Publications
622 results found
Stanković L, Mandic D, Daković M, et al., 2020, Vertex-frequency graph signal processing: A comprehensive review, Digital Signal Processing: A Review Journal, Vol: 107, ISSN: 1051-2004
© 2020 Elsevier Inc. Graph signal processing deals with signals which are observed on an irregular graph domain. While many approaches have been developed in classical graph theory to cluster vertices and segment large graphs in a signal independent way, signal localization based approaches to the analysis of data on graph represent a new research direction which is also a key to big data analytics on graphs. To this end, after an overview of the basic definitions of graphs and graph signals, we present and discuss a localized form of the graph Fourier transform. To establish analogy with classical signal processing, spectral domain and vertex domain definitions of the localization window are given next. The spectral and vertex localization kernels are then related to the wavelet transform, followed by their polynomial approximations and a study of filtering and inversion operations. For rigor, the analysis of energy representation and frames in the localized graph Fourier transform is extended to the energy forms of vertex-frequency distributions, which operate even without the requirement to apply localization windows. Another link with classical signal processing is established through the concept of local smoothness, which is subsequently related to the paradigm of signal smoothness on graphs, a lynchpin which connects the properties of the signals on graphs and graph topology. This all represents a comprehensive account of the relation of general vertex-frequency analysis with classical time-frequency analysis, an important but missing link for more advanced applications of graph signal processing. The theory is supported by illustrative and practically relevant examples.
Phan A-H, Cichocki A, Uschmajew A, et al., 2020, Tensor Networks for Latent Variable Analysis: Novel Algorithms for Tensor Train Approximation., IEEE Trans Neural Netw Learn Syst, Vol: 31, Pages: 4622-4636
Decompositions of tensors into factor matrices, which interact through a core tensor, have found numerous applications in signal processing and machine learning. A more general tensor model that represents data as an ordered network of subtensors of order-2 or order-3 has, so far, not been widely considered in these fields, although this so-called tensor network (TN) decomposition has been long studied in quantum physics and scientific computing. In this article, we present novel algorithms and applications of TN decompositions, with a particular focus on the tensor train (TT) decomposition and its variants. The novel algorithms developed for the TT decomposition update, in an alternating way, one or several core tensors at each iteration and exhibit enhanced mathematical tractability and scalability for large-scale data tensors. For rigor, the cases of the given ranks, given approximation error, and the given error bound are all considered. The proposed algorithms provide well-balanced TT-decompositions and are tested in the classic paradigms of blind source separation from a single mixture, denoising, and feature extraction, achieving superior performance over the widely used truncated algorithms for TT decomposition.
Talebi SP, Werner S, Mandic DP, 2020, Quaternion-Valued Distributed Filtering and Control, IEEE TRANSACTIONS ON AUTOMATIC CONTROL, Vol: 65, Pages: 4246-4257, ISSN: 0018-9286
Zhang X, Xia Y, Li C, et al., 2020, Complex Properness Inspired Blind Adaptive Frequency-Dependent I/Q Imbalance Compensation for Wideband Direct-Conversion Receivers, IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, Vol: 19, Pages: 5982-5992, ISSN: 1536-1276
Davies HJ, Williams I, Peters NS, et al., 2020, In-ear SpO2: a tool for wearable, unobtrusive monitoring of core blood oxygen saturation, Sensors (Basel, Switzerland), Vol: 20, ISSN: 1424-8220
The non-invasive estimation of blood oxygen saturation (SpO2) by pulse oximetry is of vital importance clinically, from the detection of sleep apnea to the recent ambulatory monitoring of hypoxemia in the delayed post-infective phase of COVID-19. In this proof of concept study, we set out to establish the feasibility of SpO2 measurement from the ear canal as a convenient site for long term monitoring, and perform a comprehensive comparison with the right index finger-the conventional clinical measurement site. During resting blood oxygen saturation estimation, we found a root mean square difference of 1.47% between the two measurement sites, with a mean difference of 0.23% higher SpO2 in the right ear canal. Using breath holds, we observe the known phenomena of time delay between central circulation and peripheral circulation with a mean delay between the ear and finger of 12.4 s across all subjects. Furthermore, we document the lower photoplethysmogram amplitude from the ear canal and suggest ways to mitigate this issue. In conjunction with the well-known robustness to temperature induced vasoconstriction, this makes conclusive evidence for in-ear SpO2 monitoring being both convenient and superior to conventional finger measurement for continuous non-intrusive monitoring in both clinical and everyday-life settings.
Li Z, Deng W, Pei W, et al., 2020, SINR analysis of mimo systems with widely linear MMSE receivers for the reception of real-valued constellations
© 2020 IEEE. Although the widely linear minimum mean-square error (WLMMSE) receiver has been widely applied in multiple-input-multiple-output (MIMO) systems, there has been no theoretical analysis to quantify its distribution of signal-to-interference-plus-noise ratio (SINR) in arbitrary fading environments. In this paper, the closed-from expression of SINR at the output of the WLMMSE detection is presented, which, in its essence, can be interpreted as the sum of a series of gamma distributed random variables. The general probability density function of SINR is derived for the first time, which is explicitly expressed in terms of the confluent Lauricella hypergeometric function. Simulations on MIMO transmission systems over Rayleigh fading channels support the analytic results.
Zhang X, Dees BS, Li C, et al., 2020, Analysis of Least Stochastic Entropy Adaptive Filters for Noncircular Gaussian Signals, IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, Vol: 67, Pages: 1364-1368, ISSN: 1549-7747
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- Citations: 2
Chen Z, Dees BS, Mandic DP, 2020, A Probabilistic Beat-to-Beat Filtering Model for Continuous and Accurate Blood Pressure Estimation
© 2020 IEEE. Cuffless technologies provide a convenient platform for remote and continuous blood pressure (BP) monitoring, however, signal recordings employed for cuffless BP estimation, which are based on the electrocardiogram (ECG) and photo-plethysmography (PPG) signals, are frequently corrupted with measurement noise and artefacts. Consequently, even a small portion of abnormal data samples can severely impact the overall signal quality and therefore lead to significantly distorted BP value estimates. To this end, a data-driven model is proposed to infer the beat-to-beat signal quality for the ECG, PPG and BP signal recordings, whereby high-quality and low-quality (outlier) beats are detected using a probabilistic model chosen according to the maximum entropy principle. Physiological rules are also imposed to guarantee that each filtered sample is physiologically meaningful. The advantages of the proposed filtering framework for both systolic blood pressure and diastolic blood pressure estimation are demonstrated through the analysis and estimation of 12,000 clinical BP recordings, consisting of over 200,000 test samples.
Phan A-H, Cichocki A, Oseledets I, et al., 2020, Tensor Networks for Latent Variable Analysis: Higher Order Canonical Polyadic Decomposition, IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, Vol: 31, Pages: 2174-2188, ISSN: 2162-237X
Stankovic L, Dakovic M, Mandic D, et al., 2020, A Low-Dimensionality Method for Data-Driven Graph Learning, Pages: 5340-5344, ISSN: 1520-6149
© 2020 IEEE. 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, followed by variations of the edge weights in the graph domain or the eigenvalues in the graph spectral domain. These domains are high-dimensional, since their dimension is at least equal to the order of the number of vertices. In this paper, we propose a numerically efficient method for estimating of the normalized Laplacian through its eigenvalues estimation and by promoting its sparsity. The minimization problem is solved in quite a low-dimensional space, related to the polynomial order of the underlying system on a graph corresponding to the the observed data. The accuracy of the results is tested on numerical example.
Dees BS, Stankovic L, Constantinides AG, et al., 2020, Portfolio Cuts: A Graph-Theoretic Framework to Diversification, Pages: 8454-8458, ISSN: 1520-6149
© 2020 IEEE. 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 assumption of the completeness of graph topology and to equip the graph model with practically relevant physical intuition, we introduce the portfolio cut paradigm. Such a graph-theoretic portfolio partitioning technique is shown to allow the investor to devise robust and tractable asset allocation schemes, by virtue of a rigorous graph framework for considering smaller, computationally feasible, and economically meaningful clusters of assets, based on graph cuts. In turn, this makes it possible to fully utilize the asset returns covariance matrix for constructing the portfolio, even without the requirement for its inversion. The advantages of the proposed framework over traditional methods are demonstrated through numerical simulations based on real-world price data.
Stankovic L, Brajovic M, Dakovic M, et al., 2020, On the decomposition of multichannel nonstationary multicomponent signals, SIGNAL PROCESSING, Vol: 167, ISSN: 0165-1684
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- Citations: 3
Kisil I, Calvi GG, Scalzo Dees B, et al., 2020, Tensor Decompositions and Practical Applications: A Hands-on Tutorial, Studies in Computational Intelligence, Pages: 69-97
© 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. The exponentially increasing availability of big and streaming data comes as a direct consequence of the rapid development and widespread use of multi-sensor technology. The quest to make sense of such large volume and variety of that has both highlighted the limitations of standard flat-view matrix models and the necessity to move toward more versatile data analysis tools. One such model which is naturally suited for data of large volume, variety and veracity are multi-way arrays or tensors. The associated tensor decompositions have been recognised as a viable way to break the “Curse of Dimensionality”, an exponential increase in data volume with the tensor order. Owing to a scalable way in which they deal with multi-way data and their ability to exploit inherent deep data structures when performing feature extraction, tensor decompositions have found application in a wide range of disciplines, from very theoretical ones, such as scientific computing and physics, to the more practical aspects of signal processing and machine learning. It is therefore both timely and important for a wider Data Analytics community to become acquainted with the fundamentals of such techniques. Thus, our aim is not only to provide a necessary theoretical background for multi-linear analysis but also to equip researches and interested readers with an easy to read and understand practical examples in form of a Python code snippets.
Stankovic L, Mandic DP, Dakovic M, et al., 2020, Demystifying the Coherence Index in Compressive Sensing [Lecture Notes], IEEE SIGNAL PROCESSING MAGAZINE, Vol: 37, Pages: 152-162, ISSN: 1053-5888
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- Citations: 2
Xiang M, Xia Y, Mandic DP, 2020, Performance Analysis of Deficient Length Quaternion Least Mean Square Adaptive Filters, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 68, Pages: 65-80, ISSN: 1053-587X
Nakamura T, Alqurashi Y, Morrell M, et al., 2020, Hearables: automatic overnight sleep monitoring with standardised in-ear EEG sensor, IEEE Transactions on Biomedical Engineering, Vol: 67, Pages: 203-212, ISSN: 0018-9294
Objective: Advances in sensor miniaturisation and computational power have served as enabling technologies for monitoring human physiological conditions in real-world scenarios. Sleep disruption may impact neural function, and can be a symptom of both physical and mental disorders. This study proposes wearable in-ear electroencephalography (ear- EEG) for overnight sleep monitoring as a 24/7 continuous and unobtrusive technology for sleep quality assessment in the community. Methods: Twenty-two healthy participants took part in overnight sleep monitoring with simultaneous ear-EEG and conventional full polysomnography (PSG) recordings. The ear- EEG data were analysed in the both structural complexity and spectral domains; the extracted features were used for automatic sleep stage prediction through supervised machine learning, whereby the PSG data were manually scored by a sleep clinician. Results: The agreement between automatic sleep stage prediction based on ear-EEG from a single in-ear sensor and the hypnogram based on the full PSG was 74.1% in the accuracy over five sleep stage classification; this is supported by a Substantial Agreement in the kappa metric (0.61). Conclusion: The in-ear sensor is both feasible for monitoring overnight sleep outside the sleep laboratory and mitigates technical difficulties associated with scalp-EEG. It therefore represents a 24/7 continuously wearable alternative to conventional cumbersome and expensive sleep monitoring. Significance: The ‘standardised’ one-size-fits-all viscoelastic in-ear sensor is a next generation solution to monitor sleep - this technology promises to be a viable method for readily wearable sleep monitoring in the community, a key to affordable healthcare and future eHealth.
Cheng H, Xia Y, Huang Y, et al., 2020, Improperness Based SINR Analysis of GFDM Systems Under Joint Tx and Rx I/Q Imbalance, IEEE Wireless Communications and Networking Conference (IEEE WCNC), Publisher: IEEE, ISSN: 1525-3511
von Rosenberg W, Hoting M-O, Mandic DP, 2019, A physiology based model of heart rate variability, BIOMEDICAL ENGINEERING LETTERS, Vol: 9, Pages: 425-434, ISSN: 2093-9868
Stankovic L, Mandic DP, Dakovic M, et al., 2019, Understanding the Basis of Graph Signal Processing via an Intuitive Example-Driven Approach, IEEE SIGNAL PROCESSING MAGAZINE, Vol: 36, Pages: 133-145, ISSN: 1053-5888
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- Citations: 1
Xiang M, Xia Y, Mandic DP, 2019, Complementary Cost Functions for Complex and Quaternion Widely Linear Estimation, IEEE SIGNAL PROCESSING LETTERS, Vol: 26, Pages: 1344-1348, ISSN: 1070-9908
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- Citations: 1
Mandic DP, Djuric PM, Cichocki A, et al., 2019, Quo Vadis ICASSP: Echoes of 2019 ICASSP in Brighton, United Kingdom Signal Processing Meets the Needs of Modern Humankind, IEEE SIGNAL PROCESSING MAGAZINE, Vol: 36, Pages: 127-134, ISSN: 1053-5888
Talebi SP, Werner S, Mandic DP, 2019, Complex-Valued Nonlinear Adaptive Filters With Applications in alpha-Stable Environments, IEEE SIGNAL PROCESSING LETTERS, Vol: 26, Pages: 1315-1319, ISSN: 1070-9908
Solo V, Greco MS, Mandic D, et al., 2019, Innovation Starts With Education ICASSP 2019 Education Panel, IEEE SIGNAL PROCESSING MAGAZINE, Vol: 36, Pages: 135-+, ISSN: 1053-5888
Oliveira V, von Rosenberg W, Montaldo P, et al., 2019, Early postnatal heart rate variability in healthy newborn infants, Frontiers in Physiology, Vol: 10, Pages: 1-12, ISSN: 1664-042X
Background: Despite the increasing interest in fetal and neonatal heart rate variability (HRV) analysis and its potential use as a tool for early disease stratification, no studies have previously described the normal trends of HRV in healthy babies during the first hours of postnatal life.Methods: We prospectively recruited 150 healthy babies from the postnatal ward and continuously recorded their electrocardiogram during the first 24 h after birth. Babies were included if born in good condition and stayed with their mother. Babies requiring any medication or treatment were excluded. Five-minute segments of the electrocardiogram (non-overlapping time-windows) with more than 90% consecutive good quality beats were included in the calculation of hourly medians and interquartile ranges to describe HRV trends over the first 24 h. We used multilevel mixed effects regression with auto-regressive covariance structure for all repeated measures analysis and t-tests to compare group differences. Non-normally distributed variables were log-transformed.Results: Nine out of 16 HRV metrics (including heart rate) changed significantly over the 24 h [Heart rate p < 0.01; Standard deviation of the NN intervals p = 0.01; Standard deviation of the Poincaré plot lengthwise p < 0.01; Cardiac sympathetic index (CSI) p < 0.01; Normalized high frequency power p = 0.03; Normalized low frequency power p < 0.01; Total power p < 0.01; HRV index p = 0.01; Parseval index p = 0.03], adjusted for relevant clinical variables. We observed an increase in several HRV metrics during the first 6 h followed by a gradual normalization by approximately 12 h of age. Between 6 and 12 h of age, only heart rate and the normalized low frequency power changed significantly, while between 12 and 18 h no metric, other than heart rate, changed significantly. Analysis with multilevel mixed effects regression analysis (multivariable) revealed that gestational age, reduced fetal movements, cardi
Zhang X, Xia Y, Li C, et al., 2019, Analysis of the Unconstrained Frequency-Domain Block LMS for Second-Order Noncircular Inputs, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 67, Pages: 3970-3984, ISSN: 1053-587X
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- Citations: 4
Xia Y, Tao S, Li Z, et al., 2019, Full Mean Square Performance Bounds on Quaternion Estimators for Improper Data, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 67, Pages: 4093-4106, ISSN: 1053-587X
Nitta T, Kobayashi M, Mandic DP, 2019, Hypercomplex Widely Linear Estimation Through the Lens of Underpinning Geometry, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 67, Pages: 3985-3994, ISSN: 1053-587X
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- Citations: 3
Stott AE, Dees BS, Kisil I, et al., 2019, A class of multidimensional NIPALS algorithms for quaternion and tensor partial least squares regression, SIGNAL PROCESSING, Vol: 160, Pages: 316-327, ISSN: 0165-1684
Powezka K, Adjei T, von Rosenberg W, et al., 2019, A pilot study of preoperative heart rate variability predicting pain during local anesthetic varicose vein surgery, JOURNAL OF VASCULAR SURGERY-VENOUS AND LYMPHATIC DISORDERS, Vol: 7, Pages: 382-386, ISSN: 2213-333X
Kanna S, Moniri A, Xia Y, et al., 2019, A Data Analytics Perspective of Power Grid Analysis-Part 2: Teaching Old Power Systems New Tricks, IEEE SIGNAL PROCESSING MAGAZINE, Vol: 36, Pages: 110-117, ISSN: 1053-5888
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