雜志介紹

Iet Signal Processing雜志介紹

《Iet Signal Processing》是一本以English為主的開放獲取國際優(yōu)秀期刊，中文名稱工業(yè)信號處理，本刊主要出版、報道工程技術-ENGINEERING, ELECTRICAL & ELECTRONIC領域的研究動態(tài)以及在該領域取得的各方面的經(jīng)驗和科研成果，介紹該領域有關本專業(yè)的最新進展，探討行業(yè)發(fā)展的思路和方法，以促進學術信息交流，提高行業(yè)發(fā)展。該刊已被國際權威數(shù)據(jù)庫SCIE收錄，為該領域相關學科的發(fā)展起到了良好的推動作用，也得到了本專業(yè)人員的廣泛認可。該刊最新影響因子為1.1，最新CiteScore 指數(shù)為3.8。

本刊近期中國學者發(fā)表的論文主要有：

• 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

英文介紹

Iet Signal Processing雜志英文介紹

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