IET Signal Processing publishes novel contributions in signal processing including: 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.
IET Signal Processing
Description
Identifiers
ISSN | 1751-9675 |
e-ISSN | 1751-9683 |
Publisher
IET
Additional information
Data set: ieee
Title history
- ( 1980 - 1988 ) IEE Proceedings F - Communications, Radar and Signal Processing
- ( 1989 - 1993 ) IEE Proceedings I - Communications, Speech and Vision
- ( 1994 - 2006 ) IEE Proceedings - Vision, Image and Signal Processing
Articles
IET Signal Processing > 2017 > 11 > 8 > 916 - 922
Calibration and higher-order statistics are standard components of image steganalysis. However, these techniques have not yet found adequate attention in audio steganalysis. Specifically, most of current studies are either non-calibrated or only based on noise removal. The goal of this study is to fill these gaps and to show that calibrated features based on re-embedding technique improve performance...
IET Signal Processing > 2017 > 11 > 8 > 986 - 997
The problem of consensus-based distributed state estimation of a non-linear dynamical system in the presence of multiplicative observation noise is investigated in this study. Generalised extended information filter (GEIF) is developed for non-linear state estimation in the information-space framework. To fuse the information contribution of local estimators, an average consensus algorithm is employed...
IET Signal Processing > 2017 > 11 > 8 > 901 - 908
A signal with discrete frequency components has a zero bispectrum if no addition or subtraction of any of the frequencies equals one of the frequency components. The authors introduce the fractional bispectrum (FBS) transform in which for signals with zero bispectrum the FBS could be non-zero. It is shown that FBS has the same property as the bispectrum for signals with a Gaussian probability density...