Search Results (855)

Periocular recognition is essential when full-face images cannot be used because of occlusion, privacy constraints, or sensor limitations, yet in many deployments, only periocular images are available at run time, while richer evidence, such as archival face photos and textual metadata, exists offline. This mismatch makes it hard to deploy conventional multimodal fusion. This motivates the notion of conditional biometrics, where auxiliary modalities are used only during training to learn stronger periocular representations while keeping deployment strictly periocular-only. In this paper, we propose Multi-Anchor Conditional Periocular Embedding (MACPE), which maps periocular, facial, and textual features into a shared anchor-conditioned space via a learnable anchor bank that preserves periocular micro-textures while aligning higher-level semantics. Training combines identity classification losses on periocular and face branches with a symmetric InfoNCE loss over anchors and a pulling regularizer that jointly aligns periocular, facial, and textual embeddings without collapsing into face-dominated solutions; captions generated by a vision language model provide complementary semantic supervision. At deployment, only the periocular encoder is used. Experiments across five periocular datasets show that MACPE consistently improves Rank-1 identification and reduces EER at a fixed FAR compared with periocular-only baselines and alternative conditioning methods. Ablation studies verify the contributions of anchor-conditioned embeddings, textual supervision, and the proposed loss design. Full article

Path tracking is a crucial part of agricultural machinery automatic navigation system (ANS) and has been extensively investigated in prior research. Although existing ANS designs perform satisfactorily under mild soil condition, path-tracking algorithms are often challenged by unknown disturbances arising from complicated field environment and machine conditions. The current literature lacks a detailed analysis of the influence of the sideslip angle under specific operating speeds and path scenarios for agricultural machinery, which serves as the primary motivation for this study. In this paper, simulations are conducted for sprayers and harvesters across various paths, curvatures, and speeds to analyze the impact of sideslip on path-tracking performance. The results indicate that under the typical low-speed and large-curvature conditions of agricultural machinery, neglecting sideslip effects leads to a mismatch between the theoretical model and the actual vehicle motion. Compared to an MPC based on a kinematic model that disregards the sideslip angle, explicitly incorporating the sideslip angle into the kinematic model reduces the maximum lateral tracking error from 0.234 m to 0.174 m for a U-shaped path, and from 0.263 m to 0.194 m for a rectangular-shaped path. Simulation at different travel speeds further demonstrates that proposed algorithm achieves smaller sideslip amplitudes and faster attenuation after completing turns compared to conventional MPC. These findings offer valuable insights for the design of path-tracking algorithms in agricultural machinery autonomous driving systems. Full article

This paper systematically analyzes the propagation, transformation, and accumulation mechanisms of multi-source noise and device non-idealities within the complete signal chain from the transmitter through the channel to the receiver, focusing on wireless optical coherent communication systems from a signal propagation perspective. It establishes the stepwise propagation process of signals and noise from the transmitter through the atmospheric turbulence channel to the coherent receiver, clarifying the coupling mechanisms and accumulation patterns of various noise sources within the propagation chain. From a signal propagation viewpoint, the study focuses on analyzing the impact mechanisms of factors, such as Mach–Zehnder modulator nonlinear distortion, atmospheric turbulence effects, 90° mixer optical splitting ratio imbalance, and dual-balanced detector responsivity mismatch, on system bit error rate performance and constellation diagrams under conditions of coexisting multiple noises. Simultaneously, by introducing differential and common-mode processes, the propagation and suppression characteristics of additive noise at the receiver end within the balanced detection structure were analyzed, revealing the dominant properties of different noise components under varying optical power conditions. Simulation results indicate that within the range of weak turbulence and engineering parameters, the impact of modulator nonlinearity on system bit error rate is relatively minor compared to channel noise. Atmospheric turbulence dominates system performance degradation through the combined effects of amplitude fading and phase perturbation, causing significant constellation spreading. Imbalanced optical splitting ratios and mismatched responsivity at the receiver weaken common-mode noise suppression, leading to variations in effective signal gain and constellation stretching/distortion. Under different signal light power and local oscillator light power conditions, the system noise exhibits distinct dominant characteristics. Full article

Unsteady flow-induced pressure fluctuations and the consequent dynamic stresses in pump-turbines are critical determinants of their operational reliability and fatigue resistance. This investigation systematically examines the spatiotemporal propagation of Rotor–Stator Interaction (RSI)-induced pressure pulsations and evaluates the corresponding dynamic stress mechanisms based on a phase-resolved fluid–structure interaction strategy. The results reveal a significant hydrodynamic duality: RSI pressure waves manifest as convective traveling waves on the pressure side but as modal standing waves on the suction side. Crucially, a severe spanwise phase mismatch is identified between the hub and shroud streamlines, which induces a periodic hydrodynamic torsional moment on the blade. Due to the rigid constraint at the blade–crown junction, this torsional tendency is restricted, resulting in high-amplitude constrained tensile stresses at the root. This explains why the stress concentration at the crown inlet is significantly higher than in other regions. Additionally, the stress spectrum shows strong load dependence, characterized by low-frequency modulations on the suction side under high-load conditions. Full article

The advancement of automated driving technologies offers potential safety and efficiency gains, yet safety remains the primary barrier to higher-level deployment. Failures in automated driving systems rarely result from a single technical malfunction. Instead, they emerge from coupled organizational, technical, human, and environmental factors, particularly in partial and conditional automation where human supervision and intervention remain critical. This study systematically identifies safety failures in automated driving systems and analyzes how they propagate across system layers and human–machine interactions. A qualitative case-based analytical approach is adopted by integrating the Swiss Cheese model and the SHELL model. The Swiss Cheese model is used to represent multilayer defensive structures, including governance and policy, perception, planning and decision-making, control and actuation, and human–machine interfaces. The SHELL model structures interaction failures between liveware and software, hardware, environment, and other liveware. The results reveal recurrent cross-layer failure pathways in which interface-level mismatches, such as low-salience alerts, sensor miscalibration, adverse environmental conditions, and inadequate handover communication, align with latent system weaknesses to produce unsafe outcomes. These findings demonstrate that autonomous driving safety failures are predominantly socio-technical in nature rather than purely technological. The proposed hybrid framework provides actionable insights for system designers, operators, and regulators by identifying critical intervention points for improving interface design, operational procedures, and policy-level safeguards in autonomous driving systems. Full article

Small-diameter synthetic grafts often fail from thrombosis, intimal hyperplasia, and compliance mismatch, highlighting the need for alternatives that better support endothelialization and remodeling. Here, we evaluated multilayer plant-based vascular grafts fabricated from decellularized leatherleaf viburnum reinforced with cross-linked gelatin, seeded with vascular smooth muscle cells and endothelial cells, and conditioned in a perfusion bioreactor to mimic physiological shear stress. Pre-implant assays confirmed effective decellularization, low residual detergent, and mechanical integrity suitable for surgical handling. In a rat abdominal aorta interposition model, plant-based grafts remained patent at 1, 4, and 24 weeks and showed higher survival than silicone controls. Ultrasound imaging demonstrated flow patterns and resistance indices similar to native vessels, and plant-based grafts maintained significantly higher endothelial cell coverage than silicone controls, reaching native-like density by 24 weeks. Histology and biochemical assays showed early collagen and elastin coverage comparable to native aorta and increased collagen by 24 weeks. Scanning electron microscopy showed smooth luminal surfaces with minimal thrombus formation, contrasting with the rougher, thrombus-prone surfaces of silicone grafts. These findings indicate that plant-based grafts support endothelialization, maintain long-term patency, and undergo favorable remodeling in vivo, supporting their potential as a biomimetic alternative for small-diameter arterial repair. Full article

For 5G tunnel communications, antennas often face critical challenges arising from severe path loss and multipath fading in confined environments, as well as polarization mismatch under dynamic propagation conditions. This paper proposes a 3.5-GHz circularly polarized (CP) endfire antenna array with bidirectional right-hand CP radiation, featuring high gain, low profile, and compact configuration. The array is implemented on a single-layer F4B substrate and integrates eight pairs of electric and magnetic dipoles to synthesize orthogonal linear field components required for CP radiation. By applying the extended method of maximum power transmission efficiency, constraints on the amplitude and phase are introduced to maximize the CP gain in the endfire direction. A 16-element linear array prototype is fabricated and tested for validation. Measurement results show that the proposed array achieves a bidirectional right-hand CP endfire gain exceeding 12.2 dBic, an impedance bandwidth from 3.1 to 3.78 GHz, and a 3 dB axial ratio bandwidth of 19.5%, demonstrating its suitability for 5G tunnel communication applications. Full article

(1) Background: In hospitals, mattresses are often relocated for cleaning or patient transfer, leading to mismatches between actual and recorded bed locations. Manual updates are time-consuming and error-prone, requiring an automatic localization system that is cost-effective and easy to deploy to ensure traceability and reduce nursing workload. (2) Purpose: This study presents a pragmatic, large-scale implementation and validation of a BLE-based localization system using RSSI measurements. The goal was to achieve reliable room-level identification of smart mattresses by leveraging existing hospital infrastructure. (3) Results: The system showed stable signals in the complex hospital environment, with a 12.04 dBm mean gap between primary and secondary rooms, accurately detecting mattress movements and restoring location confidence. Nurses reported easier operation, reduced manual checks, and improved accuracy, though occasional mismatches occurred when receivers were offline. (4) Conclusions: The RSSI-based system demonstrates a feasible and scalable model for real-world asset tracking. Future upgrades include receiver health monitoring, watchdog restarts, and enhanced user training to improve reliability and usability. (5) Method: RSSI–distance relationships were characterized under different partition conditions to determine parameters for room differentiation. To evaluate real-world scalability, a field validation involving 266 mattresses in 101 rooms over 42 h tested performance, along with relocation tests and nurse feedback. Full article

This work addresses parameter mismatch in Permanent Magnet Synchronous Motor (PMSM) drives, focusing on performance degradation caused by variations in flux linkage and inductance arising under realistic operating uncertainties. An artificial neural network (ANN) is trained to estimate these parameter shifts and update the controller model online. The procedure comprises three steps: (i) data generation using Sphere Decoding Algorithm-based Model Predictive Control (SDA-MPC) across a mismatch range of $\pm 50\%$; (ii) offline ANN training to map measured features to parameter estimates; and (iii) online ANN deployment to update model parameters within the SDA-MPC loop. MATLAB /Simulink simulations show that ANN-based compensation can improve current tracking and THD under many mismatch conditions, although in some cases—particularly when inductance is overestimated—THD may increase relative to nominal operation. When parameters return to nominal values the ANN adapts accordingly, steering the controller back toward baseline performance. The data-driven adaptation enhances robustness with modest computational overhead. Future work includes hardware-in-the-loop (HIL) testing and explicit experimental study of temperature-dependent effects. Full article

High-density 380 V–12 V $LLC$ resonant converters typically employ planar transformers with integrated leakage inductance. To achieve Zero-Voltage Switching (ZVS), an air gap is introduced to adjust the magnetizing inductance ($L_m$). However, this gap alters the internal magnetic field (H) distribution. In Center-Tapped (CT) structures, this alteration leads to asymmetric leakage inductances between the positive and negative half-cycles, causing resonant frequency mismatch and performance degradation, particularly under light-load conditions. In this work, the asymmetrical leakage inductance effect in a CT transformer for a 380 V–12 V $LLC$ resonant converter is systematically investigated. A mathematical model is derived to quantify the leakage inductance distribution, revealing that the relative position between the air gap and the windings significantly affects the symmetry. Based on this modeling analysis, the centralized assembly method is identified as the optimal solution to ensure impedance symmetry. The accuracy of the proposed model and the effectiveness of this structure are validated through Finite Element Analysis (FEA) simulations and a hardware prototype of a 250-W, 600-kHz $LLC$ converter. Results demonstrate that this method eliminates the approximately 11% leakage inductance discrepancy (1.8 μH vs. 1.6 μH), ensuring stable operation across the full load range. Full article

Biaxially stretched polytetrafluoroethylene (PTFE) membranes are promising media for high-efficiency air filtration because of their stable node–fiber microstructure and environmental durability. To clarify how resin properties and microstructure govern filtration behavior, ten PTFE resins with different average molecular weights (Mn) and particle size characteristics were processed into membranes under essentially identical biaxial stretching and sintering conditions. Resin particle size, fiber diameter and pore size distributions were quantified, and coefficients of variation (CVs), together with Spearman rank correlations, were used to analyze material–structure–performance links. Filtration efficiency, pressure drop and quality factor (QF) were measured according to ISO 29463-3 using 0.1–0.3 μm aerosols. Higher Mn combined with lower particle-size dispersion favored finer fibers and narrower pores, yielding efficiencies close to 100%, but increased pressure drop and slightly reduced QF, indicating a trade-off between efficiency and flow resistance. The sample with the lowest Mn in its group and a high machine-direction draw ratio (12×), showed pronounced fibril breakage, node coalescence, broadened pore-size distribution and degraded QF, illustrating the sensitivity of structure and performance to resin-process mismatch. Overall, the study establishes a hierarchical material–fiber–pore–performance relationship that can guide resin selection, structural tuning and process optimization of biaxially stretched PTFE membranes. Full article

This study presents a robust estimation approach for linear discrete-time systems subject to parametric uncertainties. To address model mismatch, the proposed method enhances the MHE framework, thereby improving estimation accuracy. Based on this framework, the estimator is derived by minimizing the expected estimation error. A detailed derivation is provided, along with a novel recursive formulation for the pseudo-covariance of the estimation error. The resulting estimator maintains structural similarity to the Kalman filter and supports recursive implementation. Theoretical analysis establishes convergence to a stable system, with guaranteed boundedness and asymptotic unbiasedness of the estimation error. Simulation results demonstrate that the proposed strategy maintains high effectiveness and robustness under different uncertain conditions. Full article

The threat posed by unauthorized drones to public airspace has become increasingly critical. To address the challenge of UAV detection in unaligned visible–infrared dual-spectral images, we present a novel framework that comprises two sequential stages: image alignment and object detection. The Speeded-Up Robust Features (SURF) algorithm is applied for feature matching, combined with the gray centroid method to remove mismatched feature points. A plane-adaptive pixel remapping algorithm is further developed to achieve images fusion. In addition, an enhanced YOLOv11 model with a modified loss function is employed to achieve robust object detection in the fused images. Experimental results demonstrate that the proposed method enables precise pixel-level dual-spectrum fusion and reliable UAV detection under diverse and complex conditions. Full article

Enhancing the explosive power output of the knee joints is critical for improving the agility and obstacle crossing of humanoid robots. However, a mismatch between the knee-to-CoM transmission ratio and jumping demands, together with power-loss–induced motor performance degradation at high speeds, shortens the high-power operating window and limits jump performance. To address this, this paper introduces a variable-reduction-ratio knee-joint paradigm in which the reduction ratio is coupled to the joint angle and decreases during extension. Analysis of motor output and knee kinematics motivates coupling the reduction ratio to the joint angle. A high initial ratio increases the takeoff torque, and a gradual decrease limits motor speed and power losses, extending the high-power window. A linear-actuator-driven guide-rod mechanism realizes this strategy, and parameter optimization guided by explosive jump control is employed to select the design parameters. Experimental validation demonstrates a high jump of 0.63 m on a single-joint platform (a theoretical improvement of $31.9\%$ over the optimal fixed-ratio baseline under the tested conditions). Integrated into a humanoid robot, the proposed design enables a 1.1 m long jump, a 0.5 m high jump, and a 0.5 m box jump. Full article

Cross-domain building segmentation in high-resolution remote sensing imagery underpins urban change monitoring, disaster assessment, and exposure mapping. However, differences in sensors, regions, and imaging conditions create structural and radiometric domain gaps that degrade model generalization. Most existing methods adopt model-centric domain adaptation with additional networks or losses, complicating training and deployment. We propose a data-centric framework, Hybrid Object-Based Augmentation and Histogram Matching (Hybrid OBA–HM), which improves cross-domain building segmentation without modifying the backbone architecture or using target-domain labels. The proposed framework comprises two stages: (i) object-based augmentation to increase structural diversity and building coverage, and (ii) histogram-based normalization to mitigate radiometric discrepancies across domains. Experiments on OpenEarthMap and cross-city transfer among three KOMPSAT-3A scenes show that Hybrid OBA–HM improves F1-scores from 0.808 to 0.840 and from 0.455 to 0.652, respectively, while maintaining an object-level intersection over union of 0.89 for replaced buildings. Domain-indicator analysis further reveals larger gains under stronger radiometric and geometric mismatches, indicating that the proposed framework strengthens cross-domain generalization and provides practical guidance by relating simple domain diagnostics (e.g., brightness/color and orientation mismatch indicators) to the expected benefits of augmentation and normalization when adapting to new domains. Full article