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Signal Processing, IET

Issue 9 • Date Dec. 2012

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Displaying Results 1 - 7 of 7
  • Multiuser modulation classification based on cumulants in additive white Gaussian noise channel

    Page(s): 815 - 823
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (498 KB)  

    In this study the negative impacts of interference transmitters on automatic modulation classification (AMC) have been discussed. The authors proposed two approaches for AMC in the presence of interference: single user modulation classification (SUMC) and multiuser modulation classification (MUMC). When the received power of one transmitter is larger than the other transmitters, SUMC approach recognises the modulation type of the dominant transmitter and other transmitters are treated as interferences. Alternatively when the received powers of all the transmitters are close to each other the authors propose MUMC method to recognise the modulation type of all of the transmitted signals. The features being used to recognise the modulation types of transmitted signals for the both approaches, SUMC and MUMC are higher order cumulants. The superposition property of cumulants for independent random variables is utilised for SUMC and MUMC. The authors investigated the robustness of their classifier with respect to different powers of the received signals via analytical and simulation results and the authors have shown the analytical results will be confirmed by simulations. Also the authors studied the effects of signal synchronisation error, phase jitter and frequency offset on their proposed methods via simulations in the both condition for MUMC and SUMC. View full abstract»

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  • Combining log-spectral mean subtraction at different frequency resolutions for handset-channel compensation in single utterance speaker verification

    Page(s): 824 - 828
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (230 KB)  

    Cepstral mean subtraction (CMS) is a well-known feature domain channel compensation technique employed to eliminate the effects of convolutive channel distortion. However, as the authors use in log-spectral mean subtraction (LSMS), the compensation might be applied in spectral domain before the filter-bank analysis with a higher-frequency resolution. LSMS can also be combined with CMS to further improve the recognition performance. In this study, the authors compare the performances of LSMS and CMS methods using a multi-channel, text-dependent single utterance speaker recognition database. In the experiments, the authors observe that LSMS outperforms CMS especially in the high false acceptance region. Moreover, the accuracy is further improved when the methods are combined together. With the combination, the authors achieve 15.5% relative reduction in equal error rate for no score normalisation and 9.4% for test normalisation cases when compared with the baseline CMS experiment. View full abstract»

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  • Automatic feature extraction using generalised autoregressive conditional heteroscedasticity model: an application to electroencephalogram classification

    Page(s): 829 - 838
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (489 KB)  

    Nearly 1% of the world population in all ages suffer from epilepsy. An automatic seizure detection system is an important tool, which helps diagnose epilepsy. In this study, a new approach for epileptic detection based on generalised autoregressive conditional heteroscedasticity (GARCH) model is proposed. First, the electroencephalogram (EEG) signals are decomposed into the frequency sub-bands using wavelet transform. To choose an efficient statistical model for EEG signal wavelet coefficients, the statistical characteristics of these coefficients are studied. The authors show that these coefficients are heteroscedastic. To capture this important property, GARCH model, that is, heteroscedastic, is employed for these coefficients. Moreover, the authors show that GARCH model is compatible with other properties of wavelet coefficients such as heavy tail marginal distribution. GARCH parameters are calculated for each sub-band to represent the wavelet coefficients% distribution of EEG signals. These parameters are then utilised for EEG classification. Next, Markov random field is used to feature selection. The features found are then fed to multilayer perceptron classifier with three discrete outputs: healthy volunteers, epilepsy patients during seizure-free interval and epilepsy patients during seizure. The results clearly indicate that the performance of the new method in classification of EEG signals outperforms previous methods. View full abstract»

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  • Optimal linear estimation for systems with multiplicative noise uncertainties and multiple packet dropouts

    Page(s): 839 - 848
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (534 KB)  

    This study is concerned with the optimal linear estimation problem for linear discrete-time stochastic systems with multiplicative noise uncertainties in state and measurement matrices and with multiple packet dropouts from a sensor to an estimator. Based on the projection theory, the optimal linear estimators including filter, predictor and smoother are derived in the linear minimum variance sense. In the absence of stochastic uncertainties and/or packet dropouts, the corresponding results can be obtained as the special cases of the proposed estimators. Steady-state property is also analysed. A sufficient condition for the existence of the steady-state estimators is obtained. They can be computed offline. So they have the reduced online computational cost. Simulation examples are given to demonstrate the effectiveness of the proposed estimators. View full abstract»

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  • Effects of trends and seasonalities on robustness of the Hurst parameter estimators

    Page(s): 849 - 856
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (634 KB)  

    Long-range dependence (LRD) is discovered in time series arising from different fields, especially in network traffic and econometrics. Detecting the presence and the intensity of LRD plays a crucial role in time-series analysis and fractional system identification. The existence of LRD is usually indicated by the Hurst parameters. Up to now, many Hurst parameter estimators have been proposed in order to identify the LRD property involved in a time series. Since different estimators have different accuracy and robustness performances, in this study, 13 most popular Hurst parameter estimators are summarised and their estimation performances are investigated. LRD processes with known Hurst parameters are generated as the control data set for the robustness evaluation. In addition, three types of LRD processes are also obtained as the test signals by adding noises in terms of means, trends and seasonalities to the control data set. All 13 Hurst parameter estimators are applied to these LRD processes to estimate the existing Hurst parameters. The estimation results are documented and quantified by the standard errors. Conclusions of the accuracy and robustness performances of the estimators are drawn by comparing the estimation results. View full abstract»

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  • Fast and efficient multidimensional scaling algorithm for mobile positioning

    Page(s): 857 - 861
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (206 KB)  

    Mobile station (MS) localisation that plays an important role in the process of target continuous localisation has received considerable attention. In this study, a new framework based on subspace approach for positioning an MS at minimum localisation system with the use of time-of-arrival measurements is introduced. Unlike ordinary multidimensional scaling algorithm using eigendcomposition or inverse computation to estimate the MS position, a computationally simple weighting estimator is proposed by introducing Lagrange multiplier and mean-square error weighting matrix. Computer simulations are included to corroborate the theoretical development and to contrast the estimator performance with several conventional algorithms as well as the CraméŕRao lower bound (CRLB). It is shown that the new method with low computational complexity attains the CRLB for zero-mean white Gaussian range error at moderate noise level. View full abstract»

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  • Medical image denoising on field programmable gate array using finite Radon transform

    Page(s): 862 - 870
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (750 KB)  

    This study presents the design and implementation of efficient architectures for finite Radon transform (FRAT) on a field programmable gate array (FPGA). FPGA-based architectures with two design strategies have been proposed: direct implementation of pseudo-code with a sequential or pipelined description, and a block random access memory-based approach. Various medical images modalities have been deployed for both software evaluation and hardware implementation. Xilinx DSP tool has been used to improve the implementation time and reduce the design cycle and the Xilinx software has been used for generating a hardware description language from a high-level MATLAB description. Objective evaluation of image denoising using FRAT is carried out and demonstrates promising results. Moreover, the impact of different block sizes on image reconstruction has been analysed. Performance analysis in terms of area, maximum frequency and throughput is presented and reveals significant achievements. View full abstract»

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