A frequency estimator based on the linear prediction property of sinusoidal signals has recently been proposed for the direct estimation of real tone frequency in white noise. We propose a variable step-size direct fr...
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A frequency estimator based on the linear prediction property of sinusoidal signals has recently been proposed for the direct estimation of real tone frequency in white noise. We propose a variable step-size direct frequency estimator (VS-DFE), which shows improved performance on account of the use of a dynamically updated step-size instead of a fixed step-size. A comparison of the simulation results of the proposed estimator with those of conventional estimators clearly indicates its superior performance. (C) 2010 Elsevier B.V. All rights reserved.
作者：
Ghazi, GeorgesGerardin, BenoitGelhaye, MagaliBotez, Ruxandra MihaelaUniv Quebec
Lab Appl Res Act Control Avion & AeroServoElast Ecole Technol Super 1100 Notre Dame West Montreal PQ H3C 1K3 Canada Univ Quebec
Lab Appl Res Act Control Avion & AeroServoElast Ecole Technol Super Aircraft Modeling & Simulat Level 11100 Notre Dame West Montreal PQ H3C 1K3 Canada
To compute the most efficient route that the aircraft has to fly, the flight management system (FMS) needs a mathematical representation of the aircraft performance. However, after several years of operation, various ...
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To compute the most efficient route that the aircraft has to fly, the flight management system (FMS) needs a mathematical representation of the aircraft performance. However, after several years of operation, various factors can degrade the overall performance of the aircraft. Such degradation can affect the reliability of the aircraft model, and the crew would lose confidence in the fuel planning estimated by the FMS. This paper presents the results of a study in which a new adaptive algorithm is proposed for continuously updating the FMS performance model using cruise flight data. The proposed algorithm combines aircraft performance monitoring techniques with adaptive lookup tables to model the aerodynamic characteristics of the aircraft. The methodology was applied to the well-known Cessna Citation X business aircraft, for which a research aircraft flight simulator was available. The development of this methodology was accomplished by creating an initial performance model, adapting it using flight data in cruise, and finally comparing its prediction with a series of flight data collected with the flight simulator. Results have shown that the proposed methodology was able to reduce fuel flow prediction mean errors by about 5%, whereas the standard deviation was reduced by a factor of 3.4.
The Markovian model has generally been used for cardiac electrophysiological simulations. However, the Markovian model is so stiff that speeding up the computation of the algorithms with variable time-steps always res...
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The Markovian model has generally been used for cardiac electrophysiological simulations. However, the Markovian model is so stiff that speeding up the computation of the algorithms with variable time-steps always results in simulation instability. In particular, the unstable simulations always occur at a low voltage rate or current change, while transition rates in the Markovian model are changing markedly. The uniformization (UNI) method allows for a Markovian model simulation with high stability but also a high computation cost. To save computation costs with variable time-steps, we propose a speed increasing idea that is a compromise to the trade-off between stability and acceleration by combining Chen-Chen-Luo's quadratic adaptive algorithm (CCL) method with hybrid operator splitting (HOS) into the solver (CCL + HOS solver). The computation cost of this CCL + HOS solver is approximately 24 times lower than the CCL + UNI solver, and the CCL + HOS solver can function 295 times faster in comparison to the HOS solver with a fixed time-step (DT). The suggested optimal solver should be CCL + HOS solver with a maximum time-step at 0.1ms due to its high speed with low error. Additionally, the CCL method has much better performance and stability than the hybrid method in this single-cell model simulation.
In this paper, a new adaptive algorithm for minor component analysis is proposed. The (PASTd) projection approximation subspace tracking with de deflation algorithm for principal component analysis by Yang is modified...
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In this paper, a new adaptive algorithm for minor component analysis is proposed. The (PASTd) projection approximation subspace tracking with de deflation algorithm for principal component analysis by Yang is modified by putting the minus sign to the adaptive gain with orthonormalization. Using the ODE (ordinary differential equation) approach, the convergent point of the algorithm is shown to be the desired one. Simulation results show that the proposed algorithm has better estimation accuracies than the existing adaptive algorithm. (C) 1998 Elsevier Science B.V. All rights reserved.
A scheme is presented for obtaining an input power estimate for setting the algorithm gain parameter μ separately in each frequency bin in the frequency domain LMS adaptive algorithm. This is particularly important i...
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A scheme is presented for obtaining an input power estimate for setting the algorithm gain parameter μ separately in each frequency bin in the frequency domain LMS adaptive algorithm. This is particularly important if the input has large spectral variations, and a single feedback parameter, set on the broad-band power, could result in instability in the adaptive filters in some frequency bins. The estimate is incorporated directly into the algorithm as a data dependent time-varying stochastic μ(n). Using a Gaussian data model and sample-to-sample data independence, first-order linear difference equations are derived and solved for the mean and misadjustment errors. The performance of the scheme is compared to the case for which the input power level is known a priori. For the same transient response, only about ten samples need be averaged to yield the same misadjustment error.
The transmission eigenvalue problem arising from the inverse scattering theory is of great importance in the theory of qualitative methods and in the practical applications. In this paper, we study the FEM for the tra...
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The transmission eigenvalue problem arising from the inverse scattering theory is of great importance in the theory of qualitative methods and in the practical applications. In this paper, we study the FEM for the transmission eigenvalue problem for anisotropic inhomogeneous media in omega subset of sigma l ������ (������ = 2, 3). Using the T-coercivity and the spectral approximation theory, we derive an a posteriori estimator of residual type and prove its effectiveness and reliability for eigenfunctions. In addition, we also prove the reliability of the estimator for transmission eigenvalues. The numerical experiments indicate our method is efficient and can reach the optimal order ������������������-2 ������/������ by using piecewise polynomials of degree ������ for real and complex eigenvalues.
This paper presents an adaptive algorithm in the time domain for the dynamic analysis of a simply supported beam subjected to the moving load and moving vehicle with/without varying surface roughness. By expanding var...
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This paper presents an adaptive algorithm in the time domain for the dynamic analysis of a simply supported beam subjected to the moving load and moving vehicle with/without varying surface roughness. By expanding variables at a discretized time interval, a coupled spatial-temporal problem can be converted into a series of recursive space problems that are solved by finite element method (FEM), and a piecewised adaptive computing procedure can be carried out for different sizes of time steps. The proposed approach is numerically verified via the comparison with analytical and the Runge-Kutta method-based solutions, and satisfactory results have been achieved. Copyright (C) 2011 John Wiley & Sons, Ltd.
We present a general block formulation of the least mean square (LMS) algorithm for adaptive filtering. This formulation has an exact equivalence with the original LMS algorithm, hence retaining the same convergence p...
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We present a general block formulation of the least mean square (LMS) algorithm for adaptive filtering. This formulation has an exact equivalence with the original LMS algorithm, hence retaining the same convergence properties, while allowing a reduction in arithmetic complexity, even for very small block lengths. Working with small block lengths is very interesting from an implementation point of view (large blocks result in large memory and large system delay) and, nevertheless, allows a significant reduction in the number of operations. Furthermore, trade-offs between a number of operations and a convergence rate are obtainable, by applying certain approximations to a matrix involved in the algorithm. Hence, the usual block LMS (BLMS) appears as a special case, which explains its convergence behavior according to the type of input signal (correlated or uncorrelated).
In this paper, we propose a medium access control (MAC) protocol for multimedia code division multiple access (CDMA) communications. In the proposed protocol, a base station (BS) estimates the instantaneous number of ...
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In this paper, we propose a medium access control (MAC) protocol for multimedia code division multiple access (CDMA) communications. In the proposed protocol, a base station (BS) estimates the instantaneous number of simultaneously transmitted packets in the future slots with exploiting a stochastic property of traffic. In order to carry out this estimation, we employ an adaptive algorithm. We evaluate the performance of the proposed protocol by comparing that with two different cases. One is no estimation case and the other is perfect estimation case. From these results, we clarify the advantage of the proposed MAC protocol.
When taking images in low light conditions, images often suffer from low visibility. In addition to affecting the sensory quality of images, this poor quality may also significantly limit the performance of various co...
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When taking images in low light conditions, images often suffer from low visibility. In addition to affecting the sensory quality of images, this poor quality may also significantly limit the performance of various computer vision systems. Many grey-level mapping enhancement algorithms based on classic mapping functions, such as the gamma mapping function, have been proposed in recent years to improve the visual quality of low-light images. However, the classic mapping function cannot coordinate the greyscale distribution of the bright and dark areas of the image well and may easily lead to excessive enhancement. This makes it difficult for the performance of these improved algorithms to be fully utilized. Therefore, this paper proposes a new multiparameter grey mapping method. Unlike the classic mapping function, the new mapping method is based on the enhancement strategy of compressing the bright area and then adjusting the dark area. Thus, the inherent shortcomings of the classic mapping function are fundamentally overcome. The new mapping method can not only directly control the compression of the grey space in the bright area of the image through parameters, but it can also adjust the greyscale distribution of dark areas without changing the greyscale value of the pixels in the bright area. Finally, this paper also designs an adaptive enhancement algorithm with the new mapping method as the core to verify its effectiveness and flexibility. Experimental results showed that the adaptive algorithm had excellent performance in colour rendering, brightness enhancement and noise suppression. It was also obviously better than the current similar algorithms in visual quality and quantitative tests.
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