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 sensing
IET信号处理发布了关于各种信号处理和机器学习主题的研究，涵盖了检测、估计、推理和分类问题中的各种应用、学科、模式和技术。发表的研究包括单维和高维数据分析的算法设计、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等一系列主题。对混沌理论的应用，还有更多。范围涉及的主题包括但不限于：单、多维滤波器设计与实现进展线性和非线性、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与分析经典、参数和高阶谱分析信号转换和压缩技术，包括时频分析系统建模与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，包括蒙特卡罗马尔可夫链和粒子滤波技术盲半盲信号分离技术的理论与应用语音信号分析、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向和波束形成技术生物医学信号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与压缩传感
投稿咨询
