A method utilizing single channel recordings to blindly separate the multicomponents overlapped in time and frequency domains is proposed in this paper. Based on the time varying AR model, the instantaneous frequency ...
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A method utilizing single channel recordings to blindly separate the multicomponents overlapped in time and frequency domains is proposed in this paper. Based on the time varying AR model, the instantaneous frequency and amplitude of each signal component are estimated respectively, thus the signal component separation is achieved. By using prolate spheroidal sequence as basis functions to expand the time varying parameters of the AR model, the method turns the problem of linear time varying parameters estimation to a linear time invariant parameter estimation problem, then the parameters are estimated by a recursive algorithm. The computation of this method is simple, and no prior knowledge of the signals is needed. Simulation results demonstrate validity and excellent performance of this method.
Most system recursive identification algorithms are based on the prediction error (PE) criterion. Such a recursive algorithm only considers the present estimation residual error instead of all estimation residuals. It...
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ISBN:
(纸本)9789898111326
Most system recursive identification algorithms are based on the prediction error (PE) criterion. Such a recursive algorithm only considers the present estimation residual error instead of all estimation residuals. It would result in large estimation error when the signal noise disturbs strongly. In this paper, a new identification criterion is proposed. It considers both the errors between the actual outputs and the estimation result and the difference of each estimation error. Under this criterion, a new recursive algorithm MSDCN (Multi-dimensional System Disturbed by Color Noise) is proposed. For multi-dimensional systems, weighting different values on the estimation errors and the difference of each error, MSDCN could both decrease the estimation errors and got smooth prediction curves. Several simulation examples are given to illustrate the method's anti-disturbance performance.
In this article, we consider the class of risk models with Markovian claim arrivals studied by Badescu et al. (2005) and Ramaswami (2006), among others. Under a multi-threshold dividend structure, we develop a recursi...
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In this article, we consider the class of risk models with Markovian claim arrivals studied by Badescu et al. (2005) and Ramaswami (2006), among others. Under a multi-threshold dividend structure, we develop a recursive algorithm for the calculation of the moments of the discounted dividend payments before ruin. Capitalizing on the connection between an insurer's surplus process and its corresponding fluid flow process, our approach generalizes results obtained by Albrecher and Hartinger (2007) and Zhou (2006) in the framework of the classical compound Poisson risk model (with phase-type claim sizes). Contrary to the traditional analysis of the discounted dividend payments in risk theory, we develop a sample-path-analysis procedure that allows the determination of these moments with or without ruin occurrence (separately). Numerical examples are then considered to illustrate our main results and show the contribution of each component to the moments of the discounted dividend payments.
This paper shows a new polynomial Kernel (a non-Gaussian window) suitable for the implementation of Phase Corrected Wavelet Transform, in a recursive manner, which the target is to track harmonics and inter-harmonics ...
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ISBN:
(纸本)9781424417711
This paper shows a new polynomial Kernel (a non-Gaussian window) suitable for the implementation of Phase Corrected Wavelet Transform, in a recursive manner, which the target is to track harmonics and inter-harmonics and achieve acceleration in the disturbances detection process and help the waveform characterization in Power Quality Analyzers that must be in compliance at the same time with the Power Quality EEC standards. Synthetic signals are generated and tested simulating many kinds of disturbances, tracking the fundamental, in four cycles or less, even in noisy environments.
recursive subspace model identification (RSMI) has been developed for a decade. Most of RSMIs are only applied for open loop data. In this paper, we propose a new recursive subspace model identification which can be a...
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recursive subspace model identification (RSMI) has been developed for a decade. Most of RSMIs are only applied for open loop data. In this paper, we propose a new recursive subspace model identification which can be applied under open loop and closed loop data. The key technique of this derivation of the proposed algorithm is to bring the Vector Auto Regressive with eXogenous input (VARX) models into RSMI. Numerical studies on a closed loop identification problem show the effectiveness of the proposed algorithm.
An improved algorithm that can reconstruct a symmetrical conjugate phase signal with only one single Fresnel transform intensity is presented. A method of producing a symmetrical conjugate form of an unknown phase obj...
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An improved algorithm that can reconstruct a symmetrical conjugate phase signal with only one single Fresnel transform intensity is presented. A method of producing a symmetrical conjugate form of an unknown phase object is introduced and the essential properties of the discrete Fresnel transform employed are stated as well. Numerical results show that this method, even without using any iterative procedure or constrains, can successfully retrieve the phase of a pure phase object. Compared with the methods reported in previous works, it has the merits of being noniterative, efficient and direct. [DOI: 10.1143/JJAP.47.8848]
Parameter selection for the criterion weighting matrix is concerned based on the information of both modifying the past estimation residuals and renewing the present estimation residual error. After minimizing the sys...
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Parameter selection for the criterion weighting matrix is concerned based on the information of both modifying the past estimation residuals and renewing the present estimation residual error. After minimizing the system estimation error, an optimal recursive algorithm is given. In this method the system data record can be used efficiently. The consistency of the new recursive algorithm is analyzed. Finally, some simulation examples are included to demonstrate the new method's reliability.
In this paper, we propose a new recursive subspace model identification (RSMI) based on regression and natural power method (NP) which is an array signal processing algorithm with excellent convergence properties. We ...
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In this paper, we propose a new recursive subspace model identification (RSMI) based on regression and natural power method (NP) which is an array signal processing algorithm with excellent convergence properties. We call this new algorithm as ‘R-NP'. The basic idea of the algorithm is to utilize an unstructured least squares linear regression approach at the updating observation vector step and the close relationship between RSMI with NP. This algorithm has simpler procedures than other RSMI algorithms. A numerical example illustrates that R-NP method is efficient and have a better performance in terms of transient behavior with respect to EIVPAST. In this paper, we consider the case where the order of system to be identified is a priori known.
Image segmentation is a key part in image processing field Though traditional threshold methods based on maximum entropy principle could sometimes divide the image into object and background, the size of search space ...
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ISBN:
(纸本)9781424417339
Image segmentation is a key part in image processing field Though traditional threshold methods based on maximum entropy principle could sometimes divide the image into object and background, the size of search space increase very rapidly when the number of parameters needed to determine the membership function increase, so it's time-consuming is often an obstacle. In this paper, based on the traditional threshold methods, the membership function is simplified and a new recursive scheme is introduced which decreases the computation complexity of the traditional method. The approach does not need the calculation of the membership function and increases the calculated speed. The experiments prove this novel approach is not only segment accuracy, but also decrease the computation time. Comparing with the traditional methods, the new method shows better real timing and noise restraining performance.
In this paper we introduce a so called C-Matrix w.r.t a rational interpolation problem and study the relationship between the unattainable points and C-Matrix. Finally, we present a recursive algorithm on rational int...
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In this paper we introduce a so called C-Matrix w.r.t a rational interpolation problem and study the relationship between the unattainable points and C-Matrix. Finally, we present a recursive algorithm on rational interpolation.
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