Remote Data Processing Research Articles

ASSESSMENT OF THE EVAPOTRANSPIRATION COMPONENTS DYNAMICS IN DIFFERENT AGRO-CLIMATIC ZONES OF UKRAINE USING THE PENMAN-MONTEITH-LEUNING MODEL

The results of the assessment of evapotranspiration (ET) and its components based on remote sensing data are presented in the paper. To obtain this assessment, a special software was developed, namely the scripts using the JavaScript programming language for remote data processing in the Google Earth Engine (GEE) cloud software environment. This software allows to adjust the Penman-Monteith-Leuning (PML) model for the conditions of Ukraine and visualize the spatial distribution of ET. The usage of the cloud capabilities of the service made it possible to access the collection of images and carry out their remote processing using the Penman-Monteith-Leuning algorithm calibrated according to the data of the global network of eddy covariance monitoring stations. The result of such an assessment was a composite mosaic - a spatially distributed generalized image of evapotranspiration and its three main components: transpiration from vegetation (Ec), evaporation from the soil (Es) and evaporation of precipitation intercepted by vegetation cover (Ei) on the territory of Ukraine for the 2020 growing season. Understanding the dynamics of these components helps to optimize the water resources use and develop effective irrigation schemes, especially in the climate change conditions. As a result of the analysis of the evapotranspiration components’ dynamics during the growing season, the most important component of evapotranspiration for different agro-climatic zones was determined. However, the models, which are using remote data to estimate evapotranspiration dynamics, require additional validation and comparison with field measurements to improve their accuracy. Quantitative indicators obtained through modeling should be consistent with the data from ground-based greenhouse gas flows monitoring stations, which will contribute to the improvement of the methodology and its adaptation to the conditions of different agricultural regions. In addition, the use of the maps of geospatial distributed evapotranspiration allows to identify regions with increased transpiration and potential shortage of water resources. Such maps become a valuable tool for planning and forecasting water resources, which is critically important for the agricultural sector.

Aerial Images Segmentation with Graph Neural Network

Abstract. The development of remote sensing platforms and sensors, as well as the improvement of remote data processing tools and methods, create new opportunities for automatic updating of maps. Currently, aerial photographs serve as the main source for automatic map updates due to their accessibility and significant informational value. One of the core elements for image to maps transition is accurate image segmentation. Nowadays, machine learning methods demonstrate the best results in task of image segmentation. At its core, maps represent information about a certain area in a vector form, that not only contains visual information about area, but also reflects some relations between objects in the map. This quality makes a map more convenient for human perception than an aerial photograph (raster image). This study addresses the problem of accurate aerial image segmentation with taking the advantages of using graph neural network as the more adequate model of map structure. We use graph neural network for retrieving semantic and vector information about a captured area from its aerial image. The developed framework at first phase utilizes visual transformer for retrieving deep features from the input aerial image. The graph neural network then performs clustering of the extracted deep features to obtain semantic segmentation of the image. To train and evaluate the developed framework, a special dataset is collected and annotated. It contains more than 10k aerial photographs representing various types of objects taken in different years and seasons. The evaluation results on the created dataset proved the state-of-the-art performance of the developed framework.

IoT-HGDS: Internet of Things integrated machine learning based hazardous gases detection system for smart kitchen

IoT-HGDS: Internet of Things integrated machine learning based hazardous gases detection system for smart kitchen

FPGA hardware kit for remote training platforms

This paper outlines the development of a Hardware Development Kit (HDK) for a remote training platform on FPGA Devices designed to provide university students pursuing degrees in electronic and informatics engineering (at the bachelor’s, master’s, and PhD levels) with the tools they need to learn and develop systems related to artificial intelligence, digital signal processing, image processing, digital systems, integrated and embedded systems using an FPGA chip. Through the internet or a local network, students can engage with the platform to conduct engineering experiments. The HDK is equipped with a Raspberry Pi, a screen, a camera, and LEDs to facilitate the transfer of experiment results and to aid in assessing the validity of lab experiment execution. The Raspberry Pi plays a crucial role in the HDK, providing control and monitoring, remote access, data processing, user interface, and integration with other possible systems.

Метод виділення контурів для вирішення завдань моніторингу транспортної інфраструктури

Introduction. Technologies for monitoring transport infrastructure have been rapidly evolving in recent years, absorbing innovations and the latest developments. The main direction of development and use for this technology has been the implementation of continuous monitoring and control of different aspects of transport infrastructure to ensure its safety and allow efficient and timely management. Computer vision has been playing the main role in the evolution of these technologies and has made unmanned aerial vehicles (UAVs) the most cost-efficient remote monitoring tool. The purpose of the paper. Among the main tasks in the field are monitoring traffic and the conditions of road surfaces and markings. Fast and accurate monitoring systems enable quick responses and minimize negative consequences for citizens. Despite the active development of computer vision algorithms, there is no universal algorithm that suits all scenarios. Algorithms depend on the task, conditions, and even UAV trajectory; even a slight change in the visual scene can cause suboptimal results. Lately, significant progress has been made in the development of edge detection algorithms. However, they do not consider the specifics of the task of monitoring road markings. The algorithm should consider the characteristics of the objects of interest – their geometric and color features, and the presence of many other objects in the images. The goal of this paper is to present method crafted specifically for the task of monitoring transport infrastructure. Methods. Computer vision, threshold filtering, Sobel operator, noise removal, probabilistic Hough transform, histograms. Results. The main features of the task of monitoring transport infrastructure using visual data obtained from surveillance cameras or unmanned aerial vehicles have been analyzed. Developed an algorithm for boundary extraction of point clusters using histograms. A method for edge detection in images has been developed, which addresses the shortcomings of known methods and is specifically enhanced for transport infrastructure monitoring. The method leverages the narrow specialization of the task to improve the obtained results. The foundation of the method is based on the features of the HSL color model, filtering in the saturation and lightness channels using gradients obtained from the Sobel operator, segment detection based on the probabilistic Hough transform, and a developed algorithm for boundary extraction of point clusters using histograms. Conclusion. The proposed method can be used in automated and semi-automated decision-making systems, UAV design bureaus, UAV manufacturing enterprises, and information-analytical centers to develop unmanned aviation systems and aerial monitoring technologies to enhance human safety and the economic development of the state. The use of automatic remote monitoring data processing methods allows for faster acquisition of necessary results and improves the efficiency of using geospatial data. Keywords: Computer vision, object detection, edge detection, image filtering, transportation infrastructure, information technology, monitoring.

Monitoring and management of green information in water ecological environment based on sensors and big data

Monitoring and management of green information in water ecological environment based on sensors and big data

Facilitate Driving with Geographical Proximity

Abstract: This research paper describes the development of a website based on remote computing with the aim of improving the connection between driving schools and automobile students. The primary purpose of this web application is to provide administrators, parents, and students with actual vehicle information, including clutch usage, speed, braking patterns, and vehicle location. This is achieved through the integration of remote data processing installed in the vehicle and sending the relevant data to web applications. This article discusses the importance of these systems in improving driver training and safety by allowing participants to remotely monitor vehicle operation, driving behavior and actions. We completed the literature review to examine existing telematics systems and their applications in the automotive industry. The work includes the selection and installation of remote data processing equipment, the development of user- friendly web interfaces using modern programming languages and methods, and scrupulous compliance with technical procedures. The main features of the web application include real-time data tracking, GPS tracking, important event alerts and notifications, and reporting and analysis capability. The implementation process is quite detailed, including hardware integration, front-end and back-end development, data storage and security measures.

Wireless strain-field monitoring system for motion recognition via direct-ink-writing sensor-array

Wireless strain-field monitoring system for motion recognition via direct-ink-writing sensor-array

Real-time performance evaluation and optimization of electrical substation equipment inspection algorithm based on distributed computing

Abstract Substation equipment inspection faces problems such as large amounts of data, various information types, complex analysis and calculations, and low resource integration. Distributed computing system uses networks to realize computer connectivity and collaboration, achieving in-depth sharing of computing resources, software resources, information resources, and communication resources. Therefore, firstly, this article introduces segmentation method for load balancing and recommendation calculation based on path search; secondly, an algorithm model for substation equipment inspection based on distributed computing is constructed; then, the article analyzes and discusses multi-core cluster task allocation, heterogeneous task scheduling framework, fragmented transmission mechanism, and vertical expansion mechanism; finally, case analysis and performance evaluation are carried out. Distributed computing has significant advantages in the inspection algorithm of power substation equipment. It can efficiently process large-scale data, improve computing power, ensure data redundancy and fault tolerance, provide good scalability, optimize resource utilization, and support remote data processing. By integrating multi-source data, distributed computing improves the accuracy and comprehensiveness of the algorithm, enhances security, supports complex algorithms such as machine learning and deep learning, and realizes real-time monitoring and early warning. The results show that the proposed algorithm can improve the efficiency of distributed parallel computing and analysis of substation equipment inspections and can simulate, evaluate, and screen substation equipment failure scenarios that may cause serious consequences within an effective time.

JIRIAF: JLAB Integrated Research Infrastructure Across Facilities

The JIRIAF project aims to combine geographically diverse computing facilities into an integrated science infrastructure. This project starts by dynamically evaluating temporarily unallocated or idled compute resources from multiple providers. These resources are integrated to handle additional workloads without affecting local running jobs. This paper describes our approach to launch best-effort batch tasks that exploit these underutilized resources. Our system measures the real-time behavior of jobs running on a machine and learns to distinguish typical performance from outliers. Unsupervised ML techniques are used to analyze hardware-level performance measures, followed by a real-time crosscorrelation analysis to determine which applications cause performance degradation. We then facilitate bad behavior by throttling these processes. We demonstrate that problematic performance interference can be detected and acted on, which makes it possible to continue to share resources between applications and simultaneously maintain high utilization levels in a computing cluster. For a case study, we relocated the CLAS12 data processing workflow to a remote data processing facility, preventing file migration and temporal data persistency.

HTTP embedded web server and its application for air parameters monitoring

Embedded systems find extensive applications in various aspects of daily life and their presence in the highly connected world of today is growing. With a large number of remote devices and sensors, there is a growing need for remote data processing. Microcontrollers, often integral components of such devices, can enable remote data manipulation through the implementation of a web server called embedded web servers. The implementation of a server on such a system poses challenges, such as limited resources, so it is crucial for the web server implementation to be compatible with the available resources. This paper focuses on the requirements for the implementation of a web server. Also, an embedded web server has been designed for specific application of monitoring air parameters. The implementation utilizes the LwIP stack and allows for remote access to measurement results within a local network. The paper will delve into the operational principles of both the web server and the entire system.

Effects of the Implementation of Intelligent Technology for Hand Hygiene in Hospitals: Systematic Review and Meta-analysis.

The World Health Organization recommends regular hand hygiene monitoring and feedback to improve hand hygiene behaviors and health care-associated infection rates. Intelligent technologies for hand hygiene are increasingly being developed as alternative or supplemental monitoring approaches. However, there is insufficient evidence regarding the effect of this type of intervention, with conflicting results in the literature. We conduct a systematic review and meta-analysis to evaluate the effects of using intelligent technology for hand hygiene in hospitals. We searched 7 databases from inception to December 31, 2022. Two reviewers independently and blindly selected studies, extracted data, and assessed the risk of bias. A meta-analysis was performed using the RevMan 5.3 and STATA 15.1 software. Sensitivity and subgroup analyses were also conducted. Overall certainty of evidence was appraised using the Grading of Recommendations Assessment, Development, and Evaluation approach. The systematic review protocol was registered. The 36 studies comprised 2 randomized controlled trials and 34 quasi-experimental studies. The included intelligent technologies involved 5 functions: performance reminders，electronic counting and remote monitoring，data processing，feedback，and education. Compared with usual care, the intelligent technology intervention for hand hygiene improved health care workers' hand hygiene compliance (risk ratio 1.56, 95% CI 1.47-1.66; P<.001), reduced health care-associated infection rates (risk ratio 0.25, 95% CI 0.19-0.33; P<.001), and was not associated with multidrug-resistant organism detection rates (risk ratio 0.53, 95% CI 0.27-1.04; P=.07). Three covariates, including publication year, study design, and intervention, were not factors of hand hygiene compliance or hospital-acquired infection rates analyzed by meta-regression. Sensitivity analysis showed stable results except for the pooled outcome of multidrug-resistant organism detection rates. The caliber of 3 pieces of evidence suggested a dearth of high-caliber research. Intelligent technologies for hand hygiene play an integral role in hospital. However, low quality of evidence and important heterogeneity were observed. Larger clinical trials are required to evaluate the impact of intelligent technology on multidrug-resistant organism detection rates and other clinical outcomes.

FEATURES OF CREATING MANAGEMENT ACCOUNTING OF THE ENTERPRISE BASED ON CLOUD TECHNOLOGIES

This article is devoted to the application of cloud technologies in the management accounting automation system at enterprises. The author considered the terminology of "cloud" and "cloud technologies" and adapted the definition of "cloud management accounting technology" as a technology that allows remote processing and storage of data for thepurpose of making management decisions and managing an economic object. The main advantages of using cloud technologies in management accounting were systematize. The advantages such as a business scaling and its information security, communication speed and data access, backup, update and administration of cloud technologies and databases. In this article, the author singles out specific tasks of management accounting that affect the development and use of information technologies in its automation. The tasks such as: timely provision of information to officials in order to make management decisions for each group of information users; provision of real, timely and comprehensive information about the object of management; provision of a wide range of modern tools for analysis and management decision-making. The issue of processing accounting information in the clouds and setting up communication between the internal and external environment is covered. The author also defined the procedure for processing accounting information based on cloud technologies, which consists of four gradual stages.The author has defined the features of building management accounting of the enterprise based on cloud technologies. The features among which are: taking into account the range of users for whom the management cloud of the enterprise will be available; determination of a specific list of management tasks that can be automated on the basis of cloud technologies; selection, in accordance with the first two features, of a specific list of cloud tools

A Deep Learning-Based Indoor Odor Compass

Mobile robot-based odor source localization (OSL) has broad applications in various industrial and daily-life scenarios. To this end, a deep learning-based odor compass is designed in this work. Functionally, the designed odor compass is divided into three primary modules, which are the sensing module (i.e., a sensor array composed of four metal-oxide-semiconductor (MOS) gas sensors), the communication module, and the remote data processing module (i.e., a deep learning-based algorithm). In particular, a deep learning-based odor attention (DL-OA) model is proposed to realize an end-to-end odor source direction estimation (OSDE) based on the responses of gas sensor array. Moreover, the proposed DL-OA model adopts a separated spatial-temporal attention-based encoder-decoder structure. Furthermore, the average validation error in estimating the OSD in an indoor environment is 4.98°, essentially demonstrating the effectiveness of designed odor compass.

Living Tree Moisture Content Detection Method Based on Intelligent UHF RFID Sensors and OS-PELM.

Moisture content (MC) detection plays a vital role in the monitoring and management of living trees. Its measurement accuracy is of great significance to the progress of the forestry informatization industry. Targeting the drawbacks of high energy consumption, low practicability, and poor sustainability in the current field of living tree MC detection, this work designs and implements an ultra-high-frequency radio frequency identification (UHF RFID) sensor system based on a deep learning model, with the main goals of non-destructive testing and high-efficiency recognition. The proposed MC diagnostic system includes two passive tags which should be mounted on the trunk and one remote data processing terminal. First, the UHF reader collects information from the living trees in the forest; then, an improved online sequential parallel extreme learning machine algorithm (OS-PELM) is proposed and trained to establish a specific MC prediction model. This mechanism could self-adjust its neuron network structure according to the features of the data input. The experimental results show that, for the entire living tree dataset, the MC prediction model based on the OS-PELM algorithm can identify the MC level with a root-mean-square error (RMSE) of no more than 0.055 within a measurement range of 1.2 m. Compared with the results predicted by other algorithms, the mean absolute error (MAE) and RMSE are 0.0225 and 0.0254, respectively, which are better than the ELM and OS-ELM algorithms. Comparisons also prove that the prediction model has the advantages of high precision, strong robustness, and broad applicability. Therefore, the designed MC detection system fully meets the demand of forestry Artificial Intelligence of Things.

Low-Frequency Trajectory Map Matching Method Based on Vehicle Heading Segmentation

Numerous Global Positioning System connected vehicles are collecting extensive data remotely in cities, enabling data-driven infrastructure planning. To truly benefit from this emerging technology, it is important to combine telematics and map data to make it easier to extract and mine useful information from the data. By performing map matching, data points that cannot be accurately located on the road network can be projected onto the correct road segment. As an important means of remote data processing, it has become an important pre-processing step in the field of data mining. However, due to the various errors of location devices and the complexity of road networks, map matching technology also faces great challenges. In order to improve the efficiency and accuracy of the map matching algorithm, this study proposes an offline method for low-frequency trajectory data map matching based on vehicle trajectory segmentation. First, the trajectory is segmented based on the vehicle’s travel direction. Then, the comprehensive probability of the corresponding road segment is calculated based on the spatial probability and the directional probability of each road segment around the location. Third, the k candidate matching paths under consideration are selected based on the comprehensive probability evaluation. Finally, the shortest path planning and the probability calculation of the different candidate paths are combined to find the optimal matching path. The experimental results on the real trajectory dataset in Shanghai and the road network environment show that the proposed algorithm has better accuracy, efficiency, and robustness than other algorithms.

Діагностична система аналізу log-файлів із віддалених вузлів обробки даних

To monitor and maintain the proper functioning of distributed systems, there are used log events to further analyze and identify the circumstances and causes of deviations from normal operation. Tools to achieve this goal should primarily provide: 1) rapid search and processing of log files in the process of analysis and identification of failure causes; 2) generation of offers for solving the identified problem. This article presents the results of the analysis of the exist-ing tools for logging and log file processing and substantiates the feasibility of the development of the proposed diagnostic system. The general structure of the developed diagnostic system and its functional model are described. In the paper, there are considered the functions of the main software modules: a parser that processes a log file and collects parts of messages into a single log object; a diagnostic module that processes information about an error in a log file ob-ject; and a module for accumulating and using knowledge about the known node malfunctions, ways to fix them and problems that were not previously identified. The choice of MongoDB NoSQL for saving the log file data and knowledge base storage DBMS is substantiated. A uni-fied structure for log file objects has been developed for the MongoDB environment. Diagnos-tics system control – interoperability of modules and their interaction with the database and knowledge base in the MongoDB environment – is provided by the main program LogHelper which allows the user to perform different queries on log objects, monitor their results, inspect known and not previously identified errors, work with unknown malfunctions, manually set their statuses and find a solution from an existing set of solutions. The results of the conducted research show that the developed diagnostic system is highly effective as a maintenance solu-tion for remote data processing nodes and it supports rapid identification of the malfunction and generation of proposed solutions.

The transformation of medical statistic during pandemic of new coronavirus infection (COVID-19)

The pandemic of new coronavirus infection (COVID-19) directly effected medical statistics service. The amendments to the Federal Law "On the Official Statistical Accounting and the System of State Statistics in the Russian Federation" adopted in December 2020, regulated the provision of primary statistical data according forms of Federal and industrial statistical observation in the format of electronic document signed with electronic signature. This required the development of system of collecting and processing statistical data at the Federal level applying new technological solutions. The purpose of the study. To analyze the changes in the system of collecting and processing annual reporting on medical statistics for 2020 during the pandemic of new coronavirus infection COVID-19. The analysis was made concerning both normative legal base regulating implementation of information systems and system of receiving annual reports for current and previous years. Also content analysis was applied and materials distributed Internet were used. In conditions of new coronavirus infection (COVID-19) pandemic, instead of classical system of informational interaction at face-to-face coordination of annual report data, in extremely short terms new model of informational interaction of remote coordination and processing of annual report data was developed. The updated technological scheme was applied that included data transmission, remote coordination in VKS format, informing thriugh Telegram-channels and signing finalized forms with enhanced electronic digital signature. The complicated epidemiological situation regarding morbidity of new coronavirus infection (COVID-19) and as well as adoption of amendments in Federal legislation regarding provision of statistical observation forms in format of electronic document signed with electronic signature, required revision of format of statistical reports reception in 2020. The application of developed technology of collecting and processing annual reports data on medical statistics in online format permitted to dispense with both provision of hard copies versions of forms and reports and business trips of specialists from the subjects of the Russian Federation to Moscow that reduced expenses of the subjects of the Russian Federation when submitting annual reports. The developed mechanism of signing with unqualified electronic digital signature permitted to control data integrity. The applied mechanism for signing finalized concerted forms with enhanced electronic digital signature of public authority of the subject of the Russian Federation in the field of health protection ensured juridical significance of document provided. The establishment of base for further modernization of system of collecting statistical information from primary data, including subsequent implementation of structured electronic medical documents is proposed.

Contemporary treatment trends for upper urinary tract stones in a total population analysis in Germany from 2006 to 2019: will shock wave lithotripsy become extinct?

PurposeTo describe the change in upper urinary tract stone management in Germany over a 14-year period.MethodsUsing remote data processing we analyzed the nationwide German billing data from 2006 to 2019. To analyze the clinics’ case numbers and regional trends, we used the reimbursement.INFO tool based on standardized quality reports of all German hospitals. To also cover shock wave lithotripsy (SWL) as an outpatient procedure, we analyzed the research database of the Institute for Applied Health Research with a representative anonymous sample of 4 million insured persons.ResultsThe number of inpatient interventional therapies for upper tract urolithiasis in Germany increased from 70,099 cases in 2006 to 94,815 cases in 2019 (trend p < 0.0001). In-hospital SWL declined from 41,687 cases in 2006 to 10,724 cases in 2019 (decline of 74%; trend p < 0.0001). The percentage of SWL as an outpatient procedure increased between 2013 and 2018 from 36 to 46% of all performed SWL, while total SWL case numbers declined. Contrarily, the number of ureteroscopies increased from 32,203 cases in 2006 to 78,125 cases in 2019 (increase of 143%; trend p < 0.0001). The number of percutaneous nephrolithotomy also increased from 1673 cases in 2006 to 8937 in 2019 (increase of 434%; trend p < 0.0001).ConclusionWe observed an increase in interventional therapy for upper tract urolithiasis in Germany with a dramatic shift from SWL to endoscopic/percutaneous treatment. These changes may be attributed to enormous technological advances of the endoscopic armamentarium and to reimbursement issues.

Object adaptation of Drag and Drop technology for web-system interface components

Today, in the development of information systems, cloud technologies are often used for remote computing and data processing. There are web technologies, and on their basis, libraries and frameworks have been developed for creating web applications and user interfaces designed for the operation of information systems in browsers. Ready-made JavaScript libraries have been developed to add drag and drop functionality to a web application. However, in some situations, the library may not be available, or there may be overhead or dependencies that the project does not need to use it. In such situations, an alternative solution provides the functionality of APIs available in modern browsers. The article discusses the current state of the methods of the Drag and Drop mechanism and proposes a programmatic way to improve the interface by creating a class for dragging and dropping elements when organizing work in multi-user information web systems. Drag and Drop is a convenient way to improve the interface. Grabbing an element with the mouse and moving it visually simplifies many operations: from copying and moving documents, as in file managers, to placing orders in online store services. The HTML drag and drop API uses the DOM event model to retrieve information about a dragged element and update that element after the drag. Using JavaScript event handlers, it is possible to turn any element of the web system into a drag-and-drop element or drop target. To solve this problem, a JavaScript object was developed with methods that allow you to create a copy of any object and handle all events of this object aimed at organizing the Drag and Drop mechanism. Basic algorithm of Drag and Drop technology based on processing mouse events. The software implementation is considered and the results of the practical use of object adaptation of the Drag and Drop technology for the interface components of the web system - the medical information system MedSystem, in which the application modules have the implementation of the dispatcher and the interactive window interface are presented. In the "Outpatient clinic" module, the Drag and Drop mechanism is used when working with the "Appointment sheet". In the "Hospital" module of the MedSystem medical information system, the Drag and Drop mechanism is used in the "List of doctor's appointments". The results of using object adaptation of Drag and Drop technology have shown that this mechanism organically fits into existing technologies for building web applications and has sufficient potential to facilitate and automate work in multi-user information systems and web services.