Real-time Monitoring Data Research Articles

Real time data monitoring of water resources environment based on computer remote data collection and image analysis

Real time data monitoring of water resources environment based on computer remote data collection and image analysis

Real-Time Patient Indoor Health Monitoring and Location Tracking with Optical Camera Communications on the Internet of Medical Things

Optical Camera Communication (OCC) is an emerging technology that has attracted research interest in recent decades. Unlike previous communication technologies, OCC uses visible light as the medium to transmit data from receivers and cameras to receive the data. OCC has several advantages that can be capitalized in several implementations. However, the Internet of Things (IoT) has emerged as a technology with immense potential. Numerous research endeavors support the IoT’s prospective technology that can be implemented in various sectors, including the healthcare system. This study introduces a novel implementation of the Internet of Medical Things (IoMT) system, using OCC for real-time health monitoring and indoor location tracking. The innovative system uses standard closed-circuit television CCTV setups, integrating deep learning-based OCC to monitor multiple patients simultaneously, each represented by an LED matrix. The effectiveness of the system was demonstrated through two scenarios: the first involves dual transmitters and a single camera, highlighting real-time monitoring of vital health data; the second features a transmitter with dual cameras, focusing patient movement tracking across different camera fields of view. To accurately locate and track the position of LED arrays in the camera, the system used YOLO (You Only Look Once). Data are securely transmitted to an edge server and stored using the REST API, with a web interface providing real-time patient updates. This study highlights the potential of OCC in IoMT for advanced patient care and proposes future exploration in larger healthcare systems and other IoT domains.

Flat-Silk-Cocoon-Based Wearable Flexible Piezoresistive Sensor and Its Performance.

Flexible sensors are becoming the focus of research because they are very vital for intelligent products, real-time data monitoring, and recording. The flat silk cocoon (FSC), as a special form of cocoon, has all the advantages of silk, which is an excellent biomass carbon-based material and a good choice for preparing flexible sensors. In this work, a flexible piezoresistive sensor was successfully prepared by encapsulating carbonized flat silk cocoons (CFSCs) using an elastic matrix polydimethylsiloxane (PDMS). The sensing performance of the material is 0.01 kPa-1, and the monitoring range can reach 680.57 kPa. It is proved that the sensor can detect human motion and has excellent durability (>800 cycles). In addition, a sensor array for a keyboard based on CFSCs was explored. The sensor has a low production cost and a simple preparation process, and it is sustainable and environmentally friendly. Thus, it may have potential applications in wearable devices and human-computer interactions.

Real-Time Data Acquisition and Monitoring of Three Phase Load Balance in Electricity Distribution

A real-time monitoring mechanism for three-phase electrical power distribution panels is essentially needed to ensure that the power is evenly distributed across all phases, so that the efficiency of energy usage could be maintained. This paper presents monitoring and data acquisition system for three-phase balance distribution. The system ensures balanced distribution of electrical power in order to enhance the efficiency and reliability of power systems. It leverages advanced technologies to collect, analyze, and report data, providing valuable insights into the performance of the power distribution network. Data acquisition is established by using sensor PZEM004T to measure the electricity parameters which is integrated with Arduino microcontroller. In addition, real-time monitoring system is visualized through dashboard data management for more informative data acquisition and analytic purposes. The performance is evaluated by testing the instrument and application at a distribution panel within a span of five days to monitor electrical parameters. The results show that real-time monitoring system for three phase balance works perfectly to provide actual data and phase balance condition. The phase imbalance values are higher during the peak working hours where the loads are dynamics and extensively used.

Road traffic noise exposure assessment based on spatiotemporal data fusion

The current static assessment methods for road traffic noise exposure have limitations in representing spatiotemporal variations and only account for the noise exposure of registered residents. To address these issues, this study proposes an assessment method by fusing spatiotemporal noise and population distributions. This approach estimates spatiotemporal noise distributions through real-time noise monitoring data and an update model of noise source intensity calibrated by historical noise monitoring data and road segment speed data. Meanwhile, spatiotemporal population distributions are generated by aggregating standardized spatiotemporal trajectories of users extracted from mobile phone data. The proposed method is applied to evaluate the population exposure to traffic noise in a Chinese city in different hourly periods. The results demonstrate that this method is a quantitative tool for assessing road traffic noise exposure dynamically, providing policymakers with valuable information to locate hotspots of traffic noise over short periods of time.

Scalable and Multi-Channel Real-Time Low Cost Monitoring System for PEM Electrolyzers Based on IoT Applications

This paper discusses and evaluates a novel multi-channel real-time architecture aimed at monitoring a Proton Exchange Membrane (PEM) electrolyzer, both at the individual cell and stack levels. The proposed solution includes two primary subsystems: a hardware subsystem dedicated to data acquisition (DAQ) and a software subsystem focused on monitoring purposes. The DAQ subsystem utilizes an Arduino platform, being an affordable and open-source solution. The real-time monitoring data can be encoded in JSON format, widely used as a light-weight inter-exchange data format between a variety of IoT applications. They are also available to be transferred to Excel. Indeed, and to enhance convenience, the proposed system integrates graphs displaying a template based on Excel spreadsheets, which are commonly used in industrial environments. The current, voltage, temperature, and pressure data of both individual cells and stacks were monitored and collected, being configurable under a variety of ranges. As a case study, the validation of the system involved static and dynamic operational modes using a 1.2 kW PEM electrolyzer prototype (100 A, 1 A/cm2). The results successfully provided the monitored variables across individual cells and within the stack. The proposed approach exhibits relevant key characteristics such as scalability, flexibility, user-friendliness, versatility, and affordability and are suitable to monitor PEM electrolyzers in real-time at both the cell and stack levels.

COVID-19 Pandemic Preparedness and Response in Latin American Countries: Scoping Review and Analysis Based on World Health Organization Standards.

To describe COVID-19 pandemic preparedness and response in 6 countries during 2020 and 2021. A literature search was conducted in MEDLINE, Web of Science, Epistemonikos, LILACS, and Google Scholar regarding the measures adopted to face the COVID-19 pandemic; the checklist of the international World Healths Organization's (WHO) guide "COVID-19 Strategic Preparedness and Response Plan" was applied. Most countries coordinated their response at the central level with the establishment of management teams and the activation of emergency operations centers for the coordination of the response to the pandemic. The capacity to perform epidemiological surveillance activities such as early case detection, contact tracing, and real-time monitoring of data was exceeded. There was low capacity in laboratory infrastructure, specialized human talent, and deployment of molecular testing for SARS-CoV-2 detection. This is the first review that addresses the importance of documenting how well-prepared countries in the region were to face a pandemic such as COVID-19 taking into account WHO standards and guidelines. In Latin America, the literature showed reports of heterogeneous responses to the pandemic, a gap in the literature on laboratory and diagnostic pillar activities, and a lack of literature reporting on epidemiological surveillance pillar activities in Argentina, Chile, and Ecuador.

Smart Building Control: An Android Application for Enhanced Monitoring and Management in the Internet of Things Era

In the dynamic landscape of technology, the intersection of Smart Buildings and Internet of Things (IoT) devices continues to hold substantial promise. The integration of billions of smart devices with computing systems empowers the identification, analysis, and influence of various actions in our daily lives. A prevalent approach in IoT implementation involves leveraging Android systems, where Android applications play a pivotal role in front-end management. This paper introduces an innovative Android application designed for the comprehensive management of smart buildings, providing users with real-time data monitoring and control capabilities. The application serves as a bridge, bringing users closer to the operational intricacies of smart building systems. Notably, the application is equipped to send timely alerts when predefined limits for humidity, temperature, or light consumption are surpassed, as well as during specific time periods with detected motion. These alerts prompt users to make informed adjustments and decisions, enhancing overall user engagement and responsiveness. The proposed system architecture comprises five distinct layers: Python scripts for sensor data generation, JSON files for data storage, a Flask-based Application Programming Interface (API) for data analysis and conversion, SQLite for efficient data storage, and the Android application for user interaction. This multi-layered framework ensures seamless integration and robust functionality throughout the smart building management process.

Dynamic Life Cycle Assessment Framework of Cold Food Storage Facilities

The imperative of responding to anthropogenic climate change that is driving energy infrastructure managers to implement resilience enhancing policies such as hourly scale dynamic pricing concurrently drives energy intensive industries to monitor energy usage at higher fidelity. This study proposes a Dynamic Life Cycle Assessment (DLCA) framework specifically tailored for cold food storage facilities which provide essential thermodynamic resilience to the human food web and consumes a large share of the web's energy footprint. Different from the conventional LCA, which delivers a snapshot of the environmental impacts and cost statically, DLCA can accommodate temporal variations in operating conditions and energy usage. It interacts with real-time monitoring data and fluctuating energy rates to provide a more accurate and time-sensitive environment impact and cost evaluation. Key parameters from real-time sensors and reference databases are summarized to lay the foundation for DLCA of cold food storage, like operating power and hours, shipment arrival time, and transportation fuel economy. Its feasibility and reliability are validated by scenarios that simulate different operating conditions, like shift change to utilize off-peak rate and change of packaging materials. The proposed framework allows facility operators and policymakers to pinpoint bottlenecks and seize opportunities for environmental impact and cost reduction.

A COMPARATIVE ANALYSIS WITH THE BLACK BOX MODEL & DSS SYSTEM

This article explores Decision Support Systems (DSS) in emergency management and scrutinizes the mathematical and technological perspectives of DSS. It focuses on how these DSS handle uncertainty, manage resources, and make choices during emergencies. Some features that distinguish them as strong are data analysis as well as coping with ambiguity, while other characteristics include things like real-time monitoring and visualization of data. At the same time, there are certain obstacles to their implementation, such as inadequate data generation or refusal by users. Therefore, instances from real life based on various kinds of disaster-stricken zones are illustrated in this research work. Also, we will find out the interconnection term in Emergency DSS in Uncertainty. It explores the Black Box model and designs the main processes in Emergency DSS in Uncertainty. This work analyzes quantitative adaptation, modeling and optimization, iterative improvement, mathematical formulation, system analysis utilities, and system modeling in dynamic cases.

Research on the Construction of Content and Service System of “Intelligent Fitness Station” in the Post-epidemic Era

Abstract In the post epidemic era, the construction of intelligent fitness post and sports service system has become a meaningful way to improve residents’ physical fitness and health, and this study aims to explore practical strategies in this field. A fitness station model combining medicine is adopted, and big data technology is utilized to realize real-time monitoring of physiological data and screening of health hazards for fitness groups. Meanwhile, a framework of urban brilliant sports service system is proposed to ensure the integrity and coordination of the service system. The study also used the fuzzy comprehensive evaluation and factor analysis models to assess the sports service level. The results show that the fitness model of medical integration significantly improves the physical fitness of middle-aged and elderly people. In addition, through the case study of sports public services in Province S, it was found that the ratings were high in cultural and social functional values, but the fundamental applicable values still need to be strengthened. The comprehensive evaluation results show that the overall level of sports public services in Province S is good, but there is still room for improvement.

Augmented Intelligence Forecasting and What-If-Scenario Analytics With Quantified Uncertainty for Big Real-Time Slope Monitoring Data

Augmented Intelligence Forecasting and What-If-Scenario Analytics With Quantified Uncertainty for Big Real-Time Slope Monitoring Data

Examining the Feasibility of GeoAI and IoT for Smart Flood Early Warning Systems in Local Communities for Caribbean Urban Spaces

Over the last few decades, flooding has resulted in many problems that significantly impact countries in the Caribbean. This has been especially challenging in urban areas where widespread damage has occurred. In addition, given that over 50% of the world's population lives in urban areas, these locations are deemed to be vulnerable to climate-related disaster events that would further exacerbate the challenges in the region. These urban spaces in the Caribbean have limited access to real-time flood monitoring data for formulating and supporting policies for disaster practitioners to coordinate timely preparedness and mitigation efforts. While flooding is complex, with a series of negative impacts on social and economic sectors, it is essential to provide a basis to support decision-making information on vulnerability and resilience through early warning systems (EWS). However, the main obstacle in creating early warnings in the Caribbean is the suitability and availability of data for real-time flood prediction. While many methods of flood models have been applied in the traditional ways, the methods are complex and not readily adaptable to the context of the local community spaces, especially with limited available data. The Caribbean urban landscape is dynamically changing, which requires careful monitoring for predicting flooding. In a disaster, informing the community is critical for managing flooding risk. The adoption of GeoAI and IoT prepares the next frontier of models for usage in the Caribbean to close the gaps of modeling in data scarcity in urban areas. Consequently, there is a research gap from the perspective of short-term forecasting for sudden rainfall events in urban spaces in the Caribbean. Given the early warning system culture in the Caribbean has an environment of real-time data of scarce resources, it is necessary to forge an approach for real-time forecasting flooding impact in urban spaces. The paper provides a preliminary analysis of the feasibility of machine learning and IoT use in supporting EWS in Caribbean urban spaces.

Real Time Route Adjustment of a UAV Based on Dust Measurement with an Onboard Sensor

This research is driven by a pressing requirement to improve the effectiveness and safety of unmanned aerial vehicle (UAV) operations, specifically in atmospheric particle sensing. Airborne particulates present a substantial health hazard, leading to an increasing demand for inventive solutions capable of autonomously guiding UAVs along optimal routes, steering clear of regions with heightened dust concentrations. This research aims to address this challenge by developing a sophisticated approach that integrates atmospheric particle sensors with UAV flight control systems. The approach outlined in this study relies on atmospheric sensors mounted on a UAV to measure dust levels in a specified region. The UAV, following a predetermined route over the area, detects dust pollution. It dynamically adjusts its route based on the observed dust levels, avoiding areas with high concentrations of dust. This adaptive route determination aims to identify safe paths, avoiding regions with elevated dust levels that may pose risks to human health. The flight strategy and area-scanning methodology tailored for this objective are established, enabling the UAV to execute the assigned task. The ultimate goal is to create a system that not only minimizes energy consumption but also prioritizes human health by autonomously redirecting the UAV away from potentially harmful dust levels. The real-time monitoring and telemetry data feedback mechanisms further contribute to the advancement of UAV technology for environmental sensing and risk mitigation. Additionally, the integration of the laser-based dust sensor, i.e., Gp2y10, with the ArduPilot autopilot flight computer ensures seamless coordination, with the UAV adjusting its flight path based on the sensor's output.

Innovative Approaches to Bank Security in India: Leveraging IoT, Blockchain, and Decentralized Systems against Loan Scams

This research paper explores the significant impacts of multiple loan fraud on Indian banks and financial institutions, emphasizing the resulting bad debts and financial losses. The issue is exacerbated in the real estate sector, where influential developers exploit system vulnerabilities to secure multiple loans using the same collateral. Consumers also face challenges in accessing credit due to these fraudulent practices. The study underscores the need for enhanced regulatory measures and internal controls within financial institutions. Additionally, it introduces IoTBlockFin, a decentralized system that integrates block chain and IoT technologies to securely assess customer reliability and mitigate fraud. IoTBlockFin's Advanced Proof of Work (APOW) mechanism, combined with IoT data for real-time monitoring, offers superior security, latency, and cost-effectiveness compared to centralized systems, as demonstrated by experimental results.

Dynamic prediction of steam consumption in beer production process based on Attention Mechanism CNN-BiLSTM

During the normal production process of the brewery, the steam consumption can be accurately predicted, and the boiler steam output can be planned to achieve a balance between steam production and use. In order to improve the accuracy of steam consumption prediction, this paper proposes a dynamic prediction method of steam consumption based on attention mechanism-convolution-bidirectional long short-term memory neural network (CNN-BiLSTM). In this paper, the real-time monitoring data of the brewery energy management system is selected as the experimental data for analysis, and the CNN-BiLSTM network model based on attention mechanism is compared with the results of CNN net-work, LSTM network, BiLSTM network and CNN-BiLSTM network prediction. The experimental results show that the mean absolute error, root mean square error and R-Square of the model are 0.0329, 0.0449 and 97.5%, which are better than the other four models, and can predict the steam consumption of brewery more accurately.

Lab Automation

Abstract: This lab automation project aims to revolutionize scientific workflows by implementing a comprehensive system that integrates cutting-edge technologies to streamline processes and enhance accuracy in laboratory settings. Leveraging robotics, sensor networks, and advanced software, our solution automates repetitive tasks, minimizes human error, and accelerates experimentation cycles. The system's modular design allows seamless integration into existing laboratory infrastructure, ensuring adaptability across diverse research domains. Through real-time data monitoring and analysis, our automation solution not only increases efficiency but also facilitates data-driven decision-making. This project represents a significant step towards the future of laboratory operations, fostering scientific advancements by optimizing resource utilization and promoting reproducibility in experimental outcomes

Healthcare Big Data Analysis with Artificial Neural Network for Cardiac Disease Prediction

The generation of a huge volume of structured, semi-structured and unstructured real-time health monitoring data and its storage in the form of electronic health records (EHRs) need to be processed and analyzed intelligently to provide timely healthcare. A big data analytic platform is an alternative to the traditional warehouse paradigms for the processing, analysis and storage of the tremendous volume of healthcare data. However, the manual analysis of these voluminous, multi-variate patients data is tedious and error-prone. Therefore, an intelligent solution method is highly essential to perform multiple correlation analyses for disease diagnosis and prediction. In this paper, first, a structural framework is proposed to process the huge volume of cardiological big data generated from the hospital and patients. Then, an intelligent analytical model for the cardiological big data analysis is proposed by combining the concept of artificial neural network (ANN) and particle swarm optimization (PSO) to predict the abnormalities in the cardiac health of a person. In the proposed cardiac disease prediction model, an extensive electrocardiogram (ECG) data analysis method is developed to identify the probable normal and abnormal cardiac feature points. Simulation results show the effects of a number of attributes for improving the accuracy of the cardiac disease prediction and data processing time in the cloud with an increase in the number of the cardiac patients.

Real-Time Air Density Measurement with IoT Integration

This paper presents the design and implementation of an IoT-based air density measurement system that integrates BMP180 and DHT22 sensors with an Arduino Uno microcontroller and ESP8266 Wi-Fi module. The system measures temperature, humidity, and atmospheric pressure to calculate air density in real-time, displaying the results on an LCD screen and transmitting the data to a smartphone app (Blynk) for remote monitoring. The goal of this research is to create a reliable, automated system capable of providing continuous air density measurements without the need for manual intervention. To evaluate the system’s accuracy, data were collected and compared with reference values from the Korea Meteorological Administration (KMA) for August 2023. The comparison revealed that the system produced air density measurements with an average error of less than 0.2%, demonstrating its high level of accuracy and reliability. The system is particularly suited for laboratory environments, where real-time and accurate air density measurements are essential. The use of IoT technology allows for remote data access and continuous monitoring, making the system convenient for various applications, including environmental monitoring and industrial settings where air density plays a crucial role. Future improvements could include sensor calibration enhancements and the integration of additional environmental parameters, such as CO2 levels or particulate matter, to broaden the system’s functionality. Overall, the IoT-based air density measurement system offers a cost-effective and scalable solution for real-time environmental data monitoring.

Dynamic adaptive control of boiler combustion based on improved GNG algorithm

The boiler combustion process contains complex physicochemical changes, which is a nonlinear time-varying industrial process with strong interference and multivariate strong coupling. For this kind of boiler combustion process with typical nonlinear characteristics and complex mechanism, it is difficult to establish an accurate mechanism model by conventional modeling methods, so it is difficult to meet the new requirements for the optimal control of the boiler. The large amount of data accumulated in the operation process contains rich system information, and the data-driven modeling and control method provides an effective way for the operation optimization of the unit. Data dynamic characterization and control technology is an important means of data mining, coal-fired boiler data has obvious temporal and drift characteristics, for the current data tracking and supervision algorithms mostly lack of dynamics, real-time and stability issues, design an adaptive clustering model based on the improved growth of neural gas model (GNG), the establishment of nodes based on the probability, the range of the search, the average distance of node A node generation and deletion mechanism based on probability, range search and average distance of nodes is established to realize real-time monitoring of drift data. Finally, the experiments are carried out by analyzing the dynamic data of coal-fired boiler, and the experimental results show that the model and algorithm have stronger real-time tracking ability for dynamic drift data, and can accurately and effectively monitor and control the dynamic data of coal-fired boiler.