IET Signal Processing（工业信号处理杂志）影响因子_期刊投稿发表

期刊简介

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 implementationlinear and nonlinear, fixed and adaptive digital filters and multirate filter banksstatistical signal processing techniques and analysisclassical, parametric and higher order spectral analysissignal transformation and compression techniques, including time-frequency analysissystem modelling and adaptive identification techniquesmachine learning based approaches to signal processingBayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniquestheory and application of blind and semi-blind signal separation techniquessignal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signalsdirection-finding and beamforming techniques for audio and electromagnetic signalsanalysis techniques for biomedical signalsbaseband signal processing techniques for transmission and reception of communication signalssignal processing techniques for data hiding and audio watermarkingsparse signal processing and compressive sensingSpecial Issue Call for Papers:Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf

IET Signal Processing出版关于各种信号处理和机器学习主题的研究，涵盖检测、估计、推理和分类问题中的各种应用、学科、模态和技术。发表的研究成果包括单维和高多维数据分析算法设计的进展、稀疏性、线性和非线性系统、递归和非递归数字滤波器和多速率滤波器组，以及从传感器阵列处理、基于深度卷积神经网络的方法到混沌理论应用等一系列主题。范围涵盖的主题包括：但不限于：单维和多维滤波器设计和实现的进展线性和非线性、固定和自适应数字滤波器和多速率滤波器组统计信号处理技术和分析经典、参数和高阶频谱分析信号变换和压缩技术，包括时间-频率分析系统建模和自适应识别技术基于机器学习的信号处理方法用于信号处理的贝叶斯方法，包括蒙特-卡罗马尔可夫链和粒子滤波技术盲和半盲信号分离技术的理论和应用用于语音信号的分析、增强、编码、合成和识别的信号处理技术用于音频和电磁信号的测向和波束形成技术用于生物医学信号的分析技术用于通信信号的发送和接收的基带信号处理技术用于数据隐藏和音频水印的信号处理技术稀疏信号处理和压缩感知特刊征稿：用于信号处理的智能深度模糊模型 www.example.com

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