In this study, the authors investigate average capacity of free space optics communication over Malaga atmospheric turbulence channel with pointing errors and path loss, for intensity modulated/direct detection (IM/DD...
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In this study, the authors investigate average capacity of free space optics communication over Malaga atmospheric turbulence channel with pointing errors and path loss, for intensity modulated/direct detection (IM/DD) and heterodyne detection. Various algorithms which use adaptive transmission with both types of detection are considered, such as: optimal rate adaption (ORA), optimal power and rate adaption (OPRA), channel inversion with fixed rate (CIFR) and truncated channel inversion with fixed rate (TIFR). Analytical closed-form expressions for channel capacities of ORA, OPRA and TIFR adaptive transmission are presented, and the authors prove that CIFR transmission is not feasible in the strict sense for the conditions considered. Obtained analytical results are numerically evaluated and graphically presented for different strengths of atmospheric turbulence (in weak, moderate and strong turbulence regime) for both types of detection (IM/DD and heterodyne), and for considered algorithms of adaptive transmission (ORA, OPRA and TIFR). The authors have developed expressions suitable for approximating high signal-to-noise ratio channel capacity, and they graphically present and compare the asymptotic approximations with the obtained analytical results for different strengths of turbulence for both types of detection. Also, obtained analytical results were confirmed by Monte-Carlo simulations, and graphically compared for different strengths of turbulence regimes.
Stereophonic acoustic echo cancellation has generated much interest in recent years due to the nonuniqueness and misalignment problems that are caused by the strong interchannel signal coherence. In this paper, we int...
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Stereophonic acoustic echo cancellation has generated much interest in recent years due to the nonuniqueness and misalignment problems that are caused by the strong interchannel signal coherence. In this paper, we introduce a novel adaptive filtering approach to reduce interchannel coherence which is based on a selective-tap updating procedure. This tap-selection technique is then applied to the normalized least-mean-square, affine projection and recursive least squares algorithms for stereophonic acoustic echo cancellation. Simulation results for the proposed algorithms have shown a significant improvement in convergence rate compared with existing techniques.
We introduce a novel methodology for analysing well known classes of adaptive algorithms. Combining recent developments concerning geometric ergodicity of stationary Markov processes and long existing results from the...
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We introduce a novel methodology for analysing well known classes of adaptive algorithms. Combining recent developments concerning geometric ergodicity of stationary Markov processes and long existing results from the theory of Perturbations of Linear Operators we first study the behaviour and convergence properties of a class of products of random matrices, this is turn allows for the analysis of the first and second order statistics of adaptive algorithms without the need of any restrictive conditions imposed on the data las essential boundedness), Efficient estimates of the convergence rate of adaptive algorithms during the initial transient phase are also presented. These estimates do not rely on the unrealistic Independence Assumption as it is commonly the case in existing literature. (C) 1998 John Wiley & Sons, Ltd.
Two research subjects in geosciences which lately underwent significant progress are treated in this review. In the first part, we focus on one key ingredient for the numerical approximation of the Darcy flow problem,...
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Two research subjects in geosciences which lately underwent significant progress are treated in this review. In the first part, we focus on one key ingredient for the numerical approximation of the Darcy flow problem, namely the discretization of diffusion terms on general polygonal/polyhedral meshes. We present different schemes and discuss in detail their fundamental numerical properties such as stability, consistency, and robustness. The second part of the paper is devoted to error control and adaptivity for model problems in geosciences. We present the available a posteriori estimates guaranteeing the maximal overall error and show how the different error components can be identified. These estimates are used to formulate adaptive stopping criteria for linear and nonlinear solvers, time step choice adjustment, and adaptive mesh refinement. Numerical experiments illustrate such entirely adaptive algorithms.
The aim in blind source separation is to separate linear mixtures of statistically independent non-Gaussian signals without resorting to an a priori knowledge of the sources or the mixing system. In this paper we prop...
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The aim in blind source separation is to separate linear mixtures of statistically independent non-Gaussian signals without resorting to an a priori knowledge of the sources or the mixing system. In this paper we propose a new family of adaptive algorithms that recursively compute the optimum separating system. The algorithms are of the gradient ascent type and maximize a statistical criterion that involves only second- and fourth-order cumulants. We present a complete analysis of all the stationary points in the proposed criterion for an arbitrary number of complex sources. We demonstrate that the algorithms can only converge to points where perfect separation is achieved provided that the mixing system is a square invertible matrix and all the sources have the same kurtosis sign. We also prove that the criterion is free of undesirable maxima. (C) 1999 Elsevier Science B.V. All rights reserved.
Structural properties are examined of systems with physical component values as parameters. Both state variable realizations and transfer function descriptions are investigated. The transfer functions in particular ar...
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Structural properties are examined of systems with physical component values as parameters. Both state variable realizations and transfer function descriptions are investigated. The transfer functions in particular are shown to be the ratios of polynomials with coefficients multilinear in the parameters. These structures prove useful in formulating adaptive algorithms.
This paper presents a coordinated control of electronic stability control (ESC) and active front steering (AFS) with adaptive algorithms for yaw moment distribution in integrated chassis control (ICC). In order to dis...
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This paper presents a coordinated control of electronic stability control (ESC) and active front steering (AFS) with adaptive algorithms for yaw moment distribution in integrated chassis control (ICC). In order to distribute a control yaw moment into control tire forcres of ESC and AFS, and to coordinate the relative usage of ESC to AFS, a LMS/Newton algorithm (LMSN) is adopted. To make the control tire forces zero in applying LMS and LMSN, the zero-attracting mechanism is adopted. Simulations on vehicle simulation software, CarSimA (R), show that the proposed algorithm is effective for yaw moment distribution in integrated chassis control.
A new method to achieve updating formulas for adaptive filters with arbitrary structures is presented. The well known network sensitivity formula combined with Newton or gradient optimisation schemes gives the adaptiv...
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A new method to achieve updating formulas for adaptive filters with arbitrary structures is presented. The well known network sensitivity formula combined with Newton or gradient optimisation schemes gives the adaptive algorithms. This is illustrated with an adaptive wave digital filter. The derived algorithms use a gradient which is exact in the limit, as the step size tends to zero.
This study considers a commutation error (CE) that results from a difference associated with the altered sequence in real active noise control (ANC) applications as compared with that at the derivation stage. New adap...
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This study considers a commutation error (CE) that results from a difference associated with the altered sequence in real active noise control (ANC) applications as compared with that at the derivation stage. New adaptive algorithms are developed as FxLMS/CE, FxNLMS/CE and FxRLS/CE in an aim to eliminate the CE-associated disturbance and to liberate the restriction of slow adaptation imposed on the existing adaptive algorithms in the ANC applications. Computer simulations show that the rate of convergence is greatly improved for the new adaptive algorithms as compared with that of the conventional algorithms. In parallel with the improved rate of convergence, simulations exhibit efficient ANC performance for all CE-based algorithms. The best ANC performance is seen for FxRLS/CE algorithm that can acquire similar to 2 s of convergence rate and similar to 34 dB reduction of sound pressure level for band-limited white noise. All experimental results indeed demonstrate enhanced ANC performance;the FxNLMS/CE algorithm can acquire similar to 2 s of convergence rate and similar to 20 dB reduction of sound pressure level for band-limited white noise. Our data together support the effectiveness to include CE into the FIR filter-based adaptive algorithms for superior ANC performance with respect to the convergence speed and noise reduction level. (C) 2006 Elsevier Ltd. All rights reserved.
In this article, we introduce accelerated algorithms for. linear discriminant analysis (LDA) and feature extraction from unimodal multiclass Gaussian data. Current adaptive methods based on the gradient descent optimi...
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In this article, we introduce accelerated algorithms for. linear discriminant analysis (LDA) and feature extraction from unimodal multiclass Gaussian data. Current adaptive methods based on the gradient descent optimization technique use a fixed or a monotonically decreasing step size in each iteration, which results in a slow convergence rate. Here, we use a variable step size, optimally computed in each iteration using the steepest descent method, in order to accelerate the convergence of the algorithm. Based on the new adaptive algorithm, we present a self-organizing neural network for adaptive computation of the square root of the inverse covariance matrix (Sigma(-1/2)) and use it (i) in a network for optimal feature extraction from Gaussian data and (ii) in cascaded form with a principal component analysis network for LDA. Experimental results demonstrate fast convergence and high stability of the algorithm and justify its advantages for on-line pattern recognition applications with stationary and non-stationary input data. (C) 2003 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
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