Real-time Approach Research Articles

Real-Time Classroom Behavior Analysis for Enhanced Engineering Education: An AI-Assisted Approach

Modern teaching has made significant progress, with many advanced equipment and technologies being introduced into the teaching process. Experimental teaching of engineering design courses is important. Due to limited teaching resources, engineering students need effective guidance during limited laboratory time. We will introduce artificial intelligence solutions to engineering education. We will use artificial intelligence technology for classroom behavior analysis to improve engineering design practice courses' teaching effectiveness. In an instructional milieu, image acquisition tools such as cameras are capable of real-time data capture, facilitating the identification and enumeration of students' emotional states. Concurrently, analytical software gauges the students' interaction patterns and performs comprehensive cluster analysis. Such multifaceted information provides valuable insights into the students' educational engagement, allowing educators to tailor their approach, thereby fostering enhanced pedagogical outcomes. The emotion recognition model we have developed, namely ERAM, demonstrates a rapid response rate coupled with dependable accuracy, making it a robust tool for classroom implementation. In contrast to the conventional post-lesson evaluations, our proposed technique furnishes immediate feedback throughout the instructional process. This real-time approach heralds a significant shift in instructional methodology, promoting timely intervention and adaptive teaching strategies. The control group experiment showed that intelligent systems improved teaching effectiveness by 8.44%. Intelligent systems can help teachers understand students' learning status and improve laboratory teaching quality in engineering design courses.

A Real-Time Resource Dispatch Approach for Edge Computing Devices in Digital Distribution Networks Considering Burst Tasks

Edge computing technology can effectively solve huge challenges posed by the large number of terminal devices accessing and massive data processing in digital distribution networks. Burst tasks, such as faults and data requests from the cloud, can occur at any time for edge computing devices in distribution networks. These tasks are unpredictable and usually hold the highest priority and must be completed as soon as possible. Although resources can be reserved partially at each period in the pre-scheduled operation plan, they may still be insufficient to handle burst tasks adequately. A real-time resource dispatch approach for burst tasks is developed in this study to address the above problems. The concept of flexibility for edge computing devices is presented, determining the real-time dispatch duration. Real-time resource dispatch and task handling processing are analyzed in detail, considered as task real-time dispatch models, computation process real-time dispatch constraints, and resource limitation constraints. The proposed real-time resource dispatch approach takes full advantage of the transferable characteristics for partial original plan tasks to adjust the pre-scheduled operation plan and release flexible resources for immediate processing of the burst task, completing burst tasks quickly and minimizing the impact for previous planned tasks on the edge computing device. The capability of the proposed method to efficiently deal with the burst tasks is also verified by the case study.

Ionosphere-Weighted Network Real-Time Kinematic Server-Side Approach Combined with Single-Differenced Observations of GPS, GAL, and BDS

Ionosphere-Weighted Network Real-Time Kinematic Server-Side Approach Combined with Single-Differenced Observations of GPS, GAL, and BDS

Consensus definition and diagnostic criteria for neonatal encephalopathy-study protocol for a real-time modified delphi study.

'Neonatal encephalopathy' (NE) describes a group of conditions in term infants presenting in the earliest days after birth with disturbed neurological function of cerebral origin. NE is aetiologically heterogenous; one cause is peripartum hypoxic ischaemia. Lack of uniformity in the terminology used to describe NE and its diagnostic criteria creates difficulty in the design and interpretation of research and complicates communication with families. The DEFINE study aims to use a modifiedDelphi approach to form a consensus definition for NE, and diagnostic criteria. Directed by an international steering group, we will conduct a systematic review of the literature to assess the terminology used in trials of NE, and with their guidance perform an online Real-time Delphi survey to develop a consensus diagnosis and criteria for NE. A consensus meeting will be held to agree on the final terminology and criteria, and the outcome disseminated widely. A clear and consistent consensus-based definition of NE and criteria for its diagnosis, achieved by use of a modifiedDelphi technique, will enable more comparability of research results and improved communication among professionals and with families. The terms Neonatal Encephalopathy and Hypoxic Ischaemic Encephalopathy tend to be used interchangeably in the literature to describe a term newborn with signs of encephalopathy at birth. This creates difficulty in communication with families and carers, and between medical professionals and researchers, as well as creating difficulty with performance of research. The DEFINE project will use a Real-time Delphi approach to create a consensus definition for the term 'Neonatal Encephalopathy'. A definition formed by this consensus approach will be accepted and utilised by the neonatal community to improve research, outcomes, and parental experience.

Optical sensor for BTEX detection: Integrating machine learning for enhanced sensing

Optical sensors provide a fast and real-time approach to detect benzene, toluene, ethylbenzene, and xylenes (BTEX) in environmental monitoring and industrial safety. However, detecting the concentration of a particular gas in a mixture can be challenging. Here, we develop a machine-learning model that can precisely measure BTEX concentrations simultaneously based on an absorption spectroscopy gas sensing system. The convolutional neural network (CNN) is utilized to identify the absorbance spectra for each volatile, along with their concentrations in a mixture. A synthetic data set is generated using a series of physics-based simulations to create the predictive model. The data set consists of the overall absorbance of numerous random BTEX mixtures over time, based on various percentages of the permissible exposure limit (PEL). It is worth noting that benzene has a negligible absorbance (very low PEL, 1–5 ppm) compared to other volatile gases, which makes it difficult to detect. To address this challenge, we introduce a 3-stage solution to accurately discriminate between all BTEX species, regardless of their concentration levels. As a result, the R-squared above 0.99 for toluene, ethylbenzene, and o-xylene, and the R-squared above 0.96 for benzene, is achieved, indicating the model's capability to predict BTEX concentrations.

RTGDC: a real-time ingestion and processing approach in geospatial data cube for digital twin of earth

ABSTRACT The emergence of the Digital Twin of Earth (DTE) signifies a pivotal advancement in the field of the Digital Earth, which includes observations, simulations, and predictions regarding the state of the Earth system and its temporal evolution. While the Geospatial Data Cubes (GDCs) hold promise in probing the DTE across time, existing GDC approaches are not well-suited for real-time data ingestion and processing. This paper proposes the design and implementation of a real-time ingestion and processing approach in Geospatial Data Cube (RTGDC), achieved by ingesting real-time observation streams into data cubes. The methodology employs a publish/subscribe model for efficient observation ingestion. To optimize observation processing within the data cube, a distributed streaming computing framework is utilized in the implementation of the RTGDC. This approach significantly enhances the real-time capabilities of GDCs, providing an efficient solution that brings the realization of the DTE concept within closer reach. In RTGDC, two cases involving local and global scales were implemented, and their real-time performance was evaluated based on latency, throughput, and parallel efficiency.

A lightweight real-time accurate deformation detection approach for pantograph horns

The pantograph is vital for urban rail transport, with the horn deformation directly impacting safety. In this paper, a novel method for detecting horn deformation is proposed. First, to determine the horn pose, keypoints on horn surfaces are extracted using a lightweight, real-time, and accurate network. A novel module is introduced to improve the performance of post-processing. Second, view transformation of horn keypoints is proposed. It calculates the three-dimensional angles of the horns in monocular images based on the vanishing points. Finally, the experimental results demonstrate a sound performance of the proposed horn keypoint detection network, achieving 97.73% accuracy and 2.05% false positive rate with an inference time of 3.8ms and a parameter count of 3.41M. Under different views, the converted horn angle can account for 95.75% under the error of 1°and 100% under the error of 1.5°. Therefore, the proposed method reaches the level of practical industrial application.

Real-Time Tracking Data and Machine Learning Approaches for Mapping Pedestrian Walking Behavior: A Case Study at the University of Moratuwa.

The growing urban population and traffic congestion underline the importance of building pedestrian-friendly environments to encourage walking as a preferred mode of transportation. However, a major challenge remains, which is the absence of such pedestrian-friendly walking environments. Identifying locations and routes with high pedestrian concentration is critical for improving pedestrian-friendly walking environments. This paper presents a quantitative method to map pedestrian walking behavior by utilizing real-time data from mobile phone sensors, focusing on the University of Moratuwa, Sri Lanka, as a case study. This holistic method integrates new urban data, such as location-based service (LBS) positioning data, and data clustering with unsupervised machine learning techniques. This study focused on the following three criteria for quantifying walking behavior: walking speed, walking time, and walking direction inside the experimental research context. A novel signal processing method has been used to evaluate speed signals, resulting in the identification of 622 speed clusters using K-means clustering techniques during specific morning and evening hours. This project uses mobile GPS signals and machine learning algorithms to track and classify pedestrian walking activity in crucial sites and routes, potentially improving urban walking through mapping.

Contraction assessment of abdominal muscles using automated segmentation designed for wearable ultrasound applications

PurposeWearable ultrasound devices can be used to continuously monitor muscle activity. One possible application is to provide real-time feedback during physiotherapy, to show a patient whether an exercise is performed correctly. Algorithms which automatically analyze the data can be of importance to overcome the need for manual assessment and annotations and speed up evaluations especially when considering real-time video sequences. They even could be used to present feedback in an understandable manner to patients in a home-use scenario. The following work investigates three deep learning based segmentation approaches for abdominal muscles in ultrasound videos during a segmental stabilizing exercise. The segmentations are used to automatically classify the contraction state of the muscles.MethodsThe first approach employs a simple 2D network, while the remaining two integrate the time information from the videos either via additional tracking or directly into the network architecture. The contraction state is determined by comparing measures such as muscle thickness and center of mass between rest and exercise. A retrospective analysis is conducted but also a real-time scenario is simulated, where classification is performed during exercise.ResultsUsing the proposed segmentation algorithms, 71% of the muscle states are classified correctly in the retrospective analysis in comparison to 90% accuracy with manual reference segmentation. For the real-time approach the majority of given feedback during exercise is correct when the retrospective analysis had come to the correct result, too.ConclusionBoth retrospective and real-time analysis prove to be feasible. While no substantial differences between the algorithms were observed regarding classification, the networks incorporating the time information showed temporally more consistent segmentations. Limitations of the approaches as well as reasons for failing cases in segmentation, classification and real-time assessment are discussed and requirements regarding image quality and hardware design are derived.

A real-time approach for smart building operations prediction using rule-based complex event processing and SPARQL query

A real-time approach for smart building operations prediction using rule-based complex event processing and SPARQL query

GABoT: A Lightweight Real-Time Adaptable Approach for Intelligent Fault Diagnosis of Rotating Machinery

GABoT: A Lightweight Real-Time Adaptable Approach for Intelligent Fault Diagnosis of Rotating Machinery

Making Drug Approval Decisions in the Face of Uncertainty: Cumulative Evidence versus Value of Information.

The COVID-19 pandemic underscored the criticality and complexity of decision making for novel treatment approval and further research. Our study aims to assess potential decision-making methodologies, an evaluation vital for refining future public health crisis responses. We compared 4 decision-making approaches to drug approval and research: the Food and Drug Administration's policy decisions, cumulative meta-analysis, a prospective value-of-information (VOI) approach (using information available at the time of decision), and a reference standard (retrospective VOI analysis using information available in hindsight). Possible decisions were to reject, accept, provide emergency use authorization, or allow access to new therapies only in research settings. We used monoclonal antibodies provided to hospitalized COVID-19 patients as a case study, examining the evidence from September 2020 to December 2021 and focusing on each method's capacity to optimize health outcomes and resource allocation. Our findings indicate a notable discrepancy between policy decisions and the reference standard retrospective VOI approach with expected losses up to $269 billion USD, suggesting suboptimal resource use during the wait for emergency use authorization. Relying solely on cumulative meta-analysis for decision making results in the largest expected loss, while the policy approach showed a loss up to $16 billion and the prospective VOI approach presented the least loss (up to $2 billion). Our research suggests that incorporating VOI analysis may be particularly useful for research prioritization and treatment implementation decisions during pandemics. While the prospective VOI approach was favored in this case study, further studies should validate the ideal decision-making method across various contexts. This study's findings not only enhance our understanding of decision-making strategies during a health crisis but also provide a potential framework for future pandemic responses. This study reviews discrepancies between a reference standard (retrospective VOI, using hindsight information) and 3 conceivable real-time approaches to research-treatment decisions during a pandemic, suggesting suboptimal use of resources.Of all prospective decision-making approaches considered, VOI closely mirrored the reference standard, yielding the least expected value loss across our study timeline.This study illustrates the possible benefit of VOI results and the need for evidence accumulation accompanied by modeling in health technology assessment for emerging therapies.

A kinematic real-time PPP approach with estimating signal in space range errors

Since 2013, the International GNSS Service (IGS) real-time service (RTS) has been providing precise orbit and clock corrections for ten years, which enables real-time positioning at the decimeter level. High precision positioning relies heavily on accurate orbit and clock. However, constrained by the relatively short observation duration, the accuracy of real-time products are often worse than the final products. Previous studies indicate that compensating for errors in orbit and clock can improve positioning accuracy. Based on this theory, we proposed an improved positioning model, which incorporates the signal-in-space range error (SISRE) into the ionosphere-free combination observation equation and treats it as a parameter in the Kalman filter. Through the analysis of SISRE, we find that the time-varying characteristic of the parameter follows a random walk assumption. To find the optimal parameter settings of the SISRE parameters, a two-dimensional sensitivity analysis is employed, and a set of recommended values are provided. In the simulated kinematic positioning experiments, the proposed method achieves positioning accuracies of 17.3 cm, 19.6 cm, and 18.0 cm for GPS, BDS-3, and Galileo, respectively, representing a 10–20 % improvement over traditional methods. In real-time PPP experiment, the accuracy reaches 21.7 cm, indicating a 12.9 % improvement compared to traditional methods.

Critical dynamics within the real-time FRG approach

The Schwinger-Keldysh functional renormalization group developed by Y.-y. Tan [Real-time dynamics of the O(4) scalar theory within the fRG approach, ] is employed to investigate critical dynamics related to a second-order phase transition. The effective action of model A is expanded to the order of O(∂2) in the derivative expansion for the O(N) symmetry. By solving the fixed-point equations of effective potential and wave function, we obtain static and dynamic critical exponents for different values of the spatial dimension d and the field component number N. It is found that one has z≥2 in the whole range of 2≤d≤4 for the case of N=1, while in the case of N=4, the dynamic critical exponent turns to z<2 when the dimension approach towards d=2. Published by the American Physical Society 2024

Interplay of Luminophores and Photoinitiators during Synthesis of Bulk and Patterned Luminescent Photopolymer Blends.

Four-dimensional printing with embedded photoluminescence is emerging as an exciting area in additive manufacturing. Slim polymer films patterned with three-dimensional lattices of multimode cylindrical waveguides (waveguide-encoded lattices, WELs) with enhanced fields of view can be fabricated by localizing light as self-trapped beams within a photopolymerizable formulation. Luminescent WELs have potential applications as solar cell coatings and smart planar optical components. However, as luminophore-photoinitiator interactions are expected to change the photopolymerization kinetics, the design of robust luminescent photopolymer sols is nontrivial. Here, we use model photopolymer systems based on methacrylate-siloxane and epoxide homopolymers and their blends to investigate the influence of the luminophore Lumogen Violet (LV) on the photolysis kinetics of the Omnirad 784 photoinitiator through UV-vis absorbance spectroscopy. Initial rate analysis with different bulk polymers reveals differences in the pseudo-first-order rate constants in the absence and presence of LV, with a notable increase (∼40%) in the photolysis rate for the 1:1 blend. Fluorescence quenching studies, coupled with density functional theory calculations, establish that these differences arise due to electron transfer from the photoexcited LV to the ground-state photoinitiator molecules. We also demonstrate an in situ UV-vis absorbance technique that enables real-time monitoring of both waveguide formation and photoinitiator consumption during the fabrication of WELs. The in situ photolysis kinetics confirm that LV-photoinitiator interactions also influence the photopolymerization process during WEL formation. Our findings show that luminophores play a noninnocent role in photopolymerization and highlight the necessity for both careful consideration of the photopolymer formulation and a real-time monitoring approach to enable the fabrication of high-quality micropatterned luminescent polymeric films.

Raman and NIR spectroscopy-based real-time monitoring of the membrane filtration process of a recombinant protein for the diagnosis of SARS-CoV-2

This research shows the detailed comparison of Raman and near-infrared (NIR) spectroscopy as Process Analytical Technology tools for the real-time monitoring of a protein purification process. A comprehensive investigation of the application and model development of Raman and NIR spectroscopy was carried out for the real-time monitoring of a process-related impurity, imidazole, during the tangential flow filtration of Receptor-Binding Domain (RBD) of the SARS-CoV-2 Spike protein. The fast development of Raman and NIR spectroscopy-based calibration models was achieved using offline calibration data, resulting in low calibration and cross-validation errors. Raman model had an RMSEC of 1.53 mM, and an RMSECV of 1.78 mM, and the NIR model had an RMSEC of 1.87 mM and an RMSECV of 2.97 mM. Furthermore, Raman models had good robustness when applied in an inline measurement system, but on the contrary NIR spectroscopy was sensitive to the changes in the measurement environment. By utilizing the developed models, inline Raman and NIR spectroscopy were successfully applied for the real-time monitoring of a process-related impurity during the membrane filtration of a recombinant protein. The results enhance the importance of implementing real-time monitoring approaches for the broader field of diagnostic and therapeutic protein purification and underscore its potential to revolutionize the rapid development of biological products.

A real-time epilepsy seizure detection approach based on EEG using short-time Fourier transform and Google-Net convolutional neural network

Epilepsy is one of the most common brain disorders, and seizures of epilepsy have severe adverse effects on patients. Real-time epilepsy seizure detection using electroencephalography (EEG) signals is an important research area aimed at improving the diagnosis and treatment of epilepsy. This paper proposed a real-time approach based on EEG signal for detecting epilepsy seizures using the STFT and Google-net convolutional neural network (CNN). The CHB-MIT database was used to evaluate the performance, and received the results of 97.74 % in accuracy, 98.90 % in sensitivity, 1.94 % in false positive rate. Additionally, the proposed method was implemented in a real-time manner using the sliding window technique. The processing time of the proposed method just 0.02 s for every 2-s EEG episode and achieved average 9.85- second delay in each seizure onset.

Smart Irrigation with Intrusion Detection

This paper proposes a comprehensive approach to smart irrigation by integrating intrusion detection mechanisms. By combining the functionalities of smart irrigation systems with intrusion detection systems (IDS), the proposed framework offers enhanced security and reliability in agricultural water management. The system employs a network of sensors weather, and soil moisture levels patterns, and other relevant parameters to optimize irrigation scheduling. Concurrently, it utilizes intrusion detection algorithms to identify and respond to unauthorized access attempts or anomalous behaviors within the irrigation infrastructure. The proposed approach represents a noteworthy advancement in the direction of sustainable and secure management of water in agriculture, contributing to improved crop yields, resource conservation, and overall perseverance in the face of emerging challenges. Additionally, by incorporating machine learning algorithms into the intrusion detection system, the framework is able to change and grow over time, enhancing its capacity to identify and neutralize possible threats. Furthermore, the system can more accurately predict when irrigation is needed by utilizing real-time data analysis and predictive modeling approaches. This maximizes water usage efficiency while reducing waste. This all-encompassing strategy encourages a more ecologically sustainable method of managing water resources while also strengthening the resilience of farming operations. Furthermore, the framework enables farmers to take educated decisions in real-time, maximizing productivity and lowering risks, by giving them actionable insights and alerts about security breaches and irrigation needs.

DeepMetaDroid: Real-Time Android Malware Detection Using Deep Learning and Metadata Features

The increasing prevalence of Android malware poses significant risks to mobile devices and user privacy. The traditional detection methods have limitations in keeping up with the evolving landscape of malware attacks, necessitating the development of more effective solutions. In this paper, we present DeepMetaDroid, a real-time detection approach for Android malware that leverages metadata features. By analyzing crucial metadata, including APK size, download size, permissions, certificates, and DEX files, the proposed method enables effective identification of malware and enhances mobile security. Using deep learning techniques, a lightweight Android real-time monitoring system is equipped with the trained model. These methods include long short-term memory (LSTM), gated recurrent units (GRU), convolutional neural networks (CNN), deep neural networks (DNN), and other ensemble models. Utilizing the rectified linear unit (ReLU) as the activation function, the DNN model is constructed with 32 neurons in the input layer. A one-dimensional convolutional layer with 32 neurons and a filter size of three is used as the input layer in the CNN model. The LSTM model is designed with an input layer consisting of 16 neurons. The GRU model with 32 neurons is employed in the input layer. Additionally, ensemble models that combined several architectures were developed. The proposed method offers a faster and more scalable solution for malware detection by consuming fewer resources like memory and CPU. This work ensures device security by providing real-time monitoring on Android devices to prevent users from installing malicious applications and, thus, enhance user privacy and security.

Real-time fluorescence loop-mediated isothermal amplification assays for detection of zoonotic malaria Plasmodium parasites

BackgroundNatural human infections by Plasmodium cynomolgi and P. inui have been reported recently and gain the substantial attention from Southeast Asian countries. Zoonotic transmission of non-human malaria parasites to humans from macaque monkeys occurred through the bites of the infected mosquitoes. The objective of this study is to establish real-time fluorescence loop-mediated isothermal amplification (LAMP) assays for the detection of zoonotic malaria parasites by combining real-time fluorescent technology with the isothermal amplification technique. MethodsBy using 18S rRNA as the target gene, the primers for P. cynomolgi, P. coatneyi and P. inui were newly designed in the present study. Four novel real-time fluorescence LAMP assays were developed for the detection of P. cynomolgi, P. coatneyi, P. inui and P. knowlesi. The entire amplification process was completed in 60 min, with the assays performed at 65 °C. By using SYTO-9 as the nucleic acid intercalating dye, the reaction was monitored via real-time fluorescence signal. ResultsThere was no observed cross-reactivity among the primers from different species. All 70 field-collected monkey samples were successfully amplified by real-time fluorescence LAMP assays. The detection limit for P. cynomolgi, P. coatneyi and P. knowlesi was 5 × 109 copies/µL. Meanwhile, the detection limit of P. inui was 5 × 1010 copies/µL. ConclusionThis is the first report of the detection of four zoonotic malaria parasites by real-time fluorescence LAMP approaches. It is an effective, rapid and simple-to-use technique. This presented platform exhibits considerable potential as an alternative detection for zoonotic malaria parasites.