Online ISSN
1751-9683
Print ISSN
1751-9675
IET Signal Processing
Volume 4, Issue 6, December 2010
Volumes & issues:
Volume 4, Issue 6
December 2010
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- Author(s): H. Olkkonen and J.T. Olkkonen
- Source: IET Signal Processing, Volume 4, Issue 6, p. 603 –609
- DOI: 10.1049/iet-spr.2009.0109
- Type: Article
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p.
603
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In wavelet theory the two-scale dilation equation has a central role. B-splines serve as good candidates for wavelet analysis, since they obey the two-scale dilation equation. This work describes the B-spline wavelet transform, which is based on the polyphase decomposition of the two-scale dilation equation. We construct a linear quadrature mirror filter (QMF) B-spline wavelet filter bank, which can be effectively implemented by the polyphase filters. The interpolating property of the two-scale dilation equation is applied for constructing the shift-invariant complex QMF B-spline wavelets. The validity of the B-spline wavelet transform is warranted in multi-scale analysis of neuroelectric signal waveforms. - Author(s): P. Donato ; C. De Marziani ; D. Carrica ; M. Funes ; J. Ureña
- Source: IET Signal Processing, Volume 4, Issue 6, p. 610 –617
- DOI: 10.1049/iet-spr.2009.0117
- Type: Article
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p.
610
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Complementary sequences are currently being applied to multiple fields of engineering not only when an active signal must be coded to obtain large noise immunity (proportional to the sequences length) but also when multiuser or multisensory operations need to be conducted (because of their orthogonality properties). The orthogonality between complementary sequences is only defined for sequences with the same length. This constraint limits complementary sequences implementation in cases in which all the sequences feature the same length. This paper describes the way in which orthogonal sequences of different length can be generated starting from given complementary sequences. A possible application to a noisy channel is also analysed. In the said application, a pair of complementary sequences of short length is used to code the desired information, while simultaneously an orthogonal complementary pair of larger length is used to measure the channel attenuation. - Author(s): M.J. Grimble and S. Ali Naz
- Source: IET Signal Processing, Volume 4, Issue 6, p. 618 –629
- DOI: 10.1049/iet-spr.2009.0001
- Type: Article
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p.
618
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A non-linear operator approach to estimation in discrete-time multivariable systems is described. It involves inferential estimation of a signal which enters a communication channel that contains non-linearities and transport delays. The measurements are assumed to be corrupted by a coloured noise signal correlated with the signal to be estimated. The solution of the non-linear estimation problem is obtained using non-linear operators. The signal and noise channels may be grossly non-linear and are represented in a very general non-linear operator form. The resulting so-called Wiener non-linear minimum variance estimation algorithm is relatively simple to implement. The optimal non-linear estimator is derived in terms of the non-linear operators and can be implemented as a recursive algorithm using a discrete-time non-linear difference equation. In the limiting case of a linear system, the estimator has the form of a Wiener filter in discrete-time polynomial matrix system form. A non-linear channel equalisation problem is considered for the design example. - Author(s): Y.-M. Yu and R.-C. Lo
- Source: IET Signal Processing, Volume 4, Issue 6, p. 630 –639
- DOI: 10.1049/iet-spr.2009.0131
- Type: Article
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p.
630
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A self-developed integrated system is employed to record and analyse intracortical evoked potentials from the primary somatosensory cortex of rats. Four different neural signals are recorded under no stimulation and stimulation using a toothbrush, pen shaft and toothpick separately. These evoked signals undergo preprocessing and post-processing, in that order. In order to improve the shortcoming of independent component analysis (ICA), which the magnitude and sequence of estimated independent components are ambiguous. The authors propose the dynamic dimension increasing method to form a feature vector by correlation coefficient matrix and mitigate the drawback of ICA. Then, k-means is employed to group the feature vector into different clusters. The authors use the information of monitoring subsystem to check the experimental results by using a video recording device. Finally, the presented methods are utilised to extract the features from various evoked potentials and distinguish the stimulants from different sensory signals. - Author(s): H.Q. Zhao ; X.P. Zeng ; J.S. Zhang ; Y.G. Liu ; T.R. Li
- Source: IET Signal Processing, Volume 4, Issue 6, p. 640 –649
- DOI: 10.1049/iet-spr.2009.0047
- Type: Article
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p.
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This study presents a joint adaptive non-linear filter with pipelined second-order polynomial perceptron (PSOVNN) to reduce the computational complexity and improve the non-linear processing capability of adaptive direct-form second-order Volterra (SOV) filter. The PSOVNN is a nesting modular structure comprising a number of modules that are interconnected in a chained form. Each module is implemented by a small-scale direct-form SOV neural network (SOVNN). These cascaded modules can perform a non-linear mapping from the input space to an intermediate space. In addition, the linear filter of the complete PSOVNN performs a linear mapping from the intermediate space to the output space. A modified real-time recurrent learning (RTRL) algorithm is developed, and its performance is evaluated by a series of simulation experiments. Computer simulations indicate that the proposed non-linear filter exhibits better performance over the direct-form SOV filter with less computational complexity. - Author(s): Y. Xia ; A. Amann ; B. Liu
- Source: IET Signal Processing, Volume 4, Issue 6, p. 650 –657
- DOI: 10.1049/iet-spr.2009.0153
- Type: Article
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This study is devoted to detection of abrupt changes in electrocardiogram (ECG). A linear time-variant model with Gaussian white noise is used to describe the real ECG signal, based on the estimated system parameters and tuned covariances of noise, the off-line and on-line generalised likelihood ratio (GLR) tests for ECG signal are developed for change detection. For comparison, the test algorithm uses Levinson, recursive least squares (RLS) methods to obtain the filter models parameters of ECG. Furthermore, windowed on-line GLR test algorithm is developed, which works more effectively in real-time situation. The simulation results with real data show the effectiveness of the application. - Author(s): F.-K. Chen ; G.-M. Chen ; B.-K. Su ; Y.-R. Tsai
- Source: IET Signal Processing, Volume 4, Issue 6, p. 658 –665
- DOI: 10.1049/iet-spr.2009.0216
- Type: Article
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The algebraic code excited linear prediction (ACELP) algorithm, because of low complexity and high quality in its analysis-by-synthesis optimisation, has been adopted by many speech coding standards. This study proposes the unified generalised pulse replacement (UPR) search algorithm for the stochastic codebook of ACELP speech coders. The proposed UPR algorithm discusses the search breadth, the order of the search direction and the update frequency based on the pulse replacement method. In addition, there are many derivative types of UPR algorithms discussed. The proposed approaches can achieve the lowest computational complexity with imperceptible degradation of the speech quality. Furthermore, the normalised degradation ratio based on the standard subjective quality measurement is proposed to fairly compare the performance. The experimental results will verify the claims. - Author(s): K. Cumanan ; R. Krishna ; Z. Xiong ; S. Lambotharan
- Source: IET Signal Processing, Volume 4, Issue 6, p. 666 –672
- DOI: 10.1049/iet-spr.2009.0204
- Type: Article
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Cognitive radio networks (CRNs) have the ability to utilise the radio spectrum efficiently by allowing secondary users (SUs) to communicate in the licensed frequency bands. In this study, downlink spatial multiplexing techniques are proposed to enable multiple SUs to share spectrum simultaneously without harmfully interfering the primary users (PUs). The multiuser transmitter beamformers are designed by setting constraints on the interference temperatures of the PUs and signal-to-interference and noise ratios (SINRs) of the SUs. The proposed beamformers minimise the total transmit power while achieving the required quality of services (QoSs) for each SU. Since spatial multiplexing techniques require channel state information (CSI) at the basestation, which could normally be in error, a robust spatial multiplexing technique using worst-case performance optimisation is proposed for underlay CRNs. The proposed robust beamformer has the ability to maintain SINRs of all SUs above a set of target values and interference leakage to PUs below a threshold for all possible CSI errors within a convex hull. Both the robust and the non-robust designs are formulated into a convex optimisation framework using semidefinite constraints. - Author(s): M. Grašič ; M. Kos ; Z. Kačič
- Source: IET Signal Processing, Volume 4, Issue 6, p. 673 –685
- DOI: 10.1049/iet-spr.2009.0235
- Type: Article
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p.
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This study presents a new online method for speaker segmentation and clustering in real-world environments. It analyses and discusses the difficulties of online speaker diarisation and proposes a new segmentation and clustering method, in which the Bayesian information criterion (BIC) and the normalised cross-likelihood ratio (NCLR) are combined into an online speaker diarisation system. A new decision parameter for BIC and NCLR is proposed using normalisation with reference criterion selection (NRCS), together with a window normalisation technique called window-length compensation (WLC), which normalises the criterion value according to analysed window length. The effectiveness of the proposed system and techniques in comparison to the standard offline speaker diarisation system (mClust) is demonstrated on the Slovenian Broadcast News database (BNSI) and an English Broadcast News database (the HUB-4). The online system presented in this study achieves similar performance to the BIC-based offline approach. - Author(s): L. Wang and R.C. de Lamare
- Source: IET Signal Processing, Volume 4, Issue 6, p. 686 –697
- DOI: 10.1049/iet-spr.2009.0243
- Type: Article
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p.
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This article proposes constrained adaptive algorithms based on the conjugate gradient (CG) method for adaptive beamforming. The proposed algorithms are derived for the implementation of the beamformer according to the minimum variance and constant modulus criteria subject to a constraint on the array response. A CG-based weight vector strategy is created for enforcing the constraint and computing the weight expressions. The devised algorithms avoid the covariance matrix inversion and exhibit fast convergence with low complexity. A complexity analysis compares the proposed algorithms with the existing ones. The convergence properties of the CCM criterion are studied, conditions for convexity are established and a convergence analysis for the proposed algorithms is derived. Simulation results are conducted for both stationary and non-stationary scenarios, showing the convergence and tracking ability of the proposed algorithms. - Author(s): Md. Kamrul Hasan ; M.A. Haque ; T. Islam
- Source: IET Signal Processing, Volume 4, Issue 6, p. 698 –707
- DOI: 10.1049/iet-spr.2009.0272
- Type: Article
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p.
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Channel shortening by least-squares (LS) optimisation is an attractive technique for its simplicity and computational efficiency. However, the method does not have suitable control over the frequency response of the shortened channel. As a result, the deep spectral nulls may inhibit some subcarriers to carry data bits and thereby reduce bit rate in multicarrier communication systems. Channel shortening is also proposed as a potential dereverberation technique in some recent research results. Again, shortening by LS optimisation leads to severe spectral distortion in the dereverberated speech signal. In this paper, we propose a spectrally constrained iterative LS minimisation algorithm that enforces spectral flatness in the shortening filter and thereby removes nulls without sacrificing the shortening performance. We also propose an optimal step-size for the iterative LS technique, which yields the fastest convergence rate for the gradient descent algorithm. The effectiveness of the proposed algorithm is tested for asymmetric digital subscriber line channels and speech dereverberation problems. The simulation results show that it outperforms the conventional techniques, resulting more subcarriers to carry bits when applied to communication channels and better quality of the speech signal when acoustic channels are considered. - Author(s): Y. Yang and Y. Wei
- Source: IET Signal Processing, Volume 4, Issue 6, p. 708 –719
- DOI: 10.1049/iet-spr.2009.0213
- Type: Article
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p.
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In order to suppress the pseudo-Gibbs phenomena in the thresholding denoising method and to improve denoising capability, a novel denoising method called the random interpolation average (RIA) scheme is proposed. The noisy signals are interpolated randomly so that the matching condition between the features of the noisy signal and that of the wavelet basis is changed. Then the thresholding denoising is applied to this interpolated signal to obtain the denoised signal. Thus, by each time interpolation and denoising, the authors will obtain an independent denoised signal. Finally, the pseudo-Gibbs phenomena will be suppressed by averaging all of the independent denoised signals. According to comparison of the signal-to-noise ratio (SNR) of four typical denoised signals, the threshold, shrinkage function, wavelet bases and interpolation scheme of the RIA scheme are optimised. The denoising capabilities of the thresholding denoising method, the translation-invariant scheme, the WienerChop algorithm and the RIA scheme are compared by simulations. The results indicate that the pseudo-Gibbs phenomena can be suppressed efficiently by the RIA scheme with universal threshold, firm shrinkage function, Coiflet families and third-order Lagrange interpolation. Compared to the other three methods, the SNR of the denoised signals in the RIA scheme is increased by 3.53, 0.7 and 1.4 dB, respectively. Both the smoothness and similarity of the signals are also improved by the RIA scheme. - Author(s): S.-T. Chen ; G.-D. Wu ; H.-N. Huang
- Source: IET Signal Processing, Volume 4, Issue 6, p. 720 –727
- DOI: 10.1049/iet-spr.2009.0187
- Type: Article
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This study presents an optimisation-based audio watermarking scheme using group-amplitude quantisation. To enhance the robustness, the watermark is embedded in the lowest-frequency coefficients in discrete wavelet transform (DWT). The performance of this watermarking scheme is analysed in terms of audio quality (signal-to-noise ratio (SNR)) and robustness (bit error ratio (BER)). As there is a tradeoff relationship between the audio quality and robustness, this study presents an optimisation-based group-amplitude quantisation scheme for audio watermarking. First, SNR is rewritten as a watermarking cost function in matrix form. Then an equation connecting the watermarking cost function and the group-amplitude quantisation equation is proposed. Second, the Lagrange principle is used to derive the optimisation solution. The optimal results are then applied to embed the watermark. Finally, the performance of the proposed scheme is tested. Experimental results show that the hidden data are robust against most common attacks, such as re-sampling, MP3 compression, low-pass filtering and amplitude scaling. - Author(s): L.P. Yan ; D.H. Zhou ; M.Y. Fu ; Y.Q. Xia
- Source: IET Signal Processing, Volume 4, Issue 6, p. 728 –739
- DOI: 10.1049/iet-spr.2009.0215
- Type: Article
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p.
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This study is concerned with the state estimation problem for a kind of asynchronous multirate multisensor dynamic system, where observations from different sensors are randomly missing. The system is described at the highest sampling rate with different sensors observing a single target independently with multiple sampling rates. The optimal state estimate is obtained by use of the multiscale system theory and the modified Kalman filter. This study extends the federated Kalman filter to the case of asynchronous multirate multisensor dynamic systems with measurements randomly missing. The presented algorithm is proven to be effective in the sense of linear minimum mean squared error. The feasibility and efficiency of the algorithm are illustrated by a numerical simulation example.
Shift-invariant B-spline wavelet transform for multi-scale analysis of neuroelectric signals
Algorithm for complementary-derived orthogonal sequences applied to measurements in noisy channels
Optimal minimum variance estimation for non-linear discrete-time multichannel systems
Recognition of various tactile stimuli using independent component analysis and k-means
Adaptive non-linear filter using a modular polynomial perceptron
Detection of abrupt changes in electrocardiogram with generalised likelihood ratio algorithm
Unified pulse-replacement search algorithms for algebra codebooks of speech coders
Multiuser spatial multiplexing techniques with constraints on interference temperature for cognitive radio networks
Online speaker segmentation and clustering using cross-likelihood ratio calculation with reference criterion selection
Constrained adaptive filtering algorithms based on conjugate gradient techniques for beamforming
Spectrally constrained channel shortening using least-squares optimisation
Random interpolation average for signal denoising
Wavelet-domain audio watermarking scheme using optimisation-based quantisation
State estimation for asynchronous multirate multisensor dynamic systems with missing measurements
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