663 results found
Li S, Mandic D, 2022, Von Mises-Fisher Elliptical Distribution, IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, ISSN: 2162-237X
Prochazka A, Charvat J, Vysata O, et al., 2022, Incremental deep learning for reflectivity data recognition in stomatology, NEURAL COMPUTING & APPLICATIONS, Vol: 34, Pages: 7081-7089, ISSN: 0941-0643
Davies HJ, Bachtiger P, Williams I, et al., 2022, Wearable in-ear PPG: detailed respiratory variations enable classification of COPD, IEEE Transactions on Biomedical Engineering, ISSN: 0018-9294
An ability to extract detailed spirometry-like breath-ing waveforms from wearable sensors promises to greatly improve respiratory health monitoring. Photoplethysmography (PPG) has been researched in depth for estimation of respiration rate, given that it varies with respiration through overall intensity, pulse amplitude and pulse interval. We compare and contrast the extraction of these three respiratory modes from both the ear canal and finger and show a marked improvement in the respiratory power for respiration induced intensity variations and pulse amplitude variations when recording from the ear canal. We next employ a data driven multi-scale method, noise assisted multivariate empirical mode decomposition (NA-MEMD), which allows for simultaneous analysis of all three respiratory modes to extract detailed respiratory waveforms from in-ear PPG. For rigour, we considered in-ear PPG recordings from healthy subjects, both older and young, patients with chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) and healthy subjects with artificially obstructed breathing. Specific in-ear PPG waveform changes are observed for COPD, such as a decreased inspiratory duty cycle and an increased inspiratory magnitude, when compared with expiratory magnitude. These differences are used to classify COPD from healthy and IPF waveforms with a sensitivity of 87% and an overall accuracy of 92%. Our findings indicate the promise of in-ear PPG for COPD screening and unobtrusive respiratory monitoring in ambulatory scenarios and in consumer wearables.
Xiao H, Mandic DP, 2022, Variational Embedding Multiscale Sample Entropy: A Tool for Complexity Analysis of Multichannel Systems, ENTROPY, Vol: 24
Li S, Yu Z, Xiang M, et al., 2022, Reciprocal GAN through Characteristic Functions (RCF-GAN), IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN: 0162-8828
The integral probability metric (IPM) equips generative adversarial nets (GANs) with the necessary theoretical support for comparing statistical moments in an embedded domain of the critic, while stabilising their training and mitigating the mode collapse issues. For enhanced intuition and physical insight, we introduce a generalisation of IPM-GANs which operates by directly comparing probability distributions rather than their moments. This is achieved through characteristic functions (CFs), a powerful tool that uniquely comprises all information about any general distribution. For rigour, we first theoretically prove the ability of the CF loss to compare probability distributions, and proceed to establish the physical meaning of the phase and amplitude of CFs. An optimal sampling strategy is then developed to calculate the CFs, and an equivalence between the embedded and data domains is proved under the reciprocal theory. This makes it possible to seamlessly combine IPM-GAN with an auto-encoder structure by an advanced anchor architecture, which adversarially learns a semantic low-dimensional manifold for both generation and reconstruction. This efficient reciprocal CF GAN (RCF-GAN) structure, uses only two modules and a simple training strategy to achieve the state-of-the-art bi-directional generation. Experiments demonstrate the superior performance of RCF-GAN on both regular (images) and irregular (graph) domains.
Zhang X, Xia Y, Li C, et al., 2021, A full second-order statistical analysis of strictly linear and widely linear estimators with MSE and Gaussian entropy criteria, SIGNAL PROCESSING, Vol: 192, ISSN: 0165-1684
Li Z, Deng W, Zhu Z, et al., 2021, A layer-wise distribution analysis of the WLMMSE-SIC MIMO receiver for rectilinear or quasi-rectilinear signals, SIGNAL PROCESSING, Vol: 189, ISSN: 0165-1684
Li S, Yu Z, Mandic D, 2021, A Universal Framework for Learning the Elliptical Mixture Model, IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, Vol: 32, Pages: 3181-3195, ISSN: 2162-237X
Khalifa Y, Mandic D, Sejdic E, 2021, A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals, INFORMATION FUSION, Vol: 69, Pages: 52-72, ISSN: 1566-2535
Haliassos A, Konstantinidis K, Mandic DP, 2021, Supervised Learning for Nonsequential Data: A Canonical Polyadic Decomposition Approach, IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, ISSN: 2162-237X
Ruiz-Garcia A, Schmidhuber J, Palade V, et al., 2021, Deep neural network representation and Generative Adversarial Learning, NEURAL NETWORKS, Vol: 139, Pages: 199-200, ISSN: 0893-6080
Scalzo B, Konstantinidis A, Mandic DP, 2021, Analysis of global fixed-income returns using multilinear tensor algebra, Journal of Fixed Income, Vol: 30, Pages: 32-52, ISSN: 1059-8596
Global fixed-income returns exhibit highly structured correlations across maturities and economies (data modes), and their modeling therefore requires analysis tools that are capable of directly capturing the inherent multiway couplings present in such multimodal data. Yet, current analyses typically employ “flat-view” multivariate matrix models and their associated linear algebras; these are agnostic to the global data structure and can only describe local linear pairwise relationships between data entries. To address this issue, the authors first show that global fixed-income returns naturally reside on multi-modal lattice data structures, referred to as tensors. This serves as a basis to introduce a multilinear algebraic approach, inherent in tensors, to the modeling of the global term structure underlying multiple interest rate curves. Owing to the enhanced flexibility of multilinear algebra, statistical descriptors, such as correlations, exist between tensor columns and rows (fibers), as opposed to between individual entries in standard matrix analysis. This allows for the expression of the covariance of global returns as a joint multilinear decomposition of the maturity-domain and country-domain covariances. This not only drastically reduces the number of parameters required to fully capture the global return covariance structure, but also makes it possible to devise rigorous and tractable global portfolio management strategies; the authors tailor these specifically to each of the data domains and thereby fully exploit the lattice structure of global fixed-income returns. The ability of the proposed multilinear tensor approach to compactly describe the macroeconomic environment through economically meaningful factors is validated via empirical analysis that demonstrates the existence of maturity-domain and country-domain covariances underlying the interest rate curves of eight developed economies.
Calvi GG, Dees BS, Mandic DP, 2021, A lower bound on the tensor rank based on its maximally square matrix unfolding, SIGNAL PROCESSING, Vol: 180, ISSN: 0165-1684
Xu D, Zhang S, Zhang H, et al., 2021, Convergence of the RMSProp deep learning method with penalty for nonconvex optimization, NEURAL NETWORKS, Vol: 139, Pages: 17-23, ISSN: 0893-6080
Stankovic L, Mandic D, Dakovic M, 2021, Data Analytics on Graphs, Publisher: Now Publishers, ISBN: 9781680839821
Aimed at readers with a good grasp of the fundamentals of data analytics, this book sets out the fundamentals of graph theory and the emerging mathematical techniques for the analysis of a wide range of data acquired on graph environments.
Brajović M, Stanković I, Daković M, et al., 2021, On the Number of Channels in Multicomponent Nonstationary Noisy Signal Decomposition
If acquired using multiple sensors, non-stationary multicomponent signals can be decomposed into individual components by exploiting interdependences of signals from different channels. Earlier, we have proposed a decomposition approach being able to extract individual non-stationary signal components even in the challenging cases when their domains of support overlap in the time, frequency or joint time-frequency (TF) domains. The approach is based upon the eigenvalue analysis of the multichannel autocorrelation matrix and minimizations of concentration measures calculated using TF representations. In this paper, we investigate the influence of the number of sensors (channels) and external noise variance to the outcome of the decomposition process.
Hammour GM, Mandic DP, 2021, Hearables: Making Sense from Motion Artefacts in Ear-EEG for Real-Life Human Activity Classification, 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC), Pages: 6889-6893, ISSN: 1557-170X
Xu YL, Calvi GG, Mandic DP, 2021, Tensor-Train Recurrent Neural Networks for Interpretable Multi-Way Financial Forecasting, International Joint Conference on Neural Networks (IJCNN), Publisher: IEEE, ISSN: 2161-4393
Scalzo B, Arroyo A, Stankovic L, et al., 2021, NONSTATIONARY PORTFOLIOS: DIVERSIFICATION IN THE SPECTRAL DOMAIN, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 5155-5159
Chereau JP, Scalzo B, Mandic DP, 2021, ROBUST PCA THROUGH MAXIMUM CORRENTROPY POWER ITERATIONS, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 4985-4989
Konstantinidis K, Li S, Mandic DP, 2021, KERNEL LEARNING WITH TENSOR NETWORKS, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 2920-2924
Menguc EC, Cnar S, Xiang M, et al., 2021, Online Censoring Based Weighted-Frequency Fourier Linear Combiner for Estimation of Pathological Hand Tremors, IEEE SIGNAL PROCESSING LETTERS, Vol: 28, Pages: 1460-1464, ISSN: 1070-9908
Xu YL, Mandic DP, 2021, Recurrent Graph Tensor Networks: A Low-Complexity Framework for Modelling High-Dimensional Multi-Way Sequences, 29th European Signal Processing Conference (EUSIPCO), Publisher: EUROPEAN ASSOC SIGNAL SPEECH & IMAGE PROCESSING-EURASIP, Pages: 1795-1799, ISSN: 2076-1465
Stankovic L, Brajovic M, Mandic D, et al., 2021, Improved Coherence Index-Based Bound in Compressive Sensing, IEEE SIGNAL PROCESSING LETTERS, Vol: 28, Pages: 1110-1114, ISSN: 1070-9908
Li Z, Pu R, Xia Y, et al., 2021, A Full Second-Order Analysis of the Widely Linear MVDR Beamformer for Noncircular Signals, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 69, Pages: 4257-4268, ISSN: 1053-587X
Gogineni VC, Talebi SP, Werner S, et al., 2020, Fractional-Order Correntropy Filters for Tracking Dynamic Systems in alpha-Stable Environments, IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, Vol: 67, Pages: 3557-3561, ISSN: 1549-7747
Stankovic L, Mandic D, Dakovic M, et al., 2020, Vertex-frequency graph signal processing: A comprehensive review, DIGITAL SIGNAL PROCESSING, Vol: 107, ISSN: 1051-2004
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