Search Results (627)

This paper proposes an optical three-dimensional (3D) point cloud acquisition and sketching system, which is not limited by the measurement size, unlike traditional 3D object measurement techniques. The system employs an optical displacement sensor for surface displacement scanning and a six-axis inertial sensor (accelerometer and gyroscope) for spatial attitude perception. A microprocessor control unit (MCU) is responsible for acquiring, merging, and calculating data from the sensors, converting it into 3D point clouds. Butterworth filtering and Mahoney complementary filtering are used for sensor signal preprocessing and calculation, respectively. Furthermore, a human–machine interface is designed to visualize the point cloud and display the scanning path and measurement trajectory in real time. Compared to existing works in the literature, this system has a simpler hardware architecture, more efficient algorithms, and better operation, inspection, and observation features. The experimental results show that the maximum measurement error on 2D planes is 4.7% with a root mean square (RMS) error of 2.1%, corresponding to the reference length of 10.3 cm. For 3D objects, the maximum measurement error is 5.3% with the RMS error of 2.4%, corresponding to the reference length of 9.3 cm. Finally, it was verified that this system can also be applied to large-sized 3D objects for outlines. Full article

At-rest PPG signals have been explored for detecting atrial fibrillation (AF), yet current signal-processing techniques do not achieve perfect accuracy even under low-motion artifact (MA) conditions. This study evaluates the effectiveness of a single-degree-of-freedom time–frequency (SDOF-TF) method in analyzing at-rest PPG signals for AF detection. The method leverages the influence of MA on the instant parameters of each harmonic, which is identified using an SDOF model in which the tissue–contact–sensor (TCS) stack is treated as an SDOF system. In this model, MA induces baseline drift and time-varying system parameters. The SDOF-TF method enables the quantification and removal of MA and noise, allowing for the accurate extraction of the arterial pulse waveform, heart rate (HR), heart rate variability (HRV), respiration rate (RR), and respiration modulation (RM). Using data from the MIMIC PERform AF dataset, the method achieved 100% accuracy in distinguishing AF from non-AF cases based on three features: (1) RM, (2) HRV derived from instant frequency and instant initial phase, and (3) standard deviation of HR across harmonics. Compared with non-AF, the RM for each harmonic was increased by AF. RM exhibited an increasing trend with harmonic order in non-AF subjects, whereas this trend was diminished in AF subjects. Full article

The increasing interconnection of inverter-based resources (IBRs) with low short-circuit current has weakened grid strength, making phase-locked loops (PLLs) susceptible to instability due to accumulated phase-angle error under current limiting. This study defines such instability as IBR instability induced by reduced grid robustness and proposes a root-mean-square (RMS) model-based screening method. After fault clearance, the residual q-axis voltage observed by the PLL is treated as a disturbance signal and, using the PLL synchronization equations, is analyzed with a standard second-order formulation. The maximum phase angle at which synchronization fails is defined as ${\theta }_{peak}$, and the corresponding q-axis voltage is defined as $V_{q,crit}$. This value is then mapped to a screening metric $P_{peak}$ suitable for RMS-domain assessment. The proposed methodology is applied to the IEEE 39-bus test system: the stability boundary and $P_{peak}$ are obtained in Power System Simulator for Engineering (PSSE), and the results are validated through electromagnetic transient (EMT) simulations in PSCAD. The findings demonstrate that the RMS-based screening can effectively identify operating conditions that are prone to PLL instability in weak grids, providing a practical tool for planning and operation with high IBR penetration. This screening method supports power system planning for high-penetration inverter-based resources by identifying weak-grid locations that require EMT studies to ensure secure operation after grid faults. Full article

Small wind turbines operating at low heights frequently experience rapidly fluctuating and highly turbulent wind conditions that challenge conventional reactive pitch-control strategies. Under these non-stationary regimes, sudden gusts produce overspeed events that increase mechanical stress, reduce energy capture, and compromise operational safety. Addressing this limitation requires a control scheme capable of anticipating aerodynamic disturbances rather than responding after they occur. This work proposes a hybrid anticipatory pitch-control approach that integrates a conventional PI regulator with a data-driven rotor-speed prediction model. The main novelty is that short-term rotor-speed forecasting is embedded into a standard PI loop to provide anticipatory action without requiring additional sensing infrastructure or changing the baseline control structure. Using six years of real wind and turbine-operation data, an optimized Random Forest model is trained to forecast rotor speed 20 s ahead based on a 60 s historical window, achieving a prediction accuracy of RMSE = 0.34 rpm and R² = 0.73 on unseen test data. The predicted uses a sliding-window representation of recent wind–rotor dynamics to estimate the rotor speed at a fixed horizon (t + Δt), and the predicted signal is used as the feedback variable in the PI loop. The method is validated through a high-fidelity MATLAB/Simulink model of 14 kW small horizontal-axis wind turbine, evaluated under four wind scenarios, including two previously unseen conditions characterized by steep gust gradients and quasi-stationary high winds. The simulation results show a reduction in overspeed peaks by up to 35–45%, a decrease in the integral absolute error (IAE) of rotor speed by approximately 30%, and a reduction in pitch-actuator RMS activity of about 25% compared with the conventional PI controller. These findings demonstrate that short-term AI-based rotor-speed prediction can significantly enhance safety, dynamic stability, and control performance in small wind turbines exposed to highly variable atmospheric conditions. Full article

Objectives: Shoulder joint strength and muscle activation during overhead reaching are critical for ergonomic task design, rehabilitation, and exoskeleton support. The objective of this study was to characterize maximum shoulder torque and flexor muscle activation profiles across functional elevation angles in healthy adult males. Methods: A total of 14 healthy male participants performed maximum voluntary isometric contractions at eight arm elevation angles (90–160°, sagittal plane, and standing). Shoulder torque was measured using a calibrated force sensor and normalized to each participant’s overall maximum. Electromyography (EMG) was recorded from the anterior deltoid, medial deltoid, biceps brachii, and clavicular pectoralis major; EMG for the medial deltoid, biceps brachii, and pectoralis major was normalized to muscle-specific isometric MVCs, whereas the anterior deltoid was normalized to the peak value at 90° during the main task. All EMG signals were smoothed using a 0.5 s RMS-based moving average window. Linear regression was used to analyze the torque–angle relationship, and linear mixed-effects models were used to test EMG differences across angles. Summary statistics included mean ± SD, coefficient of variation, R², p-values (significance threshold: p < 0.05), Cohen’s d, and 95% confidence intervals where appropriate. Results: Maximum torque declined with elevation angle (y = −0.6317x + 157.21; R² = 0.99), from 77.2 Nm at 90° to 43.2 Nm at 160°, with normalized values from 99.6% to 55.3%. Medial deltoid activation increased significantly with elevation (p < 0.001, from 87.5 ± 19.9% at 90° to 109.4 ± 25.6% at 150°), while pectoralis major declined sharply (p < 0.001, from 68.9 ± 24.2% at 90° to 19.8 ± 5.6% at 160°). Anterior deltoid and biceps brachii activations were high and showed no systematic change with angle (p = 0.37 and 0.81, respectively), remaining within approximately 95–102% and 70–85% of their reference levels across 90–160°. Normalization reduced inter-participant variability, clarifying muscle-specific trends. Conclusions: This study provides preliminary biomechanical reference values for shoulder torque and muscle activation across elevation angles in healthy males under isometric standing conditions, confirming an inverse torque–angle relationship and distinct muscle activation strategies at higher positions. These findings may inform ergonomic assessment and exoskeleton design, while recognizing that generalization to dynamic tasks and other populations requires caution. Full article

This work presents an experimental evaluation of a high-speed analog-to-digital conversion method based on passive reference waveform crossings combined with time-to-digital converter (TDC) time-tagging. Unlike conventional level-crossing event-driven analog-to-digital converters (ADCs) that require dynamically updated digital-to-analog converters (DACs), the proposed architecture compares the input waveform against a broadband periodic sampling function without active threshold control. Crossing instants are detected by a high-speed comparator and converted into rising and falling edge timestamps using a multi-channel TDC. A commercial ScioSense GPX2-based time-tagger with 30 ps single-shot precision was used for validation. A range of test signals—including 5 MHz sine, sawtooth, damped sine, and frequency-modulated chirp waveforms—were acquired using triangular, sinusoidal, and sawtooth sampling functions. Stroboscopic sampling was demonstrated using reference frequencies lower than the signal of interest, enabling effective undersampling of periodic radio frequency (RF) waveforms. The method achieved effective bandwidths approaching 100 MHz, with amplitude reconstruction errors of 0.05–0.30 RMS for sinusoidal signals and 0.15–0.40 RMS for sawtooth signals. Timing jitter showed strong dependence on the relative slope between the acquired waveform and sampling function: steep regions produced jitter near 5 ns, while shallow regions exhibited jitter up to 20 ns. The study has several limitations, including the bandwidth and dead-time constraints of the commercial TDC, the finite slew rate and noise of the comparator front-end, and the limited frequency range of the generated sampling functions. These factors influence the achievable timing precision and reconstruction accuracy, especially in low-gradient signal regions. Overall, the passive waveform-crossing method demonstrates strong potential for wideband, sparse, and rapidly varying signals, with natural scalability to multi-channel systems. Potential application domains include RF acquisition, ultra-wideband (UWB) radar, integrated sensing and communication (ISAC) systems, high-speed instrumentation, and wideband timed antenna arrays. Full article

Branching traits play a critical role in shaping the tree structure of fruit crops and directly influence both yield and fruit quality. Effective and well-managed branching is crucial for maximizing productivity. However, loquat trees typically exhibit weak branching ability, characterized by fewer and longer bearing shoots, along with terminal flower buds, which collectively result in lower yields per unit area. Despite their significance, research on branching characteristics in loquat remains limited. To clarify the factors influencing branching and to provide a rational and effective direction for improving the inherently weak branching performance of current loquat cultivars, we selected the loquat varieties ‘Dawuxing’ and ‘Chunhua 1’, which exhibit significant differences in leaf and branch growth. Compared to ‘Dawuxing’, ‘Chunhua 1’ has longer branches, wider stem and leaf angles, fewer lateral branches, and a looser leaf cell structure. Transcriptome analysis of terminal buds at different developmental stages revealed that differentially expressed genes in the terminal buds of central branches from the spring and summer shoots of the two cultivars were enriched in the plant hormone signal transduction pathway. Hormone-targeted metabolomics identified significant differences in the levels of abscisic acid, auxins, cytokinins, gibberellins, jasmonic acid, and strigolactones in the terminal buds of both cultivars. Through integrated analysis, two candidate genes were identified as potential regulators of branching differences between the two cultivars: EVM0025028 (EjSAPK1), SnRK2 gene a core component of the abscisic acid signaling pathway, and EVM0040331 (EjRMS3), a D14 gene involved in encoding a strigolactone receptor. These findings provide valuable genetic resources for future research on branching regulation in Eriobotrya species and offer a theoretical foundation for enhancing branching management in loquat cultivation. Full article

The Global Navigation Satellite System (GNSS)/Inertial Measurement Unit (IMU) Loosely Coupled (LC) integration framework has been widely adopted due to its simple structure, but it relies on complete GNSS position and velocity solutions, and the rapid accumulation of IMU errors can easily lead to navigation failure when fewer than four satellites are visible. In this paper, GNSS Doppler observations are fused with IMU attitude information within an LC framework. An inter-satellite differential Doppler model is introduced, and the velocity obtained from the differential Doppler solution is transformed into the navigation frame using the IMU-derived attitude, enabling three-dimensional velocity estimation in the navigation frame even when only two satellites are available. Analysis of real vehicle data collected by the GREAT team at Wuhan University shows that the Signal-to-Noise Ratio (SNR) and the geometric relationship between the Satellite Difference Vector (SDV) and the Receiver Motion Direction (RMD) are the dominant factors affecting velocity accuracy. A multi-factor threshold screening strategy further indicates that when $\mathrm{S}\mathrm{NR}>$ 40 and $\mid \mathrm{SDV}·\mathrm{RMD}\mid >0.2$, the Root Mean Square (RMS) of the velocity error is approximately 0.3 m/s and the data retention rate exceeds 44%, achieving a good balance between accuracy and availability. The results indicate that, while maintaining a simple system structure, the proposed Doppler–IMU fusion method can significantly enhance velocity robustness and positioning continuity within an LC architecture under weak GNSS conditions (when more than two satellites are visible but standalone GNSS positioning is still unavailable), and is suitable for constructing low-cost, highly reliable integrated navigation systems. Full article

Collagen VI-related myopathies (COL6-RM) encompass a spectrum of disorders characterized by muscle weakness, joint contractures, and connective tissue abnormalities resulting from mutations in the collagen VI genes. While muscle pathology has been extensively studied, tendon dysfunction has emerged as a critical yet underexplored contributor to disease severity, particularly in the development of joint contractures. Tendons from patients and animal models show disrupted collagen fibrillogenesis, altered extracellular matrix (ECM) composition, and impaired cellular mechanotransduction. Various defects in ECM remodeling pathways further exacerbate tendon pathology. Importantly, current clinical management remains limited to orthopedic interventions with modest outcomes, and targeted pharmacological strategies or gene-editing therapies are not yet available for clinical application. Therefore, understanding the basic pathogenic mechanisms underlying tendon dysfunction is essential for identifying novel therapeutic targets. This review provides a comprehensive synthesis of current understanding and recent advances concerning the role of mutated collagen VI in cellular and molecular mechanisms underlying tendon dysfunction. Emphasis is placed on the role of mutated collagen VI in the modulation of key signaling pathways related to mechanotransduction and primary cilium function in COL6-RM. By discussing these multifaceted contributions to disease pathogenesis, this review outlines future research directions in the field and highlights potential pathways for targeted therapeutic interventions. Full article

Purpose: This study aims to investigate whether two stress keratins, KRT6A or KRT17, label self-renewing stem cells (SCs) in adult mouse Meibomian gland (MG), the palpebral conjunctiva (PC) homeostasis, and to explore the mechanisms regulating their expression. Methods: KRT6A and KRT17 expression in adult mouse MG and PC were examined by single-nucleus RNA sequencing and immunofluorescence (IF). Lineage-tracing experiments were performed using Krt6a-Cre^ERT2 and Krt17-Cre^ERT2 mice carrying the Rosa26R^nTnG or Rosa26R^mTmG reporter. As Hedgehog (Hh) signaling, the histone deacetylase HDAC3, and the transcription factor KLF4 regulate KRT6A and KRT17 in other contexts, IF was conducted to assess the in vivo effects of overexpression of the Hh pathway activator GLI2ΔN, and inducible epithelial deletion of Hdac3 or Klf4 on KRT6A and KRT17 expression in the MG and PC. Results: KRT6A and KRT17 are primarily expressed in the MG central duct and ductules. KRT6A also shows robust expression in PC. Lineage tracing indicated that Krt17 labels self-renewing SCs in the MG, whereas Krt6a labels SCs in the PC. GLI2ΔN overexpression induced ectopic KRT17 expression in MG acini and PC but did not affect KRT6A expression in either MG or PC. Hdac3 deficiency caused expanded expression of KRT6A and KRT17 in MG acini, ectopic KRT17 expression in PC, and increased KRT6A expression in PC basal layer. Klf4 deletion resulted in ectopic KRT17 expression in PC but did not influence KRT6A expression in MG or PC. Conclusions: Krt6a- and Krt17-expressing cells contribute to adult PC and MG homeostasis, respectively. KRT17 expression is enhanced by GLI2ΔN, and suppressed by HDAC3 and KLF4, whereas KRT6A expression is controlled only by HDAC3. These findings provide important biological insight into tissue-specific maintenance mechanisms and may inform future therapeutic strategies for regenerating MG and PC tissues affected by SC exhaustion or dysregulation. Full article

The Overhauser magnetometer (OVM) is an electron resonance-enhanced nuclear magnetic resonance (NMR) magnetometer, which significantly enhances the Larmor signal, hence the signal-to-noise ratio (SNR) and sensitivity compared to traditional proton magnetometers (PM). In this paper, we intended to improve SNR and sensitivity only by reducing system noise. For this purpose, an equivalent circuit model of noise is established, and the contributions of sensor and transmission characteristics of the circuit are calculated quantitatively. By sensor parameter optimization, matching resistance, and preamplifier selection to reduce the noise of the system, the root mean square (rms) of system noise is 26.7 mV, which is consistent with the theoretical 23.9 mV. By reducing the noise of the system, the SNR of the Larmor signal can reach 39 dB. The measured results in the natural environment show that the sensitivity of the OVM is 0.0079 nT at 3 s cycling time. Full article

This study investigates vibration signals generated during end milling of thin-walled EN AW-7075 aluminum alloy components using a set of 24 tools with distinct cutting edge microgeometries. Five characteristic parameters describing the dynamic response of the process, including both energy-related and statistical indicators, were extracted and analyzed. The results clearly demonstrate the critical influence of tool microgeometry on process dynamics. In particular, the introduction of an additional zero-clearance flank land at the cutting edge proved decisive in suppressing vibrations. For the most favorable geometries, the root mean square (RMS) value of vibration was reduced by more than 50%, while the spectral power density (PSD) decreased by up to 70–75% compared with the least favorable configurations. Simultaneously, both time- and frequency-domain responses exhibited complex and irregular patterns, highlighting the limitations of intuitive interpretation and the need for multi-parameter evaluation. To enable a synthetic comparison of tools, the Vibration Severity Index (VSI), which integrates RMS and kurtosis into a single composite metric, was introduced. VSI-based ranking allowed the clear identification of the most dynamically stable geometry. For the selected tool, additional analysis was conducted to evaluate the influence of cutting parameters, namely feed per tooth and radial depth of cut. The results showed that the most favorable dynamic behavior was achieved at a feed of 0.08 mm/tooth and a radial depth of cut of 1.0 mm, whereas boundary conditions resulted in higher kurtosis and a more impulsive signal structure. Overall, the findings confirm that properly engineered cutting-edge microgeometry, especially the formation of additional zero-clearance flank land significantly enhances the dynamic of thin-wall milling, demonstrating its potential as an effective strategy for vibration suppression and process optimization in precision machining of lightweight structural materials. Full article

Precise, seamless, and high-rate navigation remains a major challenge, particularly when relying on low-cost sensors. With the decreasing cost of cameras, Inertial Measurement Units (IMUs), and Global Navigation Satellite System (GNSS) receivers, tightly coupled fusion frameworks, such as GVINS, have gained considerable attention. GVINS is an optimization-based factor-graph framework that integrates visual and inertial measurements with single-frequency GNSS-code pseudorange observations to provide robust and drift-free navigation. This study aimed to evaluate the potential of applying GVINS to low-cost, low-power, and single-frequency GNSS receivers, particularly those embedded in smartphones, by integrating 1 Hz GNSS measurements collected in three challenging urban scenarios into the GVINS framework to produce seamless 10 Hz positioning estimates. The experiments were conducted using an Xsens MTi-1 IMU and global-shutter (GS) cameras, as well as a Samsung A51 smartphone and a u-blox ZED-F9P GNSS receiver. GVINS was modified to process 1 Hz GNSS measurements. Differential corrections from a nearby GNSS reference station were also incorporated to assess their impact on optimization-based filters, such as GVINS. The performance of GVINS and Differential GVINS (D-GVINS) solutions using smartphone measurements was compared against standard point positioning (SPP) and differential GPS (DGPS) results obtained from the same smartphone GNSS receiver, as well as the GVINS solution derived from u-blox ZED-F9P measurements sampled at 1 Hz. Experimental results show that GVINS effectively operates with smartphone GNSS measurements, reducing 3D RMS errors by 80.4%, 64.9%, and 83.8% for the sports field, campus-walking, and campus-driving datasets, respectively, when differential corrections are applied relative to the SPP solution. These results highlight the potential of smartphone GNSS receivers within the GVINS framework: Even though they observe fewer constellations, lower signal quality, and a lower number of satellites, they can still achieve a performance comparable to that of a relatively higher-end dual-frequency GNSS receiver, the u-blox ZED-F9P. Further studies will focus on adapting the GVINS algorithm to run directly on smartphones to utilize all the available measurements, including the camera, IMU, barometer, magnetometer, and additional ranging sensors. Full article

Powered ankle exoskeletons have become efficient ability-enhancing and rehabilitation tools that support human body movements. Traditionally, the control schemes for ankle exoskeletons were implemented relying on precise physical and kinematic models. However, this approach resulted in poor coordination of human–machine coupled motion and an increase in the wearer’s energy consumption. To solve the cooperative control issue between the wearer and the ankle exoskeleton, this work introduces an adaptive impedance control method for the ankle exoskeleton that is based on the surface electromyography (sEMG) of the calf muscles. The proposed method achieves cooperative control by leveraging an experience-based fuzzy rule interpolation (E-FRI) approach to dynamically adjust the impedance model parameters. This adaptive mechanism is driven by the wearer’s calf sEMG signals, which capture the wearer’s movement state. The adaptive impedance model then computes the desired torque for the ankle exoskeleton. To validate and evaluate the system, the control method was implemented on a simplified ankle exoskeleton. Experimental validation with five healthy participants (age 19 ± 1.35 years) demonstrated significant improvements over conventional fixed-impedance approaches: mean RMS reductions of 19.7% in gastrocnemius activation and 21.4% in soleus activation during treadmill walking. This study establish a new paradigm for responsive exoskeleton control through symbiotic integration of neuromuscular signals and adaptive fuzzy inference, offering critical implications for rehabilitation robotics and assistive mobility technologies. Full article

Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) integrated navigation is one of the key methods for achieving precise positioning in complex urban environments. However, in some scenarios such as urban canyons, overpasses, and foliage occlusion, GNSS signals are frequently attenuated or interrupted, leading to degraded positioning accuracy when relying solely on INSs. To address this limitation, this study developed an improved GNSS/INS-integrated navigation algorithm based on a hybrid framework that combines a Robust Adaptive Kalman Filter (RAKF) with a Radial Basis Function (RBF) neural network. The RAKF allows a multi-criterion optimization strategy to be created to adaptively adjust the measurement noise covariance matrix according to GNSS data quality indicators such as PDOP, the number of satellites, and signal quality factors. This enhances the filter’s robustness and outlier detection capability under degraded GNSS conditions. Meanwhile, the RBF network is trained to predict pseudo-position increments, which substitute missing GNSS measurements during signal outages to maintain continuous navigation. Real-world vehicular experiments were conducted to evaluate the proposed RBF-aided RAKF (RBF-RAKF) against three other methods: the Extended Kalman Filter (EKF), standard RAKF, and RBF-aided Kalman Filter (RBF-KF). The experimental results demonstrate that during GNSS outages the proposed method achieved root mean square (RMS) positioning errors of 0.94, 1.02, and 0.21 m in the north, east, and down directions, respectively, representing improvements of over 90% compared with conventional filters. Moreover, the algorithm maintained meter-level horizontal accuracy and sub-meter vertical precision under severe GNSS signal degradation. These results confirm that the proposed RBF-RAKF algorithm provides stable and high-precision navigation performance in challenging urban environments. Full article