Search Results (1,679)

Steganalysis is a critical research direction in the field of information security. Traditional approaches typically employ convolution operations for feature extraction, followed by classification on noise residuals. However, since steganographic signals are inherently weak, convolution alone cannot fully capture their characteristics. To address this limitation, we propose a steganalysis method based on relationship mining, termed RMNet, which leverages positional relationships of steganographic signals for detection. Specifically, features are modeled as graph nodes, where both locally focused and globally adaptive dynamic adjacency matrices guide the propagation paths of these nodes. Meanwhile, the results are further constrained in the feature space, encouraging intra-class compactness and inter-class separability, thereby increasing inter-class separability of positional features and yielding a more discriminative decision boundary. Additionally, to counter signal attenuation during network propagation, we introduce a multi-scale perception module with cross-attention fusion. Experimental results demonstrate that RMNet achieves performance comparable to state-of-the-art models on the BOSSbase and BOWS2 datasets, while offering superior generalization capability. Full article

Wireless contactless human activity sensing is a new type of sensing method that uses the propagation characteristics of wireless signals to accurately perceive and understand human behavior. However, facing the huge amount of newly generated data and expanding action categories in the sensing process, the traditional model needs to be retrained frequently. This model not only brings significant computational power overhead, but also seriously affects the real-time response speed of the system. To address this problem, this paper proposes a model, WIS (Wireless Incremental Sense), which is composed of two parts. The first part is the basic sensing module NFFCN (Nearest Neighbor Feature Fusion Classification). NFFCN is a fusion classification method based on Nearest Class Mean (NCM) classifier and Random Forest (RF). By combining the local feature extraction ability of NCM and the integrated learning advantage of RF, this method can efficiently extract human behavior features from wireless signals and achieve high-precision classification. The second part is the incremental learning module NFFCN-RTST, which uses the retraining subtree (RTST) incremental strategy to optimize the model. Unlike update leaf statistics (ULS) and the Incrementally Grow Tree (IGT) incremental strategy, RTST not only updates the statistical data of leaf nodes but also dynamically adjusts the previously learned splitting function, so as to better adapt to new data and categories. In the experimental validation on the rRuler and Stan WiFi datasets, the average recognition accuracy of NFFCN reaches 87.1% and 98.4%, respectively. In the class-incremental experimental validation, the recognition accuracy of WIS reaches 87% and 95%, respectively. Full article

Point cloud quality assessment remains a critical challenge due to the high dimensionality and irregular structure of 3D data, as well as the need to align objective predictions with human perception. To solve this, we suggest a novel graph-based learning architecture that integrates perceptual features with advanced graph neural networks. Our method consists of four main stages: First, key perceptual features, including curvature, saliency, and color, are extracted to capture relevant geometric and visual distortions. Second, a graph-based representation of the point cloud is created using these characteristics, where nodes represent perceptual clusters and weighted edges encode their feature similarities, yielding a structured adjacency matrix. Third, a novel Graph Attention Network Transformer Fusion (GATF) module dynamically refines the importance of these features and generates a unified, view-specific representation. Finally, a Graph Convolutional Network (GCN) regresses the fused features into a final quality score. We validate our approach on three benchmark datasets: ICIP2020, WPC, and SJTU-PCQA. Experimental results demonstrate that our method achieves high correlation with human subjective scores, outperforming existing state-of-the-art metrics by effectively modeling the perceptual mechanisms of quality judgment. Full article

Rapid urbanization drives significant land use transformations, making the timely detection of newly constructed buildings a critical research focus. This study presents a novel unsupervised framework that integrates pixel-level change detection with object-level, mono-temporal building information to identify new constructions. Within this framework, we propose the Building Line Index (BLI) to capture structural characteristics from building edges. The BLI is then combined with spectral, textural, and the Morphological Building Index (MBI) to extract buildings. The fusion weight (φ) between the BLI and MBI was determined through experimental analysis to optimize performance. Experimental results on a case study in Wuhan, China, demonstrate the method’s effectiveness, achieving a pixel accuracy of 0.974, an average category accuracy of 0.836, and an Intersection over Union (IoU) of 0.515 for new buildings. Critically, at the object-level—which better reflects practical utility—the method achieved high precision of 0.942, recall of 0.881, and an F1-score of 0.91. Comparative experiments show that our approach performs favorably against existing unsupervised methods. While the single-case study design suggests the need for further validation across diverse regions, the proposed strategy offers a robust and promising unsupervised pathway for the automatic monitoring of urban expansion. Full article

As a fundamental element in industrial fluid transportation, the main pump fulfills an irreplaceable function in critical infrastructure, including the energy, water conservancy, petrochemical, and sewage treatment industries. As the core component of key power equipment, its operating condition is intrinsically connected to the safety, stability, and reliability of the entire system. This paper provides a systematic review of the latest advances in fault mechanism analysis and diagnosis methods for main pump systems. First, the typical structural composition and functional characteristics of the main pump system are examined, and the occurrence mechanisms and evolution rules of typical faults, such as mechanical malfunctions and performance degradation caused by hydraulic imbalance, are discussed in detail. Second, the main technical approaches to fault diagnosis are summarized and reviewed, including diagnosis methods based on signal processing, modeling, data-driven techniques, and multi-source information fusion. The advantages, limitations, and application scopes of these approaches are comparatively analyzed. On this basis, the development trends in main pump fault diagnosis technology and the key challenges faced—such as strong noise, small sample size, and multiple fault coupling—are identified and discussed. Finally, future research prospects are put forward in view of the limitations of current research. This review aims to provide theoretical insights and technical support for advancing condition monitoring, fault diagnosis, and health management of main pump systems. Full article

Synthetic Aperture Radar (SAR) three-dimensional (3D) imaging has considerable potential in disaster monitoring and topographic mapping. Conventional 3D SAR imaging techniques for unmanned aerial vehicle (UAV) formations require rigorously regulated vertical or linear flight trajectories to maintain signal coherence. In practice, however, restricted collaboration precision among UAVs frequently prevents adherence to these trajectories, resulting in blurred scattering characteristics and degraded 3D localization accuracy. To address this, a 3D reconstruction technique based on horizontal-cross configurations is proposed, which establishes a new theoretical framework. This approach reduces stringent flight restrictions by transforming the requirement for vertical baselines into geometric flexibility in the horizontal plane. For dual-UAV subsystems, a geometric inversion algorithm is developed for initial scattering center localization. For multi-UAV systems, a multi-aspect fusion algorithm is proposed; it extends the dual-UAV inversion method and incorporates basis transformation theory to achieve coherent integration of multi-platform radar observations. Numerical simulations demonstrate an 80% reduction in implementation costs compared to tomographic SAR (TomoSAR), along with a 1.7-fold improvement in elevation resolution over conventional beamforming (CBF), confirming the framework’s effectiveness. This work presents a systematic horizontal-cross framework for SAR 3D reconstruction, offering a practical solution for UAV-based imaging in complex environments. Full article

Background/Objectives: Utilization of polyetheretherketone (PEEK) cages for spinal fusion has surged in the U.S., yet comprehensive comparisons evaluating its postoperative effectiveness with alternative materials remain limited. This systematic review investigates the efficacy of PEEK cages against traditional fusion materials across various surgery types, elucidating PEEK’s impact on fusion rates, postoperative outcomes, and long-term success. Methods: A systematic search of PubMed, CINAHL, Scopus, Embase, and Web of Science was conducted through 14 October 2024. Included studies were randomized controlled trials (RCTs) comparing PEEK cages with titanium, silicon nitride, and metal-coated PEEK cages for anterior cervical discectomy and fusion (ACDF), posterior lumbar interbody fusion (PLIF), and transforaminal lumbar interbody fusion (TLIF). Article quality was assessed using GRADE criteria. Results: From 288 initially screened articles, 25 RCTs involving 2046 patients (mean follow-up 23.1 ± 18.2 months) met inclusion criteria and were determined as moderate (n = 21) or high (n = 4) quality. Fusion rates by cage material for PEEK (n = 1041), Ti-PEEK (n = 291), and titanium (n = 53) were 85.63 ± 18.00%, 80.05 ± 19.9%, and 92.75 ± 11.31%, respectively. In ACDF, titanium cages achieved higher fusion rates than PEEK (100% vs. 94%). In PLIF and TLIF, coated PEEK outperformed uncoated PEEK (75% vs. 71% and 94% vs. 84%, respectively). Uncoated PEEK achieved fusion rates of 94.04 ± 5.04% for ACDF, 71.21 ± 21.93% for PLIF, and 83.50 ± 24.66% for TLIF, with titanium outperforming PEEK in early fusion outcomes. Coated PEEK demonstrated potential improvements in fusion rates over uncoated PEEK in PLIFs and TLIFs. Conclusions: Selection of cage material for spinal fusions should be tailored to surgical requirements and patient needs. While titanium and PEEK are effective, their performance varies across contexts. New materials and surface modifications may enhance these outcomes further, warranting future research in long-term studies and development of novel materials. These findings can help surgeons choose cage materials according to procedure type, patient characteristics, and imaging needs. Full article

The detection of abnormal behavior has consistently garnered significant attention. Conventional methods employ vision-based dual-stream networks or 3D convolutions to represent spatio-temporal information in video sequences to identify normal and pathological behaviors. Nonetheless, these methodologies generally employ datasets balanced across data categories and consist solely of two classifications. In actuality, anomalous behaviors frequently display multi-category characteristics, with each category’s distribution demonstrating a pronounced long-tail phenomenon. This paper presents a video-based technique for detecting multi-category abnormal behavior, termed the Spatio-Temporal Fusion–Temporal Difference Network (STF-TDN). The system first employs a temporal difference network (TDN) model to encapsulate movie temporal dynamics via local and global modeling. To enhance recognition performance, this study develops a feature fusion module—Spatial-Temporal Fusion (STF)—which augments the model’s representational capacity by amalgamating spatial and temporal data. Furthermore, given the long-tailed distribution characteristics of the datasets, this study employs focused loss rather than the conventional cross-entropy loss function to enhance the model’s recognition capability for under-represented categories. We perform comprehensive experiments and ablation studies on two datasets. Precision is 96.3% for the Violence5 dataset and 87.5% for the RWF-2000 dataset. The results of the experiment indicate the enhanced efficacy of the proposed strategy in detecting anomalous behavior. Full article

With the continuous advancement of urban construction, inertial sensors are increasingly used in the detection of underground pipelines. However, inertial measurement units (IMUs) are susceptible to a variety of error sources, leading to the accumulation of position estimation errors over time, which severely restricts their positioning accuracy. This paper provides a systematic review of IMU calibration and drift suppression error compensation methods applicable to underground pipeline environments. Building upon this foundation, it innovatively proposes a three-tiered review framework based on “error characteristics–compensation mechanisms–application scenarios”. The framework begins with the characterization of error factors, maps them to corresponding compensation mechanisms, and then applies them to specific operating conditions. It identifies current research limitations in real-time performance, robustness, experimental validation, and standardized evaluation. Future efforts should focus on designing lightweight fusion algorithms, integrating deep learning with sensor fusion, and establishing standardized testing platforms. This paper aims to summarize the current state and development trends of inertial sensor error compensation methods, providing a reference for advancing related technologies while offering beginners a clear and systematic learning path. Full article

In strong clutter and maneuvering scenarios, radar track initiation faces the dual challenges of a low initiation rate and high false alarm rate. Although the existing deep learning methods show promise, the commonly adopted “feature flattening” input strategy destroys the intrinsic temporal structure and feature relationships of track data, limiting its discriminative performance. To address this issue, this paper proposes a novel radar track initiation method based on Dual-Attention Temporal Convolutional Network (DA-TCN), reformulating track initiation as a binary classification task for very short multi-channel time series that preserve complete temporal structure. The DA-TCN model employs the TCN as its backbone network to extract local dynamic features and innovatively constructs a dual-attention architecture: a channel attention branch dynamically calibrates the importance of each kinematic feature, while a temporal attention branch integrates Bi-GRU and self-attention mechanisms to capture the dependencies at critical time steps. Ultimately, a learnable gated fusion mechanism adaptively weights the dual-branch information for optimal characterization of track characteristics. Experimental results on maneuvering target datasets demonstrate that the proposed method significantly outperforms multiple baseline models across varying clutter densities: Under the highest clutter density, DA-TCN achieves 95.12% true track initiation rate (+1.6% over best baseline) with 9.65% false alarm rate (3.63% reduction), validating its effectiveness for high-precision and highly robust track initiation in complex environments. Full article

The vibration signals of gas turbine rolling bearings exhibit significant non-stationarity under complex operating conditions such as frequent start-stop cycles and variable speeds, posing a major challenge for fault diagnosis. To address this issue, this paper proposes a multi-channel variable-speed attention framework (MC-VSAttn). The method first constructs multi-channel inputs to capture rich fault information, then introduces a dynamic time-varying response module to adaptively model non-stationary features, and combines channel and spatial joint attention mechanisms to enhance selective attention to critical information, thereby achieving robust fault identification under complex operating conditions. Compared with existing methods, the proposed framework explicitly models the time-varying characteristics of non-stationary signals and jointly integrates multi-channel fusion with hierarchical attention, enabling more accurate and stable fault diagnosis across variable-speed scenarios. Experimental results based on the variable-speed datasets from Tsinghua University and Huazhong University of Science and Technology show that MC-VSAttn achieves accuracy rates of 99.14% and 98.23%, respectively. Further ablation experiments validate the key role of the dynamic time-varying response module and the joint attention mechanism in performance improvement. Full article

Background/Objectives: To determine the clinical features of different types of intermittent exotropia according to the distance and near angles of exodeviation. Methods: This study included 5331 patients with intermittent exotropia. The patients were divided into three groups according to the near-distance differences in their exodeviations: (1) Basic-type: difference between distant and near angles of the exodeviation < 10 prism diopters (PD); (2) Convergence insufficiency (CI)-type: near-distance angle ≥ 10 PD; (3) Divergence excess (DE)-type: distance-near angle ≥ 10 PD. The main outcome measures were demographics, clinical characteristics of exotropia, subjective symptoms, medical history, and family history. Results: Overall, 4599 (86.2%) patients had basic-type exotropia, 500 (9.4%) had CI-type, and 232 (4.4%) had DE-type exotropia. Older age and greater magnitude of myopia were associated with CI-type exotropia. A-pattern exotropia, superior oblique (SO) overaction, good fusional control, good stereoacuity, and diplopia were most common in CI-type exotropia. SO underaction and photophobia were most frequently observed in DE-type exotropia compared to the other types. Conclusions: The clinical characteristics varied among the different types of intermittent exotropia. CI-type exotropia was most frequently associated with older age and greater myopia. DE-type exotropia was associated with frequent photophobia. Full article

Underwater vehicles are widely used in underwater salvage and underwater photography. However, the processing of underwater images has always been a significant challenge. Due to low light conditions in underwater environments, images are often affected by color casts, low visibility and missing edge details. These issues seriously affect the accuracy of underwater object detection by underwater vehicles. To address these problems, an underwater low-light image enhancement method based on image fusion and color balance is proposed in this paper. First, color compensation and white balance algorithms are employed to restore the natural appearance of the images. The texture characteristics of these white-balanced images are then enhanced using unsharp masking (USM) technology. Subsequently, a dual channel dehazing is applied, the image visibility is improved and the blocking artifacts common in traditional dark channel dehazing is avoided. Finally, through multi-scale fusion, the sharpened and dehazed image are combined to obtain the final enhanced image. In quantitative analysis, PSNR (Peak Signal-to-Noise Ratio), SSIM (Structural Similarity index), UIQM (Underwater Image Quality Measurement) and UCIQE (Underwater Color Image Quality Evaluation) were 28.62, 0.8753, 0.8831 and 0.5928, respectively. The results show that the images generated by this enhancement technique have higher visibility compared with other methods. It also produces images with more details while preserving edge information. Full article

W/EUROFER functionally graded material (FGM) plasma-sprayed coatings are used as a protective layer in nuclear fusion applications. It is vital to develop a non-destructive test method to analyze interface characteristics and detect delamination in coatings. A phased array ultrasonic test method was developed in this work to analyze the coating interface characteristics. Two types of coated samples were tested: first, a W/EUROFER FGM-coated flat small sample, and secondly, a large-scale L-shape 50% W and 50% EUROFER curve-coated sample. The phased array ultrasonic test method reliably detected two separate interfaces in W/EUROFER FGM coating, and no delamination was detected, which was verified by cross-sectional image analysis. Secondly, the phased array ultrasonic test precisely detected delamination created during deposition in a large-scale L-shape 50% W and 50% EUROFER curve coated sample. The accuracy in detecting delamination was verified by cross-sectional images of the interface. The phased array ultrasonic test was found to be a reliable method for detecting delamination in multilayer coatings from small-scale to large-scale curved components. Full article

Rolling bearings constitute critical rotating components within rolling mill equipment. Production efficiency and the operational safety of the whole mechanical system are directly governed by their operational health state. To address the dual challenges of the over-reliance of conventional diagnostic methods on expert experience and the scarcity of fault samples in industrial scenarios, we propose a virtual–physical data fusion-optimized intelligent fault diagnosis framework. Initially, a dynamics-based digital twin model for rolling bearings is developed by leveraging their geometric symmetry. It is capable of generating comprehensive fault datasets through parametric adjustments of bearing dimensions and operational environments in virtual space. Subsequently, a symmetry-informed architecture is constructed, which integrates multi-scale convolutional neural networks with attention mechanisms and bidirectional gated recurrent units (MCNN-AT-BiGRU). This architecture enables spatiotemporal feature extraction and enhances critical fault characteristics. The experimental results demonstrate 99.5% fault identification accuracy under single operating conditions. It maintains stable performance under low SNR conditions. Furthermore, the framework exhibits superior generalization capability and transferability across the different bearing types. Full article