Imperial College London
Portrait Picture

ProfessorDaniloMandic

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Machine Intelligence
 
 
 
//

Contact

 

+44 (0)20 7594 6271d.mandic Website

 
 
//

Assistant

 

Miss Vanessa Rodriguez-Gonzalez +44 (0)20 7594 6267

 
//

Location

 

813Electrical EngineeringSouth Kensington Campus

//

Summary

 

Publications

Publication Type
Year
to

737 results found

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

Conference paper

Dees BS, Xu YL, Constantinides AG, Mandic DPet al., 2021, Graph Theory for Metro Traffic Modelling, International Joint Conference on Neural Networks (IJCNN), Publisher: IEEE, ISSN: 2161-4393

Conference paper

Scalzo B, Arroyo A, Stankovic L, Mandic DPet al., 2021, NONSTATIONARY PORTFOLIOS: DIVERSIFICATION IN THE SPECTRAL DOMAIN, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 5155-5159

Conference paper

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

Conference paper

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

Conference paper

Mahfouz M, Balch T, Veloso M, Mandic Det al., 2021, Learning to Classify and Imitate Trading Agents in Continuous Double Auction Markets, 2nd ACM International Conference on AI in Finance (ICAIF), Publisher: ASSOC COMPUTING MACHINERY

Conference paper

Li Z, Pu R, Xia Y, Pei W, Mandic DPet 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

Journal article

Stankovic L, Brajovic M, Mandic D, Stankovic I, Dakovic Met al., 2021, Improved Coherence Index-Based Bound in Compressive Sensing, IEEE SIGNAL PROCESSING LETTERS, Vol: 28, Pages: 1110-1114, ISSN: 1070-9908

Journal article

Menguc EC, Cnar S, Xiang M, P Mandic Det 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

Journal article

Gogineni VC, Talebi SP, Werner S, Mandic DPet al., 2020, Fractional-Order Correntropy Filters for Tracking Dynamic Systems in α-Stable Environments, IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, Vol: 67, Pages: 3557-3561, ISSN: 1549-7747

Journal article

Stankovic L, Mandic D, Dakovic M, Scalzo B, Brajovic M, Sejdic E, Constantinides AGet al., 2020, Vertex-frequency graph signal processing: A comprehensive review, DIGITAL SIGNAL PROCESSING, Vol: 107, ISSN: 1051-2004

Journal article

Phan A-H, Cichocki A, Uschmajew A, Tichavsky P, Luta G, Mandic DPet al., 2020, Tensor Networks for Latent Variable Analysis: Novel Algorithms for Tensor Train Approximation, IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, Vol: 31, Pages: 4622-4636, ISSN: 2162-237X

Journal article

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

Journal article

Zhang X, Xia Y, Li C, Yang L, Mandic DPet 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

Journal article

Davies HJ, Williams I, Peters NS, Mandic DPet 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.

Journal article

Zhang X, Dees BS, Li C, Xia Y, Yang L, Mandic DPet 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

Journal article

Phan A-H, Cichocki A, Oseledets I, Calvi GG, Ahmadi-Asl S, Mandic DPet 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

Journal article

Stankovic L, Brajovic M, Dakovic M, Mandic Det al., 2020, On the decomposition of multichannel nonstationary multicomponent signals, SIGNAL PROCESSING, Vol: 167, ISSN: 0165-1684

Journal article

Li S, Yu Z, Xiang M, Mandic Det al., 2020, Reciprocal adversarial learning via characteristic functions, ISSN: 1049-5258

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-GANs with theoretical support for basically comparing moments in an embedded domain of the critic. We generalise this by comparing the distributions rather than their moments via a powerful tool, i.e., the characteristic function (CF), which uniquely and universally comprising all the information about a distribution. For rigour, we first establish the physical meaning of the phase and amplitude in CF, and show that this provides a feasible way of balancing the accuracy and diversity of generation. We then develop an efficient sampling strategy to calculate the CFs. Within this framework, we further prove an equivalence between the embedded and data domains when a reciprocal exists, where we naturally develop the GAN in an auto-encoder structure, in a way of comparing everything in the embedded space (a semantically meaningful manifold). This efficient structure uses only two modules, together with a simple training strategy, to achieve bi-directionally generating clear images, which is referred to as the reciprocal CF GAN (RCF-GAN). Experimental results demonstrate the superior performances of the proposed RCF-GAN in terms of both generation and reconstruction.

Conference paper

Nakamura T, Alqurashi Y, Morrell M, Mandic Det 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.

Journal article

Bacciu D, Mandic DP, 2020, Tensor decompositions in deep learning, Pages: 441-450

The paper surveys the topic of tensor decompositions in modern machine learning applications. It focuses on three active research topics of significant relevance for the community. After a brief review of consolidated works on multi-way data analysis, we consider the use of tensor decompositions in compressing the parameter space of deep learning models. Lastly, we discuss how tensor methods can be leveraged to yield richer adaptive representations of complex data, including structured information. The paper concludes with a discussion on interesting open research challenges.

Conference paper

Kisil I, Calvi GG, Scalzo Dees B, Mandic DPet al., 2020, Tensor Decompositions and Practical Applications: A Hands-on Tutorial, Studies in Computational Intelligence, Pages: 69-97

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.

Book chapter

Stankovic L, Mandic DP, Dakovic M, Kisil Iet al., 2020, Demystifying the Coherence Index in Compressive Sensing [Lecture Notes], IEEE SIGNAL PROCESSING MAGAZINE, Vol: 37, Pages: 152-162, ISSN: 1053-5888

Journal article

Menguc EC, Acir N, Mandic DP, 2020, Widely Linear Quaternion-Valued Least-Mean Kurtosis Algorithm, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 68, Pages: 5914-5922, ISSN: 1053-587X

Journal article

Gogineni VC, Talebi SP, Werner S, Mandic DPet al., 2020, Fractional-Order Correntropy Adaptive Filters for Distributed Processing of α-Stable Signals, IEEE SIGNAL PROCESSING LETTERS, Vol: 27, Pages: 1884-1888, ISSN: 1070-9908

Journal article

Stankovic L, Mandic D, Dakovic M, Brajovic M, Scalzo B, Li S, Constantinides AGet al., 2020, Data Analytics on Graphs Part III: Machine Learning on Graphs, from Graph Topology to Applications, FOUNDATIONS AND TRENDS IN MACHINE LEARNING, Vol: 13, Pages: 332-530, ISSN: 1935-8237

Journal article

Stankovic L, Mandic D, Dakovic M, Brajovic M, Scalzo B, Li S, Constantinides AGet al., 2020, Data Analytics on Graphs Part II: Signals on Graphs, FOUNDATIONS AND TRENDS IN MACHINE LEARNING, Vol: 13, Pages: 158-331, ISSN: 1935-8237

Journal article

Stankovic L, Mandic D, Dakovic M, Brajovic M, Scalzo B, Li S, Constantinides AGet al., 2020, Data Analytics on Graphs Part I: Graphs and Spectra on Graphs, FOUNDATIONS AND TRENDS IN MACHINE LEARNING, Vol: 13, Pages: 1-157, ISSN: 1935-8237

Journal article

Cheng H, Xia Y, Lu Z, Huang Y, Pei W, Yang L, Mandic DPet al., 2020, Design of Improper Constellations for Optimal Data Rates in Downlink NOMA Systems, 12th International Conference on Wireless Communications and Signal Processing (WCSP), Publisher: IEEE, Pages: 436-441, ISSN: 2325-3746

Conference paper

Li S, Yu Z, Xiang M, Mandic Det al., 2020, Solving General Elliptical Mixture Models through an Approximate Wasserstein Manifold, 34th AAAI Conference on Artificial Intelligence / 32nd Innovative Applications of Artificial Intelligence Conference / 10th AAAI Symposium on Educational Advances in Artificial Intelligence, Publisher: ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE, Pages: 4658-4666, ISSN: 2159-5399

Conference paper

This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.

Request URL: http://wlsprd.imperial.ac.uk:80/respub/WEB-INF/jsp/search-html.jsp Request URI: /respub/WEB-INF/jsp/search-html.jsp Query String: id=00168709&limit=30&person=true&page=4&respub-action=search.html