Digital Signal Processing最新文献_第10页

Poisson multi-bernoulli mixture filter for enhanced tracking of spawning targets 用于增强产卵目标跟踪的泊松-多伯努利混合滤波

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-03-20 DOI: 10.1016/j.dsp.2025.105154

Iman Mirsadraei, Seyed Mohammad-Mahdi Dehghan, Reza Fatemi Mofrad

{"title":"Poisson multi-bernoulli mixture filter for enhanced tracking of spawning targets","authors":"Iman Mirsadraei, Seyed Mohammad-Mahdi Dehghan, Reza Fatemi Mofrad","doi":"10.1016/j.dsp.2025.105154","DOIUrl":"10.1016/j.dsp.2025.105154","url":null,"abstract":"<div><div>This paper introduces an enhanced Poisson multi-Bernoulli mixture (PMBM) filter for spawning targets, wherein spawning refers to the separation of one or multiple objects from an existing target. Tracking such targets poses a significant challenge due to the unknown location at which a target may spawn. Leveraging the information offered by the density of existing group targets, the proposed PMBM filter enables the prediction of spawning for all members. Through modifications based on the latest state of detected group targets in the Bernoulli components, the detection probability for spawning is enhanced, consequently reducing the error stemming from missed targets. This approach yields a favorable trade-off in computational complexity by modeling spawning through the Poisson Random Finite Set (RFS) in the filter, thereby averting the generation of Bernoulli components for spawned and undetected group targets. Monte Carlo simulations indicate that the modified PMBM filter diminishes missed targets and false alarms while enhancing tracking reliability during target spawning events.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"162 ","pages":"Article 105154"},"PeriodicalIF":2.9,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Few-shot learning for signal detection in wideband spectrograms 宽带频谱图中信号检测的少射学习

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-03-20 DOI: 10.1016/j.dsp.2025.105181

Weihao Li , Wen Deng , Keren Wang , Ling You , Zhitao Huang

{"title":"Few-shot learning for signal detection in wideband spectrograms","authors":"Weihao Li , Wen Deng , Keren Wang , Ling You , Zhitao Huang","doi":"10.1016/j.dsp.2025.105181","DOIUrl":"10.1016/j.dsp.2025.105181","url":null,"abstract":"<div><div>Signal detection in the wideband plays an important role in spectrum sensing or reconnaissance tasks. Considering the visualization benefits of the spectrogram and the great developments in deep learning object detection, an increasing number of researchers have implemented deep learning-based signal detection in wideband spectrograms, which obtained remarkable performance. Most existing detection models rely on the availability of abundant labeled training data, but for signal classes with little labeled training data, the detection performance will deteriorate significantly. In this paper, a few-shot signal detection model is proposed to solve this problem. The proposed model is pretrained on abundantly labeled base signal classes and aims to detect novel classes given only a few labeled samples. The model is built on an existing base detector designed specifically for signal detection, and a class-specific convolution kernel generator (CCKG) is proposed to generate convolution kernels by template signals for predictions of signal center frequency and shape attributes. Benefiting from a three-stage meta-learning procedure, the CCKG can play a significant role with only a few input samples. Comprehensive experiments with a simulated signal superimposed on real background dataset and a real-world dataset demonstrate that the proposed method yields significantly better performance than the well-established baseline models.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"162 ","pages":"Article 105181"},"PeriodicalIF":2.9,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Attention-based three-branch network for RGB-D indoor semantic segmentation 基于注意力的RGB-D室内语义分割三分支网络

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-03-20 DOI: 10.1016/j.dsp.2025.105178

Bo Lei, Peiyan Guo, Shaoyun Jia

{"title":"Attention-based three-branch network for RGB-D indoor semantic segmentation","authors":"Bo Lei, Peiyan Guo, Shaoyun Jia","doi":"10.1016/j.dsp.2025.105178","DOIUrl":"10.1016/j.dsp.2025.105178","url":null,"abstract":"<div><div>In indoor scene segmentation, utilizing the complementary information from RGB and depth images has demonstrated robustness and effectiveness in semantic segmentation. However, simple methods such as concatenating RGB and depth features or performing element-wise addition do not fully leverage the potential of multi-modal features. To better integrate these features, an attention-based three-branch RGB-D semantic segmentation network for indoor scenes, named ABTNet is proposed in this paper. First, this network employs a three-branch encoder architecture to extract RGB features, depth features, and fused features, effectively capturing important information while retaining the original RGB-D characteristics. Second, two modules include the Multi-modal Feature Fusion Module (MFFM) and the Multi-level Feature Refinement Module (MFRM) are presented. The MFFM filters RGB and depth features and performs adaptive fusion, while the MFRM achieves high-resolution predictions by integrating features from different levels. Experimental results demonstrate that the proposed model achieves excellent performance on both the NYUDv2 dataset and the more complex SUN-RGBD dataset.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"162 ","pages":"Article 105178"},"PeriodicalIF":2.9,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sub-Nyquist sampling and parameters measurement based on dual-channel random demodulation for FRI signals 基于双通道随机解调的频响信号亚奈奎斯特采样及参数测量

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-03-19 DOI: 10.1016/j.dsp.2025.105163

Guoxing Huang , Wenhao Sheng , Zhan Su , Jingwen Wang , Yu Zhang , Ye Wang

{"title":"Sub-Nyquist sampling and parameters measurement based on dual-channel random demodulation for FRI signals","authors":"Guoxing Huang , Wenhao Sheng , Zhan Su , Jingwen Wang , Yu Zhang , Ye Wang","doi":"10.1016/j.dsp.2025.105163","DOIUrl":"10.1016/j.dsp.2025.105163","url":null,"abstract":"<div><div>Recent developed finite rate of innovation (FRI) technology provides an efficient sub-Nyquist sampling and parameter measurements method for signals. However, due to the unique distinction of frequency spectrum, the existing FRI systems need to be designed according to the spectrum characteristics, which have poor universality. In this paper, we propose a sub-Nyquist sampling and parameter measurements system for FRI signals with non-ideal low-pass filter (LPF). Firstly in the main channel, a spread spectrum technology of random demodulation is used to distribute the frequency domain information over the entire spectrum. Then the spectrum information is obtained by filtering with non-ideal LPF and sampling with low rate analog-to-digital converter. To solve the problem of low reconstruction accuracy caused by the non-ideal effects of LPF, a dual-channel parallel measurement structure is proposed to obtain partial spectrum information of the basic signal. Finally, the random demodulation and filtering process are converted to a minimum L0 norm optimization problem, as well as a parameter estimation algorithm based on sparsity is proposed. We further design the hardware platform of the proposed system and confirm the validity through simulations and hardware experiments. The results demonstrate that the sampling method not only enhances the accuracy of parameters measurement, but also improves the flexibility of the sampling system.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"162 ","pages":"Article 105163"},"PeriodicalIF":2.9,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IDCNet: iterative dual-channel network for camouflaged object detection IDCNet：用于伪装目标检测的迭代双通道网络

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-03-19 DOI: 10.1016/j.dsp.2025.105167

Chuanjiang Wang , Baoqi Liu , Xiankai Hou , Yuepeng Li , Xiujuan Sun

{"title":"IDCNet: iterative dual-channel network for camouflaged object detection","authors":"Chuanjiang Wang , Baoqi Liu , Xiankai Hou , Yuepeng Li , Xiujuan Sun","doi":"10.1016/j.dsp.2025.105167","DOIUrl":"10.1016/j.dsp.2025.105167","url":null,"abstract":"<div><div>Aiming at the problem that current camouflaged object detection in military operations, this paper proposes an iterative dual-channel camouflaged object detection network (IDCNet) for this task. This method employs a dual-channel architecture to explicitly assign the tasks of localization and edge refinement, which consist of a robust localization channel and a global refinement channel. Position and channel attention mechanisms are integrated into each high-level feature in the robust localization channel. The localization prediction map is fused with the localization channel features through global attention to obtain the initial features for the global refinement channel. By incorporating a Mirror Multiplicative Attention mechanism and an attention-guided iterative zooming strategy, IDCNet achieves significant improvements in segmentation accuracy. This method not only demonstrates outstanding performance on military camouflaged object datasets but also exhibits excellent performance on general camouflaged object datasets. The model's Structure-measure (<span><math><msub><mrow><mi>S</mi></mrow><mrow><mi>α</mi></mrow></msub></math></span>) achieve 91.2% on the military camouflage object dataset CamouflageData. On the largest publicly disguised object dataset COD10K, the <span><math><msub><mrow><mi>S</mi></mrow><mrow><mi>α</mi></mrow></msub></math></span> reach 83.3%. These results underscore the potential of IDCNet to substantially enhance battlefield situational awareness and operational decision-making, paving the way for more robust camouflage object detection in real-world military applications.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"162 ","pages":"Article 105167"},"PeriodicalIF":2.9,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel STAP algorithm via volume cross-correlation function on the Grassmann manifold 一种基于格拉斯曼流形上体积互相关函数的STAP算法

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-03-19 DOI: 10.1016/j.dsp.2025.105164

Jia-Mian Li , Jian-Yi Chen , Bing-Zhao Li

{"title":"A novel STAP algorithm via volume cross-correlation function on the Grassmann manifold","authors":"Jia-Mian Li , Jian-Yi Chen , Bing-Zhao Li","doi":"10.1016/j.dsp.2025.105164","DOIUrl":"10.1016/j.dsp.2025.105164","url":null,"abstract":"<div><div>The performance of space-time adaptive processing (STAP) is often degraded by factors such as limited sample size and moving targets. Traditional clutter covariance matrix (CCM) estimation relies on Euclidean metrics, which fail to capture the intrinsic geometric and structural properties of the covariance matrix, thus limiting the utilization of structural information in the data. To address these issues, the proposed algorithm begins by constructing Toeplitz Hermitian positive definite (THPD) matrices from the training samples. The Brauer disc (BD) theorem is then employed to filter out THPD matrices containing target signals, retaining only clutter-related matrices. These clutter matrices undergo eigendecomposition to construct the Grassmann manifold, enabling CCM estimation through the volume cross-correlation function (VCF) and gradient descent method. Finally, the filter weight vector is computed for filtering. By fully leveraging the structural information in radar data, this approach significantly enhances both accuracy and robustness of clutter suppression. Experimental results on simulated and measured data demonstrate superior performance of the proposed algorithm in heterogeneous environments.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"162 ","pages":"Article 105164"},"PeriodicalIF":2.9,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel Dual-Branch Generative Adversarial Network for electrocardiogram data generation 一种新的用于心电图数据生成的双分支生成对抗网络

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-03-19 DOI: 10.1016/j.dsp.2025.105149

Feiyan Zhou , Tianlong Huang

{"title":"A novel Dual-Branch Generative Adversarial Network for electrocardiogram data generation","authors":"Feiyan Zhou , Tianlong Huang","doi":"10.1016/j.dsp.2025.105149","DOIUrl":"10.1016/j.dsp.2025.105149","url":null,"abstract":"<div><div>Computer-assisted electrocardiogram (ECG) analysis is vital for the clinical diagnosis of cardiovascular diseases. However, the performance of many ECG classification methods is adversely affected by data imbalance. Generative Adversarial Networks (GANs) have recently emerged as a promising approach to address this challenge. Nevertheless, existing GAN models for ECG generation typically utilize a single generator structure, which limits their ability to generate complex ECG waveforms. To address this limitation, this paper proposes a novel dual-branch GAN model that integrates the strengths of Transformer and Long Short-Term Memory (LSTM) networks, along with self-attention mechanisms, to enhance the quality of ECG generation. The effectiveness of the proposed method was validated using the internationally recognized MIT-BIH Arrhythmia Database (MIT-BIH-AR) and the Chinese Cardiovascular Disease Database (CCDD). By incorporating the ECG data generated by the proposed model into the training set, the classification accuracy on the MIT-BIH-AR database for four diseases improved from 90.98 % to 96.66 %. Similarly, the classification accuracy for ventricular premature beats on the CCDD increased from 98.51 % to 99.34 %. The experimental results demonstrate that the proposed generative model can produce more realistic ECG data, thereby enhancing the performance of subsequent classification tasks.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"162 ","pages":"Article 105149"},"PeriodicalIF":2.9,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed wavelet-learning-based fusion estimation for multirate multisensor systems with integer and non-integer multiple sample ratios 整数和非整数多样本比多速率多传感器系统的分布式小波学习融合估计

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-03-18 DOI: 10.1016/j.dsp.2025.105162

Rusheng Wang , Weihang Jin , Han Li , Bo Chen , Yan Han , Li Yu

{"title":"Distributed wavelet-learning-based fusion estimation for multirate multisensor systems with integer and non-integer multiple sample ratios","authors":"Rusheng Wang , Weihang Jin , Han Li , Bo Chen , Yan Han , Li Yu","doi":"10.1016/j.dsp.2025.105162","DOIUrl":"10.1016/j.dsp.2025.105162","url":null,"abstract":"<div><div>This paper explores the asynchronous fusion estimation problem of multirate multisensor systems under sensor time-varying sampling. First, the sensors are divided into integer and non-integer multiples based on the ratio of the sensor sampling rate to the state updating rate. Under the case of integer multiple, a synchronous state space model is established by state projection through wavelet transform, and then a Kalman-based multirate estimator is designed to calculate local estimation. Under the case of no-integer multiple, a measurement compensation mechanism is designed using the measurement information closest to the state updating instant, and then a back propagation neural network is constructed to obtain the corresponding estimation. Based on the above obtained local estimation, a learning-based fusion criterion is developed by taking into account the time difference between the state updating moment and the measurement sampling moment. Finally, the simulation results demonstrate the advantages and effectiveness of the proposed method.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"162 ","pages":"Article 105162"},"PeriodicalIF":2.9,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Controllable adaptive pruning for DCNN filters based on significant convolution kernel of input channels 基于输入通道显著卷积核的DCNN滤波器可控自适应剪枝

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-03-18 DOI: 10.1016/j.dsp.2025.105161

Jiajian Cai , Suyun Lian , Yang Zhao , Jihong Zhu , Jihong Pei

{"title":"Controllable adaptive pruning for DCNN filters based on significant convolution kernel of input channels","authors":"Jiajian Cai , Suyun Lian , Yang Zhao , Jihong Zhu , Jihong Pei","doi":"10.1016/j.dsp.2025.105161","DOIUrl":"10.1016/j.dsp.2025.105161","url":null,"abstract":"<div><div>With the development of the Internet of Things (IoT) and autonomous driving, many edge devices need to deploy deep network models to improve their performance. However, deep neural network models typically involve a substantial number of parameters and computational demands. Filter pruning can substantially compress and accelerate the deep network models, transforming them into compact and lightweight versions, and facilitating more efficient deployment on edge devices. In this paper, a controlled adaptive pruning method based on significant convolutional kernels of input channels called CAPSCK is proposed for DCNN filters. First, the significance of each group of input channel convolutional kernels is evaluated. The significant convolutional kernels (SCK) are used to measure filter importance, with interference caused by insignificant convolutional kernels minimized during evaluation. Second, a controllable adaptive pruning (CAP) evaluation is constructed. This assesses pruning sensitivity by utilizing the baseline pruning rate for each layer, which is determined based on filter importance. It reduces the subjectivity caused by manually setting the pruning rate. Experiments of pruning on multiple datasets and different networks show that CAPSCK can effectively compress and accelerate network models. For example, on CIFAR-10 with VGG16, CAPSCK achieves a compression ratio of 12.58× and an acceleration ratio of 4.14×, with only 0.16% drop in accuracy. The compression and acceleration performance on various datasets and networks surpasses several state-of-the-art methods.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"162 ","pages":"Article 105161"},"PeriodicalIF":2.9,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MdeBEIA: Multi-task deep leaning for butterfly ecological image analysis 基于多任务深度学习的蝴蝶生态图像分析

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-03-17 DOI: 10.1016/j.dsp.2025.105168

Kunkun Zhang , Xin Chen , Bin Wang

{"title":"MdeBEIA: Multi-task deep leaning for butterfly ecological image analysis","authors":"Kunkun Zhang , Xin Chen , Bin Wang","doi":"10.1016/j.dsp.2025.105168","DOIUrl":"10.1016/j.dsp.2025.105168","url":null,"abstract":"<div><div>Butterfly ecological image analysis (BEIA) is an exciting and essential field where computer vision can significantly aid in ecological research and biodiversity conservation. Although deep learning has made significant strides in BEIA, the existing models still handle tasks such as segmentation and classification independently, which constrains the potential performance improvements gained by exploiting the correlations between these tasks. In this paper, we design a multi-task deep learning model, named MdeBEIA, to perform both segmentation and classification tasks for BEIA. The MdeBEIA model features a unified encoder that extracts global semantics and spatial information from butterfly images, creating a shared feature representation for both tasks. This approach leverages the intrinsic correlations between segmentation and classification to enhance feature learning. To further boost classification performance, we integrate a Region of Interest Guidance Module (RIGM), which uses intermediate segmentation masks and a self-attention mechanism to refine feature learning by emphasizing contextual relationships. Additionally, we employ a deep mutual learning strategy to improve the model's performance and generalization ability. Experimental results show that MdeBEIA achieves a Jaccard score of 94.70 % in segmentation, surpassing the state-of-the-art by 0.93 %, with comparable inference speeds. In classification, it outperforms the state-of-the-art by 0.81 %, reaching 98.34 %.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"162 ","pages":"Article 105168"},"PeriodicalIF":2.9,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0