Search Results (3,924)

To address the common shortcomings of traditional artificial potential field methods in complex water environments, this paper proposes an improved artificial potential field obstacle avoidance method based on a scoring weighting mechanism. It also designs a real-time obstacle avoidance system for unmanned surface vehicles (USVs) primarily utilizing millimeter-wave radar as the sensing modality. This method utilizes obstacle information from millimeter-wave radar, introducing a scoring mechanism that comprehensively considers distance, azimuth, and motion state to dynamically adjust repulsive weighting within the artificial potential field. This enables adaptive obstacle avoidance decision-making in complex multi-obstacle scenarios. Compared to traditional artificial potential field methods, the proposed approach effectively mitigates local minima and unreachable target issues while enhancing obstacle avoidance path stability and safety without compromising real-time performance. Simulation analysis and real-vessel experiments validate the method’s strong feasibility and engineering applicability in complex environments. Full article

Accurate pattern transfer in the lithography process demands extreme positioning accuracy. However, various external disturbances acting on the wafer stage can lead to positioning errors. To address this issue, this paper proposes a pole-placement-based Variable-Gain Extended State Observer (VGESO). First, the trade-off between disturbance rejection and noise attenuation faced by conventional Extended State Observers (ESOs) in precision motion systems is analyzed. Then, a modified ESO structure is introduced, in which two pole-related parameters are employed to adaptively adjust the observer gains. These parameters enable effective suppression of both low-frequency disturbances and high-frequency measurement noise within their designated ranges. Finally, simulation results verify the effectiveness and superior performance of the proposed method. Full article

Micro-expressions (MEs) are involuntary facial movements that reveal genuine emotions, holding significant value in fields like deception detection and psychological diagnosis. However, micro-expression recognition (MER) is fundamentally challenged by the entanglement of subtle emotional motions with identity-specific features. Traditional methods, such as those based on Robust Principal Component Analysis (RPCA), attempt to separate identity and motion components through fixed preprocessing and coarse decomposition. However, these methods can inadvertently remove subtle emotional cues and are disconnected from subsequent module training, limiting the discriminative power of features. Inspired by the Bruce–Young model of facial cognition, which suggests that facial identity and expression are processed via independent neural routes, we recognize the need for a more dynamic, learnable disentanglement paradigm for MER. We propose LFD-TCMEN, a novel network that introduces an end-to-end learnable feature disentanglement framework. The network is synergistically optimized by a multi-task objective unifying orthogonality, reconstruction, consistency, cycle, identity, and classification losses. Specifically, the Disentangle Representation Learning (DRL) module adaptively isolates pure motion patterns from subject-specific appearance, overcoming the limitations of static preprocessing, while the Temporal-Complemented Motion Enhancement (TCME) module integrates purified motion representations—highlighting subtle facial muscle activations—with optical flow dynamics to comprehensively model the spatiotemporal evolution of MEs. Extensive experiments on CAS(ME)³ and DFME benchmarks demonstrate that our method achieves state-of-the-art cross-subject performance, validating the efficacy of the proposed learnable disentanglement and synergistic optimization. Full article

In recent years, the rapid commercialization and widespread adoption of portable and wearable electronic devices have imposed increasingly stringent performance requirements on flexible sensors, including enhanced sensitivity, stability, response speed, comfort, and integration. This trend has driven extensive research and technological advancement in sensor material systems, among which carbon-based materials have emerged as core candidates for high-performance flexible sensors due to their exceptional electrical conductivity, mechanical flexibility, chemical stability, and highly tunable structural features. Meanwhile, new sensing mechanisms and innovative device architectures continue to emerge, demonstrating significant value in real-time health monitoring, early disease detection, and motion-state analysis, thereby expanding the functional boundaries of flexible sensors in the health-care sector. This review focuses on the application progress and future opportunities of carbon-based materials in flexible sensors, systematically summarizing the critical roles and performance-optimization strategies of carbon nanotubes, graphene, carbon fibers, carbon black, and their derivative composites in various sensing systems, including strain and pressure sensing, physiological electrical signal detection, temperature monitoring, and chemical or environmental sensing. In response to the growing demands of modern health-monitoring technologies, this review also examines the practical applications and challenges of flexible sensors—particularly those based on emerging mechanisms and novel structural designs—in areas such as heart-rate tracking, blood-pressure estimation, respiratory monitoring, sweat-component analysis, and epidermal electrophysiological signal acquisition. By synthesizing the current research landscape, technological pathways, and emerging opportunities of carbon-based materials in flexible sensors, and by evaluating the design principles and practical performance of diverse health-monitoring devices, this review aims to provide meaningful reference insights for researchers and support the continued innovation and practical deployment of next-generation flexible sensing technologies. Full article

The tumor microenvironment, characterized by higher acidity, hypoxia, and dense cellular structures, plays a pivotal role in cancer progression, therapeutic resistance, and treatment response. Nanoparticle-based contrast agents enable the precise delineation of solid regions within heterogeneous tumors through advanced molecular imaging techniques. Since 1956, ultrasound (US) medical imaging has provided essential anatomical and functional insights about internal organs. More recently, magnetomotive ultrasound (MMUS) has emerged as a promising imaging modality, using a modulated magnetic field to exert force on superparamagnetic iron oxide nanoparticles (SPIONs), inducing motion in the surrounding tissues through mechanical coupling. In parallel, magnetic hyperthermia (MH), which employs localized heating by alternating magnetic fields, has demonstrated significant potential in selectively destroying cancer cells while sparing healthy tissues. This review summarizes the current state of IONP-based contrast agents, with particular emphasis on their use in MH for cancer treatment, as well as their potential in multimodal imaging, including MMUS, and photoacoustic (PA) imaging. The advantages and limitations of IONPs in tumor detection and characterization are discussed, examining the development of surface-functionalized MNPs, and analyzing how material properties and environmental factors affect their diagnostic and therapeutical performance. Finally, strategies for combining MMUS and PA modalities for pre-clinical cancer imaging are proposed. Full article

Intracellular transport is essential for cellular organization and function. This process is driven by molecular motors that ferry cargo along microtubules, but is characterized by intermittent motility, where cargoes switch between directed runs and prolonged pauses. The fundamental nature of these pauses has remained a mystery, specifically whether they are periods of motor detachment and passive drifting or states of active motor engagement. By combining single-particle tracking with large-scale motion analysis, we discovered that pauses are not passive. Instead, they are active, motor-driven states. We uncovered a unifying quantitative law: the diffusivity of a vesicle during a pause scales with the square of its velocity during a run. This parabolic relationship, D_eff ∝ v², holds true for both kinesin and dynein motors, different cargo types, and a variety of cellular perturbations. We show that this coupling arises because the number of engaged motors governs motility in both states. When we reduce motor engagement, vesicles move more slowly and become trapped in longer, less mobile pauses, collectively causing them to fail to reach their destination. Our work redefines transport pauses as an essential, motor-driven part of microtubule-based cargo delivery, revealing a quantitative principle that contributes to robust cargo transport through the crowded cellular environment. Full article

Limited computational resources prevent GNSS-based passive radar systems from processing all accessible signals, necessitating intelligent signal selection for efficient target tracking. This paper proposes an adaptive selection method based on Rényi divergence. Within the Cardinality Balanced Multi-Bernoulli (CBMeMBer) filter framework, the method establishes an optimization model that maximizes the expected information gain under a fixed signal-number constraint. To comprehensively validate performance, simulations are conducted under three scenarios: multi-target linear motion, single-target tracking (for comparison with the classical Geometric Dilution of Precision (GDOP) criterion), and multi-target nonlinear maneuvering. Results demonstrate that the proposed algorithm significantly reduces computational load while achieving tracking accuracy superior to random selection and comparable to using all satellites. Compared to the GDOP-based method, it exhibits improved steady-state tracking accuracy by leveraging its dynamic, information-driven selection mechanism. This work provides an effective solution for intelligent resource management in resource-constrained GNSS-based passive radar systems. Full article

Objective: Cardiac monitoring in community and home environments requires automated operation, cross-state robustness, and interpretable feedback under resource-constrained and uncontrolled conditions. Unlike accuracy-driven ECG–PCG studies focusing on diagnostic performance, this work emphasizes systematic modeling of cardiac electromechanical coupling for long-term monitoring and engineering feasibility validation. Methods: An automated ECG–PCG coupling analysis and semantic reporting framework is proposed, covering signal preprocessing, event detection and calibration, multimodal coupling feature construction, and rule-constrained LLM-assisted interpretation. Electrical events from ECG are used as global temporal references, while multi-stage consistency correction mechanisms are introduced to enhance the stability of mechanical event localization under noise and motion interference. A structured electromechanical feature set is constructed to support fully automated processing. Results: Experimental results demonstrate that the proposed system maintains coherent event sequences and stable coupling parameter extraction across resting, movement, and emotional stress conditions. The incorporated LLM module integrates precomputed multimodal metrics under strict constraints, improving report readability and consistency without performing autonomous medical interpretation. Conclusions: This study demonstrates the methodological feasibility of an ECG–PCG coupling analysis framework for long-term cardiac state monitoring in low-resource environments. By integrating end-to-end automation, electromechanical coupling features, and constrained semantic reporting, the proposed system provides an engineering-oriented reference for continuous cardiac monitoring in community and home settings rather than a clinical diagnostic solution. Full article

Foreground segmentation and background subtraction are critical components in many computer vision applications, such as intelligent video surveillance, urban security systems, and obstacle detection for autonomous vehicles. Although extensively studied over the past decades, these tasks remain challenging, particularly due to rapid illumination changes, dynamic backgrounds, cast shadows, and camera movements. The emergence of supervised deep learning-based methods has significantly enhanced performance, surpassing traditional approaches on the benchmark dataset CDnet2014. In this context, this paper provides a comprehensive review of recent supervised deep learning techniques applied to background subtraction, along with an in-depth comparative analysis of state-of-the-art approaches available on the official CDnet2014 results platform. Specifically, we examine several key architecture families, including convolutional neural networks (CNN and FCN), encoder–decoder models such as FgSegNet and Motion U-Net, adversarial frameworks (GAN), Transformer-based architectures, and hybrid methods combining intermittent semantic segmentation with rapid detection algorithms such as RT-SBS-v2. Beyond summarizing existing works, this review contributes a structured cross-family comparison under a unified benchmark, a focused analysis of performance behavior across challenging CDnet2014 scenarios, and a critical discussion of the trade-offs between segmentation accuracy, robustness, and computational efficiency for practical deployment. Full article

This paper proposes a fuzzy sliding mode controller optimized using particle swarm optimization (FSMC-PSO) for trajectory tracking of an autonomous underwater vehicle (AUV). Conventional sliding mode control (SMC) is well known for its robustness against external disturbances, unmodeled dynamics, and parameter uncertainties, ensuring stability under challenging operating conditions. In the proposed FSMC-PSO approach, fuzzy logic adaptively tunes the SMC parameters, while PSO optimizes the fuzzy output membership functions offline to improve tuning accuracy and overall control performance. During online operation, the optimized fuzzy system adaptively adjusts the SMC parameters with minimal computational cost. The effectiveness of the proposed method is evaluated through numerical simulations in the presence of random noise. Performance is assessed using standard tracking indices, including IAE, ITAE, ISE, ITSE, and RMSE. Comparative results show that FSMC-PSO achieves higher trajectory tracking accuracy, reduces steady-state and transient errors, and minimizes chattering compared to conventional SMC and SMC-PSO, as well as the super-twisting algorithm-based PSO (STA-PSO) controller.FSMC-PSO achieves up to an 86.58% reduction in ITAE and a 73.53% reduction in ITSE compared to classical SMC while also outperforming SMC-PSO and STA-PSO across all motion states (X, Y, and $\psi$). These results demonstrate the effectiveness of FSMC-PSO for high-precision and disturbance-resilient AUV trajectory tracking within the simulated scenarios. Full article

The integration of eye-tracking into consumer-grade virtual reality (VR) headsets presents a transformative opportunity for assessing user mental states within simulated, immersive environments. However, the validity of this built-in technology must be established against gold-standard real-world eye-tracking systems. This study employs a novel paradigm using a physically moving object to evaluate the accuracy of dynamic smooth pursuit, a key oculomotor function in mental state assessment. We rigorously validated the performance of the HTC Vive Pro Eye’s integrated eye-tracker against the Tobii Pro Glasses 3 using a high-precision OptiTrack motion capture system as ground-truth for object position. Eight participants completed both 2D and 3D gaze-tracking tasks. In the 2D condition, they tracked a dot on a screen, while in the 3D condition, they tracked a physically moving object. The real-world object trajectories captured by OptiTrack were replicated within a VR environment. Gaze data from both the VR headset and the Tobii glasses were recorded simultaneously and compared to the OptiTrack baseline using Dynamic Time Warping (DTW) to quantify accuracy. Results revealed a task-dependent performance. In the 2D task, the Tobii glasses demonstrated significantly lower DTW distances, indicating superior accuracy. Conversely, in the 3D task, the VR headset significantly outperformed the glasses, showing a closer match to the real object trajectory. This suggests that while traditional eye-trackers excel in constrained 2D contexts, integrated VR eye-tracking is more accurate for naturalistic 3D gaze pursuit. We conclude that VR headset eye-tracking is not only a reliable but also a cost-effective tool for research, particularly offering enhanced performance for studies conducted within immersive 3D simulations. Full article

To address the increasing demands for faster response and higher control accuracy in the braking systems of electric and intelligent vehicles, a novel brake-by-wire actuation unit and its braking force control methods are proposed. The braking unit employs a permanent-magnet linear motor as the driving actuator and utilizes the lever-based force-amplification mechanism to directly generate the caliper force. Compared with the “rotary motor and motion conversion mechanism” configuration in other electromechanical braking systems, the proposed scheme significantly simplifies the force-transmission path, reduces friction and structural complexity, thereby enhancing the overall dynamic response and control accuracy. Due to the strong nonlinearity, time-varying parameters, and significant thermal effects of the linear motor, the braking force is prone to drift. As a result, achieving accurate force control becomes challenging. This paper proposes a model-free adaptive control method based on compact-form dynamic linearization. This method does not require an accurate mathematical model. It achieves dynamic linearization and direct control of complex nonlinear systems by online estimation of pseudo partial derivatives. Finally, the proposed control method is validated through comparative simulations and experiments against the fuzzy PID controller. The results show that the model-free adaptive control method exhibits significantly faster braking force response, smaller steady-state error, and stronger robustness against external disturbances. It enables faster dynamic response and higher braking force tracking accuracy. The study demonstrates that the proposed brake-by-wire scheme and its control method provide a potentially new approach for next-generation high-performance brake-by-wire systems. Full article

This paper presents the design and implementation of a novel autonomous underwater robot with fully vectored propulsion based on model predictive control (MPC) to rapidly respond to the position and attitude required for autonomous operation. Specifically, the mechatronic design of the eight vector-distributed thruster layout for the robot’s fully vectored propulsion is detailed, and the software architecture based on the robot operating system (ROS) is constructed. Then, the corresponding dynamics model is established by adopting the Fossen approach for the prediction and optimization of the control process. To achieve autonomous control, an MPC-based controller is designed and implemented to calculate the control input for the specified control objective. Finally, way-point tracking and trajectory-tracking experiments are carried out in an indoor tank equipped with a motion-capture system to validate the feasibility and effectiveness of the robot’s design and control framework. In addition, the robustness of the robot system is verified by artificially perturbing the robot in the hovering state. Full article

Functional magnetic resonance imaging (fMRI) is a valuable tool for presurgical brain mapping, traditionally implemented with task-based paradigms (tb-fMRI) that measure blood oxygenation level-dependent (BOLD) signal changes during controlled motor or cognitive tasks. Tb-fMRI is a well-established tool for non-invasive localization of cortical eloquent areas, yet its dependence on patient cooperation and intact cognition limits use in individuals with aphasia, cognitive impairment, or in pediatric and other vulnerable populations. Resting-state fMRI (rs-fMRI) provides a task-free alternative by leveraging spontaneous low-frequency BOLD fluctuations to delineate intrinsic functional networks, including motor and language systems that show good spatial concordance with tb-fMRI and with direct cortical stimulation. This narrative review outlines the methodological foundations of tb-fMRI and rs-fMRI, comparing acquisition protocols, preprocessing and denoising pipelines, analytic approaches, and validation strategies relevant to presurgical planning. Particular emphasis is given to the technical and physiological foundations of BOLD imaging, statistical modeling, and the influence of motion, noise, and standardization on data reliability. Emerging evidence indicates that rs-fMRI can reliably expand mapping to patients with limited task compliance and may serve as a robust complementary modality in complex clinical contexts, though its methodological heterogeneity and absence of unified practice guidelines currently constrain widespread adoption. Future advances in harmonized preprocessing, multicenter validation, and integration with connectomics and machine learning frameworks are likely to be critical for translating rs-fMRI into routine, reliable presurgical workflows. Full article

Accurate measurement of sea-surface winds is critical for climate science, physical oceanography, and the rapidly expanding offshore wind energy sector. Buoy-based platforms—moored meteorological buoys, drifters, and floating LiDAR systems (FLS)—provide practical alternatives to fixed offshore structures, especially in deep water where bottom-founded installations are economically prohibitive. Yet these floating platforms are subject to continuous pitch, roll, heave, and yaw motions forced by wind, waves, and currents. Such six-degree-of-freedom dynamics introduce multiple error pathways into the measured wind signal. This paper synthesizes the current understanding of motion-induced measurement errors and the techniques developed to compensate for them. We identify four principal error mechanisms: (1) geometric biases caused by sensor tilt, which can underestimate horizontal wind speed by 0.4–3.4% depending on inclination angle; (2) contamination of the measured signal by platform translational and rotational velocities; (3) artificial inflation of turbulence intensity by 15–50% due to spectral overlap between wave-frequency buoy motions and atmospheric turbulence; and (4) beam misalignment and range-gate distortion specific to scanning LiDAR systems. Compensation strategies have progressed through four recognizable stages: fundamental coordinate-transformation and velocity-subtraction algorithms developed in the 1990s; Kalman-filter-based multi-sensor fusion emerging in the 2000s; Response Amplitude Operator modeling tailored to FLS platforms in the 2010s; and data-driven machine-learning approaches under active development today. Despite this progress, key challenges persist. Sensor reliability degrades under extreme sea states precisely when accurate data are most needed. The coupling between high-frequency platform vibrations and turbulence remains poorly characterized. No unified validation framework or benchmark dataset yet exists to compare methods across platforms and environments. We conclude by outlining research priorities: end-to-end deep-learning architectures for nonlinear error correction, adaptive algorithms capable of all-sea-state operation, standardized evaluation protocols with open datasets, and tighter integration of intelligent software with next-generation low-power sensors and actively stabilized platforms. Full article