Iet Signal Processing

杂志介绍

Iet Signal Processing杂志介绍

《Iet Signal Processing》是一本以English为主的开放获取国际优秀期刊，中文名称工业信号处理，本刊主要出版、报道工程技术-ENGINEERING, ELECTRICAL & ELECTRONIC领域的研究动态以及在该领域取得的各方面的经验和科研成果，介绍该领域有关本专业的最新进展，探讨行业发展的思路和方法，以促进学术信息交流，提高行业发展。该刊已被国际权威数据库SCIE收录，为该领域相关学科的发展起到了良好的推动作用，也得到了本专业人员的广泛认可。该刊最新影响因子为1.1，最新CiteScore 指数为3.8。

本刊近期中国学者发表的论文主要有：

• Mirrored coprime array design using sum-difference coarray optimisation

  Author: Dai, Minghui; Ma, Xiaofeng; Sheng, Weixing; Han, Yubing

• 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

• Novel frequency of arrival-based emitter localisation methods based on multiple moving observers

  Author: Wang, Ding; Yin, Jiexin; Chen, Can; Li, Jianyang

• 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

英文介绍

Iet Signal Processing杂志英文介绍

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