P
Phillip A. Regalia
Researcher at The Catholic University of America
Publications - 99
Citations - 3868
Phillip A. Regalia is an academic researcher from The Catholic University of America. The author has contributed to research in topics: Adaptive filter & Belief propagation. The author has an hindex of 27, co-authored 99 publications receiving 3687 citations. Previous affiliations of Phillip A. Regalia include Telecom SudParis & Centre national de la recherche scientifique.
Papers
More filters
Book
Adaptive IIR Filtering in Signal Processing and Control
TL;DR: In this paper, the Steiglitz-McBride family of algorithms hyperstable algorithms adaptive notch filters perspectives and open problems computations with lattice filters are discussed and the Beurling-Lax theorem, Hankel forms and classical identification adaptive IIR filtering in signal processing and control stability of time-varying recursive filters gradient descent algorithms
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The digital all-pass filter: a versatile signal processing building block
TL;DR: A broad overview of digital all-pass filters is provided in this paper, with emphasis placed on the concept of structural losslessness, which induces very robust performance in the face of multiplier coefficient quantization.
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On the Best Rank-1 Approximation of Higher-Order Supersymmetric Tensors
TL;DR: It is shown that a symmetric version of the above method converges under assumptions of convexity (or concavity) for the functional induced by the tensor in question, assumptions that are very often satisfied in practical applications.
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An improved lattice-based adaptive IIR notch filter
TL;DR: A novel lattice-based adaptive infinite impulse response (IIR) notch filter is developed which features independent tuning of the notch frequency and attenuation bandwidth, and the estimation of extremal frequencies is less prone to overflow instability than previously reported structures.
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On the behavior of information theoretic criteria for model order selection
A.P. Liavas,Phillip A. Regalia +1 more
TL;DR: It is shown that when the noise eigenvalues are not clustered sufficiently closely, then the AIC and the MDL may lead to overmodeling by ignoring an arbitrarily large gap between the signal and the noise Eigenvalues.