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In this paper, the approximate parallel prefix adders with minimizing hardware and timing complexities are proposed. Moreover, an approximate multiplier based on these adders is designed. The approximate structures include two approximate Sklansky adders, one approximate Ladner-Fischer adder, and one approximate Kogge-Stone adder. The proposed adders are free from carry rippling. The main strategy for approximate design is primarily based on rearranging and deleting sub-blocks and secondary reducing the critical path delay and area in the adders. In this case, we have a trade-off between accuracy, delay, and area. The proposed approximate multiplier has a serial structure that is designed based on using one approximate parallel prefix adder. The proposed approximate adders and multiplier are compared from hardware and accuracy point of view such as gate count, delay, area delay product, error rate, mean error distance, mean relative error distance, and normalized error distance. The efficacy of proposed structures in image processing applications such as image smoothing (low-pass filter) and image multiplication is performed using MATLAB. The results show the proposed approximate structures are comparable in terms of area, delay, PSNR, and mean structural similarity index metric parameters with other works. [ABSTRACT FROM AUTHOR]

Infrared thermal imaging presents a promising avenue for detecting physiological phenomena such as hot flushes in animals, presenting a non-invasive and efficient approach for monitoring health and behavior. However, thermal images often suffer from various types of noise, which can impede the accuracy of analysis. In this study, the efficacy of different noise filtering algorithms is investigated utilizing filtering algorithms as preprocessing steps to enhance U-Net convolutional neural network (CNN) performance in processing animal skin infrared image sets for hot flush recognition. This study compares the performance of four commonly used filtering methods: mean, median, Gaussian, and bilateral filters. The impact of each filtering technique on noise reduction and preservation of features critical for hot flush detection is evaluated in a machine learning hot flush detection algorithm. The optimal filtering for skin thermal imaging was a median filter which significantly improved the U-Net CNN's ability to accurately identify hot flush patterns, achieving an Intersection over Union score of 92.6% compared to 90.4% without filters. This research contributes to the advancement of thermal image processing methodologies for animal health monitoring applications, providing valuable insights for researchers and practitioners in the field of veterinary medicine and animal behavior studies utilizing autonomous thermal image segmentation. [ABSTRACT FROM AUTHOR]

This paper describes a method for integrating multiple dense point clouds using a shared landmark to generate a single real-scale integrated result for photogrammetry. It is difficult to integrate high-density point clouds reconstructed by photogrammetry because the scale differs with each photogrammetry. To solve this problem, this study places a QR code of known sizes, which is a shared landmark, in the reconstruction target environment and divides the reconstruction target environment based on the position of the QR code that is placed. Then, photogrammetry is performed for each divided environment to obtain each high-density point cloud. Finally, we propose a method of scaling each high-density point cloud based on the size of the QR code and aligning each high-density point cloud as a single high-point cloud by partial-to-partial registration. To verify the effectiveness of the method, this paper compares the results obtained by applying all images to photogrammetry with those obtained by the proposed method in terms of accuracy and computation time. In this verification, ideal images generated by simulation and images obtained in real environments are applied to photogrammetry. We clarify the relationship between the number of divided environments, the accuracy of the reconstruction result, and the computation time required for the reconstruction. [ABSTRACT FROM AUTHOR]

By applying vision sensing to realize online intelligent control on the pipeline welding process, the welding efficiency and qualification rate of pipeline girth butt joint during on-site construction of long-distance oil and gas pipelines can be effectively improved. During the pipeline external welding process, based on the designed combined line structured lights vision sensor (C-LSLVS), a partitioned time–frequency-space (PTFS) image processing algorithm is proposed to extract the laser centerline for the deformed laser lines image of double-V composite groove. The algorithm first utilizes information in the time domain to remove random interference from the image, and then, based on Radon transform (RT) and discrete Fourier transform (DFT), other stationary interferences can be eliminated and laser line characteristics can be enhanced by back-projection reconstruction in the frequency domain and space domain, so the stable and accurate extraction of laser centerline can be achieved under different situations. Subsequently, the theoretical analysis and error calculation of the pipeline groove sizes and related parameters solution model based on the local plane fitting method are carried out, and high-precision calculations of the parameters are completed. Finally, an intelligent pipeline welding system based on the C-LSLVS is constructed. In the intelligent welding experiment, the maximum detection error of the pipeline welding groove sizes did not exceed 0.20 mm, and the weld seam tracking deviation did not exceed 0.25 mm. The welding results show that the system has important application value for the development of pipeline intelligent welding. [ABSTRACT FROM AUTHOR]

Visible imaging is a fast, cheap, and accurate technique in the assessment of food quality and safety. The technique was used in the present research to detect sea foam adulterant levels in black and red peppers. The fraud levels included 0, 5, 15, 30, and 50%. Sample preparation, image acquisition and preprocessing, and feature engineering (feature extraction, selection, and classification) were the conducted steps in the present research. The efficient features were classified using artificial neural networks and support vector machine methods. The classifiers were evaluated using the specificity, sensitivity, precision, and accuracy metrics. The artificial neural networks had better results than the support vector machine method for the classification of different adulterant levels in black pepper with the metrics' values of 98.89, 95.67, 95.56, and 98.22%, respectively. Reversely, the support vector machine method had higher metrics' values (99.46, 98.00, 97.78, and 99.11%, respectively) for red pepper. The results showed the ability of visible imaging and machine learning methods to detect fraud levels in black and red pepper. [ABSTRACT FROM AUTHOR]

In this work we introduce GrapheNet, a deep learning framework based on an Inception-Resnet architecture using image-like encoding of structural features for the prediction of the properties of nanographenes. The model is validated on datasets of computed structure/property data on graphene oxide and defected graphene nanoflakes. By exploiting the planarity of quasi-bidimensional systems and through encoding structures into images, and leveraging the flexibility and power of deep learning in image processing, Graphenet achieves significant accuracy in predicting the physicochemical properties of nanographenes. This approach is able to efficiently encode structures composed of hundreds of atoms, scaling efficiently with the size of the model and enabling the prediction of the properties of large systems, which contrasts with the limitations of current atomistic-level representations for deep learning applications. The approach proposed based on image encoding exhibit a significant numerical accuracy and outperforms the computational efficiency of current representations of materials at the atomistic level, with significant advantages especially in the representation of nanostructures and large planar systems. [ABSTRACT FROM AUTHOR]

We aimed to compare the accuracy and reliability of two segmentation tools for magnetic resonance (MR) volumetry (FreeSurfer and Neurophet AQUA) at two magnetic field strengths (1.5T and 3T). We included 101 patients for the 1.5T–3T dataset and 112 for the 3T–3T dataset from three hospitals and five open-source datasets. The mean volume difference and average volume difference percentage with the change in magnetic field strength were compared between the methods. The hippocampus volume was larger with FreeSurfer than the Neurophet AQUA. In most brain regions, the Neurophet AQUA yielded a smaller average volume difference percentage (all < 10%) than FreeSurfer (all > 10%). The Neurophet AQUA exhibited more stable connectivity and regularity of the segmented components. Regarding volume, the Neurophet AQUA had effect sizes and ICCs comparable to those of FreeSurfer across the magnetic field strengths. With FreeSurfer, the original volume difference was small, whereas the average volume difference percentage was small with the Neurophet AQUA. Image segmentation took 1 h with FreeSurfer and 5 min with the Neurophet AQUA. When choosing an automatic segmentation method, the differences in image processing time and volume variability due to changes in the magnetic field strength of these methods should be considered. [ABSTRACT FROM AUTHOR]

Optical analog image processing technology is expected to provide an effective solution for high-throughput and real-time data processing with low power consumption. In various operations, optical spatial differential operations are essential in edge extraction, data compression, and feature classification. Unfortunately, existing methods can only perform low-order or selectively perform a particular high-order differential operation. Here, we propose and experimentally demonstrate a Bessel vortex modulated metalens composed of a single complex amplitude metasurface, which can perform multiple-order radial differential operations over a wide band by presetting the order of the corresponding Bessel vortex. This architecture further enables angle multiplexing to create multiple information channels that synchronously perform multi-order spatial differential operations, indicating the superiority of the proposed devices in parallel processing. Our approach may find various applications in artificial intelligence, machine vision, autonomous driving, and advanced biomedical imaging. A Bessel vortex modulated metalens is used for wide-band, multiple-order radial differential operations by presetting the order of the corresponding Bessel vortex. This enables synchronous spatial differential operations, with applications in optical data processing, machine vision and bio-imaging. [ABSTRACT FROM AUTHOR]

Body measurements are primarily made with a tape measure. In measurements taken with a tape measure, the inability to take measurements from the same part of the body each time, incorrect positioning of the tape measure, the occurrence of incorrect measurements, and the need for a person to take the measurements are significant problems in the traditional measurement method. Due to the social distancing rule that must be followed during the Covid-19 pandemic, the close contact between the person to be measured and the person taking the measurement became the starting point of this study. This study focuses on the detecting body shape changes using image processing techniques with 2D imaging. The novelty of the work is that non-contact body measurements are taken more accurately and reliably using the cosine theorem. Regular monitoring of obese patients is important in combating obesity, which is also the source of many health problems. In the monitoring of obese patients, it is necessary to determine the rate of slimming in areas where fat accumulation is intense. The error margin between the real measurements of human models and the calculated measurements was calculated as an average of ± 5.16% for waistline and an average of ± 4.58% for hip size. The cosine theorem was used instead of the ellipse formula used in the literature, and it was observed that the cosine theorem obtained results closer to reality. It is also thought that the developed system will be beneficial not only for extracting body measurements but also for extracting body measurements contactless in the textile sector. The study demonstrates the feasibility of image processing for non-contact body anthropometry and shape tracking. [ABSTRACT FROM AUTHOR]

Wire Arc Additive Manufacturing (WAAM) is a metal arc welding additive process that allows the production of large parts. Managing the quality of produced parts is a key challenge to the adoption of this technology in the industry. A particular case of this is the production of aluminum thin walls and the management of their thicknesses. Literature supports that the use of a near-infrared camera is a good way to monitor the meltpool shape to predict the manufactured thickness. Nevertheless, the post-processing applied to the image has a big influence on the accuracy of the measure. That is why, this article proposes a method to qualify image post-processing for thin wall thickness prediction from near-infrared camera images of aluminum WAAM process. A dataset from the literature is used to evaluate the accuracy of different post-processes. First, a method is proposed to evaluate the accuracy of a given post-process. Secondly, two types of post-processing are presented: post-processing based on conventional image processing and post-processing based on neural networks. Their accuracy is evaluated with the proposed method. The article concludes that the proposed method is adapted to qualify post-processes and that the proposed post-processing based on neural networks gives significant results in terms of accuracy. [ABSTRACT FROM AUTHOR]

Missing pixel imputation presents a critical challenge in image processing and computer vision, particularly in applications such as image restoration and inpainting. The primary objective of this paper is to accurately estimate and reconstruct missing pixel values to restore complete visual information. This paper introduces a novel model called the Enhanced Connected Pixel Identity GAN with Neutrosophic (ECP-IGANN), which is designed to address two fundamental issues inherent in existing GAN architectures for missing pixel generation: (1) mode collapse, which leads to a lack of diversity in generated pixels, and (2) the preservation of pixel integrity within the reconstructed images. ECP-IGANN incorporates two key innovations to improve missing pixel imputation. First, an identity block is integrated into the generation process to facilitate the retention of existing pixel values and ensure consistency. Second, the model calculates the values of the 8-connected neighbouring pixels around each missing pixel, thereby enhancing the coherence and integrity of the imputed pixels. The efficacy of ECP-IGANN was rigorously evaluated through extensive experimentation across five diverse datasets: BigGAN-ImageNet, the 2024 Medical Imaging Challenge Dataset, the Autonomous Vehicles Dataset, the 2024 Satellite Imagery Dataset, and the Fashion and Apparel Dataset 2024. These experiments assessed the model's performance in terms of diversity, pixel imputation accuracy, and mode collapse mitigation, with results demonstrating significant improvements in the Inception Score (IS) and Fréchet Inception Distance (FID). ECP-IGANN markedly enhanced image segmentation performance in the validation phase across all datasets. Key metrics, such as Dice Score, Accuracy, Precision, and Recall, were improved substantially for various segmentation models, including Spatial Attention U-Net, Dense U-Net, and Residual Attention U-Net. For example, in the 2024 Medical Imaging Challenge Dataset, the Residual Attention U-Net's Dice Score increased from 0.84 to 0.90, while accuracy improved from 0.88 to 0.93 following the application of ECP-IGANN. Similar performance enhancements were observed with the other datasets, highlighting the model's robust generalizability across diverse imaging domains. [ABSTRACT FROM AUTHOR]

Medical Object Detection (MOD) is a clinically relevant image processing method that locates structures of interest in radiological image data at object-level using bounding boxes. High-performing MOD models necessitate large datasets accurately reflecting the feature distribution of the corresponding problem domain. However, strict privacy regulations protecting patient data often hinder data consolidation, negatively affecting the performance and generalization of MOD models. Federated Learning (FL) offers a solution by enabling model training while the data remain at its original source institution. While existing FL solutions for medical image classification and segmentation demonstrate promising performance, FL for MOD remains largely unexplored. Motivated by this lack of technical solutions, we present an open-source, self-configuring and task-agnostic federated MOD framework. It integrates the FL framework Flower with nnDetection, a state-of-the-art MOD framework and provides several FL aggregation strategies. Furthermore, we evaluate model performance by creating simulated Independent Identically Distributed (IID) and non-IID scenarios, utilizing the publicly available datasets. Additionally, a detailed analysis of the distributions and characteristics of these datasets offers insights into how they can impact performance. Our framework's implementation demonstrates the feasibility of federated self-configuring MOD in non-IID scenarios and facilitates the development of MOD models trained on large distributed databases. [ABSTRACT FROM AUTHOR]

Urolithiasis is a leading urological disorder where accurate preoperative identification of stone types is critical for effective treatment. Deep learning has shown promise in classifying urolithiasis from CT images, yet faces challenges with model size and computational efficiency in real clinical settings. To address these challenges, we developed a non-invasive prediction approach for determining urinary stone types based on CT images. Through the refinement and improvement of the self-distillation architecture, coupled with the incorporation of feature fusion and the Coordinate Attention Module (CAM), we facilitated a more effective and thorough knowledge transfer. This method circumvents the extra computational expenses and performance reduction linked with model compression and removes the reliance on external teacher models, markedly enhancing the efficacy of lightweight models. achieved a classification accuracy of 74.96% on a proprietary dataset, outperforming current techniques. Furthermore, our method demonstrated superior performance and generalizability on two public datasets. This not only validates the effectiveness of our approach in classifying urinary stones but also showcases its potential in other medical image processing tasks. These results further reinforce the feasibility of our model for actual clinical deployment, potentially assisting healthcare professionals in devising more precise treatment plans and reducing patient discomfort. [ABSTRACT FROM AUTHOR]

Hyperspectral sparse unmixing, an image processing technique, leverages a spectral library enriched with endmember spectral information as a prerequisite. It decomposes the hyperspectral data to ascertain the abundance corresponding to each endmember in the spectral library. Currently, the majority of sparse unmixing methods are inadequate in high-noise environments due to their failure to comprehensively account for hyperspectral characteristics. Addressing this challenge, this paper introduces an innovative sparse unmixing approach for hyperspectral images named spectral weighted sparse unmixing based on adaptive total variation and low-rank constraints (SWSU-ATVLR). Initially, the sparse unmixing algorithm is introduced in detail. Subsequently, we present the our method. This method seamlessly integrates the low-rank, adaptive TV and spectral weighting characteristics of hyperspectral data. While preserving the low-rank attributes and sparsity of abundance, the adaptively adjusted abundance matrix exhibits a regularized horizontal and vertical difference ratio across various structures and fully utilizes spectral information, enhancing denoising efficiency. Subsequently, the ADMM algorithm is employed to solve the new model. To validate our proposed algorithm, SWSU-ATVLR method is compared and analysed in detailed experiments with several current state-of-the-art methods through simulated and real data experiments. Experimental results prove that our proposed method is superior to these state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The growing use of artificial neural network (ANN) tools for computed tomography angiography (CTA) data analysis underscores the necessity for elevated data protection measures. We aimed to establish an automated defacing pipeline for CTA data. In this retrospective study, CTA data from multi-institutional cohorts were utilized to annotate facemasks (n = 100) and train an ANN model, subsequently tested on an external institution's dataset (n = 50) and compared to a publicly available defacing algorithm. Face detection (MTCNN) and verification (FaceNet) networks were applied to measure the similarity between the original and defaced CTA images. Dice similarity coefficient (DSC), face detection probability, and face similarity measures were calculated to evaluate model performance. The CTA-DEFACE model effectively segmented soft face tissue in CTA data achieving a DSC of 0.94 ± 0.02 (mean ± standard deviation) on the test set. Our model was benchmarked against a publicly available defacing algorithm. After applying face detection and verification networks, our model showed substantially reduced face detection probability (p < 0.001) and similarity to the original CTA image (p < 0.001). The CTA-DEFACE model enabled robust and precise defacing of CTA data. The trained network is publicly accessible at www.github.com/neuroAI-HD/CTA-DEFACE. Relevance statement: The ANN model CTA-DEFACE, developed for automatic defacing of CT angiography images, achieves significantly lower face detection probabilities and greater dissimilarity from the original images compared to a publicly available model. The algorithm has been externally validated and is publicly accessible. Key Points: The developed ANN model (CTA-DEFACE) automatically generates facemasks for CT angiography images. CTA-DEFACE offers superior deidentification capabilities compared to a publicly available model. By means of graphics processing unit optimization, our model ensures rapid processing of medical images. Our model underwent external validation, underscoring its reliability for real-world application. [ABSTRACT FROM AUTHOR]

This study investigates an oil/water two-phase system to assess the potential efficacy of a novel passive mixer in enhancing the liquid-liquid interfacial area within a micro-channel contactor. In this system, two fluids are introduced into a microchannel with a diameter of 800 μm and a length of 20 cm, which is equipped with a stainless-steel helical wire measuring 250 μm in diameter. Throughout the experiments, both fluids are supplied at equal flow rates, and the dominant forces, including attachment and detachment forces, are examined. The results reveal a critical Weber number of 3.8 × 10−³, at which the first detachment occurs. A comparison between microchannels with and without the passive micromixer demonstrates that greater slug breakup occurs in the system incorporating the helical wire micromixer. This innovative configuration results in a significant reduction in slug/droplet size compared to a microchannel without a barrier, decreasing from approximately 600 μm to 390 μm at a flow rate of 0.8 mL/min. Additionally, a flow map is presented, illustrating three distinct flow regimes: flow contains long slug, Slug-droplet flow, and droplet flow regimes, with the droplet flow regime covering the largest area. The findings indicate that the implementation of this innovative passive mixer substantially increases the interfacial area, providing significant advantages for mass transfer applications. [ABSTRACT FROM AUTHOR]

The rolling elements of the induction motor are highly susceptible to faults. The detection and diagnosis of rolling element faults are accurate and reliable only when the extracted features are accurate. The paper proposes an approach for bearing and rotor fault diagnosis using deep optimal feature extraction and selection based on vibration signal analysis. The deep feature extraction is done using an ensemble deep models features extraction approach in which features are extracted from seven pretrained models are fused serially using serial-based feature fusion technique. This leads to a solution for a higher efficacy model, but at the cost of high processing time as the feature data set gets large. A unique approach termed Ensemble Feature Selection has been developed to address this issue and limit the harmful impact of unwanted features in data-driven diagnostics. The processing time is further reduced using the shallow classifier at the fully connected layer. The proposed model is tested using the data acquired in the laboratory and validated using the available online benchmark data sets. [ABSTRACT FROM AUTHOR]

Developing microphysiological cell culture platforms with a three-dimensional (3D) microenvironment has been a significant advancement from traditional monolayer cultures. Still, most of the current microphysiological platforms are limited in closed designs, i.e. are not accessible after 3D cell culture loading. Here, we report an open-top microfluidic chip which enables the generation of two sequentially loaded 3D cell cultures without physical barriers restricting the nurture, gas exchange and cellular communication. As a proof-of-concept, we demonstrated the formation of two 3D vasculatures, one in the upper and the other in the lower compartment, under three distinct flow conditions: asymmetric side-to-center, symmetric side-to-center and symmetric center-to-side. We used computational modelling to characterize initial flow pressures in cell culture compartments. We showed prominent vessel formation and branched vasculatures in upper and lower cell culture compartments with interconnecting, lumenized vessels with in vivo-relevant diameter in all flow conditions. With advanced image processing, we quantified and compared the overall vascular network volume and the total length formed in asymmetric side-to-center, symmetric side-to-center and symmetric center-to-side flow conditions. Our results indicate that the developed chip can house two distinct 3D cell cultures with merging vessels between compartments and by providing asymmetric side-to-center or symmetric center-to-side flow vascular morphogenesis is enhanced in terms of overall network length. The developed open-top microfluidic chip may find various applications in generation of tissue-specific 3D-3D co-cultures for studying cellular interactions in vascularized tissues and organs. [ABSTRACT FROM AUTHOR]

We introduce a novel end-to-end deep-learning solution for rapidly estimating a dense spherical depth map of an indoor environment. Our input is a single equirectangular image registered with a sparse depth map, as provided by a variety of common capture setups. Depth is inferred by an efficient and lightweight single-branch network, which employs a dynamic gating system to process together dense visual data and sparse geometric data. We exploit the characteristics of typical man-made environments to efficiently compress multi-resolution features and find short- and long-range relations among scene parts. Furthermore, we introduce a new augmentation strategy to make the model robust to different types of sparsity, including those generated by various structured light sensors and LiDAR setups. The experimental results demonstrate that our method provides interactive performance and outperforms state-of-the-art solutions in computational efficiency, adaptivity to variable depth sparsity patterns, and prediction accuracy for challenging indoor data, even when trained solely on synthetic data without any fine tuning. [ABSTRACT FROM AUTHOR]

Bipolar picture fuzzy sets (BPPFSs), being the most extended form of fuzzy sets (FSs), has more capability to analyze the state of any network, more intelligently. It is proven that BPPFSs having poles is most efficient in solving daily life problems with uncertainties, compared to the other extensions of fuzzy sets (FSs). The ideas of fuzzy planar graphs (FPGs) and intutiionistic fuzzy planar graphs (IFPGs) have been introduced in the literature, the concepts of bipolar picture fuzzy planar graph (BPPFPG) has not yet been explored. In this study, we fill the gap existing in the literature by introducing the idea of BPPFPGs. We present various key features and characteristics of BPPFPGs. In this context, we describe the degree of a vertex of a BPPFMG, strong multi-edge of a BPPFMG, complete BPPFMG, strength of a bipolar picture fuzzy multi-edge of a BPPFMG, a strong multi-edge of a BPPFMG etc. Moreover, we initiate the concepts of geometric and combinatorial duals of BPPFPGs. At the end, we present the utilizations of BPPFPGs in creating image segmentation and making road map designs. In this regard, we analyze image segmentation based on strong BPPFPG, and by utilizing the term "degree of planarity" of BPPFPGs, we introduce a new scheme for designing safer road map. [ABSTRACT FROM AUTHOR]

This work proposes a novel robotic exoskeleton for rehabilitation of the index finger. Though all motions of index finger are essential, the major range of motion is covered by flexion/extension motion. Hence, a Stephenson III six-bar mechanism has been synthesized for the robotic exoskeleton device for a pre-defined trajectory to address post stroke rehabilitation of patients. The flexion/extension trajectory was obtained experimentally using image processing. Based on the trajectory, a mathematical model was formulated which was used as the objective function for the optimization problem. To eliminate any defects that may be encountered during the synthesis, "loop-by-loop defect rectification" procedure was implemented along with well-established optimization algorithms such as TLBO, BWP, GWO and PSO for synthesis of the desired mechanism. It has been found that TLBO outperformed all the others as it could reduce the objective function value to 0.69849. whereas, BWP reduced it to 8.9952, GWO reduced it to 13.1388, and PSO could only reduce it to 6 × 105. Therefore, the design obtained using TLBO was considered for developing the prototype of the device. The device was validated experimentally using image processing, and it is found to cover the prescribed range of motion. Thus, the proposed exoskeleton is deemed to be a viable solution for post stroke index-finger rehabilitation. [ABSTRACT FROM AUTHOR]

Cellular senescence is a major driver of aging and age-related diseases. Quantification of senescent cells remains challenging due to the lack of senescence-specific markers and generalist, unbiased methodology. Here, we describe the Fully-Automated Senescence Test (FAST), an image-based method for the high-throughput, single-cell assessment of senescence in cultured cells. FAST quantifies three of the most widely adopted senescence-associated markers for each cell imaged: senescence-associated β-galactosidase activity (SA-β-Gal) using X-Gal, proliferation arrest via lack of 5-ethynyl-2'-deoxyuridine (EdU) incorporation, and enlarged morphology via increased nuclear area. The presented workflow entails microplate image acquisition, image processing, data analysis, and graphing. Standardization was achieved by (i) quantifying colorimetric SA-β-Gal via optical density; (ii) implementing staining background controls; and (iii) automating image acquisition, image processing, and data analysis. In addition to the automated threshold-based scoring, a multivariate machine learning approach is provided. We show that FAST accurately quantifies senescence burden and is agnostic to cell type and microscope setup. Moreover, it effectively mitigates false-positive senescence marker staining, a common issue arising from culturing conditions. Using FAST, we compared X-Gal with fluorescent C12FDG live-cell SA-β-Gal staining on the single-cell level. We observed only a modest correlation between the two, indicating that those stains are not trivially interchangeable. Finally, we provide proof of concept that our method is suitable for screening compounds that modify senescence burden. This method will be broadly useful to the aging field by enabling rapid, unbiased, and user-friendly quantification of senescence burden in culture, as well as facilitating large-scale experiments that were previously impractical. [ABSTRACT FROM AUTHOR]

To overcome the challenges in underwater object detection across diverse marine environments—marked by intricate lighting, small object presence, and camouflage—we propose an innovative solution inspired by the human retina's structure. This approach integrates a cone-rod cell module to counteract complex lighting effects and introduces a reparameterized multiscale module for precise small object feature extraction. Moreover, we employ the Wise Intersection Over Union (WIOU) technique to enhance camouflage detection. Our methodology simulates the human eye's cone and rod cells' brightness and color perception using varying sizes of deep and ordinary convolutional kernels. We further augment the network's learning capability and maintain model lightness through structural reparameterization, incorporating multi-branching and multiscale modules. By substituting the Complete Intersection Over Union (CIOU) with WIOU, we increase penalties for low-quality samples, mitigating the effect of camouflaged information on detection. Our model achieved a MAP_0.75 of 72.5% on the Real-World Underwater Object Detection (RUOD) dataset, surpassing the leading YOLOv8s model by 5.8%. Additionally, the model's FLOPs and parameters amount to only 10.62 M and 4.62B, respectively, which are lower than most benchmark models. The experimental outcomes affirm our design's efficacy in addressing underwater object detection's various disturbances, offering valuable technical insights for related oceanic image processing challenges. [ABSTRACT FROM AUTHOR]

This research work focuses on the design and implementation of a highly advanced field-programmable gate array (FPGA)-based system-on-chip (SoC) solution for real-time edge detection. By utilizing a Zynq processor and leveraging the powerful Vivado software, the aim is to overcome the significant computational challenges associated with achieving real-time edge detection. Edge detection in real-time scenarios presents several obstacles, including the possibility of missing edges due to noise and the substantial processing requirements of any edge detection technique. To address these challenges, the proposed SoC system synergistically combines the computational capabilities of an FPGA board and a Zynq processor, harnessing hardware acceleration to achieve high-performance edge detection. The OV7670 camera module serves as the primary input medium, capturing image frames for subsequent processing. These captured frames undergo initial processing before being seamlessly transferred to the FPGA fabric through customized intellectual property (IP) blocks. These IP blocks efficiently handle crucial tasks such as frame capturing, conversion to AXI Stream interface signals, and integration with the video direct memory access (VDMA) IP. The VDMA IP plays a pivotal role by facilitating high-speed data movement between the FPGA fabric and the Zynq processor IP, thereby enabling streamlined and efficient data transfer and processing. At the heart of this project lies the real-time edge detection algorithm, which is skillfully implemented on the Zynq processor. The resulting edge-detected frames are then visually presented and displayed on an output device utilizing the AXI4-Stream to Video Out IP. To ensure optimal utilization of available hardware resources, the comprehensive Vivado software suite provides a wide array of tools for designing, implementing, and programming the FPGA fabric. By leveraging FPGA-based systems, this project effectively addresses the critical need for real-time edge detection in time-sensitive scenarios. The result is a portable and manageable device that exhibits versatility, as it can be employed in various applications while reliably detecting edges in real-time situations. [ABSTRACT FROM AUTHOR]

Countersink inspection is crucial in various automated assembly lines, especially in the aerospace and automotive sectors. Advancements in machine vision introduced automated robotic inspection of countersinks using laser scanners and monocular cameras. Nevertheless, the aforementioned sensing pipelines require the robot to pause on each hole for inspection due to high latency and measurement uncertainties with motion, leading to prolonged execution times of the inspection task. The neuromorphic vision sensor, on the other hand, has the potential to expedite the countersink inspection process, but the unorthodox output of the neuromorphic technology prohibits utilizing traditional image processing techniques. Therefore, novel event-based perception algorithms need to be introduced. We propose a countersink detection approach on the basis of event-based motion compensation and the mean-shift clustering principle. In addition, our framework presents a robust event-based circle detection algorithm to precisely estimate the depth of the countersink specimens. The proposed approach expedites the inspection process by a factor of 10 × compared to conventional countersink inspection methods. The work in this paper was validated for over 50 trials on three countersink workpiece variants. The experimental results show that our method provides a standard deviation of 0.025 mm and an accuracy of 0.026 mm for countersink depth inspection despite the low resolution of commercially available neuromorphic cameras. Video Link: https://www.dropbox.com/s/pateqqwh4d605t3/final_video_new.mp4?dl=0. [ABSTRACT FROM AUTHOR]

With the rapid development of deep learning in the last decade, generating and processing real-time images have become one of critical methods in intelligent driving systems for new energy vehicles. However, the real-time images captured by sensors are susceptible to variations in various environments, including different weather and lighting conditions. To enhance the real-time image generation performance for new energy vehicles in complex environments, and improve real-time visual image processing capabilities, this study proposes an energy-efficient real-time visual image adversarial generation and processing algorithm, called as ENV-GAN. It hypothesizes a shared latent domain among mixed image domains after analyzing driving situations under various weather and lighting conditions. Mappings are established between different image domains. Besides, a multi-encoder weight-sharing technique is utilized to enhances the generative adversarial network model. Additionally, the algorithm integrates an attention module to enhance the model’s image generation. Experimental results and analysis demonstrate that the new algorithm outperforms existing algorithms in tasks such as defogging, rain removal, and lighting enhancement, offering high energy efficiency and low energy consumption. [ABSTRACT FROM AUTHOR]

Particle size is considered one of the significant characteristics used in geotechnical practices. Traditionally, sieve analysis is utilized for coarse-grained soil. However, this method could be time consuming and take much effort, especially for large scale infrastructure projects. This paper presents an efficient method for estimating gravel particle characterization utilizing image processing and artificial neural network technique (IPNN). The proposed algorithm is performed by utilizing particle boundary delineation and shape feature extraction to train a neural network model for estimating gravel size distribution curve. It is found that excellent agreement exists between the results obtained from conventional sieve analysis and neural analysis for gravel soil particles with maximum difference in passing percentages up to only 3.70%. The proposed technique shows satisfactory results for crushed stone samples with maximum difference in passing percentages about 10.90% mainly in large diameter particles. The presented technique (IPNN) could offer a promising alternative technique for material quality control process especially in large scale projects. [ABSTRACT FROM AUTHOR]

The rotary motor plays a pivotal role in various motion execution mechanisms. However, an inherent issue arises during the initial installation of the encoder grating, namely, eccentricity between the centers of the encoder grating and motor shaft. This eccentricity substantially affects the accuracy of motor angle measurements. To address this challenge, we proposed a precision encoder grating mounting system that automates the encoder grating mounting process. The proposed system mainly comprises a near-sensor detector and a push rod. With the use of a near-sensor approach, the detector captures rotating encoder grating images, and the eccentricity is computed in real-time. This approach substantially reduces the time delays in image data transmission, thereby enhancing the speed and accuracy of eccentricity calculation. The major contribution of this article is a method for real-time eccentricity calculation that leverages an edge processor within the detector and an edge-vision baseline detection algorithm. This method enables real-time determination of the eccentricity and eccentricity angle of the encoder grating. Leveraging the obtained eccentricity and eccentricity angle data, the position of the encoder grating can be automatically adjusted by the push rod. In the experimental results, the detector can obtain the eccentricity and eccentricity angle of the encoder grating within 2.8 s. The system efficiently and precisely completes a encoder grating mounting task in average 25.1 s, and the average eccentricity after encoder grating mounting is 3.8 µm. [ABSTRACT FROM AUTHOR]

In various countries worldwide, the incidence of colon cancer-related deaths has been on the rise in recent years. Early detection of symptoms and identification of intestinal polyps are crucial for improving the cure rate of colon cancer patients. Automated computer-aided diagnosis (CAD) has emerged as a solution to the low efficiency of traditional methods relying on manual diagnosis by physicians. Deep learning is the latest direction of CAD development and has shown promise for colonoscopic polyp segmentation. In this paper, we present a multi-level encoder-decoder architecture for polyp segmentation based on the Transformer architecture, termed NA-SegFormer. To improve the performance of existing Transformer-based segmentation algorithms for edge segmentation on colon polyps, we propose a patch merging module with a neighbor attention mechanism based on overlap patch merging. Since colon tract polyps vary greatly in size and different datasets have different sample sizes, we used a unified focal loss to solve the problem of category imbalance in colon tract polyp data. To assess the effectiveness of our proposed method, we utilized video capsule endoscopy and typical colonoscopy polyp datasets, as well as a dataset containing surgical equipment. On the datasets Kvasir-SEG, Kvasir-Instrument and KvasirCapsule-SEG, the Dice score of our proposed model reached 94.30%, 94.59% and 82.73%, with an accuracy of 98.26%, 99.02% and 81.84% respectively. The proposed method achieved inference speed with an Frame-per-second (FPS) of 125.01. The results demonstrated that our suggested model effectively segmented polyps better than several well-known and latest models. In addition, the proposed method has advantages in trade-off between inference speed and accuracy, and it will be of great significance to real-time colonoscopic polyp segmentation. The code is available at https://github.com/promisedong/NAFormer. [ABSTRACT FROM AUTHOR]

Leukemia is a type of blood tumour that occurs because of abnormal enhancement in WBCs (white blood cells) in the bone marrow of the human body. Blood-forming tissue cancer influences the lymphatic and bone marrow system. The early diagnosis and detection of leukaemia, i.e., the accurate difference of malignant leukocytes with little expense at the beginning of the disease, is a primary challenge in the disease analysis field. Despite the higher occurrence of leukemia, there is a lack of flow cytometry tools, and the procedures accessible at medical diagnostics centres are time-consuming. Distinct researchers have implemented computer-aided diagnostic (CAD) and machine learning (ML) methods for laboratory image analysis, aiming to manage the restrictions of late leukemia analysis. This study proposes a new Falcon optimization algorithm with deep convolutional neural network for Leukemia detection and classification (FOADCNN-LDC) technique. The main objective of the FOADCNN-LDC technique is to classify and recognize leukemia. The FOADCNN-LDC technique utilizes a median filtering (MF) based noise removal process to eradicate the image noise. Besides, the FOADCNN-LDC technique employs the ShuffleNetv2 model for the feature extraction process. Moreover, the detection and classification of the leukemia process are performed by utilizing the convolutional denoising autoencoder (CDAE) model. The FOA is implemented to select the hyperparameter of the CDAE model. The simulation process of the FOADCNN-LDC approach is performed on a benchmark medical dataset. The investigational analysis of the FOADCNN-LDC approach highlighted a superior accuracy value of 99.62% over existing techniques. [ABSTRACT FROM AUTHOR]

Microbiologically Induced Calcium Carbonate Precipitation (MICP) is a technology for improving soil characteristics, especially strength, that has been gaining increasing interest in literature during the last few years. Although a lot of influencing factors on the result of MICP are known, particle size and shape of the particles remain poorly understood. While destructive measuring of compressive strength or calcium carbonate content are important for the characterization of samples these methods give no insight into the internal structures and pore networks of the samples. X-ray microcomputed tomography (micro-CT) is a technique that is used to characterize the internals of rocks and to a certain degree MICP-treated soils. However, the impact of filtering and image processing of micro-CT Data depending on the type of MICP sample is poorly described in the literature. In this study, single fractions of local quarry were treated with MICP through the ureolytic microorganism Sporosarcina pasteurii to investigate the influence of particle size distribution on calcium carbonate content, unconfined compressive strength and the reduction of water permeability. Additionally, micro-CT was conducted to obtain insights into the resulting pore system. The impact of the Gauss filter und Non-local means filter on the resulting images and data on the pore network are discussed. The results show that particle size has a significant impact on the result of all tested parameters of biosandstone with lower particle size leading to higher strength and generally higher calcium carbonate content. Micro-CT data showed that the technology is feasible to gain valuable insights into the internal structures of biosandstone but the resolution and signal-to-noise ratio remain challenging, especially for samples with particle sizes smaller than 125 µm. Article highlights: Microbiologically Induced Calcium Carbonate Precipitation is more effective for smaller sand particle sizes Smaller particle size of sand lead to higher permeability reduction, higher compressive strength and higher calcium carbonate contents in biosandstone X-ray microcomputed tomography and image analysis is sufficient to characterize the pore system of biosandstone and detect uneven biocemented areas Usage of different digital filters influence resulting images and data from X-ray microcomputed tomography [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]

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]

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]

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]

With the increasing complexity of digital systems, managing power dissipation and energy consumption in digital circuits, particularly in emerging embedded systems for artificial intelligence and signal processing applications, has become a challenging issue. The emerging paradigm of approximate computing offers the potential to reduce energy consumption and enhance speed by trading accuracy. This paper introduces several 4-bit approximate multipliers and outlines a systematic approach for constructing extended bit-length multipliers by leveraging lower bit-length counterparts. Our evaluations confirm that, in comparison with state-of-the-art approximate multipliers, the proposed multipliers demonstrate energy reduction of up to 50, 76, and 83% in 8-bit, 16-bit, and 32-bit multipliers, respectively. Additionally, the reduction in energy-delay product (EDP) reaches up to 86, 93, and 97%, correspondingly. The efficiency of the proposed approximate multipliers has been explored and confirmed in executing various image processing algorithms, a regression model developed for stock price prediction, and in executing quadrature amplitude demodulation. [ABSTRACT FROM AUTHOR]

Access to large image volumes through camera traps and crowdsourcing provides novel possibilities for animal monitoring and conservation. It calls for automatic methods for analysis, in particular, when re-identifying individual animals from the images. Most existing re-identification methods rely on either hand-crafted local features or end-to-end learning of fur pattern similarity. The former does not need labeled training data, while the latter, although very data-hungry typically outperforms the former when enough training data is available. We propose a novel re-identification pipeline that combines the strengths of both approaches by utilizing modern learnable local features and feature aggregation. This creates representative pattern feature embeddings that provide high re-identification accuracy while allowing us to apply the method to small datasets by using pre-trained feature descriptors. We report a comprehensive comparison of different modern local features and demonstrate the advantages of the proposed pipeline on two very different species. [ABSTRACT FROM AUTHOR]

In today's urban development, Earth Pressure Balance (EPB) Tunnel Boring Machines (TBMs) play a vital role. It's crucial to design a comprehensive monitoring system to control surface settlement and prevent damage to surface structures. This study focuses on creating prediction models for estimating ground surface settlement. Two soft computing techniques, namely ANN-CFB and ANN-BP, were used for this purpose. The models were validated using operational data from the Qom metro Line A, specifically the section between A14 and A10 stations. Additional input parameters were incorporated using an image processing approach to include soil properties for each segment. As a result, the most accurate ANN technique was employed to predict ground surface settlements for the mentioned project. The correlation coefficients for training, testing, validation, and the overall result were found to be 0.99439, 0.97873, 0.96381, and 0.98824, respectively. Through sensitivity analysis, the study explored the connections between different parameters and ground surface settlement. The outcomes reveal strong agreement between predicted values and real data. Notably, the parameter 'cutter head torque' exhibited the highest impact on surface settlement (8.48%), while 'Pressiometric Modulus (Ep)' had the least impact (4.24%). [ABSTRACT FROM AUTHOR]

Hot forging is one of the common manufacturing processes for producing brass workpieces. However forging produces flash which is a thin metal part around the desired part formed with an excessive material. Using robots with vision system to manipulate this workpiece has encountered several challenging issues, e.g. the uncertain shape of flash, color, reflection of brass surface, different lighting condition, and the uncertainty surrounding the position and orientation of the workpiece. In this research, Mask region-based convolutional neural network together with image processing is used to resolve these issues. The depth camera can provide images for visual detection. Machine learning Mask region-based convolutional neural network model was trained with color images and the position of the object is determined by the depth image. A dual arm 7 degree of freedom collaborative robot with proposed grasping strategy is used to grasp the workpiece that can be in inappropriate position and pose. Eventually, experiments were conducted to assess the visual detection process and the grasp planning of the robot. [ABSTRACT FROM AUTHOR]

Potholes are a significant hazard, causing severe damage to vehicles and potentially leading to fatal accidents. The automatic detection of potholes is crucial for timely maintenance and reducing these risks. This research evaluates the performance of three pre-trained Convolutional Neural Network models—ResNet 50, ResNet 18, and MobileNet—in classifying pavement images. Initially, the models distinguish between images containing potholes and those without (Potholes vs. Normal). Subsequently, they categorize pavement images into three classes: Small Pothole, Large Pothole, and Normal. Pavement images were captured from two different heights: 3.5 feet (waist height) and 2 feet. Among the models, MobileNet v2 demonstrated the highest accuracy of 98% for detecting potholes. For images taken at 2 feet, the classification accuracies for large potholes, small potholes, and normal pavement were 87.33%, 88.67%, and 92%, respectively. For images taken from waist height, the accuracies increased to 98.67%, 98.67%, and 100%, respectively. The study highlights the potential of these models in enhancing road safety through reliable and automated pothole detection, providing valuable insights for infrastructure maintenance. Article Highlights: Potholes are dangerous and can cause serious vehicle damage. Train CNN models for the classification of potholes These CNNs, for different sizes of potholes can be used for the rehabilitation of potholes. Among all CNN modes, MobileNet v2 achieves 98% accuracy in detecting the presence of a pothole. [ABSTRACT FROM AUTHOR]

In large open-pit coal mine projects, internal dump slope failure is a serious issue for industry. Slope failure affects haul roads, mine machinery, and miners' lives and hampers coal production operations. Dump slope failure events will not occur suddenly, and progressive displacement will occur in the slope before failure. Therefore, dump slope monitoring is essential for the safety of humans and machinery. Generally, the mine slope displacement is measured using a conventional survey approach. However, conventional surveys cannot measure minor changes in a large dump area. Therefore, massive dump displacement monitoring is a tedious task for surveyors. This study used the unmanned aerial vehicle (UAV) close-range photogrammetry survey to introduce an intelligent and reliable approach for dump change detection and displacement monitoring. A UAV close-range photogrammetry survey was conducted to collect RGB images, and dump surface point cloud data (PCD) were generated using the structure-from-motion (SfM) computing algorithm. The ground control points (GCPs) were also measured using a coordinate measurement survey for geo-reference points. For dump surface PCD and 3D reconstruction, the Pix-4D Mapper image processing tool was used. PCDs were generated for two months and used for dump slope surface change and displacement monitoring using the cloud-to-cloud (C2C) computing algorithm. The PCD was compared with the reference PCD, and the results of this investigation indicated minor and major changes on the dump slope and top surfaces. This approach is inexpensive and reliable for large dump slope change detection and displacement monitoring. [ABSTRACT FROM AUTHOR]

A novel detection method is proposed to address the challenge of detecting small objects in object detection. This method augments the YOLOv8n architecture with a small object detection layer and innovatively designs a Concat-detection head to effectively extract features. Simultaneously, a new attention mechanism—Multi-Head Mixed Self-Attention (MMSA) mechanism—is introduced to enhance the feature-extraction capability of the backbone. To improve the detection sensitivity for small objects, a combination of Normalized Wasserstein Distance (NWD) and Intersection over Union (IoU) is used to calculate the localization loss, optimizing the bounding-box regression. Experimental results on the TT100K dataset show that the mean average precision (mAP@0.5) reaches 88.1%, which is a 13.5% improvement over YOLOv8n. The method’s versatility is also validated through experiments on the BDD100K dataset, where it is compared with various object-detection algorithms. The results demonstrate that this method yields significant improvements and practical value in the field of small-object detection. Detailed code can be found at . [ABSTRACT FROM AUTHOR]

Cracks are a common problem in concrete surfaces. With the continuous optimization of machine vision-based inspection systems, effective crack detection and recognition is the core of the entire system. In this study, support vector machine (SVM) was used to distinguish cracks from other regions. To complete the recognition system of the SVM, a framework consisting of an image processing and recognition model was proposed. An algorithm combining the Prewitt operator with the Otsu threshold was proposed for image segmentation. The binary image processed by the new algorithm combined with mathematical morphology can result in a more complete crack zone and fewer interference regions. After the initial parameter extraction, most of the impurity areas were screened by preliminary discrimination, removing 99% of the impurities. This processing step ensured the balance and effectiveness of the samples. To establish an automatic identification model based on SVM with a radial basis function, compactness, occupancy rate, and length–width ratio were selected as input parameters after comparing these three features with all six features of the crack. The recognition accuracy of this system reaches 97.14%, demonstrating that the proposed method is effective and satisfies practical requirements. [ABSTRACT FROM AUTHOR]

In this paper, a framework of obstacle avoidance algorithm applied to power line damage safety distance detection is constructed, and its overall architecture and key processes are described in detail. The system design covers three core modules: visual data acquisition and preliminary processing, accurate target recognition and distance measurement, and system error analysis and correction. In the visual data processing chain, we deeply analyze every step from image acquisition to preprocessing to feature extraction, aiming to enhance the adaptability of applications to complex scenes. The target recognition and distance estimation part integrates advanced technology of deep learning to improve the reliability of recognition accuracy and distance estimation. In addition, many common error sources, such as system bias, parallax discontinuity, fluctuation of illumination conditions, etc., are discussed in depth, and corresponding correction strategies are proposed to ensure the accuracy and stability of the system, which provides powerful technical support for achieving efficient and accurate safety monitoring. Specifically, by carefully adjusting the learning rate, convolution kernel size, batch size, pooling layer type, and number of hidden layer nodes, we succeeded in improving the overall accuracy from the initial average of 92.4–95%, and the error rate decreased accordingly. [ABSTRACT FROM AUTHOR]

Improving risk assessment (RA) for human-robot collaboration (HRC) is crucial, given challenges in existing RA tools. For example, simulation and testing RA tools lack realism due to simplified models, limited dynamic realism, and sensor integration constraints. This study explores how Artificial Intelligence (AI) can enhance risk assessment methods. Using a systematic literature review, the study analyzes three risk assessment methods: PFMEA, HAZOP, and FTA to which AI has been integrated. Results highlight strengths (e.g., systematic process failure identification, thorough hazard identification, complex system modeling) and limitations (e.g., limited coverage of human-robot dynamics, reliance on historical data, adaptability constraints, resource intensity, design dependency, complexity, human factor oversight, data dependency) in addressing HRC risks. Challenges and opportunities of AI in risk assessment are identified, emphasizing reliability, adaptation, safety, and method accuracy. Adhering to standards significantly improves the trustworthiness of AI-driven risk assessments, ensuring consistent, and validated results across diverse HRC scenarios. The study proposes a hybrid approach that combines multiple methods and incorporates image processing as a practical AI feature to enhance risk detection and prevention. The study advances AI-based risk assessment for HRC, offering a comprehensive overview of the current state of the art, highlighting gaps for future research, and suggesting a holistic solution. [ABSTRACT FROM AUTHOR]

Electrical discharge wire sawing (EDWS) is a hybrid processing method that can effectively remove craters and recast layers generated by discharges through the grinding action of abrasive grains. Under the same processing conditions, the material removal rate (MRR) of EDWS is higher than those of wire electrical discharge machining and fixed abrasive diamond wire sawing. A material removal model for EDWS is established based on the material removal mechanism of hybrid machining revealed in this study. An industrial camera was used to collect the wire saw status during EDWS, and image processing technology was used to obtain the wire saw deflection angle. The MRR for different process parameter settings is predicted using the established model. The effects of pulse width, feed rate, and wire velocity on the wire saw deflection angle and MRR were studied. Finally, the predicted results were verified through experiments. Results show that as the pulse width, feed rate, and wire velocity increases, the MRR increase. The order of impact on the wire saw deflection angle is feed rate > wire velocity > pulse width. In addition, the MRR predicted by the established model agrees with the experimental data, with a maximum error of 9.87%. [ABSTRACT FROM AUTHOR]

Emerging Industry 5.0 designs promote artificial intelligence services and data-driven applications across multiple places with varying ownership that need special data protection and privacy considerations to prevent the disclosure of private information to outsiders. Due to this, federated learning offers a method for improving machine-learning models without accessing the train data at a single manufacturing facility. We provide a self-adaptive framework for federated machine learning of healthcare intelligent systems in this research. Our method takes into account the participating parties at various levels of healthcare ecosystem abstraction. Each hospital trains its local model internally in a self-adaptive style and transmits it to the centralized server for universal model optimization and communication cycle reduction. To represent a multi-task optimization issue, we split the dataset into as many subsets as devices. Each device selects the most advantageous subset for every local iteration of the model. On a training dataset, our initial study demonstrates the algorithm's ability to converge various hospital and device counts. By merging a federated machine-learning approach with advanced deep machine-learning models, we can simply and accurately predict multidisciplinary cancer diseases in the human body. Furthermore, in the smart healthcare industry 5.0, the results of federated machine learning approaches are used to validate multidisciplinary cancer disease prediction. The proposed adaptive federated machine learning methodology achieved 90.0%, while the conventional federated learning approach achieved 87.30%, both of which were higher than the previous state-of-the-art methodologies for cancer disease prediction in the smart healthcare industry 5.0. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem that minimizing the sum of a nonsmooth function with a smooth one in the nonconvex setting, which arising in many contemporary applications such as machine learning, statistics, and signal/image processing. To solve this problem, we propose a new nonmonotone accelerated proximal gradient method with variable stepsize strategy. Note that incorporating inertial term into proximal gradient method is a simple and efficient acceleration technique, while the descent property of the proximal gradient algorithm will lost. In our algorithm, the iterates generated by inertial proximal gradient scheme are accepted when the objective function values decrease or increase appropriately; otherwise, the iteration point is generated by proximal gradient scheme, which makes the function values on a subset of iterates are decreasing. We also introduce a variable stepsize strategy, which does not need a line search or does not need to know the Lipschitz constant and makes the algorithm easy to implement. We show that the sequence of iterates generated by the algorithm converges to a critical point of the objective function. Further, under the assumption that the objective function satisfies the Kurdyka–Łojasiewicz inequality, we prove the convergence rates of the objective function values and the iterates. Moreover, numerical results on both convex and nonconvex problems are reported to demonstrate the effectiveness and superiority of the proposed method and stepsize strategy. [ABSTRACT FROM AUTHOR]

Solar loops are hot and magnetic structures that often have transverse displacements. In this research, we study the characteristics of three solar loops using the images observed in the radiation region with the Hinode/XRT telescope on 11 September 2017. Fluctuations of solar loops can be traced using two methods: image and spectral processing. In the first method, displacements and disturbances are measured in images, while in the second method, periodic patterns are searched in Doppler shifts by spectrometers. Our analysis method is based on image processing and solar loop seismology. In this method, the time slices can be prepared from the images and then analyzed using the wavelet method. The measurement results of time series for three loops with lengths of 113040, 79128, and 62800 km were obtained in the range of 3–18, 3–6, and 3–16 min, respectively. The phase velocity of these oscillations was calculated in the range of 209–1256, 438–879.2, and 130–697.7 km s−1 for three loops, respectively. Also, Alfven speeds were determined as 150–1012, 316–708.8, and 94–562.5 km s−1. The magnetic field related to these fluctuations was calculated by the seismological method with the approximation of the thin cylindrical model for the loops measuring 2.47–16.52, 3.6–11.56, and 1.1–9.18 Gauss. Our findings suggest that oscillating movements in the loops can be interpreted as Kink transverse oscillations. [ABSTRACT FROM AUTHOR]

This study investigates the processing methods of artistic images within the context of Smart city (SC) initiatives, focusing on the visual healing effects of artistic image processing to enhance urban residents' mental health and quality of life. Firstly, it examines the role of artistic image processing techniques in visual healing. Secondly, deep learning technology is introduced and improved, proposing the overlapping segmentation vision transformer (OSViT) for image blocks, and further integrating the bidirectional long short-term memory (BiLSTM) algorithm. An innovative artistic image processing and classification recognition model based on OSViT-BiLSTM is then constructed. Finally, the visual healing effect of the processed art images in different scenes is analyzed. The results demonstrate that the proposed model achieves a classification recognition accuracy of 92.9% for art images, which is at least 6.9% higher than that of other existing model algorithms. Additionally, over 90% of users report satisfaction with the visual healing effects of the artistic images. Therefore, it is found that the proposed model can accurately identify artistic images, enhance their beauty and artistry, and improve the visual healing effect. This study provides an experimental reference for incorporating visual healing into SC initiatives. [ABSTRACT FROM AUTHOR]