I
nte
rna
t
io
na
l J
o
urna
l o
f
E
lect
rica
l a
nd
Co
m
pu
t
er
E
ng
ineering
(
I
J
E
CE
)
Vo
l.
1
6
,
No
.
3
,
J
u
n
e
20
2
6
,
p
p
.
1227
~
1
2
3
5
I
SS
N:
2088
-
8
7
0
8
,
DOI
: 1
0
.
1
1
5
9
1
/ijece.
v
1
6
i
3
.
pp
1
2
2
7
-
1
2
3
5
1227
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ec
e.
ia
esco
r
e.
co
m
Ra
da
r
-
ba
sed g
est
ure re
co
g
nition
si
mula
tion for un
m
a
nned
a
eria
l vehicles
co
mm
a
nd inte
r
pret
a
tion
Denny
Der
m
a
wa
n
1
,
F
re
dd
y
K
urnia
wa
n
1
,
Yenni A
s
t
uti
1
,
P
a
ulu
s
Set
ia
wa
n
1
,
L
a
s
m
a
di
1
,
Uy
uu
nu
l
M
a
uid
zo
h
2
,
B
a
m
ba
ng
Su
dib
y
a
1
1
D
e
p
a
r
t
me
n
t
o
f
El
e
c
t
r
i
c
a
l
En
g
i
n
e
e
r
i
n
g
,
F
a
c
u
l
t
y
o
f
I
n
d
u
st
r
i
a
l
Te
c
h
n
o
l
o
g
y
,
I
n
st
i
t
u
t
T
e
k
n
o
l
o
g
i
D
i
r
g
a
n
t
a
r
a
A
d
i
su
t
j
i
p
t
o
,
Y
o
g
y
a
k
a
r
t
a
,
I
n
d
o
n
e
si
a
2
D
e
p
a
r
t
me
n
t
o
f
I
n
d
u
s
t
r
i
a
l
En
g
i
n
e
e
r
i
n
g
,
F
a
c
u
l
t
y
o
f
I
n
d
u
st
r
i
a
l
Te
c
h
n
o
l
o
g
y
,
I
n
st
i
t
u
t
T
e
k
n
o
l
o
g
i
D
i
r
g
a
n
t
a
r
a
A
d
i
su
t
j
i
p
t
o
,
Y
o
g
y
a
k
a
r
t
a
,
I
n
d
o
n
e
si
a
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Au
g
1
,
2
0
2
5
R
ev
is
ed
Feb
1
0
,
2
0
2
6
Acc
ep
ted
Ma
r
1
6
,
2
0
2
6
Ra
d
a
r
-
b
a
se
d
g
e
stu
re
re
c
o
g
n
it
i
o
n
h
a
s
e
m
e
rg
e
d
a
s
a
ro
b
u
st
a
lt
e
rn
a
ti
v
e
t
o
v
isio
n
-
b
a
se
d
sy
ste
m
s,
p
a
rti
c
u
lar
ly
in
e
n
v
iro
n
m
e
n
ts
wh
e
re
li
g
h
ti
n
g
a
n
d
p
riv
a
c
y
p
o
se
c
h
a
ll
e
n
g
e
s.
Th
is
stu
d
y
p
re
se
n
ts
a
sim
u
lati
o
n
a
p
p
ro
a
c
h
fo
r
re
c
o
g
n
izin
g
h
a
n
d
g
e
stu
re
s to
c
o
n
t
ro
l
u
n
m
a
n
n
e
d
a
e
rial
v
e
h
icle
s (UAV
s) u
sin
g
ra
d
a
r
sig
n
a
ls.
F
i
v
e
d
isc
re
te
g
e
st
u
re
s,
i.
e
.
,
Tak
e
Off,
Lan
d
,
M
o
v
e
F
o
rwa
rd
,
Tu
rn
Left
,
a
n
d
st
o
p
,
we
re
d
e
fin
e
d
a
n
d
m
o
d
e
led
in
M
ATLAB
to
g
e
n
e
ra
te
sy
n
th
e
ti
c
ra
d
a
r
sig
n
a
ls.
F
r
o
m
e
a
c
h
sa
m
p
le,
fo
u
r
ti
m
e
-
fre
q
u
e
n
c
y
d
o
m
a
in
fe
a
tu
re
s
we
re
e
x
trac
ted
:
d
u
ra
ti
o
n
,
m
a
x
imu
m
a
m
p
li
tu
d
e
,
d
o
m
in
a
n
t
f
re
q
u
e
n
c
y
,
a
n
d
r
o
o
t
m
e
a
n
sq
u
a
re
(RM
S
).
A
d
a
tas
e
t
o
f
5
0
0
sa
m
p
les
(
1
0
0
p
e
r
c
las
s)
wa
s
c
las
sified
u
sin
g
th
re
e
s
u
p
e
rv
ise
d
lea
rn
in
g
m
o
d
e
ls:
su
p
p
o
rt
v
e
c
to
r
m
a
c
h
in
e
(S
VM),
k
-
n
e
a
re
st
n
e
ig
h
b
o
rs
(
k
-
NN
),
a
n
d
d
e
c
isio
n
tree
.
Th
e
k
-
NN
c
las
sifier
a
c
h
iev
e
d
t
h
e
h
i
g
h
e
st
a
c
c
u
ra
c
y
o
f
9
6
%
,
d
e
m
o
n
stra
ti
n
g
th
e
fe
a
sib
il
it
y
o
f
li
g
h
twe
i
g
h
t
c
las
sifiers
fo
r
g
e
stu
re
re
c
o
g
n
it
i
o
n
u
si
n
g
l
o
w
-
c
o
m
p
lex
i
ty
fe
a
tu
re
s.
Th
e
se
re
su
lt
s
h
ig
h
li
g
h
t
th
e
p
o
te
n
ti
a
l
o
f
ra
d
a
r
-
b
a
se
d
in
terfa
c
e
s
t
o
re
p
lac
e
trad
it
i
o
n
a
l
re
m
o
te
c
o
n
tr
o
ls
i
n
UA
V
o
p
e
ra
ti
o
n
.
Th
e
p
ro
p
o
se
d
sim
u
latio
n
fra
m
e
wo
rk
c
o
n
t
rib
u
tes
t
o
t
h
e
d
e
v
e
lo
p
m
e
n
t
o
f
in
tu
it
iv
e
,
n
o
n
-
c
o
n
tac
t
h
u
m
a
n
-
m
a
c
h
in
e
in
tera
c
ti
o
n
sy
ste
m
s.
K
ey
w
o
r
d
s
:
Dec
is
io
n
tr
ee
K
-
n
ea
r
est n
eig
h
b
o
r
s
Ma
ch
in
e
lear
n
in
g
R
ad
a
r
-
b
ase
d
g
est
u
r
e
r
ec
o
g
n
iti
o
n
Un
m
an
n
ed
ae
r
ial
v
eh
icles
co
n
tr
o
l
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Yen
n
i A
s
tu
ti
Dep
ar
tm
en
t o
f
E
lectr
ical
E
n
g
i
n
ee
r
in
g
,
Facu
lty
o
f
I
n
d
u
s
tr
ial
T
ec
h
n
o
lo
g
y
,
I
n
s
titu
t T
ek
n
o
l
o
g
i D
ir
g
an
tar
a
Ad
is
u
tjip
to
Yo
g
y
ak
ar
ta,
I
n
d
o
n
esia
E
m
ail: y
en
n
iast
u
ti@
itd
a.
ac
.
id
1.
I
NT
RO
D
UCT
I
O
N
Gestu
r
e
r
ec
o
g
n
itio
n
h
as
b
ec
o
m
e
an
em
er
g
i
n
g
r
esear
ch
d
o
m
ain
d
u
e
to
its
p
o
te
n
tial
to
en
ab
le
n
atu
r
al
an
d
in
t
u
itiv
e
in
ter
ac
tio
n
b
etwe
en
h
u
m
an
s
an
d
m
ac
h
in
es.
T
r
a
d
itio
n
ally
,
v
is
io
n
-
b
ased
g
estu
r
e
r
ec
o
g
n
itio
n
u
s
in
g
r
ed
,
g
r
ee
n
,
b
l
u
e
(
R
GB
)
o
r
d
ep
th
ca
m
er
as
h
as
b
ee
n
th
e
p
r
ed
o
m
in
an
t
ap
p
r
o
ac
h
in
v
ar
io
u
s
ap
p
licatio
n
s
,
in
clu
d
in
g
g
am
i
n
g
,
r
o
b
o
tics
,
h
ea
lth
ca
r
e,
an
d
s
m
ar
t
h
o
m
e
s
[
1
]
–
[
4
]
.
Ho
we
v
er
,
v
is
io
n
-
b
ased
s
y
s
tem
s
ar
e
s
en
s
itiv
e
to
lig
h
tin
g
co
n
d
itio
n
s
,
o
cc
lu
s
io
n
,
a
n
d
p
r
iv
ac
y
c
o
n
ce
r
n
s
.
As
an
alter
n
ativ
e,
r
ad
ar
-
b
ased
g
estu
r
e
r
ec
o
g
n
itio
n
o
f
f
e
r
s
r
o
b
u
s
t
p
er
f
o
r
m
an
ce
u
n
d
er
c
h
allen
g
in
g
en
v
ir
o
n
m
e
n
tal
co
n
d
itio
n
s
,
d
o
es
n
o
t
r
eq
u
i
r
e
lin
e
-
of
-
s
ig
h
t,
an
d
en
s
u
r
es b
etter
p
r
i
v
a
cy
[
5
]
–
[
8
]
.
R
ad
ar
s
en
s
o
r
s
,
esp
ec
ially
m
i
cr
o
-
Do
p
p
ler
an
d
f
r
eq
u
e
n
cy
m
o
d
u
lated
co
n
tin
u
o
u
s
wav
e
(
FMC
W
)
r
ad
ar
s
,
h
av
e
s
h
o
w
n
p
r
o
m
is
in
g
ca
p
ab
ilit
ies
in
ca
p
tu
r
in
g
f
in
e
-
g
r
ain
ed
h
a
n
d
m
o
v
em
en
ts
b
y
an
aly
zin
g
v
a
r
iatio
n
s
in
s
ig
n
al
f
ea
tu
r
es
s
u
ch
as
D
o
p
p
ler
s
h
if
ts
an
d
am
p
litu
d
e
m
o
d
u
latio
n
s
[
9
]
–
[
1
1
]
.
T
h
e
i
n
teg
r
atio
n
o
f
r
ad
ar
tech
n
o
lo
g
y
with
m
ac
h
in
e
lear
n
in
g
tec
h
n
iq
u
es
h
as
led
to
im
p
r
o
v
e
d
ac
cu
r
ac
y
in
h
u
m
an
ac
tiv
ity
class
if
icatio
n
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.
1
6
,
No
.
3
,
J
u
n
e
20
2
6
:
1
2
2
7
-
1
2
3
5
1228
[
1
2
]
–
[
1
4
]
.
I
n
t
h
e
c
o
n
tex
t
o
f
u
n
m
an
n
ed
ae
r
ial
v
e
h
icle
(
UA
V)
co
n
tr
o
l,
c
u
r
r
en
t
s
y
s
tem
s
s
till
h
ea
v
ily
r
el
y
o
n
r
em
o
te
co
n
tr
o
ller
s
(
R
C
)
wh
ich
,
alth
o
u
g
h
r
eliab
le,
ar
e
o
f
te
n
n
o
n
-
in
tu
itiv
e,
b
u
lk
y
,
an
d
r
eq
u
ir
e
lin
e
-
of
-
s
ig
h
t
o
p
er
atio
n
.
C
o
n
s
eq
u
en
tly
,
th
er
e
is
a
g
r
o
win
g
i
n
ter
est
in
alter
n
ativ
e
co
m
m
a
n
d
i
n
ter
f
ac
es
th
at
ar
e
m
o
r
e
u
s
er
-
f
r
ien
d
ly
a
n
d
a
d
ap
tiv
e
to
d
y
n
a
m
ic
en
v
ir
o
n
m
en
ts
.
Desp
ite
th
e
ad
v
an
ce
s
in
r
ad
ar
-
b
ased
g
estu
r
e
r
ec
o
g
n
itio
n
,
lim
ited
s
tu
d
ies
h
av
e
ad
d
r
ess
ed
its
ap
p
licatio
n
f
o
r
UAV
co
n
tr
o
l
in
a
s
im
u
lated
o
r
r
ea
l
-
wo
r
ld
en
v
ir
o
n
m
en
t.
T
h
e
g
a
p
r
em
ai
n
s
in
th
e
d
esig
n
an
d
v
alid
atio
n
o
f
a
r
a
d
ar
-
b
ased
g
estu
r
e
r
ec
o
g
n
itio
n
s
y
s
tem
s
p
ec
if
ically
tailo
r
ed
to
in
ter
p
r
et
d
is
cr
ete
UAV
co
m
m
an
d
g
estu
r
es.
T
h
e
e
x
is
tin
g
ap
p
r
o
ac
h
es
eith
er
f
o
c
u
s
o
n
g
en
er
al
g
estu
r
e
r
ec
o
g
n
itio
n
o
r
lack
s
y
s
tem
-
lev
el
s
im
u
latio
n
to
ev
alu
ate
f
ea
s
ib
il
ity
in
th
e
UAV
co
m
m
a
n
d
co
n
t
ex
t
[
1
5
]
,
[
1
6
]
.
T
h
is
s
tu
d
y
a
d
d
r
ess
es
th
e
g
a
p
in
ex
is
tin
g
r
ad
a
r
-
b
ased
g
estu
r
e
r
ec
o
g
n
itio
n
r
esear
ch
b
y
f
o
cu
s
in
g
o
n
d
is
cr
ete
UAV
co
m
m
an
d
i
n
ter
p
r
etatio
n
with
in
a
s
im
u
latio
n
-
d
r
i
v
en
f
r
am
ewo
r
k
.
W
h
ile
p
r
io
r
wo
r
k
s
p
r
ed
o
m
i
n
an
tly
em
p
h
asize
v
i
s
io
n
-
b
ased
in
ter
f
ac
es
o
r
d
e
ep
-
lear
n
in
g
-
in
ten
s
iv
e
r
a
d
ar
m
o
d
els,
th
ey
o
f
ten
o
v
er
lo
o
k
s
y
s
tem
-
lev
el
f
ea
s
ib
ilit
y
an
d
co
m
p
u
tatio
n
al
c
o
n
s
tr
ain
ts
r
elev
an
t
to
UAV
p
la
tf
o
r
m
s
.
T
h
e
m
ain
f
in
d
in
g
s
d
e
m
o
n
s
tr
ate
th
at
f
iv
e
UAV
co
m
m
an
d
g
estu
r
es
ca
n
b
e
ef
f
ec
tiv
ely
d
is
tin
g
u
is
h
ed
u
s
in
g
a
m
in
im
al
s
et
o
f
tim
e
–
f
r
eq
u
en
cy
f
ea
t
u
r
es,
ac
h
iev
in
g
u
p
to
9
6
%
class
if
icati
o
n
ac
cu
r
ac
y
with
a
k
-
NN
class
if
ier
.
T
h
ese
r
esu
lts
in
d
icate
th
at
lig
h
tweig
h
t
r
ad
ar
-
b
ased
g
estu
r
e
r
ec
o
g
n
itio
n
is
a
v
iab
le
an
d
ef
f
icien
t
alter
n
ati
v
e
to
co
n
v
en
tio
n
al
r
em
o
te
co
n
tr
o
ller
s
.
T
h
e
n
o
v
el
ty
o
f
t
h
is
wo
r
k
lies
in
in
teg
r
a
tin
g
g
estu
r
e
-
d
ep
en
d
en
t
Do
p
p
l
er
m
o
d
elin
g
with
a
s
y
s
tem
atic
ev
alu
atio
n
o
f
class
ical
c
lass
if
ier
s
,
p
r
o
v
id
in
g
an
in
ter
p
r
etab
le,
co
m
p
u
tatio
n
ally
ef
f
icien
t,
an
d
em
b
ed
d
e
d
-
f
r
ien
d
ly
f
o
u
n
d
atio
n
f
o
r
n
o
n
-
c
o
n
tact
UAV
co
n
tr
o
l
an
d
f
u
t
u
r
e
r
ea
l
-
w
o
r
ld
im
p
lem
e
n
tatio
n
.
2.
M
E
T
H
O
D
T
h
is
s
tu
d
y
p
r
o
p
o
s
es
a
s
im
u
lated
r
ad
ar
-
b
ased
g
estu
r
e
r
e
co
g
n
itio
n
s
y
s
tem
f
o
r
UAV
co
m
m
an
d
in
ter
p
r
etatio
n
u
s
in
g
MA
T
L
AB
.
T
h
e
o
v
er
all
m
eth
o
d
o
lo
g
y
co
n
s
is
ts
o
f
f
iv
e
s
tag
es,
as
s
h
o
wn
i
n
Fig
u
r
e
1
:
i)
g
estu
r
e
s
ce
n
ar
io
d
ef
i
n
itio
n
,
ii)
r
ad
ar
s
ig
n
al
s
im
u
latio
n
,
i
ii)
f
ea
tu
r
e
ex
tr
ac
tio
n
,
iv
)
clas
s
if
ier
tr
ain
in
g
an
d
test
in
g
,
an
d
v
)
p
er
f
o
r
m
a
n
ce
ev
alu
atio
n
.
E
ac
h
s
tag
e
is
d
escr
ib
ed
in
d
etail
b
elo
w
t
o
en
s
u
r
e
r
e
p
r
o
d
u
cib
ilit
y
.
Fig
u
r
e
1
.
Simu
latio
n
s
tag
e
o
f
t
h
e
p
r
o
p
o
s
ed
r
a
d
ar
-
b
ased
g
estu
r
e
r
ec
o
g
n
itio
n
s
y
s
tem
2
.
1
.
G
esture
s
ce
na
rio
I
n
th
is
s
tu
d
y
,
f
iv
e
d
is
cr
ete
UAV
co
m
m
an
d
g
estu
r
es
wer
e
d
ef
in
ed
t
o
s
im
u
late
r
ad
a
r
s
ig
n
al
r
esp
o
n
s
es
f
o
r
class
if
icatio
n
.
T
h
e
s
elec
ted
g
estu
r
es
in
clu
d
e
T
a
k
eOf
f
,
L
an
d
,
M
o
v
eFo
r
war
d
,
T
u
r
n
L
e
f
t,
an
d
s
to
p
.
E
ac
h
g
estu
r
e
was
co
n
ce
p
tu
ally
d
esig
n
ed
to
p
r
o
d
u
ce
d
is
tin
g
u
is
h
ab
le
tem
p
o
r
al
an
d
s
p
ec
tr
al
p
atter
n
s
in
th
e
s
im
u
lated
r
ad
ar
s
ig
n
al,
e
n
s
u
r
in
g
th
at
th
e
f
ea
tu
r
e
s
p
ac
e
ca
p
tu
r
es
d
is
tin
ctiv
e
tem
p
o
r
al
an
d
s
p
ec
tr
al
ch
ar
ac
ter
is
tics
th
at
d
if
f
er
en
tiate
g
estu
r
e
class
es.
Fo
r
ea
ch
g
estu
r
e
ty
p
e,
a
to
ta
l
o
f
1
0
0
s
am
p
les
wer
e
g
e
n
er
ated
,
r
esu
ltin
g
in
a
co
m
p
lete
d
ataset
o
f
5
0
0
la
b
eled
r
ad
ar
s
ig
n
al
in
s
tan
ce
s
u
s
ed
f
o
r
tr
ain
in
g
an
d
ev
alu
atio
n
in
s
u
b
s
eq
u
en
t stag
es.
2
.
2
.
Ra
da
r
s
ig
na
l sim
ula
t
io
n
R
ad
ar
s
ig
n
al
s
im
u
latio
n
was
p
er
f
o
r
m
ed
in
MA
T
L
AB
u
s
in
g
s
y
n
th
etic
m
o
d
els
th
at
m
im
ic
th
e
m
icr
o
-
Do
p
p
ler
s
ig
n
atu
r
es
o
f
h
an
d
m
o
v
em
en
ts
.
A
g
e
n
er
al
-
p
u
r
p
o
s
e
co
n
tin
u
o
u
s
-
wav
e
(
C
W
)
r
ad
ar
m
o
d
el
was
ad
o
p
ted
to
r
ep
r
esen
t
p
r
ac
tical
s
y
s
tem
co
n
s
tr
ain
ts
an
d
s
ig
n
al
ch
ar
ac
ter
is
tics
,
in
s
p
ir
ed
b
y
th
e
Do
p
p
ler
p
r
o
ce
s
s
in
g
s
tag
e
o
f
m
m
W
av
e
r
ad
ar
s
en
s
o
r
s
s
u
ch
as th
e
T
I
I
W
R
6
8
4
3
AOP
[
1
7
]
.
E
ac
h
g
estu
r
e’
s
r
ad
ar
r
etu
r
n
s
ig
n
al
was m
o
d
eled
as
s
h
o
wn
in
(
1
)
.
Alth
o
u
g
h
F
MCW
r
ad
ar
is
wid
ely
u
s
ed
i
n
p
r
ac
tical
g
estu
r
e
r
ec
o
g
n
itio
n
s
y
s
tem
s
d
u
e
to
its
r
an
g
in
g
ca
p
ab
ilit
y
,
a
s
im
p
lifie
d
C
W
s
ig
n
al
m
o
d
el
was e
m
p
lo
y
ed
in
th
is
s
tu
d
y
to
f
o
cu
s
s
p
e
cif
ically
o
n
g
estu
r
e
-
in
d
u
ce
d
m
icr
o
-
Do
p
p
le
r
ch
a
r
ac
ter
is
tics
.
T
h
e
p
r
im
ar
y
o
b
jectiv
e
o
f
th
e
s
im
u
latio
n
is
n
o
t
tar
g
e
t
r
an
g
e
esti
m
atio
n
,
b
u
t
th
e
an
aly
s
is
o
f
Do
p
p
ler
f
r
eq
u
en
c
y
v
ar
iatio
n
s
an
d
am
p
litu
d
e
m
o
d
u
latio
n
p
atter
n
s
ass
o
ciate
d
with
h
an
d
m
o
tio
n
d
y
n
a
m
ics.
B
y
ab
s
tr
a
ctin
g
th
e
r
ad
a
r
m
o
d
el
t
o
a
C
W
r
ep
r
esen
tatio
n
,
g
estu
r
e
-
d
ep
en
d
en
t
k
in
em
atic
b
eh
av
io
r
ca
n
b
e
ex
p
licitly
m
o
d
eled
th
r
o
u
g
h
tim
e
-
v
ar
y
in
g
a
m
p
litu
d
e
(
)
an
d
Do
p
p
ler
f
r
eq
u
e
n
cy
s
h
if
t
(
)
,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
R
a
d
a
r
-
b
a
s
ed
g
estu
r
e
r
ec
o
g
n
i
tio
n
s
imu
la
tio
n
fo
r
u
n
ma
n
n
ed
a
eria
l v
eh
icles
…
(
Den
n
y
Derma
w
a
n
)
1229
wh
ile
m
ain
tain
in
g
co
m
p
u
tatio
n
al
s
im
p
licity
an
d
an
aly
tical
clar
ity
.
T
h
is
m
o
d
elin
g
ch
o
ice
is
well
s
u
ited
f
o
r
f
ea
s
ib
ilit
y
ev
alu
atio
n
an
d
ea
r
ly
-
s
tag
e
s
y
s
tem
d
esig
n
an
d
r
em
ain
s
r
ep
r
esen
tativ
e
o
f
th
e
Do
p
p
ler
p
r
o
ce
s
s
in
g
s
tag
e
in
FM
C
W
r
ad
ar
-
b
ased
im
p
lem
en
tatio
n
s
.
Dif
f
er
en
t
U
AV
co
m
m
an
d
g
estu
r
es
ar
e
r
ep
r
esen
ted
b
y
d
is
tin
ct
tem
p
o
r
al
p
r
o
f
iles
o
f
(
)
an
d
(
)
,
wh
er
e
f
aster
o
r
m
o
r
e
d
ir
ec
ti
o
n
al
h
an
d
m
o
v
em
en
ts
r
esu
lt
in
h
ig
h
er
Do
p
p
ler
s
h
if
ts
,
wh
ile
s
tatic
o
r
ter
m
in
atin
g
g
estu
r
es p
r
o
d
u
ce
l
o
wer
-
f
r
eq
u
en
cy
an
d
s
h
o
r
ter
-
d
u
r
atio
n
s
ig
n
atu
r
es.
(
)
=
(
)
∙
c
os
(
2
(
)
+
(
)
)
(
1
)
w
h
er
e:
(
)
:
T
im
e
-
v
ar
y
in
g
am
p
litu
d
e
(
m
o
d
eled
as Ga
u
s
s
ian
-
m
o
d
u
lated
en
v
elo
p
e)
,
(
)
: D
o
p
p
ler
f
r
eq
u
e
n
cy
s
h
if
t (
g
estu
r
e
-
d
e
p
en
d
e
n
t)
,
(
)
:
I
n
s
tan
tan
eo
u
s
p
h
ase
(
r
an
d
o
m
ized
with
in
a
r
an
g
e)
,
:
T
im
e
v
ec
to
r
(
d
u
r
atio
n
u
p
to
2
s
ec
o
n
d
s
at
1
k
Hz
s
am
p
lin
g
r
ate)
.
I
n
p
r
ac
tical
FMC
W
r
ad
ar
s
y
s
tem
s
,
th
e
s
am
e
Do
p
p
ler
in
f
o
r
m
atio
n
ca
n
b
e
ex
tr
ac
ted
af
ter
r
an
g
e
p
r
o
ce
s
s
in
g
;
th
er
ef
o
r
e,
t
h
e
p
r
o
p
o
s
ed
C
W
-
b
ased
m
o
d
elin
g
ca
n
b
e
d
ir
ec
tl
y
ex
ten
d
ed
t
o
FMC
W
im
p
le
m
en
tatio
n
s
in
f
u
tu
r
e
wo
r
k
.
T
o
em
u
late
r
ea
lis
tic
v
ar
iab
ilit
y
in
h
u
m
an
h
a
n
d
m
o
ti
o
n
,
r
a
n
d
o
m
v
a
r
iatio
n
s
wer
e
in
tr
o
d
u
c
ed
in
k
ey
s
ig
n
al
p
ar
am
eter
s
,
i
n
clu
d
in
g
s
ig
n
al
d
u
r
atio
n
,
am
p
litu
d
e,
Do
p
p
ler
f
r
eq
u
en
cy
,
an
d
p
h
ase.
T
h
ese
v
a
r
iatio
n
s
en
s
u
r
e
th
at
m
u
ltip
le
r
ea
lizatio
n
s
o
f
th
e
s
am
e
g
estu
r
e
ar
e
n
o
n
-
id
e
n
tical
wh
ile
p
r
eser
v
i
n
g
g
estu
r
e
-
s
p
ec
if
ic
ch
ar
ac
ter
is
tics
.
T
h
e
p
ar
am
ete
r
d
is
tr
ib
u
tio
n
s
an
d
r
a
n
g
es
u
s
ed
in
th
e
s
y
n
th
etic
r
a
d
ar
s
ig
n
al
g
en
er
atio
n
ar
e
s
u
m
m
ar
ized
in
T
a
b
le
1
.
R
ep
r
e
s
en
tativ
e
ex
am
p
les
o
f
t
h
e
s
im
u
lated
tim
e
-
d
o
m
ain
r
ad
a
r
s
ig
n
als
f
o
r
ea
ch
g
estu
r
e
class
ar
e
p
r
esen
ted
in
Fig
u
r
e
2
.
As
illu
s
tr
ated
,
ea
c
h
g
e
s
tu
r
e
ex
h
ib
its
d
is
tin
ct
tem
p
o
r
al
an
d
am
p
litu
d
e
ch
ar
ac
ter
is
tics
,
wh
ich
f
o
r
m
t
h
e
b
asis
f
o
r
s
u
b
s
eq
u
en
t
f
ea
tu
r
e
ex
tr
ac
tio
n
.
T
ab
le
1
.
Par
am
eter
r
an
g
es u
s
e
d
in
s
y
n
th
etic
r
a
d
ar
g
estu
r
e
s
ig
n
al
g
en
er
atio
n
P
a
r
a
me
t
e
r
D
i
st
r
i
b
u
t
i
o
n
R
a
n
g
e
D
u
r
a
t
i
o
n
U
n
i
f
o
r
m
0
.
5
–
2
s
A
mp
l
i
t
u
d
e
U
n
i
f
o
r
m
0
.
8
–
1
.
2
D
o
p
p
l
e
r
f
r
e
q
u
e
n
c
y
U
n
i
f
o
r
m
5
–
5
0
H
z
P
h
a
se
U
n
i
f
o
r
m
0
–
2
π
r
a
d
Fig
u
r
e
2
.
E
x
am
p
le
tim
e
-
d
o
m
ai
n
r
ad
ar
s
ig
n
als g
en
er
ated
f
o
r
f
i
v
e
UAV
co
m
m
an
d
g
estu
r
es
2
.
3
.
F
e
a
t
ure
ex
t
r
a
ct
io
n
T
o
r
ep
r
esen
t
ea
ch
r
ad
a
r
s
ig
n
a
l
in
a
co
m
p
ac
t
an
d
in
f
o
r
m
ativ
e
m
an
n
er
,
f
o
u
r
tim
e
-
f
r
eq
u
en
cy
d
o
m
ain
f
ea
tu
r
es
wer
e
ex
tr
ac
ted
.
T
h
ese
f
ea
tu
r
es
wer
e
ch
o
s
en
b
ased
o
n
th
eir
ab
ilit
y
to
ca
p
tu
r
e
k
e
y
ch
ar
ac
ter
is
tics
o
f
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.
1
6
,
No
.
3
,
J
u
n
e
20
2
6
:
1
2
2
7
-
1
2
3
5
1230
h
an
d
g
estu
r
es
in
r
a
d
ar
s
ig
n
als.
T
h
e
d
u
r
atio
n
f
ea
tu
r
e
q
u
an
tifi
es
th
e
ac
tiv
e
tim
e
s
p
an
o
f
th
e
g
estu
r
e,
d
eter
m
i
n
ed
b
y
id
en
tify
in
g
s
eg
m
en
ts
wh
er
e
th
e
s
ig
n
al
am
p
litu
d
e
ex
ce
ed
s
a
p
r
ed
ef
in
ed
th
r
esh
o
ld
.
T
h
e
m
ax
im
u
m
am
p
litu
d
e
r
ep
r
esen
ts
th
e
p
ea
k
v
alu
e
with
in
th
e
s
ig
n
al
e
n
v
elo
p
e
a
n
d
r
ef
lects
th
e
in
ten
s
ity
o
f
th
e
g
e
s
tu
r
e.
T
h
e
d
o
m
in
an
t
f
r
eq
u
e
n
cy
is
ca
lcu
lated
b
y
ap
p
ly
in
g
th
e
f
ast
Fo
u
r
ier
tr
a
n
s
f
o
r
m
(
FF
T
)
an
d
s
elec
tin
g
t
h
e
f
r
eq
u
en
cy
co
m
p
o
n
en
t
with
th
e
h
ig
h
est
m
ag
n
itu
d
e,
wh
ich
p
r
o
v
i
d
es
in
s
ig
h
t
in
to
th
e
v
elo
city
o
f
h
an
d
m
o
tio
n
.
Fi
n
ally
,
th
e
r
o
o
t
m
ea
n
s
q
u
ar
e
(
R
MS)
is
co
m
p
u
ted
t
o
ca
p
tu
r
e
th
e
o
v
er
all
s
ig
n
al
e
n
er
g
y
,
u
s
in
g
(
2
)
[
1
8
]
.
R
M
S
=
√
1
∑
2
=
1
(
2
)
w
h
er
e
x
i
r
ep
r
esen
ts
th
e
am
p
lit
u
d
e
s
am
p
les
an
d
N
is
th
e
to
t
al
n
u
m
b
e
r
o
f
s
am
p
les
in
th
e
s
ig
n
al.
T
h
e
f
ea
tu
r
e
v
ec
to
r
s
wer
e
s
tan
d
ar
d
ized
u
s
in
g
z
-
s
co
r
e
n
o
r
m
aliza
tio
n
f
o
r
th
e
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SVM)
m
o
d
el.
T
h
e
d
ec
is
io
n
tr
ee
(
DT
)
a
n
d
k
-
n
ea
r
e
s
t n
eig
h
b
o
r
s
(
k
-
NN)
m
o
d
els we
r
e
tr
ain
ed
o
n
r
aw
f
ea
tu
r
e
v
al
u
es.
2
.
4
.
Cla
s
s
if
ica
t
io
n
m
o
dels
T
h
r
ee
s
u
p
e
r
v
is
ed
class
if
icatio
n
alg
o
r
ith
m
s
wer
e
im
p
lem
en
ted
to
r
ec
o
g
n
ize
r
ad
ar
-
b
ased
g
estu
r
e
co
m
m
an
d
s
,
i.e
.
,
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
with
r
ad
ial
b
asis
f
u
n
ctio
n
(
R
B
F)
k
er
n
el
[
1
9
]
,
[
2
0
]
,
k
-
n
ea
r
est
n
eig
h
b
o
r
s
with
k
e
q
ua
l
to
5
an
d
E
u
clid
ea
n
d
is
tan
ce
[
2
1
]
,
[
2
2
]
,
an
d
d
ec
is
io
n
tr
ee
,
u
s
in
g
b
in
ar
y
s
p
lits
an
d
Gin
i
in
d
ex
as
th
e
s
p
lit
cr
iter
io
n
[
2
3
]
,
[
2
4
]
.
T
h
e
v
alu
e
k
=
5
was
s
elec
ted
as
a
co
m
m
o
n
ly
u
s
ed
co
m
p
r
o
m
is
e
b
etwe
en
n
o
is
e
s
en
s
itiv
ity
(
s
m
all
k
)
an
d
ex
ce
s
s
iv
e
s
m
o
o
th
in
g
(
la
r
g
e
k
)
,
p
r
o
v
id
in
g
s
tab
le
n
ei
g
h
b
o
r
h
o
o
d
v
o
tin
g
f
o
r
th
e
g
iv
en
s
am
p
le
s
ize.
T
h
e
in
p
u
t d
ataset
co
m
p
r
is
ed
5
0
0
s
am
p
les,
ea
ch
with
f
o
u
r
ex
tr
ac
ted
f
ea
tu
r
es.
T
h
e
d
ataset
was d
iv
id
ed
in
to
7
0
%
f
o
r
tr
ain
in
g
an
d
3
0
%
f
o
r
test
in
g
[
2
5
]
,
[
2
6
]
u
s
in
g
s
tr
atif
ied
s
am
p
lin
g
to
m
ain
tai
n
class
b
alan
ce
.
Prio
r
to
tr
ain
in
g
,
f
ea
tu
r
e
s
tan
d
ar
d
iza
tio
n
u
s
in
g
z
-
s
co
r
e
n
o
r
m
aliza
tio
n
(
ze
r
o
m
ea
n
an
d
u
n
it
v
ar
ian
ce
)
was
ap
p
lied
f
o
r
th
e
SVM
m
o
d
el
o
n
ly
,
wh
il
e
th
e
DT
an
d
k
-
NN
class
if
ier
s
wer
e
tr
ain
ed
o
n
r
aw
f
ea
tu
r
e
v
alu
es.
T
h
e
class
if
icatio
n
p
r
o
ce
s
s
was
ca
r
r
ied
o
u
t
in
MA
T
L
AB
.
Featu
r
e
d
ata
was
s
to
r
ed
in
a
C
SV
f
ile
an
d
im
p
o
r
ted
u
s
in
g
MA
T
L
AB
’
s
r
ea
d
ta
b
le
f
u
n
ctio
n
.
T
h
e
p
r
o
ce
s
s
ed
d
ata
wer
e
th
en
class
if
ied
u
s
in
g
SVM
with
a
R
B
F
k
er
n
el
,
k
-
NN
with
k
=
5
,
a
n
d
a
DT
cla
s
s
if
ier
.
2
.
5
.
E
v
a
lua
t
i
o
n m
et
rics
T
o
ass
ess
th
e
ef
f
ec
tiv
en
ess
o
f
t
h
e
class
if
icatio
n
m
o
d
e
ls
in
r
ec
o
g
n
izin
g
r
ad
ar
-
b
ased
g
estu
r
e
co
m
m
an
d
s
,
two
p
r
im
ar
y
p
e
r
f
o
r
m
an
ce
m
etr
ics
wer
e
em
p
lo
y
ed
,
n
am
ely
class
if
icatio
n
ac
cu
r
ac
y
a
n
d
t
h
e
co
n
f
u
s
io
n
m
atr
ix
.
C
lass
if
icati
o
n
ac
cu
r
ac
y
r
ep
r
esen
ts
th
e
p
r
o
p
o
r
ti
o
n
o
f
co
r
r
ec
tly
p
r
e
d
icted
g
estu
r
e
lab
els
r
elativ
e
to
th
e
to
tal
n
u
m
b
e
r
o
f
test
s
am
p
les.
I
t
s
er
v
es
a
s
a
n
o
v
er
all
in
d
icato
r
o
f
m
o
d
el
p
er
f
o
r
m
an
ce
an
d
is
u
s
ef
u
l f
o
r
c
o
m
p
a
r
in
g
d
if
f
er
e
n
t c
lass
if
ier
s
u
n
d
er
th
e
s
am
e
co
n
d
itio
n
s
.
I
n
a
d
d
itio
n
to
ac
c
u
r
ac
y
,
co
n
f
u
s
io
n
m
atr
ices
wer
e
u
s
ed
to
a
n
aly
ze
th
e
class
if
icatio
n
r
esu
l
ts
in
m
o
r
e
d
etail
b
y
d
is
p
lay
in
g
th
e
d
is
tr
ib
u
tio
n
o
f
tr
u
e
v
er
s
u
s
p
r
ed
icted
class
lab
els
[
2
7
]
,
[
2
8
]
.
E
ac
h
r
o
w
in
th
e
co
n
f
u
s
io
n
m
atr
ix
co
r
r
esp
o
n
d
s
to
th
e
ac
t
u
al
g
estu
r
e
class
,
wh
ile
ea
ch
co
lu
m
n
in
d
icate
s
th
e
p
r
ed
ict
ed
class
.
Diag
o
n
al
elem
en
ts
r
ef
lect
co
r
r
ec
tly
class
if
ied
in
s
tan
ce
s
,
wh
er
ea
s
o
f
f
-
d
iag
o
n
al
elem
en
ts
r
ev
ea
l
m
is
class
if
icatio
n
s
.
T
h
is
v
is
u
aliza
tio
n
en
ab
les
a
d
ee
p
e
r
u
n
d
e
r
s
tan
d
in
g
o
f
th
e
s
tr
en
g
th
s
an
d
wea
k
n
ess
es
o
f
ea
ch
m
o
d
el
in
r
ec
o
g
n
izin
g
s
p
ec
if
ic
g
estu
r
es.
All
m
etr
ics
wer
e
co
m
p
u
ted
b
ased
o
n
th
e
3
0
%
test
p
ar
titi
o
n
o
f
th
e
d
ataset,
wh
ich
co
n
ta
in
ed
1
5
0
u
n
s
ee
n
s
am
p
les
(
3
0
p
er
class
)
d
is
tr
ib
u
ted
ev
en
ly
ac
r
o
s
s
all
g
estu
r
e
class
es.
B
y
an
aly
zin
g
b
o
th
th
e
ac
cu
r
ac
y
an
d
co
n
f
u
s
io
n
m
atr
ix
,
we
wer
e
ab
le
to
ev
alu
ate
n
o
t
o
n
ly
th
e
o
v
er
all
m
o
d
el
p
er
f
o
r
m
an
ce
b
u
t
also
th
e
ten
d
en
cy
o
f
th
e
class
if
ier
s
to
co
n
f
u
s
e
ce
r
tain
g
estu
r
es,
wh
ich
is
cr
i
tical
f
o
r
d
esig
n
in
g
r
eliab
le
g
estu
r
e
-
b
ased
UAV
co
m
m
an
d
s
y
s
tem
s
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
is
s
ec
tio
n
p
r
esen
ts
th
e
ev
al
u
atio
n
r
esu
lts
o
f
t
h
e
p
r
o
p
o
s
ed
r
ad
ar
-
b
ased
g
estu
r
e
r
ec
o
g
n
itio
n
s
y
s
tem
f
o
r
UAV
co
m
m
an
d
in
ter
p
r
etatio
n
.
T
h
e
p
e
r
f
o
r
m
an
ce
o
f
t
h
r
ee
m
ac
h
in
e
lear
n
in
g
class
if
ier
s
—
SVM,
k
-
NN,
an
d
DT
—
was
co
m
p
ar
ed
u
s
in
g
a
d
ataset
o
f
5
0
0
s
im
u
lated
r
ad
a
r
g
estu
r
e
s
am
p
les.
E
ac
h
class
if
ier
was
tr
ain
ed
o
n
7
0
% o
f
th
e
d
ata
an
d
test
ed
o
n
th
e
r
em
ain
in
g
3
0
%,
u
s
in
g
s
tr
atif
ied
r
an
d
o
m
s
am
p
lin
g
to
en
s
u
r
e
class
b
alan
ce
.
3
.
1
.
Cla
s
s
if
ica
t
io
n
a
cc
ura
c
y
T
h
e
class
if
icatio
n
p
er
f
o
r
m
an
c
e
o
f
th
e
th
r
ee
m
ac
h
i
n
e
lear
n
i
n
g
m
o
d
els
—
SVM,
k
-
NN,
an
d
DT
—
wa
s
ev
alu
ated
u
s
in
g
th
e
test
p
o
r
tio
n
o
f
th
e
d
ataset,
co
m
p
r
is
in
g
1
5
0
g
estu
r
e
s
am
p
les
(
3
0
p
er
class
)
.
T
ab
le
2
p
r
esen
ts
th
e
class
if
icatio
n
ac
cu
r
ac
y
ac
h
ie
v
ed
b
y
ea
ch
m
o
d
el
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
R
a
d
a
r
-
b
a
s
ed
g
estu
r
e
r
ec
o
g
n
i
tio
n
s
imu
la
tio
n
fo
r
u
n
ma
n
n
ed
a
eria
l v
eh
icles
…
(
Den
n
y
Derma
w
a
n
)
1231
T
ab
le
2
.
C
lass
if
icatio
n
ac
cu
r
a
cy
o
f
ea
c
h
m
o
d
el
C
l
a
s
si
f
i
e
r
A
c
c
u
r
a
c
y
(
%)
S
V
M
(
R
B
F
k
e
r
n
e
l
)
8
7
.
3
3
k
-
N
N
(
k
=
5
)
96
D
e
c
i
s
i
o
n
Tr
e
e
8
4
.
6
7
Am
o
n
g
t
h
e
th
r
ee
,
th
e
k
-
NN
class
if
ier
ac
h
iev
ed
th
e
h
ig
h
e
s
t
o
v
er
all
ac
cu
r
ac
y
o
f
9
6
%,
in
d
icatin
g
ex
ce
llen
t
g
en
e
r
aliza
tio
n
ca
p
a
b
ilit
y
with
m
in
im
al
m
is
clas
s
if
icatio
n
.
T
h
e
SVM
m
o
d
el
f
o
llo
wed
with
a
n
ac
cu
r
ac
y
o
f
8
7
.
3
3
%,
wh
ich
a
ls
o
d
em
o
n
s
tr
ates
s
tr
o
n
g
class
if
icatio
n
p
er
f
o
r
m
a
n
ce
,
esp
ec
ia
lly
co
n
s
id
er
in
g
th
e
non
-
lin
ea
r
n
at
u
r
e
o
f
t
h
e
g
estu
r
e
f
ea
tu
r
e
d
is
tr
ib
u
tio
n
.
Me
a
n
wh
ile,
th
e
DT
class
if
ier
ac
h
iev
ed
an
ac
cu
r
ac
y
o
f
8
4
.
6
7
%,
s
h
o
win
g
m
o
d
e
r
ate
p
er
f
o
r
m
a
n
ce
b
u
t
s
lig
h
tly
lo
wer
r
o
b
u
s
tn
ess
co
m
p
ar
ed
to
th
e
o
th
er
two
.
I
t
s
h
o
u
ld
b
e
n
o
ted
th
at
th
is
ac
cu
r
ac
y
r
ef
lects
p
er
f
o
r
m
an
ce
o
n
a
co
n
tr
o
lled
s
y
n
th
etic
d
ataset
an
d
th
er
ef
o
r
e
r
ep
r
esen
ts
an
u
p
p
er
-
b
o
u
n
d
esti
m
ate
r
ath
er
th
an
a
d
ir
ec
t
m
ea
s
u
r
e
o
f
r
ea
l
-
w
o
r
ld
p
e
r
f
o
r
m
an
ce
.
T
h
e
s
u
p
er
io
r
p
e
r
f
o
r
m
an
ce
o
f
t
h
e
k
-
NN
m
o
d
el
s
u
g
g
ests
th
at
th
e
d
is
tr
ib
u
tio
n
o
f
r
ad
a
r
g
estu
r
e
f
ea
tu
r
es
in
th
e
f
o
u
r
-
d
im
en
s
io
n
al
f
ea
tu
r
e
s
p
ac
e
is
well
-
s
u
ited
f
o
r
in
s
ta
n
ce
-
b
ased
class
if
icatio
n
with
E
u
clid
ea
n
d
is
tan
ce
.
T
h
ese
f
in
d
in
g
s
h
ig
h
lig
h
t
th
e
ef
f
ec
tiv
en
ess
o
f
s
im
p
le
g
eo
m
etr
ic
class
if
ier
s
wh
en
ap
p
lied
to
well
-
en
g
in
ee
r
ed
f
ea
tu
r
e
v
ec
to
r
s
,
ev
en
with
o
u
t
ex
p
licit f
ea
tu
r
e
n
o
r
m
aliza
tio
n
.
3
.
2
.
Co
nfusi
o
n m
a
t
rix
a
na
l
y
s
is
T
h
e
co
n
f
u
s
io
n
m
atr
ix
o
f
th
e
b
est
-
p
er
f
o
r
m
in
g
class
if
ier
,
k
-
n
ea
r
est
n
eig
h
b
o
r
s
(
k
=
5
)
,
is
p
r
esen
ted
in
Fig
u
r
e
3
.
T
h
e
m
atr
ix
illu
s
tr
ates
th
e
n
u
m
b
er
o
f
co
r
r
ec
t
an
d
in
co
r
r
ec
t
p
r
e
d
ictio
n
s
f
o
r
ea
c
h
g
estu
r
e
class
.
T
h
e
d
iag
o
n
al
elem
e
n
ts
r
ep
r
ese
n
t
co
r
r
ec
t
class
if
icatio
n
s
,
wh
ile
th
e
o
f
f
-
d
iag
o
n
al
elem
en
ts
in
d
icate
m
is
class
if
icatio
n
s
.
Fro
m
th
e
m
atr
ix
,
it
ca
n
b
e
o
b
s
er
v
ed
th
at
th
e
m
o
v
e
f
o
r
war
d
g
estu
r
e
was
class
if
ied
with
p
er
f
ec
t
ac
cu
r
ac
y
(
3
0
o
u
t
o
f
3
0
)
,
f
o
llo
we
d
clo
s
ely
b
y
L
a
n
d
(
2
8
o
u
t
o
f
3
0
)
an
d
Sto
p
(
2
7
o
u
t
o
f
3
0
)
.
T
h
ese
r
esu
lts
in
d
icate
th
at
th
e
f
ea
tu
r
e
v
ec
to
r
s
d
er
iv
ed
f
r
o
m
th
es
e
g
estu
r
es
ar
e
s
u
f
f
icien
tly
d
i
s
tin
ct,
allo
win
g
th
e
class
if
ier
to
lear
n
r
o
b
u
s
t
d
ec
is
io
n
b
o
u
n
d
ar
ies.
T
h
e
an
aly
s
is
h
ig
h
lig
h
ts
th
e
r
eliab
ilit
y
o
f
t
h
e
k
-
NN
m
o
d
el
i
n
m
o
s
t
g
estu
r
e
class
es,
wh
ile
also
s
u
g
g
esti
n
g
th
e
n
ee
d
f
o
r
f
u
r
th
er
f
ea
tu
r
e
r
ef
in
em
e
n
t
o
r
ad
v
an
ce
d
m
o
d
elin
g
(
e.
g
.
,
e
n
s
em
b
le
m
eth
o
d
s
o
r
d
ee
p
lear
n
in
g
)
to
b
etter
d
is
tin
g
u
is
h
b
etwe
en
g
estu
r
es
with
s
im
ilar
d
y
n
am
i
c
p
r
o
f
iles
.
Fig
u
r
e
3
.
C
o
n
f
u
s
io
n
m
atr
i
x
o
f
k
-
NN
class
if
ier
r
esu
lts
3
.
3
.
I
nte
rpre
t
a
t
io
n
a
nd
a
pp
lica
t
io
n
T
h
e
r
esu
lts
o
b
tain
ed
f
r
o
m
th
e
k
-
NN
class
if
ier
,
wh
ich
ac
h
iev
ed
an
ac
cu
r
ac
y
o
f
9
6
%,
d
em
o
n
s
tr
ate
th
e
v
iab
ilit
y
o
f
r
a
d
ar
-
b
ased
h
an
d
g
estu
r
e
r
ec
o
g
n
itio
n
f
o
r
U
AV
co
m
m
an
d
i
n
ter
p
r
etatio
n
.
W
ith
f
o
u
r
lo
w
-
co
m
p
lex
ity
f
ea
tu
r
es
—
d
u
r
atio
n
,
m
ax
im
u
m
am
p
litu
d
e,
d
o
m
i
n
an
t
f
r
e
q
u
en
c
y
,
an
d
R
MS,
th
e
s
y
s
tem
was
ab
le
to
d
is
tin
g
u
is
h
f
iv
e
p
r
ed
e
f
in
ed
g
e
s
tu
r
es (
T
ak
eOf
f
,
L
an
d
,
Mo
v
eF
o
r
war
d
,
T
u
r
n
L
ef
t,
a
n
d
Sto
p
)
with
h
ig
h
r
eliab
ilit
y
.
T
h
e
m
o
d
el'
s
s
tr
o
n
g
p
er
f
o
r
m
a
n
ce
r
ein
f
o
r
ce
s
th
e
ef
f
ec
tiv
en
ess
o
f
s
im
p
le
tim
e
-
f
r
e
q
u
en
c
y
f
ea
t
u
r
es wh
en
p
r
o
p
er
l
y
en
g
in
ee
r
ed
an
d
n
o
r
m
alize
d
.
T
h
e
o
b
s
er
v
ed
m
is
class
if
icatio
n
b
etwe
en
th
e
T
u
r
n
L
e
f
t
a
n
d
St
o
p
g
estu
r
es
ca
n
b
e
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.
1
6
,
No
.
3
,
J
u
n
e
20
2
6
:
1
2
2
7
-
1
2
3
5
1232
attr
ib
u
ted
to
s
im
ilar
ities
in
t
h
eir
tem
p
o
r
al
an
d
en
er
g
y
-
r
elate
d
ch
ar
ac
ter
is
tics
wh
en
r
ep
r
esen
ted
u
s
in
g
th
e
s
elec
ted
f
ea
tu
r
e
s
et.
I
n
p
ar
tic
u
lar
,
b
o
th
g
estu
r
es
m
ay
e
x
h
ib
it
co
m
p
ar
ab
le
s
ig
n
al
d
u
r
atio
n
s
an
d
R
MS
v
alu
es,
esp
ec
ially
wh
en
th
e
tu
r
n
in
g
m
o
tio
n
is
p
e
r
f
o
r
m
ed
with
lim
ited
an
g
u
lar
v
elo
city
o
r
wh
en
th
e
s
to
p
p
in
g
g
estu
r
e
in
clu
d
es
b
r
ief
tr
an
s
itio
n
al
m
o
v
em
en
ts
.
Sin
ce
th
e
cu
r
r
en
t
f
ea
tu
r
e
s
et
d
o
es
n
o
t
ex
p
licitly
en
co
d
e
tem
p
o
r
al
s
eq
u
en
ce
in
f
o
r
m
atio
n
o
r
m
o
t
io
n
d
ir
e
ctio
n
ality
,
s
u
c
h
o
v
er
l
ap
s
ca
n
lea
d
to
am
b
i
g
u
ity
i
n
th
e
f
ea
tu
r
e
s
p
ac
e.
I
n
co
r
p
o
r
atin
g
tem
p
o
r
al
m
o
d
el
in
g
ap
p
r
o
ac
h
es,
s
u
ch
as
h
id
d
en
Ma
r
k
o
v
m
o
d
els
(
HM
Ms)
o
r
r
ec
u
r
r
en
t
n
eu
r
al
n
etwo
r
k
s
(
R
NNs),
as
well
a
s
d
ir
ec
tio
n
-
s
en
s
itiv
e
f
ea
tu
r
es
d
e
r
iv
ed
f
r
o
m
tim
e
–
f
r
e
q
u
en
c
y
r
e
p
r
esen
tatio
n
s
,
co
u
ld
p
o
ten
tially
m
itig
ate
th
is
am
b
ig
u
ity
in
f
u
t
u
r
e
im
p
lem
e
n
tatio
n
s
.
Fro
m
an
ap
p
licatio
n
s
tan
d
p
o
in
t,
th
ese
f
in
d
in
g
s
s
u
g
g
est
th
at
a
r
ad
ar
-
b
ased
g
estu
r
e
r
ec
o
g
n
iti
o
n
s
y
s
tem
co
u
ld
s
er
v
e
as
a
p
r
o
m
is
in
g
alter
n
ativ
e
to
co
n
v
en
tio
n
al
r
em
o
te
co
n
tr
o
ls
in
UAV
o
p
e
r
atio
n
s
.
T
h
e
i
n
tu
itiv
e
an
d
co
n
tactless
n
atu
r
e
o
f
g
estu
r
e
in
ter
f
ac
es
o
f
f
e
r
s
p
o
ten
tial
a
d
v
an
tag
es
in
s
ce
n
a
r
io
s
wh
er
e
p
h
y
s
ical
co
n
tr
o
l
is
im
p
r
ac
tical
o
r
u
n
s
af
e,
s
u
ch
as
d
u
r
i
n
g
d
is
aster
r
esp
o
n
s
e,
h
az
ar
d
o
u
s
e
n
v
ir
o
n
m
en
ts
,
o
r
ac
ce
s
s
ib
ilit
y
s
u
p
p
o
r
t
f
o
r
u
s
er
s
with
p
h
y
s
ical
lim
itatio
n
s
.
Mo
r
eo
v
er
,
th
e
u
s
e
o
f
r
a
d
ar
p
r
o
v
i
d
es
r
o
b
u
s
tn
ess
u
n
d
er
lo
w
-
lig
h
t
o
r
v
is
u
ally
o
b
s
tr
u
cted
co
n
d
itio
n
s
,
wh
ich
ar
e
lim
itatio
n
s
o
f
ca
m
er
a
-
b
ase
d
s
y
s
tem
s
.
T
h
e
cu
r
r
en
t
s
im
u
la
tio
n
r
esu
lts
f
o
r
m
a
f
o
u
n
d
a
t
i
o
n
f
o
r
f
u
t
u
r
e
i
m
p
l
e
m
e
n
t
a
t
i
o
n
u
s
i
n
g
r
e
a
l
r
a
d
a
r
s
e
n
s
o
r
s
a
n
d
e
m
b
e
d
d
e
d
s
y
s
t
e
m
s
.
Fu
t
u
r
e
i
m
p
r
o
v
e
m
e
n
t
s
m
a
y
i
n
v
o
l
v
e
e
x
p
a
n
d
i
n
g
t
h
e
f
ea
t
u
r
e
s
et
,
i
n
c
o
r
p
o
r
a
t
i
n
g
a
d
v
a
n
ce
d
t
e
m
p
o
r
a
l
a
n
d
d
i
r
e
c
ti
o
n
a
l
m
o
d
e
l
i
n
g
t
e
c
h
n
i
q
u
e
s
,
o
r
i
n
t
e
g
r
a
t
i
n
g
s
e
n
s
o
r
f
u
s
i
o
n
t
o
f
u
r
t
h
e
r
i
m
p
r
o
v
e
c
l
a
s
s
i
f
i
ca
t
i
o
n
r
o
b
u
s
t
n
e
s
s
i
n
d
y
n
a
m
i
c
e
n
v
i
r
o
n
m
e
n
t
s
.
Alth
o
u
g
h
t
h
e
k
-
NN
class
if
ier
ac
h
iev
ed
a
h
ig
h
ac
cu
r
ac
y
o
f
9
6
%,
it
s
h
o
u
ld
b
e
n
o
ted
t
h
at
th
e
d
ataset
u
s
ed
in
th
is
s
tu
d
y
was
s
y
n
t
h
etica
lly
g
en
er
ated
u
n
d
e
r
co
n
tr
o
lled
s
im
u
latio
n
ass
u
m
p
tio
n
s
.
As
a
r
esu
lt,
th
e
r
ep
o
r
ted
p
er
f
o
r
m
a
n
ce
r
ep
r
ese
n
ts
an
u
p
p
er
-
b
o
u
n
d
esti
m
ate
o
f
class
if
icatio
n
ac
cu
r
ac
y
r
ath
er
th
an
a
d
ir
ec
t
in
d
icatio
n
o
f
r
ea
l
-
wo
r
ld
p
e
r
f
o
r
m
an
ce
.
Nev
e
r
th
eless
,
th
e
p
r
im
ar
y
o
b
jectiv
e
o
f
th
is
wo
r
k
is
n
o
t
to
claim
s
u
p
er
io
r
ity
o
v
e
r
d
ee
p
-
lea
r
n
in
g
-
b
ased
ap
p
r
o
ac
h
es,
b
u
t
to
ev
alu
ate
th
e
f
ea
s
ib
ilit
y
o
f
u
s
in
g
lo
w
-
co
m
p
lex
ity
tim
e
–
f
r
eq
u
e
n
cy
f
ea
t
u
r
es
an
d
lig
h
tweig
h
t
class
if
ier
s
f
o
r
UAV
co
m
m
an
d
i
n
ter
p
r
et
atio
n
.
T
h
e
r
esu
lts
d
em
o
n
s
tr
ate
th
at,
ev
en
with
a
m
in
im
al
f
ea
tu
r
e
s
et,
r
a
d
ar
-
b
ased
g
estu
r
e
r
ec
o
g
n
itio
n
ca
n
ac
h
iev
e
r
eliab
le
d
is
cr
im
in
atio
n
am
o
n
g
d
is
cr
ete
UAV
co
m
m
a
n
d
s
at
th
e
s
im
u
latio
n
lev
el,
th
er
e
b
y
p
r
o
v
id
in
g
a
s
o
lid
b
aselin
e
f
o
r
s
u
b
s
eq
u
en
t
v
alid
atio
n
u
s
in
g
r
ea
l
r
ad
ar
s
en
s
o
r
s
an
d
m
o
r
e
co
m
p
lex
d
atasets
.
Fu
tu
r
e
s
tu
d
ies
will
f
o
cu
s
o
n
v
alid
atin
g
th
e
p
r
o
p
o
s
ed
f
r
am
ewo
r
k
u
s
in
g
r
ea
l
FMC
W
r
ad
ar
m
ea
s
u
r
em
en
ts
a
n
d
in
v
esti
g
atin
g
th
e
im
p
ac
t
o
f
n
o
is
e,
clu
tter
,
an
d
in
ter
-
s
u
b
je
ct
v
ar
iab
ilit
y
o
n
class
if
icatio
n
p
er
f
o
r
m
an
ce
.
C
o
m
p
ar
ed
to
d
ee
p
lear
n
in
g
-
b
ased
g
e
s
t
u
r
e
r
e
c
o
g
n
i
ti
o
n
a
p
p
r
o
a
c
h
e
s
t
h
a
t
t
y
p
i
c
a
l
l
y
r
e
q
u
i
r
e
l
a
r
g
e
d
a
t
a
s
et
s
a
n
d
h
i
g
h
c
o
m
p
u
t
a
tio
n
a
l
r
e
s
o
u
r
c
e
s
,
t
h
e
p
r
o
p
o
s
e
d
l
i
g
h
t
w
e
i
g
h
t
f
r
a
m
ew
o
r
k
e
m
p
h
a
s
i
z
es
i
n
t
e
r
p
r
e
t
a
b
i
l
it
y
an
d
f
e
a
s
i
b
i
li
t
y
f
o
r
e
a
r
l
y
-
s
t
a
g
e
UA
V
s
y
s
t
e
m
d
es
i
g
n
.
4.
CO
NCLU
SI
O
N
T
h
is
s
tu
d
y
h
as
s
u
cc
ess
f
u
lly
d
em
o
n
s
tr
ated
th
at
r
a
d
ar
-
b
ased
h
an
d
g
estu
r
e
r
ec
o
g
n
itio
n
m
a
y
s
er
v
e
as
a
v
iab
le
alter
n
ativ
e
to
co
n
v
en
ti
o
n
al
R
C
s
y
s
tem
s
with
n
o
n
-
co
n
tact,
in
tu
itiv
e
in
ter
f
ac
es.
Usi
n
g
s
im
u
lated
r
a
d
ar
s
ig
n
al
f
ea
tu
r
es
,
d
u
r
atio
n
,
m
a
x
im
u
m
am
p
litu
d
e,
d
o
m
in
a
n
t
f
r
eq
u
e
n
cy
,
a
n
d
R
MS
—
th
is
wo
r
k
ev
alu
ate
d
th
e
class
if
icatio
n
p
er
f
o
r
m
an
ce
o
f
t
h
r
ee
m
ac
h
in
e
lear
n
in
g
m
o
d
els:
SVM,
DT
,
an
d
k
-
NN.
T
h
e
b
est
-
p
er
f
o
r
m
in
g
m
o
d
el,
k
-
NN,
ac
h
iev
ed
an
ac
cu
r
ac
y
o
f
9
6
%,
co
n
f
ir
m
in
g
th
e
f
ea
s
ib
ilit
y
o
f
lig
h
tweig
h
t
class
if
ier
s
to
ef
f
e
ctiv
ely
r
ec
o
g
n
ize
f
iv
e
d
is
tin
c
t
UAV
co
m
m
an
d
g
estu
r
es.
T
h
ese
f
in
d
in
g
s
ar
e
co
n
s
is
ten
t
with
ea
r
lier
r
esear
c
h
in
g
estu
r
e
r
ec
o
g
n
itio
n
th
at
h
ig
h
lig
h
ts
th
e
p
o
ten
tial
o
f
r
a
d
ar
s
en
s
o
r
s
f
o
r
r
o
b
u
s
t
class
if
icatio
n
u
n
d
er
v
ar
ia
b
le
c
o
n
d
itio
n
s
.
T
h
is
r
esear
ch
ex
te
n
d
s
p
r
ev
i
o
u
s
wo
r
k
b
y
s
im
u
latin
g
a
co
m
p
lete
g
estu
r
e
class
if
icati
o
n
p
i
p
elin
e
tailo
r
ed
s
p
ec
if
ically
f
o
r
UAV
co
m
m
an
d
task
s
,
th
e
r
eb
y
ad
d
r
e
s
s
in
g
th
e
g
a
p
i
n
s
y
s
tem
-
lev
el
v
alid
atio
n
o
f
r
ad
ar
-
b
ased
co
n
tr
o
l
i
n
ter
f
ac
es.
Fu
r
th
er
m
o
r
e
,
th
e
co
n
f
u
s
io
n
m
atr
i
x
an
aly
s
is
r
e
v
ea
led
s
p
ec
i
f
ic
g
estu
r
e
p
air
s
(
e.
g
.
,
T
u
r
n
L
ef
t v
s
.
Sto
p
)
th
at
m
ay
b
en
ef
it f
r
o
m
en
h
an
ce
d
f
ea
t
u
r
e
d
esig
n
,
p
r
o
v
id
in
g
in
s
ig
h
t f
o
r
f
u
r
th
er
r
esear
ch
.
T
h
is
wo
r
k
ad
v
an
ce
s
th
e
s
tate
o
f
t
h
e
ar
t
b
y
p
r
o
p
o
s
in
g
a
r
ep
r
o
d
u
cib
le
an
d
e
f
f
icien
t
f
r
a
m
ewo
r
k
f
o
r
g
estu
r
e
-
b
ased
UAV
in
ter
ac
tio
n
,
with
p
o
ten
tial
ap
p
licatio
n
s
in
en
v
ir
o
n
m
en
t
s
wh
er
e
p
h
y
s
ical
co
n
tr
o
l
is
im
p
r
ac
tical
o
r
u
n
s
af
e.
Fu
tu
r
e
r
esear
ch
s
h
o
u
ld
f
o
c
u
s
o
n
im
p
lem
e
n
tin
g
r
ea
l
-
tim
e
s
y
s
tem
s
with
ac
tu
al
r
ad
ar
h
ar
d
war
e,
in
co
r
p
o
r
atin
g
tem
p
o
r
al
d
y
n
am
ics,
an
d
ex
p
a
n
d
in
g
th
e
g
estu
r
e
v
o
ca
b
u
lar
y
to
en
ab
le
m
o
r
e
co
m
p
le
x
UAV
o
p
er
atio
n
s
.
I
n
d
o
i
n
g
s
o
,
th
is
s
tu
d
y
c
o
n
tr
ib
u
tes
to
t
h
e
g
r
o
win
g
b
o
d
y
o
f
r
esear
ch
in
r
a
d
ar
-
b
ased
h
u
m
an
-
m
ac
h
in
e
in
ter
ac
tio
n
an
d
o
p
en
s
n
ew
d
ir
ec
tio
n
s
f
o
r
ad
a
p
tiv
e
UAV
co
n
tr
o
l
i
n
ter
f
ac
es.
T
h
ese
o
u
tco
m
es
alig
n
with
th
e
in
itial
g
o
al
o
f
ev
alu
atin
g
th
e
f
ea
s
ib
ilit
y
o
f
r
a
d
ar
-
b
ased
g
estu
r
e
r
ec
o
g
n
itio
n
as a
n
alter
n
ativ
e
co
n
t
r
o
l in
ter
f
ac
e
f
o
r
UAVs.
ACK
NO
WL
E
DG
M
E
N
T
S
T
h
is
r
esear
ch
was
f
u
n
d
ed
b
y
th
e
Min
is
tr
y
o
f
Hig
h
er
E
d
u
ca
tio
n
,
Scien
ce
,
an
d
T
ec
h
n
o
lo
g
y
(
Kem
en
ter
ian
Pen
d
id
i
k
an
T
i
n
g
g
i,
Sain
s
d
an
T
e
k
n
o
l
o
g
i/Kem
d
ik
tis
ain
tek
)
th
r
o
u
g
h
t
h
e
Fu
n
d
am
en
tal
B
asic
R
esear
ch
Gr
an
t
Sch
em
e.
T
h
e
wo
r
k
is
p
ar
t
o
f
th
e
r
esear
ch
p
r
o
ject
en
titl
ed
“
E
k
s
p
lo
r
asi
Sis
tem
Pen
g
en
alan
Ger
ak
an
T
an
g
an
B
er
b
asis
R
a
d
ar
p
ad
a
u
n
m
an
n
ed
ae
r
ial
v
e
h
icle
(
UAV)
”.
T
h
e
au
th
o
r
s
wo
u
ld
lik
e
to
th
an
k
th
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
R
a
d
a
r
-
b
a
s
ed
g
estu
r
e
r
ec
o
g
n
i
tio
n
s
imu
la
tio
n
fo
r
u
n
ma
n
n
ed
a
eria
l v
eh
icles
…
(
Den
n
y
Derma
w
a
n
)
1233
Dir
ec
to
r
ate
o
f
R
esear
ch
an
d
C
o
m
m
u
n
ity
Ser
v
ice,
as
well
as
th
e
s
u
p
p
o
r
tin
g
in
s
titu
tio
n
,
f
o
r
t
h
eir
f
ac
ilit
atio
n
an
d
s
u
p
p
o
r
t th
r
o
u
g
h
o
u
t t
h
e
p
r
o
ject.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
T
h
is
r
esear
ch
was
f
u
n
d
ed
b
y
th
e
Min
is
tr
y
o
f
Hig
h
er
E
d
u
ca
tio
n
,
Scien
ce
,
an
d
T
ec
h
n
o
lo
g
y
(
Kem
en
ter
ian
Pen
d
id
i
k
an
T
i
n
g
g
i,
Sain
s
d
an
T
e
k
n
o
l
o
g
i/Kem
d
ik
tis
ain
tek
)
th
r
o
u
g
h
t
h
e
Fu
n
d
am
en
tal
B
asic
R
esear
ch
Gr
an
t
Sch
em
e.
T
h
e
r
esear
ch
p
r
o
ject
is
titl
ed
“
E
k
s
p
lo
r
asi
Sis
tem
Pen
g
en
ala
n
Ger
ak
a
n
T
a
n
g
an
B
er
b
asis
R
ad
ar
p
ad
a
U
n
m
an
n
ed
Aer
ial
Veh
icle
(
UAV)
”,
u
n
d
e
r
R
esear
ch
Gr
an
t/C
o
n
tr
ac
t
No
.
1
2
6
/C
3
/DT
.
0
5
.
0
0
/PL/2
0
2
5
,
d
a
ted
Ma
y
2
8
,
2
0
2
5
.
AUTHO
R
CO
NT
RI
B
UT
I
O
NS ST
A
T
E
M
E
N
T
T
h
is
jo
u
r
n
al
u
s
es
th
e
C
o
n
tr
ib
u
to
r
R
o
les
T
ax
o
n
o
m
y
(
C
R
ed
iT
)
to
r
ec
o
g
n
ize
in
d
iv
id
u
al
au
th
o
r
co
n
tr
ib
u
tio
n
s
,
r
ed
u
ce
au
th
o
r
s
h
ip
d
is
p
u
tes,
an
d
f
ac
ilit
ate
co
llab
o
r
atio
n
.
Na
m
e
o
f
Aut
ho
r
C
M
So
Va
Fo
I
R
D
O
E
Vi
Su
P
Fu
Den
n
y
Der
m
awa
n
✓
✓
✓
✓
Fre
d
d
y
Ku
r
n
iawa
n
✓
✓
✓
Yen
n
i A
s
tu
ti
✓
✓
✓
Pau
lu
s
Setiawan
✓
✓
✓
L
asm
ad
i
✓
✓
Uy
u
u
n
u
l M
a
u
id
zo
h
✓
✓
✓
B
am
b
an
g
Su
d
ib
y
a
✓
✓
C
:
C
o
n
c
e
p
t
u
a
l
i
z
a
t
i
o
n
M
:
M
e
t
h
o
d
o
l
o
g
y
So
:
So
f
t
w
a
r
e
Va
:
Va
l
i
d
a
t
i
o
n
Fo
:
Fo
r
mal
a
n
a
l
y
s
i
s
I
:
I
n
v
e
s
t
i
g
a
t
i
o
n
R
:
R
e
so
u
r
c
e
s
D
:
D
a
t
a
C
u
r
a
t
i
o
n
O
:
W
r
i
t
i
n
g
-
O
r
i
g
i
n
a
l
D
r
a
f
t
E
:
W
r
i
t
i
n
g
-
R
e
v
i
e
w
&
E
d
i
t
i
n
g
Vi
:
Vi
su
a
l
i
z
a
t
i
o
n
Su
:
Su
p
e
r
v
i
s
i
o
n
P
:
P
r
o
j
e
c
t
a
d
mi
n
i
st
r
a
t
i
o
n
Fu
:
Fu
n
d
i
n
g
a
c
q
u
i
si
t
i
o
n
CO
NF
L
I
C
T
O
F
I
N
T
E
R
E
S
T
ST
A
T
E
M
E
NT
T
h
e
au
t
h
o
r
s
d
ec
lar
e
th
at
th
e
y
h
av
e
n
o
k
n
o
wn
c
o
m
p
etin
g
f
in
an
cial
in
ter
ests
o
r
p
er
s
o
n
al
r
el
atio
n
s
h
ip
s
th
at
co
u
ld
h
av
e
ap
p
ea
r
ed
t
o
in
f
lu
en
ce
th
e
wo
r
k
r
e
p
o
r
te
d
in
t
h
is
p
ap
er
.
Au
th
o
r
s
s
tate
n
o
co
n
f
lict o
f
in
ter
est.
DATA AV
AI
L
AB
I
L
I
T
Y
T
h
e
d
ata
th
at
s
u
p
p
o
r
ts
th
e
f
in
d
in
g
s
o
f
t
h
is
s
tu
d
y
ar
e
a
v
ailab
le
f
r
o
m
th
e
co
r
r
esp
o
n
d
in
g
a
u
th
o
r
,
u
p
o
n
r
ea
s
o
n
ab
le
r
e
q
u
est.
T
h
e
d
atas
et
was
g
en
er
ated
th
r
o
u
g
h
s
im
u
latio
n
in
MA
T
L
AB
an
d
c
o
n
tain
s
s
y
n
th
esized
r
ad
ar
s
ig
n
al
f
ea
t
u
r
es f
o
r
f
iv
e
UAV
co
m
m
an
d
g
estu
r
es.
RE
F
E
R
E
NC
E
S
[
1
]
M
.
C
h
m
u
r
sk
i
,
G
.
M
a
u
r
o
,
A
.
S
a
n
t
r
a
,
M
.
Z
u
b
e
r
t
,
a
n
d
G
.
D
a
g
a
s
a
n
,
“
H
i
g
h
l
y
-
o
p
t
i
mi
z
e
d
r
a
d
a
r
-
b
a
s
e
d
g
e
st
u
r
e
r
e
c
o
g
n
i
t
i
o
n
sy
s
t
e
m
,
”
S
e
n
so
rs
,
v
o
l
.
2
1
,
n
o
.
2
1
,
p
p
.
1
–
2
8
,
2
0
2
1
,
d
o
i
:
1
0
.
3
3
9
0
/
s
2
1
2
1
7
2
9
8
.
[
2
]
I
.
J.
Tsa
n
g
,
F
.
C
o
r
r
a
d
i
,
M
.
S
i
f
a
l
a
k
i
s,
W
.
V
a
n
Le
e
k
w
i
j
c
k
,
a
n
d
S
.
L
a
t
r
é
,
“
R
a
d
a
r
-
b
a
se
d
h
a
n
d
g
e
st
u
r
e
r
e
c
o
g
n
i
t
i
o
n
u
s
i
n
g
s
p
i
k
i
n
g
n
e
u
r
a
l
n
e
t
w
o
r
k
s,”
El
e
c
t
ro
n
i
c
s (
S
w
i
t
z
e
rl
a
n
d
)
,
v
o
l
.
1
0
,
n
o
.
1
2
,
p
p
.
1
–
2
0
,
2
0
2
1
,
d
o
i
:
1
0
.
3
3
9
0
/
e
l
e
c
t
r
o
n
i
c
s
1
0
1
2
1
4
0
5
.
[
3
]
B
.
H
u
a
n
d
J.
W
a
n
g
,
“
D
e
e
p
l
e
a
r
n
i
n
g
b
a
se
d
h
a
n
d
g
e
st
u
r
e
r
e
c
o
g
n
i
t
i
o
n
a
n
d
U
A
V
f
l
i
g
h
t
c
o
n
t
r
o
l
s,”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
rn
a
l
o
f
Au
t
o
m
a
t
i
o
n
a
n
d
C
o
m
p
u
t
i
n
g
,
v
o
l
.
1
7
,
n
o
.
1
,
p
p
.
1
7
–
2
9
,
2
0
2
0
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
1
6
3
3
-
0
1
9
-
1
1
9
4
-
7.
[
4
]
L.
Y
a
n
g
,
“
R
e
a
l
-
t
i
m
e
g
e
s
t
u
r
e
-
b
a
se
d
c
o
n
t
r
o
l
o
f
U
A
V
s
u
s
i
n
g
m
u
l
t
i
mo
d
a
l
f
u
si
o
n
o
f
F
M
C
W
r
a
d
a
r
a
n
d
v
i
si
o
n
,
”
i
n
J
o
u
rn
a
l
o
f
Ph
y
si
c
s:
C
o
n
f
e
re
n
c
e
S
e
r
i
e
s
,
2
0
2
3
,
v
o
l
.
2
6
6
4
,
n
o
.
1
,
p
p
.
1
–
1
1
,
d
o
i
:
1
0
.
1
0
8
8
/
1
7
4
2
-
6
5
9
6
/
2
6
6
4
/
1
/
0
1
2
0
0
2
.
[
5
]
Y
.
Li
u
,
Y
.
W
a
n
g
,
H
.
L
i
u
,
A
.
Z
h
o
u
,
J.
Li
u
,
a
n
d
N
.
Y
a
n
g
,
“
Lo
n
g
-
r
a
n
g
e
g
e
st
u
r
e
r
e
c
o
g
n
i
t
i
o
n
u
s
i
n
g
m
i
l
l
i
m
e
t
e
r
W
a
v
e
r
a
d
a
r
,
”
L
e
c
t
u
r
e
N
o
t
e
s
i
n
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
(
i
n
c
l
u
d
i
n
g
s
u
b
ser
i
e
s
L
e
c
t
u
r
e
N
o
t
e
s
i
n
Art
i
f
i
c
i
a
l
I
n
t
e
l
l
i
g
e
n
c
e
a
n
d
L
e
c
t
u
re
N
o
t
e
s
i
n
Bi
o
i
n
f
o
rm
a
t
i
c
s)
,
v
o
l
.
1
2
3
9
8
LN
C
S
,
p
p
.
3
0
–
4
4
,
2
0
2
0
,
d
o
i
:
1
0
.
1
0
0
7
/
9
7
8
-
3
-
0
3
0
-
6
4
2
4
3
-
3
_
3
.
[
6
]
Y
.
S
u
n
,
T.
F
e
i
,
X
.
L
i
,
A
.
W
a
r
n
e
c
k
e
,
E.
W
a
r
si
t
z
,
a
n
d
N
.
P
o
h
l
,
“
R
e
a
l
-
t
i
m
e
r
a
d
a
r
-
b
a
s
e
d
g
e
st
u
r
e
d
e
t
e
c
t
i
o
n
a
n
d
r
e
c
o
g
n
i
t
i
o
n
b
u
i
l
t
i
n
a
n
e
d
g
e
-
c
o
m
p
u
t
i
n
g
p
l
a
t
f
o
r
m,”
I
EEE
S
e
n
so
rs
J
o
u
r
n
a
l
,
v
o
l
.
2
0
,
n
o
.
1
8
,
p
p
.
1
0
7
0
6
–
1
0
7
1
6
,
2
0
2
0
,
d
o
i
:
1
0
.
1
1
0
9
/
JS
EN
.
2
0
2
0
.
2
9
9
4
2
9
2
.
[
7
]
J.
Zh
a
n
g
,
J
.
Ta
o
,
a
n
d
Z
.
S
h
i
,
“
D
o
p
p
l
e
r
-
r
a
d
a
r
b
a
se
d
h
a
n
d
g
e
s
t
u
r
e
r
e
c
o
g
n
i
t
i
o
n
s
y
s
t
e
m
u
si
n
g
c
o
n
v
o
l
u
t
i
o
n
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
s
,
”
i
n
Pro
c
e
e
d
i
n
g
s
o
f
t
h
e
2
0
1
7
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
C
o
m
m
u
n
i
c
a
t
i
o
n
s
,
S
i
g
n
a
l
Pr
o
c
e
ssi
n
g
,
a
n
d
S
y
s
t
e
m
s
,
2
0
1
9
,
v
o
l
.
4
6
3
,
p
p
.
1
0
9
6
–
1
1
1
3
,
d
o
i
:
1
0
.
1
0
0
7
/
9
7
8
-
981
-
10
-
6
5
7
1
-
2
_
1
3
2
.
[
8
]
F
.
K
h
a
n
,
S
.
L
e
e
m,
a
n
d
S
.
H
.
C
h
o
,
“
H
a
n
d
-
b
a
se
d
g
e
s
t
u
r
e
r
e
c
o
g
n
i
t
i
o
n
f
o
r
v
e
h
i
c
u
l
a
r
a
p
p
l
i
c
a
t
i
o
n
s
u
si
n
g
I
R
-
U
W
B
r
a
d
a
r
,
”
S
e
n
s
o
rs
,
v
o
l
.
1
7
,
n
o
.
4
,
p
p
.
1
–
1
8
,
2
0
1
7
,
d
o
i
:
1
0
.
3
3
9
0
/
s
1
7
0
4
0
8
3
3
.
[
9
]
C
.
Zh
a
o
,
G
.
L
u
o
,
Y
.
W
a
n
g
,
C
.
C
h
e
n
,
a
n
d
Z.
W
u
,
“
U
A
V
r
e
c
o
g
n
i
t
i
o
n
b
a
s
e
d
o
n
mi
c
r
o
-
d
o
p
p
l
e
r
d
y
n
a
mi
c
a
t
t
r
i
b
u
t
e
-
g
u
i
d
e
d
a
u
g
me
n
t
a
t
i
o
n
a
l
g
o
r
i
t
h
m
,
”
Re
m
o
t
e
S
e
n
si
n
g
,
v
o
l
.
1
3
,
n
o
.
6
,
p
p
.
1
–
1
7
,
2
0
2
1
,
d
o
i
:
1
0
.
3
3
9
0
/
r
s1
3
0
6
1
2
0
5
.
[
1
0
]
K
.
R
.
P
y
u
n
e
t
a
l
.
,
“
M
a
c
h
i
n
e
-
l
e
a
r
n
e
d
w
e
a
r
a
b
l
e
se
n
so
r
s
f
o
r
r
e
a
l
-
t
i
me
h
a
n
d
-
mo
t
i
o
n
r
e
c
o
g
n
i
t
i
o
n
:
T
o
w
a
r
d
p
r
a
c
t
i
c
a
l
a
p
p
l
i
c
a
t
i
o
n
s
,
”
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.
1
6
,
No
.
3
,
J
u
n
e
20
2
6
:
1
2
2
7
-
1
2
3
5
1234
N
a
t
i
o
n
a
l
S
c
i
e
n
c
e
Re
v
i
e
w
,
v
o
l
.
1
1
,
n
o
.
2
,
p
p
.
1
–
3
3
,
2
0
2
4
,
d
o
i
:
1
0
.
1
0
9
3
/
n
sr
/
n
w
a
d
2
9
8
.
[
1
1
]
A.
-
I
.
S
i
e
a
n
,
“
S
k
y
S
c
u
l
p
t
o
r
:
i
n
t
u
i
t
i
v
e
d
r
o
n
e
c
o
n
t
r
o
l
t
h
r
o
u
g
h
g
r
o
u
n
d
-
i
n
t
e
g
r
a
t
e
d
r
a
d
a
r
a
n
d
f
o
o
t
g
e
s
t
u
r
e
s
i
n
s
m
a
r
t
i
n
d
o
o
r
e
n
v
i
r
o
n
m
e
n
t
s
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
A
d
v
a
n
c
e
d
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
&
A
p
p
l
i
c
a
t
i
o
n
s
,
v
o
l
.
1
5
,
n
o
.
2
,
2
0
2
4
,
d
o
i
:
1
0
.
1
4
5
6
9
/
i
j
a
c
s
a
.
2
0
2
4
.
0
1
5
0
2
0
4
.
[
1
2
]
H
.
A
r
a
b
,
I
.
G
h
a
f
f
a
r
i
,
L.
C
h
i
o
u
k
h
,
S
.
Ta
t
u
,
a
n
d
S
.
D
u
f
o
u
r
,
“
M
a
c
h
i
n
e
l
e
a
r
n
i
n
g
b
a
se
d
o
b
j
e
c
t
c
l
a
ss
i
f
i
c
a
t
i
o
n
a
n
d
i
d
e
n
t
i
f
i
c
a
t
i
o
n
sc
h
e
m
e
u
si
n
g
a
n
e
mb
e
d
d
e
d
mi
l
l
i
me
t
e
r
-
w
a
v
e
r
a
d
a
r
se
n
so
r
,
”
S
e
n
s
o
rs
,
v
o
l
.
2
1
,
n
o
.
1
3
,
p
p
.
1
–
1
2
,
2
0
2
1
,
d
o
i
:
1
0
.
3
3
9
0
/
s
2
1
1
3
4
2
9
1
.
[
1
3
]
W
.
T
a
y
l
o
r
,
K
.
D
a
s
h
t
i
p
o
u
r
,
S
.
A
.
S
h
a
h
,
A
.
H
u
ssa
i
n
,
Q
.
H
.
A
b
b
a
si
,
a
n
d
M
.
A
.
I
mr
a
n
,
“
R
a
d
a
r
se
n
s
i
n
g
f
o
r
a
c
t
i
v
i
t
y
c
l
a
ssi
f
i
c
a
t
i
o
n
i
n
e
l
d
e
r
l
y
p
e
o
p
l
e
e
x
p
l
o
i
t
i
n
g
mi
c
r
o
-
d
o
p
p
l
e
r
s
i
g
n
a
t
u
r
e
s
u
s
i
n
g
mac
h
i
n
e
l
e
a
r
n
i
n
g
,
”
S
e
n
so
rs
,
v
o
l
.
2
1
,
n
o
.
1
1
,
p
p
.
1
–
1
5
,
2
0
2
1
,
d
o
i
:
1
0
.
3
3
9
0
/
s
2
1
1
1
3
8
8
1
.
[
1
4
]
R
.
U
l
l
a
h
,
Y
.
D
o
n
g
,
T.
A
r
sl
a
n
,
a
n
d
S
.
C
h
a
n
d
r
a
n
,
“
A
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
-
b
a
sed
c
l
a
s
si
f
i
c
a
t
i
o
n
met
h
o
d
f
o
r
mo
n
i
t
o
r
i
n
g
A
l
z
h
e
i
mer’
s
d
i
s
e
a
se
u
s
i
n
g
e
l
e
c
t
r
o
m
a
g
n
e
t
i
c
r
a
d
a
r
d
a
t
a
,
”
I
EE
E
T
r
a
n
s
a
c
t
i
o
n
s
o
n
M
i
c
r
o
w
a
v
e
T
h
e
o
ry
a
n
d
T
e
c
h
n
i
q
u
e
s
,
v
o
l
.
7
1
,
n
o
.
9
,
p
p
.
4
0
1
2
–
4
0
2
6
,
2
0
2
3
,
d
o
i
:
1
0
.
1
1
0
9
/
T
M
TT
.
2
0
2
3
.
3
2
4
5
6
6
5
.
[
1
5
]
G
.
L
i
u
,
Y
.
Li
u
,
S
.
F
a
n
,
W
.
C
u
i
,
K
.
X
i
a
,
a
n
d
L.
W
a
n
g
,
“
W
e
a
r
a
b
l
e
g
e
st
u
r
e
c
o
n
t
r
o
l
d
e
s
i
g
n
f
o
r
u
n
m
a
n
n
e
d
a
e
r
i
a
l
v
e
h
i
c
l
e
b
a
s
e
d
o
n
mu
l
t
i
-
se
n
s
o
r
f
u
si
o
n
,
”
Ro
b
o
t
i
c
a
,
v
o
l
.
4
3
,
n
o
.
3
,
p
p
.
8
1
6
–
8
5
0
,
2
0
2
4
,
d
o
i
:
1
0
.
1
0
1
7
/
S
0
2
6
3
5
7
4
7
2
4
0
0
2
1
9
4
.
[
1
6
]
S.
-
Y
.
S
h
i
n
,
Y
.
-
W
.
K
a
n
g
,
a
n
d
Y
.
-
G
.
K
i
m,
“
H
a
n
d
G
e
s
t
u
r
e
-
b
a
se
d
w
e
a
r
a
b
l
e
h
u
ma
n
-
d
r
o
n
e
i
n
t
e
r
f
a
c
e
f
o
r
i
n
t
u
i
t
i
v
e
m
o
v
e
m
e
n
t
c
o
n
t
r
o
l
,
”
i
n
2
0
1
9
I
EEE
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
C
o
n
su
m
e
r E
l
e
c
t
r
o
n
i
c
s
(
I
C
C
E)
,
2
0
1
9
,
p
p
.
1
–
6
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
C
E.
2
0
1
9
.
8
6
6
2
1
0
6
.
[
1
7
]
Te
x
a
s
I
n
st
r
u
m
e
n
t
s,
“
I
W
R
6
8
4
3
A
O
P
S
i
n
g
l
e
-
C
h
i
p
6
0
G
H
z
mm
W
a
v
e
se
n
so
r
,
”
D
a
t
a
s
h
e
e
t
,
2
0
2
3
.
A
c
c
e
ss
e
d
:
Ju
n
.
1
,
2
0
2
5
.
[
O
n
l
i
n
e
]
.
A
v
a
i
l
a
b
l
e
:
h
t
t
p
s:
/
/
w
w
w
.
t
i
.
c
o
m
/
l
i
t
/
d
s/
s
y
m
l
i
n
k
/
i
w
r6
8
4
3
a
o
p
.
p
d
f
[
1
8
]
D
.
P
o
l
j
a
k
a
n
d
S
.
A
n
t
o
n
i
j
e
v
i
c
,
“
S
i
mp
l
e
m
e
a
s
u
r
e
f
o
r
p
o
w
e
r
t
r
a
n
sf
e
r
e
f
f
i
c
i
e
n
c
y
o
f
c
o
u
p
l
e
d
d
i
p
o
l
e
a
n
t
e
n
n
a
s
b
a
se
d
o
n
r
o
o
t
-
m
e
a
n
sq
u
a
r
e
v
a
l
u
e
o
f
t
r
a
n
s
i
e
n
t
c
u
r
r
e
n
t
,
”
E
n
g
i
n
e
e
ri
n
g
a
n
a
l
y
si
s
w
i
t
h
b
o
u
n
d
a
ry
e
l
e
m
e
n
t
s
,
v
o
l
.
1
4
8
,
p
p
.
1
–
1
4
,
2
0
2
3
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
n
g
a
n
a
b
o
u
n
d
.
2
0
2
2
.
1
2
.
0
0
9
.
[
1
9
]
P
.
S
i
n
g
h
,
T.
H
a
s
i
j
a
,
a
n
d
K
.
R
a
m
k
u
m
a
r
,
“
M
a
l
w
a
r
e
c
l
a
ssi
f
i
c
a
t
i
o
n
t
o
st
r
e
n
g
t
h
e
n
i
n
g
d
i
g
i
t
a
l
r
e
s
i
l
i
e
n
c
e
:
C
o
m
p
a
r
i
n
g
S
V
M
k
e
r
n
e
l
a
n
d
l
o
g
i
s
t
i
c
r
e
g
r
e
ss
i
o
n
,
”
i
n
Pro
c
e
e
d
i
n
g
s
o
f
t
h
e
3
r
d
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
A
p
p
l
i
e
d
Art
i
f
i
c
i
a
l
I
n
t
e
l
l
i
g
e
n
c
e
a
n
d
C
o
m
p
u
t
i
n
g
(
I
C
AAIC)
,
2
0
2
4
,
p
p
.
1
4
6
1
–
1
4
6
6
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
A
A
I
C
6
0
2
2
2
.
2
0
2
4
.
1
0
5
7
5
7
3
6
.
[
2
0
]
Z.
X
i
a
a
n
d
M
.
W
u
,
“
R
a
d
a
r
a
u
t
o
ma
t
i
c
t
a
r
g
e
t
o
p
e
n
s
e
t
r
e
c
o
g
n
i
t
i
o
n
b
a
s
e
d
o
n
c
l
u
st
e
r
b
o
u
n
d
a
r
y
d
e
t
e
c
t
i
o
n
,
”
I
EEE
Ac
c
e
ss
,
v
o
l
.
1
4
,
p
p
.
7
3
6
5
–
7
3
7
8
,
2
0
2
6
,
d
o
i
:
1
0
.
1
1
0
9
/
A
C
C
ESS
.
2
0
2
6
.
3
6
5
2
2
3
8
.
[
2
1
]
S
.
M
i
s
h
r
a
,
H
.
D
a
s,
S
.
K
.
M
o
h
a
p
a
t
r
a
,
S
.
B
.
K
h
a
n
,
M
.
A
l
o
j
a
i
l
,
a
n
d
M
.
S
a
r
a
e
e
,
“
A
h
y
b
r
i
d
f
u
se
d
-
K
N
N
b
a
se
d
i
n
t
e
l
l
i
g
e
n
t
mo
d
e
l
t
o
a
c
c
e
ss
me
l
a
n
o
m
a
d
i
se
a
se
r
i
s
k
u
s
i
n
g
i
n
d
o
o
r
p
o
s
i
t
i
o
n
i
n
g
s
y
st
e
m,”
S
c
i
e
n
t
i
f
i
c
Re
p
o
rt
s
,
v
o
l
.
1
5
,
n
o
.
1
,
p
p
.
1
–
2
2
,
2
0
2
5
,
d
o
i
:
1
0
.
1
0
3
8
/
s
4
1
5
9
8
-
0
2
4
-
7
4
8
4
7
-
x.
[
2
2
]
Y
.
G
o
n
g
a
n
d
X
.
S
h
e
n
,
“
A
n
a
l
g
o
r
i
t
h
m
f
o
r
d
i
st
r
a
c
t
e
d
d
r
i
v
i
n
g
r
e
c
o
g
n
i
t
i
o
n
b
a
s
e
d
o
n
p
o
se
f
e
a
t
u
r
e
s
a
n
d
a
n
i
mp
r
o
v
e
d
K
N
N
,
”
El
e
c
t
r
o
n
i
c
s
(
S
w
i
t
zer
l
a
n
d
)
,
v
o
l
.
1
3
,
n
o
.
9
,
p
p
.
1
–
1
5
,
2
0
2
4
,
d
o
i
:
1
0
.
3
3
9
0
/
e
l
e
c
t
r
o
n
i
c
s
1
3
0
9
1
6
2
2
.
[
2
3
]
T.
D
a
n
i
y
a
,
M
.
G
e
e
t
h
a
,
a
n
d
K
.
S
.
K
u
mar,
“
C
l
a
ss
i
f
i
c
a
t
i
o
n
a
n
d
r
e
g
r
e
ssi
o
n
t
r
e
e
s
w
i
t
h
G
i
n
i
i
n
d
e
x
,
”
Ad
v
a
n
c
e
s
i
n
M
a
t
h
e
m
a
t
i
c
s:
S
c
i
e
n
t
i
f
i
c
J
o
u
r
n
a
l
,
v
o
l
.
9
,
n
o
.
1
0
,
p
p
.
8
2
3
7
–
8
2
4
7
,
2
0
2
0
,
d
o
i
:
1
0
.
3
7
4
1
8
/
a
msj
.
9
.
1
0
.
5
3
.
[
2
4
]
J.
S
h
a
f
e
-
P
u
r
c
e
l
l
a
n
d
A
.
D
.
S
l
e
p
k
o
v
,
“
B
o
o
st
e
d
d
e
c
i
si
o
n
t
r
e
e
s
f
o
r
n
o
n
-
r
e
so
n
a
n
t
b
a
c
k
g
r
o
u
n
d
r
e
m
o
v
a
l
i
n
h
y
p
e
r
sp
e
c
t
r
a
l
C
A
R
S
mi
c
r
o
sc
o
p
y
,
”
J
o
u
rn
a
l
o
f
Ph
y
si
c
s:
P
h
o
t
o
n
i
c
s
,
v
o
l
.
7
,
n
o
.
3
,
p
p
.
0
–
1
7
,
2
0
2
5
,
d
o
i
:
1
0
.
1
0
8
8
/
2
5
1
5
-
7
6
4
7
/
a
d
e
c
2
8
.
[
2
5
]
Y
.
Li
a
n
,
Y
.
X
u
,
L
.
H
u
,
Y
.
W
e
i
,
a
n
d
Z
.
W
a
n
g
,
“
M
a
c
h
i
n
e
l
e
a
r
n
i
n
g
-
b
a
s
e
d
b
r
a
i
n
ma
g
n
e
t
i
c
r
e
so
n
a
n
c
e
i
m
a
g
i
n
g
r
a
d
i
o
m
i
c
s
f
o
r
i
d
e
n
t
i
f
y
i
n
g
r
a
p
i
d
e
y
e
m
o
v
e
me
n
t
s
l
e
e
p
b
e
h
a
v
i
o
r
d
i
s
o
r
d
e
r
i
n
P
a
r
k
i
n
so
n
’
s
d
i
sea
se
p
a
t
i
e
n
t
s,”
B
MC
M
e
d
i
c
a
l
I
m
a
g
i
n
g
,
v
o
l
.
2
5
,
n
o
.
1
,
p
p
.
1
–
1
1
,
2
0
2
5
,
d
o
i
:
1
0
.
1
1
8
6
/
s
1
2
8
8
0
-
0
2
5
-
0
1
7
4
8
-
4.
[
2
6
]
Y
.
E.
P
r
a
w
a
t
y
a
,
N
.
H
.
D
j
a
n
g
g
u
,
R
.
R
a
h
ma
h
w
a
t
i
,
a
n
d
S
.
Lo
u
r
e
n
si
u
s,
“
U
t
i
l
i
z
i
n
g
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
f
o
r
p
r
e
d
i
c
t
i
v
e
m
a
i
n
t
e
n
a
n
c
e
o
f
p
r
o
d
u
c
t
i
o
n
mac
h
i
n
e
r
y
i
n
sm
a
l
l
a
n
d
m
e
d
i
u
m
e
n
t
e
r
p
r
i
s
e
s,
”
O
P
S
I
,
v
o
l
.
1
8
,
n
o
.
1
,
p
p
.
9
1
–
1
0
0
,
2
0
2
5
,
d
o
i
:
1
0
.
3
1
3
1
5
/
o
p
si
.
v
1
8
i
1
.
1
3
4
7
9
.
[
2
7
]
M
.
H
e
y
d
a
r
i
a
n
,
T.
E.
D
o
y
l
e
,
a
n
d
R
.
S
a
ma
v
i
,
“
M
L
C
M
:
mu
l
t
i
-
l
a
b
e
l
c
o
n
f
u
s
i
o
n
M
a
t
r
i
x
,
”
I
EE
E
Ac
c
e
ss
,
v
o
l
.
1
0
,
p
p
.
1
9
0
8
3
–
1
9
0
9
5
,
2
0
2
2
,
d
o
i
:
1
0
.
1
1
0
9
/
A
C
C
ESS
.
2
0
2
2
.
3
1
5
1
0
4
8
.
[
2
8
]
D
.
V
a
l
e
r
o
-
C
a
r
r
e
r
a
s,
J.
A
l
c
a
r
a
z
,
a
n
d
M
.
L
a
n
d
e
t
e
,
“
C
o
m
p
a
r
i
n
g
t
w
o
S
V
M
mo
d
e
l
s
t
h
r
o
u
g
h
d
i
f
f
e
r
e
n
t
met
r
i
c
s
b
a
s
e
d
o
n
t
h
e
c
o
n
f
u
s
i
o
n
mat
r
i
x
,
”
C
o
m
p
u
t
e
rs
a
n
d
O
p
e
r
a
t
i
o
n
s
R
e
se
a
rc
h
,
v
o
l
.
1
5
2
,
n
o
.
A
p
r
i
l
2
0
2
2
,
p
.
1
0
6
1
3
1
,
2
0
2
3
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
c
o
r
.
2
0
2
2
.
1
0
6
1
3
1
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
De
n
n
y
De
r
m
a
w
a
n
re
c
e
iv
e
d
th
e
M
.
E
n
g
.
d
e
g
re
e
in
e
lec
tri
c
a
l
e
n
g
i
n
e
e
rin
g
fr
o
m
Un
iv
e
rsitas
G
a
d
jah
M
a
d
a
(UG
M
),
In
d
o
n
e
sia
,
a
n
d
t
h
e
b
a
c
h
e
l
o
r’s
d
e
g
re
e
s
i
n
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
fr
o
m
S
e
k
o
lah
Ti
n
g
g
i
Tek
n
o
lo
g
i
Na
sio
n
a
l
(
S
TT
Na
s),
In
d
o
n
e
sia
.
He
is
c
u
rre
n
tl
y
th
e
h
e
a
d
,
lec
tu
re
r,
a
n
d
re
se
a
rc
h
e
r
a
t
th
e
De
p
a
rtme
n
t
o
f
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
,
In
sti
tu
t
Tek
n
o
l
o
g
i
Dirg
a
n
tara
Ad
is
u
tj
i
p
to
,
Yo
g
y
a
k
a
rta,
wh
e
re
h
e
a
lso
se
rv
e
s
a
s
p
ri
n
c
ip
a
l
in
v
e
stig
a
t
o
r
i
n
se
v
e
ra
l
g
o
v
e
r
n
m
e
n
t
-
fu
n
d
e
d
re
se
a
rc
h
p
ro
g
ra
m
s.
His
c
u
rre
n
t
re
se
a
rc
h
in
t
e
re
sts
in
c
lu
d
e
c
o
n
tro
l
a
n
d
sy
ste
m
s
e
n
g
in
e
e
rin
g
,
e
lec
tri
c
a
l
a
n
d
e
lec
tro
n
ic
e
n
g
i
n
e
e
rin
g
,
a
n
d
h
a
rd
wa
re
a
n
d
a
rc
h
i
tec
tu
re
.
He
c
a
n
b
e
c
o
n
tac
ted
v
ia em
a
il
:
d
e
n
n
y
d
e
rm
a
wa
n
@itd
a
.
a
c
.
id
Fre
d
d
y
K
u
r
n
ia
w
a
n
re
c
e
iv
e
d
th
e
m
a
ste
r’s
d
e
g
re
e
a
n
d
b
a
c
h
e
lo
r’s
d
e
g
re
e
s
in
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
fro
m
Un
i
v
e
rsitas
Ga
d
jah
M
a
d
a
(UG
M
),
I
n
d
o
n
e
sia
.
He
is
c
u
rre
n
tl
y
a
se
n
io
r
lec
t
u
re
r
a
n
d
re
se
a
rc
h
e
r
a
t
th
e
De
p
a
rtme
n
t
o
f
El
e
c
tri
c
a
l
E
n
g
in
e
e
rin
g
,
I
n
stit
u
t
Te
k
n
o
lo
g
i
Dirg
a
n
tara
Ad
isu
t
ji
p
t
o
(IT
DA
),
Yo
g
y
a
k
a
rta,
wh
e
re
h
e
a
lso
se
rv
e
s
a
s
p
rin
c
ip
a
l
i
n
v
e
stig
a
t
o
r
i
n
se
v
e
ra
l
g
o
v
e
r
n
m
e
n
t
-
fu
n
d
e
d
re
se
a
rc
h
p
r
o
g
ra
m
s.
His
c
u
rre
n
t
re
se
a
rc
h
in
tere
sts
i
n
c
lu
d
e
c
o
n
tr
o
l
a
n
d
sy
ste
m
s
e
n
g
in
e
e
ri
n
g
,
e
lec
tri
c
a
l
a
n
d
e
lec
tro
n
ic
e
n
g
in
e
e
rin
g
.
He
c
a
n
b
e
c
o
n
tac
ted
v
ia
e
m
a
il
:
fre
d
d
y
k
u
r
n
iaw
a
n
@itd
a
.
a
c
.
i
d
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
R
a
d
a
r
-
b
a
s
ed
g
estu
r
e
r
ec
o
g
n
i
tio
n
s
imu
la
tio
n
fo
r
u
n
ma
n
n
ed
a
eria
l v
eh
icles
…
(
Den
n
y
Derma
w
a
n
)
1235
Ye
n
n
i
As
tu
ti
re
c
e
iv
e
d
t
h
e
d
o
c
t
o
ra
l
d
e
g
re
e
,
m
a
ste
r’s
d
e
g
re
e
,
a
n
d
b
a
c
h
e
lo
r’s d
e
g
re
e
in
e
lec
tri
c
a
l
e
n
g
in
e
e
ri
n
g
fro
m
U
n
iv
e
rsitas
G
a
d
jah
M
a
d
a
(UG
M
),
I
n
d
o
n
e
sia
.
S
h
e
is
c
u
rre
n
t
ly
a
se
n
io
r
lec
t
u
re
r
a
n
d
re
se
a
rc
h
e
r
a
t
th
e
De
p
a
rtme
n
t
o
f
El
e
c
tri
c
a
l
E
n
g
in
e
e
rin
g
,
I
n
stit
u
t
Te
k
n
o
lo
g
i
Dirg
a
n
tara
Ad
is
u
tj
i
p
to
(IT
DA
),
Yo
g
y
a
k
a
rta.
He
r
c
u
rre
n
t
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
si
g
n
a
l
p
ro
c
e
ss
in
g
,
m
a
c
h
i
n
e
lea
rn
in
g
,
a
n
d
tele
c
o
m
m
u
n
ica
ti
o
n
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
v
ia
e
m
a
il
:
y
e
n
n
ias
t
u
ti
@it
d
a
.
a
c
.
id
.
Pa
u
lu
s
S
e
ti
a
wa
n
re
c
e
iv
e
d
t
h
e
m
a
ste
r’s
d
e
g
re
e
i
n
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
fro
m
Un
iv
e
rsitas
G
a
d
jah
M
a
d
a
(U
G
M
),
In
d
o
n
e
sia
a
n
d
b
a
c
h
e
l
o
r’s
d
e
g
r
e
e
in
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
fro
m
Un
i
v
e
rsitas
Tri
sa
k
ti
,
In
d
o
n
e
sia
.
He
is
c
u
rre
n
tl
y
th
e
se
c
re
tary
,
lec
tu
re
r,
a
n
d
re
se
a
rc
h
e
r
a
t
th
e
De
p
a
rtme
n
t
o
f
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
,
In
stit
u
t
Tek
n
o
l
o
g
i
Dir
g
a
n
tara
Ad
is
u
tj
i
p
to
(IT
DA
)
,
Yo
g
y
a
k
a
rta.
His
c
u
rre
n
t
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
c
o
n
tro
l
a
n
d
sy
s
tem
s
e
n
g
in
e
e
rin
g
,
e
lec
tri
c
a
l
a
n
d
e
lec
tro
n
ic en
g
i
n
e
e
rin
g
.
He
c
a
n
b
e
c
o
n
tac
ted
v
ia em
a
il
:
p
a
u
l
u
ss
e
ti
a
wa
n
@itd
a
.
a
c
.
id
.
La
sm
a
d
i
re
c
e
iv
e
d
th
e
m
a
ste
r’s
d
e
g
re
e
a
n
d
b
a
c
h
e
lo
r’s
d
e
g
re
e
in
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
fro
m
Un
i
v
e
rsitas
G
a
d
jah
M
a
d
a
(UG
M
),
In
d
o
n
e
sia
.
He
is
c
u
rre
n
tl
y
a
lec
tu
re
r
a
n
d
re
se
a
rc
h
e
r
a
t
th
e
De
p
a
rtme
n
t
o
f
El
e
c
tri
c
a
l
En
g
i
n
e
e
rin
g
,
I
n
st
it
u
t
Tek
n
o
l
o
g
i
Dir
g
a
n
tara
Ad
isu
tj
i
p
to
(IT
DA
),
Yo
g
y
a
k
a
rta.
His
c
u
rre
n
t
re
se
a
rc
h
i
n
tere
sts
i
n
c
lu
d
e
c
o
n
tro
l
a
n
d
sy
ste
m
s
e
n
g
in
e
e
rin
g
,
e
lec
tri
c
a
l
a
n
d
e
l
e
c
tro
n
ic
e
n
g
i
n
e
e
rin
g
.
He
c
a
n
b
e
c
o
n
tac
ted
v
ia
e
m
a
il
:
las
m
a
d
i@it
d
a
.
a
c
.
id
.
Uy
u
u
n
u
l
Ma
u
i
d
z
o
h
re
c
e
iv
e
d
th
e
m
a
ste
r’s
d
e
g
re
e
i
n
in
d
u
strial
e
n
g
in
e
e
rin
g
fr
o
m
In
stit
u
t
Tek
n
o
l
o
g
i
S
e
p
u
l
u
h
No
v
e
m
b
e
r
(IT
S
),
I
n
d
o
n
e
sia
a
n
d
b
a
c
h
e
lo
r’s
d
e
g
re
e
in
i
n
d
u
strial
e
n
g
in
e
e
rin
g
fro
m
Un
iv
e
rsitas
P
e
m
b
a
n
g
u
n
a
n
Na
sio
n
a
l
"
Ve
tera
n
"
Ja
wa
Ti
m
u
r
(UPNVJ
),
In
d
o
n
e
sia
.
S
h
e
is
c
u
rre
n
tl
y
t
h
e
H
e
a
d
o
f
Lab
o
ra
to
r
y
Di
v
isio
n
fo
r
P
e
rfo
rm
a
n
c
e
a
n
d
Erg
o
n
o
m
ics
,
a
se
n
io
r
lec
tu
re
r,
a
n
d
re
se
a
rc
h
e
r
a
t
th
e
De
p
a
rtme
n
t
o
f
I
n
d
u
strial
E
n
g
in
e
e
rin
g
,
I
n
stit
u
t
Tek
n
o
l
o
g
i
Dirg
a
n
tara
Ad
isu
t
ji
p
t
o
(IT
DA
),
Yo
g
y
a
k
a
rta.
He
r
c
u
rre
n
t
re
se
a
rc
h
in
tere
sts
in
c
l
u
d
e
i
n
d
u
strial
a
n
d
m
a
n
u
fa
c
tu
ri
n
g
e
n
g
i
n
e
e
rin
g
,
sa
fe
ty
,
risk
,
re
li
a
b
il
it
y
a
n
d
q
u
a
li
t
y
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
v
ia
e
m
a
il
:
u
y
u
u
n
u
l@it
d
a
.
a
c
.
id
.
Ba
m
b
a
n
g
S
u
d
ib
y
a
re
c
e
iv
e
d
th
e
m
a
ste
r’s
d
e
g
re
e
in
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
fr
o
m
In
stit
u
t
Tek
n
o
lo
g
i
S
e
p
u
lu
h
No
p
e
m
b
e
r
(IT
S
),
In
d
o
n
e
sia
a
n
d
b
a
c
h
e
lo
r’s
d
e
g
re
e
i
n
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
fr
o
m
In
st
it
u
t
Tek
n
o
l
o
g
i
M
e
d
a
n
,
In
d
o
n
e
sia
.
He
is
c
u
r
re
n
tl
y
a
se
n
io
r
lec
tu
re
r
a
n
d
re
se
a
rc
h
e
r
a
t
th
e
De
p
a
rtme
n
t
o
f
El
e
c
tri
c
a
l
En
g
i
n
e
e
rin
g
,
I
n
st
it
u
t
Tek
n
o
l
o
g
i
Dir
g
a
n
tara
Ad
isu
tj
i
p
to
(I
TDA),
Yo
g
y
a
k
a
rta.
His
c
u
rre
n
t
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
.
He
c
a
n
b
e
c
o
n
tac
ted
v
ia em
a
il
:
su
d
ib
y
a
.
stta@
g
m
a
il
.
c
o
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.