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Rock strength parameters such as uniaxial compressive strength and modulus of elasticity are crucial parameters in designing rock engineering structures. Owing to the importance of the aforementioned parameters, in this paper, image processing technique is coupled with artificial neural network (ANN) method for assessing the uniaxial compressive strength and modulus of elasticity of sandstones. For this reason, 102 core sandstone samples were prepared. Subsequently petrographic analyses and imaging operation for 102 images were performed. Principal component analysis was then conducted for feature reduction purposes. At last, an ANN model, which received its input data from image processing technique, was constructed for assessing the UCS and E of sandstone samples. Overall, the best performance of the network was obtained when 10 hidden nodes were used. The correlation coefficient (R) values of 0.9722 and 0.97062 for UCS and E, respectively, suggest the feasibility of the proposed model. [ABSTRACT FROM AUTHOR]

Stress whitening is a milky-white appearance occurring in a variety of polymers during the mechanical loading and plastic deformation. The current research examines the pattern of stress whitening occurrence including the onset and the progress in polypropylene by conducting gray scale, blush measurements and microscopic studies during tensile tests. Histograms of digital image processing in deformed zone showed that the distribution of the gray-level data points changed from a (a) narrow to (b) an extended with tail distribution, followed by (c) growth of a second peak with time which indicates that stress whitening intensification occurs gradually and generally through the deformation region. It means that whitening consists of whitening intensity enhancement and spread of whitened zone. Microscopic evaluations also showed that at regions close to the neck, the spherulitic structures are ruined and changed to fibrillar structures by deformation development. As a result, the origin of stress whitening from two distinct resources, (a) defects including micro-voids and voids such as craze, semi-craze or crack structures (occurring close to and far from the neck areas) and (b) orientations of fibrillar structures (occurring in the neck area), were considered completely. Moreover, it was shown that increasing the strain rate in tensile test leads to formation of more visible whitened area within the plastically deformed zone. This is the consequence of further growth of micro-voids at higher strain rates. At last, the agreement between these observations and the Mie scattering concept is discussed. [ABSTRACT FROM AUTHOR]

Introduction: In recent decades, numerous large survey projects have been initiated to enhance our understanding of the cosmos. Among these, the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) stands out as a flagship project of the Stage IV cosmology imaging surveys, offering an open-source framework for data management and processing adaptable to various instruments. Methods: In this paper, we introduce the 'obs_mccd' software package, designed to serve as a bridge linking raw data from generic mosaic-CCD instruments to the LSST data management framework. The package also facilitates the deployment of tailored configurations to the pipeline middleware. To validate our data processing pipeline, we processed a batch of realistic data from a commissioning wide-field telescope. Results: We established a prototype of the quality control (QC) system capable of assessing image quality parameters such as PSF size, ellipticity, and astrometric calibration. Our findings indicate that using a fifth-order polynomial for astrometric calibration effectively characterizes geometric distortion, achieving a median average geometric distortion residual of 0.011 pixel. Discussion: When comparing the performance of our pipeline to our inhouse pipeline applied to the same dataset, we observed that our new 'obs_mccd' pipeline offers improved precision, reducing the median average geometric distortion residual from 0.016 pixel to 0.011 pixel. This enhancement in performance underscores the benefits of the obs_mccd package in managing and processing data from wide-field surveys, and it opens up new avenues for scientific exploration with smaller, flexible survey systems complementing the LSST. [ABSTRACT FROM AUTHOR]

This paper proposes a new method for recognizing, extracting, and processing Phase-Resolved Partial Discharge (PRPD) patterns from two-dimensional plots to identify specific defect types affecting electrical equipment without human intervention while retaining the principals that make PRPD analysis an effective diagnostic technique. The proposed method does not rely on training complex deep learning algorithms which demand substantial computational resources and extensive datasets that can pose significant hurdles for the application of on-line partial discharge monitoring. Instead, the developed Cosine Cluster Net (CCNet) model, which is an image processing pipeline, can extract and process patterns from any two-dimensional PRPD plot before employing the cosine similarity function to measure the likeness of the patterns to predefined templates of known defect types. The PRPD pattern recognition capabilities of the model were tested using several manually classified PRPD images available in the existing literature. The model consistently produced similarity scores that identified the same defect type as the one from the manual classification. The successful defect type reporting from the initial trials of the CCNet model together with the speed of the identification, which typically does not exceed four seconds, indicates potential for real-time applications. [ABSTRACT FROM AUTHOR]

Empowering displays with intelligent functions enables their application in intelligent image processing and intelligent interactive displays, which is the combination of future display and artificial intelligence technologies. However, existing display technologies face significant processing and transmission burdens due to their conventional hardware separation architecture, which separates memory from the processor and display module from the processing module. To address these challenges, a highly integrated memory‐ processing‐display intelligent light‐emitting hardware (MPDIH) capable of information generation, memory, processing, and visualization is proposed. The MPDIH, based on an organic light‐sensitive layer and activated by ultraviolet light irradiation, exhibits unique photoinhibition behavior attributed to the reverse light‐induced electric field formed by the directional arrangement of photo‐generated excitons. This behavior enables dynamic regulation and autonomous learning during the device training process. Leveraging this phenomenon, intelligent image processing is successfully demonstrated, achieving a significant contrast improvement with a maximum enhancement of 261% compared to unprocessed raw signals. Furthermore, the memory‐processing‐display intelligent image processing scheme enables fashion MNIST image recognition using an artificial neural network, and achieves noticeably higher recognition accuracy (>89%) compared to the original fuzzy images (<59%). Consequently, the proposed memory‐processing‐display intelligent light‐emitting hardware holds promise for future applications in integrated photonic systems and interactive artificial intelligence displays. [ABSTRACT FROM AUTHOR]

Disease recognition has been revolutionized by autonomous systems in the rapidly developing field of medical technology. A crucial aspect of diagnosis involves the visual assessment and enumeration of white blood cells in microscopic peripheral blood smears. This practice yields invaluable insights into a patient's health, enabling the identification of conditions of blood malignancies such as leukemia. Early identification of leukemia subtypes is paramount for tailoring appropriate therapeutic interventions and enhancing patient survival rates. However, traditional diagnostic techniques, which depend on visual assessment, are arbitrary, laborious, and prone to errors. The advent of ML technologies offers a promising avenue for more accurate and efficient leukemia classification. In this study, we introduced a novel approach to leukemia classification by integrating advanced image processing, diverse dataset utilization, and sophisticated feature extraction techniques, coupled with the development of TL models. Focused on improving accuracy of previous studies, our approach utilized Kaggle datasets for binary and multiclass classifications. Extensive image processing involved a novel LoGMH method, complemented by diverse augmentation techniques. Feature extraction employed DCNN, with subsequent utilization of extracted features to train various ML and TL models. Rigorous evaluation using traditional metrics revealed Inception-ResNet's superior performance, surpassing other models with F1 scores of 96.07% and 95.89% for binary and multiclass classification, respectively. Our results notably surpass previous research, particularly in cases involving a higher number of classes. These findings promise to influence clinical decision support systems, guide future research, and potentially revolutionize cancer diagnostics beyond leukemia, impacting broader medical imaging and oncology domains. [ABSTRACT FROM AUTHOR]

Background: Skin barrier function is significantly impacted by skin moisture. Most non‐invasive evaluation techniques to measure skin surface hydration relying on its electrical properties, which are limited in scope and have unstable operations. Applying image processing for skin hydration assessment is uncommon, with an emphasis on skin‐capacitive pictures and near‐infrared images in general, which demand a certain spectrum. As a result, there is an increasing need for wide‐area skin hydration evaluation and mapping. Objective: The study aims to propose a quantitative evaluation algorithm for skin surface hydration from visible‐light images. Materials and methods: Three devices were applied to measure skin hydration: skin image capture device and two recognized commercial skin devices. A digital image processing system creates a new index, called GVR, to symbolize skin surface moisture. The CLAHE algorithm was applied to enhance the contrast of skin image, and after calculating it with the monochrome image, the skin reflectance image was segmented. The GVR was estimated using the values of the individual sites and the entire skin. The correlation coefficient between the three methods was examined using statistical analysis to assess the performance of GVR. Results: Skin hydration estimated from visible‐light images is influenced by the entire facial structure in addition to specific areas. The electrical and visible image evaluations showed a strong association with a significant difference. Conclusion: It was discovered that reflecting measures from visible images provide a quick and efficient way to quantify the moisture of the skin's surface. [ABSTRACT FROM AUTHOR]

Approximate computing is an emerging paradigm that can tolerate some loss of accuracy to improve the energy consumption and design complexity. Multiplication is one of the important computational components in various digital signal processing (DSP) applications. Improvement of multipliers, as the building blocks of an approximate computing systems, has received significant attention. Compressors are key elements in an approximate multiplier to have an optimal design with high performance. This paper proposes an approximate/exact 4–2 compressor and an exact 5–2 compressor in carbon nanotube field-effect transistors (CNTFET) technology for utilisation in an approximate multiplier. Four efficient designs for utilising the proposed compressors are introduced and analysed for a Wallace multiplier. All of the proposed circuits are analysed in 32 nm CNTFET Stanford technology and are simulated by HSPICE. In addition, as an image processing application of approximate multipliers, this paper compares the quality metrics of the image multiplication for the proposed designs and previously presented approximated multipliers. For example, the proposed schemes provide excellent results in structural similarity index up to 99%, and peak signal-to-noise ratio more than 45 dB. Also, it uses figure of metric (FOM) that includes the accuracy and quality metrics of the approximate multipliers. The simulation results demonstrate the efficiency of proposed designs. [ABSTRACT FROM AUTHOR]

Approximately 130,000 metal fuel pins were irradiated in the Experimental Breeder Reactor II (EBR-II) during its 30 years of operation to develop and characterize existing and prospective fuels. For many of the metal fuel irradiation experiments, neutron radiography imaging was performed to characterize fuel behavior, such as fuel axial expansion. While several fuel expansion results obtained from neutron radiography imaging have been published, the analysis of neutron radiography for the purpose of describing statistical properties of porous matter formed on top of the fuel pins, also referred to as fluff in previous publications, is significantly less represented in the literature with just a single paper so far. This study aims to validate and augment results reported in previous publications using automated image processing. The paper describes the statistical properties of the porous matter in terms of nine parameters derived from radiography images and correlates those parameters with such fuel properties as composition, expansion, temperature, and burnup. The reported results are based on 1097 fuel pins of eight different fuel compositions. For three major fuel types, U-10Zr, U-8Pu-10Zr, and U-19Pu-10Zr, a clear negative correlation is found between the Pu content and five parameters describing the amount of porous matter generated. The parameters describing granularity properties, however, showed either negative correlation or nonlinear dependency from fuel composition. The parameters describing the amount showed a positive correlation with fuel axial expansion, while granularity parameters showed a negative correlation with axial expansion. The dependency on cladding temperature was found to be weak. A positive correlation is demonstrated for volume parameters and fuel burnup. In general, reported results confirm and validate findings published in previous studies using a much larger number of pins and automated processing techniques, which easily lend themselves to reproducibility, thus avoiding subjective bias. [ABSTRACT FROM AUTHOR]

Yarn hairiness affects yarn and fabric quality. The existing hairiness detection methods cannot discriminate crossover fibers or hairs. In order to accurately separate and detect crossover fibers, an algorithm of separating crossover hairs is proposed. By obtaining refined hair skeletons after image pretreatment, the positions of fiber intersection point were determined, and a hair information table according to the characteristics of the hair cross-point was constructed. After classifying each hair branch skeleton and screening out the hair common skeleton, the branch hair matching table by using the two end points of the true common hair skeleton adjacent to the hair branch was constructed. Through pairing the same cross-hair branch with the principle of the closest slope at the adjacent cross-end, each complete cross-hair skeleton was defined for hair count in a field of view. The detection results show that compared with the existing photoelectric hairiness detection instrument, the algorithm can realize the crossover hair separation, and calculate the length of complete crossover hairs and curved hairs with high accuracy. On average, the developed algorithm measures hair length about 11.1% longer than the manually measured results, while commercial apparatus would report hair length 62.5% shorter than the actual hair length. [ABSTRACT FROM AUTHOR]

The performance of the tire has a very important impact on the safe driving of the car, and in the actual use of the tire, due to complex road conditions or use conditions, it will inevitably cause immeasurable wear, scratches and other damage. In order to effectively detect the damage existing in the key parts of the tire, a tire surface damage detection method based on image processing was proposed. In this method, the image of tire side is captured by camera first. Then, the collected images are preprocessed by optimizing the multi-scale bilateral filtering algorithm to enhance the detailed information of the damaged area, and the optimization effect is obvious. Thirdly, the image segmentation based on clustering algorithm is carried out. Finally, the Harris corner detection method is used to capture the "salt and pepper" corner of the target region, and the segmsegmed binary image is screened and matched based on histogram correlation, and the target region is finally obtained. The experimental results show that the similarity detection is accurate, and the damage area can meet the requirements of accurate identification. [ABSTRACT FROM AUTHOR]

Pavement management has traditionally relied on human-based decisions. In many countries, however, the pavement stock has recently increased, while the number of management experts has declined, posing the challenge of how to efficiently manage the larger stock with fewer resources. Compared to efficient computer-based techniques, human-based methods are more prone to errors that compromise analysis and decisions. This research built a robust probabilistic pavement management model with a safety metric output using inputs from image processing tested against the judgment of experts. The developed model optimized road pavement safety. The study explored image processing techniques considering the trade-off between processing cost and output accuracy, with annotation precision and intersection over union (IoU) set objectively. The empirical applicability of the model is shown for selected roads in Japan. [ABSTRACT FROM AUTHOR]

The utilization of tempered blast‐furnace slag through the direct fiber forming process to produce high‐value thermal insulation materials offers a dual benefit: it efficiently utilizes the latent heat in the unused slag and significantly increases the value of blast‐furnace slag utilization. However, measuring the melting properties of iron slag at high temperatures is challenging. In this study, the melting behavior of SiO2 in a high‐temperature molten pool was investigated. We employ dynamic visual data (video stream) captured via a non‐contact charge coupled device video recording system to extract SiO2 contours through image processing. The change in image centroid characteristics is used to establish a convolution function relationship, and MATLAB's traversal search algorithm determines the centroid position of SiO2. Given that SiO2 is proportionate to crucible pixels, the area of the SiO2 is calculated through pixel statistics within these contours. A new indirect method is then proposed to process image information to obtain SiO2 volume and mass at different time points. An exponential fitting yields the melting rate function of SiO2. Finally, this indirect method has been compared with shape from shading, quantitative characterization, and dimensional analysis techniques. Besides, the strengths and limitations of each method have been discussed. Our findings reveal that the indirect solution method presented here boasts straightforward calculation steps and imposes minimal image format requirements, which provides theoretical and technical support for the direct fiber forming process of blast‐furnace slag. [ABSTRACT FROM AUTHOR]

Given the abundance of images related to operations that are being captured and stored, it behooves firms to innovate systems using image processing to improve operational performance that refers to any activity that can save labor cost. In this paper, we use deep learning techniques, combined with classic image/signal processing methods, to propose a pipeline to solve certain types of object counting and layer characterization problems in firm operations. Using data obtained by us through a collaborative effort with real manufacturers, we demonstrate that the proposed pipeline method is able to achieve higher than 93% accuracy in layer and log counting. Theoretically, our study conceives, constructs, and evaluates proof of concept of a novel pipeline method in characterizing and quantifying the number of defined items with images, which overcomes the limitations of methods based only on deep learning or signal processing. Practically, our proposed method can help firms significantly reduce labor costs and/or improve quality and inventory control by recording the number of products in real time, more accurately and with minimal up-front technological investment. The codes and data are made publicly available online through the INFORMS Journal on Computing GitHub site. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0260) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0260). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Potato is one of the most nutritious foods rich in fiber and essential vitamins and minerals. However, potato plants may suffer from diseases such as early blight, late blight, septoria leaf spot or skin diseases such as greening, silver scurf, common scab, black rot etc. These diseases may lead to huge losses in the potato yield annually. Hence, an early prediction of such diseases is required so that they can be controlled at an early stage. Several machine learning, IoT based and image processing techniques have been utilized for this purpose, and this paper aims to survey all such methods and prediction models. A total of 116 studies were analyzed, and out of them, 77 relevant studies belonging to (60%) journals, (34%) conferences and (6%) other sources have been surveyed. It has been observed that most of the selected studies (64%) have used RGB image data (proprietary or PlantVillage dataset). Also, majority of the studies (48%) have worked with imbalanced data. Late blight and early blight are the two diseases that have gained the most attention by the researchers, being included in 58% and 34% of the studies. Moreover, feature extraction/selection has been used only with image data (RGB and hyperspectral). Convolutional Neural Network is the most widely used classifier, followed by Support Vector Machines. This review will help the researchers gain an insight into the work done for disease detection in potato crop and what techniques or diseases need intervention so that yield losses can further be minimized. [ABSTRACT FROM AUTHOR]

Osteoporosis is a disease in which bone mass and structural strength decrease, leading to increased fragility and susceptibility to fractures in the face, neck, spine, wrist, etc. It's a disease that doesn't display any symptoms until a break occurs; in other words, it's a silent illness. Sometime it also utilised frequently in dentistry application such that to detect osteoporosis during the operation or implanting on maxilla. This body of work shows that, despite major breakthroughs in medicine, there remains a unique role for the development of novel tools for the diagnosis of osteoporosis. In this work, we present an osteoporosis detection system developed using image processing and support vector machine (SVM) techniques. Compared to prior studies in this area, the data collected by the technology established in this study—which includes 50 sample photographs of the tibia—is of high quality. The proposed method achieves an impressively high level of accuracy (83.6% with 50 samples) because to the inclusion of the histogram feature and the use of tissue properties during feature extraction. [ABSTRACT FROM AUTHOR]

Recently, there has been a rapid increase in the volume of medical image repositories because of the maximized utilization of digitized image data in hospitals. As a result, there is a complexity in querying and handling such large databases, which has led to the development of a novel Content-Based Medical Image Retrieval (CBMIR) technique. In this work, the CBMIR technique is developed using three types of visual features (i.e., color, texture and shape) along with 12 distance measures, and is optimized with the Crossover-Gravitational Search Algorithm (CRO-GSA). For ease, we named this technique Content-Based Medical Image Retrieval using Visual features with CRO-GSA (CBMIRVC). The initial step of our CBMIRVC technique is to extract three types of visual features from the images. Then, each type of feature is employed with a suitable distance measure, which is used for the computation of image similarities between the medical images in the database and a provided query image. The optimization of our proposed CBMIRVC technique is done by the CRO-GSA algorithm. This algorithm optimizes the CBMIRVC technique by identifying the optimal combinations of visual features and similarity measures. Additionally, optimal weights are computed in accordance with the three types of features for the three similarities. Experimental validations were done using retrieved cancer-related and endoscopic color images from databases holding numerous image categories. The experimental outcome shows that our proposed CBMIRVC technique outperforms other conventional image retrieval techniques by effectively retrieving similar images from large databases. [ABSTRACT FROM AUTHOR]

Inspection robots for early detection of potential risks in severe environments require a high accuracy like human experts. A fine mechanism is crucial for extracting target components from noisy signals. We have proposed a detection system for submillimeter-width cracks in concrete surfaces of social infrastructures, such as bridges, by using morphological component analysis (MCA), for aerial image-inspection robots. Traditional schemes like PCA have relied on linear decomposition for the separation of target signal and noise components. Recent advancement in signal decomposition focuses on the enhancement of linearity in the separation by introducing a set of nonlinear basis functions to represent the raw signal even when multiple factors are mixed in a nonlinear manner. In this sense, MCA is a core technique to be able to isolate target components to represent submillimeter-width cracks from others. We proposed a proper pre- and post-processing operations to attach MCA, which demonstrated a high accuracy yet coarse and fine image components have to be integrated redundantly. In the present study, we successfully found a simpler mechanism to set the single basis function to extract the target by introducing a new thresholding mechanism. It suggests a high potential of MCA for inspection robots for various purposes. [ABSTRACT FROM AUTHOR]

The aim of this study is to propose an image processing device based on load balancing algorithm, which can realize efficient and accurate data processing in physical fitness detection. The characteristics of optical sensor image processing equipment and the demand of physical fitness detection are studied and analyzed. Then, according to the principle and strategy of load balancing, a load balancing algorithm suitable for iot optical sensor image processing equipment is designed. The algorithm can automatically adjust the task allocation according to the load of the device and ensure the load balance of each device. Finally, the experiment is carried out in the actual scene, and the comparison with the traditional method is analyzed. The experimental results show that the iot optical sensor image processing equipment based on load balancing algorithm has excellent performance in the physical fitness detection. It can effectively reduce the load pressure of the equipment and improve the speed and accuracy of data processing. The application potential of the equipment is huge, and it can play an important role in sports training, health monitoring and other fields. [ABSTRACT FROM AUTHOR]

In traditional aerobics training, the movement control of athletes mainly depends on the observation of human eyes and the guidance of coaches. However, due to the limited visual perception of the human eye, it is impossible to accurately capture and analyze the small changes and details of the athletes, resulting in the limitation of the training effect. In this study, a fine motor control system for aerobics athletes based on optical imaging equipment detection and image processing technology is designed to provide accurate postural and movement feedback. In this study, high resolution optical imaging equipment was used to monitor and capture athletes' body posture in real time. Then the image processing algorithm is used to analyze and identify the athlete's posture, and extract the key movements and technical points. Finally, the results of the analysis are fed back to the athletes and coaches to help them adjust and improve. Through experiments, the system can accurately detect and analyze the athlete's posture and movement, and provide fine movement control and feedback. When athletes use this system for training, they can better understand and grasp the correct postural essentials, improve their technical level and training effect. [ABSTRACT FROM AUTHOR]

Fruit fly courtship behaviors composed of a series of actions have always been an important model for behavioral research. While most related studies have focused only on total courtship behaviors, specific courtship elements have often been underestimated. Identifying these courtship element details is extremely labor intensive and would largely benefit from an automatic recognition system. To address this issue, in this study, we established a vision-based fly courtship behavior recognition system. The system based on the proposed image processing methods can precisely distinguish body parts such as the head, thorax, and abdomen and automatically recognize specific courtship elements, including orientation, singing, attempted copulation, copulation and tapping, which was not detectable in previous studies. This system, which has high identity tracking accuracy (99.99%) and high behavioral element recognition rates (> 97.35%), can ensure correct identification even when flies completely overlap. Using this newly developed system, we investigated the total courtship time, and proportion, and transition of courtship elements in flies across different ages and found that male flies adjusted their courtship strategy in response to their physical condition. We also identified differences in courtship patterns between males with and without successful copulation. Our study therefore demonstrated how image processing methods can be applied to automatically recognize complex animal behaviors. The newly developed system will largely help us investigate the details of fly courtship in future research. [ABSTRACT FROM AUTHOR]

In this paper, image processing techniques are employed to investigate the growth and detachment characteristics of initial bubbles in vertical glass tubes. The study explores the effects of different continuous phases (water and Therminol®66), nozzle diameters, air flow rates, and continuous phase liquid levels on the detachment diameter and shape of bubbles. Furthermore, based on the detachment characteristics obtained under various experimental conditions, a novel predictive model for bubble detachment diameter is established in both water and Therminol®66. The results show that the bubble detachment diameter in the continuous phase therminol® 66 is larger than the bubble detachment diameter in water, and that the shape of the bubbles in therminol® 66 is close to spherical, with an ellipsoidal shape in water. With the increase in nozzle diameter and airflow, the bubble detachment diameter increases, and the shape is stretched in the vertical direction. With the increase in the continuous phase liquid level, the bubble detachment diameter decreases, and the shape change is not significant. In addition, the accuracy of the bubble detachment diameter prediction model was validated through experimental data. Compared with the experimental values, the average absolute errors were 5.04% and 5.11% in water and Therminol®66, respectively. This study aims to investigate the characteristics of initial bubble growth and detachment, providing valuable references for nozzle design. [Display omitted] • The growth characteristics of initial bubbles under different working fluids are studied • The shape and diameter of the bubble detachment process are studied under different operating conditions. • A new bubble detachment diameter prediction model is established [ABSTRACT FROM AUTHOR]

An image processing-based Firebug Swarm Optimization (FSO) algorithm is developed in this article to detect maximum power point tracking (MPPT) under shadowing conditions on PV arrays. After capturing an image and applying image processing techniques, the proposed method can detect the shading portion of photovoltaic array (PV). The image processing results can then be used to modify the current rating in power equations. The Firebug swarm optimization (FSO) algorithm is metaheuristic algorithm that identifies and controls switching configurations under partial shading patterns by detecting the maximum global peak point (GPP) of output power generated by multiple configurations. This research is proposed with an image processing edge detection algorithm called the Otsu-Canny algorithm for detecting the partial shade on PV array. The FSO algorithm is used to rearrange the shaded photovoltaic array to significantly reduce the power and efficiency losses. MATLAB simulations were carried out using a 280 W PV panel to verify the efficiency of the proposed FSO MPPT technique with an image processing-based algorithm. The proposed FSO MPPT scheme's performance is compared to current cutting-edge MPPT methods. Under partial shading conditions, the proposed algorithm successfully detected the maximum peak point of a photovoltaic array, increasing power output and efficiency. [ABSTRACT FROM AUTHOR]

This study aims to explore the application of computer simulation method based on light image processing in the monitoring of metabolic regulation data in exercise training. The study collected light image data during exercise training, including several parameters such as skin reflexes, heart rate response and skin temperature. Then, using computer simulation technology, the data were processed and analyzed to obtain metabolic regulation data during exercise. Finally, according to the analysis results, the training effect is evaluated and the training plan is adjusted. The results of the data analysis show that by monitoring the metabolic regulation data, the effect of exercise training can be more accurately evaluated and help adjust the training plan to achieve better training results. This method has the advantages of non-invasive, real-time and accurate, and is expected to be widely used in the field of sports training monitoring and regulation, and further improve the training effect and the formulation of individual training plans. [ABSTRACT FROM AUTHOR]

High-resolution remote sensing image fusion technology can combine multiple remote sensing images to display images more richly and accurately, and make them present more comprehensive and accurate image features. Because this technology has a high utilization rate of image processing, it has research significance. The objective of this study is to improve the accuracy and reliability of infrared image processing technology in motion recognition. In order to achieve this goal, this paper focuses on the research of high resolution image processing technology to improve the quality and clarity of infrared images. Image enhancement algorithm is used to reduce the noise and blur effect in infrared images, image recovery technology is used to improve the resolution and detail representation ability of infrared images, and finally image segmentation and feature extraction algorithm are used to extract the motion features in infrared images. [ABSTRACT FROM AUTHOR]

The significant goal of this article is to highlight the utilization of digital imaging techniques for pathogenic disease detection in banana crops (Musa). The disease occurs in a variety of geographical climatic ranges, posing a threat to banana cultivators worldwide. The inevitable pathogenic invasions in banana plants remarkably influence crop yield loss and consequently agriculture. Subsequently, to enhance cultivation quality and quantity aspects, we involve computer vision models that have been established for automated disease prediction. Moreover, we interpreted the growth evaluation and understanding of different varieties of biotic disease properties that cause damage to the crops. Hence this review aims to address the major challenges in disease detection for identifying disease patterns in stressed plants. It involves classifying infection regions based on different factors such as the type of infectious neighboring plants, and the geometric illusions on the leaf surface area. Additionally, the review addressed the recent strategies of cloud-based computer vision in agriculture which will aid researchers in understanding how digital imaging technologies can be used for pathogen detection in banana plants. Further, this survey ensures a comprehensive review of pathogen diagnosing techniques through artificial intelligence (AI) approaches like convolutional neural networks (CNN), and deep learning (DL) to visually analyze disease patterns based on digital image processing. [ABSTRACT FROM AUTHOR]

In ophthalmic diagnostics, achieving precise segmentation of retinal blood vessels is a critical yet challenging task, primarily due to the complex nature of retinal images. The intricacies of these images often hinder the accuracy and efficiency of segmentation processes. To overcome these challenges, we introduce the cognitive DL retinal blood vessel segmentation (CoDLRBVS), a novel hybrid model that synergistically combines the deep learning capabilities of the U-Net architecture with a suite of advanced image processing techniques. This model uniquely integrates a preprocessing phase using a matched filter (MF) for feature enhancement and a post-processing phase employing morphological techniques (MT) for refining the segmentation output. Also, the model incorporates multi-scale line detection and scale space methods to enhance its segmentation capabilities. Hence, CoDLRBVS leverages the strengths of these combined approaches within the cognitive computing framework, endowing the system with human-like adaptability and reasoning. This strategic integration enables the model to emphasize blood vessels, accurately segment effectively, and proficiently detect vessels of varying sizes. CoDLRBVS achieves a notable mean accuracy of 96.7%, precision of 96.9%, sensitivity of 99.3%, and specificity of 80.4% across all of the studied datasets, including DRIVE, STARE, HRF, retinal blood vessel and Chase-DB1. CoDLRBVS has been compared with different models, and the resulting metrics surpass the compared models and establish a new benchmark in retinal vessel segmentation. The success of CoDLRBVS underscores its significant potential in advancing medical image processing, particularly in the realm of retinal blood vessel segmentation. [ABSTRACT FROM AUTHOR]

The document titled "Application of Information Theory to Computer Vision and Image Processing" provides an overview of human vision and its application to machine vision systems. It explains the process of human vision, from light entering the eyes to electrical signals being transmitted to the brain. The document also discusses the role of machine vision in cybernetic systems and its applications in various fields. It includes summaries of several articles related to information theory, measurement methods, and data processing tools for machine vision systems, covering topics such as signal processing, image enhancement, and text cluster identification. Additionally, the document provides a summary of contributions to a special issue on the application of information theory to computer vision and image processing, covering various topics and presenting unique approaches to address specific challenges in their respective fields. The authors hope that readers will find inspiration for their own research and publications by exploring the articles in this special issue. [Extracted from the article]

Approximate computing is one of the methods to improve performance in various error-resilient applications such as image and video processing. Multipliers are part of their computing unit that often requires many resources. In this paper, an approximate (8; 2) compressor is designed and compared with other available approximate compressors for quality, power consumption, delay and area in the circuit. The proposed approximate compressor is used in $ 8 \times 8 $ 8 × 8 and $ 16 \times 16 $ 16 × 16 multipliers. To show the quality of the proposed compressor, the proposed approximate multiplier of $ 8 \times 8 $ 8 × 8 used to multiply two images in MATLAB tools, then qualitative parameters of SSIM and PSNR were measured and acceptable results were obtained. Also, the MED and NED accuracy parameters indicate the acceptable error rate in the output of the proposed $ 8 \times 8 $ 8 × 8 multiplier circuit. Finally, we synthesized the proposed approximate compressor and multiplier designs by a Synopsys Design Compiler. The proposed $ 16 \times 16 $ 16 × 16 multiplier can improve delay, area and Power Delay Products by 5%, 17%, and 8%, respectively, compared to other similar existing approximate multipliers. [ABSTRACT FROM AUTHOR]

Approximate computing is a new approach to reducing power consumption and complexity, increasing performance, and can generate a trade-off between accuracy and power-delay-area efficiency in error-resilient applications. As multiplication is applied in multimedia processing, and it is time-consuming, implementing efficient circuits for multipliers is essential. This article presents novel recursive approximate multipliers and new approximate multipliers based on partial products grouping (clustering). These proposed approximate multipliers are applied in the structure of error-resilient image processing applications: image multiplication, image sharpening, and smoothing. Application-level simulation results show that the proposed multipliers improve the accuracy of their counterparts, while the circuit-level simulation results demonstrate acceptable delay and power consumption. Three Figures Of Merit (FOMs) were introduced to compromise between circuit evaluation criteria and error analysis metrics. By examining these FOMs, the proposed circuits have created a suitable trade-off between circuit and error evaluation criteria compared to other circuits. [ABSTRACT FROM AUTHOR]

Each year, numerous tornadoes occur in forested regions of the United States. Due to the substantial number of fallen trees and accessibility issues, many of these tornadoes remain poorly documented and evaluated. The process of documenting tree damage to assess tornado intensity is known as the treefall method, an established and reliable technique for estimating near-surface wind speed. Consequently, the demand for documenting fallen trees has increased in recent years. However, the treefall method proves to be extremely expensive and time-consuming, requiring a laborious assessment of each treefall instance. This research proposes a novel approach to evaluating treefall in large, forested regions using deep learning-based automated detection and advanced image processing techniques. The developed treefall method relies on high-resolution aerial imagery from a damaged forest and involves three main steps: (1) instance segmentation detection, (2) estimating tree taper and predicting fallen tree directions, and (3) obtaining subsampled treefall vector results indicating the predominant flow direction in geospatial coordinates. To demonstrate the method's effectiveness, the algorithm was applied to a tornado track rated EF-4, which occurred on 10 December 2021, cutting through the Land Between the Lakes National Recreation Area in Kentucky. Upon observation of the predicted results, the model is demonstrated to accurately predict the predominant treefall angles. This deep-learning-based treefall algorithm has the potential to speed up data processing and facilitate the application of treefall methods in tornado evaluation. [ABSTRACT FROM AUTHOR]

Considering the trend of scaling down conventional technologies toward next-generation devices and the importance of new practical and efficient procedures in increasing the efficiency of digital computing circuits, this paper presents three full adder circuits. Suggested circuits, named Design-1 to Design-3, are proposed based on FinFET technology with channel length of 11 nm. In order to optimize energy consumption and area occupation, they have been implemented based on approximate computing with 4, 10, and 12 transistors, respectively. The main characteristics of the presented circuits lie in their utilization of the gate diffusion input technique, which has resulted in the generation of only three errors in their output. Diverse objectives for error generation have been incorporated into these circuits, positioning them as highly accurate competitors when compared to the most state-of-the-art designs. Also, due to the use of low number of transistors, which has led to low internal nodes, these circuits are in a favorable position in terms of power consumption and speed. To evaluate these circuits, extensive simulations are conducted considering various implementations, including single-bit configurations and integration within 4-bit ripple carry adders with 2 and 4 approximate bits. Based on the figure of merits used consist of diverse design parameters, Design-1 with the least number of transistors has a difference of about 85% improvement compared to the nearest competitor, while design-2 and design-3 circuits are also placed in desirable rankings. [ABSTRACT FROM AUTHOR]

Freshwater fauna is facing an uphill task for survival in the Ganga Basin, India, due to a range of factors causing habitat degradation and fragmentation, necessitating conservation interventions. As part of the ongoing efforts to conserve the freshwater fauna of the Basin, we are working on rehabilitating rescued freshwater chelonians. We carry out various interventions to restore rescued individuals to an apparent state of fitness for their release in suitable natural habitats. Morphometric measurements are crucial to managing captive wild animals for assessing their growth and well-being. Measurements are made using manual methods like vernier caliper that are prone to observer error experience and require handling the specimens for extended periods. Digital imaging technology is rapidly progressing at a fast pace and with the advancement of technology. We acquired images of turtles using smartphones along with manual morphometric measurements using vernier calipers of the straight carapace length and straight carapace width. The images were subsequently processed using ImageJ, a freeware and compared with manual morphometric measurements. A significant decrease in the time spent in carrying out morphometric measurements was observed in our study. The difference in error in measurements was, however, not significant. A probable cause for this may have been the extensive experience of the personnel carrying out the measurements using vernier caliper. Digital image processing technology can cause a significant reduction in the stress of the animals exposed to handling during measurements, thereby improving their welfare. Additionally, this can be used in the field to carry out morphometric measurements of free-ranging individuals, where it is often difficult to capture individuals, and challenges are faced in obtaining permission to capture specimens. [ABSTRACT FROM AUTHOR]

Many animal species, including mice, form societies of numerous individuals for survival. Understanding the interactions between individual animals is crucial for elucidating group behavior. One such behavior in mice is huddling, yet its analysis has been limited. In this study, we propose a cost‐effective method for monitoring long‐term huddling behavior in mice using online image processing with OpenCV. This method treats a single mouse or a group of mice as a cluster of pixels (a 'blob') in video images, extracting and saving only essential information such as areas, coordinates, and orientations. This approach reduces data storage needs to 1/200000th of what would be required if the video were recorded in its compressed form, thereby enabling long‐term behavioral analysis. To validate the performance of our algorithm, ~2000 video frames were randomly chosen. We manually counted the number of clusters of mice in these frames and compared them with the number of blobs automatically detected by the algorithm. The results indicated a high level of consistency, exceeding 90% across the selected video frames. Initial observations of both male and female groups suggested some variations in huddling behavior among male and female groups; however, these results should be interpreted cautiously due to a small sample. Group behavior is known to be disrupted in several neuropsychiatric disorders, such as autism. Various mouse models of these disorders have been proposed. Our measurement system, when combined with drug or genetic modification screening, could provide a valuable tool for high‐throughput analyses of huddling behavior. [ABSTRACT FROM AUTHOR]

Reconfigurable logic‐in‐memory device, albeit a promising hardware platform for constructing parallel computing architectures, still suffers from high power‐consumption and low area‐efficiency arising from the circuit redundancy in silicon (Si) based technical path. 2D materials are identified as potential building blocks to significantly reduce the circuit footprints with outstanding power‐efficiency, owing to their atomically thin nature and unique electronic properties. However, the present 2D logic devices are primarily based on single channel materials with homogeneous transport polarities, limiting the device reconfigurability for multi‐functional applications. Here, 2D p–n junction based dual‐gate Gaussian‐type transistors for simplified reconfigurable logic circuits are reported. All fundamental Boolean logic operators are implemented in a single device with electrically driven reconfigurability, giving rise to an ultra‐low transistor consumption down to 13% of the traditional circuits. The device is also demonstrated as a reliable image processing unit for various computing tasks (pixel processing, comparing, and ciphering) by executing the corresponding logic operations. Moreover, in situ memory of the Boolean logics is achieved by engineering the dielectric properties of the transistor without compromising its reconfigurability. These findings provide a potential approach to achieving reconfigurable logic‐in‐memory operations at the hardware level, with significant implications for the advancement of parallel computing. [ABSTRACT FROM AUTHOR]

Background: Plants are known to be infected by a wide range of pathogenic microbes. To study plant diseases caused by microbes, it is imperative to be able to monitor disease symptoms and microbial colonization in a quantitative and objective manner. In contrast to more traditional measures that use manual assignments of disease categories, image processing provides a more accurate and objective quantification of plant disease symptoms. Besides monitoring disease symptoms, computational image processing provides additional information on the spatial localization of pathogenic microbes in different plant tissues. Results: Here we report on an image analysis tool called ScAnalyzer to monitor disease symptoms and bacterial spread in Arabidopsis thaliana leaves. Thereto, detached leaves are assembled in a grid and scanned, which enables automated separation of individual samples. A pixel color threshold is used to segment healthy (green) from chlorotic (yellow) leaf areas. The spread of luminescence-tagged bacteria is monitored via light-sensitive films, which are processed in a similar manner as the leaf scans. We show that this tool is able to capture previously identified differences in susceptibility of the model plant A. thaliana to the bacterial pathogen Xanthomonas campestris pv. campestris. Moreover, we show that the ScAnalyzer pipeline provides a more detailed assessment of bacterial spread within plant leaves than previously used methods. Finally, by combining the disease symptom values with bacterial spread values from the same leaves, we show that bacterial spread precedes visual disease symptoms. Conclusion: Taken together, we present an automated script to monitor plant disease symptoms and microbial spread in A. thaliana leaves. The freely available software (https://github.com/MolPlantPathology/ScAnalyzer) has the potential to standardize the analysis of disease assays between different groups. [ABSTRACT FROM AUTHOR]

With the continuous deepening of the urbanization process and the progress of science and technology, people transform nature and develop nature on a larger and larger scale, among which the most iconic transformation is a variety of building structures built by people. And with the passage of time, the building structure in the perennial wind and sun, there will be signs of "illness", if not timely treatment, it will have a huge impact on the stability and safety of the building structure. Based on this, in this paper, according to the characteristics of crack identification on the surface of concrete structure, background subtraction algorithm is selected for image noise reduction processing. Through three steps of digital image noise reduction, crack extraction and crack parameter identification, the quantitative recognition of cracks is completed and a complete system of crack parameter identification is formed. The experimental results show that the machine learning model of building structure health monitoring and damage recognition algorithm proposed in this paper has excellent statistical performance, and the relative error accuracy of recognition can be controlled within 10%. [ABSTRACT FROM AUTHOR]

In coffee processing, there are several important stages, one of which is roasting. The roasting process is an important determinant of coffee quality. Determination of coffee quality can be done using digital image processing methods to produce parameters and quality classifications precisely, make images of better quality so that photos and moving images can be easily understood. This analysis uses a Systematic Literature Review (SLR) for the identification, evaluation, and interpretation of all available research results on the topics discussed. The purpose of this study was to identify and analyze the main quality parameters and the best digital image processing methods used in classifying the quality of roasted coffee beans. From the results of the analysis of 31 journals, it is known that the parameters for evaluating the quality of roasted coffee are color parameters, texture parameters, and shape parameters. The color parameters consist of Red Green Blue (RGB), Grayscale, Hue Saturation Intensity (HSI), and L*a*b* features. The texture parameters consist of energy, entropy, homogeneity, and contrast. As for the feature shape parameters, they are area, circumference, diameter, and percentage of roundness. Results of the analysis show that the main parameter that plays an important role in assessing the quality of roasting coffee is the color parameter. This can be seen from the function of the color parameter in quality identification based on the image of the roasted coffee beans. The quality parameters used are image capture, image resolution, training data, testing data, iterations, and accuracy values. In addition, the resulting image processing methods used for quality classification include Backpropagation (BP), Learning Vector Quantization (LVQ), and K-Nearest Neighbor (KNN). Based on results of the analysis, the best method for classifying the quality of roasting results is Backpropagation, and it is known that the accuracy value of this method has a high range of values. [ABSTRACT FROM AUTHOR]

This document provides summaries of various research papers published in the journal Sensors. The summaries cover a wide range of topics, including personalized medicine, laser ranging, memristive interfaces, face and person detection, IoT data visualization, monitoring elderly activities, auditory illusions, brain connectivity, corona discharges, thermal data analysis, vital signal detection, and eye-blink artifact removal. The document showcases the contributions and findings of each study, offering valuable insights for researchers and library patrons interested in these areas of study. [Extracted from the article]

• Postoperative prognosis based on the expression level of ACTH for Cushing's disease with an accuracy of 0.83. • A computationally efficient single-linkage clustering algorithm for image analysis. • Novel medical image registration and classification methods on Cushing's disease for unsupervised image registration and supervised image classification. Due to the abnormal secretion of adreno-cortico-tropic-hormone (ACTH) by tumors, Cushing's disease leads to hypercortisonemia, a precursor to a series of metabolic disorders and serious complications. Cushing's disease has high recurrence rate, short recurrence time and undiscovered recurrence reason after surgical resection. Qualitative or quantitative automatic image analysis of histology images can potentially in providing insights into Cushing's disease, but still no software has been available to the best of our knowledge. In this study, we propose a quantitative image analysis-based pipeline CRCS, which aims to explore the relationship between the expression level of ACTH in normal cell tissues adjacent to tumor cells and the postoperative prognosis of patients. CRCS mainly consists of image-level clustering, cluster-level multi-modal image registration, patch-level image classification and pixel-level image segmentation on the whole slide imaging (WSI). On both image registration and classification tasks, our method CRCS achieves state-of-the-art performance compared to recently published methods on our collected benchmark dataset. In addition, CRCS achieves an accuracy of 0.83 for postoperative prognosis of 12 cases. CRCS demonstrates great potential for instrumenting automatic diagnosis and treatment for Cushing's disease. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power generation has attracted widespread interest as a clean and sustainable energy source, with increasing global attention given to renewable energy. However, the operation and monitoring of PV power generation systems often result in large amounts of data containing missing values, outliers, and noise, posing challenges for data analysis and application. Therefore, PV data cleaning plays a crucial role in ensuring data quality, enhancing data availability and reliability. This study proposes a PV data cleaning method based on Rasterized Data Image Processing (RDIP) technology, which integrates rasterization and image processing techniques to select optimal contours and extract essential data. To validate the effectiveness of our method, we conducted comparative experiments using three data cleaning methods, including our RDIP algorithm, the Pearson correlation coefficient interpolation method, and cubic spline interpolation method. Subsequently, the cleaned datasets from these methods were utilized for power prediction using two linear regression models and two neural network models. The experimental results demonstrated that data cleaned using the RDIP algorithm improved the short-term forecast accuracy by approximately 1.0% and 3.7%, respectively, compared to the other two methods, indicating the feasibility and effectiveness of the RDIP approach. However, it is worth noting that the RDIP technique has limitations due to its reliance on integer parameters for grid division, potentially leading to coarse grid divisions. Future research efforts could focus on optimizing the selection of binarization thresholds to achieve better cleaning results and exploring other potential applications of RDIP in PV data analysis. [ABSTRACT FROM AUTHOR]

This study proposes a novel approach for detecting and recognizing asphalt pavement cracks. Image processing techniques including discrete Fourier transform (DFT), steerable filter (SF), and integral projection (IP) are combined to extract features from pavement images. The new method employs DFT as a low-pass filter to enhance the original digital image. Accordingly, a crack salient map is constructed by the utilization of SF. Based on such salient map, IP of the image is established and its statistical properties are computed to characterize the condition of a pavement image. A soft computing model that integrates the artificial neural network, Levenberg–Marquardt (LM) backpropagation training, and Differential Flower Pollination (DFP) algorithm is put forward for the task of pavement recognition. The integration of LM and DFP aims at combining the global search capability of metaheuristic and the local search efficiency of the steepest descent-based backpropagation approach. During the optimization process of DFP, LM algorithm is performed with a randomly selected member of the population to boost the convergence rate and enhance the searching effectiveness. This new training framework of ANN is, therefore, named as ANN with Random Boosted Differential Flower Pollination (RBDFP-ANN). A data set of pavement images including four class labels (no crack, longitudinal crack, transverse crack, and alligator crack) has been collected to train and verify the RBDFP-ANN. Experimental results supported by statistical test confirm that the proposed model achieves a superior prediction accuracy rate (89.25%) over other benchmark methods. Accordingly, the RBDFP-ANN model is very potential to be used by transportation agencies in the task of asphalt pavement inspection. [ABSTRACT FROM AUTHOR]

Morphable polishing tools are capable of finishing diamond turned surfaces with roughness in the nanometre range. The material removal rate of morphable tools polishing is challenging to calculate due to the labyrinth and dimple textures. This paper introduces a multi-scale theoretical model for predicting the material removal rate of morphable polishing tools. The model includes the polishing velocity, polishing pressure, material removal by an abrasive, and number of effective abrasives. First, polishing velocity is obtained by tool kinematics analysis. Second, polishing pressure is obtained based on image processing. Theoretical polishing pressure is calculated by polishing pressure equations. Morphable tool texture images are binarized and conducted with Boolean product of the theoretical polishing pressure images to get the morphable tool polishing pressure. The morphable tool polishing pressure and 12-image averaged pressure are conducted with Boolean product of the polishing velocity to obtain the instant and average material removal rate. Then, the polishing pressure and material removal rate model are validated through pressure measurement and polishing tests, respectively. It is found that the polishing spot is ellipse shape with long axis (10.6 mm) and short axis (9.6 mm). The material removal rate for smooth tools along the short axis is almost constant while it steadily increased along the negative long axis. For labyrinth tools, the material removal rate along the short axis and the long axis is a trapezoidal shape and scalene triangular shape. For dimple tools, the material removal rate along the long axis and the short axis are similar to isosceles triangular shape. The model results agree well with the experimental data and quantify how morphable tool texture influences the polishing pressure component and influences the material removal rate, which makes the polishing process more predictive. The image processing–based method can be further applied to obtain polishing pressure and material removal rate of different complicated polishing tools. [ABSTRACT FROM AUTHOR]

This study presents a novel photodiode technology with augmented optoelectronic capabilities through the incorporation of quantum dots (QDs). The photodiode, composed of Indium Gallium Zinc Oxide‐Tellurium (IGZO‐Te), is engineered for enhanced light sensitivity and tailored optoelectronic interactions. The primary application of this advanced photodiode lies in the domain of artificial intelligence and machine learning, where it plays a pivotal role in simulating synaptic connections for image processing tasks. Due to the exceptional optical and electrical properties of the IGZO‐Te‐QDs active material combination, the photodiode demonstrates consistent and dependable optical synaptic functions linked to learning. This is achieved through conduction modulation and time duration modulation of light stimulus. In addition, the opto‐synaptic functionalities such as short‐term and long‐term memory has also been successfully demonstrated not only on single device but also on 6 × 6 photodiodes array device. These findings underscore the significant potential of the hybrid structure of IGZO‐Te with QDs in optical artificial neuromorphic chips. [ABSTRACT FROM AUTHOR]