Search Results (3,852)

Conventional contact-based monitoring of heart rate (HR) presents challenges such as patient discomfort, skin irritation, and poor long-term adherence, motivating the development of contactless, video-based sensing systems. This study proposes a robust hybrid framework combining advanced signal processing with machine learning to enhance HR estimation accuracy from facial video. The methodology integrates a two-stage geometric stabilization pipeline with dense facial tessellation to mitigate motion. Eulerian Video Magnification (EVM) amplifies subtle color variations, followed by chrominance-based roi filtering. Signal recovery utilizes a sliding-window Principal Component Analysis (PCA) for local coherence, followed by Second-Order Blind Identification (SOBI), with a Light Gradient Boosting Machine (LightGBM) classifier employed to automatically identify physiological sources. Validated on the challenging COHFACE dataset, the approach achieves a Mean Absolute Error (MAE) of 1.50 bpm, a Root Mean Square Error (RMSE) of 3.07 bpm, and a Pearson Correlation Coefficient (PCC) of 0.97 on the test set. The method demonstrates robustness across diverse lighting conditions, outperforming traditional algorithms and achieving parity with state-of-the-art deep learning models, while offering an interpretable solution for contactless health monitoring. Full article

Additive manufacturing enables the development of low-cost, self-built robotic systems; however, their performance is typically not characterized by validated metrics. The paper presents a photogrammetric concept intended for system-independent application to characterize planar positioning accuracy and repeatability without access to internal controller data. The method is based on a Raspberry Pi 4 camera system, image processing in Python 3.12.0 and OpenCV 4.12.0, and a universal additively manufactured robot tool attachment. Two position estimation strategies are investigated: a marker-based approach using ArUco markers and a markerless blob-analysis method based on a ruby sphere. Camera calibration is evaluated using different patterns, with a compact CharUco board exhibiting the lowest RMS reprojection error (~1 px). Experimental validation follows selected elements of ISO 9283:1998 and comprises 30 repetitions at five target poses for linear and axial motion strategies. The results show lower positional deviations for marker-based methods compared to the markerless approach, with a two-marker configuration yielding the lowest mean deviation under the investigated conditions. Sub-millimeter positioning accuracy and repeatability are achieved, and linear motion exhibits lower repeatability deviations than axial motion. The proposed approach provides a cost-effective and flexible solution for external robot characterization, particularly suited for self-built and resource-constrained systems. Full article

Predicting the maneuverability of ships equipped with twin azimuth thrusters remains challenging due to their complex hydrodynamic interactions. This study develops an integrated framework that combines Computational Fluid Dynamics (CFD) with an enhanced Manoeuvring Mathematical Group (MMG) Model. Using the platform supply vessel Hai Yang Shi You 661 as a case study, all requisite hydrodynamic derivatives and propeller coefficients were efficiently obtained through CFD-based captive model tests, including oblique towing and Planar Motion Mechanism tests, conducted in STAR-CCM+ 2206. A core contribution of this work is the systematic evaluation of how hydrodynamic model fidelity affects prediction accuracy. Numerical turning circle simulations were executed with three models of increasing complexity: one with only linear derivatives, a second incorporating nonlinear higher-order terms, and a third, full model that additionally includes nonlinear velocity coupling terms. The results, rigorously validated against full-scale trial data, demonstrate that while the basic CFD-MMG approach is feasible, the inclusion of nonlinear coupling terms is critical for achieving accurate predictions in large-amplitude maneuvers. This enhancement reduced the maximum error in tactical diameter prediction from over 25% to approximately 11.8%. Consequently, this study provides a validated and cost-effective framework for maneuvering the prediction of azimuth-thruster vessels and offers clear, quantitative guidance on the necessary level of model complexity for practical engineering applications. Full article

The influence of a robot’s manipulation can be observed in a robotic measurement system. Different robot end-effector trajectories yield different robot end-effector accuracy and repeatability errors. Trajectory parameters, robot motion type, velocity, and length of motion were identified as influential sources. A robot arm was used to insert measuring objects into the measurement device for dimensional measurements. In the first part, the measurement datasets for linear and joint robot motions were compared for three different velocities and four motion lengths. The influence of the number of active joints in the robot’s motion was compared for two velocities and four magnitudes of joint rotation. Dimensional measurement variability was analysed using measurement system analysis (MSA), and the statistical influence of trajectory parameters was further addressed by analysis of variance (ANOVA). All identified trajectory parameters have a statistically significant impact on measurement variability, reflecting the robot end-effector’s accuracy and repeatability errors. Linear motion provides higher measurement variability up to $20\%$, a velocity increase that is typically up to 25–35% and motion length that is typically up to 15–35%. Full article

Hand range of motion (ROM) measurement is crucial for diagnosing joint limitations, tracking rehabilitation progress, and creating personalized treatment plans. In recent years, exergames combined with dedicated game controllers have emerged as promising tools to complement traditional hand rehabilitation; however, their validity as motor function assessment tools remains insufficiently explored. This study evaluates the validity of the eJamar game controller as a tool for measuring hand ROM and hand grip strength (HGS), by comparing its outputs with standard goniometry and dynamometry. In a prior technical validation using a robotic arm under controlled conditions, the device showed a mean error of approximately 1.5°, indicating high measurement precision under ideal conditions. In the clinical validation with 32 patients undergoing hand rehabilitation, performance was movement-dependent. Pronation and supination showed strong agreement (MAE < 3°) and higher agreement compared with other movements, whereas flexion, extension, and radial-ulnar deviation exhibited weaker correlations and substantially higher errors (around 20°). In contrast, grip strength measurements for more and less affected hands, respectively, showed high correlation (0.88–0.91) and moderate agreement (ICC 0.81–0.66) with MAE values around 4 kg-f. Overall, results suggest that the eJamar shows preliminary suitability for assessing HGS and forearm pronation and supination in clinical settings. However, for HGS, agreement should be interpreted with caution due to the observed bias and error levels, indicating that further validation and calibration are required before stronger clinical claims can be made. For wrist flexion, extension, and radial-ulnar deviation, the device currently shows limited accuracy and requires further improvement. Full article

Indoor mobile robots often experience degraded localization accuracy and robustness when relying on a single positioning modality. In addition, conventional pose computation based on Euler-parameterized transformations can be computationally involved and susceptible to singularities, while practical fusion schemes may not adequately suppress measurement errors. This paper proposes an indoor robot localization method, termed IMU_UWB_ESKF, which tightly fuses inertial and UWB measurements using a Lie-group state representation. IMU- and UWB-derived quantities are formulated on the associated Lie algebra, enabling numerically stable pose propagation and measurement updates. To mitigate sensor noise and reduce drift, a dual-stage Kalman filtering strategy is adopted: an EKF-based measurement correction is first performed, followed by a multi-dimensional error-state Kalman filter for refined fusion. The proposed pipeline is implemented on a wheeled-robot platform under ROS, integrating real-time IMU/UWB parameter extraction, pose transformation, and online state estimation. Experimental results demonstrate stable real-time localization with improved robustness and accuracy under dynamic motion, indicating the method’s applicability to indoor navigation tasks. Full article

Underwater wireless optical communication (UWOC) for autonomous underwater vehicles is severely compromised by the coupling of oceanic optical turbulence and platform motion. Traditional static statistical models fail to capture the temporal evolution of these stochastic processes, hindering effective real-time beam tracking. This paper proposes a unified dynamic framework and a hybrid intelligent control strategy to address beam misalignment in turbulent environments. First, a physically motivated stochastic differential equation (SDE) model is derived from the Radiative Transfer Equation via diffusion approximation. Validated by an inverse Fokker–Planck approach, this model accurately reconstructs drift fields for diverse channel conditions, serving as a dynamic generator for time-varying fading. Second, to maintain robust link alignment, a hybrid Fuzzy-Reinforcement Learning control strategy is developed. This approach integrates the interpretability of fuzzy logic with the adaptive optimization of Q-learning, incorporating a supervisor mechanism to handle deep fading events. Numerical simulations and hardware-in-the-loop (HIL) experiments demonstrate the system’s efficacy. The proposed controller achieves a median alignment error of 3.64 mm and reduces transient errors by over 80% compared to classical PID controllers during signal recovery. These results confirm that the proposed framework significantly enhances link stability and tracking robustness for AUVs in complex random media. Full article

To address the severe multiple-seed pickup problem during the seed-filling process of an air-suction seed metering device for small-seed crops with multiple seeds per hill, a combined seed-cleaning mechanism consisting of an upper seven-tooth seed-cleaning device and a lower seed-cleaning blade was developed based on an analysis of the causes of multiple pickup. Mathematical models of seed motion and force were established to describe the interaction between the seven-tooth seed-cleaning device and the seed population during the cleaning process. The installation position and adjustment mechanism of the device on the seed chamber housing were determined, and its tooth-profile parameters and major operating positions were theoretically analyzed. Accordingly, the design method and calculation models for the key parameters of the seven-tooth seed-cleaning device were established. A quadratic regression orthogonal rotational combination experiment was conducted using three factors affecting cleaning performance: the distance between the apex of the first tooth and the corresponding suction hole, the operating speed of the seed metering device, and the negative pressure. Regression equations were established and response surface analysis was performed. With the seed-cleaning qualification rate as the optimization objective, the optimal parameter combinations were obtained as follows: for millet, 3.36 mm, 3.59 km/h, and 1.43 kPa; for broomcorn millet, 3.49 mm, 4.22 km/h, and 2.11 kPa; and for rapeseed, 3.15 mm, 3.73 km/h, and 1.52 kPa. To reduce the influence of random error, 200 repeated bench tests were conducted for each seed type under its corresponding optimal parameter combination at operating speeds of 2.0–5.0 km/h. The seed-cleaning qualification rates for millet, broomcorn millet, and rapeseed were all above 90%, meeting the design requirements of the seed-cleaning mechanism. This study provides a theoretical basis and technical reference for seed-cleaning mechanisms for air-suction precision seed metering devices for small-seed crops with multiple seeds per hill. Full article

Hybrid CNC and additive manufacturing platforms often rely on host-assisted or otherwise overdimensioned control architectures to achieve deterministic multi-axis motion, increasing system cost and complexity. This paper presents a fully microcontroller-based timer–DMA motion execution architecture that eliminates the need for external processors or FPGA-based execution, enabling deterministic multi-axis synchronization under the tested conditions in a simpler, more cost-effective way. The proposed framework integrates motion planning, precise step-time computation, and hardware-assisted pulse generation within a unified embedded control architecture. The main novelty lies in the systematic use of timer and DMA peripherals to offload time-critical pulse execution from the microcontroller core, allowing it to focus on motion planning and precise step-time computation. Unlike segmentation-based approaches, the duration of each individual step is calculated directly without fixed-interval segmentation, enabling high motion resolution while avoiding per-step interrupts that introduce jitter at high motion speeds. The architecture was validated on a hybrid platform capable of both milling and material extrusion. Experimental results confirmed real-time feasibility within practical on-chip memory limits and demonstrated very small interpolation errors caused mainly by timer quantization, comparable to those observed in host-processor-based motion systems. Machining and additive-manufacturing experiments further confirmed stable execution and accurate trajectory tracking under real operating conditions. Full article

Accurate absolute radiometric calibration is critical for ensuring the data quality of spaceborne Synthetic Aperture Radar (SAR) systems and supporting quantitative remote sensing applications. Absolute radiometric calibration generally relies on ground reference targets with known radar cross-section (RCS) deployed at dedicated calibration sites. Such ground-based calibration methods are costly and time-consuming, and calibration frequency is constrained by the distribution of calibration sites and the satellite revisit cycles. Additionally, for specialized SAR missions, such as deep space exploration, deploying calibration equipment on the observed extraterrestrial surface is infeasible. This study proposes a space-based absolute calibration concept using a small calibration satellite carrying a well-characterized reference (e.g., a passive reflector or an active transponder) and flying in formation with the SAR satellite. The relative motion ensures a side-looking acquisition geometry, enabling the SAR to image the accompanying target and derive calibration factors. The overall calibration process is divided into two stages: determination of an in-orbit calibration factor using the calibration satellite, followed by its transformation to accommodate ground imaging conditions. This method effectively isolates the radar system gain to characterize the intrinsic hardware response. Furthermore, by operating entirely in space, it avoids atmospheric and ground-clutter distortions, ensuring a fully space-based, end-to-end calibration process dominated primarily by sensor systematic errors. Moreover, it allows for more frequent and flexible calibration, eliminating reliance on ground calibration sites and infrastructure. The feasibility and advantages of the proposed concept are demonstrated through comprehensive simulations, covering orbit analysis, echo simulation, and image processing. Full article

Camera rotation estimation is a key component in video stabilization and motion analysis. In many practical scenarios, inertial measurements are unavailable or temporally unreliable, while classical geometric pipelines degrade under blur, low texture, and low illumination. This paper investigates whether substantially downsampled optical flow can retain sufficient structure for accurate frame-to-frame rotation regression. We present VisualRNet, a lightweight rotation-specific visual regression framework trained with cross-modal IMU supervision. Our design uses coordinate-aware feature encoding, depthwise separable convolutions, lightweight attention, and a compact 6D rotation head to model the spatial structure of rotational flow fields. On Deep-FVS, VisualRNet achieves a mean rotation error of 0.3151${}^{\circ }$ on the test set. The VisualRNet regression head contains 7.7 K parameters, 0.002 GFLOPs, and runs at 729 FPS, while the full pipeline with the FastFlowNetv2 frontend contains 1.374 M parameters, 7.194 GFLOPs, and runs at approximately 113 FPS. A cross-camera adaptation experiment on TUM VI further indicates that the learned motion representation can be aligned to a new camera system with limited calibration data. These results support low-resolution optical flow as a practical input for visual rotation estimation and suggest particular value in stabilization-oriented and cost-sensitive applications where approximate rotational trend matters more than full scene geometry. Full article

This study presents the design, simulation, and experimental validation of a novel sector-shaped thread rolling machine aimed at improving forming efficiency, structural compactness, and process controllability compared with conventional linear thread rolling systems. A systematic engineering framework integrating mechanism design, curved-die implementation, motion control, finite-element simulation, and experimental verification is established. DEFORM-3D simulations are performed to investigate the effects of friction coefficient and die spacing on material flow and thread profile formation, and the results are used to guide machine construction and parameter optimization. Experimental results demonstrate that the proposed mechanism can simultaneously form four screws within a single rotation cycle, significantly enhancing production efficiency. Under optimized parameters, the relative errors of pitch diameter and helix angle are maintained within 5%, showing good agreement with simulation predictions. The findings confirm the feasibility, controllability, and stable forming capability of the proposed system, providing a practical and efficient solution for next-generation compact and high-productivity thread rolling equipment. Full article

Indoor localization in GNSS-denied environments remains a significant challenge due to the low sampling frequency and high variability of wireless signal measurements. This~paper presents a wireless communication-based indoor localization method that integrates Wi-Fi received signal strength indication (RSSI) measurements with optical initialization and inertial sensor fusion. The proposed approach eliminates the need for labor-intensive fingerprinting and specialized infrastructure by leveraging existing Wi-Fi networks. Optical pose estimation using ArUco markers provides accurate initial position and orientation, enabling alignment between sensor coordinate systems and reducing inertial drift. During tracking, inertial measurements compensate for motion between sparse Wi-Fi observations by virtually translating historical RSSI samples, allowing statistically consistent averaging and improved distance estimation. A simplified factor graph framework is employed to fuse heterogeneous measurements while maintaining computational efficiency suitable for real-time operation on mobile devices. Experimental validation using a robot-based ground-truth reference system demonstrates sub-meter localization accuracy with an average positioning error of approximately 0.40~m. The proposed method provides a low-cost and scalable solution for indoor positioning and navigation applications such as access-controlled environments, exhibitions, and large public venues. Full article

A model predictive control (MPC) strategy is proposed based on state observation and updating for image-based visual servoing (IBVS) tasks of micro aerial vehicles (MAVs). This control strategy enables precise pose adjustment of MAVs without relying on the global positioning system (GPS). Specifically, image features are first defined on a virtual image plane to decouple the translational motion of the MAV. Subsequently, a linear velocity observer is developed to provide high-quality real-time velocity information for the MAV during IBVS execution. Furthermore, the image dynamics on the virtual image plane are linearized using a first-order Taylor expansion, and a linear MPC controller is formulated to efficiently compute the optimal control inputs. Moreover, the state inputs to the MPC controller are updated at each control cycle to eliminate errors accumulated during the rolling optimization based on the linearized dynamics, thereby ensuring the precision of IBVS. Simulation and experimental results demonstrate the performance of the proposed observer and control strategy. Full article

Continuous wrist angle estimation based on surface electromyography (sEMG) is often affected by signal variability and prediction instability. Although regression models provide instantaneous outputs, their predictions may exhibit temporal fluctuations and limited robustness due to the non-stationary nature of sEMG signals. To address this issue, we propose an uncertainty-aware probabilistic fusion post-processing framework for continuous wrist motion estimation. The proposed approach decouples regression and uncertainty modeling, enabling plug-in compatibility with feature-based regression models. A local Gaussian process regression (LGPR) model is employed to estimate predictive uncertainty from a sliding feature window. The instantaneous regression output is then fused with the LGPR prediction through a Bayesian-inspired Gaussian formulation, resulting in a closed-form adaptive gain that dynamically adjusts smoothing strength according to predictive variance. Experimental results from both open-loop wrist joint motion estimation and closed-loop myoelectric control tasks demonstrate that our method outperforms existing methods in key performance indicators, including task completion time, trajectory smoothness, and trajectory tracking error. Full article