Time-Frequency Brown Bag Seminar
Wednesday, March 24, 1999
12:30pm
EQuad E415
Speaker: Brani Vidakovic, ISDS, Duke University
Title: Bayesian Strategies for Wavelet Denoising
Abstract:
After a short introduction to Bayesian modeling in the
wavelet domain I will discuss two applications,
one in smoothing the periodograms of stationary time series,
and the second in processing and denoising turbulence signals.
For the second application a short description is provided.
Fully developed turbulence is a self-similar
phenomena with universal and stable second order
properties. That makes wavelets an appropriate tool
in describing, summarizing, and modeling the
turbulence measurements.
The following two tasks are discussed:
-
Wavelet based ``Townsend's Denoising'' in which
the measured atmospheric turbulence signal
(wind velocity components, temperature, humidity,
ozone and CO2 concentrations, etc.) is separated
on the attached part (that preserves energy and fluxes)
and the detached part (an energyless component).
-
Denoising turbulence signals from the instrumentation
noise [ozone measurements] is performed with help of Bayesian models
in the wavelet domain.
A novel shrinkage technique of ``supervised'' shrinkage
is introduced with a motivation to preserve fluxes.
The research is done jointly with
Gabriel Katul
from NSOE, Duke University. More information can be found in
Discussion Paper 98-30 at:
http://www.stat.duke.edu/papers/working-papers-98.html
.
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