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Signal Processing, IET

Issue 5 • Date August 2011

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Displaying Results 1 - 9 of 9
  • Comments on 'Three uncertainty relations for real signals associated with linear canonical transform'

    Page(s): 441 - 442
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (79 KB)  

    It is the purpose of this note to point out that there could be some exceptions to the uncertainty relations for real signals associated with linear canonical transform. View full abstract»

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  • Modulation classification of linearly modulated signals in slow flat fading channels

    Page(s): 443 - 450
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (417 KB)  

    In this study, the authors study the modulation classification of linearly modulated signals including amplitude shift keying (ASK), phase shift keying (PSK) and quadrature amplitude modulation (QAM) signals. The authors consider an unknown frequency non-selective slowly fading channel with an unknown variance additive white Gaussian noise. The authors treat this classification problem as a multi-hypotheses test which is invariant under the complex scale. In such a case, the authors objective is to find uniformly most powerful (UMP) test in the class of invariant decisions. However, the authors find out that the UMPI test does not exist; instead, they provide a most powerful invariant (MPI) PSK signal classifier for known signal to noise ratio and use it as the upper performance bound for any invariant classifier. The authros also propose a hybrid likelihood ratio test (HLRT) solution which can be employed for the classification of linearly modulated signals, inter-family and intra-family. The authors also explain the efficient implementation of these algorithms in some steps. In order to reduce the computational cost, the authors propose quasi-HLRT classifiers for PSK signals. Some simulation examples are provided that show the power of the proposed algorithms in the classification of linearly modulated signals. View full abstract»

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  • Grassmannian beamforming and null space broadcasting protocols for cognitive radio networks

    Page(s): 451 - 460
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (637 KB)  

    Novel Grassmannian beamforming techniques based on two null space broadcasting protocols are proposed for a cognitive radio network. A secondary user basestation (SBS) with multiple secondary users sharing the primary user spectrum is considered. A protocol for feeding back the channel state information (CSI) under the constraint on the availability of a finite number of bits for feedback is proposed. The SBS informs all the secondary users of the null space of the channel matrices seen between itself and the primary users. The secondary users choose the best beamforming vectors from a Grassmannian codebook as those that are closer to the null space of the primary user channels and those that maximise their projection onto the signal space of the corresponding secondary users. An improvement on the ergodic channel capacity has been observed. The interference leakage to the primary users owing to the quantisation of the CSI has been analytically quantified and verified using simulation results. View full abstract»

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  • Analysis and investigation of emotion identification in biased emotional talking environments

    Page(s): 461 - 470
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (347 KB)  

    Speakers usually use certain words more frequently in expressing their emotions since they have learned the connection between certain words and their corresponding emotions. The work of this research is devoted to the analysis and investigation of emotion identification in two separate and different talking environments based on classifiers called suprasegmental hidden Markov models. The first talking environment is unbiased towards any emotional state, whereas the second talking environment is biased towards different emotional states. Each emotional talking environment is composed of six emotions. The results of this work show that emotion identification performance in the second talking environment outperforms that in the first talking environment. Based on subjective assessment by human judges, emotion identification performance in the biased talking environment leads that in the unbiased talking environment. View full abstract»

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  • Dual optimal filters for parameter estimation of a multivariate autoregressive process from noisy observations

    Page(s): 471 - 479
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (364 KB)  

    This study deals with the estimation of a vector process disturbed by an additive white noise. When this process is modelled by a multivariate autoregressive (M-AR) process, optimal filters such as Kalman or H filter can be used for prediction or estimation from noisy observations. However, the estimation of the M-AR parameters from noisy observations is a key issue to be addressed. Off-line or iterative approaches have been proposed recently, but their computational costs can be a drawback. Using on-line methods such as extended Kalman filter and sigma-point Kalman filter are of interest, but the size of the state vector to be estimated is quite high. In order to reduce this size and the resulting computational cost, the authors suggest using dual optimal filters. In this study, the authors propose to extend to the multi-channel case the so-called dual Kalman or H filters-based scheme initially proposed for single-channel applications. The proposed methods are first tested with a synthetic M-AR process and then with an M-AR process corresponding to a mobile fading channel. The comparative simulation study the authors carried out with existing techniques confirms the effectiveness of the proposed methods. View full abstract»

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  • Sparsity regularised recursive least squares adaptive filtering

    Page(s): 480 - 487
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (477 KB)  

    The authors propose a new approach for the adaptive identification of sparse systems. This approach improves on the recursive least squares (RLS) algorithm by adding a sparsity inducing weighted ℓ1 norm penalty to the RLS cost function. Subgradient analysis is utilised to develop the recursive update equations for the calculation of the optimum system estimate, which minimises the regularised cost function. Two new algorithms are introduced by considering two different weighting scenarios for the ℓ1 norm penalty. These new ℓ1 relaxation-based RLS algorithms emphasise sparsity during the adaptive filtering process, and they allow for faster convergence than standard RLS when the system under consideration is sparse. The authors test the performance of the novel algorithms and compare it with standard RLS and other adaptive algorithms for sparse system identification. View full abstract»

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  • Adaptive decision-feedback generalised sidelobe canceller against desired signal and interference non-stationarity

    Page(s): 488 - 498
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (968 KB)  

    Adaptive generalised sidelobe canceller (GSC) implemented with the least-mean-square (LMS) algorithm is a well-proven approach to effectively suppress unwanted interference. However, when the directions-of-arrival (DOAs) of the desired signal and interference sources change over time, its performance may be largely degraded because of the fact that not only the interference-cancelling filter of GSC does not properly cancel the `moving` interference, but also both the signal-matched filter and blocking matrix of GSC deviate from their best states for the `moving` desired signal. Recently, a robust adaptive decision-feedback (DF) GSC has been proposed to partly solve the problem, but if the DOAs of all sources vary significantly and continuously, the adaptive DFGSC will eventually lose track of the source motion. In this study, the authors present a new modification for the DFGSC structure, which provides additional robustness in this kind of non-stationary signal environment. Specifically, they extend the use of Householder transformation together with the LMS algorithm for DFGSC, and a new update method is introduced for the whole beamforming processor. In addition, the tracking behaviour of this specially designed adaptive structure is studied and analysed, leading to the derivation of optimal step size for the interference-cancelling filter. Simulation results show that this modified adaptive DFGSC is very effective in mitigating the described non-stationary scenario. View full abstract»

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  • New efficient window function, replacement for the hamming window

    Page(s): 499 - 505
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (621 KB)  

    A new simple window function is presented, which for the same window order (M), has a main-lobe width less than or equal to that of the Hamming window, while offering about 2-4.5-dB smaller peak side-lobe amplitude. Furthermore, just like the Hamming window, it is computationally efficient for signal spectrum analysis; this is because of the fact that, the sum of window coefficients with its shifted version by M/2 samples (i.e. 50% overlap) is constant for the overlapped region. The new window is obtained by adding the third harmonic of the cosine function to the Hamming window, and finding the appropriate amplitudes of DC term, cosine function, and its third harmonic to minimise the peak side-lobe amplitude. A comparison with the Kaiser and Dolph-Chebyshev windows is also performed. Finite impulse response filters designed by windowing method show the efficiency of the new window. View full abstract»

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  • Evaluation of multiclass support vector machine classifiers using optimum threshold-based pruning technique

    Page(s): 506 - 513
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (375 KB)  

    Support vector machine (SVM) is the state-of-the-art classifier used in real world pattern recognition applications. One of the design objectives of SVM classifiers using non-linear kernels is reducing the number of support vectors without compromising the classification accuracy. To meet this objective, decision-tree approach and pruning techniques are proposed in the literature. In this study, optimum threshold (OT)-based pruning technique is applied to different decision-tree-based SVM classifiers and their performances are compared. In order to assess the performance, SVM-based isolated digit recognition system is implemented. The performances are evaluated by conducting various experiments using speaker-dependent and multispeaker-dependent TI46 database of isolated digits. Based on this study, it is found that the application of OT technique reduces the minimum time required for recognition by a factor of 1.54 and 1.31, respectively, for speaker-dependent and multispeaker-dependent cases. The proposed approach is also applicable for other SVM-based multiclass pattern recognition systems such as target recognition, fingerprint classification, character recognition and face recognition. View full abstract»

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