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In other words, the advantage of wavelet transform is that the wavelet coef
cient
re
ects in a simple and precise manner the properties of the underlying function.
Wavelet transform theory and its application to multiresolution signal decomposi-
tion has been thoroughly developed and well documented over the past few years
[ 19 , 20 ]. Daubechies [ 19 ] introduced the concept of the orthogonal wavelet, gen-
erally referred to as Daubechies wavelet. Indeed, multiresolution analysis and DWT
have a strong potential for exploring various dynamic features of any time-
dependent data.
In an orthonormal multi-resolution analysis, a signal, f
ð
t
Þ e
V 1 , is decomposed
, such that f ð t Þ ¼ P 0 g i ð t Þ
into an in
rst
level decomposition is done by projecting onto two orthogonal subspaces, V 0 and
W 0 , where V 1 ¼
nite series of detail functions, g i ð t Þ
. The
V 0
W 0 and
are the direct sum operator. The projection
produces f 0 ð
Þ e
ð
Þ
and g 0 ð
Þ e
t
V 0 , a low resolution approximation of f
t
t
W 0 , the
ð
Þ
to f 0 ð
Þ
detail lost in going from f
t
t
. The decomposition continues by projecting
f 0 ð
Þ
t
onto V 1 and W 1 , and so on. The orthonormal bases of V j and W j are given by
2 j = 2
2 j x
w j ; k ¼
k
Þ
ð 4 : 32 Þ
2 j = 2
2 j x
u j ; k ¼
k
Þ
ð 4 : 33 Þ
where
t
Þ
is the mother wavelet and
t
Þ
is the scaling function [ 20 ] where
Z
x
Þ
dt
¼
0
, wð
0
Þ ¼
0
ð 4 : 34 Þ
Z
x
Þ
dt
¼
1
, uð
1
Þ ¼
1
ð 4 : 35 Þ
where
w Þ
and
w Þ
are the Fourier transform of
t Þ
and
t Þ
, respectively. The
projection equations are
1
d k 2 ð j = 2 Þ
2 j t
g i ð
t
Þ ¼
k
Þ
ð 4 : 36 Þ
k
1
d k
¼ h
f j 1 ð
t
Þ; w j ; k i
ð 4 : 37 Þ
1
c k 2 ð j = 2 Þ
2 j t
f i ð
t
Þ ¼
k
Þ
ð 4 : 38 Þ
k 1
c k
¼ h f j 1 ð t Þ; u j ; k i
ð 4 : 39 Þ
where d k
and c k
is the L 2 inner
product. The nested sequence of subspaces, V j , constitutes the multiresolution
analysis. Conditions for the multiresolution to be orthonormal are (1)
are the projection coef
cients and
h:; :i
w j ; k and
/ j ; k
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