Real-time Processing Research Articles

The MASLIS: A five-year review of Costa Rica's near real-time seismic intensity processing system

The MASLIS is an online system that displays the earthquake’s seismic intensity in near-real time. The system analyzes the data from Costa Rica’s strong motion network. When the intensity exceeds a specified intensity threshold at a number of sites, the system sends several warning messages. When it was first developed in 2019, the system was tested using scenario earthquakes. After five years of operation, this article briefly discusses its performance using actual earthquake data.

Hardware simulation of real-time wavelength corrected phase projection

We demonstrate the real-time signal processing operation of a dispersion-free phase projection algorithm intended for atmospheric correction of multi-aperture optical phased arrays. It uses interferometric phase measurements at multiple sensing wavelengths, offset by 50 GHz, to compute a phase correction at a third, remote wavelength. This is useful where phase sensing cannot be implemented at the wavelength of interest, enabling interferometric level control from wavelength offset targeting beacons or guidestars. The digital signal processing implementation we demonstrate has a residual temporal phase error of 4×10−4rad/Hz while being capable of 100 MHz throughput with 0.53 µs latency, making it a viable approach for either feedback or feed-forward atmospheric correction in segmented piston-phase control systems.

Improving Performance of Uncombined PPP-AR Model with Ambiguity Constraints

With the advancement of multi-frequency and multi-constellation GNSS signals and the introduction of observable-specific bias (OSB) products, the uncombined precise point positioning (PPP) model has grown more prevalent. However, this model faces challenges due to the large number of estimated parameters, resulting in strong correlations between state parameters, such as clock errors, ionospheric delays, and hardware biases. This can slow down the convergence time and impede ambiguity resolution. We propose two methods to improve the triple-frequency uncombined PPP-AR model by integrating ambiguity constraints. The first approach makes use of the resolved ambiguities from dual-frequency ionosphere-free combined PPP-AR processing and incorporates them as constraints into triple-frequency uncombined PPP-AR processing. While this approach requires the implementation of two filters, increasing computational demands and thereby limiting its feasibility for real-time applications, it effectively reduces parameter correlations and facilitates ambiguity resolution in post-processing. The second approach incorporates fixed extra-wide-lane (EWL) and wide-lane (WL) ambiguities directly, allowing for rapid convergence, and is well suited for real-time processing. Results show that, compared to the uncombined PPP-AR model, integrating N1 and N2 constraints reduces averaged convergence time from 8.2 to 6.4 min horizontally and 13.9 to 10.7 min vertically in the float solution. On the other hand, integrating EWL and WL ambiguity constraints reduces the horizontal convergence to 5.9 min in the float solution and to 4.6 min for horizontal and 9.7 min for vertical convergence in the fixed solution. Both methods significantly enhance the ambiguity resolution in the uncombined triple-frequency PPP model, increasing the validated fixing rate from approximately 80% to 89%.

GPThingSim: A IoT Simulator Based GPT Models Over an Edge-Cloud Environments

The Internet of Things encapsulates a vision of a world in which billions of objects, called connected devices, are endowed with intelligence, communication capabilities, and integrated sensing and actuation capabilities. They often outsource their storage and computing needs to more powerful resources in the cloud. In Edge computing, it is the device itself that collects and processes data and must make real-time decisions. A model that combines the edge and the cloud allows for instant processing of large amounts of data, making devices increasingly intelligent. One fundamental aspect of Artificial Intelligence, particularly Machine Learning (ML), is that it requires a substantial amount of data to learn. The GPT model can be adapted to operate within a cloud environment, which enables instant processing of large quantities of data. In this article, our approach relies on a hybrid of Cloud computing and Edge computing, merging the best features of both approaches by using Edge for real-time processing and the cloud for storage and large-scale data analysis. We will explore the possibilities of integrating the GPT model into IoT devices. This extension allows devices to offer processing capabilities at the local level, which can be extremely beneficial for many applications. We propose a platform for simulating connected objects augmented by the LSTM neural network model to predict the energy consumption of IoT devices in a smart city scenario.

Face-Looking as a Real-Time Process in Mind-Mindedness: Timely Coordination Between Mothers' Gaze on Infants' Faces and Mind-Related Comments.

Maternal mind-mindedness refers to a caregiver's tendency to respond to their infants as individuals with their own thoughts, feelings, desires, and beliefs. Although previous studies have focused on maternal speech in quantifying mind-mindedness, maternal mind-mindedness should manifest not only as mind-related comments but also through non-verbal behaviors during infant-mother interactions. In this study, we investigated the relationship between maternal gaze at the infant's face and typical verbal measurement of mind-mindedness in free-flowing interactions. Forty 11- to 13-month-old infants and their mothers participated in the study; the mothers were asked to wear a head-mounted eye tracker to measure their gaze during infant-mother free-play interactions. We measured the proportion of time mothers looked at the infant's face when it was present in the mother's field of view and examined the relationship between the face-looking proportion and verbal measurement of mothers' mind-mindedness. Mothers who displayed appropriate mind-related comments looked at the faces of their infants more frequently. Moreover, their looking was coordinated in a timely manner with appropriate mind-related comments compared with other comments. Our findings suggest that mothers looking at infants' faces supports comments regarding infants' mental states and shed new light on real-time behaviors underlying mothers' mentalization processes.

Design of mean filter using field programmable gate arrays for digital images

In this paper, we design and analysis of mean filter using field programmable gate arrays (FPGAs) for digital images, FPGAs are integrated circuits consisting of interconnections that connect programmable internal hardware blocks allows users to customize operations for a specific application. FPGA is an ideal choice for real-time image processing, these FPGA devices are controlled in Verilog or VHDL languages, allowing to design at different levels and adapt to design changes or even support new applications throughout the life of the component. Digital image filtering is the most important task in image processing and with the help of computers, image recognition involves identifying and classifying objects in an image. This paper design of mean filter for digital image processing, implementation and analysis of image processing algorithms on FPGAs. The results obtained on the FPGA are compared and analyzed with the results by MATLAB software.

Aqua-stream: an IoT based smart water management system for sustainable living

<p class="03IJISAEAbstract">Aqua-stream, an innovative internet of things (IoT) enabled water management system, utilizes the power of long short-term memory (LSTM) networks, a sophisticated time-series forecasting machine learning technique with Kafka. Aqua-stream seamlessly integrates LSTM within the Kafka streaming architecture for efficient real-time data processing, ensuring quick responses to emerging water management needs. LSTM is employed for real-time anomaly detection, dynamically analyzing streaming data to prevent leaks through automated shut-off valves. The system’s comprehensive dashboard utilizes LSTM insights for live water quality analysis; adaptive scheduling based on individual preferences and personalized recommendations, enhancing cost-effective water management. This streamlined approach extends to the smart gardening system, where LSTM guides automation for optimal plant care incorporating sensors to monitor soil moisture, temperature, and sunlight levels. This system automatically adjusts watering and lighting to ensure optimal conditions for plant growth. Users can control and monitor their garden remotely via a smartphone, facilitating plant care while saving water and energy. Aqua-stream redefines home water management, offering a holistic solution that combines intelligent water conservation with smart gardening for a sustainable and connected living experience. Aqua-stream represents a seamless integration of LSTM-based machine learning and IoT technologies, offering an intelligent, yet simplified, solution for sustainable and connected living.</p>

Travel time prediction for an intelligent transportation system based on a data-driven feature selection method considering temporal correlation

Travel-time prediction is a critical component of Intelligent Transportation Systems (ITS), offering vital information for tasks such as accident detection, congestion management, and traffic flow optimisation. Accurate predictions are highly dependent on the selection of relevant features. In this study, a two-stage methodology is proposed which consists of two layers of Optimisation Algorithm and one Data-Driven method (OA2DD) to enhance the accuracy and efficiency of travel-time prediction. The first stage involves an offline process where interconnected optimisation algorithms are employed to identify the optimal set of features and determine the most effective machine learning model architecture. In the second stage, the real-time process utilises the optimised model to predict travel times using new data from previously unseen parts of the dataset. The proposed OA2DD method was applied to a case study on the M50 motorway in Dublin. Results show that OA2DD improves the convergence curve and reduces the number of selected features by up to 50%, leading to a 56% reduction in computational costs. Furthermore, using the selected features from OA2DD, reduced the prediction error by up to 29% compared to the full feature set and other feature selection methods, demonstrating the method's effectiveness and robustness.

Real-time Detection of Abnormal Financial Transactions Using Generative Adversarial Networks: An Enterprise Application

This paper presents a novel real-time financial fraud detection framework utilizing Generative Adversarial Networks (GANs) for enterprise applications. The proposed system addresses critical challenges in fraud detection, including class imbalance, real-time processing requirements, and enterprise scalability. Implementing a sophisticated multi-layered architecture, the system integrates advanced preprocessing techniques with an optimized GAN model explicitly designed for fraud pattern recognition. The framework incorporates parallel processing capabilities and adaptive batch processing mechanisms to maintain high throughput while ensuring sub-second latency. The experimental evaluation uses a subset of the European Credit Card Transaction dataset, comprising 50,000 transactions with a balanced representation achieved through strategic sampling and SMOTE technique. The proposed model achieves 97.8% accuracy, 96.5% precision, and 95.8% recall, demonstrating competitive performance compared to traditional machine learning approaches. Real-time performance analysis shows consistent sub-100ms latency while maintaining robust performance under varying load conditions. The system demonstrates linear scalability up to 32 nodes, with high availability and failover capabilities. The comprehensive assessment validates the framework's effectiveness in enterprise environments, providing practical solutions for financial institutions facing evolving fraud challenges. This research contributes to the advancement of financial security technology through the innovative application of adversarial learning in fraud detection.

Network intrusion detection in big datasets using Spark environment and incremental learning

<p class="Abstract">Internet of things (IoT) systems have experienced significant growth in data traffic, resulting in security and real-time processing issues. Intrusion detection systems (IDS) are currently an indispensable tool for self-protection against various attacks. However, IoT systems face serious challenges due to the functional diversity of attacks, resulting in detection methods with machine learning (ML) and limited static models generated by the linear discriminant analysis (LDA) algorithm. The process entails adjusting the model parameters in real time as new data arrives. This paper proposes a new method of an IDS based on the LDA algorithm with the incremental model. The model framework is trained and tested on the IoT intrusion dataset (UNSW-NB15) using the streaming linear discriminant analysis (SLDA) ML algorithm. Our approach increased model accuracy after each training, resulting in continuous model improvement. The comparison reveals that our dynamic model becomes more accurate after each batch and can detect new types of attacks.</p>

STQD-Det: Spatio-Temporal Quantum Diffusion Model for Real-Time Coronary Stenosis Detection in X-Ray Angiography.

Detecting coronary stenosis accurately in X-ray angiography (XRA) is important for diagnosing and treating coronary artery disease (CAD). However, challenges arise from factors like breathing and heart motion, poor imaging quality, and the complex vascular structures, making it difficult to identify stenosis fast and precisely. In this study, we proposed a Quantum Diffusion Model with Spatio-Temporal Feature Sharing to Real-time detect Stenosis (STQD-Det). Our framework consists of two modules: Sequential Quantum Noise Boxes module and spatio-temporal feature module. To evaluate the effectiveness of the method, we conducted a 4-fold cross-validation using a dataset consisting of 233 XRA sequences. Our approach achieved the F1 score of 92.39% with a real-time processing speed of 25.08 frames per second. These results outperform 17 state-of-the-art methods. The experimental results show that the proposed method can accomplish the stenosis detection quickly and accurately.

Securing the internet of things frontier: a deep learning ensemble for cyber-attack detection in smart environments

<span lang="EN-US">This study presents a novel and innovative approach using deep learning (DL) ensemble technique to improve the security of internet of things (IoT) by identifying intricate cyber-attacks. By utilising advanced DL models like deep neural network (DNN) and long short-term memory (LSTM), our approach significantly enhances the accuracy of categorization compared to basic models. The initial binary classifier achieved an accuracy of 85.2%, while the multi-class classifier achieved an accuracy of 79.7%. Both classifiers continually enhanced, achieving accuracies of 99.34% and 98.26%, respectively, after 100 epochs. Real-time scenario evaluations showed that the average execution time per sample record was 0.9439 ms, confirming its efficiency. The DL ensemble exhibited improved performance in comparison to traditional models, indicating its potential for wider implementation in IoT security. The study not only emphasises significant improvements in accuracy, but also emphasises the method’s ability to perform well across many evaluation measures. This study presents a thorough and pragmatic method for identifying cyber-attacks in IoT settings. The stacked ensemble technique outperforms earlier models and fulfils real-time processing requirements, offering substantial advancements in IoT security. These findings enhance both the theoretical comprehension and practical application, establishing a novel benchmark for protecting intelligent IoT systems.</span>

An OpenCV and Single-Camera Facial Recognition Real-Time Attendance System

Abstract--Traditional attendance mechanisms have fallen short of providing an actual guarantee in attendance, especially in advanced commercial and academic milieus, based on the general practice of proxy attendance. This paper details the implementation of an attendance system based on technologies like cameras and facial recognition capabilities. By prohibiting unlawful attendance using proxy mechanisms, this system seeks to improve the accuracy of attendance tracking. We present a strong framework that takes, processes, and validates facial photos in real- time by utilising developments in computer vision and machine learning. This provides a creative way to get around the drawbacks of traditional attendance systems. Keywords—Facial recognition, Accuracy of attendance tracking, Computer vision, Machine learning, Real-time processing, Attendance validation, Advanced commercial and academic milieus.

Water Trash Detection System: An Innovative Approach to Monitoring Aquatic Pollution

Abstract: Water pollution, particularly from non-biodegradable waste such as plastics, is one of the most pressing environmental issues facing aquatic ecosystems today. This paper presents an innovative solution for detecting and classifying waste in water bodies using machine learning techniques, specifically You Only Look Once (YOLO) and Convolutional Neural Networks (CNNs). The Water Trash Detection System (WTDS) can process both static images and real-time video streams to efficiently detect various types of aquatic trash, including plastic bottles, bags, and fishing debris. The system generates detailed reports on detected waste, providing environmental organizations and researchers with actionable data for monitoring and cleaning polluted water bodies. The combination of YOLO for real-time object detection and CNNs for waste classification allows the WTDS to deliver high accuracy and real-time processing, which is essential for effective environmental management.

Locomotion Algorithm for Onroad Autonomous Robots with Real Time Response

Abstract: This research introduces a novel locomotionalgorithm for autonomous on-road robots that are intended to realtime garbage collection from dustbins.The proposed system incorporates a cutting-edge object identification framework called YOLO (You Only Look Once) to recognise roadways, obstructions, and dustbins. The autonomous robot, equipped with a robust hardware platform and a camera, navigates urban environments efficiently, ensuring seamless obstacle avoidance and precise dustbin localization.Our method makes use of real-time processing to facilitate adaptive decision-making and dynamic path planning, which improves the robot's operational effectiveness. The algorithm's efficacy in varioussettings is illustrated by the experimental findings, underscoring its potential for scalable implementation in smart city endeavours. Recent advances in autonomous urban cleaning systems form the basis for this study, which focuses on real-time processing capabilities to enable adaptive decisionmaking and smooth navigation. Experimental validation illustratesthe suggested locomotion algorithm's performance in avariety of urban environments, highlighting its practical application in autonomous garbage management systems. This research aims to address waste management difficulties and contribute to the development of autonomous urban cleaning technology, thereby supporting the realisation of smarter and cleaner cities. By providing a long-term solution to the problems associated with urban waste management, this research advances autonomous urban cleaning systems.

Enhancing financial portfolio management with predictive analytics and scalable data modeling techniques

The financial industry faces an increasingly complex landscape for portfolio management, where data-driven insights are crucial for optimizing asset allocation and managing risk. This paper explores the integration of predictive analytics and scalable data modeling techniques in enhancing financial portfolio management. By leveraging machine learning algorithms, big data architectures, and real-time data processing, predictive analytics can forecast asset trends, detect market anomalies, and assess portfolio risk with high precision. We evaluate several predictive models, including time-series forecasting, neural networks, and ensemble methods, for their efficacy in financial prediction. The study also discusses the role of scalable data modeling frameworks, such as Apache Spark and cloud-based data lakes, in handling vast volumes of unstructured data across different markets and asset classes. Findings indicate that predictive analytics, when paired with robust data models, can deliver real-time, actionable insights, enhancing decision-making for fund managers and institutional investors. Furthermore, this paper highlights best practices for implementing scalable models in financial institutions, addressing challenges like data latency, model interpretability, and system scalability. By adopting these advanced analytics frameworks, portfolio managers can achieve improved risk-adjusted returns, better asset diversification, and enhanced adaptability to market volatility.

Ultraweak light-modulated heterostructure with bidirectional photoresponse for static and dynamic image perception

The human visual system’s adaptability to varying brightness levels has inspired the development of optoelectronic neuromorphic devices. However, achieving bidirectional photoresponse, essential for mimicking these functions, often requires high operation voltages or high light intensities. Here, we propose a bidirectional ZnO/CsPbBr3 heterostructure based neuromorphic image sensor array (10 × 10 pixels) capable of ultraweak light stimulation. The device demonstrates positive and negative photoconductivity through the ionization and deionization of oxygen vacancies in the ZnO channel, extendable to other ZnO/perovskites and IGZO/perovskites heterostructures. Operating at a reduced bias voltage of 2.0 V, the array achieves synaptic weight updates under green (525 nm) and UV (365 nm) light with light intensities ranging from as low as 45 nW/cm² to 15.69 mW/cm², mimicking basic synaptic functions and visual adaptation. It performs multiple image pre-processing tasks, including background denoising and encoding spatiotemporal motion, achieving 92% accuracy in pattern recognition and 100% accuracy in motion clustering. This straightforward strategy highlights a potential for intelligent visual systems capable of real-time image processing under low voltage and dark conditions.

Real-Time Risk Assessment in Insurance: A Deep Learning Approach to Predictive Modeling

The insurance industry is experiencing a paradigm shift driven by the integration of big data analytics and machine learning technologies. This article presents a comprehensive framework for implementing advanced analytics in insurance operations, focusing on risk assessment optimization and predictive modeling applications. Building upon established actuarial methods, this article demonstrates how modern machine learning algorithms and real-time data processing enhance underwriting accuracy and claim processing efficiency. This article examines the integration of multiple data sources, including IoT sensors, telematics, and external databases, highlighting their collective impact on risk assessment precision. This analysis indicates significant improvements in underwriting accuracy and claims processing speed through the implementation of automated analytics workflows. This article also addresses critical challenges in data quality, regulatory compliance, and integration complexity, providing strategic recommendations for insurers pursuing digital transformation. This article contributes to the growing body of knowledge in insurance analytics by establishing a scalable framework for big data integration while maintaining regulatory compliance and operational efficiency

A conceptual framework for data-driven business optimization: Enhancing operational efficiency and strategic growth in U.S. small enterprises

Small enterprises play a vital role in the U.S. economy, yet many face significant challenges in optimizing operations and achieving sustainable growth due to resource constraints and market dynamics. This study proposes a conceptual framework for data-driven business optimization, aimed at enhancing operational efficiency and fostering strategic growth in U.S. small enterprises. Leveraging advancements in data analytics, the framework integrates predictive modeling, real-time data processing, and machine learning algorithms to enable informed decision-making and proactive strategy formulation. The framework emphasizes three core components: · Data Acquisition and Management, which involves collecting structured and unstructured data from internal and external sources to build a robust data repository; · Analytics and Insights Generation, utilizing advanced tools to identify patterns, forecast trends, and detect operational inefficiencies; and · Strategic Implementation, applying data-driven insights to streamline workflows, reduce costs, and capitalize on emerging opportunities. By aligning data strategies with organizational goals, the framework ensures a systematic approach to addressing challenges and unlocking new growth avenues. Furthermore, this study explores the role of emerging technologies, including artificial intelligence and Internet of Things (IoT) devices, in enhancing the scalability and adaptability of the framework. It also examines the challenges of adopting data-driven approaches, such as limited technological expertise and data privacy concerns, offering practical recommendations to overcome these barriers. Case studies of successful implementations in diverse sectors—retail, manufacturing, and services—highlight the framework's potential to deliver measurable outcomes, including increased productivity, improved customer satisfaction, and competitive advantage. This research provides small enterprises with actionable insights into leveraging data as a strategic asset, fostering innovation, and achieving resilience in a rapidly evolving business landscape.

Harnessing LLMs for Financial Forecasting: A Systematic Review of Advances in Stock Market Prediction and Portfolio Optimization

Abstract: This review paper examines the rapidly evolving landscape of Large Language Models (LLMs) in financial analysis, synthesizing recent advances and applications in this transformative field. We trace the progression from traditional natural language processing (NLP) methods to contemporary language models in financial applications, showing how these technologies are reshaping market analysis, risk assessment, fraud detection, and investment decision-making across various financial sectors. The paper offers an in-depth overview of LLM architectures and approaches used in finance, covering both general-purpose models adapted for financial tasks and specialized models tailored for industry needs. Through systematic analysis of the latest research and empirical studies, we assess the capabilities of LLMs in processing diverse financial data sources, including real-time market data, news articles, social media sentiment, earnings calls, and regulatory filings, enhancing insights and predictive accuracy. We identify key challenges in LLM implementation, such as the need for real-time data processing, high accuracy, and interpretability, crucial for trust and adoption in high-stakes contexts. Additionally, we explore emerging trends and future research directions, highlighting both the transformative potential and limitations of LLMs as they redefine analytical frameworks and decision support in finance. This review underscores the need for ongoing research to bridge gaps and fully realize LLMs’ potential in reshaping financial analysis and practices