Real-time Monitoring System Research Articles

A novel low-cost sensors system for real-time multipollutant indoor air quality monitoring – Development and performance

Indoor air pollution poses a significant health risk, thus demanding effective indoor air quality (IAQ) monitoring strategies. Low-cost sensors (LCS) have gained attraction for IAQ monitoring, but their data accuracy and robustness remain key challenges. This study addresses gaps in the literature by developing and evaluating a novel low-cost monitor prototype for IAQ monitoring. A detailed design methodology based on requirements analysis was used to create two identical low-cost monitors (LCM) prototypes for multipollutant monitoring, including PM2.5, CO2, CO, O3, NO2, temperature and relative humidity. The LCM were deployed in an in-field monitoring campaign alongside research-grade instruments. The uncorrected sensor signals showed linear response compared to research-grade instruments with high Pearson Correlation Coefficients for 1-min mean: PM2.5 (0.97), CO2 (0.81–0.89), CO (0.95–0.98), and O3 (0.80–0.85). The concentration range of pollutants was observed to play a crucial role in the response and accuracy of the LCS. Kalman filter was implemented with optimised parameters to reduce noise from the signal of ozone sensors. Univariate and multivariate linear models were used to field calibrate the sensors. Linear regression models, with high coefficients of determination (>0.8) and low error values, imply that the developed LCM prototypes can be reliably used for indicative monitoring. In most of the cases, univariate linear models using only the sensor response of LCS as explanatory variables showed significant improvement. Future work will involve longer monitoring campaigns in different microenvironments, evaluation of calibration drift and to assess failure episodes in long-term use.

Charge monitoring of test masses in gravitational waves interferometers

The sensitivity of gravitational waves (GWs) detectors is impacted by several sources of noise. One such source is related to the deposition of charge on the test masses (TMs), since its interaction with the surrounding electrical fields introduces an unwanted non-gravitational force on the TM. The charging process is still not completely understood and the charge deposition has historically never been consistently monitored, therefore we propose to implement a real-time monitoring system to overcome these limitations. In particular, in our work we explored 32 candidate sensor locations. Using simulation, we considered more than 100,000 different Gaussian-distributed charge located on either the front or back face of the TM. Utilizing dimensional reduction theories, like the PCA, we established sensor selection criteria to individuate the most relevant sensors. Exploiting Neural Networks (NNs), trained on the simulations, we determined the charge distributions.

Development of an Internet of Things-based Environmental Information System for Realtime Monitoring of Air Conditions

This study aims to develop an environmental information system called the Environmental Monitoring System (EENNOS) to monitor air environmental conditions. ENNOS is an Internet of Things (IoT) based system that monitors air environmental conditions in the form of temperature, humidity and carbon dioxide. The research was conducted using the 4D development method which consisted of the Define, Design, Develop, and Disseminate stages. EENNOS was developed using a DHT 22 sensor for DHT 22 temperature and humidity and a carbon dioxide sensor with MQ-135. The controller used is Arduino uno ESP8266 with output sending using the internet to the database and presented on the dashboard and displayed on a 16x2 liquid crystal screen. The output is in the form of dashboard innovations that are registered with copyrights and scientific publications in the form of articles in the Journal of Environmental Sciences.

Causal technological model for predicting void fraction and energy consumption in material extrusion process of polylactic acid

The rapid expansion of Additive Manufacturing (AM) technologies within the framework of Industry 4.0 raises important questions about their sustainability. Specifically, the widespread use of Material Extrusion (MEx) processes for polymeric materials necessitates a thorough evaluation of the possible balance between performance efficiency and sustainability, highlighting the need for further research into optimizing process parameters to ensure high product quality and reduced energy consumption. The present work focuses on Fused Filament Fabrication (FFF) technology, investigating the relationship between sustainability and print quality. A causal technology model is proposed to elucidate this relationship, underlining the impact of process parameters on defect generation and energy use. A real-time monitoring system for energy consumption measurements was employed to create an energy model that accounts for each component of the CreatBot F430 printer. An image analysis procedure was performed on a special configuration of the HIROX RH-2000 digital microscope to determine the void fraction of each sample. An experimental campaign was conducted, producing single-layer samples in polylactic acid (PLA) following a full factorial plan with four process parameters. A void fraction of up to 18 % was observed, with energy consumption per sample ranging between 638 J and 8843 J. Statistical analysis was performed to assess the impact of each process parameter and to determine operational ranges through the Response Surfaces Method. These results showed good agreement with the predicted conditions, demonstrating the model's effectiveness in supporting decision-making processes in technology selection.

ATC-CNN-Based IoT Framework for Real-Time Cardiovascular Monitoring: A Comparative Analysis with Deep Learning Models

Background: Cardiovascular diseases (CVDs), including hypertension, coronary heart disease, and ventricular fibrillation, remain the leading cause of death worldwide. The COVID-19 pandemic accelerated the adoption of telecardiology to minimize in-person interactions, enhancing access to cardiovascular care, especially in remote areas. However, traditional real-time monitoring (RTM) systems often require patient visits, creating a need for cost-effective, user-friendly alternatives that leverage IoT and AI for improved patient management. Methods: This study developed an IoT-enabled RTM system integrated with an Adaptive Thresholding Classifier-Convolutional Neural Network (ATC-CNN) framework for managing cardiovascular patients (CP). Data from a heart failure dataset in the UCI Machine Learning Archive was used to train and evaluate the model. Multiple deep learning (DL) algorithms, including MLP, CNN, LSTM, RNN, and the proposed ATC-CNN, were assessed based on accuracy, precision, recall, and F1 scores. Results: The ATC-CNN model achieved an accuracy of 0.9831, outperforming other DL models, including MLP, CNN, LSTM, and RNN. It demonstrated superior capabilities in handling complex cardiovascular data, offering reliable and timely diagnosis with high precision and recall. The integration of IoT and AI facilitated continuous monitoring and real-time data analysis, addressing key limitations of traditional RTM systems. Conclusion: The ATC-CNN framework shows great potential for revolutionizing cardiovascular care by enhancing remote monitoring capabilities and clinical decision-making. It provides an effective, real-time solution for early diagnosis and intervention, particularly in remote and underserved areas. Future research should focus on expanding datasets and refining the model for broader adaptability in diverse healthcare settings.

Carbon-based Wearable Pressure Sensor System for Real-time Step Frequency Monitoring

Abstract Wearable pressure sensors have attracted great attention in human motion and health monitoring due to their sensitivity, flexibility and portability that can provide continuous physiological information recording. However, current pressure sensors used for wearable motion monitoring suffer from low sensitivity, poor resistance to interference, and the inability to be mass-produced. In this study, we proposed the utilization of multi-walled carbon nanotubes (MWNTs) as sensing materials, fabric as substrates, and screen printing process for fabricating flexible pressure sensors. The fabricated sensors exhibited a high sensitivity at low pressure, and good stability of more than 3000 cycles. Moreover, we designed a wearable step frequency monitoring system for real-time recording of footstep frequency during human walking activities. The results demonstrated that the system accurately monitors frequency under different walking speeds and footstep activities during various movements, providing a promising strategy to develop wearable and wireless sensing system for real-time motion monitoring.

A Review of Negligible Factors Inimical to Building Failures in Nigeria: Architectural View

Building failures pose significant risks to public safety, property, and the environment, making it imperative to identify and address the negligible factors contributing to such failures in Nigeria. This review examines the architectural perspective of building failures in Nigeria, focusing on factors that play a role in mitigating risks and promoting safer construction practices. Through an analysis of recent literature and empirical evidence, the study highlights key findings, conclusions, and recommendations aimed at enhancing construction safety standards nationwide. The findings of the review reveal the multifaceted nature of building failures, influenced by regulatory frameworks, construction materials, skilled labor, community engagement, technological integration, economic incentives, and environmental considerations. Strengthening regulatory enforcement, promoting the use of high-quality construction materials, and investing in skilled labor development emerge as critical strategies for mitigating risks and improving construction safety outcomes. Community engagement initiatives and technological innovations, such as building information modeling (BIM) and real-time monitoring systems, are also identified as effective measures for enhancing transparency and accountability in construction projects. Based on the findings, the study concludes that addressing the negligible factors inimical to building failures requires a holistic approach, encompassing regulatory reforms, capacity building, and technological advancements. Furthermore, the study emphasizes the importance of fostering collaboration between government agencies, industry stakeholders, and local communities to promote construction safety and sustainable development goals. The recommendations proposed include enhancing regulatory enforcement mechanisms, promoting the use of high-quality construction materials, and investing in skilled labor development programs. Additionally, leveraging technological innovations and fostering community engagement initiatives are identified as key strategies for enhancing construction safety standards and fostering sustainable development in Nigeria. Overall, this review provides valuable insights and actionable recommendations for policymakers, construction professionals, and community stakeholders seeking to address the negligible factors contributing to building failures in Nigeria. By implementing the recommendations outlined in this study, stakeholders can work collaboratively to create safer, more resilient built environments that promote the well-being of communities and support sustainable development goals.

Effect of withering/spreading on the physical and chemical properties of tea: A review.

Withering and spreading, though slightly differing in their parameters, share the same aim of moisture reduction in tea leaves, and they have a strong impact on the physical and chemical properties of tea. Even though researchers tend to pay close attention to the characteristic crafts of different teas, increasing investigations begin to focus on the withering process due to its profound effects on the composition and content of quality-related compounds. This review provides an overview of tea withering process to address questions comprehensively during withering. Hence, it is expected in this review to figure out factors that affect withering results, the way withering influences the physical and chemical properties of withered leaves and tea quality, and intelligent technologies and devices targeted at withering processes to promote the modernization of the tea industry. Herein, several key withering parameters, including duration, temperature, humidity, light irradiation, airflow, and more, are tailored to different tea types, demanding further exploration of advanced withering devices and real-time monitoring systems. The development of real-time monitoring technology enables objective and real-time adjustment of withering status in order to optimize withering results. Tea quality, including taste, aroma, and color quality, is first shaped during withering due to the change of composition and content of quality-related metabolites through (non)enzymatic reactions, which areeasily influenced by the factors above. A thorough understanding of withering is key to improving tea quality effectively and scientifically.

Real-time Vertical Air Quality Monitoring System Development for Urban Scale

Real-time Vertical Air Quality Monitoring System Development for Urban Scale

Evaluation of geological hazard susceptibility in Xixia Xichuan area based on support vector machine

Abstract Geological hazards seriously affect the personal and property safety of regional residents, in the Xichuan area, a real-time monitoring and early warning system could have provided timely alerts before the landslide, potentially averting the tragic loss of life and destruction.So it is very important to carry out geological hazard susceptibility assessment and hazard prevention. The Xixia area of Xichuan is selected as the study area, and the support vector machine model is used to evaluate the susceptibility of geological hazards in the study area. Through Kendall factor correlation analysis, the susceptibility evaluation indexes of the study area are selected, the evaluation index system of the study area is established, and the thematic layers and attribute data of the evaluation indexes are extracted through GIS technology. The MATLAB platform was used to establish linear kernel function (LN-SVM), polynomial kernel function (PL-SVM), radial basis kernel function (RBF-SVM), sigmoid kernel function (Sigmoid-SVM) based on the four support vector machine models of the study area were used to evaluate and classify the susceptibility of geological hazards. ROC curves were plotted to check the accuracy of the evaluation results of the four kernel functions, and the most suitable kernel function model for the study area was selected. The results show that the RBF-SVM model has high evaluation accuracy and is most suitable for the evaluation of geological hazard susceptibility in the study area. Finally, GIS technology was applied to partition the evaluation results into susceptibility zones. The evaluation results show that 86.14% of the geological hazards in Xixia Xichuan area are of medium and high susceptibility, and the evaluation results have certain practical significance for the prevention and control of geological hazards in Xixia Xichuan area.

A real-time cardiac output monitoring system based on photoplethysmography

Abstract Cardiac output is a critical parameter for assessing the cardiovascular system, and thermodilution is commonly used as CO monitoring in clinical settings. Thermodilution cannot be applied in continuous monitoring due to invasion. Photoplethysmography signal originates from the cardiovascular system. We proposed a CO continuous monitoring system based on photoplethysmography and deep learning method. PPG waveform parameters are extracted during the pre-processing stage, and fed into the deep learning model to calculate cardiac output. The system employs an analog front-end chip and an HPS_FPGA architecture to capture PPG signals. The pre-processing and the deep learning model-driven algorithm are designed in Python on the custom Linux for HPS. It realizes the real-time processing of the PPG signals and the continuous monitoring of cardiac output. Validated by an FPGA, the system reported average cardiac output monitoring results of approximately 5.09 L/min at rest and about 7.39 L/min after exercise.

Low voltage distribution intelligent control system based on distributed photovoltaic grid connection

Abstract This article mainly studied the use of distributed photovoltaic power generation technology to achieve intelligent control of low-voltage distribution networks. With the rapid development of new energy, distributed photovoltaic power generation technology has become an important means to alleviate the current energy crisis and environmental problems. This article first elaborated on the theory and research status of distributed photovoltaic power generation systems. Based on this, artificial intelligence technology was applied to distributed photovoltaic power generation systems for real-time monitoring to enhance their stable and reliable performance. When the simulated grid voltage was set to 220V, the system output voltage was also 220V, showing a deviation of 0.0%, proving that the system has excellent regulation accuracy at this voltage level. This article is an innovative research that integrates distributed photovoltaic power generation with intelligent control, opening up a practical and feasible technical path for the large-scale utilization of clean energy. Therefore, this study has significant academic value and practical significance.

Design and Development of a Real-Time Monitoring System for Highway Road Surface Anomalies

The recent unprecedented increase in highway networks in India has increased the need to maintain safe and smooth roads. The increasing number of road surface anomalies such as cracks, surface irregularities, and anomalies can lead to vehicle damage and accidents. This research work presents the design and development of a real-time monitoring system for detecting anomalies in highway road surfaces. The proposed system utilizes the machine vision model integrated with cameras, sensors, and edge computing to provide timely and accurate alerts for the drivers and also to the road maintenance authorities. The proposed solution is designed specially to work under different environmental conditions and enable large-scale deployment. The output generated from the proposed model is visual feedback of the detected anomalies and its severity analysis, enabling quick road maintenance actions.

P014 APPLICATION OF AI IN HYPERTENSION MANAGEMENT: A REVIEW

Background and Objective: The integration of artificial intelligence (AI) in healthcare has revolutionized the management of various diseases, including hypertension. This review explores the potential of AI applications in improving hypertension diagnosis, treatment, and patient outcomes. Methods: A comprehensive literature review was conducted using databases such as PubMed, Scopus, and IEEE Xplore. Studies published between 2010 and 2023 were included, focusing on AI models used for hypertension management, such as machine learning algorithms and predictive analytics. Results: AI has demonstrated significant accuracy in predicting hypertension risk factors, optimizing treatment plans, and monitoring patient adherence. Machine learning models, such as neural networks and decision trees, have shown up to 95% accuracy in early hypertension detection. Furthermore, AI-driven personalized treatment plans have resulted in improved patient outcomes and reduced healthcare costs. Conclusions: AI holds tremendous potential in transforming hypertension management by enhancing diagnostic precision and personalizing treatment strategies. Future research should focus on integrating AI with electronic health records and developing real-time monitoring systems to further improve patient care.

Design of an Improved Method for P-cycle Protection and Dynamic Bandwidth Allocation in Elastic Optical Networks for Healthcare Applications

The acceptance of a modern-day health care system with telemedicine and remote patient monitoring requires extremely reliable and flexible network infrastructures. Thus, the need for very changeable Elastic Optical Networks, with dynamic bandwidth allocation for extremely diversified health care-oriented information, such as real-time video consultation, continuous patient monitoring, and huge medical imaging files. However, the current network infrastructures struggle very often with the optimization of reliability against resource utilization, particularly for unanticipated failures, which will bring service disruption to the healthcare services and damage patient outcomes. To address these limitations, this paper proposes an advanced network protection model using P-cycle protection in EONs to enhance network resilience by minimizing resource consumption. P-cycles refer to backup loops that are preconfigured such that, upon failure of a link, the traffic could be rerouted instantaneously. Unlike the conventionally used protection schemes, the restoration of P-cycles can occur in much faster duration with less resource loss. In this model, we propose an intelligent adaptation of P-cycles consistent with the dynamic bandwidth requirements of healthcare applications. It does manage to achieve these functionalities through the inclusion of algorithms for traffic-aware P-cycle design to optimize the capabilities of the utilized network for recovery through the least wastage of resources. There are huge implications of P-cycle protection introduced in EONs for healthcare networks. It further increases the reliability of systems for real-time patient monitoring by ensuring non-stop data transmission to decrease system latency in the event of a loss of packets caused by a network failure. The principal beneficiaries of telemedicine are uninterrupted services for high-quality video consultation as well as reliable, massive transmission of medical data in the form of MRI and CT images. This is because of the positive results that are beginning to come from health care, which is becoming more reliable and expedient, especially in rural and critical scenarios.

Sacred journeys and pilgrimages: health risks associated with travels for religious purposes.

Pilgrimages and travel to religious Mass Gatherings (MGs) are part of all major religions. This narrative review aims to describe some characteristics, including health risks, of the more well known and frequently undertaken ones. A literature search was conducted using keywords related to the characteristics (frequency of occurrence, duration, calendar period, reasons behind their undertaking and the common health risks) of Christian, Muslim, Hindu, Buddhist and Jewish religious MGs. About 600 million trips are undertaken to religious sites annually. The characteristics varies between religions and between pilgrimages. However, religious MGs share common health risks, but these are reported in a heterogenous manner. European Christian pilgrimages reported both communicable diseases, such as norovirus outbreaks linked to the Marian Shrine of Lourdes in France, and noncommunicable diseases (NCD). NCD predominated at the Catholic pilgrimage to the Basilica of Our Lady of Guadalupe in Mexico, which documented 11 million attendees in one week. The Zion Christian Church Easter gathering in South Africa, attended by about 10 million pilgrims, reported mostly motor vehicles accidents. Muslim pilgrimages, such as the Arbaeen (20 million pilgrims) and Hajj documented a high incidence of respiratory tract infections, up to 80% during Hajj. Heat injuries and stampedes have been associated with Hajj. The Hindu Kumbh Mela pilgrimage, which attracted 100 million pilgrims in 2013, documented respiratory conditions in 70% of consultations. A deadly stampede occurred at the 2021 Jewish Lag BaOmer MG. Communicable and NCD differ among the different religious MGs. Gaps exists in the surveillance, reporting, and data accessibility of health risks associated with religious MGs. A need exists for the uniform implementation of a system of real-time monitoring of diseases and morbidity patterns, utilising standardised modern information-sharing platforms. The health needs of pilgrims can then be prioritised by developing specific and appropriate guidelines.

Real-Time Monitoring System for Peatland Fire Potential Based on Internet of Things

This research aims to develop a real-time monitoring system for peatland fire potential based on the Internet of Things (IoT) with a focus on early detection of potential peatland fires. The main problem to be solved is the lack of an effective system in the early detection of potential peatland fires, which can cause serious environmental impacts. The method used involves the use of air temperature, air humidity, soil moisture, and fire detection sensors integrated with alarm-based alerts. Data collection is done in real-time to provide a deeper understanding of peatland conditions and potential fire risks. The research results show that the developed system is capable of providing accurate and fast information related to peatland conditions, thus helping to prevent and reduce the impact of peatland fires. With this system, it is expected to increase efficiency in early fire detection and minimize the losses caused by peatland fires.

The application of deep learning technology in integrated circuit design

This study addresses the intricate challenge of circuit layout optimization central to integrated circuit (IC) design, where the primary goals involve attaining an optimal balance among power consumption, performance metrics, and chip area (collectively known as PPA optimization). The complexity of this task, evolving into a multidimensional problem under multiple constraints, necessitates the exploration of advanced methodologies. In response to these challenges, our research introduces deep learning technology as an innovative strategy to revolutionize circuit layout optimization. Specifically, we employ Convolutional Neural Networks (CNNs) in developing an optimized layout strategy, a performance prediction model, and a system for fault detection and real-time monitoring. These methodologies leverage the capacity of deep learning models to learn from high-dimensional data representations and handle multiple constraints effectively. Extensive case studies and rigorous experimental validations demonstrate the efficacy of our proposed deep learning-driven approaches. The results highlight significant enhancements in optimization efficiency, with an average power consumption reduction of 120% and latency decrease by 1.5%. Furthermore, the predictive capabilities are markedly improved, evidenced by a reduction in the average absolute error for power predictions to 3%. Comparative analyses conclusively illustrate the superiority of deep learning methodologies over conventional techniques across several dimensions. Our findings underscore the potential of deep learning in achieving higher accuracy in predictions, demonstrating stronger generalization abilities, facilitating superior design quality, and ultimately enhancing user satisfaction. These advancements not only validate the applicability of deep learning in IC design optimization but also pave the way for future advancements in addressing the multidimensional challenges inherent to circuit layout optimization.

Research of real-time corn yield monitoring system with DNN-based prediction model.

The real-time monitoring of corn yield by a combine harvester is a critical data source for constructing the yield histogram, which significantly benefits precision management and decision-making in modern precision agriculture. While widely used, the current photoelectric sensor-based yield monitoring method has limitations. It detects the corn height on each scraper and calculates the yield through a geometric formula. However, it neglects the noticeable difference in the corn stacking patterns affected by factors such as feeding volume, terrain, and driving speed. This oversight often results in low accuracy and poor stability in the prediction of corn yield, highlighting the need for a more advanced approach. To resolve this, we employ EDEM discrete element simulation to demonstrate the large difference of corn stacking patterns on the scraper of the elevator corresponding to feeding volume. Then, we develop a real-time monitoring system on our self-developed double elevator testing rig for carrying out a composite dataset for training three machine learning algorithm-based models, namely Deep Neural Networks (DNN), Gradient Boosting Machine (GBM), and Random Forest (RF). Importantly, these models have undergone rigorous validation under various feeding volumes, ensuring their robustness and reliability. The auxiliary elevator speed is meticulously set at 150r/min, 225r/min, and 450r/min, providing a comprehensive performance assessment. The results denote that the DNN model performs best and is stable, with a coefficient of determination (R2) of 0.998, root mean square error (RMSE) of 0.526, and mean absolute error (MAE) of 0.425. The paper also performs field experiments to test the proposed three prediction models and the system. The results also denote the DNN-based prediction model's best performance for the lowest relative error of 2.29% and the highest average accuracy of 97.85%. Consequently, the proposed real-time corn yield monitoring system achieves high accuracy and reliability for the combine harvester applications.

Development of real-time energy monitoring system using IoT base

This paper aims to develop a real-time energy monitoring system based on smart metering to enhance energy efficiency in the single-phase residential sector. Based on the concept of low-cost IoT devices, this intelligent meter system is designed to monitor household energy consumption. It provides real-time information on a graphical Node-RED Dashboard, making it easy for households to track and manage their energy usage. The hardware of the power metering system included the Node MCU ESP8266, the PZEM-004T, and a cloud server built on the Raspberry Pi for storing electricity consumption data by using Node-RED to connect devices via an application programming interface system. This system can help analyze the electricity consumption behavior in the residential sector. It is a guideline for selecting the electricity rate between the TOD and TOU rates. The results found that in the electrical energy measurements of households 1 and 2, the mean deviations were 0.8758% and 0.5523%, respectively. The electricity cost-saving results when changing the electricity tariff from the TOD rate of households 1 and 2 to the TOU rate. It was found that electricity costs can be saved by 0.2807% and 1.0936%, respectively. The most critical variable is electricity consumption during peak periods. In the case study of household 1, if the electricity consumption during the peak period is less than 13%, it will be appropriate to select the Time of Use rate. In household 2, if there is electricity consumption during the peak period, less than 38% would be appropriate to choose the type of electricity user with a TOU rate.