Real-time Event Research Articles

An advanced real-time crash prediction framework for combined hard shoulder running and variable speed limits system using transformer

One of the important applications of real-time crash prediction analysis lies in the field of proactive traffic management, where instantaneous crash risk evaluation and dynamic decision-making are prerequisites. This research proposes an integrated and advanced real-time crash risk prediction framework for Variable Speed Limits (VSL) and Hard Shoulder Running (HSR) implemented freeways considering their operational periods. Statistical methods are utilized to identify the significant crash contributing factors (related to traffic, roadway geometry, and weather conditions) and explain their relationships with crashes. Time-Embedded Transformer models are proposed to predict the likelihood of real-time crash events. The sensitivity and false alarm rate of the proposed AM peak model are found to be 0.76 and 0.27, respectively, whereas the values are 0.78 and 0.24, respectively for the PM peak model. Additionally, the results indicate substantial improvements in model prediction performance (i.e., an increment of sensitivity values by 7.04% and 8.33% in the AM and PM models, respectively) after incorporating the general safety condition of a freeway segment as an input feature while estimating real-time crash prediction models. Practitioners and policymakers could apply this method to obtain a more accurate real-time crash likelihood estimation, identify important crash precursors, dynamically update algorithms, and enhance safety aspects while operating traffic management strategies on freeways.

Tracking and measuring local protein synthesis in vivo.

Detecting when and how much of a protein molecule is synthesized is important for understanding cell function, but current methods either cannot be performed in vivo or have poor temporal resolution. Here, we developed a technique to detect and quantify subcellular protein synthesis events in real time in vivo. This Protein Translation Reporting (PTR) technique uses a genetic tag that produces a stoichiometric ratio of a small peptide portion of a split fluorescent protein and the protein of interest during protein synthesis. We show that the split fluorescent protein peptide can generate fluorescence within milliseconds upon binding the larger portion of the fluorescent protein, and that the fluorescence intensity is directly proportional to the number of molecules of the protein of interest synthesized. Using PTR, we tracked and measured protein synthesis events in single cells over time in vivo. We use different color split fluorescent proteins to detect multiple genes or alleles in single cells simultaneously. We also split a photoswitchable fluorescent protein to photoconvert the reconstituted fluorescent protein to a different channel to continually reset the time of detection of synthesis events.

A Cloud Infrastructure for Health Monitoring in Emergency Response Scenarios

Wearable devices have a significant impact on society, and recent advancements in modern sensor technologies are opening up new possibilities for healthcare applications. Continuous vital sign monitoring using Internet of Things solutions can be a crucial tool for emergency management, reducing risks in rescue operations and ensuring the safety of workers. The massive amounts of data, high network traffic, and computational demands of a typical monitoring application can be challenging to manage with traditional infrastructure. Cloud computing provides a solution with its built-in resilience and elasticity capabilities. This study presents a Cloud-based monitoring architecture for remote vital sign tracking of paramedics and medical workers through the use of a mobile wearable device. The system monitors vital signs such as electrocardiograms and breathing patterns during work sessions, and it is able to manage real-time alarm events to a personnel management center. In this study, 900 paramedics and emergency workers were monitored using wearable devices over a period of 12 months. Data from these devices were collected, processed via Cloud infrastructure, and analyzed to assess the system’s reliability and scalability. The results showed a significant improvement in worker safety and operational efficiency. This study demonstrates the potential of Cloud-based systems and Internet of Things devices in enhancing emergency response efforts.

Real-Time Ambulatory ECG Does Not Expedite Care.

Ambulatory ECG (AECG) monitoring is pivotal to the diagnosis of arrhythmias and can be performed with near "real-time" notification of abnormalities. There are limited data on the relative benefit of real-time monitoring compared with traditional Holter monitoring. This is a retrospective observational analysis of University of Utah Health patients who underwent ambulatory ECG studies from 2010 to 2022. The study cohort was stratified by patients with an ambulatory ECG that provides real-time event notification (non-Holter) versus those who do not (Holter). The outcomes were cardiac implantable electronic device procedure, ablation procedure, emergency department/hospitalization visit, and initiation of anticoagulation out to 6 months. We identified 20 259 patients, 16 650 with non-Holter studies and 3609 with Holter studies. Holter patients were younger (mean 52 versus 55, P<0.001), more often women (60.2% versus 57%, P<0.001), and had lower mean CHADS2-VA2Sc scores (1.7 versus 2.1, P<0.001). The median time to ablation procedure was 74 versus 72 (P=0.5), for Holter versus non-Holter, respectively. Median days to new cardiac implantable electronic device implantation was 54 days versus 52 (P=0.6); initiation of anticoagulation among patients not already treated was 42 versus 31 days (P=0.03). Time to first emergency department visit or hospitalization was 63 versus 57 (P=0.6). In multivariable models, there were no significant differences in time to intervention between Holter and non-Holter for each outcome. Real-time monitoring demonstrates mixed results in terms of reducing time to intervention, with the significant benefit limited to oral anticoagulation initiation. It is time to revisit clinical scenarios where real-time ambulatory monitoring may not improve health care efficiency.

Near Real-Time Responsive Flood Event Representation: An Open-Source Interactive Web Application Architecture

Abstract. The adoption of accessible Digital Twin technology in flood applications has been impeded by a notable lack of practical examples. Arguably, the most distinguishing feature of Digital Twin technology is the integration of near real-time data analytics through IoT sensor connectivity, which has remained underutilised in flood applications and therefore indicates a critical research gap. This project endeavoured to develop a comprehensive and replicable open-source architecture framework, specifically tailored for near real-time responsive flood event representation. The resultant interactive web application integrated near real-time river height and rain fall data streams and performed on-demand data analytics. Key results include the establishment of a back-end spatial database, a coupled physical city and digital space model, and a functional holistic front-end user interface. Additionally, this research aligned with the Gemini Principles by emphasising data interoperability and maintaining an information feedback loop. The study also aimed to configure near real-time IoT sensor connections, implement event triggers, and deliver interactive visualisations in an easily accessible format. The research addressed key questions surrounding the effective integration of IoT sensor data, identification of crucial flood indicators and parameters, and rapid quantification of flood event impacts. Ultimately, this research project demonstrates how Digital Twin technology can swiftly provide decision-makers with crucial insights during flood disaster events.

Synchronization evaluation of digital twin for a robotic assembly system using computer vision

Traditional production processes are profoundly transforming in the era of Industry 4.0 and smart manufacturing. Smart manufacturing, a core element of Industry 4.0, employs advanced technologies such as automation, robotics, AI, big data analytics, and machine learning to boost productivity and manufacturing performance. Central to this evolution is the Digital Twin (DT), digital replicas of physical assets that blend real-time data with advanced analytics and simulations. Ensuring synchronization between the digital replica and its physical counterpart is crucial for the success of Digital Twins. This paper addresses the challenge of achieving and quantifying synchronization within DTs, focusing on replicating physical system behaviour and measuring deviations or delays. The study delves into the critical aspects of synchronization within digital twin applications, focusing on its implications for a robotic assembly system. The research successfully harnessed YOLOv8 to facilitate real-time event tracking and synchronization characterization, highlighting the potential of computer vision in enhancing synchronization accuracy and, consequently, the efficiency and reliability of manufacturing processes.

Deep feature fusion with computer vision driven fall detection approach for enhanced assisted living safety

Assisted living facilities cater to the demands of the elderly population, providing assistance and support with day-to-day activities. Fall detection is fundamental to ensuring their well-being and safety. Falls are frequent among older persons and might cause severe injuries and complications. Incorporating computer vision techniques into assisted living environments is revolutionary for these issues. By leveraging cameras and complicated approaches, a computer vision (CV) system can monitor residents’ movements continuously and identify any potential fall events in real time. CV, driven by deep learning (DL) techniques, allows continuous surveillance of people through cameras, investigating complicated visual information to detect potential fall risks or any instances of falls quickly. This system can learn from many visual data by leveraging DL, improving its capability to identify falls while minimalizing false alarms precisely. Incorporating CV and DL enhances the efficiency and reliability of fall detection and allows proactive intervention, considerably decreasing response times in emergencies. This study introduces a new Deep Feature Fusion with Computer Vision for Fall Detection and Classification (DFFCV-FDC) technique. The primary purpose of the DFFCV-FDC approach is to employ the CV concept for detecting fall events. Accordingly, the DFFCV-FDC approach uses the Gaussian filtering (GF) approach for noise eradication. Besides, a deep feature fusion process comprising MobileNet, DenseNet, and ResNet models is involved. To improve the performance of the DFFCV-FDC technique, improved pelican optimization algorithm (IPOA) based hyperparameter selection is performed. Finally, the detection of falls is identified using the denoising autoencoder (DAE) model. The performance analysis of the DFFCV-FDC methodology was examined on the benchmark fall database. A widespread comparative study reported the supremacy of the DFFCV-FDC approach with existing techniques.

Design and Evaluation of Real-Time Data Storage and Signal Processing in a Long-Range Distributed Acoustic Sensing (DAS) Using Cloud-Based Services.

In cloud-based Distributed Acoustic Sensing (DAS) sensor data management, we are confronted with two primary challenges. First, the development of efficient storage mechanisms capable of handling the enormous volume of data generated by these sensors poses a challenge. To solve this issue, we propose a method to address the issue of handling the large amount of data involved in DAS by designing and implementing a pipeline system to efficiently send the big data to DynamoDB in order to fully use the low latency of the DynamoDB data storage system for a benchmark DAS scheme for performing continuous monitoring over a 100 km range at a meter-scale spatial resolution. We employ the DynamoDB functionality of Amazon Web Services (AWS), which allows highly expandable storage capacity with latency of access of a few tens of milliseconds. The different stages of DAS data handling are performed in a pipeline, and the scheme is optimized for high overall throughput with reduced latency suitable for concurrent, real-time event extraction as well as the minimal storage of raw and intermediate data. In addition, the scalability of the DynamoDB-based data storage scheme is evaluated for linear and nonlinear variations of number of batches of access and a wide range of data sample sizes corresponding to sensing ranges of 1-110 km. The results show latencies of 40 ms per batch of access with low standard deviations of a few milliseconds, and latency per sample decreases for increasing the sample size, paving the way toward the development of scalable, cloud-based data storage services integrating additional post-processing for more precise feature extraction. The technique greatly simplifies DAS data handling in key application areas requiring continuous, large-scale measurement schemes. In addition, the processing of raw traces in a long-distance DAS for real-time monitoring requires the careful design of computational resources to guarantee requisite dynamic performance. Now, we will focus on the design of a system for the performance evaluation of cloud computing systems for diverse computations on DAS data. This system is aimed at unveiling valuable insights into performance metrics and operational efficiencies of computations on the data in the cloud, which will provide a deeper understanding of the system's performance, identify potential bottlenecks, and suggest areas for improvement. To achieve this, we employ the CloudSim framework. The analysis reveals that the virtual machine (VM) performance decreases significantly the processing time with more capable VMs, influenced by Processing Elements (PEs) and Million Instructions Per Second (MIPS). The results also reflect that, although a larger number of computations is required as the fiber length increases, with the subsequent increase in processing time, the overall speed of computation is still suitable for continuous real-time monitoring. We also see that VMs with lower performance in terms of processing speed and number of CPUs have more inconsistent processing times compared to those with higher performance, while not incurring significantly higher prices. Additionally, the impact of VM parameters on computation time is explored, highlighting the importance of resource optimization in the DAS system design for efficient performance. The study also observes a notable trend in processing time, showing a significant decrease for every additional 50,000 columns processed as the length of the fiber increases. This finding underscores the efficiency gains achieved with larger computational loads, indicating improved system performance and capacity utilization as the DAS system processes more extensive datasets.

Triggering dark showers with conditional dual auto-encoders

Abstract We present a family of conditional dual auto-encoders (CoDAEs) for generic and model-independent new physics searches at colliders. New physics signals, which arise from new types of particles and interactions, are considered in our study as anomalies causing deviations in data with respect to expected background events. In this work, we perform a normal-only anomaly detection, which employs only background samples, to search for manifestations of a dark version of strong force applying (variational) auto-encoders on raw detector images, which are large and highly sparse, without leveraging any physics-based pre-processing or strong assumption on the signals. The proposed CoDAE has a dual-encoder design, which is general and can learn an auxiliary yet compact latent space through spatial conditioning, showing a neat improvement over competitive physics-based baselines and related approaches, therefore also reducing the gap with fully supervised models. It is the first time an unsupervised model is shown to exhibit excellent discrimination against multiple dark shower models, illustrating the suitability of this method as an accurate, fast, model-independent algorithm to deploy, e.g. in the real-time event triggering systems of large hadron collider experiments such as ATLAS and CMS.

A Novel Method to Integrate Hydropower Plants into Resource Adequacy Assessment Studies

This paper presents a novel methodology for modeling hydropower plants (HPPs) with and without pumping capability in resource adequacy assessment studies. The proposed method is based on the premise that HPPs should maximize their contribution to system adequacy within their technical constraints by using the energy reserves in their upper reservoirs without significantly deviating from their market schedule. The approach of this paper differs from the conventional operating policies for incorporating HPPs into resource adequacy assessment studies, which either adhere to a fixed market schedule or perform peak shaving, and are inelastic to real-time events or do not resort to realistic temporal correlations between natural water inflows on upper reservoirs and the water discharge needs to cover demand peaks, respectively. The modeling approach focuses on large-reservoir HPPs with natural inflows and is generic enough to deal with both stations incorporating pumping capabilities and those without. It utilizes the state-of-the-art Monte Carlo simulation technique to form the availability of system assets and determine the loss of load incidents. The market schedule and level of reservoir fulfillment for the HPPs are retrieved from a cost-optimal power system simulation algorithm executed offline before the application of the resource adequacy assessment. The effectiveness of the proposed methodology is demonstrated through its implementation in a case study of a power system experiencing different levels of adequacy, comparing the obtained results with various traditional HPP modeling methods from the literature.

HabitSense: A Privacy-Aware, AI-Enhanced Multimodal Wearable Platform for mHealth Applications

Wearable cameras provide an objective method to visually confirm and automate the detection of health-risk behaviors such as smoking and overeating, which is critical for developing and testing adaptive treatment interventions. Despite the potential of wearable camera systems, adoption is hindered by inadequate clinician input in the design, user privacy concerns, and user burden. To address these barriers, we introduced HabitSense, an open-source1, multi-modal neck-worn platform developed with input from focus groups with clinicians (N=36) and user feedback from in-wild studies involving 105 participants over 35 days. Optimized for monitoring health-risk behaviors, the platform utilizes RGB, thermal, and inertial measurement unit sensors to detect eating and smoking events in real time. In a 7-day study involving 15 participants, HabitSense recorded 768 hours of footage, capturing 420.91 minutes of hand-to-mouth gestures associated with eating and smoking data crucial for training machine learning models, achieving a 92% F1-score in gesture recognition. To address privacy concerns, the platform records only during likely health-risk behavior events using SECURE, a smart activation algorithm. Additionally, HabitSense employs on-device obfuscation algorithms that selectively obfuscate the background during recording, maintaining individual privacy while leaving gestures related to health-risk behaviors unobfuscated. Our implementation of SECURE has resulted in a 48% reduction in storage needs and a 30% increase in battery life. This paper highlights the critical roles of clinician feedback, extensive field testing, and privacy-enhancing algorithms in developing an unobtrusive, lightweight, and reproducible wearable system that is both feasible and acceptable for monitoring health-risk behaviors in real-world settings.

Adaptive Detection in Real-Time Gait Analysis through the Dynamic Gait Event Identifier.

The Dynamic Gait Event Identifier (DGEI) introduces a pioneering approach for real-time gait event detection that seamlessly aligns with the needs of embedded system design and optimization. DGEI creates a new standard for gait analysis by combining software and hardware co-design with real-time data analysis, using a combination of first-order difference functions and sliding window techniques. The method is specifically designed to accurately separate and analyze key gait events such as heel strike (HS), toe-off (TO), walking start (WS), and walking pause (WP) from a continuous stream of inertial measurement unit (IMU) signals. The core innovation of DGEI is the application of its dynamic feature extraction strategies, including first-order differential integration with positive/negative windows, weighted sleep time analysis, and adaptive thresholding, which together improve its accuracy in gait segmentation. The experimental results show that the accuracy rate of HS event detection is 97.82%, and the accuracy rate of TO event detection is 99.03%, which is suitable for embedded systems. Validation on a comprehensive dataset of 1550 gait instances shows that DGEI achieves near-perfect alignment with human annotations, with a difference of less than one frame in pulse onset times in 99.2% of the cases.

Enabling Seamless Connectivity: Networking Innovations in Wireless Sensor Networks for Industrial Application.

The wide-ranging applications of the Internet of Things (IoT) show that it has the potential to revolutionise industry, improve daily life, and overcome global challenges. This study aims to evaluate the performance scalability of mature industrial wireless sensor networks (IWSNs). A new classification approach for IoT in the industrial sector is proposed based on multiple factors and we introduce the integration of 6LoWPAN (IPv6 over low-power wireless personal area networks), message queuing telemetry transport for sensor networks (MQTT-SN), and ContikiMAC protocols for sensor nodes in an industrial IoT system to improve energy-efficient connectivity. The Contiki COOJA WSN simulator was applied to model and simulate the performance of the protocols in two static and moving scenarios and evaluate the proposed novelty detection system (NDS) for network intrusions in order to identify certain events in real time for realistic dataset analysis. The simulation results show that our method is an essential measure in determining the number of transmissions required to achieve a certain reliability target in an IWSNs. Despite the growing demand for low-power operation, deterministic communication, and end-to-end reliability, our methodology of an innovative sensor design using selective surface activation induced by laser (SSAIL) technology was developed and deployed in the FTMC premises to demonstrate its long-term functionality and reliability. The proposed framework was experimentally validated and tested through simulations to demonstrate the applicability and suitability of the proposed approach. The energy efficiency in the optimised WSN was increased by 50%, battery life was extended by 350%, duplicated packets were reduced by 80%, data collisions were reduced by 80%, and it was shown that the proposed methodology and tools could be used effectively in the development of telemetry node networks in new industrial projects in order to detect events and breaches in IoT networks accurately. The energy consumption of the developed sensor nodes was measured. Overall, this study performed a comprehensive assessment of the challenges of industrial processes, such as the reliability and stability of telemetry channels, the energy efficiency of autonomous nodes, and the minimisation of duplicate information transmission in IWSNs.

A survey of large language models for cyber threat detection

With the increasing complexity of cyber threats and the expanding scope of cyberspace, there exist progressively more challenges in cyber threat detection. It is proven that most previous threat detection models may become inadequate due to the escalation of hacker attacks. However, recent research has shown that some of these problems can be effectively addressed by Large Language Models (LLMs) directly or indirectly. Nowadays, a growing number of security researchers are adopting LLMs for analyzing various cyber threats. According to the investigation, we found that while there are numerous emerging reviews on the utilization of LLMs in some fields of cyber security, there is currently a lack of a comprehensive review on the application of LLMs in the threat detection stage. Through retrieving and collating existing works in recent years, we examined various threat detection and monitoring tasks for which LLMs may be well-suited, including cyber threat intelligence, phishing email detection, threat prediction, logs analysis, and so on. Additionally, the review explored the specific stages of different detection tasks in which LLMs are involved, evaluating the points at which LLMs are optimized. For instance, LLMs have been found to enhance the interpretability of log analysis in real-time anomaly event discovery. Additionally, we discussed some tasks where LLMs may not be suitable and explored future directions and challenges in this field. By providing a detailed status update and comprehensive insights, this review aims to assist security researchers in leveraging LLMs to enhance existing detection frameworks or develop domain-specific LLMs.

Detecting Toe-Off and Initial Contact in Real-Time With Self-Adapting Thresholds.

This research introduces an adaptive control algorithm designed to determine gait phase in real-time using an inertial measurement unit (IMU) affixed to the shank. Focusing on detecting specific gait events, primarily initial contact (IC) and toe-off (TO), the algorithm utilizes dynamic thresholds and ratios that facilitate accurate event determination adaptively across a range of walking speeds. Built-in safety checks further ensure precision and minimize false detections. We validated the algorithm with eight participants walking at varying speeds. The algorithm demonstrated promising results in detecting IC and TO events with mean lead of 8.95 ms and 4.42 ms and detection success rate of 100% and 99.72%, respectively. These results are consistent with benchmarks from established algorithms (Hanlon and Anderson, 2009, "Real-Time Gait Event Detection Using Wearable Sensors," Gait Posture, 30(4), pp. 523-527; Maqbool et al., 2017, "A Real-Time Gait Event Detection for Lower Limb Prosthesis Control and Evaluation," IEEE Trans. Neural Syst. Rehabil. Eng.: Publ. IEEE Eng. Med. Biol. Soc., 25(9), pp. 1500-1509). Moreover, the algorithm's self-adaptive nature ensures it can be used in scenarios of varying movement, offering a promising solution for real-time gait phase detection.

EXPRESS: The Role of Real-Time Event Monitoring in Dynamic Response to Disruptions

This paper investigates a risk-averse firm’s investment strategy in real-time event-monitoring technologies coupled with dynamic disruption response decisions. The firm does not know how long the disruption will last, and the event-monitoring solutions provide the firm with a quicker update regarding the length of the disruption. When the firm receives an update, it has the flexibility to revise its initial response action. We model a two-stage stochastic program to study this problem where the risk aversion is modeled in the form of a Service-at-Risk (SaR) constraint. Our paper makes four important contributions. First, it characterizes the optimal recourse strategies given an update and identifies the conditions when the firm benefits from the recourse. Second, we characterize the optimal investment and initial response strategies where we find that the investment behavior is non-monotone. Third, our paper shows an interesting impact of risk aversion on the investment decision such that the firm may benefit from investing in event monitoring under moderate degrees of risk aversion, but not necessarily under low or high degrees of risk aversion. Fourth, we find that a firm’s benefit from event monitoring is more profound when the capacity of contingency response is moderately limited.

A Novel Approach for Enhanced Real-Time Event Diagnosis for Grid Connected Microgrid with Multiple Distributed Energy Resources (DERs)

A Novel Approach for Enhanced Real-Time Event Diagnosis for Grid Connected Microgrid with Multiple Distributed Energy Resources (DERs)

Supremacy in the Electromagnetic Spectrum

Supremacy in the electromagnetic spectrum provides additional options and increased flexibility in any competition, even other than military conflicts, in addition to greater control of the scale of escalation. It gives policy makers and military leaders the decision space to manage events in real time while denying equivalent decision space to adversaries. At the same time, supremacy in the electromagnetic spectrum requires the collection, analysis and validation of robust information in the following key areas: electromagnetic spectrum sensors, communications, data links, radars, jamming, directed energy and infrared systems, engineering data describing the performance, characteristics and signature information of associated equipment, weapons and platforms, combat support data and modelling and simulation support. The authors describe these topics through a detailed analysis of the importance and role of supremacy in the electromagnetic spectrum.

Object and Event Detection Pipeline for Rink Hockey Games

All types of sports are potential application scenarios for automatic and real-time visual object and event detection. In rink hockey, the popular roller skate variant of team hockey, it is of great interest to automatically track player movements, positions, and sticks, and also to make other judgments, such as being able to locate the ball. In this work, we present a real-time pipeline consisting of an object detection model specifically designed for rink hockey games, followed by a knowledge-based event detection module. Even in the presence of occlusions and fast movements, our deep learning object detection model effectively identifies and tracks important visual elements in real time, such as: ball, players, sticks, referees, crowd, goalkeeper, and goal. Using a curated dataset consisting of a collection of rink hockey videos containing 2525 annotated frames, we trained and evaluated the algorithm’s performance and compared it to state-of-the-art object detection techniques. Our object detection model, based on YOLOv7, presents a global accuracy of 80% and, according to our results, good performance in terms of accuracy and speed, making it a good choice for rink hockey applications. In our initial tests, the event detection module successfully detected an important event type in rink hockey games, namely, the occurrence of penalties.

Nexikon Society (Society Management System)

The “The Nexikon Society” is an offers an integrated platform aimed at facilitating effective communication, information sharing, and issue resolution within residential communities. This platform serves as a centralized hub where society members can access real-time updates, notices, and event announcements, fostering a stronger sense of community engagement and collaboration. Through the Nexikon website, residents have easy access to various community-related information, including updates on ongoing projects, upcoming events, and important notices. Additionally, the platform allows members to view profiles of their neighbour’s, facilitating networking and social interactions within the community. A significant feature of Nexikon is its complaint management system, which enables members to report issues or concerns pertaining to the society. This feature ensures that grievances are addressed promptly and promotes transparency and accountability among society administrators. Moreover, the website restricts database modification privileges to the administrator, ensuring the integrity and security of the data. The administrator is tasked with managing member information, overseeing platform operations, and performing essential tasks to ensure its smooth functioning. Keywords— Society Management System, Residential Communities, Communication, Information Dissemination, Issue Resolution, Administrator, Database Management.