Real-time System Research Articles

Rapid and facile electrochemical detection and degradation of carbendazim in the spiked environmental trials using reduced graphene oxide/titanium dioxide-based sensor

This study aims to develop a rapid, facile, and sensitive sensor for electroanalytical carbendazim (CAR) detection. A novel and simple sensor was fabricated by incorporating a composite matrix of titanium dioxide nanoparticles (TiO2-NPs) with reduced graphene oxide (rGO) loaded carbon paste within a polytetrafluoroethylene (PTFE) tube for investigation. Characterization of the synthesized TiO2-NPs was conducted using SEM, XRD, and AFM techniques to assess their structural, morphological and functional features that could support understanding the electrocatalytic activity at the modified sensor (CPE/rGO/TiO2). Comparative analysis with the carbon paste electrode (CPE) revealed that CPE/rGO/TiO2 demonstrated sensitivity, achieving approximately a 6-fold higher detection current in CAR analysis. Cyclic voltammetry (CV), linear sweep voltammetry (LSV) and square wave voltammetry (SWV) approaches were employed for CAR's electrochemical detection and determination. The physicochemical attributes of the electrode activity were investigated under optimum experimental conditions, including electrolyte pH and accumulation time. The CPE/rGO/TiO2 exhibited a limit of detection of 7.66 nM, with a sensitivity of 1.08 µA.µM−1.cm−2 within a wide range of concentration linearity. The selectivity of CPE/rGO/TiO2 against interference metal ions with CAR was assessed. In real-time applications, the developed electrode was tested for the analysis of CAR in spiked soil and water samples, demonstrating significant detection capacity with good recovery. Moreover, the electrode exhibited stability across multiple measurements, highlighting CPE/rGO/TiO2 as a promising sensor for CAR detection.

Smart Water Quality Monitoring with IoT Wireless Sensor Networks.

Traditional laboratory-based water quality monitoring and testing approaches are soon to be outdated, mainly because of the need for real-time feedback and immediate responses to emergencies. The more recent wireless sensor network (WSN)-based techniques are evolving to alleviate the problems of monitoring, coverage, and energy management, among others. The inclusion of the Internet of Things (IoT) in WSN techniques can further lead to their improvement in delivering, in real time, effective and efficient water-monitoring systems, reaping from the benefits of IoT wireless systems. However, they still suffer from the inability to deliver accurate real-time data, a lack of reconfigurability, the need to be deployed in ad hoc harsh environments, and their limited acceptability within industry. Electronic sensors are required for them to be effectively incorporated into the IoT WSN water-quality-monitoring system. Very few electronic sensors exist for parameter measurement. This necessitates the incorporation of artificial intelligence (AI) sensory techniques for smart water-quality-monitoring systems for indicators without actual electronic sensors by relating with available sensor data. This approach is in its infancy and is still not yet accepted nor standardized by the industry. This work presents a smart water-quality-monitoring framework featuring an intelligent IoT WSN monitoring system. The system uses AI sensors for indicators without electronic sensors, as the design of electronic sensors is lagging behind monitoring systems. In particular, machine learning algorithms are used to predict E. coli concentrations in water. Six different machine learning models (ridge regression, random forest regressor, stochastic gradient boosting, support vector machine, k-nearest neighbors, and AdaBoost regressor) are used on a sourced dataset. From the results, the best-performing model on average during testing was the AdaBoost regressor (a MAE¯ of 14.37 counts/100 mL), and the worst-performing model was stochastic gradient boosting (a MAE¯ of 42.27 counts/100 mL). The development and application of such a system is not trivial. The best-performing water parameter set (Set A) contained pH, conductivity, chloride, turbidity, nitrates, and chlorophyll.

Real-Time News Aggregation Using ReactJS: A Case Study and Analysis

Abstract: Real-time news aggregation has become increasingly vital in the digital age, where access to upto-date information is crucial for individuals and organizations alike. This paper presents a comprehensive study on the development of a real-time news application using ReactJS, a popular JavaScript library for building user interfaces. The study explores the significance of real-time updates in news applications, reviews existing literature on news aggregation platforms and ReactJS development, and identifies gaps in the current research landscape. The proposed system leverages ReactJS's flexibility and efficiency to create an intuitive user interface for accessing and interacting with real-time news content. Through integration with external APIs such as News API, the application fetches news articles from various sources and presents them to users in a timely manner.

Fall Detection System based on iOS Smartphone Sensors

Activities of daily living (ADLs) are a crucial aspect of human life, especially in remote health monitoring and fall detection. As smartphones have become an integral part of our daily routines, with their ability to perform complex calculations, connect to the internet, and incorporate various sensors, researchers have been inspired to explore human activity recognition systems. This paper focuses on accelerometer and gyroscope data from iOS-based smartphones. We developed a data collection app to record fall types (e.g., Falling Right, Falling Left) and fall-like activities (e.g., Sitting Fast, Jumping). Volunteers carried smartphones naturally in their pockets during experiments, presenting a challenge of noise but enhancing user comfort. We applied different Machine Learning algorithms (Decision Trees, Random Forest, Logistic Regression, k-Nearest Neighbor, XGBoost, LightGBM, and Neural Networks) to analyze the collected dataset. In contrast to typical studies, our approach replicated real-world smartphone usage. The paper presents and analyzes promising results from the study. Furthermore, we implemented the trained model as a real-time mobile application for potential users. This research illustrates the potential of smartphones in fall detection and opens the way for user-friendly solutions in remote health monitoring.

Real Time Chat Application using React and Firebase

Abstract: This paper presents the development of a real-time chat application leveraging modern web technologies, including React for front-end development, Tailwind CSS for styling, and Firebase for backend services. The chat application addresses the need for seamless communication among users in various contexts. We discuss the architectural design, implementation details, and performance evaluation of the application. Our findings demonstrate the effectiveness and scalability of the proposed solution, paving the way for future enhancements and applications in real-time communication systems.

Research and Development of Online Monitoring Protection Sensors for Paper Books Based on TiO2 NT/MoS2

NO2 is a prevalent environmental pollutant, and its reaction with water produces nitric acid, which is one of the main factors contributing to the degradation of books and paper. Therefore, it is crucial to develop a real-time monitoring system for NO2 gas content in the air and establish timely response measures to delay book aging and provide effective protection. In this study, TiO2 nanotubes (NTs) were fabricated using the anodic oxidation method, followed by the preparation of TiO2 NT/MoS2 composites through hydrothermal synthesis. It was observed that flaky MoS2 is attached to the surface of TiO2 nanotubes, forming aggregated structures resembling flower balls. The TiO2 NT/MoS2 nanocomposites were found to exhibit a rapid response with a 5 s response time and an 80 s recovery time towards 367 ppm NO2 at 260 °C. The gas response to 100 ppm NO2 vapor was 3.3, which is higher than all the other gases under the same concentration. Our experimental results demonstrate that compared to pure TiO2 NTs, TiO2 NT/MoS2 composites exhibit a larger specific surface area along with higher sensitivity and faster response times towards various concentrations of NO2 gas.

ОСНОВНІ РЕЖИМИ РОБОТИ І ГРАФІЧНИЙ ІНТЕРФЕЙС КОРИСТУВАЧА КОМПЛЕКСУ ДЛЯ ЕКСПЕРИМЕНТАЛЬНИХ ДОСЛІДЖЕНЬ МАГНІТНИХ ПОЛІВ І ДІАГНОСТУВАННЯ ЕЛЕКТРОЕНЕРГЕТИЧНОГО ОБЛАДНАННЯ

Hardware and software monitoring complex is designed to study the relationship between changes in the patterns of the magnetic field intensity distribution with power equipment failures. In experimental studies with predetermined techni-cal faults artificially introduced into the object the complex will provide useful and reliable results by registering changes in the magnetic field induction in the spatial, temporal and frequency domains. The choice and flexible adjust-ment of data collection tools by the researcher will ensure the use of the most appropriate method for measuring, con-verting and displaying information about the investigated faults. The article presents the main operating modes and graphical user interfaces of a personal computer and a mobile data acquisition system that jointly convert, store, ana-lyze and display measurement information received from various magnetic sensors. The generalized functional specifi-cation of the complex software, the design of the graphical user interface (GUI) as parts of the real-time operating sys-tem (RTOS) of the mobile data acquisition system (DAQ) and PC software will also be used in the development of prob-lem-oriented computer diagnostic systems for electric power equipment. Ref. 12, fig. 3.

Real-Time Data Processing Method for CO2 Dispersion Interferometer on EAST

Abstract In this paper, a real-time data processing system is designed for the carbon dioxide dispersion interferometer (CO2-DI) on EAST to measure electron density and provide real-time feedback control. The system utilizes the parallel and pipelining capabilities of Field-Programmable Gate Array (FPGA) to digitize and process the intensity of signals from the detector. Finally, the real-time electron density signals are exported through a Digital-to-Analog Converter (DAC) module in the form of analog signals. The system has been successfully applied in the CO2-DI system to provide low-latency electron density data to the plasma control system on EAST. Experimental results of the latest campaign with long-pulse discharges on EAST (2022-2023) demonstrate that the system can respond effectively in the case of rapid density changes, which means it is reliable and accurate for future electron density calculation.

Water Quality Monitoring System: Literature Survey and Design

Abstract: Given the significant obstacles that water pollution poses to the principles of green globalisation, it is more important than ever to guarantee a reliable and safe source of drinking water. This research presents a practical and affordableapproach to meet this need by proposing a real-time waterquality monitoring system that makes use of the Internet ofThings (IoT). The proposed system effectively incorporates awide range of sensors that are specifically designed to monitor physical and chemical parameters that are critical for assessingthe quality of water. These parameters include temperature, pH,turbidity, and water flow.

Development of Dynamic Image Recognition System for Hand Sign Language into Audio and Visual Output using Artificial Neural Networks

Abstract: This system provides a simple solution to a complex problem faced by deaf and dumb community. This paper visualizes the development web application of a real-time image recognition system whose main aim is to convert or translate the hand signs used in sign language in textual or visual and audio form output by using artificial neural networks. This system used computer vision to see signs as an input for processing and makes accurate predictions by using deep learning methods in realtime. By using artificial neural networks (ANN), the system achieves better performance in predicting or recognizing hand signs. The system makes communication between people using sign language and people who don’t know sign language effective by providing output as an audio and texted / visual, which even helps people in both ways. The project aims only to provide the society a solution by helping people who use sign language to express themselves to make it more effective and give them an ability to speak by using the technologies.

Eco-Flow: Water Management System Using UV Sterilization

Abstract: In our contemporary world, the accessibility of clean and potable water is a pervasive issue, particularly in residential complexes and apartment buildings where shared waterstorage facilities pose a significant challenge. The lack of awareness and diligence in maintaining these communal tanks exposes residents to waterborne diseases. The current hurdle in water monitoring and filtering systems is the creation of a cost- effective, scalable solution that can be easily implemented across diverse environments. This project introduces EcoFlow, an innovative and budget- friendly solution for water level monitoring and purification. The system utilizes UV lights and activated carbon to efficiently filter water, eliminating harmful microorganisms and removing impurities, chemicals, and odors. By addressing the unique challenges of different regions, EcoFlow aims to contribute to the broader adoption of UV and activated carbon-based water treatment systems. Beyond its filtration capabilities, EcoFlow incorporates a real-time water level monitoring system, empowering users with vital information for sustainable and equitable access to clean water

Emerging applications and ongoing challenges in WSN assisted IoT networks

In current scenario, Wireless Sensor Networks (WSN) based on Internet of Things (IoT) have seen a significant growth in research area. IoT is a network that enables sensors, and other events to interact with each other without human interference. In addition, wireless network is an essential part of the Internet of Things, generating an abundance of real-time applications. WS Ns can be recognized by their distributed sensor nodes and application fields including environmental monitoring, healthcare, agriculture, industrial automation, and smart cities. Therefore, an extensive usage of such networks is the outcome of their adaptability in variety of fields. The present study digs into the immense scope of WSN applications and the daunting challenges that come alongside. However, these applications face a slew of obstacles, including those related to energy, scalability, security, quality of service, network dependability, and data management. Further, the study focuses on gathering insights into the changing WSN technology environment by examining these applications and the issues they encounter, allowing to satisfy the demands of both existing and forthcoming deployments. As WSN technology is improving, the present study contributes by investigating prospective solutions, such as machine learning integration, energy harvesting, and standardization efforts.

Enhancing Security and Sensors Emerging Internet of Things (IoT) Technology of Homophone-Based Encryption using MANET‐IoT Networks Technique

The emerging Internet of Things (IoT) technology brings several benefits to the real-time applications of the smart city project. The IoT-enabled technologies are driven by the networks like Mobile Ad hoc Network (MANET), Vehicular Ad Hoc Network (VANET), Wireless Sensor Network (WSN), Underwater Wireless Sensor Network (UWSN), etc. The core functionality of IoT-enabled technologies mainly depends on routing functionality. The focus of this work is on the IoT-enabled MANET, i.e., IoT-MANET routing methods. There are several types of research conducted so far on the efficiency of MANET in different areas such as routing protocol, MAC protocol, queue, etc. Routing protocols are the major domain on which the performance of IoT-MANET has been analyzed. There are different types of routing protocols have introduced for the MANETs. The use and selection routing methods are done as per the real-time application needs. An average Ad hoc On-Demand Vector (AODV) routing protocol had shown good results under any type of network. The increase in internet usage for communication has led to a significant growth in the volume of data being transmitted over the internet, including computer data, text, video, audio, sound, and images. However, due to limited network capacities, efficient compression methods are needed to reduce the amount of data while preserving its quality and ensuring timely transmission. Data confidentiality is a crucial aspect of network security, and the homophone-based encryption with compression (HEC) technique offers a viable solution in this regard.

Microwave-assisted hydrothermal synthesis and gas sensing properties of ZnSn(OH)6, ZnSnO3, and Zn2SnO4/SnO2 hierarchical nano-/hetero-structures

Although semiconducting metal oxide sensors present reasonable sensitivity, an improved lower detection limit and/or selectivity would allow broadening real-time monitoring applications. This work reports the growth mechanism and gas sensing performance of zinc tin oxide-based structures synthesised via a microwave-assisted hydrothermal route. The synthesised materials were characterised by X-ray diffraction (XRD), Raman and Fourier-transform infrared (FTIR) spectroscopy, scanning and scanning transmission electron microscopy (SEM and STEM), X-ray photoelectron spectroscopy (XPS), energy-dispersive X-ray spectroscopy (EDS), and nitrogen adsorption/desorption experiments. Gas sensor measurements showed that ZnSnO3 presents an outstanding lower detection limit to nitrogen dioxide (NO2), in which a 10-fold increase in electrical resistance is expected in the presence of 1 ppb NO2 at an operating temperature of 150 ˚C. Moreover, the Zn2SnO4/SnO2 heterostructure exhibited superior selectivity to NO2 relative to hydrogen (H2) and carbon monoxide (CO), exhibiting a sensor response ∼1500 times higher for the oxidising gas. Hence, it is demonstrated that nanostructures’ growth engineering can realise lower detection limits and ultra-selective high-performance gas sensor devices through a greater surface area and enhanced contact potential barriers.

Design an Effective, Faster Region-Based Convolutional Neural Network with Optimization for the Human Tracking System

Nowadays, real-time Human Tracking System (HTS) is a crucial topic in computer vision and image processing with applications like robotic perception, scene understanding, video surveillance, image compression, medical image analysis, and augmented reality, among many others. In this paper, we design a Faster Region-based Convolutional Neural Network (FR-CNN) with Crow Search Optimization (FR-CNN-CSO) architecture to improve computational complexity and enhance the performance of HTS. The system is implemented in a Python environment with video input. Remove unnecessary data from the gathered datasets during preprocessing. Next, feature extraction is processed using Histograms of Oriented Gradients (HOG). Then update, the extracted features into a designed FR-CNN model for identifying and tracking a person using crow search fitness. The main goal of the developed approach is to attain accurate prediction results and improve the computational complexity by achieving less execution time. Finally, the experimental outcomes show the reliability of the designed system by other conventional techniques in terms of accuracy, precision, recall, F-measure, and execution time.

Implementation of Technological Upgradation in Public Distribution System in India for Ensuring Availability, Accessibility and Affordability

The largest initiative to reduce poverty in India is the public distribution system (PDS), which uses a network of fair-pricing shops to supply highly subsidised food grains in order to fulfil necessities of households living below poverty line. PDS's primary objective is to buy food grains from farmers. This is to ensure that grain is distributed, stored, and transported so that people in need have access to food. The Indian government has thus upgraded the PDS's technology in an attempt to increase its transparency. To close this gap between technology and citizen benefits, e-government makes use of technical intermediaries like ration Integrated Weight Management System (IWMS), Management Information System (MIS), Inventory management system, etc. The focus of the government is to enact a number of administrative and technological changes under the centralised online real-time electronic public distribution system in order to empower end users of the PDS supply. In this context this paper deals with the number of difficulties when distribution system switches from manual to automated transactions, dealer of fair price shop, who employs the technologies. Based on secondary literature, the objective of this paper is to understand the challenges associated with implementing technology at the point of sale during distribution, as well as study the status of the implementation technological based distribution system. Int. J. Soc. Sc. Manage. Vol. 11, Issue-(1-2): 1-6.

Single-metalens-assisted polarization imaging and edge detection for target recognition

Simultaneous capture of various light information, including polarization and edge information of the objects, has consistently been a fundamental concern within the field of target recognition. However, these tasks are typically accompanied by bulky optical components and active illumination methods, which significantly restricts their use in compact and lightweight applications. Here, we demonstrate a metalens-assisted imaging system that can simultaneously achieve polarization imaging and optoelectronic edge detection in a single shot with low consumption. The dielectric metalens is designed to achieve polarization imaging by dispersing the input polarized light into two orthogonal components, resulting in optoelectronic isotropic edge detection of two-dimensional images after digital post-processing. Compared with the algorithmic methods using a convolution kernel, the proposed system has a much lower computational complexity. The work presented in this study demonstrates the potential applications in machine vision and paves the way for the development of compact target recognition and real-time image processing systems.

Predict the Heart Attack Possibilities Using Machine Learning

Heart disease remains one of the leading causes of mortality worldwide, making early detection and prevention crucial. Machine learning techniques offer promising avenues for predicting heart attack possibilities by analyzing patient data and identifying risk factors. This study explores the development of a predictive model using machine learning algorithms to assess the likelihood of a heart attack based on individual patient characteristics and medical history. The dataset comprises a comprehensive range of features including demographic information, lifestyle factors, medical history, and results from diagnostic tests such as electrocardiograms (ECG), cholesterol levels, and blood pressure readings. Preprocessing techniques such as data cleaning, normalization, and feature engineering are applied to prepare the dataset for analysis. Looking ahead, the article identifies promising avenues for future research, including the integration of multimodal data sources, real-time risk assessment systems, and collaborative efforts to develop standardized benchmarks and evaluation protocols. By synthesizing the collective knowledge gleaned from decades of research, this historical review aims to inform and inspire ongoing endeavors in leveraging machine learning for proactive cardiovascular health management and prevention strategies.

Automatic Identification of Emergency Sounds for Self-Driving Cars

As the development of Self-Driving Cars (SDCs) advances, one important feature that requires attention is their ability to sense and respond to emergencies on the road. Drivers of emergency vehicles prioritize speed over safety during emergencies to save lives, potentially leading to accidents. However, manual cars sometimes may not understand the urgency of the situation, further increasing the risk of collisions. In such cases, self-driving cars offer a better solution to replace manual cars and minimize road accidents. These cars with emergency sound detections offer a level of responsiveness, accuracy, and consistency that surpasses manual cars, contributing to improved road safety and efficiency in emergencies. Therefore, the key objective of the research is to improve the listening capability of self-driving cars to detect emergency vehicles with the help of a hybrid feature extraction technique. The suggested technique leverages a combination of Complex Morlet Wavelet and Co-occurrence Matrix to obtain statistical features from the emergency sounds. The proposed technique can work with the input length of 1.2 seconds of raw waveforms. This research work investigates that self-driving cars can accurately identify emergency vehicles by examining the distinctive emergency sound patterns emitted by the emergency vehicle with the highest accuracy of 94%. At the same time, the proposed technique reduces the computational cost by 20 – 40 milliseconds when compared with other techniques. The result of this work not only provides better accuracy but also reduces detection time, which is a crucial requirement for real-time applications such as self-driving cars.

DRIVER DROWSINESS DETECTION AND CREATING SAFETY MEASURES USING OPENCV,PYTHON

Accidents due to driver drowsy driving is significantly increasing in high ratio now days. So we proposed an AI based solution to reduce the driver drossy driving accident by alerting the driver when they fall asleep. This is an brief abstract that explains system working and principles. This paper present a novel, Real-Time Driver Drowsiness Detection System(DDDS) that utilizes a multi-model approach for fatigue assessment. The system incorporates computer vision and machine learning techniques to analyze facial features, eye movements, and physical signal(if applicable) captured through a webcam processing techniques enhance data processing techniques enhanced data quality for better analysis.Facial landmarks are identified to locate the eyes, and features like Eye Aspect Ratio(EAR) are used to detect signs of fatigue. The system employs a multimodal approach, utilizing computer vision techniques to analyze facial features and eye movements captured through a webcam. Specifically, the DDDS leverages the Haar cascade algorithm for efficient facial landmark detection and the Histogram of Oriented Gradients (HOG) algorithm for feature extraction from facial regions of interest. This combination allows for accurate localization and analysis of key features like eyes. Additionally, the system explores the potential of physiological data (e.g., heart rate variability) for a more comprehensive analysis (if applicable). Machine learning techniques (if applicable) can be employed on the combined features, including those extracted using Haar and HOG, for enhanced fatigue classification. Upon detecting drowsiness, the DDDS triggers customizable alerts (visual, audio, or haptic) to warn the driver. The system additionally explores the potential of physiological data (e.g., heart rate variability) for fatigue detection, offering a more comprehensive approach (if applicable).A machine learning model can be employed on the combined features for robust fatigue classification. Upon detection of drowsiness,the DDDS triggers customizable alerts ( visual, audio, or haptic) to warn the driver. This system addresses limitations of existing solution by: 1.Real-Time processing:Ensuring immediate detection and response to fatigue signs. 2.Multi feature analysis: Combining facial features, eye movements,and physiological data for robust detection. 3.Customizable alerts: Providing drivers control over alert type for a personalized experience. The evaluation methodology employs a dataset of [data type e.g.., image, videos] to access the system’s performance.Metrics like accuracy,precision,recall,and F1-score will be used to evaluate the effectiveness of the DDDS in detecting drowsy driving. This research aims to contribute to safer roads by developing a comprehensive and adaptable Driver Drowsiness Detection System. II.KEYWORDS: Drowsy Driving, Driver Monitoring,Computer Vision, Machine Learning,Eye Tracking, Fatigue Detection,Physiological Signs(if applicable)