IET Signal Processing

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

【译文】IET信号处理期刊发表关于信号处理和机器学习领域的广泛研究，涵盖了检测、估计、推理和分类问题中的各种应用、学科、模态和技术。发表的研究包括分析单维和多维数据、稀疏性、线性和非线性系统、递归和非递归数字滤波器以及多速率滤波器组的算法设计方面的进步，以及从传感器阵列处理、基于深度卷积神经网络的近似方法到混沌理论应用等众多主题。涵盖的主题包括但不限于：单和多维滤波器设计及实现的进步、线性和非线性、固定和自适应数字滤波器以及多速率滤波器组、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，包括时频分析、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、信号处理的贝叶斯方法，包括蒙特卡洛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与应用、用于语音信号分析、增强、编码、合成和识别的信号处理技术、音频和电磁信号的定位和波束成形技术、生物医学信号的信号处理分析技术、通信信号传输和接收的基带信号处理技术、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。