Herein, we propose a method for interrogating a fiber grating sensor using a dual-phase-shifted fiber grating and an optoelectronic oscillator (OEO). Combining the advantages of fiber grating sensing technology and microwave photonic technology, fiber gratings in a system serve both as sensing probes and as filtering units in a microwave photonic filter of the OEO. When the measurand changes the features of the sensing fiber grating, the frequency produced by the OEO also changes. This is not influenced by varying environmental temperatures, thus avoiding the cross-sensitivity problem of fiber grating sensors. Through the OEO, the wavelength change of the grating is reflected as a corresponding change in the microwave frequency. Furthermore, we experimentally demonstrate such a dual-passband microwave photonic filter using the phase-shifted fiber grating and construct an OEO ring based on the filter. Our experimental results show that the proposed system can provide an interrogation sensitivity of about 36 MHz/με. The implementation of this device holds promise for the development of a novel method of interrogating fiber grating sensors to achieve high-resolution and high-speed measurements. The proposed method can also help promote further developments in practical applications of optical fiber sensing technology.

Herein, an ultra-weak fiber Bragg grating hydrophone array system based on Fizeau interference is demonstrated. The ultra-weak fiber Bragg grating hydrophone array is constructed via on-line preparation of a fiber Bragg grating using a drawing tower. The amplitude, frequency, and phase of underwater acoustic signals in a wide frequency range of 2-2000 Hz are simultaneously measured using the signal demodulated by the arc-tangent algorithm. The phase acoustic-pressure sensitivity of the system hydroacoustic signals at 2 Hz reaches -135.81 dB(re rad/μPa). The sensitivity is 2755.49 μPa/Hz, and the signal-to-noise ratio is 43.785 dB. The experimental results of sensor multiplexing prove that the system can demodulate underwater acoustic information at different locations. Furthermore, the phase acoustic-pressure sensitivity of the system increases with the increasing cavity length of the sensor. The system exhibits very good stability, indicating the possibility of extensive applications in high-sensitivity underwater acoustic sensing, very low frequency acoustic detection, deep-sea monitoring, and other fields.

A low-frequency acoustic sensor based on fiber optic interferometer was designed and fabricated for detection of infrasound signals with a frequency less than 20 Hz. The sensor contains a metallic diaphragm that is a circular nickel membrane with a thickness of 5 μm and a radius of 1/2 inch (1 inch=2.54 cm), and the diaphragm is flatly fixed to a port of the metal cylinder in a peripherally stretched manner. The end face of the single-mode fiber protruding into the cylinder and the inner surface of the diaphragm constitute an extrinsic fiber Fabry-Perot interferometer (EFPI) for detecting the diaphragm's vibration caused by sound. In order to optimize the sensor performance, the effects of the prestress applied to the diaphragm and the diaphragm thickness on the first-order resonant frequency of the sensor are analyzed based on the principle of elastic mechanics and finite element simulation software. The simulation results show that the first-order resonant frequency is sensitive to prestress rather than to the diaphragm thickness under the condition of the diaphragm thickness being within a certain small range. The above-prepared fiber optic infrasound sensor was characterized with the B&K 4193-L-004 standard acoustic sensor and the 42AE standard infrasound source. The results show that the sensor has good frequency response in the range from 0.1 Hz to 20 Hz and the sensitivity to 1 Hz sound is as high as 285 mV/Pa.

Region proposal networks (RPN) in region-based full-convolutional networks (R-FCN) follow the RPN of faster region convolutional neural networks. In this paper, a full-convolution object detection network based on clustering region generation is proposed to solve the problems of the artificially fixed sizes and quantities of anchor boxes and excessively generated proposals. K-means clustering on the ground-truth box of the training samples is used to optimize the sizes and numbers of the anchor boxes in order to replace the fixed boxes in the R-FCN. Furthermore, to enhance the generalization ability of the model, an online hard example mining is used to train the datasets based on the backbone network of ResNet. The experimental results show that the accuracy of the detection results of the proposed algorithm is significantly higher than that of the R-FCN.

To solve geometric distortion and misalignment generated in the traditional microscopic image stitching process and avoid low matching rate and high computational burden caused by rare features, a stitching algorithm based on the regional frog leaping search algorithm and image contour matching is proposed. First, the image contour curves of continuous acquisition and overlapping regions were extracted. Then, the clue-aware trajectory similarity or mean square error distance was introduced to calculate the similarity and discrete distance between image contour curves. Finally, the regional frog leaping algorithm was used in the decision domain, and by updating the tweet decibel and frog leaping strategy, optimal contour matching for the image was searched. Consequently, the images were stitched quickly and accurately. Experimental results verify that the proposed algorithm demonstrates high stitching precision and strong robustness. In addition, the simplified matching strategy reduces the amount of calculation and exhibits strong timeliness.

This study proposes a perspective transformation algorithm for light field image based on total variational projection and hole restoration. The proposed method estimates disparity maps for all subaperture views of the input light field image. The light field image with the specified posture in the specified virtual camera position is generated via back projection and reprojection optimization. Using the image restoration algorithm based on the disparity priority, we restore the central subaperture view image after perspective transformation. The central subaperture views are used for content distribution in horizontal and vertical directions, and thus the rest subaperture view images are restored orderly. Experimental results show that the perspective transformed light field images generated by the proposed method meet requirements, and the covered regions are filled correctly. The proposed method can be applied to a series of light field image editing.

This study proposes a medical image fusion algorithm based on improved guided filtering and dual-channel pulse coupled neural networks (PCNN) to solve the problems of blurring edge and complementary information insufficiency in current multimodal medical image fusion. First, medical source images are transformed with a non-subsampled contourlet, and the dual-channel PCNN is used to fuse the low-frequency sub-bands. The sum of the modified Laplacian energy is used as the input of the dual-channel PCNN, and the improved spatial frequency is considered as the connection strength. Then, improved guided filtering is used to fuse the high-frequency sub-bands of the source images. Finally, the fusion of the low-frequency sub-bands and that of the high-frequency sub-bands are inverted by the non-subsampled contourlet transforming to obtain the fused image. Experimental results show that the proposed algorithm effectively retains the characteristic information of the source images and objectively evaluates the mutual information, information entropy, and spatial frequency.

Insulator image segmentation is the basic operation used to conduct insulator recognition and extraction using image processing. To segment insulator images accurately, an improved unit-linking pulse-coupled neural network (UL-PCNN)-based insulator image segmentation method is proposed in the present study. First, the link input and coupled parameters of the original UL-PCNN model are improved based on the relationship between a neuron and its neighbors. Next, the improved model is used to segment an insulator image to obtain multiple output images. Finally, the gradient algorithm is used to calculate the edges of the original image and output images, and the mean square error (MSE) of the edge of the original image and MSE of each output image are calculated. The output image with the smallest MSE is considered as the optimal result of insulator image segmentation. The experimental results demonstrate that this improved method can accurately segment insulator images in different environments and has good anti-noise performance.

A convolutional neural network (CNN) can extract hierarchical features in an image, and the extracted images include a large amount of detailed information contained in the image. However, CNN-based methods do not take full advantage of all hierarchical features extracted by the network. To make full use of all hierarchical features and enhance feature reuse and information flow, we design a residual dense network suitable for hyperspectral image classification. The residual dense network combines residual and dense networks, including shallow feature extraction, residual dense units, and dense feature fusion. Firstly, shallow features of the original image are extracted using a convolution operation, which is input to the residual dense unit. Secondly, the output of the residual dense unit establishes a shortcut connection with each convolution layer and output layer in the next unit, thereby realizing continuous information transmission. Subsequently, dense features extracted from the two units are added to the shallow features to form global residual learning, which realizes the fusion of all hierarchical features. The fused features are then used for hyperspectral image classification. Experimental results demonstrate that the proposed method can obtain 98.71%, 99.31%, and 97.91% classification accuracies on the Indian Pines, University of Pavia, and Salinas data, respectively, which effectively improves the classification accuracy of hyperspectral images and enhances the stability of classification methods.

Time-slicing images captured by laser-based active range-gated imaging technique are easily affected by the energy of the laser pulse and gate time. Consequently, the contrasts of time-slicing images are poor. Furthermore, the edge information and structural information are seriously lacked in the reconstruction of three-dimensional imaging. Therefore, an image preprocessing method for time-slicing images based on guided filtering combined with bilateral filtering is proposed to overcome this limitation. The proposed method changes the distribution of pixel information of time-slicing images within the error range to obtain high-quality three-dimensional reconstruction results. First, the guided filtering is employed to enhance edge information and recover structural information within the image. Then, the bilateral filtering is employed to compensate for defects introduced in the previous step (i.e., increasing invalid points). As a result, the uniformity of the image is stabilized. Experimental results demonstrate that three-dimensional reconstruction after applying the proposed preprocessing method achieves better performance in enhancing edge information, structural information, and image uniformity without sacrificing distance accuracy.

Q-type asphere has broad application prospect which has been applied in optical system design increasingly. But there is no easy or high-efficiency way to test the Q-type aspherical surface precisely until now. Aimed at the testing of Q-type asphere in the process of ultra-precision turning, a method based on MATLAB and Taylor Horbson PGI-1240 aspheric profilometer is proposed. First, we have fabricated a single crystal cupreous Q-type asphere whose aperture is 11.8 mm by Nanoform 700 single point diamond ultra turning lathe. And the testing result shows that, the PV and RMS values of Q-type asphere surface shape error are 0.1963 μm and 0.03412 μm. It meets the requirement that PV value should be less than 0.2 μm as well as RMS value should be less than 0.04 μm. This testing method can get precise surface shape error and provide data support for next stage of turning.

To realize automatic detection of bullet appearance defects and to overcome the limitations associated with traditional machine vision methods, i.e., excessive time required to manually design a target feature and generalization ability is poor in defect detection, we use the K-means++ algorithm to improve the anchor frame generation method and propose a bullet appearance defect detection model based on the improved faster region-convolutional neural network (R-CNN). The proposed model uses a CNN that can automatically extract target features and has strong generalization ability. The detection model is combined with ZFNet, VGG_CNN_M_1024, and VGG16, respectively. Results demonstrate that the detection accuracy of the detection model combined with VGG16 is higher than the others. The results show that that of the improved model demonstrates 97.75% accuracy and the speed reaches 28 frame·s -1.

Based on a virtual structured-light data coding algorithm, three-dimensional (3D) data can be encoded into the phase of a two-dimensional (2D) color fringe image to complete the data compression. In the process of decoding a 2D color fringe image to 3D data, phase calculation and phase unwrapping are required. Due to the compression and storage of the 2D image, a quantization error exists at the edge of the phase index map, which will cause a local error in the unwrapping phase and will result in a 3D data decoding error in the z direction. To overcome this limitation, the decoding process of virtual structured-light 3D data is improved. The proposed algorithm can revise the local phase error and enhance the 3D data decoding accuracy efficiently. In the proposed algorithm, deviation between absolute phase unwrapping and relative phase unwrapping is calculated. Since the deviation is fixed, phase compensation is performed when the local deviation is changed sharply, and the local error of the decoded 3D data caused by the edge quantization error of the phase index map is suppressed. Experiments are conducted to verify the effectiveness of the proposed algorithm, which makes the virtual structured-light 3D data encoding algorithm more robust after compressing and storing the encoded 2D images. In comparison with the median filtering algorithm, the proposed algorithm can reduce the root-mean-square error of data decoding by an average of 9.7%.

To address the problem of stereo matching algorithms having low matching accuracy in non-occluded regions, especially in the weak-textured regions, a non-local stereo matching algorithm based on edge constraint iteration was proposed. Firstly, the proposed method combined the color and gradient information to construct a matching cost computation function. Secondly, the minimum spanning tree structures of left and right images were constructed, and the cost volumes were aggregated according to the smoothness information of the image. Subsequently, a disparity map obtained by the winner-takes-all strategy was used for edge detection. The image edges were then used as constraints to re-aggregate the cost volumes and optimize the results. Finally, dense disparity maps were obtained by the disparity refinement process. The experimental results demonstrate that, for 31 pairs of images from the Middlebury test platform, the average mismatching rate in non-occluded regions of the proposed algorithm is 8.35%. Compared with five existing methods, the proposed algorithm can effectively improve matching accuracy in non-occluded regions.

This paper proposes a saliency detection method based on deep residual networks and multiscale edge residual learning to address the problems of low salient values and blurred edges in images having complex backgrounds. Further, an edge residual block is proposed, and an edge residual network is constructed based on the deep residual network using the edge residual block for the salient graph edge supervised learning. In addition, the edge features are learned while training the network by constructing a three-category model based on the background, foreground, and edge, which can make the target edge more accurate. The output uses atrous convolutions to construct a multiscale atrous convolution unit for integrating and extracting the multiscale features based on the global information. Finally, the proposed algorithm is tested in an ablation study using two datasets (SED2 and ECSSD) and compared with various existing algorithms based on the common evaluation indicators. The experimental results demonstrate that the proposed method exhibits high accuracy and recall rate, maintains good integrity for the significant target, and distinguishes the significant target and background from the edge contour regions.

This study proposed a human action recognition algorithm based on the Bi-LSTM-Attention model to solve the problem of low action recognition rate. This problem was caused by the inability of long short term memory (LSTM) networks to effectively extract correlative informations before and after actions. The proposed algorithm first extracted 20 image frames from each video and used the Inceptionv3 model to extract deep features from these frames. Then, forward and backward Bi-LSTM neural networks were constructed to learn the temporal information in the feature vectors. The influences of network weights on recognition results were adaptively perceived using the attention mechanism. This step was performed so that the model could achieve more accurate recognition based on the relationship between informations acquired before and after performing the given action. Finally, the videos were connected via a fully-connected layer to a Softmax classifier for classification. Comparison between the Action Youtobe and KTH human action datasets and existing methods revealed that the proposed algorithm effectively improved the action recognition rate.

This study proposes a method for the accurate and real-time measurement of the angle between two planes based on the concentric circle mark and the principle of projective transformation. First, the curve equation corresponding to the concentric circle mark in the obtained image is fitted. Second, several vanishing points are calculated according to the invariant cross and harmonic ratios. The vanishing line equation of the plane, where the concentric circles are located, is fitted to determine the vanishing points of the two plane normals. Third, the angle between the two normals is calculated by combining known camera internal parameters. The angle between the two planes is the supplementary angle. The simulation results show that the method is feasible. The actual image experiments show that the absolute error is less than 1°, while the relative error is less than 1%. This method requires only the internal parameters of the camera and has no special requirements for the size of the concentric circle mark and shoot angle. Moreover, the method has high flexibility and can provide a convenient and efficient scheme for the space angle measurement.

To lay a foundation for the non-invasive examination of tumor cells, the associations between Raman shift characteristics and morphological parameters of cytochemical staining are analyzed in lung adenocarcinoma cells and physiological mesothelial cells. A549 cells and Met-5A cells are analyzed using Raman spectroscopy to calculate the Raman spectral shift characteristics. A549 cells and Met-5A cells are stained with H&E and PAP solutions to calculate their morphological image characteristics. In both cell lines, Raman shifts are observed at 1082, 1155, and 1304 cm-1. Energy standard deviations of A549 and Met-5A cells are recognized by the images. PAP staining results are significantly different (p0.05) between the cell lines. The mean color value of cell image is significantly different (p<0.05) between the two stains. The Raman shift characteristics of lung cancer cells are then analyzed in terms of tumor substance expression and accumulation structure based on the affinity characteristics of the chemical substances. Experimental results show that clinical histopathological staining reveals the characteristics of nucleic acid and protein expression, and Raman spectroscopy reveals Raman peak shifts of nucleic acids, protein expression, and the lipid structure in lung cancer cells, laying a foundation for Raman spectroscopy to be used to set the substance peak of tumor cells.

While passing through an ultra-short-pulse measurement system, the pulse will be broadened owing to the presence of transmission elements. The broadening of the pulse results in significant errors in the final measurement results. To address this problem, a total-reflective ultra-short-pulse measurement system is designed to realize the measurement of shorter ultra-short pulse. In this system, Fresnel bi-mirror is used to generate time delays and a cylindrical mirror, thin crystals, and gratings are used to generate spectra, preventing the generation of dispersion in the system. Under the premise of satisfying the sampling rate requirement, the sampling range of the frequency-resolved optical gating trace is analyzed to obtain the required time delay and frequency-doubling bandwidth. With this constraint, the relationship between the width of the pulse to be measured and parameters of Fresnel bi-mirror and cylindrical mirror is deduced herein.

The study of secondary optical design of extended sources is significant in the applications of high-power light-emitting diodes in the lighting field. Herein, first, the causes of degradation in the illumination effect in light distribution systems for uniform illumination under extended source conditions are analyzed. Then, a refractive index pre-compensation method is proposed. Based on this method, a light distribution lens for uniform illumination is designed for extended sources with aperture of 8 mm. Simulation results show that the length of the collapse edge of the spot significantly decreases such that the slope improves with respect to the light distribution lens, and thus, the effective illumination area on the target surface increases. Consequently, the average illuminance of the target area increases. The normalized root-mean-square of the effective illumination area further decreases, and the uniformity of illuminance improves. The illumination efficiency improves because the light energy is able to further concentrate on the effective illumination area. This method provides a new approach to realize uniform illumination for extended source.

A two-dimensional photonic crystal optical attenuator with temperature control based on the Fabry-Pérot cavity is proposed. The structure utilizes the linear modulation of the refractive index of the photonic crystal induced by thermooptic effect of the doped silicon. The change in refractive index causes variations in the photonic crystal bandgap and optical path difference of the Fabry-Pérot cavity. This causes linear variation in reflectivity as a function of temperature within a specific temperature domain, which realizes the controllable attenuation of the incident light. A commercial electromagnetic simulation software, CST, is used to simulate the reflectance spectrum of the photonic crystal with the Fabry-Pérot cavity after silicon doping, showing that the spectrum curve shifts as a function of temperature. The variation pattern of reflectivity is simulated for this structure as a function of temperature at several specific frequencies. It is found that the curve has relatively good linearity at a frequency of 0.284 THz. The simulation is conducted for incidence angles of 1°, 3°, 5°, and 8°, respectively, whereas the linear fitting is performed for the temperature-reflectivity curve at 3°, through which the characterization formula is obtained. A temperature-controlled photonic crystal optical attenuator with a tuning amplitude of 0-7.68% is designed.

Silicon-based optical waveguide phase shifters are key components in silicon-based photoelectronic systems. Transparent conductive oxide (TCO) films are expected to be applicable to the next generation of silicon-based optical waveguide phase shifters with high modulation speed, low insertion loss, and CMOS-compatibility due to their tunable permittivity under a gate voltage. However, the high optical absorption coefficient of the TCO has limited their application in electro-optic phase shifters. We propose a compact and low-insertion-loss silicon-based optical waveguide phase shifter based on TCOs with high electron mobility. We demonstrate that the mobility of the TCO material is closely related to their insertion loss. Based on theoretical calculations and numerical simulations, we propose a silicon-based optical waveguide phase shifter based on high-mobility cadmium oxide (CdO, μ=300 cm 2·V-1·s-1) materials. For π-phase shift at 1550 nm, this CdO-based phase shifter shows a low-insertion loss of 1.4 dB, device length of 127 μm, and modulation bandwidth of 300 GHz. It provides a new strategy for the development of high-speed silicon-based optical waveguide phase shifters.

Surface-lattice resonances generated by using noble-metallic nanoparticle arrays can effectively suppress radiation loss, thereby improving the resonance quality factor and increasing localized field intensity. Surface-lattice resonances have been widely used for designing high-performance micro-nano photonic devices. Realizing the simultaneous excitation of orthogonal and parallel surface-lattice resonances is crucial for manipulating the collective responses of nanoparticle arrays, which is also useful for practical applications. Herein, we design an array structure composed of L-shaped nanoantennas. Furthermore, we investigate the optical response caused by the coupling between the Rayleigh anomaly and bonding/antibonding modes. We find that the two kinds of surface-lattice resonances can be excited simultaneously in extinction spectra. Calculation results reveal that the bonding/antibonding modes can be used to couple with Rayleigh anomaly, thereby leading to the formation of orthogonal and parallel surface-lattice resonances. These properties make asymmetric nanoparticle arrays become promising platforms for designing micro-nano photonic devices.

A theoretical model of the scanning control for a fiber-optic interferometric phased array based on phase modulation is proposed to overcome the limitation of scanning control for a fiber-optic interferometric phased array in practice. This model uses optical fibers as the phased-array elements, adopts paraxial and far-field approximations, and uses the scanning-control voltage loaded on the phase modulator to realize the scanning of the beam. A [-π, π] charging scheme of scanning voltage is proposed. The relationship among scanning angle, element spacing, and parameters of the phase modulator is numerically analyzed, and the influences of the phased-array path number and modulation coefficient on scanning resolution are verified numerically as well. Results show that the voltage is controlled within ±Vπ (half-wave voltage) by the [-π, π] charging scheme; when the spacing of the phased-array elements is constant, the scanning angle increases linearly with an increase in the length-to-height ratio of the phase modulator in the small-angle range; the higher the number of phased-array beams and the smaller the modulation coefficient, the higher the resolution. The maximum scanning angle can reach 0.1 rad when the length-to-height ratio of the phase modulator is 103 orders of magnitude and the spacing of the fiber-array elements is 125 μm, and the scanning resolution is 210 when the modulation coefficient is 0.3 and the number of fiber-optic elements is 50.

Herein, we investigate a scheme to generate continuous double-mode squeezed vacuum states based on a beam splitter by assuming that a beam splitter can cause entanglement, and the entangled conditions are solved. For a 50∶50 beam splitter, the phase shift influencing factor is analyzed and extracted. The relationship between the relative squeezing angle of quantum state output and input and the phase shift influencing factor is analyzed based on the quantum state conversion and the Wigner function. Subsequently, the Duan criterion is used to determine whether the output is entangled, and logarithmic negativity is defined for measuring the degree of entanglement. The results indicate the presence of a periodic effect in the entanglement characteristics of the outputs is due to the beam-splitter phase shift influencing factor and the relative squeezing angle provided as input; in a half-single pass, outputs are the processes associated with unrelated properties, partially entangled properties, and maximally entangled properties. Increasing the degree of squeezing provided as the input will cause an increase in output when the maximally entangled states are obtained as outputs. This study provides some concepts for using single-mode squeezed vacuum states to produce the maximally entangled states.

Current continuous-variable quantum key distribution systems suffer from low computing speed for data reconciliation. Herein, we address this problem by implementing a parallel acceleration for a multidimensional data reconciliation algorithm based on a central processing unit/graphics processing unit (CPU/GPU) heterogeneous platform. To meet the special requirements of heterogeneous parallel computing, we propose a static two-way crosslinked list to store a hyperscale low-density parity-check matrix. We also propose a parallel reconciliation algorithm. A simulation experiment is carried out on the heterogenous platform with a code length of 2.048×105. Reconciliation speed, key transmission distance, and reconciliation efficiency are calculated based on the simulation results of the convergence signal-to-noise ratio and time of reconciliation. Results show that when the code length is 2.048×105, the reconciliation speed of parallel acceleration on the CPU/GPU heterogeneous platform is five times faster than that on the CPU platform.

Remote sensing image classification is a specific application of the pattern recognition technology in the remote sensing field. This study proposes an atrous convolution model based on encoder-decoder (DeepLab-v3+) for performing remote sensing image classification with respect to the inaccurate edge classification and low classification accuracy problems encountered while processing remote sensing image classification using ordinary convolutional neural networks. First, the satellite image data are marked, and the DeepLab-v3+ model is trained using a calibration dataset. This model can extract edge features exhibiting considerable robustness from the remote sensing image. Finally, the classification results of the remote sensing image is obtained. When compared with other classification methods, the proposed method achieves higher classification accuracy, more robust edge features, and better classification results when applied on a remote sensing dataset.

Holographic stereogram technology is a three-dimensional display technology that can be used with the naked eye. Several holographic stereogram products have been successfully commercialized and have realized amazing visual experiences. However, in practical applications, several problems remain, such as an inadequate field of view (FOV) in the reconstructed image applications, excessive number of sampled images required to print a high-quality holographic stereogram, and lack of simulation results used for comparison with experimental results. In recent years, several research groups have conducted in-depth research on these issues. This study introduces the two most widely used holographic stereogram techniques, and then summarizes recent progresses in enlarging the FOV of a holographic stereogram, reducing the number of required samples,and numerical reconstruction of holographic stereograms.

Semantic segmentation, which classifies all pixels in an image and divides the image into several regions with specific semantic categories, is a key technology in the field of computer vision. In recent years, convolutional neural networks (CNNs) have been making breakthroughs and have demonstrated great potential in using deep learning to perform semantic segmentation. Herein, beginning with the definition of semantic segmentation, existing challenges in the field of semantic segmentation are discussed. Based on CNN principles, several datasets used for semantic segmentation algorithm evaluation are compared in detail, and recent deep learning methods based on decoders, information fusion, and recurrent neural networks in semantic segmentation are summarized. Finally, future development trends (e.g. enriching database scenes, improving real-time performance of algorithms, and researching the semantic segmentation) of three-dimensional point cloud data in semantic segmentation are summarized.

Microorganisms play an important role in the ecosystem; thus, the identification of microorganisms is an important task. Herein, surface-enhanced Raman spectroscopy (SERS) is applied to identify pinus massoniana lamb pollen (PML), boletus bicolor peck (BBP) spores, boletus speciosus frost (BSF) spores, and tylopilus plumbeoviolaceus spores (TPS). Spectroscopy results indicate that, after the surface enhancement, the locations of the conventional Raman signal of PML are at 1702, 1680, 1513, 1382, 1243, 1011, and 793 cm-1, with observable Raman peaks at 1698, 1653, 1592, 1516, 1403, 1288, 1210, 813, and 562 cm-1. Three boletus spores identified via the conventional Raman spectroscopy do not exhibit any Raman peak, whereas the surface-enhanced Raman spectra of PML, BBP spores, BSF spores, and TPS are significantly different in the range of 1700-1100 cm-1. Experimental results validate that SERS can effectively identify PML, BBP spores, BSF spores, and TPS.

To explore the feasibility of using fractional differentials in the hyperspectral estimation of wheat leaf water content, we select the Fukang experimental science base of Xinjiang University as the study area. Based on springtime wheat-field spectral data and wheat leaf water content data, we calculate the fractional differentials of the spectrum from the 0-order to the 2-order in 0.2-order steps; further, we analyze their correlations with the water content of the wheat leaves. We then use the successive projection algorithm (SPA) to select the optimal combination of bands for estimating the leaf water content from bands passing the 0.01 significance test. Finally, we establish a back propagation (BP) neural network model for estimating the water content of spring wheat leaves. The results show that fractional differentials can refine the trend of correlation between wheat leaf water content and the wheat leaves' spectral data. After fractional differential processing, the number of bands for which the correlation coefficients pass the 0.01 significance test first increases and subsequently decreases; in addition, the optimal order of fractional differentials is also different in different bands. Sensitive bands selected by the SPA are mainly concentrated in the red and near infrared bands, and the number of water sensitive bands is highest (reaching 13) after 1.2-order differential processing. Among the models considered herein, the BP neural network model with the 6-4-1 structure based on the 1.8-order differential is the best model, with the following specifications: the root-mean-square error of the modeling group is 0.701, the determination coefficient of the modeling group is 0.751, the root-mean-square error of the verification group is 0.227, the determination coefficient of the verification group is 0.917, and the relative analysis error of the verification group is 3.253. These conclusions show that the stability and predictive ability of the model using fractional differentials are better than those of integer differentials, and it provides a well-defined reference for the quantitative inversion of hyperspectral data to estimate the water content of spring wheat.