The inverse Legendre moment transform plays an important role in the field of image analysis applications. In this paper, a recursive algorithm based on Clenshaw's recurrence formula is derived for the fast comput...
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The inverse Legendre moment transform plays an important role in the field of image analysis applications. In this paper, a recursive algorithm based on Clenshaw's recurrence formula is derived for the fast computation of the inverse Legendre moment transform for signal and image reconstruction purposes. The reconstruction can be implemented effectively using recursive equations. The presented algorithm is particularly suitable for parallel very large-scale integrated circuit implementation due to its regular, modular, and simple filter structure.
In this work, we present a simple theoretical analysis of a compact rectangular microstrip resonator with stubs. The analytical model is based on a recursive algorithm for a transmission line loaded with capacitive st...
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In this work, we present a simple theoretical analysis of a compact rectangular microstrip resonator with stubs. The analytical model is based on a recursive algorithm for a transmission line loaded with capacitive stubs. We calculate the input impedance and the resonant frequency of this resonator and analyse the dependence of these parameters on the number and dimensions of the stubs. The numerical results are compared with those obtained by other methods and with experimental data. (C) 2004 Wiley Periodicals, Inc.
We establish consistency and derive asymptotic distributions for estimators of the coefficients of a subset vector autoregressive (SVAR) process. Using a martingale central limit theorem, we first derive the asymptoti...
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We establish consistency and derive asymptotic distributions for estimators of the coefficients of a subset vector autoregressive (SVAR) process. Using a martingale central limit theorem, we first derive the asymptotic distribution of the subset least squares (LS) estimators. Exploiting the similarity of closed form expressions for the LS and Yule-Walker (YW) estimators, we extend the asymptotics to the latter. Using the fact that the subset Yule-Walker and recently proposed Burg estimators satisfy closely related recursive algorithms, we then extend the asymptotic results to the Burg estimators. All estimators are shown to have the same limiting distribution. (C) 2003 Elsevier Inc. All rights reserved.
The main goal of this paper is to compare recursive algorithms such as Turing machines with such super-recursive algorithms as inductive Turing machines. This comparison is made in a general setting of dual complexity...
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The main goal of this paper is to compare recursive algorithms such as Turing machines with such super-recursive algorithms as inductive Turing machines. This comparison is made in a general setting of dual complexity measures such as Kolmogorov or algorithmic complexity. To make adequate comparison, we reconsider the standard axiomatic approach to complexity of algorithms. The new approach allows us to achieve a more adequate representation of static system complexity in the axiomatic context. It is demonstrated that for solving many problems inductive Turing machines have much lower complexity than Turing machines and other recursive algorithms. Thus, inductive Turing machines are not only more powerful, but also more efficient than Turing machines. (C) 2003 Elsevier B.V. All rights reserved.
A new approach for measuring the shape and surface of an object observed from a single view is proposed. The proposed approach is based on using a single hidden layer wavelet neural network (WNN) to approximate comple...
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ISBN:
(纸本)7121002159
A new approach for measuring the shape and surface of an object observed from a single view is proposed. The proposed approach is based on using a single hidden layer wavelet neural network (WNN) to approximate complex nonlinear reflectance map functions in which example data are not explicitly given. The shape from shading problem is formulated as the minimization of an intensity error function with respect to the network weights. Due to the properties of the WNN. the representation of network topology can be definitely developed, and the training problem can be transformed into a convex optimization process, the global minimum can be obtained and the learning speed also increases. Experimental results on synthetic images demonstrate the new algorithm has performed well and compared favorably to the traditional ones.
We present a new accurate algorithm (REFUND) for computing the fundamental matrix (or closely related group inverse matrix) of a finite regular Markov chain. This algorithm is developed within the framework of the sta...
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We present a new accurate algorithm (REFUND) for computing the fundamental matrix (or closely related group inverse matrix) of a finite regular Markov chain. This algorithm is developed within the framework of the state reduction approach exemplified by the GTH (Grassmann, Taksar, Heyman)/S (Sheskin) algorithm for recursively finding invariant measure. The rst (reduction) stage of the GTH/S algorithm is shared by REFUND, as well as by an earlier algorithm FUND developed for the fundamental matrix by Heyman in 1995, and by a modified version of Heyman and O'Leary in 1998. Unlike FUND, REFUND is recursive, being based on an explicit formula relating the group inverse matrix of an initial Markov chain and the group inverse matrix of a Markov chain with one state removed. Operation counts are approximately the same: Theta (7/3n(3)) for REFUND versus Theta (8/3n(3)) for FUND. Numerical tests indicate that REFUND is accurate. The structure of REFUND makes it easily combined with the other algorithms based on the state reduction approach. We also discuss the general properties of this approach, as well as connections to the optimal stopping problem and to tree decompositions of graphs related to Markov chains.
Two spanning trees rooted at some vertex r in a graph G are said to be independent if for each vertex v of G, v not equal r, the paths from r to v in two trees are vertex-disjoint. A set of spanning trees of G is said...
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Two spanning trees rooted at some vertex r in a graph G are said to be independent if for each vertex v of G, v not equal r, the paths from r to v in two trees are vertex-disjoint. A set of spanning trees of G is said to be independent if they are pairwise independent. A set of independent spanning trees is optimal if the average path length of the trees is the minimum. Any k-dimensional hypercube has k independent spanning trees rooted at an arbitrary vertex. In this paper, an O(kn) time algorithm is proposed to construct k optimal independent spanning trees on a k-dimensional hypercube, where n = 2(k) is the number of vertices in a hypercube.
Principal components analysis is an important and well-studied subject in statistics and signal processing. The literature has an abundance of algorithms for solving this problem, where most of these algorithms could ...
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Principal components analysis is an important and well-studied subject in statistics and signal processing. The literature has an abundance of algorithms for solving this problem, where most of these algorithms could be grouped into one of the following three approaches: adaptation based on Hebbian updates and deflation, optimization of a second-order statistical criterion (like reconstruction error or output variance), and fixed point update rules with deflation. In this paper, we take a completely different approach that avoids deflation and the optimization of a cost function using gradients. The proposed method updates the eigenvector and eigenvalue matrices simultaneously with every new sample such that the estimates approximately track their true values as would be calculated from the current sample estimate of the data covariance matrix. The performance of this algorithm is compared with that of traditional methods like Sanger's rule and APEX, as well as a structurally similar matrix perturbation-based method.
A new approach for measuring the shape and surface of an object observed from a single view is proposed. The proposed approach is based on using a single hidden layer wavelet neural network (WNN) t
A new approach for measuring the shape and surface of an object observed from a single view is proposed. The proposed approach is based on using a single hidden layer wavelet neural network (WNN) t
The Hartley transform is more efficient and economical than the Fourier transform for real series data processing in several applications. This paper presents a block decomposition representation of discrete Hartley t...
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The Hartley transform is more efficient and economical than the Fourier transform for real series data processing in several applications. This paper presents a block decomposition representation of discrete Hartley transform (DHT) coefficient matrix in view of algebra, from which a new fast recursive algorithm is derived. As to the DHT calculation of N = 2(t) real sequence, the arithmetic complexity is M = 1/2 N log(2) N - 3/2 N + 2 real multiplications and A = 3/2 N log(2) N - 4/3 N + 3/2 + (-1)(t-1) 1/6 real additions. Obviously, this recursive algorithm belongs to that class with the lowest arithmetic complexity. (C) 2001 Elsevier Science B.V. All rights reserved.
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