Online ISSN
1751-9683
Print ISSN
1751-9675
IET Signal Processing
Volume 6, Issue 2, April 2012
Volumes & issues:
Volume 6, Issue 2
April 2012
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- Author(s): H. Zhang ; Y. Shi ; A. Saadat Mehr
- Source: IET Signal Processing, Volume 6, Issue 2, p. 73 –78
- DOI: 10.1049/iet-spr.2010.0367
- Type: Article
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The problem of equalisation for communication channels with inter symbol interference (ISI) is investigated in this study. One practical yet challenging constraint for a channel with high transmission rate is incorporated into the modelling of the equalisation system: the communication channel is subject to uncertainties, which are assumed to be within a polytope with finite vertices. By using the augmentation method, the filtering error system of the equalisation problem is also characterised as a system with polytopic uncertainties. Sufficient conditions on the stability and the ℋ∞ performance for the filtering error system are obtained. A design method for the equaliser is proposed such that the filtering error system can achieve minimal ℋ∞ performance index even with the channel uncertainties. Two illustrative design examples demonstrate the design procedure and the effectiveness of the proposed method. - Author(s): J. Zhang ; X.P. Gao ; Y.Q. Li
- Source: IET Signal Processing, Volume 6, Issue 2, p. 79 –90
- DOI: 10.1049/iet-spr.2010.0186
- Type: Article
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In this study, a novel four-layer architecture of wavelet network is proposed for function learning. Compared to conventional three-layer wavelet networks, the proposed one exploits adaptive wavelet neuron selection technique according to input information, so that the widespread structural redundancy is avoided. Meanwhile, it controls the scale of problem solution. Based on the proposed architecture, two wavelet networks including single-wavelet neural network and multi-wavelet neural network are built and verified for function learning. The experimental results demonstrate that our models are remarkably superior to some of the well-established three-layer wavelet networks including Zhang's model and Pati's model in terms of both speed and accuracy. Compared with Huang's real-time neural network, the proposed models have significantly better accuracy with basically similar speed. - Author(s): C.-N. Ko
- Source: IET Signal Processing, Volume 6, Issue 2, p. 91 –98
- DOI: 10.1049/iet-spr.2011.0025
- Type: Article
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In this study, a time-varying learning algorithm (TVLA) using particle swarm optimisation (PSO) method is presented to optimise radial basis function neural networks (RBFNNs) for identification of non-linear systems. First, support vector regression (SVR) method is adopted to determine the number of hidden layer nodes, the initial parameters of the kernel and the initial weights of RBFNNs. After initialisation, an annealing robust TVLA (ARTVLA) is then applied to train the RBFNNs. In the ARTVLA, the determination of the learning rate would be an important issue for the trade-off between stability and speed of convergence. A simple and computationally efficient optimisation method, PSO, is adopted to simultaneously find a set of promising learning rates to overcome the stagnation for searching optimal solutions in training procedure of RBFNNs. The proposed SVR-based RBFNNs with ARTVLA (SVR–ARTVLA–RBFNNs) have good performance for system identification only using few hidden layer nodes. Three examples of a non-linear system, including two benchmarks and a real data set, are illustrated to show the feasibility and superiority of the proposed SVR–ARTVLA–RBFNNs for identification of non-linear systems. - Author(s): C. Guerrero-Mosquera and A. Navia-Vázquez
- Source: IET Signal Processing, Volume 6, Issue 2, p. 99 –106
- DOI: 10.1049/iet-spr.2010.0135
- Type: Article
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A new method for eye movement artefacts removal based on independent component analysis (ICA) and recursive least squares (RLS) is presented. The proposed algorithm combines the effective ICA capacity of separating artefacts from brain waves, together with the online interference cancellation achieved by adaptive filtering. Eye blink, saccades, eyes opening and closing produce changes of potentials at frontal areas. For this reason, the method uses as a reference the electrodes closest to the eyes Fp1, Fp2, F7 and F8, which register vertical and horizontal eye movements in the electroencephalogram (EEG) caused by these activities as an alternative of using extra dedicated electrooculogram (EOG) electrodes, which could not always be available and could be subject to larger variability. Both reference signals and EEG components are first projected into ICA domain and then the interference is estimated using the RLS algorithm. The component related to EOG artefact is automatically eliminated using channel localisations. Results from experimental data demonstrate that this approach is suitable for eliminating artefacts caused by eye movements, and the principles of this method can be extended to certain other artefacts as well, whenever a correlated reference signal is available. - Author(s): J.S. Pereira and H.J.A. da Silva
- Source: IET Signal Processing, Volume 6, Issue 2, p. 107 –113
- DOI: 10.1049/iet-spr.2010.0195
- Type: Article
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107
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In this study, the authors propose a method to obtain many different sets of N orthogonal perfect periodic autocorrelation sequences, with length N, derived from Walsh-Hadamard codes and orthogonal Gold codes. Moreover, these orthogonal perfect discrete Fourier transform (DFT) sequences can be transformed into real orthogonal perfect sequences or into bipolar codes with better correlation properties than other well known bipolar codes used in code division multiple access (CDMA) systems. - Author(s): X. Yang
- Source: IET Signal Processing, Volume 6, Issue 2, p. 114 –121
- DOI: 10.1049/iet-spr.2010.0201
- Type: Article
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114
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A new method for the dual estimation in dynamic state-space model was proposed in this study with a focus on sequential Bayesian learning about time-varying state and static parameter simultaneously. The proposed algorithm combines auxiliary particle filtering (APF) with particle swarm optimisation (PSO) to achieve computational efficiency and stability. The PSO provides the mechanism for generating new parameter values for the particle filtering at each time step. By properly choosing the fitness function of PSO, the algorithm produces the recursive maximum-likelihood estimation of the parameter. It is shown that PSO can be integrated with APF in the simulation-based sequential frame for dual estimation. The algorithm is tested on Markov switching stochastic volatility model with promising results compared with existing ones. - Author(s): M. Marazin ; R. Gautier ; G. Burel
- Source: IET Signal Processing, Volume 6, Issue 2, p. 122 –131
- DOI: 10.1049/iet-spr.2010.0343
- Type: Article
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To enhance the quality of transmissions, all digital communication systems use error-correcting codes. By introducing some redundancy in the informative binary data stream, they allow one to better withstand channel impairments. The design of new coding schemes leads to a perpetual evolution of the digital communication systems and, thus, cognitive radio receivers have to be designed. Such receivers will be able to blind estimate the transmitter parameters. In this study, an algebraic method dedicated to the blind identification of punctured convolutional encoders is presented. The blind identification of such encoders is of great interest, because convolutional encoders are embedded in most digital transmission systems where the puncturing principle is used to increase the code rate to reduce the loss of the information data rate because of the redundancy introduced by the encoder. After a brief recall of the principle of puncturing codes and the construction of the equivalent punctured code, a new method dedicated to the blind identification of both the mother code and the puncturing pattern is developed when the received bits are erroneous. Finally, case studies are presented to illustrate the performances of our blind identification method. - Author(s): S. Zhu and Z. Ding
- Source: IET Signal Processing, Volume 6, Issue 2, p. 132 –142
- DOI: 10.1049/iet-spr.2010.0352
- Type: Article
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This study deals with the localisation of all nodes in a network, also called as network localisation, based on pairwise distance measurements. The case of a fully connected network is considered, where ‘fully connected’ refers to that within the whole network every pair of nodes directly connect to each other, thus their pairwise distance can be measured and available. For the localisation of such a network, the multi-dimensional scaling (MDS) algorithm can provide a relative localisation solution, but only a coarse solution when there are measurement errors. To bridge the gap in the localisation performance between the MDS-based and optimum network localisation, the authors propose an efficient subsequent refinement, that is, the iterative least square (LS)/weighted least square (WLS) refinement for the widely existing independent zero-mean Gaussian measurement errors. Analysis and simulation study show that with sufficiently small measurement errors the proposed improved network localisation scheme can achieve, in very limited iterations, the LS/WLS solution, which exhibits the localisation performance the same as the Cramer–Rao lower bound. The authors also extend the proposed refinement to the absolute localisation case with sufficient position-known anchors that are fully and directly connected to all sensors of the network. - Author(s): G. Chen and W.-P. Zhu
- Source: IET Signal Processing, Volume 6, Issue 2, p. 143 –147
- DOI: 10.1049/iet-spr.2010.0262
- Type: Article
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Denoising is a very important preprocessing step in signal/image processing. In this study, a new signal denoising algorithm is proposed by using neighbouring wavelet coefficients. The dual-tree complex wavelet transform is employed because of its property of approximate shift invariance, which is very important in signal denoising. Both translation-invariant (TI) and non-TI versions of the denoising algorithm are considered. Experimental results show that the proposed method outperforms other existing methods in the literature for denoising both artificial and real-life noisy signals. - Author(s): G. Liu ; X. Zeng ; Y. Liu
- Source: IET Signal Processing, Volume 6, Issue 2, p. 148 –158
- DOI: 10.1049/iet-spr.2010.0265
- Type: Article
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p.
148
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To address the drawbacks of continuous partial differential equations, a diffusion method based on spectral graph theory and random walk with restart kernel is proposed, which uses non-subsampled contourlet transform to capture the geometric feature of image. Specifically, a new graph weighting function is constructed based on the geometric feature. Moreover, a second-order random walk with restart kernel was generated. The derivation shows that the proposed method is equivalent to the denoising methods based on partial differential equations. The simulation results demonstrate that the proposed method can effectively reduce Gaussian noise and preserve image edge with superior performance compared with other graph-based partial differential equation methods. - Author(s): Y. Wang and Y.C. Jiang
- Source: IET Signal Processing, Volume 6, Issue 2, p. 159 –169
- DOI: 10.1049/iet-spr.2010.0208
- Type: Article
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p.
159
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A novel approach for inverse synthetic aperture radar (ISAR) imaging of three-dimensional (3-D) rotation target is presented in this study. It is based on the assumption that the manoeuverability is not too severe, and the received signal in a range bin can be characterised as multi-component linear frequency modulated (LFM) signal. The two-order match Fourier transform (TMFT) is used to estimate the parameters of the LFM signal, and the asymptotic statistical performance is analysed simultaneously. Combined with the Range-instantaneous-Doppler (RID) algorithm, the high-quality instantaneous ISAR images can be obtained. The results of simulated and real data demonstrate the effectiveness of the method proposed.
Robust equalisation for inter symbol interference communication channels
Efficient wavelet networks for function learning based on adaptive wavelet neuron selection
Identification of non-linear systems using radial basis function neural networks with time-varying learning algorithm
Automatic removal of ocular artefacts using adaptive filtering and independent component analysis for electroencephalogram data
Orthogonal perfect discrete Fourier transform sequences
Particle swarm optimisation particle filtering for dual estimation
Algebraic method for blind recovery of punctured convolutional encoders from an erroneous bitstream
Bridging gap between multi-dimensional scaling-based and optimum network localisation via efficient refinement
Signal denoising using neighbouring dual-tree complex wavelet coefficients
Image denoising by random walk with restart Kernel and non-subsampled contourlet transform
Inverse synthetic aperture radar imaging of three-dimensional rotation target based on two-order match Fourier transform
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