Image Data Processing Research Articles

Combustion visualization analysis of alternative fuels in the pulverized coal injection raceway through laminar flow reactor

Currently, the steelmaking process uses a pulverized coal injection (PCI) system that serves as the heat source and reductant for ironmaking (blast furnace and FINEX) where system uses expensive high-grade coal and high operating costs. Hydrogen steelmaking is currently being developed to achieve carbon-free operation. To achieve a soft-landing during this phase of rapid change, the use of biomass and inexpensive, thermal coal, and coke dust is necessary. Research on their combustion characteristics is necessary to apply these alternative fuels to PCI. Therefore, this study analyzed the combustion characteristics of ignition delay, devolatilization, and char combustion using a laminar flow reactor visualization equipment that simulates blast furnace (BF) and FINEX PCI tuyere, using flame image data processing. The ignition time were generally longer in BF than in FINEX, and the char combustion length and time also showed the same trend due to the high oxygen rate which indicate under 2 ms on ignition delay, under 16 ms on char combustion. Also, the volatile cloud was qualitatively shown in the image to be highest in thermal coal and biomass with high volatile matter. Based on the correlation and theoretical calculation with proximate analysis and the results, ignition delay time had a combined effect of volatile matter and moisture except coke dust, and char combustion time affected unburned carbon. The combustion chemical characteristics were discussed with chemical percolation devolatilization (CPD) model parameter. Through SEM image and BET analysis, the surface area has been increased more than 10 times after combustion. Consequently, the biomass and high moisture thermal coal could cofired within 10 % and coke dust could be cofired within 9 %, respectively.

SPR humidity dynamic monitoring method via PVA sensing membrane thickness variation and image processing techniques

Humidity monitoring is paramount in diverse applications, industrial, and medical applications. Surface Plasmon Resonance (SPR) is an optical detection technique capable of sensing various environmental parameters through changes in reflected optical spectra and has garnered significant attention. Typically, SPR sensing employs a single-point detection strategy with the sample at a fixed concentration to achieve optimal sensitivity, limiting its application in dynamic environmental testing. This study proposes an image-based SPR humidity monitoring method, integrating SPR with image processing, enabling dynamic parameter reconstruction, and achieving high responsiveness. Au-PVA is used as a sensing film. To attain the best sensing film thickness, sensing film thicknesses ranging from 94.0 nm to 243.3 nm were tested. Through optimizing film thickness and image data processing, high precision and dynamic responsiveness were achieved. Experimental results demonstrate a response time of 84 ms and an average relative prediction error of 1.57 % for the sensor. Our research holds significant promise for dynamic and accurate humidity detection.

Optimization Algorithms for Real-Time Image Processing in IoT Networks

This study explores the application of optimization algorithms, with a focus on Particle Swarm Optimization (PSO), to enhance real-time image processing in Internet of Things (IoT) networks. Given the constraints of computational power and energy resources in IoT devices, efficient processing of image data is a critical challenge. The proposed PSO-based framework aims to minimize processing time and energy consumption while maintaining high accuracy in image analysis tasks. Experimental results demonstrate that PSO can reduce processing time by 10% and energy consumption by 15% on average, with a slight improvement in accuracy. Comparisons with traditional and other evolutionary optimization techniques reveal that PSO provides a superior balance between efficiency and performance, making it particularly well-suited for real-time IoT applications. The findings suggest that the integration of advanced optimization algorithms like PSO into IoT systems can significantly enhance their operational efficiency, scalability, and effectiveness, particularly in resource-constrained environments.

Deep Learning Model Construction of Urban Planning Image Data Processing and Health Intelligence System

Deep Learning Model Construction of Urban Planning Image Data Processing and Health Intelligence System

Analysis of Agricultural Land Classification in East Kotawaringin District, Central Kalimantan Province using Landsat 8-OLI Image

Purpose:This research aims to map agricultural land in East Kotawaringin Regency, Kalimantan Province, and analyze it using Landsat 8 OLI satellite imagery in 2023.Methodology:The method used in this research is supervised classification, which will then carry out a data analysis process related to natural conditions in the field. Based on the results of satellite image data processing, there are five categories of land cover classification in the East Kotawaringin Regency area: built-up land, empty land, fields/moors, gardens and forests.Findings:According to the accuracy test results, the overall accuracy value of satellite imagery in East Kotawaringin Regency is 87%. Community activities, such as economic activities and meeting needs for public facilities and housing, influence the distribution of existing land cover areas.Implication:The classification results obtained later through maps of agricultural land in East Kotawaringin Regency can be used to find various studies, especially on expanding the agricultural sector.

Introducing ARTiMiS: A Low-Cost Flow Imaging Microscope for Microalgal Monitoring.

Manual microscopy is the gold standard for phytoplankton monitoring in diverse engineered and natural environments. However, it is both labor-intensive and requires specialized training for accuracy and consistency, and therefore difficult to implement on a routine basis without significant time investment. Automation can reduce this burden by simplifying the measurement to a single indicator (e.g., chlorophyll fluorescence) measurable by a probe, or by processing samples on an automated cytometer for more granular information. The cost of commercially available flow imaging cytometers, however, poses a steep financial barrier to adoption. To overcome these labor and cost barriers, we developed ARTiMiS: the Autonomous Real-Time Microbial 'Scope. The ARTiMiS is a low-cost flow imaging microscopy-based platform with onboard software capable of providing taxonomically resolved quantitation of phytoplankton communities in real-time. ARTiMiS leverages novel multimodal imaging and onboard machine learning-based data processing that is currently optimized for a curated and expandable database of industrially relevant microalgae. We demonstrate its operational limits, performance in identification of laboratory-cultivated microalgae, and potential for continuous monitoring of complex microalgal communities in full-scale industrial cultivation systems.

Integrative approach of omics and imaging data to discover new insights for understanding brain diseases.

Treatments that can completely resolve brain diseases have yet to be discovered. Omics is a novel technology that allows researchers to understand the molecular pathways underlying brain diseases. Multiple omics, including genomics, transcriptomics and proteomics, and brain imaging technologies, such as MRI, PET and EEG, have contributed to brain disease-related therapeutic target detection. However, new treatment discovery remains challenging. We focused on establishing brain multi-molecular maps using an integrative approach of omics and imaging to provide insights into brain disease diagnosis and treatment. This approach requires precise data collection using omics and imaging technologies, data processing and normalization. Incorporating a brain molecular map with the advanced technologies through artificial intelligence will help establish a system for brain disease diagnosis and treatment through regulation at the molecular level.

ANALISIS PERKEMBANGAN LAHAN TERBANGUN BERDASARKAN METODE SUPERVISED CLASSIFICATION MENGGUNAKAN GOOGLE EARTH ENGINE (STUDI KASUS: DESA CIPUTI, KECAMATAN PACET, KAB.CIANJUR)

Monitoring the development of built-up areas can be done by observing remote sensing time series data such as Satellite Imagery. Google Earth Engine (GEE) makes it easy for users to access satellite image data, data processing and data analysis. GEE provides various machine learning algorithms to extract land cover data. This research aims to analyze the development of built-up areas using time series of remote sensing data, namely Sentinel 2A images recorded in 2019 and 2023 and comparing Random Forest (RF), Classification and Regression Tree (CART), Support Vector Machine (SVM) and Gradient Tree Boost (GTB) algorithms and predicts built-up areas in 2027. Based on the results of this research, RF is the algorithm with the highest accuracy in mapping land cover in Ciputri Village with an Overall Accuracy (OA) of 92% and a Kappa Coefficent (KC) of 0.89 in both the 2019 and 2023 classification results, while the lowest accuracy is the SVM algorithm. A comparison of the built-up land area between the 2019 and 2023 classification results shows a decrease in the built-up land area of 3.08 ha. Meanwhile, the prediction results for 2027 show an increase in built-up areas to 114.72 ha.

Defect monitoring system of the internal structures of a sodium fast reactor using an artificial intelligence model

This study developed a defect-monitoring system with an artificial intelligence model, YOLOv7, which is tailored for processing image data from an ultrasonic visualization system within sodium fast reactor (SFR) internal structures. For the safety of SFR internal structures, although it is a crucial inspection for defect monitoring, it is difficult to identify structural defects because of the invisible environment. Therefore, we applied the YOLOv7 model in this study; however, we encountered challenges including decreased accuracy with complex defect shapes and complications from data augmentation during pre-training. To solve these problems, we additionally applied the enhanced super-resolution generative adversarial network for higher resolution and a Sobel noise-filtering algorithm to enhance the defect detection accuracy. And we evaluated our system by comparing it with a confidence score. This underscores the effectiveness of the approach in enhancing the defect detection capabilities. Therefore, this defect-monitoring system should be designed to preemptively identify internal structure deformations and enhance SFR safety and maintenance practices.

TomoPyUI: a user-friendly tool for rapid tomography alignment and reconstruction.

The management and processing of synchrotron and neutron computed tomography data can be a complex, labor-intensive and unstructured process. Users devote substantial time to both manually processing their data (i.e. organizing data/metadata, applying image filters etc.) and waiting for the computation of iterative alignment and reconstruction algorithms to finish. In this work, we present a solution to these problems: TomoPyUI, a user interface for the well known tomography data processing package TomoPy. This highly visual Python software package guides the user through the tomography processing pipeline from data import, preprocessing, alignment and finally to 3D volume reconstruction. The TomoPyUI systematic intermediate data and metadata storage system improves organization, and the inspection and manipulation tools (built within the application) help to avoid interrupted workflows. Notably, TomoPyUI operates entirely within a Jupyter environment. Herein, we provide a summary of these key features of TomoPyUI, along with an overview of the tomography processing pipeline, a discussion of the landscape of existing tomography processing software and the purpose of TomoPyUI, and a demonstration of its capabilities for real tomography data collected at SSRL beamline 6-2c.

A novel edge computing approach to astronomical image data processing based on sCMOS camera using SoC

The ever-growing deluge of astronomical data challenges traditional server-based processing, hindering real-time analysis and scientific discovery. This paper proposes a novel approach: edge computing directly on an sCMOS camera using a System-on-Chip (SoC) architecture currently developed at Creotech Instruments. We present a custom-designed camera equipped with an FPGA-based SoC, enabling on-board preprocessing and feature extraction of astronomical images. This significantly reduces data transmission, minimizes latency, and empowers real-time decision-making for critical observations. We showcase the camera's capabilities through real-world scenarios, demonstrating its usability in astronomy.

Georectifying drone image data over water surfaces without fixed ground control: Methodology, uncertainty assessment and application over an estuarine environment

Light-weight consumer-grade drones have the potential to provide geospatial image data to study a broad range of oceanic processes. However, rigorously tested methodologies to effectively and accurately geolocate and rectify these image data over mobile and dynamic water surfaces, where temporally fixed points of reference are unlikely to exist, are limited. We present a simple to use automated workflow for georectifying individual aerial images using position and orientation data from the drone's on-board sensor (i.e. direct-georectification). The presented methodology includes correcting for camera lens distortion and viewing angle and exploits standard mathematics and camera data processing techniques. The method is used to georectify image datasets from test flights with different combinations of altitude and camera angle. Using a test site over land, directly-georectified images, as well as the same images georectified using standard photogrammetry software, are evaluated using a network of known ground control points. The novel methodology performs well with the camera at nadir (both 10 m and 25 m above ground level) and exhibits a mean spatial accuracy of ±1 m. The same accuracy is achieved when the camera angle is 30° at 10 m above ground level but decreases to ±2.9 m at 30° and 25 m. The accuracy changes because the uncertainties are a function of the altitude and angle of the camera versus the ground. Drone in-flight positioning errors can reduce the accuracy further to ±5 m with the camera at 30° and 25 m. An ensemble approach is used to map the uncertainties within the camera field-of-view to show how they change with viewing distance and drone position and orientation. The complete approach is demonstrated over an estuarine environment that includes the shoreline and open water, producing results consistent with the land-based field-tests of accuracy. Overall, the workflow presented here provides a low cost and agile solution for direct-georectification of drone-captured image data over water surfaces. This approach could be used for collecting and processing image data from drones or ship-mounted cameras to provide observations of ocean colour, sea-ice, ocean glitter, sea surface roughness, white-cap coverage, coastal water quality, and river plumes. The Python scripts for the complete image georectification workflow, including uncertainty map generation, are available from https://github.com/JamieLab/SArONG.

Processing and Integration of Multimodal Image Data Supporting the Detection of Behaviors Related to Reduced Concentration Level of Motor Vehicle Users

This paper introduces a comprehensive framework for the detection of behaviors indicative of reduced concentration levels among motor vehicle operators, leveraging multimodal image data. By integrating dedicated deep learning models, our approach systematically analyzes RGB images, depth maps, and thermal imagery to identify driver drowsiness and distraction signs. Our novel contribution includes utilizing state-of-the-art convolutional neural networks (CNNs) and bidirectional long short-term memory (Bi-LSTM) networks for effective feature extraction and classification across diverse distraction scenarios. Additionally, we explore various data fusion techniques, demonstrating their impact on improving detection accuracy. The significance of this work lies in its potential to enhance road safety by providing more reliable and efficient tools for the real-time monitoring of driver attentiveness, thereby reducing the risk of accidents caused by distraction and fatigue. The proposed methods are thoroughly evaluated using a multimodal benchmark dataset, with results showing their substantial capabilities leading to the development of safety-enhancing technologies for vehicular environments. The primary challenge addressed in this study is the detection of driver states not relying on the lighting conditions. Our solution employs multimodal data integration, encompassing RGB, thermal, and depth images, to ensure robust and accurate monitoring regardless of external lighting variations

Magnetic Particle Imaging-Guided Thermal Simulations for Magnetic Particle Hyperthermia.

Magnetic particle hyperthermia (MPH) enables the direct heating of solid tumors with alternating magnetic fields (AMFs). One challenge with MPH is the unknown particle distribution in tissue after injection. Magnetic particle imaging (MPI) can measure the nanoparticle content and distribution in tissue after delivery. The objective of this study was to develop a clinically translatable protocol that incorporates MPI data into finite element calculations for simulating tissue temperatures during MPH. To verify the protocol, we conducted MPH experiments in tumor-bearing mouse cadavers. Five 8-10-week-old female BALB/c mice bearing subcutaneous 4T1 tumors were anesthetized and received intratumor injections of Synomag®-S90 nanoparticles. Immediately following injection, the mice were euthanized and imaged, and the tumors were heated with an AMF. We used the Mimics Innovation Suite to create a 3D mesh of the tumor from micro-computerized tomography data and spatial index MPI to generate a scaled heating function for the heat transfer calculations. The processed imaging data were incorporated into a finite element solver, COMSOL Multiphysics®. The upper and lower bounds of the simulated tumor temperatures for all five cadavers demonstrated agreement with the experimental temperature measurements, thus verifying the protocol. These results demonstrate the utility of MPI to guide predictive thermal calculations for MPH treatment planning.

High-precision rail movement measurement under ambient-light changes via video image progressing

Abstract The advancement of train technology makes monitoring railroad lines increasingly important. The use of cameras to measure rail movement can be done in real-time monitoring. The camera, however, is easily influenced by variations in ambient light intensity. Therefore, this research proposes a method to address the issue. The system can calculate rail displacement without being affected by ambient light intensity and monitor rail displacement in real-time. By using a flexible field programmable gate array-based framework that uses parallel and pipelined architecture to process image data, the system’s efficiency is improved by 24.7%. The system can accurately locate and measure displacement in complex environments despite interference from light and surroundings. Following experimental validation, the detection system achieves 0.07 mm precision and 95.2% detection accuracy in varying light.

Design and Implementation of Intelligent Robot Control System Integrating Computer Vision and Mechanical Engineering

With the rapid development of science and technology and industry, robot technology has become a key pillar in many fields, especially in industrial manufacturing, robots have been widely used in automated operations. However, in the face of the complex and changeable demands of modern industry, simple mechanized operation can no longer be satisfied, and intelligence has become the inevitable trend of robot technology development. By combining computer vision technology, this study provides robots with powerful environmental awareness and target recognition capabilities, thus achieving higher-level autonomous operation and decision-making. Aiming at the intelligent sorting system, this research adopts XYZ mechanical arm as the execution platform, equipped with TCS34725 color sensor to identify the color of articles, and integrates Hikvision industrial camera and NVIDIA Jetson TX2 image processor to capture and process image data, so as to achieve accurate positioning of articles. In the aspect of software design, this research realizes the function of accurately sorting different color items from the conveyor belt by combining color recognition algorithm and image processing technology with mechanical engineering control. The test results of the system show that the system has high accuracy and stability in color recognition and object positioning, and the trajectory accuracy of the manipulator meets the requirements, which verifies the realization of the research goal. The intelligent robot control system designed in this paper not only improves the level of industrial automation and reduces labor costs, but also broadens the application fields of robots in complex environments.

Разработка программы для персонализированного подхода в лечении субретинальных кровоизлияний

Abctract Software development for a personalized approach in the treatment of subretinal hemorrhages R.R. Fayzrakhmanov, E.D. Вosov, V.A. Bogdanova, I.I. Tumanov, G.O. Karpov N.I. Pirogov National Medical Surgical Center, Moscow, Russian Federation Institute of Advanced Training of Physicians N.I. Pirogov National Medical Surgical Center, Moscow, Russian Federation Design Bureau «Rus’», Moscow, Russian Federation Purpose. Development and clinical application of software for individual calculation of the volume of fibrinolytic infusion solution for surgical treatment of submacular hemorrhages. Material and methods. Based on the processing of optical coherence tomography data using threedimensional visualization, the program 3D volume calculator was developed. As a vitreoretinal intervention, a two-port 27G surgery was performed with controlled subretinal administration of a calculated recombinant prourokinase solution through a 38G cannula and hexafluoroethane tamponade at 20% of the volume of the vitreal cavity. Results. Developed software makes it possible to measure the volume of submacular hemorrhage, and also calculates an individualized amount of subretinal infusion solution for reliable displacement of the blood clot to the periphery. 2 weeks after surgery for submacular hemorrhage, complete dislocation was observed in 40 cases (93%). Conclusion. Сurrently, minimally invasive surgery and a personalized approach are the fundamental directions in modern ophthalmology. Application of the proposed software in clinical practice has proven its effectiveness in relation to morphological and functional perspectives in the treatment of submacular hemorrhages. Key words: submacular hemorrhage, software, calculation of fibrinolytic volume, minimally invasive surgery

Implementing ABCD studyⓇ MRI sequences for multi-site cohort studies: Practical guide to necessary steps, preprocessing methods, and challenges

Large multi-site studies that combine magnetic resonance imaging (MRI) data across research sites present exceptional opportunities to advance neuroscience research. However, scanner or site variability and non-standardised image acquisition protocols, data processing and analysis pipelines can adversely affect the reliability and repeatability of MRI derived brain measures. We implemented a standardised MRI protocol based on that used in the Adolescent Brain Cognition Development (ABCD)Ⓡ study in two sites, and across four MRI scanners.Twice repeated measurements of a single healthy volunteer were obtained in two sites and in four 3T MRI scanners (vendors: Siemens, Philips, and GE). Imaging data included anatomical scans (T1 weighted, T2 weighted), diffusion weighted imaging (DWI) and resting state functional MRI (rs-fMRI). Standardised containerized pipelines were utilised to pre-process the data and different image quality metrics and test-retest variability of different brain metrics were evaluated.The implementation of the MRI protocols was possible with minor adjustments in acquisition (e.g. repetition time (TR), higher b-values) and exporting (DICOM formats) of images due to different technical performance of the scanners. This study provides practical insights into the implementation of standardised sequences and data processing for multisite studies, showcase the benefits of containerised preprocessing tools, and highlights the need for careful optimisation of multisite image acquisition.

A novel adaptive gramian angle field based intelligent fault diagnosis for motor rolling bearings

Abstract The Gramian Angle Field (GAF) can encode one-dimensional vibration signals into two-dimensional feature maps containing time correlation information. However, the feature representation ability of traditional GAF-encoded images is limited, making it challenging to further improve fault diagnosis accuracy. To address these issues, an AGAF-CNN intelligent fault diagnosis method is proposed in this paper. In addition to traditional GAF encoding, an adaptive adjustment parameter is introduced that automatically determines its value based on the dynamic distribution characteristics of the input data. This ensures that the resulting feature map after two-dimensional encoding contains optimal fault discriminant features. By leveraging CNN’s capability to process image data, the proposed method extracts deep-level feature representations from the vibration signal feature map and performs accurate diagnosis. The approach is validated using a rolling bearing dataset from Case Western Reserve University in the United States, and superior diagnostic accuracy is demonstrated compared to other vibration signal coding methods combined with CNN-based fault diagnosis methods. The theoretical and practical advantages of AGAF feature coding are explained comprehensively.

Transition zone parameter development in multi-material powder bed fusion: a general approach

Power bed fusion of metals using a laser beam (PBF-LB/M) offers unique possibilities to manufacture functionally graded materials (FGM) consisting of different alloys. These so-called multi-material parts enable their material properties to be tailored to local material requirements. In this paper, a new methodical approach for the production of metal FGM with transition zones oriented in different directions and manufacturing sequences of the different materials is investigated. Existing approaches for the manufacturing of these transition zones were enhanced with graded parameter variations, spatial laser movement modulation techniques (wobbling), and geometric approximations using a step structure. For the validation of the approach and the characterization of the transition zones, the manufactured samples were investigated and characterized using optical microscopy and hardness profile measurements. Furthermore, the density of the transition zones was analyzed by image data processing. The feasibility of the presented methods is shown and the production of defect-free transition zones with controlled expansions for functionally graded materials via PBF-LB/M achieved