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k
,
lead
in
g
to
a
b
etter
s
o
lu
tio
n
i
n
th
e
g
iv
en
s
ce
n
ar
io
,
ass
is
tin
g
r
escu
e
wo
r
k
er
s
in
d
is
aster
zo
n
es,
an
d
in
c
r
ea
s
in
g
b
o
r
d
er
s
ec
u
r
ity
.
T
h
e
p
ap
er
c
o
m
p
r
is
es
m
o
d
u
les,
an
au
to
-
n
eu
r
al
ar
ch
itectu
r
e
g
en
e
r
ato
r
,
an
d
a
p
r
o
ce
s
s
th
at
u
n
ites
th
em
[
1
2
]
.
First,
th
er
m
al
d
ata
o
f
th
e
v
ictim
’
s
p
o
s
itio
n
is
co
m
b
in
ed
with
d
ata
ab
o
u
t
th
e
ter
r
ain
,
wh
ich
h
as
b
ee
n
co
lo
r
e
d
in
t
h
e
d
ar
k
.
T
h
en
a
f
u
s
ed
2
d
im
en
s
io
n
al
(
D
)
t
o
p
o
lo
g
ical
m
o
d
el
is
u
s
ed
to
d
ep
ict
th
e
b
lo
ck
ad
e,
f
lo
o
d
e
d
,
an
d
d
eb
r
i
s
lo
ca
tio
n
s
,
as
well
a
s
n
ig
h
t
v
is
io
n
im
ag
es
th
at
h
av
e
b
e
en
co
lo
r
ized
[
1
3
]
.
W
ith
th
er
m
al
v
is
io
n
,
g
e
o
g
r
a
p
h
ic
g
eo
-
tec
h
n
o
lo
g
ies
ca
n
h
el
p
p
eo
p
le
m
ak
e
d
ec
is
io
n
s
with
en
h
a
n
ce
d
r
escu
e
s
tr
ateg
ies.
Acc
o
r
d
in
g
ly
,
u
s
in
g
a
d
r
o
n
e
-
b
ased
n
i
g
h
t m
ap
p
in
g
m
eth
o
d
is
h
elp
f
u
l f
o
r
r
ap
i
d
r
es
p
o
n
s
e
team
s
[
1
4
]
.
A
m
u
lti
-
m
o
d
al
im
ag
e
f
u
s
io
n
s
y
s
tem
th
at
p
r
o
tects
im
p
o
r
tan
t
in
f
o
r
m
atio
n
f
r
o
m
th
e
s
en
d
er
p
ictu
r
es
b
y
d
ec
o
m
p
o
s
in
g
th
e
in
p
u
t
im
ag
es
u
s
in
g
d
is
cr
ete
wav
elet
tr
a
n
s
f
o
r
m
(
DW
T
)
at
f
o
u
r
lev
els
an
d
NSC
T
at
two
lev
els.
T
h
is
tech
n
iq
u
e
s
h
o
ws
a
s
ig
n
if
ican
t
im
p
r
o
v
em
en
t
i
n
p
ix
el
clar
ity
a
n
d
m
ain
tain
s
d
ata
at
t
h
e
f
u
s
ed
im
ag
e
’
s
co
r
n
er
s
an
d
ed
g
es
with
o
u
t
an
y
l
o
s
s
[
1
5
]
.
A
m
ac
h
in
e
v
is
io
n
ap
p
licatio
n
u
tili
zin
g
an
im
ag
e
p
r
o
ce
s
s
in
g
m
eth
o
d
estab
lis
h
ed
a
n
ew
way
o
f
r
ice
s
ee
d
class
if
icatio
n
,
wh
ich
ap
p
lies
h
ash
in
g
tech
n
iq
u
es
p
r
e
-
p
r
o
ce
s
s
in
g
o
f
im
ag
e
p
r
e
d
ictio
n
[
1
6
]
.
L
in
ea
r
r
eg
r
ess
io
n
alg
o
r
ith
m
,
wh
ic
h
ca
lcu
lates
th
e
v
alu
e
o
f
a
d
ep
en
d
e
n
t
v
ar
ia
b
le,
estab
lis
h
es a
n
in
d
ep
en
d
en
t
v
ar
iab
le
[
1
7
]
.
T
h
e
s
co
p
e
is
to
ex
p
an
d
an
in
te
r
n
et
o
f
th
in
g
s
(
I
o
T
)
s
y
s
tem
th
at
ass
i
s
ts
s
u
s
tain
ab
ilit
y
an
d
en
er
g
y
ef
f
ic
ien
cy
b
y
p
r
o
v
id
in
g
r
ea
l
-
tim
e
o
b
s
er
v
in
g
an
d
in
s
ig
h
ts
in
to
p
o
wer
u
tili
za
tio
n
[
1
8
]
.
T
h
e
b
en
ef
its
o
f
in
co
r
p
o
r
atin
g
I
o
T
an
d
g
eo
g
r
ap
h
ic
in
f
o
r
m
ati
o
n
s
y
s
tem
s
in
ag
r
icu
ltu
r
e,
wit
h
p
ar
ticu
lar
ac
ce
n
t
o
n
th
e
p
o
s
s
ib
le
to
tr
an
s
f
o
r
m
s
o
il
o
b
s
er
v
in
g
a
n
d
n
u
tr
ien
ts
m
an
ag
em
en
t
to
r
aise
cr
o
p
o
u
t
p
u
t
wh
ile
m
in
im
izin
g
n
eg
ativ
e
en
v
ir
o
n
m
en
tal
af
f
ec
t
s
[
1
9
]
.
On
l
y
co
n
tem
p
o
r
a
r
y
im
ag
e
an
d
s
ig
n
al
p
r
o
ce
s
s
in
g
a
p
p
r
o
ac
h
es
m
ay
r
ev
ea
l
cr
u
cial
in
s
ig
h
ts
in
to
e
lectr
o
e
n
ce
p
h
alo
g
r
a
m
(
EEG
)
d
ata.
I
m
ag
e
p
r
o
ce
s
s
in
g
tech
n
iq
u
es
m
eticu
lo
u
s
ly
an
aly
ze
E
E
G
im
ag
es
to
r
ev
ea
l
u
n
d
ete
ctab
le
p
atter
n
s
.
Ad
v
an
ce
d
te
m
p
o
r
al
an
d
f
r
eq
u
en
cy
d
o
m
ai
n
s
ig
n
al
p
r
o
ce
s
s
in
g
m
ay
d
etec
t
s
eizu
r
e
p
r
ec
u
r
s
o
r
s
[
2
0
]
.
I
t
ea
s
ier
to
d
ea
l
d
etails
a
n
d
ass
ets
cr
ea
tin
g
m
o
r
e
c
o
h
esiv
e
ec
o
s
y
s
tem
[
2
1
]
.
I
n
cr
ea
s
ed
co
n
n
ec
tiv
ity
an
d
p
r
o
d
u
ctiv
ity
in
th
e
clo
u
d
ar
e
b
o
th
b
en
ef
its
o
f
in
ter
o
p
er
ab
il
ity
.
I
t
ex
p
an
d
s
o
u
r
k
n
o
wled
g
e
o
f
ep
ilep
s
y
an
d
f
o
r
esh
ad
o
ws
a
h
u
m
an
e
m
ed
ical
tech
n
o
lo
g
y
.
C
lo
u
d
co
m
p
u
tin
g
,
im
ag
e
p
r
o
ce
s
s
in
g
,
an
d
s
ig
n
al
p
r
o
ce
s
s
in
g
en
a
b
le
u
s
to
u
n
d
er
s
tan
d
s
eizu
r
es
at
a
b
asic
lev
el
r
ath
er
th
an
m
er
ely
p
r
ed
ict
th
em
.
T
h
e
r
esu
lts
o
f
th
is
in
v
esti
g
ati
o
n
ar
e
s
ig
n
if
ica
n
t
f
o
r
cr
ea
tin
g
p
er
s
o
n
alize
d
e
p
ilep
s
y
tr
ea
tm
en
t
p
r
o
g
r
am
s
[
2
2
]
.
T
h
e
d
ata
is
s
to
r
ed
,
an
aly
ze
d
,
an
d
s
h
o
wn
u
s
in
g
T
h
in
g
Sp
e
ak
,
a
clo
u
d
-
b
ased
I
o
T
p
latf
o
r
m
.
I
t
h
as
a
s
im
p
le
in
ter
f
ac
e
wh
ich
ca
n
b
e
u
tili
ze
d
to
o
b
s
er
v
e
wate
r
le
v
els,
o
b
s
er
v
e
d
ata
f
r
o
m
th
e
p
ast,
an
d
e
x
p
an
d
d
is
co
v
er
in
g
wh
ich
ca
n
b
e
ap
p
lied
to
h
an
d
le
wate
r
r
eso
u
r
ce
s
b
etter
[
2
3
]
.
P
o
wer
q
u
ality
o
b
s
er
v
in
g
i
n
PV
g
r
i
d
s
y
s
tem
s
,
th
is
r
esear
ch
o
f
f
er
s
an
in
v
en
ti
v
e
s
tr
ateg
y
ap
p
ly
in
g
I
o
T
[
2
4
]
.
T
h
e
s
co
p
e
is
to
ex
ten
d
an
I
o
T
s
y
s
tem
th
at
h
elp
s
s
u
s
tain
ab
ilit
y
an
d
en
er
g
y
ef
f
ic
ien
cy
[
2
5
]
.
2.
M
E
T
H
O
D
Data
p
r
o
ce
s
s
in
g
an
d
p
r
o
d
u
ct
ar
e
b
o
th
s
ec
tio
n
s
o
f
th
e
m
et
h
o
d
o
lo
g
y
,
k
n
o
wn
as
f
lig
h
t
p
la
n
n
in
g
an
d
d
ata
ac
q
u
is
itio
n
.
I
n
th
e
f
lig
h
t
p
lan
n
in
g
an
d
d
ata
ac
q
u
is
itio
n
’
p
h
ase,
ef
f
icien
cy
is
o
f
th
e
u
tm
o
s
t
im
p
o
r
tan
ce
b
ec
au
s
e
r
esear
ch
er
s
m
u
s
t
g
ath
er
an
d
co
llate
in
f
r
ar
ed
im
a
g
es
o
f
th
e
s
tu
d
y
ar
ea
.
Pro
p
er
f
lig
h
t
p
lan
n
in
g
is
d
o
n
e
o
n
ce
th
e
r
e
q
u
ir
em
e
n
ts
ar
e
i
n
v
esti
g
ated
.
T
h
e
n
th
e
f
lig
h
t
tr
ajec
to
r
y
is
ca
lcu
lated
,
an
d
o
t
h
er
v
a
r
iab
les
lik
e
m
ax
im
u
m
f
li
g
h
t
tim
e
ar
e
d
ef
i
n
ed
to
p
er
f
o
r
m
a
s
m
o
o
th
au
t
o
n
o
m
o
u
s
f
lig
h
t.
A
s
elf
-
f
ly
i
n
g
f
l
ig
h
t
g
ath
er
s
in
f
r
ar
ed
im
ag
er
y
f
r
o
m
o
v
e
r
lap
p
in
g
s
tu
d
y
ar
ea
s
.
T
o
e
n
s
u
r
e
ac
c
u
r
ate
d
ata
co
llectio
n
,
th
ese
im
ag
es
h
av
e
b
ee
n
o
r
g
an
ized
an
d
f
ilter
e
d
.
T
h
e
f
ir
s
t
s
tep
,
“
d
ata
p
r
o
ce
s
s
in
g
a
n
d
d
ata
p
r
o
d
u
ct
,
”
is
to
co
n
v
er
t
th
e
i
n
f
r
ar
e
d
im
ag
es
in
to
R
GB
b
an
d
s
u
s
in
g
th
e
I
R
-
R
GB
im
ag
e
tr
an
s
f
o
r
m
atio
n
.
Fo
r
a
s
p
ec
if
ic
s
tu
d
y
ar
ea
,
s
ev
er
al
c
o
lo
r
e
d
R
GB
im
ag
es
ar
e
m
o
s
aice
d
to
g
eth
er
to
cr
ea
te
a
n
R
GB
m
o
s
a
ic.
Ad
d
itio
n
ally
,
I
R
im
ag
es
ar
e
u
s
ed
to
p
r
o
ce
s
s
s
elec
ted
ar
ea
s
in
to
I
R
m
o
s
aics.
As
a
r
esu
lt,
a
s
u
b
s
eq
u
en
t
m
o
s
aic
is
u
s
ed
to
co
v
er
th
e
ar
ea
r
eq
u
ir
e
d
.
A
n
ew
I
R
-
R
GB
im
ag
e
f
u
s
io
n
s
cr
ip
t
is
n
o
w
av
aila
b
le
f
o
r
d
o
wn
lo
ad
,
allo
win
g
y
o
u
to
c
o
m
b
in
e
th
e
f
u
s
ed
i
m
ag
e
with
I
R
an
d
R
GB
f
ea
tu
r
es.
Usi
n
g
th
e
f
u
s
ed
im
ag
e,
it is
p
o
s
s
ib
le
to
u
s
e
th
er
m
al
an
d
o
p
tic
al
r
em
o
te
s
en
s
in
g
tech
n
iq
u
es to
m
ap
th
e
v
ictim
’
s
lo
ca
tio
n
.
T
h
is
,
as
s
h
o
wn
in
Fig
u
r
e
1
,
u
tili
ze
s
an
au
to
n
e
u
r
al
ar
ch
itectu
r
e
g
en
er
ato
r
to
p
r
o
ce
s
s
n
ig
h
t
tim
e
ter
r
ain
m
ap
p
in
g
in
f
o
r
m
atio
n
,
alo
n
g
with
th
er
m
al
im
ag
er
y
,
t
o
ass
is
t
in
lo
ca
tin
g
v
ictim
s
in
a
n
atu
r
al
d
is
aster
.
T
h
e
f
o
llo
win
g
n
ig
h
t v
is
io
n
co
l
o
r
izatio
n
alg
o
r
ith
m
s
h
av
e
b
ee
n
f
o
u
n
d
.
T
h
is
p
ap
er
’
s
NC
C
A
au
to
n
eu
r
al
ar
ch
itectu
r
e
alg
o
r
ith
m
p
r
esen
tatio
n
in
clu
d
es
th
e
n
etwo
r
k
co
d
e
d
f
ea
tu
r
e
s
et.
T
h
is
co
r
r
esp
o
n
d
en
ce
allo
ws
u
s
to
lear
n
h
o
w
to
r
en
d
er
ac
cu
r
ate
e
d
g
e
m
ap
s
,
g
e
o
m
etr
y
,
an
d
co
lo
u
r
wh
ile
im
p
r
o
v
in
g
o
u
r
ac
cu
r
ac
y
.
W
o
r
k
in
l
o
w
-
lig
h
t
c
o
n
d
itio
n
s
b
ec
au
s
e
o
f
u
s
in
g
a
n
etwo
r
k
o
f
f
ea
tu
r
e
m
a
p
s
.
T
h
is
m
ea
n
s
it
is
p
o
s
s
ib
le
to
id
en
tify
m
is
s
in
g
f
e
atu
r
es
an
d
co
m
p
lete
th
e
g
ap
s
in
m
is
s
in
g
d
at
a
s
eg
m
en
ts
,
s
o
y
o
u
ca
n
g
et
wo
r
k
d
o
n
e
in
th
ese
co
n
s
id
er
atio
n
s
.
T
o
b
u
ild
a
n
etwo
r
k
o
f
co
d
ed
f
ea
tu
r
e
s
ets,
t
h
e
s
ce
n
e
an
d
o
b
jec
t
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
6
,
No
.
2
,
No
v
em
b
er
20
24
:
1
3
3
8
-
1
3
4
6
1340
f
ea
tu
r
es
ar
e
g
r
o
u
p
e
d
a
n
d
lab
el
led
in
o
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d
er
t
o
cr
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te
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ets
o
f
f
ea
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r
e
v
ec
t
o
r
s
with
d
if
f
er
e
n
t
l
ig
h
tin
g
c
o
n
d
itio
n
s
.
T
h
e
alg
o
r
ith
m
of
n
etwo
r
k
co
d
ed
f
ea
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r
e
s
et
is
ex
p
lain
e
d
in
[
2
6
]
.
Fig
u
r
e
1
.
I
ll
u
s
tr
atio
n
o
f
n
ig
h
t t
im
e
m
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ik
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eg
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im
p
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e
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is
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ess
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d
lo
ca
lizatio
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s
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tili
zin
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er
m
al
im
ag
e
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s
io
n
Yo
u
ca
n
u
s
e
an
im
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e
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o
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m
at
(
a
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ar
am
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e
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)
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at
m
ap
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ea
ch
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ain
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g
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e
to
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e
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ig
h
t
co
n
ce
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atio
n
o
f
lig
h
t
f
ield
,
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lo
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alu
es,
a
n
d
lig
h
t
i
n
ten
s
ity
.
R
ef
er
to
Fig
u
r
e
1
f
o
r
clar
if
icatio
n
.
W
h
en
v
iewin
g
o
b
jects
f
r
o
m
d
i
f
f
er
en
t
a
n
g
les,
o
r
ie
n
tatio
n
s
,
an
d
lig
h
tin
g
co
n
d
itio
n
s
,
y
o
u
s
h
o
u
ld
u
s
e
th
e
T
u
b
u
le
Net
ar
tific
ial
in
tellig
en
ce
(
AI
)
m
o
d
u
le
I
to
class
if
y
th
e
o
b
jects.
T
o
o
r
g
an
ize
im
ag
es,
y
o
u
m
u
s
t
u
s
e
ed
g
e
m
ask
s
,
th
e
d
is
tr
ib
u
tio
n
o
f
th
e
o
b
jects
in
th
e
im
ag
e,
an
d
co
lo
r
.
Yo
u
s
h
o
u
ld
tr
ain
y
o
u
r
p
r
o
g
r
am
m
e
r
to
d
if
f
er
en
tiate
b
etwe
en
u
p
a
n
d
d
o
w
n
-
s
am
p
led
im
ag
es
to
en
h
a
n
ce
o
b
ject
r
ec
o
g
n
itio
n
.
T
h
e
p
r
o
ce
s
s
th
e
n
co
n
tin
u
es
b
y
tr
an
s
f
e
r
r
in
g
to
t
h
e
T
u
b
u
le
Net
m
o
d
u
le
I
I
,
wh
ich
will
co
n
d
u
ct
lig
h
t
f
ield
ass
o
ciatio
n
tr
ain
in
g
u
s
in
g
th
e
p
r
ev
io
u
s
ly
class
if
ied
o
u
tp
u
t
an
d
th
e
s
eg
m
en
t
ed
ed
g
e
m
ask
d
er
iv
e
d
f
r
o
m
it.
I
n
ten
s
ity
an
d
lu
m
in
escen
ce
in
th
e
s
ce
n
e
u
n
d
er
s
tan
d
in
g
,
T
u
b
u
le
Net
-
b
ase
d
f
ea
tu
r
e
p
o
o
lin
g
m
o
d
u
le
I
I
I
is
u
s
ed
to
id
en
tify
wh
at
is
p
r
esen
t
in
th
e
s
ce
n
e
a
n
d
s
ea
r
ch
f
o
r
o
b
jects
with
in
it
(
h
y
p
er
p
a
r
am
eter
s
)
.
E
n
s
u
r
e
t
h
at
th
e
co
l
o
r
izatio
n
is
co
r
r
ec
t
b
y
tr
y
in
g
s
ev
er
al
tim
e
s
.
I
t
is
n
ec
ess
ar
y
to
tr
ain
m
o
d
u
le
I
V
o
f
t
h
e
T
u
b
u
le
Net
,
wh
ich
was
d
ev
elo
p
ed
f
o
llo
win
g
s
tep
s
1
an
d
2
.
R
esu
lts
f
r
o
m
m
o
d
u
le
I
I
I
ca
n
b
e
u
s
ed
to
co
m
p
ar
e
m
o
d
u
le
I
V
t
o
m
o
d
u
le
I
I
I
.
Mo
d
u
le
I
I
I
s
h
o
u
ld
b
e
r
etr
ain
e
d
af
ter
it
h
as
b
ee
n
f
e
d
er
r
o
r
s
an
d
co
m
p
l
icate
d
m
ask
s
s
o
th
at
th
e
er
r
o
r
v
alu
e
co
n
v
er
g
es
to
its
m
ax
im
u
m
.
C
o
n
v
er
g
en
ce
a
n
d
p
er
ce
p
tu
al
v
alid
atio
n
ef
f
e
ctiv
ely
p
r
o
m
o
te
o
n
ly
th
e
m
o
s
t
r
elev
an
t
r
esu
lts
.
On
ce
th
e
n
o
is
e
v
alu
es
h
av
e
b
ee
n
f
ed
in
to
th
e
ch
ec
k
,
th
e
p
r
o
g
r
am
u
s
es
th
is
in
f
o
r
m
atio
n
to
s
u
g
g
est
im
p
r
o
v
em
e
n
ts
to
th
e
s
o
u
n
d
.
E
ac
h
p
ix
el
lear
n
s
th
e
o
v
er
all
co
lo
r
d
is
tr
ib
u
tio
n
b
y
lo
o
k
in
g
at
th
e
en
tire
im
ag
e.
T
u
b
u
le
Net
was
d
ev
elo
p
ed
u
s
in
g
T
u
b
u
le
-
b
ased
n
eu
r
o
n
s
,
wh
ich
lear
n
ed
n
o
t
th
e
wh
o
le
b
u
t
o
n
ly
th
e
s
p
ec
if
ic
tim
es to
f
ir
e
in
f
o
r
m
atio
n
to
ac
tiv
ate
in
ter
co
n
n
e
cted
co
m
p
o
n
e
n
ts
o
r
f
o
r
ap
p
licatio
n
s
s
u
ch
as g
am
e
th
eo
r
y
.
T
h
e
“
T
u
b
u
le
”
is
a
wo
r
d
u
s
ed
to
d
escr
ib
e
a
s
et
o
f
n
eu
r
al
n
etwo
r
k
s
g
r
o
u
p
ed
in
im
p
r
ess
iv
e
n
u
m
b
er
s
an
d
f
o
u
n
d
clo
s
e
(
2
)
.
C
ateg
o
r
y
th
eo
r
y
a
n
d
f
o
r
m
al
g
r
a
m
m
a
r
ar
e
b
o
th
in
v
o
lv
e
d
in
ac
tiv
a
tin
g
th
e
T
u
b
u
le
s
.
T
o
tr
an
s
m
it
weig
h
ted
in
f
o
r
m
atio
n
,
a
T
u
b
u
le
in
th
e
n
etwo
r
k
m
u
s
t
b
e
ac
tiv
ated
.
At
th
e
b
eg
in
n
in
g
o
f
th
e
ex
p
er
im
en
t,
th
e
ac
tiv
atio
n
t
h
r
esh
o
ld
is
s
et
to
o
n
e.
T
h
e
co
n
v
en
tio
n
al
d
ee
p
lear
n
in
g
n
etw
o
r
k
r
ep
r
esen
ted
b
y
n
u
m
b
er
1
is
id
en
tical
to
th
e
r
ec
tifie
d
lin
ea
r
u
n
it
(
R
e
LU
)
ac
tiv
atio
n
f
u
n
ctio
n
with
in
p
u
t
an
d
o
u
tp
u
t
co
n
v
o
l
u
tio
n
al
lay
er
s
,
wh
ich
is
a
R
e
L
U
ac
tiv
atio
n
f
u
n
ctio
n
.
T
h
e
n
etwo
r
k
cr
ea
ted
b
y
T
u
b
u
l
e
s
’
tu
b
u
lar
s
tr
u
ctu
r
e
is
“
h
id
d
en
.
”
T
u
b
u
le
Net
(
2
)
co
n
n
ec
ts
to
th
e
lin
e
s
eq
u
e
n
cin
g
p
o
o
l
wh
en
ev
e
r
th
e
ac
tiv
at
io
n
f
u
n
ctio
n
in
th
at
T
u
b
u
le
Net
(
2
)
is
ac
tiv
ated
.
W
h
en
th
e
last
o
d
d
ac
tiv
ato
r
in
th
e
T
u
b
u
le
Net
’
s
m
o
d
u
les
is
ac
tiv
ated
,
th
e
T
u
b
u
l
e
Net
is
th
en
p
ar
t
o
f
th
e
n
etwo
r
k
.
T
h
e
th
r
esh
o
ld
v
alu
e
n
o
w
f
u
n
ctio
n
s
b
ased
o
n
th
e
v
ar
io
u
s
in
p
u
t
v
al
u
es
f
r
o
m
th
e
m
o
d
u
les.
B
ec
au
s
e
th
is
p
r
o
ce
d
u
r
e
s
to
r
es
v
alu
es
in
m
em
o
r
y
,
th
e
co
n
n
ec
tio
n
is
o
p
tim
ized
.
C
h
an
g
in
g
th
e
o
r
d
er
o
f
in
p
u
ts
u
s
ed
in
th
e
ca
lcu
la
tio
n
g
iv
es
a
wid
e
r
an
g
e
o
f
r
esp
o
n
s
es
f
o
r
in
p
u
ts
with
s
im
ilar
ch
ar
ac
ter
is
tics
.
Gr
ee
n
-
tin
ted
n
i
g
h
t v
is
io
n
is
u
s
ed
to
in
ter
p
r
et
an
d
v
is
u
alize
im
ag
es
.
First,
p
er
f
o
r
m
t
h
e
f
o
llo
win
g
a
ctio
n
s
:
w
h
en
d
o
in
g
b
atch
im
a
g
e
an
aly
s
is
,
y
o
u
will
n
ee
d
t
o
ex
tr
ac
t
th
e
co
lo
r
an
d
lig
h
t
in
ten
s
ity
v
alu
e
s
f
o
r
ea
ch
b
atch
o
f
im
ag
es
an
d
th
en
s
av
e
th
ese
v
alu
es
as
an
in
d
ex
o
f
p
ar
am
ete
r
s
f
o
r
ea
ch
tr
ain
in
g
im
ag
e
.
T
h
a
n
k
s
to
th
e
T
u
b
u
le
Net
AI
m
o
d
u
le
I
,
I
ca
n
b
e
u
s
ed
to
class
if
y
a
wid
e
r
a
n
g
e
o
f
o
b
jects
an
d
v
iew
t
h
em
f
r
o
m
v
ar
io
u
s
an
g
les,
o
r
ie
n
tatio
n
s
,
an
d
lig
h
t
f
ield
s
,
th
an
k
s
t
o
th
e
T
u
b
u
le
Net
AI
m
o
d
u
l
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
A
u
to
d
ig
itiz
a
tio
n
o
f a
eria
l ima
g
es to
ma
p
g
e
n
era
tio
n
fr
o
m
U
A
V
feed
(
R
a
j
u
Ja
g
a
d
ee
s
h
K
a
n
n
a
n
)
1341
I
.
C
lass
if
y
in
g
an
o
b
ject
is
b
y
ap
p
ly
in
g
an
ed
g
e
m
ask
a
n
d
a
co
lo
r
d
is
tr
ib
u
tio
n
.
Yo
u
s
h
o
u
ld
also
tr
ain
y
o
u
r
p
r
o
g
r
a
m
to
d
if
f
e
r
en
tiate
b
et
wee
n
u
p
s
am
p
led
an
d
d
o
wn
s
am
p
led
im
ag
es
to
en
h
an
ce
o
b
ject
r
ec
o
g
n
itio
n
.
Allo
w
th
e
m
o
d
u
le
to
lear
n
ab
o
u
t
o
b
ject
-
to
-
ass
o
ciate
d
lig
h
t
f
ield
ass
o
ciatio
n
co
n
ce
r
n
in
g
t
h
e
co
lo
r
d
is
tr
ib
u
tio
n
f
o
r
ea
ch
p
ix
el
an
d
co
n
n
ec
t
th
e
m
o
d
u
le
’
s
f
i
n
d
in
g
s
to
th
e
T
u
b
u
le
Net
m
o
d
u
le
I
I
,
wh
ich
h
as
b
ee
n
g
iv
e
n
th
e
p
ar
am
eter
i
n
d
ex
d
er
i
v
ed
f
r
o
m
s
tep
1
.
Please
ex
ec
u
te
th
e
T
u
b
u
le
Net
-
b
ased
f
ea
tu
r
e
p
o
o
lin
g
m
o
d
u
le
I
I
I
.
Un
til y
o
u
ar
e
s
atis
f
ied
with
th
e
co
lo
r
izatio
n
r
esu
lt,
k
ee
p
r
ep
ea
tin
g
th
e
lo
o
p
.
B
y
th
e
r
esu
lts
o
f
s
tep
s
1
an
d
2
,
T
u
b
u
le
Net
m
o
d
u
le
I
V
s
h
o
u
l
d
b
e
d
e
v
elo
p
ed
.
R
esu
lts
f
r
o
m
m
o
d
u
le
I
I
I
ca
n
b
e
u
s
ed
to
c
o
m
p
ar
e
m
o
d
u
le
I
V
to
m
o
d
u
le
I
I
I
.
M
o
d
u
le
I
I
I
s
h
o
u
ld
b
e
r
etr
ain
e
d
af
ter
it
h
as
b
ee
n
f
ed
e
r
r
o
r
s
an
d
co
m
p
licated
m
ask
s
s
o
th
at
th
e
er
r
o
r
v
alu
e
co
n
v
er
g
es
to
its
m
ax
im
u
m
.
Af
ter
co
n
v
er
g
e
n
ce
an
d
p
er
ce
p
tu
al
v
alid
atio
n
h
a
v
e
b
ee
n
co
m
p
let
ed
,
th
e
b
est
-
s
u
ited
r
esu
lts
s
h
o
u
ld
b
e
p
u
s
h
ed
f
o
r
war
d
.
Activ
a
tio
n
o
f
ea
c
h
T
u
b
u
le
r
esu
lts
in
th
e
tr
an
s
m
is
s
io
n
o
f
in
f
o
r
m
atio
n
b
etwe
en
T
u
b
u
le
s
clo
s
e
to
o
n
e
an
o
th
er
,
as
s
h
o
wn
in
Fig
u
r
e
2
.
C
h
ec
k
in
g
f
o
r
co
r
r
ec
tn
ess
an
d
im
p
r
o
v
in
g
th
e
b
eh
a
v
io
r
f
o
llo
ws
th
e
later
v
alu
es.
I
f
we
wer
e
to
b
o
il
it
d
o
wn
to
its
ess
en
ce
,
it
’
s
d
eter
m
in
in
g
th
e
co
lo
r
d
is
tr
ib
u
tio
n
o
f
ea
ch
p
ix
el.
Fig
u
r
e
2.
D
iag
r
a
m
o
f
s
u
b
-
m
o
d
u
les in
T
u
b
u
le
Net
Alth
o
u
g
h
T
u
b
u
le
Net
is
esta
b
lis
h
ed
o
n
T
u
b
u
le
-
b
ased
n
e
u
r
o
n
s
th
at
lear
n
th
e
s
y
s
tem
’
s
co
m
p
lete
m
ec
h
an
ics,
r
ath
er
th
an
s
im
p
ly
lear
n
in
g
a
p
o
r
tio
n
o
f
it,
th
e
s
y
s
tem
ca
n
o
n
ly
f
ir
e
d
ata
i
n
a
p
ar
ticu
lar
p
atter
n
to
ac
tiv
ate
in
ter
co
n
n
ec
ted
co
m
p
o
n
en
ts
o
r
n
eu
r
al
s
ets
s
im
ilar
to
th
o
s
e
alr
ea
d
y
ac
tiv
ated
.
I
d
ea
lly
,
th
e
n
eu
r
al
n
etwo
r
k
s
s
h
o
u
ld
b
e
a
r
r
an
g
e
d
s
o
th
at
th
er
e
ar
e
an
o
d
d
n
u
m
b
er
o
f
n
eu
r
al
n
etwo
r
k
s
in
a
g
iv
en
s
et;
th
e
r
esu
lt
s
h
o
u
ld
b
e
o
n
e
T
u
b
u
le
o
r
g
r
o
u
p
o
f
n
e
u
r
al
n
etwo
r
k
s
.
Fo
r
e
v
e
r
y
T
u
b
u
le
in
th
e
s
y
s
tem
,
a
n
a
ctiv
atio
n
f
u
n
ctio
n
is
d
ev
elo
p
e
d
in
th
e
f
o
r
m
o
f
a
f
ac
to
r
b
ased
o
n
ca
teg
o
r
y
t
h
eo
r
y
.
Du
r
in
g
t
h
e
ea
r
l
y
s
tag
es
o
f
d
e
v
elo
p
m
en
t,
th
er
e
is
a
u
n
if
o
r
m
ac
tiv
atio
n
th
r
esh
o
ld
.
I
n
p
u
t
an
d
o
u
tp
u
t
p
r
o
ce
d
u
r
es
ar
e
d
escr
ib
ed
in
d
etail.
T
h
e
y
ar
e
d
ee
p
lear
n
in
g
n
etwo
r
k
s
in
th
e
co
n
v
en
tio
n
a
l
s
en
s
e,
b
u
t
in
s
tead
o
f
u
s
in
g
th
e
R
e
L
U
ac
tiv
atio
n
f
u
n
c
tio
n
,
th
ey
u
s
e
th
e
co
n
v
o
l
u
tio
n
al
lay
er
(
C
ONV
)
a
ctiv
atio
n
f
u
n
ctio
n
.
T
h
e
h
id
d
en
n
etwo
r
k
is
f
o
r
m
e
d
b
y
th
e
n
o
n
s
tr
u
ctu
r
al
n
etwo
r
k
th
at
ex
is
ts
with
in
T
u
b
u
le
s
.
A
co
n
n
ec
tio
n
is
estab
lis
h
ed
to
th
e
n
ex
t
T
u
b
u
le
Net
in
t
h
e
p
o
o
l
ea
ch
tim
e
th
e
ac
tiv
atio
n
f
u
n
ctio
n
o
f
a
T
u
b
u
le
Net
in
a
lin
e
s
eq
u
en
cin
g
p
o
o
l
is
ac
tiv
ated
.
T
h
e
d
ata
is
s
h
ar
ed
wh
en
th
e
last
o
d
d
ac
tiv
ato
r
in
a
T
u
b
u
le
Net
i
s
ac
tiv
ated
,
as sh
o
wn
in
F
ig
u
r
e
3
.
I
t
d
ep
en
d
s
o
n
th
e
ty
p
e
o
f
in
p
u
t
v
alu
es
th
at
a
r
e
u
s
ed
in
t
h
e
m
o
d
u
le
f
o
r
ea
ch
m
o
d
u
le.
Sig
n
if
ican
t
o
p
tim
izatio
n
o
f
t
h
e
co
n
n
ec
tio
n
ca
n
b
e
attain
ed
b
y
s
to
r
in
g
v
alu
es
in
m
em
o
r
y
.
C
h
an
g
in
g
th
e
o
r
d
er
o
f
in
p
u
ts
u
s
ed
in
th
e
ca
lcu
latio
n
g
iv
es
a
wid
e
r
an
g
e
o
f
r
esp
o
n
s
es
f
o
r
in
p
u
ts
with
s
im
ilar
ch
ar
ac
ter
i
s
tics
.
An
ex
ten
s
iv
e
s
et
o
f
B
ay
esian
f
ea
tu
r
es
(
s
u
ch
as
th
e
cla
s
s
if
icatio
n
o
f
n
o
d
es
estab
lis
h
ed
o
n
th
e
co
m
p
lian
ce
th
em
e,
as
well
a
s
p
o
s
itiv
e
o
r
n
eg
ativ
e
class
if
icatio
n
)
is
u
s
ed
to
em
u
late
d
if
f
er
en
tial
d
en
d
r
itic
ev
o
lu
tio
n
.
I
n
th
is
ex
am
p
le,
3
D
p
er
-
p
ix
el
im
a
g
in
g
is
u
s
ed
to
id
en
tify
d
if
f
er
en
tial
ch
an
g
es
in
d
en
d
r
itic
g
r
o
wth
ac
r
o
s
s
d
if
f
er
en
t
f
ea
tu
r
e
s
ets
.
T
h
e
p
r
im
ar
y
an
d
s
u
p
e
r
-
lev
el
ca
teg
o
r
y
d
if
f
er
en
tial
d
en
d
r
itic
ev
o
lu
tio
n
we
s
im
u
late
d
ep
en
d
s
o
n
wh
eth
er
th
e
n
o
d
es
’
n
e
u
r
al
weig
h
ts
ar
e
c
o
n
f
o
r
m
ed
o
r
n
o
n
-
c
o
n
f
o
r
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
6
,
No
.
2
,
No
v
em
b
er
20
24
:
1
3
3
8
-
1
3
4
6
1342
Fig
u
r
e
3
.
I
ll
u
s
tr
atio
n
o
f
s
u
b
-
m
o
d
u
les
in
T
u
b
u
le
Net
W
h
en
th
e
ac
tiv
atio
n
f
u
n
ctio
n
o
f
a
T
u
b
u
le
Net
is
tr
ig
g
er
ed
,
a
co
n
n
ec
tio
n
is
m
ad
e
to
th
e
n
ex
t
T
u
b
u
le
Net
in
th
e
T
u
b
u
le
Net
s
eq
u
e
n
ce
p
o
o
l,
wh
ich
is
f
o
r
m
atted
r
an
d
o
m
ly
.
W
h
en
th
e
p
r
ec
ed
i
n
g
o
d
d
ac
tiv
ato
r
i
n
ev
er
y
elem
en
t
o
f
th
e
T
u
b
u
le
Net
(
s
q
u
ar
e
b
o
x
ab
o
v
e
)
ac
tu
ates
o
v
er
th
e
th
r
esh
o
ld
,
th
e
wh
o
le
T
u
b
u
le
Net
will
ac
tiv
ate
o
v
er
th
e
th
r
esh
o
l
d
.
F
r
o
m
t
h
is
p
o
in
t,
th
e
wh
o
le
T
u
b
u
le
Net
(
s
q
u
ar
e
b
o
x
a
b
o
v
e
)
will
d
is
tr
ib
u
te
d
ata
b
ased
o
n
h
o
w
m
u
ch
ea
c
h
m
o
d
u
le
weig
h
s
.
Fo
r
ea
ch
ty
p
e
o
f
i
n
p
u
t
v
alu
e
r
ec
eiv
ed
,
th
e
th
r
es
h
o
ld
v
alu
e
f
o
r
ea
ch
m
o
d
u
le
v
ar
ies.
T
h
e
co
n
n
ec
tio
n
is
o
p
tim
ized
,
an
d
v
alu
es
ar
e
s
to
r
ed
in
t
h
e
m
e
m
o
r
y
.
I
n
o
t
h
er
wo
r
d
s
,
r
ep
ly
to
r
elate
d
in
p
u
t sig
n
als v
a
r
y
d
e
p
e
n
d
in
g
o
n
th
e
s
er
ies o
f
in
p
u
t
s
ig
n
als p
r
o
v
i
d
ed
.
3.
E
XP
E
R
I
M
E
N
T
A
L
SE
T
UP
Du
r
in
g
th
e
test
in
g
,
we
u
tili
z
ed
4
DJI
Par
r
o
t
d
r
o
n
es.
E
ac
h
d
r
o
n
e
is
p
r
o
h
i
b
ited
b
y
a
c
u
s
to
m
-
b
u
ilt
au
to
p
ilo
t
r
u
n
n
in
g
o
n
Nv
id
ea
-
J
etso
n
Nan
o
.
E
ac
h
d
r
o
n
e
’
s
d
e
alin
g
u
n
it
co
n
tain
s
a
q
u
ad
-
co
r
e
C
PU
AR
M
A5
7
@
1
.
4
3
GHz
,
Me
m
o
r
y
o
f
4
GB
6
4
-
b
it
64
-
b
it
lo
w
-
p
o
wer
d
o
u
b
le
d
ata
r
ate
(
L
PDDR
4
)
2
5
.
6
GB
/s
,
4
K
g
o
p
r
o
b
lack
C
am
er
a
u
n
its
,
th
r
o
u
g
h
Z
ig
b
ee
with
Xiao
m
i
r
ep
ea
ter
f
o
r
r
em
o
te
s
tr
ea
m
in
g
o
f
f
u
n
ct
io
n
s
,
an
d
d
r
o
n
e
lo
g
r
ep
o
r
t.
I
m
p
ar
tially
we
r
eq
u
ir
e
to
en
v
elo
p
a
5
×
5
km
r
eg
i
o
n
with
s
ev
er
al
d
r
o
n
es
in
a
s
war
m
b
ac
k
g
r
o
u
n
d
;
th
u
s
f
o
r
th
e
d
atab
ase,
we
d
is
tr
ib
u
ted
a
p
ar
s
e
s
er
v
e
r
f
o
r
r
ea
l
-
t
im
e
tr
an
s
m
is
s
io
n
am
o
n
g
ev
e
r
y
d
r
o
n
e.
Du
r
in
g
th
e
ex
p
er
im
en
t,
all
d
r
o
n
es
s
tar
te
d
f
r
o
m
t
h
e
s
am
e
p
o
s
itio
n
wi
th
a
p
u
r
p
o
s
e
to
co
llab
o
r
ativ
e
ly
co
v
e
r
th
e
s
lam
f
u
n
ctio
n
in
d
e
f
in
ite
lan
d
co
at.
Hen
ce
,
th
e
d
r
o
n
e
lan
d
i
n
g
p
a
d
is
u
n
d
eter
m
in
ed
at
th
e
s
tar
t
o
f
th
e
ex
p
er
im
en
t.
Als
o
,
th
e
b
atter
y
o
f
ev
e
r
y
d
r
o
n
e
ca
n
last
ab
o
u
t
2
0
m
in
u
tes
a
t
m
ax
;
as
a
r
esu
lt,
we
h
av
e
p
r
o
g
r
am
t
h
e
d
r
o
n
es
to
s
y
s
tem
th
e
lan
d
in
g
r
e
g
io
n
e
n
er
g
etica
lly
n
ea
r
to
o
n
e
a
n
o
th
er
with
a
1
0
-
1
5
m
v
ar
iatio
n
.
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
On
ly
th
e
m
o
s
t
ex
p
en
s
iv
e
li
g
h
t
d
etec
tio
n
an
d
r
an
g
in
g
(
L
I
DAR
)
d
ata
an
d
v
er
tices
an
d
th
eir
to
p
o
lo
g
ical
r
elatio
n
s
h
ip
s
ar
e
u
s
ed
,
as we
ll
as
th
e
d
ata
an
d
r
elatio
n
s
h
ip
s
o
n
h
ig
h
-
ca
p
ac
it
y
h
a
r
d
d
r
iv
es to
r
ed
u
ce
co
s
ts
.
A
b
r
o
ad
r
an
g
e
o
f
jo
b
s
,
in
clu
d
in
g
law
en
f
o
r
ce
m
en
t,
t
h
e
f
ir
e
s
er
v
ice,
an
d
th
e
th
e
r
m
o
g
r
ap
h
y
p
r
o
f
ess
io
n
,
ar
e
b
ec
o
m
in
g
s
u
b
s
tan
tially
m
o
r
e
d
iv
e
r
s
e
an
d
ex
citin
g
b
ec
au
s
e
o
f
th
e
ac
ce
s
s
ib
ilit
y
to
af
f
o
r
d
ab
le
ae
r
ial
im
ag
in
g
.
Usi
n
g
a
th
er
m
al
ca
m
er
a
o
n
a
d
r
o
n
e
h
elp
s
to
f
in
d
m
is
s
in
g
p
er
s
o
n
s
m
o
r
e
q
u
ick
l
y
.
Sm
all
u
n
m
an
n
ed
ae
r
ial
v
eh
icles
(
UAVs)
ar
e
p
o
r
tab
le
an
d
co
m
p
ac
t,
m
ak
in
g
th
em
s
u
itab
le
f
o
r
s
ea
r
ch
an
d
r
escu
e
o
p
er
atio
n
s
.
A
co
n
tr
ib
u
tio
n
to
law
e
n
f
o
r
ce
m
en
t
’
s
m
is
s
io
n
o
f
en
c
o
u
r
a
g
in
g
an
d
im
p
o
s
in
g
non
-
d
is
cr
im
in
ato
r
y
p
o
lices
s
f
o
r
s
u
s
tain
ab
le
g
r
o
wth
is
d
r
iv
en
b
y
p
r
o
v
id
in
g
th
em
with
th
e
to
o
ls
an
d
r
eso
u
r
ce
s
th
ey
n
ee
d
to
ac
co
m
p
lis
h
th
is
task
.
On
e
o
f
th
e
o
p
er
ato
r
s
tak
es
co
n
tr
o
l
o
f
th
e
d
r
o
n
e
wh
ile
th
e
o
th
er
k
ee
p
s
an
ey
e
o
n
th
e
d
o
wn
lin
k
f
ee
d
in
th
e
s
ce
n
e
to
s
ea
r
ch
f
o
r
s
ig
n
s
o
f
life
.
T
h
e
d
r
o
n
e
is
co
n
tr
o
lled
b
y
two
p
eo
p
le
wh
o
s
it
at
tw
o
co
n
tr
o
l
s
tatio
n
s
.
Ho
wev
er
,
s
in
ce
v
is
ib
le
s
p
ec
tr
u
m
ca
m
er
as
ca
n
n
o
t
d
is
tin
g
u
is
h
b
etwe
en
d
if
f
er
e
n
t
lig
h
tin
g
c
o
n
d
itio
n
s
,
s
u
ch
as
f
o
liag
e,
b
r
u
s
h
,
l
o
w
lig
h
t,
an
d
s
h
ad
o
ws,
it
m
ay
b
e
ch
allen
g
in
g
to
u
s
e
th
ese
ca
m
e
r
as
in
s
ce
n
e
s
th
at
f
ea
tu
r
e
t
h
ese
v
ar
io
u
s
co
n
d
itio
n
s
,
s
u
ch
as
f
o
liag
e,
b
r
u
s
h
,
lo
w
lig
h
t
an
d
s
h
ad
o
ws
.
M
ak
in
g
it
m
o
r
e
c
h
allen
g
in
g
to
s
ee
th
e
co
n
tr
ast is
m
ad
e
m
o
r
e
d
if
f
icu
lt
b
y
k
ee
p
in
g
y
o
u
r
b
o
d
y
tem
p
er
atu
r
e
an
d
t
h
e
s
u
r
r
o
u
n
d
in
g
e
n
v
i
r
o
n
m
en
t
h
id
d
e
n
.
T
h
e
d
ev
elo
p
ed
s
o
lu
tio
n
b
r
i
n
g
s
to
g
eth
er
th
e
b
est
o
f
wo
r
ld
s
(
in
f
r
ar
e
d
an
d
co
lo
r
e
d
R
GB
im
ag
er
y
)
,
m
ak
in
g
th
e
u
s
e
o
f
in
f
r
a
r
ed
im
ag
er
y
ea
s
ier
an
d
allo
win
g
p
ilo
ts
to
lead
o
th
er
s
to
th
e
ta
r
g
et
m
o
r
e
clea
r
l
y
u
s
in
g
lan
d
m
ar
k
s
an
d
r
ef
er
e
n
ce
p
o
in
ts
wh
ile
also
en
h
an
cin
g
s
ea
r
ch
o
p
er
atio
n
s
b
y
in
cr
ea
s
in
g
t
h
e
co
n
tr
ast
b
etwe
en
th
e
im
ag
er
y
an
d
th
e
p
h
y
s
ical
s
ce
n
e.
Af
ter
u
s
in
g
th
e
p
r
esen
t
ed
s
o
lu
tio
n
,
r
esu
lts
ac
h
iev
ed
i
n
th
e
9
4
%
n
atu
r
al
lig
h
tin
g
co
n
d
itio
n
r
eq
u
ir
ed
o
n
ly
1
0
-
1
5
tr
ain
in
g
s
am
p
les.
T
h
e
o
v
er
all
d
ig
itized
m
a
p
o
f
th
e
R
o
o
r
k
ee
ar
ea
f
r
o
m
th
e
UAV
f
ee
d
is
s
h
o
wn
in
Fig
u
r
e
4
.
In
Fig
u
r
e
4
,
th
e
ar
ea
p
atr
o
lled
b
y
a
d
r
o
n
e
d
u
r
in
g
th
e
n
ig
h
t
tim
e
h
o
u
r
s
a
r
e
d
is
p
lay
ed
u
s
in
g
a
d
ig
itized
m
ap
.
T
h
e
th
ir
d
g
r
ap
h
ic,
wh
ich
s
h
o
ws
th
e
o
u
tco
m
es
o
f
th
e
p
r
esen
ted
m
ec
h
an
is
m
an
d
th
e
c
u
r
r
en
tly
u
s
ed
s
tate
-
of
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th
e
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ar
t a
lg
o
r
ith
m
,
is
d
em
o
n
s
tr
ated
in
Fig
u
r
e
5
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
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N:
2
5
0
2
-
4
7
52
A
u
to
d
ig
itiz
a
tio
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o
f a
eria
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ma
p
g
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era
tio
n
fr
o
m
U
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feed
(
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a
j
u
Ja
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d
ee
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n
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1343
Fig
u
r
e
6
s
h
o
ws
a
m
ap
o
f
o
u
r
co
n
f
id
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ce
in
t
h
e
s
itu
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n
.
C
o
r
r
elatio
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o
f
m
ap
d
etails
with
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e
d
ef
au
lt
o
r
th
o
g
r
ap
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ic
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to
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r
am
m
etr
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c
m
eth
o
d
in
Fig
u
r
e
6
(
a
)
v
e
r
s
u
s
p
r
esen
ted
m
eth
o
d
Fig
u
r
e
6
(
b
)
.
E
v
e
r
y
d
o
t
r
ep
r
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ts
o
n
e
test
in
s
tan
ce
.
T
h
e
d
ash
ed
lin
e
at
f
iv
e
C
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s
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o
n
d
s
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o
n
s
tr
ates
th
e
cu
t
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o
f
f
tim
e
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f
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e
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b
jectiv
e
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o
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it
h
m
u
tili
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d
a
m
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th
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s
etu
p
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r
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ce
s
s
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Fig
u
r
e
4
.
T
h
e
o
v
er
all
d
ig
itized
m
ap
o
f
th
e
R
o
o
r
k
ee
ar
ea
f
r
o
m
th
e
UAV
f
ee
d
Fig
u
r
e
5
.
C
o
m
p
a
r
is
o
n
o
f
th
e
p
r
esen
ted
m
ec
h
an
is
m
with
s
tate
-
of
-
th
e
-
ar
t a
lg
o
r
ith
m
s
f
o
r
r
u
n
ti
m
e
UAV
co
n
tr
o
l
an
d
f
ee
d
d
ig
itizatio
n
(
a)
(
b
)
Fig
u
r
e
6
.
C
o
r
r
elatio
n
o
f
m
ap
d
etails with
(
a)
th
e
d
ef
au
lt o
r
th
o
g
r
ap
h
ic
p
h
o
to
g
r
a
m
m
etr
ic
m
e
th
o
d
an
d
(
b
)
th
e
p
r
esen
ted
m
et
h
o
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
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m
p
Sci
,
Vo
l.
3
6
,
No
.
2
,
No
v
em
b
er
20
24
:
1
3
3
8
-
1
3
4
6
1344
5.
CO
NCLU
SI
O
N
T
h
is
p
ap
er
ex
is
ts
an
au
to
n
eu
r
al
ar
ch
itectu
r
e
g
en
e
r
ato
r
f
o
r
o
b
s
er
v
atio
n
i
n
th
e
d
a
r
k
.
T
h
is
al
lo
ws
u
s
to
lear
n
h
o
w
to
r
en
d
er
ac
cu
r
ate
ed
g
e
m
ap
s
,
g
eo
m
etr
y
,
an
d
co
l
o
u
r
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ile
im
p
r
o
v
in
g
o
u
r
ac
cu
r
ac
y
.
T
h
e
r
esear
ch
p
r
o
p
o
s
ed
a
s
o
f
t
f
r
am
ewo
r
k
f
o
r
cr
is
is
m
ap
p
in
g
to
aid
in
m
ap
p
in
g
th
e
s
tate
o
f
th
e
ae
r
ial
lan
d
s
ca
p
e
in
d
is
aster
-
s
tr
ick
en
ar
ea
s
,
allo
win
g
s
tr
at
eg
ic
r
escu
e
o
p
e
r
atio
n
s
to
b
e
m
o
r
e
ef
f
ec
ti
v
ely
p
lan
n
ed
.
T
h
e
d
ig
itized
m
ap
in
wh
ich
p
o
o
led
ed
g
es
o
f
t
h
e
f
ea
tu
r
e
class
es
ar
e
r
etr
iev
ed
with
th
e
p
o
o
led
s
et
o
f
p
r
ev
i
o
u
s
n
etwo
r
k
d
ata
t
o
f
ac
ilit
ate
s
im
p
le
m
ap
p
in
g
o
f
f
ea
tu
r
es,
an
d
its
f
o
llo
win
g
ca
te
g
o
r
izatio
n
as
th
e
m
is
s
in
g
f
ea
t
u
r
e
b
ec
au
s
e
o
f
p
o
o
r
lig
h
tin
g
s
itu
atio
n
is
im
p
r
o
v
ed
.
T
h
e
o
u
tco
m
e
o
f
th
is
r
esear
ch
f
u
lf
ills
th
e
o
b
jectiv
e
s
et
b
y
Un
ited
Natio
n
s
E
n
v
is
io
n
2
0
3
0
Go
al
1
6
:
Pea
ce
,
J
u
s
tice,
an
d
Str
o
n
g
I
n
s
titu
tio
n
s
.
Atten
u
atio
n
o
f
h
y
p
e
r
p
ar
am
eter
is
s
till
a
m
an
u
al
b
u
r
d
en
.
T
h
is
wo
r
k
en
s
u
r
es
th
e
ex
p
a
n
s
io
n
o
f
s
u
ch
p
r
o
to
co
l
will e
n
h
an
ce
th
e
d
r
o
n
e
m
ap
p
in
g
i
n
th
e
d
a
r
k
f
o
r
d
e
f
en
s
e
an
d
f
ac
ilit
ate
s
o
lv
in
g
is
s
u
e
d
elib
er
ate
d
esig
n
in
g
o
f
d
is
aster
r
ea
ctio
n
s
in
tr
ag
e
d
y
s
tr
ik
e
r
eg
io
n
s
at
n
ig
h
t
th
at
s
tay
s
a
im
p
o
r
tan
t
is
s
u
e
an
d
th
e
u
n
ch
ar
ted
r
e
g
io
n
s
.
T
h
e
f
u
tu
r
e
wo
r
k
o
f
th
is
r
esear
ch
is
to
u
p
en
d
th
e
s
ec
u
r
ity
in
th
e
s
en
s
in
g
f
ield
en
v
ir
o
n
m
e
n
t.
RE
F
E
R
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NC
E
S
[
1
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.
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0
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[
3
]
B
.
W
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n
g
,
Y
.
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u
,
a
n
d
Y
.
-
Q
.
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[
4
]
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[
5
]
P.
Y
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,
Z.
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.
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,
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[
6
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K
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EEE
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C
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.
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8
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7
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5
3
2
.
[
7
]
B
.
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g
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EEE
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ra
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[
8
]
S
.
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d
D
.
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[
9
]
A
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J
.
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5
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1
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.
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[
1
2
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p
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[
1
3
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.
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l
,
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.
[
1
4
]
S
.
I
i
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u
k
a
,
E
.
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m
o
-
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a
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[
1
5
]
V
.
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.
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6
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A
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[
1
7
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S
.
Th
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ma
s
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d
A
.
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r
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sh
n
a
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m
e
n
tatio
n
a
n
d
Co
n
tro
l
En
g
in
e
e
ri
n
g
fr
o
m
M
a
d
u
ra
i
Ka
m
a
ra
j
Un
iv
e
rsity
,
M
a
d
u
ra
i,
Tam
il
n
a
d
u
,
In
d
ia.
He
se
c
u
re
d
M
a
ste
r
o
f
En
g
i
n
e
e
rin
g
in
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o
m
p
u
ter
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c
ien
c
e
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n
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n
g
i
n
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rin
g
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t
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a
n
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m
a
n
iam
S
u
n
d
a
ra
n
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r
U
n
iv
e
rsit
y
,
Ti
ru
n
e
lv
e
li
,
Tam
il
n
a
d
u
,
In
d
ia.
He
wa
s
a
wa
rd
e
d
P
h
.
D.
in
th
e
field
o
f
C
o
m
p
u
ter
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c
ien
c
e
a
n
d
En
g
in
e
e
rin
g
a
t
An
n
a
Un
iv
e
rsity
,
Ch
e
n
n
a
i,
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il
n
a
d
u
I
n
d
ia.
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c
a
rries
b
o
th
i
n
d
u
stry
a
n
d
a
c
a
d
e
m
ic
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x
p
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rien
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e
fo
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m
o
re
th
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n
2
0
y
e
a
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h
a
s
p
r
e
se
n
ted
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p
a
p
e
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n
n
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ti
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tern
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m
p
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s.
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e
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h
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ired
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ss
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ti
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tern
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r
fo
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p
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v
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e
d
j
o
u
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a
ls.
His
m
a
jo
r
a
re
a
o
f
in
tere
st
in
c
l
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d
e
s
c
y
b
e
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p
h
y
sic
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l
sy
ste
m
s,
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o
m
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in
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n
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m
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c
a
n
b
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ta
c
ted
a
t
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m
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d
r_
rj
k
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tma
il
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c
o
m
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Pro
f.
K
a
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n
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sh
Pr
a
ta
p
Ya
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a
v
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u
ru
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m
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h
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DBM,
M
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h
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a
n
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t
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a
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d
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v
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o
n
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ra
tes
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k
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L
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fr
o
m
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tern
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ti
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a
l
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s/In
sti
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He
a
lt
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c
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ti
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m
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ric
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lt
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v
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n
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p
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VC)a
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w
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ra
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tate
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c
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n
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fo
rm
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ti
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h
n
o
lo
g
y
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c
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ll
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il
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u
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v
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s
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n
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Un
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lt
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re
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n
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re
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r,
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h
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tt
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rh
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o
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n
try
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sb
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d
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n
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q
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l
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sa
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o
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th
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a
.
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h
a
s
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+
y
e
a
rs
o
f
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x
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e
rien
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in
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h
in
g
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re
se
a
rc
h
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a
d
m
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n
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n
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n
d
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stry
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wa
s
Dire
c
to
r
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r
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y
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rs
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iffere
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stit
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p
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v
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d
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t
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lh
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re
g
i
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n
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S
I
ET
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h
a
z
iab
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d
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n
d
M
IET
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KCCIT
M
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IIM
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re
a
ter
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n
d
fo
rm
e
r
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n
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si
d
e
n
t
o
f
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a
n
g
a
m
Un
iv
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rsity
,
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il
wa
ra
,
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jas
th
a
n
.
His
e
x
p
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rti
se
is
NA
AC/AICTE
-
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QS
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tar
In
tern
a
ti
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l
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ti
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ra
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k
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g
s
y
ste
m
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
d
rk
p
y
a
d
a
v
7
3
2
@g
m
a
il
.
c
o
m
.
Dr
.
Ba
la
su
b
r
a
m
a
n
i
a
n
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r
e
e
d
e
v
i
is
p
re
se
n
tl
y
wo
r
k
in
g
a
s
p
ro
fe
s
so
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n
d
HO
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n
De
p
a
rtme
n
t
o
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m
p
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ter
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e
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n
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g
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t
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ri
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a
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m
In
stit
u
te
o
f
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h
n
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l
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y
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h
e
c
o
m
p
lete
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ste
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g
re
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in
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m
p
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n
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g
wit
h
Disti
n
c
ti
o
n
fro
m
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i
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a
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d
o
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h
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g
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fro
m
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n
a
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e
n
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a
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a
m
il
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d
ia.
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h
e
h
a
s m
o
re
th
a
n
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f
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e
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h
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s
p
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e
d
p
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p
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rs
i
n
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c
o
p
u
s
(S
CI)
a
n
d
IEE
E.
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r
re
se
a
rc
h
in
tere
st
’
s
li
e
s
in
t
h
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a
re
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o
f
p
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ra
m
m
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lan
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u
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g
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m
a
c
h
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l
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rn
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a
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ima
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ro
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h
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s
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o
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ted
a
c
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ly
with
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ip
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h
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ti
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b
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a
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b
o
d
ies
li
k
e
IS
TE
,
CS
I,
IEI
,
IEE
E,
IAENG
,
a
n
d
IRO
.
S
h
e
c
a
n
b
e
c
o
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tac
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:
h
o
d
c
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@s
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e
d
u
.
in
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
5
0
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52
In
d
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J
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&
C
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p
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,
Vo
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3
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No
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2
,
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v
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b
er
20
24
:
1
3
3
8
-
1
3
4
6
1346
Dr
.
J
e
h
a
n
Chelli
a
h
re
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e
iv
e
d
th
e
BE
d
e
g
re
e
i
n
t
h
e
De
p
a
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t
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lec
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Co
m
m
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n
ica
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n
g
in
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fro
m
M
a
d
u
ra
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Ka
m
a
ra
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Un
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,
Tam
il
Na
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u
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In
d
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in
1
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.
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p
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.
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.
d
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in
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il
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u
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n
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in
2
0
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7
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e
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u
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De
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a
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n
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S
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k
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Ch
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n
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re
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s
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c
a
n
b
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c
o
n
tac
ted
a
t
e
m
a
il
:
sm
u
re
sju
r@g
m
a
il
.
c
o
m
.
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