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Journal articleXia Y, Mandic DP, 2017,
A Full Mean Square Analysis of CLMS for Second-Order Noncircular Inputs
, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 65, Pages: 5578-5590, ISSN: 1053-587X -
Conference paperDionelis N, Brookes, 2017,
Speech Enhancement Using Modulation-Domain Kalman Filtering with Active Speech Level Normalized Log-Spectrum Global Priors
, 25th European Signal Processing Conference (EUSIPCO), Publisher: IEEE, ISSN: 2076-1465We describe a single-channel speech enhancement algorithm that is based on modulation-domain Kalman filtering that tracks the inter-frame time evolution of the speech logpower spectrum in combination with the long-term average speech log-spectrum. We use offline-trained log-power spectrum global priors incorporated in the Kalman filter prediction and update steps for enhancing noise suppression. In particular, we train and utilize Gaussian mixture model priors for speech in the log-spectral domain that are normalized with respect to the active speech level. The Kalman filter update step uses the log-power spectrum global priors together with the local priors obtained from the Kalman filter prediction step. The logspectrum Kalman filtering algorithm, which uses the theoretical phase factor distribution and improves the modeling of the modulation features, is evaluated in terms of speech quality. Different algorithm configurations, dependent on whether global priors and/or Kalman filter noise tracking are used, are compared in various noise types.
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Conference paperPapayiannis C, Evers C, Naylor PA, 2017,
Sparse parametric modeling of the early part of acoustic impulse responses
, 25th European Signal Processing Conference (EUSIPCO), Publisher: IEEE, Pages: 678-682, ISSN: 2076-1465Acoustic channels are typically described by their Acoustic Impulse Response (AIR) as a Moving Average (MA) process. Such AIRs are often considered in terms of their early and late parts, describing discrete reflections and the diffuse reverberation tail respectively. We propose an approach for constructing a sparse parametric model for the early part. The model aims at reducing the number of parameters needed to represent it and subsequently reconstruct from the representation the MA coefficients that describe it. It consists of a representation of the reflections arriving at the receiver as delayed copies of an excitation signal. The Time-Of-Arrivals of reflections are not restricted to integer sample instances and a dynamically estimated model for the excitation sound is used. We also present a corresponding parameter estimation method, which is based on regularized-regression and nonlinear optimization. The proposed method also serves as an analysis tool, since estimated parameters can be used for the estimation of room geometry, the mixing time and other channel properties. Experiments involving simulated and measured AIRs are presented, in which the AIR coefficient reconstruction-error energy does not exceed 11.4% of the energy of the original AIR coefficients. The results also indicate dimensionality reduction figures exceeding 90% when compared to a MA process representation.
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Conference paperHafezi S, Moore AH, Naylor PA, 2017,
Multiple DOA estimation based on estimation consistency and spherical harmonic multiple signal classification
, European Signal Processing Conference, EUSIPCO 2017, Pages: 1240-1244© EURASIP 2017. A common approach to multiple Direction-of- Arrival (DOA) estimation of speech sources is to identify Time- Frequency (TF) bins with dominant Single Source (SS) and apply DOA estimation such as Multiple Signal Classification (MUSIC) only on those TF bins. In the state-of-the-art Direct Path Dominance (DPD)-MUSIC, the covariance matrix, used as the input to MUSIC, is calculated using only the TF bins over a local TF region where only a SS is dominant. In this work, we propose an alternative approach to MUSIC in which all the SS-dominant TF bins for each speaker across TF domain are globally used to improve the quality of covariance matrix for MUSIC. Our recently proposed Multi-Source Estimation Consistency (MSEC) technique, which exploits the consistency of initial DOA estimates within a time frame based on adaptive clustering, is used to estimate the SS-dominant TF bins for each speaker. The simulation using spherical microphone array shows that our proposed MSEC-MUSIC significantly outperforms the state-of-the-art DPD-MUSIC with less than 6:5° mean estimation error and strong robustness to widely varying source separation for up to 5 sources in the presence of realistic reverberation and sensor noise.
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Journal articleReynolds SC, Abrahamsson T, Schuck R, et al., 2017,
ABLE: an activity-based level set segmentation algorithm for two-photon calcium imaging data
, eNeuro, Vol: 4, Pages: 1-13, ISSN: 2373-2822We present an algorithm for detecting the location of cells from two-photon calcium imaging data. In our framework, multiple coupled active contours evolve, guided by a model-based cost function, to identify cell boundaries. An active contour seeks to partition a local region into two subregions, a cell interior and exterior, in which all pixels have maximally ‘similar’ time courses. This simple, local model allows contours to be evolved predominantly independently. When contours are sufficiently close, their evolution is coupled, in a manner that permits overlap. We illustrate the ability of the proposed method to demix overlapping cells on real data. The proposed framework is flexible, incorporating no prior information regarding a cell’s morphology or stereotypical temporal activity, which enables the detection of cells with diverse properties. We demonstrate algorithm performance on a challenging mouse in vitro dataset, containing synchronously spiking cells, and a manually labelled mouse in vivo dataset, on which ABLE achieves a 67.5% success rate.
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Journal articleXia Y, Douglas SC, Mandic DP, 2017,
Performance analysis of the deficient length augmented CLMS algorithm for second order noncircular complex signals
, Signal Processing, Vol: 144, Pages: 214-225, ISSN: 0165-1684The augmented complex LMS (ACLMS) algorithm deals with second order noncircular (improper) input signals, based on widely linear modelling and the use of full second order statistical information. In current analyses of ACLMS, it is implicitly or explicitly assumed that the length of the adaptive filter is equal to that of the unknown system's impulse response (optimal model order). In many applications, however, the length of the adaptive filter is smaller than required, the so called deficient length case, which renders the analysis for a ‘sufficient length’ ACLMS inadequate. To this end, we examine the statistical behaviour of the ACLMS algorithm in undermodelling situations. Exact expressions are developed to completely characterise both the transient and steady-state mean and mean square performances of the deficient length ACLMS for general second order noncircular Gaussian input signals. This is achieved using the recently introduced approximate uncorrelating transform (AUT), in order to jointly diagonalise the covariance and pseudo-covariance matrices with a single singular value decomposition (SVD), which both simplifies the analysis and enables a link between the degree of input noncircularity and the steady state mean square error (MSE) performance of the deficient length ACLMS. Simulations in system identification settings support the analysis.
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Journal articleNakamura T, Goverdovsky V, mandic D, 2017,
In-ear EEG biometrics for feasible and readily collectable real-world person authentication
, IEEE Transactions on Information Forensics and Security, Vol: 13, Pages: 648-661, ISSN: 1556-6013The use of EEG as a biometrics modality has been investigated for about a decade, however its feasibility in real-world applications is not yet conclusively established, mainly due to the issues with collectability and reproducibility. To this end, we propose a readily deployable EEG biometrics system based on a ‘one-fits-all’ viscoelastic generic in-ear EEG sensor (collectability), which does not require skilled assistance or cumbersome preparation. Unlike most existing studies, we consider data recorded over multiple recording days and for multiple subjects (reproducibility) while, for rigour, the training and test segments are not taken from the same recording days. A robust approach is considered based on the resting state with eyes closed paradigm, the use of both parametric (autoregressive model) and non-parametric (spectral) features, and supported by simple and fast cosine distance, linear discriminant analysis and support vector machine classifiers. Both the verification and identification forensics scenarios are considered and the achieved results are on par with the studies based on impractical on-scalp recordings. Comprehensive analysis over a number of subjects, setups, and analysis features demonstrates the feasibility of the proposed ear-EEG biometrics, and its potential in resolving the critical collectability, robustness, and reproducibility issues associated with current EEG biometrics.
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Journal articleAntonello N, De Sena E, Moonen M, et al., 2017,
Room Impulse Response Interpolation Using a Sparse Spatio-Temporal Representation of the Sound Field
, IEEE/ACM Transactions on Audio Speech and Language Processing, Vol: 25, Pages: 1929-1941, ISSN: 2329-9290© 2017 IEEE. Room Impulse Responses (RIRs) are typically measured using a set of microphones and a loudspeaker. When RIRs spanning a large volume are needed, many microphone measurements must be used to spatially sample the sound field. In order to reduce the number of microphone measurements, RIRs can be spatially interpolated. In the present study, RIR interpolation is formulated as an inverse problem. This inverse problem relies on a particular acoustic model capable of representing the measurements. Two different acoustic models are compared: the plane wave decomposition model and a novel time-domain model, which consists of a collection of equivalent sources creating spherical waves. These acoustic models can both approximate any reverberant sound field created by a far-field sound source. In order to produce an accurate RIR interpolation, sparsity regularization is employed when solving the inverse problem. In particular, by combining different acoustic models with different sparsity promoting regularizations, spatial sparsity, spatio-spectral sparsity, and spatio-temporal sparsity are compared. The inverse problem is solved using a matrix-free large-scale optimization algorithm. Simulations show that the best RIR interpolation is obtained when combining the novel time-domain acoustic model with the spatio-temporal sparsity regularization, outperforming the results of the plane wave decomposition model even when far fewer microphone measurements are available.
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Journal articleElMikaty M, Stathaki P, 2017,
Detection of cars in high-resolution aerial images of complex urban environments
, IEEE Transactions on Geoscience and Remote Sensing, Vol: 55, Pages: 5913-5924, ISSN: 0196-2892Detection of small targets, more specifically cars, in aerial images of urban scenes, has various applications in several domains, such as surveillance, military, remote sensing, and others. This is a tremendously challenging problem, mainly because of the significant interclass similarity among objects in urban environments, e.g., cars and certain types of nontarget objects, such as buildings' roofs and windows. These nontarget objects often possess very similar visual appearance to that of cars making it hard to separate the car and the noncar classes. Accordingly, most past works experienced low precision rates at high recall rates. In this paper, a novel framework is introduced that achieves a higher precision rate at a given recall than the state of the art. The proposed framework adopts a sliding-window approach and it consists of four stages, namely, window evaluation, extraction and encoding of features, classification, and postprocessing. This paper introduces a new way to derive descriptors that encode the local distributions of gradients, colors, and texture. Image descriptors characterize the aforementioned cues using adaptive cell distributions, wherein the distribution of cells within a detection window is a function of its dominant orientation, and hence, neither the rotation of the patch under examination nor the computation of descriptors at different orientations is required. The performance of the proposed framework has been evaluated on the challenging Vaihingen and Overhead Imagery Research data sets. Results demonstrate the superiority of the proposed framework to the state of the art.
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Journal articleLyu S, Ling C, 2017,
Boosted KZ and LLL algorithms
, IEEE Transactions on Signal Processing, Vol: 65, Pages: 4784-4796, ISSN: 1053-587XThere exist two issues among popular lattice reduction algorithms that should cause our concern. The first one is Korkine-Zolotarev (KZ) and Lenstra-Lenstra-Lovász (LLL) algorithms may increase the lengths of basis vectors. The other is KZ reduction suffers worse performance than Minkowski reduction in terms of providing short basis vectors, despite its superior theoretical upper bounds. To address these limitations, we improve the size reduction steps in KZ and LLL to set up two new efficient algorithms, referred to as boosted KZ and LLL, for solving the shortest basis problem with exponential and polynomial complexity, respectively. Both of them offer better actual performance than their classic counterparts, and the performance bounds for KZ are also improved. We apply them to designing integer-forcing (IF) linear receivers for multi-input multioutput communications. Our simulations confirm their rate and complexity advantages.
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Conference paperRossi G, Leung KK, 2017,
Optimal CSMA/CA protocol for safety messages in vehicular Ad-Hoc networks
, IEEE Symposium on Computers and Communications (ISCC) 2017, Publisher: IEEEVehicular ad-hoc networks (VANETs) that enablecommunication among vehicles have recently attracted signif-icant interest from researchers, due to the range of practicalapplications they can facilitate, particularly related to roadsafety. Despite the stringent performance requirements for suchapplications, the IEEE 802.11p standard still uses the carriersensing medium access/collision avoidance (CSMA/CA) protocol.The latter when used in broadcast fashion employs a randomlyselected backoff period from a fixed contention window (CW)range, which can cause performance degradation as a result ofvehicular density changes. Concerns regarding the robustnessand adaptiveness of protocols to support time-critical applica-tions have been raised, which motivate this work. This paperinvestigates how the maximum CW size can be optimised toenhance performance based on vehicular density. A stochasticmodel is developed to obtain the optimal maximum CW that canbe integrated in an amended CSMA/CA protocol to maximisethe single-hop throughput among adjacent vehicles. Simulationsconfirm our optimised protocol can greatly improve the channelthroughput and transmission delay performance, when comparedto the standardised CSMA/CA, to support safety application inVANETs.
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Journal articleAdjei T, von Rosenberg W, Goverdovsky V, et al., 2017,
Pain prediction from ECG in vascular surgery
, IEEE Journal of Translational Engineering in Health and Medicine, Vol: 5, ISSN: 2168-2372Varicose vein surgeries are routine outpatient procedures, which are often performed under local anaesthesia. The use of local anaesthesia both minimises the risk to patients and is cost effective, however, a number of patients still experience pain during surgery. Surgical teams must therefore decide to administer either a general or local anaesthetic based on their subjective qualitative assessment of patient anxiety and sensitivity to pain, without any means to objectively validate their decision. To this end, we develop a 3-D polynomial surface fit, of physiological metrics and numerical pain ratings from patients, in order to model the link between the modulation of cardiovascular responses and pain in varicose vein surgeries. Spectral and structural complexity features found in heart rate variability signals, recorded immediately prior to 17 varicose vein surgeries, are used as pain metrics. The so obtained pain prediction model is validated through a leave-one-out validation, and achieved a Kappa coefficient of 0.72 (substantial agreement) and an area below a receiver operating characteristic curve of 0.97 (almost perfect accuracy). This proof-of-concept study conclusively demonstrates the feasibility of the accurate classification of pain sensitivity, and introduces a mathematical model to aid clinicians in the objective administration of the safest and most cost-effective anaesthetic to individual patients.
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Conference paperLi C, Gan L, Ling C, 2017,
Coprime Sensing by Chinese Remaindering over Rings
, 12th International Conference on Sampling Theory and Applications (SAMPTA), Publisher: IEEE, Pages: 561-565 -
Conference paperMurray-Bruce J, Dragotti PL, 2017,
Spatiotemporal Sampling Trade-off for Inverse Diffusion Source Problems
, 12th International Conference on Sampling Theory and Applications (SAMPTA), Publisher: IEEE, Pages: 55-59We consider the spatiotemporal sampling of diffusion fields induced by M point sources, and study the associated inverse problem of recovering the initial parameters of the unknown sources. In particular, we focus on characterising qualitatively the error of the obtained source estimates. To achieve this, we obtain an expression with which we can trade the sensor density for performance accuracy. In other words, by evaluating the optimal sampling instant for a given sensor density-and using the corresponding field samples at that instant-we can expect to obtain an improvement in the estimation performance when compared to an arbitrary sampling instant. Finally, several numerical simulations are presented, to support the theoretical results obtained.
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Conference paperHuang J-J, Dragotti PL, 2017,
Sparse Signal Recovery Using Structured Total Maximum Likelihood
, 12th International Conference on Sampling Theory and Applications (SAMPTA), Publisher: IEEE, Pages: 639-643In this paper, we consider the sparse signal recovery problem when the dictionary is a Fourier frame. Based on the annihilation relation, the sparse signal recovery from noisy observations is posed as a structured total maximum likelihood (STML) problem. The recent structured total least squares (STLS) approach for finite rate of innovation signal recovery can be viewed as a particular version of our method. We transform the STML problem which has an additional logdet term into a form similar to the STLS problem. It can be effectively tackled using an iterative quadratic maximum likelihood like algorithm. From simulation results, our proposed STML approach outperforms the STLS based algorithm and the state-of-the-art sparse recovery algorithms.
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Journal articleFrossard P, Dragotti PL, Ortega A, et al., 2017,
Introduction to the COOPERATIVE SPECIAL ISSUE ON GRAPH SIGNAL PROCESSING IN THE IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING AND THE IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS
, IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS, Vol: 3, Pages: 448-450, ISSN: 2373-776X -
Journal articleFrossard P, Dragotti PL, Ortega A, et al., 2017,
Introduction to the Cooperative Special Issue on Graph Signal Processing in the IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS and the IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
, IEEE Journal of Selected Topics in Signal Processing, Vol: 11, Pages: 771-773, ISSN: 1932-4553The papers in this special issue are intended to address some of the main research challenges in Graph Signal Processing by presenting a collection of the latest advances in the domain. These papers examine key representation, learning and processing aspects for signals living on graphs and networks, as well as new methods and applications in graph signal processing. Numerous applications rely on the processing of high dimensional data that reside on irregular or otherwise unordered structures that are naturally modeled as networks. The need for new tools to process such data has led to the emergence of the field of graph signal processing, which merges algebraic and spectral graph theoretic concepts with computational harmonic analysis to process signals on structures such as graphs. This important new paradigm in signal processing research, coupled with its numerous applications in very different domains, has fueled the rapid development of an inter-disciplinary research community that has been working on theoretical aspects of graph signal processing and applications to diverse problems such as big data analysis, coding and compression of 3D point clouds, biological data processing, and brain network analysis.
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Journal articleXia Y, Mandic DP, 2017,
Complementary Mean Square Analysis of Augmented CLMS for Second-Order Noncircular Gaussian Signals
, IEEE SIGNAL PROCESSING LETTERS, Vol: 24, Pages: 1413-1417, ISSN: 1070-9908 -
Journal articleJoudeh H, Clerckx B, 2017,
Rate-Splitting for Max-Min Fair Multigroup Multicast Beamforming in Overloaded Systems
, IEEE Transactions on Wireless Communications, Vol: 16, Pages: 7276-7289, ISSN: 1536-1276In this paper, we consider the problem of achieving max-min fairness amongst multiple co-channel multicast groups through transmit beamforming. We explicitly focus on overloaded scenarios in which the number of transmitting antennas is insufficient to neutralize all inter-group interference. Such scenarios are becoming increasingly relevant in the light of growing low-latency content delivery demands, and also commonly appear in multibeam satellite systems. We derive performance limits of classical beamforming strategies using DoF analysis unveiling their limitations; for example, rates saturate in overloaded scenarios due to inter-group interference. To tackle interference, we propose a strategy based on degraded beamforming and successive interference cancellation. While the degraded strategy resolves the rate-saturation issue, this comes at a price of sacrificing all spatial multiplexing gains. This motivates the development of a unifying strategy that combines the benefits of the two previous strategies. We propose a beamforming strategy based on rate-splitting (RS) which divides the messages intended to each group into a degraded part and a designated part, and transmits a superposition of both degraded and designated beamformed streams. The superiority of the proposed strategy is demonstrated through DoF analysis. Finally, we solve the RS beamforming design problem and demonstrate significant performance gains through simulations.
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Conference paperSharma D, Jost U, Naylor PA, 2017,
Non-Intrusive Bit-Rate Detection of Coded Speech
, 25th European Signal Processing Conference (EUSIPCO), Publisher: IEEE, Pages: 1799-1803, ISSN: 2076-1465 -
Conference paperParada PP, Sharma D, van Waterschoot T, et al., 2017,
Robust Statistical Processing of TDOA Estimates for Distant Speaker Diarization
, 25th European Signal Processing Conference (EUSIPCO), Publisher: IEEE, Pages: 86-90, ISSN: 2076-1465 -
Conference paperLi Z, Xia Y, Pei W, et al., 2017,
A DFT Enhanced Complex LMS for Digital Adaptive Spur Cancellation
, 2017 22nd International Conference on Digital Signal Processing (DSP), Publisher: IEEE, ISSN: 1546-1874 -
Conference paperXia Y, Qiao L, Yang Q, et al., 2017,
Widely Linear Adaptive Frequency Estimation for Unbalanced Three-Phase Power Systems with Multiple Noisy Measurements
, 2017 22nd International Conference on Digital Signal Processing (DSP), Publisher: IEEE, ISSN: 1546-1874 -
Journal articleDragotti P, Murray-Bruce J, 2017,
A Sampling Framework for Solving Physics-driven Inverse Source Problems
, IEEE Transactions on Signal Processing, Vol: 65, Pages: 6365-6380, ISSN: 1053-587XPartial differential equations are central to describing many physical phenomena. In many applications these phenomena are observed through a sensor network, with the aim of inferring its underlying properties. Leveraging from certain results in sampling and approximation theory, we present a new framework for solving a class of inverse source problems for physical fields governed by linear partial differential equations. Specifically, we demonstrate that the unknown field sources can be recovered from a sequence of, so called, generalised measurements by using multidimensional frequency estimation techniques. Next we show that---for physics-driven fields---this sequence of generalised measurements can be estimated by computing a linear weighted-sum of the sensor measurements; whereby the exact weights (of the sums) correspond to those that reproduce multidimensional exponentials, when used to linearly combine translates of a particular prototype function related to the Green's function of our underlying field. Explicit formulae are then derived for the sequence of weights, that map sensor samples to the exact sequence of generalised measurements when the Green's function satisfies the generalised Strang-Fix condition. Otherwise, the same mapping yields a close approximation of the generalised measurements. Based on this new framework we develop practical, noise robust, sensor network strategies for solving the inverse source problem, and then present numerical simulation results to verify their performance.
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Conference paperCampello A, Liu L, Ling C, 2017,
Multilevel Code Construction for Compound Fading Channels
, IEEE International Symposium on Information Theory (ISIT), Publisher: IEEE, Pages: 1008-1012 -
Conference paperLyu S, Campello A, Ling C, et al., 2017,
Compute-and-Forward over Block-Fading Channels Using Algebraic Lattices
, IEEE International Symposium on Information Theory (ISIT), Publisher: IEEE, Pages: 1848-1852 -
Journal articleClerckx B, Dai M, 2017,
Multiuser Millimeter Wave Beamforming Strategieswith Quantized and Statistical CSIT
, IEEE Transactions on Wireless Communications, Vol: 16, Pages: 7025-7038, ISSN: 1536-1276To alleviate the high cost of hardware in mmWave systems, hybrid analog/digital precoding is typically employed. In the conventional two-stage feedback scheme, the analog beamformer is determined by beam search and feedback to maximize the desired signal power of each user. The digital precoder is designed based on quantization and feedback of effective channel to mitigate multiuser interference. Alternatively, we propose a one-stage feedback scheme which effectively reduces the complexity of the signalling and feedback procedure. Specifically, the second-order channel statistics are leveraged to design digital precoder for interference mitigation while all feedback overhead is reserved for precise analog beamforming. Under a fixed total feedback constraint, we investigate the conditions under which the one-stage feedback scheme outperforms the conventional twostage counterpart. Moreover, a rate splitting (RS) transmission strategy is introduced to further tackle the multiuser interference and enhance the rate performance. Consider (1) RS precoded by the one-stage feedback scheme and (2) conventional transmission strategy precoded by the two-stage scheme with the same firststage feedback as (1) and also certain amount of extra secondstage feedback. We show that (1) can achieve a sum rate comparable to that of (2). Hence, RS enables remarkable saving in the second-stage training and feedback overhead.
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Journal articleHuang Y, Clerckx B, 2017,
Large-Scale Multi-Antenna Multi-Sine Wireless Power Transfer
, IEEE Transactions on Signal Processing, Vol: 65, Pages: 5812-5827, ISSN: 1053-587XWireless Power Transfer (WPT) is expected to be a technology reshaping the landscape of low-power applications such as the Internet of Things, RF identification (RFID) networks, etc. To that end, multi-antenna multi-sine waveforms adaptive to the Channel State Information (CSI) have been shown to be a promising building block of WPT. However, the current design is computationally too complex to be applied to large-scale WPT, where the transmit signal is sent across a large number (tens) of antennas and frequencies. In this paper, we derive efficient singleuser and multi-user algorithms based on a generalizable optimization framework, in order to design transmit waveforms that maximize the weighted-sum/minimum rectenna DC output voltage. The study highlights the significant effect of the nonlinearity introduced by the rectification process on the design of waveforms in single/multi-user systems. Interestingly, in the single-user case, the optimal spatial domain beamforming, obtained prior to the frequency domain power allocation optimization, turns out to be Maximum Ratio Transmission (MRT). On the contrary, in the general multi-user weighted sum criterion maximization problem, the spatial domain beamforming optimization and the frequency domain power allocation optimization are coupled. Assuming channel hardening, low-complexity algorithms are proposed based on asymptotic analysis, to maximize the two criteria. The structure of the asymptotically optimal spatial domain precoder can be found prior to the optimization. The performance of the proposed algorithms is evaluated. Numerical results confirm the inefficiency of the linear model-based design for the single and multi-user scenarios. It is also shown that as nonlinear modelbased designs, the proposed algorithms can benefit from an increasing number of sinewaves at a computational cost much lower than the existing method. Simulation results highlight the significant benefits of the large-scale WPT architecture to
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Journal articleKappel SL, Looney D, Mandic DP, et al., 2017,
Physiological artifacts in scalp EEG and ear-EEG.
, BioMedical Engineering OnLine, Vol: 16, ISSN: 1475-925XBACKGROUND: A problem inherent to recording EEG is the interference arising from noise and artifacts. While in a laboratory environment, artifacts and interference can, to a large extent, be avoided or controlled, in real-life scenarios this is a challenge. Ear-EEG is a concept where EEG is acquired from electrodes in the ear. METHODS: We present a characterization of physiological artifacts generated in a controlled environment for nine subjects. The influence of the artifacts was quantified in terms of the signal-to-noise ratio (SNR) deterioration of the auditory steady-state response. Alpha band modulation was also studied in an open/closed eyes paradigm. RESULTS: Artifacts related to jaw muscle contractions were present all over the scalp and in the ear, with the highest SNR deteriorations in the gamma band. The SNR deterioration for jaw artifacts were in general higher in the ear compared to the scalp. Whereas eye-blinking did not influence the SNR in the ear, it was significant for all groups of scalps electrodes in the delta and theta bands. Eye movements resulted in statistical significant SNR deterioration in both frontal, temporal and ear electrodes. Recordings of alpha band modulation showed increased power and coherence of the EEG for ear and scalp electrodes in the closed-eyes periods. CONCLUSIONS: Ear-EEG is a method developed for unobtrusive and discreet recording over long periods of time and in real-life environments. This study investigated the influence of the most important types of physiological artifacts, and demonstrated that spontaneous activity, in terms of alpha band oscillations, could be recorded from the ear-EEG platform. In its present form ear-EEG was more prone to jaw related artifacts and less prone to eye-blinking artifacts compared to state-of-the-art scalp based systems.
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Journal articleHafezi S, Moore AH, Naylor PATRICK, 2017,
Augmented Intensity Vectors for Direction of Arrival Estimation in the Spherical Harmonic Domain
, IEEE Transactions on Audio, Speech and Language Processing, Vol: 25, Pages: 1956-1968, ISSN: 1558-7916Pseudointensity vectors (PIVs) provide a means of direction of arrival (DOA) estimation for spherical microphone arrays using only the zeroth and the first-order spherical harmonics. An augmented intensity vector (AIV) is proposed which improves the accuracy of PIVs by exploiting higher order spherical harmonics. We compared DOA estimation using our proposed AIVs against PIVs, steered response power (SRP) and subspace methods where the number of sources, their angular separation, the reverberation time of the room and the sensor noise level are varied. The results show that the proposed approach outperforms the baseline methods and performs at least as accurately as the state-of-the-art method with strong robustness to reverberation, sensor noise, and number of sources. In the single and multiple source scenarios tested, which include realistic levels of reverberation and noise, the proposed method had average error of 1.5∘ and 2∘, respectively.
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