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In this paper, ZnO/ g-C3N4 series composite photocatalysts were prepared by thermal polymerization and hydrothermal method. The structure, morphology and light absorption properties of the prepared samples were characterized and tested. The photocatalytic performance of these samples was evaluated by the photocatalytic degradation of methylene blue (MB) and the photolysis aquatic hydrogen under visible light irradiation. The experimental results indicate that the performances of the ZnO/ g-C3N4 composite photocatalysts are superior to those of ZnO and g-C3N4 . Moreover, when the molar ratio of ZnO and g-C3N4 is 1:1, the photocatalytic performance of the prepared ZnO/ g-C3N4 composite samples is the best. On the one hand, the degradation rate of the 1:1 composite sample reaches 94. 36% after only 30 min, and the degradation rate is 5. 6 and 6. 7 times that of ZnO and g-C3 N4, respectively. On the other hand, the photolysis aquatic hydrogen of the 1:1 sample after 6 h is 11. 75 mmol, and the photolysis aquatic hydrogen rate is 7 times that of g-C3N4 . The results show that the ZnO/ g-C3N4 composite photocatalyst has excellent performance in pollutant degradation and photolysis aquatic hydrogen. In addition, the photocatalytic degradation and photolysis aquatic hydrogen mechanism of ZnO/ g-C3N4 series composite photocatalyst are also studied. [ABSTRACT FROM AUTHOR]

Bacteria are one of the most common human pathogens, which not only seriously jeopardize human health and public health safety, but also bring about huge medical expenditures. Rapid and accurate bacterial detection is important for the treatment of bacterial infections. The spectral detection method can not only obtain the classification, content and functional status of bacteria promptly and in real time, but also has the advantages of simple operation and non-invasive, and has great potential in the field of bacterial detection. This paper describes the research and application of Raman spectroscopy, terahertz spectroscopy, visible and near-infrared light spectroscopy, fluorescence spectroscopy in bacterial detection, and the molecular mechanism of visible and near-infrared light spectroscopy, light targets, including bacterial retinal plasmids containing retinal chromophores, bacteriophytochromes with tetrapyrrole chromophores, photoactive proteins with p-coumarate chromophores, photo-oxidative stress structural domains with flavin-like mononucleotide chromophores, cryptochromes with flavin-adenine dinucleotide chromophores. And blue-light sensing domains with flavin-adenine dinucleotides are described. Finally, optimization strategies for bacterial detection techniques are proposed with respect to the advantages and disadvantages of existing bacterial spectral detection techniques, hoping to provide help for the research of bacterial spectral detection. [ABSTRACT FROM AUTHOR]

Copper based sulfide photocatalysts have attracted extensive attention of researchers due to their narrow band gap, local surface plasmon resonance effect, good absorption ability to visible light, rich in reserves and non-toxic properties. However, the high recombination rate of photogenerated electrons and holes, and the low utilization efficiency of visible light hinder their application in the field of photocatalysis. Therefore, researchers have tried different modification strategies to improve their photocatalytic performance. This paper focuses on the modification strategies of copper based sulfides, mainly discusses the modification of the photocatalytic performance of copper based sulfides by morphology regulation, crystal phase regulation, and semiconductor heterojunction, etc., and analyzes the effect of different modification methods on the improvement of the photocatalytic performance. The application of copper based sulfides in the photocatalytic degradation of organic pollutants, photocatalytic splitting water for hydrogen production, photocatalytic reduction of CO2, etc. are discussed, and the development direction of copper based sulfide modification is prospected. [ABSTRACT FROM AUTHOR]

Objective This paper introduces a method for determining the calorific value of natural gas by using the correlation of sound velocity and visible light spectrum, so that domestic natural gas quality detection and energy measurement workers and enterprises can more effectively carry out the research and standardization of this method, and provide more diversified choices for the determination methods of calorific value of natural gas. Methods Based on the comparative analysis of the advantages and disadvantages of various natural gas calorific value determination methods and related standards, the method principle of using sound velocity and visible light spectrum correlation to determine the calorific value of natural gas is introduced, and the calorific value data of a group of gas reference materials determined by gas chromatography and the introduced correlation method are analyzed. Results The measured value of calorific value of natural gas determined by the correlation between sound velocity and visible light spectrum is in good agreement with the calculated value according to GB/T 11062-2020 Natural gas-Calculation of calorific values, density, relative density and Wobbe indices from composition. For six binary gas reference materials simulating natural gas, the relative deviation is between 0.08%-0.42%; For five kinds of multi-component （up to 14 components） gas reference materials simulating natural gas, the relative deviation is-0.45%-0.07%. Conclusions The feasibility of measuring the calorific value of natural gas by using the correlation between sound velocity and visible light spectrum is preliminarily verified. This method has the characteristics and advantages that multiple data can be obtained within 1 s, synchronized with flow measurement, and the measurement is not limited by gas components and not effected by H2. It is suggested to further carry out the research on accuracy, measurement range, precision and field application, and timely establish standard methods, level 0 test devices of calorific value direct combustion, gas compression factor, density and other test devices. [ABSTRACT FROM AUTHOR]

[Objectives]In order to eliminate the interference of soil background in the process of spectral indices measurement and overcome the dependence of existing spectral detection methods on structural light,the self-adaption segmentation algorithm of crop canopy region was studied in this paper. [Methods]In this paper,two cameras with narrow-band filters were used to build the measurement system,and the geometric installation parameters of the two cameras were calibrated. The ratio of the valley gray value of the narrow-band image gray histogram to the maximum gray value was used as a normalization threshold to achieve optical environment adaptability of narrow-band image segmentation. On the basis of realizing the segmentation of soil and canopy in the near-infrared narrow-band image,the canopy regions of the narrow-band images in the visible light band were derived by geometric transformation,and the narrow-band image of visible light could be segmented. [Results]In the height range of 80-140 cm,a measurement height was set every 10 cm,and each group collected 5 sets of narrow-band images(770 and 660 nm)of green radish with an average height of 25 cm. After the image acquisition was completed,the camera with the 660 nm filter was replaced with 770 nm to retake 5 photos as the reference images for the 660 nm images’ segmentation effect. The results showed that each average overlap of the segmentation region of the 660 nm image and the segmentation region of the reference image was greater than 99%. At the same time,the non-normalized thresholds and normalized thresholds of the 770 nm narrow-band images of 50 green radish photographed in the range of 10 000-26 000 lx were calculated,and their correlation coefficients with light intensity were 0.586 6 and 0.091 6,respectively. [Conclusions]The segmentation method based on normalized threshold proposed in this paper has strong adaptability to the changes of light environment. Combined with normalized threshold and geometric transformation,it can realize the segmentation of narrow-band images and provide effective basic data for constructing spectral indices which eliminate soil interference such as normalized difference vegetation index(NDVI). [ABSTRACT FROM AUTHOR]

In this paper, the orthorhombic nano-spherical tin dioxide and orthorhombic bismuth tungstate which are aggregated from flake to spherical structure were prepared by hydrothermal method, and then they were compounded to prepare the composite photocatalytic material of tin dioxide / bismuth tungstate. X-ray diffraction (XRD), scanning electron microscope (SEM), specific surface area tester ( BET ) and UV-Vis spectrophotometer were used to characterize the structure, morphology, specific surface area, pore volume and aperture and optical properties of the composite samples. The photocatalytic activity of tin dioxide / bismuth tungstate composites was studied by iodine tungsten lamp to simulate sunlight and using tin dioxide, bismuth tungstate and tin dioxide / bismuth tungstate composites as catalysts to degrade rhodamine B(RhB). The degradation rates of rhodamine B by tin dioxide, bismuth tungstate and tin dioxide / bismuth tungstate are 9%, 22% and 30%, respectively. Experiments show that the photocatalytic activity of tin dioxide / bismuth tungstate composite is higher than that of single tin dioxide and bismuth tungstate under visible light. [ABSTRACT FROM AUTHOR]

Accurate and effective drought monitoring using remote sensing technology is essential for regional and global drought warning and forecasting. Especially, soil moisture (SM) is one of the key potential factors affecting agricultural drought. At present, most studies ignore the fact that soil moisture is a complex nonlinear coupling system，the research only using visible light, near infrared, short-wave infrared, thermal infrared and other remote sensing drought index has certain limitations in SM inversion. A new method of retrieving farmland SM of winter wheat at returning green stage based on MODIS and radial basis function neural network (RBFNN) is presented in this paper. Firstly, the adaptability of various MODIS-derived drought indices is analyzed in the period of seedling establishment of winter wheat, including soil water content, crop morphological change in water requirement, crop canopy water content and temperature and other parameters. Secondly, the correlation between the original remote sensing drought indices and the soil relative humidity 10 cm soil layer was analyzed. A comprehensive evaluation set of apparent thermal inertia (ATI), vegetation supply water index (VSWI), enhanced vegetation index (EVI), normalized difference infrared index band7 (NDIIB7), normalized multi-band drought index (NMDI) and temperature condition index (TCI) are selected to invert soil moisture of farmland. Finally, combining MODIS remote sensing drought index with RBFNN, the SM of farmland was retrieved synergistically, and the retrieved results were compared with those of BP-NN and linear regression (LR) models. The experimental study is conducted in Henan province of China, MODIS reflectance products (MOD09A1) and temperature products (MOD11A2) at returning green stage of winter wheat from 2001 to 2012 were used to extract remote sensing drought indices, and the global land cover product of MODIS (MCD12Q1) is selected to obtain cropland distributions. Soil moisture data is derived from the “China crop growth and development and farmland soil moisture ten-day dataset”, which contains the soil relative humidity of 10 and 20 cm soil layer observed every 10 days by 17 soil moisture stations. The results show that the average accuracy of SM inversion model using RBFNN and multiple drought indices is 93.27%, which is increased by 2.92 and 9.97 percentage points compared with BP-NN and LR model, respectively. Most of the data points of the three models are concentrated around the 1:1 line, which indicate that there is a good correlation between the predicted value and the measured value. Compared with BP-NN model and LR model, the deviation between predicted value of RBFNN model and the 1:1 line is the smallest, the higher the regression correlation coefficient is, the higher the determination coefficient is. SM is significantly correlated with ATI, NDIIB7 and VSWI in the early growth stage of winter wheat. Multi-band remote sensing drought monitoring indices can comprehensively reflect the changes of crop physiology and morphology under soil water stress, and also determine the retrieval accuracy of the humidity model simultaneously. In this paper, only winter wheat in returning green stage is selected as the research object, and the comprehensive evaluation index set selected is not suitable for SM inversion of other growth stages of winter wheat, the adaptability of different remote sensing drought indices in different growth stages needs further experimental study. The study provides a new case for regional SM inversion from remote sensing-based drought monitoring indices and neural network. [ABSTRACT FROM AUTHOR]

Mechanical weeding can reduce the use of pesticides and is of great significance to ensure high yield of crops. Real-time and accurate identification of crops is a key technical problem needs to be solved in mechanical weeding equipment. Because of the subjectivity of feature extraction process in weed recognition, the accuracy of traditional methods in actual field environment is low. In recent years, the method of weed identification based on convolution neural network has been widely studied. Although the accuracy is obviously improved, there are still problems such as large parameters and poor real-time performance. In order to solve the above problems, a four-channel input image is constructed by collecting near infrared and visible images of sugar beet in the field, and a lightweight convolution neural network based on codec structure is proposed. In this paper, Sugarbeet and weed images collected from a farm in Bonn, Germany, in 2016 were used as data sets, which covered images of different growth stages of sugar beet, and 226 pictures of which were randomly selected as training sets, and the remaining 57 pictures were used as test sets. The experimental data set was composed of three channels of visible light image and one channel of near infrared image, which are merged into a four-channel image by pixel level superposition, and the depth-wise separable convolution was used in the deep model. Firstly, the input feature image was convoluted in 2 dimensions convolution kernel and the number of channels was expanded. Then, the 1×1 convolution kernel was used to make the 3 dimensions convolution which combined channel feature and compressed the channels to enhance the nonlinear mapping ability of the model. In order to avoid the problem of the gradient disappearing, the residual block was used to connect the input and output of the depth-wise separate convolution. Finally, the coding and decoder structure was designed and the shallow features were combined with deep features to refine the segmentation effect. Due to the imbalance of pixel proportions of soil, crops and weeds, the weighted loss function was used to optimize the model. The segmentation accuracy, parameters and operating efficiency of models at different input resolutions and different width factor were introduced to evalute the model. When the width factor was 1, the segmentation accuracy of the model increased with the increase of the input image resolution, the model accuracy of four channel input was higher than that of the model based on original visible image input, which showed that the near-infrared image features can compensate the defects of ordinary RGB images to some extent, and make the model more suitable for the dark environment. Under the same input image resolution, the model with a width factor of 2 or 4 performs better than the model with a width factor of 1. With the increases of width factor, the parameters of the model increase greatly. The amount of calculation is related to the size of the input image, so the frame rate gradually decreases with the increase of the size of input image. The experimental results show that the optimal model in this paper is a four channel input model with a width coefficient of 2, and the average intersection union ration is 87.58%, the average pixel accuracy is 99.19%, the parameters are 525 763 and the frame rate is 42.064 frames/s. The model has high segmentation and recognition accuracy and good real-time performance, and can provide theoretical basis for the development of intelligent mechanization weeding equipment. [ABSTRACT FROM AUTHOR]

In this paper, a method is proposed to locate the insulator automatically in aerial image based on binarized normed gradients (BING) and convolutional neural networks (CNNs). Firstly, we extract insulator candidate windows with BING algorithm. Secondly, we identify the windows containing insulator with convolution neural networks. Finally, the weighted iteration of the window set with high overlap is used to acquire the final insulator positioning results. The method proposed in this paper is validated with the transmission line aerial images obtained by the actual inspection of the large-scale unmanned helicopter of Guangdong Grid Co. Experiment shows that the recall of insulators with complex background is 90. 5% and the positioning accuracy is 92%, which means the proposed method can effectively locate the insulators in aerial image with complex background, the method also has strong versatility, and can he adapted to the visible light image of different background. [ABSTRACT FROM AUTHOR]

Aiming at the problem of insufficient background information and low contrast of the fusion result of infrared and visible light images, this paper proposed a fusion method combining guided filtering and fast co-occurrence filtering. First, it decomposed source images into a detail layer and a base layer by Gaussian filtering. Then it simplified the co-occurrence filtering by removing the range filter and global statistical co-occurrence information to form a fast co-occurrence filter, and then used it to fuse the detail layer. In addition, it introduced a window factor, and determined window for guided filter by the ratio of the image size and the window factor, then used it to fuse the base layer. Experimental results show that the algorithm increases the background details of the image and improves the contrast between the person and the background. Subjective and objective experimental analysis verifies the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

The rapid and accurate seedling situation acquisition is an important prerequisite for farmland fine management, and also the basis for promoting the development of precision agriculture. It was found that UAV remote sensing images combined with machine vision technology had obvious advantages in crop detection in the field. However, current research mainly focused on crops such as corn, wheat, and rape, and only realized the extraction of emergence rate or coverage. In fact, there were few reports on the research of cotton overall seedling situation acquisition. In order to solve the problems of time-consuming and inefficient manual collection of cotton seedling information, this article explored a new method of extracting seedlings based on unmanned aerial vehicles (UAV) visible light remote sensing images. Firstly, cotton images in the 3-4 leaf stage were captured by the UAV equipped with a high-resolution visible light sensor. Meanwhile, the typical images were selected for experimentation after a series of preprocessing operations, such as correction, stitching, and cropping. The separation of cotton from the background (soil, mulch) was a primary prerequisite for obtaining cotton seedling situation information. The segmentation effect of eight color indexes on cotton image were compared and analyzed and the green-blue difference index (GBDI) color index was selected in this paper to realize the segmentation of cotton and background by combining with the Otsu threshold segmentation method because GBDI component was found to have fewer impurities and more complete segmentation. In order to avoid the influence of weed noise on the follow-up experiment, morphology and grid method for weed noise elimination were adopted, and the results showed that the grid method was more effective than the morphological method in removing weeds. A mapping relationship between morphological characteristics and the number of cotton plants was established to estimate the number of cotton. Because conglutinated cotton was difficult to be segmented by morphological operation, 10 morphological features were extracted as candidate variables to establish SVM plant number estimation model. A total of 3710 sample data were obtained in this experiment, 80% of which were randomly selected for classification modeling, while the remaining 20% were used for testing. Based on the person correlation analysis, 6 features whose correlation coefficient more than 0.7 were selected.The model was applied to the whole image to obtain the number of emerging cotton plants in the study area, and then the seedling emergence rate, canopy coverage and evenness of cotton plant growth were calculated, consecutively. The results showed that the classification accuracy of SVM plant number estimation model reached 97.17%, the statistical error ranged from 0.8% to 4.7%, and the average error was 2.52%. The error of the method decreased with the increase of monitoring area, which indicated that the model had better applicability in larger cotton fields. The cotton emergence rate, canopy coverage and growth uniformity were 64.89%, 7.17%, 10.98% respectively. The method based on the UAV visible light image effectively improved the efficiency of cotton field seedling acquisition, and the research results can provide technical support for subsequent cotton field management and fine plant protection. [ABSTRACT FROM AUTHOR]

To handle the LED nonlinearity distortion caused by the high PAPR of Flip-OFDM in visible light communication systems, this paper proposed a dynamic compensation based algorithm to reduce the LED nonlinearity distortion since the Flip-OFDM signal was unipolar. Firstly, the algorithm sleeted partial transmit sequence coefficient by minimizing the negative signal amplitude of Flip-OFDM, and clipped the Flip-OFDM signal to ensure that the signal was in the quasi-linear operate region of LED. Ten, it calculated the compensatory signal according to the positive signal sequence dynamically, and added to the corresponding zero position of negative signal sequence. Finally, it recovered positive signals according to compensatory signals in receiving terminal by combined detection methods of maximum likelihood and decision threshold algorithms, which could reduce the clipping noise. Performance analysis and simulation results show that compared with PTS-Clipping algorithm, the proposed algorithm can significantly restrain PAPR and clipping noise, and have up to about 5 dB improvement for SNR where the BER is in the order of 10 3, effectively reduce LED nonlinearity distortion to improve OFDM systems performance. [ABSTRACT FROM AUTHOR]