Search Results (120)

This research provides a new systematic solution to the essential issue of self-noise interference in underwater acoustic sensing signals induced by unmanned surface vehicles (USVs) operating at sea. The self-noise pertains to the near-field interference noise generated by the growing diversity and volume of acoustic equipment utilized by USVs. The generating mechanism of self-noise is clarified, and a self-noise propagation model is developed to examine its three-dimensional coupling properties within spatiotemporal fluctuation environments in the time-frequency-space domain. On this premise, the YOLOv11 object identification framework is innovatively applied to the delay-Doppler (DD) feature maps of self-noise, thereby overcoming the constraints of traditional time-frequency spectral approaches in recognizing noise with delay spread and overlapping characteristics. A comprehensive comparison with traditional models like YOLOv8 and SSD reveals that the suggested delay-Doppler YOLO (DD-YOLO) algorithm attains an average accuracy of 87.0% in noise source identification. An enhanced denoising method, termed optimized time-frequency regularized overlapping group shrinkage (OTFROGS), is introduced, using structural sparsity alongside non-convex regularization techniques. Comparative experiments with traditional denoising methods, such as the normalized least mean square (NLMS) algorithm, wavelet threshold denoising (WTD), and the original time-frequency regularized overlapping group shrinkage (TFROGS), reveal that OTFROGS outperforms them in mitigating USV self-noise. This study offers a dependable technological approach for optimizing the performance of USV acoustic systems and proposes a theoretical framework and methodology applicable to different underwater acoustic sensing contexts. Full article

This study investigates on-edge seizure detection that aims to resolve two major constraints that hold the deployment of deep learning models in clinical settings at present. First, centralized training requires gathering and consolidating data across institutions, which poses a serious issue of privacy. Second, a high computational overhead inherent in inference imposes a crushing burden on resource-limited edge devices. Hence, we propose NeuroFed-LightTCN, a federated learning (FL) framework, incorporating a lightweight temporal convolutional network (TCN), designed for resource-efficient and privacy-preserving seizure detection. The proposed framework integrates depthwise separable convolutions, grouped with structured pruning to enhance efficiency, scalability, and performance. Furthermore, asynchronous aggregation is employed to mitigate training overhead. Empirical tests demonstrate that the network can be reduced fully to 70% with a 44.9% decrease in parameters (65.4 M down to 34.9 M and an inferencing latency of 56 ms) and still maintain 97.11% accuracy, a metric that outperforms both the non-FL and FL TCN optimizations. Ablation shows that asynchronous aggregation reduces training times by 3.6 to 18%, and pruning sustains performance even at extreme sparsity: an F1-score of 97.17% at a 70% pruning rate. Overall, the proposed NeuroFed-LightTCN addresses the trade-off between computational efficiency and model performance, delivering a viable solution to federated edge-device learning. Through the interaction of federated-optimization-driven approaches and lightweight architectural innovation, scalable and privacy-aware machine learning can be a practical reality, without compromising accuracy, and so its potential utility can be expanded to the real world. Full article

Predictive maintenance (PdM) is vital to maritime operations; however, the traditional deep learning solutions currently offered heavily depend on centralized data aggregation, which is impractical under the limited connectivity, privacy concerns, and resource constraints found in maritime vessels. Federated Learning addresses privacy by training models locally, yet most FL methods assume homogeneous client architectures and exchange full model weights, leading to heavy communication overhead and sensitivity to system heterogeneity. To overcome these challenges, we introduce FLUID, a dynamic, model-agnostic FL framework that combines client clustering, structured pruning, and student–teacher knowledge distillation. FLUID first groups vessels into resource tiers and calibrates pruning strategies on the most capable client to determine optimal sparsity levels. In subsequent FL rounds, clients exchange logits over a small reference set, decoupling global aggregation from specific model architectures. We evaluate FLUID on a real-world heavy-fuel-oil purifier dataset under realistic heterogeneous deployment. With mixed pruning across clients, FLUID achieves a global $R^2$ of 0.9352, compared with 0.9757 for a centralized baseline. Predictive consistency also remains high for client-based data, with a mean per-client MAE of 0.02575 ± 0.0021 and a mean RMSE of 0.0419 ± 0.0036. These results demonstrate FLUID’s ability to deliver accurate, efficient, and privacy-preserving PdM in heterogeneous maritime fleets. Full article

Task-oriented medical dialogue systems face two fundamental challenges: the explosion of state-action space caused by numerous diseases and symptoms and the sparsity of informative signals during interactive diagnosis. These issues significantly hinder the accuracy and efficiency of automated clinical reasoning. To address these problems, we propose MA-HRL, a multi-agent hierarchical reinforcement learning framework that decomposes the diagnostic task into specialized agents. A high-level controller coordinates symptom inquiry via multiple worker agents, each targeting a specific disease group, while a two-tier disease classifier refines diagnostic decisions through hierarchical probability reasoning. To combat sparse rewards, we design an information entropy-based reward function that encourages agents to acquire maximally informative symptoms. Additionally, medical knowledge graphs are integrated to guide decision-making and improve dialogue coherence. Experiments on the SymCat-derived SD dataset demonstrate that MA-HRL achieves substantial improvements over state-of-the-art baselines, including +7.2% diagnosis accuracy, +0.91% symptom hit rate, and +15.94% symptom recognition rate. Ablation studies further verify the effectiveness of each module. This work highlights the potential of hierarchical, knowledge-aware multi-agent systems for interpretable and scalable medical diagnosis. Full article

The challenge of achieving high-precision direction-of-arrival (DOA) estimation with enhanced degrees of freedom (DOFs) under a limited number of physical array elements remains a critical issue in array signal processing. To address this limitation, this paper makes the following three key contributions: (1) a novel moving sparse array synthesis model incorporating time-frequency-spatial joint processing for coprime frequencies signal sources; (2) an optimized coprime frequencies-based unmanned aerial vehicle array (CF-UAVA) configuration with derived closed-form expressions for the distribution of synthesized array; and (3) two DOA estimation methods: a group sparsity-based approach universally applicable to the proposed aperture synthesis model and a joint group sparsity and virtual array interpolation tailored for the proposed CF-UAVA configuration. Comprehensive simulation results demonstrate the superior DOA estimation accuracy and increased DOFs achieved by our proposed aperture synthesis model and DOA estimation algorithms compared to conventional approaches. Full article

Ground texture-based localization leverages environment-invariant, planar-constrained features to enhance pose estimation robustness, thus offering inherent advantages for seamless localization. However, traditional feature extraction methods struggle with reliable performance under large-scale rotations and texture sparsity in the case of ground texture-based localization. This study addresses these challenges through a learning-based feature extraction framework—Ground Texture Rotation-Equivariant Keypoints and Descriptors (GT-REKD). The GT-REKD framework employs group-equivariant convolutions over the cyclic rotation group, augmented with directional attention and orientation-encoding heads, to produce dense keypoints and descriptors that are exactly invariant to 0–360° in-plane rotations. The experimental results for ground texture localization show that GT-REKD achieves 96.14% matching in pure rotation tests, 94.08% in incremental localization, and relocalization errors of 5.55° and 4.41 px (≈0.1 cm), consistently outperforming baseline methods under extreme rotations and sparse textures, highlighting its applicability to visual localization and simultaneous localization and mapping (SLAM) tasks. Full article

Knowledge graph-enhanced recommendation has gained increasing attention for its ability to provide structured semantic context. However, most existing approaches struggle with two critical challenges: the sparsity of long-tail relations in knowledge graphs and the lack of adaptability to users’ dynamic preferences. In this paper, we propose GLARA, a novel recommendation framework that combines semantic abstraction and behavioral adaptation through a two-stage modeling process. First, a Virtual Relational Knowledge Graph (VRKG) is constructed by clustering semantically similar relations into higher-level virtual groups, which alleviates relation sparsity and enhances generalization. Then, a global Local Weighted Smoothing (LWS) module and a local Graph Attention Network (GAT) are integrated to jointly refine item and user representations: LWS propagates information within each virtual relation subgraph to improve semantic consistency, while GAT dynamically adjusts neighbor importance based on recent interaction signals. Extensive experiments on Last.FM and MovieLens-1M demonstrate that GLARA outperforms state-of-the-art methods, achieving up to 5.8% improvements in NDCG@20, especially in long-tail and cold-start scenarios. Additionally, case studies confirm the model’s interpretability by tracing recommendation paths through clustered semantic relations. This work offers a flexible and interpretable solution for robust recommendation under sparse and dynamic conditions. Full article

In modern data management systems, structured query language (SQL) databases, as a mature and stable technology, have become the standard for processing structured data. These databases ensure data integrity through strongly typed schema definitions and support complex transaction management and efficient query processing capabilities. However, data sparsity—where most fields in large table sets remain unused by most queries—leads to inefficiencies in access optimization. We propose a grouping-based approach (GARMT) that partitions SQL queries into fixed-size groups and applies a modified FP-Growth algorithm (GFP-Growth) to identify frequent table access patterns. Experiments on a real-world dataset show that grouping significantly reduces runtime—by up to 40%—compared to the ungrouped baseline while preserving rule relevance. These results highlight the practical value of query grouping for efficient pattern discovery in sparse database environments. Full article

Traditional sparse code multiple access (SCMA) systems, which transmit user codewords through fixed subcarrier allocations, exhibit vulnerability to external jamming and interference. To address this challenge, we propose a novel SCMA codebook design incorporating the frequency-hopping (FH) technique in this paper. The construction of FH-SCMA codebooks is developed by applying cyclic shifting operations to the factor graph matrix of the conventional SCMA codebooks, where the cyclic shifting patterns are governed by chaotic Bernoulli FH sequences. Through a comprehensive case study, the critical properties of the proposed FH-SCMA codebooks—the uniformity and the sparsity, along with its error-rate performance—are illustrated in detail. Through the proposed FH-SCMA codebooks, the subcarriers of FH-SCMA are randomly hopped over within the resource-block group, while retaining the sparsity requirement, thereby facilitating the multi-user detection at the receiver. The proposed FH-SCMA system (codebooks) achieves superior performance under jamming scenarios compared to both the traditional SCMA and the previous pseudo-random FH-SCMA. Full article

Adaptive optics (AO)-corrected retina flood illumination imaging technology is widely used for investigating both structural and functional aspects of the retina. Given the inherent low-contrast nature of original retinal images, it is necessary to perform image restoration. Total variation (TV) regularization is an efficient regularization technique for AO retinal image restoration. However, a main shortcoming of TV regularization is its potential to experience the staircase effects, particularly in smooth regions of the image. To overcome the drawback, a new image restoration model is proposed for AO retinal images. This model utilizes the overlapping group sparse total variation (OGSTV) as a regularization term. Due to the structural characteristics of AO retinal images, only partial information regarding the PSF is known. Consequently, we have to solve a more complicated myopic deconvolution problem. To address this computational challenge, we propose an ADMM-MM-LAP method to solve the proposed model. First, we apply the alternating direction method of multiplier (ADMM) as the outer-layer optimization method. Then, appropriate algorithms are employed to solve the ADMM subproblems based on their inherent structures. Specifically, the majorization–minimization (MM) method is applied to handle the asymmetry OGSTV regularization component, while a modified version of the linearize and project (LAP) method is adopted to address the tightly coupled subproblem. Theoretically, we establish the complexity analysis of the proposed method. Numerical results demonstrate that the proposed model outperforms the existing state-of-the-art TV model across several metrics. Full article

Spammer group detection is necessary for curbing collusive review spammers on online shopping websites. However, the current detection approaches ignore exploring deep-level suspicious user review relationships and learning group features with low discrimination, which affects detection performance. Furthermore, the interpretation of detection results is easily influenced by noise features and unimportant group structures, leading to suboptimal interpretation performance. Aimed at addressing these concerns, we propose an interpretable review spammer group detection model based on knowledge distillation and counterfactual generation. First, we analyze user review information to generate a suspicious user review relationship graph, combining a graph agglomerative hierarchical clustering approach to discover candidate groups. Second, we devise a knowledge distillation network to learn discriminative candidate group features for detecting review spammer groups. Finally, we design a counterfactual generation model to search important subgraph structures for interpreting the detection results. The experiments indicate that the improvements in our model’s Precision@k and Recall@k are among the top-1000 state-of-the-art solutions on the Amazon, YelpChi, YelpNYC, and YelpZip datasets, which are [13.37%, 72.63%, 37.46%, and 18.83%] and [17.34%, 43.81%, 41.22%, and 21.05%], respectively. The Fidelities of our interpretation results under different Sparsity are around 6%, 7%, 7%, and 6% higher than that of the state-of-the-art solutions on the Amazon, YelpChi, YelpNYC, and YelpZip datasets, respectively. Full article

Location data collected from mobile devices via global positioning system often lack semantic information and can form sparse trajectories in space and time. This study investigates whether user age groups can be accurately classified solely from such sparse spatial–temporal trajectories. We propose a feature extraction method based on a Gaussian mixture model (GMM), which assigns representative points (RPs) by clustering the location data and aggregating user trajectories into these RPs. We then construct three machine learning (ML) models—support vector classifier (SVC), random forest (RF), and deep neural network (DNN)—using the GMM-based features and compare their performance with that of the improved DNN (IDNN), which is an existing feature extraction approach. In our experiments, we introduced a missing value ratio ${\theta }_{\mathrm{th}}$ to quantify trajectory sparsity and analyzed the effect of trajectory sparsity on the classification accuracy and generalizability performance of the ML models. The results indicate that GMM-based features outperform IDNN-based features in both classification accuracy and generalization performance. Notably, the RF model achieved the highest accuracy, whereas the SVC model displayed stable generalizability. As the missing value ratio ${\theta }_{\mathrm{th}}$ increases, the IDNN becomes more susceptible to overfitting, whereas the GMM-based approach preserves accuracy and robustness. These findings suggest that sparse trajectories can still offer meaningful classification performance with appropriate feature design and model selection even without semantic information. This approach holds promise for domains where large-scale, sparse trajectory data are common, including urban planning, marketing analysis, and public policy. Full article

Image denoising is a classic yet challenging problem in low-level image processing. Traditional image denoising approaches using convex regularized prior (e.g., $L_1$-norm) often bring bias problems. To address this issue, a novel prior model based on a family of non-convex functions and group sparsity residual (GSC) prior constraint for image denoising is studied. We propose a generalized non-convex GSC prior model for the image denoising problem. We first utilize the group-sparse representation (GSR) before exploiting image prior information. Specifically, to further improve the image denoising performance of the GSC prior model, we employ several typical non-convex surrogate functions for the sparsity constraint. Then, a fast and efficient thresholding algorithm is proposed to minimize the resulting optimization problem. The experimental results have demonstrated that our proposed method can achieve the best reconstruction results compared with other image denoising approaches. Full article

Recommendation systems offer an effective solution to information overload, finding widespread application across e-commerce, news platforms, and beyond. By analyzing interaction histories, these systems automatically filter and recommend items that are most likely to resonate with users. Recently, with the swift advancement of social networking, group recommendation has emerged as a compelling research area, enabling personalized recommendations for groups of users. Unlike individual recommendation, group recommendation must consider both individual preferences and group dynamics, thereby enhancing decision-making efficiency for groups. One of the key challenges facing recommendation algorithms is data sparsity, a limitation that is even more severe in group recommendation than in traditional recommendation tasks. While various group recommendation methods attempt to address this issue, many of them still rely on single-view modeling or fail to sufficiently account for individual user preferences within a group, limiting their effectiveness. This paper addresses the data sparsity issue to improve group recommendation performance, overcoming the limitations of overlooking individual user recommendation tasks and depending on single-view modeling. We propose MCSS (multi-view collaborative training and self-supervised learning), a novel framework that harnesses both multi-view collaborative training and self-supervised learning specifically for group recommendations. By incorporating both group and individual recommendation tasks, MCSS leverages graph convolution and attention mechanisms to generate three sets of embeddings, enhancing the model’s representational power. Additionally, we design self-supervised auxiliary tasks to maximize the data utility, further enhancing performance. Through multi-task joint training, the model generates refined recommendation lists tailored to each group and individual user. Extensive validation and comparison demonstrate the method’s robustness and effectiveness, underscoring the potential of MCSS to advance state-of-the-art group recommendation. Full article

Non-destructive testing (NDT) enables the determination of internal defects and flaws in concrete structures without damaging them, making it a common application in current bridge concrete inspections. However, due to the complexity of the internal structure of this type of concrete, limitations regarding measurement point placement, and the extensive detection area, accurate defect detection cannot be guaranteed. This paper proposes a method that combines the Simultaneous Algebraic Reconstruction Technique with Group Sparsity Regularization (SART-GSR) to achieve tomographic imaging of bridge concrete under sparse measurement conditions. Firstly, a mathematical model is established based on the principles of the tomographic imaging of bridge concrete; secondly, the SART algorithm is used to solve for its velocity values; thirdly, on the basis of the SART results, GSR is applied for optimized solution processing; finally, simulation experiments are conducted to verify the reconstruction effects of the SART-GSR algorithm compared with those of the SART and ART algorithms. The results show that the SART-GSR algorithm reduced the relative error to 1.5% and the root mean square error to 89.76 m/s compared to the SART and ART algorithms. This improvement in accuracy makes it valuable for the tomographic imaging of bridge concrete and provides a reference for defect detection in bridge concrete. Full article