11 accepted papers
The emergence of in-ear wearable sensors, so called Hearables, offers a convenient and cost-effective tool for long-term health monitoring. Traditionally, extracting frequency domain features from these signals relies on Power Spectral Density (PSD), which is highly susceptible to the effects of sto…
A new quaternion-valued adaptive filtering algorithm based on extrapolated weight methods is proposed. The proposed algorithm belongs to the class of conjugate direction algorithms [1]. This class of extrapolation (momentum) based algorithms is preferred to RLS-based algorithms when the matrix inver…
Linear adaptive systems are a well-known staple in numerous signal processing applications. Recently, significant activity and performance gains have been achieved in multilayer neural networks for deep learning applied to practical data processing applications. In this paper, we describe the import…
The least-mean-magnitude-phase (LMMP) algorithm is useful for complex-valued signal processing applications where precise control of magnitude and/or phase error information can provide improved estimation performance. Because it is a gradient procedure, however, the convergence speed of the algorit…
This work sheds a new light on the spectral whitening effects of the sliding discrete Fourier transform (DFT) and uses it as a basis for a novel technique for circularity-preserving spectral estimation. This makes it possible to utilise full available spectral information, unlike the existing method…
Real world complex-valued signals typically exhibit rotation-dependent distributions (noncircularity), and significant performance gains in learning algorithms can be obtained by accounting for information beyond the standard second-order noncircularity (impropriety). To this end, we introduce a new…
The Wiener filter is a well-known signal processing method for improving a noisy signal's quality. The Wiener filter requires either knowledge of or estimates of the power spectra of the signal-of-interest and of the undesired noise, leading to implementation challenges. In this paper, we show how a…
Widely linear processing has been shown to be superior to the traditional strictly linear processing in quaternion minimum mean square error (MMSE) estimation. However, a quantifiable performance difference between strictly and widely linear processing and the relationship between the performance an…
The least-mean-magnitude-phase (LMMP) algorithm is useful for complex-valued signal processing applications where control of magnitude and/or phase error information is needed to achieve good overall performance. Due to the highly-nonlinear nature of the update terms in the LMMP and related methods,…
In this paper, we develop a procedure for evaluating the performance of a single moving sensor system to estimate the range to a stationary emitter based on the discrete-time collection of bearing measurements along the trajectory travelled. We describe a numerical procedure to calculate the range p…
Current approaches to the mean-square analyses of the complex-least-mean-square (CLMS) and augmented CLMS (ACLMS) algorithms can be challenging due to the difficulty in diagonalising the augmented covariance matrix. By employing the recently introduced approximate uncorrelating transform (AUT), whic…