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Semantic relation graph reasoning network for visual question answering 面向视觉问答的语义关系图推理网络
International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2588837
Hong Lan, Pufen Zhang
{"title":"Semantic relation graph reasoning network for visual question answering","authors":"Hong Lan, Pufen Zhang","doi":"10.1117/12.2588837","DOIUrl":"https://doi.org/10.1117/12.2588837","url":null,"abstract":"In order to answer semantically-complicated questions about an image, a Visual Question Answering (VQA) model needs to fully understand the visual scene in the image, especially the dynamic interaction between different objects. This task inherently requires reasoning the visual relationships among the objects of image. Meanwhile, the visual reasoning process should be guided by the information of the question. In this paper, we proposed a semantic relation graph reasoning network, the process of semantic relation reasoning is guided by the cross-modal attention mechanism. In addition, a Gated Graph Convolutional Network (GGCN) constructed based on cross-modal attention weights that novelly injects the semantic interaction information between objects into their visual features, and the features with relational awareness are produced. In particular, we trained a semantic relationship detector to extract the semantic relationship between objects for constructing the semantic relation graph. Experiments demonstrate that proposed model outperforms most state-of-the-art methods on the VQA v2.0 benchmark datasets.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114501632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
An implementation method of ethernet MAC controller IP core 一种以太网MAC控制器IP核的实现方法
International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2588975
Kui Xiong, Zhimei Zhou, Xiaoyong Wang, Jie Zhou
{"title":"An implementation method of ethernet MAC controller IP core","authors":"Kui Xiong, Zhimei Zhou, Xiaoyong Wang, Jie Zhou","doi":"10.1117/12.2588975","DOIUrl":"https://doi.org/10.1117/12.2588975","url":null,"abstract":"Ethernet Media Access Control (MAC) controller is an indispensable IP core in Field-Programmable Gate Array (FPGA), in order to realize the independent intellectual property rights of MAC controller IP core. This paper designs a MAC controller which supports Media Independent interface (MII) / Gigabit Media Independent Interface (GMII) and supports full duplex / half duplex. According to the definition of Ethernet frame format, MAC control frame structure and Station (STA) management frame format in IEEE 802.3 protocol, the overall structure of MAC controller and the function of each module are designed. Advanced High-performance Bus (AHB) and Advanced Peripheral Bus (APB) are used to realize separate access of data cache and configuration register to improve the transmission efficiency of MAC controller bus. The results of Electronic Design Automation (EDA) and FPGA board level verification show that the MAC controller meets the design requirements of data transmission.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121980903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
DOA estimation in sparse array based on matrix completion 基于矩阵补全的稀疏阵列DOA估计
International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2581218
Jinying Gao, Yibin Rui, Yuan Gao, Yuhang Li
{"title":"DOA estimation in sparse array based on matrix completion","authors":"Jinying Gao, Yibin Rui, Yuan Gao, Yuhang Li","doi":"10.1117/12.2581218","DOIUrl":"https://doi.org/10.1117/12.2581218","url":null,"abstract":"This paper presents a novel matrix completion algorithm, penalty decomposition method based augmented Lagrange multipliers (PD-ALM), to improve the performance of Direction Of Arrival (DOA) in sparse array. In PD-ALM algorithm, we apply the penalty decomposition method to solve low-rank matrix completion problem directly. Firstly, we reconstruct a low rank matrix using the special structure of received signals of uniform linear array (ULA). Then, PD-ALM algorithm is used to complete the received signals of the sparse array. Finally, we apply Multiple Signal Classification (MUSIC) algorithm to estimate direction of arrival. The numerical experiments are provided to validate the effectiveness of the proposed algorithm.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134090224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Ground surveillance radar target classification based on 2D CNN 基于二维CNN的地面监视雷达目标分类
International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2581289
Yuhang Li, Yibin Rui, Yuan Gao, Jinying Gao
{"title":"Ground surveillance radar target classification based on 2D CNN","authors":"Yuhang Li, Yibin Rui, Yuan Gao, Jinying Gao","doi":"10.1117/12.2581289","DOIUrl":"https://doi.org/10.1117/12.2581289","url":null,"abstract":"In this paper, a new approach for classifying targets captured by low-resolution Ground Surveillance Radar is proposed. Radar target is detected by the Doppler effect in radar echo signal. Those signals can be disposed in various domains to gain unique features of targets which can be used in radar target classification and enhance its effectiveness. The proposed approach consists of two steps, transforming original signals from 1D to 2D and constructing deep 2D convolution neural networks(CNN). In first step, Toeplitz matrix is made use of reconstructing Radar signal, to build a 2D plane of data. Reconstruction does not change the characteristic distribution of the signal but maps the signal from one to two dimensions in a rearranged method. Whilst,it makes possible of using 2D CNN to train the data. In second step, we take advantage of the “bottleneck” block to create 2D CNN, which guarantee the depth of CNN and ease the problem of vanishing/exploding gradients in back propagation process. method was tested on actual collected database including human and car, which achieve 99.7% accuracy on the original test set and 97% accuracy after adding noise.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127254691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Adaptive synchrosqueezing transform based instantaneous frequency rate estimation 基于自适应同步压缩变换的瞬时频率估计
International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2581384
Yuan Gao, Yibin Rui, Jinying Gao, Yuhang Li
{"title":"Adaptive synchrosqueezing transform based instantaneous frequency rate estimation","authors":"Yuan Gao, Yibin Rui, Jinying Gao, Yuhang Li","doi":"10.1117/12.2581384","DOIUrl":"https://doi.org/10.1117/12.2581384","url":null,"abstract":"The synchrosqueezing transform(SST), a kind of reassignment method, aims to sharpen the time-frequency representation. In this paper, we consider synchrosqueezing transform based short-time fourier transform with instantaneous frequency rate of change to analyze nonlinear and nonstationary signal, called the adaptive synchrosqueezing transform (ASST). Compared with SST, the window width of ASST is adaptively adjusted with the instantaneous frequency rate estimation which is extracted at the signal ridge. The proposed method can generate a more energy concentrated TF representation for the non-stationary signals with fast-varying frequencies. Simulation results are provided to demonstrate the effectiveness of the proposed method.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124310273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A robust adaptive weighted CFAR detector based on truncated statistics 基于截断统计量的鲁棒自适应加权CFAR检测器
International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2581348
Renhong Xie, Junfeng Wei, Xing Wang, Bohao Dong, Peng Li, Yibin Rui
{"title":"A robust adaptive weighted CFAR detector based on truncated statistics","authors":"Renhong Xie, Junfeng Wei, Xing Wang, Bohao Dong, Peng Li, Yibin Rui","doi":"10.1117/12.2581348","DOIUrl":"https://doi.org/10.1117/12.2581348","url":null,"abstract":"Constant false alarm rate (CFAR) detectors are widely used in modern radar system to declare the presence of targets. One or more outliers will appear in the reference cell under the multiple strong interferences situation, and the clutter power estimation will increase, which will affect the detection threshold calculation, the detection probability of CFAR detectors decrease and the alarm rates increase significantly. This paper proposes an adaptive weighted truncation statistic CFAR (AWTS-CFAR) algorithm and achieves good performance. By improving the truncation process, the truncated larger value is adaptively weighted with the smaller value in the reference cell. Since AWTS-CFAR makes the larger value in the reference cell also participate in the calculation of the background clutter power estimation, even if the truncation threshold is selected to be smaller, AWTS-CFAR will not cause too much loss of constant false alarm, and will suppress clutter edge effect as much as possible in the clutter edge environment.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122504518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Specific emitter identification based on Signal-Graph Capsule Network (SGCN) 基于信号图胶囊网络(SGCN)的特定发射机识别
International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2589342
Tingpeng Li, Zhixi Feng, Bowen Zhang, Shuyuan Yang
{"title":"Specific emitter identification based on Signal-Graph Capsule Network (SGCN)","authors":"Tingpeng Li, Zhixi Feng, Bowen Zhang, Shuyuan Yang","doi":"10.1117/12.2589342","DOIUrl":"https://doi.org/10.1117/12.2589342","url":null,"abstract":"As a typical pattern recognition problem, specific emitter identification (SEI) is a crucial step to achieve efficient spectrum sensing. In this work, an emitter identification method based on Signal Graph Capsule Network, which refered as SGCN, is proposed. First, emitter signal is transformed into an undirected graph according to the Euclidean distance from its sampling point, and then take the undirected graph as the input of the network. Second, optimizing the topological structural characteristics by graph convolution operation on this undirected graph. Finally, by introduce the capsule network to improve the generalization ability and enhance the robustness. Extensive analysis and experiments on 30 individual emitters signals demonstrates the attentiveness of the proposed model.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122580281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Sparse tensor recovery based method for MIMO radar high resolution three-dimensional imaging 基于稀疏张量恢复的MIMO雷达高分辨率三维成像方法
International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2589502
Tao Pu, N. Tong, W. Feng, Bin Xue, Pengcheng Wan
{"title":"Sparse tensor recovery based method for MIMO radar high resolution three-dimensional imaging","authors":"Tao Pu, N. Tong, W. Feng, Bin Xue, Pengcheng Wan","doi":"10.1117/12.2589502","DOIUrl":"https://doi.org/10.1117/12.2589502","url":null,"abstract":"To improve the imaging quality and reduce the computation burden, this paper proposes a sparse tensor recovery based method for multiple-input multiple-output (MIMO) radar 3D imaging. Firstly, by constructing the sensing matrices in the range direction and angle directions in a pseudo polar coordinate, the sparse tensor recovery model for target 3D imaging is established. Then, the tensor sequential order one negative exponential (Tensor-SOONE) function is proposed to measure the sparsity of the received signal tensor. At last, the gradient projection (GP) method is employed to effectively solve the sparse tensor recovery problem to get the 3D image of targets. Compared to conventional imaging methods, the proposed method can achieve a high-resolution 3D image of targets with reduced sampling number. Compared to existing sparse recovery based imaging methods, the proposed method has a higher accuracy and robustness, while the computational complexity is relatively small. Simulations verify the effectiveness of the proposed method.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117136116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Mainlobe interference suppression algorithm based on BMP and L2 norm constraint 基于BMP和L2范数约束的主瓣干扰抑制算法
International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2589256
Lu Zhang, Baixiao Chen, Rui Song, Wanjie Song
{"title":"Mainlobe interference suppression algorithm based on BMP and L2 norm constraint","authors":"Lu Zhang, Baixiao Chen, Rui Song, Wanjie Song","doi":"10.1117/12.2589256","DOIUrl":"https://doi.org/10.1117/12.2589256","url":null,"abstract":"The algorithms of blocking matrix preprocessing (BMP) and eigen-projection matrix preprocessing (EMP) to suppress mainlobe interference will lead to the main peak offset. Aiming at dealing with the problem, the conventional methods, such as diagonal loading, diagonal loading combined with linear constraint, whitened, and weight coefficient compensation are used. Guided by the eigen-projection and L2 norm constraint, this paper presents a novel method based on blocking matrix preprocessing and L2 norm constraint. The improved BMP algorithm not only can overcome the deviation of the peak value, but also can achieve excellent performance at low snapshots in comparison with traditional methods. Besides, the accurate direction of sidelobe interference is not required, so the improved method is easy to implement in engineering. The biggest advantage is that the proposed method can maintain the high output signal to interference-plus-noise ratio (SINR) in the case of strong mainlobe jamming. Theoretical analysis and simulations demonstrate that the proposed method has the optimal performance for mainlobe jamming suppression.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"41 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128485702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
An improved depth learning method for surface defect detection 一种改进的深度学习表面缺陷检测方法
International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2589588
Haifeng Lv, Baoming Pu
{"title":"An improved depth learning method for surface defect detection","authors":"Haifeng Lv, Baoming Pu","doi":"10.1117/12.2589588","DOIUrl":"https://doi.org/10.1117/12.2589588","url":null,"abstract":"In order to solve the problems of low recognition rate, incapability of autonomous detection and weak generality of the existing surface defect detection methods, an improved depth learning surface defect detection method is proposed. This method improves the convolution neural network model in depth learning and divides it into two modules: segmentation module and decision module. After preprocessing, the image is input to the segmentation module for training, and then the output of the segmentation module and network features are used as input to the decision module to detect defects in the image. In the improved model, the convolution layer and convolution kernel size in the segmentation module are optimized, and a new convolution network model is constructed. In downsampling, the maximum pool is used instead of the maximum stride, and the loss function and activation function are designed at the same time. Experiments show that the method has a high defect detection accuracy rate of 99%, realizes autonomous detection, and has certain universality.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126225647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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