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The rapid development of information and communication technology provides an opportunity for scientists to develop a quantitative analytical method that is built on a mobile gadget as a detector. In this paper, we report a low-cost, simple and portable analytical method for mercury ion quantification based on digital image colorimetry coupled with a smartphone application. A small amount of silver nanoparticles (AgNPs) was used as a colorimetric agent that is selective only to mercury ions. The yellowish brown color of AgNPs instantly changed to colorless after the addition of mercury ions due to a redox reaction. To increase the portability, we attached the AgNPs onto a medium to create a paper-based analytical device. The final data processing of the colorimetric analysis was conducted using an android application available on the Google Play Store, called “Mercury Detector”. The proposed method has good sensitivity, with a detection limit of 0.86 ppb, which is comparable to those of bulk and more expensive instruments. This allows for the detection of mercury ions as low as 2 ppb (10 nM), which is also the maximum contaminant level permitted in drinking water by the US Environmental Protection Agency. The proposed method was applied to real samples that provide satisfactory results on accuracy (2.4%) and precision (2.5%).

Abstract Cronobacter spp. are recognized as a world-wide emerging opportunistic food-borne pathogens that can persist in various food and processing environment for a long time. Therefore, the prevention and detection of them are particularly important. In this study, a micro-spot paper-based analytical device (μPADs) was created by combining PVC pad with filter paper. Detection is achieved by measuring the color change (from colorless to indigo) when a species-specific enzyme associated with the Cronobacter spp. of interest reacts with a chromogenic substrate. When combined with the optimization of specific enrichment process, the method allows for a testing time of 10 h or less and is capable of detecting live bacteria on the inoculated surface of samples in concentrations as low as 101 CFU cm−2. We are surprised to discover that C. dublinensis species and their subspecies had the highest ability to produce α-glucosidase in all genus of Cronobacter spp.. This work demonstrated that the manufacturing method is novel, simple, well reproducible (RSD less than 5%) and low-cost (less than $ 0.15 per micro-spot). Graphical abstract Image 1 Highlights • The fabrication of μPADs using PVC pad is described for the first time, which is simple and low-cost (less than $ 0.15). • The colorimetric detection of Cronobacter spp. using μPADs for the first time. • We found that C. dublinensis and its subspecies had the highest ability to produce α-glucosidase. • The LOD and RSD (n = 6) are 101 CFU cm−2 and less than 5%. In addition, the detection time is about 10 h. [ABSTRACT FROM AUTHOR]

In this paper, we describe a validated paper-based microfluidic analytical device for the simultaneous quantification of two important biomarkers of renal function in urine. This paper platform provides an inexpensive, simple, and easy to use colorimetric method for the quantification of creatinine (CRN) and uric acid (UA) in urine samples. The microfluidic paper-based analytical device (μPAD) consists of a main channel with three identical arms, each containing a circular testing zone and a circular uptake zone. Creatinine detection is based on the Jaffé reaction, in which CRN reacts with picrate to form an orange-red product. Uric acid quantification is based on the reduction of Fe 3+ to Fe 2+ by UA, which is detected in a colorimetric reaction using 1,10-phenanthroline. Under optimum conditions, obtained through chemometrics, the concentrations of the analytes showed good linear correlations with the effective intensities, and the method presented satisfactory repeatability. The limits of detection and the linear ranges, respectively, were 15.7 mg L −1 and 50–600 mg L −1 for CRN and 16.5 mg L −1 and 50–500 mg L −1 for UA. There were no statistically significant differences between the results obtained using the μPAD and a chromatographic comparative method (Student's t -test at 95% confidence level). [ABSTRACT FROM AUTHOR]

The paper elaborates on methods of digital image compression. The focus is on the compression method that represents a raster image as a set of multiply thinned sub-images. Sub-images are processed consecutively to generate special reference images. The difference between the synthesized reference image and original sub-image forms a divergence array. The algorithm introduces a discrete error into the divergence array to provide the actual bit-depth reduction. However, the introduction of the error inevitably impairs the quality of the decompressed image. The aim is to make sure that the parametric smoothing of divergence arrays can lessen this quality impairment without changing the bit depth reduction originally provided by the method. Numerical experiments on real digital images are carried out to prove that the use of parametric smoothing improves noticeably the efficiency of the image compression method under discussion. [ABSTRACT FROM AUTHOR]

This paper describes the development and application of a paper-based analytical device (μPAD) for the determination of hydrogen peroxide, an important adulterant in milk. The method employs the reaction between hydrogen peroxide and guaiacol, catalyzed by peroxidase, producing a red product, which is then quantified by digital imaging. Experimental design methodology was used to optimize the experimental conditions. The linear concentration range was from 12.5 × 10−4 to 150 × 10−4 mol L−1, resulting in the regression equation A B = 0.02466 (±0.00192) + 17.053 (±0.750) C, with an excellent correlation coefficient (r = 0.986). The relative standard deviations obtained were 1.1 and 1.3% (intra-day), and 4.8 and 2.9% (inter-day), for 25.0 × 10−4 and 100 × 10−4 mol L−1 of hydrogen peroxide, respectively. The limits of detection and quantification were 3.54 × 10−4 and 11.8 × 10−4 mol L−1, respectively, with standard deviation of the blank of 0.002012. The proposed method was successfully applied for the determination of peroxide in milk samples, with recoveries between 92.2 and 109%. The proposed device constitutes a valuable analytical tool for the identification of hydrogen peroxide adulteration and offers advantages including low cost, simplicity, portability, and no (or minimal) requirement for sample pretreatment. Unlabelled Image • Bioactive paper platform as solid support for H 2 O 2 analysis using digital image • Determination of hydrogen peroxide in adulterated milk • The proposed method represents an advantageous alternative to most reported methods. • It was developed of a simple, low-cost, portable, and eco-friendly method. [ABSTRACT FROM AUTHOR]

[Display omitted] • For the first time a test strip is developed to determine Cu2+ in cachaças. • Precise, simple and fast qualitative and quantitative methods. • The methods enable producers to make an adequate control of Cu2+ levels in beverages. • The proposal is cheap and can be easily disseminated in the productive environment. In this work we propose for the first time, a paper-based test strip to analyse Cu2+ content in sugar cane spirits, which, due to its simplicity, high portability and fast analytical response (3 min), can be easily applied to in situ analyses by producers. The test strip was developed aiming: i) identify qualitatively the Cu2+ content in sugar cane spirits, and, ii) determine quantitatively the Cu2+ content using a digital image method employing a smartphone. The paper-based test strip was functionalized with cuprizone and optimized through a Box-Behnken, an experimental design for obtaining the best reaction conditions. Based on qualitative method with naked eyes approach performed by six volunteers analyst untrained, the method present a percentage of accuracy of 93%. For the quantitative analysis, it was determined the metal content at a level of statistical agreement with the reference method, as well as it was obtained the dynamic linear range from 2 to 13 mg L−1 with limits of detection and quantification of 0.034 and 0.103 mg L−1, respectively. Furthermore, the quantitative method showed a reliable precision with an RSD of 4.3% (n = 10) and the recovery of Cu2+ ranged from 80 to 103.8%. [ABSTRACT FROM AUTHOR]

In the petroleum and coal industries, digital image technology and acoustic emission technology are employed to study rock properties, but both exhibit flaws during data processing. Digital image technology is vulnerable to interference from fractures and scaling, leading to potential loss of image data; while acoustic emission technology is not hindered by these issues, noise from rock destruction can interfere with the electrical signals, causing errors. The monitoring errors of these techniques can undermine the effectiveness of rock damage analysis. To address this issue, this paper focuses on the restoration of image data acquired through digital image technology, leveraging deep learning techniques, and using soft and hard rocks made of similar materials as research subjects, an improved Incremental Transformer image algorithm is employed to repair distorted or missing strain nephograms during uniaxial compression experiments. The concrete implementation entails using a comprehensive training set of strain nephograms derived from digital image technology, fabricating masks for absent image segments, and predicting strain nephograms with full strain detail. Additionally, we adopt deep separable convolutional networks to optimize the algorithm's operational efficiency. Based on this, the analysis of rock damage is conducted using the repaired strain nephograms, achieving a closer correlation with the actual physical processes of rock damage compared to conventional digital image technology and acoustic emission techniques. The improved incremental Transformer algorithm presented in this paper will contribute to enhancing the efficiency of digital image technology in the realm of rock damage, saving time and money, and offering an innovative approach to traditional rock damage analysis. [ABSTRACT FROM AUTHOR]

The most extensive data storage size is significantly increasing, and storing such data, including images to make them obtainable over the network, has become a significant problem. Thus, efficient compression techniques are necessary because the original images need much disk space. Generally, the primary purpose of image compression is to decrease the amount of data required for representing digital images. Image compression techniques can be categorized into lossless and lossy. Rounding Intensity followed by the Dividing Technique (FIRD) is a known lossy technique utilized in the proposed method that aims to reduce the range of the intensities and increase redundancy, achieving better compression performance. The main idea of this paper is to separate the (8*8 block of mage) into two parts: the matrix of hundreds and the single matrix. The elements in the hundreds matrix will be divided into 10, and the resulted matrix will be compressed using the Huffman technique. The elements in the single matrix will be rounded as follow: 0,1,2,3, and 4 to 2, and the elements 5, 6, 7, 8, and 9 are rounded to 7. Then, the Huffman technique will be applied to the resulting matrix. The previously generated values are combined, and the initial results indicate a reduction in distortion with better compression performance. This paper deals with whether the proposed technique enhancing the quality of Compressed Images using Rounding Intensity Followed by the Dividing Technique (E-RFID) can be used as a distinctive option in the distortion reduction process image a high compression ratio. In addition, image compression technology and quality enhancement with high compression explain each step's work, which will be presented in detail. The research factors have been positively impacted by improving the results and comparing them through MSE, PSNR, and MAE. The E-RIFD technique experiments showed an improved rate in MSE 66.82% and PSNR 18.31%, while the MAE improvement rate was 21.09% for grayscale images. However, when testing color images, the results show that the rate of improvement in MSE was 66.59% and PSNR 15.28, while the improvement rate in the MAE was 18.64%. [ABSTRACT FROM AUTHOR]

The paper firstly establishes the so-called n-times iterated connected sum of a simple closed k-surface in Z 3 , denoted by C k n , k ∈ { 6 , 18 , 26 } . Secondly, for a simple closed 18-surface M S S 18 , we prove that there are only two types of connected sums of it up to 18-isomorphism. Besides, given a simple closed 6-surface M S S 6 , we prove that only one type of M S S 6 ♯ M S S 6 exists up to 6-isomorphism, where ♯ means the digital connected sum operator. Thirdly, we prove the digital k-contractibility of C k n : = M S S k ♯ ⋯ ♯ M S S k ⏞ n − times , k ∈ { 18 , 26 } , which leads to the simply k-connectedness of C k n , k ∈ { 18 , 26 } , n ∈ N . Fourthly, we prove that C 6 2 and C k n do not have the almost fixed point property (AFPP, for short), k ∈ { 18 , 26 } . Finally, assume a closed k-surface S k (⊂ Z 3) which is (k , k ¯) -isomorphic to (X , k) in the picture (Z 3 , k , k ¯ , X) and the set X is symmetric according to each of x y -, y z -, and x z -planes of R 3 . Then we prove that S k does not have the AFPP. In this paper given a digital image (X , k) is assumed to be k-connected and its cardinality | X | ≥ 2 . [ABSTRACT FROM AUTHOR]

This paper proposes a novel deep learning model incorporating attention mechanisms for the analysis of slope stochastic fields. Initially, a deep learning model is designed to digitally image the stochastic field features of soil strength variability. This is achieved by discretizing the slope soil stochastic field using the Karhunen-Loeve expansion method and transforming the discrete results into digital images. These images are then used to establish a Convolutional Neural Network (CNN) surrogate model that maps the implicit relationship between stochastic field images and slope functional function values, thus calculating the probability of slope failure. The precision of the CNN surrogate model is enhanced through Bayesian optimization and five-fold cross-validation. Moreover, to overcome the limitations of existing data-driven landslide stability prediction models, this study also introduces a Spatial-Temporal Attention (STA) mechanism. By combining the CNN with Long Short-Term Memory (LSTM) networks, the model can accurately approximate the actual results of slope stability calculations in scenarios of high-dimensional representation imaging of stochastic fields and low-probability slope instability. Consequently, this significantly improves the computational efficiency of slope reliability analysis considering stochastic field simulations. [ABSTRACT FROM AUTHOR]

Digital image steganography serves as a technology facilitating covert communication through digital images by subtly incorporating secret data into a cover image. This practice poses a potential threat, as criminals exploit steganography to transmit illicit content, thereby jeopardizing information security. Consequently, it becomes imperative to implement defensive strategies against steganographic techniques. This paper proposes a novel defense mechanism termed "image vaccine" to safeguard digital images from steganography. The process of "vaccinating" an image renders it immune to steganographic manipulation. Notably, when criminals attempt to embed secret data into vaccinated images, the presence of such hidden information can be detected with a 100% probability, ensuring the consistent identification of stego images. This proactive approach enables the interception of stego image transmission, thereby neutralizing covert communication channels. [ABSTRACT FROM AUTHOR]

The rapid development of information and communication technology provides an opportunity for scientists to develop a quantitative analytical method that is built on a mobile gadget as a detector. In this paper, we report a low-cost, simple and portable analytical method for mercury ion quantification based on digital image colorimetry coupled with a smartphone application. A small amount of silver nanoparticles (AgNPs) was used as a colorimetric agent that is selective only to mercury ions. The yellowish brown color of AgNPs instantly changed to colorless after the addition of mercury ions due to a redox reaction. To increase the portability, we attached the AgNPs onto a medium to create a paper-based analytical device. The final data processing of the colorimetric analysis was conducted using an android application available on the Google Play Store, called "Mercury Detector". The proposed method has good sensitivity, with a detection limit of 0.86 ppb, which is comparable to those of bulk and more expensive instruments. This allows for the detection of mercury ions as low as 2 ppb (10 nM), which is also the maximum contaminant level permitted in drinking water by the US Environmental Protection Agency. The proposed method was applied to real samples that provide satisfactory results on accuracy (2.4%) and precision (2.5%). [ABSTRACT FROM AUTHOR]

In research, there is a growing interest in using artificial intelligence to find solutions to difficult scientific problems. In this paper, a deep learning algorithm has been applied using images of samples of materials used for road surfaces. The photographs showed cross-sections of random samples taken with a CT scanner. Historical samples were used for the analysis, located in a database collecting information over many years. The deep learning analysis was performed using some elements of the VGG16 network architecture and implemented using the R language. The learning and training data were augmented and cross-validated. This resulted in the high level of 96.4% quality identification of the sample type and its selected structural features. The photographs in the identification set were correctly identified in terms of structure, mix type and grain size. The trained model identified samples in the domain of the dataset used for training in a very good way. As a result, in the future such a methodology may facilitate the identification of the type of mixture, its basic properties and defects. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Intelligent Science & Technology is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The present paper investigates digital topological properties of an alignment of fixed point sets which can play an important role in fixed point theory from the viewpoints of computational or digital topology. In digital topology-based fixed point theory, for a digital image (X , k) , let F (X) be the set of cardinalities of the fixed point sets of all k-continuous self-maps of (X , k) (see Definition 4). In this paper we call it an alignment of fixed point sets of (X , k) . Then we have the following unsolved problem. How many components are there in F (X) up to 2-connectedness? In particular, let C k n , l be a simple closed k-curve with l elements in Z n and X : = C k n , l 1 ∨ C k n , l 2 be a digital wedge of C k n , l 1 and C k n , l 2 in Z n . Then we need to explore both the number of components of F (X) up to digital 2-connectivity (see Definition 4) and perfectness of F (X) (see Definition 5). The present paper addresses these issues and, furthermore, solves several problems related to the main issues. Indeed, it turns out that the three models C 2 n n , 4 , C 3 n − 1 n , 4 , and C k n , 6 play important roles in studying these topics because the digital fundamental groups of them have strong relationships with alignments of fixed point sets of them. Moreover, we correct some errors stated by Boxer et al. in their recent work and improve them (see Remark 3). This approach can facilitate the studies of pure and applied topologies, digital geometry, mathematical morphology, and image processing and image classification in computer science. The present paper only deals with k-connected spaces in DTC. Moreover, we will mainly deal with a set X such that X ♯ ≥ 2 . [ABSTRACT FROM AUTHOR]

This paper discusses a feasible software solution that enables automatic marking and grading of scripts. Technology keeps expanding, and more advanced innovations are being implemented with time. The marking and allocation of grades for examination scripts through human efforts are gradually becoming a thing of the past. Hence, machines and software applications are introduced to make the entire marking and grading of examination scripts more efficient, fast, and less tedious. Computer vision is an artificial intelligence (AI) knowledge domain that ensures devices obtain useful information from digital images, videos, and other visual inputs. Image processing and recognition, a unique part of computer vision alongside the python programming language and the OpenCV library was employed for this project. These are the most used in developing most recent applications that utilize, to some extent, artificial intelligence to attain specific desired results. The result of the project seeks to develop a maintainable android software application that uses image processing technology to scan patterns or images and grades results of multiple-choice question scripts based on a set marking scheme. This ensures that desired results are obtained while increasing efficiency and productivity. [ABSTRACT FROM AUTHOR]

Image processing software is designed to manipulate the digital images by capturing and converting them into digital form and to accomplish manipulations on traffic data. In this paper the methodology for detecting vehicles on highways is presented. Due to urbanization all over the world, the growth of traffic is shattered since few years and we entered in to the era of big data for transportation. The present study involves the understanding of fundamental elements of traffic, the problems in collecting the data, and their suitable corrections (speed, lateral and longitudinal) to be applied, extraction of error free macroscopic data, modelling and analysis of the macroscopic data. TRAZER is used for trajectory data extraction and the macroscopic relationships investigated by the trajectory data. Problems with TRAZER raw data extraction and corrections to it are discussed. The curves illustrate the basics traffic flow relationships between the three parameters such as density, flow and speed and from the analysis it is observed that the data is fitting Greenshields model. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to develop a method for predicting the body weight of beef cattle using meta-analysis based on digital image processing. Materials and Methods: The meta-analysis process commenced by collecting studies with the keywords "beef cattle," "correlation," "digital image," and "body weight" from Google Scholar and Science Direct. The obtained studies were reviewed papers based on their titles, abstracts, and content, and then categorized by authors, year, country, sample size, and correlation coefficient. A digital image of body measurements used included wither and hip height, chest depth, heart girth, body length, and top view. The statistical analysis was conducted by calculating effect sizes using the correlation coefficient and sample sizes. Results: The results of the meta-analysis, based on 3,017 cattle from 13 selected studies, showed the highest and lowest correlation coefficients for the top view variable and hip height. Based on cattle breed, significant differences (p < 0.05) were observed in the wither height variable with correlation coefficients of 0.94, 0.79, and 0.66 for Hanwoo, Holstein, and Simmental, respectively. Based on sex, significant differences (p < 0.05) were seen in the wither height variable, with correlation coefficients of 0.73 for males and 0.90 for females, while for hip height, the values were 0.70 and 0.87, respectively. Conclusion: In conclusion, to achieve the best accuracy in predicting the body weight of beef cattle based on a digital image, the top view variable can be used. However, for ease of field experimentation, body length or chest depth can also be used while taking breed and sex categories into the model. [ABSTRACT FROM AUTHOR]

In order to more accurately realize the automatic identification and quantitative characterization of coral reef multi-scale pore structure, this paper has carried out such research. On the basis of using multiple information acquisition technology, it can effectively collect field measured data and indoor observation data to form a high-precision digital image of the development characteristics of multi-scale coral reef pore structure. By constructing the multi-scale structure information correlation and fusion function of coral reef pore structure, it can formulate the positioning and search strategy for the key areas of coral reef pore structure development characteristics. In this paper, a fine identification and quantitative characterization method suitable for the pore structure of "millimeter–micron–nanometer" is formed. At the same time, combined with the actual project requirements, the multi-scale pore structure feature area location method of coral reef is constructed, which combines the distribution characteristics, variation characteristics, fractal characteristics and shape characteristics in a certain range, and realizes the search of pore key areas between different scales and different regions of the same scale through the similarity matching algorithm, which can effectively realize the automatic search of key areas in the "millimeter–micron–nanometer"-scale image of coral reefs. Finally, the correctness and feasibility of this method are verified by multi-angle example data comparison. It shows that this method can effectively solve the productivity prediction and evaluation problem of coral reef oil and gas reservoirs. [ABSTRACT FROM AUTHOR]

In the recent trends and technology we found that the digital image plays an important role. The digital images are widely used in various fields. The validation is now becomes a challenging task because of advancement in the tools and technology. The various tools are used to modify the digital image to create fake digital images. The several research has been carried out in the field of image forgery detection. In this paper attempted to review the existing technique to identify the forgery in an image. Accordingly related papers are reviewed and analyzed. The active and passive are the two approaches in the image forgery detection technique. The digital signature and digital watermarking are the two methods in active approach, and in passive approach various techniques are there such as copy-move, splicing, resampling etc., In this paper we describe the comparative analysis of different types of image forgery detection technique along with future work to be carried out. [ABSTRACT FROM AUTHOR]

Digital image security and rectitude are the top major issues in today's informative world. Water marking is a specific method that is used for copyright protection and firm verified. Watermark should be strong and gradual. Digital image watermarking is an embedding data such as an image converting into digital signal using a strong media QR code. It is very difficult to analyse the digital image watermarking has applied to data. This is a competent way to prevent illegal copyright from the modern multimedia network with modern technology in printing and scanning, creating imitate documents which has become a difficult task that makes it very complicated to understand between actual and artificial. So, it is a very big challenge for serving security or digital data. This paper put forward an idea or proclamation to provide and innovative method to verify a digital document. The advanced method of QR code is brought here which permits us to embed a digital data such as an image in QR code. In this paper we bring up and innovative method where the image is encoded in QR that is code in encoded method. If a hacker tried to modify or meddle with the data then he/she will fail to do that so. This is caused because of the encryption key to which the hacker is not aware of it. It is unfeasible to scan or access to the encrypted QR code as the codes are robust. [ABSTRACT FROM AUTHOR]

Copyright of Iraqi Journal of Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

At present, watermarking techniques play an important role in protecting digital images. To date, many classical watermarking schemes have been developed to protect images based on spatial and transform domains. However, classical watermarking schemes are less resilient to many attacks. Recently, deep learning-based watermarking made a significant contribution to image content security and received attention for various popular applications. In this paper, we use convolutional neural networks (CNNs) to propose an interesting watermarking technique for digital images. Initially, latent features of cover and secret images are extracted using an encoder network and later concatenated to generate a marked image. On the receiver side, a denoising autoencoder network is used to remove noise variations from the received image and later to extract the secret mark image using a CNN. Our technique not only imperceptibly hides an image inside a cover image but also outperforms other state-of-the-art schemes in terms of visual quality and robustness according to simulation results and performance comparisons. [ABSTRACT FROM AUTHOR]

The complex mechanical characteristics of the Xiyu conglomerate significantly influence the resistance and deformation features of its caverns' surrounding rock, thereby constraining the construction of related water diversion tunnels. This paper introduces an improved SegFormer framework developed for the detection of mesoscale geomaterial structures. Computerized tomography (CT) scan images of the Xiyu conglomerate were employed to establish a high-precision numerical model. From the results of segmentation, the proposed algorithm outperformed UNet, HRNet, and the original SegFormer neural network. The segmentation results were used to calculate the porosity, and biaxial compression numerical simulation experiments based on the real structure were carried out using the particle flow code (PFC). We observed the failure process of the model and obtained the shear strength of the Xiyu conglomerate. We explored the causes and influencing factors of the anisotropy of the Xiyu conglomerate from the microstructure perspective and provide a micro-observation basis for establishing an anisotropic mechanical model. [ABSTRACT FROM AUTHOR]

Cracking in concrete structures is a ubiquitous problem and affects the serviceability of such structures. In many cases, it is imperative to properly measure and monitor a crack's size to identify locations for repair and rehabilitation. This paper describes two field studies that used digital image processing to measure the width of cracks in concrete structures. The first field study compared crack measurements from digital image processing, a handheld microscope, and a crack gauge card. In the second field study, digital image processing was used to measure end region cracks in precast pretensioned concrete girders. Guidance is provided for engineers who wish to use digital image processing in field studies. Conditions where digital image processing may lead to errors are identified, and limitations of the methods are discussed. In general, the studies demonstrate that image processing methods can efficiently measure the size of cracks in concrete structures in field settings and that image-based measurements are comparable to microscope measurements. [ABSTRACT FROM AUTHOR]

This paper proposes a new cover selection method for steganography. We focus on the scenario that the available images for selection contain diversified sources, i.e., nature images and metaverse images. For the scenario, we design a targeted strategy to evaluate the suitability for steganography of a candidate image, which selects images according to the undetectability against steganalytic tools symmetrically. Firstly, steganalytic features of the candidate images are extracted. Then, the features are fed on a steganalytic classifier, and the possibility of carrying secret data is calculated for cover selection. As a result, the selected images are the best candidates to resist steganalysis. Experimental results show that our method performs better than existing cover selection schemes when checked by steganalytic tools. [ABSTRACT FROM AUTHOR]

Abstract In the petroleum and coal industries, digital image technology and acoustic emission technology are employed to study rock properties, but both exhibit flaws during data processing. Digital image technology is vulnerable to interference from fractures and scaling, leading to potential loss of image data; while acoustic emission technology is not hindered by these issues, noise from rock destruction can interfere with the electrical signals, causing errors. The monitoring errors of these techniques can undermine the effectiveness of rock damage analysis. To address this issue, this paper focuses on the restoration of image data acquired through digital image technology, leveraging deep learning techniques, and using soft and hard rocks made of similar materials as research subjects, an improved Incremental Transformer image algorithm is employed to repair distorted or missing strain nephograms during uniaxial compression experiments. The concrete implementation entails using a comprehensive training set of strain nephograms derived from digital image technology, fabricating masks for absent image segments, and predicting strain nephograms with full strain detail. Additionally, we adopt deep separable convolutional networks to optimize the algorithm’s operational efficiency. Based on this, the analysis of rock damage is conducted using the repaired strain nephograms, achieving a closer correlation with the actual physical processes of rock damage compared to conventional digital image technology and acoustic emission techniques. The improved incremental Transformer algorithm presented in this paper will contribute to enhancing the efficiency of digital image technology in the realm of rock damage, saving time and money, and offering an innovative approach to traditional rock damage analysis.

The big data of digital image contains massive information, which are from many aspects and need to be effectively fused. The multi-level information fusion method of big data in digital image is studies this paper, so as to get accurate information and make effective emergency strategies in time. According to the reasonable sleep scheduling mechanism, the perception information of the nodes in the cluster environment acquisition system, the sampling data are transmitted to the cluster head node, cluster head node is constructed to estimate sampling data in linear regression model, and the model parameters expressing the characteristics of the data are upload to the base station according to the query statistics needs. After dimensionality reduction of high-dimensional data, the hierarchical visualization of information is realized by the expression of radar map. After the radar coordinate system is mapped by the original processing data, different variables are allocated in different directions of radar map. In this paper, the multi-level information of big data in digital image is cleaned and processed, and the radar image is to make hierarchical visualized fusion. [ABSTRACT FROM AUTHOR]

Geotechnical heterogeneous materials contain a large number of defects, such as granular structures of different types and spatial geometric distribution, cracks, and voids, and have a complex internal meso-structure. Moreover, the mechanical properties and defect structures of various meso-media have an important influence on the fracture mechanism, which makes the analysis of the fracture process of geotechnical materials a nonequilibrium and nonlinear three-dimensional mechanical problem in space. However, due to the complexity of mathematical theory, the limitation of laboratory test and field observation, test conditions, and technology, it is very difficult to study the three-dimensional fracture process of geotechnical materials, and numerical simulation has become one of the powerful tools to solve these problems. This paper first introduces the concept of digital image and the development of geotechnical engineering, and then analyses the types of engineering materials used in geotechnical engineering construction. A finite element method for mechanical analysis based on digital images is proposed. The method can be used to study the influence of the internal microstructure of geotechnical engineering materials on the mechanical response of materials. By analyzing the images, it can be concluded that the aggregate content is 36.1%, the matrix content is 48.9%, and the rest is the proportion of interface elements. The digital image numerical analysis method in this paper is based on two-dimensional space, but according to the principle of stereological logic transformation, it can be applied to three-dimensional finite element analysis. [ABSTRACT FROM AUTHOR]

This paper continues a series in which we study deficiencies in previously published works concerning fixed point assertions for digital images. [ABSTRACT FROM AUTHOR]

Freezing sets and cold sets have been introduced as part of the theory of fixed points in digital topology. In this paper, we introduce a generalization of these notions, the limiting set, and examine properties of limiting sets. [ABSTRACT FROM AUTHOR]

Relating image contours and regions and their attributes according to connectivity based on incidence or adjacency is a crucial task in numerous applications in the fields of image processing, computer vision and pattern recognition. In this paper, the crucial incidence topological information of 2-dimensional images is extracted in an efficient manner through the computation of a new structure called the HomDuRAG of an image; that is, the dual graph of the HomRAG (a topologically consistent extended version of the classical RAG). These representations are derived from the two traditional self-dual square grids (in which physical pixels play the role of 2-dimensional cells) and encapsulate the whole set of topological features and relations between the three types of objects embedded in a digital image: 2-dimensional (regions), 1-dimensional (contours) and 0-dimensional objects (crosses). Here, a first version of a fully parallel algorithm to compute this new representation is presented, whose timing complexity order (in the worst case and supposing one processing element per 0-cell) is O (l o g (M × N)) , M and N being the height and width of the image. Efficient implementations of this parallel algorithm would allow images to be processed in real time, as well as permit us to uncover fast algorithms for contour detection and segmentation, opening new perspectives within the image processing field. [ABSTRACT FROM AUTHOR]

Copyright of Informatics & Mathematical Methods in Simulation / Informatika ta Matematičnì Metodi v Modelûvannì is the property of Odessa Polytechnic University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Vegetation encroaching on overhead power lines can cause short circuit faults and pose a major threat to the security and stability of power grids. Therefore, establishing an effective visual detection algorithm to oversee potential circuit failures of the power lines is critical to the ongoing inspection of vegetation encroachment. This paper establishes a deep learning-based detection framework that utilizes the images obtained from vision sensors mounted on power transmission towers. The proposed detection framework includes three cascaded modules: (1) detection of vegetation regions based on the Faster Region Convolution Neural Network (Faster R-CNN), (2) detection of power lines based on the Hough transform, and (3) detection of vegetation encroachment based on an advanced stereovision (SV) algorithm. In particular, the proposed SV algorithm converts the detected two-dimensional (2D) image data of the vegetation and power lines to three-dimensional (3D) height and location results in order to obtain precise geographical locations. Case studies using field captured images provided by a Transmission System Operator (TSO) demonstrate the effectiveness of the proposed framework in detecting vegetation failures, thus improving overall reliability and reducing economic loss. [ABSTRACT FROM AUTHOR]

The quality and authenticity of images is essential for data presentation, especially in the life sciences. Questionable images may often be a first indicator for questionable results, too. Therefore, a tool that uses mathematical methods to detect suspicious images in large image archives can be a helpful instrument to improve quality assurance in publications. As a first step towards a systematic screening tool, especially for journal editors and other staff members who are responsible for quality assurance, such as laboratory supervisors, we propose a basic classification of image manipulation. Based on this classification, we developed and explored some simple algorithms to detect copied areas in images. Using an artificial image and two examples of previously published modified images, we apply quantitative methods such as pixel-wise comparison, a nearest neighbor and a variance algorithm to detect copied-and-pasted areas or duplicated images. We show that our algorithms are able to detect some simple types of image alteration, such as copying and pasting background areas. The variance algorithm detects not only identical, but also very similar areas that differ only by brightness. Further types could, in principle, be implemented in a standardized scanning routine. We detected the copied areas in a proven case of image manipulation in Germany and showed the similarity of two images in a retracted paper from the Kato labs, which has been widely discussed on sites such as pubpeer and retraction watch. [ABSTRACT FROM AUTHOR]

Securing information became essential to exchange multimedia information safely. These exchanged data need to be transformed in a well-managed, secure, and reliable manner. This paper focuses on securing multimedia images via cryptography during transmission among users using an effective selection from several Pseudorandom Number Generators (PRNG). This paper implements several PRNG techniques involved within consecutive cryptoprocesses of substitution and transposition that have proven a secure process. In the study, different PRNGs are tested to encrypt images in forms of grayscale and colored RGB images compared to current similar approaches. The work experimentation is aiming at investigating and identifying suitability and reliability through security measures standard parameters. The research is showing proper PRNG selection as an attractive, significant work worth remarking for image cryptography. [ABSTRACT FROM AUTHOR]

Copyright of Eastern-European Journal of Enterprise Technologies is the property of PC TECHNOLOGY CENTER and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we have established some new results by extending some existing theorems in the setting of Digital Metric Space. We also proved some results in Digital Metric Space which were established earlier in the context of Complete Metric Space by different authors. [ABSTRACT FROM AUTHOR]

In this paper the concept of soft continuity is focused on for digital images by using soft sets which is defined on κ - adjacent digital images. Also the definitions of digital soft isomorphism and digital soft retraction are given. Some theorems are obtained deal with soft isomorphism and soft retraction for digital images and some numerical examples are presented in dimension 2. Finally digital soft retraction is obtained as a soft topological invariant. [ABSTRACT FROM AUTHOR]

Background: Several advanced methods for evaluating wrinkles are currently available, however, with limitations in their application because wrinkle structures change in response to facial expressions and surrounding environments. A Visual Illusion‐based image feature enhancement System (VIS) was used to develop a real‐time evaluation method. Objectives: This study expands the VIS application into the wrinkle evaluation method by adjusting VIS to evaluate facial wrinkles, evaluating the age‐dependent wrinkles, and validating it for real‐time wrinkle evaluation. Methods: Wrinkles in various Japanese men and women were evaluated using VIS and the current methods. Furthermore, the effectiveness of an eye cream containing niacinamide was evaluated before and after the 4‐week treatment. Results: VIS qualitatively detects even fine wrinkles and numerically records them without any special instrument. Moreover, VIS can be applied to moving images, revealing the effectiveness of the antiwrinkle formulation qualitatively and quantitatively even when the subjects are smiling. Conclusion: This paper presents an epoch‐making wrinkle evaluation method that is qualitative and quantitative, with high sensitivity in real‐time and relies solely on digital images without any difficulties. Therefore, these results imply that this method enables the wrinkle evaluation under real‐life conditions. [ABSTRACT FROM AUTHOR]

Security of digital data is an important task in the present era. In this paper, we propose a new scheme of digital image encryption and decryption method based on three-dimensional (3D) Arnold cat map (ACM) and elliptic curve. In this proposed encryption method, we have applied 3D ACM on the digital color image which performs the dual encryption first, it performs the permutation and second, it performs the substitution of image pixels. After that, elliptic curve cryptography (ECC) is used to encrypt the image, for this a mapping method is proposed to convert the pixels of the image as points on the elliptic curve. Further, a mapping inverting method is proposed for decryption and then 3D inverse Arnold cat map (iACM) is applied to get the original image. The statistical and security analyses are done on various images and the experimental results show the robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Informatics & Mathematical Methods in Simulation / Informatika ta Matematičnì Metodi v Modelûvannì is the property of Odessa Polytechnic University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Guangxi Normal University - Natural Science Edition is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper covers the problem of determination of defects and contamination in malting barley grains. The analysis of the problem indicated that although several attempts have been made, there are still no effective methods of identification of the quality of barley grains, such as the use of information technology, including intelligent sensors (currently, quality assessment of grain is performed manually). The aim of the study was the construction of a reduced set of the most important graphic descriptors from machine-collected digital images, important in the process of neural evaluation of the quality of BOJOS variety malting barley. Grains were sorted into three size fractions and seed images were collected. As a large number of graphic descriptors implied difficulties in the development and operation of neural classifiers, a PCA (Principal Component Analysis) statistical method of reducing empirical data contained in the analyzed set was applied. The grain quality expressed by an optimal set of transformed descriptors was modelled using artificial neural networks (ANN). The input layer consisted of eight neurons with a linear Postsynaptic Function (PSP) and a linear activation function. The one hidden layer was composed of sigmoid neurons having a linear PSP function and a logistic activation function. One sigmoid neuron was the output of the network. The results obtained show that neural identification of digital images with application of Principal Component Analysis (PCA) combined with neural classification is an effective tool supporting the process of rapid and reliable quality assessment of BOJOS malting barley grains. [ABSTRACT FROM AUTHOR]