43 accepted papers
We present a novel approach to anomaly detection by integrating Generalized Hyperbolic (GH) processes into kernel-based methods. The GH distribution, known for its flexibility in modeling skewness, heavy tails, and kurtosis, helps to capture complex patterns in data that deviate from Gaussian assump…
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…
Respiratory rate (RR) is a critical health indicator often monitored under inconvenient scenarios, limiting its practicality for continuous monitoring. Photoplethysmography (PPG) sensors, increasingly integrated into wearable devices, offer a chance to continuously estimate RR in a portable manner.…
Time series regression models are typically trained using the mean squared error (MSE) and thus rely critically on time-aligned reference data. However, the MSE loss is often inadequate when processing real-world data, such as physiological signals, as misalignment between two signals can cause a la…
Despite the recent theoretical boom in Sample Entropy based algorithms for the analysis of physiological and pathological systems, the major issue which prevents their more widespread use remains that of large computational load, particularly in the studies of quantification of structural richness i…
The utility of Graph Neural Networks (GNN) for the paradigm of forecasting short-term stock price movements is investigated. In particular, a finance-specific graph pooling operation, referred to as StockPool, is introduced to efficiently coarsen the stock graph. This is achieved by employing domain…
During speech perception, a listener’s electroencephalogram (EEG) reflects acoustic-level processing as well as higher-level cognitive factors such as speech comprehension and attention. However, decoding speech from EEG recordings is challenging due to the low signal-to-noise ratios of EEG signals.…
Hyperparameter optimisation is a prerequisite for state-of-the- art performance in machine learning, with current strategies including Bayesian optimisation, hyperband, and evolutionary methods. While such methods have been shown to improve performance, none of these is designed to explicitly take a…
Stock market returns are typically analyzed using standard regression models yet they reside on irregular domains, a natural scenario for graph signal processing. This motivates us to consider a market graph as an intuitive way to represent the relationships between financial assets. Traditional met…
Denoising diffusion probabilistic models (diffusion models for short) require a large number of iterations in inference to achieve the generation quality that matches or surpasses the state-of-the-art generative models, which invariably results in slow inference speed. Previous approaches aim to opt…
The attention mechanism is at the core of modern Natural Language Processing (NLP) models, owing to its ability to focus on the most contextually relevant part of a sequence. However, current attention models rely on "flat-view" matrix methods to process tokens embedded in vector spaces; this result…
The rapid development in sensor technology has made it convenient to acquire data from multi-channel systems but has also high-lighted the need for the analysis of nonlinear dynamical properties at a higher level - the so-called structural complexity. Traditional single-scale entropy measures, such…
Tensor network (TN) methods have proven their considerable potential in deterministic regression and classification related paradigms, but remain underexplored in probabilistic settings. To this end, we introduce a variational inference framework for supervised learning in the context of TNs, referr…
Classical portfolio optimization methods typically determine an optimal capital allocation through the implicit, yet critical, assumption of statistical time-invariance. Such models are inadequate for real-world markets as they employ standard time-averaging based estimators which suffer significant…
Principal component analysis (PCA) is considered a quintessential data analysis technique when it comes to describing linear relationships between the features of a dataset. However, the well-known lack of robustness of PCA for non-Gaussian data and/or outliers often makes its practical use unreliab…
In many graph signal processing applications, finding the topology of a graph is part of the overall data processing problem rather than a priori knowledge. Most of the approaches to graph topology learning are based on the assumption of graph Laplacian sparsity, with various additional constraints,…
Investment returns naturally reside on irregular domains, however, standard multivariate portfolio optimization methods are agnostic to data structure. To this end, we investigate ways for domain knowledge to be conveniently incorporated into the analysis, by means of graphs. Next, to relax the assu…
Generative adversarial nets (GANs) have become a preferred tool for tasks involving complicated distributions. To stabilise the training and reduce the mode collapse of GANs, one of their main variants employs the integral probability metric (IPM) as the loss function. This provides extensive IPM-GA…
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…
Complex least mean square (CLMS) based adaptive computation of discrete orthogonal transforms has been extensively investigated in the literature. However, all of these results provide only a means for the calculation of either forward orthogonal transforms or their inverse orthogonal transforms, se…
The future Smart Grid represents an extraordinary opportunity to transform the ways we currently approach energy into a new era of low-carbon, renewable, and efficient solutions which will ultimately have a significant impact on both the environment and economy. This effort requires close collaborat…
Past decades have witnessed excessive use of the Support Vector Machines (SVMs) in financial contexts. Despite their success, given the inherently multivariate nature of financial indices, the vector-based nature of SVM will inevitably lead to a loss of information, owing to its inability to fully e…
In order to accommodate for modern adaptive filtering applications, the classic adaptive filtering paradigm is considered from a more general perspective. The new formulation allows for time dependent variations in the state of the system and more importantly it relaxes the Gaussian assumption to th…
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…
A novel method for common and individual feature analysis from exceedingly large-scale data is proposed, in order to ensure the tractability of both the computation and storage and thus mitigate the curse of dimensionality, a major bottleneck in modern data science. This is achieved by making use of…
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 hyperscanning method simultaneously acquires and relates cerebral data from two participants while performing cooperative activities. The aim of this work is to evaluate the performance of our novel EEG recording concept, termed ear-EEG, against on-scalp EEG as an alternative, user-friendly data…
An augmented nonlinear complex LMS (ANCLMS) algorithm is proposed to adaptively mitigate both the linear and nonlinear self-interference (SI) components in a full-duplex direct-conversion transceiver (DCT). A data prewhitening scheme, which exploits the known SI signal distributions, is also adopted…
Partial Least Squares (PLS) has been gaining popularity as a multivariate data analysis tool due to its ability to cater for noisy, collinear and incomplete data-sets. However, most PLS solutions are designed as block-based algorithms, rendering them unsuitable for environments with streaming data a…
A resource effective extension to the class of distributed real-time diffusion Kalman filters is proposed. The proposed scheme removes the need to share measurement variables explicitly, by sharing only the state estimates and state error covariance matrices which implicitly contain the information…
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…
The Electrocardiogram (ECG) collected in real-life scenarios is often noisy and contaminated with motion artefacts. This study proposes a new framework to analyse the heart rate variability (HRV) in mobile scenarios by introducing novel R-peak detection and HRV detrending algorithms. The R-peak dete…
The recent introduction of η-Hermitian matrices A = A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ηH</sup> has opened a new avenue of research in quaternion signal processing. However, the exploitation of this matrix structure has been limited, perh…
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…
Cooperative tasks require coordinated joint actions among the participants, to the extent that a failure in an individual's action may have catastrophic consequences on the task of the group as a whole. One such activity is choir singing, where highly synchronised performance of the individual singe…
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,…
Motivated by the need for accurate frequency estimation in power systems, a novel algorithm for estimating the fundamental frequency of both balanced and unbalanced three-phase power systems, which is robust to noise and distortions, is developed. This is achieved through the use of quaternions in o…
Very often data we encounter in practice is a collection of matrices rather than a single matrix. These multi-block data often share some common features, due to the background in which they are measured. In this study we propose a new concept of linked blind source separation (BSS) that aims at dis…
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…
Multichannel data-driven time-frequency algorithms, such as the multivariate empirical mode decomposition (MEMD), have emerged as important tools in the analysis of inter-channel dependencies that arise in multivariate data. Such methods employ uniform projection schemes on hyperspheres in order to…
A widely linear quaternion recursive total least squares (WL-QRTLS) algorithm is introduced for the processing of ℚ-improper processes contaminated by noise. The total least squares for quaternions (QTLS) is a generalisation of the real-valued total least squares and is introduced rigorously, starti…
It is essential to measure physiological parameters such as heart rate variability and respiratory rate of drivers to evaluate their performance. The results from this measurement can be used to assess the state of body and mind, for instance concentration and stress. However, current systems only w…