The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform
S. Golden - One of the best experts on this subject based on the ideXlab platform.
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Maximum likelihood estimation, analysis, and applications of Exponential Polynomial signals
IEEE Transactions on Signal Processing, 1999Co-Authors: S. Golden, Benjamin FriedlanderAbstract:We model complex signals by approximating the phase and the logarithm of the time-varying amplitude of the signal as a finite order Polynomial. We refer to a signal that has this form as an Exponential Polynomial signal (EPS). We derive an iterative maximum-likelihood (ML) estimation algorithm to estimate the unknown parameters of the EPS model. The initialization of the ML algorithm can be performed by using the result of a related paper. A statistical analysis of the ML algorithm is performed using a finite-order Taylor expansion of the mean squared error (MSE) of the estimate about the variance of the additive noise. This perturbation analysis gives a method of predicting the MSE of the estimate for any choice of the signal parameters. The MSE from the perturbation analysis is compared with the MSE from a Monte Carlo simulation and the Cramer-Rao Bound (CRB). The CRB for this model is also derived.
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Estimation and statistical analysis of Exponential Polynomial signals
IEEE Transactions on Signal Processing, 1998Co-Authors: S. Golden, Benjamin FriedlanderAbstract:We derive and analyze a suboptimal algorithm for estimating the unknown parameters of an Exponential Polynomial signal (EPS) model. The EPS model represents a signal as an exponent raised to a complex Polynomial of time. That is, the phase and the logarithm of the amplitude are approximated as finite-order Polynomials of time.
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ICASSP - Estimation and statistical analysis for Exponential Polynomial signals
1995 International Conference on Acoustics Speech and Signal Processing, 1Co-Authors: S. GoldenAbstract:In this paper we approximate arbitrary complex signals by modeling both the logarithm of the amplitude and the phase of the complex signal as finite-order Polynomials in time. We refer to a signal of this type as an Exponential Polynomial signal (EPS). We propose an algorithm to estimate any desired coefficient for this signal model. We also show how the mean-squared error of the estimate can be determined by using a first-order perturbation analysis. A Monte Carlo simulation is used to verify the validity of the perturbation analysis. The performance of the algorithm is illustrated by comparing the mean-squared error of the estimate to the Cramer-Rao bound for a particular example.
Benjamin Friedlander - One of the best experts on this subject based on the ideXlab platform.
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Maximum likelihood estimation, analysis, and applications of Exponential Polynomial signals
IEEE Transactions on Signal Processing, 1999Co-Authors: S. Golden, Benjamin FriedlanderAbstract:We model complex signals by approximating the phase and the logarithm of the time-varying amplitude of the signal as a finite order Polynomial. We refer to a signal that has this form as an Exponential Polynomial signal (EPS). We derive an iterative maximum-likelihood (ML) estimation algorithm to estimate the unknown parameters of the EPS model. The initialization of the ML algorithm can be performed by using the result of a related paper. A statistical analysis of the ML algorithm is performed using a finite-order Taylor expansion of the mean squared error (MSE) of the estimate about the variance of the additive noise. This perturbation analysis gives a method of predicting the MSE of the estimate for any choice of the signal parameters. The MSE from the perturbation analysis is compared with the MSE from a Monte Carlo simulation and the Cramer-Rao Bound (CRB). The CRB for this model is also derived.
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Estimation and statistical analysis of Exponential Polynomial signals
IEEE Transactions on Signal Processing, 1998Co-Authors: S. Golden, Benjamin FriedlanderAbstract:We derive and analyze a suboptimal algorithm for estimating the unknown parameters of an Exponential Polynomial signal (EPS) model. The EPS model represents a signal as an exponent raised to a complex Polynomial of time. That is, the phase and the logarithm of the amplitude are approximated as finite-order Polynomials of time.
Fucai Qian - One of the best experts on this subject based on the ideXlab platform.
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Frequency Distribution Model of Wind Speed Based on the Exponential Polynomial for Wind Farms
Sustainability, 2019Co-Authors: Lingzhi Wang, Jun Liu, Fucai QianAbstract:This study introduces and analyses existing models of wind speed frequency distribution in wind farms, such as the Weibull distribution model, the Rayleigh distribution model, and the lognormal distribution model. Inspired by the shortcomings of these models, we propose a distribution model based on an Exponential Polynomial, which can describe the actual wind speed frequency distribution. The fitting error of other common distribution models is too large at zero or low wind speeds. The proposed model can solve this problem. The Exponential Polynomial distribution model can fit multimodal distribution wind speed data as well as unimodal distribution wind speed data. We used the linear-least-squares method to acquire the parameters for the distribution model. Finally, we carried out contrast simulation experiments to validate the effectiveness and advantages of the proposed distribution model.
Jia Zhu - One of the best experts on this subject based on the ideXlab platform.
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Fiber spectrum extraction for LAMOST based on 2-D Exponential Polynomial model
Experimental Astronomy, 2012Co-Authors: Bo Zhang, Jia ZhuAbstract:A spectrum extraction method based on a two-dimensional (2-D) Exponential Polynomial model is proposed in this paper. It is presented for extracting the flux of the fiber spectrum from a two-dimensional image observed by Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). Due to the effects of the environments and the instruments, centers of the fiber spectra deviate from the ideal, and the energy is expanded to the neighbors in the form of a point-spread function (PSF) in two dimensions. Thus we use a 2-D model to fit the PSF. In spatial orientation and wavelength orientation, the flat-field images and the calibration lamp images are respectively used to evaluate the parameters of the 2-D Exponential Polynomial model. With the 2-D Exponential Polynomial model, the spectrum flux is extracted based on the least square theory. The experiments are processed through simulated profiles to demonstrate the effectiveness of the proposed algorithm. Especially, its superiority of restraining noise and fitting asymmetric fiber profiles is proved.
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Exponential Polynomial fitting for fibre spectrum ccd profiles
Publications of the Astronomical Society of Australia, 2010Co-Authors: Zhangqin Zhu, Jia Zhu, Hanqin Qin, Chong WangAbstract:A fibre spectrum profile fitting method based on the least-squares method is presented in this article. For each spectrum of one fibre in spatial orientation, two Exponential functions are employed to approximate the profile. Experiments are performed with both simulated profiles and observed profiles to demonstrate the effectiveness of the algorithm. Specially, the proposed method has a better performance for profiles that are asymmetric or composed of multi-Gaussian functions.
Fiorenzo A Fazzolari - One of the best experts on this subject based on the ideXlab platform.
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generalized Exponential Polynomial and trigonometric theories for vibration and stability analysis of porous fg sandwich beams resting on elastic foundations
Composites Part B-engineering, 2018Co-Authors: Fiorenzo A FazzolariAbstract:Abstract The present article investigates the free vibration and elastic stability behaviour of three-dimensional functionally graded sandwich beams featured by two different types of porosity, with arbitrary boundary conditions and resting on Winkler-Pasternak elastic foundations. The investigation is carried out by using the method of series expansion of displacement components. Various hierarchical refined Exponential, Polynomial, and trigonometric higher-order beam theories are developed in a generalized manner and are validated and assessed against 3D FEM results. The weak-form of the governing equations (GEs) is derived via Hamilton's Principle. The GEs are then solved by using the Ritz method, whose accuracy is significantly enhanced by orthogonalizing the algebraic Ritz functions by virtue of the Gram-Schmidt process. Convergence and accuracy are comprehensively analysed by testing 86 quasi-3D beam theories. Moreover, the effect of significant parameters such as slenderness ratio, volume fraction index, porosity coefficient, elastic foundation coefficients, FG sandwich beam typology as well as boundary conditions, on the circular frequency parameters and critical buckling loads, is discussed.
