Imperial College London

ProfessorDaniloMandic

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Professor of Signal Processing
 
 
 
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Contact

 

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

 
 
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Assistant

 

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

 
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Location

 

813Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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685 results found

Qing Z, Ni J, Li Z, Menguc EC, Chen J, Mandic DPet al., 2023, Performance analysis of the augmented complex-valued least mean kurtosis algorithm, SIGNAL PROCESSING, Vol: 203, ISSN: 0165-1684

Journal article

Talebi SP, Godsill SJ, Mandic DP, 2023, Filtering Structures or alpha-Stable Systems, IEEE CONTROL SYSTEMS LETTERS, Vol: 7, Pages: 553-558, ISSN: 2475-1456

Journal article

Powezka K, Pettipher A, Hemakom A, Adjei T, Normahani P, Mandic DP, Jaffer Uet al., 2022, A Pilot Study of Heart Rate Variability Synchrony as a Marker of Intraoperative Surgical Teamwork and Its Correlation to the Length of Procedure., Sensors (Basel), Vol: 22

OBJECTIVE: Quality of intraoperative teamwork may have a direct impact on patient outcomes. Heart rate variability (HRV) synchrony may be useful for objective assessment of team cohesion and good teamwork. The primary aim of this study was to investigate the feasibility of using HRV synchrony in surgical teams. Secondary aims were to investigate the association of HRV synchrony with length of procedure (LOP), complications, number of intraoperative glitches and length of stay (LOS). We also investigated the correlation between HRV synchrony and team familiarity, pre- and intraoperative stress levels (STAI questionnaire), NOTECHS score and experience of team members. METHODS: Ear, nose and throat (ENT) and vascular surgeons (consultant and registrar team members) were recruited into the study. Baseline demographics including level of team members' experience were gathered before each procedure. For each procedure, continuous electrocardiogram (ECG) recording was performed and questionnaires regarding pre- and intraoperative stress levels and non-technical skills (NOTECHS) scores were collected for each team member. An independent observer documented the time of each intraoperative glitch. Statistical analysis was conducted using stepwise multiple linear regression. RESULTS: Four HRV synchrony metrics which may be markers of efficient surgical collaboration were identified from the data: 1. number of HRV synchronies per hour of procedure, 2. number of HRV synchrony trends per hour of procedure, 3. length of HRV synchrony trends per hour of procedure, 4. area under the HRV synchrony trend curve per hour of procedure. LOP was inversely correlated with number of HRV synchrony trends per hour of procedure (p < 0.0001), area under HRV synchrony trend curve per hour of procedure (p = 0.001), length of HRV synchrony trends per hour of procedure (p = 0.002) and number of HRV synchronies per hour of procedure (p < 0.0001). LOP was positively correlated with: FS (

Journal article

Scalzo B, Stanković L, Daković M, Constantinides AG, Mandic DPet al., 2022, A class of doubly stochastic shift operators for random graph signals and their boundedness., Neural Netw, Vol: 158, Pages: 83-88

A class of doubly stochastic graph shift operators (GSO) is proposed, which is shown to exhibit: (i) lower and upper L2-boundedness for locally stationary random graph signals, (ii) L2-isometry for i.i.d. random graph signals with the asymptotic increase in the incoming neighbourhood size of vertices, and (iii) preservation of the mean of any graph signal - all prerequisites for reliable graph neural networks. These properties are obtained through a statistical consistency analysis of the proposed graph shift operator, and by exploiting the dual role of the doubly stochastic GSO as a Markov (diffusion) matrix and as an unbiased expectation operator. For generality, we consider directed graphs which exhibit asymmetric connectivity matrices. The proposed approach is validated through an example on the estimation of a vector field.

Journal article

Menguc EC, Xiang M, Mandic DP, 2022, Online censoring based complex-valued adaptive filters, SIGNAL PROCESSING, Vol: 200, ISSN: 0165-1684

Journal article

Kisil I, Calvi GG, Konstantinidis K, Xu YL, Mandic DPet al., 2022, Accelerating Tensor Contraction Products via Tensor-Train Decomposition [Tips & Tricks], IEEE SIGNAL PROCESSING MAGAZINE, Vol: 39, Pages: 63-70, ISSN: 1053-5888

Journal article

Davies HJ, Williams I, Hammour G, Yarici M, Stacey MJ, Seemungal BM, Mandic DPet al., 2022, In-ear SpO2 for classification of cognitive workload, IEEE Transactions on Cognitive and Developmental Systems, Pages: 1-1, ISSN: 2379-8920

The brain is the most metabolically active organ in the body, which increases its metabolic activity, and thus oxygen consumption, with increasing cognitive demand. This motivates us to question whether increased cognitive workload may be measurable through changes in blood oxygen saturation. To this end, we explore the feasibility of cognitive workload tracking based on in-ear SpO2 measurements, which are known to be both robust and exhibit minimal delay. We consider cognitive workload assessment based on an N-back task with randomised order. It is shown that the 2-back and 3-back tasks (high cognitive workload) yield either the lowest median absolute SpO2 or largest median decrease in SpO2 in all of the subjects, indicating a measurable and statistically significant decrease in blood oxygen in response to increased cognitive workload. This makes it possible to classify the four N-back task categories, over 5 second epochs, with a mean accuracy of 90.6%, using features derived from in-ear pulse oximetry, including SpO2, pulse rate and respiration rate. These findings suggest that in-ear SpO2 measurements provide sufficient information for the reliable classification of cognitive workload over short time windows, which promises a new avenue for real time cognitive workload tracking.

Journal article

Thornton M, Mandic D, Reichenbach T, 2022, Robust decoding of the speech envelope from EEG recordings through deep neural networks, JOURNAL OF NEURAL ENGINEERING, Vol: 19, ISSN: 1741-2560

Journal article

Took CC, Mandic D, 2022, Weight sharing for LMS algorithms: Convolutional neural networks inspired multichannel adaptive filtering, DIGITAL SIGNAL PROCESSING, Vol: 127, ISSN: 1051-2004

Journal article

Davies HJ, Williams I, Mandic DP, 2022, Tracking Cognitive Workload in Gaming with In-Ear [Formula: see text]., Annu Int Conf IEEE Eng Med Biol Soc, Vol: 2022, Pages: 4913-4916

The feasibility of using in-ear [Formula: see text] to track cognitive workload induced by gaming is investigated. This is achieved by examining temporal variations in cognitive workload through the game Geometry Dash, with 250 trials across 7 subjects. The relationship between performance and cognitive load in Dark Souls III boss fights is also investigated followed by a comparison of the cognitive workload responses across three different genres of game. A robust decrease in in-ear [Formula: see text] is observed in response to cognitive workload induced by gaming, which is consistent with existing results from memory tasks. The results tentatively suggest that in-ear [Formula: see text] may be able to distinguish cognitive workload alone, whereas heart rate and breathing rate respond similarly to both cognitive workload and stress. This study demonstrates the feasibility of low cost wearable cognitive workload tracking in gaming with in-ear [Formula: see text], with applications to the play testing of games and biofeedback in games of the future.

Journal article

Normahani P, Sounderajah V, Mandic D, Jaffer Uet al., 2022, Machine learning-based classification of arterial spectral waveforms for the diagnosis of peripheral artery disease in the context of diabetes: A proof-of-concept study, VASCULAR MEDICINE, Vol: 27, Pages: 450-456, ISSN: 1358-863X

Journal article

Li S, Mandic D, 2022, Von Mises-Fisher Elliptical Distribution, IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, ISSN: 2162-237X

Journal article

Prochazka A, Charvat J, Vysata O, Mandic Det al., 2022, Incremental deep learning for reflectivity data recognition in stomatology, NEURAL COMPUTING & APPLICATIONS, Vol: 34, Pages: 7081-7089, ISSN: 0941-0643

Journal article

Davies HJ, Bachtiger P, Williams I, Molyneaux PL, Peters NS, Mandic Det al., 2022, Wearable in-ear PPG: detailed respiratory variations enable classification of COPD, IEEE Transactions on Biomedical Engineering, Vol: 69, 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.

Journal article

Arroyo A, Scalzo B, Stanković L, Mandic DPet al., 2022, DYNAMIC PORTFOLIO CUTS: A SPECTRAL APPROACH TO GRAPH-THEORETIC DIVERSIFICATION, Pages: 5468-5472, ISSN: 1520-6149

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 methods for estimating asset-return covariance operate under the assumption of statistical time-invariance, and are thus unable to appropriately infer the underlying structure of the market graph. To this end, this work introduces a class of graph spectral estimators which cater for the nonstationarity inherent to asset price movements, as a basis to represent the time-varying interactions between assets through a dynamic spectral market graph. Such an account of the time-varying nature of the asset-return covariance allows us to introduce the notion of dynamic spectral portfolio cuts, whereby the graph is partitioned into time-evolving clusters, thus allowing for robust and online asset allocation. The advantages of the proposed framework over traditional methods are demonstrated through numerical case studies using real-world price data.

Conference paper

Talebi SP, Werner S, Xia Y, Took CC, Mandic DPet al., 2022, A Joint Particle Filter for Quaternion-Valued a-Stable Signals via the Characteristic Function, Pages: 390-394

The filtering paradigm is revisited through the perspective of characteristic functions. This results in the derivation of a novel particle filtering technique for sequential estimation/tracking of quaternion-valued a-stable random signals. Importantly, the derived particle filter incorporates an efficient information fusion format and collaborative/distributed estimation framework to accommodate the push toward use of sensor networks. The distributed setting provides for the distribution of computational complexity among agents of a sensor network, while allowing each agent to retain an estimate of the state. Furthermore, the quaternion-valued structure allows for the derivation of a rigorous algorithm that is advantageous when dealing with signals of a multidimensional nature commonly encountered in sensor arrays.

Conference paper

Konstantinidis K, Xu YL, Zhao Q, Mandic DPet al., 2022, VARIATIONAL BAYESIAN TENSOR NETWORKS WITH STRUCTURED POSTERIORS, Pages: 3638-3642, ISSN: 1520-6149

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, referred to as the Bayesian Tensor Network (BTN). This is achieved by making use of the multi-linear nature of tensor networks which allows us to construct a structured variational model which scales linearly with data dimensionality. The so imposed low rank structure on the tensor mean and Kronecker separability of the local covariances makes it possible to efficiently induce weight dependencies in the posterior distribution. This is shown to enhance model expressiveness at a drastically lower parameter complexity compared to the standard mean-field approach. A comprehensive validation of the proposed framework demonstrates the competitiveness of BTNs against existing structured Bayesian neural network approaches, while exhibiting enhanced interpretability, computational efficiency, and ability to yield credibility intervals.

Conference paper

Chen Z, Tan X, Wang K, Pan S, Mandic D, He L, Zhao Set al., 2022, INFERGRAD: IMPROVING DIFFUSION MODELS FOR VOCODER BY CONSIDERING INFERENCE IN TRAINING, Pages: 8432-8436, ISSN: 1520-6149

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 optimize the choice of inference schedule over a few iterations to speed up inference. However, this results in reduced generation quality, mainly because the inference process is optimized separately, without jointly optimizing with the training process. In this paper, we propose InferGrad, a diffusion model for vocoder that incorporates inference process into training, to reduce the inference iterations while maintaining high generation quality. More specifically, during training, we generate data from random noise through a reverse process under inference schedules with a few iterations, and impose a loss to minimize the gap between the generated and ground-truth data samples. Then, unlike existing approaches, the training of InferGrad considers the inference process. The advantages of InferGrad are demonstrated through experiments on the LJSpeech dataset showing that InferGrad achieves better voice quality than the baseline WaveGrad under same conditions while maintaining the same voice quality as the baseline but with 3x speedup (2 iterations for InferGrad vs 6 iterations for WaveGrad).

Conference paper

Xu YL, Konstantinidis K, Li S, Stanković L, Mandic DPet al., 2022, LOW-COMPLEXITY ATTENTION MODELLING VIA GRAPH TENSOR NETWORKS, Pages: 3928-3932, ISSN: 1520-6149

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 results in exceedingly high parameter complexity which is prohibitive for practical applications. To this end, we introduce a novel Tensorized Graph Attention (TGA) mechanism, which leverages on the recent Graph Tensor Network (GTN) framework to efficiently process tensorized token embeddings via attention based graph filters. Such tensorized token embeddings are shown to effectively bypass the Curse of Dimensionality, reducing the parameter complexity of the attention mechanism from an exponential to a linear one in the embedding dimensions. The expressive power of the TGA framework is further enhanced by virtue of domain-aware graph convolution filters. Simulations across benchmark NLP paradigms verify the advantages of the proposed framework over existing attention models, at drastically lower parameter complexity.

Conference paper

Li S, Yu Z, Xiang M, Mandic Det 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.

Journal article

Talebi SP, Werner S, Mandic DP, 2022, Fractional-Order Learning Systems

From the inaugural steps of McCulloch and Pitts to put forth a composition for an electrical brain, that combined with the conception of an adaptive leaning mechanism by Widrow and Hoff has given rise to the phenomena of intelligent machines, machine learning techniques have gained the status of a miracle solution in a myriad of scientific fields. At the heart of these techniques lies iterative optimisation processes that are derived based on first, and in some cases, second-order derivatives. This manuscript, however, aims to expand the mentioned framework to that of using fractional-order derivatives. The entire format of adaptation is revised form the perspective of fractional-order calculus and the appropriate framework for taking full advantage of the fractional-order calculus in learning and adaptation paradigms is formulated. For rigour, the structure of behavioural analysis and performance prediction of this novel class of learning machines is also forged.

Conference paper

Occhipinti E, Davies HJ, Hammour G, Mandic DPet al., 2022, Hearables: Artefact removal in Ear-EEG for continuous 24/7 monitoring

Ear-worn devices offer the opportunity to measure vital signals in a 24/7 fashion, without the need of a clinician. These devices are however prone to motion artefacts, so that entire epochs of artefact-corrupt recordings are routinely discarded. This work aims at reducing the impact of artefacts introduced by a series of common real life daily activities such as talking, chewing, and walking while recording Electroencephalogram (EEG) from the ear canal. The approach used employs multiple external sensors, such as microphones and an accelerometer as means to capture the artefact. The proposed algorithm is a combination of Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD) with Adaptive Noise Cancellation (ANC), where each pair (EEG and motion sensors) of Intrinsic Mode Functions (IMFs) within NA-MEMD is fed independently to multiple Normalised Least Mean Square (NLMS) adaptive filters. The resulting denoised IMFs are then added up again to reconstruct the denoised EEG signal. Results across multiple subjects show that the so denoised EEG signals have reduced power in the frequency range occupied by artefacts. Also, different sensors provide different denoising performance in the tested artefacts, with the microphones being more sensitive to artefacts which cause internal motion within the ear-canal, such as chewing, and the accelerometer being more suitable for artefacts which come from full body movements of the subjects, such as walking.

Conference paper

Xiao H, Chanwimalueang T, Mandic DP, 2022, MULTIVARIATE MULTISCALE COSINE SIMILARITY ENTROPY, Pages: 5997-6001, ISSN: 1520-6149

The rapid development in sensor technology has made it convenient to acquire data from multi-channel systems but has also highlighted the need for the analysis of nonlinear dynamical properties at a higher level - the so-called structural complexity. Traditional single-scale entropy measures, such as the amplitude based Sample Entropy (SampEn), are designed to give a quantification of irregularity and randomness. Its enhanced versions, Multiscale Sample Entropy (MSampEn) and Multivariate Multiscale Sample Entropy (MMSE), are capable of detecting the structure within a signal at high scales and for multivariate data, however, the scaling process comes at a cost of the reduction of the number of sample points that results in reduced stability and limitations regarding the selection of the embedding dimension. In addition, the analyses of structure on the basis of MSampEn and MMSE require relatively high scales, yet without prior-knowledge of the scale degree. To this end, we propose a new multivariate entropy method based on the recently introduced Cosine Similarity Entropy (CSE). The proposed Multivariate Multiscale Cosine Similarity Entropy (MMCSE) is based on angular distance which makes it possible to assess long-term correlation within a system at both a low and large scales, and thus assess the true structural complexity in a more physically meaningful way. Both synthetic and real world signals are utilized to examine the performance of the proposed approach, with the resulting simulations supporting the approach.

Conference paper

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

Journal article

Mahfouz M, Balch T, Veloso M, Mandic Det al., 2021, Learning to classify and imitate trading agents in continuous double auction markets

Continuous double auctions such as the limit order book employed by exchanges are widely used in practice to match buyers and sellers of a variety of financial instruments. In this work, we develop an agent-based model for trading in a limit order book and show (1) how opponent modelling techniques can be applied to classify trading agent archetypes and (2) how behavioural cloning can be used to imitate these agents in a simulated setting. We experimentally compare a number of techniques for both tasks and evaluate their applicability and use in real-world scenarios.

Conference paper

Li Z, Deng W, Zhu Z, Mandic DPet 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

Journal article

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

Journal article

Stankovic L, Lerga J, Mandic D, Brajovic M, Richard C, Dakovic Met al., 2021, From Time-Frequency to Vertex-Frequency and Back, MATHEMATICS, Vol: 9

Journal article

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