Search Results (6,803)

The three-dimensional (3D) documentation of the Similaun mummy (Ötzi the Iceman) and the associated Copper Age equipment and clothing presents unique challenges due to diverse material properties and strict conservation constraints. This study presents a comprehensive digital preservation workflow, primarily utilizing Structure from Motion (SfM) close-range photogrammetry (a method that reconstructs precise 3D geometry from overlapping 2D digital photographs), integrated with Image-Based Modeling (IBM) and Neural Radiance Field (NeRF) algorithms (a machine learning approach that models a complex scene as a continuous volumetric function method). To overcome the non-Lambertian properties of the mummy’s protective ice layer and wet skin (surfaces that reflect light specularly rather than diffusely, creating glares that can disorient standard reconstruction algorithms), a specialized Polarized Light Photography (PLP) strategy was implemented using custom-built hardware. This integration required advanced anatomical segmentation to resolve postural discrepancies caused by taphonomic processes. The resulting web-based application provides the scientific community with a metrically accurate digital twin, featuring interactive tools for cross-sectioning and X-ray visualization. By adopting a Free/Libre and Open-Source Software (FLOSS) ecosystem, this project establishes a sustainable, modular framework for future forensic investigations and diachronic monitoring, ensuring the long-term digital life of one of the world’s most significant archaeological finds. Full article

This paper develops a process ontology of spacetime grounded in the primacy of motion. Standard interpretations of relativity are commonly understood in terms of a four-dimensional manifold of events, with motion represented by world-lines connecting temporally ordered event-points. Although mathematically and empirically successful, this framework encourages an event-based metaphysics in which motion is derivative and temporal becoming plays no fundamental ontological role. Full article

Background: Medication non-adherence is a major driver of poor therapeutic outcomes, and computer vision methods that observe facial movements offer a non-contact route to verifying oral medication intake. Objective: To map and synthesize the existing literature on computer vision techniques applicable to the monitoring of medication intake, focusing on face tracking methods, oral movement detection and deglutition recognition, and to assess their potential in supporting automatic medication adherence verification systems. Eligibility criteria: Peer-reviewed articles, conference papers, patents, theses and preprints published from 2016 onward, written in English or Portuguese, applying facial landmark tracking or face analysis to ingestion-related movements (mouth opening, hand-to-mouth motion, pill placement, mastication or deglutition); studies confined to object/pill detection without facial analysis, or to general food intake without transferability to medication, were excluded. Sources of evidence: A systematic screening of 362 initial records was conducted across six main electronic databases and repositories: Google Scholar, PubMed, ScienceDirect, arXiv, IEEE Xplore, and Espacenet. Charting methods: Data were charted with a standardized, pilot-tested extraction form capturing bibliographic attributes, dataset type, experimental environment, face tracking approach, tools/models, and target movements; extraction was performed by a single reviewer. Following the screening process, a final selection of 34 relevant studies was included for detailed analysis and mapping. Results: Among the 34 included studies, 14 employ facial landmarks, 11 utilize temporal deep learning models, 6 apply facial action models and 3 rely on hybrid multimodal approaches that combine video analysis, object detection and temporal modeling. Tasks such as detecting mouth opening or tracking pill-to-mouth movement show promising results, while accurately detecting deglutition remains a technical challenge due to high sensitivity and individual variability. Limitations: The majority of the literature relies on private or institutional datasets (31 studies) and operates in controlled laboratory environments (22 studies); only 2 studies evaluated their methods via independent external datasets, which limits the generalization of current solutions to real-world telemonitoring scenarios. Conclusions: The literature indicates the existence of solid technical foundations for developing automated medication intake verification systems. To advance the field toward practical deployment, future research must address the need for more diverse datasets, real-world validation and more robust, adaptable modeling frameworks. Full article

Hybrid CNN–Transformer (HCT) synthesis for automated neurodevelopmental diagnostics is an effective approach to constructing intelligent detection systems that are not merely oriented toward feature classification but primarily toward solving spatiotemporal pattern recognition problems in motor disorder assessment. In neurodevelopmental diagnostics, existing automated methods rely on fixed, single-model architectures that process spatial or temporal motion features independently, failing to adapt to the heterogeneous motor irregularities characteristic of developmental coordination disorder and degrading detection sensitivity and generalization across diverse patient populations. There is therefore a pressing need for models capable of simultaneously capturing intra-frame spatial coordination patterns and inter-frame temporal movement dependencies against interrelated diagnostic criteria including accuracy, sensitivity, and motor irregularity specificity. To address this challenge, this paper proposes HCT, a novel framework that integrates ResNet-based spatial feature extraction from optical flow maps and pose estimation skeletons with multi-head self-attention Transformer encoding for modeling long-range temporal dependencies across multi-frame motion sequences. Unlike conventional single-stream approaches, where spatial and temporal processing remain confined to independent architectures, HCT decouples spatiotemporal feature learning through a cross-modal fusion pipeline, constructing a unified discriminative architecture that captures motor coordination dependencies between motion imaging inputs and multiple diagnostic criteria simultaneously. The convolutional encoder generates diverse joint displacement features, which are consolidated through cross-modal attention fusion into a robust, unified embedding with enhanced generalization and resilience to inter-individual motor variability. Integration within neurodevelopmental assessment frameworks facilitates reliable developmental coordination disorder classification, motor irregularity prediction, and interpretable diagnostic decision support, advancing the accuracy, flexibility, and clinical validity of intelligent motor disorder diagnostic systems. Full article

As offshore wind development moves toward deeper waters, floating offshore wind turbines have become essential for carbon-neutral energy systems. This study experimentally investigates the hydrodynamic coefficients of typical column–heave-plate components under forced oscillations, focusing on the influence of pitch-induced inclination. A circular column without a heave plate and a circular column equipped with a hexagonal heave plate were tested under heave and surge motions with varying periods, amplitudes, and static inclination angles. The static inclinations were used to represent the attitude variation of platform components during large-amplitude pitch responses. Added mass and damping coefficients were identified using the least squares method. The results show that for the heave-plate-equipped column, increasing the inclination from 0° to 5° and 10° reduced the nondimensional heave added mass by approximately 4.3% and 5.9%, respectively, and reduced the nondimensional heave damping by approximately 7.1% and 6.8%. The corresponding reductions in surge added mass were approximately 5.3% and 10.5%, whereas the reductions in surge damping reached approximately 8.2% and 16.4%, indicating that the surge damping is most sensitive to static inclination. These variations may be associated with the altered geometric projection and disturbed flow symmetry of the inclined component, which may affect the attached-fluid volume and energy-dissipation process during forced oscillation. Future studies should further verify the corresponding local separation and vortex-formation mechanisms through detailed flow-field measurements, PIV, or CFD. Full article

High nonlinearity and limited computational resources create persistent challenges for achieving autonomous, stable, and accurate locomotion in insect-scale crawling robots, hindering their practical deployment. In this study, we build the mathematical models of the insect-scale micro crawling robot named PLioBot and propose a locomotion control strategy that eliminates the need for gait transitions. The locomotion transformation of the PLioBot prototype, from driving inputs to mechanical motion outputs, is decomposed into multiple motion processes. This study analyzes the theoretical models underlying these motion processes and develops the locomotion simulation model for the PLioBot based on the mathematical models and the multibody dynamics simulation tool. The locomotion control strategy with low computational requirements is designed based on a closed-loop PID controller, which regulates the robot’s locomotion by independently adjusting the step lengths of its left and right legs. The locomotion co-simulation system is established to validate the control strategy. The simulation results confirm that this control strategy enables the PLioBot to perform straight-line locomotion and turning without requiring any gait transition. Full article

UAV visual tracking remains challenging because aerial imagery frequently contains small targets, visually similar distractors, camera motion, occlusion, and rapid appearance variation. To improve target representation and template–search matching under these conditions, we propose SiamDC, an anchor-free Siamese tracker built upon SiamCAR. SiamDC introduces a Dual-channel Collaborative Enhancement (DCE) module that jointly models spatial dependencies and inter-channel relationships within the template and search branches and further transfers branch-specific channel relationships reciprocally between them. In addition, a Cross-Correlation Feature Fusion (CFF) module is developed to perform a cascaded matching process: pixel-wise correlation first preserves fine-grained spatial correspondence, after which the correlation responses are fused with the search representation and further processed by channel-preserving depth-wise cross-correlation. Experiments on DTB70, UAV123, and UAV20L show consistent improvements over the SiamCAR baseline and competitive performance against the evaluated trackers while retaining real-time tracking capability. Under the standardized efficiency evaluation protocol, SiamDC requires 55.80 M parameters and 26.90 GFLOPs and achieves a network-forward speed of 44.1 FPS on an NVIDIA RTX 3080. Full article

Attitude estimation is critical for the stability and reliability of launch vehicle flight missions, especially under complex dynamic conditions with external disturbances and sensor uncertainties. To address the limitations of conventional estimation methods that ignore the coupling between thrust vector control (TVC) and attitude states, this paper proposes a robust attitude estimation framework based on the Right Invariant Extended Kalman Filter (IEKF). Two key innovations are incorporated: first, the control model of the launch vehicle is established as a TVC model, which explicitly characterizes the coupling between TVC inputs (thrust magnitude and gimbal deflections) and launch vehicle dynamics, instead of treating TVC effects as external disturbances. Second, TVC motion constraints are introduced into the classic IEKF filtering process, embedding TVC as a deterministic input into the state propagation model to enhance the structural rationality of the estimator. To verify the effectiveness of the proposed method, simulations of the launch vehicle ascent trajectory are conducted, with three comparative configurations tested under normal and sensor anomaly scenarios. The simulation results demonstrate that the proposed attitude estimation method, integrated with TVC modeling and motion constraints, is significantly superior to traditional methods in both accuracy and robustness, effectively suppressing state estimation drift and maintaining stable performance even under sensor degradation or outages. Full article

Rapidly deployable floating bridges face coupled challenges in compact deployment, structural load-bearing, and controllable module maneuvering, which cannot be fully evaluated through a single-stage structural or hydrodynamic assessment. To close this gap, this study proposes a rigid–flexible hybrid floating bridge composed of rigid deck plates, inflatable buoyancy bladders, scissor linkages, and integrated waterjet propulsors. A multi-stage assessment was conducted through inflation and calm-water maneuvering tests, gas–solid coupled finite-element analysis, and hydrodynamic and mooring simulations. The inflation experiment revealed three stages in the inflation process of the rigid–flexible specimen, including filling, transition, and pressurization stages. A remotely controlled scale model completed longitudinal, lateral, rotational, and compound motions, demonstrating the feasibility of module-level maneuvering under manual remote control. The finite-element results showed that increasing the initial internal pressure improved the load-bearing capacity and reduced local plastic deformation of the upper deck, while further improvement became limited above 70 kPa. Under the specified wave–current conditions, the ten-module assembly exhibited maximum mooring tension, horizontal displacement, and rotation of 34.7 kN, 0.276 m, and 5.525°, respectively. These results demonstrate the potential of the proposed configuration for bearing capacity, rapid deployment, and resistance to the investigated current and wave conditions while providing a multi-stage framework for further engineering design. Full article

Accurate remaining useful life (RUL) prediction of lithium-ion batteries is critical for reliable operation of the system. The performance evolution of lithium-ion batteries shows long-range dependence and capacity regeneration. However, existing performance evolution models fail to properly consider the joint effect of long-range dependence and capacity regeneration, and consequently have a deficiency in mechanism interpretability, which may limit the prediction accuracy of RUL. To address this gap, this paper separately characterizes the degradation and regeneration processes of the discharge capacity, and proposes a novel discharge capacity evolution model incorporating fractional Brownian motion and the Poisson capacity regeneration process. For the estimation problem of the model parameters caused by the Poisson regeneration, we approximately transform the proposed model to one with independent increments according to the weak convergence theorem, and then develop a maximum likelihood estimation method. From a limit perspective and using the theory of total probability, we derive the distribution of RUL, and provide the point estimation of RUL. Finally, we utilize the data set of lithium-ion batteries produced by NASA to verify the effectiveness of the proposed method, and the mean absolute errors of the proposed method are declined by at least 24% compared to the existing methods. Full article

During high-speed multi-joint coordination, the nonlinear joint-to-cylinder mapping may increase the velocity and jerk peaks of the hydraulic cylinders, while simultaneous multi-cylinder motion may cause flow-peak superposition and increase the risk of exceeding the pump-flow limit. Addressing the limitations of traditional joint-space trajectory planning, which struggles to balance actuator-space smoothness, nonlinear inverse kinematics robustness, and multi-cylinder total-flow constraints, this paper proposes a multi-objective trajectory-planning method in the hydraulic-cylinder actuator space. First, a kinematic model is constructed based on the modified Denavit–Hartenberg method and hydraulic-cylinder closed-loop cosine mapping to evaluate effective moment arms and transmission sensitivity. Subsequently, a method combining Monte Carlo global search and Levenberg–Marquardt local iteration is adopted to solve inverse kinematics without explicitly computing the Moore–Penrose pseudoinverse of the Jacobian. On this basis, analytic quintic splines incorporating asymmetric perturbation terms are constructed, and a non-dominated sorting genetic algorithm II bi-objective optimization model for minimizing the motion time and the maximum absolute jerk in the actuator space is established, incorporating the total-flow hard constraint. Simulation results demonstrate that the motion time of the compromise solution is 7.71 s, the maximum absolute jerk in the actuator space is 22.94 mm/s³, and the total flow throughout the process is lower than 105 L/min. This method keeps the planned total-flow demand within the pump-flow capacity and reduces the risk that the planned actuator speeds cannot be maintained because of insufficient flow supply, providing a planning basis for the stable operation of heavy-duty hydraulic demolition robots. Full article

Skeleton-based action recognition has attracted considerable research interest because skeleton data are inherently robust to illumination changes, viewpoint variation, background clutter, and camera motion. Nevertheless, extracting informative representations from skeleton sequences remains a challenging problem, as it requires capturing both the spatial co-occurrence patterns among body joints and the fine-grained kinematic cues that distinguish different actions. In this paper, we propose a single-stream architecture that constructs an action-specific skeleton graph directly from motion and processes it with a kinematic-aware Transformer. Rather than relying on a fixed skeleton topology, a motion-guided dynamic-graph construction module infers a per-frame adjacency matrix from short-term motion cues through a differentiable edge predictor and Gumbel-Softmax sparsification, allowing the model to discover action-driven connections between distant joints that lack direct bone connectivity (e.g., coordinated hand motion during clapping). Each joint is described by kinematic node features that combine its 3D position, instantaneous velocity, and limb-angle encodings within a single descriptor, so that both motion dynamics and higher-order limb configurations are available to the spatial encoder from the outset. A graph-attention network (GAT) encodes the spatial configuration of every frame over the learned graph, and the resulting sequence of frame descriptors is processed by a Transformer encoder that models long-range temporal dependencies; a learnable classification token aggregates the sequence, and a multi-layer perceptron (MLP) produces the final action classification. The entire model is trained end-to-end from action labels alone. We conduct a comprehensive ablation study and evaluate the proposed method on the large-scale NTU RGB+D 60 and NTU RGB+D 120 benchmarks, where the results demonstrate that our approach achieves competitive performance compared to state-of-the-art architectures. Full article

Flexible pressure sensors serve as core sensing components for wearable health monitoring systems, electronic skins for soft robots, and flexible human–machine interaction devices. Benefiting from the interfacial electric double-layer polarization effect, iontronic sensing delivers far higher pressure response sensitivity than conventional parallel-plate capacitive sensors, endowing it with distinctive advantages in the detection of weak physiological signals. Nevertheless, current dense ionic thin-film dielectric layers suffer from limited deformation space under compression and poor low-pressure sensing capability. Mainstream high-precision micropillar arrays are fabricated via photolithography, 3D printing, and metal etching molds, which require costly equipment and complicated fabrication procedures, making large-area mass production unfeasible. Random frosted concave-convex microstructures feature disordered dimensions, leading to severe device hysteresis and narrow linear ranges, which fail to achieve ultrahigh sensitivity alongside a wide pressure detection range simultaneously. To address the aforementioned multiple bottlenecks, this paper proposes a low-cost resin template replication process to fabricate TPU-based ionic thin-film dielectric layers with ordered micropillar array microstructures. Combined with inkjet-printed silver conductive PI flexible electrodes, an iontronic flexible pressure sensor with a sandwich layered structure is constructed. Multi-dimensional investigations including microscopic morphology characterization, electromechanical sensing performance calibration, and human wearable application tests are systematically implemented to thoroughly elucidate the synergistic enhancement mechanism of the arrayed micropillars. Test results demonstrate that the effective pressure detection range of the sensor spans 0–1038 kPa, accommodating ultra-low pressures such as pulse signals as well as medium-to-high-pressure loads including joint bending. The sensitivity reaches 23.27 kPa⁻¹ within the low-pressure range of 0–200 kPa and remains stable at 3.52 kPa⁻¹ in the high-pressure range of 200–1038 kPa, with piecewise linear fitting correlation coefficients of 0.93 and 0.96 respectively. Both the response time and recovery time of the device are 40 ms, and the hysteresis error throughout the loading-unloading cycle is merely 2.62%. After 20,000 consecutive cyclic loading-unloading tests, the peak capacitance output only decays by 5.1%, verifying outstanding mechanical fatigue resistance and electrical stability. Validations in multi-scenario applications prove that the sensor can accurately capture human physiological and motion signals including radial artery pulses, laryngeal deformation induced by multi-syllable vocalization, and multi-angle bending of fingers and elbow joints, suitable for home-based health monitoring, quantitative rehabilitation training, flexible tactile interaction and other scenarios. The entire fabrication process eliminates high-precision micro-nano processing equipment such as photolithography systems, plasma etchers and 3D printers; only general chemical raw materials and conventional laboratory instruments are adopted. The reusable templates enable low manufacturing costs and large-area coating forming, offering a novel low-cost technical solution for the engineering implementation and industrialization of high-performance iontronic flexible pressure sensors. Full article

To improve trajectory estimation for highly maneuvering aerial targets under time-varying noise, an adaptive multiple-model filtering framework is developed based on Kalman filtering. The framework integrates online model-probability updating, higher-order error-propagation correction, and adaptive noise adjustment to accommodate motion-mode transitions, nonlinear estimation errors, and measurement uncertainty. Simulation results show that, at a relative velocity of $800 \mathrm{m}/\mathrm{s}$, the proposed method reduces the position root-mean-square error (RMSE) by $57.73\%$ compared with the raw measurements and outperforms the individual motion-model filters. A field-programmable gate array (FPGA)-based laboratory validation platform is further developed, and the experimental results are consistent with the simulation results. The measured single-frame processing latency is $152 \mathsf{\mu }\mathrm{s}$, demonstrating the effectiveness and real-time feasibility of the proposed framework for maneuvering-target trajectory estimation. Full article

The application and performance of Nonlinear Model Predictive Control (NMPC) are critically limited by the computational efficiency of solving nonlinear combinatorial optimization problems. To address this challenge, this study proposes an efficient optimization strategy that employs a neural network to solve the constrained L-1 norm minimization problem within the NMPC framework, thereby enhancing motion control performance. Inspired by the flexible representational capacity and powerful optimization capabilities of neural networks, we explicitly encode the NMPC objective function into a network architecture. The optimal control solution is then obtained efficiently through network training. We validate the proposed strategy in a tractor path-tracking control task, detailing the processes of network construction and optimization. Benefiting from the inherent parallelism and computational efficiency of neural networks, the resulting controller demonstrates excellent real-time performance. Specifically, with prediction horizons set to 5, 10, and 20 steps, the solution times are reduced to less than 0.12, 0.28, and 0.84 s, respectively, under typical operating constraints. Full article