Search Results (102)

Reliable target-free vision-based modal identification remains challenging because accurate image correspondence does not necessarily guarantee physically reliable structural measurements. This study presents a Physics-Aware Adaptive Deep-Feature Tracking (PA-ADFT) framework that integrates learned feature extraction, stereo-temporal correspondences, physics-aware measurement validation, and triangulation to reconstruct three-dimensional camera-coordinate displacement trajectories for structural dynamic analysis. The framework was experimentally validated under controlled laboratory conditions through five repeated free-vibration tests on a single healthy aluminum cantilever beam at 10 mm tip-release amplitude, using synchronized stereo cameras and strain gauges. PA-ADFT maintained 7–9 accepted structural measurement points with 90–95% median tracking coverage across five experiments and accurately identified the first bending natural frequency with a median value of 7.02 Hz, compared to 7.03 Hz from strain measurements and 7.06 Hz from the finite-element model. A staged comparison showed that this frequency is already recovered by SuperPoint–LightGlue–Lucas–Kanade tracking; the subsequent geometric, temporal, and kinematic gates preserve track coverage and three-dimensional point identity. Calibrated stereo triangulation recovered the camera-coordinate trajectory in three dimensions, preserving the dominant first-mode bending behavior in the vertical displacement component. Vertical displacement was validated quantitatively against reference measurements; the depth component was reconstructed but not independently validated. Together with physics-aware measurement checks, this supports target-free stereo vision as a viable method for identifying first-mode bending in a single-specimen, single-amplitude laboratory setup. Full article

This work presents a novel AlGaN/GaN high-electron-mobility transistor (HEMT) structure, named the Advanced Embedded-Gate HEMT (AEG-HEMT), designed to enhance the influence and utilization of polarization charges for improving device performance. In GaN-based HEMTs, spontaneous and piezoelectric polarization effects at the AlGaN/GaN interface induce a strong built-in electric field that enables the formation of a high-density two-dimensional electron gas (2DEG). The proposed AEG-HEMT embeds the gate into the AlGaN barrier and introduces an adjacent N-type Silicon-doped AlGaN field plate to optimize the vertical and lateral electric field distributions. Our results show that the embedded gate significantly increases the vertical electric field, thereby enhancing the coupling between the gate bias and polarization charges, which improves 2DEG confinement and carrier density. Additionally, the N-type Si-doped field plate reduces electric field crowding at the gate edge, improving breakdown voltage and current capability. Compared to conventional HEMTs, the AEG-HEMT achieves higher drain current, improved transconductance, and lower recombination rates, validating the advantages of polarization charge enhancement through structural design. Full article

Accurate stereo visual–inertial measurement is essential for mobile robots operating in Global Navigation Satellite System (GNSS)-denied and structurally complex environments. In stereo visual–inertial odometry (VIO), pose and trajectory outputs depend strongly on the point measurements delivered by the visual front end before sensor-fusion update. In sparse-texture but structurally regular scenes, tracked point features may exhibit poor persistence, uneven spatial distribution, and local tracking noise, even when informative line structures are present. Existing point–line VIO methods can improve positioning accuracy by introducing line landmarks or line residuals, but they usually modify the estimator state, measurement model, and Jacobian treatment. We present a scene-adaptive line-aware visual measurement conditioning method for stereo VIO front ends with point-measurement updates. The method uses 2-D image-line segments as lightweight structural priors and applies bounded normal-direction conditioning to reliable point measurements before a fixed-interface VIO back-end update. A sparse pruning safeguard removes only highly inconsistent long-lived tracks under strong structural support, while a scene-level confidence gate attenuates the intervention when line evidence is weak or unstable. The method is instantiated and evaluated in an S-MSCKF pipeline. On the reported EuRoC MAV sequences, it reduces the sequence-averaged absolute trajectory error (ATE) RMSE by approximately 13% relative to S-MSCKF, with 3–27% reductions on machine-hall sequences. On three real-world robot measurement sequences with an RTK-aided inertial reference, the mean Sim(2)-aligned planar position error decreases from 8.72 m to 7.31 m, and the mean yaw error decreases from $8.02°$ to $6.76°$; an additional scale-preserving SE(2) evaluation reveals sequence-dependent planar behavior and residual metric-scale sensitivity. Candidate-level stereo-consistency diagnostics show subpixel mean and 95th-percentile image-domain perturbations without systematic vertical-stereo bias, while the final reliability-weighted primary-view update is analytically bounded by approximately 0.221 pixels in the reported implementation. Runtime profiling reports an average front-end time of 33.34 ms on the tested CPU platform, close to the 33.3 ms frame period of the 30 Hz stereo input, although the $\mu +3\sigma$ runtime of 47.23 ms exceeds a strict frame-by-frame 30 Hz budget. These results suggest that line-aware front-end conditioning can improve visual measurement quality in structured stereo visual–inertial sensing without modifying the evaluated back-end interface. Full article

Saltwater intrusion in regulated estuaries is commonly assessed using intrusion distance; however, its response to barrage operation may also depend on arrival timing, vertical stratification, and bathymetrically influenced near-bed transport. This study investigated these processes during a controlled saltwater intrusion experiment conducted on 17–18 September 2019 in the freshwater reach upstream of the Nakdong River Estuary Barrage, South Korea. One barrage gate was opened for 51 min, with an officially reported seawater inflow of approximately 1.01 × 10⁶ t. Repeated vertical profiles of water temperature and salinity were collected at Vessels A and B (3.0, 7.0 km upstream). The salinity stratification index ($SI$) was defined as the difference between near-bottom and surface salinities. Before intrusion, salinity at both stations was approximately 0.1 psu. At Vessel A, near-bottom salinity increased abruptly at approximately 12:40 on 17 September, and the maximum recorded salinity later reached 7.83 psu. At Vessel B, near-bottom salinity began increasing progressively at approximately 20:00 on 17 September, and the maximum recorded salinity reached 4.69 psu at approximately 10:00 on 18 September. Saline water appeared earlier at Vessel A (3.0 km) and showed a substantially delayed response at Vessel B (7.0 km), indicating spatially heterogeneous adjustment of the saline bottom layer rather than uniform propagation. Mean $SI$ values were $3.34\pm 2.60$ psu at Vessel A and $2.49\pm 1.98$ psu at Vessel B, confirming predominantly near-bed salt-wedge propagation beneath persistent freshwater surface flow. The observed bottom-intensified salinity structure was consistent with bathymetry-driven transport through deeper sections of the S-shaped channel, producing an abrupt intrusion at the 3.0 km station and delayed but sustained stratification at the 7.0 km station. Subsequent freshwater discharge is consistent with freshwater flushing, with an inferred retreat pathway involving seaward retreat and flushing of the saline layer, and the observed retreat pattern suggesting an influence of bathymetry. Surface salinity observations and single-distance criteria may underestimate saltwater penetration in regulated estuaries. Assessments should therefore consider intrusion distance, arrival time, vertical stratification, and near-bottom transport pathways. Full article

Tropical cyclone (TC) intensity estimation over the western North Pacific remains affected by uncertainties in satellite observations, best-track records, and rapidly evolving inner-core structures. To further exploit information on cloud-system vertical structure and its temporal evolution, this study introduces FY-4A/B cloud-top height (CTH) products and develops CTH-TCNet, a three-branch gated-fusion model that integrates infrared brightness temperature, CMORPH precipitation, and multidimensional CTH information for TC intensity estimation. The model consists of a CNN-based spatial branch, an LSTM-based temporal branch representing CTH evolution over the preceding 12 h, and a shortcut branch preserving current-time CTH statistics. Systematic ablation experiments show that the contribution of CTH is closely related to its representation and fusion strategy. Directly adding a single-time-step two-dimensional CTH field as an additional spatial channel provides no further performance gain, whereas historical CTH evolution and current-time CTH statistics provide complementary information. Jointly representing these two types of information through the temporal and shortcut branches yields the best performance. The final model achieves an MAE of 6.26 kt and an RMSE of 7.41 kt on the test sets. Intensity-stratified results further show that CTH generally provides larger improvements for TY, STY, and Super TY than for TS. Interpretability analyses indicate that, as TC intensity increases, the model exhibits greater reliance on CTH temporal evolution and structural information from the inner-core and eyewall-adjacent regions, with these dependence patterns being broadly consistent with known characteristics of TC inner-core convective organization and eyewall-related structures. These results indicate that FY-4A/B CTH provides valuable complementary structural and temporal information for satellite-based TC intensity estimation. Full article

Accurate and timely monitoring of terrestrial oil spills is vital for ecological conservation and safe oilfield operations. To address the challenges of segmenting terrestrial oil spills in UAV remote sensing imagery, including blurred boundaries, irregular shapes, and complex background interference, we propose Fluid-SegFormer, a fluid-aware semantic segmentation model based on the lightweight SegFormer architecture. Fluid-SegFormer employs a Mix Transformer (MiT-B0) encoder to extract hierarchical multi-scale features and integrates a hierarchical fluid-aware optimization framework. Specifically, the Local Noise Gating (LNG) module suppresses background noise, the Horizontal–Vertical Perception Attention (HVPA) module enhances the structural representation of irregular oil spill regions, and the Fluid Soft Boundary Refinement Decoder (FSBRD) recovers fine boundary details. Experiments on a newly constructed high-resolution UAV terrestrial oil spill dataset demonstrate that Fluid-SegFormer achieves an mIoU of 87.84%, an IoU of 77.56%, and a Precision of 91.42%, effectively balancing computational efficiency and segmentation accuracy. These results demonstrate the potential of Fluid-SegFormer for practical deployment in UAV-based oil spill monitoring on edge devices. Full article

To address trench-bottom electric-field concentration and the limited BV–R_on,sp trade-off of the conventional shielded-gate trench MOSFET (Conventional SGT), a floating-P-island-assisted silicon SGT MOSFET is proposed. Sentaurus TCAD is used to investigate the effects of increasing the number of floating P-islands from 0 to 3 on BV, R_on,sp, FOM, and electric-field distribution. Within this range, the three-floating-P-island device (3FPI-SGT) provides a favorable BV–R_on,sp trade-off; its drift-region doping matching, dynamic characteristics, and parameter sensitivity are further analyzed. Local depletion around multiple vertically discrete P-islands generates secondary electric-field peaks that share the potential drop with the main trench-bottom peak, reducing field crowding and improving voltage utilization of the drift region. After optimization, the maximum BV and FOM reach 177.7 V and 11.48 MW·cm⁻², respectively. At an identical R_on,sp of 2.62 mΩ·cm², BV increases by 54.6%. At V_DS = 80 V, C_oss and C_rss decrease by approximately 12.8% and 12.5%, while total switching energy increases by only approximately 2.5%. A clear BV advantage is retained under ±20% single-factor deviations in key P-island parameters. Thus, the proposed structure significantly improves the silicon SGT MOSFET BV–R_on,sp trade-off with a limited dynamic-performance penalty. Full article

The service gates of high-head water release structures are mostly radial gates with sudden lateral enlargement and vertical drop. As the control transition section from pressurized flow to open-channel flow, this flow passage not only meets the layout requirements for gate sealing, but also possesses the function of aerating high-velocity flow to protect the baseplate and side walls of the open-channel section. However, as application conditions become increasingly complex, several projects have experienced cavitation erosion damage on the side walls after prolonged operation under high water levels and partial-opening conditions. To address this problem, this study takes the sediment discharge tunnel of a hydroproject as the research object, using a combination of physical model tests and numerical simulation. The hydraulic and cavitation characteristics of the open-channel section under various gate openings are analyzed, and the cavitation mechanism on the side walls is revealed. The shape of the sudden lateral enlargement and vertical drop is proposed according to the requirements of the gate sealing arrangement. Meanwhile, for existing and planned similar projects with partial opening operation requirements, recommendations for cavitation-reduction measures and operational methods are also presented, with the aim of providing reference for similar projects. Full article

LiDAR-based 3D object detection for autonomous driving must balance localization accuracy with real-time inference, while sparse point measurements make small-scale objects such as pedestrians and cyclists particularly challenging to represent at long range. This paper presents PES-PointPillars, an enhanced PointPillars detector with three coordinated design changes. First, pinwheel-shaped convolution (PConv) replaces selected backbone convolutions to expand horizontal and vertical receptive fields for sparse structural patterns. Second, an Improved Inter-Layer Feature Correlation (I-EFC) module uses soft gating and adaptive thresholding to fuse multi-level features through continuous, input-dependent weights. Third, a Smooth L1-NWD (SNWD) loss applies normalized Wasserstein distance to planar position and scale while retaining Smooth L1 regression for vertical position, height, and orientation. Using the parameter settings and configuration of the original PointPillars implementation, the locally executed PES-PointPillars experiment achieves Moderate 3D average precision values of 77.1% for cars, 46.7% for pedestrians, and 62.9% for cyclists at 68.3 FPS on the KITTI validation split. Relative to the source-reported PointPillars reference, the corresponding numerical differences are 2.1, 3.2, and 3.8 percentage points. The reported component-wise and staged ablations show category-dependent gains, with the complete model providing the strongest aggregate performance among the evaluated configurations. Full article

Reconstructing high-resolution hyperspectral images (HR-HSIs) from low-resolution hyperspectral images (LR-HSIs) and high-resolution multispectral images (HR-MSIs) is an important multimodal remote sensing task for applications requiring both fine spatial details and reliable spectral characterization. However, existing Transformer-based HSI–MSI fusion methods still face difficulty in jointly preserving geometric structures and spectral continuity. Specifically, flattening two-dimensional image structures into one-dimensional token sequences tends to weaken local spatial connectivity, while standard self-attention does not explicitly model inter-band dependency, which may lead to structural distortion and spectral inconsistency in the fused results. To address these issues, this paper proposes an Adaptive Polyline Path Masked Attention Network (AdaPPMA-Net) for HSI-MSI fusion. First, an Adaptive Polyline Path Masked Attention mechanism is developed to explicitly encode horizontal and vertical geometric continuity, while a gating strategy is introduced to adaptively regulate positional constraints and suppress redundant dependencies. Second, a spectral enhancement module is embedded into the Transformer block to strengthen inter-band dependency modeling and alleviate the loss of spectral continuity during token interaction. Third, a spatial–spectral refinement (SSRefine) module is designed to recalibrate fused spatial–spectral representations, thereby improving reconstruction quality in the decoding stage. Extensive experiments on four public datasets demonstrate that AdaPPMA-Net consistently outperforms several state-of-the-art methods across multiple quantitative metrics. In particular, on the Washington DC Mall dataset, the proposed method raises PSNR by 4.2975 dB and lowers RMSE, ERGAS, and SAM by 39.03%, 39.35%, and 38.02%, respectively, compared with the strongest competing method. These results indicate that AdaPPMA-Net provides a more effective solution for high-fidelity multimodal remote sensing fusion. Full article

Vertically linked commodity markets—global futures, regional spot, and farm-gate prices—transmit information through directed cross-market channels whose strength varies with latent volatility regimes. Standard deep learning forecasters absorb both the directed cross-market dependence and the regime dependence of intrinsic-mode-aligned latent components into shared model weights, with no explicit architectural mechanism that exposes either as an inspectable structure. This paper proposes HVB-RA, a modular framework that combines two such mechanisms with a per-tier Variational Mode Decomposition and bidirectional LSTM backbone: (i) a directed cross-market attention layer in which the upstream-to-downstream topology is supplied from domain knowledge and the time-varying upstream-source attention intensities at the farm-gate tier (the regional-spot tier, with a single upstream key, reduces algebraically to a fixed residual upstream fusion) are extracted from data, and (ii) a regime-informed modal-weighting layer that mixes two trainable softmax weight profiles over IMF-aligned latent components through a filtered Markov-switching state probability fitted in a separate stage. An auxiliary post hoc projection enforces an exact linear constraint defined by long-run sample-mean ratios across tiers; the paper does not claim that these descriptive ratios are cointegrating relations or equilibrium coefficients. The framework is evaluated on three tiers of daily natural-rubber prices spanning 2038 trading days, against three external benchmarks (random walk, ARIMA(2,0,2), and an exogenous-only LSTM) and a contemporary neural hierarchical-interpolation forecaster (NHITS). Root mean squared error is reported per tier-horizon cell; a decision-aware income-smoothing metric quantifies the operational value of $h=5$ farm-gate forecasts under a 5-day selling rule; and a within-method comparison evaluates the marginal contribution of the auxiliary constraint projection. On the present single-regime test window, HVB-RA attains a lower point error than the contemporary NHITS baseline at every tier-horizon cell, while no method—including HVB-RA—improves on the random-walk floor at most cells; the regime-conditional components of the architecture are not identifiable because every calibration and test origin is classified as a high-volatility regime by the trained Markov-switching model. The paper contributes to machine learning and knowledge extraction by demonstrating how time-varying upstream-source attention intensities at the farm-gate tier and regime-dependent latent-component-weight profiles—two forms of latent structure typically absorbed into model weights—can be exposed as explicit, inspectable, and individually testable components of a multi-tier forecasting architecture, and by providing a reproducibility package documenting the conditions under which each component is expected to be identifiable. Full article

This research investigates a vertically structured Ge-on-Si avalanche photodetector (APD) fabricated in a separate absorption, charge, and multiplication configuration. The application of ramp gating enables reverse bias beyond the punch-through voltage, allowing the device to operate in linear avalanche mode. A significant dark avalanche current is observed under steady conditions, exhibiting linear multiplication approximately proportional to the input gating and thermal generation rate. Notably, this linear behavior persists even at voltages beyond the Geiger mode. The observed results are attributed to Ge/Si interface traps caused by the 4.18% lattice mismatch and deep-level traps introduced during fabrication. Under 1550 nm short-wave infrared normal-incidence pulsed illumination, the device exhibits negative differential resistance, attributed to illumination-induced self-quenching of electric field in multiplication region and modification of the barrier at the Ge/Si interface. A light-induced slow transient decrease in the absolute dark-state current is followed by a sustained inverse quenching effect, restoring the large dark-state current. These findings offer insights into the dynamic behavior of Ge-on-Si APDs, with potential implications for advanced optoelectronic applications. Full article

UAV remote sensing provides centimetre-level imagery for fine-grained cotton growth monitoring, yet existing segmentation models face three challenges: cotton fields exhibit a pronounced row and column structure that standard convolutions struggle to capture; conventional decoders fuse features statically, suppressing fine boundary cues; and the pixel-level class distribution is severely imbalanced. We present DCA-DeepLab, built on DeepLabv3+ with three task-specific components: a Dual-Coordinate Attention Gating (DCAG) module that decouples horizontal and vertical dependencies to encode row and column structures; a Multi-Scale Attention-Guided Modulated Feature Merging (MSAM-MFM) module that reweights semantic and detail features at each location; and an adaptive pixel-level modulated focal loss (APMFL), which focuses training on hard, minority-class pixels. We construct a cotton growth dataset of 11,745 UAV patches with four semantic classes. On this dataset and the public LoveDA benchmark, DCA-DeepLab attained the highest mIoU among the compared methods (51.74% and 51.71%), exceeding the strongest cotton baseline by 1.10 percentage points. Relative to DeepLabv3+, the Vigorous and Sparse minority-class IoUs improved by 3.51 and 1.91 percentage points, respectively, and Vigorous recall rose from 51.85% to 60.04%, with only 3.9% more parameters. These results show that encoding directional structure and adaptively balancing class contributions benefits fine-grained UAV crop segmentation. Full article

We study a minimal quantum pre-processing filter for image feature extraction built from angle embeddings and two Control-NOT (CNOT) gates. Our goal is to assess whether such a lightweight quantum front-end can benefit classical classifiers and to investigate whether its induced entanglement—measured via average single-qubit von Neumann entropy—relates to predictive performance. The circuit admits three spatially symmetric layouts (diagonal, vertical, and horizontal), each producing distinct feature transformations. Experiments show that the filter can provide modest gains in shallow learning settings, but it does not consistently outperform strong classical baselines. Notably, we find no reliable relationship between entanglement and classification accuracy: variations in average entropy fail to consistently track performance. These results suggest that the utility of simple quantum filters is determined more by dataset structure and model capacity than by entanglement magnitude, offering practical guidance for the design of hybrid quantum–classical learning pipelines. Full article

Infrared focal plane array detectors produce column stripe noise due to inter-detector response variations. Existing single-frame correction methods operate exclusively on the degraded infrared image and cannot reliably distinguish column noise from genuine vertical scene structures. With the increasing availability of co-registered visible-light cameras in modern electro-optical/infrared payloads, we propose to exploit the visible image as a structural guide for infrared destriping. Through a cross-modal correlation analysis, we show that the structural correspondence between RGB and infrared images is spatially non-uniform, motivating a selective rather than uniform fusion strategy. Based on this observation, we propose CMSP (Cross-Modal Scene Prior), a lightweight single-frame denoising architecture that selectively applies RGB guidance where it is beneficial. The proposed AdaptiveSPADE module blends RGB-guided modulation with standard instance normalization through a learned per-pixel confidence map, while a dual-path output head separately estimates pixel-wise residuals and column-constant stripe patterns. Evaluated on three public RGB–IR datasets, CMSP achieves 51.91 dB PSNR on M3FD, outperforming the best baseline by 5.79 dB with only 638 K parameters. A downstream evaluation on real stripe noise demonstrates that CMSP not only removes artifacts but also preserves the fine structures critical for infrared small target detection. Ablation studies confirm that adaptive gating more than doubles the benefit of RGB guidance compared to uniform modulation, and prevents degradation when cross-modal alignment is weak. Full article