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I
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CT
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15
,
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1
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Ma
r
ch
20
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84
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N:
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ical
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d
co
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itiv
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d
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atin
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m
o
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e
cr
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cial
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th
e
p
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m
is
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s
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i
f
f
ic
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lt:
it
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o
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atin
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o
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e
r
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tin
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an
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to
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ile,
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d
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b
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r
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n
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f
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ch
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lo
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ical
an
d
p
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ac
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As
p
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Hea
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esti
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ates
th
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m
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s
o
f
p
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s
u
f
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ies
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f
atalities
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a
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r
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lis
t
o
f
c
o
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tr
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f
ac
to
r
s
[
1
]
.
Acc
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r
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in
g
to
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Am
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Au
to
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Ass
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s
[
2
]
.
Acc
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in
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ased
Natio
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Hig
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way
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at
m
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ely
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[
3
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.
Ov
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,
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in
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(
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ac
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r
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g
to
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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t J I
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&
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n
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N:
2252
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8
7
7
6
F
u
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lo
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b
a
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ed
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tig
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s
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ly
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85
s
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s
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o
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e
d
in
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[
4
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Acc
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5
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.
Fro
m
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s
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tiv
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we
p
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two
alter
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eth
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lin
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e
f
atig
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[
6
]
.
Du
r
i
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t
h
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in
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s
tag
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d
ee
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lear
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in
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tech
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iq
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ased
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d
etec
t
h
u
m
a
n
c
o
g
n
itiv
e
p
ar
a
m
eter
s
s
u
ch
as
s
leep
in
ess
,
d
r
iv
e
r
’
s
ag
e
an
d
h
ea
d
p
o
s
tu
r
e
[
7
]
.
L
ater
,
d
ee
p
lear
n
in
g
tech
n
iq
u
es
a
n
d
ar
tific
ial
in
tell
ig
en
ce
(
AI
)
is
u
s
ed
to
ex
tr
ac
t
s
ig
n
if
ican
t
f
ea
tu
r
es
f
r
o
m
th
e
im
ag
e,
af
te
r
wh
ich
th
e
in
f
o
r
m
atio
n
is
f
ed
in
to
a
f
u
zz
y
in
f
e
r
en
ce
s
y
s
tem
to
ass
ess
th
e
in
atten
tio
n
lev
el
o
f
th
e
d
r
iv
er
[
8
]
.
T
h
e
f
ir
s
t
s
u
g
g
ested
m
eth
o
d
is
ca
r
r
ied
o
u
t
u
s
in
g
a
co
n
v
o
lu
tio
n
al
n
e
u
r
al
n
etwo
r
k
(
C
NN
)
,
wh
ich
is
a
class
o
f
n
eu
r
al
n
etwo
r
k
th
at
e
x
tr
ac
t
f
ea
tu
r
es
f
r
o
m
th
e
d
ata
s
eq
u
en
ce
s
(
f
o
r
in
s
tan
ce
,
u
s
in
g
wea
th
er
d
ata
f
r
o
m
th
e
last
s
ev
en
d
ay
s
to
esti
m
ate
th
e
wea
th
er
to
m
o
r
r
o
w)
[
9
]
.
T
h
e
m
o
d
el
will
b
e
ab
le
to
f
o
r
ec
ast
wh
eth
er
o
r
n
o
t
th
e
d
r
iv
er
is
d
r
o
wsy
s
in
ce
th
e
C
NN
ar
ch
i
tectu
r
e
is
ab
le
to
d
etec
t
th
e
im
ag
e
p
atter
n
a
n
d
lo
ca
te
tr
e
n
d
s
th
r
o
u
g
h
o
u
t
th
e
s
eq
u
en
c
e
o
f
i
m
ag
es
[
1
0
]
.
T
h
e
s
ec
o
n
d
m
et
h
o
d
,
in
co
n
tr
ast,
u
s
es
co
m
b
in
e
d
tech
n
iq
u
es
o
f
d
e
ep
lear
n
i
n
g
an
d
AI
to
p
r
e
-
p
r
o
ce
s
s
th
e
d
r
iv
er
'
s
p
h
o
to
s
[
1
1
]
.
T
h
e
h
is
to
g
r
am
o
f
o
r
ien
ted
g
r
a
d
ien
ts
(
HOG)
th
at
p
in
p
o
in
t
f
ac
ial
elem
en
ts
n
am
ely
m
o
u
th
o
r
ey
es
wh
er
ea
s
lin
ea
r
s
u
p
p
o
r
ti
n
g
v
ec
to
r
m
ac
h
in
e
(
SVM)
will
b
e
u
s
ed
to
id
en
tify
th
e
f
ac
e
[
1
2
]
.
I
n
th
is
s
tu
d
y
,
a
c
o
m
b
in
atio
n
o
f
HOG
an
d
lin
ea
r
SVM
was e
m
p
lo
y
ed
to
id
e
n
tify
th
e
d
r
iv
er
'
s
f
ac
e.
T
h
is
s
ec
tio
n
p
r
o
v
id
es
a
s
u
m
m
ar
y
o
f
th
e
m
eth
o
d
s
an
d
s
tr
ateg
ies
u
s
ed
b
ef
o
r
e
to
id
en
tif
y
tire
d
n
ess
.
T
h
e
f
ir
s
t
ap
p
r
o
ac
h
is
b
ased
o
n
d
r
i
v
in
g
h
ab
its
an
d
d
ep
en
d
s
h
ea
v
ily
o
n
th
e
ch
ar
ac
ter
is
tics
o
f
th
e
ca
r
,
th
e
co
n
d
itio
n
o
f
th
e
r
o
a
d
s
,
an
d
th
e
d
r
iv
e
r
.
I
t
h
as
b
ee
n
u
tili
ze
d
b
ef
o
r
e
d
r
iv
i
n
g
p
atter
n
s
s
h
o
u
ld
b
e
esti
m
ated
u
s
in
g
d
ev
iatio
n
s
f
r
o
m
later
al
o
r
lan
e
p
o
s
itio
n
s
o
r
s
teer
in
g
wh
ee
l
m
o
v
em
e
n
ts
.
Dr
iv
er
ass
is
tan
ce
s
y
s
tem
is
i
m
p
lem
en
ted
with
a
s
in
g
le
lo
w
-
co
s
t
ca
m
er
a
th
at
is
p
lace
d
in
n
er
o
f
t
h
e
au
to
m
o
b
ile,
th
is
s
o
lu
tio
n
is
n
o
n
-
in
v
a
s
iv
e
an
d
d
o
es
n
o
t
d
ep
en
d
o
n
wea
r
ab
le
g
ad
g
ets
[
1
3
]
.
Fo
r
f
ac
e
r
ec
o
g
n
itio
n
,
a
Haa
r
-
c
ascad
e
a
p
p
r
o
ac
h
is
em
p
lo
y
ed
,
an
d
f
o
r
p
r
o
ce
s
s
in
g
a
n
d
class
if
icatio
n
,
s
im
p
le
n
e
u
r
al
n
etwo
r
k
ar
ch
it
ec
tu
r
e
is
u
s
ed
[
1
4
]
.
C
o
n
v
e
r
tin
g
v
id
eo
f
r
am
es
to
g
r
ay
s
ca
le
is
th
e
f
ir
s
t
s
tep
[
1
5
]
.
T
h
e
Haa
r
-
ca
s
ca
d
e
a
p
p
r
o
ac
h
is
th
en
u
tili
ze
d
to
tr
im
f
ac
es
f
r
o
m
im
ag
es
in
two
s
tag
es
co
n
s
id
er
in
g
th
at
it
is
a
s
im
p
le,
ac
cu
r
ate
an
d
r
ap
id
p
r
o
ce
s
s
o
f
f
ac
e
d
etec
tio
n
[
1
6
]
.
T
h
e
v
alid
atio
n
o
f
th
e
Haar
-
ca
s
ca
d
e
ap
p
r
o
ac
h
is
d
o
n
e
b
y
s
tr
atif
ied
k
-
f
o
ld
(
STKF)
[
1
7
]
.
T
h
e
o
u
tp
u
t
im
ag
es
ac
q
u
ir
ed
th
r
o
u
g
h
t
h
e
p
r
e
-
p
r
o
ce
s
s
in
g
an
d
f
ac
e
d
etec
tio
n
s
u
b
-
s
y
s
tem
ar
e
s
ep
ar
ated
in
to
f
iv
e
eq
u
al
h
alv
es [
1
8
]
.
R
ec
o
g
n
izin
g
f
atig
u
e
b
ased
o
n
v
is
u
al
cu
es.
A
tr
ied
-
an
d
-
tr
u
e
m
eth
o
d
f
o
r
d
etec
tin
g
d
r
iv
e
r
d
r
o
wsi
n
ess
is
to
u
s
e
a
v
id
eo
ca
m
er
a
to
r
ec
o
r
d
ey
elid
m
o
v
em
e
n
t a
n
d
g
az
e
.
Vis
u
al
in
d
icato
r
s
o
f
f
atig
u
e
in
clu
d
e
d
r
o
o
p
in
g
p
o
s
tu
r
e,
y
aw
n
in
g
,
f
r
eq
u
en
t
n
o
d
d
in
g
,
s
lo
w
ey
elid
m
o
v
em
en
t,
a
s
lu
g
g
is
h
f
ac
ial
ex
p
r
ess
io
n
,
an
d
a
d
ec
r
ea
s
ed
d
eg
r
ee
o
f
ey
e
o
p
en
in
g
.
Nu
m
e
r
o
u
s
s
tu
d
ies
h
av
e
b
ee
n
co
n
d
u
cted
o
n
th
ese
s
tr
ateg
ies.
Yet,
th
ese
m
eth
o
d
s
f
r
eq
u
e
n
tly
ex
h
ib
it
s
en
s
itiv
ity
to
o
u
ts
id
e
elem
en
ts
lik
e
b
r
ig
h
tn
es
s
o
r
t
h
e
d
r
iv
er
'
s
lo
o
k
.
I
n
th
is
r
esear
ch
,
r
aw
d
ata
f
r
o
m
m
u
ltip
le
d
ata
s
ets
in
th
e
liter
atu
r
e
is
u
s
ed
to
id
en
tif
y
d
r
iv
er
wea
r
in
ess
u
tili
zin
g
(
mu
lti
-
task
C
o
n
NN)
.
I
n
th
is
s
tu
d
y
,
th
e
d
lib
p
ac
k
ag
e
is
u
s
ed
to
r
ec
o
g
n
ize
a
n
d
t
r
ac
k
d
r
iv
er
s
'
f
ac
es
in
th
e
r
ea
l
-
tim
e
f
ilm
.
T
h
e
f
ac
e,
m
o
u
th
,
an
d
ey
e
a
r
ea
s
wer
e
th
en
d
ef
in
ed
u
s
in
g
th
e
d
lib
tec
h
n
iq
u
e
o
n
th
ese
s
ca
led
p
ictu
r
es.
T
h
e
m
o
u
t
h
an
d
ey
e
ar
e
o
p
e
n
ed
o
r
clo
s
ed
,
an
d
th
e
o
p
en
in
g
is
d
esig
n
ated
ac
co
r
d
in
g
to
th
e
clo
s
ed
co
n
d
itio
n
,
in
th
ese
d
ef
i
n
ed
p
lace
s
.
“
1
”
r
ep
r
esen
ts
o
p
en
s
tates,
wh
ile
“
0
”
r
ep
r
esen
ts
clo
s
ed
s
tates.
W
e
w
er
e
ab
le
to
ac
h
ie
v
e
an
8
5
% a
c
cu
r
ac
y
r
ate
o
v
er
all.
T
h
e
s
y
s
tem
s
f
o
r
f
ac
ial
lan
d
m
ar
k
d
etec
tio
n
,
b
lin
k
d
etec
tio
n
,
a
n
d
y
awn
d
etec
tio
n
wer
e
th
e
m
ain
to
p
ics
o
f
th
is
liter
at
u
r
e
r
e
v
iew
[
1
9
]
.
Am
o
n
g
t
h
e
tech
n
i
q
u
es
u
s
ed
to
id
en
tify
s
leep
in
ess
ar
e
d
e
ep
C
NN,
co
m
p
u
ter
v
is
io
n
,
b
eh
av
io
r
al
m
ea
s
u
r
es,
an
d
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
,
ea
ch
o
f
wh
ich
h
as a
d
v
an
ta
g
es,
d
r
awb
ac
k
s
,
an
d
v
ar
y
in
g
d
eg
r
ee
s
o
f
ac
cu
r
ac
y
[
2
0
]
.
T
ec
h
n
o
lo
g
ies
b
ased
o
n
e
y
e
a
s
p
ec
t
r
at
i
o
(
E
A
R
)
an
d
m
o
u
t
h
a
s
p
e
c
t
r
a
t
io
(
MA
R
)
h
av
e
b
ee
n
in
v
esti
g
a
ted
f
o
r
th
e
d
etec
tio
n
o
f
b
li
n
k
s
an
d
y
awn
s
[
2
1
]
.
“
C
o
m
p
u
ter
v
is
io
n
-
b
ased
d
r
o
wsi
n
ess
d
etec
tio
n
f
o
r
m
o
to
r
ized
v
eh
icles
with
web
p
u
s
h
n
o
tific
atio
n
s
,
”
th
e
titl
e
claim
s
.
I
n
th
is
wo
r
k
,
th
ey
d
escr
ib
e
a
co
m
p
u
ter
v
is
io
n
-
b
a
s
ed
s
y
s
tem
f
o
r
id
en
tify
in
g
th
e
d
r
o
wsi
n
ess
in
ca
r
s
,
co
m
p
lete
with
aler
t
s
o
u
n
d
s
an
d
web
p
u
s
h
n
o
tific
atio
n
s
.
T
h
e
d
r
iv
er
will
b
e
in
f
o
r
m
e
d
b
y
th
ese
m
ess
ag
es,
en
ab
lin
g
th
em
to
p
r
ev
e
n
t
an
ac
cid
en
t.
T
o
h
elp
th
e
d
r
iv
er
s
tay
f
o
c
u
s
ed
,
th
e
s
y
s
tem
ca
n
als
o
s
en
d
o
u
t
a
n
aler
t
t
h
at
s
h
o
ws
lo
ca
l
co
f
f
ee
s
h
o
p
s
.
I
n
lig
h
t
o
f
th
is
,
th
e
s
y
s
tem
s
u
cc
ess
f
u
lly
id
en
tifie
d
t
h
e
d
r
iv
e
r
'
s
s
leep
in
es
s
th
r
o
u
g
h
o
u
t
th
e
t
r
ial
r
u
n
.
T
h
e
ey
es'
o
p
en
n
ess
o
r
clo
s
u
r
e
was
d
eter
m
in
ed
u
s
in
g
th
e
E
AR
.
A
b
u
zz
er
p
r
o
v
id
ed
an
aler
t,
a
n
d
af
ter
th
at
th
e
u
s
er
.
T
h
ese
will
b
e
d
o
n
e
with
an
em
p
h
asis
o
n
s
p
ee
d
an
d
r
eliab
ilit
y
,
wh
ich
ar
e
n
ec
ess
ar
y
f
o
r
th
e
r
ea
l
-
wo
r
ld
,
ch
allen
g
i
n
g
ap
p
licatio
n
o
f
p
r
ev
en
tin
g
ca
r
ac
cid
en
ts
an
d
m
ak
in
g
d
r
iv
in
g
an
d
r
o
ad
s
s
af
er
.
T
h
is
wo
r
k
in
t
en
d
s
to
p
u
t
to
g
eth
e
r
a
s
y
s
tem
th
at
ca
n
ass
ess
a
d
r
iv
er
'
s
s
tate
o
f
f
atig
u
e
b
ase
d
o
n
f
ac
ial
im
ag
e
s
eq
u
en
c
es.
T
h
e
d
r
i
v
er
-
b
ased
ad
v
an
ce
d
d
r
iv
i
n
g
ass
is
tan
ce
s
y
s
tem
(
ADAS
)
s
y
s
tem
f
o
r
id
en
tify
in
g
s
leep
in
ess
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co
n
s
tr
ain
ed
in
to
two
k
ey
f
ac
to
r
s
.
W
e
h
av
e
d
e
v
elo
p
ed
t
h
is
wo
r
k
with
a
p
ar
t
o
f
h
as
two
k
e
y
co
n
s
tr
ain
ts
:
ea
r
ly
d
etec
tio
n
an
d
m
in
im
izin
g
f
alse
p
o
s
itiv
es.
T
o
p
r
ev
en
t
f
al
s
e
p
o
s
itiv
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th
at
wo
u
ld
an
n
o
y
th
e
d
r
iv
er
an
d
f
o
r
ce
th
em
to
tu
r
n
o
f
f
th
e
ADAS
with
o
u
t
u
s
in
g
th
e
r
em
ain
i
n
g
f
ea
tu
r
es,
th
e
s
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s
tem
is
d
esi
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ed
to
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n
ly
aler
t
th
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d
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ess
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icu
lt
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eter
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am
e
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ate
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ich
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er
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an
d
s
y
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tem
m
u
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t
ex
ch
an
g
e
d
ata
wh
en
ca
p
t
u
r
in
g
th
e
d
r
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r
.
D
u
e
to
t
h
e
v
ast
c
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n
t
o
f
f
r
am
es
p
er
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ec
o
n
d
(
FP
S)
to
b
e
an
al
y
z
e
,
a
h
ig
h
f
r
am
e
r
ate
will
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v
er
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ad
t
h
e
s
y
s
tem
,
b
u
t
a
lo
w
FP
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ca
n
h
av
e
a
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etr
i
m
en
tal
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f
ec
t
o
n
s
y
s
tem
p
e
r
f
o
r
m
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ce
.
I
n
th
is
f
ield
,
h
av
in
g
s
u
f
f
icien
t
FP
S
is
ess
e
n
tial
f
o
r
a
p
p
r
ec
iatin
g
im
ag
e
s
eq
u
en
ce
elem
e
n
ts
with
ex
tr
e
m
ely
litt
le
d
u
r
atio
n
s
,
lik
e
b
lin
k
s
.
I
n
o
r
d
e
r
to
p
r
e
d
ict
d
r
iv
er
d
r
o
wsi
n
ess
,
th
is
r
esear
ch
s
u
g
g
ests
an
ad
v
a
n
ce
d
ap
p
r
o
ac
h
th
at
ass
o
ciate
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
15
,
No
.
1
,
Ma
r
ch
20
26
:
84
-
92
86
d
ee
p
lear
n
in
g
an
d
AI
tech
n
i
q
u
es
to
tak
e
o
u
t
v
ar
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f
ea
t
u
r
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f
r
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m
th
e
p
h
o
to
s
an
d
th
en
in
co
r
p
o
r
ates
th
o
s
e
f
ea
tu
r
es
in
to
a
f
u
zz
y
lo
g
ic
-
b
ased
s
y
s
tem
.
Ho
wev
er
,
th
e
p
r
o
ce
s
s
s
tr
u
ctu
r
e
is
d
ep
icted
in
Fig
u
r
e
1
,
w
h
ich
in
clu
d
es th
r
e
e
p
h
ases
: p
r
e
-
p
r
o
ce
s
s
in
g
,
an
aly
s
is
,
an
d
alar
m
ac
tiv
atio
n
.
Fig
u
r
e
1
.
Dr
iv
e
r
d
r
o
wsi
n
ess
es
tim
atio
n
m
o
d
u
les
2.
P
RO
P
O
SE
D
M
E
T
H
O
D
W
h
ile
th
e
d
r
iv
e
r
is
b
eh
in
d
th
e
wh
ee
l,
a
ca
m
er
a
r
ec
o
r
d
s
th
e
d
r
iv
er
'
s
f
ac
e
an
d
s
tr
ea
m
s
it
as
v
id
eo
.
T
h
e
s
o
f
twar
e
th
en
ev
alu
ates
th
e
v
i
d
eo
f
o
r
s
ig
n
s
o
f
f
atig
u
e,
s
leep
in
ess
,
an
d
d
r
o
wsi
n
ess
in
ten
s
ity
[
2
2
]
.
T
h
e
d
r
iv
e
r
'
s
f
ac
ial
tr
ac
k
in
g
,
lev
el
o
f
ex
h
a
u
s
tio
n
,
an
d
id
en
tific
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n
o
f
i
m
p
o
r
tan
t
f
ac
ial
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eg
io
n
s
b
ased
o
n
ey
e
clo
s
u
r
e
a
n
d
y
awn
in
g
a
r
e
th
e
ess
en
tial
f
ac
to
r
s
th
at
s
h
o
u
ld
b
e
s
tu
d
ied
f
o
r
an
aly
s
is
at
th
is
p
o
in
t [
2
3
]
.
Fin
ally
,
an
y
d
r
o
wsi
n
ess
is
f
o
u
n
d
f
r
o
m
th
e
d
r
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v
er
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a
v
o
ice
aler
t
is
g
iv
en
.
T
h
e
p
r
e
-
p
r
o
ce
s
s
in
g
m
o
d
u
le
r
ec
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es
th
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im
ag
es
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d
is
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esp
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n
s
ib
le
f
o
r
tr
an
s
f
o
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m
in
g
t
h
em
in
to
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ata
th
at
th
e
s
leep
in
e
s
s
d
etec
tio
n
m
o
d
el
m
ay
u
s
e
[
2
4
]
.
T
h
e
an
al
y
s
is
m
o
d
u
le
r
ec
eiv
e
s
th
e
p
r
e
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p
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ce
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s
ed
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ata
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te
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wh
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it
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ec
u
tes
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tiv
ities
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elate
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o
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atig
u
e
d
etec
tio
n
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d
d
eter
m
in
es
th
e
d
r
iv
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'
s
lev
el
o
f
d
r
o
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n
ess
at
th
at
m
o
m
en
t
u
s
in
g
d
ata
f
r
o
m
th
e
p
r
ec
ed
in
g
6
0
s
ec
o
n
d
s
[
2
5
]
.
T
h
e
alar
m
ac
tiv
atio
n
m
o
d
u
le
t
h
en
r
ec
eiv
es
th
e
ca
lcu
lated
lev
el
o
f
tire
d
n
ess
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d
d
ec
id
es
wh
eth
er
o
r
n
o
t
to
n
o
tify
th
e
d
r
iv
er
b
ased
o
n
p
a
s
t
lev
els
o
f
d
r
o
wsi
n
ess
[
2
6
]
.
As
was
p
r
ev
io
u
s
ly
m
en
tio
n
ed
,
th
e
m
ain
o
b
jectiv
e
o
f
alar
m
ac
tiv
atio
n
s
y
s
tem
is
to
v
er
if
y
th
e
ac
cu
r
ac
y
o
f
f
alse p
o
s
itiv
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p
r
o
d
u
ce
d
b
y
th
e
m
o
d
u
le
(
w
h
en
d
i
v
er
aw
ak
e
g
iv
e
an
in
tim
atio
n
o
f
d
r
o
wsi
n
ess
aler
t
s
)
,
f
alse p
o
s
itiv
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ca
n
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p
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et
th
e
d
r
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d
in
cr
ea
s
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th
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c
h
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ce
t
h
at
th
e
s
y
s
tem
will
b
e
tu
r
n
ed
o
f
f
[
2
7
]
.
T
h
is
is
o
n
e
o
f
th
e
r
ea
s
o
n
s
r
esear
ch
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s
ar
e
ex
p
er
im
en
tin
g
with
m
o
v
ies
r
a
th
er
th
an
f
r
a
m
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tig
h
ten
i
n
g
u
p
th
e
test
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g
p
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o
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d
u
r
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as
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e
class
if
icat
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1
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m
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tef
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ld
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e
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ee
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ed
“
d
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eg
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r
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at
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is
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ef
o
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e
o
r
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ter
th
at
o
n
e
alar
m
is
ac
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ated
.
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h
e
h
u
m
a
n
-
co
m
p
u
ter
in
te
r
f
ac
e
s
y
s
tem
i
n
ch
ar
g
e
o
f
war
n
i
n
g
th
e
d
r
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v
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ia
v
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al
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d
/o
r
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d
ito
r
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es
will
r
ec
eiv
e
t
h
e
s
y
s
tem
'
s
d
ec
is
io
n
o
n
ce
it
h
as
b
ee
n
d
ec
id
ed
wh
eth
er
o
r
n
o
t
t
o
in
f
o
r
m
th
e
d
r
iv
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(
a
y
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o
p
o
s
s
ib
le
o
u
tco
m
e)
.
Fig
u
r
e
2
illu
s
tr
ates
th
e
th
r
ee
m
o
d
u
les
o
f
ea
ch
p
o
s
s
ib
le
s
o
lu
tio
n
:
p
r
e
-
p
r
o
ce
s
s
in
g
,
an
aly
s
is
,
an
d
alar
m
ac
tiv
atio
n
.
Fig
u
r
e
2
.
Ar
c
h
itectu
r
e
m
o
d
u
le
s
2
.
1
.
Wo
r
k
f
lo
w
H
i
g
h
-
r
e
s
o
l
u
ti
o
n
c
a
m
e
r
as
a
r
e
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s
e
d
t
o
m
o
n
i
t
o
r
a
n
d
c
a
p
t
u
r
e
i
m
a
g
e
s
,
w
it
h
t
h
e
f
r
a
m
e
s
b
e
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n
g
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x
t
r
a
c
t
ed
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n
d
i
v
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d
u
a
l
l
y
a
n
d
a
l
e
r
ts
b
ei
n
g
p
r
o
v
i
d
e
d
[
2
8
]
.
H
a
a
r
c
as
c
a
d
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cl
a
s
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f
i
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r
s
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p
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M
AR
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A
R
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e
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d
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h
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s
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t
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l
t
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f
f
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.
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s
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v
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d
t
o
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w
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k
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d
r
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r
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a
n
d
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t
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i
ll
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i
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n
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n
t
i
l
t
h
e
d
r
i
v
e
r
r
es
p
o
n
d
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
F
u
z
z
y
lo
g
ic
-
b
a
s
ed
d
r
iver fa
tig
u
e
p
r
ed
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n
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ystem
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r
s
a
fe
a
n
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fr
ien
d
ly
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(
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va
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)
87
2
.
2
.
F
a
cia
l
f
e
a
t
ures
a
nd
g
esture
det
ec
t
io
n
Fra
m
e
ac
q
u
is
itio
n
:
to
r
ec
o
r
d
th
e
d
r
iv
er
'
s
f
ield
o
f
v
iew,
a
to
p
-
of
-
th
e
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lin
e
d
ig
ital
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m
er
a
is
m
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u
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ted
i
n
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e
au
to
m
o
b
ile
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d
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et
to
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o
s
e
m
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d
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ess
th
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cu
r
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en
t
co
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itio
n
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ea
l
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tim
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id
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ath
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d
f
r
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m
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ar
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k
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.
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p
o
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itio
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e
d
r
i
v
er
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s
f
ac
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in
a
v
id
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e
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s
ed
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o
m
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in
in
g
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SVM
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etec
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s
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Fig
u
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3
s
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ical
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Fig
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3
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u
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3
.
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ates
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ar
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m
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t
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u
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4
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ial
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ates.
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R
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Fig
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4
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e
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ies f
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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ates
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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DATA AV
AI
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AB
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Der
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d
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
F
u
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g
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(
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)
91
RE
F
E
R
E
NC
E
S
[
1
]
G
.
Li
u
,
M
.
Zh
o
u
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B
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Evaluation Warning : The document was created with Spire.PDF for Python.