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y
s
t
e
m
o
b
t
a
i
n
e
d
g
o
o
d
m
e
a
s
u
r
e
s
f
o
r
v
e
r
i
f
i
c
a
t
i
o
n
[
6
]
.
Do
r
o
z
et
a
l.
[
7
]
in
tr
o
d
u
ce
d
a
n
e
w
m
eth
o
d
f
o
r
v
er
i
f
icat
io
n
t
h
e
h
a
n
d
w
r
iti
n
g
s
i
g
n
at
u
r
es.
I
n
th
is
m
et
h
o
d
th
e
s
i
g
n
atu
r
e
w
as d
escr
ib
ed
b
y
cr
ea
ti
n
g
a
co
m
p
le
x
ch
ar
ac
ter
i
s
tic
f
o
r
ea
ch
s
i
g
n
at
u
r
e,
th
e
cr
ea
ted
ch
ar
ac
ter
is
tics
w
er
e
b
ased
o
n
d
ep
en
d
en
cies
an
al
y
s
is
b
et
w
ee
n
t
h
e
d
y
n
a
m
i
c
ch
ar
ac
ter
is
tics
w
h
ic
h
r
eg
i
s
t
er
ed
in
th
e
tab
les.
C
o
m
p
le
x
ch
ar
ac
ter
i
s
tics
w
er
e
u
tili
ze
d
to
cr
ea
te
v
ec
to
r
s
f
o
r
d
escr
ib
in
g
ch
ar
a
cter
i
s
tics
,
b
y
u
s
in
g
t
h
e
s
u
g
g
ested
m
ea
s
u
r
es,
t
h
e
ele
m
e
n
t
s
o
f
v
ec
to
r
s
w
er
e
co
m
p
u
ted
.
E
v
alu
a
te
d
th
e
s
i
m
ilar
it
y
b
et
w
ee
n
t
h
e
s
ig
n
a
tu
r
es
h
ad
b
ee
n
ac
co
m
p
li
s
h
ed
b
y
d
eter
m
in
in
g
th
e
s
i
m
ilar
it
y
o
f
co
r
r
esp
o
n
d
in
g
v
ec
to
r
s
o
f
th
e
co
m
p
ar
ed
s
ig
n
atu
r
es.
9
6
.
6
7
%
w
as
th
e
r
esu
lts
o
b
tain
ed
b
y
t
h
is
m
e
th
o
d
[
7
]
.
Ma
lik
a
n
d
A
r
o
v
a
[
3
]
p
r
esen
te
d
a
m
et
h
o
d
f
o
r
s
ig
n
at
u
r
e
r
ec
o
g
n
i
tio
n
.
E
u
ler
n
u
m
b
er
w
a
s
u
s
ed
to
s
tu
d
y
th
e
s
i
g
n
atu
r
es
o
f
v
ar
io
u
s
p
eo
p
le.
T
h
eir
ad
o
p
ted
m
eth
o
d
co
n
s
is
ts
o
f
t
h
r
ee
p
h
ase
s
.
I
n
th
e
f
ir
s
t
p
h
ase,
t
w
o
s
e
ts
o
f
f
ea
t
u
r
e
v
ec
to
r
s
w
er
e
g
e
n
er
ate
d
b
y
e
x
tr
ac
ti
n
g
t
h
e
f
ea
t
u
r
es
o
f
tr
ain
i
n
g
s
i
g
n
atu
r
e
s
d
ataset
a
n
d
f
ea
tu
r
e
s
o
f
test
i
n
g
s
ig
n
at
u
r
es.
I
n
th
e
s
ec
o
n
d
p
h
as
e,
u
s
i
n
g
Ma
n
h
at
tan
d
is
ta
n
ce
cl
ass
i
f
ier
to
co
m
p
ar
e
b
et
w
ee
n
t
h
e
tr
ain
i
n
g
s
i
g
n
atu
r
e
f
ea
t
u
r
e
an
d
t
h
e
s
et
o
f
t
h
e
f
ea
t
u
r
e
o
f
th
e
tes
ti
n
g
s
ig
n
at
u
r
es.
T
h
e
r
esu
lt
o
f
r
ec
o
g
n
itio
n
w
a
s
d
is
p
la
y
ed
to
t
h
e
u
s
er
in
th
e
last
p
h
a
s
e.
T
h
e
tr
ain
an
d
test
d
ata
w
h
ic
h
u
s
ed
in
d
ataset
h
ad
a
litt
le
ch
an
g
e
b
u
t
t
h
e
s
u
g
g
e
s
ted
m
et
h
o
d
ca
n
b
e
ex
ten
d
ed
to
d
ataset
w
h
ic
h
h
ad
a
g
r
ea
t c
h
a
n
g
e
o
f
th
e
tr
ai
n
an
d
test
d
at
a
[
3
]
.
Hed
j
az
et
a
l.
[
8
]
in
tr
o
d
u
ce
d
an
ap
p
r
o
ac
h
f
o
r
r
ec
o
g
n
i
tio
n
th
e
o
f
f
lin
e
s
i
g
n
at
u
r
e.
B
in
ar
y
s
tati
s
ti
ca
l
i
m
a
g
e
f
ea
t
u
r
es
(
B
SIF)
an
d
lo
ca
l
b
in
ar
y
p
atter
n
s
(
L
B
P
)
w
er
e
u
s
ed
to
ex
tr
ac
t
t
h
e
f
ea
t
u
r
es.
T
w
o
p
u
b
lic
d
atasets
w
er
e
u
s
ed
:
MCYT
-
7
5
an
d
GP
D
-
1
0
0
.
B
y
u
tili
z
in
g
a
K
-
n
ea
r
es
t
n
eig
h
b
o
r
(
KNN)
class
if
ier
a
p
er
f
o
r
m
an
ce
o
f
r
ec
o
g
n
itio
n
r
ea
ch
es
9
7
.
3
%
f
o
r
MCYT
-
7
5
an
d
9
6
.
1
%
f
o
r
GPDS
-
1
0
0
[
8
]
.
T
h
e
m
ain
co
n
tr
ib
u
tio
n
o
f
t
h
is
p
ap
er
as
f
o
llo
w
:
to
in
tr
o
d
u
ce
d
an
ef
f
ici
en
t te
ch
n
iq
u
e
f
o
r
o
f
f
li
n
e
s
i
g
n
a
tu
r
e
r
ec
o
g
n
itio
n
d
ep
en
d
in
g
o
n
ex
tr
ac
ti
n
g
t
h
e
lo
ca
l
f
ea
t
u
r
e
b
y
u
til
izin
g
t
h
e
h
aa
r
wav
elet
s
u
b
b
an
d
s
an
d
e
n
er
g
y
.
2.
P
RO
P
O
SE
D
SYS
T
E
M
T
h
is
w
o
r
k
ai
m
s
to
d
ev
elo
p
an
ef
f
icie
n
t
tec
h
n
iq
u
e
f
o
r
o
f
f
li
n
e
s
i
g
n
atu
r
e
r
ec
o
g
n
itio
n
d
ep
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d
in
g
o
n
ex
tr
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ti
n
g
t
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lo
ca
l
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t
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e
h
aa
r
w
av
ele
t
s
u
b
b
an
d
s
an
d
e
n
er
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y
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T
h
e
p
r
o
p
o
s
ed
ap
p
r
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ac
h
is
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m
p
o
s
ed
o
f
t
h
e
f
o
llo
w
i
n
g
s
tep
s
:
T
h
e
la
y
o
u
t
o
f
th
e
s
u
g
g
ested
s
i
g
n
at
u
r
e
s
y
s
te
m
is
ap
p
ea
r
ed
in
Fig
u
r
e
1
.
T
h
e
f
lo
w
o
f
t
h
e
p
r
o
p
o
s
ed
w
o
r
k
is
as
f
o
llo
w
s
:
t
h
e
s
y
s
te
m
co
n
s
i
s
ts
o
f
t
h
r
ee
m
a
in
p
h
a
s
es:
p
r
e
-
p
r
o
ce
s
s
i
n
g
p
h
a
s
e,
f
ea
t
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r
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ex
tr
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n
,
b
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ar
iza
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tch
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etail
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n
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o
d
u
ce
d
in
t
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n
ex
t sectio
n
s
.
Fig
u
r
e
1
.
T
h
e
lay
o
u
t o
f
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y
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te
m
m
o
d
el
2
.
1
.
P
re
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pro
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s
ing
ph
a
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T
h
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p
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r
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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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.
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ata
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∗
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as d
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(
,
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=
{
−
(
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<
0
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9
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1
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h
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t
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1
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C
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n
tr
ast
T
h
e
s
u
b
j
ec
tiv
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cr
iter
ia
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h
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j
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al
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ties
o
f
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m
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e
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m
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1
2
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.
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n
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s
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tain
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R
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th
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2
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1
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4
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B
ina
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h
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[
1
3
]
.
T
h
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m
o
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[
1
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I
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30
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2
.
1
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5
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h
[
1
5
,
1
6
]
.
2
.
2
.
DWT
a
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f
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t
ure
ex
t
ra
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io
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s
e
T
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w
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m
e
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a
d
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f
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[
1
7
-
1
9
]
.
T
h
e
lo
w
an
d
h
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ter
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DW
T
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ca
les.
2
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2
.
1
.
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a
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Haa
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d
co
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to
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s
[
1
8
-
2
1
]
.
T
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its
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[2
2
-
2
6
]
.
2
.
2
.
2
.
Appl
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DWT
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Fo
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4
-
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as sh
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Fig
u
r
e
2
.
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(
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M
a
t
ching
ph
a
s
e
I
n
th
i
s
p
h
a
s
e,
g
e
n
er
atio
n
o
f
t
h
e
te
m
p
late
f
r
o
m
tr
ai
n
i
n
g
s
a
m
p
le
s
an
d
f
ea
tu
r
e
m
atc
h
i
n
g
ar
e
u
s
e
d
to
o
b
tain
th
e
s
i
m
ilar
it
y
d
eg
r
ee
b
et
w
ee
n
th
e
tr
ain
i
n
g
s
a
m
p
le
s
an
d
en
t
er
ed
s
ig
n
at
u
r
es.
Nu
m
er
ical
v
a
lu
e
is
t
h
e
o
u
tp
u
t
o
f
m
atc
h
in
g
p
h
a
s
e,
s
o
h
ig
h
r
esu
l
t
in
d
icate
th
at
t
h
e
s
i
g
n
at
u
r
e
s
a
m
p
le
b
elo
n
g
s
to
th
e
s
a
m
e
s
ig
n
atu
r
e.
No
r
m
alize
d
m
ea
n
s
q
u
ar
e
d
if
f
er
e
n
ce
s
(
NM
SD)
an
d
n
o
r
m
a
lized
m
ea
n
ab
s
o
lu
te
d
if
f
er
en
ce
(
NM
A
D)
ar
e
u
s
ed
to
d
ete
r
m
i
n
e
th
e
s
i
m
ilar
it
y
d
eg
r
ee
.
2
.
3
.
1
.
G
ener
a
t
io
n t
he
t
e
m
pl
a
t
e
I
n
o
r
d
er
to
g
en
er
ate
th
e
te
m
p
la
te
a
n
u
m
b
er
o
f
s
ig
n
at
u
r
e
ar
e
u
t
ilized
f
o
r
ea
ch
i
n
d
iv
id
u
al
as sa
m
p
les f
o
r
tr
ain
i
n
g
.
T
h
e
te
m
p
late
o
f
an
i
n
d
iv
id
u
al
co
n
tain
s
all
f
ea
tu
r
e
s
w
h
ic
h
ex
tr
ac
ted
f
r
o
m
all
t
h
e
tr
ain
i
n
g
s
a
m
p
le
s
.
Fo
r
ea
ch
p
er
s
o
n
a
m
ea
n
f
ea
t
u
r
e
v
e
cto
r
an
d
co
n
ce
r
n
i
n
g
s
ta
n
d
ar
d
d
ev
iatio
n
ar
e
k
ep
t
i
n
d
ata
b
as
e.
T
h
e
f
o
llo
w
i
n
g
i
n
(
9
,
1
0
)
ar
e
u
s
ed
to
ca
lcu
late
th
e
m
ea
n
a
n
d
s
ta
n
d
ar
d
d
ev
iatio
n
.
̅
(
,
)
=
1
∑
(
,
,
)
=
1
(
9
)
(
,
)
=
√
1
∑
(
(
,
,
)
−
̅
(
,
)
)
2
=
1
(
1
0
)
w
h
er
e
is
a
p
er
s
o
n
n
u
m
b
er
,
is
a
s
a
m
p
le
n
u
m
b
er
an
d
is
a
f
ea
t
u
r
e
n
u
m
b
er
,
F
ac
ts
t
h
e
f
ea
t
u
r
es
v
ec
to
r
an
d
r
ep
r
esen
t th
e
to
tal
n
u
m
b
e
r
o
f
s
a
m
p
les.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
Offlin
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ig
n
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tu
r
es ma
tch
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in
g
h
a
a
r
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ve
let
s
u
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a
n
d
s
(
Zin
a
h
S
.
A
b
d
u
lja
b
b
a
r
)
2907
Fig
u
r
e
2
.
Me
th
o
d
s
o
f
f
ea
t
u
r
e
e
x
tr
ac
tio
n
2
.
3
.
2
.
F
ea
t
ures
m
a
t
ching
Fo
r
th
e
p
u
r
p
o
s
e
o
f
f
ea
t
u
r
es
m
atch
i
n
g
,
s
tatis
t
ical
an
al
y
s
i
s
i
s
u
s
ed
.
NM
SD
a
n
d
NM
A
D
ar
e
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o
p
ted
t
o
d
is
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v
er
th
e
n
ea
r
es
t sto
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ed
m
a
tch
w
it
h
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e
g
iv
e
n
e
n
ter
ed
s
ig
n
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r
e.
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o
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s
eq
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n
tl
y
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t
h
e
o
u
tp
u
t o
f
t
h
is
s
ta
g
e
is
to
d
eter
m
in
e
w
h
et
h
er
th
e
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ig
n
at
u
r
es sa
m
p
le
s
b
elo
n
g
to
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e
s
a
m
e
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ig
n
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o
t
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(
1
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2
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.
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(
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3.
RE
SU
L
T
S
T
h
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
tech
n
i
q
u
es
u
s
ed
i
n
t
h
is
p
ap
er
w
as
e
v
alu
a
ted
an
d
co
m
p
ar
ed
to
o
th
er
ex
is
ti
n
g
tech
n
iq
u
es.
F
u
r
t
h
er
m
o
r
e,
th
e
p
er
f
o
r
m
a
n
ce
o
f
t
h
e
ef
f
ic
ien
t
tech
n
iq
u
e
f
o
r
o
f
f
li
n
e
s
i
g
n
atu
r
e
r
ec
o
g
n
itio
n
w
a
s
ev
alu
a
ted
in
ter
m
s
o
f
it
s
r
ec
o
g
n
i
tio
n
a
n
d
co
m
p
ar
ed
to
o
th
er
f
ea
tu
r
e
s
elec
tio
n
tec
h
n
iq
u
es.
T
h
r
ee
d
if
f
er
en
t
s
et
s
o
f
f
ea
tu
r
es
ar
e
u
ti
lized
b
y
p
ar
t
itio
n
i
n
g
th
e
s
i
g
n
a
tu
r
e
i
m
a
g
e
i
n
to
n
o
n
o
v
er
lap
p
in
g
b
lo
ck
s
wh
er
e
d
if
f
er
en
t
b
lo
ck
s
izes
ar
e
u
s
ed
.
C
E
D
AR
s
i
g
n
a
tu
r
e
d
atab
ase
i
s
u
s
ed
as
a
d
at
aset
f
o
r
test
i
n
g
p
u
r
p
o
s
e
.
A
ll
t
h
e
e
x
p
er
i
m
e
n
ts
an
d
th
e
r
esu
lts
ac
h
ie
v
ed
ar
e
p
r
esen
ted
in
th
i
s
s
ec
tio
n
.
3
.
1
.
P
er
f
o
rm
a
nce
a
na
ly
s
i
s
T
h
e
p
er
f
o
r
m
an
ce
o
f
t
h
e
h
aa
r
w
av
e
let
s
u
b
b
an
d
s
an
d
en
er
g
y
tec
h
n
iq
u
e
s
an
d
th
e
f
ea
tu
r
e
ex
tr
ac
tio
n
tech
n
iq
u
es w
as
a
n
al
y
ze
d
.
Fro
m
C
E
D
AR
d
atab
ase
[
1
9
]
th
e
d
ataset
is
u
tili
ze
d
in
t
h
e
test
i
n
g
p
r
o
ce
s
s
.
T
h
e
d
ataset
is
co
n
s
i
s
ted
o
f
5
5
p
er
s
o
n
s
an
d
t
w
el
v
e
s
i
g
n
atu
r
e
s
a
m
p
les
f
o
r
ea
ch
p
er
s
o
n
,
ea
ch
s
i
g
n
a
tu
r
e
i
m
ag
e
i
s
a
.
b
m
p
f
ile
t
y
p
e
w
it
h
2
4
b
it/p
ix
el.
Fiv
e
f
ea
tu
r
es
(
L
L
,
H
L
,
L
H,
H
H
an
d
p
o
w
er
)
ar
e
u
tili
ze
d
b
y
ap
p
l
y
i
n
g
1
-
le
v
el
d
ec
o
m
p
o
s
itio
n
o
f
h
aa
r
w
a
v
ele
t tr
an
s
f
o
r
m
.
2
1
*
2
3
is
s
elec
ted
as th
e
s
ize
o
f
b
lo
ck
w
it
h
t
h
e
m
eth
o
d
s
o
f
m
atch
in
g
an
d
f
o
r
o
n
e
s
et
o
f
f
ea
t
u
r
es,
r
es
u
lts
ar
e
d
escr
ib
ed
in
T
ab
les
1
,
2
an
d
3
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1693
-
69
30
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
,
Vo
l.
18
,
No
.
6
,
Dec
em
b
er
2
0
2
0
:
2
9
0
3
-
2910
2908
T
ab
le
1
.
T
h
e
r
ate
o
f
r
ec
o
g
n
itio
n
u
s
in
g
t
h
e
en
er
g
y
w
i
th
o
n
e
s
e
t o
f
f
ea
t
u
r
es
M
a
t
c
h
i
n
g
M
e
t
h
o
d
s
LL
LH
HL
HH
P
o
w
(
P
E
)
N
M
A
D
8
8
.
1
8
2
6
3
.
6
3
6
7
6
.
3
6
4
5
5
.
3
0
3
6
8
.
1
8
2
N
M
S
D
9
7
.
7
2
7
9
0
.
6
0
6
9
3
.
7
8
8
8
1
.
9
7
0
9
0
.
0
0
0
T
ab
le
2
.
T
h
e
r
ate
o
f
r
ec
o
g
n
itio
n
u
s
in
g
P
N
=
0
.
5
w
it
h
o
n
e
s
e
t o
f
f
ea
t
u
r
es
M
a
t
c
h
i
n
g
M
e
t
h
o
d
s
LL
LH
HL
HH
P
o
w
(
P
N
)
N
M
A
D
6
7
.
4
2
4
4
3
.
7
8
8
5
0
.
0
0
0
3
9
.
6
9
7
4
5
.
0
0
0
N
M
S
D
8
7
.
1
2
1
6
5
.
3
0
3
7
0
.
3
0
3
5
4
.
2
4
2
6
0
.
1
5
2
T
ab
le
3
.
T
h
e
r
ate
o
f
r
ec
o
g
n
itio
n
u
s
in
g
P
N
=
0
.
7
5
w
it
h
o
n
e
s
et
o
f
f
ea
t
u
r
es
M
a
t
c
h
i
n
g
M
e
t
h
o
d
s
LL
LH
HL
HH
P
o
w
(
P
N
)
N
M
A
D
6
9
.
6
9
7
4
6
.
0
6
1
5
3
.
9
3
9
4
1
.
3
6
4
4
6
.
6
6
7
N
M
S
D
8
9
.
3
9
4
6
8
.
7
8
8
7
4
.
242
5
6
.
3
6
4
6
4
.
6
9
7
T
en
f
ea
tu
r
es
(
L
L
-
L
H,
L
L
-
HL
,
L
L
-
H
H,
L
L
-
p
o
w
,
L
H
-
H
L
,
L
H
-
HH,
L
H
-
p
o
w
,
H
L
-
HH,
H
L
-
p
o
w
a
n
d
HH
-
p
o
w
)
ar
e
th
e
r
esu
lt
s
o
f
co
m
b
i
n
i
n
g
t
h
e
t
w
o
f
ea
t
u
r
es
o
f
h
aa
r
w
a
v
elet
tr
an
s
f
o
r
m
(
t
w
o
s
ets
o
f
f
ea
tu
r
es).
T
h
e
r
ec
o
g
n
itio
n
r
ates
ar
e
p
r
esen
ted
in
T
ab
les
4
s
h
o
w
ed
t
h
e
r
ate
o
f
r
ec
o
g
n
itio
n
u
s
i
n
g
t
h
e
e
n
er
g
y
w
it
h
t
w
o
s
e
ts
o
f
f
ea
tu
r
es
,
T
ab
le
5
s
h
o
w
d
t
h
e
r
ate
o
f
r
ec
o
g
n
itio
n
u
s
i
n
g
P
N
=
0
.
5
w
it
h
t
w
o
s
ets
o
f
f
ea
t
u
r
es
5
an
d
6
b
y
u
s
in
g
th
e
m
atch
i
n
g
m
eth
o
d
s
w
it
h
b
l
o
ck
s
ize
eq
u
al
s
to
2
1
*
2
3
.
T
ab
le
6
s
h
o
w
t
h
e
r
ate
o
f
r
ec
o
g
n
iti
o
n
u
s
i
n
g
P
N
=
0
.
7
5
w
it
h
t
w
o
s
et
s
o
f
f
ea
t
u
r
es
T
ab
le
4
.
T
h
e
r
ate
o
f
r
ec
o
g
n
itio
n
u
s
in
g
t
h
e
en
er
g
y
w
i
th
t
w
o
s
e
ts
o
f
f
ea
tu
r
es
M
a
t
c
h
i
n
g
M
e
t
h
o
d
s
LL
-
LH
LL
-
HL
LL
-
HH
LL
-
P
o
w
(P
E
)
LH
-
HL
LH
-
HH
LH
-
P
o
w
(P
E
)
HL
-
HH
HL
-
P
o
w
(P
E
)
HH
-
P
o
w
(P
E
)
N
M
A
D
8
2
.
4
2
4
2
8
8
.
1
8
1
8
7
9
.
2
4
24
8
1
.
6
6
6
7
8
3
.
1
8
1
8
6
6
.
0
6
0
6
7
0
.
7
5
7
6
6
9
.
6
9
7
0
7
5
.
4
5
4
5
6
1
.
9
6
9
7
N
M
S
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I
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69
30
T
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18
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No
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6
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Dec
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2
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2
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2910
2910
ACK
NO
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RE
F
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R
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NC
E
S
[1
]
B
.
M
.
Al
-
M
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q
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leh
,
A
.
M
.
Qa
id
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S
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8
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p
p
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2
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[2
]
A
.
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m
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S
.
Ya
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w
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ter
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3
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p
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6
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.
[4
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Disc
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1
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p
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[5
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3
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1
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[6
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ry
,
R
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C
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F
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T
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v
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51
,
no
.
2
,
pp.
1
6
5
-
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7
4
,
2
0
1
3
.
[7
]
R
.
Do
r
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P
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Orc
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,
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.
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i
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lex
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e
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tu
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s
,
”
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o
u
rn
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l
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f
M
e
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ica
l
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rm
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ti
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s
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3
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p
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0
1
4
.
[8
]
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.
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z
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,
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.
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m
il
i,
H
.
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u
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b
a
,
“
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ig
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tu
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Re
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Us
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a
ry
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e
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KN
N
,
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ter
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rn
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o
me
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l
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8
.
[9
]
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M
.
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.
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ter
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ter
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.
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0
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sa
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,
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3
D
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Pro
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2
.
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2
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.
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in
g
h
,
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.
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v
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.
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s
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ter
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g
,
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.
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3
]
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.
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ich
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lak
,
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.
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a
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rp
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,
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e
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ro
p
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v
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l.
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1
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o
.
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,
p
p
.
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-
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8
,
2
0
1
9
.
[1
4
]
P
u
n
e
e
t,
N
.
K
.
G
a
rg
,
“
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a
riza
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T
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c
h
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se
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f
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r
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re
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le
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m
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g
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s
,
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ter
n
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ti
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n
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l
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o
u
r
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l
o
f
C
o
mp
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ter
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p
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ti
o
n
,
v
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l
.
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1
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n
o
.
1
,
p
p
.
8
-
11,
2
0
1
3
.
[1
5
]
F
.
Ha
se
e
n
a
,
R
.
Clara
,
“
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e
rf
o
rm
a
n
c
e
A
n
a
l
y
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s
o
f
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h
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n
in
g
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rd
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m
,
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ter
n
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l
C
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fer
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d
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me
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ts i
n
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o
mp
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h
n
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ies
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v
ol
.
4
,
n
o
.
2
,
2
0
1
8
.
[1
6
]
D
.
G
u
p
ta,
S
.
Ch
o
u
b
e
y
,
“
Dis
c
re
t
e
W
a
v
e
let
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ra
n
s
f
o
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f
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r
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m
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g
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in
g
,
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ter
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t
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o
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l
.
4
.
n
o
.
3
,
2
0
1
5
.
[1
7
]
M
.
J.
G
o
m
e
z
,
C
.
Ca
ste
jo
n
,
Ju
a
n
C
.
G
a
rc
ia
,
“
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v
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e
w
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t
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d
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n
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in
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p
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ra
n
s
f
o
r
m
to
Dia
g
n
o
se
Cra
c
k
e
d
Ro
to
rs
,
”
Al
g
o
rith
ms
,
v
o
l
.
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,
no
.
1
,
p
p
.
1
-
1
3
,
2
0
1
6
.
[
1
8
]
I
.
S
h
a
r
i
f
,
S
.
K
h
a
r
e
,
“
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o
m
p
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v
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n
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h
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l
A
r
c
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t
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m
m
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m
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d
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n
f
o
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t
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o
n
S
c
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e
n
c
e
s
,
v
o
l
.
XL
-
8
,
2014.
[1
9
]
F
V
C
2
0
0
4
,
“
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in
g
e
rp
ri
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t
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e
rif
ica
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p
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2
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0
4
,”
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0
0
4
.
[
On
l
in
e
].
A
v
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b
le:
h
tt
p
:/
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c
sr.u
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2
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/
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0
]
Z.
H.
S
a
li
h
,
G
.
T
.
Ha
sa
n
,
a
n
d
M
.
A
.
M
o
h
a
m
m
e
d
,
"
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v
e
sti
g
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te
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ly
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lev
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ra
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ted
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ro
m
u
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ro
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n
d
p
o
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c
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rn
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it
ies
,
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2
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9
th
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n
ter
n
a
ti
o
n
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l
Co
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fer
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o
n
El
e
c
tro
n
ics
,
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m
p
u
ter
s a
n
d
Arti
fi
c
ia
l
In
tell
ig
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n
c
e
(
ECA
I)
,
2
0
1
7
.
[2
1
]
Z.
H.
S
a
li
h
,
G
.
T
.
Ha
sa
n
,
M
.
A
.
M
o
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m
m
e
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e
t
a
l
.
,
"
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tu
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teg
ra
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tab
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tri
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ti
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m
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d
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ter
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S
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ms
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Co
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p
u
ter
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c
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c
e
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)
,
p
p
.
4
4
3
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4
7
,
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0
1
9
.
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2
]
N.
Q.
M
o
h
a
m
m
e
d
,
M
.
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.
A
h
m
e
d
,
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.
A
.
M
o
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m
m
e
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,
e
t
a
l.
,
"
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m
p
a
ra
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a
l
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e
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o
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rc
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se
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n
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o
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ra
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s,"
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0
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d
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ter
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o
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e
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),
p
p
.
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4
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1
9
.
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3
]
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.
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.
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m
m
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,
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A
.
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o
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m
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,
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A
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sa
n
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e
t
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l.
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re
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s: I
ss
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t
h
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t),
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p
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8
,
2
0
1
9
.
[2
4
]
N.
D.
Zak
i,
N.
Y.
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sh
im
,
Y.
M
.
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o
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e
n
,
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.
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o
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re
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trica
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g
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n
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o
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ti
c
s
,
v
o
l.
9
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o
.
6
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p
p
.
1
4
1
1
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4
1
9
,
2
0
2
0
.
[2
5
]
H.
R.
Ib
ra
h
e
e
m
,
Z.
F
.
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ss
a
in
,
S
.
M
.
A
li
,
M
.
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lj
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.
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n
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ti
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,
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n
e
w
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ty
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n
d
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T
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KOM
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T
e
lec
o
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n
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n
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o
mp
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tro
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v
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l
.
1
8
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o
.
3
,
p
p
.
1
6
8
8
-
1
9
4
,
2
0
2
0
.
[2
6
]
O.
A
.
Ha
m
m
o
o
d
,
N.
Niz
a
m
,
M
.
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fa
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