Real-time Data Processing Research Articles

Revolutionizing Supply Chain Management: Real-time Data Processing and Concurrency

In the contemporary business landscape, effective supply chain management (SCM) is paramount for organizations seeking to thrive amidst evolving market dynamics and heightened customer expectations. This research paper presents a pioneering approach to SCM that harnesses cutting-edge technologies, namely Kafka and Akka, to revolutionize data integration and decision-making processes. By leveraging Kafka as a robust distributed event streaming platform and Akka as a versatile toolkit for developing concurrent and distributed applications, our system facilitates seamless communication and coordination across diverse nodes within the supply chain network. This paper elucidates the intricacies of the proposed architecture, detailing the implementation methodology and performance evaluation metrics. Through a comprehensive examination, we demonstrate how our solution enhances supply chain visibility, fosters operational agility, and enables real-time responsiveness to market fluctuations and customer demands. Moreover, practical use cases exemplify the transformative impact of our approach on inventory management optimization, order fulfillment efficiency, and logistics optimization. Furthermore, we delve into the challenges encountered during implementation and deployment, offering insights into potential mitigative strategies. Finally, we outline avenues for future research, exploring emerging trends and opportunities in the realm of SCM empowered by Kafka and Akka technologies.

Managing the E-commerce Data Deluge through Text Analytics and Web Management (Overview of Amazon.com)

Today, more than 80% of the big data handled in the e-commerce industry is text and unstructured data. Text analytics is an automated process for analyzing text and extracting useful information from it. It can discover trends and relationships in data. Web analytics is the collection, processing, and analysis of data in order to draw conclusions to optimize usability on a website. Web analytics can be used to improve the usability of a site by analyzing user behavior patterns such as time spent on the site, abandonment rates, most frequently accessed products, click-through rates, etc. It can also help analyze the interests of different user demographics, as it tracks granular details such as user demographics, age and gender, geography, and devices used as data. In order to obtain UpToDate information, the business can utilize business intelligence for real-time data processing, then they can practice stream analysis to analyse continuous flow of data. For instance, the business can collect instant information in Twitter or other social media and analyse it by using social media analysis. For website management, business can practice web analysis to analyse the customer’s behaviours. Tracking the customer’s activity, page view and conversion rate is important for business to analyse how to improve the website performance. Text analytics of comments received on Amazon can be used to group text data and produce results in terms of word frequency distribution and sentiment analysis. Text analytics could be used for decision making, improving service quality, and developing new business models.

Improvement of Student Interaction Analysis in Online Education Platforms Through Interactive Mobile Technology and Machine Learning Integration

The emergence of online education platforms, driven by interactive mobile technology, has significantly reshaped traditional educational paradigms and underscored the critical need for advanced analysis and improvement of student interactions. Effective analysis of student interaction is crucial for enhancing teaching quality and optimizing the learning experience in these digitally enriched environments. Traditional analysis frameworks often face challenges such as inaccuracies in anomaly detection and inefficiencies in data handling, particularly when handling extensive datasets typical of online platforms. This study introduces a novel approach to enhancing student interaction analysis systems by leveraging the synergy between machine learning and advanced interactive mobile technologies. Initially, the study proposes an advanced anomaly detection method tailored for identifying irregular student interactions. This method utilizes a blend of machine learning algorithms and the real-time data processing capabilities of mobile technology. Furthermore, to address the complexities of data transmission in mobile-based online education ecosystems, a state-ofthe- art congestion control algorithm has been developed. This algorithm optimizes data flow, significantly enhancing transmission stability and efficiency. The integration of interactive mobile technology with machine learning offers a robust and dynamic framework for analyzing student interactions, thereby facilitating a more engaging and effective online educational experience. This research contributes to the advancement of online education quality and efficiency by emphasizing the role of interactive mobile technology in shaping future learning environments.

Gas Level and Leakage Detection with Automatic Booking

Gas level and leakage detection is crucial for ensuring safety in various environments such as homes, industries, and laboratories. In this paper, we present a novel approach utilizing embedded C language, MQ2 sensor, ESP32 microcontroller, buzzer, and alert messaging system for efficient gas monitoring and detection. The proposed system employs the MQ2 sensor, capable of detecting various gases including LPG, methane, and carbon monoxide, interfaced with an ESP32 microcontroller for real-time data processing and analysis. Embedded C programming language is utilized for firmware development to facilitate efficient resource management and enhance system performance. Upon detecting abnormal gas levels or leakage, the system triggers an audible alarm through the buzzer and simultaneously sends alert messages to designated recipients via messaging protocols such as SMS. This enables timely response and preventive measures to mitigate potential hazards associated with gas leakage incidents. Additionally, an automatic gas booking feature is integrated, where upon gas depletion, the system automatically sends an SMS to the designated gas provider, facilitating seamless replenishment of gas supply.. Experimental results demonstrate the effectiveness and reliability of the proposed system in accurately detecting gas levels and identifying leakages in different environments. The system's low-cost implementation and versatility make it suitable for deployment in various applications, contributing to enhanced safety standards and risk mitigation strategies

Integrated Two-Level New Energy Monitoring and Big Data Centers: A Comprehensive Approach for Urban Sustainability

This study examines the transition to green energy in Wenzhou City and suggests a novel plan for integrating an energy monitoring and data center. The aim is to improve the city's sustainability through the building of a large-scale wind-solar integrated energy monitoring system by a group in Wenzhou City. The solution uses the Internet of Things and intelligent data analysis technology to realize real-time data collection, processing and operation and maintenance decision support for distributed power stations. The system adopts storage-computer separation and data encryption technology to ensure data security and system stability and improves data processing efficiency through containerization and microservice architecture. In addition, the study also explores the application potential of this technical scheme in regional energy planning, which provides a new idea for Wenzhou to achieve green energy transformation and low-carbon development.

Phenomena observed in electron EBRT using a pulse-by-pulse Radioluminescence dosimetry system with cloud-based analytics

The pulse-by-pulse characteristics of electron beams from a clinical linear accelerator (linac) at high dose rates are crucial for ensuring precise dose delivery in radiation therapy. In this research, we employ a radioluminescence scintillator made from Ge-doped silica optical fiber to analyse electron beams at standard therapy dose rates 600 cGy/min with energy of 15 MeV. The scintillator is combined with a photomultiplier tube (PMT) and a photon-counting setup to achieve microsecond (μs) gating times, providing high temporal resolution. For sub-microsecond (sub-μs) temporal resolution, we utilize a multi-pixel photon counter and a rapid digital oscilloscope. Our study examines the peak time and the time it takes for pulses to return to baseline, uncovering the effects of afterglow at elevated dose rates and its potential implications for dosimetry precision. Our results indicate that monitor chambers, which have lower temporal resolution than the radioluminescence system outlined in this study, may struggle to accurately track high-rate dose delivery, emphasizing the necessity for advanced monitoring methods in FLASH radiotherapy. The dosimetric data was further analysed for deeper analytics on a cloud-based system. The dosimetric data underwent advanced analysis on a cloud-based platform, employing a prototype analytics software system comprised of Dosi-Client and Dosi-Cloud Backend components for real-time data acquisition and processing. This innovative approach revealed significant insights into radiation dosimetry, including the detection of anomalies through a reconstruction convolutional autoencoder, enhancing the precision and reliability of dosimetric measurements.

From cloud and fog computing to federated-fog computing: A comparative analysis of computational resources in real-time IoT applications based on semantic interoperability

In contemporary computing paradigms, the evolution from cloud computing to fog computing and the recent emergence of federated-fog computing have introduced new challenges pertaining to semantic interoperability, particularly in the context of real-time applications. Fog computing, by shifting computational processes closer to the network edge at the local area network level, aims to mitigate latency and enhance efficiency by minimising data transfers to the cloud. Building upon this, federated-fog computing extends the paradigm by distributing computing resources across diverse organisations and locations, while maintaining centralised management and control. This research article addresses the inherent problematics in achieving semantic interoperability within the evolving architectures of cloud computing, fog computing, and federated-fog computing. Experimental investigations are conducted on a diverse node-based testbed, simulating various end-user devices, to emphasise the critical role of semantic interoperability in facilitating seamless data exchange and integration. Furthermore, the efficacy of federated-fog computing is rigorously evaluated in comparison to traditional fog and cloud computing frameworks. Specifically, the assessment focuses on critical factors such as latency time and computational resource utilisation while processing real-time data streams generated by Internet of Things (IoT) devices. The findings of this study underscore the advantages of federated-fog computing over conventional cloud and fog computing paradigms, particularly in the realm of real-time IoT applications demanding high performance (lowering CPU usage to 20%) and low latency (with picks up to 300ms). The research contributes valuable insights into the optimisation of processing architectures for contemporary computing paradigms, offering implications for the advancement of semantic interoperability in the context of emerging federated-fog computing for IoT applications.

A comparative review of subsea navigation technologies in offshore engineering projects

Subsea navigation technologies play a critical role in enhancing precision and efficiency in offshore engineering projects. This comparative review examines the evolution and application of subsea navigation systems, with a focus on improving precision in offshore construction and surveying. The study evaluates various subsea navigation technologies, including acoustic positioning systems, inertial navigation systems, and optical imaging systems, highlighting their capabilities and limitations. The review begins by discussing the historical development of subsea navigation technologies, tracing their evolution from early acoustic systems to modern integrated solutions. It then explores the key components and operation principles of each technology, providing insights into their functionalities and suitability for different offshore applications. The study also examines the challenges and advancements in subsea navigation, including issues related to signal interference, accuracy, and real-time data processing. It discusses how these challenges have been addressed through technological innovations, such as improved sensor technologies and advanced data fusion algorithms. Furthermore, the review assesses the practical application of subsea navigation technologies in offshore engineering projects, including pipeline installation, subsea infrastructure maintenance, and underwater surveying. It analyzes case studies and industry practices to illustrate the effectiveness of these technologies in improving precision, reducing costs, and mitigating risks in offshore operations. Overall, this comparative review provides a comprehensive overview of subsea navigation technologies in offshore engineering projects. It highlights the evolution, capabilities, and applications of these technologies, emphasizing their role in enhancing precision and efficiency in offshore construction and surveying. The study concludes with recommendations for future research and development to further improve the performance and reliability of subsea navigation systems in offshore operations.

Evaluation of Student Success Across the Subjects using MERN Stack

bstract: The Evaluation of Student Success across the Subjects is an essential tool in educational institutions, facilitating the efficient management and dissemination of student performance data. This paper presents the design and implementation of a SRMS using the MERN stack, an innovative technology stack comprising MongoDB, Express.js, React.js, and Node.js. The proposed system leverages the full-stack JavaScript environment to enhance the interactivity, real-time data processing, and user experience of the SRMS.React.js is employed on the frontend to provide an intuitive and dynamic user interface, making it easier for users to interact with the system effectively. We discuss the development process, focusing on the integration of these technologies, and demonstrate how the MERN stack facilitates rapid development and deployment of a web-based application. The system's performance is evaluated based on criteria such as response time, data integrity, and user satisfaction. Initial results indicate that the SRMS is not only faster and more reliable but also provides enhanced accessibility and user engagement compared to traditional systems. This paper aims to contribute to the academic community by providing insights into the application of the MERN stack in developing educational tools, thereby suggesting a pathway for future research and development in educational technology systems

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.

Safety Helmet for Miners

Abstract: The safety helmets basically aim at safety of miners reducing the risks of accidents. It also highlights the hazardous environments of mining and potential dangers that the workers face while working in mines. Safety helmets will be containing infrared sensors which keep track of the environment of the miner. It highlights the inherent risks miners face, including poor visibility, potential collisions, and hazardous conditions. The helmets are capable of alerting the miners from dangers. Infrared sensors can sense the heat signatures and obstacles in real-time resulting in spatial awareness. The helmets also consist of Arduino to enable real-time data processing and communication integration. The helmet also consists of LoRa which provide wireless communication between helmets and receiver ends, which would notify them in case of any emergency. The abstract concludes by enhancing the importance of IR-sensors, LoRa and Arduino which interfaces all of the above equipment.

GPS-Based Precise Orbit Determination of LEO Satellites Using Space-Based Double-Differenced Observations

One of the important GPS applications in space is precise orbit determination (POD) of Low-Earth Orbit (LEO) satellites. Thousands of LEO satellites are currently in orbit. One of the challenges is how to efficiently and precisely determine the orbits to satisfy the relative and absolute accuracy needs of missions. Currently, GPS-based POD of LEO satellites can be performed using either un-differenced (UD) or ground-based double-differenced (DD) observations. The UD POD needs both precise GPS satellite orbits and clocks; the DD POD needs not only the precise orbits, but also global ground reference receivers. Therefore, the GPS-based LEO POD is based on either global ground stations or precise GNSS clocks, which are not convenient for near real-time or real-time data processing. The GPS orbits can be precisely predicted in certain time; the clocks are not. For some formation flying satellite missions (two or more LEO satellites), the absolute orbit accuracy requirements are not as stringent as the relative requirements. The problem is how to perform LEO POD without global station data and precise GPS clocks in near real-time or real-time to achieve the mission orbit accuracy requirement. Based on this motivation, we investigated the GPS-based LEO POD using space-based DD observations without using global ground station GPS data and precise GPS clock products. For this study, we processed the real GPS observations from two LEO satellites. The absolute and relative orbit accuracy is assessed using several tests, including analysis of the orbit fits, external orbit comparisons, Satellite Laser Ranging (SLR) and K-band Ranging (KBR) residuals.

VOICE CONTROL CAR USING MACHINE LEARNING

The "Voice-Controlled Car Using Machine Learning" project aims to create an innovative and user- friendly vehicle control system by integrating machine learning technology. This system provides a hands-free driving experience, making it safer and more convenient for drivers. The project involves developing a smart car prototype equipped with various sensors and microcontrollers. Machine learning algorithms are implemented to recognize voice commands and translate them into specific actions for the vehicle, such as accelerating, braking, turning, and even parking. The core components of the system include a speech recognition module, a control interface, and a real-time data processing unit. The project not only demonstrates the potential of machine learning in the automotive industry but also promotes a more accessible and user-centric approach to vehicular control. Keywords: “Speech Recognition”, command interpretation”, audio processing”, Wake word Detection”.

CredChecker:-Credit Card Fraud Detection WebApp

The proposed app will serve as a crucial tool for detecting and managing credit card fraud, focusing on high-risk areas such as densely populated urban centers. The primary objective is to minimize financial losses incurred due to fraudulent activities. A key advantage lies in its seamless integration with financial institutions and law enforcement agencies, facilitating efficient collaboration between users and relevant authorities in case of suspected fraud incidents. This integration is pivotal in achieving the app's fundamental mission of delivering real-time fraud alerts, including transaction details, potential fraudulent activities, and recommended actions. The application will feature advanced functionalities tailored for individual users to enhance their awareness and preparedness against fraudulent transactions. These tools empower users to set up personalized fraud detection plans, allowing them to proactively monitor their accounts and swiftly respond to suspicious activities. To ensure the reliability and promptness of information, the app will leverage a combination of cuttingedge technologies, including machine learning algorithms and real-time data processing.

In Situ Monitoring of the Curing of Highly Filled Epoxy Molding Compounds: The Influence of Reaction Type and Silica Content on Cure Kinetic Models.

Monitoring of molding processes is one of the most challenging future tasks in polymer processing. In this work, the in situ monitoring of the curing behavior of highly filled EMCs (silica filler content ranging from 73 to 83 wt%) and the effect of filler load on curing kinetics are investigated. Kinetic modelling using the Friedman approach was applied using real-time process data obtained from in situ DEA measurements, and these online kinetic models were compared with curing analysis data obtained from offline DSC measurements. For an autocatalytic fast-reacting material to be processed above the glass transition temperature Tg and for an autocatalytic slow-reacting material to be processed below Tg, time-temperature-transformation (TTT) diagrams were generated to investigate the reaction behavior regarding Tg progression. Incorporating a material containing a lower silica filler content of 10 wt% enabled analysis of the effects of filler content on sensor sensitivity and curing kinetics. Lower silica particle content (and a larger fraction of organic resin, respectively) favored reaction kinetics, resulting in a faster reaction towards Tg1. Kinetic analysis using DEA and DSC facilitated the development of highly accurate prediction models using the Friedman model-free approach. Lower silica particle content resulted in enhanced sensitivity of the analytical method, leading, in turn, to more precise prediction models for the degree of cure.

Intelligent manufacturing: bridging the gap between the Internet of Things and machinery to achieve optimized operations

The access gateway layer in the IoT interior design bridging the gap between several destinations. The capabilities include message routing, message identification, and a service. IoT intelligence can help machinery industries optimize their operations with perspectives on factory processes, energy use, and help efficiency. Automation can bring in improved operations, lower destruction, and greater manufacture. IoT barriers are exactly developed for bridging the gap between field devices and focused revenues and industrial applications, maximizing intelligent system performance and receiving and processing real-time operational control data that the network edge. The creation of powerful, flexible, and adjustable Human Machine Interfaces (HMI) can enable associates with information and tailored solutions to increase productivity while remaining safe. An innovative strategy for data-enabled engineering advances based on the Internet of Manufacturing Things (IoMT) is essential for effectively utilizing physical mechanisms. The proposed method HMI-IoMT has been gap analysis to other business processes turns into a reporting process that can be utilized for improvement. Implementing a gap analysis in production or manufacturing can bring the existing level of manpower allocation closer to an ideal level due to balancing and integrating the resources. Societal growth and connection are both aided in the built environment. Manufacturing operations are made much more productive with the help of automation and advanced machinery. Increasing the output of products and services is possible as a result of this efficiency, which allows for the fulfillment of an expanding population's necessities.

Enhancing Workplace Efficiency and Security Through Intelligent Employee Surveillance

This project aims to develop an innovative system for accurately tracking employees' working hours based on their presence within designated workspace areas, particularly their work cabins. Leveraging advanced technologies such as image annotation, preprocessing, and augmentation, as well as robust object detection models, this study addresses the need for efficient employee surveillance and time management solutions in contemporary workplaces. The methodology involved detailed annotation and enhancement of image data, enabling precise representation of cabin areas and identification of individual employees within images. Subsequently, a state-of-the-art object detection model, YOLOv8, was utilized to train using this annotated dataset, achieving an impressive accuracy of more than 90% in recognizing and tracking employee presence within the specified cabin regions. Through small incremental changes based on previous insights and optimization, the project achieved high levels of accuracy in inferring employees' working hours based on their occupancy within the designated workspace. By differentiating between time spent inside cabins (considered as working time) and time spent outside these areas (considered as non-working time), the system offers an automated and objective approach to time tracking, eliminating the need for manual input or subjective assessments. Future scopes for this research include exploring the integration of additional sensors or data sources to further enhance the accuracy and granularity of employee activity tracking. Additionally, advancements in machine learning algorithms and hardware may enable real-time processing and analysis of surveillance data, leading to more proactive management of employee productivity and well-being. Moreover, the application of this technology could extend beyond traditional office settings to various industries, such as manufacturing, retail, and healthcare, where precise monitoring of employee activities is crucial for optimizing operations and ensuring compliance with regulations. This research underscores the potential of advanced computer vision techniques, particularly YOLOv8, in revolutionizing employee surveillance and time management practices. By providing real-time monitoring capabilities and ensuring compliance with work regulations, this approach holds promise for enhancing workplace productivity, transparency, and accountability.

A Comprehensive Analysis of AI/ML-enabled Predictive Maintenance Modelling for Advanced Driver-Assistance Systems

Advanced Driver-Assistance Systems (ADAS) are changing driver-vehicle interactions to improve road safety and reduce distractions. Technological advances like ADAS and AI in cars present societal challenges and opportunities. It shows how AI aids human-machine communication by improving motor skills. The auto industry is interested in ADAS because it can increase energy efficiency, safety, and comfort. Numerous studies have shown its benefits. ADAS and vehicle networking show promise, but establishing a sound control system is challenging. Model Predictive Control (MPC) is one answer to these problems. To manage higher-level connectivity and automation, the paper analyses and implements key research. It also finds issues and recommends solutions. The latest driverless car ADAS improvements have dramatically increased passenger safety. These systems are safer and more automated using sensors and ECUs. Most ADAS have RADAR, cameras, ultrasonic, and LiDAR. This work uses AI/ML-enabled Predictive Maintenance modelling to improve ADAS safety and longevity. AI and ML in Advanced Driver Assistance Systems (ADAS) are significant vehicle safety and reliability advances. AI/ML-enabled predictive maintenance detects and fixes ADAS component faults. ADAS predictive maintenance using AI/ML can detect issues, improve driver safety, and boost vehicle efficiency. Advanced sensor arrays and control units are needed for adaptive cruise control, traffic sign recognition, and lane-keeping assistance. AI/ML algorithms discover issues and enable early interventions in predictive maintenance models. Predictive maintenance is examined utilizing classical machine learning, deep learning, and reinforcement learning. Integration of numerous AI/ML models, real-time data processing, customization based on vehicle usage patterns, scalability, and adaptability of predictive maintenance models to new ADAS technologies are research gaps. 

Alcohol Detection and Over-Speed Alert System for Vehicles

Road accidents stemming from alcohol impairment and over-speeding remain critical global issues, necessitating proactive safety measures. To address these challenges, we present an integrated system combining alcohol detection and over-speed alert functionalities for vehicles. Leveraging advanced sensor technologies and real-time data processing, the system detects alcohol presence via breath or touch-based sensors and triggers visual and audible warnings upon exceeding permissible limits, thus deterring drunk driving. Simultaneously, GPS and vehicle speed sensors monitor real-time speed, activating alerts when predefined thresholds are surpassed to caution against over-speeding. Through synergistic integration, this system offers comprehensive safety measures, evaluated through simulations and real-world testing to demonstrate effectiveness in reducing accidents caused by alcohol impairment and over-speeding, thereby contributing to a safer transportation ecosystem. Keywords: alcohol detection, integrated system, over-speed alert, road safety.

Voice Recognition Based Vital Parameter Monitoring Patient BED

In healthcare protocols continuous monitoring of vital parameters plays a crucial role in patient care. Integrating advanced technologies for both monitoring and patient comfort enhances the efficiency of healthcare delivery. This paper presents the design and development of a novel patient bed system equipped with vital parameter monitoring capabilities and adjustable head position controlled through voice commands. The proposed system leverages state-of-the-art sensors to monitor vital parameters such as heart rate, blood pressure, oxygen saturation, and temperature. These sensors are seamlessly integrated into the bed, providing non-intrusive and continuous monitoring of the patient's health status. Real-time data acquisition and processing algorithms ensure accurate and timely assessment of the patient's condition. Furthermore, the patient bed is designed with an adjustable head position feature, allowing for optimal comfort and support. Unlike conventional manual or automated adjustment mechanisms, our system employs voice control technology for intuitive and hands-free operation. Patients can simply issue verbal commands to adjust the head position according to their preferences, enhancing overall comfort and facilitating better rest.