In this paper, we briefly describe two interior-point algorithms for semidefinite programming. At each iteration, both these algorithms compute search directions by solving a linear system. We discuss some preliminary...
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In this paper, we briefly describe two interior-point algorithms for semidefinite programming. At each iteration, both these algorithms compute search directions by solving a linear system. We discuss some preliminary experiments for moderately sized, block diagonal semidefinite programs, comparing direct and iterative methods for solving the linear systems.
Basic algorithms and LAPACK-based Fortran software for multivariable system identification by subspace techniques are briefly described. Deterministic and combined deterministic-stochastic identification problems are ...
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Basic algorithms and LAPACK-based Fortran software for multivariable system identification by subspace techniques are briefly described. Deterministic and combined deterministic-stochastic identification problems are dealt with using two approaches. A state space model is computed from input-output data sequences. Multiple data sequences collected by possibly independent identification experiments can be handled. Sequential processing of large data sets is provided as an option. Illustrative numerical examples are included.
The basic design freedom of a (generalized) Gabor transform is the choice of (i) the time-frequency lattice constants and (ii) the analysis prototype (atom, window). The design of the synthesis prototype is subject to...
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The basic design freedom of a (generalized) Gabor transform is the choice of (i) the time-frequency lattice constants and (ii) the analysis prototype (atom, window). The design of the synthesis prototype is subject to linear constraints (Wexler-Raz (1990) condition) depending on the redundancy of the presentation. For multidimensional signals the design freedom can be greatly increased by the consideration of nonseparable situations: (i) nonseparable prototypes and/or (ii) nonseparable position-frequency sampling lattices. We present such a general theory for the Gabor expansion of 2D signals. Our main result is a generalized biorthogonality condition connecting the analysis and synthesis prototype. The theory is illustrated by a simple numerical experiment.
We briefly survey how to use libraries of (orthonormal) bases of well-behaved waveforms, including wavelets and lapped orthogonal transforms, so as to obtain fast numerical algorithms for the expansion of functions an...
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ISBN:
(纸本)0819418447
We briefly survey how to use libraries of (orthonormal) bases of well-behaved waveforms, including wavelets and lapped orthogonal transforms, so as to obtain fast numerical algorithms for the expansion of functions and operators in these bases. The most important applications are fast approximate matrix multiplication, and application of matrices to vectors.
Deconvolution of images of the same object from multiple sensors with different point spread functions (PSF), as shown by Berenstein and Patrick, can be a well-posed problem in the sense of distributions if the PSF sa...
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ISBN:
(纸本)081941929X
Deconvolution of images of the same object from multiple sensors with different point spread functions (PSF), as shown by Berenstein and Patrick, can be a well-posed problem in the sense of distributions if the PSF satisfy some suitable conditions. More precisely, if these operators are represented by compactly supported distributions, a corresponding set of deconvolvers, also given by compactly supported distributions, may exist. Nevertheless, it must be observed that this inverse operator is not particularly useful if the multiple images which must be deconvolved are affected by noise, because continuity in the sense of distributions is too weak. This is the reason why a more effective approach is provided by the inverse methods typical of regularization theory. We have considered the case described by Berenstein and Patrick, in which the input function consists of the sum of two Gaussian pulses and the PSF are the characteristic functions of the intervals (-1, 1) and (- (root)2, 2). The two images we have obtained have been affected by Gaussian noise and then simulated data have been inverted by using various regularization techniques; in particular, in the case of iterative methods, it has also been possible to introduce the positivity constraint. The comparison between the reconstructions we have obtained and the input function allows to estimate the greater efficiency of the regularized multiple operators deconvolution, compared with the inversion of a single image, when linear filtering is applied. On the contrary the performance of the nonlinear constrained iterative method seems not to be particularly sensitive to the use of two images instead of one. An explanation of this fact is given and an example, where the use of multiple images can be advantageous, is presented.
We present an algorithm-independent theory of statistical accuracy attainable in emission tomography. Let f denote the tracer density as a function of position (i.e., f is the underlying image). We consider the proble...
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ISBN:
(纸本)081941929X
We present an algorithm-independent theory of statistical accuracy attainable in emission tomography. Let f denote the tracer density as a function of position (i.e., f is the underlying image). We consider the problem of estimating (Phi) (f) equalsV (integral) (phi) (x)f(x)dx, where (phi) is a smooth function, given n independent observations distributed according to the Radon transform of f. Assuming only that f is bounded above and below away from 0, we construct minimum-variance unbiased (MVU) estimators for (Phi) (f). By definition, the variavnce of the MVU estimator is a best-possible lower bound (depending on (phi) and f) on the variance of unbiased estimators of (Phi) (f). The analysis gives a geometrical explanation of when and by how much estimators based on the standard filtered-backpropagation reconstruction algorithm may be improved.
An optimization method is developed based on ellipsoidal trust regions that are defined by conic functions. It provides a powerful unifying theory from which can be derived a variety of interesting and potentially use...
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An optimization method is developed based on ellipsoidal trust regions that are defined by conic functions. It provides a powerful unifying theory from which can be derived a variety of interesting and potentially useful optimization algorithms, in particular, conjugate-gradient-like algorithms for nonlinear minimization and Karmarkar-like interior-point algorithms for linear programming.
The proceedings contains 212 papers. Topics discussed include nonlinear control, robot control, stochastic control, Riemann geometry in parametrization and control theory, numericalmethods, adaptive control applicati...
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The proceedings contains 212 papers. Topics discussed include nonlinear control, robot control, stochastic control, Riemann geometry in parametrization and control theory, numericalmethods, adaptive control applications, discrete time signals, systems, and control, frequency domain identification, neural networks, real life control design challenge problems, system stability, optimal control, control of computer and telecommunication networks, robustness analysis, hybrid control systems, time delay systems, Kalman filtering, backstepping, robotics, sampled data systems, least squares identification, neural adaptive estimation and control, aircraft applications, linear and semilinear systems, H-infinity control, and target tracking.
An alternate approach to speech synthesis based on numerical solution of Navier-Stokes (NS) and Reynolds-Averaged-Navier-Stokes (RANS) equations is described. Unlike the traditional methods based on linear acoustic th...
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The proceedings contains 191 papers. Topics discussed include nonlinear control, adaptive control of robot manipulators, stochastic systems, nonlinear observer design, numericalmethods, robustness, parameter varying ...
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The proceedings contains 191 papers. Topics discussed include nonlinear control, adaptive control of robot manipulators, stochastic systems, nonlinear observer design, numericalmethods, robustness, parameter varying systems, identification, fuzzy logic control, computer aided control system design, linear systems, nonlinear H-infinity control, control challenges by broadband networks, robust stability and control, discrete event systems, partial differential equations, behavioral approach to system theory, Lyapunov second method, mechanical systems, feedback linearization, industrial applications of control systems, time varying systems, multivariable systems, knowledge based systems, aerospace and vehicular control, control and performance of computer and communication networks, infinite dimensional system control.
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