Journal Title：Iet Signal Processing

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

IET Signal Processing 发表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、模式和技术。发表的研究包括用于分析单维和高多维数据、稀疏性、线性和非线性系统、递归和非递归数字滤波器和多速率滤波器组的算法设计进展，以及一系列主题从传感器阵列处理、基于深度卷积神经网络的方法到混沌理论的应用等等。

范围涵盖的主题包括但不限于：

单维和多维滤波器设计与实现的进展

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

统计信号处理技术与分析

经典、参数和高阶光谱分析

信号转换和压缩技术，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

用于分析、增强、编码、合成和识别语音信号的信号处理技术

音频和电磁信号的测向和波束成形技术

生物医学信号分析技术

用于传输和接收通信信号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

论文特刊征集：

用于信号处理的智能深度模糊模型 - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf