Search Results (8,422)

In recent years, the use of UAVs has become increasingly widespread, and the public safety risks posed by unauthorized UAV flights have become increasingly prominent, creating an urgent need for effective detection and identification of UAV targets. However, such targets are small in size, have low contrast, and exhibit an extremely low signal-to-noise ratio; conventional detection methods generally suffer from insufficient feature discrimination, missed detections, and false alarms in complex backgrounds. To address these challenges, this paper proposes a Frequency-Guided Cross-scale Refinement Network (FGCR-Net). Based on an encoder-decoder architecture, this network achieves end-to-end collaborative optimization through cross-layer feature fusion, side-channel prediction refinement, and frequency-domain background suppression. First, a multi-path selective cross-layer fusion module (SCFM) is designed. This module employs coordinated modeling via both channel and spatial paths, supplemented by adaptive weighting with learnable coefficients, to perform differentiated selective fusion of the encoder’s fine-grained features and the decoder’s semantic features, thereby bridging the semantic gap at jump connections; Second, we designed a Cross-Scale Adaptive Fusion Enhancement Attention Module (CAFEM), which cascades multi-receptive-field hollow convolutions, strip pooling, and a bidirectional semantic guidance mechanism to perform cross-scale refinement on the side outputs of each decoder layer, thereby alleviating the issues of blurred boundaries and false alarms caused by inconsistent quality of multi-scale prediction maps and insufficient cross-layer consistency; finally, we design a Frequency-Guided Semantic Enhancement Module (FGSEM), which uses the Fast Fourier Transform (FFT) to decouple encoder features into the frequency domain. By leveraging low-frequency energy to predict the background confidence map and applying spatially selective suppression to high-frequency components, this module distinguishes, from a frequency-domain perspective, the high-frequency responses of complex backgrounds and targets that are highly similar in the spatial domain. Experiments on MSDS-UAV, a self-built multi-scenario UAV dataset for small targets, demonstrate that our method consistently outperforms existing state-of-the-art methods across multiple performance metrics, with Pixel Accuracy, Mean Intersection over Union, and Probability of Detection reaching 92.76%, 70.91%, and 92.69%, respectively; Compared to the baseline model, these three metrics improved by 1.90, 3.20, and 3.76 percentage points, respectively, fully validating the effectiveness and superiority of the proposed method. Full article

Addressing the dual threats of malicious jamming and time-varying fading faced by wireless communication links in complex dynamic electromagnetic adversarial environments, existing intelligent anti-jamming methods predominantly focus on single-dimensional resource optimization under quasi-static channels. This focus neglects the nonlinear superposition effects of multi-path deep fading and dynamic strong jamming in the time-frequency domain, making it challenging for systems to balance transmission reliability and system energy efficiency in physical environments where fading and suppression coexist. To address this issue, this study proposes a joint intelligent anti-jamming method for channel switching and transmit power control based on a Deep Q-Network (DQN). Initially, a composite communication environment model incorporating Markov time-varying fading and jamming is constructed. Subsequently, the joint resource scheduling problem is formulated as a Markov Decision Process. The environment state space is reconstructed by integrating continuous channel state estimation and jamming observation features, accompanied by the design of a highly aggregated two-dimensional discrete action space for both channel and power. Finally, a composite reward function evaluating both communication success rates and power consumption costs is proposed to guide the agent in multi-dimensional resource joint optimization. Simulation results demonstrate that the proposed algorithm effectively extracts implicit features under the composite state of fading and jamming. When encountering extreme deep fading or full-band blocking, the agent strategically triggers a silent mechanism to avoid exorbitant invalid energy consumption penalties, while precisely matching interference-free channels with the minimum effective transmit power during favorable communication windows. Simulation results show that compared with traditional xx algorithms, the proposed method significantly improves the dynamic successful transmission rate and system energy efficiency in complex, highly dynamic scenarios, achieving an effective optimization of anti-jamming reliability and low power overhead. Full article

Partial discharge (PD) detection is a crucial means for the early warning of incipient insulation defects in arresters. However, under strong electromagnetic interference and background noise, PD signals are prone to distortion, making it difficult to accurately determine the pulse onset front and thus severely degrading the accuracy of time-delay estimation. To address the difficulty of time-delay estimation under low signal-to-noise ratio (SNR) and multi-channel aliasing conditions, this paper proposes a method for arrester PD detection and high-precision time-delay estimation based on joint denoising and improved cross-correlation. First, a joint denoising strategy that integrates singular value decomposition (SVD), variational mode decomposition adaptively optimized by the sparrow search algorithm (SSA-VMD), and the Teager energy operator (TEO) is constructed. This strategy suppresses white noise and periodic narrowband interference while effectively extracting the oscillatory onset characteristics of PD pulses. Second, an enhanced time-delay estimation method based on HB-weighted generalized quadratic cross-correlation is introduced. By employing the dual mechanisms of HB frequency-domain weighting and amplitude weighting to sharpen the correlation peak, the estimation robustness under low SNR is improved. Simulation results show that the proposed method attains an accuracy of 99.9911%, significantly outperforming conventional cross-correlation, PHAT-SCOT, and NLMS methods. Finally, experiments are conducted on a needle-plate discharge platform. In multiple comparative experiments with different spatial distance differences (ranging from <30 cm to >50 cm), the maximum relative error is kept within 0.6%, verifying the reliability and accuracy of the proposed algorithm under controlled laboratory conditions. This method can provide a new approach for online monitoring and accurate fault location of arresters in power systems. Full article

Massive Multiple-Input Multiple-Output (MIMO) is a key enabling technology for fifth-generation (5G) and beyond wireless communication systems because of its ability to greatly enhance both spectral efficiency (SE) and energy efficiency (EE). However, maximizing these two performance metrics simultaneously remains a challenging multi-objective optimization problem due to the conflicting effects of the transmit power, antenna deployment, and circuit power consumption. This paper investigates the EE–SE trade-off in a downlink Massive MIMO system with Minimum Mean Square Error (MMSE) channel estimation (CE) and different linear combining and precoding techniques. A power optimization framework based on transmit power allocation and antenna configuration is analyzed to identify operating points that maximize EE while maintaining high SE. Performance analysis of the number of base station (BS) antennas in MIMO systems, user equipment density, transmit power, and inter-cell interference on system performance is evaluated through numerical simulations. The results demonstrate that appropriately selecting the number of transmit antennas and optimizing the transmit power significantly improve the EE–SE trade-off. Furthermore, although increasing the number of antennas enhances SE, EE exhibits a non-monotonic behavior because of the additional circuit power required by the radio-frequency hardware. The findings confirm that MMSE-based CE provides higher spectral efficiency than the MR, ZF, RZF, and S-MMSE schemes, albeit at increased computational complexity, offering useful design guidelines for energy-efficient Massive MIMO networks. Full article

Background: Early detection of dementia is essential for timely intervention, yet existing diagnostic approaches remain costly, invasive, or dependent on specialized expertise. Electroencephalography (EEG) offers a non-invasive and accessible alternative; however, purely data-driven deep learning models may overlook clinically established neurophysiological biomarkers, particularly in the challenging detection of mild cognitive impairment (MCI). Methods: We propose the Clinical EEG Feature-Augmented Network (CEFA-Net), a knowledge-guided deep learning framework that systematically integrates automatic representation learning from raw multichannel EEG with clinically validated neurophysiological biomarkers. The architecture combines three complementary convolutional pathways capturing multi-scale temporal dynamics with domain-informed feature representations, enabling both data-driven discovery and clinically grounded interpretation. Task-specific optimization strategies—including focal loss, class-aware augmentation, and validation-guided ensemble weighting—were employed to enhance robustness under class imbalance. The model was evaluated on the large-scale the Chung-Ang University Hospital EEG (CAUEEG) dataset (1379 recordings from 1155 patients) across binary abnormality detection and three-class dementia staging tasks. Results: CEFA-Net achieved 81.02% accuracy (macro F1: 81.15%) for dementia staging and 87.15% accuracy (macro F1: 87.41%) for abnormality detection, outperforming baseline methods by 6.75–9.10 percentage points (p < 0.001). Notably, the proposed framework substantially improved MCI detection (F1-score: 78%), representing a 14-point gain over traditional machine learning approaches. Ablation analyses confirmed that clinical biomarker integration and multi-model fusion provide complementary diagnostic value. In an additional patient-disjoint evaluation using the no-overlap partitions, CEFA-Net achieved 85.40% accuracy for abnormality detection and 73.80% accuracy for dementia staging, demonstrating generalization to subjects completely excluded from the training data. Conclusions: These findings demonstrate that knowledge-guided integration of clinical biomarkers with deep representation learning can significantly enhance EEG-based dementia detection. CEFA-Net offers a clinically aligned and computationally efficient solution, supporting its potential for real-world screening and early diagnostic workflows. Full article

A long-standing account of retinal direction selectivity states that asymmetric inhibition suppresses responses to motion in the null direction. We use this idea as a computational prior in the Directional Inhibition Network (DI-Net), a two-stage model for direction classification from a before/after image pair. The first stage is a deterministic, parameter-free, anti-coincidence encoder that produces eight spatial maps of local directional evidence. The second is a compact convolutional network that combines this evidence into a global direction estimate. Because only the second stage is learned, the intermediate code remains directly inspectable. Experiments use object-conditioned pairs derived from DIS5K, with controlled translations and corruption applied only at test time. For one-pixel motion, DI-Net achieves 0.993 Accuracy on clean pairs and 0.747 under 10% corruption, close to Lucas–Kanade in the same evaluation. Fixed-channel voting reduces clean Accuracy to 0.713, whereas a convolutional network trained directly on the image pair degrades much more sharply under corruption. A multi-step version of the encoder also improves direction classification for displacements from 1 to 16 pixels compared with a parameter-matched one-step control. Tests on selected DAVIS-derived pairs show no statistically resolved difference between DI-Net and the evaluated optical-flow baselines. Taken together, these results support DI-Net as an interpretable, retina-inspired computational model for controlled motion-direction tasks rather than as a physiological account of retinal processing. Full article

This paper investigates hazard assessment and mitigation measures for rainfall-induced debris flow at a highway tunnel portal in Guangdong, China. The tunnel is situated at the outlet of a steep gully with a channel length of 2.38 km, an elevation difference of 682 m, and a gradient of 23.4%. Under extreme rainfall conditions, the portal faces severe risks of scouring and inundation that threaten the structural safety and operational stability of the highway. A high-resolution digital elevation model was established via UAV oblique photogrammetry, and debris flow processes were simulated using HEC-RAS 6.4. The hydrologic behavior under 20-year, 50-year, and 100-year recurrence intervals is first investigated in the research area. After that, the Bingham flow model is used for the debris flow simulation. Results show that as the return period increases from 20 to 100 years, the maximum flow depth at the tunnel portal rises from 1.92 to 2.21 m and the maximum flow velocity rises from 5.8 to 7.8 m/s, indicating that flow velocity is more sensitive to rainfall intensity than flow depth. These simulated flow depths far exceed the 0.5 m flood level stipulated in the Chinese highway tunnel design code, indicating a serious threat to tunnel safety. Based on a quantitative comparison between single-dam and multi-dam schemes, a multi-dam combination with different heights at four positions along the gully is proposed for segmented interception. This multi-dam scheme successfully reduces the flow depth at the tunnel portal to zero without requiring any individual dam to exceed 10 m in height, demonstrating a favorable balance between engineering feasibility and disaster mitigation effectiveness. Full article

Accurate regulation of combustion temperature is critical for objectively evaluating the fire-resistance performance of cables. However, existing temperature control strategies mainly rely on centralized regulation methods, which struggle to simultaneously address the nonlinear coupling among multiple heat sources, spatial thermal non-uniformity, and dynamic temperature fluctuations. To address these challenges, a multi-channel self-adaptive temperature control method based on distributed optimization and computational modeling is proposed in this study. First, a data-driven computational model based on an attention-enhanced multi-channel convolutional neural network is developed to characterize the complex nonlinear relationship between distributed heat inputs and the resulting temperature field, enabling accurate thermal state perception and prediction. Subsequently, a data-driven NSGA-III optimization algorithm is introduced to achieve dynamic allocation and coordinated optimization of heat flux among multiple independent heating channels. Furthermore, a deep reinforcement learning-based adaptive decision framework is established to realize autonomous adjustment of heating strategies under varying testing conditions. The proposed framework integrates thermal modeling, distributed optimization, and intelligent decision-making to achieve real-time adaptive control of multi-source heating systems. Experimental validation on practical fire-resistance testing equipment demonstrates that the proposed framework achieves an R² of 0.9745 with an MAE of 19.90 °C in the closed-loop control evaluation and provides improved spatial thermal uniformity compared with conventional control strategies. Full article

Rice diseases and pest-related damage severely threaten agricultural productivity and global food security. While deep learning has advanced automated crop diagnostics, distinguishing visually similar disease symptoms and damage patterns remains challenging due to subtle visual variations and complex backgrounds. To address this challenge, we introduce DASO-RiceNet (Dual-Attention Semantic Optimization Network), a deep learning framework for fine-grained rice disease and damage classification. The architecture utilizes a multi-stage residual backbone for feature extraction and a sequential dual-attention module combining channel and spatial attention to emphasize diagnostically relevant features while suppressing background information. Evaluated on a ten-class rice disease and damage dataset under a consistent experimental protocol, DASO-RiceNet achieved an accuracy of 0.965, macro precision of 0.963, macro recall of 0.966, macro F1-score of 0.964, and micro F1-score of 0.965, outperforming the evaluated CNN- and transformer-based baseline models. Furthermore, Grad-CAM and LIME provided qualitative insights into model predictions, with the examined examples showing attention to visually apparent symptom-related regions. These results demonstrate the potential of DASO-RiceNet for automated rice disease and damage classification and precision-agriculture applications. Full article

This paper investigates robust output-feedback consensus of linear fractional-order multi-agent systems under external disturbances and communication constraints. A fully distributed framework is developed by integrating local dynamic observers, fractional adaptive edge couplings, and dynamic event-triggered communication. The resulting protocol requires neither the network size nor algebraic connectivity. An explicit locally verifiable triggering condition is derived directly from the weighted broadcast-error term, while positivity of the fractional internal variable and Hölder continuity of Caputo trajectories are used to exclude Zeno behavior. A three-channel fractional bounded-real analysis characterizes the consensus, observer-to-coupling, and observer-to-output mappings and yields a generalized $H_{\infty }$ attenuation bound for the actual plant disagreement. In a six-agent benchmark, the proposed trigger requires 366 transmissions, compared with 3164 for a static trigger and 6000 for periodic communication, corresponding to reductions of $88.43\%$ and $93.90\%$, respectively, with comparable consensus accuracy. Finite-energy disturbance tests, attenuation-certificate analysis, numerical sensitivity studies, and a fractional servo synchronization example further demonstrate the effectiveness and applicability of the proposed method. Full article

Energy labeling converts otherwise difficult-to-observe product energy performance into standardized and comparable information, thereby reshaping market incentives for energy-efficient production; however, whether these incentives translate into more effective utilization of firms’ quasi-fixed productive capacity remains insufficiently understood. This study examines the impact of the China Energy Label (CEL) on firm-level capacity utilization using 41,106 firm-year observations for 3862 Chinese A-share listed firms from 2001 to 2024. Exploiting the staggered inclusion of product categories in the CEL catalogue, we estimate a multi-period difference-in-differences model with firm and year fixed effects, with capacity utilization (CU) measured using a stochastic frontier production function. We also conduct a battery of identification and robustness checks, including event-study tests, the Sun–Abraham estimator, and leave-one-batch-out analyses. The results show that CEL increases CU by approximately 1.2 percentage points under a specification that accounts for time-varying city- and industry-level shocks. The positive effect persists across a range of identification and robustness checks. Mechanism analyses provide suggestive evidence that CEL is positively associated with green managerial cognition, breakthrough innovation, and internationalization. These findings are consistent with these factors serving as potential channels through which CEL may improve CU. The effect is more pronounced among state-owned and loss-making firms. Overall, the findings indicate that energy labeling can improve production-side efficiency by promoting more effective use of firms’ existing productive capacity. Full article

A decline in olfactory ability represents one of the earliest signs of Alzheimer’s disease (AD) and can be valuable information for early diagnosis at the stage of mild cognitive impairment (MCI). Nevertheless, the underlying neurophysiological mechanisms of olfactory impairment have not been systematically studied and, so far, have not been applied to make objective diagnoses using electroencephalography (EEG). To fill this gap, this proof-of-concept study investigates the possibility of using olfactory-evoked EEG complexity to discriminate between healthy subjects (HSs) and MCI patients. To this purpose, a publicly available olfactory oddball EEG-recording dataset was considered. First, a strategy for cleaning the EEG signals was implemented and applied, including exclusion of participants, channels, and epochs affected by substantial artifacts and noise. Then, a dedicated preprocessing pipeline was implemented: in particular, the cleaned signals were partitioned into three temporal intervals according to the stimulus onsets (i.e., pre-stimulus, early post-stimulus, and late post-stimulus periods). For each window of interest, a novel metric—namely, the Multivariate Multiscale Multi-Frequency Entropy (M3FrEn)—was computed across 10 temporal scales. The obtained results showed significant main effects of group and stimulus, as well as a significant group-by-stimulus interaction across all scales, as assessed by linear mixed-effects models. Post hoc analysis revealed a significantly reduced stimulus-related entropy modulation in MCI subjects compared to healthy controls across several early post-stimulus scales, with the strongest effect at scale 6 (adjusted $p=0.0189$, $Hedges’g=-2.88$). An exploratory, fully nested subject-level classification analysis, in which feature selection was performed independently within each cross-validation fold, achieved an accuracy of approximately 92% with all classifiers, consistently relying on the early post-stimulus feature. These preliminary findings suggest that M3FrEn captures olfactory-related EEG alterations in MCI, providing proof-of-concept evidence for its potential as a candidate biomarker. Full article

Fluvial sand bodies represent one of the most significant reservoir types in hydrocarbon exploration. Restricted by climatic conditions, sedimentary environments and the properties of provenance parent rocks, sedimentary characteristics and sand-body architectures exhibit substantial spatial variations across different river systems and along individual river segments. Research on modern sedimentary processes of the Weihe River provides critical insights for advancing continental fluvial sedimentology theories and reconstructing paleoriver sedimentary models for analogous basins. Integrating high-resolution satellite image interpretation combined with systematic field geological surveys across typical river reaches, this study systematically characterizes the spatial differentiation of river patterns, sedimentary signatures, sand-body architectures and their primary controlling factors within the Weihe Basin. The results reveal a distinct three-segment spatial differentiation pattern of fluvial styles in the Weihe Basin: braided rivers dominate the Baoji–Zhouzhi reach; low-sinuosity meandering rivers occur in the reach from Zhouzhi to Lintong; and high-sinuosity meandering rivers prevail in the downstream segments below Lintong. Unique hydrodynamic regimes associated with each river type control sedimentary partitioning and sand-body development. Braided rivers feature intense hydrodynamic force and coarse-grained sediments, with sedimentary assemblages consisting of gravelly channel deposits, mid-channel bars and floodplain deposits, which form thick stacked sand bodies via multi-stage sedimentary superimposition. Low-sinuosity meandering rivers possess moderate hydrodynamic energy and are dominated by sandy-gravel deposits, yielding a complete sedimentary succession composed of channel fills, point bars, natural levees and floodplains. High-sinuosity meandering rivers are characterized by weak hydrodynamic conditions and fine grain sizes dominated by sandstone and mudstone units, developing diverse sedimentary facies including channels, crevasse splays and oxbow lake deposits. The spatial heterogeneity of the sedimentary system across the Weihe Basin is synergistically controlled by the channel gradient, provenance attributes, sediment grain size and sediment concentration. This study clarifies the sedimentary evolutionary laws of modern rivers under semi-arid and semi-humid climatic conditions, enriches fundamental fluvial sedimentology theories, and supplies a modern sedimentary analog for paleoriver identification, paleoenvironmental reconstruction and hydrocarbon exploration targeting fluvial reservoir systems. Full article

Polarimetric synthetic aperture radar (PolSAR) calibration quality assessment is essential for verifying the reliability of polarimetric calibration results and ensuring the accuracy of subsequent quantitative applications. The corner-reflector-based assessment is accurate but depends on field deployment and maintenance, whereas the distributed-target-based methods is easier to automate but is sensitive to mixed scattering within image patches and to non-unique histogram peaks. In addition, the parameter distribution may contain multiple peaks, affecting the uniqueness and stability of assessment. To address these problems, this paper proposes a robust distributed-target-based PolSAR calibration quality assessment (RD-PCQA) method without corner reflectors (CRs) to address these problems. The proposed method first uses hypothesis testing of confidence interval method for PolSAR calibration (PCHTCI) to extract high-quality distributed targets and combines the polarimetric correlation coefficient R_HHVV to select volume-scattering-dominant targets, thereby improving the physical consistency of samples used for channel imbalance amplitude (CIA) estimation. Second, a high-proportion distributed-target constraint is used to refine the samples for channel imbalance phase (CIP) and polarimetric crosstalk estimation, reducing the influence of nonideal scatterers on parameter estimation. Finally, a unique peak searching strategy based on progressively enlarged statistical scales is proposed to suppress the effect of multi-peak distributions on assessment result. Experiments were conducted using three GF-3 PolSAR images acquired over the SAR calibration site in Etuoke Banner, Ordos, Inner Mongolia, China, with CR results used as references, and considering finite sample uncertainty. The experimental results show that, compared with the conventional distributed-target-based method, the proposed method is closer to the corresponding CR mean in all comparisons, with the mean absolute deviations for CIA, CIP and crosstalk scenarios reduced to 0.024 dB, 2.043° and 3.069 dB, respectively. Therefore, it demonstrates the effectiveness and practical potential of the proposed method for PolSAR calibration quality assessment without CRs. Full article

This paper proposes Phase-PCANet, a new image representation method for fingerprint liveness detection. Phase-PCANet integrates both local and global phase features. The local phase, capturing fine-grained edges and textures, is extracted via short-time Fourier transform with singular value decomposition; the global phase, encoding the holistic structural layout, is obtained from a full-image Fourier transform. These two phase components are separately fed into an improved PCANet, which employs dual binary coding, i.e., scalar-based intra-channel coding and vector-similarity-based inter-channel coding, to preserve within-channel structures and cross-channel correlations. Multi-stage features from different PCA layers within each phase path are aggregated, and the resulting deep features from both paths are concatenated to form the final image representation. Experiments on the LivDet 2011, 2013, and 2015 databases verify the effectiveness of the proposed method. Full article