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In turn, this gives rise to a simple and unified treatment of the Caratheodory and Nehari moment problems.
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In turn, this gives rise to a simple and unified treatment of the Caratheodory and Nehari moment problems.
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A fundamental problem with the DWT, however, is the treatment of finite length data sequences.
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In the second part, where we explore connections with Maxwell's equations, the main novelty is the treatment of the corresponding electro-magnetic boundary value problem by recasting it as a 'half' Dirichlet problem for a suitable Dirac operator.
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The numerical treatment of two-dimensional scattering in inhomogeneous media
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It is shown that the one-dimensional sampling sets correspond to Bessel sequences of complex exponentials that are not Riesz bases for $L^2[-R,R].$ A signal processing application in which such sampling sets arise naturally is described in detail.
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The Discrete Wavelet Transform (DWT) is of considerable practical use in image and signal processing applications.
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Among all image transforms, the classical (Euclidean) Fourier transform has had the widest range of applications in image processing.
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The projectively adapted properties of theSL(2, ?)-harmonic analysis, as applied to the problems, in image processing, are confirmed by computational tests.
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Such systems play an important role in time-frequency analysis and digital signal processing.
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Duality theorems, atomic decompositions, and factorization of functions are treated.
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While our proofs, constructions and numerical examples are given for eigenvalue problems for the Laplacian operator in the plane, other elliptic operators can be treated similarly.
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This gives analyses of some endpoint cases of results treated in [3].
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A slightly elevated fasting blood glucose profile, 1.5 fold higher serum insulin levels and impaired glucose tolerance was prevalent in ethanol treated rats.
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Briefly, MCF-7 cells were treated with the compounds for 24 h followed by treatment with BP (0.5 μM).
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In the process we study the properties of different homogeneous models for ${\mathbb H}H(n).$
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We exploit an analogy between the trigonometric moment problem and prediction theory for a stationary stochastic process.
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We exploit an analogy between the trigonometric moment problem and prediction theory for a stationary stochastic process.
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Using this characterization we recapture Daubechies' orthonormal wavelets bypassing the spectral factorization process.
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Consider the fractional Brownian motion process $B_H(t), t\in [0,T]$,
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