Based on lidar observational data of atmospheric particulate matter, ground monitoring station data, and wind field data measured by an ultrasonic anemometer on a tower (later referred to as tower wind field data), a pollution process that occurred from January 16 to 18, 2018 in the northern Zhejiang Province and its characteristics were comprehensively analyzed. The results show that the pollution process is primarily caused by regional pollutant transport. The lidar results show that pollutants in northwest cities begin to be transported to Ningbo via a northwest wind at 12:00 on January 16, causing the concentration of particulate matter to increase rapidly. The wind direction switches to a weaker southern wind at 18:00 on January 17, resulting in the dissipation of the pollutants. The 532 nm range-square-corrected signal is in agreement with the variation trend of the near-ground pollutant concentration; the depolarization ratio and wavelength dependence show that the pollutants at a height of 0.5-1 km and those near the ground are of two different types. The results of the HYSPLIT backward trajectory model show that the polluted air mass comes from the northwest city of Ningbo and is transported at a high speed, consistent with results analyzed from the tower wind field data. Therefore, the lidar observational results can effectively characterize the spatial distribution and evolution of the particulate matter concentration and provide a theoretical basis for air pollution monitoring and early warning systems.

A feature extraction and recognition algorithm for fiber intrusion signals is proposed based on ensemble empirical-mode decomposition (EEMD) coupled with a random vector-function linked (RVFL) neural network to accurately identify the type of intrusion signal on a distributed optical fiber. The proposed algorithm starts with the preprocessing for the collected fiber intrusion signals,including minimum-maximum normalization processing and the removal of low frequency noise using the db3 wavelet. Then, the intrusion signals are decomposed by the EEMD to obtain five groups of intrinsic mode functions (IMF). Subsequently, the energy ratio of each component of the IMF is calculated, and three feature vectors are filtered using the analysis of variance. Finally, the feature vectors are sent into the RVFL neural network to be trained for the completion of the signal recognition. The experimental results validate that the proposed algorithm can accurately distinguish between different intrusion signals with high recognition rate.

To improve the spectrum utilization rate and reduce the establishment delay and blocking rate of an optical path, a joint-optimization routing spectrum allocation (CODS-RSA) algorithm based on the delay and spectrum utilization rate is proposed herein. The CODS-RSA algorithm establishes an optical connection request service database by calculating a control model, a spectrum bit diagram, and an optimal allocation algorithm during a preliminary calculation period. Then, the CODS-RSA algorithm performs routing spectrum allocation based on the optical connection request service database and real-time update calculation. The simulation results show that preliminary calculations of the CODS-RSA algorithm reduce the optical path’s establishment delay; the allocated spectra of optimal allocation algorithm and the spectrum bit diagram improve the spectrum utilization; the service request’s blocking rate is relatively reduced as well.

A new type of non-scanning correlation demodulation system based on a new compound optical wedge structure is introduced in order to improve the correlation demodulation resolution of compound fiber Fabry-Perot (FP) sensors. Based on the desired demodulation range of the optical thickness of each cavity of the compound fiber FP sensors along the pixel arrangement direction of the linear array CCD, an air-gap optical wedge structure having different tilt angles and thickness ranges is designed and adopted to achieve the compound synchronous demodulation of multiple FP cavity lengths. The demodulation performance of the designed compound optical wedge structure is simulated and analyzed for compound fiber FP sensors having basal and air-cavity lengths of 600 μm and 80 μm, respectively. Simulation results show that such sensors with multiple FP cavities and different optical thicknesses are demodulated synchronously and accurately using the proposed non-scanning correlation demodulation system based on the compound optical wedge structure. The demodulation resolution for a compound fiber FP sensor can be better than that of a single optical wedge. This conclusion is particularly true for a compound fiber FP sensors comprising cavities with large cavity length differences.

In coherent optical communication systems, phase noise of the laser causes the signal to rotate in the complex plane; therefore, the estimation and recovery of carrier phase are required at the receiving end. Herein, when using the M-th power carrier phase recovery algorithm to estimate the phase, we simplify the summing and averaging of adjacent N symbols to reduce the influence of additive Gaussian noise. The proposed algorithm considers the phase shift caused by the residual frequency offset, phase noise, and additive Gaussian noise together. Then, the algorithm estimates the total phase shift caused by these three factors and resumes the modulation phase. The performance of the proposed algorithm is compared with that of the traditional M-th power carrier phase recovery algorithm in the simulation. After the phase recovery by the proposed algorithm, the error between the signal and original modulation phases is only 10 -16 rad. However, when using the traditional algorithm, the error can reach 0.3 rad, implying that the proposed algorithm can recover the modulation phase more accurately and has a higher estimation accuracy. In the simulation, even without compensating for the frequency offset, phase recovery can be realized using the proposed algorithm, while the traditional algorithm can only recover the phase of the signal after compensating the frequency offset. In addition, the complexity of the algorithm is reduced by reducing the summing and averaging step as well.

Based on the Visual Geometry Group (VGG-19) model of convolutional neural networks (CNN), influences of the edge information in an input texture feature map on the natural texture are studied when the CNN convolves the input texture. When the input image is convoluted by the VGG using the CNN, the feature map is processed in an average pooling manner to prevent overfitting, which protects the edge information of the feature map to some extent and the generation effect is better than that obtained via max-pooling processing. The edge information of each layer of feature map is extracted and superimposed on the feature map, which preserves the edge structure information of the texture image well. Experimental results demonstrate that the proposed method achieves a good texture generation effect.

Resultsshow that the proposed algorithm can completely detect moving objects in the sample videos while quickly removing ghosts and shadows. The proposed algorithm's detection accuracy is 21.53% higher than that of the existing Vibe algorithm.

The accuracy of head pose estimation is easy to be affected by illumination, occlusion and other disturbances in practical applications and a large number of calculations are difficult to meet timeliness of practical applications. In order to solve these problems, a method based on multi-scale convolutional neural network is proposed. The feature extraction of the input head pose image is performed by using different scale convolution kernels, which enriches the image features while preserving the image information, and enhances the robustness of the algorithm to the interference factors. At the same time, the 1×1 convolution is introduced to reduce the network structure parameters, reduce the computational complexity of the system, and improve the timeliness of the algorithm. The result of experiment shows that the recognition rates of the proposed algorithm on Pointing'04 and CAS-PEAL-R1 databases are 96.5% and 98.9%, respectively. The method shows good robustness to illumination, expression, occlusion and other disturbances, and has better operation and speed.

Traditional facial expression recognition often uses a single image to extract features, train, and recognize; however, subtle changes in dynamic facial expressions are not recognized. This study proposes a multi-expression sequence fusion recognition method based on probabilistic cooperative representation using the changes in facial expression before and after time. First, 68 feature points of facial expression are located using an active appearance model (AAM). Then, the AAM features of three adjacent facial expressions are combined using the the proposed method. Finally, the classification advantages of probabilistic cooperative representation are used for recognition. Experimental results indicate that the proposed method can grasp the temporal change information of expression on the CK+ expression database. Moreover, this method can achieve higher recognition rates compared with traditional expression recognition algorithms.

A new method based on gold image subtraction (GIS) combined with the Fourier transform displacement theorem (FTDT) curve matching algorithm was proposed to detect the defects of printed fabrics caused by color and pattern diversity. First, a Gaussian filter was applied to printed fabrics to eliminate the influence of noise on the defect detection results. Then, GIS was employed for image matching and defect segmentation. Finally, a curve matching algorithm based on FTDT was adapted to calculate the relative offset of the cross-printing defects. Experiment results indicate that the algorithm can accurately segment the defect and provide detailed information about the defect. The algorithm can also accurately obtain the relative offset of cross-printing defects, which has a certain guiding significance for industrial production.

This study proposes a machine vision-based surface defect detection algorithm to enhance the effect and efficiency of detecting surface defects in polyvinyl chloride (PVC) pipes for industrial production. The algorithm performs image preprocessing and defect detection. Image preprocessing includes steps such as edge traversal, fringe detection, and Gamma transformation. Defect detection mainly includes horizontal and vertical projection, fast region growing for connected region marking, and block processing. The proposed algorithm accelerates the Gamma transformation and region growing, and it can also be used to optimally detect surface defects in PVC pipes, while avoiding false detection. Results of tests and actual factory inspections suggest that the proposed algorithm achieves a detection accuracy of 97.6%, with a real-time detection speed of >60 m/min, and a minimum defect detection area of 0.05 mm 2. Moreover, a unilateral jitter of <5 mm does not cause any false alarms and the missed detection rate is 0 when the pipe runs at a speed of 45 m/min, which meets actual production needs.

Aiming at the problems of insufficient usage of context information and unclear image edge segmentation in image semantic segmentation, a network model based on multi-scale feature extraction and fully connected conditional random fields is proposed. RGB and depth images are input into the network in a multi-scale form, and their features are extracted by a Convolutional neural network. Depth information is added to supplement the RGB feature map and obtain a rough semantic segmentation, which is optimized by the fully connected conditional random fields. Finally, fine semantic segmentation results are obtained. This proposed method improves the precision of semantic segmentation and optimizes the image edge segmentation, which has a practical application.

For three-dimensional (3D) reconstruction of targets, existing image-processing algorithms require a single background, and they significantly depend on the experimental environment. Therefore, an image-preprocessing algorithm for 3D reconstruction of targets in complex backgrounds is proposed. First, to maximize the target detail information, Gaussian filtering, Gamma conversion, and histogram-equalization processing are performed on the acquired images to remove image noise and suppress complex backgrounds. Then, the Grab cut and Deeplab algorithms are combined to solve the problems of long time consumed on Grab cut and edges blurred on Deeplab, effectively separating the target from complex backgrounds. A test platform for the car model is built and sixteen sets of target images are obtained to verify the algorithm. Considering two sets of targets as examples, the effects of the proposed algorithm and the traditional 3D-reconstruction image-preprocessing algorithm are compared. The segmentation accuracy of the proposed algorithm is 0.9986, the sensitivity is 0.9889, and the specificity is 0.9991, which are higher than those of the traditional algorithm. The point-cloud noise rate of the traditional algorithm is 22.7%, which is reduced to 1.15% by the proposed algorithm. The average reconstruction time of the proposed algorithm is 2.245 s, which is 60.6% of the time of the traditional algorithm. These results prove that the proposed image-preprocessing algorithm offers superior 3D image reconstruction under complex backgrounds.

An efficient and fast convolution neural network for millimeter-wave images that uses deconvolution and a shortcut connection is proposed. The proposed network retains the low-order fine-grained features of the image and significantly improves the detection speed to 27 frame/s from 9 frame/s of original frame. The RCNN (Regions with Convolutional Neural Networks) part of the Faster RCNN is removed. To achieve better network convergence, the initial candidate box size is designed based on thought clustering. The online hard example mining is applied to optimize the loss function of the Faster RCNN such that the imbalance problem between positive and negative samples in millimeter wave images is solved and the training speed is improved significantly. By using the proposed algorithm, the accuracy of 87.6% and the detection rate of 81.2% are obtained on the test set. Compared with mainstream algorithms, the proposed algorithm improves the F1 score by approximately 5%.

In this study, a training model was constructed to identify several abnormal behaviors in video sequences. A convolutional neural network (CNN) was used to extract features, and the features were then optimized using a gradient-based optimization algorithm known as Adam algorithm. The adaptive pooling layer was introduced for feature discrimination to reduce the computational complexity of the network and rapidly identify abnormal behaviors in video sequences. The recognition rate reaches 87.6% after using the Adam algorithm for model optimization. The recognition rate reaches 91.9% when the adaptive pooling layer is introduced. CNN is faster and more accurate than the improved dense trajectories and the two-stream networks in detecting abnormal behaviors in video sequences. Compared with the temporal segment networks and temporal relation networks, the CNN has a lower accuracy but a faster speed.

Based on the previous studies, visible near-infrared hyperspectral (350-1700 nm) data of 193 samples from sandy ginger black soil in northern Anhui province are firstly used to optimize the nine models by combing the nonlinear and linear kernel functions. Then, model combination and secondary optimization are performed via three integrated learning algorithms based on the random forest, boosting tree, and gradient boosting decision tree (GBDT). Four single models, including partial least squares of Sigmoid function, linear support vector regression, radial basis support vector regression, and support vector regression of Sigmoid function, are selected and combined by model comparison. After optimization of the integrated algorithms, it is found that the prediction results of the GBDT algorithm are optimal. The determination coefficient of the GBDT algorithm is 0.86, which is 17.8% higher than that of the single model, and the relative analysis error coefficient is 2.55, which is significantly improved from grade B to A. The GBDT algorithm not only improves the accuracy, but also has low overfitting degree and good generalization performance. Therefore, the GBDT algorithm can be combined with the advantages of multiple models and improve the accuracy of the prediction results of soil available phosphorus through hyperspectral model integration.

This study presents a novel subjective image quality assessment for large samples to solve existing problems in subjective assessments of image quality databases, such as less distortion levels and insufficient analysis of experimental results. The proposed method is based on a double-stimulus continuous quality scale and employs a simplified, two-level subjective assessment scale. We obtain a quality sequence of sample images by integrating circularly, selecting the best quality, and adjusting the sequence. Then, fuzzy clustering is used to analyze the quality sequence. The probability of image quality sequence in fuzzy clustering analysis is taken as its matching degree, which establishes a fuzzy similarity matrix of samples. We obtain the image quality score by normalizing the probability, establishing the fuzzy similarity relationship, and building a fuzzy equivalence relation, classification, and scoring. We test the subjective assessment for a 64-distortion-level image. The results demonstrate that the image quality scores accurately reflect the variation of just-noticeable difference, assessment accuracy is up to 94%, standard deviation of the image quality scores is from 0 to 7, and the mean value of standard deviation is 3.08 (percentile system), which is much less than the current level of other image quality databases. The proposed method demonstrates high accuracy and stability, and is suitable for subjective assessments of image quality databases and the study of human visual characteristics.

This study proposes a method of object shading and display of the object’s visible parts based on OpenGL, which is used to solve the problems of large calculation amount and low accuracy when the blanking process of a complex structure object is realized by a numerical method. By enabling depth buffers and depth testing, the computer graphics acceleration card is used in a hardware level to quickly achieve the goal of hidden and surface element distance information. The calculation efficiency and accuracy are improved. Moreover, the cosine of the angle between the target element and the detection line of sight is obtained by a single illumination. Subsequently, the target reflectivity distribution is obtained by a simulation considering the temporal and spatial distribution characteristics of the laser pulse. The convolution model is then used to calculate the laser echo waveform of the target. The effects of the laser pulse width and the target surface material on the quality of laser reflection tomography are analyzed using the presented method.

Chemical oxygen demand (COD) is an important indicator of organic matter pollution in water source and one of the necessary parameters for monitoring water quality. Detecting the COD based on ultraviolet spectrophotometry is an important method for online real-time monitoring of pollution sources. This study designs a COD online detection sensor based on a dual-light source to solve the problems of COD online detection using the traditional reagent method. The influences of turbidity, light-source temperature, and water-sample temperature on measurement are analyzed theoretically. Methods of temperature and turbidity compensations are then proposed. Experimental results show that the repeatability error of the sensor is 3.12%, whereas the maximum relative error of the actual water-sample comparison experiment is 6.18%. The turbidity and temperature compensations are accurate. The designed COD sensor can accurately measure the COD concentration of the water samples to meet the technical requirements of water quality online monitoring equipment.

A diode-pumped 424 nm blue laser is designed based on intra-cavity sum-frequency mixing. A type-I phase-matched LBO crystal is used to combine a 1030 nm Yb∶YAG laser and a 720 nm Pr∶YLF laser via sum-frequency mixing with a complex V-shaped cavity. An 18 mW, 424 nm laser is obtained by setting the pump powers of the Yb∶YAG and Pr∶YLF crystals to 5 W and 3.1 W, respectively. In this way, an all-solid-state 424 nm blue laser with a stable output is realized with an optical-to-optical conversion efficiency of 0.2%. The beam quality factors are approximately 1.62 and 1.50 in the horizontal and vertical directions, respectively.

In this study, we investigate the laser spectra of the InGaAs/GaAs/InGaP quantum well lasers. Single-mode operation characteristics can be observed in the Fabry-Perot (FP) cavity laser, which exist in a large operating current range (36-68 mA). Mode-hop free wavelength tuning can be observed at a current of 14 mA. The maximum side-mode suppression ratio (SMSR) under single-mode operation of the laser is 29.8 dB at 20 ℃ with an injection current of 62 mA, while SMSR values are generally greater than 20 dB under other injection currents. Furthermore, the maximum output power (single side) of the laser under single-mode operation reaches 12.5 mW. Accordingly, the aforementioned single-mode operation characteristics can be observed for the FP cavity lasers fabricated with similar structures and materials in devices exhibiting different strip widths and cavity lengths. This unique feature results in considerable application potential in applications requiring a single-frequency laser source.

Herein, a broadband chaotic signal generator with a bandwidth of 50 GHz is designed and developed, based on the perturbation of amplified spontaneous emission (ASE) noise in conjunction with semiconductor laser mutual injection. The generator primarily contains two distributed-feedback semiconductor lasers (DFB-SL) and one semiconductor optical amplifier (SOA). Chaotic laser is obtained by perturbing the SL with ASE noise generated by the SOA. In addition, the beating effect caused by the SL mutual injection is exploited to achieve spectrum shaping and bandwidth enhancement. With a feedback strength of 9.096%, frequency detuning of 32.75 GHz, and coupling strength of 1.966, the chaotic laser with a bandwidth exceeding 50 GHz, flatness of ±2.5 dB, and linewidth of 0.56 nm is generated by the signal generator. This broadband chaotic signal generator, with large bandwidth and high flatness, solves the problems of uneven spectrum and narrow bandwidth of chaotic lasers, and further promotes the industrial application of chaotic laser.

Point cloud registration is an important step in the processing of terrestrial three-dimensional laser scanning data. Aiming at the scene with small terrain fluctuation, we propose an automatic point cloud registration method based on projection distribution entropy. Initially, information entropy is used to describe the intensity of point cloud projection distribution. Following this, a coarse registration is achieved by seeking an optimal point cloud distribution between two point clouds. Consequently, the transformation parameters are determined between the two point clouds with different distributions and supplied as an input to the iterative closest point algorithm to achieve a fine registration. Compared with the automatic point cloud registration method based on features, the proposed method's main concern is the consistency of the overall distributions of the clouds. Results show that the proposed method shows a robust and accurate registration outcome, especially for the point cloud scene with great change of perspective and multiple repetitive symmetrical structures.

To address the problems of missing and inaccurate prints during the medical infusion bag printing process that impact the medical quality, an intelligent detection and defect classification method based on a support vector machine is proposed for infusion bags. The selected defect classification targets, which are to be classified based on the analysis of the defect characteristics of medical infusion bags during the production process, include the product name offset, product name rotation, and product name stain. These three features, including the location relation between the region of interest and the monitoring region, rotation angle of region of interest and monitoring region, and filling degree are used as the input vectors of the support vector machine to train the classifier. Further, a radial basis function and an one-to-one classification method are used in this experiment. The average operation time and recognition accuracy are considered to be the evaluation criteria for comparing various experiments. The experimental results demonstrate that the recognition accuracy of the proposed method can become 96.7%, satisfying the requirements of commercial production.

In recent years, the liquid lenses are widely used in imaging, biometrics, optoelectronics, and lab-on-a-chip. A liquid lens is successfully developed herein based on the frequency-modulated dielectrophoresis effect. The focal length of the lens is varied by adjusting the output frequency of the input power. The relationships among the frequency, voltage, contact angle, focal length, and lens aperture are obtained. When the applied voltage frequency of the dielectrophoresis lens is varied within 0-20 kHz, the focal length, lens aperture, and lens response time are 6-8 cm, 2-3.25 mm, and about 375 ms, respectively. The as-prepared lens has the advantages of a simple structure, fast driving, precise adjustment, low energy consumption, high resolution, and large dynamic range.

In this study, the discomfort glare caused by light emitting diode (LED) modules with different spectral power distributions is investigated. Further, red/green/blue/white (R/G/B/W) four-color LED source modules are designed and developed. The glare effect of the modules is determined for three different background luminances. Subject assessment of each condition is provided according to a seven-level scale. Results show that different glare effects are caused by the R/G/B/W four-color LED sources for three different background luminance. A high subjective rating score is obtained at a large luminance of the four modules within the same background luminance, causing more discomfort glare. Simultaneously, the largest glare is caused by B-LED source, whereas the smallest glare is caused by W-LED source in those modules. The unified glare rating (RUGR) model is used to experimentally verify the applicability of four-color LED source modules. Results show that the correlation between calculated value of RUGR and subjective discomfort glare rating score of the B-LED source is the worst, whereas the W-LED source has the best correlation (R2=0.951) when the background luminance Lb is 19.967 cd·m -2.

Periodic silver nanoparticles and nanohole arrays were prepared through tailored nanosphere lithography, and the optical properties of the fabricated nanostructures were investigated. First, a hexagonal close-packed monolayer comprising polystyrene (PS) nanospheres was made on a glass substrates as a mask. Then, the silver film was deposited in the voids between the nanospheres using an electron beam evaporator. After deposition, the PS nanospheres were removed, affording an array of silver nanoparticles. If the PS nanospheres were tailored through reactive-ion etching prior to the silver deposition step, the nanoparticles merged with the adjacent particles and produced periodic silver nanohole arrays. A series of nanostructures were prepared, and their surface plasmon resonance characteristics were analyzed by measuring the transmission spectra. The optical properties of the related nanostructures were numerically simulated using the finite difference time domain, the results of which were consistent with those of the experiments.

Beginning from Maxwell's equations and by considering the second-order nonlinear polarization related to the optical activity as the perturbation of linear polarization, we investigate the propagation behavior of the paraxial Gaussian light which acts as incident light in the optically active medium Bi12SiO20 and utilize the Minkowski and Abraham momenta to obtain the expressions of optical momentum and angular momentum in Bi12SiO20 crystals. Further, we obtain that the ratio of Minkowski momentum to Abraham momentum is not equal to the square of the refractive index of the crystal because of the existence of optical activity. In the Minkowski picture, because of the coupling of the angular momentum between the light and the crystal, the angular momentum of the light field is not conserved, whereas the total angular momentum of the light and crystal is conserved.

A micro-pulse lidar and a compact optical backscatter aerosol detector (COBALD) are deployed for the combined observation of aerosol vertical structures at Kunming from July to August 2017. The synchronous observation and comparison methods of these two instruments are introduced. The differences in the backscatter ratio profiles and vertical distribution characteristics of the aerosols, which are simultaneously measured by the lidar and COBALD, are analyzed. The observation results of the two instruments show good consistency with each other. The correlation coefficient between the backscatter ratio measured by lidar and that measured by COBALD is 0.87 in the absence of a cloud at a height of 1-4 km, and the corresponding root-mean-square error is 0.752. Atmospheric observation results indicate that the developed micro-pulse lidar is an effective tool for continuous detection of the evolution processes of aerosol vertical structures. Synchronous fusion with sounding data from the COBALD can reduce hypothetical parameters in the retrieve algorithm of lidar signals. In conclusion, the combined observation of these two instruments has certain application.

This study proposes a fiber Bragg grating (FBG) sensor based on a singlemode-multimode-singlemode tapered structure that can effectively increase strain sensitivity. The grating is located at the tapered-multimode fiber region. A multimode fiber has the advantages of large aperture, easy coupling, easy writing of gratings, and the ability to withstand large strain. The use of tapered-multimode fiber gratings for strain sensing provides high accuracy and sensitivity. We produce multiple sensors with different tapers by changing the stretched length of the multimode fiber. Firstly, a 1.7-cm-long multimode fiber is welded between two singlemode fibers. Then, the middle multimode fiber section is tapered to 0.8, 0.9, and 1.0 cm, respectively. Subsequently, a 7-mm-long Bragg grating is written on the side of the multimode fiber cone. Changes in external parameters, such as temperature and strain, are monitored by analyzing the change of the resonant wavelength in the reflection spectrum. The experimental results show that the strain detection range of the sensor is 0-960 με,the highest strain sensitivity of the sensor can reach 15.5 pm/με, and the temperature sensitivity is 10.5 pm/℃. Furthermore, the thinner the cone radius is, the more obvious the increase in strain sensitivity is. The strain sensitivity is considerably improved compared with that of the conventional FBG, and the temperature sensing characteristics remain unchanged; hence, the temperature-induced strain error is only 0.677 με/℃ without temperature compensation, which reduces the cross-sensitivity between strain and temperature.

The environmental perception of unmanned vehicles is a vital technology for automatic driving. The usage of three-dimensional (3D) LiDAR for obstacle detection becomes a popular research topic. In this paper, we first introduce the classification of obstacle detection methods for an unmanned vehicle according to different sensors. The basic principle of obstacle detection based on 3D LiDAR is then introduced in detail along with an analysis of the traditional method of obstacle detection using 3D LiDAR. Deep learning is an important method for two-dimensional object detection and classification. We analyze the characteristics of the 3D LiDAR point clouds and the challenges of deep learning for point clouds. Finally, we analyze the research status and development trend of deep learning in the point cloud obstacle detection application and introduce relevant datasets in the field of automatic driving, such as KITTI and ApolloScape.

Laser beams with evenly distributed or specifically modulated intensity profiles are required in many practical applications, thereby necessitating theoretical and experimental studies in laser beam shaping techniques. A variety of laser beam shaping methods, wherein the beam integration plays an important role as a result of its simple principle and wide applicability, have been proposed thus far. Therefore, it has great value in engineering. This study introduces the usage of optical components, such as prisms, mirrors, and micro-lens arrays, in beam integration systems. In this paper, the typical optical paths and recent progress are presented along with a discussion of the characteristics of these shaping methods.