efficiency. Our proposed method has potential in applications for real-time haze removal in images and videos.

The vertical wind in a tunnel will change the heat convection and smoke distribution in the tunnel and influence the temperature-sensing error. This study starts from the relevant state departments for the mandatory acceptance of tunnel fire detection (i.e., the alarm signal must be generated within 60 s after ignition). A fiber grating temperature sensing fire detection technology used to collect the temperature field of multiple ignition experiments under different wind speeds and ignition positions is then developed. The temperature field distribution and its dynamic changes under different working conditions are analyzed. Subsequently, an intelligent algorithm for correcting the fire point position based on the temperature field is proposed. The problem of the position error of the fire alarm caused by the longitudinal wind is corrected according to the temperature distribution and the dynamic change of the tunnel fire. In addition, the fire location can be intelligently determined within the specified time.

This study aims to investigate the effect of microstructure on mechanical properties of the GH3536 alloy fabricated by selective laser melting (SLM) technology. Different powder batches are utilized to fabricate the specimens. The microstructures, room temperature tensile property, and high temperature endurance performance are then tested, and the invalidation mechanism is analyzed. The results show that the microstructure of the SLM GH3536 alloy is mainly austenitic phase with M23C6 inside the grain and at the grain boundary. The amount of grain boundary decreases with the increasing grain size, leading to an improvement of the high temperature endurance performance, but a degradation of the tensile property at room temperature. The massive carbide at the grain boundary will reduce the room temperature tensile property and the high temperature endurance performance of the SLM GH3536 alloy. However, the chain carbide at the boundary can improve the room temperature tensile property and high temperature endurance performance.

The variable overlap rate is investigated on the basis of ultrasonic vibrations in the cladding experiment to improve the current limitations in the overlap rate selection. Furthermore, the performance of cladding layer with a low overlap rate is investigated. The initial overlap rates are set to 33%, 50%, and 66%, and observations and comparisons of the macroscopic appearance between the 33% overlap rate and other overlap rates of the cladding layer are conducted. A comparative experiment that employs a 33% overlap rate of the cladding layer without ultrasonic vibration is also performed. The comparative samples are first processed via electrical discharge machining, followed by observations of their metallographic microstructures and evaluations of their hardness and wear resistance. The results show that the macroscopic appearance of the cladding layer of the sample with a 33% overlap rate exhibits wavy stripes, whereas no wavy stripes are observed at the other overlap rates. The good cladding effect is obtained using a 33% overlap rate, with the ultrasonic vibrations deteriorating the quality of laser cladding layer. This analysis outlines a useful approach for selecting the overlap rate when applying ultrasonic vibrations to laser cladding experiments.

Stellite 6 coating is cladded on the stainless steel using a fiber laser. The effect of heat treatment on the microstructure of the coating is investigated at various temperatures. Further, a tribometer with a pin-on-disk configuration at high temperatures (600 ℃, 700 ℃, 800 ℃, and 900 ℃) is used to evaluate the sliding wear behavior of the coating during interaction with alumina for identifying the applicable working temperature range of the coating. The results denote that the coating predominantly comprises carbide eutectics and a Co-rich solid solution exhibiting fcc and hcp structures. The microstructure of the coating is thermally stable at 700 ℃. However, the eutectics are critically decomposed with the secondary precipitation of fine particles after heat treatments at 800 ℃ and 900 ℃. The transformation temperature from fcc to hcp structures of Co-rich solid solution is 700-800 ℃. In case of the Stellite 6 coating, wear at high temperature can be observed during the initial stage of the friction process. The wear loss drastically decreases once the glaze layer is formed. Among the friction temperatures, the wear loss at 700 ℃ is observed to be the lowest. Ploughing and adhesion are the wear mechanisms at 600 ℃. Slight glaze layer is formed on the surface of the coating, which is loosely bound with the coating. At 700 ℃ and 800 ℃, the coating exhibits abrasion and adhesion, and compact glaze layer is formed and bound tightly with the coating. At 900 ℃, the coating is considerably softened, leading to tough plastic deformation during the initial stage of the friction process. Therefore, the applicable working temperature range of the laser-cladding Stellite 6 coating is 700-800 ℃.

This study proposed a defect detection method based on the reflection moiré images to efficiently detect defects on the polished surface of a workpiece. The proposed method located defects by detecting variations in the moiré image reflected by the polished surface. The illumination model of the polished surface was analyzed, and the SHEN-Castan algorithm was used to suppress the edge-step effect of the moiré. Defects were detected and located after performing defect extraction to remove false defect via Gabor transform and maximum entropy segmentation. Experimental results and statistics show that the proposed method can detect surface defects on different polished workpiece surfaces at a detection rate of >92%. The proposed method can be used to independently set the detection resolution ratio of a system, thereby increasing the system’s adaptability to detect different types of defects and considerably improving its extensibility, universality, and practicability. Thus, the proposed method can efficiently detect defects on highly reflective polished workpiece surfaces and has great theoretical as well as economic value.

The scale-invariant feature transform (SIFT) algorithm is the representative approach for key-point detection in the field of digital image processing. A binocular distance measurement system is established herein based on the improved SIFT algorithm by using open computing language (OpenCL) parallel computing as an acceleration method, and a profound study on how to speed up the operation of SIFT algorithm is performed. First, the integral mean blur is selected to speed up the SIFT algorithm operation. OpenCL parallel computing is then used to accelerate it. The parallel optimization of the algorithm is made to be implemented on NVIDIA GPU hardware platforms. The original SIFT matching method is improved to obtain an accurate parallax. Consequently, the matching efficiency has been greatly improved. Finally, a binocular distance measurement system heterogeneous computing experimental platform is constructed. The experimental platform performs a real-time processing on the acquired images. The feasibility of parallel acceleration based on the SIFT algorithm is verified. An intermediate calculation process and the distance measurement results can be directly obtained in the system. The experimental results show that compared with the previous accelerated optimization work to the SIFT, the computational time consumption of the proposed approach is much less than that in the original method.

A non-contact satellite motion parameter identification method is proposed for the derotation of satellites that roll in space while simultaneously spinning and precessing. The algorithm involves three main steps. First, the point-cloud registration is used to obtain the pose transformation between two adjacent point clouds, from which the trajectories of the points in the satellite’s point cloud are obtained. Second, because the points on the spin axis only rotate around the moving axis, the principal component analysis and circle-fitting method are used to find a point on the spin axis and thereby determine the direction and position of the precession axis. Finally, the rolling satellite’s parameters are found by solving a set of nonlinear equations, established based on the relationships between the overall pose transformation and the two motions. The simulation results show that the proposed method can accurately identify the motion parameters of a rolling satellite for certain noise levels.

Existing methods of person reidentification based on convolutional neural network lack discriminative information, due to occlusion and complex backgrounds. To solve these problems, a method based on multi-scale convolutional feature fusion is proposed herein. In the training phase, pyramid pooling is used to extract multiple eigenvectors containing global features and multi-scale local features for blocking and pooling of the convolutional feature map. Afterward, each feature vector is classified independently, and the weights and features on the last inner layer of each class are normalized to improve the classification performance. Finally, a gradient descent algorithm is applied to optimize the sum of losses for each classification. In the recognition phase, pooled multiple feature vectors are concatenated into a new vector for similarity matching. The efficiency of the proposed algorithm is verified on datasets Market-1501 and DukeMTMC-reID, in which the results indicate that features obtained by the proposed model are more discriminative and that the Rank-1 accuracy and average accuracy are both better than most state-of-the-art algorithms.

In this study, silica sol with decane as a solvent was prepared by the sol modification and solvent replacement method. A film with good uniformity was coated on 50 mm×50 mm×10 mm square KDP crystals using the single spin coating method. The optical properties of the film coated on the KDP crystals were tested using a spectrophotometer. The peak transmittance value of the KDP crystal substrate coated with the third harmonic frequency and the fundamental frequency of the second harmonic frequency antireflective films could achieve more than 99.5% at 378 nm and 835 nm, respectively. The defect control in the preparation process of the coating film was realized by the combination of the filtration and the ultrasonic cleaning technology. The third harmonic frequency antireflective film with defect control was coated on a high-cleanliness fused silica substrate and pretreated by laser conditioning before testing. The laser-induced damage threshold of the third harmonic frequency antireflective film reached (14.0 ± 2.1) J·cm-2 measured by a laser with 355 nm wavelength and 3 ns pulse width.

A wavelength selective switch (WSS) is the core device of the current- and next-generation reconfigurable optical add-drop multiplexer and dynamic all-optical network, which is used to provide high port counts and spectral modulation fitness. In this study, we simulate a WSS system using the ZEMAX optical software. Further, a Fourier lens group and double cylinder lens are employed to improve the optical structure of WSS. The WSS optical structure is designed and optimized by using a merit function, a spot diagram, and an optical transfer function. Subsequently, the imaging capability of the entire WSS optical system is gradually improved. Finally,a 65-port WSS optical system with a minimum spectral filtering bandwidth of 8 GHz is obtained.

Based on Kirchhoff approximation method, a numerical computation model of Mueller matrix full angle distribution is constructed herein, and Mueller matrix distributions of random rough surfaces of metals and dielectrics are calculated. Results show that there are obvious difference between Mueller matrix distributions of metals and dielectrics; six components of the Mueller matrix of dielectric surfaces are close to 0, whereas those of metal surfaces are non-zero; additionally, the difference is independent of the surface roughness. This difference can act as the robust criterion to distinguish metal and dielectric targets, and can provide new tools in target detection and recognition.

The large number of raw point cloud data collected with three-dimensional laser scanners presents a challenge during the subsequent data processing. Unfortunately, the existing curvature-based point cloud compression methods can lead to loss of details in the sub-feature regions. Therefore, we propose a curvature-grading-based compression method for point cloud data in this study. First, the feature distribution is obtained by estimating the curvature of every point. Then, the curvature level of each point is acquired based on the logarithmic function and its normalized curvature. Finally, voxelized grids are created over the input point cloud and are used to perform grading compression according to the levels. The experimental results denote that the proposed method can preserve the details of raw data while reducing the amount of data, resulting in an efficient pathway to compress the point cloud data.

Black phosphorus is a new type of two-dimensional semiconductor material that has attracted extensive attention because of its tunable direct band gap and high carrier mobility. Compared with bulk black phosphorus, low-dimensional black phosphorus has a greater potential application value because of its structural and performance advantages. In recent years, many research groups have successfully prepared low-dimensional black phosphorus with different morphologies and applied them to the active layer, electron transport layer, and hole transport layer of solar cells, such that the conversion efficiency is improved to different degrees. This study introduces a preparation method of low-dimensional black phosphorus, focuses on its research progress in the field of solar cells, puts forward the problems that must be solved, and forecasts the future development trend of low-dimensional black phosphorus.

Herein, based on generalized Lorenz-Mie theory (GLMT), the scattering characteristics of single spherical aerosol particles illuminated by a high-order vector Bessel vortex beam are investigated. The electromagnetic field expression of each component of the Bessel vortex beam propagating along z axis in a rectangular coordinate system is transformed into an expression of each component in the spherical coordinate system. The beam factors of the Bessel vortex beam are calculated by the integral localized approximation method in GLMT, and then the scattering of single spherical aerosol particles illuminated by the axially-incident Bessel vortex beam is calculated. Taking the ammonium-nitrate spherical particles as examples, the distribution of variation in the scattering angle for the differential scattering cross-section of the interaction between aerosol particles and vector Bessel vortex beams which have different topological charges and half-cone angles are discussed by numerical calculation. Furthermore, variations of the extinction, absorption, and scattering cross-sections with the size parameters of uniformly spherical aerosol particles are discussed as well. Results show that, with increasing topological charge of the Bessel vortex beam, the differential scattering cross-section of particles gradually decreases; the absorption, scattering, and extinction cross-sections of the vector Bessel vortex beam with a certain topological charge overall trend to decrease as the half-cone angle of the beam increases.

Laser-induced breakdown spectroscopy (LIBS) was applied in this study to quantitatively detect heavy metal lead (Pb) in paint coatings. By optimizing experimental parameters, the plasma spectrum of the paint coating within 308-417 nm was collected using a multi-channel spectrometer. Univariate and multivariate linear regression together with competitive adaptive reweighted sampling-partial least squares (CARS-PLS) analyses were later performed on the obtained Pb data of paint coatings to achieve more accurate quantitative results. Among the three analytical models, CARS-PLS displayed the highest accuracy; the determination coefficient of the calibration set was 0.991, and the relative errors of 4# and 7# of the predicted samples were 1.4% and 1.5%, respectively. Thus, CARS variable screening effectively screened important Pb-related data, and accurately detected Pb in the paint coating when combined with PLS.