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Modern bioimaging core facilities at research institutions are essential for managing and maintaining high‐end instruments, providing training and support for researchers in experimental design, image acquisition and data analysis. An important task for these facilities is the professional management of complex multidimensional bioimaging data, which are often produced in large quantity and very different file formats. This article details the process that led to successfully implementing the OME Remote Objects system (OMERO) for bioimage‐specific research data management (RDM) at the Core Facility Cellular Imaging (CFCI) at the Technische Universität Dresden (TU Dresden). Ensuring compliance with the FAIR (findable, accessible, interoperable, reusable) principles, we outline here the challenges that we faced in adapting data handling and storage to a new RDM system. These challenges included the introduction of a standardised group‐specific naming convention, metadata curation with tagging and Key–Value pairs, and integration of existing image processing workflows. By sharing our experiences, this article aims to provide insights and recommendations for both individual researchers and educational institutions intending to implement OMERO as a management system for bioimaging data. We showcase how tailored decisions and structured approaches lead to successful outcomes in RDM practices. [ABSTRACT FROM AUTHOR]

In the image analysis of the hydrophobic flocculation process of coal particles, the recognition accuracy of floc characteristic parameters is affected by bubbles, floc superposition, and floc morphology. An image processing method was studied and designed based on the PyCharm Community platform for clarity recognition and floc segmentation of coal particle floc characteristics. Firstly, the critical value of image clarity was determined to be 1.31 by using the Laplace convolution accounting method after the average gray level correction, and the fuzzy flocs whose clarity was less than the critical value were eliminated. Then, by comparing the segmentation effect of the threshold, edge, and region segmentation algorithm on the flocs, the histogram bimodal method was determined to be the best image processing segmentation method for fine coal floc collection. The fractal theory was used to verify the effect of image processing. The results showed that the surface fractal dimension of the floc image after deblurring and segmentation was maintained near 2.05, indicating that the processed image can obtain more stable and reliable floc characteristic parameters. [ABSTRACT FROM AUTHOR]

This study introduces a novel approach that addresses the limitations of existing methods by integrating 2D image processing with 3D point cloud analysis, enhanced by interpretable neural networks. Unlike traditional methods that rely on either 2D or 3D data alone, our approach leverages the complementary strengths of both data types to improve detection accuracy in environments adversely affected by welding spatter and smoke. Our system employs an improved Faster R-CNN model with a ResNet50 backbone for 2D image analysis, coupled with an innovative orthogonal plane intersection line extraction algorithm for 3D point cloud processing. By incorporating explainable components such as visualizable feature maps and a transparent region proposal network, we address the "black box" issue common in deep learning models.This architecture enables a more transparent decision-making process, providing technicians with necessary insights to understand and trust the system's outputs. The Faster-RCNN structure is designed to break down the object detection process into distinct, understandable steps, from initial feature extraction to final bounding box refinement. This fusion of 2D-3D data analysis and interpretability not only improves detection performance but also sets a new standard for transparency and reliability in automated welding systems, facilitating wider adoption in industrial applications. [ABSTRACT FROM AUTHOR]

Sandwich structures are known for their exceptional mechanical properties and widespread applications. This study integrates additive manufacturing (AM) technology, experiments, theoretical equations, and numerical simulations to investigate a novel lightweight sandwich structure with a core based on Triply Periodic Minimal Surfaces (TPMS). The core incorporates Primitive, Neovius, and Diamond structures and is designed and manufactured using AM. Three‐point bending experiments are conducted to assess bending performance, failure modes, and energy absorption capability. A numerical model is established to analyze behavior under bending loads, validated through comparison with theoretical equations and numerical simulations, focusing on stress concentration areas. A parametric study investigates the effects of skin material, thickness, and TPMS core relative density on bending performance and energy absorption capacity. Furthermore, a functionally graded core layer with a gradient change in relative density is designed to study its influence on bending performance. The parameters of the core have a decisive impact on TPMS sandwich panel performance. This study demonstrates that sandwich structures with TPMS cores can be designed to possess desirable bending performance and energy absorption capacity, providing insights for future engineering applications. Highlights: A novel TPMS sandwich structure is fabricated using 3D printing technology.The gradient relative density core is introduced.The bending performance and failure behavior of the structure are evaluated.A parametric study was conducted to investigate the effects on performance. [ABSTRACT FROM AUTHOR]

Deep learning applications have witnessed significant advancements across diverse domains, revolutionizing tasks such as image recognition, disease classification, cancer detection, natural language processing, and autonomous decision-making. However, striking a balance between performance and cost remains a fundamental challenge. Performance, which encompasses accuracy, efficiency, and effectiveness, is crucial for the real-world applicability of deep learning models. Attaining high performance often involves using resource-intensive models with intricate architectures, extensive datasets, and hyperparameter tuning. While these factors lead to improved performance, they also result in increased costs, including computational resources, time, and energy consumption. This research addresses this challenge by proposing a novel low-cost deep learning model based on Sharpened Cosine Similarity (LC-SCS) for breast cancer classification using thermal images. In our study on a DMR-IR dataset, we also utilized pre-trained models: ResNet-101, VGG-16, Inception-V3, ResNet-50, VGG-19, and Xception. The proposed LC-SCS model achieved an impressive accuracy of 94%, trailing just 4% behind the leading VGG-19 model, while maintaining a low computational cost. It achieved recall and precision scores of 0.95 each, with an F1-score of 0.94. The LC-SCS model excels in computational efficiency, demanding only 1.85 GFLOPs and 1.2 million parameters. The memory requirement is minimal at 865.35 MB, and it exhibits a latency of just 0.003 s. Additionally, the CPU execution time for prediction is 3.59 s. Comparatively, the best-performing pre-trained VGG-19 model achieved 98% accuracy but incurred significantly higher costs. The proposed LC-SCS model showcases a remarkable 298.63 times reduction in GFLOPs, a 116.31 times reduction in total parameters, a 4.57 times reduction in memory requirements for model weights, a 5 times reduction in latency, and a 3.33 times reduction in CPU execution time for prediction, thus making it as an exceptionally resource-efficient model, particularly in scenarios with limited computational resources. [ABSTRACT FROM AUTHOR]

Automatic skin lesion subtyping is a crucial step for diagnosing and treating skin cancer and acts as a first level diagnostic aid for medical experts. Although, in general, deep learning is very effective in image processing tasks, there are notable areas of the processing pipeline in the dermoscopic image regime that can benefit from refinement. Our work identifies two such areas for improvement. First, most benchmark dermoscopic datasets for skin cancers and lesions are highly imbalanced due to the relative rarity and commonality in the occurrence of specific lesion types. Deep learning methods tend to exhibit biased performance in favor of the majority classes with such datasets, leading to poor generalization. Second, dermoscopic images can be associated with irrelevant information in the form of skin color, hair, veins, etc.; hence, limiting the information available to a neural network by retaining only relevant portions of an input image has been successful in prompting the network towards learning task-relevant features and thereby improving its performance. Hence, this research work augments the skin lesion characterization pipeline in the following ways. First, it balances the dataset to overcome sample size biases. Two balancing methods, synthetic minority oversampling TEchnique (SMOTE) and Reweighting, are applied, compared, and analyzed. Second, a lesion segmentation stage is introduced before classification, in addition to a preprocessing stage, to retain only the region of interest. A baseline segmentation approach based on Bi-Directional ConvLSTM U-Net is improved using conditional adversarial training for enhanced segmentation performance. Finally, the classification stage is implemented using EfficientNets, where the B2 variant is used to benchmark and choose between the balancing and segmentation techniques, and the architecture is then scaled through to B7 to analyze the performance boost in lesion classification. From these experiments, we find that the pipeline that balances using SMOTE and uses the adversarially trained segmentation network achieves the best baseline performance of 91% classification accuracy with EfficientNet B2. Based on the scaling experiments, we find that optimal performance is reached with the B6 architecture that classifies with a 97% accuracy. Furthermore, the proposed pipeline for lesion characterization outperforms the state of the art performance on the ISIC dataset. [ABSTRACT FROM AUTHOR]

The image object recognition and detection technology are widely used in many scenarios. In recent years, big data has become increasingly abundant, and big data-driven artificial intelligence models have attracted more and more attention. Evolutionary computation has also provided a powerful driving force for the optimization and improvement of deep learning models. In this paper, we propose an image object detection method based on self-supervised and data-driven learning. Differ from other methods, our approach stands out due to its innovative use of multispectral data fusion and evolutionary computation for model optimization. Specifically, our method uniquely combines visible light images and infrared images to detect and identify image targets. Firstly, we utilize a self-supervised learning method and the AutoEncoder model to perform high-dimensional feature extraction on the two types of images. Secondly, we fuse the extracted features from the visible light and infrared images to detect and identify objects. Thirdly, we introduce a model parameter optimization method using evolutionary learning algorithms to enhance model performance. Validation on public datasets shows that our method achieves comparable or superior performance to existing methods. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) are popular in image processing but are vulnerable to adversarial attacks, which makes their deployment in security-sensitive systems risky. Adversarial attacks reduce the performance of DNNs by generating adversarial examples (AEs). In this paper, we propose a novel method called Trans-IFFT-FGSM (Transformer Inverse Finite Fourier Transform Fast Gradient Sign Method) to generate adversarial examples. Unlike others, we apply multiple steps, adding imperceptible perturbation and saving input noise information to create strong AEs, while emphasizing simplicity, efficiency, robustness through iterations, and analytical precision on specific models. We evaluate and compare perturbation generated by Trans-IFFT-FGSM and other attack methods, including FGSM, PGD, DeepFool, and C &W on ImageNet and MNIST, and evaluation results suggest that Trans-IFFT-FGSM achieves a high attack success rate (ASR) and attack accuracy. In addition, we compare Trans-IFFT-FGSM and other attack methods under the existence of a defense method, which denoises the AEs generated by these methods, and the evaluation results also suggest Trans-IFFT-FGSM outperforms other methods. [ABSTRACT FROM AUTHOR]

The field of computer vision has witnessed significant interest in the area of salient object recognition. The utilization of this technology becomes advantageous in various applications, such as image segmentation, image attention retargeting, image cropping, and image understanding. The primary challenges encountered by underwater images pertain to diminished contrast, distorted colors, and an overall suboptimal visual appearance. The present study introduces an innovative and resilient method for detecting saliency in underwater photos. The RGB picture input undergoes enhancement and is subsequently subjected to gradient domain filtration. The filtered image is subjected to a pyramid decomposition. The computation of saliency is performed on three distinct scales using a process consisting of two parallel phases. In the first stage, saliency is calculated by employing cellular automata at various scales. Initially, the filtered image is subjected to a decomposition process into superpixels. Subsequently, the k-means clustering technique is employed to calculate matrices that represent color dissimilarity and geodesic distance. The application of cellular automata follows the fusion of the matrices. The map undergoes additional optimization and filtering processes in order to achieve saliency at a specific scale. The computation of a main saliency map involves the process of scale integration. In the second stage, the computation of saliency is achieved by applying a homomorphic filter to each scale. The natural logarithm of the filtered image is computed. The spatial domain of the image undergoes a transformation to the frequency domain. The Butterworth high-pass filter is utilized. The frequency domain of the image is subsequently converted back to its spatial domain. The computation of the inverse logarithm of the image is performed. The guided filter is employed to acquire saliency on a specific scale. The computation of the secondary saliency map involves the process of scale integration. The final output is obtained by applying multiplicative fusion to both primary and secondary saliency maps. Utilizing cutting-edge methodologies, a thorough evaluation that includes both qualitative and quantitative analyses evaluates the effectiveness of the suggested strategy. In comparison to alternative state-of-the-art methodologies, the findings indicate that the suggested methodology exhibits high levels of precision and dependability. [ABSTRACT FROM AUTHOR]

In order to solve the problems of IspecHyper multi-rotor UAV hyperspectral imaging system, such as lack of data processing supporting software, large error in collecting multi-band hyperspectral data, lack of coordinates, and inability to automatically stitch. Taking the citrus planting base of Jiaolong Village, Taiping Town, Wuming District as the research area, this paper studies the hyperspectral image processing method of IspecHyper multi-rotor UAV. Firstly, the IspecHyper-VM200 imaging system was used to obtain high-definition photos and multi-band hyperspectral image data of the study area. Secondly, using high-definition photos as data sources, high-resolution UAV orthophotos were formed by PXI4D Mapper software preprocessing and ENVI software image geometric correction. Finally, the ENVI software was used to cut the distorted boundary data of multi-band hyperspectral images, and the geometric correction was completed based on the UAV orthophoto image, and then the hyperspectral reflectance image product was formed through image mosaic and spectral conversion calculation. The experimental results show that the technical method formed in this paper can effectively solve the problems of hyperspectral image processing of IspecHyper multi-rotor UAV, and provide technical reference for hyperspectral image processing of UAV. [ABSTRACT FROM AUTHOR]

Public safety is a critical concern, typically addressed through security checks at entrances of public places, involving trained officers or X-ray scanning machines to detect prohibited items. However, many places like hospitals, schools, and event centres lack such resources, risking security breaches. Even with X-ray scanners or manual checks, gaps can be exploited by individuals with malicious intent, posing significant security risks. Additionally, traditional methods, relying on manual inspections and conventional image processing techniques, are often inefficient and prone to high error rates. To mitigate these risks, we propose a real-time detection model – EnhanceNet using a customized Scale-Enhanced Pooling Network (SEP-Net) integrated into the YOLOv4. The innovative SEP-Net enhances feature representation and localization accuracy, significantly contributing to the model’s efficacy in detecting prohibited items. We annotated a custom dataset of nine classes and evaluated our models using different input sizes (608 and 416). The 608 input size achieved a mean Average Precision (mAP) of 74.10% with a detection speed of 22.3 Frames per Second (FPS). The 416 input size showed superior performance, achieving a mAP of 76.75% and a detection speed of 27.1 FPS. These demonstrate that our models are accurate and efficient, making them suitable for real-time applications. [ABSTRACT FROM AUTHOR]

In the field of image processing, common noise types include Gaussian noise, salt and pepper noise, speckle noise, uniform noise and pulse noise. Different types of noise require different denoising algorithms and techniques to maintain image quality and fidelity. Traditional image denoising methods not only remove image noise, but also result in the detail loss in the image. It cannot guarantee the clean removal of noise information while preserving the true signal of the image. To address the aforementioned issues, an image denoising method combining an improved threshold function and wavelet transform is proposed in the experiment. Unlike traditional threshold functions, the improved threshold function is a continuous function that can avoid the pseudo Gibbs effect after image denoising and improve image quality. During the process, the output image of the finite ridge wave transform is first combined with the wavelet transform to improve the denoising performance. Then, an improved threshold function is introduced to enhance the quality of the reconstructed image. In addition, to evaluate the performance of different algorithms, different densities of Gaussian noise are added to Lena images of black, white, and color in the experiment. The results showed that when adding 0.010.01 variance Gaussian noise to black and white images, the peak signal-to-noise ratio of the research method increased by 2.58dB in a positive direction. The mean square error decreased by 0.10dB. When using the algorithm for denoising, the research method had a minimum denoising time of only 13ms, which saved 9ms and 3ms compared to the hard threshold algorithm (Hard TA) and soft threshold algorithm (Soft TA), respectively. The research method exhibited higher stability, with an average similarity error fluctuating within 0.89%. The above results indicate that the research method has smaller errors and better system stability in image denoising. It can be applied in the field of digital image denoising, which can effectively promote the positive development of image denoising technology to a certain extent. [ABSTRACT FROM AUTHOR]

Stripe noise is a prevalent issue in infrared imaging systems, characterized by its distinctive directional features, which often appear as vertical lines across the image. This type of noise can significantly degrade the quality of the captured images, making it crucial to address and mitigate its effects. This paper presents an effective strategy to tackle this problem by transforming it from a 2D image issue into a 1D signal problem, enabling efficient resolution of stripes in infrared images. By understanding the characteristics of stripe noise, the proposed algorithm effectively solves the problem by first computing the column average of the noisy image, extracting stripe components from this one-dimensional signal, and effectively removing the stripes without blurring image details. This approach has been tested on numerous images with varying noise levels, demonstrating exceptional denoising performance compared to state-of-the-art methods. The results show marked improvements in visual quality, especially around edges and smooth areas, without requiring complex algorithms or iterative processes. [ABSTRACT FROM AUTHOR]

Introduction: Paediatric forearm fractures are a prevalent reason for medical consultation, often requiring diagnostic X-rays that present a risk due to ionising radiation, especially concerning given the sensitivity of children's tissues. This paper explores the efficacy of ultrasound imaging, particularly through the development of the SonoBox system, as a safer, non-ionising alternative. With emerging evidence supporting ultrasound as a viable method for fracture assessment, innovations like SonoBox will become increasingly important. Materials and methods: In our project, we want to advance ultrasound-based, contact-free, and automated cross-sectional imaging for diagnosing paediatric forearm fractures. To this end, we are building a technical platform that navigates a commercially available ultrasound probe around the extremity within a water-filled tank, utilising intelligent robot control and image processing methods to generate a comprehensive ultrasound tomogram. Safety and hygiene considerations, gender and diversity relevance, and the potential reduction of radiation exposure and examination pain are pivotal aspects of this endeavour. Results: Preliminary experiments have demonstrated the feasibility of rapidly generating ultrasound tomographies in a water bath, overcoming challenges such as water turbulence during probe movement. The SonoBox prototype has shown promising results in transmitting position data for ultrasound imaging, indicating potential for autonomous, accurate, and potentially painless fracture diagnosis. The project outlines further goals, including the construction of prototypes, validation through patient studies, and development of a hygiene concept for clinical application. Conclusion: The SonoBox project represents a significant step forward in paediatric fracture diagnostics, offering a safer, more comfortable alternative to traditional X-ray imaging. By automating the imaging process and removing the need for direct contact, SonoBox has the potential to improve clinical efficiency, reduce patient discomfort, and broaden the scope of ultrasound applications. Further research and development will focus on validating its effectiveness in clinical settings and exploring its utility in other medical and veterinary applications. [ABSTRACT FROM AUTHOR]

Black ice is responsible for dangerous road-related incidents that can cause collisions and harm vehicle drivers and pedestrians. Visual examination and weather forecasts are two standard traditional methods for detecting black ice on roads, but they are often inaccurate and may not deliver the vehicle driver with up-to-date information on road conditions. The evolution of Industry 4.0 enabling technologies such as wireless sensor network (WSN), Internet of Things (IoT), cloud computing, and machine learning (ML) has been capable of detecting events in real time. This study aims to analyse the integration of the WSN, IoT, ML, and image processing for black ice detection. The qualitative research method is followed in this study, where the problems of black ice detection are studied. Following this, the role of Industry 4.0 enabling technologies is analyzed in detail for black ice detection. According to the study, we can detect black ice using different methods, but some methods need to be refined if we talk about the prediction. By merging different technologies, we can improve the overall architecture and create an algorithm that works with images and physical variables like temperature, humidity, due point, and road temperature, which were responsible for black ice formation, and predict the chances of black ice formation by training the system. [ABSTRACT FROM AUTHOR]

AbstractIn this experimental study, we delve into the performance of an active mixer that utilizes liquid metal droplets. Our mixer configuration follows an innovative Y-type design, where two liquid metal-based pumping mechanisms operate within the minichannel inlet branches. By applying alternating current voltage to both sides of the liquid metal, we induce a surface tension gradient, propelling the fluid from the reservoirs toward the minichannel. Notably, the two entering liquids are distinctly colored, allowing us to quantify mixing using a Mixing Index (MI) definition and image processing techniques. Our investigation centers on four key variables governing mixer performance: applied voltage, liquid metal droplet diameter, input angle between the branches, and minichannel width. Given the experimental nature of our research, we employ a central composite design method within the framework of design of experiments (DoE). From our obtained experimental results, we establish correlations between MI and the other variables. Our examination of the four mentioned parameters revealed that liquid metal droplet diameter exerts the most significant influence on the mixing index. Following this, in descending order of impact, we find applied voltage, input angle, and channel width. In the optimal dimensions of our mixer, we achieve a remarkable maximum MI of 0.867. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the applicability of the high-resolution WaveFront Phase Imaging Sensor (WFPI) in eyes with Fuchs' Endothelial Corneal Dystrophy (FECD) through qualitative and quantitative analysis using a custom-designed Automatic Guttae Detection Method (AGDM). The ocular phase was measured using the t · eyede aberrometer and then was processed to obtain its High-Pass Filter Map (HPFM). The subjects were pathological and healthy patients from the Fundación Jiménez-Díaz Hospital (Madrid, Spain). The AGDM was developed and applied in pupils with 3 and 5 mm of diameter. A set of metrics were extracted and evaluated like the Root-Mean-Square error (RMS), Number of guttae, Guttae Area, and Area of Delaunay Triangulation (DT). Finally, a Support Vector Machine (SVM) model was trained to classify between pathological and healthy eyes. Quantitatively, the HPFM reveals a dark spots pattern according to the ophthalmologist's description of the slit-lamp examination of guttae distribution. There were significant statistical differences in all the metrics when FECD and Healthy groups were compared using the same pupil size; but comparing both pupil sizes for the same group there were significant differences in most of the variables. This sensor is a value tool to objectively diagnose and monitor this pathology through wavefront phase changes. [ABSTRACT FROM AUTHOR]

Reversible solid oxide cells (r -SOCs) are regarded as a solution form of clean energy generation towards a green hydrogen energy transition. However, during transportation, installation, and operation, they are required to withstand dynamic conditions as they face thermal, mechanical, and vibrational effects. Assessing the interplay of the thermomechanical and dynamical impacts on r-SOC materials during the mode-switch from electrolysis to fuel cell operation is the subject of this current comprehensive study. Advanced FEM-based homogenisation techniques have been harnessed in predicting and evaluating the detailed transients during static and dynamic loading. The intricate 3D thermal profiles throughout the transient reversible dual-modus operation from a typical r-SOC study have been derived using advanced image processing. Orthotropic material properties were incorporated into a unified macroscopic scale model by deriving the complex 3D microstructure geometry characteristics of the cell materials and component layers. The comprehensive thermomechanical and pre-stressed modal analysis results showed that the static and dynamic forces in fuel cell mode are more pronounced, whereas, in electrolysis operation, the dynamical behaviour highly depends on geometrical and operating conditions. Smaller component size shows higher natural frequencies and more intense vibration, leading to higher deformation attributed to complex mode shapes and reduced flexural rigidity. These insights are essential for optimizing r-SOC design parameters, enhancing their dynamic and structural reliability throughout their dual-mode operation lifespan. • Influence of cell size and thickness on static and dynamic r-SOC behaviour. • 3D physical r-SOC microstructure model generation and virtually characterisation. • Thermal field derivation using data recognition and image processing techniques. • 3D thermomechanical stresses, natural frequencies, and mode shapes are determined. • Comparison of thermomechanical and dynamic effects on reversible operation. [ABSTRACT FROM AUTHOR]

The need for effective and precise detection technologies to lessen the impact of pests on agricultural output is a global concern. This research introduces a new method for detecting pests using image processing techniques and artificial neural networks (ANNs). A strong solution that can identify pests in different agricultural contexts is offered by our suggested technique, which uses learning and image analysis. Artificial neural networks (ANNs) allow for the training of specialised models that can identify different types of pests using visual features collected from digital photos. In addition, picture preparation methods are used to boost model performance and feature extraction. Extensive studies on real-world datasets reveal that the suggested method outperforms standard pest detection approaches in terms of efficiency and accuracy, proving its efficacy. A dependable and automated approach for early pest identification is offered by this study, which advances the area of precision agriculture. This allows for earlier intervention and minimises crop loss. We use artificial neural networks (ANN) because of their capacity to learn complicated patterns straight from picture data; this method is based on the latest developments in deep learning as well as image processing. Additionally, input pictures are preprocessed using feature extraction methods such morphological operations and histogram equalisation to successfully decrease noise and improve the recovered features' discriminative strength. In order to test how well our method works, we use a wide range of datasets that include different types of pests and their environments. Achieving excellent accuracy rates in pest identification tasks, the results show that our technology is resilient and scalable across multiple agricultural situations. Additionally, we contrast our technique with more conventional ways to pest identification, such as chemical-based methods and human inspection, to demonstrate the benefits of automation and efficiency that our system provides. Our research highlights the incredible power of AI and image processing to transform pest control approaches. This might lead to more resilient and sustainable agricultural systems that can withstand new threats. [ABSTRACT FROM AUTHOR]

In recent times, geological remote sensing has greatly enhanced the ability to access alteration zones and identify potential sites for hydrothermal deposits quickly and cost-effectively. This study utilizes satellite image processing methods to map the alteration zones in the exploration area of the Zafarghand. The study area is located in the NE of Isfahan and falls within the central structural zone of Iran. The Zafarghand porphyry system exhibits phyllic, potassic, propylitic, and argillic alteration halos. Alterations in this study were detected using ASTER sensor imagery. Each pixel's digital number value from the satellite images, organized in a matrix, serves as a sample in a systematic grid. The singularity method algorithm was then applied as an effective structural tool to identify geochemical anomalies in the digital pixel values from ASTER images. The findings demonstrate that the singularity method, due to its structural attributes, has been successful in decision-making and highly effective on determining promising areas in the study area, especially for propylitic and phyllic alterations. [ABSTRACT FROM AUTHOR]

Antimicrobial Susceptibility Test (AST) is a highly effective diagnostic method for determining the most suitable antibiotic and its optimal dosage. Conventional AST methods, such as the Disc diffusion test, are cost-effective but require 48–72 h to obtain results. On the other hand, advanced technological methods like PCR provide results in a much shorter time, approximately 6 h, but are more expensive and require a laboratory environment. This work presents the development of a cost-effective Automated AST smart system. The system incorporates automation through the utilization of IoT, image processing, and Deep Learning algorithms to effectively categorize the results. A semantic segmentation model was developed using CNN dilated convolutions for the automatic identification of inhibition zones on agar plates. The typical computation time on the model is 45 s. When evaluating the accuracy of segmentation performance, we take into account metrics such as F1 score, IoU, Mean accuracy, Weighted IoU, and BF score. The obtained results for Mean accuracy is 96.79, Weighted IoU is 0.9331, and BF score is 0.6944. It is possible to remotely monitor inhibition zones using the suggested approach. Periodic observations are made of the zones, and their susceptibility categorization is calculated every 15 min. Using this advanced technique, it is possible to test three samples simultaneously, with the ability to test each sample against a maximum of 12 antibiotics. With the suggested automation setup, test time is significantly reduced to 4–6 h, resulting in a more cost-effective solution. [ABSTRACT FROM AUTHOR]

Pistachio nuts are a type of nut that is widely consumed around the world due to their high nutritional value and pleasant taste. Pistachios are usually sold in their shells, either open or closed. However, closed-shell pistachios are not well received by consumers, resulting in a lower commercial value. It is essential to be able to distinguish between open and closed pistachio shells in order to ensure quality control during production processes and processing. This can be done manually or by using mechanical devices. Manual inspection and categorization of pistachio nuts have traditionally been done by workers, but this process is inefficient in terms of time and money. Mechanical separation of open and closed-shell pistachio can damage the kernels of open-shell nuts due to the needle mechanism used in the sorting process. This study aims to classify pistachio nuts using a machine visionbased system and evaluate its applicability in terms of classification accuracy. The system is evaluated on the Antep pistachio species, which can be distinguished from other pistachio varieties, such as Siirt and Urfa pistachios, based on their shape, size, and taste properties. The machine vision system in this study classifies pistachio nuts into closed and open shell classes in a completely automated manner. In this study, 1,000 Antep pistachio nuts images were obtained and examined, including 500 open and 500 closed nuts. The images were pre-processed and prepared for feature extraction. From the images, a total of 14 color features were extracted. Although the single feature was used, promising classification accuracy rates of 95.6%, 94.8%, and 93.6% from the Random Forest, Support Vector Machine (SVM), and Logistic Regression were achieved, respectively. The performances of classifiers were compared to each other. Almost similar performances were detected. These results demonstrate that the Random Forest classifier is the most effective algorithm for classifying open and closed Antep pistachio nuts. [ABSTRACT FROM AUTHOR]

【Objective】Traditional methods for detecting potato pollen viability rely on visual counting, which can be inefficient and inaccurate. In this study, a method for quickly detecting pollen viability was proposed based on PaddlePaddle deep learning framework by comparing different models.【Method】First, the pollens were stained with 2,3,5-triphenyltetrazolium chloride（TTC）and imaged using a microscope. The images were annotated by Photoshop（PS）. Viable and total pollens were labeled respectively, then the label images were converted into single-channel images. Three models, SegFormer, U-Net and DeepLabV3, were used for training to distinguish viable pollens and total pollens. Finally, a Python OpenCV program was used to count the pollen number and calculate pollen viability.【Result】Compared with other models, SegFormer demonstrated the best performance in various evaluation indexes of the two datasets. Compared with manual recognition, the OpenCV program enabled fast and batch counting with less error.【Conclusion】Potato pollen viability can be detected quickly and accurately by image processing technology. This method was used to quickly identify the pollen viability of 200 F2 individuals, providing a soild foundation for the collection of pollen viability in potato. [ABSTRACT FROM AUTHOR]

The adoption of automated methods for the identification and assessment of tomato-related disorders is highly sought-after in the agriculture sector. Using this technology is crucial for reducing wasteful spending, increasing the efficiency of treatments, and ultimately growing more resilient crops by reducing losses in agricultural output and maximising the effectiveness of these processes. An automated method has been suggested for accurately identifying and classifying diseases using a single photograph. The described method for disease detection in tomato plants makes use of a computer vision-based technique. Image processing, ML, and deep learning are just a few of the methods that this strategy uses. The goal of this approach is to prevent tomato crops from being damaged by various illnesses by reducing the need of conventional procedures. Bacterial spot, early blight, late blight, leaf mould, spider mites, target spot, spotted spider mite, mosaic virus, and yellow leaf curl are all examples of these illnesses. The following ten diseases frequently strike tomato crops in India. By utilising picture segmentation in combination with the Enhanced OPTICS algorithm (EOPTICSA), the affected area of the tomato plant may be precisely detected and defined after image pre-processing procedures have been used. It may be necessary to look for certain visual signs in order to diagnose the previously mentioned illnesses. The primary goal of this study was to evaluate the efficacy of the EOPTICSA method for detecting diseases in plant leaves. To eliminate the geometric features associated with colour, texture, and leaf arrangement in the provided plant pictures, image segmentation and edge detection methods are employed. Using these methods allows us to achieve our goal. Various efficacy measures are used to assess and provide a technique recommendation. This research shows that when performance metrics are used to implement these strategies, the suggested strategy outperforms the current methods in terms of accuracy, precision, and F1-score. The process of detecting sickness involves several consecutive steps. Capturing images, segmenting them, detecting edges, and determining the infection's severity are all steps in this process. To accomplish the goal of recognising and categorising different types of diseases that might impact tomato plants, the method of transfer learning is employed. As soon as the problem is identified, it is recommended to take proactive measures to help individuals and organisations involved in agriculture address the effects of these disorders using appropriate measures. [ABSTRACT FROM AUTHOR]

With the advantages of parallel processing, high‐speed operation, and ultra‐low power consumption, optical computing presents immense potential in this age of information explosion. As a fundamental optical computing component, optical differentiator has extensive applications, particularly excelling in the real‐time processing and recognition of high‐frame‐rate image information. An ideal optical differentiator needs to adapt to different application scenarios, such as arbitrary operating wavelengths, complex or even transparent targets. Furthermore, performing differential operations of varying orders on target optical field can extract multi‐level information. Unfortunately, most current differential computing devices can only meet part of the requirements, which limits the attained information dimensions and hinders the development of their practical application. Herein, a universal platform based on liquid crystals (LCs) is proposed to achieve 0th‐, 1st‐, and 2nd‐order differential operations of amplitude‐typed or even phase‐typed objects within a broadband range. The switching of different differentiation orders can be accomplished by straightforward adjusting the applied voltages of the differentiator. This approach is capable of extracting more feature information including bright‐field, outline, and edges images of versatile targets. With advanced characteristics of multi‐dimension, broadband and multifunction, the work can flourish applications in fields of image processing, biomedical imaging and optical analog computation, etc. [ABSTRACT FROM AUTHOR]

Multidimensional multiplexing technologies have been proven to be a promising scheme to meet the demands of high‐capacity optical interconnects and optical processing networks. However, challenges remain to realize multidimensional hybrid multiplexing data transmission and signal processing between few‐mode fiber transmission links and on‐chip optical processing networks, since the optical coupling between fiber and chip is almost exclusively in the single‐mode regime. Here, a multidimensional fiber‐to‐chip optical processing system is proposed and demonstrated for hybrid wavelength‐, mode‐, and polarization‐division multiplexing signals, where multidimensional coupling between few‐mode fiber transmission link and on‐chip multi‐mode processing network is realized by using a 3D photonic integrated (de)multiplexers on a glass chip as well as 2D photonic integrated (de)multiplexers on a silicon chip, and the on‐chip multidimensional optical processing network composed by parallel cascaded micro‐ring resonator array performs a function of reconfigurable optical add/drop multiplexer. By operating wavelength‐division multiplexing in conjunction with mode‐division multiplexing and polarization‐division multiplexing, the communication bandwidth of fiber‐to‐chip optical processing systems can be further scaled. [ABSTRACT FROM AUTHOR]

This article highlights the expanding issue of e-waste caused by the accessibility and widespread utilisation of electronics. Because precious metals in e-waste have high value, it is vital to recycle them while minimising their loss. Batteries in e-waste are identified and located using image processing techniques, such as semantic segmentation, which categorizes each pixel in an image. The article describes a modified U-Net Convolutional Neural Network approach with pre-processing procedures to assure clean raw photos for image segmentation of the battery component. Key matrices were used to analyse the output of three distinct CNN models with loss functions. The study comes to the conclusion that the improved model for battery segmentation of X-ray images is the modified U-Net with dice coefficient. The development of more efficient e-waste recycling methods with the help of this research could lead to a more sustainable future. [ABSTRACT FROM AUTHOR]

Background: Focusing on the complicated pathological features, such as blurred boundaries, severe scale differences between symptoms, and background noise interference, we aim to enhance the reliability of multiple lesions joint segmentation from medical images. Purpose: Propose a novel reliable multi‐scale wavelet‐enhanced transformer network, which can provide accurate segmentation results with reliability assessment. Methods: Focusing on enhancing the model's capability to capture intricate pathological features in medical images, this work introduces a novel segmentation backbone. The backbone integrates a wavelet‐enhanced feature extractor network and incorporates a multi‐scale transformer module developed within the scope of this work. Simultaneously, to enhance the reliability of segmentation outcomes, a novel uncertainty segmentation head is proposed. This segmentation head is rooted in the SL, contributing to the generation of final segmentation results along with an associated overall uncertainty evaluation score map. Results: Comprehensive experiments are conducted on the public database of AI‐Challenge 2018 for retinal edema lesions segmentation and the segmentation of Thoracic Organs at Risk in CT images. The experimental results highlight the superior segmentation accuracy and heightened reliability achieved by the proposed method in comparison to other state‐of‐the‐art segmentation approaches. Conclusions: Unlike previous segmentation methods, the proposed approach can produce reliable segmentation results with an estimated uncertainty and higher accuracy, enhancing the overall reliability of the model. The code will be release on https://github.com/LooKing9218/ReMultiSeg. [ABSTRACT FROM AUTHOR]

Surgical robotics application in the field of minimally invasive surgery has developed rapidly and has been attracting increasingly more research attention in recent years. A common consensus has been reached that surgical procedures are to become less traumatic and with the implementation of more intelligence and higher autonomy, which is a serious challenge faced by the environmental sensing capabilities of robotic systems. One of the main sources of environmental information for robots are images, which are the basis of robot vision. In this review article, we divide clinical image into direct and indirect based on the object of information acquisition, and into continuous, intermittent continuous, and discontinuous according to the target-tracking frequency. The characteristics and applications of the existing surgical robots in each category are introduced based on these two dimensions. Our purpose in conducting this review was to analyze, summarize, and discuss the current evidence on the general rules on the application of image technologies for medical purposes. Our analysis gives insight and provides guidance conducive to the development of more advanced surgical robotics systems in the future. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) refers to a diabetes complexity that immensely impacts the eyes. This is classified into 5 various stages of the severity in accordance with the international convention. Despite that, optimization of a grading model to have a robust generalizability needs a huge number of balanced training data that is very complicated to gather, especially for greater levels of severity. A vast amount of medical data is complex and has a very high-priced method which requires cooperation between the clinics and researchers. The issue is usually attempted to be figured out with the usage of the traditional methods of data augmentation by making certain changes to images of retina dataset for instance rotation, cropping, size and zooming. In this suggested paper, the latest methods or techniques of data augmentation is exhibited which is called as deep convolutional generative adversial network (DC-GAN) and variational auto encoders (VAE). This is a particular method which is responsible for the production of artificial medical images. In addition to this, to improve DR, we can also take the aid of the classification models which are resnet50, densenet201, InceptionV3 and VGG19 for the purpose of classification of the eye related diseases. The proposed method is depicted on the Asia Pacific Tele-Ophthalmology Society (APTOS)-Blindness dataset. First, the present-day online data augmentation techniques have been utilized, and the artificial images of retina are produced by the ease of DCGAN. Then, a method of classifying is used for both techniques. Ultimately, after the method training which is done by using the real & synthetic clinical images and the outcome exhibits which the proposed model determines every stage or phase of DR and achieve the accuracy of 98.66% with using of ResNet-50 which is contrary to the current existing techniques. [ABSTRACT FROM AUTHOR]

Medical imaging fusion is the process of combining pictures from various imaging modalities to create a single image that may be used in clinical settings. Robust methods for merging image data from several modalities are being developed in the field of multimodal medical imaging. Deep learning (DL) has been widely researched in two areas: pattern recognition and image processing. We will demonstrate a multimodal image fusion with DL implementation that considers the characteristics of medical diagnostic imaging as well as the demands of clinical practice. For the past three years, pixel-level picture fusion has been a hot topic. This paper proposes a new multimodal medical picture fusion technique for a wide range of medical diagnostic challenges. Image fusion is crucial in biomedical research and clinical diagnostics for biomedical image processing and therapy planning. The most convincing argument for fusion is obtaining a significant amount of critical information from the input photographs. We show how a well-organized multimodal medical image fusion technique can be utilized to integrate computed tomography (CT) and magnetic resonance imaging (MRI) data in this study. Using convolutional neural networks (CNNs), the quantum-behaved particle swarm optimization (QPSO) algorithm was used to create a method for integrating multimodal medical pictures. In order to improve the overall quality and efficiency of QPSO, it was chosen to add the metrics of image entropy, standard deviation, average gradient (AG), spatial frequency (SF), and visual information fidelity (VIF). In experiments, multimodal medical images are utilized to evaluate a variety of parameters, including performance and algorithm stability. When compared to the other possibilities, the recommended technique outperformed them in the evaluations. On a range of quantitative metrics, this method outperforms the alternatives. [ABSTRACT FROM AUTHOR]

Indonesia is currently struggling with stunting, wasting, obesity, and micronutrient deficiencies. Nutritional imbalance, which varies by gender, age, and activity level, contributes to these issues. This research developed a food detector with an innovative camera sensor to accurately measure food calories. The study adopted a developmental approach, using a pre-experimental design and "post-test only" research. The study included various food ingredients with calorie counts. Camera sensors were used instead of load cell sensors to weigh food and convert it to calories. Camera sensory testing was done on processed food samples. A number of food image data tests showed high accuracy. With test results close to real data, the device showed promising accuracy. This model system used camera resolution to detect calories, helping people measure and manage their diets in quality and quantity. We hope this technology will continue to improve, making it more accessible and aiding nutritional management. [ABSTRACT FROM AUTHOR]

Introduction: Central venous catheter (CVC) tip placement guided by chest X‐ray (CXR) landmarks is currently prone to inconsistency and malpositioning. This study aims to better define the relationship between the cavoatrial junction (CAJ) and selected X‐ray landmarks. Methods: Chest CTs of 100 patients were retrospectively assessed. CT images were converted to a 'virtual CXR' using a digital workstation, enabling simultaneous localisation of the CAJ and evaluation of CXR landmarks. Vertical distances between the CAJ and selected landmarks were measured for each patient. Measurements were assessed for correlation with age and compared between age groups and sexes. Results: The mean vertical distance of the following landmarks above the CAJ was found: the carina (46.2 mm), the intersection of the bronchus intermedius and the right heart border (7.6 mm) and the superior inflection of the right heart border (Sup‐RHB) (13.0 mm). The maximum lateral bulge of the right heart border (Lat‐RHB) was 18.4 mm below the CAJ. A new landmark: the mid‐superior right heart border, defined as the mid‐point between the Sup‐RHB and Lat‐RHB, was the closest to the CAJ, lying 2.6 mm below the CAJ. Conclusion: We propose that the CVC tip can be placed at the mid‐superior right heart border landmark. [ABSTRACT FROM AUTHOR]

Significance: Minimally invasive surgery (MIS) has shown vast improvement over open surgery by reducing post-operative stays, intraoperative blood loss, and infection rates. However, in spite of these improvements, there are still prevalent issues surrounding MIS that may be addressed through hyperspectral imaging (HSI). We present a laparoscopic HSI system to further advance the field of MIS. Aim: We present an imaging system that integrates high-speed HSI technology with a clinical laparoscopic setup and validate the system's accuracy and functionality. Different configurations that cover the visible (VIS) to near-infrared (NIR) range of electromagnetism are assessed by gauging the spectral fidelity and spatial resolution of each hyperspectral camera. Approach: Standard Spectralon reflectance tiles were used to provide ground truth spectral footprints to compare with those acquired by our system using the root mean squared error (RMSE). Demosaicing techniques were investigated and used to measure and improve spatial resolution, which was assessed with a USAF resolution test target. A perception-based image quality evaluator was used to assess the demosaicing techniques we developed. Two configurations of the system were developed for evaluation. The functionality of the system was investigated in a phantom study and by imaging ex vivo tissues. Results: Multiple configurations of our system were tested, each covering different spectral ranges, including VIS (460 to 600 nm), red/NIR (RNIR) (610 to 850 nm), and NIR (665 to 950 nm). Each configuration is capable of achieving real-time imaging speeds of up to 20 frames per second. RMSE values of 3.51 ± 2.03%, 3.43 ± 0.84%, and 3.47%were achieved for the VIS, RNIR, and NIR systems, respectively. We obtained sub-millimeter resolution using our demosaicing techniques. Conclusions: We developed and validated a high-speed hyperspectral laparoscopic imaging system. The HSI system can be used as an intraoperative imaging tool for tissue classification during laparoscopic surgery. [ABSTRACT FROM AUTHOR]

Quantum-dot cellular automata (QCA) are one of the most promising alternatives to traditional VLSI technology despite significant current obstacles. The QCA has the advantages of very low power dissipation, faster switching speed, and extremely low circuit area, which can be used in designing nano-scale image processing circuits. Morphological operations and processing of digital image processing is a significant topic for researchers because it is widely used for analyzing, enhancing, and modifying images to extract meaningful information or improve their visual quality. Image processing is also used for image retrieval and enhancement, image compression, object recognition, machine vision, and medical applications. QCA technology, as a new and leading technology with great potential, can play a fundamental role in morphological operations, processing digital images, image editing, medical imaging, facial recognition, and autonomous vehicles. In recent years, researchers in this field have presented many circuits, but they have many flaws in terms of speed, accuracy, and area consumption, and the need to create more efficient circuits is felt more than ever. Therefore, in this article, a new design for morphological operations and processing digital images is presented using QCA technology. This paper presents a new efficient QCA-based implementation of image processing based on the direct interactions between the QCA cells. This circuit uses two majority gates of five new inputs to produce the output and produces the desired output. In addition, a comparison and analysis of the area and clocking complexity, design cost, and energy dissipation through simulation using QCADesigner and QCADesigner-E are done. The results show that the presented circuit produces the expected and correct output results in 0.75 clock phases, and the obtained results show the high speed and low consumption space of the presented circuit. In addition, the presented circuit performs better than the previous best circuits in terms of quantum cost and delay, and according to the mentioned advantages, it can be used to improve and expand other circuits in image processing. [ABSTRACT FROM AUTHOR]

Background: Lung cancer is a major health concern globally, being the primary cause of cancer-related deaths. It accounts for approximately one–sixth of all cancer fatalities. Objective: The goal of this study is to develop an effective method for the early detection of lung tumors using computed tomography (CT) images. This method aims to identify lung tumors of various sizes and shapes, which is a significant challenge due to the variability in tumor characteristics. Methods: The research utilizes CT images of the lungs in sagittal view from the LID-IDRI database. To tackle the issue of tumor variability in size, shape, and number, the study proposes a novel image processing technique. This technique involves detecting tumor clusters using a weighted average-based automatic thresholding method. This method focuses on maximizing inter-class variance and is supplemented by further classification and segmentation processes. Results: The proposed image processing technique was tested on a dataset of 315 lung CT images. It demonstrated a high level of accuracy, achieving a 98.96% success rate in identifying lung tumors. Conclusion: The study introduces a highly effective method for the detection of lung tumors in CT images, irrespective of their size and shape. The technique's high accuracy rate suggests it could be a valuable tool in the early diagnosis of lung cancer, potentially leading to improved patient outcomes. [ABSTRACT FROM AUTHOR]

Corneal ulcer is a prevalent ocular condition that requires ophthalmologists to diagnose, assess, and monitor symptoms. During examination, ophthalmologists must identify the corneal ulcer area and evaluate its severity by manually comparing ocular staining images with severity indices. However, manual assessment is time-consuming and may provide inconsistent results. Variations can occur with repeated evaluations of the same images or with grading among different evaluators. To address this problem, we propose an automated corneal ulcer grading system for ocular staining images based on deep learning techniques and the Hough Circle Transform. The algorithm is structured into two components for cornea segmentation and corneal ulcer segmentation. Initially, we apply a deep learning method combined with the Hough Circle Transform to segment cornea areas. Subsequently, we develop the corneal ulcer segmentation model using deep learning methods. In this phase, the predicted cornea areas are utilized as masks for training the corneal ulcer segmentation models during the learning phase. Finally, this algorithm uses the results from these two components to determine two outputs: (1) the percentage of the ulcerated area on the cornea, and (2) the severity degree of the corneal ulcer based on the Type–Grade (TG) grading standard. These methodologies aim to enhance diagnostic efficiency across two key aspects: (1) ensuring consistency by delivering uniform and dependable results, and (2) enhancing robustness by effectively handling variations in eye size. In this research, our proposed method is evaluated using the SUSTech-SYSU public dataset, achieving an Intersection over Union of 89.23% for cornea segmentation and 82.94% for corneal ulcer segmentation, along with a Mean Absolute Error of 2.51% for determining the percentage of the ulcerated area on the cornea and an Accuracy of 86.15% for severity grading. [ABSTRACT FROM AUTHOR]

Most of the current optical image watermarking schemes are designed for robust watermarking, primarily aimed at protecting image copyrights. However, these robust optical watermarking schemes cannot determine if an image has been tampered with. To address this issue, we propose an optical fragile watermarking scheme based on single-pixel ptychography and variational image decomposition. In our proposed fragile watermarking scheme, the complex watermark image is encoded by the single-pixel ptychography system and embedded into the high-frequency detail component of the color host image obtained through variational image decomposition. Our proposed fragile watermarking scheme is highly sensitive to common image tampering, image processing and geometric attacks. Additionally, by incorporating human fingerprint keys during the encoding and encryption process of the watermark image, the scheme associates the watermarking process with user identity, thereby enhancing security. Extensive numerical simulations validate the feasibility, security and fragility of our proposed optical fragile watermarking scheme. [ABSTRACT FROM AUTHOR]

Photonic Band Gap (PBG) crystals have shown their significant role in optical data processing. Again it is seen that in optical communication and computation, Frequency Shift Keying (FSK) can extend a potential application. In PBG also the FSK can show its usefulness. The main novelty of the proposed work is the implementation of an optical FSK system designed on a 2D PBG crystal. The proposed structure contains a square-shaped lattice formed by GaAsInP-doped rods placed in an air-based substrate along with photonic crystal-based Semiconductor Optical Amplifiers (pc-SOAs). The authors also presented a theoretical model that successfully demonstrates the operation of the FSK system in the optical domain with simulation experiment. This work uses a moderate input light power and operates at a very high-speed (Tbps). Here, a ring cavity resonator with a constant biasing signal is incorporated to realize the FSK model. The intensity-encoded Boolean signal and the photonic crystal-based two Semiconductor Optical Amplifiers (pc-SOAs) are used here to establish the FSK scheme where the Boolean 1 (presence of optical signal) is encoded by another light having a frequency υ1 and the Boolean 0 (absence of light) is encoded by another frequency of light υ2. As the FSK is developed in the optical domain using the switching character of PBG, hence a moderate light power is enough to conduct this implementation. In this communication, the optical FSK is designed and analyzed by a simulation process with Finite-Difference-Time-Domain (FDTD) and Plane Wave Expansion (PWE) techniques. [ABSTRACT FROM AUTHOR]

Ship fire detection presents significant challenges in computer vision-based approaches due to factors such as the considerable distances from which ships must be detected and the unique conditions of the maritime environment. The presence of water vapor and high humidity further complicates the detection and classification tasks for deep learning models, as these factors can obscure visual clarity and introduce noise into the data. In this research, we explain the development of a custom ship fire dataset, a YOLO (You Only Look Once)-v10 model with a fine-tuning combination of dehazing algorithms. Our approach integrates the power of deep learning with sophisticated image processing to deliver comprehensive solutions for ship fire detection. The results demonstrate the efficacy of using YOLO-v10 in conjunction with a dehazing algorithm, highlighting significant improvements in detection accuracy and reliability. Experimental results show that the YOLO-v10-based developed ship fire detection model outperforms several YOLO and other detection models in precision (97.7%), recall (98%), and mAP@0.50 score (89.7%) achievements. However, the model reached a relatively lower score in terms of F1 score in comparison with YOLO-v8 and ship-fire-net model performances. In addition, the dehazing approach significantly improves the model's detection performance in a haze environment. [ABSTRACT FROM AUTHOR]

In the production process of high-end PP-R pipes, mixing different colored raw material particles can result in uneven color in the final product, affecting its appearance quality. in addition, color mixing can reduce the physical properties of the pipes, impacting their durability and safety. To address this issue, we propose a visual, non-destructive inspection solution based on image processing technology. The solution aims to enhance detection efficiency and accuracy by reducing background interference and enabling adaptive adjustments in various environments. Initially, the K-Means image segmentation algorithm is employed to eliminate complex background factors from the original image, significantly improving image segmentation accuracy. Subsequently, the Gaussian mixture model algorithm is utilized to automatically extract the color threshold of the foreground image after background removal, facilitating adaptive algorithm adjustments. Finally, the mean value algorithm is introduced to swiftly and accurately identify plastic particles of different colors using the automatically obtained color thresholds. Experimental results demonstrate that this method can quickly and accurately identify different color particles and effectively support the rejection of impurity particles. Through this approach, the algorithm achieves an average detection accuracy of 99.3%. [ABSTRACT FROM AUTHOR]

Pests are widely distributed in nature, characterized by their small size, which, along with environmental factors such as lighting conditions, makes their identification challenging. A lightweight pest detection network, HCFormer, combining convolutional neural networks (CNNs) and a vision transformer (ViT) is proposed in this study. Data preprocessing is conducted using a bottleneck-structured convolutional network and a Stem module to reduce computational latency. CNNs with various kernel sizes capture local information at different scales, while the ViT network's attention mechanism and global feature extraction enhance pest feature representation. A down-sampling method reduces the input image size, decreasing computational load and preventing overfitting while enhancing model robustness. Improved attention mechanisms effectively capture feature relationships, balancing detection accuracy and speed. The experimental results show that HCFormer achieves 98.17% accuracy, 91.98% recall, and a mean average precision (mAP) of 90.57%. Compared with SENet, CrossViT, and YOLOv8, HCFormer improves the average accuracy by 7.85%, 2.01%, and 3.55%, respectively, outperforming the overall mainstream detection models. Ablation experiments indicate that the model's parameter count is 26.5 M, demonstrating advantages in lightweight design and detection accuracy. HCFormer's efficiency and flexibility in deployment, combined with its high detection accuracy and precise classification, make it a valuable tool for identifying and classifying crop pests in complex environments, providing essential guidance for future pest monitoring and control. [ABSTRACT FROM AUTHOR]

Objectives: The circular coded marker is widely used in close-range photogrammetry, and has excellent characteristics that are independent of translation, rotation, and scaling. The robust recognition and decoding of the circular coded markers are of great significance to the subsequent 3D reconstruction. Methods: Aiming at the identification of coded marks in complex environments such as cluttered backgrounds and uneven lighting, a two-step contour screening strategy is proposed. First, coarse screening is performed based on the contour length and roundness, and then fine screening is performed based on statistical analysis on gray-scale distribution. Results: The experimental results show that when the shooting angle is less than 75° and the size of the code point is greater than 30 pixels, the proposed method can guarantee high accuracy in recognition and decoding. In a complex environment, the recognition accuracy and decoding accuracy are 99.53% and 100%, respectively, and the false recognition rate is 0%. Conclusions: The proposed method can effectively reduce the false recognition rate and have a good recognition effect in a complex environment. [ABSTRACT FROM AUTHOR]

This study introduces a sophisticated neural network structure for segmenting breast tumors. It achieves this by combining a pretrained Vision Transformer (ViT) model with a UNet framework. The UNet architecture, commonly employed for biomedical image segmentation, is further enhanced with depthwise separable convolutional blocks to decrease computational complexity and parameter count, resulting in better efficiency and less overfitting. The ViT, renowned for its robust feature extraction capabilities utilizing self-attention processes, efficiently captures the overall context within images, surpassing the performance of conventional convolutional networks. By using a pretrained ViT as the encoder in our UNet model, we take advantage of its extensive feature representations acquired from extensive datasets, resulting in a major enhancement in the model's ability to generalize and train efficiently. The suggested model has exceptional performance in segmenting breast cancers from medical images, highlighting the advantages of integrating transformer-based encoders with efficient UNet topologies. This hybrid methodology emphasizes the capabilities of transformers in the field of medical image processing and establishes a new standard for accuracy and efficiency in activities related to tumor segmentation. [ABSTRACT FROM AUTHOR]

Natural risks comprise a whole range of disasters and dangers, requiring comprehensive management through advanced assessment, forecasting, and warning systems. Our specific focus is on landslides in difficult terrains. The evaluation of landslide risks employs sophisticated multicriteria models, such as the weighted sum GIS approach, which integrates qualitative parameters. Despite the challenges posed by the rugged terrain in Northern Algeria, it is paradoxically home to a dense population attracted by valuable hydro-agricultural resources. The goal of our research is to study landslide risks in these areas, particularly in the Mila region, with the aim of constructing a mathematical model that integrates both hazard and vulnerability considerations. This complex process identifies threats and their determining factors, including geomorphology and socio-economic conditions. We developed two algorithms, the analytic hierarchy process (AHP) and the fuzzy analytic hierarchy process (FAHP), to prioritize criteria and sub-criteria by assigning weights to them, aiming to find the optimal solution. By integrating multi-source data, including satellite images and in situ measurements, into a GIS and applying the two algorithms, we successfully generated landslide susceptibility maps. The FAHP method demonstrated a higher capacity to manage uncertainty and specialist assessment errors. Finally, a comparison between the developed risk map and the observed risk inventory map revealed a strong correlation between the thematic datasets. [ABSTRACT FROM AUTHOR]

Fire detection and extinguishing systems are critical for safeguarding lives and minimizing property damage. These systems are especially vital in combating forest fires. In recent years, several forest fires have set records for their size, duration, and level of destruction. Traditional fire detection methods, such as smoke and heat sensors, have limitations, prompting the development of innovative approaches using advanced technologies. Utilizing image processing, computer vision, and deep learning algorithms, we can now detect fires with exceptional accuracy and respond promptly to mitigate their impact. In this article, we conduct a comprehensive review of articles from 2013 to 2023, exploring how these technologies are applied in fire detection and extinguishing. We delve into modern techniques enabling real-time analysis of the visual data captured by cameras or satellites, facilitating the detection of smoke, flames, and other fire-related cues. Furthermore, we explore the utilization of deep learning and machine learning in training intelligent algorithms to recognize fire patterns and features. Through a comprehensive examination of current research and development, this review aims to provide insights into the potential and future directions of fire detection and extinguishing using image processing, computer vision, and deep learning. [ABSTRACT FROM AUTHOR]

Remote emission sensing (RES) is a state-of-the-art technique for monitoring thousands of vehicles on the road every day to detect high emitters. Modern commercial RES systems use absorption spectroscopy to measure the ratio of pollutants to CO2 from vehicle exhaust gases. In this work, we present an approach to enable direct concentration measurements by spectroscopic techniques in RES through measurement of the absorption path length. Our gas schlieren imaging sensor (GSIS) system operates on the principle of background-oriented schlieren (BOS) imaging in combination with advanced image processing and deep learning techniques to calculate detected exhaust plume sizes. We performed a qualitative as well as a quantitative analysis of vehicle exhaust and plume dimensions with the GSIS system. We present the system details and results from the GSIS system in the lab in comparison to a BOS model based on flow simulations, the results from characterization measurements in the lab with defined gas mixtures and temperatures, and the results from measurements on the road from different vehicles. [ABSTRACT FROM AUTHOR]

BACKGROUND: The incidence of kidney tumors is progressively increasing each year. The precision of segmentation for kidney tumors is crucial for diagnosis and treatment. OBJECTIVE: To enhance accuracy and reduce manual involvement, propose a deep learning-based method for the automatic segmentation of kidneys and kidney tumors in CT images. METHODS: The proposed method comprises two parts: object detection and segmentation. We first use a model to detect the position of the kidney, then narrow the segmentation range, and finally use an attentional recurrent residual convolutional network for segmentation. RESULTS: Our model achieved a kidney dice score of 0.951 and a tumor dice score of 0.895 on the KiTS19 dataset. Experimental results show that our model significantly improves the accuracy of kidney and kidney tumor segmentation and outperforms other advanced methods. CONCLUSION: The proposed method provides an efficient and automatic solution for accurately segmenting kidneys and renal tumors on CT images. Additionally, this study can assist radiologists in assessing patients' conditions and making informed treatment decisions. [ABSTRACT FROM AUTHOR]

Agriculture has undergone a remarkable transformation, transitioning from traditional methods that were used for centuries to technology-driven practices. The advent of image processing and computational intelligence has revolutionized crop production and plant health monitoring. From drones capturing detailed crop growth data to sensors meticulously measuring soil moisture levels, the possibilities are boundless. This review delves into the cutting-edge research advancements in the application of image processing and computational intelligence techniques for botanical fields, with a particular focus on plant health monitoring. First, it provides a comprehensive overview of the diverse imaging sensors employed in agriculture, including visible, near-infrared, thermal, and hyperspectral imaging. Subsequently, it carefully analyzes the advantages and limitations of each sensor type, along with illustrative examples of their utilization in plant health monitoring. The review further explores the application of machine learning and deep learning for automated plant disease identification, highlighting the critical need for standardized datasets, benchmarking protocols, and domain-specific knowledge for effective implementation. In conclusion, the review emphasizes the future challenges and trends in this rapidly evolving field. It serves as a valuable resource, providing insights into the latest trends in computer vision-based plant disease monitoring and identifying gaps that demand further attention from the scientific community. [ABSTRACT FROM AUTHOR]

The global impact of COVID‐19 has resulted in millions of individuals being afflicted, with a staggering mortality toll of over 16 000 over a span of 2 years. The dearth of resources and diagnostic techniques has had an impact on both emerging and wealthy nations. In response to this, researchers from the domains of engineering and medicine are using deep learning methods to create automated algorithms for detecting COVID‐19. This work included the development and comparison of a collaborative deep‐learning model for the identification of COVID‐19 using CT scan images, in comparison to previous deep learning‐based methods. The model underwent an ablation study using publicly accessible COVID‐19 CT imaging datasets, with encouraging outcomes. The suggested model might aid doctors and academics in devising tools to expedite the process of determining the optimal therapeutic approach for health professionals, hence reducing the risk of potential problems. [ABSTRACT FROM AUTHOR]