Real-time Data Research Articles

Application of Deep Learning Classification Model for Regional Evaluation of Roof Pressure Support Evolution Effects Over Time in Coal Mining Face

Hydraulic support leg pressure serves as a crucial indicator for assessing work face support quality. Current evaluation methods for support quality primarily concentrate on static analyses-like inadequate initial support force, pressure overrun, and uneven bracket force-while neglecting dynamic column pressure changes. This paper introduces a model for assessing hydraulic support quality using deep learning techniques. Real-time data is preprocessed into a spatio-temporal pressure sub-matrix sample, which is then inputted into the model. This process assesses the support quality type and characterizes its dynamic evolution within the area. The model facilitates the identification of dynamic support quality effects in the working face area, aiding operators in making targeted adjustments to hydraulic support status. Experimental results revealed that the optimized LeNet-5 network-adjusting parameters like convolutional layer count, kernel size, and ReLU activation function-achieved the highest classification accuracy of 85.25% for support quality, surpassed other networks. Furthermore, the improved LeNet-5 network outperformed other networks in both F1 score and recall. Additionally, the improved LeNet-5 network achieved faster convergence to the optimal solution, accelerated training speed. This highlighted its advantages in evaluating the spatio-temporal support quality of hydraulic supports in smart mining operations.

Orbital mechanics with the Global Positioning System

The recent availability of relatively inexpensive dual-frequency receivers for signals from Global Navigation Satellite Systems (GNSS) provides access to ultra-precise, real-time data such as positions and velocities of dozens of satellites orbiting the Earth. We discuss how these data can be obtained, processed, and analyzed either with or without the actual purchase of a GNSS receiver. The positional information can be used to verify Kepler's three laws at lowest order as well as to reveal the presence of higher-order perturbations such as the oblateness of the Earth and the gravitational influences of the Sun and Moon on these satellites. The supplementary material includes both introductory laboratory exercises and Python scripts used to gather and process data suitable for intermediate courses.

EV Battery Management using Adaptive Kalman Filter and ECC

This initiative addresses the critical concerns of enhancing battery management and easing the calculation of battery capacity in electric vehicles. By using Coulomb counting method, the State of Charge (SoC) of Electric Vehicle (EV) batteries is estimated by analysing real-time battery data through simulations and interpolation techniques. The primary objective is to offer precise SoC estimation to mitigate the range anxiety. Furthermore, this research proposes methods of estimating the SoC of Lithium-Ion batteries and an enhanced coulomb counting model with Adaptive Kalman Filter to estimate the state of charge with higher accuracy. This comprehensive approach seeks to address critical challenges in EV battery management contributing significantly to the electric vehicle technology for the society.

Crop Monitoring and Smart Farming Through Mobile App

This study presents the development and implementation of a mobile application aimed at revolutionizing crop monitoring and smart farming practices. The application integrates advanced technologies such as remote sensing and the Internet of Things (IoT) and provides real-time data analysis and decision-making support for farmers. By leveraging the ubiquity and accessibility of mobile devices, this solution enables farmers to remotely monitor crop health, soil conditions, and environmental parameters. Through intuitive interfaces and data visualization tools, the application empowers farmers to make informed decisions, optimize resource utilization, and enhance productivity in agricultural operations. This research highlights the potential of mobile technology to drive innovation and sustainability in modern farming practices.

A Medical Cyber-physical system for predicting maternal health in developing countries using machine learning

It is essential to monitor any health issues during pregnancy to ensure a safe delivery because pregnancy is crucial for both mother and child. However, developing countries have poor access to healthcare, making managing possible health risks during pregnancy challenging. An Internet of Things (IoT)-based Medical Cyber-Physical System (MCPS) can offer a valuable and affordable solution for anticipating and controlling health hazards during pregnancy to solve this issue. This paper presents the design and development of an MCPS for recognizing health risks in pregnant women in developing countries. The system collects key health metrics using temperature, blood pressure, glucose levels, and heart rate sensors. It automatically considers risk factors to predict health risks using Machine Learning (ML) and sends them to the nearest clinic or hospital. Patients can manually enter their risk factors into the program and talk with a doctor through it. The efficacy of the proposed MCPS is evaluated using a dataset of pregnant women, and the results demonstrate that the system can accurately detect health issues during pregnancy. Medical experts can.enhance maternal and fetal health outcomes using the systems real-time data collecting and processing capabilities. Despite restricted access to healthcare in developing countries, the proposed MCPS provides a valuable and economical method of addressing pregnancy-related health risks. The MCPS can assist medical personnel in making quick and informed choices, enhancing the level of care provided to expectant mothers and their unborn children.

Nudging visitors into pro-environmental behaviour: assessing the use of a tidal information pillar for effective dynamic zoning

In the past few years, more human-wildlife conflicts have occurred as a result of increased human visitations, particularly in areas where space is scarce and functions need to co-exist. Nature organisations are increasingly challenged to improve nature protection on the one hand, while offering visitors high quality nature experiences on the other hand. To encourage both go hand in hand, nudging has recently received more attention. Nudging is a tool to subtly change human behaviour without imposing strict rules. It is, however, hardly applied in nature areas. Currently, experiments are taking place in the Dutch part of the Wadden Sea with alternating signage (nudging message based on real-time data of the area) aided by a tidal information pillar to encourage dynamic zoning. At low tide the signage indicates: freely accessible, at high tide the advice: ‘do not enter’. The purpose is to address visitors’ sense of responsibility, nudge pro-environmental behaviour and reduce human-wildlife conflicts by limiting access. This article assesses the effectiveness of the alternating nudging signage, and especially the effectiveness of the signage ‘high tide – do not enter’. The results show that the signage on the pillar is most effective when visitors stop at the pillar and read the advice. Among these visitors, about half turn around. Among the visitors who do not encounter the signage and the pillarn, the majority goes on to access the area. With this study, we aim to contribute to the knowledge on nudging as a tool to promote pro-environmental behaviour in nature areas.

Improving Cardiopulmonary Resuscitation (CPR): Integrating Internet of Medical Things (IoMT) and Machine Learning (ML) - A Review

This review explores the pivotal role of cardiopulmonary resuscitation (CPR) in the chain of survival during cardiac events and delves into the challenges and advancements in CPR techniques and technologies. While manual interventions and automated devices have improved survival rates, they present limitations such as rescuer fatigue and lack of real-time feedback. The emergence of the Internet of Medical Things (IoMT) and machine learning (ML) algorithms offers transformative opportunities to enhance CPR rescue efforts by facilitating real-time data acquisition, remote monitoring, and adaptive feedback. However, challenges including interoperability and data security must be addressed for effective integration. The study discusses major findings from related literature, gaps in research, and future directions, highlighting the potential of integrating IoMT and ML to improve CPR outcomes and revolutionize healthcare delivery. Finally, it concludes with recommendations for optimizing CPR strategies and advancing technology for better patient outcomes.

Smart Slot Assignment for Urban EV Charging Using Folium API Integration

Abstract: Efficient management of electric vehicle (EV) charging distribution in urban areas is crucial amid increasing demand and varying traffic conditions. This study proposes a novel web-based solution that integrates Folium Map API for realtime location tracking and Python Flask backend with MySQL database storage to allocate charging demands. By leveraging real-time traffic information from the Folium Map API, the proposed model optimizes charging resource allocation across multiple stations and road networks. The web interface, built using HTML, CSS, and JavaScript, allows users to interact with the system seamlessly. Charging allocation is dynamically adjusted based on real-time data, prioritizing maxi- mum returns. Additionally, an emergency charge counter facility is introduced to cater to unforeseen charging needs, enhancing user flexibility. The system offers two interfaces and a dashboard for both station administrators and users, en- abling pre-booking services for travelers during their journey, facilitating access to nearby charging stations, and providing a facility for prebooking. These features contribute to improved station revenue and pile utilization, highlighting the efficacy of the proposed web-based approach. By leveraging advanced web technologies and APIs, this comprehensive strategy enhances charging infrastructure efficiency, fosters the widespread adoption of electric vehicles, and contributes to sustainable urban mobility management.

Research on An IOT Based Smart E-Fuel Station using ESP-32

Abstract: The Internet of Things (IoT) is altering traditional fuel stations in the fast-changing energy and transportation landscape. The ESP32 microcontroller's capabilities are utilized in the design and implementation of an Internet of Things (IoT) smart e-fuel station shown in this research. By adding real-time monitoring, automation, and data analytics, the system seeks to improve the effectiveness, security, and user experience of traditional filling stations. Actuators for controlling pumps and queuing traffic are important parts of theproposed system, along with sensor networks for tracking fuel levels, traffic movement, and environmental conditions. The central center for data processing, aggregation, and communication is the ESP32 microcontroller, which makes it easier for different subsystems to work together seamlessly.

ALVoyage – An AI Travel Planner

Abstract: The document discusses the ALVoyage AI travel planner, which integrates with Gemini and Weather APIs and utilizes Firebase authentication. ALVoyage offers personalized travel planning by leveraging AI technology to curate itineraries based on user preferences. It seamlessly integrates with the Gemini API to generate informative content and incorporates real-time weather data from the OpenWeatherMap API. Firebase authentication ensures user security and privacy. The document also highlights the benefits and key features of Gemini and OpenWeatherMap APIs. The methodology section outlines the system architecture and components of ALVoyage, including user authentication, UI/UX design, Gemini API integration, Weather API integration, and user reviews. The results and discussion section emphasizes the system's performance, user feedback, and the future scope of the project. The conclusion highlights the potential of AI-powered travel planning apps and the need for further research and development in the field.

Fall Detection System for Elderly Care

Abstract: This project introduces a revolutionary Fall Detection System for Elderly Care, focusing on swift detection and immediate response to falls, particularly among wheelchair-bound seniors. In response to the escalating challenges posed by an aging population, Especially concerning the well-being of the elderly, the development of innovative solutions has become imperative. Central to these concerns is the prevalence of falls among seniors, which not only jeopardizes their physical health but also threatens their independence. Conventional fall detection methods have proven inadequate in providing timely assistance, underscoring the critical need for more advanced technologies. This project addresses this pressing need through the creation of a comprehensive Fall Detection System, with a specific focus on wheelchair-bound individuals. By seamlessly integrating state-of-the-art hardware and software components, the system aims to revolutionize elderly care by prioritizing safety and autonomy. At its core, the system utilizes the MPU6050 sensor module, renowned for its precision in monitoring body position and movement. Through the implementation of sophisticated algorithms, the software component meticulously analyzes real-time sensor data to swiftly detect falls. Upon detection of a fall event, the system autonomously triggers alerts to nearby healthcare services or designated contacts, ensuring a rapid response and timely provision of assistance to the elderly individual in need.

Efficient Soil Health Monitoring and Irrigation Management System Using Fuzzy Logic

Abstract: The need for sustainable agriculture is acutely felt due to ever increasing global water crisis. Efficient irrigation management is crucial in this regard. The proposed system employs a network of soil moisture sensor distributed over a agricultural field, where continuous acquisition of real time data on soil moisture levels is done. Soil moisture measurement is a vital parameter in terms of precision agriculture, which affects the irrigation scheduling for ensuring optimal crop growth. Traditional methods related face challenges adapting to changing soil patterns at different conditions. The system proposes application of fuzzy logic algorithm for processing of data. Key advantages of using fuzzy logic in soil moisture measurement include improved precision in moisture detection, enhanced ability to make more informed irrigation decisions. The proposed system delivers a model for promoting water conservation, enhancing crop productivity and maintaining soil health.

Real-Time Video Streaming Applications in Military 5G Networks: A Technical Review

Abstract: The integration of advanced telecommunications technologies into military operations marks a pivotal evolution in modern warfare strategies. With the advent of military-grade 5G networks, there arises an unprecedented capacity for real-time data streaming and enhanced operational transparency on the battlefield. This literature review critically examines the deployment of real-time streaming applications within such networks, focusing on a technology stack comprising React Native, Node.js, MongoDB, Docker, and WebRTC. These technologies collectively form the backbone of a system designed to leverage the full capabilities of 5G’s high throughput and ultra-low latency, essential for the demands of contemporary military engagements.

Crop Recommendation System Using Machine Learning

Abstract: This study describes how machine learning techniques were used to successfully construct a crop recom- mendation system. With an emphasis on maximizing resource utilization and advancing agricultural sustainability, the system is a useful tool for helping farmers choose their crops. The system delivers customized crop suggestions by ana- lyzing multiple aspects, including soil conditions and climate, by utilizing machine learning algorithms and incorporating real-time data. This novel strategy seeks to improve farming methods by giving farmers customized insights that help them make more effective and sustainable farming decisions

Drive Alert Solutions Synopsis

Abstract: In today's technological landscape, "Safety First: Drive Alert" pioneers a novel approach to enhance road safety by integrating AI and real-time data analysis. Developed with Flutter and advanced machine learning algorithms, this solution serves as a proactive tool for tracking driver locations and identifying high-risk accident-prone areas. With two-level alerts at 500m and 200m, it offers early warnings to drivers, mitigating potential hazards and preventing accidents. This innovation not only delivers critical real-time information to drivers but also signifies technology's transformative potential in addressing safety issues. By promoting a safer driving environment, it embodies innovation's ethos and underscores technology's role in protecting lives on the road

IoT Based Transformer Monitoring System using ESP-32

Abstract: The integration of Internet of Things (IoT) technology with power transformer monitoring systems presents a significant advancement in electrical grid management. This paper explores the development of an IoT-based transformer monitoring system utilizing the ESP-32 microcontroller. The primary objective is to enhance the reliability and efficiency of transformers by providing real-time data on their operational status and health. The system leverages the ESP-32's capabilities in wireless communication and data processing to monitor critical parameters such as temperature, load current, voltage, and oil levels. Data is transmitted to a centralized cloud platform for analysis and visualization, enabling predictive maintenance and rapid response to potential failures. The proposed system promises to reduce downtime and maintenance costs while ensuring the longevity and optimal performance of transformers. This research highlights the design, implementation, and testing phases of the monitoring system, demonstrating its effectiveness through various case studies and field trials.

Real Time Monitoring of Temperature And Humidity Using LabVIEW and ML

Abstract: This project presents a Real-Time Temperature and Humidity Monitoring System built on LabVIEW, featuring ML predictive analytics. It facilitates real-time data collection from diverse sensors, ensuring precise environmental monitoring. The ML algorithms within the system forecast future temperature and humidity trends, aiding proactive decision-making. Users enjoy an intuitive graphical interface for visualizing data, analysing historical trends, and receiving alerts for threshold variations. Additionally, the system provides remote monitoring, data logging, and reporting, enhancing its usability and accessibility. This integrated solution caters to the crucial requirement for accurate environmental monitoring, with applications spanning industries like manufacturing, agriculture, and climate control, ultimately enhancing efficiency and decision-making in these sectors.

Empowering Collectors with Real-Time Weather Insights for Optimal Collection Conditions

Abstract: A cutting-edge Air QualitCy Monitoring System leveraging Node MU was developed to tackle the mounting concerns surrounding air pollution. The system comprehensively monitored various parameters including Suspended Particulate Matter (SPM), Carbon Dioxide, Ozone, Carbon Monoxide, Smoke, temperature, and humidity. To capture pollutant data, sensors such as MQ4, MQ135, MQ9, and DHT11 were employed. These sensors gathered data, which were then processed using an Analog to Digital converter and analyzed utilizing the software capabilities of Node MCU. The experiment was conducted in Rajarhat, New Town, Kolkata, and the obtained results were compared against data from the local environmental control authority. The comparison highlighted the system's effectiveness in providing real-time data for informed decision-making amidst the heightened air pollution prevalent across Indian cities.

Compact Pollution Control and Sound Indicator Device

Abstract: The increasing urbanisation of cities globally has intensified worries about car pollution, leading to substantial environmental damage and public health risks. Current Pollution Under Control (PUC) systems, which primarily focus on reducing exhaust emissions, often encounter usability problems and neglect the issue of noise pollution. This study presents a new and innovative approach called the Compact Pollution Control and Sound Indicator Device (CPCSID), which aims to overcome these limitations. The project includes state-of-the-art sensors capable of discriminating and measuring several exhaust gases generated by cars, including nitrogen oxides (NOx), carbon monoxide (CO), and carbon dioxide (CO2). Additionally, it contains an integrated microphone to assess engine-generated noise levels. The LCD panel presents real-time data of pollution and noise, providing consumers with practical insights into the environmental effects of their car. In addition, our invention is integrated with a GSM module, which allows it to send SMS notifications to car owners. These warnings aim to promote environmentally aware behaviour and enable quick responses to reduce pollution. The small size and easy-to-use design make it suited for both individual car owners and fleet managers, providing a complete solution for reducing automobile pollution in urban areas.

Advanced Tactical Helmet for Military Use

Abstract: Advanced features on a tactical helmet designed for military use. Soldiers in today's military operations must have improved situational awareness, strong communication skills, and superior protection when fighting. A new generation of tactical helmets with enhanced features is required because the standard helmets now in use do not adequately fulfil these criteria. To improve army performance and survivability The helmet has several extra features in addition to its extensive ESP32 CAM for real-time data visualization, communication systems for seamless inter-squad communication, and improved ballistic protection materials. Among these characteristics include the incorporation of a helmet camera's First-Person-View (FPV) live video feed into a base station, along with the base station's face detection capabilities for enhanced identification. SolidWorks was used in the design process to produce a unique 3D-printed helmet, while ESP32 and ESP8266 were used to power a custom mainboard that effectively controlled the helmet's functions. this creative solution aims to significantly improve military operations, increase survivability, and ensure the safety of soldiers on the battlefield.