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This paper studies optimal input excitation design for parametric frequency response estimation. The objective is to minimize the uncertainty of functions of the frequency response estimate at a specified frequency ω while limiting the power of the input signal. We focus on least-squares estimation of Finite Impulse Response (FIR) models and minimum variance input design. The optimal input problem...
In this paper, we show that Sudoku puzzles can be formulated and solved as a sparse linear system of equations. We begin by showing that the Sudoku ruleset can be expressed as an underdetermined linear system: Ax = b, where A is of size m times n and n > m. We then prove that the Sudoku solution is the sparsest solution of Ax = b, which can be obtained by lo norm minimization, i.e. min ||x:||0...
In this paper, we propose optimal methods for preconditioning an ill-conditioned linear system of equations, obtained when interpolating missing data in a band-limited sequence. The optimal preconditioning weights are obtained by solving an eigenvalue optimization problem via semidefinite programming. The so-obtained optimal weights are compared with a commonly used set of heuristic weights in terms...
Parameter estimation when the true model structure is unknown is a commonly occurring task in measurement problems. In a sparse modeling scenario, the number of possible models grows exponentially with the total number of parameters. The full set of models therefore becomes computationally infeasible to handle. We propose a method, based on successive model reduction, for finding a sound and computationally...
Parameter estimation when the true model structure is unknown is a commonly occurring task in measurement problems. In a sparse modeling scenario, the number of possible models grows exponentially with the total number of parameters. The full set of models therefore becomes computationally infeasible to handle. We propose a method, based on successive model reduction, for finding a sound and computationally...
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