Search Results (765)

Accurate delineation of rural settlements at large spatial extents is fundamental to territorial spatial governance, rural revitalization, and the improvement of human living environments. However, in medium-resolution remote sensing imagery, rural settlement patches are typically small, morphologically complex, and easily confused with other impervious surfaces. As a result, existing products still fall short in characterizing these features. Here, we propose a lightweight Transformer-based semantic segmentation model, SELPFormer, and develop a multi-source fusion automatic labeling pipeline that integrates Global Impervious Surface Dynamics dataset, OpenStreetMap spatial priors, and nighttime lights constraints. Built upon SegFormer as the backbone, SELPFormer introduces a lightweight pyramid pooling module at the deepest feature level to aggregate multi-scale global context and embeds an SCSE channel–spatial attention mechanism into deep features to suppress background interference. In addition, it incorporates an efficient local attention module into multi-scale lateral connections to enhance boundary and texture representations, thereby jointly improving small-object recognition and fine boundary preservation. We evaluate the proposed method using Landsat multispectral imagery covering five provinces on the North China Plain. SELPFormer achieves IoU = 74.23%, mIoU = 86.43%, F1 = 85.21%, OA = 98.69%, and Kappa = 0.8452 under a unified training and evaluation protocol, yielding IoU gains of +1.44, +3.98, and +12.35 percentage points over SegFormer, U-Net, and DeepLabV3+, respectively. SELPFormer has 15.44 M parameters and attains a parameter efficiency of 3.93% IoU per million parameters and an ROC-AUC of 0.993, indicating strong threshold-independent discriminative capability. These results indicate that the proposed method can effectively extract rural settlements from medium-resolution imagery and provides a generic “global–channel–local” collaborative framework for model design and data construction. Full article

In adaptive optics systems, high spatial resolution detection is a core prerequisite for achieving accurate wavefront correction. High spatial resolution wavefront measurement based on the traditional Shack-Hartmann technique is limited by the density of the microlens array. In contrast, off-axis digital holography technology is applied in wavefront measurement systems of adaptive optics systems due to its advantages of high spatial resolution, non-contact measurement, and full-field measurement. However, during the demodulation of its interference fringes, the accurate extraction of the complex amplitude of the +1st-order diffraction order directly determines the precision of wavefront reconstruction. Traditional frequency-domain filtering methods suffer from drawbacks such as reliance on manual threshold setting, poor adaptability to irregular spectra, and localization deviations caused by multi-region interference, making it difficult to meet the dynamic application requirements of adaptive optics. To address these issues, this study proposes a spectrum extraction method based on the Canny operator for adaptive edge extraction and centroid localization. The method first locks the rough range of the +1st-order spectrum through multi-stage peak screening, then achieves complete segmentation of spectrum spots by combining adaptive histogram equalization with edge closing and filling, resolves centroid indexing errors via maximum connected component screening, and ultimately accomplishes accurate extraction through Gaussian window filtering. Simulation experimental results show that, in comparison with two classical spectrum filtering methods, the centroid estimation error of the proposed method remains below 0.245 pixels under different noise intensity conditions. Moreover, the root mean square error of the residual wavefront corresponding to the reconstructed wavefront of the proposed method is reduced by 89.0% and 87.2% compared with those of the two classical methods, respectively. We further carried out measurement experiments based on a self-developed atmospheric turbulence test bench. The experimental results demonstrate that the proposed method exhibits higher-precision spectral centroid localization capability, which provides a reliable technical support for the high-precision measurement of dynamic distortion induced by atmospheric turbulence. Full article

To address the malfunction and unreliable operation of traditional open discharge gaps in high-altitude environments (with sandstorms and low pressure), which are prone to interference from factors like electrode corrosion and contamination, this study proposes an intelligent sealed neutral point protection device for transformers. Its core is a sealed discharge gap filled with nitrogen gas, effectively isolating it from external conditions and significantly stabilizing the power frequency discharge voltage. Innovatively, an active breakdown technology is introduced. Overvoltage signals at the transformer neutral point are acquired in real time via a capacitive voltage divider. After processing by a microcontroller unit (MCU), if both the amplitude and duration meet the preset thresholds, the MCU triggers a pulse to actively induce a discharge at the gap’s low-voltage end, enabling controlled breakdown. This allows the transient discharge voltage to be raised to 3–4 times the steady-state value, avoiding overlap with the surge arrester’s residual voltage. Tests confirm that the gap breaks down stably only when both amplitude and duration conditions are met, remaining reliable otherwise. This design successfully resolves the critical issues of failure and maloperation under both steady-state and transient overvoltages in high-altitude settings, significantly improving protection selectivity and reliability, and offering a novel solution for transformer safety in such regions. Full article

Addressing the challenges in vision-based sensor networks, this study proposes a novel fire detection framework combining Multi-Feature Fusion and Adaptive Support Vector Machine (A-SVM). First, a high-dimensional feature vector is constructed by fusing HSI color space statistics, Local Binary Pattern (LBP) dynamic textures, and Wavelet Transform shape features. A baseline SVM classifier is then trained on source domain data. Second, to overcome the difficulty of acquiring labeled samples in target domains (e.g., strong daytime interference or low nighttime illumination), a small-sample domain adaptation mechanism is introduced. This mechanism fine-tunes the source model parameters using only a few labeled samples from the target domain via regularization constraints. Experimental results demonstrate that, compared with traditional color thresholding methods and unadapted baseline SVMs, the proposed method increases the F1-score by 19% and 30% in typical daytime and nighttime cross-domain scenarios, respectively. This study effectively achieves low-cost, high-precision, and robust cross-scenario fire detection, making it highly suitable for deployment on resource-constrained edge computing nodes within smart sensor networks. Full article

To address the slow convergence and sensitivity to a low signal-to-noise ratio (SNR) of the minimum-entropy autofocus (MEA) algorithm in the refocusing of moving targets, this paper proposes a sparsity-assisted minimum-entropy autofocus algorithm. Within the framework of the traditional gradient descent MEA with variable step size, the proposed method introduces soft-thresholding-based sparse reconstruction to make moving targets more prominent and suppress background clutter in the image domain. A joint metric combining image entropy and the Hoyer sparsity measure is then constructed, and a three-point adaptive, variable step-size search is employed to reduce the number of evaluations of the cost function, thereby effectively mitigating clutter interference and significantly accelerating the optimization while maintaining good focusing quality. Simulation and real-data experiments demonstrate that, under complex phase errors and different SNR conditions, the proposed algorithm outperforms the conventional variable-step MEA in terms of image entropy, image sparsity, and runtime, while keeping the phase error estimation accuracy within a small range. These results indicate that the proposed method can achieve satisfactory moving-target focusing performance and exhibits promising engineering applicability. Full article

Offshore oil exploration and the volume of imported crude oil shipping have increased steadily, elevating the risk of oil spills. An advanced offshore oil film identification method is proposed to realize the accurate and robust recognition and segmentation of oil films from marine radar images in offshore oil spill detection. This method integrates feature engineering with an improved Beetle Antennae Search (BAS) optimization algorithm, aiming to address the key issues of low discrimination between oil films and complex marine backgrounds and insufficient spill boundary localization accuracy in radar image analysis. First, the raw radar image was transformed into the Cartesian coordinate system, and a filtering procedure was applied to attenuate interference. Subsequently, the gray distribution and local contrast of the denoised image was further improved. Afterwards, the complexity of the grayscale distribution within each feature map was quantified using Shannon entropy. The Top-K feature maps with the highest entropy values were subsequently used to construct an information-rich subset. The subset was then processed through a pixel-wise averaging strategy to generate a coupled feature image. Then, Otsu threshold was used to refine ocean wave regions. Finally, the oil films were segmented with an improved BAS optimization algorithm. The fitness function of the improved BAS algorithm was augmented through the integration of edge fitting accuracy, and a target-proximity penalization scheme. Through an adaptive step-length modulation paradigm and Perceptual Mechanism, it can achieve a marked improvement in search accuracy and achieving precise segmentation of oil slicks. The detection accuracy of the proposed method is significantly enhanced relative to the traditional BAS algorithm and existing marine radar oil spill detection methods. The IOU, Dice, recall and F1-score reached 81.2%, 89.6%, 85.2%, and 90.1% respectively. This method not only advances the methodological rigor of spill detection but also provides critical data support for the development of more effective control and remediation practices. Full article

This article discusses the fundamental limitations of Light Fidelity (Li-Fi) systems, an emerging visible light communication technology that is constrained by line-of-sight dependency and optical attenuation. Unlike existing adaptive modulation approaches that focus solely on improving signal processing, we present an integrated framework that combines three key contributions: (1) an adaptive modulation optimization algorithm that selects among OOK, PAM, and OFDM schemes based on instantaneous signal-to-noise ratio thresholds, achieving a 30–40% range extension compared to fixed modulation references; (2) a method for spatial optimization of access points (APs) using the L-BFGS-B algorithm to determine the optimal location of APs, taking into account lighting constraints and coverage uniformity; and (3) comprehensive system-level modeling incorporating shot noise, thermal noise, inter-symbol interference, and dynamic shadowing effects for realistic performance evaluation. Through extensive simulations on multiple room geometries (6 m × 5 m to 20 m × 15 m) and AP configurations (one to six APs), we demonstrate that the proposed adaptive system achieves an average throughput 60% higher than that of fixed OOK, while maintaining 98.7% coverage in a 10 m × 8 m environment with two optimally placed APs. The framework provides practical design guidelines for Li-Fi deployment, including an analysis of computational complexity $O(M\times N)$ for coverage assessment, $O(I\times D^3)$ for access point optimization) and a characterization of convergence behavior. A comparative analysis with state-of-the-art techniques (optical smart reflective surfaces, machine learning-based blockage prediction, and Li-Fi/RF hybrid configurations) positions our lightweight algorithmic approach as suitable for resource-constrained deployment scenarios, where system-level integration and practical feasibility take precedence over innovation in individual components. Full article

Background/Objectives: Migraine and diabetes mellitus are highly prevalent chronic diseases, and their comorbidity presents management challenges, particularly when wearable medical technologies are used concurrently. Remote electrical neuromodulation (REN; Nerivio^®) is an FDA-cleared non-pharmacological migraine therapy, and continuous glucose monitoring (CGM) systems are widely used in diabetes care. However, the safety and compatibility of simultaneous co-use have not yet been evaluated. This technical compatibility study aimed to assess whether REN operation affects CGM performance or interferes with glucose measurement integrity in diabetic adults. Methods: Twenty-one adults with diabetes using Dexcom G6/G7 or FreeStyle Libre 2/3 participated in a single-arm interventional study. During a 45 min session, participants operated the REN and CGM devices simultaneously on their smartphones, and the REN device was paused three times to compare CGM readings between REN ON and RED OFF conditions. The primary outcome was the mean absolute relative difference (MARD_{REN ON/OFF}), evaluated against a prespecified 5% threshold. Statistical analysis included the Wilcoxon test, with subgroup analysis by the CGM device family. Results: The median MARD_{REN ON/OFF} across all participants was 1.61% (IQR 0.84–2.44%), significantly below the 5% threshold (p < 0.001). All participants achieved MARD_{REN ON/OFF} < 5%. Subgroup analyses were consistent: the median MARD_{REN ON/OFF} was 1.70% (IQR 0.90–2.45%) for Dexcom and 1.05% (IQR 0.83–1.50%) for Abbott. No technical interference, Bluetooth disruptions, missed data transmission, or adverse events were observed. Conclusions: Simultaneous use of Nerivio^® REN and CGM systems in adults with diabetes is compatible and safe, with no evidence of interference or significant deviations in glucose readings. These findings support the integrated and reliable use of REN and CGM wearables in adults with diabetes managing comorbid conditions. Full article

Wind turbine (WT) misalignment fault diagnosis is constrained by critical signal processing challenges: weak fault features, intense background noise, and poor generalization. This study proposes a lightweight method for high-precision fault diagnosis. A fixed-threshold wavelet denoising method with the scene-specific pre-optimized parameter a (0 < a ≤ 1.3) is proposed: the parameter a is determined via offline grid search using the feature retention rate (FRR) as the objective function for typical wind farm operating scenarios. A multi-scale depthwise separable CNN (MS-DSCNN) captures multi-scale spatial features via 3 × 1 and 5 × 1 kernels, reducing computational complexity by 73.4% versus standard CNNs. An attention-based minimal peephole LSTM (AttMPLSTM) enhances temporal feature measurement, using minimal peephole connections for long-term dependencies and channel attention to weight fault-relevant signals. Joint L1–L2 regularization mitigates overfitting and environmental interference, improving model robustness. Validated on a WT test bench, the Adams simulation dataset, and the CWRU benchmark, the model achieves a 90.2 ± 1.4% feature retention rate (FRR) in signal processing, an over 98% F1-score for fault classification, and over 99% accuracy. With 2.5 s single-epoch training and a 12.8 ± 0.5 ms single-sample inference time, the reduced parameters enable real-time deployment in embedded systems, advancing signal processing for rotating machinery fault diagnosis. Full article

Modern healthcare systems increasingly depend on wearable Internet of Medical Things (IoMT) devices for the continuous monitoring of patients’ physiological parameters. It remains challenging to differentiate between genuine physiological anomalies, sensor faults, and malicious cyber interference. In this work, we propose a hybrid fusion framework designed to attribute the most plausible source of an anomaly, thereby supporting more reliable clinical decisions. The proposed framework is developed and evaluated using two complementary datasets: CICIoMT2024 for modelling security threats and a large-scale intensive care cohort from MIMIC-IV for analysing key vital signs and bedside interventions. The core of the system combines a supervised XGBoost classifier for attack detection with an unsupervised LSTM autoencoder for identifying physiological and technical deviations. To improve clinical realism and avoid artefacts introduced by quantised or placeholder measurements, the physiological module incorporates quality-aware preprocessing and missingness indicators. The fusion decision policy is calibrated under prudent, safety-oriented constraints to limit false escalation. Rather than relying on fixed fusion weights, we train a lightweight fusion classifier that combines complementary evidence from the security and clinical modules, and we select class-specific probability thresholds on a dedicated calibration split. The security module achieves high cross-validated performance, while the clinical model captures abnormal physiological patterns at scale, including deviations consistent with both acute deterioration and data-quality faults. Explainability is provided through SHAP analysis for the security module and reconstruction-error attribution for physiological anomalies. The integrated fusion framework achieves a final accuracy of 99.76% under prudent calibration and a Matthews Correlation Coefficient (MCC) of 0.995, with an average end-to-end inference latency of 84.69 ms (p95 upper bound of 107.30 ms), supporting near real-time execution in edge-oriented settings. While performance is strong, clinical severity labels are operationalised through rule-based proxies, and cross-domain fusion relies on harmonised alignment assumptions. These aspects should be further evaluated using realistic fault traces and prospective IoMT data. Despite these limitations, the proposed framework offers a practical and explainable approach for IoMT-based patient monitoring. Full article

In the diagnostics of plasmas heated by neutral beam injection (NBI), which serves as a fundamental heating technique, critical core parameters such as ion temperatures and rotational velocities can be measured through NBI’s associated diagnostic methods. However, conventional spectral analysis methods applied in NBI-based Beam Emission Spectroscopy diagnostics face a significant limitation: a relatively high false detection rate during characteristic peak detection and boundary determination. This issue stems from three primary factors: persistent noise interference, overlapping spectral peaks, and dynamic broadening effects. To address this critical issue, we propose a spectral feature extraction method based on morphological segmentation and optimized clustering, with three key innovations that work synergistically: (1) an adaptive chunking algorithm driven by gradient, Laplacian, and curvature features to dynamically partition spectral regions, laying a foundation for localized analysis; (2) a hierarchical residual iteration mechanism combining dynamic thresholding and Gaussian template subtraction to enhance weak peak signals; (3) optimized DBSCAN clustering integrated with morphological closure to refine peak boundaries accurately. Among them, the adaptive chunking technique is distinct from general adaptive methods: its chunking granularity can be dynamically adjusted according to peak structures and can accurately adapt to low signal-to-noise ratio (SNR) scenarios. Experimental results based on measured data from the EAST device demonstrate that the adaptive chunking strategy maintains a missed detection rate of 0–20% across the full signal-to-noise ratio (SNR) range, with false positive rates limited to 16.67–50.00%. Notably, it achieves effective peak detection even under extremely low SNR conditions. Full article

Classical beamforming (CBF) beamforming constrains the accuracy and quality of underwater acoustic imaging by producing wide main-lobes that reduce resolution, high sidelobes that cause leakage, and point-spread functions that blur targets. Existing approaches typically address only one of these issues at a time, limiting their ability to resolve multiple, interrelated problems simultaneously. In this study, we introduce a double-compression deconvolution high-resolution beamforming method designed to enhance multi-beam sonar imaging using an underwater uniform linear array. The proposed approach formulates imaging as a sparse deconvolution problem and suppresses off-target interference through two sparse constraints, thereby improving the sonar’s resolving capability. During sparse reconstruction, an auxiliary-parameter iterative shrinkage-threshold algorithm is employed to recover azimuthal sparse signals with higher accuracy. Simulations and controlled pool experiments demonstrate that, relative to classical beamforming, the proposed method significantly improves resolution, suppresses off-target interference, expands the imaging intensity dynamic range, and yields clearer target representations. This study provides an effective strategy to mitigate intrinsic limitations in high-resolution underwater sonar imaging. Full article

In the eddy current testing (ECT) of magnetized ferromagnetic pipelines, permeability perturbations near defects cause magnetic distortion that primarily modulates the imaginary part of the ECT sensor’s impedance, leading to confusion between inner and outer wall defect signals. To address this interference, this study thoroughly analyzes the modulation mechanism of permeability changes on impedance and investigates the feasibility of detecting solely the real part to enhance discrimination reliability. This understanding leads to the proposal of a solution employing an LC resonant circuit, capitalizing on its characteristic of zero imaginary part impedance at the resonant frequency, to effectively suppress the permeability-related signal interference. Experimental results demonstrate the effectiveness of the proposed approach: the magnetization response test confirms the insensitivity of the LC sensor to permeability perturbations, and the defect discrimination experiment shows that the sensor achieves a standard deviation ratio of 2.25 and a peak-to-peak ratio of 4.42 between inner and outer wall defect signals. The findings indicate that the LC resonant sensor can reliably distinguish between inner and outer wall defects through simple amplitude thresholding, thereby improving the reliability of inspections for magnetized pipelines in industrial applications. Full article

Cancer stem cells (CSCs) represent a small but highly resilient tumor subpopulation responsible for sustained growth, metastasis, therapeutic resistance, and recurrence. Their survival is supported by aberrant activation of developmental and inflammatory pathways, including Wnt/β-catenin, Notch, Hedgehog, PI3K/Akt/mTOR, STAT3, and NF-κB, as well as epithelial–mesenchymal transition (EMT) programs and niche-driven cues. Increasing evidence shows that phytochemicals, naturally occurring bioactive compounds from medicinal plants, can disrupt these networks through multi-targeted mechanisms. This review synthesizes current findings on prominent phytochemicals such as curcumin, sulforaphane, resveratrol, EGCG, genistein, quercetin, parthenolide, berberine, and withaferin A. Collectively, these compounds suppress CSC self-renewal, reduce sphere-forming capacity, diminish ALDH⁺ and CD44⁺/CD24⁻ fractions, reverse EMT features, and interfere with key transcriptional regulators that maintain stemness. Many phytochemicals also sensitize CSCs to chemotherapeutic agents by downregulating drug-efflux transporters (e.g., ABCB1, ABCG2) and lowering survival thresholds, resulting in enhanced apoptosis and reduced tumor-initiating potential. This review further highlights the translational challenges associated with poor solubility, rapid metabolism, and limited bioavailability of free phytochemicals. Emerging nanotechnology-based delivery systems, including polymeric nanoparticles, lipid carriers, hybrid nanocapsules, and ligand-targeted formulations, show promise in improving stability, tumor accumulation, and CSC-specific targeting. These nanoformulations consistently enhance intracellular uptake and amplify anti-CSC effects in preclinical models. Overall, the consolidated evidence supports phytochemicals as potent modulators of CSC biology and underscores the need for optimized delivery strategies and evidence-based combination regimens to achieve meaningful clinical benefit. Full article

Tomatoes are an important economic crop in China, and crop diseases often lead to a decline in their yield. Deep learning-based visual recognition methods have become an approach for disease identification; however, challenges remain due to complex background interference in the field and the diversity of disease manifestations. To address these issues, this paper proposes the SXA-YOLO (an improvement based on YOLO, where S stands for the SAAPAN architecture, X represents the XIoU loss function, and A denotes the AsDDet module) symmetric perception recognition model. First, a comprehensive symmetry architecture system is established. The backbone network creates a hierarchical feature foundation through C3k2 (Cross-stage Partial Concatenated Bottleneck Convolution with Dual-kernel Design) and SPPF (the Fast Pyramid Pooling module) modules; the neck employs a SAAPAN (Symmetry-Aware Adaptive Path Aggregation Architecture) bidirectional feature pyramid architecture, utilizing multiple modules to achieve equal fusion of multi-scale features; and the detection head is based on the AsDDet (Adaptive Symmetry-aware Decoupled Detection Head) module for functional decoupling, combining dynamic label assignment and the XIoU (Extended Intersection over Union) loss function to collaboratively optimize classification, regression, and confidence prediction. Ultimately, a complete recognition framework is formed through triple symmetric optimization of “feature hierarchy, fusion path, and task functionality.” Experimental results indicate that this method effectively enhances the model’s recognition performance, achieving a P (Precision) value of 0.992 and an mAP50 (mean Average Precision at 50% IoU threshold) of 0.993. Furthermore, for ten categories of diseases, the SXA-YOLO symmetric perception recognition model outperforms other comparative models in both p value and mAP50. The improved algorithm enhances the recognition of foliar diseases in tomatoes, achieving a high level of accuracy. Full article