发布求助
文献互助 智能选刊 最新文献

2019 27th European Signal Processing Conference (EUSIPCO)最新文献

筛选
英文 中文
Learning Tensor-structured Dictionaries with Application to Hyperspectral Image Denoising 学习张量结构字典及其在高光谱图像去噪中的应用
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8902593
C. Dantas, Jérémy E. Cohen, R. Gribonval
{"title":"Learning Tensor-structured Dictionaries with Application to Hyperspectral Image Denoising","authors":"C. Dantas, Jérémy E. Cohen, R. Gribonval","doi":"10.23919/EUSIPCO.2019.8902593","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8902593","url":null,"abstract":"Dictionary learning, paired with sparse coding, aims at providing sparse data representations, that can be used for multiple tasks such as denoising or inpainting, as well as dimensionality reduction. However, when working with large data sets, the dictionary obtained by applying unstructured dictionary learning methods may be of considerable size, which poses both memory and computational complexity issues. In this article, we show how a previously proposed structured dictionary learning model, HO-SuKro, can be used to obtain more compact and readily-applicable dictionaries when the targeted data is a collection of multiway arrays. We introduce an efficient alternating optimization learning algorithm, describe important implementation details that have a considerable impact on both algorithmic complexity and actual speed, and showcase the proposed algorithm on a hyperspectral image denoising task.","PeriodicalId":319233,"journal":{"name":"2019 27th European Signal Processing Conference (EUSIPCO)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132251226","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: 10
Two-Way MIMO Decode-and-Forward Relaying Systems with Tensor Space-Time Coding 具有张量空时编码的双向MIMO译码转发中继系统
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8902719
W. Freitas, G. Favier, A. D. Almeida, M. Haardt
{"title":"Two-Way MIMO Decode-and-Forward Relaying Systems with Tensor Space-Time Coding","authors":"W. Freitas, G. Favier, A. D. Almeida, M. Haardt","doi":"10.23919/EUSIPCO.2019.8902719","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8902719","url":null,"abstract":"In this paper, we present a new closed-form semiblind receiver for a two-way decode-and-forward (DF) relaying system. The proposed receiver jointly estimates the symbol and channel matrices involved in the two-way relaying system by exploiting tensor structures of the received signals at the relay and the destination, without using training sequences. The proposed receiver exploits a cross-coding approach using a third-order tensor space-time code (TSTC) at the relay, and it does not require a channel reciprocity between uplink and downlink phases, which can be of interest in frequency division duplex relaying systems. The advantages of this DF receiver compared with the amplify-and-forward (AF) receivers of [14] are three-fold: 1) use of the DF protocol which makes it possible to attenuate the propagation errors compared to the AF protocol, at the cost of an additional decoding at the relay, 2) a cross-coding approach which allows the suppression of interference between sources and therefore greatly simplifies the receivers, and 3) the closed-form aspect of the receivers based on a least squares (LSs) Kronecker product factorization algorithm. Parameter identifiability and computational complexity are analysed, and simulation results are provided to corroborate the effectiveness of the proposed semiblind receiver and coding scheme when compared with the AF receivers of [11].","PeriodicalId":319233,"journal":{"name":"2019 27th European Signal Processing Conference (EUSIPCO)","volume":"168 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134233382","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
A New Signal-Selective Wide-Band Ambiguity Function 一种新的信号选择性宽带模糊函数
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8903148
A. Napolitano
{"title":"A New Signal-Selective Wide-Band Ambiguity Function","authors":"A. Napolitano","doi":"10.23919/EUSIPCO.2019.8903148","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8903148","url":null,"abstract":"A new signal-selective wide-band cross-ambiguity function is introduced. It performs a sinusoidally weighted correlation of a signal with a time-scaled and delayed version of a reference signal. If the reference signal is almost cyclostationary and the frequency of the weighting sinusoid is one of its cycle frequencies properly scaled, the new function, referred to as the wide-band cyclic cross-correlation function, exhibits the signal selectivity properties that are typical of cyclostationarity-based techniques. The new function is exploited for the localization and speed estimation of a wide-band moving source whose signal impinges on two sensors in a severe noise and interference environment.","PeriodicalId":319233,"journal":{"name":"2019 27th European Signal Processing Conference (EUSIPCO)","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134366750","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
Tensor-Train Modeling for Mimo-OFDM Tensor Coding-and-Forwarding Relay Systems Mimo-OFDM张量编码转发中继系统的张量训练建模
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8902770
Yassine Zniyed, R. Boyer, A. Almeida, G. Favier
{"title":"Tensor-Train Modeling for Mimo-OFDM Tensor Coding-and-Forwarding Relay Systems","authors":"Yassine Zniyed, R. Boyer, A. Almeida, G. Favier","doi":"10.23919/EUSIPCO.2019.8902770","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8902770","url":null,"abstract":"In this paper, we consider a new one-way two-hop amplify-and-forward (AF) relaying scheme with a tensor space-time coding under frequency-selective fading channels. The signals received at the destination of the multi-input multi-output (MIMO) system define a 6-order tensor which satisfies a tensor-train decomposition (TTD). We propose a new TTD based receiver for a joint channel and symbol estimation. The proposed receiver avoids the use of long training sequences and resort to very few pilots to provide unique estimates of the individual channel matrices and the symbol matrix. Numerical simulations show the performance of the new proposed TTD-based semi-blind receiver.","PeriodicalId":319233,"journal":{"name":"2019 27th European Signal Processing Conference (EUSIPCO)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115723411","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: 8
Segmentation of Head and Neck Tumours Using Modified U-net 基于改进U-net的头颈部肿瘤分割
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8902637
Baixiang Zhao, J. Soraghan, G. D. Caterina, D. Grose
{"title":"Segmentation of Head and Neck Tumours Using Modified U-net","authors":"Baixiang Zhao, J. Soraghan, G. D. Caterina, D. Grose","doi":"10.23919/EUSIPCO.2019.8902637","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8902637","url":null,"abstract":"A new neural network for automatic head and neck cancer (HNC) segmentation from magnetic resonance imaging (MRI) is presented. The proposed neural network is based on U-net, which combines features from different resolutions to achieve end-to-end locating and segmentation of medical images. In this work, the dilated convolution is introduced into U-net, to obtain larger receptive field so that extract multi-scale features. Also, this network uses Dice loss to reduce the imbalance between classes. The proposed algorithm is trained and tested on real MRI data. The cross-validation results show that the new network outperformed the original U-net by 5% (Dice score) on head and neck tumour segmentation.","PeriodicalId":319233,"journal":{"name":"2019 27th European Signal Processing Conference (EUSIPCO)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125096092","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: 8
Discrete-Valued Vector Reconstruction by Optimization with Sum of Sparse Regularizers 稀疏正则化和优化的离散值向量重构
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8902940
Ryo Hayakawa, K. Hayashi
{"title":"Discrete-Valued Vector Reconstruction by Optimization with Sum of Sparse Regularizers","authors":"Ryo Hayakawa, K. Hayashi","doi":"10.23919/EUSIPCO.2019.8902940","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8902940","url":null,"abstract":"In this paper, we propose a possibly nonconvex optimization problem to reconstruct a discrete-valued vector from its underdetermined linear measurements. The proposed sum of sparse regularizers (SSR) optimization uses the sum of sparse regularizers as a regularizer for the discrete-valued vector. We also propose two proximal splitting algorithms for the SSR optimization problem on the basis of alternating direction method of multipliers (ADMM) and primal-dual splitting (PDS). The ADMM based algorithm can achieve faster convergence, whereas the PDS based algorithm does not require the computation of any inverse matrix. Moreover, we extend the ADMM based approach for the reconstruction of complex discrete-valued vectors. Note that the proposed approach can use any sparse regularizer as long as its proximity operator can be efficiently computed. Simulation results show that the proposed algorithms with nonconvex regularizers can achieve good reconstruction performance.","PeriodicalId":319233,"journal":{"name":"2019 27th European Signal Processing Conference (EUSIPCO)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134201305","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
Dual-threshold Based Local Patch Construction Method for Manifold Approximation And Its Application to Facial Expression Analysis 基于双阈值的流形逼近局部斑块构建方法及其在面部表情分析中的应用
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8902603
S. Happy, A. Dantcheva, A. Routray
{"title":"Dual-threshold Based Local Patch Construction Method for Manifold Approximation And Its Application to Facial Expression Analysis","authors":"S. Happy, A. Dantcheva, A. Routray","doi":"10.23919/EUSIPCO.2019.8902603","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8902603","url":null,"abstract":"In this paper, we propose a manifold based facial expression recognition framework which utilizes the intrinsic structure of the data distribution to accurately classify the expression categories. Specifically, we model the expressive faces as the points on linear subspaces embedded in a Grassmannian manifold, also called as expression manifold. We propose the dual-threshold based local patch (DTLP) extraction method for constructing the local subspaces, which in turn approximates the expression manifold. Further, we use the affinity of the face points from the subspaces for classifying them into different expression classes. Our method is evaluated on four publicly available databases with two well known feature extraction techniques. It is evident from the results that the proposed method efficiently models the expression manifold and improves the recognition accuracy in spite of the simplicity of the facial representatives.","PeriodicalId":319233,"journal":{"name":"2019 27th European Signal Processing Conference (EUSIPCO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131568889","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
Compressive Chirp Transform for Estimation of Chirp Parameters 啁啾参数估计的压缩啁啾变换
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8903119
Luay Ali Al Irkhis, A. Shaw
{"title":"Compressive Chirp Transform for Estimation of Chirp Parameters","authors":"Luay Ali Al Irkhis, A. Shaw","doi":"10.23919/EUSIPCO.2019.8903119","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8903119","url":null,"abstract":"This paper develops a new algorithm for estimating the parameters of multiple chirp signals in noise. The proposed method uses Compressive Sensing (CS) formulation of the Discrete Chirp Fourier Transform (DCFT) basis to achieve superior estimator performance. Unlike Fourier or time-frequency based approaches, DCFT incorporates the underlying chirp signal model parameters in formulating the transform [1] –[4]. In this work a CS formulation exploits the parametric DCFT basis for fast recovery to achieve highly accurate parameter estimation results in polynomial time using Orthogonal Matching Pursuit (OMP). The performance of the proposed algorithm has been compared with existing methods via simulations.","PeriodicalId":319233,"journal":{"name":"2019 27th European Signal Processing Conference (EUSIPCO)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133552320","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: 5
Asymptotic Karlin-Rubin’s Theorem with Application to Signal Detection in a Subspace Cone 渐近Karlin-Rubin定理及其在子空间圆锥信号检测中的应用
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8902793
Sabrina Bourmani, François-Xavier Socheleau, Dominique Pastor
{"title":"Asymptotic Karlin-Rubin’s Theorem with Application to Signal Detection in a Subspace Cone","authors":"Sabrina Bourmani, François-Xavier Socheleau, Dominique Pastor","doi":"10.23919/EUSIPCO.2019.8902793","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8902793","url":null,"abstract":"We first propose an asymptotic formulation of Karlin-Rubin’s theorem that relies on the weak convergence of a sequence of random vectors to design Asymptotically Uniformly Most Powerful (AUMP) tests dedicated to composite hypotheses. This general property of optimality is then applied to the problem of testing whether the energy of a signal projected onto a known subspace exceeds a specified proportion of its total energy. The signal is assumed unknown deterministic and it is observed in independent and additive white Gaussian noise. Such a problem can arise when the signal to be detected obeys the linear subspace model and when it is corrupted by unknown interference. It can also be relevant in machine learning applications where one wants to check whether an assumed linear model fits the analyzed data. For this problem, where it is shown that no Uniformly Most Powerful (UMP) and no UMP invariant tests exist, an AUMP invariant test is derived.","PeriodicalId":319233,"journal":{"name":"2019 27th European Signal Processing Conference (EUSIPCO)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132618212","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
Joint unmixing-deconvolution algorithms for hyperspectral images 高光谱图像的联合解混-反卷积算法
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8902950
Yingying Song, E. Djermoune, D. Brie, C. Richard
{"title":"Joint unmixing-deconvolution algorithms for hyperspectral images","authors":"Yingying Song, E. Djermoune, D. Brie, C. Richard","doi":"10.23919/EUSIPCO.2019.8902950","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8902950","url":null,"abstract":"This paper combines supervised linear unmixing and deconvolution problems to increase the resolution of the abundance maps for industrial imaging systems. The joint unmixing-deconvolution (JUD) algorithm is introduced based on the Tikhonov regularization criterion for offline processing. In order to meet the needs of industrial applications, the proposed JUD algorithm is then extended for online processing by using a block Tikhonov criterion. The performance of JUD is increased by adding a non-negativity constraint which is implemented in a fast way using the quadratic penalty method and fast Fourier transform. The proposed algorithm is then assessed using both simulated and real hyperspectral images.","PeriodicalId":319233,"journal":{"name":"2019 27th European Signal Processing Conference (EUSIPCO)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132976650","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
首页 上一页
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