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Evaluation Warning : The document was created with Spire.PDF for Python.
T
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b
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6
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ti
-
attac
k
ca
p
ab
ilit
y
o
f
s
u
ch
ap
p
r
o
ac
h
ag
ain
s
t th
e
n
o
is
e
attac
k
s
is
g
o
i
n
g
to
b
e
en
h
an
ce
d
;
f
u
r
th
er
m
o
r
e,
r
o
b
u
s
tn
ess
r
eg
ar
d
in
g
s
u
ch
a
p
p
r
o
ac
h
is
g
o
i
n
g
to
b
e
en
h
a
n
ce
d
[
7
,
8
]
.
T
h
e
p
r
esen
ted
s
tu
d
y
d
ev
elo
p
s
DW
T
-
DC
T
-
SV
D
(
s
in
g
u
lar
v
alu
e
d
ec
o
m
p
o
s
itio
n
)
b
ased
ap
p
r
o
ac
h
o
f
th
e
im
ag
e
en
cr
y
p
tio
n
ac
c
o
r
d
in
g
t
o
d
ig
ital
wate
r
m
ar
k
i
n
g
ap
p
r
o
ac
h
es;
th
e
r
esu
lts
ar
e
s
h
o
win
g
th
at
th
e
d
ev
el
o
p
ed
ap
p
r
o
ac
h
h
as
th
e
a
b
ilit
y
f
o
r
r
esis
tin
g
th
e
m
ajo
r
ity
o
f
attac
k
s
;
th
e
ef
f
ec
tiv
en
ess
o
f
th
e
s
u
g
g
ested
s
ch
em
e
h
as
b
ee
n
h
o
wev
er
in
ac
ce
p
ta
b
le
in
ter
m
s
o
f
Ga
u
s
s
ian
n
o
is
e
attac
k
s
.
T
h
er
e
f
o
r
e,
th
e
s
tu
d
y
will
s
p
ec
if
y
u
tili
zin
g
th
e
im
ag
e
d
en
o
is
in
g
f
o
r
b
o
o
s
tin
g
an
ti
-
attac
k
ab
ilit
y
ag
ain
s
t th
e
n
o
is
e
attac
k
s
.
2.
SI
NG
U
L
AR
VA
L
U
E
DE
CO
M
P
O
SI
T
I
O
N
(
SV
D)
SVD
ca
n
b
e
d
ef
in
e
d
as
m
atr
ix
tr
an
s
f
o
r
m
atio
n
ap
p
r
o
ac
h
th
at
d
ep
en
d
s
o
n
th
e
eig
en
v
al
u
e.
E
a
ch
o
n
e
o
f
th
e
im
a
g
es
c
o
u
ld
b
e
p
r
o
v
id
e
d
as
m
atr
ix
,
SVD
m
ig
h
t
b
e
d
ec
o
m
p
o
s
in
g
th
e
m
atr
ix
to
s
u
m
o
f
v
ar
i
o
u
s
m
a
tr
ices.
Als
o
,
SVD
is
n
’
t
as
s
o
ciate
d
to
tr
an
s
f
o
r
m
atio
n
b
etwe
en
f
r
eq
u
e
n
cy
an
d
s
p
atial
d
o
m
ain
,
y
et
im
ag
e’
s
s
in
g
u
lar
v
alu
e
h
as
ex
ce
llen
t
s
tab
ilit
y
;
al
s
o
,
it
is
ty
p
ically
co
m
b
in
in
g
with
th
e
tr
an
s
f
o
r
m
alg
o
r
it
h
m
s
in
th
e
f
ield
o
f
im
ag
e
p
r
o
ce
s
s
in
g
.
I
n
t
h
e
ca
s
e
wh
en
d
is
tu
r
b
an
ce
s
ar
e
ap
p
lied
to
a
n
i
m
ag
e,
s
in
g
u
lar
v
alu
e
wo
n
’
t
b
e
to
o
m
u
ch
m
o
d
if
ie
d
.
Als
o
,
m
atr
ix
’
s
s
in
g
u
lar
v
ec
to
r
h
as
in
v
ar
ian
ce
in
ter
m
s
o
f
r
o
tatio
n
,
tr
an
s
latio
n
,
a
n
d
s
o
o
n
.
T
h
u
s
,
s
in
g
u
lar
v
alu
e
m
ig
h
t
ef
f
icien
tly
r
e
f
lect
th
e
m
atr
ix
’
s
p
r
o
p
er
ties
.
I
n
th
e
ca
s
e
wh
en
b
ein
g
u
tili
ze
d
to
im
ag
e’
s
m
atr
ix
,
s
in
g
u
la
r
v
alu
e
in
ad
d
itio
n
to
its
s
p
an
n
ed
v
ec
to
r
s
p
ac
e
r
eg
a
r
d
in
g
th
e
im
ag
e
m
ig
h
t
b
e
r
ef
lectin
g
v
ar
io
u
s
f
ea
tu
r
es
an
d
co
m
p
o
n
en
ts
o
f
im
ag
e.
I
m
ag
e
’
s
alg
eb
r
aic
ch
ar
ac
ter
is
tics
m
ig
h
t
b
e
s
p
ec
if
ied
,
also
SVD
h
as
b
e
en
m
ajo
r
ly
u
tili
ze
d
in
th
e
im
ag
e
p
r
o
ce
s
s
in
g
.
Du
e
t
o
its
r
o
tatio
n
in
v
ar
ian
ce
an
d
s
tab
ilit
y
,
th
e
m
ajo
r
ity
o
f
p
r
esen
t
alg
o
r
ith
m
s
o
f
im
ag
e
en
cr
y
p
tio
n
h
a
v
e
b
ee
n
o
n
t
h
e
b
asis
o
f
SVD
t
h
at
h
a
v
e
elev
a
ted
r
o
b
u
s
tn
ess
[
9
-
11
]
.
An
ex
c
ellen
t
ap
p
r
o
ac
h
f
o
r
co
m
p
u
tin
g
eig
e
n
v
ec
to
r
s
a
n
d
e
ig
en
v
alu
es
o
f
d
ata
m
atr
ix
X
(
Kx
M)
h
as
b
ee
n
with
th
e
u
s
e
o
f
SVD
s
p
ec
if
ied
as
f
o
llo
ws
[
12
]
:
=
[
1
2
…
…
…
…
+
1
+
2
…
…
…
2
(
−
1
)
+
1
(
−
1
)
+
2
…
…
.
]
(
1
)
T
h
e
th
eo
r
e
m
o
f
SVD
in
d
icatin
g
th
at
^x
M
m
atr
ix
X
m
ig
h
t
b
e
d
ec
o
m
p
o
s
ed
to
th
e
n
ex
t m
atr
ic
es’
p
r
o
d
u
ct:
=
∑
∗
(
2
)
I
n
wh
ich
U
r
e
p
r
ese
n
tin
g
K
x
K
o
r
th
o
n
o
r
m
al
m
atr
i
x
wh
ich
c
o
n
tain
lef
t sin
g
u
lar
v
ec
to
r
s
th
a
t a
r
e
ar
r
an
g
ed
co
lu
m
n
wis
e
=
[
1
1
…
…
1
1
2
…
…
1
1
…
…
1
]
(
3
)
V
r
ep
r
esen
tin
g
M
x
M
o
r
t
h
o
n
o
r
m
al
m
atr
ix
r
elate
d
to
th
e
r
ig
h
t sin
g
u
lar
v
ec
t
o
r
s
,
=
[
1
1
…
…
1
1
2
…
…
1
1
…
…
1
]
(
4
)
wh
ile
∑
r
ep
r
esen
tin
g
K
x
M
m
at
r
ix
r
eg
ar
d
in
g
t
h
e
n
o
n
n
eg
ativ
e
r
ea
l sin
g
u
lar
v
alu
es:
∑
=
[
1
0
0
0
2
0
.
.
.
.
.
.
.
.
.
.
.
.
0
0
0
0
0
.
.
.
.
.
.
.
.
.
.
.
.
0
0
0
]
(
5
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1693
-
6
9
3
0
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
,
Vo
l.
18
,
No
.
6
,
Dec
em
b
e
r
2
0
2
0
:
30
8
0
-
30
8
7
3082
Du
e
to
s
u
ch
SVD’
s
p
r
o
p
e
r
ties
,
in
th
e
p
ast
two
y
ea
r
s
,
s
o
m
e
w
ater
m
ar
k
in
g
ca
lcu
latio
n
s
wer
e
s
u
g
g
ested
with
r
eg
ar
d
to
s
u
ch
s
y
s
tem
.
T
h
e
m
ajo
r
co
n
ce
p
t
o
f
s
u
ch
ap
p
r
o
ac
h
h
as
b
ee
n
d
is
co
v
er
in
g
th
e
co
v
er
im
ag
e’
s
SVD
an
d
th
en
ch
an
g
in
g
its
s
o
litar
y
q
u
alities
f
o
r
in
s
tallin
g
wate
r
m
ar
k
.
A
f
ew
o
f
t
h
e
SVD
-
b
ase
d
ca
lcu
latio
n
s
h
a
v
e
b
ee
n
s
p
ec
if
ied
as
SVD
-
s
itu
ated
,
it
m
ig
h
t
b
e
in
d
icate
d
th
at
th
e
lo
n
e
SVD
ar
ea
h
as
b
ee
n
ap
p
lied
f
o
r
im
p
lan
tin
g
wate
r
m
ar
k
to
p
ictu
r
e
.
R
ec
en
t
ly
,
a
f
ew
o
f
h
alf
an
d
h
alf
S
VD
-
b
ased
ca
lcu
latio
n
s
wer
e
s
u
g
g
ested
,
in
wh
ich
th
e
d
if
f
er
en
t
ty
p
es
o
f
c
h
an
g
e
s
s
p
ac
e
in
v
o
lv
in
g
DC
T
,
DW
T
,
an
d
f
ast
Had
am
ar
d
tr
an
s
f
o
r
m
.
wer
e
u
s
ed
f
o
r
in
s
er
tin
g
th
e
wate
r
m
ar
k
to
p
ict
u
r
e
[
13
]
.
3.
DCT T
RANSF
O
R
M
T
h
is
ap
p
r
o
ac
h
h
as
s
o
lid
en
er
g
y
co
n
ce
n
tr
atio
n
p
r
o
p
er
ties
in
th
e
lo
w
f
r
eq
u
en
cy
p
a
r
t
f
o
llo
win
g
a
tr
an
s
f
o
r
m
.
Als
o
,
s
ig
n
al’
s
s
tatis
tical
ch
ar
ac
ter
is
tic
s
h
as
b
ee
n
clo
s
e
to
th
e
p
r
o
ce
s
s
o
f
Ma
r
k
o
v
,
DC
T
’
s
de
-
co
r
r
elate
d
p
er
f
o
r
m
a
n
ce
h
a
s
b
ee
n
clo
s
e
to
th
e
p
er
f
o
r
m
a
n
ce
r
eg
ar
d
in
g
K
-
L
tr
an
s
f
o
r
m
;
th
e
latter
p
r
o
v
id
e
d
o
p
tim
u
m
de
-
c
o
r
r
elate
d
p
er
f
o
r
m
an
ce
,
t
h
u
s
DC
T
h
as
b
ee
n
m
ajo
r
ly
u
tili
ze
d
in
th
e
im
ag
e
p
r
o
ce
s
s
in
g
lik
e
im
a
g
e
en
cr
y
p
tio
n
an
d
im
ag
e
c
o
m
p
r
e
s
s
io
n
.
I
n
co
m
p
ar
is
o
n
to
DFT,
co
m
p
u
tatio
n
s
in
th
e
DC
T
h
av
e
b
ee
n
in
th
e
r
ea
l
d
o
m
ain
,
elim
in
atin
g
th
e
co
m
p
lex
o
p
er
atio
n
s
as
well
as
en
h
a
n
cin
g
s
p
ee
d
.
Als
o
,
DC
T
h
as
r
o
tatio
n
,
tr
an
s
latio
n
,
an
d
s
ca
lin
g
in
v
ar
ian
ce
r
elate
d
to
Fo
u
r
ier
tr
an
s
f
o
r
m
th
at
m
i
g
h
t
b
e
ef
f
icien
tly
r
esis
tin
g
th
e
g
eo
m
etr
ic
attac
k
s
.
Du
e
to
s
u
ch
b
en
e
f
its
,
DC
T
h
as
ex
ce
lle
n
t
p
er
f
o
r
m
a
n
ce
in
im
ag
e
en
cr
y
p
tio
n
f
ield
a
n
d
was
u
tili
ze
d
r
ec
en
tly
in
a
lo
t o
f
s
tu
d
ies
[
13
,
14
]
.
C
h
an
g
es in
d
is
cr
ete
co
s
in
e
h
as
b
ee
n
a
p
r
o
ce
s
s
to
ch
a
n
g
e
f
lag
to
r
u
d
im
en
tar
y
r
ec
u
r
r
e
n
ce
p
ar
t
s
.
Als
o
,
it
is
d
ea
lin
g
with
th
e
p
ictu
r
e
as
en
tire
ty
o
f
s
in
u
s
o
id
s
r
elate
d
t
o
f
r
eq
u
e
n
cies
an
d
f
lu
ctu
ati
n
g
e
x
ten
ts
.
W
ith
r
eg
ar
d
to
th
e
in
f
o
r
m
atio
n
p
ictu
r
e,
x
,
t
h
e
DC
T
co
ef
f
icien
ts
f
o
r
ch
an
g
ed
y
ield
p
ictu
r
e,
y
,
h
av
e
b
ee
n
p
r
o
ce
s
s
ed
as
s
h
o
wn
in
(
5
)
.
Fu
r
th
e
r
m
o
r
e
,
x
,
r
ep
r
esen
tin
g
in
f
o
im
a
g
eh
av
in
g
N
x
M
p
ix
els,
x
(
m
,
n
)
h
as b
ee
n
p
ix
el’
s
p
o
wer
in
p
u
s
h
m
,
also
s
eg
m
en
t
n
r
elate
d
to
p
ictu
r
e,
y
(
u
,
v
)
r
e
p
r
esen
tin
g
DC
T
co
ef
f
icien
t
in
th
e
p
u
s
h
u
,
wh
ile
th
e
s
ec
tio
n
v
o
f
DC
T
n
etwo
r
k
[
15
,
16
]
.
(
,
)
=
√
2
√
2
∑
∑
(
,
)
cos
(
2
+
1
)
2
−
1
=
0
−
1
=
0
cos
(
2
+
1
)
2
(6
)
An
im
ag
e
h
as b
ee
n
r
e
-
c
o
n
s
tr
u
cted
v
ia
u
s
in
g
th
e
in
v
er
s
e
DC
T
o
p
er
ati
o
n
as sh
o
w
in
(
6
)
:
(
,
)
=
√
2
√
2
∑
∑
(
,
)
c
os
(
2
+
1
)
2
−
1
=
0
−
1
=
0
c
os
(
2
+
1
)
2
(
7
)
4.
DIS
CR
E
T
E
WA
VE
L
E
T
T
R
ANSF
O
RM
(
DW
T
)
T
h
e
wav
elets
h
av
e
b
ee
n
u
tili
ze
d
in
im
ag
e
p
r
o
ce
s
s
in
g
f
o
r
co
m
p
r
ess
io
n
,
wate
r
m
ar
k
in
g
,
s
am
p
le
ed
g
e
de
tectio
n
,
co
d
in
g
a
n
d
d
e
n
o
is
in
g
o
f
th
e
in
ter
esti
n
g
f
ea
t
u
r
es
with
r
eg
ar
d
to
s
u
b
s
eq
u
en
t
cla
s
s
if
icatio
n
.
T
h
e
n
ex
t
s
u
b
-
s
ec
tio
n
s
ar
e
d
is
cu
s
s
in
g
th
e
d
en
o
is
in
g
o
f
im
ag
e
th
r
o
u
g
h
t
h
r
esh
o
ld
in
g
DW
T
co
ef
f
icien
ts
[
17
-
20
]
.
4
.
1
.
DWT
o
f
i
m
a
g
e
da
t
a
I
m
ag
es
ar
e
p
r
o
v
id
ed
as
2
D
c
o
ef
f
icien
ts
’
ar
r
ay
.
E
ac
h
o
n
e
o
f
th
e
co
ef
f
icien
t
s
ar
e
r
ep
r
es
en
tin
g
th
at
p
o
in
t’
s
b
r
ig
h
tn
ess
d
e
g
r
ee
.
T
h
e
m
ajo
r
ity
o
f
t
h
e
h
er
b
al
p
h
o
to
g
r
ap
h
s
ar
e
s
h
o
win
g
th
e
s
m
o
o
th
c
o
lo
r
atio
n
v
ar
iatio
n
s
with
o
p
tim
u
m
d
etails
r
e
p
r
esen
tin
g
s
h
ar
p
ed
g
es
f
r
o
m
s
im
p
le
v
er
s
io
n
s
.
T
h
e
clea
n
v
a
r
iatio
n
s
in
co
lo
r
atio
n
m
ig
h
t
b
e
lab
elled
as
lo
w
-
f
r
eq
u
e
n
cy
v
er
s
io
n
s
,
in
wh
ich
th
e
p
o
in
ty
v
a
r
iatio
n
s
m
ig
h
t
b
e
lab
elled
as
ex
ce
s
s
iv
e
-
f
r
eq
u
en
c
y
v
er
s
io
n
s
.
A
ls
o
,
lo
w
-
f
r
eq
u
en
c
y
co
m
p
o
n
en
ts
(
f
o
r
in
s
tan
ce
,
s
m
o
o
th
v
er
s
io
n
s
)
ar
e
s
h
o
win
g
th
e
p
h
o
to
g
r
ap
h
s
’
b
ase
,
in
wh
ich
ex
ce
s
s
iv
e
-
f
r
e
q
u
en
c
y
co
m
p
o
n
en
ts
(
f
o
r
in
s
tan
ce
e
d
g
e
s
p
r
o
v
i
d
in
g
th
e
d
etails
)
h
a
v
e
b
ee
n
u
p
lo
ad
e
d
u
p
o
n
lo
w
-
f
r
eq
u
e
n
cy
co
m
p
o
n
en
ts
f
o
r
r
ef
in
in
g
th
e
im
ag
e
,
th
u
s
cr
ea
t
in
g
in
-
d
e
p
th
im
ag
e
s
.
Als
o
,
th
e
ea
s
y
v
er
s
io
n
s
h
av
e
b
ee
n
s
ig
n
if
ican
t
in
c
o
m
p
ar
is
o
n
to
d
etails.
A
lo
t
o
f
ap
p
r
o
ac
h
es
m
ig
h
t
b
e
u
tili
ze
d
f
o
r
d
if
f
er
en
tiatin
g
b
etwe
en
th
e
p
h
o
to
g
r
ap
h
in
f
o
r
m
atio
n
an
d
ea
s
y
v
ar
iatio
n
s
.
An
e
x
am
p
le
o
f
s
u
c
h
ap
p
r
o
ac
h
es
h
as
b
ee
n
pi
ctu
r
e
d
ec
o
m
p
o
s
itio
n
th
r
o
u
g
h
DW
T
r
e
-
m
o
d
elin
g
.
Var
io
u
s
lev
els
o
f
de
-
co
m
p
o
s
itio
n
r
elate
d
to
DW
T
ca
n
b
e
s
ee
n
i
n
th
e
Fig
u
r
e
1
.
4
.
2
.
I
ma
g
e’
s
inv
er
s
e
DWT
Var
io
u
s
d
ata
class
es
h
av
e
b
e
en
co
llected
t
o
r
e
-
co
n
s
tr
u
cted
im
ag
e
with
th
e
u
s
e
o
f
r
ev
er
s
e
wav
elet
tr
an
s
f
o
r
m
.
Als
o
,
p
air
o
f
th
e
lo
w
an
d
h
ig
h
-
p
ass
f
ilter
s
h
av
e
b
ee
n
u
tili
ze
d
th
r
o
u
g
h
o
u
t
th
e
p
r
o
ce
s
s
o
f
re
-
co
n
s
tr
u
ctio
n
.
T
h
e
f
ilter
s
h
a
v
e
b
ee
n
in
d
icate
d
t
o
as
s
y
n
t
h
esis
f
ilter
p
air
.
T
h
e
p
r
o
ce
s
s
o
f
f
ilter
in
g
h
as
b
ee
n
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
I
mp
r
o
ve
d
a
n
ti
-
n
o
is
e
a
tta
ck
a
b
ilit
y
o
f ima
g
e
en
cryp
tio
n
a
l
g
o
r
ith
m
u
s
in
g
.
.
.
(
Mo
h
a
n
a
d
N
a
jm
A
b
d
u
lw
a
h
ed
)
3083
th
e
o
p
p
o
s
ite
o
f
tr
a
n
s
f
o
r
m
atio
n
;
th
e
p
r
o
ce
s
s
is
s
tar
ti
n
g
f
r
o
m
h
ig
h
est
lev
el.
Fu
r
th
er
m
o
r
e,
f
ilter
s
h
av
e
b
ee
n
in
itially
u
tili
ze
d
co
lu
m
n
-
wis
e,
af
ter
th
a
t r
o
w
-
wis
e
lev
el
b
y
lev
el
till
r
e
ac
h
in
g
lo
west lev
el.
(
a)
(
b
)
(
c)
Fig
u
r
e
1
.
DW
T
Dec
o
m
p
o
s
itio
n
lev
els
;
(
a)
s
in
g
le
lev
el
d
ec
o
m
p
o
s
itio
n
,
(
b
)
two
lev
el
d
ec
o
m
p
o
s
itio
n
,
(
c)
th
r
ee
lev
el
d
ec
o
m
p
o
s
itio
n
5.
ST
RA
T
E
G
I
E
S O
F
I
M
AG
E
DE
NO
I
SI
NG
U
T
I
L
I
Z
I
NG
DWT
W
ith
r
eg
ar
d
to
d
ig
ital
im
ag
e
p
r
o
c
ess
in
g
,
im
ag
es
a
r
e
s
o
m
etim
es
attac
k
ed
v
ia
d
if
f
e
r
en
t
n
o
is
es
an
d
th
e
im
ag
e’
s
q
u
ality
is
g
o
in
g
to
b
e
r
ed
u
ce
d
;
if
th
e
im
ag
e
n
o
is
e
m
ig
h
t
b
e
ef
f
icien
tly
f
ilter
ed
o
u
t
o
r
n
o
t,
it
is
g
o
in
g
to
b
e
af
f
ec
tin
g
s
u
b
s
eq
u
en
t
p
r
o
ce
s
s
in
g
lik
e
im
ag
e
d
ec
r
y
p
tio
n
,
ed
g
e
d
etec
tio
n
,
o
b
ject
s
eg
m
e
n
tatio
n
,
an
d
f
ea
tu
r
e
ex
tr
ac
tio
n
[
21
,
22
]
.
W
ith
r
eg
ar
d
to
d
ig
ital
im
ag
e
p
r
o
ce
s
s
in
g
,
i
m
ag
es
ar
e
s
o
m
etim
es
attac
k
ed
v
ia
d
if
f
er
en
t
n
o
is
es
an
d
th
e
im
ag
e’
s
q
u
ality
is
g
o
in
g
to
b
e
r
ed
u
ce
d
;
if
th
e
im
ag
e
n
o
is
e
m
ig
h
t
b
e
e
f
f
icien
tly
f
ilter
ed
o
u
t
o
r
n
o
t,
it
is
g
o
in
g
to
b
e
af
f
ec
tin
g
s
u
b
s
eq
u
en
t
p
r
o
ce
s
s
in
g
lik
e
im
ag
e
d
ec
r
y
p
tio
n
,
e
d
g
e
d
etec
tio
n
,
o
b
jec
t
s
eg
m
en
tatio
n
,
an
d
f
ea
tu
r
e
ex
tr
ac
ti
o
n
[
21
,
22
]
.
T
h
e
n
ex
t p
h
ases
ar
e
d
escr
ib
in
g
th
e
p
r
o
ce
s
s
o
f
im
a
g
e
d
en
o
is
in
g
.
-
DW
T
r
elate
d
to
a
n
o
is
y
im
ag
e
will b
e
esti
m
ated
.
-
Af
t
er
th
e
DW
T
r
ep
r
esen
tatio
n
d
o
n
e,
d
e
-
n
o
is
in
g
is
d
o
n
e
u
s
in
g
s
o
f
t
-
th
r
esh
o
ld
in
g
b
y
m
o
d
if
ied
u
n
iv
er
s
al
th
r
esh
o
ld
esti
m
atio
n
(
MU
T
E
)
.
Pro
v
id
i
n
g
am
b
ien
t
n
o
is
e
is
a
c
o
lo
r
ed
,
a
th
r
esh
o
ld
d
e
p
en
d
e
n
t
o
n
lev
el
ap
p
lied
to
ea
ch
lev
el
o
f
f
r
eq
u
en
cy
wa
s
p
r
o
p
o
s
ed
in
[
7
,
23
]
.
T
h
e
v
al
u
e
o
f
th
r
esh
o
ld
ap
p
lied
to
t
h
e
c
o
ef
f
icien
ts
o
f
esti
m
ated
tim
e
-
f
r
eq
u
en
c
y
u
s
in
g
MU
T
E
[
23
]
is
ex
p
r
ess
ed
as
:
=
.
,
√
2
l
og
(
)
(
8
)
wh
er
e
N
is
len
g
th
o
f
s
ig
n
al,
,
is
n
o
is
e
esti
m
ated
s
tan
d
ar
d
d
ev
iatio
n
f
o
r
lev
el
k
,
an
d
c
i
s
th
e
(
m
o
d
if
ied
u
n
iv
er
s
al
th
r
esh
o
ld
f
ac
to
r
)
0<
<1
.
T
h
e
n
o
is
e
v
ar
ian
ce
will
b
e
co
m
p
u
ted
with
th
e
u
s
e
o
f
th
e
n
ex
t
r
o
b
u
s
t
m
ed
ian
esti
m
ato
r
:
=
(
|
(
,
)
|
)
0
.
6745
(
9
)
I
n
wh
ich
(
,
)
r
ep
r
esen
tin
g
all
co
ef
f
icien
ts
r
elate
d
to
wav
elet
d
eta
ils
in
lev
el
k
[
24
]
.
-
So
f
t
th
r
esh
o
l
d
will
b
e
u
tili
ze
d
to
s
u
b
-
b
an
d
co
e
f
f
icien
ts
wi
th
r
eg
a
r
d
to
ea
ch
o
f
th
e
s
u
b
-
b
an
d
s
,
e
x
clu
d
in
g
lo
w
-
p
ass
o
r
ap
p
r
o
x
im
atio
n
s
u
b
-
b
an
d
[
25
]
.
,
(
,
)
=
{
(
(
,
)
)
(
|
(
,
)
|
−
)
|
(
,
)
|
>
0
|
(
,
)
|
≤
(
10
)
I
n
wh
ich
r
ep
r
esen
tin
g
th
r
esh
o
ld
v
alu
e
in
th
e
lev
el
k
,
al
s
o
,
(
,
)
r
ep
r
esen
tin
g
wav
elet
d
etail
co
ef
f
icien
ts
f
o
llo
win
g
th
e
p
r
o
ce
s
s
o
f
th
r
esh
o
ld
in
g
in
lev
el
k
.
-
I
m
ag
e
h
as
b
ee
n
r
e
-
c
o
n
s
tr
u
cted
th
r
o
u
g
h
u
s
in
g
in
v
er
s
e
DW
T
f
o
r
o
b
tain
i
n
g
d
en
o
i
s
ed
im
ag
e.
Fig
u
r
e
2
s
h
o
win
g
th
e
d
ata
f
lo
w
d
ia
g
r
am
r
elate
d
t
o
th
e
d
e
n
o
is
in
g
p
r
o
ce
s
s
o
f
an
i
m
ag
e.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1693
-
6
9
3
0
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
,
Vo
l.
18
,
No
.
6
,
Dec
em
b
e
r
2
0
2
0
:
30
8
0
-
30
8
7
3084
Fig
u
r
e
2
.
Data
f
l
o
w
d
iag
r
am
o
f
im
ag
e
d
e
n
o
is
in
g
6.
E
NCRY
P
T
I
O
N
T
E
CH
N
I
Q
UE
S AC
CO
RDING
T
O
DW
T
-
DCT
-
S
VD
T
H
RO
UG
H
U
T
I
L
I
Z
I
NG
DE
NO
I
SI
NG
AP
P
RO
ACH
E
S
US
I
NG
DW
T
On
th
e
b
asi
s
o
f
th
e
p
r
esen
ted
DW
T
-
DC
T
-
SV
D
en
cr
y
p
tio
n
t
ec
h
n
iq
u
es
with
t
h
e
u
s
e
o
f
n
o
r
m
al
im
ag
e
as
h
o
s
t
im
ag
e,
u
s
in
g
th
e
ap
p
r
o
ac
h
es
o
f
d
en
o
is
in
g
p
r
io
r
to
im
ag
e
d
ec
r
y
p
tio
n
f
o
r
en
h
an
cin
g
th
e
an
ti
-
attac
k
ca
p
ab
ilit
y
r
elate
d
to
s
u
ch
ap
p
r
o
ac
h
ag
ain
s
t
n
o
is
e
attac
k
s
.
Al
s
o
,
n
ew
wo
r
k
f
lo
w
h
as
b
ee
n
s
h
o
wn
in
th
e
Fig
u
r
e
3.
Acc
o
r
d
in
g
to
th
e
Fig
u
r
e
3
,
th
e
p
r
o
ce
s
s
es
o
f
en
cr
y
p
tio
n
an
d
d
e
cr
y
p
tio
n
m
ig
h
t
b
e
p
r
o
v
id
ed
in
th
e
f
o
llo
win
g
way
:
-
Step
1
: Sele
ctin
g
o
r
ig
in
al
a
n
d
h
o
s
t im
ag
es o
f
s
am
e
s
ize;
-
Step
2
:
Utilizin
g
DW
T
to
th
e
two
im
ag
e,
also
g
e
ttin
g
4
s
u
b
-
b
an
d
s
f
o
r
ea
ch
o
n
e
o
f
th
e
im
ag
es;
f
o
llo
win
g
u
tili
zin
g
DC
T
o
n
th
e
s
u
b
-
b
a
n
d
s
,
ap
p
ly
in
g
SVD
f
o
r
ea
c
h
o
n
e
o
f
th
e
s
u
b
-
b
an
d
s
an
d
co
m
p
o
s
ed
th
e
c
o
i
n
c
i
d
e
n
t
s
u
b
-
b
a
n
d
s
t
o
w
a
r
d
s
o
r
i
g
i
n
a
l
a
n
d
h
o
s
t
i
m
a
g
e
s
;
a
f
t
e
r
t
h
a
t
,
a
p
p
l
y
i
n
g
t
h
e
i
n
v
e
r
s
e
-
D
W
T
a
s
w
e
l
l
a
s
t
h
e
i
n
v
e
r
s
e
-
D
C
T
f
o
r
g
e
t
t
i
n
g
e
n
c
r
y
p
t
e
d
i
m
a
g
e
,
s
u
c
h
p
r
o
c
e
s
s
m
i
g
h
t
b
e
t
r
e
a
t
e
d
a
s
D
W
T
-
D
C
T
-
S
V
D
e
n
c
r
y
p
t
i
o
n
a
p
p
r
o
a
c
h
;
-
Step
3
:
T
h
r
o
u
g
h
th
e
en
cr
y
p
ted
im
ag
e’
s
tr
an
s
m
is
s
io
n
,
it
m
ig
h
t
b
e
attac
k
ed
th
r
o
u
g
h
th
e
n
o
is
in
g
attac
k
s
;
u
s
in
g
co
n
v
en
tio
n
al
d
en
o
is
in
g
ap
p
r
o
ac
h
es
o
r
t
h
e
lin
ea
r
C
NN
m
o
d
el
-
b
ased
a
p
p
r
o
ac
h
f
o
r
f
i
lter
in
g
attac
k
ed
en
cr
y
p
ted
im
ag
e;
-
Step
4
:
E
n
cr
y
p
ted
im
a
g
e
is
g
o
in
g
to
b
e
d
ec
r
y
p
ted
,
also
th
e
p
r
o
ce
s
s
o
f
d
ec
r
y
p
tio
n
is
g
o
in
g
t
o
b
e
h
an
d
led
as
en
cr
y
p
tio
n
’
s
in
v
er
s
e
p
r
o
ce
d
u
r
e;
af
ter
th
at,
g
ettin
g
th
e
d
ec
r
y
p
ted
im
ag
e.
Fig
u
r
e
3
.
Su
g
g
ested
m
o
d
el
7.
P
E
RF
O
RM
AN
CE
M
E
ASU
RE
S
T
h
e
co
m
m
o
n
m
ea
s
u
r
em
en
t p
a
r
am
eter
s
with
r
eg
ar
d
to
th
e
r
el
iab
ilit
y
o
f
im
ag
e
in
v
o
l
v
es m
ea
n
ab
s
o
lu
te
er
r
o
r
,
n
o
r
m
alize
d
m
ea
n
s
q
u
a
r
e
er
r
o
r
(
NM
SE
)
,
m
ea
n
s
q
u
a
r
e
er
r
o
r
(
MSE
)
,
an
d
p
ea
k
s
i
g
n
al
-
to
-
n
o
is
e
r
atio
(
PS
NR
)
.
SNR
o
v
er
4
0
d
B
o
f
f
er
s
o
p
tim
u
m
q
u
ality
o
f
t
h
e
i
m
ag
e
wh
ich
is
clo
s
e
t
o
o
r
ig
in
al
im
ag
e;
SNR
with
30
-
40
d
B
g
en
er
ally
p
r
o
d
u
cin
g
ex
ce
llen
t
q
u
ality
o
f
th
e
im
ag
e
with
ad
eq
u
ate
d
is
to
r
tio
n
s
;
S
NR
wi
th
2
0
-
30
d
B
p
r
esen
tin
g
b
ad
q
u
ality
o
f
th
e
i
m
ag
e;
SNR
n
o
t
m
o
r
e
t
h
an
2
0
d
B
g
en
er
atin
g
u
n
d
esira
b
le
im
ag
e
[
26
]
.
Fu
r
t
h
er
m
o
r
e,
th
e
ca
lcu
latio
n
ap
p
r
o
ac
h
e
s
f
o
r
NM
SE
an
d
PS
NR
[
27
]
h
av
e
b
ee
n
p
r
o
v
id
ed
i
n
th
e
f
o
llo
win
g
way
:
(
11
)
I
n
wh
ich
MSE
r
e
p
r
esen
tin
g
M
SE
b
etwe
en
o
r
ig
in
al
im
a
g
e
(
)
as we
ll a
s
d
en
o
is
ed
im
ag
e
(
̂
)
with
s
ize
M×
N:
(1
2
)
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
I
mp
r
o
ve
d
a
n
ti
-
n
o
is
e
a
tta
ck
a
b
ilit
y
o
f ima
g
e
en
cryp
tio
n
a
l
g
o
r
ith
m
u
s
in
g
.
.
.
(
Mo
h
a
n
a
d
N
a
jm
A
b
d
u
lw
a
h
ed
)
3085
8.
RE
SU
L
T
S
AND
D
I
SCU
SS
I
O
N
S
T
h
is
s
tu
d
y
u
tili
ze
d
2
d
is
tin
ctiv
e
alg
o
r
ith
m
s
with
r
e
g
ar
d
to
d
ig
ital
im
ag
e’
s
wate
r
m
ar
k
in
g
,
also
f
o
r
ea
c
h
o
n
e
o
f
th
e
s
ch
em
es,
th
er
e
ar
e
3
ty
p
es o
f
r
esu
lts
as f
o
llo
ws:
-
T
h
e
i
m
ag
e
wate
r
m
ar
k
in
g
/
d
ewa
ter
m
ar
k
in
g
with
n
o
im
ag
e
at
tack
.
-
T
h
e
i
m
ag
e
wate
r
m
ar
k
in
g
/
d
ewa
ter
m
ar
k
in
g
with
th
e
Gau
s
s
ian
n
o
is
e
im
ag
e
attac
k
.
W
ith
r
eg
ar
d
to
all
th
e
s
ets o
f
im
ag
es,
th
er
e
h
av
e
b
ee
n
3
r
esu
l
ts
r
elate
d
to
ea
ch
alg
o
r
ith
m
.
T
h
e
r
ec
o
v
e
r
im
ag
e’
s
q
u
ality
h
a
s
b
ee
n
esti
m
ated
v
ia
MSE
an
d
PS
NR
.
Hig
h
PS
NR
v
alu
es
r
ep
r
esen
tin
g
h
ig
h
er
q
u
ality
r
elate
d
to
th
e
r
ec
o
v
e
r
im
ag
e
b
ec
a
u
s
e
o
f
s
m
all
er
r
o
r
s
i
n
th
e
al
g
o
r
ith
m
o
f
im
ag
e
ex
tr
ac
tio
n
.
Als
o
,
t
h
e
MSE
n
ea
r
ze
r
o
s
is
th
e
s
im
ilar
ity
m
ea
s
u
r
e
b
etwe
en
2
im
ag
es.
T
h
e
s
tu
d
y
s
elec
t
ed
im
ag
e
ca
m
er
m
a
n
f
o
r
s
h
o
win
g
th
e
r
esu
lts
.
Dec
r
y
p
ted
an
d
en
c
r
y
p
ted
im
ag
es
ca
n
b
e
s
ee
n
in
Fig
u
r
e
4
.
Acc
o
r
d
in
g
to
th
e
r
esu
lts
,
it
ca
n
b
e
s
ee
n
th
at
th
e
en
cr
y
p
te
d
im
ag
e
h
as
b
ee
n
co
m
p
ar
a
b
le
to
h
o
s
t
im
ag
e.
Pu
t
d
if
f
er
en
tly
,
s
ec
r
et
im
ag
e
’
s
in
f
o
r
m
atio
n
was
s
u
cc
ess
f
u
lly
h
id
d
en
in
en
cr
y
p
ted
im
ag
e.
W
ith
r
eg
ar
d
to
th
e
d
ec
r
y
p
ted
im
ag
e,
it
ca
n
b
e
in
d
ic
ated
th
at
th
e
s
ec
r
et
im
ag
e’
s
d
etails
ar
e
v
is
ib
le,
als
o
s
p
ec
if
y
in
g
th
at
all
th
e
4
r
esu
lts
ar
e
m
ee
tin
g
th
e
ex
p
ec
tatio
n
s
,
also
th
e
en
c
r
y
p
tio
n
ap
p
r
o
ac
h
o
n
th
e
b
asis
o
f
DW
T
-
DC
T
-
SVD
s
y
s
tem
i
s
o
f
ad
eq
u
ate
p
er
f
o
r
m
an
ce
.
(
a)
(
b
)
(
c)
(
d
)
Fig
u
r
e
4
.
R
esu
lts
f
o
r
alg
o
r
ith
m
o
n
e
im
a
g
e
en
cr
y
p
tio
n
with
n
o
n
o
is
e
attac
k
;
(
a)
h
o
s
t im
ag
e,
(
b
)
o
r
ig
i
n
al
im
ag
e,
(
c)
e
n
cr
y
p
ted
im
ag
e,
(
d
)
d
ec
r
y
p
ted
im
ag
e
T
h
e
s
tu
d
y
u
tili
ze
d
t
h
e
DC
T
-
D
W
T
-
SVD
n
o
is
e
alg
o
r
ith
m
o
n
t
h
e
h
o
s
t
im
ag
e
f
o
r
wate
r
m
ar
k
i
n
g
o
r
ig
in
al
im
ag
e.
Af
ter
th
at,
th
e
Gau
s
s
ian
im
ag
e
with
t
h
e
v
ar
ian
ce
attac
k
s
h
as b
ee
n
a
p
p
lied
to
th
e
wa
ter
m
ar
k
im
ag
e,
also
it
h
as
b
ee
n
d
ewa
ter
m
ar
k
ed
a
n
d
th
e
ex
tr
ac
ted
wate
r
m
ar
k
ed
i
m
ag
e
ca
n
b
e
s
ee
n
in
th
e
Fig
u
r
e
5
.
T
ab
le
1
,
s
h
o
win
g
th
e
s
u
g
g
ested
m
eth
o
d
’
s
p
e
r
f
o
r
m
an
ce
o
n
th
e
n
o
is
e
p
o
wer
with
v
ar
ia
n
ce
0
.
1
o
n
th
e
b
asis
o
f
Dau
b
ec
h
ies wa
v
elet
b
iases
in
co
m
p
ar
is
o
n
to
th
e
c
ase
with
n
o
n
o
is
e
attac
k
.
T
h
e
v
alu
es
o
f
MSE
an
d
PS
NR
h
av
e
b
ee
n
esti
m
ated
ac
co
r
d
in
g
to
n
o
is
e
p
o
wer
v
al
u
e.
T
ab
le
1
.
Per
f
o
r
m
an
ce
o
n
t
h
e
n
o
is
e
p
o
wer
with
v
ar
ia
n
ce
0
.
1
o
n
th
e
b
asis
o
f
Dau
b
ec
h
ies wa
v
elet
b
iases
in
co
m
p
ar
is
o
n
t
o
th
e
ca
s
e
with
n
o
n
o
is
e
attac
k
C
a
se
P
S
N
R
M
S
E
N
o
A
t
t
a
c
k
2
1
2
0
.
0
0
3
4
2
.
3
0
0
.
0
0
8
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1693
-
6
9
3
0
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
,
Vo
l.
18
,
No
.
6
,
Dec
em
b
e
r
2
0
2
0
:
30
8
0
-
30
8
7
3086
(
a)
(
b
)
(
c)
(
d
)
(
e)
(f)
Fig
u
r
e
5
.
I
m
ag
e
e
n
cr
y
p
tio
n
r
e
s
u
lts
with
th
e
Gau
s
s
ian
n
o
is
e
attac
k
;
(
a)
h
o
s
t im
ag
e,
(
b
)
o
r
ig
i
n
al
im
ag
e,
(
c)
e
n
cr
y
p
ted
im
ag
e,
(
d
)
g
au
s
s
ian
n
o
is
e
en
cr
y
p
ted
im
ag
e,
(
e)
en
cr
y
p
ted
im
a
g
e
af
ter
d
en
o
is
in
g
,
(
f
)
d
e
cr
y
p
te
d
im
ag
e
d
en
o
is
in
g
9.
CO
NCLU
SI
O
N
S
Th
e
r
esu
lts
o
f
th
is
s
tu
d
y
ar
e
s
u
g
g
esti
n
g
th
at
th
e
DC
T
-
DW
T
-
SVD
b
ased
wate
r
m
ar
k
in
g
ap
p
r
o
ac
h
as
well
as
th
e
d
en
o
is
in
g
alg
o
r
ith
m
u
tili
zin
g
DW
T
h
as
b
ee
n
p
r
o
v
id
in
g
o
p
tim
u
m
p
e
r
f
o
r
m
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r
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eg
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w
ater
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h
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n
d
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Ha
m
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y
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]
O.
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o
ru
y
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,
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t
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l
.
,
"
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v
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lu
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ti
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L
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NIKA
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e
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o
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o
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p
p
.
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9
6
8
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4
,
De
c
e
m
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r
2019.
[3
]
E.
H.
Ra
c
h
m
a
wa
n
to
,
e
t
a
l
.,
"
A
n
imp
ro
v
e
d
se
c
u
rit
y
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n
d
m
e
ss
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g
e
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a
p
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it
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u
sin
g
AES
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n
d
Hu
ff
m
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n
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ste
g
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n
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p
h
y
,
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E
L
KOM
NIKA
T
e
l
e
c
o
mm
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n
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mp
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ti
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tro
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ics
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o
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l
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o
.
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p
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4
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9
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t
o
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e
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2
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.
[4
]
Z.
Li
u
,
"
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o
m
p
a
ra
ti
v
e
e
v
a
l
u
a
ti
o
n
s
o
f
ima
g
e
e
n
c
ry
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ti
o
n
a
l
g
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rit
h
m
s,"
Au
c
k
lan
d
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i
v
e
rsity
o
f
Tec
h
n
o
l
o
g
y
,
2
0
1
8
.
[5
]
Z.
Li
u
,
e
t
a
l
.
,
"
Do
u
b
le
ima
g
e
e
n
c
ry
p
ti
o
n
b
y
u
si
n
g
Arn
o
l
d
tran
sfo
rm
a
n
d
d
isc
re
te
fra
c
ti
o
n
a
l
a
n
g
u
lar
tran
sfo
rm
,
"
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t
ics
a
n
d
L
a
se
rs
in
En
g
i
n
e
e
rin
g
,
v
o
l.
5
0
,
n
o
.
2
,
pp
.
2
4
8
-
2
5
5
,
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e
b
ru
a
r
y
2
0
1
2
.
[6
]
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Div
e
c
h
a
a
n
d
N.
Ja
n
i,
"
Im
p
l
e
m
e
n
tatio
n
a
n
d
p
e
rf
o
rm
a
n
c
e
a
n
a
ly
sis
o
f
DCT
-
DWT
-
S
VD
b
a
se
d
wa
term
a
rk
in
g
a
lg
o
rit
h
m
s
fo
r
c
o
lo
r
ima
g
e
s,"
2
0
1
3
I
n
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
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n
telli
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e
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t
S
y
ste
ms
a
n
d
S
i
g
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a
l
Pr
o
c
e
ss
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g
(IS
S
P)
,
p
p
.
2
0
4
-
208
,
M
a
rc
h
2
0
1
3
.
[7
]
Y.
Y.
Al
-
A
b
o
o
si,
R
.
S
.
Iss
a
,
a
n
d
A.
k
h
a
li
d
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ss
im,
"
Im
a
g
e
d
e
n
o
s
in
g
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u
n
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e
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ter
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ise
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te
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sfo
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h
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iffere
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t
n
o
ise
le
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l
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stim
a
ti
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n
,
"
T
E
L
KOM
NIKA
T
e
l
e
c
o
mm
u
n
ica
t
io
n
,
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m
p
u
ti
n
g
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e
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tro
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ics
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n
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tro
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l.
1
8
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o
.
3
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p
.
1
4
3
9
-
1
4
4
6
,
J
u
n
e
2
0
2
0
.
[8
]
R.
Th
il
lai
n
a
y
a
g
i
a
n
d
K.
S
e
n
t
h
il
K
u
m
a
r,
"
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m
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i
n
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ti
o
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o
f
wa
v
e
let
tr
a
n
sfo
rm
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n
d
sin
g
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lar
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l
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e
d
e
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o
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p
o
siti
o
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se
d
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o
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tras
t
e
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h
a
n
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e
m
e
n
t
tec
h
n
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u
e
fo
r
targ
e
t
d
e
tec
ti
o
n
i
n
UA
V
re
c
o
n
n
a
issa
n
c
e
th
e
rm
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l
ima
g
e
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J
o
u
rn
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l
o
f
M
o
d
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ti
c
s,
v
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l.
6
6
,
n
o
.
3
,
p
p
.
6
0
6
-
6
1
7
,
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n
u
a
ry
2
0
1
9
.
[9
]
N.
M
.
M
a
k
b
o
l,
B.
E
.
Kh
o
o
,
a
n
d
T.
H.
Ra
ss
e
m
,
"
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c
k
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b
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g
h
u
m
a
n
v
isu
a
l
sy
ste
m
c
h
a
ra
c
teristics
,
"
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e
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ro
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1
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p
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
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A.
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v
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n
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.
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se
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1
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iza
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.
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ima
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n
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.
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tu
ti
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b
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st
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u
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i
o
w
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term
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rk
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g
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se
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o
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tra
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sfo
r
m
d
o
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a
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o
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re
ss
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e
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m
p
li
n
g
fra
m
e
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rk
,
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T
E
L
KOM
NIKA
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e
l
e
c
o
mm
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n
ica
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o
n
,
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o
mp
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ti
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e
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tro
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l
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1
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no.
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p
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1
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9
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p
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0
2
0
.
[1
2
]
Ha
n
d
it
o
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Ku
r
n
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n
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ra
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e
t
a
l.
,
"
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e
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o
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p
a
riso
n
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twe
e
n
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VD
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CT
a
n
d
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VD
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ig
i
tal
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term
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,
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ter
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l
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e
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Da
t
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a
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d
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fo
rm
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t
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c
ien
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e
.
IOP
C
o
n
f.
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e
rie
s:
J
o
u
rn
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l
o
f
Ph
y
sic
s:
Co
n
f.
S
e
rie
s,
v
o
l.
9
7
1
.
2
0
1
7
.
[1
3
]
Zh
a
n
g
,
Li
n
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a
n
d
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y
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n
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"
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a
l
DCT
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ig
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term
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iza
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i
a
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n
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p
p
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2
8
0
0
3
-
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8
0
2
3
,
2
0
1
9
.
[1
4
]
L.
No
v
a
m
iza
n
ti
,
G
.
Bu
d
ima
n
,
a
n
d
I.
S
a
fit
ri,
"
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o
d
ifi
e
d
DCT
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b
a
se
d
a
u
d
i
o
wa
term
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rk
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g
o
p
ti
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iza
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sin
g
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e
n
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ti
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s
a
lg
o
rit
h
m
,"
T
EL
KOM
NIK
A
T
e
l
e
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o
mm
u
n
ic
a
ti
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n
,
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o
mp
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n
g
,
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e
c
tro
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1
6
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o
.
6
,
p
p
.
2
6
5
1
-
2
6
6
0
,
De
c
e
m
b
e
r
2018.
[1
5
]
K.
R.
Ra
o
a
n
d
P
.
Yip
,
"
Disc
r
e
te
c
o
sin
e
tran
sfo
rm
:
a
lg
o
r
it
h
m
s,
a
d
v
a
n
tag
e
s,
a
p
p
li
c
a
ti
o
n
s
,"
Aca
d
e
mic
P
re
ss
Pro
fes
sio
n
a
l
,
A
u
g
u
st 1
9
9
0
.
[1
6
]
G
.
Bu
d
ima
n
,
L.
No
v
a
m
iza
n
ti
,
a
n
d
I.
Iwu
t,
"
G
e
n
e
ti
c
s
a
lg
o
rit
h
m
o
p
t
imiz
a
ti
o
n
o
f
DW
T
-
DCT
b
a
se
d
ima
g
e
Wate
rm
a
rk
in
g
,
"
J
o
u
r
n
a
l
o
f
Ph
y
si
c
s: Co
n
fer
e
n
c
e
S
e
rie
s
,
v
o
l.
7
9
5
,
n
o
.
1
,
p
p
.
0
1
2
0
3
9
,
F
e
b
r
u
a
ry
2
0
1
7
.
[1
7
]
M
.
M
isit
i,
e
t
a
l
.
,
"
Wav
e
let
to
o
lb
o
x
,
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h
e
M
a
t
h
W
o
rk
s In
c
.
,
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ti
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k
,
M
A,
v
o
l
.
1
5
,
p
p
.
2
1
,
1
9
9
6
.
[1
8
]
D.
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u
p
ta
a
n
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.
Ch
o
u
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e
y
,
"
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re
te
wa
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e
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rm
fo
r
ima
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e
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r
o
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e
ss
in
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,
"
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ter
n
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ti
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l
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o
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rn
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rg
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e
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h
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y
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n
d
A
d
v
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n
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e
d
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g
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g
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o
l.
4
,
p
p
.
5
9
8
-
6
0
2
,
2
0
1
5
.
[1
9
]
D.
Ba
lea
n
u
,
"
Ad
v
a
n
c
e
s
in
wa
v
e
let
th
e
o
ry
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n
d
t
h
e
ir
a
p
p
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ti
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s
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e
e
rin
g
,
p
h
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s
a
n
d
tec
h
n
o
l
o
g
y
,"
B
o
D
–
B
o
o
k
s
o
n
De
ma
n
d
,
2
0
1
2
.
[2
0
]
W.
S
.
S
a
ri,
E.
H.
Ra
c
h
m
a
wa
n
to
,
a
n
d
C.
A.
S
a
ri,
"
A
G
o
o
d
P
e
rfo
rm
a
n
c
e
OTP
e
n
c
ry
p
ti
o
n
i
m
a
g
e
b
a
se
d
o
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