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Iet Signal ProcessingSCIE

国际简称：IET SIGNAL PROCESS 参考译名：工业信号处理

中科院分区
4区
CiteScore分区
Q2
JCR分区
Q4

基本信息：: ISSN：1751-9675; E-ISSN：1751-9683; 是否OA：开放; 是否预警：否; TOP期刊：否

出版信息：: 出版地区：ENGLAND; 出版商：Wiley; 出版语言：English; 出版周期：Bi-monthly; 出版年份：2007; 研究方向：工程技术-工程：电子与电气

评价信息：: 影响因子：1.1; H-index：35; CiteScore指数：3.8; SJR指数：0.396; SNIP指数：0.675

发文数据：: Gold OA文章占比：83.26%; 研究类文章占比：100.00%; 年发文量：63; 自引率：0.0588...; 开源占比：0.4959; 出版撤稿占比：0; 出版国人文章占比：0; OA被引用占比：0.0053...

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英文简介期刊介绍 CiteScore数据中科院SCI分区 JCR分区发文数据常见问题

英文简介Iet Signal Processing期刊介绍

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

Special Issue Call for Papers:

Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf

期刊简介Iet Signal Processing期刊介绍

《Iet Signal Processing》自2007出版以来，是一本工程技术优秀杂志。致力于发表原创科学研究结果，并为工程技术各个领域的原创研究提供一个展示平台，以促进工程技术领域的的进步。该刊鼓励先进的、清晰的阐述，从广泛的视角提供当前感兴趣的研究主题的新见解，或审查多年来某个重要领域的所有重要发展。该期刊特色在于及时报道工程技术领域的最新进展和新发现新突破等。该刊近一年未被列入预警期刊名单，目前已被权威数据库SCIE收录，得到了广泛的认可。

该期刊投稿重要关注点：

预计审稿时间：约9.0个月
工程技术
ENGINEERING, ELECTRICAL & ELECTRONIC
SCIE
中科院4区
非预警

Cite Score数据（2024年最新版）Iet Signal Processing Cite Score数据

CiteScore：3.8
SJR：0.396
SNIP：0.675

学科类别	分区	排名	百分位
大类：Engineering 小类：Electrical and Electronic Engineering	Q2	349 / 797	56%
大类：Engineering 小类：Signal Processing	Q2	65 / 131	50%

CiteScore 是由Elsevier(爱思唯尔)推出的另一种评价期刊影响力的文献计量指标。反映出一家期刊近期发表论文的年篇均引用次数。CiteScore以Scopus数据库中收集的引文为基础，针对的是前四年发表的论文的引文。CiteScore的意义在于，它可以为学术界提供一种新的、更全面、更客观地评价期刊影响力的方法，而不仅仅是通过影响因子(IF)这一单一指标来评价。

历年Cite Score趋势图

中科院SCI分区Iet Signal Processing 中科院分区

中科院 2023年12月升级版 综述期刊：否 Top期刊：否

大类学科	分区	小类学科	分区
工程技术	4区	ENGINEERING, ELECTRICAL & ELECTRONIC 工程：电子与电气	4区

中科院分区表 是以客观数据为基础，运用科学计量学方法对国际、国内学术期刊依据影响力进行等级划分的期刊评价标准。它为我国科研、教育机构的管理人员、科研工作者提供了一份评价国际学术期刊影响力的参考数据，得到了全国各地高校、科研机构的广泛认可。

中科院分区表 将所有期刊按照一定指标划分为1区、2区、3区、4区四个层次，类似于“优、良、及格”等。最开始，这个分区只是为了方便图书管理及图书情报领域的研究和期刊评估。之后中科院分区逐步发展成为了一种评价学术期刊质量的重要工具。

历年中科院分区趋势图

JCR分区Iet Signal Processing JCR分区

2023-2024 年最新版

按JIF指标学科分区	收录子集	分区	排名	百分位
学科：ENGINEERING, ELECTRICAL & ELECTRONIC	SCIE	Q4	278 / 352	21.2%

按JCI指标学科分区	收录子集	分区	排名	百分位
学科：ENGINEERING, ELECTRICAL & ELECTRONIC	SCIE	Q3	261 / 354	26.41%

JCR分区的优势在于它可以帮助读者对学术文献质量进行评估。不同学科的文章引用量可能存在较大的差异，此时单独依靠影响因子(IF)评价期刊的质量可能是存在一定问题的。因此，JCR将期刊按照学科门类和影响因子分为不同的分区，这样读者可以根据自己的研究领域和需求选择合适的期刊。

历年影响因子趋势图

本刊中国学者近年发表论文

1、Mirrored coprime array design using sum-difference coarray optimisation
Author: Dai, Minghui; Ma, Xiaofeng; Sheng, Weixing; Han, Yubing

Journal: IET SIGNAL PROCESSING. 2023; Vol. 17, Issue 1, pp. -. DOI: 10.1049/sil2.12160
2、Estimating the visibility in foggy weather based on meteorological and video data: A Recurrent Neural Network approach
Author: Chen, Jian; Yan, Ming; Qureshi, Muhammad Rabea Hanzla; Geng, Keke

Journal: IET SIGNAL PROCESSING. 2023; Vol. 17, Issue 1, pp. -. DOI: 10.1049/sil2.12164
3、Novel frequency of arrival-based emitter localisation methods based on multiple moving observers
Author: Wang, Ding; Yin, Jiexin; Chen, Can; Li, Jianyang

Journal: IET SIGNAL PROCESSING. 2023; Vol. 17, Issue 1, pp. -. DOI: 10.1049/sil2.12161
4、A novel method for deinterleaving radar signals: First-order difference curve based on sorted TOA difference sequence
Author: Xie, Min; Zhao, Chuang; Zhao, Yongjun; Hu, Dexiu; Wang, Zewen

Journal: IET SIGNAL PROCESSING. 2023; Vol. 17, Issue 1, pp. -. DOI: 10.1049/sil2.12162
5、A novel denoising method for non-linear and non-stationary signals
Author: Wu, Honglin; Wang, Zhongbin; Si, Lei; Tan, Chao; Zou, Xiaoyu; Liu, Xinhua; Li, Futao

Journal: IET SIGNAL PROCESSING. 2023; Vol. 17, Issue 1, pp. -. DOI: 10.1049/sil2.12165
6、Radar signal recognition exploiting information geometry and support vector machine
Author: Cheng, Yuqing; Guo, Muran; Guo, Limin

Journal: IET SIGNAL PROCESSING. 2023; Vol. 17, Issue 1, pp. -. DOI: 10.1049/sil2.12167
7、Fractional Fourier cosine and sine Laplace weighted convolution and its application
Author: Xiang, Yi; Yuan, Sha; Feng, Qiang

Journal: IET SIGNAL PROCESSING. 2023; Vol. 17, Issue 2, pp. -. DOI: 10.1049/sil2.12170
8、A novel end-to-end deep separation network based on attention mechanism for single channel blind separation in wireless communication
Author: Ma, Hao; Zheng, Xiang; Yu, Lu; Zhou, Xingyu; Chen, Yufan

Journal: IET SIGNAL PROCESSING. 2023; Vol. 17, Issue 2, pp. -. DOI: 10.1049/sil2.12173