IEEE open journal of signal processing最新文献_第5页

Quaternion-Based Acoustic SLAM for 3D Drone Localization and Source Mapping 基于四元数的声学SLAM三维无人机定位与源映射

IF 2.7

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-03-01 DOI: 10.1109/OJSP.2026.3689806

Hala Abualsaud;Peter Gerstoft

引用次数: 0

Exact Integration of Stationary Gaussian Process Kernels 平稳高斯过程核的精确积分

IF 2.7

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-01-20 DOI: 10.1109/OJSP.2026.3656062

Ralph J. McDougall;Tim Hargreaves;Simon J. Godsill

{"title":"Exact Integration of Stationary Gaussian Process Kernels","authors":"Ralph J. McDougall;Tim Hargreaves;Simon J. Godsill","doi":"10.1109/OJSP.2026.3656062","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3656062","url":null,"abstract":"In applications such as object tracking and financial time series, it is often desirable to model stochastic processes that are the integral of another stationary process, leading potentially to non-stationarity and infinite variance. In tracking, for example, an object’s velocity may be modeled by a stationary Gaussian Process (GP), while the position over time is an integrated GP (IGP). Such ‘integrated’ processes are also GPs, and we derive closed-form expressions for their kernels when the base kernel is from the Matérn, squared-exponential, rational quadratic, or Laplace class. Particular attention is given to the Matérn kernel, as it has special cases that allow for exact integration in terms of elementary functions. We show that mixtures of these special Matérn kernels also include the family of discrete state-space models (SSMs), thereby allowing integration of these SSMs without a discrete accumulation approximation. We provide an optimized filtering implementation for SSMs with integrated GP dynamics and compare our method with integrated tracking models from the literature on a real-world dataset of agile soccer/football player tracking.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"257-265"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11359464","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Semi-Blind Joint Channel and Symbol Estimation for Beyond Diagonal Reconfigurable Surfaces 超对角可重构曲面的半盲联合信道与符号估计

IF 2.6

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-06-22 DOI: 10.1109/OJSP.2026.3705980

Gilderlan Tavares de Araújo;André L. F. de Almeida;Bruno Sokal;Gabor Fodor

{"title":"Semi-Blind Joint Channel and Symbol Estimation for Beyond Diagonal Reconfigurable Surfaces","authors":"Gilderlan Tavares de Araújo;André L. F. de Almeida;Bruno Sokal;Gabor Fodor","doi":"10.1109/OJSP.2026.3705980","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3705980","url":null,"abstract":"The beyond-diagonal reconfigurable intelligent surface (BD-RIS) is a recent architecture in which scattering elements are interconnected to enhance the degrees of freedom for wave control, yielding performance gains over traditional single-connected RISs. For BD-RIS, channel estimation, which is well studied for conventional RIS, becomes more challenging due to complex connections and a larger number of coefficients. Prior works have relied on pilot-assisted estimation followed by data decoding. This paper introduces a semi-blind tensor-based approach to joint channel and symbol estimation that reduces the need for dedicated training sequences by directly leveraging data symbols. We consider a practical scenario with time-varying user terminal–RIS channels under mobility. By reformulating the received signal from a tensor-decomposition perspective, we develop two semi-blind receivers: a two-stage method that transforms the fourth-order PARATUCK model into a third-order PARAFAC model, and a single-stage iterative process based on the fourth-order TUCKER decomposition. Identifiability conditions for reliable joint recovery are derived, and numerical results demonstrate the performance advantages and trade-offs of the proposed schemes over existing solutions.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"688-703"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11573652","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance of Sequential and Quickest Detection Based on Machine Learning 基于机器学习的顺序快速检测性能

IF 2.6

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-07-30 DOI: 10.1109/OJSP.2026.3718771

Ryan J. Harvey;Paolo Braca;Leonardo M. Millefiori;Peter K. Willett

{"title":"Performance of Sequential and Quickest Detection Based on Machine Learning","authors":"Ryan J. Harvey;Paolo Braca;Leonardo M. Millefiori;Peter K. Willett","doi":"10.1109/OJSP.2026.3718771","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3718771","url":null,"abstract":"With the rapid proliferation of Machine Learning (ML) and Deep Learning (DL) based decision systems, properly characterizing their often unpredictable performance is a key challenge. In this work we introduce data-driven analogues to the Sequential Probability Ratio Test (SPRT), and Page Test, denoted the Sequential Data-Driven Decision Function (S-D3F) and Quickest Data-Driven Decision Function (Q-D3F), respectively. It is shown that the S-D3F and Q-D3F inherit many key properties from the SPRT and Page test which allow for a reliable extension of their performance prediction strategies to these data-driven approaches via the process of statistical characterization. As a result, one is able to perform sequential analysis and quickest detection using a ML driven detector while attaining near-optimal performance and reliable performance modeling; that is, the performance can be predicted from the training data itself. The reliability of these predictions is demonstrated via extensive numerical experimentation, including a challenging low-SNR passive acoustic detection problem via an ambient acoustic dataset injected with a realistic synthetic signal.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"841-865"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11629565","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148780980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TR-ENF: A Trust-Region Optimization Framework for Peak-Agnostic Electric Network Frequency Detection in Audio TR-ENF：音频中峰值不可知电网频率检测的信任域优化框架

IF 2.6

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-09-16 DOI: 10.1109/OJSP.2026.3734810

Christos Korgialas;Ioannis Tsingalis;Constantine Kotropoulos

{"title":"TR-ENF: A Trust-Region Optimization Framework for Peak-Agnostic Electric Network Frequency Detection in Audio","authors":"Christos Korgialas;Ioannis Tsingalis;Constantine Kotropoulos","doi":"10.1109/OJSP.2026.3734810","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3734810","url":null,"abstract":"Electric Network Frequency (ENF) acts as a fingerprint in multimedia forensics. ENF detection remains challenging in short audio segments at low signal-to-noise ratios (SNR) because frequency and amplitude estimation are non-convexly coupled. Existing detectors commonly separate spectral frequency estimation from amplitude fitting, introducing discretization and initialization effects that are most pronounced on short windows. The proposed TR-ENF framework jointly estimates ENF frequency and amplitude in a continuous three-parameter space using exact gradients and Hessians, positive-definite Hessian shifting, and frequency box constraints. Experiments on the ENF-WHU benchmark show a TR-ENF accuracy of 87.0% on 5 s segments and accuracies above 90% for 8–10 s segments, with the 9 s condition providing the most concentrated bootstrap distribution. Under the common empirical median operating rule, the point estimates are competitive with the evaluated likelihood-ratio baselines. A matched 5 s paired bootstrap shows that TR-ENF retains the highest point accuracy, with differences of <inline-formula><tex-math>$+1$</tex-math></inline-formula> to <inline-formula><tex-math>$+4$</tex-math></inline-formula> percentage points. Threshold-free ROC/DET analyses provide complementary evidence, with the strongest 5 s AUC and EER obtained by TR-ENF. Robustness is further characterized through SNR sensitivity, frequency-box and hyperparameter ablations, convergence trajectories, and bootstrap analysis.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"989-1005"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11694287","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Super-Resolution DOA Estimation for Multi Frequency Signals of Polarization Sensitive Array Based on Atomic Norm Minimization 基于原子范数最小化的极化敏感阵列多频信号超分辨方位估计

IF 2.7

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-03-09 DOI: 10.1109/OJSP.2026.3671700

Xuejing Hu;Tao Chen;Haolin Xi;Nan Wang

{"title":"Super-Resolution DOA Estimation for Multi Frequency Signals of Polarization Sensitive Array Based on Atomic Norm Minimization","authors":"Xuejing Hu;Tao Chen;Haolin Xi;Nan Wang","doi":"10.1109/OJSP.2026.3671700","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3671700","url":null,"abstract":"This paper presents a super-resolution direction-of-arrival (DOA) estimation framework for multi-frequency (MF) signals received by polarization-sensitive arrays, based on atomic norm minimization (ANM). To overcome the aperture limitation of compact arrays, a non-redundant virtual array construction method is proposed, transforming frequency diversity into spatial diversity to extend the effective aperture without increasing sensor count. A gridless joint estimation scheme is developed by embedding polarization angle and phase difference into the atomic set. The resulting problem is formulated as a convex semidefinite program (SDP), with closed-form recovery rules for polarization parameter extraction. Theoretical performance is analyzed via the Cramér–Rao Bound (CRB), confirming estimation consistency and identifiability. Simulation results demonstrate that the proposed method achieves super-resolution accuracy even with only two sensors. Real-world experimental validation further confirms its robustness and practical feasibility. Overall, the framework provides an efficient and scalable solution for high-resolution array signal processing, with broad applicability to radar, sonar, wireless localization, and integrated sensing and communications (ISAC) systems.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"393-409"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11425778","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual Representation-Based Light Field View Synthesis using Deformable Convolutional and Deep Residual Channel Attention Networks 基于可变形卷积和深度残差通道注意网络的双表示光场视图合成

IF 2.6

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-09-01 DOI: 10.1109/OJSP.2026.3729157

Muhammad Zubair;Paulo Nunes;Caroline Conti;Luís Ducla Soares

{"title":"Dual Representation-Based Light Field View Synthesis using Deformable Convolutional and Deep Residual Channel Attention Networks","authors":"Muhammad Zubair;Paulo Nunes;Caroline Conti;Luís Ducla Soares","doi":"10.1109/OJSP.2026.3729157","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3729157","url":null,"abstract":"Light Field (LF) cameras simultaneously capture both intensity values and directional information of light rays in a single exposure, providing a unique perspective for computational photography and 3D geometry perception. However, existing LF cameras are constrained by sensor resolution, limiting their ability to capture high spatial and angular resolutions simultaneously. To mitigate these issues, various learning-based methods have been proposed to increase the angular resolution of captured LFs, known as LF View Synthesis (LFVS). Many of these methods either neglect essential geometric cues or rely on neural networks with large receptive fields, which restrict their ability to accurately exploit LF structural characteristics. In response to these challenges, this paper introduces a dual representation-based LFVS method that employs deformable convolutional and Deep Residual Channel Attention (DRCA) networks. The proposed method includes two main modules: (i) Coarse Light Field View Synthesis (CLFVS) for initial LFVS, and (ii) Coarse-To-Fine Refinement (CFR) for final quality enhancement. The CLFVS module relies on deformable convolutions to adaptively extract LF features using two parallel networks: (i) Spatial Feature Extraction (SPFE) network using a depth-dependent LFVS approach, and (ii) Angular Feature Extraction (AFE) network using a non-depth-dependent LFVS approach. The CFR module refines the CLFVS output using a DRCA network, which employs dense residual connections between residual groups instead of conventional convolutional layers. The DRCA network employs Residual Channel Attention Blocks (RCABs) to model inter-channel dependencies, selectively enhancing meaningful features while suppressing irrelevant ones. The proposed method achieves state-of-the-art performance on synthetic and real-world LF benchmarks.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"902-916"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11673011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148985920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DepthPlay-Agent: Agentic Learning and Fusion for Depth Estimation deepplay - agent：深度估计的代理学习和融合

IF 2.6

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-06-15 DOI: 10.1109/OJSP.2026.3703724

Fahad Majeed;Maria Nazir;Zhihe Lu;Marco Agus;Jens Schneider

{"title":"DepthPlay-Agent: Agentic Learning and Fusion for Depth Estimation","authors":"Fahad Majeed;Maria Nazir;Zhihe Lu;Marco Agus;Jens Schneider","doi":"10.1109/OJSP.2026.3703724","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3703724","url":null,"abstract":"Monocular depth estimation in complex, dynamic environments remains challenging due to rapid object motion, texture repetitions, occlusions, and strong geometric constraints inherent in structured scenes. To address these challenges, we propose DepthPlay-Agent (DEAN), an agentic multi-model fusion framework that builds upon SOTA depth estimator backbones such as UNet++, HybridDepth, DINOv3, and ZoeDepth under a learnable Mixture-of-Experts controller. This controller embeds scene context through transformers and uses task priors to dynamically assign pe-pixel fusion weights. A domain-aware refinement module further enforces geometric consistency using planar and semantic segmentation cues. Beyond static fusion, DEAN introduces an agentic inference layer that dynamically regulates expert contributions and refinement strategies, enabling adaptive and interpretable decision-making. Experiments across four benchmarks (SoccerNet-Depth, KITTI, MPI-Sintel, NYU Depth V2) demonstrate that DEAN consistently achieves SOTA performance, improving AbsRel by up to 5% and reducing SILog by 8% over strong baselines. By coupling multi-modal intelligence with structured geometric reasoning, DEAN establishes a new paradigm for adaptive, context-aware depth estimation in dynamic real-world domains.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"678-687"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11563622","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

See What’s Wrong: Visual Counterfactual Explanations for Anomaly Detection via Energy-Guided Restoration 参见什么是错的：通过能量引导恢复异常检测的视觉反事实解释

IF 2.6

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-07-10 DOI: 10.1109/OJSP.2026.3712191

Eirini Cholopoulou;Dimitris K. Iakovidis

{"title":"See What’s Wrong: Visual Counterfactual Explanations for Anomaly Detection via Energy-Guided Restoration","authors":"Eirini Cholopoulou;Dimitris K. Iakovidis","doi":"10.1109/OJSP.2026.3712191","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3712191","url":null,"abstract":"Visual Anomaly Detection (VAD) aims to identify patterns in images that deviate from normality in real-world applications, such as industrial quality inspection and medical diagnosis. While recent generative and reconstruction-based methods have been proposed in the literature, they remain prone to hallucinations, altering or over-correcting normal regions without explicit guidance. Furthermore, these approaches operate as black boxes, which limits user trust. To address these limitations, this paper proposes a novel methodology that regularizes the generative restoration process using a learned signal prior. Unlike traditional methods that blindly restore the entire input and provide only anomaly scores, the proposed approach combines Energy-Based Modeling with diffusion-guided inpainting to generate counterfactual explanations for VAD. More specifically it introduces a lightweight EBM that utilizes a contrastive energy loss to associate high-energy values with potential abnormalities, producing a spatial prior that subsequently constrains the restoration process only to potential anomalies, preserving normal image context and minimizing hallucinations. The restored images then serve as intuitive counterfactual explanations that illustrate how a sample would appear if it were normal, enabling a direct pixel-level comparison between the input and the energy-guided output. Experiments on the MVTec AD and VisA datasets demonstrate that the proposed method achieves an average pixel-level localization AUROC score of 97.5% and 97.3%, respectively, outperforming state-of-the-art reconstruction methods while providing visual counterfactual explanations that preserve the original context.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"822-832"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11603436","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148626186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rigid Body Localization via Gaussian Belief Propagation With Quadratic Angle Approximation 二次角近似高斯信念传播刚体定位

IF 2.6

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-09-07 DOI: 10.1109/OJSP.2026.3731121

Niclas Führling;Hyeon Seok Rou;Giuseppe Thadeu Freitas de Abreu;David González G.;Osvaldo Gonsa

{"title":"Rigid Body Localization via Gaussian Belief Propagation With Quadratic Angle Approximation","authors":"Niclas Führling;Hyeon Seok Rou;Giuseppe Thadeu Freitas de Abreu;David González G.;Osvaldo Gonsa","doi":"10.1109/OJSP.2026.3731121","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3731121","url":null,"abstract":"Gaussian belief propagation (GaBP) is a technique that relies on linearized error and input-output models to yield low-complexity solutions to complex estimation problems, which has been recently shown to be effective in the design of range-based GaBP schemes for stationary and moving rigid body localization (RBL) in three-dimensional (3D) space, as long as the relative rotation between the prior position and the target rigid body is sufficiently small. In this article we present a novel range-based RBL scheme via GaBP that relaxes the latter limitation significantly. To this end, the proposed method incorporates a quadratic angle approximation to linearize the relative orientation between the prior and the target rigid body, enabling high precision estimates of corresponding rotation angles even for large deviations. Leveraging the resulting linearized model, we derive the corresponding message-passing (MP) rules to obtain estimates of the translation vector and rotation matrix of the target rigid body, relative to a prior reference frame. Numerical results corroborate the good performance of the proposed angle approximation itself, as well as the consequent RBL performance in terms of root mean square error (RMSE) in comparison to the state-of-the-art (SotA), while maintaining a low computational complexity.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"944-956"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11680541","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0