Real-time Response Research Articles

Optimized Task Deployment in Dynamic Voltage and Frequency Scaling-Enabled Network-on-Chip Systems: Enhancing Energy Efficiency and Real-Time Responsiveness

In modern multi-design computing systems, which employ dynamic voltage and frequency scaling (DVFS) and network-on-chip (NoC) communications, the optimization of task deployment is precarious for enhancing overall system performance. It introduces a comprehensive methodology that integrates task allocation, scheduling, frequency management, redundancy handling, and diverse data routing approaches. The aim is to optimize energy intake, real-time responsiveness, and system heftiness. The system design features a primary processing element associated with three slave computing units (CUs) within a 2D mesh network, with the primary CU connected to a co-processor for dependent task scheduling. This research also proposes innovative algorithms for co-processor and real-time operating system (RTOS) scheduling to reduce latency and boost power efficiency. These methodologies aim to maximize job scheduling efficiency in RTOS environments, thus refining overall system performance.

Mapping Variable Wildfire Source Areas Through Inverse Modeling

Global climate change is leading to increased wildfire activity in many parts of the world, and with increasing development, a heightened threat to communities in the wildland urban interface. Evaluating the potential for fire to affect communities and critical infrastructure is essential for effective response decision-making and resource prioritization, including evacuation planning, with changing weather conditions during the fire season. Using a receptor–pathway–source assessment framework, we estimate the potential source area from which a wildfire could spread to a community in British Columbia by projecting fire growth outward from the community’s perimeter. The outer perimeter of the source area is effectively an evacuation trigger line for the forecast period. The novel aspects of our method are inverting fire growth in both space and time by reversing the wind direction, the time course of hourly weather, and slope and aspect inputs to a time-evolving fire growth simulation model Prometheus. We also ran a forward simulation from the perimeter of a large fire that was threatening the community to the community edge and back. In addition, we conducted a series of experiments to examine the influence of varying environmental conditions and ignition patterns on the invertibility of fire growth simulations. These cases demonstrate that time-evolving fire growth simulations can be inverted for practical purposes, although caution is needed when interpreting results in areas with extensive non-fuel cover or complex community perimeters. The advantages of this method over conventional simulation from a fire source are that it can be used for pre-attack planning before fire arrival, and following fire arrival, it does not require having an up-to-the-minute map of the fire location. The advantage over the use of minimum travel time methods for inverse modeling is that it allows for changing weather during the forecast period. This procedure provides a practical tool to inform real-time wildfire response decisions around communities, including resource allocation and evacuation planning, that could be implemented with several time-evolving fire growth models.

Support vector regression model for the prediction of buildings’ maximum seismic response based on real monitoring data

Maximum drift ratio (MDR), one of the engineering demand parameters (EDPs), provides fundamental physical value for predicting building damage. Existing machine learning based prediction models mainly rely on numerical simulation data or structural experiments and are not appropriate for prediction of seismic response of real structures. The New Earthquake Data (NDE1.0) is the most comprehensive publicly available dataset of actual structural seismic response observations. Currently the prediction models using NDE1.0 are mainly based on linear or log-linear regression. In this study, based on the NDE1.0 flatfile, we develop a full-feature support vector regression (SVR) based MDR prediction model (SVR-MDR), treating all the available 41 characteristic parameters including structural information as input feature. To improve the model’s efficiency and practical applicability, we also establish a reduced-feature SVR model (RSVR-MDR) by selecting 10 fundamental parameters based on SHapley Additive exPlanations (SHAP) values and the accessibility of features. Our results demonstrate that SVR-MDR model outperform other machine learning models such as kernel ridge regression and decision tree models, and SVR-MDR and RSVR-MDR models outperform conventional loglinear regression and multinomial models, because SVR can map the complex nonlinear function of multiple variables and consider the available information of buildings especially the fundamental frequency. The proposed RSVR-MDR model have promising potential application for post-event seismic damage assessment and post-event emergency response in near real time.

A New Method for Complex Impedance Measurement of Power Transformers via a Continuous Wavelet Transform

The Fourier transform is widely accepted as the time-to-frequency conversion procedure, although it has some limitations. Currently, measurements in the time domain are usually transient (non-periodic waveforms) within a finite window time and discrete (non-continuous) sampled signals. The accuracy of the Fourier transform decreases as the window time and sampling frequency decrease. This is where the wavelet transform proves to be a valuable tool in this analysis. This paper presents a novel method for estimating the complex electrical impedance of power transformers by analyzing transient electrical signals with the continuous wavelet transform. The great importance of knowing the complex electrical impedance of the transformer is that it allows knowing the state and condition of the internal parts, such as the core and the windings, whose behavior depends on the frequency with which the transformer is fed. The wavelet transform is employed in the proposed method to improve the analysis of the frequency response (FRA), following the same procedure commonly used with the Fourier transform. The proposed method is validated by performing an experimental test on a 28 MVA power transformer. The results show that the new method using the continuous wavelet transform is a power tool that enhances the extraction of the total electrical impedance curve (magnitude–phase) compared to the Fourier transform. This enables real-time frequency response analysis in transformers, facilitating accurate diagnosis.

Analytical Performance of a Novel Nanopore Sequencing for SARS-CoV-2 Genomic Surveillance.

The genomic analysis of SARS-CoV-2 has served as a crucial tool for generating invaluable data that fulfils both epidemiological and clinical necessities. Long-read sequencing technology (e.g., ONT) has been widely used, providing a real-time and faster response when necessitated. A novel nanopore-based long-read sequencing platform named QNome nanopore has been successfully used for bacterial genome sequencing and assembly; however, its performance in the SARS-CoV-2 genomic surveillance is still lacking. Synthetic SARS-CoV-2 controls and 120 nasopharyngeal swab (NPS) samples that tested positive by real-time polymerase chain reaction were sequenced on both QNome and MGI platforms in parallel. The analytical performance of QNome was compared to the short-read sequencing on MGI. For the synthetic SARS-CoV-2 controls, despite the increased error rates observed in QNome nanopore sequencing reads, accurate consensus-level sequence determination was achieved with an average mapping quality score of approximately 60 (i.e., a mapping accuracy of 99.9999%). For the NPS samples, the average genomic coverage was 89.35% on the QNome nanopore platform compared with 90.39% for MGI. In addition, fewer consensus genomes from QNome were determined to be good by Nextclade compare with MGI (p < 0.05). A total of 9004 mutations were identified using QNome sequencing, with substitutions being the most prevalent, in contrast, 10 997 mutations were detected on MGI (p < 0.05). Furthermore, 23 large deletions (i.e., deletions≥ 10 bp) were identified by QNome while 19/23 were supported by evidence from short-read sequencing. Phylogenetic analysis revealed that the Pango lineage of consensus genomes for SARS-CoV-2 sequenced by QNome concorded 83.04% with MGI. QNome nanopore sequencing, though challenged by read quality and accuracy compared to MGI, is overcoming these issues through bioinformatics and computational advances. The advantage of structure variation (SV) detection capabilities and real-time data analysis renders it a promising alternative nanopore platform for the surveillance of the SARS-CoV-2.

Intelligent edge-fog interplay for healthcare informatics: A blockchain perspective

This paper explores artificial intelligence (AI) and edge-fog interplay to strengthen healthcare informatics (HCI), while also considering the blockchain perspective for securing HCI to transform cloud-based HCI to edge-fog-based HCI to serve real-time responses for critical healthcare applications. This article discusses that AI is vital in providing better healthcare, precision medicine, and personalized treatments. A comprehensive review of edge-fog interplay-based HCI and blockchain-based HCI demonstrated the need for integrating blockchain and edge-fog interplay for successful HCI. Subsequently, some current research projects explore edge computing, fog computing, and blockchain in the healthcare sector to securely store and share patient data, thereby providing real-time data analysis, which is also highlighted. The latter part reflects some essential aspects of blockchain in healthcare, such as immutability, trustworthiness, and traceability. We also present a case study on AI-edge-fog interplay, and blockchain on heart stroke prediction for HCI that examines the practical application of the amalgamation of such technologies required to refine the healthcare sector to support the proposed analysis. Finally, this work discusses the future challenges of integrating AI, edge-fog interplay, and blockchain in the field of HCI.

Enhanced Accuracy in State-of-Charge Estimation for Lithium-Ion Batteries in Electric Vehicles Using Augmented Adaptive Extended Kalman Filter

Accurate assessment of state of charge (SoC) is crucial for the safety and efficiency of lithium-ion batteries used in electric vehicles (EVs). For precise SoC estimation in EV BMSs, an Augmented Adaptive Extended Kalman Filter (AAEKF) has been proposed in this research. To improve the precision of SoC estimation, AAEKF approach combines Kalman filtering methods with adaptive robust control concepts. The effectiveness of the AAEKF algorithm is shown by simulation results and confirmed by comparison with data from LiFePO4 battery research. The proposed AAEKF SoC estimation matches measurement results with errors of less than 3%. Adaptive Extended Kalman Filter (AEKF) is quite sensitive to the accuracy of the battery model, as errors in this region can lead to large estimate errors. Faster convergence to actual SoC, improved real-time responsiveness, accuracy, and resilience to disturbances and parameter fluctuations are all results of AAEKF's efficient handling of nonlinearities in battery systems, adaptation to system changes, errors (Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), and overall performance. The maximum estimate error stays under 5% at various external temperatures.

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.

Multimodal Human–Robot Interaction Using Gestures and Speech: A Case Study for Printed Circuit Board Manufacturing

In recent years, technologies for human–robot interaction (HRI) have undergone substantial advancements, facilitating more intuitive, secure, and efficient collaborations between humans and machines. This paper presents a decentralized HRI platform, specifically designed for printed circuit board manufacturing. The proposal incorporates many input devices, including gesture recognition via Leap Motion and Tap Strap, and speech recognition. The gesture recognition system achieved an average accuracy of 95.42% and 97.58% for each device, respectively. The speech control system, called Cellya, exhibited a markedly reduced Word Error Rate of 22.22% and a Character Error Rate of 11.90%. Furthermore, a scalable user management framework, the decentralized multimodal control server, employs biometric security to facilitate the efficient handling of multiple users, regulating permissions and control privileges. The platform’s flexibility and real-time responsiveness are achieved through advanced sensor integration and signal processing techniques, which facilitate intelligent decision-making and enable accurate manipulation of manufacturing cells. The results demonstrate the system’s potential to improve operational efficiency and adaptability in smart manufacturing environments.

Real-Time Soil Nutrient Monitoring Using NPK Sensors: Enhancing Precision Agriculture

Prediction of various parameters in the agriculture field using sensors is a significant topic nowadays. However, in many scenarios, the sensor data does not accurately detect the real parameter(/s) in the agriculture field. The sensor data may vary due to various external factors, whereas the real parameters don’t vary too much for a particular agriculture field. The present work introduces a modified neural network approach to predict real agricultural parameters from sensor data with accuracy caused by several external factors and demonstrates enhanced predictive accuracy and adaptability. The neural network takes the sensor data as input in various weather conditions and tries to find out the original real parameters of that sensor data. The real-time sensor data was collected from multiple agricultural sites. The results demonstrated high predictive accuracy, with the neural network outperforming traditional statistical methods in forecasting soil moisture and other vital variables. Additionally, the model’s ability to generalize across different environmental conditions enhances its applicability in various crop management scenarios. The study concludes that neural networks hold significant potential for improving the efficiency of smart agriculture systems by providing timely, data driven insights for farmers and agronomists. Further research will explore the integration of deep learning models and edge computing to enhance scalability and realtime responsiveness in field applications. The aforementioned research highlights the significance of NPK sensors in sustainable farming methods, namely in enabling accurate nutrient management via real-time data.

AI-driven threat intelligence for real-time cybersecurity: Frameworks, tools, and future directions

AI-driven threat intelligence is transforming cybersecurity by enhancing real-time threat detection, analysis, and response capabilities. This paper reviews state-of-the-art AI frameworks, machine learning models, and tools that support threat intelligence, providing a survey of current research in the field and identifying challenges and future directions for real-time cybersecurity. Techniques such as supervised and unsupervised learning, reinforcement learning, and natural language processing (NLP) contribute to the robustness of threat detection, while evolving frameworks and ethics guide AI implementation in security operations. By addressing the increasing sophistication of cyber threats, AI-driven approaches aim to create a proactive, dynamic cybersecurity posture that can keep up with evolving cyber adversaries.

A governance framework model for cloud computing: role of AI, security, compliance, and management

The rapid adoption of cloud computing has transformed how organizations manage their IT resources, necessitating robust governance frameworks to address the complexities and risks inherent in cloud environments. This review proposes a comprehensive governance framework model that integrates the roles of artificial intelligence (AI), security, compliance, and management to enhance the effectiveness of cloud operations. AI plays a critical role in optimizing resource allocation and improving decision-making processes within cloud governance. By leveraging machine learning algorithms, organizations can achieve dynamic resource management, predictive analytics, and automated compliance monitoring, which enhance operational efficiency and reduce human error. Furthermore, the integration of AI in security management facilitates real-time threat detection and response, allowing organizations to proactively mitigate risks associated with data breaches and cyberattacks. Security is a paramount concern in cloud governance, given the shared responsibility model between cloud providers and clients. This framework emphasizes the implementation of comprehensive security measures, including data encryption, identity management, and incident response protocols, to safeguard sensitive information and maintain customer trust. Compliance with regulatory requirements is essential in ensuring organizational accountability and minimizing legal risks. The proposed governance model incorporates automated compliance checks and reporting mechanisms, ensuring adherence to industry-specific regulations such as GDPR and HIPAA. Moreover, effective management of cloud resources is crucial for optimizing performance and controlling costs. The governance framework outlines best practices for lifecycle management, cost optimization, and resource allocation, enabling organizations to achieve their strategic objectives. This governance framework model underscores the importance of integrating AI, security, compliance, and management for a holistic approach to cloud governance, providing organizations with the necessary tools to navigate the complexities of cloud computing while maximizing its benefits.

The Real-Time Observation of Electric Vehicle Operating Points Using an Extended Kalman Filter

Electric Vehicles (EVs) are set to play a crucial role in the energy transition. Although EVs offer significant environmental benefits, their technology still faces major challenges related to performance optimization, energy efficiency improvement, and cost reduction. A key point to address these challenges is the accurate identification of the speed/torque operating points of the drive systems. However, this identification is generally achieved using mechanical sensors, which are fragile, bulky, and expensive. This paper aims to develop, implement, and validate a speed/torque observer in real time based on the Extended Kalman Filter (EKF) approach for an EV equipped with an Open-End Winding Induction Motor with Dual Inverter (OEWIM-DI). The implementation of the EKF is based on the state modeling of the OEWIM-DI, enabling the observation of the torque and speed using voltage and current measurements. The validation of this approach is conducted experimentally on the FPGA and DS1104 boards. The results show that this approach offers excellent performance in terms of accuracy, stability, and real-time response speed. These results suggest that the proposed method could significantly contribute to the advancement of EV technology by providing a more robust and cost-effective alternative to traditional mechanical sensors while improving the overall efficiency and performance of EV drive systems.

A Methodology for Detecting Loss of Support Under PCC Pavement Based on Real-Time Structural Acceleration Response

Loss of support is a concealed distress in PCC pavements, weakening the support condition under PCC slabs and causing other distresses such as cracks, which accelerate the pavement deterioration. Currently, the traditional loss of support detection method uses the falling weight deflectometer (FWD) to obtain maximum deflection response, which requires traffic closure and makes it impossible to conduct real-time monitoring. To realize real-time dynamic health monitoring of PCC pavement, this study proposes a methodology for detecting loss of support under PCC pavement based on real-time structural acceleration response. Specifically, this study constructed PCC pavements without loss of support, with loss of support at the slab edge, and with loss of support at the slab center. The slab corner and center were loaded and monitored, where loss of support easily occurs. Acceleration signals were collected using self-developed MEMS acceleration sensors suitable for monitoring PCC pavements. Analysis of the time-domain signals and frequency spectrums shows that both the maximum acceleration response and the main frequency at the loss of support region would significantly increase when the loss of support region is loaded. Two critical values are proposed, including the amplitude ratio of acceleration signals at the slab corner and center [Formula: see text] and the ratio of signal energy within the high-frequency range to that within the low-frequency range (ER). The results indicate that by deploying sensors at the slab corner and center, the loss of support condition can be evaluated based on [Formula: see text] and ER when the slab corner is loaded. Compared to traditional deflection detection methods, the proposed methodology offers a better integral structural monitoring for detecting loss of support. The findings can be used for future detection of support loss under PCC pavements subjected to traffic load, enabling real-time monitoring and maintenance guidance for PCC pavements that have been open to traffic.

Trap Assisted Dynamic Mechanoluminescence Toward Self-Referencing and Visualized Strain Sensing.

Strain sensors utilizing mechanoluminescent (ML) materials have garnered significant attention and application due to their advantages, such as self-powering, non-contact operation, and real-time response. However, ML-based strain sensing techniques typically rely on the establishing of a mathematical relationship between ML intensity and mechanical parameters. The absolute ML intensity is vulnerable to environmental factors, which can result in measurement errors. Herein, an color-resolved visualized dynamic ML and self-referencing strain sensing is investigated in Ca9Al(PO4)7: Tb3+, Mn2+. By analyzing the ML performance under various mechanical stimulations and adjustable strain parameters, a relationship between strain and the ML intensity ratio of Tb3+/Mn2+ is aimed to bed established. This will enable the development of a self-referencing and visualized strain sensing technology. Through a comparison of luminescence characteristics under continuous mechanical stimulation (stretching) and continuous X-ray irradiation, it is discovered that the ratiometric dynamic ML is primarily driven by the dynamic filling and continuous release of carriers form traps, which compensates for the ML of Mn2+. Leveraging the self-referencing and color-resolved (from green to red) visualized ML characteristics, an application scenario for monitoring human joint movement is developed. This approach offers new insights into the use of dynamic ML materials in strain sensing and human-machine interaction.

A Review of Gait Coordination Control Methods for Lower Limb Exoskeleton Robots Based on Visual Models

The ongoing evolution of exoskeleton technology has rendered the exclusive reliance on a single vision technology an impractical approach to addressing the complexities of diverse environments and individual requirements. This paper therefore explores the potential of combining traditional visual models with movement intention prediction based on surface electromyography (sEMG) and virtual reality (VR) technology in the context of gait control. The study concluded that traditional visual models are capable of providing accurate environmental perception and gait planning; however, they are deficient in forecasting user intentions. Surface electromyography (sEMG) technology can supplement this deficiency with muscle activity signals, enabling the system to respond dynamically to user needs. Concurrently, virtual reality (VR) technology furnishes users with a more personalized training program through immersive environmental simulation and feedback. The combination of these three technologies not only enhances the system's real-time responsiveness but also exhibits commendable adaptability in complex terrain and rehabilitation training. This paper examines the three fundamental technologies of lower limb exoskeleton robots in gait control and their fusion applications, with the objective of enhancing the system's intelligence and adaptability, providing a direction for the future development of lower limb exoskeleton technology, and highlighting the challenges and potential of multimodal data fusion and computational optimisation.

An audience response study on Korean translation of culture-specific items in licensed musicals

ABSTRACT This study explores the Korean translation of culture-specific items (CSIs) in licensed musicals by analyzing audience responses to different translation strategies. Musicals such as Les Misérables, Urinetown, Little Women, Rent, Blood Brothers, and The Spitfire Grill were examined. Foreignized and domesticated translations of units of measurement, idioms, proper nouns, and customs were performed consecutively, followed by audience surveys to assess comprehension and preferences. Results show that audiences generally find foreignized translations less understandable, though preferences vary by CSI. Audiences are receptive to content adaptation and free translation, but they react negatively to the domestication of currency units and proper names. In contrast, there is a strong preference for the domestication of social and legal culture, customs, and humor. This study, using live performances to gather real-time audience responses, provides valuable insights for translators. However, it acknowledges limitations due to the time constraints of live performances, which restricted the depth of analysis.

A Smart Irrigation System Using the IoT and Advanced Machine Learning Model

The rapid advancement of IoT (Internet of Things) technologies and sophisticated machine learning models is driving innovation in irrigation systems, laying the foundation for more effective and eco-friendly smart agricultural procedures. This systematic literature review strives to uncover the advancements and challenges in the advancement and implementation of IoT-based smart irrigation systems integrated with advanced machine learning techniques. By analyzing 43 relevant studies published between 2017 and 2024, the research focuses on the ability of these technologies have evolved to meet the challenges of modern agriculture irrigation system. Predictive analytics, anomaly detection, and adaptive control—that enhance irrigation precision and decision-making processes. Employing the PRISMA methodology, this review uncovers the strengths and limitations of current systems, highlighting significant achievements in real-time data utilization and system responsiveness. However, it also brings attention to unresolved issues, including the complexities of data integration, network reliability, and the scalability of IoT-based frameworks. Additionally, the study identifies crucial gaps in standardization and the need for flexible solutions that can adapt to diverse environmental conditions. By offering a comprehensive analysis, this review provides key insights for advancing smart irrigation technologies, emphasizing the importance of continued research in overcoming the existing barriers to wider adoption and effectiveness in various agricultural settings.

Lightweight real-time vehicle collision warning based on deep learning multi-scale feature fusion

With the widespread application of deep learning technology in the field of intelligent driving, machine vision has become the preferred technology for vehicle detection tasks. However, high-precision object detection often involves a large number of parameters and significant computational complexity, making algorithm deployment challenging and hindering real-time system response. This study adopts the lightweight detection model YOLOv8n as the foundational framework, optimizing it to develop BiFCNet-YOLOv8, which aims to balance detection accuracy with computational efficiency for in-vehicle system deployment. In our research, the more efficient Bi-FPN replaces the traditional PANet, enhancing feature interactions across different scales through bidirectional pathways. Additionally, the CBAM attention mechanism is incorporated between the Neck and Head layers to further refine information and enhance local feature representation. Comparative experiments show that the improved model increases Precision by 1.71% and mAP0.5:0.95 by 3.23%, with a parameter growth of only 6.43M and a FLOPS increase of only 15.83B. The detection algorithm integrates TTC and DeepSORT to enhance the driving warning function. Road tests confirm that this algorithm can effectively and accurately detect the position of the vehicle ahead. In simulation tests, key parameters such as relative speed and longitudinal acceleration meet the requirements for collision warning systems. This study provides a lightweight and efficient detection algorithm solution for vehicle collision warning tasks.

Securing a Generative AI-Powered Healthcare Chatbot.

In Generative Artificial Intelligence (AI), Large Language Models (LLMs) like GPT-4, Gemini, Claude, and Llama, significantly impact healthcare by aiding in patient care, medical research, and administrative tasks. AI-powered chatbots offer real-time responses and manage chronic diseases, improving patient outcomes and operational efficiency. However, these models pose security and ethical challenges, necessitating robust data privacy, adversarial training, and ethical guidelines. This paper proposes a secure, ethical pipeline for deploying AI healthcare chatbots, integrating advanced privacy-preserving techniques and continuous security assessments to enhance data privacy, resilience, and user trust.