Search Results (1,883)

Magnetic anomaly detection (MAD) is a passive technique for detecting ferromagnetic targets, but weak magnetic anomaly signals are often submerged in background noise. Existing stochastic resonance (SR)-based MAD methods mainly focus on target detection and generally provide limited capability for waveform and amplitude reconstruction. To address this problem, this paper proposes a weak magnetic anomaly signal enhancement method based on an adaptive variable-structure stochastic resonance (AVSSR) system. A potential function capable of switching among monostable, bistable, and multistable structures is designed to improve the adaptability of SR processing under different noise conditions. The noisy vector magnetic signals are processed by the AVSSR system, and the normalized sliding-window standard deviation is combined with a scaling factor to reconstruct the magnetic anomaly signal’s waveform and amplitude. The system parameters are optimized using the differential evolution algorithm. Simulation results show that the proposed method can effectively reconstruct magnetic anomaly signals under Gaussian white noise and colored $1/f^{\alpha }$ noise, even at an input SNR of −15 dB. Comparisons with the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method and an adaptive multistable SR method demonstrate better waveform preservation and more stable amplitude reconstruction. Experimental results using measured $B_t$ signals further verify its practical applicability. Full article

Structural damage classification in composite UAV wings is a key challenge in Structural Health Monitoring (SHM), particularly under barely visible impact damage conditions. Fiber Bragg Grating (FBG) sensor networks provide high-resolution strain data; however, systematic experimental benchmarking of lightweight neural architectures trained on real FBG datasets remains limited, especially under sensor degradation scenarios. This work presents a four-phase benchmarking study of Multilayer Perceptron (MLP) configurations using strain measurements from a composite UAV wing instrumented with 32 FBG sensors across five damage states and 210 loading experiments. The framework evaluates optimization strategies, hyperparameter sensitivity, architectural depth, and robustness under controlled sensor dropout, Gaussian noise, and wavelength drift perturbations. Results indicate that compact architectures with progressive dimensional reduction (256–128–64) trained using adaptive optimizers (AdamW and Nadam) achieve the best balance between macro-F1 performance (up to 0.85 during validation), stability, and computational efficiency. Robustness analysis shows gradual performance degradation under sensor loss, suggesting distributed strain-field learning. These findings provide practical guidelines for selecting computationally efficient and robust neural models for deployable FBG-based SHM systems in aerospace applications. Full article

Real-time binaural beat synthesis in dynamic acoustic environments is challenged by carrier non-stationarity, interaural phase discontinuities, and processing delay in conventional digital signal processing pipelines. This study proposes a predictive dual-stage neural framework for phase-coherent auditory synthesis under non-stationary acoustic conditions. The framework decouples real-time carrier estimation from phase-coherent signal generation through two specialized modules. An intelligent acoustic sensing module (AI-1) estimates time-varying carrier information across harmonic, fluctuating, and broadband acoustic profiles using a causal neural front-end with an adaptive confidence-driven strategy. A predictive phase-coherent generator (AI-2) then forecasts short-horizon carrier trajectories and drives a discrete-time phase accumulator to maintain continuous phase evolution during binaural beat embedding. Objective evaluation under multiple acoustic profiles and noise conditions shows that the proposed framework maintains strong phase continuity, with a Phase Coherence Factor greater than 0.91, and low artifact levels, with a Signal-to-Artifact Ratio greater than 39.8 dB, under the evaluated conditions. Additional comparisons with conventional DSP baselines, stronger classical F0 estimators, a lightweight neural F0 tracker, and component-wise ablation variants further demonstrate that the performance improvement arises from the combination of adaptive carrier estimation and predictive phase-coherent actuation, rather than from carrier estimation alone. Hardware profiling shows a combined INT8 inference time of 2.4 ms per frame on a resource-constrained Raspberry Pi Zero 2W-class edge device. Importantly, this inference time and the sub-millisecond phase-accumulator resolution should not be interpreted as sub-millisecond end-to-end physical audio latency. The complete system still includes buffering, framing, neural inference, and output processing delay; the proposed method instead reduces effective phase-boundary misalignment through short-horizon predictive compensation. These results support the proposed framework as a lightweight engineering solution for real-time phase-continuous auditory synthesis in dynamic listening environments. The reported PCF and SAR values should be interpreted as signal-level indicators of phase continuity and artifact suppression, rather than as evidence of listener comfort, perceptual preference, or neurophysiological efficacy. Full article

Terrestrial laser scanning (TLS) represents an efficient method for acquiring spatial data in urban environments, while the quality of resulting geometric outputs is significantly influenced by subsequent point cloud processing. This article focuses on analyzing the accuracy of automatic building footprint extraction from classified TLS point clouds, with an emphasis on the role of data cleaning methods. The study area is located in the city center of Žiar nad Hronom, where urban structures were monitored using TLS. For detailed analysis, three objects were selected—an apartment building, a garage, and an industrial building—representing different levels of geometric complexity. To simulate realistic processing conditions, classification results obtained from different software (Leica Cyclone 3DR, Trimble RealWorks, and LiDAR360) were used. Their quality was evaluated using standard metrics such as Precision, Recall, and F1-score. These classifications also served as input scenarios containing typical errors, such as point clusters, vegetation near buildings, or misclassified terrain elements. Subsequently, selected point cloud cleaning methods were applied to these datasets, specifically statistical outlier removal, noise filter, and label connected components. The accuracy of the extracted building footprints was evaluated by comparison with reference data obtained from geodetic measurements. The results show that automatic classification alone is not sufficient to achieve accurate building footprints, and that data cleaning plays a decisive role. For example, in the case of the apartment building, statistical filtering reduced the area from 1052 m² to approximately 854 m² (reference value: 706 m²) and significantly improved positional accuracy (centroid shift reduced from 0.455 m to 0.077 m). Similarly, for the industrial building, the area was reduced from 215 m² to approximately 165 m² (reference: 148 m²) while maintaining the correct number of corner points. In contrast, noise filter method proved to be less reliable, as removing up to 25–30% of points often did not lead to improvements in footprint geometry. The results highlight the importance of systematic point cloud cleaning as a key step in automated building footprint extraction and demonstrate that a properly selected combination of methods can significantly improve accuracy even in noisy datasets. The article also provides practical guidance for efficient TLS data processing in geoinformatics applications. Full article

In terms of the detection of colorectal polyps during a colonoscopy, the accuracy of the diagnosis is key to effective prevention and treatment, and can be hindered by manual identification. Colorectal polyps are abnormal tissue growths in the colon or rectum, and their sizes, shapes and textures can make them difficult to find. Researchers have now turned to deep learning techniques and the YOLOv11 detection framework in particular to provide a method to automate the recognition and accurate identification of these abnormal growths. Specifically, the proposed method modifies the conventional YOLOv11 detection workflow by generating bounding box annotations from polyp segmentation masks, applying region-aware data preprocessing and augmentation, and training the detector under region-guided supervision to enhance localization precision and detection robustness. polyp segmentation masks are utilized to generate bounding box annotations which not only contribute exact spatial supervision but also avoid manual box labeling inconstancy. Region-aware data preprocessing and augmentation pay more attention to polyp-relevant regions and suppress background noise, which leads to clearer feature discrimination for small or irregular polyps. Additionally, region-guided supervision serves as explicit guidance for localizing objects with the anatomical polyp regions, which largely helps achieve accurate boundaries and prevent false detections. The proposed YOLOv11-based polyp detection system was tested and evaluated on the publicly available Kvasir-SEG dataset, which is comprised of annotated colonoscopy images. Enhanced data pre-processing and exhaustive training with appropriate choice of hyper-parameters fortified the reliability and useability of the model. The results confirmed high-grade results, and gave an Intersection over Union score of 0.9764, and an overall correctness rate of 99.00%, with well-balanced precision, recollection and F1-scores. Coming in with a mean Average Precision (mAP) of 0.9937 at a Intersection over Union threshold of 0.5 and 0.9935 over the full spectrum of thresholds from 0.5 to 0.95, this shows that the model is able to consistently and reliably detect polyps. The proposed system was also compared with Segment Anything Model, YOLO-Seg, and SAM2 and confirmed the efficacy of its method. Full article

Time-of-Flight (ToF) 3D imaging suffers from diverse systematic and non-systematic errors that limit its practical performance and reliability. Reliable simulation is critical for understanding these error mechanisms and guiding performance improvement. Therefore, this paper proposes a multi-error coupling simulation framework for ToF 3D imaging based on optical path unit decomposition. By decomposing the full light propagation chain and systematically integrating established typical error mechanisms into their corresponding physical stages, we produce simulation results that closely match real-world sensor measurements. Validated through laboratory and real-scene experiments, the proposed method outperforms mainstream approaches in RMSE, PSNR, and relative error metrics, accurately reproducing the depth distortion and noise characteristics of real ToF sensors. This multi-error coupled modeling method effectively bridges the gap between simulation and actual measurement, offering a credible reference for ToF system error evaluation, parameter optimization, and performance enhancement. Full article

Capacitive sensing offers a low-power, non-optical route for automated insect monitoring, but architecture-level benchmarking under shared geometry remains limited. Rather than presenting a general framework, this study proposed a configuration-specific laboratory benchmark comparing four sigma-delta and charge-transfers in a 6 mm dual-arc conduit at 25 °C, targeting six adult terrestrial arthropod species spanning a 25-fold range of the body cross-sectional area. Static measurements showed a strong linear relationship between ΔC_static and body cross-sectional area (17.96 fF/mm², r = 0.995), supporting first-pass conduit sizing and detectability screening. In contrast, transit amplitudes were not monotonic with body size because posture, motion, and gap occupancy affected waveform shape. Under chamber conditions, static sensitivity degraded by less than 3.2% across all architectures from RH 40% to 80%. However, under the deployment-oriented noise model, SNR_FR degradation was substantially higher for charge-transfer devices (64.8–66.8%) than for Σ–Δ devices (≤35.5%), because the composite noise floor amplifies the effect of humidity-induced baseline drift. These results generated a conduit-specific reference dataset for preliminary capacitance-to-digital converter (CDC) selection within the tested 6 mm dual-arc geometry. In addition, the experimental validation focused on laboratory baseline noise characterization, long-term drift, and trap-integrated testing in temperature-controlled environments and natural-locomotion trials, providing critical information on configuration-specific architectures and body-size-scaling reference. This study serves as an initial step toward real-world capacitive insect sensing. Future studies will investigate additional conduit geometries and insect species to improve the robustness of the proposed framework. Full article

In automotive crash testing, trustworthy crash-test curves are essential for reliable crashworthiness assessment, yet automated anomaly detection is difficult due to limited labeled abnormal cases, event-level data scarcity, and distribution shifts across vehicle models and sensor configurations. This paper proposes MVCA-AD (Multi-View Context Augmentation for Anomaly Detection) for single-channel crash-test curves. MVCA-AD generates multiple context-rich views using deterministic time- and frequency-domain transformations to amplify subtle anomalous patterns under limited labeled supervision. A trend-aware modulation module and cross-view attention fuse these views to improve sensitivity to critical segments such as impact spikes and gradual transitions while remaining robust to noise. Experiments on three subsets derived from physical full-scale crash tests show that MVCA-AD improves Precision, Recall, F1-score, and area under the ROC curve (AUC) over strong baselines and achieves stable performance under event-level grouped evaluation across heterogeneous head and B-pillar crash-test signals. The proposed approach supports crash-test data quality control by automatically identifying abnormal curves for downstream crashworthiness assessment workflows. Full article

Cross-noise fault diagnosis remains challenging due to the mismatch between training and testing noise conditions, which degrades feature reliability and model generalization. To address this issue, this paper proposes a reliability-guided adaptive feature fusion framework (RGAF-Net). The method focuses on sample-wise adaptive feature fusion, where the enhanced wide first-layer convolutional neural network(WDCNN) backbone is employed to improve multi-scale feature extraction under noisy environments. In addition, a dual-path architecture is introduced to provide complementary representations, including globally robust structural representations and locally detail-sensitive structural responses. Furthermore, a lightweight reliability estimation module is designed to characterize the signal degradation tendency under noisy conditions of each input sample, based on which a sample-wise routing mechanism dynamically adjusts feature contributions during feature fusion. Experiments on two public bearing datasets (PU and JNU) under cross-noise settings demonstrate that the proposed method achieves improved performance compared with representative approaches, particularly under severe noise conditions. For example, on the JNU dataset at −10 dB, the proposed method improves the Macro-F1 score by over 19 percentage points compared with the baseline WDCNN. Ablation studies and visualization analyses further demonstrate the effectiveness and adaptive fusion behavior of the proposed framework. The results indicate that the proposed method provides an effective solution for robust fault diagnosis under noise mismatch scenarios. Full article

Individual tree crown (ITC) detection from remotely sensed data is fundamental to forest inventory and ecological monitoring, but deep learning approaches remain constrained by limited labelled training data. We systematically evaluate three transformer detectors (the Detection Transformer (DETR), Deformable DETR, and DETR with Improved DeNoising Anchor Boxes (DINO)) paired with two backbones, ImageNet-pretrained ResNet-50 and a Masked Autoencoder (MAE) pretrained on unlabelled Canopy Height Model (CHM) data. These are benchmarked against a classical local maximum and watershed pipeline and Faster R-CNN across four test sets spanning boreal, temperate mixed-wood, and diverse North American forest types at 0.25–1.0 m resolution. Spatially held-out test regions with a one-patch buffer band eliminate sliding-window leakage; headline configurations are reported as mean ± standard deviation across three random seeds. With multi-resolution MAE pretraining, the practical lower bound for non-degenerate single-dataset transformer detection lies between ∼200 and ∼1200 patches. Without MAE pretraining, DETR fails at every dataset size we tested. Multi-dataset joint training reaches $F_1=0.84\pm 0.01$ on the boreal test set and 0.45–0.68 across the temperate-mixed-wood and NEON test sets, while Faster R-CNN narrowly wins on the smallest training pool. Standard DETR with ResNet-50 collapses regardless of the length of training schedule, but the same architecture with an MAE backbone reaches $F_1=0.83\pm 0.01$ at that schedule, showing that DETR’s reported instability is conditional on the combination of backbone initialization and training budget rather than architectural. Resolution and backbone interact: ResNet-50 wins at 0.25 m, and MAE wins at 0.5–1.0 m, consistent with the eight-pixel MAE patch-matching crown scale only at coarser resolutions. Full article

Robust vessel detection from low-Signal-to-Noise Ratio (SNR) Distributed Acoustic Sensing (DAS) data benefits from exploiting spatial correlations among adjacent channels. The Cross-Channel Attention Fusion Network (CASFNet) is presented, utilizing a Cross-Channel Attention Fusion (CASF) mechanism to dynamically model dependencies among adjacent channels. This approach, based on a dual-component spectrogram representation, adaptively fuses local spatial context, enhancing signal coherence under low-SNR conditions. Experiments on real-world DAS data demonstrate superior accuracy and robustness compared to state-of-the-art methods, achieving a detection accuracy of 99.24% and an F1-score of 99.19%. Ablation results confirm the effectiveness of this spatial fusion strategy for vessel monitoring using submarine DAS data. Full article

Rapid and reliable seismic monitoring requires accurate waveform inference, together with robustness to noise, incomplete sensing, and unstable predictions. This study investigates a quality-controlled, waveform-centered, AI-assisted framework for seismic event detection, P- and S-phase picking, graph-aware inter-station refinement, and rapid hazard-related characterization. The framework includes optional DAS, MEMS, and high-rate GNSS branches; however, the primary empirical validation is based on real waveform-centered IRIS records from the Almaty seismic region, not on a fully synchronized multimodal field deployment. The dataset includes seven seismic stations, HHZ waveforms sampled at 100 Hz, 219 seismic events, 1260 event traces, and 240 s P-centered windows from 1 January 2023 to 31 December 2024. Optional auxiliary branches are evaluated through controlled branch-availability, reduced-input, fallback, and stress-test scenarios. Under the standard-condition benchmark, the proposed framework achieved a precision of 0.941, recall of 0.932, F1 score of 0.936, false-alarm rate of 0.051, detection latency of 173 ms, and P- and S-pick mean absolute errors of 31 ms and 54 ms. Under controlled low-SNR testing, it retained an F1 score of 0.846. The findings support waveform-centered, quality-controlled monitoring, while broader cross-domain and fully synchronized multimodal validation remain necessary. Full article

Multimodal Arabic sentiment analysis has gained increasing attention due to the growing volume of user-generated multimedia content. However, effectively integrating textual, acoustic, and visual modalities remains challenging because of modality imbalance and weak cross-modal alignment. This study proposes a Directional Semantic Enhancement approach with Gated Fusion to address these limitations. The objective is to explicitly model similarity-guided semantic transfer between modalities while adaptively regulating information flow during fusion. The proposed architecture consists of four main stages: modality encoding, directional semantic enhancement, gated fusion, and classification. Directional semantic interactions enable structured cross-modal knowledge exchange, while adaptive gating mechanisms balance original and enhanced representations to mitigate modality-specific noise. Extensive experiments are conducted on the Ar-MuSA benchmark dataset, which contains 8700 multimodal samples. The proposed approach achieves 89.89% accuracy and an F1-score of 0.8989 with a latent dimension of 1024, outperforming early fusion, late fusion, and recent state-of-the-art methods. The study highlights the importance of controlled cross-modal alignment and provides a scalable approach for robust multimodal sentiment understanding in Arabic multimedia environments. Full article

Attention Deficit Hyperactivity Disorder (ADHD) is a heterogeneous neurodevelopmental disorder whose diagnosis is mainly based on behavioral assessment and is often delayed due to clinical complexity and limited availability of specialists. Resting-state functional magnetic resonance imaging (rs-fMRI) provides a valuable source of information for supporting automated and objective diagnosis. However, existing studies often do not fully capture the complex interactions of functional connectivity between different brain regions. To address this limitation, this work proposes a graph-based deep learning framework for ADHD classification from rs-fMRI that combines functional connectivity modeling with graph representation learning. The approach used Phase-Locking Value (PLV)-based connectivity estimation and Graph Sample and Aggregate (GraphSAGE) to jointly capture regional brain activity and inter-regional interactions in a scalable and efficient manner. GraphSAGE improves robustness to noise and inter-subject variability by aggregating information from stable local graph neighborhoods. This integration allows the model to learn discriminative connectivity-aware representations while remaining robust to signal variability and adaptable to multi-site data. The proposed framework was evaluated on the publicly available ADHD-200 dataset across multiple acquisition sites as well as on a combined multi-site dataset. The results indicate consistent performance across individual sites and on the combined dataset. The model achieved an Accuracy of 0.89, an AUC of 0.96, and a Specificity of 0.96 on the combined dataset, outperforming several existing methods in this setting. By integrating PLV-based connectivity with GraphSAGE learning, the approach provides an effective and scalable solution for automated ADHD classification from rs-fMRI data, contributing to data-driven approaches for the analysis of neurodevelopmental disorders. Full article

To improve the data quality of underwater point clouds acquired by UAV-borne LiDAR bathymetry, a scanline-based sliding window filtering method is proposed based on an analysis of scanline data characteristics. Scanline data of underwater point clouds are first extracted from raw point clouds, and the radius outlier removal algorithm is employed to eliminate outliers. Taking the acquisition time of scanline points as the X-axis and elevation as the Y-axis, a 3D problem is simplified into a 2D representation, and a sliding window is constructed along the scanline. Robust least-squares fitting is applied within the window. The median absolute deviation of the fitting residuals is adopted to calculate the terrain feature values for quantifying the terrain complexity, followed by an adaptive filtering threshold determination according to terrain feature values. Fine filtering of the individual scanlines is performed using a point-by-point sliding window. Experimental results demonstrate that the proposed method is adaptable to various terrain conditions, achieving a noise recall rate ≥ 96%, an overall filtering accuracy ≥99%, and an F1-score ≥ 0.9. Particularly, the precision rate in flat-water areas reached 97.37%. Overall, the proposed filtering method effectively separates noise points while preserving detailed terrain features and supports UAV-borne LiDAR bathymetry for mapping complex shallow-water regions. Full article