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Signal Processing, IET

Issue 5 • Date Oct. 2010

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Displaying Results 1 - 14 of 14
  • Blind multiuser spreading sequences estimation algorithm for the direct-sequence code division multiple access signals

    Page(s): 465 - 478
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (634 KB)  

    By combining the multiple signal classification (MUSIC)-based algorithm, proposed by Haghighat and Soleymani, and the segmentation-based idea, a blind multiuser spreading sequences estimation algorithm for the direct-sequence code division multiple access (DS-CDMA) signals is presented in this study. The main idea of the proposed algorithm is to divide the spreading sequences into short-time segments so as to recover them in a sequential procedure. First, the received signal samples are divided into K collections of temporal windows. Each collection is corresponding to a group of short-time segments of the spreading sequences. Secondly, by performing a sub-segmentation-based estimation scheme on the first collection of temporal windows, the first short-time segments of all the users' spreading sequences are extracted. Finally, with the help of the segments acquired in advance, the remaining segments of the spreading sequences can be recovered chip-by-chip through K-1 subspace projection loops. This algorithm can be applied not only to the synchronous short-code (SC) DS-CDMA signals, but also to the long-code (LC) DS-CDMA signals in non-cooperative contexts, even with minus SNR. Simulations are presented to illustrate the performance of the proposed method. In addition, for the synchronous SC-DS-CDMA signals, the computational cost of the proposed algorithm is much lower than that of the MUSIC-based algorithm. Furthermore, a derivation for blind synchronisation of the synchronous SC-DS-CDMA signals is also presented. View full abstract»

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  • Classification of low level surface electromyogram using independent component analysis

    Page(s): 479 - 487
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (432 KB)  

    There is an urgent need for a simple yet robust system to identify natural hand actions and gestures for controlling prostheses and other computer-assisted devices. Surface electromyogram (SEMG) is a non-invasive measure of the muscle activities but is not reliable because there are a multiple simultaneously active muscles. This study proposes the use of independent component analysis (ICA) for SEMG to separate activity from different muscles. A mitigation strategy to overcome shortcomings related to order and magnitude ambiguity related to ICA has been developed. This is achieved by using a combination of unmixing matrix obtained from FastICA analysis and weight matrix derived from training of the supervised neural network corresponding to the specific user. This is referred to as ICANN (independent component analysis neural network combination). Experiments were conducted and the results demonstrate a marked improvement in the accuracy. The other advantages of this system are that it is suitable for real time operations and it is easy to train by a lay user. View full abstract»

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  • New approaches to finite impulse response systems identification using higher-order statistics

    Page(s): 488 - 501
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (314 KB)  

    In this study, new approaches for the identification of finite impulse response (FIR) systems using higher-order statistics are proposed. The unknown model parameters are obtained using optimisation algorithms. In fact, the proposed method consists first in defining an optimisation problem and second in using an appropriate algorithm to resolve it. Moreover, a new method is developed for estimating the order of FIR models using only the output cumulants. The results presented in this study illustrate the performance of the proposed methods and compare them with a range of existing approaches. View full abstract»

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  • Voice phishing detection technique based on minimum classification error method incorporating codec parameters

    Page(s): 502 - 509
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (482 KB)  

    The authors propose an effective voice phishing detection algorithm based on a Gaussian mixture model (GMM) employing the minimum classification error (MCE) technique. The detection of voice phishing is performed based on the GMM using decoding parameters of the 3GPP2 selectable mode vocoder (SMV) codec directly extracted from the decoding process of the transmitted speech information in the mobile phone. The authors' approach is further improved by the MCE scheme in that different weights are assigned to each likelihood ratio and is considered to be new. The experimental results show that the proposed method is effective in discriminating between true statements and lies. View full abstract»

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  • Brain-computer interface for single-trial eeg classification for wrist movement imagery using spatial filtering in the gamma band

    Page(s): 510 - 517
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (388 KB)  

    The aim of this study is to discriminate between the left and right wrist movements imagery in four different directions. To achieve this goal, the authors have applied spatial filtering on the EEG signal in the gamma frequency band to extract key features to perform classification. Specifically, the original EEG signal is transformed in to a spatial pattern and applied to the radial basis function (RBF) classifier. The authors demonstrate that spatial filtering method in multichannel EEG effectively extracts discriminant information from single-trial EEG for left and right wrist movement imagery. An average recognition rate of approximately 89% was achieved in all the four type of movements (extension, flexion, pronation and supination) between left and right wrist in five healthy subjects. The results are comparable to the highest rates reported in the literature. View full abstract»

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  • Fault detection of a vibration mechanism by spectrum classification with a divergence-based kernel

    Page(s): 518 - 529
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (639 KB)  

    The present study describes a frequency spectrum classification method for fault detection of the LP gas pressure regulator using support vector machines (SVMs). Conventional diagnosis methods are inefficient because of problems such as significant noise and non-linearity of the detection mechanism. In order to address these problems, a machine learning method with a divergence-based kernel is introduced into spectrum classification. The authors use the normalised frequency spectrum directly as input with the divergence-based kernel. The proposed method is applied to the vibration spectrum classification of the rubber diaphragm in a pressure regulator. As a result, the classification performance using the divergence-based kernel is shown to be better than when using common kernels such as the Gaussian kernel or the polynomial kernel. The high classification performance is achieved by using an inexpensive sensor system and the machine learning method. The proposed method is widely applicable to other spectrum classification applications without limitation on the generality if the spectra are normalised. View full abstract»

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  • Adaptive sensor selection in wireless sensor networks for target tracking

    Page(s): 530 - 536
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (346 KB)  

    Sensor selection in wireless sensor networks (WSN) is considered for target tracking. A decentralised estimation scenario is considered based on the extended information filter. A cost function using the geometrical dilution of precision (GDOP) measure is derived for active sensor selection. Accordingly, a new adaptive sensor selection (ASS) algorithm is proposed in which the number of active sensors is adaptively determined based on the absolute local innovations vector. Simulation results show that the tracking accuracy of the ASS is comparable to that of the other algorithms, but with less energy consumption. View full abstract»

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  • Z-domain counterpart to prony's method for exponential-sinusoidal decomposition

    Page(s): 537 - 547
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (530 KB)  

    Prony's method has applications in exponential sinusoidal modelling, parametric modelling, filter design, system modelling and system identification. Similarly to Pade' approximation, Prony's method and refinements thereof are major tools for statistical signal analysis, system auto-regressive moving average (ARMA) modelling and least-squares digital filter design. In this study, a z-domain counterpart to Prony's method is proposed as a spectral analysis approach to exponential-sinusoidal decomposition in the presence of noise contamination. The approach is particularly effective in the case where the signal components have 'well behaved' frequencies, meaning that they are multiples of the fundamental frequency. Spectral weighting is applied to power spectra over the z-plane. Spectral peaks of signals contaminated by noise are used to estimate the amplitude, frequency, damping and phase of damped sinusoidal components. The present approach requires no a priori knowledge of the number of damped sinusoidal components present in the contaminated signal, and hence no knowledge of the system order. As expected, however, the analysed signal duration should be long enough to reveal signal properties in the presence of noise. In the case where signal components are not well behaved, spectral leakage would necessitate windowing and higher resolution frequency analysis in order to identify the successive components with improved accuracy. View full abstract»

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  • Joint quantisation strategies for low bit-rate sinusoidal coding

    Page(s): 548 - 559
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (470 KB)  

    Although there are speech coding standards producing high-quality speech above 4 kbps, below that transparent quality has not been achieved yet. There is still room for improvement at lower bit rates, especially at 2.4 kbps and below, which is an area of interest for military and security applications. Strategies for achieving high-quality speech using sinusoidal coding at very low bit rates are discussed. Previous work in the literature on combining several frames in a metaframe and performing variable bit allocation within the metaframe is extended. Experiments have been carried out to find an optimum metaframe size compromise between delay and quantisation gains. Metaframe classification and quantisation according to the metaframe class are used for better efficiency. A method for voicing determination from the linear prediction coefficient (LPC) shape is also presented. The proposed techniques have been applied to the SB-LPC vocoder to produce speech at 1.2 and 0.8 kbps, and compared to the original SB-LPC vocoder at 2.4/1.2 kbps as well as an established standard (Mixed Excitation Linear Predictive - MELP - vocoder) at 2.4/1.2/0.6 kbps in a listening test. It has been found that the proposed techniques have been effective in reducing the bit rate while not compromising the speech quality. View full abstract»

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  • Novel class of stable wideband recursive digital integrators and differentiators

    Page(s): 560 - 566
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (487 KB)  

    Designs of wideband recursive digital integrators and differentiators are presented. The integrators are obtained by interpolating some of the popular digital integration techniques. The procedure consists of obtaining an integrator and then modifying its transfer function appropriately to get a stable differentiator. The proposed integrators and differentiators accurately approximate the ideal integrator and ideal differentiator over the whole Nyquist frequency range and compare favourably with the existing integrators and differentiators. The suggested integrators and differentiators are of third-order and highly suitable for real-time applications, where linear phase property is not necessary. View full abstract»

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  • Family of affine projection adaptive filters with selective partial updates and selective regressors

    Page(s): 567 - 575
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (517 KB)  

    In this study the concepts of selective partial updates (SPU) and selective regressors (SR) in the affine projection adaptive filtering algorithm are combined and the family of affine projection algorithms (APAs) with SPU and SR features are established. These algorithms are computationally efficient. The mean-square performance of the presented algorithms are analysed based on the energy conservation arguments of Sayed's group. This analysis does not need to assume a Gaussian or white distribution for the regressors. The authors demonstrate the performance of the presented algorithms through simulations. The good agreement between theoretically predicted and actually observed performances is also demonstrated. View full abstract»

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  • Energy-proportion based scheme for audio watermarking

    Page(s): 576 - 587
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (360 KB)  

    This study presents a audio watermarking that embeds information by energy-proportion scheme. By using normalised energy instead of probability, this study rewrites the entropy in information theory as an energy-proportion function. In order to guarantee the robustness of watermarks, low-frequency coefficients of discrete wavelet transform (DWT) are mapped into the domain of this function. Then, the characteristic curve of energy-proportion function (CCEP) is obtained. Moreover, some characteristics and properties of this function are analysed and proved. Based on CCEP and the properties of energy-proportion function, this study proposes a novel audio watermarking scheme. In addition, the proposed energy-proportion scheme can extract the watermark without original audio signal. Finally, performance of the proposed scheme is assessed by signal-to-noise ratio (SNR), mean opinion score, embedding capacity and BER. Experimental results demonstrate that the embedded data are robust against most signal processing and attacks, such as re-sampling, MP3 compression, low-pass filtering and amplitude scaling. View full abstract»

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  • Modelling cardiovascular physiological signals using adaptive hermite and wavelet basis functions

    Page(s): 588 - 597
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (663 KB)  

    This study presented a unified perspective of adaptive basis functions to compare Hermite decomposition and wavelet transform for the analysis of cardiovascular physiological signals. Three different algorithms were presented to carry out physiological signal modelling with adaptive Hermite basis functions (HBFs), orthonormal wavelet basis functions (OWBFs) and adaptive wavelet basis functions (AWBFs). The modelling with OWBFs is computationally efficient. However, the concomitant restrictions in mathematics make OWBFs not optimal for compact modelling. In contrast, the optimised AWBFs can model cardiovascular physiological signals compactly with the cost of losing orthonormality. It not only sacrifices the fast implementation but also degrades AWBFs in discriminant analysis. In summary, merely HBFs achieve a balanced performance in compact modelling and discriminant analysis. View full abstract»

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  • Channels estimation in OFDM system over rician fading channel based on comb-type pilots arrangement

    Page(s): 598 - 602
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (207 KB)  

    Orthogonal frequency-division multiplexing (OFDM) is a transmission technique that is based on many orthogonal carriers that are transmitted simultaneously. Channel estimation techniques for OFDM systems, based on comb-type pilot arrangement, over frequency-selective Rician and time-varying fading channel are investigated. The advantage of comb-type pilot arrangement, in channel estimation, is the ability to track the variation in the channel, which is the main reason for inter-carrier interference modelled as an additive white Gaussian noise, leading to an increase in the noise level. The estimation of the channel at the pilot frequencies is based on least-square (LS) method. Several interpolation methods have been used to estimate the channel response at the data frequency. In this study, simulation results for Rician channel model were examined and bit error rate/signal-to-noise ratio performance, for various conditions, were considered, and the pilot arrangement was used with the LS estimation and low-pass interpolation techniques, leading to a reduction in the effect of Doppler frequency. View full abstract»

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