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International Conference on Signal Processing Systems最新文献

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Graph-based detection and reduced-complexity LDPC decoding over 2D intersymbol interference channels 二维码间干扰信道上基于图的LDPC检测与低复杂度解码
International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2581263
Zhiliang Qin, Yingying Li, Yu Qin, Qidong Lu, Xiaowei Liu
{"title":"Graph-based detection and reduced-complexity LDPC decoding over 2D intersymbol interference channels","authors":"Zhiliang Qin, Yingying Li, Yu Qin, Qidong Lu, Xiaowei Liu","doi":"10.1117/12.2581263","DOIUrl":"https://doi.org/10.1117/12.2581263","url":null,"abstract":"In this paper, we propose a fully graph-based iterative detection and decoding scheme for low-density parity-check (LDPC) coded generalized two-dimensional (2D) intersymbol interference (ISI) channels. The 2D detector consists of a downtrack detector based on the symbol-level sum-product algorithm (SPA) and a bit-level SPA-based crosstrack detector. A LDPC decoder based on simplified check node operations is used to provide soft information for the 2D channel detector. Numerical results show that the proposed receiver significantly reduces the decoding complexity and also achieves better performance as compared with the trellis-based BCJR detector over 2×2 2D channels.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"37 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":"123889087","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
Remote sensing vehicle detection based on embedded system 基于嵌入式系统的遥感车辆检测
International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2588843
Haoxiang Su, Z. Dong, Fan Yang, Yu Lin
{"title":"Remote sensing vehicle detection based on embedded system","authors":"Haoxiang Su, Z. Dong, Fan Yang, Yu Lin","doi":"10.1117/12.2588843","DOIUrl":"https://doi.org/10.1117/12.2588843","url":null,"abstract":"At present, remote sensing image vehicle detection based on deep learning has achieved certain results, but most of them rely on powerful PC computing power and cannot be deployed in satellites, so they cannot provide support for satellite in-orbit detection. Aiming at this problem, this paper proposes a remote sensing image vehicle detection method based on YOLOv5 model and successfully deploys it in Jetson TX2 embedded equipment that can be deployed on a satellite platform. Experiments have proved that the algorithm proposed in this article detects vehicle targets in a 12000*12000 pixels wide remote sensing image in an embedded device, and the detection time is only about 1 minute and 20 seconds at the fastest.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"18 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":"126405120","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
Comparison of five common land cover supervised classification algorithms based on GF-2 and Landsat8 data 基于GF-2和Landsat8数据的5种常见土地覆盖监督分类算法比较
International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2589321
Jiakun Li, Jianhua Huang, Xiaomao Chen, Yang Bai, Huien Shi, Yu Xiao
{"title":"Comparison of five common land cover supervised classification algorithms based on GF-2 and Landsat8 data","authors":"Jiakun Li, Jianhua Huang, Xiaomao Chen, Yang Bai, Huien Shi, Yu Xiao","doi":"10.1117/12.2589321","DOIUrl":"https://doi.org/10.1117/12.2589321","url":null,"abstract":"With the development of remote sensing technology and the differences in remote sensing image classification, it is particularly important to be able to accurately use classification methods to classify images and to compare classification algorithms. In this paper, taking Yangshuo County as the research area, five common supervised classifications, namely support vector machine (SVM), maximum likelihood classification (MLC), neural network (NN), spectral angle mapping (SAM) and spectral information divergence (SID), are used to classify the land cover of remote sensing image data of GF- 2、Landsat8 and its fusion in the same area. The classification results are obtained and compared. Moreover, the overall classification accuracy (OA) and Kappa coefficient are used to evaluate the performance of the image classification algorithm. The results show that both MLC and SVM perform best on these three data sets. For higher spatial resolution GF-2 and fusion data, the OA and Kappa coefficients of both image data classifiers is 10% higher than those of Landsat8 data with higher spectral resolution.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"55 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":"122485728","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 least mean M-estimate adaptive filtering algorithm based on geometric algebra for system identification 基于几何代数的鲁棒最小均值m估计自适应滤波算法用于系统辨识
International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2589392
Shaohui Lv, Haiquan Zhao
{"title":"A robust least mean M-estimate adaptive filtering algorithm based on geometric algebra for system identification","authors":"Shaohui Lv, Haiquan Zhao","doi":"10.1117/12.2589392","DOIUrl":"https://doi.org/10.1117/12.2589392","url":null,"abstract":"In this paper, a novel robust algorithm called geometric algebra least mean M-estimate (GA-LMM) is proposed, which is the extension of the conventional LMM algorithm in GA space. To further improve the convergence performance, variable step-size GA-LMM (VSS-GA-LMM) algorithm is also proposed, which effectively balances the trade-off between convergence rate and steady-state misalignment. Finally, a multidimensional system identification problem is considered to verify the performance of the proposed GA-LMM and VSS-GA-LMM algorithms. Simulation results show that the proposed algorithms are superior to other GA-based algorithms in terms of convergence rate and steady-state misalignment in impulsive noise environments.","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":"121230015","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
A learned detector method for point cloud registration 一种点云配准的学习检测器方法
International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2589346
Liyin Zhang, Yi Yang, Z. Xiong, Liu Chao
{"title":"A learned detector method for point cloud registration","authors":"Liyin Zhang, Yi Yang, Z. Xiong, Liu Chao","doi":"10.1117/12.2589346","DOIUrl":"https://doi.org/10.1117/12.2589346","url":null,"abstract":"In this paper, we propose a Detector-Net method for point cloud registration which learns a 3D feature detector of a specific descriptor. Different from the traditional detectors, deep neural network is used to generate this detector and manual annotation of feature points is not required. Instead, we leverage the aligned point cloud to deduce distinguishing points to generate training data. The indoor point cloud dataset is used as the training set, and experimental results show that the Detector-Net has better accuracy among traditional detectors.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"11719 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":"129045276","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
Fast factorized back-projection for circular synthetic aperture sonar imaging 圆形合成孔径声纳成像的快速分解反投影
International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2581552
Sai Zeng, Wei Fan, Xuanmin Du
{"title":"Fast factorized back-projection for circular synthetic aperture sonar imaging","authors":"Sai Zeng, Wei Fan, Xuanmin Du","doi":"10.1117/12.2581552","DOIUrl":"https://doi.org/10.1117/12.2581552","url":null,"abstract":"Circular synthetic aperture sonar (CSAS) has attracted great attention in the field of high-resolution SAS imaging. Time domain circular synthetic aperture imaging algorithm can adapt the non-uniform sampling on azimuth direction caused by platform non-uniform velocity, it has the advantage of lower memory demand and suitable for parallel computation. However, exact time domain demands huge computation resources. The Fast Factorized Back-Projection (FFBP) time domain imaging algorithms can reduce the computation load dramatically. In this work, the FFBP imaging algorithm has been used in circular SAS trajectories for experiment data. The first step is to split the entire aperture into several subapertures, then, processing the data in sub-apertures with back-projection method. The last step is to obtain the full-aperture CSAS image by merging all sub-images obtained from the sub-aperture processing. The experiment results have been validated the FFBP imaging algorithm compare with reference simulation result. What’s more, the result also shows that the FFBP imaging quality decrease with the approximation error of FFBP increased.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"51 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":"131589565","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
The segmentation algorithm of marine warship image based on mask RCNN 基于掩模RCNN的舰船图像分割算法
International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2589235
Hongyan Chen, Xinle Yu
{"title":"The segmentation algorithm of marine warship image based on mask RCNN","authors":"Hongyan Chen, Xinle Yu","doi":"10.1117/12.2589235","DOIUrl":"https://doi.org/10.1117/12.2589235","url":null,"abstract":"This paper proposes a warship image segmentation algorithm based on Mask RCNN network. Based on the Tensorflow+ Keral deep learning framework, the Mask-RCNN network structure was constructed. The segmentation of the image of warship at sea level was achieved by using the supervised learning method and tagging of the data set. Mask R-CNN is the most advanced convolutional neural network algorithm, which is mainly used for object detection and object instance segmentation of natural images. Due to the difficulty in obtaining warship samples and the insufficient number of data sets, the method of data enhancement is adopted to expand the data set. Through parameter adjustment and experimental verification, the mAP of warship reaches 0.603, which can meet the requirements of high-precision segmentation. The experimental results show that the Mask RCNN model has a very good effect on the image segmentation of naval ships at sea.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"76 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":"131614169","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: 3
Robust and imperceptible digital speech watermarking 鲁棒和难以察觉的数字语音水印
International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2589645
Akanksha J. N., Diya Dhiraj, Hemantha Reddy, Shikha Tripathi
{"title":"Robust and imperceptible digital speech watermarking","authors":"Akanksha J. N., Diya Dhiraj, Hemantha Reddy, Shikha Tripathi","doi":"10.1117/12.2589645","DOIUrl":"https://doi.org/10.1117/12.2589645","url":null,"abstract":"The concept of Speech watermarking has risen to be an efficient and promising solution to safeguard speech signals in today’s world of swiftly advancing communication technologies. In this paper, Robust Principal Component Analysis (RPCA) and Formant Manipulation (FM) have been used to embed the watermark into the host speech signal. RPCA involves obtaining the sparse component of the speech signal for accurate embedding, extraction of the watermark and FM involves modifying the formants by exploiting the properties of Line Spectral Frequencies (LSFs). A non-blind watermark detection scheme has been proposed to detect the watermark which demonstrates better stability and accuracy. Results of performance evaluation reveal that the proposed technique is robust and the watermark embedded is imperceptible. Also, the robustness of the method is verified by testing against several speech processing attacks.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"7 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":"130292755","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
Automatic target recognition method for low-resolution ground surveillance radar based on 1D-CNN 基于1D-CNN的低分辨率地面监视雷达目标自动识别方法
International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2581319
Renhong Xie, Bohao Dong, Peng Li, Yibin Rui, Xing Wang, Junfeng Wei
{"title":"Automatic target recognition method for low-resolution ground surveillance radar based on 1D-CNN","authors":"Renhong Xie, Bohao Dong, Peng Li, Yibin Rui, Xing Wang, Junfeng Wei","doi":"10.1117/12.2581319","DOIUrl":"https://doi.org/10.1117/12.2581319","url":null,"abstract":"This paper proposes a low-resolution ground surveillance radar automatic target recognition(ATR) method based on onedimensional convolutional neural network (1D-CNN), which solves the problem of overfitting using complex CNN for data classification. First, the target recognition algorithm combines the time-domain waveform, power spectrum, and power transform spectrum into the three channels of the established 1D-CNN input. After that, the autoencoder is used to reduce the feature dimension and improve the classifier's ability to select parameters autonomously. Finally, the Bayesian hyperparameter optimization method is used to optimize hyperparameters, which not only simplifies the network structure, but also reduces the parameter calculation scale. We tested our method with the collected data to classify people and cars, and the results showed that the recognition accuracy rate has reached 99%. Compared with the traditional artificial feature extraction target recognition method, our model has better recognition performance and adaptability.","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":"121430072","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
An expectation maximization solution for RSS target localization by Gaussian mixture noise analysis 基于高斯混合噪声分析的RSS目标定位期望最大化解
International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2589432
Kang Li, Jinghua Li, Yutao Jiao, Guoru Ding, Shihua Dong
{"title":"An expectation maximization solution for RSS target localization by Gaussian mixture noise analysis","authors":"Kang Li, Jinghua Li, Yutao Jiao, Guoru Ding, Shihua Dong","doi":"10.1117/12.2589432","DOIUrl":"https://doi.org/10.1117/12.2589432","url":null,"abstract":"RSS-based target localization algorithms are usually derived from channel path-loss model where the measurement noise is generally assumed to obey Gaussian distribution. In this paper, we approximate the realistic measurement noise distribution by a Gaussian mixture model and proposed an improved mixture noise analysis-based RSS target localization algorithm employing expectation maximization, called Gaussian mixture-expectation maximization (GMEM) approach, to estimate target coordinates iteratively, which can be efficiently used for tackling unknown parameters of maximum likelihood estimation and non-convex optimization. Simulations show a considerable performance gain of our proposed localization algorithm in 2-D wireless sensor network.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"1 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":"128450484","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
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