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Signal Processing, IET

Issue 4 • Date June 2013

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Displaying Results 1 - 9 of 9
  • Adaptive efficient sparse estimator achieving oracle properties

    Publication Year: 2013 , Page(s): 259 - 268
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (415 KB)  

    Compressed Sensing is the new trend in the signal processing context which aims to sample a compressible signal with a rate less than the Nyquist lower bound sampling rate. The main challenge arises due to the non-convex optimisation problem to be solved in the reconstruction stage. This paper introduces a suitable objective function in order to simultaneously recover the true support of the underlying sparse signal while achieving an acceptable estimation error. Inspired by the well-known Lasso objective function, we have developed an objective function based on a new penalty denoted by the Linearised Exponentially Decaying (LED) penalty. The comprehensive analysis of the LED based objective function shows that the new approach satisfies the oracle properties, as opposed to the conventional Lasso objective function. Furthermore, we have developed a Sequential Adaptive Coordinate-wise (SAC) solution for the proposed objective function. The simulation results for the proposed LED-SAC reconstruction algorithm are given and compared with other state of the art methods. It is shown that LED-SAC approaches the least mean squared error criterion. Moreover, compared to the other methods, LED-SAC has much more adaptation rate in terms of tracking the variations in the support of the underlying sparse signal. View full abstract»

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  • Detection of weak signals based on empirical mode decomposition and singular spectrum analysis

    Publication Year: 2013 , Page(s): 269 - 276
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1350 KB)  

    A novel method for the detection of weak signals embedded in non-stationary backgrounds is derived based on empirical mode decomposition and singular spectrum analysis in the present study. Simulated example reveals that the new method performs well in the detection of the characteristic components and especially the weak signals. Finally, the method is applied to the experimental signals of gearbox and the useful weak fault components can be exactly captured, which shows that the method presented in this study provides an effective means to the detection of weak signals. View full abstract»

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  • Duality of linear estimation for multiplicative noise systems with measurement delay

    Publication Year: 2013 , Page(s): 277 - 284
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (276 KB)  

    This study firstly investigates the estimation problem for systems with multiplicative noise and measurement delay. Based on the innovation analysis approach, the estimators are developed in terms of a Riccati equation and a Lyapunov equation. The equations are of the same dimension as the plant; therefore compared with the augmentation approach, the presented approach lessens the computational demand. Then the linear quadratic regulation (LQR) problem for input delay systems is discussed based on non-augmented approach, and the controller is given in terms of a backward Riccati equation and a backward Lyapunov equation. Finally, the authors establish a duality between the estimation problem for measurement delay systems with multiplicative noise and the LQR problem for deterministic input delay systems with constraint conditions. View full abstract»

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  • Weighted non-linear criterion-based adaptive generalised eigendecomposition

    Publication Year: 2013 , Page(s): 285 - 295
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (607 KB)  

    Generalised eigendecomposition problem for a symmetric matrix pencil is reinterpreted as an unconstrained minimisation problem with a weighted non-linear criterion. The analytical results show that the proposed criterion has a unique global minimum which corresponds to the principal generalised eigenvectors, thus guaranteeing the global convergence via iterative methods to search the minimum. A gradient-based adaptive algorithm and a fixed point iteration-based adaptive algorithm are derived for the generalised eigendecomposition, which both work in parallel and avoid the error propagation effect of sequential-type algorithms. By applying the stochastic approximation theory, the global convergence of the proposed adaptive algorithm is proved. The performance of the proposed method is evaluated by simulations in terms of convergence rate, estimation accuracy as well as tracking capability. View full abstract»

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  • Resource-efficient and scalable solution to problem of real-data polyphase discrete Fourier transform channelisation with rational over-sampling factor

    Publication Year: 2013 , Page(s): 296 - 305
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (848 KB)  

    The study describes the results of research carried out into the design of a parallel and resource-efficient solution to the real-data polyphase discrete Fourier transform (DFT), or PDFT. The solution is able to exploit both the real-valued nature of the data and the parallel processing capabilities of the computing technology - assumed to be a field-programmable gate array - to yield a solution with a low size, weight and power requirement. A parallel computing architecture has been devised, based upon batch processing, whereby pipelined operation of the polyphase filter bank (PFB) is achieved using shared resources and pipelined operation of the real-data DFT using the resource-efficient regularised fast Hartley transform (RFHT). The PFB outputs are appropriately re-ordered for input to the RFHT by means of a suitably defined finite state machine. The resulting design, which includes a flexible up-sampling capability (with rational over-sampling factor) to address the problem of adjacent channel interference, trade-off time complexity against space complexity in order to satisfy the associated timing constraints. The solution is also scalable, in terms of the number of channels, so that it might be easily adapted, for new or multiple applications, at minimal re-design effort and cost. View full abstract»

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  • Gaussian mixture model approximation of total spatial power spectral density for multiple incoherently distributed sources

    Publication Year: 2013 , Page(s): 306 - 311
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (218 KB)  

    Practically, the spatial power spectral density (PSD) of single or multiple incoherently distributed (ID) sources is often unknown, and the total spatial PSD is suitable to model the spatial distribution characteristic of signals if the number of multiple ID sources is also unknown. In this study, the Gaussian mixture model (GMM) is employed to characterise the total spatial PSD of multiple ID sources, and two algorithms are proposed to estimate the parameters of the GMM. The first one is the covariance fitting method for multiple ID sources with Gaussian PSD, and the other is the iterative expectation maximisation (EM) algorithm. Simulation studies demonstrate that the EM algorithm outperforms other methods in approximating the shape of the total spatial PSD, especially for small spatial spread. View full abstract»

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  • Parallel computing for efficient time-frequency feature extraction of power quality disturbances

    Publication Year: 2013 , Page(s): 312 - 326
    Cited by:  Papers (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1212 KB)  

    Fast signal processing implementation techniques for detection and classification of power quality (PQ) disturbances are the need of the hour. Hence in this work, a parallel computing approach has been proposed to speed up the feature extraction of PQ signals to facilitate rapid building of classifier models. Considering that the Fourier, the one-dimensional discrete wavelet, the time-time and the Stockwell transforms have been used extensively to extract pertinent time-frequency features from non-stationary and multi-frequency PQ signals, acceleration approaches using data and task parallelism have been employed for parallel implementation of the above time-frequency transforms. In the first approach, data parallelism was applied to the Stockwell transform and the time-time transform-based feature extraction methods separately to alleviate capability problems. Also, data parallelism was applied to Fourier and wavelet-based feature extraction methods independently to alleviate capacity problems. Secondly, a combination of task and data parallelism was applied to speed up S-transform based three-phase sag feature extraction. Experiments were conducted using shared-memory and distributed memory architectures to try out the effectiveness of the proposed parallel approaches. The performances of these parallel implementations were analysed in terms of computational speed and efficiency in comparison with the sequential approach. View full abstract»

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  • Rao-Blackwellised particle filtering and smoothing for jump Markov non-linear systems with mode observation

    Publication Year: 2013 , Page(s): 327 - 336
    Cited by:  Papers (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (557 KB)  

    This study is concerned with the problem of filtering and fixed-lag smoothing for jump Markov non-linear systems when the mode information can be extracted from an image sensor. Based on the idea of Rao-Blackwellisation, the authors present a general theoretical framework to derive the recursive estimates by employing the particle filtering method. A suboptimal image-enhanced Rao-Blackwellised particle filter is proposed, in which the mode state is estimated by using random sampling and the continuous state as well as the relevant likelihood function are approximated as Gaussian distributions. The one-step fixed-lag smoothing result is also obtained for such systems with lagged mode observations. Performance comparison of the proposed algorithms with the existing methods is provided through a manoeuvring target tracking simulation study. View full abstract»

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  • Distributed multichannel speech enhancement based on perceptually-motivated Bayesian estimators of the spectral amplitude

    Publication Year: 2013 , Page(s): 337 - 344
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (694 KB)  

    In this study, the authors propose multichannel weighted Euclidean (WE) and weighted cosh (WCOSH) cost function estimators for speech enhancement in the distributed microphone scenario. The goal of the work is to illustrate the advantages of utilising additional microphones and modified cost functions for improving signal-to-noise ratio (SNR) and segmental SNR (SSNR) along with log-likelihood ratio (LLR) and perceptual evaluation of speech quality (PESQ) objective metrics over the corresponding single-channel baseline estimators. As with their single-channel counterparts, the perceptually-motivated multichannel WE and WCOSH estimators are functions of a weighting law parameter, which influences attention of the noisy spectral amplitude through a spectral gain function, emphasises spectral peak (formant) information, and accounts for auditory masking effects. Based on the simulation results, the multichannel WE and WCOSH cost function estimators produced gains in SSNR improvement, LLR output and PESQ output over the single-channel baseline results and unweighted cost functions with the best improvements occurring with negative values of the weighting law parameter across all input SNR levels and noise types. View full abstract»

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