Search Results (1,021)

Early detection is crucial for preventing heart disease. Advances in health technology, particularly wearable devices for automated heartbeat detection and machine learning, can enhance early diagnosis efforts. However, previous studies on heartbeat classification inference systems have primarily relied on batch processing, which introduces delays. To address this limitation, a real-time system utilizing stream processing with a distributed computing architecture is needed for continuous, immediate, and scalable data analysis. Real-time ECG inference is particularly crucial for immediate heartbeat classification, as human heartbeats occur with durations between 0.6 and 1 s, requiring inference times significantly below this threshold for effective real-time processing. This study implements a real-time heartbeat classification inference system using distributed stream processing with LSTM-512, LSTM-256, and FCN models, incorporating RR-interval, morphology, and wavelet features. The system is developed as a distributed web-based application using the Flask framework with distributed backend processing, integrating Polar H10 sensors via Bluetooth and Web Bluetooth API in JavaScript. The implementation consists of a frontend interface, distributed backend services, and coordinated inference processing. The frontend handles sensor pairing and manages real-time streaming for continuous ECG data transmission. The backend processes incoming ECG streams, performing preprocessing and model inference. Performance evaluations demonstrate that LSTM-based heartbeat classification can achieve real-time performance on distributed edge devices by carefully selecting features and models. Wavelet-based features with an LSTM-Sequential architecture deliver optimal results, achieving 99% accuracy with balanced precision-recall metrics and an inference time of 0.12 s—well below the 0.6–1 s heartbeat duration requirement. Resource analysis on Jetson Orin devices reveals that Wavelet-FCN models offer exceptional efficiency with 24.75% CPU usage, minimal GPU utilization (0.34%), and 293 MB memory consumption. The distributed architecture’s dynamic load balancing ensures resilience under varying workloads, enabling effective horizontal scaling. Full article

The increasing adoption of digital technologies, robotic systems, and IoT applications in sectors such as medicine, agriculture, and industry drives a surge in data generation and necessitates secure and efficient encryption. For resource-constrained systems, lightweight yet robust cryptographic algorithms are critical. This study addresses the security demands of IoRT systems by proposing an enhanced chaos-based encryption method. The approach integrates the lightweight structure of NIST-standardized Ascon-AEAD128 with the randomness of the Zaslavsky map. Ascon-AEAD128 is widely used on many hardware platforms; therefore, it must robustly resist both passive and active attacks. To overcome these challenges and enhance Ascon’s security, we integrate into Ascon the keys and nonces generated by the Zaslavsky chaotic map, which is deterministic, nonperiodic, and highly sensitive to initial conditions and parameter variations.This integration yields a chaos-based Ascon variant with a higher encryption security relative to the standard Ascon. In addition, we introduce exploratory variants that inject non-repeating chaotic values into the initialization vectors (IVs), the round constants (RCs), and the linear diffusion constants (LCs), while preserving the core permutation. Real-time tests are conducted using Raspberry Pi 3B devices and ROS 2–based IoRT robots. The algorithm’s performance is evaluated over 100 encryption runs on 12 grayscale/color images and variable-length text transmitted via MQTT. Statistical and differential analyses—including histogram, entropy, correlation, chi-square, NPCR, UACI, MSE, MAE, PSNR, and NIST SP 800-22 randomness tests—assess the encryption strength. The results indicate that the proposed method delivers consistent improvements in randomness and uniformity over standard Ascon-AEAD128, while remaining comparable to state-of-the-art chaotic encryption schemes across standard security metrics. These findings suggest that the algorithm is a promising option for resource-constrained IoRT applications. Full article

To maintain product reliability and stabilize performance, it is essential to prioritize the identification and resolution of latent defects. Advanced products such as high-precision electronic devices and semiconductors are susceptible to performance degradation over time due to environmental factors and electrical stress. However, conventional performance testing methods typically evaluate products based solely on predefined acceptable ranges, making it difficult to predict long-term degradation, even for products that pass initial testing. In particular, products exhibiting borderline values close to the threshold during initial inspections are at a higher risk of exceeding permissible limits as time progresses. Therefore, to ensure long-term product stability and quality, a novel approach is required that enables the early prediction of potential defects based on test data. In this context, the present study proposes a machine learning-based framework for predicting latent defects in products that are initially classified as normal. Specifically, we introduce the Sigma Deviation Count Labeling (SDCL) method, which utilizes a Gaussian distribution-based approach. This method involves preprocessing the dataset consisting of initially passed test samples by removing redundant features and handling missing values, thereby constructing a more robust input for defect prediction models. Subsequently, outlier counting and labeling are performed based on statistical thresholds defined by 2σ and 3σ, which represent potential anomalies outside the critical boundaries. This process enables the identification of statistically significant outliers, which are then used for training machine learning models. The experiments were conducted using two distinct datasets. Although both datasets share fundamental information such as time, user data, and temperature, they differ in the specific characteristics of the test parameters. By utilizing these two distinct test datasets, the proposed method aims to validate its general applicability as a Predictive Anomaly Testing (PAT) approach. Experimental results demonstrate that most models achieved high accuracy and geometric mean (GM) at the 3σ level, with maximum values of 1.0 for both metrics. Among the tested models, the Support Vector Machine (SVM) exhibited the most stable classification performance. Moreover, the consistency of results across different models further supports the robustness of the proposed method. These findings suggest that the SDCL-based PAT approach is not only stable but also highly adaptable across various datasets and testing environments. Ultimately, the proposed framework offers a promising solution for enhancing product quality and reliability by enabling the early detection and prevention of latent defects. Full article

Water scarcity is an escalating global challenge, driven by climate change and population growth. With only 2.5% of Earth’s freshwater readily accessible, there is an urgent need to explore sustainable alternatives. Textile-based fog collectors are advanced tools which have shown great potential and have gained remarkable attention across the world. This review critically evaluates emerging technologies for freshwater generation, including desalination (thermal and reverse osmosis (RO)), fog and dew harvesting, atmospheric water extraction, greywater reuse, and solar desalination systems, e.g., WaterSeer and Desolenator. Key performance metrics, e.g., water yield, energy input, and water collection efficiency, are summarized. For instance, textile-based fog harvesting devices can yield up to 103 mL/min/m², and modern desalination systems offer 40–60% water recovery. This work provides a comparative framework to guide future implementation of water-scarcity solutions, particularly in arid and semi-arid regions. Full article

Background: Optical coherence tomography angiography (OCTA) enables non-invasive, high-resolution visualization of the retinal microvasculature and is increasingly utilized in pediatric ophthalmology. However, its clinical application in children is limited by the absence of age-specific normative data. Methods: In this cross-sectional study, macular vessel density (VD) and foveal avascular zone (FAZ) area were assessed in 118 healthy Caucasian children aged 4 to 17 years. OCTA scans were obtained using the OCT Topcon Triton^® device with 3 × 3 mm and 6 × 6 mm macular cubes. Vascular metrics from the superficial (SCP) and deep capillary plexuses (DCP) were analyzed in relation to demographic, refractive, biometric, and structural OCT parameters. Correlation and multivariate linear regression analyses were performed to evaluate associations. Results: Age-stratified reference percentiles for macular VD and FAZ area in SCP and DCP are presented for 118 children. Key associations included: (1) Increased macular thickness correlated with higher VD in the fovea and inner ring (SCP and DCP, all p < 0.05); (2) Thicker maculas were associated with smaller FAZ areas (SCP: r = −0.72, DCP: r = −0.58, both p < 0.001); (3) Older age was linked to reduced VD in the inner macular ring and smaller FAZ area (SCP and DCP, all p < 0.001); and (4) longer axial length correlated with lower central VD (SCP: r = −0.27, DCP: r = −0.37, both p < 0.05). No significant sex-based differences were observed. Conclusions: This study provides normative OCTA data for macular VD and FAZ area in healthy Caucasian children and identifies key associations with ocular parameters. These findings support improved diagnostic accuracy and clinical decision-making in pediatric retinal evaluation. Full article

In complex electromagnetic environments, traditional static absorbers struggle to meet dynamic control requirements. Tunable absorbers based on metasurfaces have emerged as a research hotspot due to their ability to flexibly control electromagnetic wave properties. This paper provides a systematic review of research progress in tunable absorbers across the microwave, terahertz, and infrared bands, with a focus on analyzing the physical mechanisms, material systems, and performance characteristics of five dynamic control methods: electrical control, magnetic control, optical control, temperature control, and mechanical control. Electrical control achieves rapid response through materials such as graphene and varactor diodes; magnetic control utilizes ferrites and other materials for stable tuning; optical control relies on photosensitive materials for ultrafast switching; temperature control employs phase-change materials for large-range reversible regulation; and mechanical control expands tuning freedom through structural deformation. Research indicates that multi-band compatibility faces challenges due to differences in structural scale and physical mechanisms, necessitating the integration of emerging materials and synergistic control strategies. This paper summarizes the core performance metrics and typical applications of absorbers across various bands and outlines future development directions such as multi-field synergistic control and low-power design, providing theoretical references and technical pathways for the development of intelligent tunable absorber devices. Full article

Thorough investigation into Tesla valve (TV) design was conducted across a large design of experiments (DOE) consisting of four varying geometric parameters and six different Reynolds number regimes in order to develop an optimized pressure drop device utilizing machine learning (ML) methods. A non-standard TV design was geometrically parameterized, and an automation suite was created to cycle through numerous combinations of parameters. Data were collected from completed computational fluid dynamics (CFD) simulations. TV designs were tested in the restricted flow direction for overall differential pressure, and overall minimum pressure with consideration to the onset of cavitation. Qualitative observations were made on the effects of each geometric parameter on the overall valve performance, and particular parameters showed greater influence on the pressure drop compared to classically optimized parameters used in previous TV studies. The overall minimum pressure demonstrated required system pressure for a valve to be utilized such that onset to cavitation would not occur. Data were utilized to train an ML model, and an optimized geometry was selected for maximized pressure drop. Multiple optimization efforts were made to meet design pressure drop goals versus traditional diodicity metrics, and two geometries were selected to develop a final design tool for overall pressure drop component development. Future work includes experimental validation of the large dataset, as well as further validation of the design tool for use in industry. Full article

The structural parameters of Stewart platforms play a critical role in enhancing dynamic performance, improving motion accuracy, and enabling effective control strategies. However, practical applications face several key limitations, including the metric balancing for optimization, the limited singularity-free workspace, and low computational efficiency. To overcome those shortcomings, this work proposes a multi-objective optimal design of the structural parameters for Stewart platform based on Non-dominated Sorting Genetic Algorithm III (NSGA-III) and Kolmogorov–Arnold Network (KAN). Firstly, under the stroke constraints of the Stewart platform, this work focuses on optimizing the platform’s key structural parameters. This approach enables both the optimization of existing equipment and the design of new devices. Secondly, this work employs KAN to establish a model that characterizes the relationship between the structural parameters and diverse postures within the maximum singularity-free workspace. This approach not only enhances computational efficiency but also ensures high precision. Finally, this study proposes six performance metrics and utilizes NSGA-III to optimize the structural parameters, thereby achieving a trade-off among these diverse objectives. Simulation and experimental results demonstrate that KAN significantly outperforms the Multi-Layer Perceptron (MLP) in predicting workspace postures. Compared with MLP, KAN achieves higher prediction accuracy and lower error rates across both training and test datasets. When comparing NSGA-III with NSGA-II, the proposed approach demonstrates modest improvements in most performance metrics while preserving acceptable trade-offs between the optimization objectives. Full article

This paper presents the implementation of a real-time nonlinear state observer applied to an erbium-doped fiber laser system. The observer is designed to estimate population inversion, a state variable that cannot be measured directly due to the physical limitations of measurement devices. Taking advantage of the fact that the laser intensity can be measured in real time, an observer was developed to reconstruct the dynamics of population inversion from this measurable variable. To validate and strengthen the estimate obtained by the observer, a Recurrent Wavelet First-Order Neural Network (RWFONN) was implemented and trained to identify both state variables: the laser intensity and the population inversion. This network efficiently captures the system’s nonlinear dynamic properties and complements the observer’s performance. Two metrics were applied to evaluate the accuracy and reliability of the results: the Euclidean distance and the mean square error (MSE), both of which confirm the consistency between the estimated and expected values. The ultimate goal of this research is to develop a neural control architecture that combines the estimation capabilities of state observers with the generalization and modeling power of artificial neural networks. This hybrid approach opens up the possibility of developing more robust and adaptive control systems for highly dynamic, complex laser systems. Full article

With the rapid development of wearable devices and intelligent human–machine interaction technologies, the demand for high-precision pressure sensors has soared. Piezoresistive pressure sensors excel due to their simple structure, low cost, and high sensitivity, among which flexible piezoresistive pressure sensors based on porous polymers have become a research focus, thanks to their unique 3D porous structure and excellent performance. This review summarizes recent advances: it introduces key performance metrics and the piezoresistive sensing mechanism; outlines porous structure preparation methods (phase separation, 3D printing, electrospinning) with their principles, advantages, and limitations; examines conductive fillers (carbon-based, polymer, metal, MXene) with their properties and applications; and highlights flexible substrates (silicone, polyurethane, polyimide, natural polymers) in ensuring mechanical compliance and device integration. Studies show material innovation, structural optimization, and process improvement can significantly enhance sensor accuracy, stability, and durability, helping break traditional performance bottlenecks. Future prospects are broad in tactile sensing, biomedical monitoring, and human–machine interaction, providing references for related research and industrial development. Full article

Virtual reality (VR) technology has emerged as a promising alternative to conventional perimetry for assessing visual fields. However, the clinical validity of commercially available VR-based perimetry devices remains uncertain due to variability in hardware, software, and testing protocols. A systematic review was conducted following PRISMA guidelines to evaluate the validity of VR-based perimetry compared to the Humphrey Field Analyzer (HFA). Literature searches were performed across MEDLINE, Embase, Scopus, and Web of Science. Studies were included if they assessed commercially available VR-based visual field devices in comparison to HFA and reported visual field outcomes. Devices were categorized by regulatory status (FDA, CE, or uncertified), and results were synthesized narratively. Nineteen studies were included. Devices such as Heru, Olleyes VisuALL, and the Advanced Vision Analyzer showed promising agreement with HFA metrics, especially in moderate to advanced glaucoma. However, variability in performance was observed depending on disease severity, population type, and device specifications. Limited dynamic range and lack of eye tracking were common limitations in lower-complexity devices. Pediatric validation and performance in early-stage disease were often suboptimal. Several VR-based perimetry systems demonstrate clinically acceptable validity compared to HFA, particularly in certain patient subgroups. However, broader validation, protocol standardization, and regulatory approval are essential for widespread clinical adoption. These devices may support more accessible visual field testing through telemedicine and decentralized care. Full article

This project introduces an add-on device that monitors key data points essential for evaluating the daily performance of a photovoltaic (PV) array. It is designed for homeowners who are transitioning to solar energy for economic or environmental benefits. The goal is to enhance the operational understanding for users by offering a smart system capable of detecting inefficiencies, especially for users with limited technical knowledge of PV components. The device measures five parameters: light intensity, temperature, humidity, current, and voltage. These metrics have been successfully integrated and are fully operational. This paper details the electronic design, hardware prototype, and data collected to validate the system’s performance. Full article

The dye-sensitized solar cell (DSSC) is a promising candidate, offering an attractive substitute for conventional silicon-based photovoltaic technologies. The performance advantages of the DSSC have led to a surge in research activity reflected in the number of publications over the years. To deliver data-driven analysis of DSSC performance, machine learning models have been applied. As a first step, a literature-based database has been developed and after the data preprocesses, Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), xgboost (XGB), and Artificial Neural Network (ANN) algorithms were applied with stratified train-test splits. The performance of the models has been assessed via metrics, and the model interpretability relied on SHAP analysis. Based on the employed metrics and the confusion matrix, DT, RF, and KNN are the most accurate models for predicting DSSC efficiency on the developed dataset. Furthermore, it was revealed that synthesis temperature and the thickness of thin film were identified as the dominant drivers, followed by precursor and dye. Mid-tier contributors were morphological structure, electrolyte concentrations, and the absorption maximum. The results suggest that in optimizing the manufacturing process, targeted tuning of the synthesis temperature, the thickness of thin film, the precursor, and the dye are likely to improve the performance of the device. Therefore, experimental effort should concentrate on these factors. Full article

The acceleration of global population aging has driven a surge in demand for health monitoring among older adults. However, traditional mobility assessment methods mostly rely on invasive measurements or laboratory-grade equipment, making it difficult to achieve continuous monitoring in daily scenarios. This study investigated the correlation between dynamic gait characteristics and static body metrics to enhance the understanding of elderly mobility and overall health. A sensor-based framework was implemented, which utilizes the Short Physical Performance Battery (SPPB), combined with PoseNet (a vision-based sensor) for dynamic gait analysis, and the InBody bioelectrical impedance device for static body composition assessment. Key variables comprised the dynamic metric mean directional shift and static metrics, including skeletal muscle index (SMI), skeletal muscle mass (SMM), body fat percentage (PBF), visceral fat area (VFA), and intracellular water. Nineteen elderly participants aged 60–89 years underwent assessments; among them, 16 were males (84.21%), and 3 were females (15.79%), 50% were in the 80–89 age group, 95% did not live alone, and 90% were married. Dynamic gait data were analyzed for center displacement and horizontal directional shifts. A Pearson correlation analysis revealed that the mean directional shift positively correlated with SMI ($\rho =0.561$, $p<0.01$), SMM ($\rho =0.496$, $p<0.01$), and intracellular water ($\rho =0.497$, $p<0.01$), highlighting the role of muscle strength in movement adaptability. Conversely, negative correlations were found with PBF ($\rho =-0.256$) and VFA ($\rho =-0.342$, $p<0.05$), suggesting that greater fat mass impedes dynamic mobility. This multimodal integration of dynamic movement patterns and static physiological metrics may enhance health monitoring comprehensiveness, particularly for early sarcopenia risk detection. The findings demonstrate the framework’s potential, indicating mean directional shift as a valuable dynamic health indicator. Full article

Background: Mixed martial arts (MMA) requires athletes to generate repeated bursts of high-intensity effort with minimal recovery time. Despite the sport’s reliance on anaerobic power, there are minimal data assessing pre-competition physiological capacity in MMA fighters. This study aimed to evaluate anaerobic performance using the Wingate Anaerobic Test (WAnT) and Countermovement Jump (CMJ) in professional MMA athletes, and to examine relationships between performance metrics across weight classes. Methods: Twelve professional male MMA fighters (age 29.00 ± 4.80 years, weight 85.60 ± 13.90 kg) completed both CMJ and WAnT assessments using sensor-integrated devices (Just Jump mat and Wattbike Pro). CMJ height and WAnT variables (peak power, average power, and fatigue index) were measured. Pearson correlations were used to examine the relationships between CMJ and Wingate outputs. Independent t-tests compared performance between lighter (<83.9 kg) and heavier (≥83.9 kg) weight groups. Results: CMJ performance showed significant positive correlations with both average power (r = 0.71, p < 0.001) and peak power (r = 0.61, p = 0.004). Peak power was also positively correlated with fatigue index (r = 0.84, p < 0.001), suggesting greater fatigue in higher power-producing athletes. Finally, the heavier weight group of fighters produced significantly (p = 0.03) more peak power when compared to the lighter weight group. Conclusions: The findings support the use of CMJ and WAnT testing as practical tools for evaluating anaerobic performance in MMA athletes. These assessments can help guide individualized training strategies, particularly when accounting for weight group specific differences in power and fatigue dynamics. Full article