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av
er
ag
e
id
en
tific
atio
n
ac
cu
r
a
cy
ca
n
b
e
as
h
ig
h
as
9
8
%
f
o
r
s
p
ec
if
ic
co
n
d
itio
n
[
2
]
.
Au
to
m
atio
n
an
d
f
ast
id
en
tific
atio
n
o
f
C
s
-
1
3
7
g
am
m
a
s
o
u
r
ce
b
ased
-
o
n
C
NN
als
o
p
r
esen
ted
b
y
u
s
in
g
Gea
n
t4
s
im
u
latio
n
d
ata,
an
d
p
r
eser
v
e
co
n
f
id
e
n
ce
lev
el
o
f
9
0
%
[
4
]
.
I
n
r
ea
l
m
ea
s
u
r
e
m
en
t
c
o
n
d
itio
n
s
,
th
e
ac
cu
r
ac
y
m
ig
h
t
b
e
d
ec
r
ea
s
e
d
u
e
to
p
er
f
o
r
m
an
ce
v
ar
iatio
n
s
o
f
d
et
ec
to
r
af
f
ec
ted
b
y
tem
p
er
atu
r
e
ch
an
g
es,
p
o
wer
s
u
p
p
ly
n
o
is
e,
f
r
o
n
t
-
e
n
d
cir
cu
it
n
o
is
e,
an
d
q
u
an
tizatio
n
e
r
r
o
r
s
o
n
an
alo
g
to
d
ig
ital c
o
n
v
e
r
ter
(
ADC)
[
2
3
]
,
[
2
4
]
.
T
ab
le
1
.
R
esear
ch
o
n
th
e
d
e
v
e
lo
p
m
en
t o
f
r
ad
i
o
n
u
clid
e
i
d
en
ti
f
icatio
n
s
y
s
tem
D
a
t
a
s
e
t
M
e
t
h
o
d
A
l
g
o
r
i
t
h
m
M
o
d
e
l
A
c
c
u
r
a
c
y
Y
e
a
r
Ref
M
o
n
t
e
-
C
a
r
l
o
si
mu
l
a
t
i
o
n
C
N
N
En
e
r
g
y
-
w
e
i
g
h
t
e
d
Cs
-
1
3
7
:
8
4
%;
Co
-
6
0
:
8
0
%
2
0
2
3
[
1
4
]
M
o
n
t
e
-
C
a
r
l
o
si
mu
l
a
t
i
o
n
ANN
B
a
c
k
p
r
o
p
a
g
a
t
i
o
n
Cs
-
1
3
7
:
9
6
.
5
%;
C
o
-
6
0
:
9
7
.
5
%
2
0
2
2
[
2
]
M
A
EST
R
O
so
f
t
w
a
r
e
b
a
se
d
o
n
S
D
K
R
-
L
d
e
c
o
n
v
o
l
u
t
i
o
n
a
n
d
f
u
z
z
y
P
e
a
k
se
a
r
c
h
N
o
t
m
e
n
t
i
o
n
e
d
,
f
o
c
u
s
e
d
o
n
c
o
n
f
i
d
e
n
c
e
n
u
c
l
i
d
e
2
0
1
9
[
1
0
]
A
N
S
I
N
4
2
.
3
4
l
i
b
r
a
r
y
i
s
o
t
o
p
e
p
e
a
k
e
n
e
r
g
i
e
s
W
a
v
e
l
e
t
t
r
a
n
sf
o
r
m f
o
r
p
e
a
k
e
x
t
r
a
c
t
i
o
n
B
a
y
e
s
i
a
n
st
a
t
i
st
i
c
N
o
t
m
e
n
t
i
o
n
e
d
,
f
o
c
u
s
e
d
o
n
p
e
a
k
mea
s
u
r
e
m
e
n
t
2
0
1
5
[
1
1
]
Mo
s
t
C
NN
m
o
d
els
ar
e
d
esig
n
ed
an
d
tr
ain
ed
u
s
in
g
d
atasets
b
ased
-
o
n
p
ar
ticle
s
im
u
latio
n
s
,
an
d
o
n
ly
a
f
ew
s
tu
d
ies
u
s
e
ex
p
er
im
en
tal
d
ata
tak
en
in
th
e
lab
o
r
at
o
r
y
.
I
n
th
is
s
tu
d
y
,
we
co
llect
d
ata
tak
en
f
r
o
m
lab
o
r
ato
r
y
ex
p
er
im
en
ts
b
y
m
ea
s
u
r
in
g
th
e
r
ad
iatio
n
s
p
ec
tr
u
m
o
f
s
ev
er
al
r
ad
iatio
n
s
o
u
r
ce
s
at
d
if
f
er
e
n
t
d
o
s
e
r
ates.
T
h
r
ee
C
NN
m
o
d
els
with
d
if
f
er
en
t
a
r
ch
itectu
r
es
wer
e
co
n
s
tr
u
cte
d
f
r
o
m
lar
g
e
n
u
m
b
er
o
f
e
x
p
er
im
en
tal
d
ata,
wh
ich
is
th
e
m
ain
c
o
n
tr
ib
u
tio
n
o
f
th
is
s
tu
d
y
.
T
h
e
p
er
f
o
r
m
a
n
ce
o
f
ea
ch
m
o
d
el
is
ev
alu
ated
u
s
in
g
a
cc
u
r
ac
y
,
p
r
ec
is
io
n
,
an
d
r
ec
all
to
f
in
d
th
e
o
p
tim
al
ar
ch
itectu
r
e.
T
h
e
r
esu
ltin
g
m
o
d
el
ca
n
th
en
b
e
im
p
lem
en
ted
in
an
em
b
ed
d
e
d
d
ev
ice
u
tili
zin
g
th
e
tin
y
m
ac
h
i
n
e
lear
n
in
g
(
T
in
y
ML
)
p
latf
o
r
m
.
2.
M
E
T
H
O
D
2
.
1
.
Acquis
it
io
n o
f
g
a
m
ma
e
nerg
y
s
pect
rum
Gam
m
a
en
er
g
y
s
p
ec
tr
u
m
wa
s
co
llected
th
r
o
u
g
h
lab
o
r
ato
r
y
ex
p
e
r
im
en
t
u
s
in
g
2
in
ch
R
2
D
-
NaI
-
2
NaI
(
T
l)
s
cin
till
atio
n
d
etec
to
r
,
co
u
p
led
with
u
p
to
2
K
ch
an
n
els
o
f
I
n
t
-
1K
-
NaI
-
5
0
PMT
-
1000
m
u
lti
-
ch
an
n
e
l
an
aly
ze
r
(
MCA)
f
r
o
m
B
r
i
d
g
ep
o
r
t
I
n
s
tr
u
m
en
ts
.
Hig
h
v
o
ltag
e
s
u
p
p
ly
,
p
er
f
o
r
m
a
n
ce
-
en
h
a
n
cin
g
f
ield
-
p
r
o
g
r
a
m
m
ab
le
g
ate
a
r
r
ay
(
FP
GA)
,
an
d
em
b
e
d
d
ed
AR
M
p
r
o
ce
s
s
o
r
ar
e
in
teg
r
ated
in
th
e
d
etec
to
r
wh
ich
is
co
n
n
ec
ted
to
co
m
p
u
ter
th
r
o
u
g
h
u
n
iv
er
s
al
s
er
ial
b
u
s
(
US
B
)
.
R
ad
io
ac
tiv
e
s
o
u
r
ce
s
u
s
ed
in
t
h
is
ex
p
er
im
en
t
ar
e
lis
ted
in
T
ab
le
2
.
Fig
u
r
e
1
d
e
s
cr
ib
es
th
e
co
n
f
ig
u
r
atio
n
o
f
t
h
e
ex
p
er
im
en
t.
T
h
e
d
etec
to
r
i
s
p
lace
d
o
n
a
s
tatic
d
etec
to
r
s
tan
d
,
wh
ile
t
h
e
r
ad
io
n
u
clid
e
s
o
u
r
ce
is
p
lace
d
o
n
a
r
ad
io
n
u
clid
e
h
o
ld
e
r
r
o
d
th
at
ca
n
b
e
ad
j
u
s
ted
b
ac
k
an
d
f
o
r
th
d
r
iv
en
b
y
lin
ea
r
ac
tu
ato
r
.
Ar
d
u
i
n
o
is
u
s
ed
to
c
o
n
tr
o
l th
e
m
o
v
em
e
n
t o
f
t
h
e
lin
ea
r
a
ctu
ato
r
.
T
ab
le
2
.
R
ad
io
n
u
clid
es u
s
ed
f
o
r
d
ataset
in
Octo
b
er
2
0
2
2
R
a
d
i
o
n
u
c
l
i
d
e
s
γ
E
n
e
r
g
y
(
k
e
V
)
H
a
l
f
-
l
i
f
e
(
y
e
a
r
)
M
a
n
u
f
a
c
t
u
r
e
d
a
t
e
(
mm
/
d
d
/
y
y
)
I
n
i
t
i
a
l
a
c
t
i
v
i
t
y
(
μC
i
)
Est
i
m
a
t
e
d
a
c
t
i
v
i
t
y
(
μ
C
i
)
137
Cs
6
6
2
3
0
.
0
5
0
1
/
0
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/
1
9
0
.
1
0
0
.
0
9
60
Co
1
,
1
7
3
;
1
,
332
5
.
2
7
0
4
/
0
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1
9
1
.
0
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0
.
5
9
134
Cs
5
6
9
,
6
0
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,
7
9
6
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0
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1
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0
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7
7
0
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0
7
152
Eu
1
2
2
;
344
;
779
;
9
6
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;
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,
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;
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1
3
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5
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9
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5
6
A
p
y
th
o
n
-
b
ased
a
p
p
licatio
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was
d
ev
elo
p
ed
t
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co
n
tr
o
l
th
e
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o
u
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ce
m
o
v
em
en
t
an
d
to
co
llect
th
e
s
p
ec
tr
u
m
d
ata
th
r
o
u
g
h
USB
p
o
r
ts
.
I
n
th
e
ex
p
er
im
en
t,
v
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n
o
f
d
o
s
e
r
ate
was
o
b
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ed
b
y
ac
q
u
is
itio
n
o
f
s
p
ec
tr
u
m
d
ata
at
v
ar
io
u
s
d
is
tan
ce
s
tar
tin
g
f
r
o
m
2
0
to
1
0
0
cm
with
1
0
cm
in
ter
v
al,
an
d
m
ea
s
u
r
em
en
t
tim
e
f
r
o
m
5
to
6
0
s
ec
o
n
d
s
with
5
s
ec
o
n
d
in
cr
em
en
t.
Sp
ec
t
r
u
m
d
ata
at
ea
ch
d
is
tan
ce
an
d
tim
e
co
m
b
in
atio
n
f
o
r
ce
r
tain
r
ad
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n
u
clid
e
was
tak
e
n
5
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tim
es
to
ac
co
m
m
o
d
ate
d
etec
to
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p
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f
o
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m
a
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f
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.
Var
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f
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clid
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o
u
r
ce
s
ar
e
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n
u
m
er
ated
in
th
e
f
o
r
m
o
f
4
r
a
d
io
n
u
clid
es.
T
h
er
ef
o
r
e,
th
e
co
l
lecte
d
s
p
ec
tr
u
m
f
o
r
ea
ch
r
ad
io
n
u
clid
e
is
1
2
×
9
×5
0
=
5
,
4
0
0
d
ata,
r
esu
ltin
g
to
tal
o
f
s
p
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tr
u
m
was 2
1
,
6
0
0
d
ata
f
o
r
4
r
ad
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n
u
clid
es.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
2
,
Ap
r
il
20
25
:
2
2
8
2
-
2
2
9
0
2284
(
a)
(
b
)
Fig
u
r
e
1
.
Gam
m
a
en
er
g
y
s
p
ec
tr
u
m
d
ata
co
llectio
n
s
y
s
tem
,
(
a)
h
ar
d
war
e
co
m
p
o
n
en
ts
o
f
th
e
d
ata
co
llectio
n
s
y
s
tem
,
an
d
(
b
)
b
lo
ck
d
iag
r
a
m
th
at
s
h
o
ws th
e
co
n
n
e
ctio
n
b
et
wee
n
ea
ch
co
m
p
o
n
e
n
t
2
.
2
.
Da
t
a
s
et
prepa
ra
t
io
n
a
nd
net
wo
rk
a
rc
hite
ct
ure
Sp
ec
tr
u
m
im
a
g
es
p
r
e
p
ar
atio
n
is
illu
s
tr
ated
in
Fig
u
r
e
2
.
T
h
e
p
r
o
ce
s
s
b
eg
in
s
with
ac
q
u
is
itio
n
o
f
g
am
m
a
en
er
g
y
s
p
ec
tr
u
m
,
u
s
i
n
g
a
s
cin
till
ato
r
d
etec
to
r
wh
ich
will
p
r
o
d
u
ce
r
ad
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n
in
t
en
s
ity
d
ata
b
o
th
f
o
r
b
ac
k
g
r
o
u
n
d
a
n
d
r
ad
io
n
u
clid
e
s
to
r
ed
in
to
1
D
v
ec
to
r
.
T
h
e
av
e
r
ag
e
b
ac
k
g
r
o
u
n
d
an
d
ea
ch
r
ad
io
n
u
clid
e
s
p
ec
tr
u
m
ar
e
n
o
r
m
alize
d
as sh
o
wn
b
y
(
1
)
in
wh
ich
′
=
×
255
(
1
)
w
h
e
r
e
′
is
r
a
d
i
a
t
i
o
n
i
n
t
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n
s
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a
f
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r
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m
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d
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c
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h
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a
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l
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is
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k
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am
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u
ltip
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(
th
e
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g
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d
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i
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th
e
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ten
s
ity
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h
is
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o
r
m
aliza
tio
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s
s
will
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lt
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r
ed
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ib
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ted
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th
e
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g
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5
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ch
,
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tak
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ith
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r
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es
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n
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f
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tiv
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ea
t.
Sin
ce
th
e
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ad
iatio
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s
p
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tr
u
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ata
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r
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r
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u
r
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e
o
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th
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ea
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t
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ize
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wh
ich
ca
n
b
e
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ed
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ce
d
o
v
e
r
f
itti
n
g
[
1
9
]
.
Af
ter
o
b
tain
i
n
g
th
e
s
p
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m
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ata
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ap
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o
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atr
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r
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m
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el.
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h
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s
f
o
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eth
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r
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r
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tical
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n
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o
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izo
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tal
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ca
n
n
in
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[
2
0
]
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ian
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et
a
l
.
[
2
1
]
h
as
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ee
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co
m
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ar
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d
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aly
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ased
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e
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o
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o
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et
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r
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d
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et
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m
a
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u
r
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s
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el
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o
f
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m
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en
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h
ese
1
0
2
4
c
h
an
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els
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e
d
iv
i
d
ed
b
y
3
2
f
o
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ea
ch
r
o
w,
r
esu
ltin
g
in
3
2
r
o
ws
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d
3
2
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lu
m
n
s
.
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h
e
f
ir
s
t
r
o
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f
r
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e
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3
is
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illed
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o
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atio
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im
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f
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r
ad
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in
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ap
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icate
s
th
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h
ig
h
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in
ten
s
ity
in
f
o
r
m
ati
o
n
f
r
o
m
th
e
en
u
m
er
atio
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at
th
at
p
o
s
itio
n
.
T
h
e
h
ig
h
est
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ten
s
ity
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s
h
o
wn
in
y
ello
w
an
d
th
e
lo
west
in
d
ar
k
b
lu
e.
T
h
is
im
ag
e
r
ep
r
esen
ta
tio
n
was
u
s
ed
as
a
d
ataset
to
tr
ain
an
d
test
class
if
icatio
n
m
o
d
el.
C
NN
is
ch
o
s
en
to
clas
s
if
y
g
am
m
a
r
ad
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n
s
o
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r
ce
s
s
in
ce
it
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ty
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e
o
f
n
eu
r
al
n
etwo
r
k
well
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u
ited
f
o
r
p
r
o
ce
s
s
in
g
s
tr
u
ctu
r
ed
ar
r
a
y
s
o
f
d
ata,
em
p
lo
y
i
n
g
m
u
ltip
le
lay
er
s
o
f
lin
ea
r
an
d
n
o
n
-
lin
ea
r
o
p
er
atio
n
s
lear
n
ed
s
im
u
ltan
eo
u
s
ly
in
an
en
d
-
to
-
e
n
d
m
an
n
er
[
2
5
]
,
[
2
6
]
.
W
e
tr
ai
n
ed
a
C
NN
u
s
in
g
p
r
ep
ar
e
d
PNG
im
ag
e
d
ataset
as
in
p
u
t
lay
e
r
to
id
e
n
tify
r
ad
io
n
u
clid
es.
I
n
th
is
s
tu
d
y
,
th
r
ee
C
NN
ar
ch
itectu
r
es
a
r
e
u
s
ed
to
tr
ai
n
th
e
m
o
d
el,
wh
ich
ar
e
VGG
-
1
6
,
Alex
Net
an
d
Xce
p
tio
n
as
s
h
o
wn
in
Fig
u
r
e
3
.
I
n
th
e
co
n
te
x
t o
f
d
ee
p
lear
n
in
g
,
VGG
-
1
6
,
Alex
Net,
an
d
Xce
p
tio
n
ar
e
p
r
o
m
in
en
t
C
NN
ar
ch
itectu
r
es
a
s
s
h
o
w
n
in
Fig
u
r
e
3
(
a)
-
(
c)
,
ea
ch
r
e
p
r
esen
tin
g
d
is
tin
ct
ev
o
lu
tio
n
ar
y
s
tag
es
in
d
ev
elo
p
in
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d
ee
p
n
eu
r
al
n
etwo
r
k
s
f
o
r
im
ag
e
r
ec
o
g
n
itio
n
task
s
.
T
h
ese
ar
ch
itectu
r
es
v
ar
y
s
ig
n
if
ican
tly
in
th
eir
d
esig
n
p
r
in
cip
les,
lay
er
co
m
p
o
s
itio
n
s
,
an
d
co
m
p
u
tatio
n
al
co
m
p
le
x
ities
.
As
s
h
o
wn
i
n
Fig
u
r
e
3
,
Xce
p
tio
n
h
as
m
o
r
e
lay
er
s
co
m
p
ar
ed
t
o
VGG
-
1
6
an
d
Alex
Net
p
r
im
a
r
ily
d
u
e
to
its
ar
ch
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r
e
p
h
ilo
s
o
p
h
y
,
wh
ic
h
is
ce
n
ter
ed
ar
o
u
n
d
d
e
p
th
wis
e
s
ep
a
r
ab
le
co
n
v
o
lu
tio
n
.
T
h
e
c
h
o
i
ce
b
etwe
en
th
ese
ar
ch
itectu
r
es
d
ep
e
n
d
s
o
n
th
e
s
p
ec
if
ic
r
eq
u
ir
em
en
ts
o
f
th
e
task
,
in
clu
d
in
g
ac
cu
r
ac
y
,
co
m
p
u
tatio
n
al
ef
f
icien
c
y
,
an
d
r
eso
u
r
ce
av
ailab
ilit
y
.
(
a)
(
b
)
(
c)
Fig
u
r
e
3
.
I
ll
u
s
tr
atio
n
o
f
t
h
r
ee
C
NN
ar
ch
itectu
r
es,
(
a)
VGG
-
1
6
,
(
b
)
Alex
Net,
an
d
(
c)
Xce
p
tio
n
I
n
th
is
s
tu
d
y
,
th
e
im
ag
e
d
ataset
was
d
iv
id
ed
with
a
r
atio
o
f
7
0
%
f
o
r
tr
ain
in
g
d
ata,
2
0
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
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8
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I
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t J E
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Vo
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15
,
No
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2
,
Ap
r
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20
25
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2
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r
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ize
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3.
RE
SU
L
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S AN
D
D
I
SCU
SS
I
O
N
3
.
1
.
T
ra
ns
f
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rm
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t
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da
t
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m
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f
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s
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ag
e
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ataset
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s
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wn
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u
r
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4
.
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h
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f
ig
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r
e
s
h
o
ws
ex
am
p
le
o
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th
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tr
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s
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3
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s
h
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wn
.
(
a)
(
b
)
(
c)
(
d
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Fig
u
r
e
4
.
E
x
am
p
le
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f
f
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an
s
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s
p
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to
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(
a)
Cs
-
137
,
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b
)
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,
(
c)
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,
a
n
d
(
d
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Eu
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5
2
3
.
2
.
E
v
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t
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f
t
r
a
ini
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Acc
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ith
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ticu
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tly
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lic
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ar
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tics
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wit
h
a
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ig
h
d
eg
r
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o
f
ac
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[
2
7
]
–
[
3
0
]
.
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r
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lt
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ased
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e
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[
3
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]
.
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h
e
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ain
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h
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wn
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n
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5
,
wh
ich
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icate
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atter
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[
2
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Mo
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
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I
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15
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No
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2
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Ap
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R
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n
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p
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ted
in
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b
ed
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ed
d
ev
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in
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ML
p
latf
o
r
m
[
2
5
]
.
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ce
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ed
d
e
d
d
ev
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as
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eso
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s
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s
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f
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d
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4.
CO
NCLU
SI
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u
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m
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p
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a
t
th
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alg
o
r
ith
m
was
s
u
itab
le
f
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id
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tific
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.
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ain
in
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m
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s
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ch
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at
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h
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tim
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cc
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is
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y
lo
w.
T
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e
r
esu
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ac
h
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th
e
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m
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m
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at
9
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f
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all
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7
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Fu
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k
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th
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T
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m
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e
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f
in
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to
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p
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e
d
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etec
tio
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ev
ice.
RE
F
E
R
E
NC
E
S
[
1
]
I
.
P
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[
2
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.
Q
i
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t
a
l
.
,
“
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a
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t
w
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k
,
”
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u
c
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r
En
g
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T
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p
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2
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2
7
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,
Ja
n
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.
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.
[
3
]
C
.
Li
e
t
a
l
.
,
“
A
n
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r
a
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o
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