|
Brani
Vidakovic; Peter Muller |
|
26
páginas que presentan un tutorial general sobre wavelet. |
|
Se
incluye el desarrollo del concepto de wavelet, los diferentes tipos y
aparece un ejemplo |
|
"sencillo"
basado en la wavelet más fundamental, también existe un programa de
wavelet |
|
El
tutorial no muestra ni discute nada con respecto a wavelet en sistemas de
potencia.
|
|
Wavelets
and signal processing |
|
IEEE
Signal Processing Magazine , Volume: 8 Issue: 4 , Oct. 1991 |
|
A
simple, nonrigorous, synthetic view of wavelet theory is presented for
both review and |
|
tutorial
purposes. The discussion includes nonstationary signal analysis, scale
versus |
|
frequency,
wavelet analysis and synthesis, scalograms, wavelet frames and orthonormal |
|
bases,
the discrete-time case, and applications of wavelets in signal processing.
The main |
|
definitions
and properties of wavelet transforms are covered, and connections among
the |
|
various
fields where results have been developed are shown.
|
|
Power
electronics, power quality and modern analytical tools: the impact on |
|
electrical
engineering education |
|
Ribeiro,
P.F.; Rogers, D.A. |
|
Frontiers
in Education Conference, 1994. Twenty-fourth Annual Conference. |
|
The
new power electronics context characterized by the proliferation of
sensitive |
|
electronics
equipment supplied by an electrical network with very high levels of
distortion, |
|
which
are in part generated by the massive utilization of power electronics
applications, |
|
creates
an environment in which traditional circuit modeling analysis and
techniques |
|
cannot
be applied straightforwardly. High harmonic distortion, voltage notches,
high |
|
frequency
noise, etc., are among the typical situations in which sensitive
electronic |
|
devices
are being operated. As a consequence of the new electrical environment,
the |
|
currents
and voltages on the electrical network substantially and randomly deviate
from a |
|
sinusoidal
form. Thus the state of the electrical system cannot be fully analyzed by |
|
traditional
methods. Due to the consequent dynamics of distortion generation,
propagation |
|
and
interaction with the system, one would need a more powerful technique to
efficiently |
|
analyze
the system performance in the presence of nonstationary distortions. This
paper |
|
briefly
presents the basic concepts for some of the new analytical tools for
signal |
|
processing
and identification, their similarities and differences with respect to
traditional |
|
techniques,
and underlines how these new techniques are changing engineering design |
|
and
ultimately Specifically, wavelet theory, genetic algorithms, expert
systems, fuzzy logic, |
|
and
neural network concepts are reviewed for their potential applications in
power quality |
|
Wavelets
and wideband correlation processing |
|
IEEE
Signal Processing Magazine , Volume: 11 Issue: 1 , Jan. 1994 |
|
This
tutorial presents the application of wavelet transforms to wideband
correlation |
|
processing.
One major difference between most applications of wavelets and the work |
|
presented
is the choice of mother wavelet. It focuses on nonorthogonal, continuous |
|
mother
wavelets, whereas most applications use the orthogonal mother wavelets
that |
|
were
advanced by Daubechies (1988). The continuous wavelet transform then
provides |
|
an
additional tool for studying and gaining insight into wideband correlation
processing. In |
|
order
to understand when wideband processing may be required, its counterpart, |
|
narrowband
processing, is presented and its limitations are discussed. Identifying
those |
|
applications
requiring wideband processing and presenting techniques to implement the |
|
processing
are two of the goals of this tutorial article. The underlying tool is the
wavelet |
|
An
introduction to wavelets |
|
IEEE
Computational Science and Engineering , Volume: 2 Issue: 2 , Summer 1995 |
|
Wavelets
were developed independently by mathematicians, quantum physicists,
electrical |
|
engineers
and geologists, but collaborations among these fields during the last
decade |
|
have
led to new and varied applications. What are wavelets, and why might they
be |
|
useful
to you? The fundamental idea behind wavelets is to analyze according to
scale. |
|
Indeed,
some researchers feel that using wavelets means adopting a whole new mind-set |
|
or
perspective in processing data. Wavelets are functions that satisfy
certain |
|
mathematical
requirements and are used in representing data or other functions. Most of |
|
the
basic wavelet theory has now been done. The mathematics have been worked
out in |
|
excruciating
detail, and wavelet theory is now in the refinement stage. This involves |
|
generalizing
and extending wavelets, such as in extending wavelet packet techniques.
The |
|
future
of wavelets lies in the as-yet uncharted territory of applications.
Wavelet |
|
techniques
have not been thoroughly worked out in such applications as practical data |
|
analysis,
where, for example, discretely sampled time-series data might need to be |
|
analyzed.
Such applications offer exciting avenues for exploration.
|
|
Exploring
the power of wavelet analysis |
|
Galli,
A.W.; Heydt, G.T.; Ribeiro, P.F. |
|
IEEE
Computer Applications in Power , Volume: 9 Issue: 4 , Oct. 1996 |
|
Wavelets
are a recently developed mathematical tool for signal analysis. Informally,
a |
|
wavelet
is a short-term duration wave. Wavelets are used as a kernel function in
an |
|
integral
transform, much in the same way that sines and cosines are used in Fourier |
|
analysis
or the Walsh functions in Walsh analysis. To date, the primary application
of |
|
wavelets
has been in the areas of signal processing, image compression, subband
coding, |
|
medical
imaging, data compression, seismic studies, denoising data, computer
vision and |
|
sound
synthesis. Here, the authors describe how wavelets may be used in the
analysis of |
|
power
system transients using computer implementation.
|
|
Wavelets
and time-frequency analysis |
|
Hess-Nielsen,
N.; Wickerhauser, M.V. |
|
Proceedings
of the IEEE , Volume: 84 Issue: 4 , April 1996 |
|
We
present a selective overview of time-frequency analysis and some of its
key |
|
problems.
In particular we motivate the introduction of wavelet and wavelet packet |
|
analysis.
Different types of decompositions of an idealized time-frequency plane
provide |
|
the
basis for understanding the performance of the numerical algorithms and
their |
|
corresponding
interpretations within the continuous models. As examples we show how to |
|
control
the frequency spreading of wavelet packets at high frequencies using |
|
nonstationary
filtering and study some properties of periodic wavelet packets. |
|
Furthermore
we derive a formula to compute the time localization of a wavelet packet |
|
from
its indexes which is exact for linear phase filters, and show how this
estimate |
|
deteriorates
with deviation from linear phase.
|
|
Proceedings
of the IEEE , Volume: 84 Issue: 4 , April 1996 |
|
The
author looks ahead to see what the future can bring to wavelet research.
He tries to |
|
find a
common denominator for "wavelets" and identifies promising
research directions |
|
and
challenging problems.
|
|
Where
do wavelets come from? A personal point of view |
|
Proceedings
of the IEEE , Volume: 84 Issue: 4 , April 1996 |
|
The
development of wavelets is an example where ideas from many different
fields |
|
combined
to merge into a whole that is more than the sum of its parts. The subject
area of |
|
wavelets,
developed mostly over the last 15 years, is connected to older ideas in
many |
|
other
fields, including pure and applied mathematics, physics, computer science,
and |
|
engineering.
The history of wavelets can therefore be represented as a tree with roots |
|
reaching
deeply and in many directions. In this picture, the trunk would correspond
to the |
|
rapid
development of "wavelet tools" in the second half of the 1980's,
with shared efforts |
|
by
researchers from many different fields; the crown of the tree, with its
many branches, |
|
would
correspond to different directions and applications in which wavelets are
now |
|
becoming
a standard part of the mathematical tool kit, alongside other more
established |
|
techniques.
The author gives here a highly personal version of the development of |
|
Wavelet
based signal processing: where are we and where are we going? |
|
Digital
Signal Processing Proceedings, 1997. DSP 97., 1997 13th International |
|
Conference
on , Volume: 1 , 1997 |
|
This
article discusses the history of modern wavelet based signal processing
and then |
|
reviews
the present state of the art. It also speculates about the future of this
exciting |
|
field.
The history of wavelets and wavelet based signal processing is fairly
recent. Its |
|
roots
in signal expansion go back to early geophysical and image processing
methods and |
|
in DSP
to filter bank theory and subband coding.
|
|
A
Friendly Guide To Wavelets |
|
Proceedings
of the IEEE , Volume: 86 Issue: 11 , Nov. 1998 |
|
Un
comentario sobre el libro a friendly guide to wavelet
|
|
A
tutorial on wavelets from an electrical engineering perspective .2. The |
|
IEEE
Antennas and Propagation Magazine , Volume: 40 Issue: 6 , Dec. 1998 |
|
The
wavelet transform is described from the perspective of a Fourier
transform. The |
|
relationships
among the Fourier transform, the Gabor (1946) transform (windowed Fourier |
|
transform),
and the wavelet transform are described. The differences are also outlined, |
|
to
bring out the characteristics of the wavelet transform. The limitations of
the wavelets in |
|
localizing
responses in various domains are also delineated. Finally, an adaptive
window is |
|
presented
that may be optimally tailored to suit one's needs, and hence, possibly,
the |
|
scaling
functions and the wavelets.
|
|
A
tutorial on wavelets from an electrical engineering perspective. I.
Discrete |
|
Sarkar,
T.K.; Su, C.; Adve, R.; Salazar-Palma, M.; Garcia-Castillo, L.; Boix, R.R. |
|
IEEE
Antennas and Propagation Magazine , Volume: 40 Issue: 5 , Oct. 1998 |
|
The
objective of this paper is to present the subject of wavelets from a
filter-theory |
|
perspective,
which is quite familiar to electrical engineers. Such a presentation
provides |
|
both
physical and mathematical insights into the problem. It is shown that
taking the |
|
discrete
wavelet transform of a function is equivalent to filtering it by a bank of |
|
constant-Q
filters, the non-overlapping bandwidths of which differ by an octave. The |
|
discrete
wavelets are presented, and a recipe is provided for generating such
entities. One |
|
of the
goals of this tutorial is to illustrate how the wavelet decomposition is
carried out, |
|
starting
from the fundamentals, and how the scaling functions and wavelets are
generated |
|
from
the filter-theory perspective. Examples (including image compression) are |
|
presented
to illustrate the class of problems for which the discrete wavelet
techniques are |
|
ideally
suited. It is interesting to note that it is not necessary to generate the
wavelets or |
|
the
scaling functions in order to implement the discrete wavelet transform.
Finally, it is |
|
shown
how wavelet techniques can be used to solve operator/matrix equations. It
is |
|
shown
that the "orthogonal-transform property" of the discrete wavelet
techniques does |
|
not
hold in numerical computations.
|
|
A
literature survey of wavelets in power engineering applications |
|
CHIEN-HSING
LEE , YAW-JUEN WANG *AND WEN-LIANG HUANG ** |
|
Proc.
Natl. Sci. Counc. ROC(A) |
|
Vol.
24, No. 4, 2000. pp. 249-258 |
|
The
use of wavelet analysis is a new and rapidly growing area of research
within |
|
mathematics,
physics, and engineering.
In this paper, we present a literature |
|
survey
of the various applications of wavelets
in power engineer-ing. |
|
We
start by describing the wavelet properties that are most important for
power |
|
engineering
applications and then
review their uses in the analysis of non-stationary |
|
signals
occurring in power systems.
Next, we provide a lit-erature survey
of recent |
|
wavelet
developments in power engineering applications. These include detection, |
|
local-ization,
dentification, classification, compression, storage, and network/system |
|
ianalysis
of power disturbance
sig-nals. In
each case, we provide the reader with some |
|
general
background information and a brief
explanation.
|
|
Wavelet
analysis for power system transients |
|
Galli,
A.W.; Nielsen, O.M. |
|
IEEE
Computer Applications in Power , Volume: 12 Issue: 1 , Jan. 1999 |
|
Page(s):
16, 18, 20, 22, 24 -25 |
|
The
purpose of this tutorial is to introduce the basics of wavelet analysis
and propose how |
|
this
new mathematical tool may be applied in power engineering. Frequently,
newcomers |
|
to
wavelet analysis become discouraged due to the oftentimes elusive
mathematical rigor |
|
of the
subject and the variety of nomenclatures that are used in various arenas.
This |
|
tutorial
presents wavelet analysis in such a way that the reader can easily grasp
the |
|
rudiments
and begin investigating the use of this powerful tool in a variety of
applications |
|
related
to power engineering.
|
|
Prolog
to sampling-50 years after Shannon |
|
Proceedings
of the IEEE , Volume: 88 Issue: 4 , April 2000 |
|
Prólogo
del paper de 50 años despues de Shannon.
|
|
Sampling-50
years after Shannon |
|
Proceedings
of the IEEE , Volume: 88 Issue: 4 , April 2000 |
|
This
paper presents an account of the current state of sampling, 50 years after
Shannon's |
|
formulation
of the sampling theorem. The emphasis is on regular sampling, where the
grid |
|
is
uniform. This topic has benefitted from a strong research revival during
the past few |
|
years,
thanks in part to the mathematical connections that were made with wavelet |
|
theory.
To introduce the reader to the modern, Hilbert-space formulation, we
reinterpret |
|
Shannon's
sampling procedure as an orthogonal projection onto the subspace of |
|
band-limited
functions. We then extend the standard sampling paradigm for a
presentation |
|
of
functions in the more general class of "shift-in-variant"
function spaces, including |
|
splines
and wavelets. Practically, this allows for simpler-and possibly more |
|
realistic-interpolation
models, which can be used in conjunction with a much wider class |
|
of (anti-aliasing)
prefilters that are not necessarily ideal low-pass. We summarize and |
|
discuss
the results available for the determination of the approximation error and
of the |
|
sampling
rate when the input of the system is essentially arbitrary; e.g.,
nonbandlimited. |
|
We
also review variations of sampling that can be understood from the same
unifying |
|
perspective.
These include wavelets, multiwavelets, Papoulis generalized sampling,
finite |
|
elements,
and frames. Irregular sampling and radial basis functions are briefly
mentioned.
|
|
Wavelet
transforms in power systems. I. General introduction to the wavelet |
|
Chul
Hwan Kim; Raj Aggarwal |
|
Power
Engineering Journal , Volume: 14 Issue: 2 , April 2000 |
|
This
tutorial gives an introduction to the field of the wavelet transform. It
is the first of |
|
two
tutorials which are intended for engineers applying or considering to
apply WTs to |
|
power
systems. They show how the WT-a powerful new mathematical tool-can be |
|
employed
as a fast and very effective means of analysing power system transient |
|
waveforms,
as an alternative to the traditional Fourier transform. The focus of the |
|
tutorials
is to present concepts of wavelet analysis and to demonstrate the
application of |
|
the WT
to a variety of transient signals encountered in electrical power systems.
|
|
Wavelet
transforms in power systems. II. Examples of application to actual |
|
Chul
Hwan Kim; Aggarwal, R. |
|
Power
Engineering Journal , Volume: 15 Issue: 4 , Aug. 2001 |
|
This
is the second in a series of two and illustrates some practical
applications of the |
|
wavelet
transform to power systems: protection/fault detection, detection of power
quality |
|
disturbances
and analysis of the partial discharge phenomenon in GIS (gas-insulated |
|
substations).
Emphasis is placed on a number of practical issues. |
|
