58,190 research outputs found
Bell-shaped nonstationary refinable ripplets
We study the approximation properties of the class of nonstationary refinable
ripplets introduced in \cite{GP08}. These functions are solution of an infinite
set of nonstationary refinable equations and are defined through sequences of
scaling masks that have an explicit expression. Moreover, they are
variation-diminishing and highly localized in the scale-time plane, properties
that make them particularly attractive in applications. Here, we prove that
they enjoy Strang-Fix conditions and convolution and differentiation rules and
that they are bell-shaped. Then, we construct the corresponding minimally
supported nonstationary prewavelets and give an iterative algorithm to evaluate
the prewavelet masks. Finally, we give a procedure to construct the associated
nonstationary biorthogonal bases and filters to be used in efficient
decomposition and reconstruction algorithms. As an example, we calculate the
prewavelet masks and the nonstationary biorthogonal filter pairs corresponding
to the nonstationary scaling functions in the class and construct the
corresponding prewavelets and biorthogonal bases. A simple test showing their
good performances in the analysis of a spike-like signal is also presented.
Keywords: total positivity, variation-dimishing, refinable ripplet, bell-shaped
function, nonstationary prewavelet, nonstationary biorthogonal basisComment: 30 pages, 10 figure
Study of random process theory aids digital data processing
Study of techniques for all random process technology, including stationary, nonstationary, and Gaussian bivariate, aids digital data processing. It presents material on digital filtering, correlation function, optimal spectral smoothing, deterministic data processing, and nonstationary spectrum and correlation analyses
Modeling Non-Stationary Processes Through Dimension Expansion
In this paper, we propose a novel approach to modeling nonstationary spatial
fields. The proposed method works by expanding the geographic plane over which
these processes evolve into higher dimensional spaces, transforming and
clarifying complex patterns in the physical plane. By combining aspects of
multi-dimensional scaling, group lasso, and latent variables models, a
dimensionally sparse projection is found in which the originally nonstationary
field exhibits stationarity. Following a comparison with existing methods in a
simulated environment, dimension expansion is studied on a classic test-bed
data set historically used to study nonstationary models. Following this, we
explore the use of dimension expansion in modeling air pollution in the United
Kingdom, a process known to be strongly influenced by rural/urban effects,
amongst others, which gives rise to a nonstationary field
The Averaged Periodogram for Nonstationary Vector Time Series
Averaged periodogram; nonstationary processes; fractional Brownian motion.Averaged periodogram, nonstationary processes, fractional Brownian motion.
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