Calibrating the linearity between grayscale and element content for X-ray KES imaging of alloys

Doped elements in alloys significantly impact their performance.Conventional methods usually sputter the surface material of the sample,or their performance is limited to the surface of alloys owing to their poor penetration ability.The X-ray K-edge subtraction(KES)method exhibits great potential for the nondestructive in situ detection of element contents in alloys.However,the signal of doped elements usually deteriorates because of the strong absorption of the principal component and scattering of crystal grains.This in turn prevents the extensive application of X-ray KES imaging to alloys.In this study,methods were developed to calibrate the linearity between the grayscale of the KES image and element content.The methods were aimed at the sensitive analysis of elements in alloys.Furthermore,experiments with phantoms and alloys demonstrated that,after elaborate calibration,X-ray KES imaging is capable of nondestructive and sensitive analysis of doped elements in alloys.

Grayscale Mapping of Historically Irrigated LandsUsing GIS

This document describes the use of grayscale mapping and GIS for identification of historical irrigated lands. Historical irrigated lands form the basis for water rights—a private property right in New Mexico that is bought and sold on the open market. Identification of irrigated land on historical photography is both a science and an art. Grayscale mapping of historic black and white photographs can aid in the identification of irrigated lands. GIS allows historical images to be geo-referenced and area computations to be performed on polygons that define the irrigated lands.

Single Photon Compressive Imaging Based on Digital Grayscale Modulation Method

In single-pixel imaging or computational ghost imaging,the measurement matrix has a great impact on the performance of the imaging system,because it involves modulation of the optical signal and image reconstruction.The measurement matrix reported in the existing literatures is first binarized and then loaded onto the digital micro-mirror device(DMD)for optical modulation,that is,each pixel can only be modulated into on-off states.In this paper,we propose a digital grayscale modulation method for more efficient compressive sampling.On the basis of this,we demonstrate a single photon compressive imaging system.A control and counting circuit,based on field-programmable gate array(FPGA),is developed to control DMD to conduct digital grayscale modulation and count single-photon pulse output from the photomultiplier tube(PMT)simultaneously.The experimental results show that the imaging reconstruction quality can be improved by increasing the sparsity ratio properly and compressive sampling ratio(SR)of these gray-scale matrices.However,when the compressive SR and sparsity ratio are increased appropriately to a certain value,the reconstruction quality is usually saturated,and the imaging reconstruction quality of the digital grayscale modulation is better than that of binary modulation.

High-resolution grayscale image hidden in a laser beam

Images perceived by human eyes or recorded by cameras are usually optical patterns with spatially varying intensity or color profiles.In addition to the intensity and color,the information of an image can be encoded in a spatially varying distribution of phase or polarization state.Interestingly,such images might not be able to be directly viewed by human eyes or cameras because they may exhibit highly uniform intensity profiles.Here,we propose and experimentally demonstrate an approach to hide a high-resolution grayscale image in a square laser beam with a size of less than half a millimeter.An image with a pixel size of 300×300 nm is encoded into the spatially variant polarization states of the laser beam,which can be revealed after passing through a linear polarizer.This unique technology for hiding grayscale images and polarization manipulation provides new opportunities for various applications,including encryption,imaging,optical communications,quantum science and fundamental physics.

Grayscale image statistics of COVID-19 patient CT scans characterize lung condition with machine and deep learning

Background:Grayscale image attributes of computed tomography(CT)of pulmonary scans contain valuable information relating to patients with respiratory ailments.These attributes are used to evaluate the severity of lung conditions of patients confirmed to be with and without COVID-19.Method:Five hundred thirteen CT images relating to 57 patients(49 with COVID-19;8 free of COVID-19)were collected at Namazi Medical Centre(Shiraz,Iran)in 2020 and 2021.Five visual scores(VS:0,1,2,3,or 4)are clinically assigned to these images with the score increasing with the severity of COVID-19-related lung conditions.Eleven deep learning and machine learning techniques(DL/ML)are used to distinguish the VS class based on 12 grayscale image attributes.Results:The convolutional neural network achieves 96.49%VS accuracy(18 errors from 513 images)successfully distinguishing VS Classes 0 and 1,outperforming clinicians’visual inspections.An algorithmic score(AS),involving just five grayscale image attributes,is developed independently of clinicians’assessments(99.81%AS accuracy;1 error from 513 images).Conclusion:Grayscale CT image attributes can be successfully used to distinguish the severity of COVID-19 lung damage.The AS technique developed provides a suitable basis for an automated system using ML/DL methods and 12 image attributes.

Integrating image processing and deep learning for effective analysis and classification of dust pollution in mining processes

A comprehensive evaluation method is proposed to analyze dust pollution generated in the production process of mines.The method employs an optimized image-processing and deep learning framework to characterize the gray and fractal features in dust images.The research reveals both linear and logarithmic correlations between the gray features,fractal dimension,and dust mass,while employing Chauvenel criteria and arithmetic averaging to minimize data discreteness.An integrated hazardous index is developed,including a logarithmic correlation between the index and dust mass,and a four-category dataset is subsequently prepared for the deep learning framework.Based on the range of the hazardous index,the dust images are divided into four categories.Subsequently,a dust risk classifcation system is established using the deep learning model,which exhibits a high degree of performance after the training process.Notably,the model achieves a testing accuracy of 95.3%,indicating its efectiveness in classifying diferent levels of dust pollution,and the precision,recall,and F1-score of the system confrm its reliability in analyzing dust pollution.Overall,the proposed method provides a reliable and efcient way to monitor and analyze dust pollution in mines.

A Multi-Stream Scrambling and DNA Encoding Method Based Image Encryption

Information security has emerged as a key problem in encryption because of the rapid evolution of the internet and networks.Thus,the progress of image encryption techniques is becoming an increasingly serious issue and considerable problem.Small space of the key,encryption-based low confidentiality,low key sensitivity,and easily exploitable existing image encryption techniques integrating chaotic system and DNA computing are purposing the main problems to propose a new encryption technique in this study.In our proposed scheme,a three-dimensional Chen’s map and a one-dimensional Logistic map are employed to construct a double-layer image encryption scheme.In the confusion stage,different scrambling operations related to the original plain image pixels are designed using Chen’s map.A stream pixel scrambling operation related to the plain image is constructed.Then,a block scrambling-based image encryption-related stream pixel scrambled image is designed.In the diffusion stage,two rounds of pixel diffusion are generated related to the confusing image for intra-image diffusion.Chen’s map,logistic map,and DNA computing are employed to construct diffusion operations.A reverse complementary rule is applied to obtain a new form of DNA.A Chen’s map is used to produce a pseudorandom DNA sequence,and then another DNA form is constructed from a reverse pseudorandom DNA sequence.Finally,the XOR operation is performed multiple times to obtain the encrypted image.According to the simulation of experiments and security analysis,this approach extends the key space,has great sensitivity,and is able to withstand various typical attacks.An adequate encryption effect is achieved by the proposed algorithm,which can simultaneously decrease the correlation between adjacent pixels by making it near zero,also the information entropy is increased.The number of pixels changing rate(NPCR)and the unified average change intensity(UACI)both are very near to optimal values.

Fractal and microscopic quantitative characterization of unclassified tailings flocs

A series of laboratory investigations are conducted to analyze the effect of flocculant type on the spatial morphology and microstructural characteristics of flocs during the flocculation and settling of tailings.Four flocculant types(i.e.,ZYZ,JYC-2,ZYD,and JYC-1)are considered in this study.The fractal characteristics and internal structures of tailings flocs with different flocculant types and settlement heights are analyzed by conducting scanning electron microscopy and X-ray micro-computed tomography scanning experiments based on the fractal theory.Results show that unclassified tailings flocs are irregular clusters with fractal characteristics,and the flocculation effect of the four flocculant types has the following trend:ZYZ>JYC-2>ZYD>JYC-1.The size and average grayscale value of tailings flocs decrease with the increase in settlement height.The average grayscale values at the top and bottom are 144 and 103,respectively.The settlement height remarkably affects the pore distribution pattern,as reflected in the constructed three-dimensional pore model of tailings flocs.The top part of flocs has relatively good penetration,whereas the bottom part of flocs has mostly dispersed pores.The number of pores increases exponentially with the increase in settlement height.By contrast,the size of pores initially increases and subsequently decreases with the increase in settlement height.

COLOR AC-PDP ENHANCEMENT OF LOW LEVELS IMAGE QUALITY WITH DYNAMIC MODIFYING SUB-FIELDS CODE

Based on the "Grayscales average distribution" method which equally distributes the input gray levels to output gray levels, three improved methods named: "Reduce the gray range expressed by the less significant subfields", "Low levels preset" and "Modify the exponent of inverse-gamma function" are proposed in this paper. Using these methods, the inverse-gamma relation subfields code can be obtained easily which can improve the low level expressions of AC-PDP. And a program, "gray scales distribution validate program", which can enhance the expressions of the demanded gray levels range, is also proposed in this paper.

Value of duplex doppler ultrasonography in non-invasive assessment of children with chronic liver disease

AIM:To investigate the value of duplex Doppler ultrasonography (US) in the assessment of the hemodynamics of the portal and hepatic veins in a cohort of children with chronic liver disease (CLD) and to detect any relationship between the US changes,etiology and severity (or stage) of CLD. METHODS:We prospectively enrolled 25 children with biopsy-proven CLD. Thirteen had cirrhosis (aged 8.9 ± 2.0 years) and 12 had chronic hepatitis (aged 9.3 ± 2.3 years). Gray scale and color-coded duplex Doppler US were performed for all,as well as 30 healthy age and sex-matched controls. Findings were correlated with clinical,laboratory and histopathological characteristics. RESULTS:Prominent caudate lobe was detected in 100% of cirrhotics,but none of the chronic hepatitis or controls. Thickened lesser omentum and loss of the triphasic waveform of the hepatic vein were present in 69.2% and 53.8% of cirrhotics vs 33.3% and 8.3% of chronic hepatitis respectively. Portal vein flow velocity was significantly lower (P < 0.0001) and the congestion index was significantly higher (P < 0.005) in both patient groups compared to controls. Child-Pugh's staging showed a positive correlation with both abnormal hepatic vein waveform and direction of portal blood flow; and a negative correlation with both hepatic and portal vein flow velocities. No correlation with the etiology of CLD could be detected. CONCLUSION:Duplex Doppler added to grayscale US can detect significant morphologic and portal hemodynamic changes that correlate with the severity (stage) of CLD,but not with etiology.

Iterative circle fitting based on circular attracting factor

An intuitive method for circle fitting is proposed. Assuming an approximate circle(CA,n) for the fitting of some scattered points, it can be imagined that every point would apply a force to CA,n, which all together form an overall effect that "draws" CA,n towards best fitting to the group of points. The basic element of the force is called circular attracting factor(CAF) which is defined as a real scalar in a radial direction of CA,n. An iterative algorithm based on this idea is proposed, and the convergence and accuracy are analyzed. The algorithm converges uniformly which is proved by the analysis of Lyapunov function, and the accuracy of the algorithm is in accord with that of geometric least squares of circle fitting. The algorithm is adopted to circle detection in grayscale images, in which the transferring to binary images is not required, and thus the algorithm is less sensitive to lightening and background noise. The main point for the adaption is the calculation of CAF which is extended in radial directions of CA,n for the whole image. All pixels would apply forces to CA,n, and the overall effect of forces would be equivalent to a force from the centroid of pixels to CA,n. The forces from would-be edge pixels would overweigh that from noisy pixels, so the following approximate circle would be of better fitting. To reduce the amount of calculation, pixels are only used in an annular area including the boundary of CA,n just in between for the calculation of CAF. Examples are given, showing the process of circle fitting of scattered points around a circle from an initial assuming circle, comparing the fitting results for scattered points from some related literature, applying the method proposed for circular edge detection in grayscale images with noise, and/or with only partial arc of a circle, and for circle detection in BGA inspection.

Cement Pavement Surface Crack Detection Based on Image Processing

This article introduces the application of image recognition technology in cement pavement crack detection and put forward to method fordetermining threshold about grayscale stretching. This algorithm is designed for binarization which has a self-adaptive characteristic. After theimage is preprocessed, we apply 2D wavelet and Laplace operator to process the image. According to the characteristic of pixel of gray image, analgorithm designed on binarization for Binary image. The feasibility of this method can be verified the image processed by comparing with theresults of three algorithms: Otsu method, iteration method and fixed threshold method.

Semi-automated detection of rangeland runoff and erosion control berms using high-resolution topography data

An inventory of topographic modifications is essential to addressing their impacts on hydrological and morphological processes in human-altered watersheds.However,such inventories are generally lacking.This study presents two workflows for semi-automatic detection of linear earthen runoff and erosion control berms in rangelands using high-resolution topographic data.The workflows consist of initial object identification by applying either morphological grayscale reconstruction(MGR)or the Geomorphon(GEO)method,followed by identification refinements through filters based on objects’horizontal and vertical information.Three sites were selected within the Altar Valley,Arizona,in the southwestern United States.One site was used for developing workflows and optimizing filter thresholds,and the other two sites were used to validate workflows.The results showed that:1)The MGR-based workflow methodology could produce final precision and detection rates of up to 92%and 75%,respectively,and take less than 5 s for a 10.1 km^(2) site;2)The workflow based on the MGR method yielded greater identification accuracy than did the GEO workflow;3)Object length,orientation,and eccentricity were important characteristics for identifying earthen berms,and are sensitive to general channel flow direction and berm shape;4)Manual interrogation of topographic data and imagery can significantly improve identification precision rates.The proposed workflows will be useful for developing inventories of runoff and erosion control structures in support of sustainable rangeland management.