IET Signal Processing

Compressed Sensing is the new trend in the signal processing context which aims to sample a compressible signal with a rate less than the Nyquist lower bound sampling rate. The main challenge arises due to the non-convex optimisation problem to be solved in the reconstruction stage. This paper introduces a suitable objective function in order to simultaneously recover the true support of the under... View full abstract»

A novel method for the detection of weak signals embedded in non-stationary backgrounds is derived based on empirical mode decomposition and singular spectrum analysis in the present study. Simulated example reveals that the new method performs well in the detection of the characteristic components and especially the weak signals. Finally, the method is applied to the experimental signals of gearb... View full abstract»

This study firstly investigates the estimation problem for systems with multiplicative noise and measurement delay. Based on the innovation analysis approach, the estimators are developed in terms of a Riccati equation and a Lyapunov equation. The equations are of the same dimension as the plant; therefore compared with the augmentation approach, the presented approach lessens the computational de... View full abstract»

Generalised eigendecomposition problem for a symmetric matrix pencil is reinterpreted as an unconstrained minimisation problem with a weighted non-linear criterion. The analytical results show that the proposed criterion has a unique global minimum which corresponds to the principal generalised eigenvectors, thus guaranteeing the global convergence via iterative methods to search the minimum. A gr... View full abstract»

The study describes the results of research carried out into the design of a parallel and resource-efficient solution to the real-data polyphase discrete Fourier transform (DFT), or PDFT. The solution is able to exploit both the real-valued nature of the data and the parallel processing capabilities of the computing technology - assumed to be a field-programmable gate array - to yield a solution w... View full abstract»

Practically, the spatial power spectral density (PSD) of single or multiple incoherently distributed (ID) sources is often unknown, and the total spatial PSD is suitable to model the spatial distribution characteristic of signals if the number of multiple ID sources is also unknown. In this study, the Gaussian mixture model (GMM) is employed to characterise the total spatial PSD of multiple ID sou... View full abstract»

Fast signal processing implementation techniques for detection and classification of power quality (PQ) disturbances are the need of the hour. Hence in this work, a parallel computing approach has been proposed to speed up the feature extraction of PQ signals to facilitate rapid building of classifier models. Considering that the Fourier, the one-dimensional discrete wavelet, the time-time and the... View full abstract»

This study is concerned with the problem of filtering and fixed-lag smoothing for jump Markov non-linear systems when the mode information can be extracted from an image sensor. Based on the idea of Rao-Blackwellisation, the authors present a general theoretical framework to derive the recursive estimates by employing the particle filtering method. A suboptimal image-enhanced Rao-Blackwellised par... View full abstract»

In this study, the authors propose multichannel weighted Euclidean (WE) and weighted cosh (WCOSH) cost function estimators for speech enhancement in the distributed microphone scenario. The goal of the work is to illustrate the advantages of utilising additional microphones and modified cost functions for improving signal-to-noise ratio (SNR) and segmental SNR (SSNR) along with log-likelihood rati... View full abstract»