发布求助
文献互助 智能选刊 最新文献

International Conference on Signal Processing Systems最新文献

筛选
英文 中文
Rethinking network for classroom video captioning 重新思考网络课堂视频字幕
International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2589435
Mingjian Zhu, Chenrui Duan, Changbin Yu
{"title":"Rethinking network for classroom video captioning","authors":"Mingjian Zhu, Chenrui Duan, Changbin Yu","doi":"10.1117/12.2589435","DOIUrl":"https://doi.org/10.1117/12.2589435","url":null,"abstract":"Many people believe that the understanding of classroom activities can benefit the parents and education experts to analyze the teaching situation. However, employing workers to supervise the events in the classroom costs lots of human resources. The deployment of surveillance video systems is considered to be a good solution to this problem. Converting videos captured by cameras into descriptions can further reduce data transmission and storage costs. In this paper, we propose a new task named Classroom Video Captioning (CVC), which aims at describing the events in classroom videos with natural language. We collect classroom videos and annotate them with sentences. To tackle the task, we employ an effective architecture called rethinking network to encode the visual features and generate the descriptions. The extensive experiments on our dataset demonstrate that our method can describe the events in classroom videos satisfactorily.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"25 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":"115835834","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
Evolutionary programming: a population-based machine learning model for coded MIMO channels 进化编程:基于群体的编码MIMO信道机器学习模型
International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2588917
Xingming Li, Jigang Yu, Zhiliang Qin, Yuanhao Sun, Qidong Lu, Xiaowei Liu
{"title":"Evolutionary programming: a population-based machine learning model for coded MIMO channels","authors":"Xingming Li, Jigang Yu, Zhiliang Qin, Yuanhao Sun, Qidong Lu, Xiaowei Liu","doi":"10.1117/12.2588917","DOIUrl":"https://doi.org/10.1117/12.2588917","url":null,"abstract":"The conventional channel detector based on the maximum a posteriori (MAP) algorithm for coded multiple-input multiple-output (MIMO) multiuser systems has a computational complexity growing exponentially with the product of the number of users, the number of transmit antennas, and the symbol constellation size. In this paper, we consider the multiuser detection problem from a combinatorial optimization viewpoint and develop a low-complexity iterative receiver based on the evolutionary programming (EP) technique. Simulation results show that with the proposed receiver, the performance of coded multiuser systems approaches that of the iteratively MAP-decoded single-user (SU) MIMO system at a significantly reduced computational complexity even for unknown channel scenarios.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"150 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":"134289290","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
Bistatic-ISAR cross-range scaling and distortion correction 双静态isar跨距离缩放和畸变校正
International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2589513
Likang Deng, Shuanghui Zhang, Yongxiang Liu
{"title":"Bistatic-ISAR cross-range scaling and distortion correction","authors":"Likang Deng, Shuanghui Zhang, Yongxiang Liu","doi":"10.1117/12.2589513","DOIUrl":"https://doi.org/10.1117/12.2589513","url":null,"abstract":"For bistatic inverse synthetic aperture radar (Bi-ISAR) cross-range scaling (CRS), it needs to estimate the effective rotational velocity (ERV) and correct linear-geometry distortion at the same time. In this paper, the effective rotational velocity (ERV), rotational center (RC) and ratio of linear-geometry distortion (RLGD) are jointly estimated by optimizing the image quality, which is measured by the image entropy. After parameter estimation and phase compensation, the image without linear-geometry distortion is generated by the matched Fourier transform (MFT). Numerical results validate that the proposed method works robust under different signal to noise ratio (SNR) conditions.","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":"117183789","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
Cylindrical phased array radar signal processing design and FPGA implementation 圆柱相控阵雷达信号处理设计与FPGA实现
International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2581423
Renhong Xie, Xing Wang, Bohao Dong, Junfeng Wei, Peng Li, Yibin Rui
{"title":"Cylindrical phased array radar signal processing design and FPGA implementation","authors":"Renhong Xie, Xing Wang, Bohao Dong, Junfeng Wei, Peng Li, Yibin Rui","doi":"10.1117/12.2581423","DOIUrl":"https://doi.org/10.1117/12.2581423","url":null,"abstract":"Cylindrical phased array radar has an important role in low-altitude target surveillance, and signal processing is one of the important component modules of the radar system. Cylindrical radar as one kind of phased-array radar has the characteristics of full azimuth range of multi beam and huge data which makes high demands of signal processing. FPGA occupies an important position in radar signal processing because of its characteristics of high-speed and real-time. In this paper, signal processing scheme based on Xilinx's FPGA Kintex-7 and multi-core digital signal processor (DSP) is proposed, which mainly implements functions such as data reception, pulse compression, and moving target detection (MTD) processing etc. By comparing the actual results with the matlab simulation results, it is shown that this scheme has a good performance in stablility with fast processing speed. Moreover, it has obvious advantages in the design and provides great value for engineering.","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":"129786248","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
Attentive deep CNN for speaker verification 细心的深度CNN说话者验证
International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2581351
Yong-bin Yu, Min-hui Qi, Yi-fan Tang, Quan-xin Deng, Chenhui Peng, Feng Mai, T. Nyima
{"title":"Attentive deep CNN for speaker verification","authors":"Yong-bin Yu, Min-hui Qi, Yi-fan Tang, Quan-xin Deng, Chenhui Peng, Feng Mai, T. Nyima","doi":"10.1117/12.2581351","DOIUrl":"https://doi.org/10.1117/12.2581351","url":null,"abstract":"In this paper, an end-to-end speaker verification system based on attentive deep convolutional neural network (CNN) is highlighted. It takes log filter bank coefficients as input and measures speaker similarity between a test utterance and enrollment utterances by cosine similarity for verification. The approach utilizes the channel attention module of convolutional block attention module (CBAM) to increase representation power by giving different weights to feature maps. In addition, softmax is used to pre-train for initializing the weights of the network and tuple-based end-to-end (TE2E) loss function is responsible for fine-tune in evaluation stage, such a strategy not only results in notable improvements over the baseline model but also allows for direct optimization of the evaluation metric. Experimental results on VoxCeleb dataset indicates that proposed model achieves an equal error rate (EER) of 3.83%, which is slightly worse than x-vectors while outperforms i-vectors.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"19 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":"122983733","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
Identification of skin melanoma based on microscopic hyperspectral imaging technology 基于显微高光谱成像技术的皮肤黑色素瘤识别
International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2588969
T. Fan, Yanxi Long, Xisheng Zhang, Zijing Peng, Qingli Li
{"title":"Identification of skin melanoma based on microscopic hyperspectral imaging technology","authors":"T. Fan, Yanxi Long, Xisheng Zhang, Zijing Peng, Qingli Li","doi":"10.1117/12.2588969","DOIUrl":"https://doi.org/10.1117/12.2588969","url":null,"abstract":"Screening and diagnosing of the melanoma are crucial for the early diagnosis. As the deterioration of melanoma, it can be easily separated from the other materials based on the spectral features and spatial features. With the image of microscopic hyperspectral, this paper applies spectral math to preprocess the image firstly and the utilizes three traditional supervised classifications-maximum likelihood classification (MLC), convolution neural networks (CNN) and support vector machine (SVM) to make the segmentation after preprocess. Finally, we evaluate the accuracy of results generated by three to get the best segmentation method among them. This experiment shows practical value in pathological diagnosis.","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":"114062899","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
Robust subspace tracking methods based on fast and stable DPM algorithm 基于快速稳定DPM算法的鲁棒子空间跟踪方法
International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2581672
Qiming Chen, Ming Zhang
{"title":"Robust subspace tracking methods based on fast and stable DPM algorithm","authors":"Qiming Chen, Ming Zhang","doi":"10.1117/12.2581672","DOIUrl":"https://doi.org/10.1117/12.2581672","url":null,"abstract":"The data projection method can be used in tracking the signal subspace. However, when the signal is interfered by impulse noise, the data projection method will lose its tracking ability during the period impulse noise appears. In this paper, two stable and low complexity subspace tracking algorithms are proposed to solve this problem. The basic idea is to multiply a weight coefficient before the step size of the fast and stable data projection method. When the impulse noise occurs, the weight coefficient will reduce significantly. As a result, the step size will shrink and the adverse effect of subspace tracking caused by the outlier data will be reduced. In addition, both algorithms are verified through numerical simulations, and the effect caused by signal-to-noise ratio is discussed in the simulations.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"30 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":"121739869","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
Machine fault diagnosis based on multi-head deep learning network 基于多头深度学习网络的机器故障诊断
International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2581262
Qidong Lu, Yu Qin, Yingying Li, Zhiliang Qin, Xiaowei Liu
{"title":"Machine fault diagnosis based on multi-head deep learning network","authors":"Qidong Lu, Yu Qin, Yingying Li, Zhiliang Qin, Xiaowei Liu","doi":"10.1117/12.2581262","DOIUrl":"https://doi.org/10.1117/12.2581262","url":null,"abstract":"Feature extraction and utilization is of great importance for the problem of machine fault diagnosis. In this paper, multihead deep learning network is proposed to achieve machine health status classification using features of different sizes. Firstly, statistical characteristics which reflect machine signal status of time domain and frequency domain are summarized to compose feature vectors as one-dimensional network input. Secondly, Mel power spectrum and its incremental characteristics are utilized as two-dimensional network input of three channels. Lastly, the multi-head network is introduced to analyze both one-dimensional and two-dimensional features using two different sub neural networks and classify the machine health status according to the joint feature analyzing result. The experiments on bearing working status database of Case Western Reserve University show that the proposed method has good mechanical signal classification ability and better stability. Moreover, our final test accuracy of fault diagnosis on 16 kinds of bearing working signals can reach up to about 99.53%.","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":"127826512","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
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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信
小红书