Kenneth C. Chou

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Abstruet-A current topic of great interest is the multiresolu-tion analysis of signals and the development of multiscale signal processing algorithms. In this paper, we describe a framework for modeling stochastic phenomena at multiple scales and for their efficient estimation or reconstruction given partial and/or noisy measurements which may also be at(More)
A current topic of great interest is the multi-resolution analysis of signals and the development of multi-scale or multigrid algorithms. In this paper we describe part of a research effort aimed at developing a corresponding theory for stochastic processes described at multiple scales and for their efficient estimation or reconstruction given partial(More)
An overview is provided of the several components of a research effort aimed at the development of a theory of multiresolution stochastic modeling and associated techniques for optimal multiscale statistical signal and image processing. As described, a natural framework for developing such a theory is the study of stochastic processes indexed by nodes on(More)
In this paper we describe and analyze a class of multiscale stochastic processes which are modeled using dynamic representations evolving in scale based on the wavelet transform. The statistical structure of these models is Markovian in scale, and in addition the eigenstructure of these models is given by the wavelet transform. The implication of this is(More)
Motivated by the recently-developed theory of multiscale signal models and wavelet transforms, we introduce stochastic dynamic models evolving on homogeneous trees. In particular we introduce and investigate both AR and state models on trees. Our analysis yields generalizations of Levinson and Schur recursions and of Kalman filters, Riccati equations, and(More)
In this paper a method of estimating the conductivity in a bounded 2-D domain at multiple spatial resolutions given boundary excitations and measurements is presented. The problem is formulated as a maximum-likelihood estimation problem and an algorithm that consists of a sequence of estimates at successively finer scales is presented. By exploiting the(More)
In this paper we describe and analyze a class of multiscale stochastic processes which are modeled using dynamic representations evolving in scale based on the wavelet transform. The statistical structure of these models is Markovian in scale, and in addition the eigenstructure of these models is given by the wavelet transform. The implication of this is(More)