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IET Signal Processing
基本信息
期刊名称 | IET Signal Processing IET SIGNAL PROCESS |
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期刊ISSN | 1751-9675 |
期刊官方网站 | https://onlinelibrary.wiley.com/journal/17519683 |
是否OA | Yes |
出版商 | John Wiley & Sons Inc. |
出版周期 | Bi-monthly |
文章处理费 | 登录后查看 |
始发年份 | 2007 |
年文章数 | 63 |
最新影响因子 | 1.1(2023) scijournal影响因子 greensci影响因子 |
中科院SCI期刊分区
大类学科 | 小类学科 | Top | 综述 |
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工程技术4区 | ENGINEERING, ELECTRICAL & ELECTRONIC 工程:电子与电气4区 | 否 | 否 |
CiteScore
CiteScore排名 | CiteScore | SJR | SNIP | ||
---|---|---|---|---|---|
学科 | 排名 | 百分位 | 3.8 | 0.396 | 0.675 |
Engineering Electrical and Electronic Engineering |
349/797 | 56% |
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Computer Science Signal Processing |
65/131 | 50% |
补充信息
自引率 | 10.70% |
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H-index | 35 |
SCI收录状况 |
Science Citation Index Expanded |
官方审稿时间 | 登录后查看 |
网友分享审稿时间 | 数据统计中,敬请期待。 |
接受率 | 登录后查看 |
PubMed Central (PMC) | http://www.ncbi.nlm.nih.gov/nlmcatalog?term=1751-9675%5BISSN%5D |
投稿指南
期刊投稿网址 | https://mc.manuscriptcentral.com/theiet-spr |
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收稿范围 | IET Signal Processing publishes research on a diverse range of signal processing and machine learning topics, covering a variety of applications, disciplines, modalities, and techniques in detection, estimation, inference, and classification problems. The research published includes advances in algorithm design for the analysis of single and high-multi-dimensional data, sparsity, linear and non-linear systems, recursive and non-recursive digital filters and multi-rate filter banks, as well a range of topics that span from sensor array processing, deep convolutional neural network based approaches to the application of chaos theory, and far more. Topics covered by scope include, but are not limited to: advances in single and multi-dimensional filter design and implementation linear and nonlinear, fixed and adaptive digital filters and multirate filter banks statistical signal processing techniques and analysis classical, parametric and higher order spectral analysis signal transformation and compression techniques, including time-frequency analysis system modelling and adaptive identification techniques machine learning based approaches to signal processing Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques theory and application of blind and semi-blind signal separation techniques signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals direction-finding and beamforming techniques for audio and electromagnetic signals analysis techniques for biomedical signals baseband signal processing techniques for transmission and reception of communication signals signal processing techniques for data hiding and audio watermarking sparse signal processing and compressive sensing |
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