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  • Journal article
    Evers C, Naylor PA, 2018,

    Optimized self-localization for SLAM in dynamic scenes using probability hypothesis density filters

    , IEEE Transactions on Signal Processing, Vol: 66, Pages: 863-878, ISSN: 1053-587X

    In many applications, sensors that map the positions of objects in unknown environments are installed on dynamic platforms. As measurements are relative to the observer's sensors, scene mapping requires accurate knowledge of the observer state. However, in practice, observer reports are subject to positioning errors. Simultaneous localization and mapping addresses the joint estimation problem of observer localization and scene mapping. State-of-the-art approaches typically use visual or optical sensors and therefore rely on static beacons in the environment to anchor the observer estimate. However, many applications involving sensors that are not conventionally used for Simultaneous Localization and Mapping (SLAM) are affected by highly dynamic scenes, such that the static world assumption is invalid. This paper proposes a novel approach for dynamic scenes, called GEneralized Motion (GEM) SLAM. Based on probability hypothesis density filters, the proposed approach probabilistically anchors the observer state by fusing observer information inferred from the scene with reports of the observer motion. This paper derives the general, theoretical framework for GEM-SLAM, and shows that it generalizes existing Probability Hypothesis Density (PHD)-based SLAM algorithms. Simulations for a model-specific realization using range-bearing sensors and multiple moving objects highlight that GEM-SLAM achieves significant improvements over three benchmark algorithms.
  • Journal article
    Clerckx B, 2018,

    Wireless information and power transfer: nonlinearity, waveform design, and rate-energy tradeoff

    , IEEE Transactions on Signal Processing, Vol: 66, Pages: 847-862, ISSN: 1053-587X

    The design of wireless information and power transfer (WIPT) has so far relied on an oversimplified and inaccurate linear model of the energy harvester. In this paper, we depart from this linear model and design WIPT considering the rectifier nonlinearity. We develop a tractable model of the rectifier nonlinearity that is flexible enough to cope with general multicarrier modulated input waveforms. Leveraging that model, we motivate and introduce a novel WIPT architecture relying on the superposition of multicarrier unmodulated and modulated waveforms at the transmitter. The superposed WIPT waveforms are optimized as a function of the channel state information so as to characterize the rate-energy region of the whole system. Analysis and numerical results illustrate the performance of the derived waveforms and WIPT architecture and highlight that nonlinearity radically changes the design of WIPT. We make key and refreshing observations. First, analysis (confirmed by circuit simulations) shows that modulated and unmodulated waveforms are not equally suitable for wireless power delivery, namely, modulation being beneficial in single-carrier transmissions but detrimental in multicarrier transmissions. Second, a multicarrier unmodulated waveform (superposed to a multicarrier modulated waveform) is useful to enlarge the rate-energy region of WIPT. Third, a combination of power splitting and time sharing is in general the best strategy. Fourth, a nonzero mean Gaussian input distribution outperforms the conventional capacity-achieving zero-mean Gaussian input distribution in multicarrier transmissions. Fifth, the rectifier nonlinearity is beneficial to system performance and is essential to efficient WIPT design.
  • Journal article
    Xiang M, Enshaeifar S, Stott AE, Took CC, Xia Y, Kanna S, Mandic DPet al., 2018,

    Simultaneous diagonalisation of the covariance and complementary covariance matrices in quaternion widely linear signal processing

    , Signal Processing, Vol: 148, Pages: 193-204, ISSN: 0165-1684

    Recent developments in quaternion-valued widely linear processing have established that the exploitation of complete second-order statistics requires consideration of both the standard covariance and the three complementary covariance matrices. Although such matrices have a tremendous amount of structure and their decomposition is a powerful tool in a variety of applications, the non-commutative nature of the quaternion product has been prohibitive to the development of quaternion uncorrelating transforms. To this end, we introduce novel techniques for a simultaneous decomposition of the covariance and complementary covariance matrices in the quaternion domain, whereby the quaternion version of the Takagi factorisation is explored to diagonalise symmetric quaternion-valued matrices. This gives new insights into the quaternion uncorrelating transform (QUT) and forms a basis for the proposed quaternion approximate uncorrelating transform (QAUT) which simultaneously diagonalises all four covariance matrices associated with improper quaternion signals. The effectiveness of the proposed uncorrelating transforms is validated by simulations on both synthetic and real-world quaternion-valued signals.
  • Journal article
    Wang Z, Ling C, 2018,

    On the geometric ergodicity of Metropolis-Hastings algorithms for lattice Gaussian sampling

    , IEEE Transactions on Information Theory, Vol: 64, Pages: 738-751, ISSN: 0018-9448

    Sampling from the lattice Gaussian distribution has emerged as an important problem in coding, decoding, and cryptography. In this paper, the classic Metropolis-Hastings (MH) algorithm in Markov chain Monte Carlo methods is adopted for lattice Gaussian sampling. Two MH-based algorithms are proposed, which overcome the limitation of Klein's algorithm. The first one, referred to as the independent Metropolis-Hastings-Klein (MHK) algorithm, establishes a Markov chain via an independent proposal distribution. We show that the Markov chain arising from this independent MHK algorithm is uniformly ergodic, namely, it converges to the stationary distribution exponentially fast regardless of the initial state. Moreover, the rate of convergence is analyzed in terms of the theta series, leading to predictable mixing time. A symmetric Metropolis-Klein algorithm is also proposed, which is proven to be geometrically ergodic.
  • Conference paper
    Varasteh M, Rassouli B, Clerckx B, 2018,

    Wireless Information and Power Transfer over an AWGN channel: Nonlinearity and Asymmetric Gaussian Signaling

    , 2017 IEEE Information Theory Workshop (ITW), Publisher: IEEE, Pages: 181-183, ISSN: 2475-420X
  • Journal article
    Looney D, Adjei T, Mandic DP, 2018,

    A Novel Multivariate Sample Entropy Algorithm for Modeling Time Series Synchronization

    , ENTROPY, Vol: 20, ISSN: 1099-4300
  • Journal article
    Stathaki P, ElMikaty M, 2018,

    Car detection in aerial images of dense urban areas

    , IEEE Transactions on Aerospace and Electronic Systems, Vol: 54, Pages: 51-63, ISSN: 0018-9251

    With the ever-increasing demand in the analysis and understanding of aerial images in order to remotely recognise targets, this paper introduces a robust system for the detection and localisation of cars in images captured by air vehicles and satellites. The system adopts a sliding-window approach. It compromises a window-evaluation and a window-classification sub-systems. The performance of the proposed framework was evaluated on the Vaihingen dataset. Results demonstrate its superiority to the state of the art.
  • Journal article
    Schuck R, Go MA, Garasto S, Reynolds S, Dragotti PL, Schultz SRet al., 2018,

    Multiphoton minimal inertia scanning for fast acquisition of neural activity signals

    , Journal of Neural Engineering, Vol: 15, ISSN: 1741-2552

    Objective: Multi-photon laser scanning microscopy provides a powerful tool for monitoring the spatiotemporal dynamics of neural circuit activity. It is, however, intrinsically a point scanning technique. Standard raster scanning enables imaging at subcellular resolution; however, acquisition rates are limited by the size of the field of view to be scanned. Recently developed scanning strategies such as Travelling Salesman Scanning (TSS) have been developed to maximize cellular sampling rate by scanning only select regions in the field of view corresponding to locations of interest such as somata. However, such strategies are not optimized for the mechanical properties of galvanometric scanners. We thus aimed to develop a new scanning algorithm which produces minimal inertia trajectories, and compare its performance with existing scanning algorithms.
 Approach: We describe here the Adaptive Spiral Scanning (SSA) algorithm, which fits a set of near-circular trajectories to the cellular distribution to avoid inertial drifts of galvanometer position. We compare its performance to raster scanning and TSS in terms of cellular sampling frequency and signal-to-noise ratio (SNR).
 Main Results: Using surrogate neuron spatial position data, we show that SSA acquisition rates
 are an order of magnitude higher than those for raster scanning and generally exceed those achieved by TSS for neural densities comparable with those found in the cortex. We show that this result also holds true for in vitro hippocampal mouse brain slices bath loaded with the synthetic calcium dye Cal-520 AM. The ability of TSS to "park" the laser on each neuron along the scanning trajectory, however, enables higher SNR than SSA when all targets are precisely scanned. Raster scanning has the highest SNR but at a substantial cost in number of cells scanned. To understand the impact of sampling rate and SNR on functional calcium imaging, we used the Crame ́r-Rao Bound on e
  • Journal article
    Zhang P, Gan L, Ling C, Sun Set al., 2018,

    Atomic norm denoising-based joint channel estimation and faulty antenna detection for massive MIMO

    , IEEE Transactions on Vehicular Technology, Vol: 67, Pages: 1389-1403, ISSN: 0018-9545

    We consider joint channel estimation and faulty antenna detection for massive multiple-input multiple-output systems operating in time-division duplexing mode. For systems with faulty antennas, we show that the impact of faulty antennas on uplink data transmission does not vanish even with unlimited number of antennas. However, the signal detection performance can be improved with a priori knowledge on the indices of faulty antennas. This motivates us to propose the approach for simultaneous channel estimation and faulty antenna detection. By exploiting the fact that the degrees of freedom of the physical channel matrix are smaller than the number of free parameters, the channel estimation and faulty antenna detection can be formulated as an extended atomic norm denoising problem and solved efficiently via the alternating direction method of multipliers. Furthermore, we improve the computational efficiency by proposing a fast algorithm and show that it is a good approximation of the corresponding extended atomic norm minimization method. Numerical simulations are provided to compare the performances of the proposed algorithms with several existing approaches and demonstrate the performance gains of detecting the indices of faulty antennas.
  • Journal article
    Xia Y, Kanna S, Mandic DP, 2018,

    Maximum likelihood parameter estimation of unbalanced three-phase power signals

    , IEEE Transactions on Instrumentation and Measurement, Vol: 67, Pages: 569-581, ISSN: 0018-9456

    Accurate detection of the system parameters in unbalanced three-phase power systems is a prerequisite for the optimal operation and control of future smart grids. However, theoretical and practical performance bounds of various estimators for unbalanced systems are only just being established. To this end, we introduce the appropriate Cramer- Rao lower bounds (CRLBs) for frequency estimation, based on the αβ-transformed unbalanced voltage contaminated with noise. Next, for rigor, the maximum likelihood estimation (MLE) method for frequency estimation is introduced as a maximizer of an “augmented periodogram.” The underlying augmented complex statistics is shown to cater for all the available secondorder information, including the noncircularity associated with unbalanced systems. To find the ML solution, Newton's iterative method is employed and its initialization is implemented by a discrete Fourier transform-based dichotomous search technique. We show that the MLE of phases and amplitudes of both the positive and negative phase-sequence components within the αβ-transformed voltage can be generically derived based on the ML frequency estimates. In this way, a unified framework is provided to accurately detect voltage characteristics of the positive and negative phase-sequence components within an unbalanced three-phase power system when its frequency experiences off-nominal conditions. Simulations verify that the proposed MLE approaches theoretical CRLBs for all parameters under consideration.
  • Journal article
    Liu J, Ling C, 2018,

    Adaptive compressed sensing using intra-scale variable density sampling

    , IEEE Sensors Journal, Vol: 18, Pages: 547-558, ISSN: 1530-437X

    Adaptive sensing has the potential to achieve near optimal performance by using current measurements to design subsequential sensing vectors. Existing adaptive sensing methods are usually based on recursive bisection or known structures of certain sparse representations. They suffer from either wasting extra measurements for detecting large coefficients, or missing these coefficients because of violations of these structures. In this paper, intra-scale variable density sampling (InVDS) is presented to capture the heterogeneous property of coefficients. First, Latin hypercube sampling with good uniformity is employed to find areas containing large coefficients. Then, the neighborhoods of K largest coefficients are measured according to the block-sparsity or clustering property. Finally, the denoising-based approximate message passing algorithm is introduced to enhance the performance of image reconstruction. The probability that our sampling method fails to obtain large coefficients is analyzed. The superiority of InVDS is validated by numerical experiments with wavelet, discrete cosine, and Hadamard transforms.
  • Journal article
    Xia Y, Mandic DP, 2018,

    Augmented Performance Bounds on Strictly Linear and Widely Linear Estimators With Complex Data

    , IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 66, Pages: 507-514, ISSN: 1053-587X
  • Journal article
    Stankovic L, Mandic D, Dakovic M, Brajovic Met al., 2018,

    Time-frequency decomposition of multivariate multicomponent signals

    , SIGNAL PROCESSING, Vol: 142, Pages: 468-479, ISSN: 0165-1684
  • Conference paper
    Mital N, Gunduz D, Ling C, 2018,

    Coded caching in a multi-server system with random topology

    , IEEE Wireless Communications and Networking Conference (WCNC), Publisher: IEEE, ISSN: 1525-3511

    Cache-aided content delivery is studied in a multi-server system with P servers and K users, each equipped with a local cache memory. In the delivery phase, each user connects randomly to any ρ out of P servers. Thanks to the availability of multiple servers, which model small base stations with limited storage capacity, user demands can be satisfied with reduced storage capacity at each server and reduced delivery rate per server; however, this also leads to reduced multicasting opportunities compared to a single server serving all the users simultaneously. A joint storage and proactive caching scheme is proposed, which exploits coded storage across the servers, uncoded cache placement at the users, and coded delivery. The delivery latency is studied for both successive and simultaneous transmission from the servers. It is shown that, with successive transmission the achievable average delivery latency is comparable to that achieved by a single server, while the gap between the two depends on ρ, the available redundancy across servers, and can be reduced by increasing the storage capacity at the SBSs.
  • Journal article
    Xiang M, Kanna S, Mandic DP, 2017,

    Performance analysis of quaternion-valued adaptive filters in nonstationary environments

    , IEEE Transactions on Signal Processing, Vol: 66, Pages: 1566-1579, ISSN: 1053-587X

    Quaternion adaptive filters have been widely used for the processing of three-dimensional (3-D) and 4-D phenomena, but complete analysis of their performance is still lacking, partly due to the cumbersomeness of multivariate quaternion analysis. This causes difficulties in both understanding their behavior and designing optimal filters. Based on a thorough exploration of the augmented statistics of quaternion random vectors, this paper extends an analysis framework for real-valued adaptive filters to the mean and mean square convergence analyses of general quaternion adaptive filters in nonstationary environments. The extension is nontrivial, considering the noncommutative quaternion algebra, only recently resolved issues with quaternion gradient, and the multidimensional augmented quaternion statistics. Also, for rigor, in order to model a nonstationary environment, the system weights are assumed to vary according to a first-order random-walk model. Transient and steady-state performance of a general class of quaternion adaptive filters is provided by exploiting the augmented quaternion statistics. An innovative quaternion decorrelation technique allows us to develop intuitive closed-form expressions for the performance of quaternion least mean square (QLMS) filters with Gaussian inputs, which provide new insights into the relationship between the filter behavior and the complete second-order statistics of the input signal, that is, quaternion noncircularity. The closed-form expressions for the performance of strictly linear, semiwidely linear, and widely linear QLMS filters are investigated in detail, while numerical simulations for the three classes of QLMS filters with correlated Gaussian inputs support the theoretical analysis.
  • Journal article
    Wang Y, Brookes DM, 2017,

    Model-Based Speech Enhancement in the Modulation Domain

    , IEEE/ACM Transactions on Audio, Speech and Language Processing, Vol: 26, Pages: 580-594, ISSN: 2329-9304

    This paper presents an algorithm for modulationdomain speech enhancement using a Kalman filter. The proposed estimator jointly models the estimated dynamics of the spectral amplitudes of speech and noise to obtain an MMSE estimation of the speech amplitude spectrum with the assumption that the speech and noise are additive in the complex domain. In order to include the dynamics of noise amplitudes with those of speech amplitudes, we propose a statistical “Gaussring” model that comprises a mixture of Gaussians whose centres lie in a circle on the complex plane. The performance of the proposed algorithm is evaluated using the perceptual evaluation of speech quality (PESQ) measure, segmental SNR (segSNR) measure and shorttime objective intelligibility (STOI) measure. For speech quality measures, the proposed algorithm is shown to give a consistent improvement over a wide range of SNRs when compared to competitive algorithms. Speech recognition experiments also show that the Gaussring model based algorithm performs well for two types of noise.
  • Journal article
    De Sena E, Brookes DM, Naylor PA, van Waterschoot Tet al., 2017,

    Localization Experiments with Reporting by Head Orientation: Statistical Framework and Case Study

    , Journal of the Audio Engineering Society, Vol: 65, Pages: 982-996, ISSN: 0004-7554

    This research focuses on sound localization experiments in which subjects report the position of an active sound source by turning toward it. A statistical framework for the analysis of the data is presented together with a case study from a large-scale listening experiment. The statistical framework is based on a model that is robust to the presence of front/back confusions and random errors. Closed-form natural estimators are derived, and one-sample and two-sample statistical tests are described. The framework is used to analyze the data of an auralized experiment undertaken by nearly nine hundred subjects. The objective was to explore localization performance in the horizontal plane in an informal setting and with little training, which are conditions that are similar to those typically encountered in consumer applications of binaural audio. Results show that responses had a rightward bias and that speech was harder to localize than percussion sounds, which are results consistent with the literature. Results also show that it was harder to localize sound in a simulated room with a high ceiling despite having a higher direct-to-reverberant ratio than other simulated rooms.
  • Conference paper
    Weiss S, Goddard NJ, Somasundaram S, Proudler IK, Naylor PAet al., 2017,

    Identification of Broadband Source-Array Responses from Sensor Second Order Statistics

    , Sensor Signal Processing for Defence Conference (SSPD), Publisher: IEEE, Pages: 35-39

    This paper addresses the identification of source-sensor transfer functions from the measured space-time covariance matrix in the absence of any further side information about the source or the propagation environment. Using polynomial matrix decomposition techniques, the responses can be narrowed down to an indeterminacy of a common polynomial factor. If at least two different measurements for a source with constant power spectral density are available, this indeterminacy can be reduced to an ambiguity in the phase response of the source-sensor paths.
  • Journal article
    Hemakom A, Powezka K, Goverdovsky V, Jaffer U, Mandic DPet al., 2017,

    Quantifying team cooperation through intrinsic multi-scale measures: respiratory and cardiac synchronization in choir singers and surgical teams

    , Royal Society Open Science, Vol: 4, ISSN: 2054-5703

    A highly localized data-association measure, termed intrinsic synchrosqueezing transform (ISC), is proposed for the analysis of coupled nonlinear and non-stationary multivariate signals. This is achieved based on a combination of noise-assisted multivariate empirical mode decomposition and short-time Fourier transform-based univariate and multivariate synchrosqueezing transforms. It is shown that the ISC outperforms six other combinations of algorithms in estimating degrees of synchrony in synthetic linear and nonlinear bivariate signals. Its advantage is further illustrated in the precise identification of the synchronized respiratory and heart rate variability frequencies among a subset of bass singers of a professional choir, where it distinctly exhibits better performance than the continuous wavelet transform-based ISC. We also introduce an extension to the intrinsic phase synchrony (IPS) measure, referred to as nested intrinsic phase synchrony (N-IPS), for the empirical quantification of physically meaningful and straightforward-to-interpret trends in phase synchrony. The N-IPS is employed to reveal physically meaningful variations in the levels of cooperation in choir singing and performing a surgical procedure. Both the proposed techniques successfully reveal degrees of synchronization of the physiological signals in two different aspects: (i) precise localization of synchrony in time and frequency (ISC), and (ii) large-scale analysis for the empirical quantification of physically meaningful trends in synchrony (N-IPS).
  • Journal article
    Luo K, Manikas A, 2017,

    Joint transmitter–receiver optimization in multitarget MIMO radar

    , IEEE Transactions on Signal Processing, Vol: 65, Pages: 6292-6302, ISSN: 1053-587X

    Although, a significant attention has been drawn to the concept of multiple-input multiple-output (MIMO) radar and in particular on the transmit beamforming design and multitarget localization, the cooperation between Tx and Rx has been rarely investigated. In this paper, a novel joint Tx and Rx optimization approach for localization is proposed for MIMO radar where the transmit beamforming design and the estimation of the target's complex path gain are jointly derived by solving an optimization problem that takes account various target parameters. Moreover, the proposed transmit beamforming design controls the power distribution per individual target. The performance of the proposed method is evaluated using computer simulation studies and compared with techniques which simply combine Tx beamforming design with multitarget localization methods.
  • Journal article
    Talebi SP, Mandic DP, 2017,

    Distributed Particle Filtering of alpha-Stable Signals

    , IEEE SIGNAL PROCESSING LETTERS, Vol: 24, Pages: 1862-1866, ISSN: 1070-9908
  • Journal article
    Chanwimalueang T, Mandic DP, 2017,

    Cosine Similarity Entropy: Self-Correlation-Based Complexity Analysis of Dynamical Systems

    , Entropy, Vol: 19, ISSN: 1099-4300

    The nonparametric Sample Entropy (SE) estimator has become a standard for the quantification of structural complexity of nonstationary time series, even in critical cases of unfavorable noise levels. The SE has proven very successful for signals that exhibit a certain degree of the underlying structure, but do not obey standard probability distributions, a typical case in real-world scenarios such as with physiological signals. However, the SE estimates structural complexity based on uncertainty rather than on (self) correlation, so that, for reliable estimation, the SE requires long data segments, is sensitive to spikes and erratic peaks in data, and owing to its amplitude dependence it exhibits lack of precision for signals with long-term correlations. To this end, we propose a class of new entropy estimators based on the similarity of embedding vectors, evaluated through the angular distance, the Shannon entropy and the coarse-grained scale. Analysis of the effects of embedding dimension, sample size and tolerance shows that the so introduced Cosine Similarity Entropy (CSE) and the enhanced Multiscale Cosine Similarity Entropy (MCSE) are amplitude-independent and therefore superior to the SE when applied to short time series. Unlike the SE, the CSE is shown to yield valid entropy values over a broad range of embedding dimensions. By evaluating the CSE and the MCSE over a variety of benchmark synthetic signals as well as for real-world data (heart rate variability of three different cardiovascular pathologies), the proposed algorithms are demonstrated to be able to quantify degrees of structural complexity in the context of self-correlation over small to large temporal scales, thus offering physically meaningful interpretations and rigor in the understanding the intrinsic properties of the structural complexity of a system, such as the number of its degrees of freedom.
  • Journal article
    von Rosenberg W, Chanwimalueang T, Goverdovsky V, Peters NS, Papavassiliou C, Mandic DPet al., 2017,

    Hearables: feasibility of recording cardiac rhythms from head and in-ear locations

    , Royal Society Open Science, Vol: 4, ISSN: 2054-5703

    Mobile technologies for the recording of vital signs and neural signals are envisaged to underpin the operation of future health services. For practical purposes, unobtrusive devices are favoured, such as those embedded in a helmet or incorporated onto an earplug. However, these locations have so far been underexplored, as the comparably narrow neck impedes the propagation of vital signals from the torso to the head surface. To establish the principles behind electrocardiogram (ECG) recordings from head and ear locations, we first introduce a realistic three-dimensional biophysics model for the propagation of cardiac electric potentials to the head surface, which demonstrates the feasibility of head-ECG recordings. Next, the proposed biophysics propagation model is verified over comprehensive real-world experiments based on head- and in-ear-ECG measurements. It is shown both that the proposed model is an excellent match for the recordings, and that the quality of head- and ear-ECG is sufficient for a reliable identification of the timing and shape of the characteristic P-, Q-, R-, S- and T-waves within the cardiac cycle. This opens up a range of new possibilities in the identification and management of heart conditions, such as myocardial infarction and atrial fibrillation, based on 24/7 continuous in-ear measurements. The study therefore paves the way for the incorporation of the cardiac modality into future ‘hearables’, unobtrusive devices for health monitoring.
  • Conference paper
    Kanna S, Mandic DP, 2017,

    Stability of Distributed Extended Kalman Filters

    , 2017 22nd International Conference on Digital Signal Processing (DSP), Publisher: IEEE, ISSN: 1546-1874
  • Conference paper
    Xiang M, Douglas SC, Mandic DP, 2017,

    The Quaternion Least Mean Magnitude Phase Adaptive Filtering Algorithm

    , 2017 22nd International Conference on Digital Signal Processing (DSP), Publisher: IEEE, ISSN: 1546-1874
  • Conference paper
    Liu T, Stathaki T, 2017,

    Enhanced pedestrian detection using deep learning based semantic image segmentation

    , Digital Signal Processing (DSP) 2017, Publisher: IEEE

    Pedestrian detection and semantic segmentation arehighly correlated tasks which can be jointly used for betterperformance. In this paper, we propose a pedestrian detectionmethod making use of semantic labeling to improve pedestriandetection results. A deep learning based semantic segmentationmethod is used to pixel-wise label images into 11 common classes.Semantic segmentation results which encodes high-level imagerepresentation are used as additional feature channels to beintegrated with the low-level HOG+LUV features. Some falsepositives, such as falsely detected pedestrians located on a tree,can be easier eliminated by making use of the semantic cues.Boosted forest is used for training the integrated feature channelsin a cascaded manner for hard negatives mining. Experimentson the Caltech-USA pedestrian dataset show improvements ondetection accuracy by using the additional semantic cues.
  • Conference paper
    Stankovic L, Brajovic M, Dakovic M, Mandic Det al., 2017,

    Two-Component Bivariate Signal Decomposition Based on Time-Frequency Analysis

    , 2017 22nd International Conference on Digital Signal Processing (DSP), Publisher: IEEE, ISSN: 1546-1874
  • Conference paper
    Wang Z, Ling C, 2017,

    On the geometric ergodicity of Gibbs algorithm for lattice gaussian sampling

    , 2017 IEEE Information Theory Workshop (ITW), Publisher: IEEE, Pages: 269-273, ISSN: 2475-420X

    Sampling from the lattice Gaussian distribution is emerging as an important problem in coding and cryptography. In this paper, the conventional Gibbs sampling algorithm is demonstrated to be geometrically ergodic in tackling with lattice Gaussian sampling, which means its induced Markov chain converges exponentially fast to the stationary distribution. Moreover, as the exponential convergence rate is dominated by the spectral radius of the forward operator of the Markov chain, a comprehensive analysis is given and we show that the convergence performance can be further enhanced by usages of blocked sampling strategy and choices of selection probabilities.
  • Journal article
    Huang Y, Clerckx B, 2017,

    Waveform Design for Wireless Power Transfer with Limited Feedback

    , IEEE Transactions on Wireless Communications, Vol: 17, Pages: 415-429, ISSN: 1536-1276

    Waveform design is a key technique to jointly exploit a beamforming gain, the channel frequency selectivity, and the rectifier nonlinearity, so as to enhance the end-to-end power transfer efficiency of wireless power transfer (WPT). Those waveforms have been designed, assuming perfect channel state information at the transmitter. This paper proposes two waveform strategies relying on limited feedback for multi-antenna multi-sine WPT over frequency-selective channels. In the waveform selection strategy, the energy transmitter (ET) transmits over multiple timeslots with every time a different waveform precoder within a codebook, and the energy receiver (ER) reports the index of the precoder in the codebook that leads to the largest harvested energy. In the waveform refinement strategy, the ET sequentially transmits two waveforms in each stage, and the ER reports one feedback bit indicating an increase/decrease in the harvested energy during this stage. Based on multiple one-bit feedback, the ET successively refines waveform precoders in a tree-structured codebook over multiple stages. By employing the framework of the generalized Lloyd’s algorithm, novel algorithms are proposed for both strategies to optimize the codebooks in both space and frequency domains. The proposed limited feedback-based waveform strategies are shown to outperform a set of baselines, achieving higher harvested energy.
  • Journal article
    Tonoyan Y, Chanwimalueang T, Mandic DP, Van Hulle MMet al., 2017,

    Discrimination of emotional states from scalp- and intracranial EEG using multiscale Renyi entropy

    , PLOS One, Vol: 12, ISSN: 1932-6203

    A data-adaptive, multiscale version of Rényi’s quadratic entropy (RQE) is introduced for emotional state discrimination from EEG recordings. The algorithm is applied to scalp EEG recordings of 30 participants watching 4 emotionally-charged video clips taken from a validated public database. Krippendorff’s inter-rater statistic reveals that multiscale RQE of the mid-frontal scalp electrodes best discriminates between five emotional states. Multiscale RQE is also applied to joint scalp EEG, amygdala- and occipital pole intracranial recordings of an implanted patient watching a neutral and an emotionally charged video clip. Unlike for the neutral video clip, the RQEs of the mid-frontal scalp electrodes and the amygdala-implanted electrodes are observed to coincide in the time range where the crux of the emotionally-charged video clip is revealed. In addition, also during this time range, phase synchrony between the amygdala and mid-frontal recordings is maximal, as well as our 30 participants’ inter-rater agreement on the same video clip. A source reconstruction exercise using intracranial recordings supports our assertion that amygdala could contribute to mid-frontal scalp EEG. On the contrary, no such contribution was observed for the occipital pole’s intracranial recordings. Our results suggest that emotional states discriminated from mid-frontal scalp EEG are likely to be mirrored by differences in amygdala activations in particular when recorded in response to emotionally-charged scenes.

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