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Curl propensity is a critical-to-quality (CTQ) property of paper, as it causes severe problems during printing and other final conversion operations. The main papermaking factor causing the curl phenomenon is the existence of a fiber orientation (FO) gradient across the thickness direction (or ZD), also known as two-sidedness. Therefore, a methodology that characterizes the FO across the ZD is fundamental for papermakers. In this work, we propose and validate an efficient and cost-effective protocol based on sheet splitting and image analysis. Besides assessing the level of FO two-sidedness, the methodology also provides insights into the flow dynamics in the draining zone of the forming section of the paper machine and the drying stresses built into the paper. This information is relevant for monitoring, optimizing, and troubleshooting activities in the paper industry. [ABSTRACT FROM AUTHOR]

BACKGROUND: This work proposes the use of a simple, inexpensive method for the hydrothermal synthesis of fluorescent carbon dot nanoparticles (CDs) from rice starch aimed at the determination of Hg2+ in water. The proposed method involved using a paper‐based analytical device coupled to a 3D plate, with a UV‐LED chamber and a smartphone for the acquisition and analysis of the fluorescence digital images of the CDs. RESULTS: The size of the carbon dots ranged from 0.5 to 3 nm, with an average particle size of ~1 nm. The functionalization of carbon dots with methimazole allowed a high selective for Hg2+ determination. The results obtained showed a linear response R2 of 0.997 and Hg2+ concentration in the range of 0.5–45.0 μmol L−1 with a limit of detection and limit of quantitation of 0.23 and 0.62 μmol L−1, respectively. The results of the study show that there were no significant differences, at 95% confidence level, between the data obtained from the application of the proposed method and the reference method. CONCLUSION: The proposed method is in line with the principles of green chemistry, as it involves the use of renewable sources for starch extraction and a hydrothermal synthesis process that does not employ toxic reagents. In addition, the method employs only 15 μL reagent/sample. © 2024 Society of Chemical Industry (SCI). [ABSTRACT FROM AUTHOR]

We have achieved a successful synthesis of a label-free, flexible, portable, and colour-indicating biosensor using cholesteric nanostructure liquid crystals (LCs), commonly referred to as cholesteric LCs (CLCs), on paper fibres. This innovative approach holds significant promise for rapid screening in precision medicine and other applications. We incorporated optically active nematic LCs in various configurations within CLCs, allowing them to rotate along different axes. When exposed to light, these LCs reflected light at specific wavelengths. By employing polarised optical microscopy and a charged couple device (CCD), we conducted titration experiments with bovine serum albumin (BSA) on the paper substrate and quantified protein concentrations based on the observed colour shifts. Utilising internal Python pixel analysis software, we evaluated the red, green, and blue colour components, which served as biomarkers for different BSA concentrations. Our results demonstrated a linear correlation between BSA concentration and colour variations. This CLC-based paper-substrate biosensor exhibits the potential to efficiently screen a large number of samples, thereby enhancing the diagnostic accuracy in clinical settings. Combining the colour-indicating and quantifiable attributes of CLCs with the affordability and portability of paper substrates, we have developed a versatile biosensor. Its ease of use, rapid response, and high sensitivity make it a promising tool for future disease detection endeavours. [ABSTRACT FROM AUTHOR]

The ARMID@ Venezia project aims to safeguard extra-ordinary collections. both handwritten and printed, preserved in the library of the Benedetto Marcello Conservatory of Venice, heavily damaged by the exceptionally high tide of November, 2019. The project was moreover conceived to integrate various competencies from art history, conservation science, archival systems, and music philology, in order to propose a comprehensive and innovative perception on the conservation of documents. The project involves digitalization (through high-resolution photographic reproduction), virtual restoration and a detailed investigation of the materials used (supports, inks, pigments, dyes, etc.) for future physical restoration. Only non-invasive and non-destructive techniques are being employed, such as imaging analysis in the visible (including macro - and micro-photographic examination), near infrared (NIR), and near ultraviolet (UV-induced fluorescence or UVF, and reflected UV or UVR) range, on representative artefacts. In addition, non-invasive spectroscopic techniques, such as Fiber Optics Reflectance Spectroscopy (FORS), p.-Raman and X-Ray (XRF) were also applied for a detailed chemical characterization of materials used to support the results obtained through imaging analyses. In this article, the results of multispectral imaging (MSI) conducted in three different categories of music sources, paper manuscripts, parchment manuscripts, and printed books, are discussed and compared with spectroscopic techniques, confirming the benefits of photographic investigation in these artworks. The great assortment of available documents, in terms of constitutive materials, historical period of production and state of conservation, is a strong point of this project in order to test the potential holistic approach to safeguard the documents collected in the library. [ABSTRACT FROM AUTHOR]

Forensic document examination has been considered one of the most important fields in the forensic science. For this, a quick, effective, and cheap techniques for evaluation of forensic evidence is highly required. This study explores the comparison of A4 document paper by chemical and physical analysis. 16 of white A4 paper from different brands were analyzed by FTIR for investigation of structural characteristics. Video Spectral Comparator 8000 has been used to compare the A4 papers under different light source. The inorganic filler composition in the paper is also explored by ICP‐OES. The calcium carbonate content in document papers play a discriminant role which found to be between 16.02 % and 35.54 %. Also, water contact angle has been utilized in order to examine the surface of paper which is generally related to the inorganic filler and other additives. The WCA results ranged between 83° and 111° that enable quick and easy way to compare A4 document papers. The experimental results have demonstrated that besides FTIR methods, other methods such as ICP‐OES for elemental composition, WCA for wetting test, VSC for visualization under different light of source could have been applied for comparison of document papers in case of criminal cases. [ABSTRACT FROM AUTHOR]

A turn-on fluorescent aptasensor based on a paper-based microfluidic chip was developed to detect arsenite via aptamer competition strategy and smartphone imaging. The chip was prepared by wax-printing hydrophilic channels on filter paper. It is portable, low-cost, and environmentally friendly. Double-stranded DNA consisting of aptamer and fluorescence-labeled complementary strands was immobilized on the reaction zone of the paper chip. Due to the specific strong binding between aptamer and arsenite, the fluorescent complementary strand was squeezed out and driven by capillary force to the detection area of the paper chip, so that the fluorescent signal arose in the detection area under the excitation wavelength of 488 nm. Arsenite can be quantified by using smartphone imaging and RGB image analysis. Under the optimal conditions, the paper-based microfluidic aptasensor exhibited excellent linear response over a wide range of 1 to 1000 nM, with a detection limit as low as 0.96 nM (3σ). [ABSTRACT FROM AUTHOR]

Wearable eye‐tracking technology is an essential component in AR/VR devices. Conventional Pupil‐Corneal Reflection (PCR) eye‐tracking technology often presents challenges due to high costs and bulky designs, particularly for compact AR and smart glasses. To overcome these limitations, our next‐generation eyetracking technology relies on AI image analysis, significantly reducing the number of required IR LEDs and image sensors. This simplification enhances flexibility and lowers production costs while maintaining accuracy. Our breakthrough solution makes eye tracking more accessible for the masses in AR/VR devices in the new era of spatial computing and the metaverse. [ABSTRACT FROM AUTHOR]

A semi-hydroponic system was developed to assess canola (Brassica napus L.) root architectural traits. Four cultivars were grown under controlled conditions in germination paper rolls immersed in half-strength Hoagland's solution. Eight parameters, including total root length, total root surface area, average root diameter, tip number, total primary root length, total lateral root length, total tertiary root length, and basal link length, were analyzed using the WinRHIZO software after 7, 14, and 21 days. The results suggested that 14 days in the semi-hydroponic system were optimal for accurate root trait assessment, as clear differences were observed while maintaining ease of handling and scanning. [ABSTRACT FROM AUTHOR]

Computer vision is one of the artificial intelligence's most challenging fields, enabling computers to interpret, analyse and derive meaningful information from the visual world. There are various utilizations of computer vision algorithms, and most of them, from simpler to more complicated, have an object and shape recognition in common. Traditional pen and paper tests are designed in a pre-established format and consist of numerous basic shapes, which designate the important parts of the test itself. With that in mind, many computer vision applications regarding pen and paper tests arise as an opportunity. Massive courses and large schooling organizations mostly conduct their exams in paper format and assess them manually, which imposes a significant burden on the teaching staff. Any kind of automatization that will facilitate the grading process is highly desirable. Hence, an automated answer recognition system in assessment was developed to mitigate the problems above. The system uses images of scanned test pages obtained from the test scanning process and performs the necessary image manipulation steps to increase target recognition accuracy. Further, it manages to identify regions of interest containing multiple-choice questions and contours. Finally, the system verifies obtained results using the knowledge of the whereabouts of the test template regions of interest. [ABSTRACT FROM AUTHOR]

Medieval paper, a handmade product, is made with a mould which leaves an indelible imprint on the sheet of paper. This imprint includes chain lines, laid lines and watermarks which are often visible on the sheet. Extracting these features allows the identification of the paper stock and gives information about the chronology, localisation and movement of manuscripts and people. Most computational work for feature extraction of paper analysis has so far focused on radiography or transmitted light images. While these imaging methods provide clear visualisation of the features of interest, they are expensive and time consuming in their acquisition and not feasible for smaller institutions. However, reflected light images of medieval paper manuscripts are abundant and possibly cheaper in their acquisition. In this paper, we propose algorithms to detect and extract the laid and chain lines from reflected light images. We tackle the main drawback of reflected light images, that is, the low contrast attenuation of chain and laid lines and intensity jumps due to noise and degradation, by employing the spectral total variation decomposition and develop methods for subsequent chain and laid line extraction. Our results clearly demonstrate the feasibility of using reflected light images in paper analysis. This work enables feature extraction for paper manuscripts that have otherwise not been analysed due to a lack of appropriate images. We also open the door for paper stock identification at scale. [ABSTRACT FROM AUTHOR]

This study proposes a method to determine the two‐dimensional absorption capacity of paper towels. Individual plies were tested to minimize the effects of transverse wicking on wetting. The method involves arbitrary point source injection of liquid into the towel surface while recording microscopic images of the wetted areas. The samples were selected from the main manufacturing processes: conventional wet pressing and through‐air drying. Wetting graphs that illustrate the dynamic and static wetting behaviors were generated. The driving force was found to have a negligible effect on the maximum wetted area. The maximum wetted area and the paper grammage were used to determine the two‐dimensional absorption capacity. The results were validated by the basket‐immersion test method. [ABSTRACT FROM AUTHOR]

Image pre-processing is crucial for large fleet management. Many traffic videos are collected by closed-circuit television (CCTV), which has a fixed area monitoring for image analysis. This paper adopts the front camera installed in large vehicles to obtain moving traffic images, whereas CCTV is more limited. In practice, fleets often install cameras with different resolutions due to cost considerations. The cameras evaluate the front images with traffic lights. This paper proposes fuzzy enhancement with RGB and CIELAB conversions to handle multiple resolutions. This study provided image pre-processing adjustment comparisons, enabling further model training and analysis. This paper proposed fuzzy enhancement to deal with multiple resolutions. The fuzzy enhancement and fuzzy with brightness adjustment produced images with lower MSE and higher PSNR for the images of the front view. Fuzzy enhancement can also be used to enhance traffic light image adjustments. Moreover, this study employed You Only Look Once Version 9 (YOLOv9) for model training. YOLOv9 with fuzzy enhancement obtained better detection performance. This fuzzy enhancement made more flexible adjustments for pre-processing tasks and provided guidance for fleet managers to perform consistent image-enhancement adjustments for handling multiple resolutions. [ABSTRACT FROM AUTHOR]

Modern μ -CT scans offer non-destructive three-dimensional microstructure analysis of various materials. Despite small density contrasts, this technology also accurately captures the complex network structure of paper and paperboard. It provides detailed insight into the fiber orientation distribution in paper, which was previously unattainable with existing measurement methods. The image analysis results are applicable for numerical fiber network models, and simulations based on these models can significantly improve our understanding of the microstructural influence on macrostructural paper properties, such as strength, stiffness, and curl tendency. This work presents a new method for investigating the microstructural fiber orientation in paperboard. Orientation tensors obtained from μ -CT scan image processing are extracted and evaluated. Based on the direction of the orientation tensor's first eigenvector, the fiber orientation distribution is determined and then approximated by periodic and non-periodic probability density functions (PDFs). In this study, 40 commercial paperboard samples, each consisting of three plies, were analyzed. From a given set of commonly used PDFs, the best fitting ones have been identified based on their original location in the paper roll and on their ply. It was found that the von Mises PDF provided the most accurate representation of fiber orientation distribution in the middle ply, while the Elliptical PDF was the most suitable for the outer plies. Moreover, a more pronounced fiber orientation anisotropy was observed at the edges of the paper roll than in its center. The best PDFs and their function parameters are provided to allow for direct usage in numerical microstructure models of paperboard, thus enhancing their representativeness. [ABSTRACT FROM AUTHOR]

Federated learning is an emerging technology that enables the decentralised training of machine learning-based methods for medical image analysis across multiple sites while ensuring privacy. This review paper thoroughly examines federated learning research applied to medical image analysis, outlining technical contributions. We followed the guidelines of Okali and Schabram, a review methodology, to produce a comprehensive summary and discussion of the literature in information systems. Searches were conducted at leading indexing platforms: PubMed, IEEE Xplore, Scopus, ACM, and Web of Science. We found a total of 433 papers and selected 118 of them for further examination. The findings highlighted research on applying federated learning to neural network methods in cardiology, dermatology, gastroenterology, neurology, oncology, respiratory medicine, and urology. The main challenges reported were the ability of machine learning models to adapt effectively to real-world datasets and privacy preservation. We outlined two strategies to address these challenges: non-independent and identically distributed data and privacy-enhancing methods. This review paper offers a reference overview for those already working in the field and an introduction to those new to the topic. [ABSTRACT FROM AUTHOR]

During the storage of celluloid pictures for animation films over half a century, an interleave paper adhered to acrylic paint. The purpose of this study is to establish a methodology to cleanly remove the paper from the paint. A layered film, a replica of the celluloid pictures, adhered with paper was prepared and immersed in water or ethanol. The effect of these solvents on the peeling behavior was investigated using a peel test. The maximum peel force for the dry layered film in was distributed at ~0.5 N, independently of the peel speed. The peel force was significantly reduced after the layered film was immersed in pure water or ethanol. A morphological observation revealed that the dry paper was peeled off via the cohesive failure of the paper. After the layered film was immersed in pure water, the paper was also peeled off via cohesive failure. The layered film immersed in ethanol was peeled off at the paper/paint interface. To clear the effect of the volume change in the paint on peel behavior, the relative volume was determined via image analysis. The relative volume of paint was 1.56 in pure water and 1.37 in ethanol. It can be considered that the large difference in the volume of paint induces a large shear stress at the paint/paper interface. [ABSTRACT FROM AUTHOR]

A user-friendly, cost-effectively, portable, and environmentally friendly colorimetric sensor for the quantitative determination of formaldehyde was developed based on the combining of microfluidic paper-based analytical device (μPAD), headspace microextraction (HSME), and digital image colorimetry. Coupling HSME and μPAD led to enhancements in selectivity and sensitivity of the sensor through sample cleanup and analyte enrichment. To construct the μPAD-HSME device, two pieces of paper as the sample and detection zone were placed facing each other so that a small common and sealed space was created between them. The color change occurred when the analyte in the gaseous form crossed this gap and reached the detection zone. Colorimetric sensing in the detection zone was performed based on the Hantzsch reaction. The color change in the detection zone was recorded by a smartphone and digital images were processed using image analysis software based on the RGB model. The influence of some key variables on the sensitivity of the method including derivatization reagent composition, sample volume, extraction temperature, and extraction time was studied and optimized. The linear dynamic range of the method was obtained in two ranges of 0.10–0.75 and 0.75–5.0 mg L−1 with a limit of detection of 0.03 mg L−1. The recoveries were in the range 80–126% for the quantification of formaldehyde in textile, milk, and wastewater samples. [ABSTRACT FROM AUTHOR]

Flow and spontaneous imbibition phenomena in porous media are important for various industrial applications, including printing and medical lateral flow assays. Their quantitative characterization is important to better understand and select the appropriate raw materials. However, standard methods often require time-consuming tests, and/or expensive equipment. Different time scales must be considered, limiting the range of possible characterization tools. A novel experimental approach based on image analysis for characterizing spontaneous imbibition processes is presented. Hence, ultra-fast diffusion may be quantitatively characterized. Models are issued from the literature to consider physical phenomena at small (milliseconds) and medium range (seconds) of time scales. The obtained experimental data fit with theoretical results, providing valuable insights into the understanding of fluid flow behavior at different time scales. Furthermore, the identification of some physical properties for either the fluid, or the substrate, based on the theoretical models are possible, as the contact angle, which remains to be otherwise challenging. This study contributes to bridging the gap between spontaneous imbibition and capillary phenomena at different time scales, their modeling, and a characterization of material and/or fluid properties paving the way for enhanced understanding and control of fluid behavior in porous media. Different papers are considered to illustrate the method. [ABSTRACT FROM AUTHOR]

Especially in the field of paper and print post-processing on an industrial scale, continuous cutting operations generate paper dust. Despite preventive measures like dedusting units, a high paper web velocity induces the release of particulate dust. As a result, the particles settle and accumulate on machine components and in the surrounding area. Resuspended particles around a potential ignition source represent a fire hazard. The prevention of such fire incidents requires frequent servicing and maintenance. However, the shutdown time on industrial scale applications remains cost-intensive. In this research, a cutting plotter executes reproducible knife edge cutting sequences to identify significant parameters affecting the paper dust formation on a macro and micro scale. For this purpose, an extraction system collects the exposed particles along the cutting line. The applied methods include gravimetric determination of the dust mass and dynamic image analysis to characterise particle size and shape. Final results indicate a significant influence of the blade wear condition. [ABSTRACT FROM AUTHOR]

A pipeline robot suitable for miniature pipeline detection, namely π-II, was proposed in this paper. It features six wheel-leg mobile mechanisms arranged in a staggered manner, with a monocular fisheye camera located at the center of the front end. The proposed robot can be used to capture images during detection in miniature pipes with an inner diameter of 120 mm. To efficiently identify the robot's status within the pipeline, such as navigating in straight pipes, curved pipes, or T-shaped pipes, it is necessary to recognize and classify these specific pipeline landmarks accurately. For this purpose, the residual network model ResNet18 was employed to learn from the images of various pipeline landmarks captured by the fisheye camera. A detailed analysis of image characteristics of some common pipeline landmarks was provided, and a dataset of approximately 908 images was created in this paper. After modifying the outputs of the network model, the ResNet18 was trained according to the proposed datasets, and the final test results indicate that this modified network has a high accuracy rate in classifying various pipeline landmarks, demonstrating a promising application prospect of image detection technology based on deep learning in miniature pipelines. [ABSTRACT FROM AUTHOR]

In insecure communication environments where the communication bandwidth is limited, important image data must be compressed and encrypted for transmission. However, existing image compression and encryption algorithms suffer from poor image reconstruction quality and insufficient image encryption security. To address these problems, this paper proposes an image-compression and encryption scheme based on a newly designed hyperchaotic system and two-dimensional compressed sensing (2DCS) technique. In this paper, the chaotic performance of this hyperchaotic system is verified by bifurcation diagrams, Lyapunov diagrams, approximate entropy, and permutation entropy, which have certain advantages over the traditional 2D chaotic system. The new 2D chaotic system as a pseudo-random number generator can completely pass all the test items of NIST. Meanwhile, this paper improves on the existing 2D projected gradient (2DPG) algorithm, which improves the quality of image compression and reconstruction, and can effectively reduce the transmission pressure of image data confidential communication. In addition, a new image encryption algorithm is designed for the new 2D chaotic system, and the security of the algorithm is verified by experiments such as key space size analysis and encrypted image information entropy. [ABSTRACT FROM AUTHOR]

A semi-hydroponic system was developed to assess canola (Brassica napus L.) root architectural traits. Four cultivars were grown under controlled conditions in germination paper rolls immersed in half-strength Hoagland's solution. Eight parameters, including total root length, total root surface area, average root diameter, tip number, total primary root length, total lateral root length, total tertiary root length, and basal link length, were analyzed using the WinRHIZO software after 7, 14, and 21 days. The results suggested that 14 days in the semi-hydroponic system were optimal for accurate root trait assessment, as clear differences were observed while maintaining ease of handling and scanning. [ABSTRACT FROM AUTHOR]

With the continuous progress of science and technology, image processing techniques have been used increasingly in recent years. Image processing plays an indispensable role in the fields of computer vision, artificial intelligence, pattern recognition, and related fields. Improvements in basic algorithms and the development of new algorithms have resulted in considerable innovation and progress. This paper is devoted to finding new game applications in a branch of image processing. It introduces an analysis model proposed by the author and discusses the relationship between roughness in the frequency domain and visual image interpretation. By using the concept of roughness, we separated the image features into meaningful information and residual information and analysed the image in the frequency domain. The results were compared with those of traditional image processing methods. The starting point is the visual identification of a feature based on human interpretation. The image information was separated into meaningful features and the residual component to reduce the redundancy of the model. This allowed for a sparse representation of the feature information in the image. By analysing the meaningful features and residual components of an image separately, we established a relationship between the results and the original images. Parameters such as texture, morphology, and the degree of blurring were considered and we developed a parameter called "frequency roughness". The algorithm incorporates the concepts of frequency and roughness and the roughness is determined in the frequency domain. The frequency roughness algorithm successfully separated the rough features in the frequency domain and calculated the residual value in an image. This model provided more accurate image processing results than comparable methods. This paper includes an analysis and game applications of the proposed model for de-blurring, image enhancement, recognition, and other image processing tasks. Some game applications were successful, whereas others require further investigation. [ABSTRACT FROM AUTHOR]

Conditional image retrieval (CIR), which involves retrieving images by a query image along with user-specified conditions, is essential in computer vision research for efficient image search and automated image analysis. The existing approaches, such as composed image retrieval (CoIR) methods, have been actively studied. However, these methods face challenges as they require either a triplet dataset or richly annotated image-text pairs, which are expensive to obtain. In this work, we demonstrate that CIR at the image-level concept can be achieved using an inverse mapping approach that explores the model's inductive knowledge. Our proposed CIR method, called Backward Search, updates the query embedding to conform to the condition. Specifically, the embedding of the query image is updated by predicting the probability of the label and minimizing the difference from the condition label. This enables CIR with image-level concepts while preserving the context of the query. In this paper, we introduce the Backward Search method that enables single and multi-conditional image retrieval. Moreover, we efficiently reduce the computation time by distilling the knowledge. We conduct experiments using the WikiArt, aPY, and CUB benchmark datasets. The proposed method achieves an average mAP@10 of 0.541 on the datasets, demonstrating a marked improvement compared to the CoIR methods in our comparative experiments. Furthermore, by employing knowledge distillation with the Backward Search model as the teacher, the student model achieves a significant reduction in computation time, up to 160 times faster with only a slight decrease in performance. The implementation of our method is available at the following URL: https://github.com/dhlee-work/BackwardSearch. [ABSTRACT FROM AUTHOR]

Purpose: Historical newspaper collections provide a wealth of information about the past. Although the digitization of these collections significantly improves their accessibility, a large portion of digitized historical newspaper collections, such as those of KBR, the Royal Library of Belgium, are not yet searchable at article-level. However, recent developments in AI-based research methods, such as document layout analysis, have the potential for further enriching the metadata to improve the searchability of these historical newspaper collections. This paper aims to discuss the aforementioned issue. Design/methodology/approach: In this paper, the authors explore how existing computer vision and machine learning approaches can be used to improve access to digitized historical newspapers. To do this, the authors propose a workflow, using computer vision and machine learning approaches to (1) provide article-level access to digitized historical newspaper collections using document layout analysis, (2) extract specific types of articles (e.g. feuilletons – literary supplements from Le Peuple from 1938), (3) conduct image similarity analysis using (un)supervised classification methods and (4) perform named entity recognition (NER) to link the extracted information to open data. Findings: The results show that the proposed workflow improves the accessibility and searchability of digitized historical newspapers, and also contributes to the building of corpora for digital humanities research. The AI-based methods enable automatic extraction of feuilletons, clustering of similar images and dynamic linking of related articles. Originality/value: The proposed workflow enables automatic extraction of articles, including detection of a specific type of article, such as a feuilleton or literary supplement. This is particularly valuable for humanities researchers as it improves the searchability of these collections and enables corpora to be built around specific themes. Article-level access to, and improved searchability of, KBR's digitized newspapers are demonstrated through the online tool (https://tw06v072.ugent.be/kbr/). [ABSTRACT FROM AUTHOR]

Underwater image enhancement technology is crucial for the human exploration and exploitation of marine resources. The visibility of underwater images is affected by visible light attenuation. This paper proposes an image reconstruction method based on the decomposition–fusion of multi-channel luminance data to enhance the visibility of underwater images. The proposed method is a single-image approach to cope with the condition that underwater paired images are difficult to obtain. The original image is first divided into its three RGB channels. To reduce artifacts and inconsistencies in the fused images, a multi-resolution fusion process based on the Laplace–Gaussian pyramid guided by a weight map is employed. Image saliency analysis and mask sharpening methods are also introduced to color-correct the fused images. The results indicate that the method presented in this paper effectively enhances the visibility of dark regions in the original image and globally improves its color, contrast, and sharpness compared to current state-of-the-art methods. Our method can enhance underwater images in engineering practice, laying the foundation for in-depth research on underwater images. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach to enhance the performance of the stochastic gradient descent (SGD) algorithm by incorporating a modified decay step size based on .... The proposed step size integrates a logarithmic term, leading to the selection of smaller values in the final iterations. Our analysis establishes a convergence rate of O ... for smooth non-convex functions without the Polyak-Łojasiewicz condition. To evaluate the effectiveness of our approach, we conducted numerical experiments on image classification tasks using the Fashion-MNIST and CIFAR10 datasets, and the results demonstrate significant improvements in accuracy, with enhancements of 0:5% and 1:4% observed, respectively, compared to the traditional p1 t step size. [ABSTRACT FROM AUTHOR]

Because the human eye cannot visually detect the results of direct bilirubin test papers accurately and quantitatively, this study proposes four different highly collimated mini light-emitting diodes (HC mini-LEDs) as light sources for detection. First, different concentrations of bilirubin were oxidized to biliverdin by FeCl3 on the test paper, and pictures were obtained with a smartphone. Next, the red, green, and blue (RGB) channels of the pictures were separated to average grayscale values, and their linear relationship with the direct bilirubin concentration was analyzed to detect bilirubin on the test paper noninvasively and quantitatively. The experimental results showed that when green HC mini-LEDs were used as the light sources and image analysis was performed using the G channel, for a direct bilirubin concentration range of 0.1–2 mg/dL, the G channel determination coefficient (R2) reached 0.9523 and limit of detection was 0.459 mg/dL. The detection method proposed herein has advantages such as rapid analysis, noninvasive detection, and digitization according to RGB grayscale changes in the images of the detection test paper. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue in the journal Applied Sciences titled "Artificial Intelligence Technology in Medical Image Analysis." The special issue explores the applications of artificial intelligence (AI) in medical imaging and its impact on diagnostic and therapeutic processes. The use of AI-powered tools in image interpretation has shown exceptional capabilities in detecting and diagnosing medical conditions from imaging data, particularly in radiology. AI also contributes to improving image quality, automating routine tasks, and streamlining healthcare workflows. However, challenges such as data privacy, ethics, and regulatory frameworks need to be addressed for responsible implementation. The special issue includes several research papers that present advancements in automated medical decision support, age estimation, quality assurance, orthotic insole recommendation, tumor identification, thalamus segmentation, medical image classification, hyperparameter optimization, lung disease classification, and thoracic cavity segmentation. These papers demonstrate the potential of AI in improving accuracy, efficiency, and personalized treatment in medical image analysis. The integration of AI into healthcare requires collaboration between AI researchers, healthcare professionals, and regulatory bodies to ensure responsible and effective deployment. The future of AI in medical image analysis holds promise for improved diagnostic accuracy, early disease detection, and personalized treatment strategies. [Extracted from the article]

The objective of the medical image analysis is to increase the effectiveness of the diagnosis options. The Coevolution Neural Network (CNN) is the predominant neural network architecture used in Deep Learning (DL) for medical image analysis. Recently, various innovative technics of DL such as different activation functions, optimization technics, and loss functions have enhanced the performance of CNNs. The Deep Learning CNN (DL-CNN) assists as valuable tool to assist radiologist in diagnosis and improves efficiency and accuracy. Numerous DL-CNN methods have been published to analyze medical images. This paper compiles the performance metrics of DL-CNN, as presented by various authors. This paper reviews the image analysis of six different diseases, viz., lung cancer, colorectal cancer, liver cancer, stomach cancer, breast cancer, and brain tumors. [ABSTRACT FROM AUTHOR]

The deep sea has abundant resources and is regarded as the "sixth continent" that humans can use. Although research on the deep sea has made some progress, its exploration is not deep enough. The acquisition and analysis of deep-sea images can provide important technical means for deep-sea exploration and the acquisition of information. However, there is currently no complete underwater image dataset. For this reason, this paper is the first deep-sea visual dataset of the South China Sea, contains over 100 TB videos and 1 million high quality images. This paper also applies the dataset to underwater image enhancement and image superresolution, and the experiment proves the effectiveness of the dataset. [ABSTRACT FROM AUTHOR]

Video processing is the most emergent area of image processing in Research and Development. This paper, we present some advanced technology interrelated to video and image processing, which will provide a super-resolution high-quality most prominent visible light video for better human visualization and background motions or gesticulations of the human brain. There are two approaches for increasing the current resolution level of an image. The first one is the improvement of spatial resolution by reducing the size of pixels through the techniques of sensor manufacturing by about 40 $ {\mathcal{M}}{\textrm{m}^2} $ M m 2 for a 0.35 $ {\mathcal{M}}\textrm{m} $ M m CMOS process and the second approach is the enhancement of chip size that also enhances the capacitance. In video processing, we mainly focus on object images, i.e. any form of signal processing in which the input is an image and the output is a photograph or a frame of video. So, it will be better to converge over techniques of digital image processing. In the proposed algorithm (MHI Simulink model), edge extraction is performed to find the precious area of MRI and CT-Scan images. Using FPGA, edge detection methods have been implemented over scanned images to get better accuracy. The accuracy of the proposed method has been compared with Prewitt and Sobel's edge detection techniques. The proposed method has given better accuracy than Prewitt and Sobel methods. Finally, this paper shows future directions for researchers to enhance the characteristics of digital image and video processing. [ABSTRACT FROM AUTHOR]

Retained surgical items (RSIs) pose significant risks to patients and healthcare professionals, prompting extensive efforts to reduce their incidence. RSIs are objects inadvertently left within patients' bodies after surgery, which can lead to severe consequences such as infections and death. The repercussions highlight the critical need to address this issue. Machine learning (ML) and deep learning (DL) have displayed considerable potential for enhancing the prevention of RSIs through heightened precision and decreased reliance on human involvement. ML techniques are finding an expanding number of applications in medicine, ranging from automated imaging analysis to diagnosis. DL has enabled substantial advances in the prediction capabilities of computers by combining the availability of massive volumes of data with extremely effective learning algorithms. This paper reviews and evaluates recently published articles on the application of ML and DL in RSIs prevention and diagnosis, stressing the need for a multi-layered approach that leverages each method's strengths to mitigate RSI risks. It highlights the key findings, advantages, and limitations of the different techniques used. Extensive datasets for training ML and DL models could enhance RSI detection systems. This paper also discusses the various datasets used by researchers for training the models. In addition, future directions for improving these technologies for RSI diagnosis and prevention are considered. By merging ML and DL with current procedures, it is conceivable to substantially minimize RSIs, enhance patient safety, and elevate surgical care standards. [ABSTRACT FROM AUTHOR]

Recent research has documented the relationship between the promotion of 'ideal', 'fit' bodies in social media, body image and associated body concerns and conditions. This article expands this scholarship, focusing specifically on gender, body disaffection and social media. Thus far, body disaffection has mostly been understood through a psychological framing, as a pathology residing within an individual and strongly associated with poor body image because of internalizing media images. In this paper, drawing on feminist new materialism, I offer a framing of body disaffection as a relational phenomenon. The paper draws on a mixed method study in England, with over 1000 young people examining their experiences with a range of digital health technologies. I focus specifically on their engagement with social media, to explore the relationship between ideal images and body concerns. Far from being a simple process of internalization of negative perceptions or image one has of their body, disaffection is formed through the body via a complex process of entanglement with social media and other elements. I outline how disaffection materialises as part of an assemblage of elements, including discourses, humans, bodies, digital objects and platforms. The paper reveals how entanglements with social media can generate powerful affects such as shame, pleasure and belonging along gendered lines, which may have significant implications for young people's relationships with their bodies. I analyse how social media events focused on the 'transformation' of bodies generate powerful affects, which open or limit capacities for what 'boys' or 'girls' bodies might become in deeply gendered and sometimes harmful ways. [ABSTRACT FROM AUTHOR]

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As remote sensing technology has advanced, the use of satellites and similar technologies has become increasingly prevalent in daily life. Now, it plays a crucial role in hydrology, agriculture, and geography. Nevertheless, because of the distinct qualities of remote sensing, including expansive scenes and small, densely packed targets, there are many challenges in detecting remote sensing objects. Those challenges lead to insufficient accuracy in remote sensing object detection. Consequently, developing a new model is essential to enhance the identification capabilities for objects in remote sensing imagery. To solve these constraints, we have designed the OD-YOLO approach that uses multi-scale feature fusion to improve the performance of the YOLOv8n model in small target detection. Firstly, traditional convolutions have poor recognition capabilities for certain geometric shapes. Therefore, in this paper, we introduce the Detection Refinement Module (DRmodule) into the backbone architecture. This module utilizes Deformable Convolutional Networks and the Hybrid Attention Transformer to strengthen the model's capability for feature extraction from geometric shapes and blurred objects effectively. Meanwhile, based on the Feature Pyramid Network of YOLO, at the head of the model framework, this paper enhances the detection capability by introducing a Dynamic Head to strengthen the fusion of different scales features in the feature pyramid. Additionally, to address the issue of detecting small objects in remote sensing images, this paper specifically designs the OIoU loss function to finely describe the difference between the detection box and the true box, further enhancing model performance. Experiments on the VisDrone dataset show that OD-YOLO surpasses the compared models by at least 5.2% in mAP50 and 4.4% in mAP75, and experiments on the Foggy Cityscapes dataset demonstrated that OD-YOLO improved mAP by 6.5%, demonstrating outstanding results in tasks related to remote sensing images and adverse weather object detection. This work not only advances the research in remote sensing image analysis, but also provides effective technical support for the practical deployment of future remote sensing applications. [ABSTRACT FROM AUTHOR]

The main scientific results of the 16th International Conference on Pattern Recognition and Information Processing (PRIP-2023), Minsk, Republic of Belarus, October 2023, are reviewed and analyzed. The history of this series of conferences is outlined, and its significant role in the development of the theory and practice of image analysis, pattern recognition, and artificial intelligence is indicated. A list of articles in the special issue is provided, prepared from reports selected by the PRIP-2023 Program Committee. [ABSTRACT FROM AUTHOR]

The article presents a new technique for analyzing phenomena occurring during the measurement of the strength properties of paper in the conditions of compression of the tested samples with forces acting in the paper plane. The technique is based on collecting data on the current distance of the clamps holding the tested sample and the force exerted on the sample using a universal testing machine and on the simultaneous recording of image sequence of the sample during the measurement. Next, the resulting images are subjected to processing and analysis, the purpose of which is to extract information about the shape of the sample edge in all phases of the measurement. Its advantage is the ability to determine the deflection arrow of the sample and describe its shape using the selected function given by the analytical parametric formula. It will be helpful in further research on the development of an analytical model describing the phenomena occurring during paper compression, and a method to determine the mechanism of paper destruction and the corresponding maximum force that destroys a paper sample. [ABSTRACT FROM AUTHOR]

This article analyses Italian illustrated editions of Little Women published between 1908 and 1945. After an overview of the publishing history of the novel in Italy, the paper will examine Italian illustrations as hermeneutic tools in order to examine their ideological function in the representation of gender. The belated reception of Alcott's novel in Italy, caused by its representation of a nonconformist educational model for girls, is mirrored in the tension between the nationalistic drive and the influence of foreign models that can be pinpointed in the illustrated editions. By analyzing adaptations of American illustrations, original Italian creations, and omissions of iconic illustrations, this paper will unveil how much the visual element is embedded in the Italian historical and cultural context, as the choice (or omission) of particular images impacted the interpretation of the book. By examining the Italian editions of Little Women published in this time frame, we will also be able to retrace the links with the American illustrated history of the book and bear testimony to the powerful impact of the 1934 film version on printed editions. [ABSTRACT FROM AUTHOR]

The visual signal object detection technology of deep learning, as a high-precision perception technology, can be adopted in various image analysis applications, and it has important application prospects in the utilization and protection of marine biological resources. While the marine environment is generally far from cities where the rich computing power in cities cannot be utilized, deploying models on mobile edge devices is an efficient solution. However, because of computing resource limitations on edge devices, the workload of performing deep learning-based computationally intensive object detection on mobile edge devices is often insufficient in meeting high-precision and low-latency requirements. To address the problem of insufficient computing resources, this paper proposes a lightweight process based on a neural structure search and knowledge distillation using deep learning YOLOv8 as the baseline model. Firstly, the neural structure search algorithm was used to compress the YOLOv8 model and reduce its computational complexity. Secondly, a new knowledge distillation architecture was designed, which distills the detection head output layer and NECK feature layer to compensate for the accuracy loss caused by model reduction. When compared to YOLOv8n, the computational complexity of the lightweight model optimized in this study (in terms of floating point operations (FLOPs)) was 7.4 Gflops, which indicated a reduction of 1.3 Gflops. The multiply–accumulate operations (MACs) stood at 2.72 G, thereby illustrating a decrease of 32%; this saw an increase in the AP50, AP75, and mAP by 2.0%, 3.0%, and 1.9%, respectively. Finally, this paper designed an edge computing service architecture, and it deployed the model on the Jetson Xavier NX platform through TensorRT. [ABSTRACT FROM AUTHOR]

Detecting aircraft in remote sensing images poses a formidable challenge due to the diverse characteristics of aircraft, including type, size, pose, and intricate backgrounds. Traditional algorithms encounter difficulties in manually extracting features from numerous candidate regions. This paper introduces an innovative aircraft detection approach that combines corner clustering with a diverse set of Deep Learning (DL) models. The proposed method involves two main stages: region proposal and classification. In the region proposal stage, initial candidate regions are generated using a mean-shift clustering algorithm applied to corners detected on binary images. Subsequently, a comprehensive set of classifiers, encompassing CNN, DenseNet, MobileNetV2, Inception v3, Random Forest (R.F), ResNet50, ResNeXT, Support Vector Machine (SVM), VGG16, Xception, EfficientNet, and InceptionResNetv2, is employed for feature extraction and classification. The presented approach demonstrates superior accuracy and efficiency compared to conventional methods. By leveraging the autonomous learning capabilities of CNN and DL models on extensive datasets, the methodology generates a reduced yet high-quality set of candidate regions. Inspired by the detection methodology employed by image analysts, the approach adopts a coarse-to-fine strategy using CNN and DL models. The first CNN proposes coarse candidate regions, and the second identifies individual airplanes within these regions in finer detail. This framework results in a decreased number of candidate regions compared to existing literature while extracting distinctive deep features. Experimental evaluations on Google Earth images validate the efficiency of the proposed method, underscoring its potential for practical applications in both civilian and military contexts. [ABSTRACT FROM AUTHOR]

Existing image analysis algorithms cannot achieve consistency with human visual classification results when classifying particles based on angular levels. To address this issue, this paper proposes an image analysis method based on triangle side ratio to quantify particle angularity, referred to as a TSR method. The proposed method utilizes a primary parameter, Mean Angularity, to assess the mean angularity level, and employs three auxiliary parameters to offer insights into the Sharpest Angularity, the Flat Proportion, and the Number of Angularity. When quantifying the angularity, the method further provides the count of convex angles. Each parameter can reflect different characteristic information of the angularity. When using the mean angularity level to order particles, the TSR method achieves the same results as visual classification, and furthermore introduces a range of values for the main parameter corresponding to the different angularity levels. The TSR method is simpler and more stable, since the particle parameters can be calculated directly without contour smoothing, and consistent results are achieved for different shapes with the same degree of angular sharpness. The results of the study on lunar soil, volcanic rock, mechanism stone, and stream stone, show that the TSR method can objectively and comprehensively analyze and quantify the particle angularity. [ABSTRACT FROM AUTHOR]

Automatic pavement disease detection aims to address the inefficiency in practical detection. However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. Therefore, this paper proposes a novel approach based on the lightweight Transformer Patch Labeling Network (LTPLN) to enhance the efficiency of automatic pavement disease detection and overcome the limitations of existing methods. Firstly, the input images undergo histogram equalization preprocessing to enhance image quality. Subsequently, the images are evenly partitioned into small patch blocks, serving as inputs to the enhanced Transformer model. This enhancement strategy involves integrating feature map labels at each layer of the model to reduce computational complexity and enhance model lightweightness. Furthermore, a depthwise separable convolution module is introduced into the Transformer architecture to introduce convolutional bias and reduce the model's dependence on large amounts of data. Finally, an iterative training process utilizing the label distillation strategy based on expectation maximization is employed to update the labels of patch blocks and roughly locate the positions of pavement diseases under weak supervision. Experimental results demonstrate that compared to the baseline model, the proposed enhanced model achieves a reduction of 2.5G Flops computational complexity and a 16% speed improvement on a private pavement disease dataset, with only a 1.2 percentage point decrease in AUC accuracy. Moreover, compared to other mainstream image classification models, this model exhibits more balanced performance on a public dataset, with improved accuracy and speed that better align with the practical requirements of pavement inspection. These findings highlight the significant performance advantages of the LTPLN model in automatic pavement disease detection tasks, making it more efficiently applicable in real-world scenarios. [ABSTRACT FROM AUTHOR]

The paper presents new insights into the particle kinematics and tribological aspects and their effects on the non-dilative interface shear response from novel experimental investigations. A custom-designed apparatus that enables image analysis of particulate-continuum materials interactions from the bottom of the interface plane while shearing was developed. The effect of influential factors on the frictional mechanism, particle kinematics, and subsequently on the friction coefficient was investigated by performing experiments on three types of sands at different normal stresses with a transparent acrylic sheet and smooth geomembrane. The results demonstrated that the frictional response of the acrylic sheet and geomembrane was comparable, indicating that their particle kinematics at the interface could be similar. However, the critical normal and peak shear stresses differed due to the materials' hardness. The image and micro-topographical analysis of the tested interfaces revealed that the box fixity, particle shape, and normal stress influence particle kinematics and shear-induced surface changes. The fixed box has shown restricted particle movements compared to the conventional box. Angular and smooth spherical particles exhibited lesser kinematics despite a huge difference in the shape and shear-induced surface changes. Rough spherical particles have larger displacements and shear-induced surface changes than smooth spherical particles. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny 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.)

Purpose: In recent years, the use of deep learning for medical image segmentation has become a popular trend, but its development also faces some challenges. Firstly, due to the specialized nature of medical data, precise annotation is time‐consuming and labor‐intensive. Training neural networks effectively with limited labeled data is a significant challenge in medical image analysis. Secondly, convolutional neural networks commonly used for medical image segmentation research often focus on local features in images. However, the recognition of complex anatomical structures or irregular lesions often requires the assistance of both local and global information, which has led to a bottleneck in its development. Addressing these two issues, in this paper, we propose a novel network architecture. Methods: We integrate a shift window mechanism to learn more comprehensive semantic information and employ a semi‐supervised learning strategy by incorporating a flexible amount of unlabeled data. Specifically, a typical U‐shaped encoder‐decoder structure is applied to obtain rich feature maps. Each encoder is designed as a dual‐branch structure, containing Swin modules equipped with windows of different size to capture features of multiple scales. To effectively utilize unlabeled data, a level set function is introduced to establish consistency between the function regression and pixel classification. Results: We conducted experiments on the COVID‐19 CT dataset and DRIVE dataset and compared our approach with various semi‐supervised and fully supervised learning models. On the COVID‐19 CT dataset, we achieved a segmentation accuracy of up to 74.56%. Our segmentation accuracy on the DRIVE dataset was 79.79%. Conclusions: The results demonstrate the outstanding performance of our method on several commonly used evaluation metrics. The high segmentation accuracy of our model demonstrates that utilizing Swin modules with different window sizes can enhance the feature extraction capability of the model, and the level set function can enable semi‐supervised models to more effectively utilize unlabeled data. This provides meaningful insights for the application of deep learning in medical image segmentation. Our code will be released once the manuscript is accepted for publication. [ABSTRACT FROM AUTHOR]

Infrared thermography (IRT) is a non-destructive testing technique that can detect the internal defects of materials. In the detection of austenitic stainless-steel pipes with large curvature, image noise caused by uneven heating is difficult to avoid. Traditional image processing methods are less effective. According to previous works, a supervised neural network was proposed in this paper using Unet network and convolutional block attention module. Existing image processing method and networks were used to compare with the proposed method. The results show that the proposed method can remove the noise caused by uneven heating, and detect all subsurface defects in stainless-steel pipe. [ABSTRACT FROM AUTHOR]

Breast cancer is the second most prevalent cause of cancer death and the most common malignancy among women, posing a life-threatening risk. Treatment for breast cancer can be highly effective, with a survival chance of 90% or higher, especially when the disease is detected early. This paper introduces a groundbreaking deep U-Net framework for mammography breast cancer images to perform automatic detection of abnormalities. The objective is to provide segmented images that show areas of tumors more accurately than other deep learning techniques. The proposed framework consists of three steps. The first step is image preprocessing using the Li algorithm to minimize the cross-entropy between the foreground and the background, contrast enhancement using contrast-limited adaptive histogram equalization (CLAHE), normalization, and median filtering. The second step involves data augmentation to mitigate overfitting and underfitting, and the final step is implementing a convolutional encoder-decoder network-based U-Net architecture, characterized by high precision in medical image analysis. The framework has been tested on two comprehensive public datasets, namely INbreast and CBIS-DDSM. Several metrics have been adopted for quantitative performance assessment, including the Dice score, sensitivity, Hausdorff distance, Jaccard coefficient, precision, and F1 score. Quantitative results on the INbreast dataset show an average Dice score of 85.61% and a sensitivity of 81.26%. On the CBIS-DDSM dataset, the average Dice score is 87.98%, and the sensitivity reaches 90.58%. The experimental results ensure earlier and more accurate abnormality detection. Furthermore, the success of the proposed deep learning framework in mammography shows promise for broader applications in medical imaging, potentially revolutionizing various radiological practices. [ABSTRACT FROM AUTHOR]

Bright print images help any product on the shelf stand out, attract the consumer, and add a unique value to the product. This research work is to empirically identify significant factors and optimize printing press parameters like press speed (x1: 5000, 7000, and 9000 sheets per hour), ink viscosity (x2: 15, 25, 35 Pa·s), and blanket rubber hardness (x3: 65, 70 and 75 shore A) to achieve prints with rich chroma yet with low dot gain in the lithography process, the most economical and widely used process today in the field of label and packaging industry. The parameters used during the printing process can affect the physical dot gain and the color of printed images. To study this, measurements were taken of the dot gain (y1) and color (measured in terms of lightness (y2) and chroma (y3)) at three different dot areas - highlight (25 %), middle tone (50 %), and shadow (75 %). Box-Behnken Design was used to test fifteen different print conditions. The second-order polynomial model's fit quality was good concerning the responses. The optimum printing machine conditions determined to minimize dot gain and lightness and maximize chroma were a press speed of 5000 sheets per hour, 30 Pa·s ink viscosity, and blanket rubber having 70 A shore hardness. The optimized values agreed with the predicted responses were acceptable, considering the right balance of minimum dot gain, higher chroma, and lower lightness to give the vibrant yet controlled halftone dot area in print production. [ABSTRACT FROM AUTHOR]