Search Results (4,301)

Biofuels represent a sustainable alternative that supports global energy development without compromising environmental balance. This work introduces a novel hardware–software platform for the experimental characterization of biomass solid yield during the slow pyrolysis process, integrating physical experimentation with advanced computational modeling. The hardware consists of a custom-designed pyrolizer equipped with temperature and weight sensors, a dedicated control unit, and a user-friendly interface. On the software side, a two-step kinetic model was implemented and coupled with three optimization algorithms, i.e., Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Nelder–Mead (N-M), to estimate the Arrhenius kinetic parameters governing biomass degradation. Slow pyrolysis experiments were performed on wheat straw (WS), pruning waste (PW), and biosolids (BS) at a heating rate of 20 °C/min within 250–500 °C, with a 120 min residence time favoring biochar production. The comparative analysis shows that the N-M method achieved the highest accuracy (100% fit in estimating solid yield), with a convergence time of 4.282 min, while GA converged faster (1.675 min), with a fit of 99.972%, and PSO had the slowest convergence time at 6.409 min and a fit of 99.943%. These results highlight both the versatility of the system and the potential of optimization techniques to provide accurate predictive models of biomass decomposition as a function of time and temperature. Overall, the main contributions of this work are the development of a low-cost, custom MATLAB-based experimental platform and the tailored implementation of optimization algorithms for kinetic parameter estimation across different biomasses, together providing a robust framework for biomass pyrolysis characterization. Full article

Radon exposure is the second leading cause of lung cancer worldwide, yet monitoring strategies remain limited, expensive, and unevenly applied. Recent advances in the Internet of Things (IoT) offer the potential to change radon surveillance through low-cost, real-time, distributed sensing networks. This review consolidates emerging research on IoT-based radon monitoring, drawing from both primary radon studies and analogous applications in environmental IoT. A search across six major databases and relevant grey literature yielded only five radon-specific IoT studies, underscoring how new this research field is rather than reflecting a shortcoming of the review. To enhance the analysis, we delve into sensor physics, embedded system design, wireless protocols, and calibration techniques, incorporating lessons from established IoT sectors like indoor air quality, industrial safety, and volcanic gas monitoring. This interdisciplinary approach reveals that many technical and logistical challenges, such as calibration drift, power autonomy, connectivity, and scalability, have been addressed in related fields and can be adapted for radon monitoring. By uniting pioneering efforts within the broader context of IoT-enabled environmental sensing, this review provides a reference point and a future roadmap. It outlines key research priorities, including large-scale validation, standardized calibration methods, AI-driven analytics integration, and equitable deployment strategies. Although radon-focused IoT research is still at an early stage, current progress suggests it could make continuous exposure assessment more reliable, affordable, and widely accessible with clear public health benefits. Full article

Wireless pipeline robots often suffer from localization drift and position loss due to electromagnetic attenuation and shielding in complex pipeline configurations, which hinders accurate pipeline reconstruction. This paper proposes a trajectory reconstruction method based on Geometric Constraint–Long Short-Term Memory (GC-LSTM). First, a motor control system based on Field-Oriented Control (FOC) was developed for the proposed pipeline robot; second, trajectory errors are mitigated by exploiting pipeline geometric characteristics; third, a Long Short-Term Memory (LSTM) network is used to predict and compensate the robot’s velocity when odometer slip occurs; finally, multi-sensor fusion is employed to obtain the reconstructed trajectory. In straight-pipe tests, the GC-LSTM method reduced the maximum deviation and mean absolute deviation by 69.79% and 72.53%, respectively, compared with the Back Propagation (BP) method, resulting in a maximum deviation of 0.0933 m and a mean absolute deviation of 0.0351 m. In bend-pipe tests, GC-LSTM reduced the maximum deviation and the mean absolute deviation by 60.48% and 69.91%, respectively, compared with BP, yielding a maximum deviation of 0.2519 m and a mean absolute deviation of 0.0850 m. The proposed method significantly improves localization accuracy for wireless pipeline robots and enables more precise reconstruction of pipeline environments, providing a practical reference for accurate localization in pipeline inspection applications. Full article

Lower limb functional degeneration has become prevalent, notably reducing the core strength that drives motor control. Squats are frequently used in lower limb training, improving overall muscle strength. However, performing continuously with improper techniques can lead to dynamic knee instability. It worsens with little to no motivation to perform these power training motions. Hence, it is crucial to have a gaming-based exercise tracking system to adaptively enhance the user experience without causing injury or falls. In this work, 28 healthy subjects performed exergame-based squat training, and dynamic kinematic features were recorded. The five features acquired from a depth camera-based inertial measurement unit (IMU) (1—Knee shakiness, 2—Knee distance, and 3—Squat depth) and an Anima forceplate sensor (4—Sway velocity and 5—Sway area) were assessed using a Spearman correlation coefficient-based feature selection method. An input vector that defines knee instability is used to train and test the Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) models for binary classification. The results showed that knee instability events can be successfully classified and achieved a high accuracy of 96% in both models with sets 1, 2, 3, 4, and 5 and 1, 2, and 3. The feature selection results indicate that the LSTM network with the proposed combination of input features from multimodal sensors can successfully perform real-time tracking of knee instability. Furthermore, the findings demonstrate that this multimodal approach yields improved classifier performance with enhanced accuracy compared to using features from a single modality during lower limb therapy. Full article

This study presents the development and evaluation of an automated ultraviolet-C irradiation system for maize seed treatment, emphasizing disinfection performance, environmental control, and vision-based monitoring. The system features dual 8-watt ultraviolet-C lamps, sensors for temperature and humidity, and an air extraction unit to regulate the microclimate of the chamber. Without air extraction, radiation stabilized within one minute, with internal temperatures increasing by 5.1 °C and humidity decreasing by 13.26% over 10 min. When activated, the extractor reduced heat build-up by 1.4 °C, minimized humidity fluctuations (4.6%), and removed odors, although it also attenuated the intensity of ultraviolet-C by up to 19.59%. A 10 min ultraviolet-C treatment significantly reduced the fungal infestation in maize seeds by 23.5–26.25% under both extraction conditions. Thermal imaging confirmed localized heating on seed surfaces, which stressed the importance of temperature regulation during exposure. Notable color changes ($\Delta E>2.3$) in treated seeds suggested radiation-induced pigment degradation. Ultraviolet-C intensity mapping revealed spatial non-uniformity, with measurements limited to a central axis, indicating the need for comprehensive spatial analysis. The integrated computer vision system successfully detected seed contours and color changes under high-contrast conditions, but underperformed under low-light or uneven illumination. These limitations highlight the need for improved image processing and consistent lighting to ensure accurate monitoring. Overall, the chamber shows strong potential as a non-chemical seed disinfection tool. Future research will focus on improving radiation uniformity, assessing effects on germination and plant growth, and advancing system calibration, safety mechanisms, and remote control capabilities. Full article

The phase shift (PS) between spontaneous slow oscillations of cerebral and systemic hemodynamics reliably reflects the state of cerebral autoregulation (CA). However, CA measurements are performed retrospectively after studying the signals from the analysis sensors. At the same time, CA-oriented therapy is becoming increasingly important with the receipt of data on the state of CA in real time, especially in intensive care units. We offer a hardware and software complex for transcranial Dopplerography, which uses a non-invasive method and allows for continuous measurement of cerebral blood flow to assess the rate of CA in real time. The hardware and software complex uses sensors to measure the PS between spontaneous slow oscillations of blood flow velocity (BFV) in the middle cerebral arteries (MCAs) and systemic arterial pressure (BP) in the Mayer wave range and performs wavelet analysis of sensor signals. An examination of 30 volunteers, with an average age of 28 ± 8 years, and 15 patients, with an average age of 57 ± 16 years, with various neurovascular pathologies confirms the feasibility of using the developed hardware and software complex for continuous monitoring of PS in real time to study the mechanisms of cerebral blood flow regulation. Full article

Motion capture (MoCap) data derived from wearable Inertial Measurement Units is essential to applications in sports science and healthcare robotics. However, a significant amount of the potential of this data is limited due to missing data derived from sensor limitations, network issues, and environmental interference. Such limitations can introduce bias, prevent the fusion of critical data streams, and ultimately compromise the integrity of human activity analysis. Despite the plethora of data imputation techniques available, there have been few systematic performance evaluations of these techniques explicitly for the time series data of IMU-derived MoCap data. We address this by evaluating the imputation performance across three distinct contexts: univariate time series, multivariate across players, and multivariate across kinematic angles. To address this limitation, we propose a systematic comparative analysis of imputation techniques, including statistical, machine learning, and deep learning techniques, in this paper. We also introduce the first publicly available MoCap dataset specifically for the purpose of benchmarking missing value imputation, with three missingness mechanisms: missing completely at random, block missingness, and a simulated value-dependent missingness pattern simulated at the signal transition points. Using data from 53 karate practitioners performing standardized movements, we artificially generated missing values to create controlled experimental conditions. We performed experiments across the 53 subjects with 39 kinematic variables, which showed that discriminating between univariate and multivariate imputation frameworks demonstrates that multivariate imputation frameworks surpassunivariate approaches when working with more complex missingness mechanisms. Specifically, multivariate approaches achieved up to a 50% error reduction (with the MAE improving from 10.8 ± 6.9 to 5.8 ± 5.5) compared to univariate methods for transition point missingness. Specialized time series deep learning models (i.e., SAITS, BRITS, GRU-D) demonstrated a superior performance with MAE values consistently below 8.0 for univariate contexts and below 3.2 for multivariate contexts across all missing data percentages, significantly surpassing traditional machine learning and statistical methods. Notable traditional methods such as Generative Adversarial Imputation Networks and Iterative Imputers exhibited a competitive performance but remained less stable than the specialized temporal models. This work offers an important baseline for future studies, in addition to recommendations for researchers looking to increase the accuracy and robustness of MoCap data analysis, as well as integrity and trustworthiness. Full article

Microcontroller units (MCUs) serve as the core components of embedded systems. In the era of smart IoT, embedded devices are increasingly deployed on mobile platforms, leading to a growing demand for low-power consumption. As a result, low-power technology for MCUs has become increasingly critical. This paper systematically reviews the development history and current technical challenges of MCU low-power technology. It then focuses on analyzing system-level low-power optimization pathways for integrating MCUs with artificial intelligence (AI) technology, including lightweight AI algorithm design, model pruning, AI acceleration hardware (NPU, GPU), and heterogeneous computing architectures. It further elaborates on how AI technology empowers MCUs to achieve comprehensive low power consumption from four dimensions: task scheduling, power management, inference engine optimization, and communication and data processing. Through practical application cases in multiple fields such as smart home, healthcare, industrial automation, and smart agriculture, it verifies the significant advantages of MCUs combined with AI in performance improvement and power consumption optimization. Finally, this paper focuses on the key challenges that still need to be addressed in the intelligent upgrade of future MCU low power consumption and proposes in-depth research directions in areas such as the balance between lightweight model accuracy and robustness, the consistency and stability of edge-side collaborative computing, and the reliability and power consumption control of the sensor-storage-computing integrated architecture, providing clear guidance and prospects for future research. Full article

Inertial measurement unit (IMU)-based gait biometrics have attracted increasing attention for unobtrusive identity recognition. While recent studies often fuse signals from multiple sensor positions and time–frequency features, the actual contribution of each sensor location and signal modality remains insufficiently explored. In this work, we present a comprehensive quantitative analysis of the role of different IMU placements and feature domains in gait-based identity recognition. IMU data were collected from three body positions (shank, waist, and wrist) and processed to extract both time-domain and frequency-domain features. An attention-gated fusion network was employed to weight each signal branch adaptively, enabling interpretable assessment of their discriminative power. Experimental results show that shank IMU dominates recognition accuracy, while waist and wrist sensors primarily provide auxiliary information. Similarly, the contribution of time-domain features to classification performance is the greatest, while frequency-domain features offer complementary robustness. These findings illustrate the importance of sensor and feature selection in designing efficient, scalable IMU-based identity recognition systems for wearable applications. Full article

This study aimed to develop a non-contact, marker-less machine learning model to estimate forearm pronation–supination angles using 2D hand landmarks derived from MediaPipe Hands, with inertial measurement unit sensor angles used as reference values. Twenty healthy adults were recorded under two camera conditions: medial (in-camera) and lateral (out-camera) viewpoints. Five regression models were trained and evaluated: Linear Regression, ElasticNet, Support Vector machine (SVM), Random Forest, and Light Gradient Boosting Machine (LightGBM). Among them, LightGBM achieved the highest accuracy, with a mean absolute error of 5.61° in the in-camera setting and 4.65° in the out-camera setting. The corresponding R² values were 0.973 and 0.976, respectively. The SHAP analysis identified geometric variations in the palmar triangle as the primary contributors, whereas elbow joint landmarks had a limited effect on model predictions. These results suggest that forearm rotational angles can be reliably estimated from 2D images, with an accuracy comparable to that of conventional goniometers. This technique offers a promising alternative for functional evaluation in clinical settings without requiring physical contact or markers and may facilitate real-time assessment in remote rehabilitation or outpatient care. Full article

Aimed at addressing the increasingly serious problem of adulteration in Xihu Longjing, a catechin-targeted nano-enhanced visual and fluorescent dual-mode sensor array was constructed by nano porphyrins and quantum dots (QDs) for the precise analysis of Xihu Longjing from adjacent origins. This sensor array realizes the quantitative analysis of catechin enantiomers in Xihu Longjing through the selective combination of sensing units. It can accurately identify adjacent Xihu Longjing teas with different grades and storage times and can precisely detect samples with a low proportion of adulteration. At the same time, the flavor quality and antioxidant performance of Xihu Longjing tea can also be quantitatively evaluated. The dual-mode sensor array design proposed in this study provides a new idea for detecting minor differences in food authenticity and has significant application value for quality control in the tea industry. Full article

Vibration monitoring plays a crucial role in assessing the condition and operational safety of electric drive systems. In many industrial applications, scalar control is widely used due to its simplicity and reliability, yet its influence on vibration transmission within interconnected machines remains insufficiently explored. This study addresses the problem of understanding how mechanical vibrations are transmitted between a scalar-controlled induction motor coupled with an AC generator. A comparative experimental investigation was conducted using two different configurations of drive units, incorporating either an induction or a synchronous generator. Vibrations were measured at various operating speeds and analysed using different sensor types to ensure repeatability and reliability of the results. The findings have revealed distinct patterns of vibration transmission between the motor and generator, highlighting the importance of drive system configuration and measurement methodology. A novel approach to data presentation is proposed by normalising vibration levels between machines, offering a clearer interpretation of vibration amplification or damping effects. The results contribute to the development of diagnostic techniques and the optimisation of scalar-controlled drive designs. Full article

This study aims to develop and implement a robust monitoring system to continuously assess water quality parameters, such as total dissolved solids (TDS), pH, and turbidity. This system enhances the quality control process for water refilling stations by adopting the Internet of Things and sensors. Sensors are integrated with a central monitoring unit to provide real-time water quality data. The station enhances water quality management practices, customer trust, and compliance with regulatory standards. Prototype integrated sensors were also developed to replace TDS sensors. The sensor-based monitoring system demonstrated the feasibility and benefits in ensuring a safe and reliable water supply. Full article

Traditional road asset management relies on periodic, often inefficient, inspections. Digital Twins offer a paradigm shift towards proactive, data-driven maintenance by creating a real-time virtual replica of physical infrastructure. This paper proposes a comprehensive, formalized framework for a highway Digital Twin, structured into three integrated components: a Physical Space, which defines key performance indicators through mathematical state vectors; a Data Interconnection layer for real-time data processing; and a Virtual Space equipped with hybrid models. We provide a formal definition of these state vectors and a dynamic synchronization mechanism between the physical and virtual spaces. In this study, we focused on pavement condition assessment by using a data-driven component using accessible technology. This study show the synergy between the Digital Twin and deep learning, specifically by integrating advanced analytical models within the Virtual Space for intelligent pavement condition assessment. To validate this approach, a case study was conducted to classify road surface anomalies using low-cost Inertial Measurement Unit (IMU) data. We evaluated several machine learning classifiers and introduced a novel parallel Gated Recurrent Unit network. The results demonstrate that our proposed architecture achieved superior performance, with an accuracy of 89.5% and an F1-score of 0.875, significantly outperforming traditional methods. The findings validate the viability of the proposed Digital Twin framework and highlight its potential to achieve high-precision pavement monitoring using low-cost sensor data, a critical step towards intelligent road infrastructure management. Full article

Background: This study used inertial measurement unit (IMU)-based posture angle estimates to define proxy risk labels and investigated whether these labels can be predicted from demographic, anthropometric, and lifestyle variables through machine learning analysis. Methods: Thirty healthy individuals aged 18–25 years were included. Demographic and anthropometric data and information on daily living activities were collected. The IMU sensors were placed at vertebral levels C1, C7, T5, T12, and L5. Participants were instructed to stand in an upright posture, followed by a relaxed daily posture. Anatomic postural changes between these positions were analyzed. Cervical lordosis, thoracic kyphosis, lumbar lordosis, and scoliosis risks were predicted using machine learning algorithms, including Random Forest (RF) and Artificial Neural Networks (ANN). Results: Incorrect postures during desk work and phone use were associated with an increased likelihood of posture-related deviations, such as cervical lordosis, thoracic kyphosis, and lumbar lordosis. Conversely, daily physical activity reduced these deviations. Using LOSO and stratified cross-validation with imbalance handling, balanced accuracies ranged between 0.55 and 0.82 across targets, with majority-class baselines between 0.53 and 0.87. For cervical lordosis risk, RF achieved a 0.82 balanced accuracy (95% CI: 0.74–0.97), while other categories showed a moderate but consistent performance. AUPRC values exceeded baseline levels across all models. Conclusions: IMU-based posture angle estimates can be used to identify posture-related risk categories. In this study, ML models have demonstrated predictive relationships with demographic, anthropometric, and lifestyle variables. These findings provide exploratory evidence based on IMU-derived proxy labels in a small cohort of healthy young adults. They represent exploratory indicators of postural deviation rather than clinical outcomes and may motivate future studies on preventive strategies. Importantly, the results remain underpowered relative to the a priori power targets and should be interpreted qualitatively. Full article