Mapping soil organic matter in cultivated land based on multi-year composite images on monthly time scales

Rapid and accurate acquisition of soil organic matter(SOM)information in cultivated land is important for sustainable agricultural development and carbon balance management.This study proposed a novel approach to predict SOM with high accuracy using multiyear synthetic remote sensing variables on a monthly scale.We obtained 12 monthly synthetic Sentinel-2 images covering the study area from 2016 to 2021 through the Google Earth Engine(GEE)platform,and reflectance bands and vegetation indices were extracted from these composite images.Then the random forest(RF),support vector machine(SVM)and gradient boosting regression tree(GBRT)models were tested to investigate the difference in SOM prediction accuracy under different combinations of monthly synthetic variables.Results showed that firstly,all monthly synthetic spectral bands of Sentinel-2 showed a significant correlation with SOM(P<0.05)for the months of January,March,April,October,and November.Secondly,in terms of single-monthly composite variables,the prediction accuracy was relatively poor,with the highest R^(2)value of 0.36 being observed in January.When monthly synthetic environmental variables were grouped in accordance with the four quarters of the year,the first quarter and the fourth quarter showed good performance,and any combination of three quarters was similar in estimation accuracy.The overall best performance was observed when all monthly synthetic variables were incorporated into the models.Thirdly,among the three models compared,the RF model was consistently more accurate than the SVM and GBRT models,achieving an R^(2)value of 0.56.Except for band 12 in December,the importance of the remaining bands did not exhibit significant differences.This research offers a new attempt to map SOM with high accuracy and fine spatial resolution based on monthly synthetic Sentinel-2 images.

Image Processing for Denoising Using Composite Adaptive Filtering Methods Based on RMSE

As one of the carriers for human communication and interaction, images are prone to contamination by noise during transmission and reception, which is often uncontrollable and unknown. Therefore, how to denoise images contaminated by unknown noise has gradually become one of the research focuses. In order to achieve blind denoising and separation to restore images, this paper proposes a method for image processing based on Root Mean Square Error (RMSE) by integrating multiple filtering methods for denoising. This method includes Wavelet Filtering, Gaussian Filtering, Median Filtering, Mean Filtering, Bilateral Filtering, Adaptive Bandpass Filtering, Non-local Means Filtering and Regularization Denoising suitable for different types of noise. We can apply this method to denoise images contaminated by blind noise sources and evaluate the denoising effects using RMSE. The smaller the RMSE, the better the denoising effect. The optimal denoising result is selected through comprehensively comparing the RMSE values of all methods. Experimental results demonstrate that the proposed method effectively denoises and restores images contaminated by blind noise sources.

Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

From text to image:challenges in integrating vision into ChatGPT for medical image interpretation

Large language models(LLMs),such as ChatGPT developed by OpenAI,represent a significant advancement in artificial intelligence(AI),designed to understand,generate,and interpret human language by analyzing extensive text data.Their potential integration into clinical settings offers a promising avenue that could transform clinical diagnosis and decision-making processes in the future(Thirunavukarasu et al.,2023).This article aims to provide an in-depth analysis of LLMs’current and potential impact on clinical practices.Their ability to generate differential diagnosis lists underscores their potential as invaluable tools in medical practice and education(Hirosawa et al.,2023;Koga et al.,2023).

Graphene Aerogel Composites with Self‑Organized Nanowires‑Packed Honeycomb Structure for Highly Efficient Electromagnetic Wave Absorption

With vigorous developments in nanotechnology,the elaborate regulation of microstructure shows attractive potential in the design of electromagnetic wave absorbers.Herein,a hierarchical porous structure and composite heterogeneous interface are constructed successfully to optimize the electromagnetic loss capacity.The macro–micro-synergistic graphene aerogel formed by the ice template‑assisted 3D printing strategy is cut by silicon carbide nanowires(SiC_(nws))grown in situ,while boron nitride(BN)interfacial structure is introduced on graphene nanoplates.The unique composite structure forces multiple scattering of incident EMWs,ensuring the combined effects of interfacial polarization,conduction networks,and magnetic-dielectric synergy.Therefore,the as-prepared composites present a minimum reflection loss value of−37.8 dB and a wide effective absorption bandwidth(EAB)of 9.2 GHz(from 8.8 to 18.0 GHz)at 2.5 mm.Besides,relying on the intrinsic high-temperature resistance of SiC_(nws) and BN,the EAB also remains above 5.0 GHz after annealing in air environment at 600℃ for 10 h.

Advancements and Challenges in Organic–Inorganic Composite Solid Electrolytes for All‑Solid‑State Lithium Batteries

To address the limitations of contemporary lithium-ion batteries,particularly their low energy density and safety concerns,all-solid-state lithium batteries equipped with solid-state electrolytes have been identified as an up-and-coming alternative.Among the various SEs,organic–inorganic composite solid electrolytes(OICSEs)that combine the advantages of both polymer and inorganic materials demonstrate promising potential for large-scale applications.However,OICSEs still face many challenges in practical applications,such as low ionic conductivity and poor interfacial stability,which severely limit their applications.This review provides a comprehensive overview of recent research advancements in OICSEs.Specifically,the influence of inorganic fillers on the main functional parameters of OICSEs,including ionic conductivity,Li+transfer number,mechanical strength,electrochemical stability,electronic conductivity,and thermal stability are systematically discussed.The lithium-ion conduction mechanism of OICSE is thoroughly analyzed and concluded from the microscopic perspective.Besides,the classic inorganic filler types,including both inert and active fillers,are categorized with special emphasis on the relationship between inorganic filler structure design and the electrochemical performance of OICSEs.Finally,the advanced characterization techniques relevant to OICSEs are summarized,and the challenges and perspectives on the future development of OICSEs are also highlighted for constructing superior ASSLBs.

Moment Redistribution Effect of the Continuous Glass Fiber Reinforced Polymer-Concrete Composite Slabs Based on Static Loading Experiment

This study aimed to investigate the moment redistribution in continuous glass fiber reinforced polymer(GFRP)-concrete composite slabs caused by concrete cracking and steel bar yielding in the negative bending moment zone.An experimental bending moment redistribution test was conducted on continuous GFRP-concrete composite slabs,and a calculation method based on the conjugate beam method was proposed.The composite slabs were formed by combining GFRP profiles with a concrete layer and supported on steel beams to create two-span continuous composite slab specimens.Two methods,epoxy resin bonding,and stud connection,were used to connect the composite slabs with the steel beams.The experimental findings showed that the specimen connected with epoxy resin exhibited two moments redistribution phenomena during the loading process:concrete cracking and steel bar yielding at the internal support.In contrast,the composite slab connected with steel beams by studs exhibited only one-moment redistribution phenomenon throughout the loading process.As the concrete at the internal support cracked,the bending moment decreased in the internal support section and increased in the midspan section.When the steel bars yielded,the bending moment further decreased in the internal support section and increased in the mid-span section.Since GFRP profiles do not experience cracking,there was no significant decrease in the bending moment of the mid-span section.All test specimens experienced compressive failure of concrete at the mid-span section.Calculation results showed good agreement between the calculated and experimental values of bending moments in the mid-span section and internal support section.The proposed model can effectively predict the moment redistribution behavior of continuous GFRP-concrete composite slabs.

Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net(MU-Net)on Spine Magnetic Resonance Images

Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people everywhere.Due to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the spine.The semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar diseases.It is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation accuracy.This work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra S1.Pseudo-colour mask images were generated and used as ground truth for training the model.The work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley Data.The proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.

Uniaxial experimental study of the acoustic emission and deformation behavior of composite rock based on 3D digital image correlation(DIC)

In this paper, uniaxial compression tests were carried out on a series of composite rock specimens with different dip angles, which were made from two types of rock-like material with different strength. The acoustic emission technique was used to monitor the acoustic signal characteristics of composite rock specimens during the entire loading process. At the same time, an optical non-contact 3 D digital image correlation technique was used to study the evolution of axial strain field and the maximal strain field before and after the peak strength at different stress levels during the loading process. The effect of bedding plane inclination on the deformation and strength during uniaxial loading was analyzed. The methods of solving the elastic constants of hard and weak rock were described. The damage evolution process, deformation and failure mechanism, and failure mode during uniaxial loading were fully determined. The experimental results show that the θ = 0?–45?specimens had obvious plastic deformation during loading, and the brittleness of the θ = 60?–90?specimens gradually increased during the loading process. When the anisotropic angle θincreased from 0?to 90?, the peak strength, peak strain,and apparent elastic modulus all decreased initially and then increased. The failure mode of the composite rock specimen during uniaxial loading can be divided into three categories:tensile fracture across the discontinuities(θ = 0?–30?), slid-ing failure along the discontinuities(θ = 45?–75?), and tensile-split along the discontinuities(θ = 90?). The axial strain of the weak and hard rock layers in the composite rock specimen during the loading process was significantly different from that of the θ = 0?–45?specimens and was almost the same as that of the θ = 60?–90?specimens. As for the strain localization highlighted in the maximum principal strain field, the θ = 0?–30?specimens appeared in the rock matrix approximately parallel to the loading direction,while in the θ = 45?–90?specimens it appeared at the hard and weak rock layer interface.

Quasi-static tensile deformation and fracture behavior of a highly particle-filled composite using digital image correlation method

Polymer bonded explosives (PBXs) are highly particle-filled composite materials.This paper experimentally studies the tensile deformation and fracture behavior of a PBX simulation by using the semi-circular bending (SCB) test.The deformation and fracture process of a pre-notched SCB sample with a random speckle pattern is recorded by a charge coupled device camera.The displacement and strain fields on the observed surface during the loading process are obtained by using the digital image correlation method.The crack opening displacement is calculated from the displacement fields,the initiation and propagation of the crack are analyzed.In addition,the damage evolution and fracture mechanisms of the SCB sample are analyzed according to the strain fields and the correlation coefficient fields at different loading steps.

DAMAGE AND FRACTURE EVALUATION OF GRANULAR COMPOSITE MATERIALS BY DIGITAL IMAGE CORRELATION METHOD

This paper presents the applications of digital image correlation technique to the mesoscopic damage and fracture study of some granular based composite materials including steel- fiber reinforced concrete,sandstone and crystal-polymer composite.The deformation fields of the composite materials resulted from stress localization were obtained by the correlation computation of the surface images with loading steps and thus the related damage prediction and fracture parameters were evaluated.The correlation searching could be performed either directly based on the gray levels of the digital images or from the wavelet transform(WT)coefficients of the transform spectrum.The latter was developed by the authors and showed higher resolution and sensitivity to the singularity detection. Because the displacement components came from the rough surfaces of the composite materials without any coats of gratings or fringes of optical interferometry,both surface profiles and the deformation fields of the composites were visualized which was helpful to compare each other to analyze the damage of those heterogeneous materials.

Identification of the elastic stiffness of composites using the virtual fields method and digital image correlation

This paper presents an effective methodology for characterizing the mechanical parameters of composites using digital image correlation combined with the virtual fields method.By using a three-point bending test configuration,this method can identify all mechanical parameters of the material with merely a single test.Successful results verified that this method is especially effective for characterizing composite materials.In this study,the method is applied to measure the orthotropic elastic parameters of fiber-reinforced polymer-matrix composites before and after the hygrothermal aging process.The results indicate that the hygrothermal aging environment significantly influences the mechanical property of a composite.The components of the parameters in the direction of the fiber bundle decreased significantly.From the accuracy analysis,we found that the actual measurement accuracy is sensitive to a shift of the horizontal edges and rotation of the vertical edges.

Revealing the role of local shear strain partition of transformable particles in a TRIP-reinforced bulk metallic glass composite via digital image correlation

The coupling effects of the metastable austenitic phase and the amorphous matrix in a transformation-induced plasticity(TRIP)-reinforced bulk metallic glass(BMG)composite under compressive loading were investigated by employing the digital image correlation(DIC)technique.The evolution of local strain field in the crystalline phase and the amorphous matrix was directly monitored,and the contribution from the phase transformation of the metastable austenitic phase was revealed.Local shear strain was found to be effectively consumed by the displacive phase transformation of the metastable austenitic phase,which relaxed the local strain/stress concentration at the interface and thus greatly enhanced the plasticity of the TRIP-reinforced BMG composites.Our current study sheds light on in-depth understanding of the underlying deformation mechanism and the interplay between the amorphous matrix and the metastable crystalline phase during deformation,which is helpful for design of advanced BMG composites with further improved properties.

Monthly composites from Sentinel-1 and Sentinel-2 images for regional major crop mapping with Google Earth Engine

Rapid and accurate access to large-scale,high-resolution crop-type distribution maps is important for agricultural management and sustainable agricultural development.Due to the limitations of remote sensing image quality and data processing capabilities,large-scale crop classification is still challenging.This study aimed to map the distribution of crops in Heilongjiang Province using Google Earth Engine(GEE)and Sentinel-1 and Sentinel-2 images.We obtained Sentinel-1 and Sentinel-2 images from all the covered study areas in the critical period for crop growth in 2018(May to September),combined monthly composite images of reflectance bands,vegetation indices and polarization bands as input features,and then performed crop classification using a Random Forest(RF)classifier.The results show that the Sentinel-1 and Sentinel-2 monthly composite images combined with the RF classifier can accurately generate the crop distribution map of the study area,and the overall accuracy(OA)reached 89.75%.Through experiments,we also found that the classification performance using time-series images is significantly better than that using single-period images.Compared with the use of traditional bands only(i.e.,the visible and near-infrared bands),the addition of shortwave infrared bands can improve the accuracy of crop classification most significantly,followed by the addition of red-edge bands.Adding common vegetation indices and Sentinel-1 data to the crop classification improved the overall classification accuracy and the OA by 0.2 and 0.6%,respectively,compared to using only the Sentinel-2 reflectance bands.The analysis of timeliness revealed that when the July image is available,the increase in the accuracy of crop classification is the highest.When the Sentinel-1 and Sentinel-2 images for May,June,and July are available,an OA greater than 80%can be achieved.The results of this study are applicable to large-scale,high-resolution crop classification and provide key technologies for remote sensing-based crop classification in small-scale agricultural areas.

Edge Detection of Composite Insulators Hydrophobic Image Based on Improved Canny Operator

The detection of hydrophobicity is an important way to evaluate the performance of composite insulators, which is helpful to the safe operation of composite insulators. Image processing technology is used to judge the hydrophobicity of composite insulators, which makes detection results more accurate and overcomes the subjective drawbacks of traditional detection methods.?As the traditional Canny operator requires manual intervention in selecting the variance of the Gaussian filter and the threshold, the paper presents a method of edge detection based on improved Canny operator. First, the adaptive median filter replaces the Gaussian filter, which can eliminate the impact from the variance of Gaussian filter and remove noise according to the characteristics of the image itself. Then the Ostu threshold method is used to select the best threshold automatically, which makes the edge detection be more continuous and reduce the presence of fake edges. The results show that the operator is applicable to all hydrophobic images.

Image Storage and Real-Time Distorted Image Correction by Using Photorefractivity in a Stable Photorefractive Polymer Composite

Using a highly stable and excellently performed three-component photorefractive(PR)polymer composite,poly(N-vinylcarbazole):1-n-butoxyl-2,5-dimethyl-4-(4-nitrophenylazo)benzene:2,4,7-tnnitro-9-fluorenone,optical image storage was demonstrated.The dark storage time was 5-7h.The PR response time was measured to be about 200 ms at an intensity of 1 W/cm^(2) with an applied electric Held of 84 V/μm.A proof-of-principle experiment on real-time correction of distorted images based on phase conjugation of four-wave mixing geometry was also carried out.

Adaptive Boundary and Semantic Composite Segmentation Method for Individual Objects in Aerial Images

There are two types of methods for image segmentation.One is traditional image processing methods,which are sensitive to details and boundaries,yet fail to recognize semantic information.The other is deep learning methods,which can locate and identify different objects,but boundary identifications are not accurate enough.Both of them cannot generate entire segmentation information.In order to obtain accurate edge detection and semantic information,an Adaptive Boundary and Semantic Composite Segmentation method(ABSCS)is proposed.This method can precisely semantic segment individual objects in large-size aerial images with limited GPU performances.It includes adaptively dividing and modifying the aerial images with the proposed principles and methods,using the deep learning method to semantic segment and preprocess the small divided pieces,using three traditional methods to segment and preprocess original-size aerial images,adaptively selecting traditional results tomodify the boundaries of individual objects in deep learning results,and combining the results of different objects.Individual object semantic segmentation experiments are conducted by using the AeroScapes dataset,and their results are analyzed qualitatively and quantitatively.The experimental results demonstrate that the proposed method can achieve more promising object boundaries than the original deep learning method.This work also demonstrates the advantages of the proposed method in applications of point cloud semantic segmentation and image inpainting.

Dendritic Learning-Incorporated Vision Transformer for Image Recognition

Dear Editor,This letter proposes to integrate dendritic learnable network architecture with Vision Transformer to improve the accuracy of image recognition.In this study,based on the theory of dendritic neurons in neuroscience,we design a network that is more practical for engineering to classify visual features.Based on this,we propose a dendritic learning-incorporated vision Transformer(DVT),which out-performs other state-of-the-art methods on three image recognition benchmarks.

Infrared and Visible Image Fusion Based on Res2Net-Transformer Automatic Encoding and Decoding

A novel image fusion network framework with an autonomous encoder and decoder is suggested to increase thevisual impression of fused images by improving the quality of infrared and visible light picture fusion. The networkcomprises an encoder module, fusion layer, decoder module, and edge improvementmodule. The encoder moduleutilizes an enhanced Inception module for shallow feature extraction, then combines Res2Net and Transformerto achieve deep-level co-extraction of local and global features from the original picture. An edge enhancementmodule (EEM) is created to extract significant edge features. A modal maximum difference fusion strategy isintroduced to enhance the adaptive representation of information in various regions of the source image, therebyenhancing the contrast of the fused image. The encoder and the EEM module extract features, which are thencombined in the fusion layer to create a fused picture using the decoder. Three datasets were chosen to test thealgorithmproposed in this paper. The results of the experiments demonstrate that the network effectively preservesbackground and detail information in both infrared and visible images, yielding superior outcomes in subjectiveand objective evaluations.

Using restored two-dimensional X-ray images to reconstruct the three-dimensional magnetopause

Astronomical imaging technologies are basic tools for the exploration of the universe,providing basic data for the research of astronomy and space physics.The Soft X-ray Imager(SXI)carried by the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)aims to capture two-dimensional(2-D)images of the Earth’s magnetosheath by using soft X-ray imaging.However,the observed 2-D images are affected by many noise factors,destroying the contained information,which is not conducive to the subsequent reconstruction of the three-dimensional(3-D)structure of the magnetopause.The analysis of SXI-simulated observation images shows that such damage cannot be evaluated with traditional restoration models.This makes it difficult to establish the mapping relationship between SXIsimulated observation images and target images by using mathematical models.We propose an image restoration algorithm for SXIsimulated observation images that can recover large-scale structure information on the magnetosphere.The idea is to train a patch estimator by selecting noise–clean patch pairs with the same distribution through the Classification–Expectation Maximization algorithm to achieve the restoration estimation of the SXI-simulated observation image,whose mapping relationship with the target image is established by the patch estimator.The Classification–Expectation Maximization algorithm is used to select multiple patch clusters with the same distribution and then train different patch estimators so as to improve the accuracy of the estimator.Experimental results showed that our image restoration algorithm is superior to other classical image restoration algorithms in the SXI-simulated observation image restoration task,according to the peak signal-to-noise ratio and structural similarity.The restoration results of SXI-simulated observation images are used in the tangent fitting approach and the computed tomography approach toward magnetospheric reconstruction techniques,significantly improving the reconstruction results.Hence,the proposed technology may be feasible for processing SXI-simulated observation images.