2022 14th International Conference on Signal Processing Systems (ICSPS)最新文献_第3页

Aperture Resource Optimization Method for Low Sidelobe Linear Opportunistic Array Based on PSO-CVX Algorithm 基于PSO-CVX算法的低旁瓣线性机会阵列孔径资源优化方法

2022 14th International Conference on Signal Processing Systems (ICSPS) Pub Date : 2022-11-01 DOI: 10.1109/ICSPS58776.2022.00043

Yanwei Zhang, Hailin Li, Yuanjin Tian, Xiao Dong, Ke Wang

引用次数: 0

A Research on Acoustic Hear-Through Technology Based on an Active Noise Cancellation Headphone System 基于有源降噪耳机系统的声学透听技术研究

2022 14th International Conference on Signal Processing Systems (ICSPS) Pub Date : 2022-11-01 DOI: 10.1109/ICSPS58776.2022.00041

Mengyao Jia, Ning Han, Lingchen Zhou

引用次数: 0

Across-scale Local Difference L1-norm Principal Component Analysis Network for Facial Expression Recognition 面部表情识别的跨尺度局部差分l1范数主成分分析网络

2022 14th International Conference on Signal Processing Systems (ICSPS) Pub Date : 2022-11-01 DOI: 10.1109/ICSPS58776.2022.00075

Zhengyan Zhang, Jin Hui, G. Lu, Weijia Huang, Xia Li

{"title":"Across-scale Local Difference L1-norm Principal Component Analysis Network for Facial Expression Recognition","authors":"Zhengyan Zhang, Jin Hui, G. Lu, Weijia Huang, Xia Li","doi":"10.1109/ICSPS58776.2022.00075","DOIUrl":"https://doi.org/10.1109/ICSPS58776.2022.00075","url":null,"abstract":"Facial expression recognition has long been a research hotspot in the fields of artificial intelligence and computer vision due to its various applications. In this paper, we proposed a novel method named across-scale local difference L1-norm principal component analysis network (ALDL1-PCANet) to extract powerful and discriminative expression features. Based on the idea of PCANet model, we construct a multiscale space to calculate across-scale local differences of expression images to obtain the holist and local information. Then, we implement L1-norm PCA to learn the convolution filters of two stages from the across-scale local differences. Afterwards, we encode the output images by binary hash and concatenate all the block-wise histograms to form expression features. Finally, we employ support vector machine (SVM) with linear kernel for classification. Extensive experiments are conducted on both controlled and uncontrolled expression databases, including CK+, JAFFE, ISED and BAUM-2i. Experimental results demonstrate our proposed method outperforms the most of existing methods by effectively extracting powerful and discriminative features from both acted and spontaneous expressions.","PeriodicalId":330562,"journal":{"name":"2022 14th International Conference on Signal Processing Systems (ICSPS)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122947732","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

Deep Multi-feature Fusion CNNs with Gemstone Image Classification Algorithm 基于宝石图像分类算法的深度多特征融合cnn

2022 14th International Conference on Signal Processing Systems (ICSPS) Pub Date : 2022-11-01 DOI: 10.1109/ICSPS58776.2022.00066

Haoyuan Huang, Rongcheng Cui

引用次数: 0

Median Value Filtering Method for Non-Circular Signal DOA Estimation with Nested Arrays in the Presence of Impulsive Noise Scenarios 脉冲噪声条件下嵌套阵列非圆信号DOA估计的中值滤波方法

2022 14th International Conference on Signal Processing Systems (ICSPS) Pub Date : 2022-11-01 DOI: 10.1109/ICSPS58776.2022.00054

Xu-dong Dong, Jun Zhao, Meng Sun, Xiaofei Zhang

{"title":"Median Value Filtering Method for Non-Circular Signal DOA Estimation with Nested Arrays in the Presence of Impulsive Noise Scenarios","authors":"Xu-dong Dong, Jun Zhao, Meng Sun, Xiaofei Zhang","doi":"10.1109/ICSPS58776.2022.00054","DOIUrl":"https://doi.org/10.1109/ICSPS58776.2022.00054","url":null,"abstract":"Direction of arrival (DOA) estimation algorithms in the impulsive noise environment are mostly based on fractional low-order statistics (FLOS), which is not only complicated in calculation, but also poor in adaptable to strong impulsive noise. In order to mitigate the impulsive noise, a new direction finding method-global median value filtering method has recently been proposed, which exploits the characteristic that the impulsive noise appears with randomness. However, this algorithm can not detect more sources than the number of sensors. In this paper, a median filtering method based on non-circular signals is proposed, and it is extended to nested array scenarios, which expands the virtual array aperture and improves the number of estimated sources and DOA estimation performance. Then the traditional second order moment DOA estimation method can be used to process the covariance matrix of the filtered received data. To further reduce the computational complexity of this algorithm, the reduced dimensional MUSIC (RD-MUSIC) method is introduced for DOA estimation. Simulation results show that the method has better DOA estimation performance than existing methods, especially in the strong impulsive noise environment.","PeriodicalId":330562,"journal":{"name":"2022 14th International Conference on Signal Processing Systems (ICSPS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120991364","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

Power Allocation for Non-Coherent Multi-Carrier FSK Underwater Acoustic Communication Systems with Uneven Transmission Source Level 传输源电平不均匀的非相干多载波FSK水声通信系统的功率分配

2022 14th International Conference on Signal Processing Systems (ICSPS) Pub Date : 2022-11-01 DOI: 10.1109/ICSPS58776.2022.00114

Yunfei Zhao, L. Wan, Yougan Chen, En Cheng, Feng Xu, Lei Liang

引用次数: 0

Low-Flying Moving Target Detection and Imaging Algorithm of Spaceborne SAR Based on Two-Dimensional Velocity Search 基于二维速度搜索的星载SAR低空运动目标检测与成像算法

2022 14th International Conference on Signal Processing Systems (ICSPS) Pub Date : 2022-11-01 DOI: 10.1109/ICSPS58776.2022.00081

Ruian Li, He Yan, Chen Wu, Rui Zhao, Jindong Zhang, Daiyin Zhu

{"title":"Low-Flying Moving Target Detection and Imaging Algorithm of Spaceborne SAR Based on Two-Dimensional Velocity Search","authors":"Ruian Li, He Yan, Chen Wu, Rui Zhao, Jindong Zhang, Daiyin Zhu","doi":"10.1109/ICSPS58776.2022.00081","DOIUrl":"https://doi.org/10.1109/ICSPS58776.2022.00081","url":null,"abstract":"Using spaceborne SAR to detect low-flying moving targets is a difficult problem in the application field of SAR. The existence of ground clutter and sea clutter, mutual interference in the detection of slow and high-velocity targets, azimuth Doppler folding of moving targets, range cell migration and other problems lead to serious defocusing of high-speed moving targets, which is difficult to effectively detect in SAR image. Ultimately, these lead to the difficulty of motion parameter estimation and imaging. Therefore, when the traditional SAR imaging algorithm is used to image the moving target of the spaceborne SAR, the target is usually in a defocused state, which leads to the degradation of the detection performance. In this paper, combined with the BP algorithm, a spaceborne SAR moving target detection algorithm based on two-dimensional velocity (range velocity and azimuth velocity) search is designed to match the Doppler parameters. After realizing the ergodic focusing of the moving target at a uniform speed, target detection is followed. Then the detection probability and imaging quality of the moving target can be improved by comparing the associated amplitude values at different search speeds. The simulation results verify the feasibility and effectiveness of the algorithm.","PeriodicalId":330562,"journal":{"name":"2022 14th International Conference on Signal Processing Systems (ICSPS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131892465","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

Multi-view SAR Target Recognition Using Bidirectional Conv-LSTM Network 基于双向卷积lstm网络的多视点SAR目标识别

2022 14th International Conference on Signal Processing Systems (ICSPS) Pub Date : 2022-11-01 DOI: 10.1109/ICSPS58776.2022.00076

Zhe Hu, Gong Zhang, Dai-Yin Zhu

{"title":"Multi-view SAR Target Recognition Using Bidirectional Conv-LSTM Network","authors":"Zhe Hu, Gong Zhang, Dai-Yin Zhu","doi":"10.1109/ICSPS58776.2022.00076","DOIUrl":"https://doi.org/10.1109/ICSPS58776.2022.00076","url":null,"abstract":"Deep neural networks are widely used in synthetic aperture radar (SAR) automatic target recognition (ATR) due to their excellent performance. The imaging mechanism of SAR images differs from that of optical images in that SAR images are highly angle sensitive. However, current SAR ATR methods based on deep learning frameworks generally lack the use of spatial correlation information between multi-view SAR images. In this paper, we propose a multi-view SAR image recognition method based on a bidirectional convolutional long and short term memory (Conv-LSTM) network. Firstly, we use a Log-Gabor filter to extract angle-stable monogenic features to reduce inter-class differences. Secondly, feature dimensionality reduction is performed using a multilayer perceptron (MLP) network. Finally, a bidirectional LSTM network is used to integrate the SoftMax classifier for target recognition. The experimental results on the MSTAR dataset and the self-made dataset show that the average recognition accuracy of our proposed method can reach more than 99%. The results of our method outperform other existing methods, indicating the effectiveness and application potential of our algorithm.","PeriodicalId":330562,"journal":{"name":"2022 14th International Conference on Signal Processing Systems (ICSPS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132004706","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

Visual Garment Collar Style Identification using Deep Neural Networks 基于深度神经网络的视觉服装领型识别

2022 14th International Conference on Signal Processing Systems (ICSPS) Pub Date : 2022-11-01 DOI: 10.1109/ICSPS58776.2022.00073

Zhenxing Li, Diming Zhang, Yuanjiang Li, Lu Wang

{"title":"Visual Garment Collar Style Identification using Deep Neural Networks","authors":"Zhenxing Li, Diming Zhang, Yuanjiang Li, Lu Wang","doi":"10.1109/ICSPS58776.2022.00073","DOIUrl":"https://doi.org/10.1109/ICSPS58776.2022.00073","url":null,"abstract":"Modeling fashion style is key for fashion industry. While traditional manually analysis is slow and inefficient, we aim to automate it using deep neural networks. Particularly, in this paper, we propose a VGG16-based collar recognition network that is more suitable for commercial use to solve the problem of difficult collar recognition in the apparel field. Furthermore, to enable our research and further research using deep learning, we create a dataset called COLLAR 2000. To achieve the commercial network for recognition accuracy, network stability, network complexity and other requirements, we explored traditional machine learning methods as well as mature neural networks, respectively, by adjusting the parameters of PCA, SVM, LeNet, AlexNet, VGG16 and combining migration learning methods to make them more suitable for garment collar recognition. The experimental results show that the VGG16-based collar recognition network is the best for collar recognition (81.7% recognition accuracy, 83.0%precision, 81.7% recall, 0.82 F1-Score), the network stability and complexity are moderate, which is more suitable for commercial use.","PeriodicalId":330562,"journal":{"name":"2022 14th International Conference on Signal Processing Systems (ICSPS)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116231027","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

Improved Minimum Description Length CFAR Based on Weighted Log-Likelihood Estimation 基于加权对数似然估计的改进最小描述长度CFAR

2022 14th International Conference on Signal Processing Systems (ICSPS) Pub Date : 2022-11-01 DOI: 10.1109/ICSPS58776.2022.00035

Zongmin Liu, Renhong Xie, Weilin Wang, Ziye Wang, Yongnan Zhou, Xiaoyan Liu, Peng Li, Yibin Rui

{"title":"Improved Minimum Description Length CFAR Based on Weighted Log-Likelihood Estimation","authors":"Zongmin Liu, Renhong Xie, Weilin Wang, Ziye Wang, Yongnan Zhou, Xiaoyan Liu, Peng Li, Yibin Rui","doi":"10.1109/ICSPS58776.2022.00035","DOIUrl":"https://doi.org/10.1109/ICSPS58776.2022.00035","url":null,"abstract":"Minimum description length CFAR (MDL-CFAR) can improve the detection performance in clutter edge environment, but the detection performance is poor in multi-target environment. In this paper, an improved minimum description length CFAR based on weighted log-likelihood estimation is proposed. The minimum description length method is used for clutter edge location determination, and the final selected sample data set is subjected to weighted log-likelihood estimation to obtain the background clutter power estimate. Comparing the advantages and disadvantages of CFAR algorithm based on minimum description length and CFAR detection algorithm based on weighted log-likelihood estimation, the proposed improved minimum description length CFAR based on weighted log-likelihood estimation (WLL-MDL-CFAR), which combines the advantages of WLL-CFAR and MDL-CFAR algorithms, effectively improves the detection performance in different environments. And at the same time, the ability to maintain a constant false alarm in the clutter-edge environment is guaranteed.","PeriodicalId":330562,"journal":{"name":"2022 14th International Conference on Signal Processing Systems (ICSPS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115320974","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