Eurasip Journal on Advances in Signal Processing最新文献_第5页

A novel approach to extracting useful information from noisy TFDs using 2D local entropy measures 利用二维局部熵量从噪声 TFD 中提取有用信息的新方法

IF 1.9 4区工程技术

Eurasip Journal on Advances in Signal Processing Pub Date : 2020-04-25 DOI: 10.1186/s13634-020-00679-2

Ana Vrankovic, J. Lerga, N. Saulig

引用次数: 10

Model set adaptive filtering algorithm using variational Bayesian approximations and Rényi information divergence 使用变异贝叶斯近似和雷尼信息发散的模型集自适应过滤算法

IF 1.9 4区工程技术

Eurasip Journal on Advances in Signal Processing Pub Date : 2020-04-20 DOI: 10.1186/s13634-020-00670-x

Tianli Ma, Chaobo Chen, Song Gao

引用次数: 1

Multi-task learning for abstractive text summarization with key information guide network 利用关键信息引导网络进行抽象文本摘要的多任务学习

IF 1.9 4区工程技术

Eurasip Journal on Advances in Signal Processing Pub Date : 2020-04-19 DOI: 10.1186/s13634-020-00674-7

Weiran Xu, Chenliang Li, Ming-Ying Lee, Chi Zhang

引用次数: 19

Regularized supervised Bayesian approach for image deconvolution with regularization parameter estimation 利用正则化参数估计图像解卷积的正则化监督贝叶斯方法

IF 1.9 4区工程技术

Eurasip Journal on Advances in Signal Processing Pub Date : 2020-04-07 DOI: 10.1186/s13634-020-00671-w

Bouchra Laaziri, S. Raghay, A. Hakim

引用次数: 4

High-dimensional neural feature design for layer-wise reduction of training cost 分层降低训练成本的高维神经特征设计

IF 1.9 4区工程技术

Eurasip Journal on Advances in Signal Processing Pub Date : 2020-03-29 DOI: 10.1186/s13634-020-00695-2

Alireza M. Javid, Arun Venkitaraman, M. Skoglund, S. Chatterjee

引用次数: 0

A variable step-size diffusion LMS algorithm with a quotient form 具有商数形式的可变步长扩散 LMS 算法

IF 1.9 4区工程技术

Eurasip Journal on Advances in Signal Processing Pub Date : 2020-03-24 DOI: 10.1186/s13634-020-00672-9

M. O. B. Saeed, A. Zerguine

引用次数: 4

Stationary time-vertex signal processing. 平稳时间-顶点信号处理。

IF 1.9 4区工程技术

Eurasip Journal on Advances in Signal Processing Pub Date : 2019-01-01 Epub Date: 2019-08-20 DOI: 10.1186/s13634-019-0631-7

Andreas Loukas, Nathanaël Perraudin

引用次数: 40

Superimposed signaling inspired channel estimation in full-duplex systems. 全双工系统中受叠加信号启发的信道估计。

IF 1.9 4区工程技术

Eurasip Journal on Advances in Signal Processing Pub Date : 2018-01-01 Epub Date: 2018-01-24 DOI: 10.1186/s13634-018-0529-9

Abbas Koohian, Hani Mehrpouyan, Ali A Nasir, Salman Durrani, Steven D Blostein

{"title":"Superimposed signaling inspired channel estimation in full-duplex systems.","authors":"Abbas Koohian, Hani Mehrpouyan, Ali A Nasir, Salman Durrani, Steven D Blostein","doi":"10.1186/s13634-018-0529-9","DOIUrl":"https://doi.org/10.1186/s13634-018-0529-9","url":null,"abstract":"Residual self-interference (SI) cancellation in the digital baseband is an important problem in full-duplex (FD) communication systems. In this paper, we propose a new technique for estimating the SI and communication channels in a FD communication system, which is inspired from superimposed signaling. In our proposed technique, we add a constant real number to each constellation point of a conventional modulation constellation to yield asymmetric shifted modulation constellations with respect to the origin. We show mathematically that such constellations can be used for bandwidth efficient channel estimation without ambiguity. We propose an expectation maximization (EM) estimator for use with the asymmetric shifted modulation constellations. We derive a closed-form lower bound for the mean square error (MSE) of the channel estimation error, which allows us to find the minimum shift energy needed for accurate channel estimation in a given FD communication system. The simulation results show that the proposed technique outperforms the data-aided channel estimation method, under the condition that the pilots use the same extra energy as the shift, both in terms of MSE of channel estimation error and bit error rate. The proposed technique is also robust to an increasing power of the SI signal.","PeriodicalId":49203,"journal":{"name":"Eurasip Journal on Advances in Signal Processing","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13634-018-0529-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37579841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Joint frequency offset, time offset, and channel estimation for OFDM/OQAM systems. 联合频率偏移，时间偏移和信道估计OFDM/OQAM系统。

IF 1.9 4区工程技术

Eurasip Journal on Advances in Signal Processing Pub Date : 2018-01-01 Epub Date: 2018-01-08 DOI: 10.1186/s13634-017-0526-4

Ali Baghaki, Benoit Champagne

{"title":"Joint frequency offset, time offset, and channel estimation for OFDM/OQAM systems.","authors":"Ali Baghaki, Benoit Champagne","doi":"10.1186/s13634-017-0526-4","DOIUrl":"https://doi.org/10.1186/s13634-017-0526-4","url":null,"abstract":"Among the multicarrier modulation techniques considered as an alternative to orthogonal frequency division multiplexing (OFDM) for future wireless networks, a derivative of OFDM based on offset quadrature amplitude modulation (OFDM/OQAM) has received considerable attention. In this paper, we propose an improved joint estimation method for carrier frequency offset, sampling time offset, and channel impulse response, needed for the practical application of OFDM/OQAM. The proposed joint ML estimator instruments a pilot-based maximum-likelihood (ML) estimation of the unknown parameters, as derived under the assumptions of Gaussian noise and independent input symbols. The ML estimator formulation relies on the splitting of each received pilot symbol into contributions from surrounding pilot symbols, non-pilot symbols and additive noise. Within the ML framework, the Cramer-Rao bound on the covariance matrix of unbiased estimators of the joint parameter vector under consideration is derived as a performance benchmark. The proposed method is compared with a highly cited previous work. The improvements in the results point to the superiority of the proposed method, which also performs close to the Cramer-Rao bound.","PeriodicalId":49203,"journal":{"name":"Eurasip Journal on Advances in Signal Processing","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13634-017-0526-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37593490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Fast dictionary learning from incomplete data. 快速字典学习从不完整的数据。

IF 1.9 4区工程技术

Eurasip Journal on Advances in Signal Processing Pub Date : 2018-01-01 Epub Date: 2018-02-22 DOI: 10.1186/s13634-018-0533-0

Valeriya Naumova, Karin Schnass

{"title":"Fast dictionary learning from incomplete data.","authors":"Valeriya Naumova, Karin Schnass","doi":"10.1186/s13634-018-0533-0","DOIUrl":"10.1186/s13634-018-0533-0","url":null,"abstract":"This paper extends the recently proposed and theoretically justified iterative thresholding and K residual means (ITKrM) algorithm to learning dictionaries from incomplete/masked training data (ITKrMM). It further adapts the algorithm to the presence of a low-rank component in the data and provides a strategy for recovering this low-rank component again from incomplete data. Several synthetic experiments show the advantages of incorporating information about the corruption into the algorithm. Further experiments on image data confirm the importance of considering a low-rank component in the data and show that the algorithm compares favourably to its closest dictionary learning counterparts, wKSVD and BPFA, either in terms of computational complexity or in terms of consistency between the dictionaries learned from corrupted and uncorrupted data. To further confirm the appropriateness of the learned dictionaries, we explore an application to sparsity-based image inpainting. There the ITKrMM dictionaries show a similar performance to other learned dictionaries like wKSVD and BPFA and a superior performance to other algorithms based on pre-defined/analytic dictionaries.","PeriodicalId":49203,"journal":{"name":"Eurasip Journal on Advances in Signal Processing","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13634-018-0533-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35882807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11