Real-time System Research Articles

FPGA implementation of automatic seizure detection in EEG signals using machine learning algorithm

This work presents a novel approach that harnesses the capabilities of field programmable gate arrays (FPGA) to enable instantaneous monitoring of electroencephalography (EEG) signals for the detection and prediction of seizure patterns. The integration of FPGA technology with the perceptron learning algorithm holds promises for enhancing real-time healthcare monitoring systems and contributing to improved patient care and safety. The proposed work addresses the challenges presented at predicting seizures from EEG data. The first challenge is to handle massive EEG data that poses memory issues. To tackle this, the research employs cellular automata (Rule 90 and Rule 150) to reduce data size by 85.71%, making it more manageable. The second is to implement a linearly classified perceptron algorithm using FPGA to predict and detect seizures from real-time EEG data. The third is clock synchronization between stored data and real EEG data for comparison purposes. The proposed system is continuously compared to real-time EEG signals using a single perceptron neural network, and alert signals are generated based on preactivation values, with a continuous alert issued when the value reaches 3/4th sample match. These early alerts empower individuals to take preventive actions. The model is implemented using an FPGA Zynq-7000 series, which consumes 270mW of power. The design utilizes 118 Lookup Tables (LUTs).

Virtualized Fault Injection Framework for ISO 26262-Compliant Digital Component Hardware Faults

Simulation-based Fault Injection (FI) is crucial for validating system behaviour in safety-critical applications, such as the automotive industry. The ISO 26262 standard’s Part 11 extension provides failure modes for digital components, driving the development of new fault models to assess software-implemented mechanisms against random hardware failures (RHF). This paper proposes a Fault Injection framework, QEFIRA, and shows its ability to achieve the failure modes proposed by Part 11 of the ISO 26262 standard and estimate relevant metrics for safety mechanisms. QEFIRA uses QEMU to inject permanent and transient faults during runtime, whilst logging the system state and providing automatic post-execution analysis. Complemented with a confusion matrix, it allows us to gather standard compliant metrics to characterise and evaluate different designs in the early stages of development. Comparatively to the native QEMU implementation, the tool only shows a slowdown of 1.4× for real-time microcontroller-based applications.

ESenTRy: an on-site earthquake early warning system based on the instrumental modified Mercalli intensity

AbstractEarthquake early warning systems (EEWSs) are real-time seismology-based applications that purpose to reduce earthquake damage by taking some precautions for environments that are likely to be damaged. These systems are divided into two categories as on-site and regional. In this study, an on-site EEWS called ESenTRy, consisting of an accelerometer, GPS and communication components, has been designed. In this scope, a Windows operating system-based software has been developed using the .NET Framework and C# language. The developed software has the ability to read the Güralp Compressed Format strong motion data in real-time, compute the strong motion indices such as destructive intensity, real-time intensity and instrumental modified Mercalli intensity, issue an alarm in the target area and alert the defined Modbus clients. To test the reliability of ESenTRy, two simulations have been performed using the strong motion data belonging to the magnitude 7.7 Kahramanmaraş-Pazarcık earthquake that occurred in Türkiye on February 6, 2023, at 01:17:32 UTC and magnitude 5.9 Düzce-Gölyaka earthquake that occurred in Türkiye on December 23, 2022, at 01:08:15 UTC. In the test applications, depending on the distance between the stations and earthquake epicenters, the time difference between the Level-2 alarm trigger times and S-wave arrival times varied between 1.5 and 16 s, while the time difference between the Level-2 alarm trigger times and P wave arrival times varied between 0.5 and 29.5 s. The flowchart diagrams of the developed algorithm and results obtained from the simulations are presented in detail.

Narrative review of patient-specific 3D visualization and reality technologies in skull base neurosurgery: enhancements in surgical training, planning, and navigation

Recent advances in medical imaging, computer vision, 3-dimensional (3D) modeling, and artificial intelligence (AI) integrated technologies paved the way for generating patient-specific, realistic 3D visualization of pathological anatomy in neurosurgical conditions. Immersive surgical simulations through augmented reality (AR), virtual reality (VR), mixed reality (MxR), extended reality (XR), and 3D printing applications further increased their utilization in current surgical practice and training. This narrative review investigates state-of-the-art studies, the limitations of these technologies, and future directions for them in the field of skull base surgery. We begin with a methodology summary to create accurate 3D models customized for each patient by combining several imaging modalities. Then, we explore how these models are employed in surgical planning simulations and real-time navigation systems in surgical procedures involving the anterior, middle, and posterior cranial skull bases, including endoscopic and open microsurgical operations. We also evaluate their influence on surgical decision-making, performance, and education. Accumulating evidence demonstrates that these technologies can enhance the visibility of the neuroanatomical structures situated at the cranial base and assist surgeons in preoperative planning and intraoperative navigation, thus showing great potential to improve surgical results and reduce complications. Maximum effectiveness can be achieved in approach selection, patient positioning, craniotomy placement, anti-target avoidance, and comprehension of spatial interrelationships of neurovascular structures. Finally, we present the obstacles and possible future paths for the broader implementation of these groundbreaking methods in neurosurgery, highlighting the importance of ongoing technological advancements and interdisciplinary collaboration to improve the accuracy and usefulness of 3D visualization and reality technologies in skull base surgeries.

Real-time body movement tracking of athletes via a CNN-based approach

Sports injuries frequently stem from improper poses and movements. This paper explores the potential of using video and deep neural network model in mitigating such risks. A novel real-time body movement tracking system is designed to enable athletes to analyze and refine their techniques without the need for intrusive sensors. Central to our approach is the development of a user-friendly graphical interface, facilitating the effortless display and examination of tracked movements. This system not only enhances athletic performance but also aims to significantly reduce the sports injuries.

Modified receiver architecture in software-defined radio for real-time modulation classification

Automatic modulation classification (AMC) is an important process for future communication systems with prominent applications from spectrum management, and secure communication, to cognitive radio. The requirement for an efficient AMC classifier is due to its capability in blind modulation recognition, which is a difficult task in real scenarios where the limitations of traditional hardware and the complexity of channel impairments are involved. Therefore, this paper proposes a complete real-time AMC system based on software-defined radio and deep learning architecture. The system demodulation performance is verified through simulations and real channel impairment conditions to ensure reliability. With at most 6 times reduced number of parameters, two proposed models convolutional long short-term memory deep neural network and residual long short-term memory neural network also show a general improvement in classification accuracy compared with reference studies. The performance of these models at real-time AMC is tested with suitable processing time for practical applications.

A Real-Time and Privacy-Preserving Facial Expression Recognition System Using an AI-Powered Microcontroller

This study proposes an edge computing-based facial expression recognition system that is low cost, low power, and privacy preserving. It utilizes a minimally obtrusive cap-based system designed for the continuous and real-time monitoring of a user’s facial expressions. The proposed method focuses on detecting facial skin deformations accompanying changes in facial expressions. A multi-zone time-of-flight (ToF) depth sensor VL53L5CX, featuring an 8 × 8 depth image, is integrated into the front brim of the cap to measure the distance between the sensor and the user’s facial skin surface. The distance values corresponding to seven universal facial expressions (neutral, happy, disgust, anger, surprise, fear, and sad) are transmitted to a low-power STM32F476 microcontroller (MCU) as an edge device for data preprocessing and facial expression classification tasks utilizing an on-device pre-trained deep learning model. Performance evaluation of the system is conducted through experiments utilizing data collected from 20 subjects. Four deep learning algorithms, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, and Deep Neural Networks (DNN), are assessed. These algorithms demonstrate high accuracy, with CNN yielding the best result, achieving an accuracy of 89.20% at a frame rate of 15 frames per second (fps) and a maximum latency of 2 ms.

Mask detection using automated machine learning techniques: A response strategy to the COVID-19 pandemic

Amidst the global backdrop of the COVID-19 pandemic, the imperative of judicious mask usage has emerged as a pivotal facet of public health. Conventional monitoring methods, such as manual checks, prove inadequate in addressing the demands of a pandemic of this scale. Although traditional machine learning techniques offer a potential solution for mask detection, their time-consuming nature poses challenges for real-time applications. In response, this study delves into the realm of automated machine learning techniques, focusing on the EasyDL platform due to its user-friendly interface and robust algorithms. This study explores automated machine learning for efficient mask detection during the COVID-19 pandemic. Using the EasyDL platform, we achieved a 94.3% precision rate in mask detection with only 213 minutes of training on a 6006-image dataset. This approach proves more time-effective than traditional methods, making it suitable for real-time applications and large-scale monitoring. The combination of high accuracy and efficiency showcases the potential of automated machine learning in public health, enabling swift responses to health threats. To sum up, this research symbolizes a significant progress in terms of applying artificial intelligence to address the chanlleges of the public health. It could be estimated that these discoveries will stimulate further researches, and pave the way for developing more efficient and more effective mask detecting tools, which would be likely to contribute to the progress of public health management in the future.

Fabrication of SrTiO3 anchored rGO/g-C3N4 photocatalyst for the removal of mixed dye from wastewater: dual photocatalytic mechanism

A metal-free combination of rGO/g-C3N4-coupled SrTiO3 (SRN) ternary nanocomposite prepared via a wet impregnation method for UV–Vis light photocatalytic applications. Various physicochemical properties of the samples were investigated by several spectroscopic techniques including X-ray diffraction (XRD), FT-IR, Raman, field emission scanning electron microscopy with energy dispersive X-ray spectroscopy (FE-SEM-EDX), high-resolution transmission electron microscopy (HR-TEM), UV–Vis, photoluminescence (PL), X-ray photoelectron spectroscopy (XPS) and Brunauer–Emmett–Teller (BET) surface area analysis. The data suggest agglomerated SRT nanoparticles are dispersed and distributed throughout the surface of the rGO sheets and GCN nanostructures. The photocatalytic performance of the SRN towards combined mixed dye and its degradation activities were evaluated towards the most common industrial effluents, Rhodamine B (RhB) and Methylene blue (MB), under UV–Vis light illumination. The results revealed that the degradation efficiency of the SRN photocatalyst shows excellent performance compared with that of the binary composition and the pure SrTiO3 (SRT) sample. The reaction rate constant for RhB was estimated to be 0.0039 min−1 and for MB to be 0.0316 min−1, which are 3.26 (RhB) and 4.21 (MB) times faster than the pure SRT sample. The enhanced degradation efficiency was attained not only by interfacial formation but also by the speedy transportation of electrons across the heterojunction. After 5 runs of the photocatalytic recylic process, the SRN photocatalyst exhibited ultimate stability without structural changes, and no noticeable degradation was observed. The outcomes of the ternary SRN nanocomposite manifest a dual photocatalytic scheme, the photocatalytic enrichment could be caused by the Z-scheme charge transfer process between GCN, SRT, and rGO nanocomposite, which helps effectual charge separation and keeps a high redox potential. From the results, SRN sample provides insight into the integration of an effective and potential photocatalyst for wastewater treatment toward real-time environmental remediation applications.

Sputtered thin film deposited laser induced graphene based novel micro-supercapacitor device for energy storage application

Pioneering flexible micro-supercapacitors, designed for exceptional energy and power density, transcend conventional storage limitations. Interdigitated electrodes (IDEs) based on laser-induced graphene (LIG), augmented with metal-oxide modifiers, harness synergies with layered graphene to achieve superior capacitance. This study presents a novel one-step process for sputtered plasma deposition of HfO2, resulting in enhanced supercapacitance performance. Introducing LIG-HfO2 micro-supercapacitor (MSC) devices with varied oxygen flow rates further boosts supercapacitance performance by introducing oxygen functional groups. FESEM investigations demonstrate uniform coating of HfO2 on LIG fibers through sputtering. Specific capacitance measurements reveal 6.4 mF/cm2 at 5 mV/s and 4.5 mF/cm2 at a current density of 0.04 mA/cm2. The LIG-HfO2 devices exhibit outstanding supercapacitor performance, boasting at least a fourfold increase over pristine LIG. Moreover, stability testing indicates a high retention rate of 97% over 5000 cycles, ensuring practical real-time applications.

A novel scale-bridging method for MSMA linking continuum thermodynamics constitutive formulations to lumped system-level models

Abstract This work introduces a novel scale-bridging method between a continuum thermodynamics constitutive model and a lumped system-level model for magnetic shape memory alloys (MSMA). With this method, system models for real-time operations are generated based on virtual experiments using the constitutive model. The proposed method addresses the fact that, while constitutive models for MSMA typically only require small sets of parameters as input, their evaluation is still computationally expensive. System models for control engineering, however, require extensive experimental parameterization, while their evaluation is highly time-efficient. The proposed scale-bridging method has the potential to combine a small parameterization effort and a low computational cost of the real-time system model. Additionally, the constitutive model is utilized to investigate whether it can determine the individual behavior of MSMA samples. This is important since the inherent model parameters, while valid for ideal single crystals, deviate for non-ideal MSMA sample behavior.
To this end, the MSMA constitutive model, based on a global variational principle originally proposed by Kiefer et al.~is supplemented by various extensions, including a more robust algorithmic treatment. A parameter identification procedure is introduced to optimize the constitutive model parameters based on an outer hysteresis curve for a particular load case. By conducting virtual experiments with the constitutive model, data sets are generated to parameterize Preisach hysteresis models as numerical approximations of the constitutive models. The resulting hysteresis models are compared with physical experiments using an MSMA test bench for different load cases. It is shown that the proposed scale-bridging method successfully generates hysteresis models derived from constitutive models. While maintaining accuracy comparable to strictly phenomenological models across various load cases (as validated through physical MSMA test bench experiments), these models require significantly less parameterization effort than classical system models. This translates to faster model creation and broader applicability.

Evaluation of WRF-Chem air quality forecasts during the AEROMMA and STAQS 2023 field campaigns

ABSTRACT A real-time air quality forecasting system was developed using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to provide support for flight planning activities during the NOAA Atmospheric Emissions and Reactions Observed from Megacities to Marine Areas (AEROMMA) and NASA Synergistic TEMPO Air Quality Science (STAQS) 2023 field campaigns. The forecasting system operated on two separate domains centered on Chicago, IL, and New York City, NY, and provided 72-hour predictions of atmospheric composition, aerosols, and clouds. This study evaluates the Chicago-centered forecasting system’s 1-, 2-, and 3-day ozone (O3) forecast skill for Chiwaukee Prairie, WI, a rural area downwind of Chicago that often experiences high levels of O3 pollution. Comparisons to vertical O3 profiles collected by a Tropospheric Ozone Lidar Network (TOLNet) instrument revealed that forecast skill decreases as forecast lead time increases. When compared to surface measurements, the forecasting system tended to underestimate O3 concentrations on high O3 days and overestimate on low O3 days at Chiwaukee Prairie regardless of forecast lead time. Using July 25, 2023, as a case study, analyses show that the forecasts underestimated peak O3 levels at Chiwaukee Prairie during this regionwide bad air quality day. Wind speed and direction data indicates that this underestimation can partially be attributed to lake breeze simulation errors. Surface fine particulate matter (PM2.5) measurements, Geostationary Operational Environmental Satellite-16 (GOES-16) aerosol optical depth (AOD) data, and back trajectories from the NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model show that transported Canadian wildfire smoke impacted the Lake Michigan region on this day. Errors in the forecasted chemical composition and transport of the smoke plumes also contributed to underpredictions of O3 levels at Chiwaukee Prairie on July 25, 2023. The results of this work help identify improvements that can be made for future iterations of the WRF-Chem forecasting system. Implications: Air quality forecasting is an important tool that can be used to inform the public about upcoming high pollution days so that individuals may plan accordingly to limit their exposure to health-damaging air pollutants. Forecasting also helps scientists make decisions about where to make observations during air quality field campaigns. A variety of observational datasets were used to evaluate the accuracy of an air quality forecasting system that was developed for NOAA and NASA field campaigns that occurred in the summer of 2023. These evaluations inform areas of improvement for future development of this air quality forecasting system.

The 5G Era: A Comprehensive Review of Recent Advancements and Applications

The fifth generation is a promising and transformative leap in network capabilities, bringing out applications beyond traditional technologies. This review paper discusses the novel technologies shaping 5G, studying their impact and potential. We explore technological advancements like Network Function Virtualization (NFV) and Software- Defined Networking (SDN) for their role in creating flexible and efficient networks. Services like Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (uRLLC), and Massive Machine-Type Communication (mMTC) are also examined, along with their details, and role in shaping the benefits of 5G for various fields. This paper also investigates how 5G addresses the aspects like energy efficiency enhancements and network security enhancements brought about in 5G. We discuss the details of integration of Artificial Intelligence (AI) for network optimization, security, automation and user experience, along with its impact in various domains like healthcare, manufacturing, education and transportation. Further, the paper explores the incorporation of edge computing and its role in supporting real-time applications. Finally, we examine the growing importance of Non-Public Networks (NPNs) tailored for private use cases, another aspect that adds to the benefits and novelty of the fifth generation. This review provides a comprehensive overview of the key technological advancements driving the 5G revolution, highlighting their role in transforming communication infrastructures and other key use cases, supporting a new era of communication.

An automated qualitative analysis of real-time systems using Timed Petri net and SPIN

An automated qualitative analysis of real-time systems using Timed Petri net and SPIN

Exploring Ficus religiosa inflorescence powder as an eco-friendly and sustainable solution for the removal of crystal violet with a disposal solution.

This study investigates the potential of using Ficus religiosa inflorescence (peepal tree) as an efficient solution for removing crystal violet from simulated and industrial wastewater. Various analyses were conducted to understand the adsorbent's structure, including particle morphology, BET surface area, FTIR, and pHZPC. The adsorption process was studied under different physicochemical factors such as temperature, concentration, contact time, and pH. Results revealed rapid adsorption, with 94.15% removal efficiency within the first 15min at neutral pH. The highest observed adsorption capacity was 198.03mgg-1, following pseudo-second-order kinetics (R2 = 0.99), indicating chemisorption. The Langmuir model accurately described the adsorption pathway (R2 = 0.99), showing monolayer adsorption. Thermodynamic analysis indicated an exothermic, feasible, and spontaneous process with increased entropy. The adsorbent could be easily regenerated using a 1:1 MeOH/H2O mixture for up to three cycles, yielding up to 73.86%. Real-time application with industrial effluent containing crystal violet showed up to 44.70% adsorption. The experiments demonstrated reliability with evaluated standard deviations (0.017935-0.000577) and relative standard deviations (0.439-0.673%), confirming statistical reliability. In conclusion, it presents a sustainable and eco-friendly approach for removing crystal violet dye from diverse wastewater sources.

Artificial intelligence based diagnosis of sulcus: assesment of videostroboscopy via deep learning.

To develop a convolutional neural network (CNN)-based model for classifying videostroboscopic images of patients with sulcus, benign vocal fold (VF) lesions, and healthy VFs to improve clinicians' accuracy in diagnosis during videostroboscopies when evaluating sulcus. Videostroboscopies of 433 individuals who were diagnosed with sulcus (91), who were diagnosed with benign VF diseases (i.e., polyp, nodule, papilloma, cyst, or pseudocyst [311]), or who were healthy (33) were analyzed. After extracting 91,159 frames from videostroboscopies, a CNN-based model was created and tested. The healthy and sulcus groups underwent binary classification. In the second phase of the study, benign VF lesions were added to the training set, and multiclassification was executed across all groups. The proposed CNN-based model results were compared with five laryngology experts' assessments. In the binary classification phase, the CNN-based model achieved 98% accuracy, 98% recall, 97% precision, and a 97% F1 score for classifying sulcus and healthy VFs. During the multiclassification phase, when evaluated on a subset of frames encompassing all included groups, the CNN-based model demonstrated greater accuracy when compared with that of the five laryngologists (%76 versus 72%, 68%, 72%, 63%, and 72%). The utilization of a CNN-based model serves as a significant aid in the diagnosis of sulcus, a VF disease that presents notable challenges in the diagnostic process. Further research could be undertaken to assess the practicality of implementing this approach in real-time application in clinical practice.

Evaluation of the analytical and clinical performance of the Abbott Alinity m instrument for the quantification of HIV-1 RNA plasma viral load in a reference laboratory in Rio de Janeiro, Brazil.

We sought to evaluate the analytical performance of the Alinity m system (Abbott Molecular) and to compare the clinical performance of HIV-1 assays on the Alinity m and m2000 RealTime platforms (Abbott Molecular). The sensitivity, precision, and accuracy of the Alinity m instrument were determined using a panel of standard samples (n = 46). The carryover effect was assessed by analyzing HIV-negative clinical samples (n = 20). Clinical performance of the Alinity m and m2000 RealTime platforms was compared using surplus HIV-positive patient plasma samples (n = 39). The Alinity m HIV-1 assay demonstrated 100% sensitivity, a high precision (coefficient of variation (s/x̄) × 100 ≤1.5% [SD ≤ 0.05] logarithm to base 10 [log10] copies/mL), and partial accuracy over the quantification range. Analysis of clinical samples suggested that the Alinity m HIV-1 assay does not cause carryover effect and produced a mean bias of 0.209 log10 copies/mL (95% CI, 0.153-0.265) compared with the m2000 RealTime System. The Alinity m instrument's performance correlated to that of the m2000 RealTime platform and showed excellent sensitivity, precision, and accuracy, despite producing overquantification not clinically relevant for disease management. Furthermore, use of the Alinity m platform can reduce turnaround time.

Elaborating and performing optimization approaches for web-based 3D spatial analysis of 3D city models

ABSTRACT Three-dimensional (3D) geographic information systems (GIS) are vital for urban planning and architectural design, enabling detailed visualization and analysis of urban landscapes. However, the growing size of 3D city data and the transition to web environments cause performance challenges for real-time applications. This study addresses the need for optimizing 3D spatial analyses for the web using Quadtree data structures and multiprocessing tools. Optimization scenarios based on data size and hardware configurations are evaluated, and the best hosting plans for GIS applications on cloud services are also considered.

Impact of actuation and sensor measurement delays on stability of real-time hybrid aeroelastic simulation system

Impact of actuation and sensor measurement delays on stability of real-time hybrid aeroelastic simulation system

A Comparative Analysis of Machine Learning Models in News Categorization

The constant stream of news nowadays highlights the necessity for meticulous assessment to ensure that the information accurately reaches its intended audience with the least amount of delay least delay. Despite the flexibility and efficiency of Deep Learning (DL) models, their intricate training and substantial resource demands pose significant challenges for their deployment in real-time applications. In this regard, this study evaluates the performance of resource-efficient Machine Learning (ML) techniques – Multinomial Naive Bayes (MNB), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) – in categorizing news. Based on the results, all the evaluated models attain a commendable level of accuracy in news categorization. Notably, the SVM excels, achieving an accuracy rate of 98% and a mean squared error of 0.28. This performance exemplifies the robust effectiveness of classical ML models in the categorization of news, particularly when enhanced by a suitably tailored preprocessing pipeline.