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lec
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n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
E
n
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p
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…
(
R
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ma
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La
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)
215
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c
t
i
o
n
2
.
S
e
c
t
i
o
n
3
d
i
s
c
u
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s
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t
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p
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b
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i
n
s
e
ct
i
o
n
4
.
T
h
e
c
o
n
c
l
u
s
i
o
n
is
d
is
c
u
s
s
e
d
in
s
e
c
ti
o
n
5
.
2.
L
I
T
E
R
AT
U
RE
SU
RVE
Y
T
o
o
p
tim
ize
th
e
s
u
r
g
ical
r
o
u
te
wh
ile
s
till
s
atis
f
y
in
g
all
o
f
th
e
cr
iter
ia
o
f
th
e
p
r
o
ce
d
u
r
e
an
d
av
o
id
in
g
an
y
p
o
te
n
tially
d
an
g
e
r
o
u
s
lo
ca
tio
n
s
,
m
an
y
au
t
o
m
ated
s
u
r
g
ical
p
ath
p
lan
n
i
n
g
ap
p
r
o
ac
h
es h
a
v
e
b
ee
n
s
u
g
g
ested
.
T
h
e
s
u
r
g
ical
c
o
u
r
s
e
o
f
h
ar
d
d
ev
ices,
s
u
ch
as
p
u
n
ctu
r
e
n
e
ed
les,
f
lex
ib
le
in
s
tr
u
m
e
n
ts
,
s
u
ch
as
ca
th
eter
s
an
d
n
ee
d
les,
o
r
m
ed
ical
r
o
b
o
ts
m
ay
b
e
p
lan
n
e
d
to
u
s
e
th
ese
ap
p
r
o
ac
h
es
[
1
]
.
W
h
en
d
ep
l
o
y
ed
as
au
to
n
o
m
o
u
s
s
y
s
tem
s
in
u
n
f
am
iliar
ar
ea
s
,
p
ath
p
lan
n
in
g
b
ec
o
m
es e
v
en
m
o
r
e
im
p
o
r
tan
t f
o
r
co
n
tin
u
u
m
r
o
b
o
ts
.
I
n
s
u
c
h
ca
s
es,
wh
ile
p
lo
ttin
g
a
r
o
u
te,
it'
s
im
p
o
r
tan
t
to
th
i
n
k
a
b
o
u
t
m
o
r
e
th
a
n
ju
s
t
th
e
b
eg
i
n
n
in
g
an
d
en
d
i
n
g
p
o
in
ts
;
y
o
u
als
o
n
ee
d
to
th
in
k
ab
o
u
t
th
e
cu
r
r
en
t
en
v
ir
o
n
m
e
n
tal
cir
cu
m
s
tan
ce
s
an
d
th
e
s
y
s
tem
's
s
tatu
s
[
2
]
.
Hav
in
g
a
r
o
b
o
t
lead
th
e
p
r
o
b
e
is
an
o
th
e
r
ad
d
-
o
n
t
h
at
lets
y
o
u
au
t
o
m
ate
th
e
wh
o
le
p
r
o
ce
s
s
.
E
v
en
th
o
u
g
h
n
o
f
u
lly
au
to
n
o
m
o
u
s
s
y
s
tem
is
n
o
w
ac
ce
s
s
ib
le,
th
er
e
ar
e
s
ev
er
al
r
o
b
o
tic
u
ltra
s
o
u
n
d
s
y
s
tem
s
d
etailed
in
th
e
liter
atu
r
e
f
o
r
u
s
e
in
v
ar
io
u
s
s
o
f
t
tis
s
u
e
d
iag
n
o
s
tic
ap
p
licatio
n
s
[
3
]
.
On
e
d
ef
in
it
io
n
o
f
p
ath
p
lan
n
in
g
is
th
e
p
r
o
ce
s
s
b
y
wh
ich
a
m
o
b
ile
ag
en
t f
in
d
s
a
m
eth
o
d
t
o
g
et
f
r
o
m
o
n
e
lo
ca
tio
n
to
an
o
th
er
in
th
e
s
tate
s
p
ac
e.
T
h
e
m
o
b
ile
ag
en
t'
s
v
o
lu
m
e
an
d
lo
ca
tio
n
p
o
s
e
ar
e
im
p
o
r
tan
t
f
ac
to
r
s
to
co
n
s
id
er
in
en
g
in
ee
r
in
g
ap
p
licatio
n
s
.
As
an
ex
am
p
le
o
f
a
wo
r
k
p
lace
,
we
m
ay
th
in
k
o
f
th
e
s
tate
s
p
ac
e
wh
er
e
th
e
m
o
b
ile
ag
en
t'
s
v
o
lu
m
e
an
d
p
o
s
itio
n
p
o
s
e
ar
e
r
elev
an
t
f
ac
to
r
s
[
4
]
.
T
h
er
e
ar
e
th
r
ee
m
ain
t
y
p
es
o
f
p
ath
-
p
lan
n
i
n
g
alg
o
r
ith
m
s
f
o
r
f
lex
i
b
le
n
ee
d
le
p
u
n
ctu
r
e:
n
u
m
er
ical,
in
v
er
s
e
s
o
lu
tio
n
,
an
d
s
ea
r
c
h
m
eth
o
d
.
O
n
e
d
r
awb
ac
k
o
f
th
ese
alg
o
r
ith
m
s
is
th
at
th
e
y
ar
e
n
o
t v
er
y
r
ea
l
-
tim
e
an
d
o
n
ly
aim
to
r
e
d
u
ce
th
e
p
ath
len
g
th
o
r
tim
e,
with
o
u
t
g
u
a
r
a
n
teein
g
th
at
th
e
p
ath
s
o
b
tain
ed
ar
e
o
p
tim
al
[
5
]
.
Plan
n
in
g
a
r
o
b
o
t'
s
r
o
u
te
in
s
u
ch
a
way
th
at
it
ef
f
icien
tly
an
d
m
eth
o
d
ically
co
v
e
r
s
all
it
s
t
ar
g
et
ar
ea
is
k
n
o
wn
as
co
v
er
ag
e
p
ath
p
lan
n
in
g
.
W
h
en
co
m
p
lete
co
v
er
i
n
g
o
f
an
ar
ea
is
r
eq
u
ir
ed
,
s
u
c
h
a
s
in
en
v
ir
o
n
m
e
n
tal
m
o
n
ito
r
in
g
o
r
clea
n
in
g
,
th
is
i
s
o
f
ten
u
s
ed
[
6
]
.
C
o
m
p
lete
c
o
v
er
ag
e
p
ath
p
lan
n
in
g
(
C
C
PP
)
is
cr
u
cial
f
o
r
an
au
to
n
o
m
o
u
s
u
n
d
er
wate
r
h
elic
o
p
ter
(
AUH)
in
th
e
n
ea
r
-
b
o
tto
m
r
eg
io
n
s
wee
p
in
u
n
k
n
o
wn
c
o
n
d
itio
n
s
,
wh
ich
is
a
co
m
m
o
n
ap
p
licatio
n
s
itu
atio
n
.
W
e
p
r
o
v
id
e
a
c
o
m
p
r
eh
en
s
iv
e
co
v
er
ag
e
r
o
u
te
p
lan
n
in
g
m
eth
o
d
f
o
r
AUH
u
s
in
g
a
s
in
g
le
b
ea
m
ec
h
o
s
o
u
n
d
er
[
7
]
.
T
h
is
m
eth
o
d
in
co
r
p
o
r
ates
b
o
th
th
e
in
itial
p
ath
p
lan
n
in
g
an
d
an
o
n
lin
e
lo
ca
l
co
llis
io
n
av
o
id
a
n
ce
m
ec
h
an
is
m
.
R
o
b
o
t
r
o
u
te
p
la
n
n
in
g
an
d
co
n
tr
o
l
d
u
r
in
g
m
in
im
ally
in
v
asiv
e
p
r
o
ce
d
u
r
es
(
MI
Ps
)
is
v
er
y
d
em
an
d
in
g
.
Ob
s
tacle
s
an
d
d
an
g
er
o
u
s
lo
ca
tio
n
s
,
in
clu
d
in
g
ar
ea
s
o
f
ca
r
d
iac
ca
lcif
icatio
n
,
m
u
s
t
b
e
a
v
o
id
e
d
,
an
d
u
n
ex
p
ec
ted
co
n
tact
with
lu
m
en
walls
m
u
s
t
b
e
r
e
d
u
ce
d
[
8
]
.
R
esear
ch
er
s
in
th
e
f
ield
o
f
m
u
lti
-
r
o
b
o
t
co
o
p
er
atio
n
h
av
e
f
o
cu
s
ed
o
n
i
n
v
e
s
tig
atin
g
v
ar
io
u
s
r
o
u
te
p
la
n
n
i
n
g
s
tr
ateg
ies
to
let
m
an
y
r
o
b
o
ts
in
ter
ac
t
co
h
esiv
e
ly
an
d
av
o
id
cr
ash
es
[
9
]
.
On
e
in
n
o
v
ativ
e
ar
ea
o
f
s
tu
d
y
is
a
u
to
n
o
m
o
u
s
r
o
b
o
tic
s
u
r
g
er
y
,
wh
ich
s
ee
k
s
to
elim
in
ate
th
e
n
ee
d
f
o
r
h
u
m
an
s
u
r
g
eo
n
s
b
y
d
ev
el
o
p
in
g
an
d
im
p
lem
en
tin
g
r
o
b
o
tic
d
ev
ices
th
at
ca
n
ca
r
r
y
o
u
t
s
u
r
g
ical
o
p
er
atio
n
s
in
d
ep
en
d
e
n
tly
.
Su
r
g
ical
r
o
b
o
tic
d
ev
ice
s
with
au
to
n
o
m
o
u
s
c
o
n
tr
o
l
h
av
e
t
h
e
p
o
ten
tial
to
m
in
im
ize
tis
s
u
e
in
ju
r
y
,
p
e
r
f
o
r
m
clev
e
r
m
o
v
es,
an
d
in
cr
e
ase
ac
cu
r
ac
y
[
1
0
]
.
I
n
ter
v
en
tio
n
al
s
u
r
g
ical
r
o
b
o
t
s
h
av
e
b
ee
n
th
e
s
u
b
ject
o
f
m
an
y
attem
p
ts
to
m
an
i
p
u
late
th
o
s
e
u
s
in
g
r
ein
f
o
r
ce
m
e
n
t
lear
n
in
g
(
RL
)
ap
p
r
o
ac
h
es.
T
h
ese
in
clu
d
e
r
o
u
te
p
lan
n
in
g
,
s
u
r
g
ical
tr
a
in
in
g
s
y
s
tem
s
,
an
d
s
u
r
g
er
ies
in
v
o
lv
i
n
g
s
u
r
r
o
g
ate
s
u
r
g
eo
n
s
.
I
n
co
n
tr
ast
to
h
u
m
a
n
d
o
cto
r
s
,
wh
o
o
f
ten
n
ee
d
m
u
ltimo
d
al
in
p
u
t
to
ex
ec
u
te
th
eir
p
r
o
ce
d
u
r
es,
th
e
s
e
tech
n
iq
u
es
u
s
u
ally
d
e
p
en
d
o
n
s
in
g
le
-
m
o
d
al
s
u
r
g
ical
d
a
ta
[
1
1
]
.
I
t
is
u
s
u
a
l
p
r
ac
tice
to
u
s
e
b
asic
r
o
u
te
p
l
an
n
in
g
alg
o
r
ith
m
s
o
r
a
p
p
r
o
ac
h
es
f
o
r
a
v
o
id
i
n
g
o
b
s
tacle
s
.
Alth
o
u
g
h
th
ey
wo
r
k
well
in
co
n
tr
o
lled
en
v
ir
o
n
m
en
ts
,
th
ey
ar
en
'
t
ad
ap
tab
le
en
o
u
g
h
to
d
ea
l
with
d
if
f
er
e
n
t
an
d
u
n
f
o
r
eseen
s
itu
atio
n
s
.
A
p
o
ten
tial su
b
s
titu
te
th
at
h
as a
r
is
en
i
n
r
ec
en
t tim
es is
m
ac
h
in
e
lear
n
in
g
(
ML
)
[
1
2
]
.
W
h
en
s
en
d
in
g
item
s
to
d
is
tr
ib
u
tio
n
h
u
b
s
,
ce
r
tain
m
ajo
r
o
n
lin
e
r
etailer
s
m
ay
g
r
o
u
p
s
im
ilar
-
s
ized
co
n
tain
er
s
to
g
eth
e
r
.
Plan
n
in
g
tech
n
iq
u
es,
s
u
ch
a
h
ei
g
h
t
-
f
ir
s
t
s
elec
tio
n
ap
p
r
o
ac
h
o
r
a
s
elec
tio
n
m
eth
o
d
wit
h
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
9
,
No
.
1
,
Ju
ly
20
25
:
2
1
4
-
2
2
4
216
estab
l
is
h
ed
s
tack
p
atter
n
s
,
m
ak
e
it
ea
s
y
to
m
o
d
if
y
th
e
a
u
t
o
m
atio
n
in
t
h
ese
s
itu
atio
n
s
[
1
3
]
.
W
ith
in
a
g
i
v
en
s
p
ac
e
o
r
ch
am
b
er
,
p
ath
p
la
n
n
i
n
g
is
th
e
p
r
o
ce
s
s
o
f
f
ig
u
r
in
g
o
u
t
h
o
w
a
r
o
b
o
t
will
g
et
f
r
o
m
o
n
e
p
o
in
t
t
o
an
o
th
er
.
I
n
m
an
y
ca
s
es,
esp
ec
ially
in
co
n
f
in
ed
,
co
m
p
l
icate
d
,
an
d
in
te
r
io
r
s
p
ac
es,
m
o
r
e
o
p
tim
izatio
n
an
d
r
ef
in
em
en
t
o
f
th
e
r
o
b
o
t'
s
g
en
er
ated
r
o
u
te
is
r
eq
u
ir
e
d
[
1
4
]
.
C
o
m
p
u
tin
g
a
s
er
ies
o
f
v
alid
co
n
f
ig
u
r
atio
n
s
th
at
co
n
n
ec
ts
two
p
o
in
ts
,
o
n
e
at
th
e
b
e
g
in
n
in
g
a
n
d
o
n
e
at
th
e
e
n
d
,
with
o
u
t
co
llis
io
n
s
is
th
e
task
at
h
an
d
in
th
e
r
o
u
te
p
lan
n
in
g
is
s
u
e
f
o
r
m
o
b
ile
r
o
b
o
ts
.
W
h
ee
led
m
o
b
ile
r
o
b
o
ts
n
ee
d
o
b
s
tacle
av
o
id
a
n
ce
a
n
d
n
av
i
g
atio
n
ca
p
ab
ilit
ies
f
o
r
m
an
y
p
r
ac
tical
u
s
es
[
1
5
]
.
T
h
e
f
r
a
m
ewo
r
k
is
lik
e
a
b
ea
co
n
in
th
e
d
ar
k
h
u
m
a
n
-
r
o
b
o
t
in
ter
ac
tio
n
(
HR
I
)
w
o
r
ld
,
s
h
o
win
g
th
e
way
to
in
ter
ac
tio
n
s
th
at
ar
e
b
o
th
s
u
cc
ess
f
u
l
a
n
d
p
ain
less
.
C
ar
ef
u
l
tu
n
in
g
o
f
co
n
tr
o
l
s
tr
ateg
ies,
alg
o
r
ith
m
s
f
o
r
r
o
u
te
p
lan
n
i
n
g
,
an
d
m
eth
o
d
s
f
o
r
av
o
id
i
n
g
o
b
s
tacle
s
h
as b
ee
n
ca
r
r
ied
o
u
t [
1
6
]
.
T
o
av
o
id
im
p
ed
im
e
n
ts
an
d
r
o
b
o
t
c
o
llis
io
n
s
,
th
e
p
r
im
ar
y
g
o
al
o
f
r
o
u
te
p
lan
n
in
g
is
to
d
i
s
co
v
er
th
e
o
p
tim
al
p
ath
.
Saf
er
an
d
q
u
ick
er
n
av
ig
atio
n
is
ac
h
iev
ed
b
y
s
p
litt
in
g
th
e
co
n
tin
u
o
u
s
r
o
u
te
in
to
o
p
tim
u
m
s
u
b
-
p
ath
ed
g
es.
T
h
e
r
o
u
te
p
lan
n
i
n
g
alg
o
r
ith
m
s
m
ay
b
e
b
r
o
ad
l
y
class
if
ied
in
to
two
ty
p
es:
i
n
d
o
o
r
an
d
o
u
t
d
o
o
r
.
I
n
d
o
o
r
al
g
o
r
ith
m
s
a
r
e
u
s
ed
w
h
en
th
e
s
ize
an
d
c
o
m
p
lex
ity
o
f
th
e
ar
e
n
a
o
r
wo
r
k
p
lace
ar
e
k
n
o
wn
in
ad
v
an
ce
,
wh
er
ea
s
o
u
td
o
o
r
alg
o
r
ith
m
s
a
r
e
u
s
ed
wh
en
th
ese
d
etails
ar
e
u
n
k
n
o
wn
[
1
7
]
.
T
h
e
m
o
s
t
c
o
m
m
o
n
lo
ca
l
r
o
u
te
p
lan
n
in
g
alg
o
r
ith
m
s
n
o
w
in
u
s
e
ar
e
b
e
h
av
io
r
d
ec
o
m
p
o
s
it
io
n
(
BD
)
,
ca
s
ed
lear
n
in
g
(
CL
)
,
an
d
DW
A,
o
r
d
y
n
am
ic
win
d
o
w
ap
p
r
o
ac
h
.
T
o
co
m
p
lete
th
e
wh
o
le
m
o
v
in
g
jo
b
,
B
D
-
b
ased
alg
o
r
ith
m
s
b
r
ea
k
d
o
wn
t
h
e
r
o
u
te
p
lan
n
in
g
in
to
s
ep
ar
ate
p
ar
ts
,
ca
lled
b
eh
av
io
r
al
p
r
im
itiv
es
[
1
8
]
.
T
o
m
ak
e
s
am
p
le
-
b
ase
d
p
r
o
ce
d
u
r
es
m
o
r
e
ef
f
ec
tiv
e,
m
an
y
way
s
h
av
e
b
ee
n
s
u
g
g
ested
,
s
u
ch
as
m
u
lti
-
d
ir
ec
tio
n
al
s
ea
r
ch
s
am
p
lin
g
an
d
b
i
-
d
ir
ec
tio
n
al
s
ea
r
ch
s
am
p
lin
g
.
I
n
r
ea
l
-
wo
r
ld
s
ce
n
ar
io
s
,
it
is
d
if
f
icu
lt
t
o
c
o
n
n
ec
t
tr
ee
s
w
h
ile
y
et
a
d
h
er
i
n
g
to
t
h
e
d
i
f
f
er
en
tial
lim
itatio
n
s
o
f
r
o
b
o
t
d
y
n
am
ic
s
[
1
9
]
.
T
o
o
v
e
r
co
m
e
th
e
s
h
o
r
tco
m
in
g
s
o
f
ex
is
tin
g
an
y
tim
e
m
o
tio
n
p
la
n
n
in
g
m
eth
o
d
s
,
th
e
s
u
g
g
ested
alg
o
r
ith
m
tak
es
laten
cy
tim
e
an
d
th
e
ch
o
s
en
r
o
u
te
s
m
o
o
th
i
n
g
lev
el
in
to
ac
co
u
n
t
d
u
r
in
g
p
lan
n
i
n
g
m
o
tio
n
,
u
s
in
g
an
in
itial p
ath
an
d
an
o
n
lin
e
-
o
p
tim
ized
d
o
u
b
le
-
lay
er
s
tr
u
ctu
r
e
[
2
0
]
.
User
s
ca
n
'
t
g
et
a
b
etter
g
r
asp
o
f
th
in
g
s
lik
e
r
o
u
te
p
lan
n
i
n
g
alg
o
r
ith
m
s
,
an
d
ar
m
m
o
tio
n
s
,
with
o
u
t
s
im
u
lato
r
s
.
I
t
will
b
e
m
u
c
h
s
im
p
ler
to
u
n
d
er
s
tan
d
a
r
o
b
o
t's
ch
ar
ac
ter
is
tics
b
ef
o
r
e
u
s
in
g
it
in
r
ea
l
life
if
s
im
u
lato
r
s
ar
e
av
ailab
le.
On
ce
th
e
s
im
u
latio
n
is
s
u
cc
es
s
f
u
l,
th
e
n
ex
t
s
tep
is
to
d
esig
n
th
e
r
o
u
te
[
2
1
]
.
T
h
e
ab
ilit
y
o
f
th
e
a
u
to
n
o
m
o
u
s
r
o
b
o
t
to
d
o
its
m
is
s
io
n
with
litt
le
o
r
n
o
h
u
m
an
in
v
o
lv
em
e
n
t
is
its
d
ef
in
in
g
f
ea
tu
r
e.
I
n
ac
tiv
ities
th
at
p
eo
p
le
ar
e
u
n
ab
le,
u
n
willin
g
,
o
r
u
n
ab
le
t
o
ac
co
m
p
lis
h
,
s
u
ch
as
in
lo
ca
ti
o
n
s
with
a
h
ig
h
r
is
k
o
f
in
f
ec
tio
n
o
r
wh
e
r
e
d
is
ea
s
es sp
r
ea
d
q
u
ick
l
y
,
au
to
n
o
m
o
u
s
r
o
b
o
ts
m
ay
o
p
er
ate
alo
n
g
s
id
e
h
u
m
an
s
o
r
e
v
en
tak
e
th
eir
p
lace
[
2
2
]
.
Ma
n
y
m
eta
-
h
eu
r
is
tic
o
p
tim
izatio
n
m
eth
o
d
s
h
av
e
f
o
u
n
d
p
r
ac
tical
s
o
lu
tio
n
s
to
d
if
f
icu
lt
o
p
tim
izatio
n
is
s
u
es.
Mo
r
e
an
d
m
o
r
e
o
p
tim
izatio
n
alg
o
r
ith
m
s
,
b
o
th
s
im
p
le
an
d
ad
v
an
ce
d
,
h
av
e
b
ee
n
s
u
g
g
este
d
[
2
3
]
,
in
r
esp
o
n
s
e
to
th
e
m
et
a
-
h
eu
r
is
tic
alg
o
r
ith
m
s
'
g
r
o
win
g
p
o
p
u
lar
ity
in
s
o
lv
in
g
r
ea
l
-
wo
r
ld
o
p
tim
izatio
n
is
s
u
es.
Gr
ap
h
s
ea
r
ch
,
h
e
u
r
is
tic
o
p
tim
izatio
n
,
a
n
d
in
cr
em
en
t
al
s
ea
r
ch
ar
e
ju
s
t
a
f
ew
wa
y
s
th
at
r
o
u
te
p
lan
n
in
g
alg
o
r
ith
m
s
h
av
e
b
ee
n
tr
ad
itio
n
ally
ca
teg
o
r
ized
[
2
4
]
.
Fin
d
in
g
a
s
er
ies
o
f
s
tates
v
ia
s
ce
n
ar
i
o
s
th
at
ca
n
tr
a
n
s
p
o
r
t
th
in
g
s
f
r
o
m
th
eir
s
tar
tin
g
p
o
i
n
t
to
th
eir
d
esti
n
atio
n
w
h
ile
a
v
o
id
in
g
p
o
r
tio
n
s
o
f
th
e
s
ea
r
c
h
s
p
ac
e
th
at
a
r
e
n
o
t
ac
ce
s
s
ib
le
is
th
e
r
o
u
te
p
lan
n
in
g
is
s
u
e.
I
n
s
ev
er
al
co
n
tex
ts
,
p
a
th
p
lan
n
in
g
is
an
ess
en
tial step
[
2
5
]
.
3.
M
E
T
H
O
D
R
o
b
o
tic
au
to
n
o
m
o
u
s
s
u
r
g
er
y
h
as
r
ev
o
lu
tio
n
ized
m
e
d
ical
r
e
s
ea
r
ch
b
y
im
p
r
o
v
in
g
s
u
r
g
ical
ac
cu
r
ac
y
an
d
ef
f
icien
cy
.
Desp
ite
its
p
r
o
m
is
e,
m
ax
im
izin
g
r
esu
lts
an
d
ef
f
icien
c
y
is
d
if
f
icu
lt.
r
o
u
te
p
lan
n
in
g
,
wh
ic
h
d
eter
m
in
es
th
e
b
est
r
o
u
te
f
o
r
s
u
r
g
ical
eq
u
i
p
m
en
t
to
n
eg
o
tiat
e
co
m
p
licated
an
at
o
m
ical
s
tr
u
ctu
r
es,
is
cr
u
cial
to
r
o
b
o
tic
s
u
r
g
er
y
s
u
cc
ess
.
Fig
u
r
e
1
s
h
o
ws th
e
f
o
u
r
r
o
u
te
p
la
n
n
in
g
ty
p
es with
two
s
u
b
ca
te
g
o
r
i
es e
ac
h
.
Fig
u
r
e
1
.
C
u
r
r
e
n
t p
ath
p
lan
n
i
n
g
m
eth
o
d
s
ar
e
class
if
ied
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:
2502
-
4
7
5
2
E
n
h
a
n
ce
d
p
erfo
r
ma
n
ce
a
n
d
e
fficien
cy
o
f ro
b
o
tic
a
u
t
o
n
o
mo
u
s
p
r
o
ce
d
u
r
es
…
(
R
a
ma
n
La
th
a
)
217
T
h
is
r
esear
ch
ex
am
in
es
h
o
w
r
o
u
te
p
lan
n
in
g
alg
o
r
ith
m
s
im
p
r
o
v
e
r
o
b
o
tic
au
t
o
n
o
m
o
u
s
s
u
r
g
ical
o
u
tco
m
es
an
d
ef
f
icien
cy
v
ia
d
etailed
ex
am
in
atio
n
an
d
a
n
aly
s
is
.
T
h
is
p
r
o
ject
aim
s
t
o
ev
alu
ate
r
o
b
o
tic
au
to
n
o
m
o
u
s
s
u
r
g
er
y
r
o
u
te
p
la
n
n
in
g
alg
o
r
ith
m
s
an
d
d
eter
m
i
n
e
th
e
b
est
way
s
to
im
p
r
o
v
e
s
u
r
g
ical
r
esu
lts
.
T
h
e
r
esear
ch
ev
alu
ates
alg
o
r
ith
m
s
u
n
d
er
v
ar
io
u
s
s
u
r
g
ical
s
itu
at
io
n
s
an
d
an
ato
m
ical
d
if
f
icu
lti
es
to
id
en
tify
th
eir
s
tr
en
g
th
s
an
d
wea
k
n
ess
es
an
d
p
r
o
v
id
e
r
o
u
te
p
lan
n
in
g
s
u
g
g
esti
o
n
s
.
T
h
is
ca
teg
o
r
izatio
n
is
b
ased
o
n
r
o
u
te
co
n
s
tr
u
ctio
n
a
n
d
r
etu
r
n
m
et
h
o
d
s
.
An
ex
p
lan
a
tio
n
o
f
th
ese
ca
teg
o
r
ies an
d
wh
y
th
ey
ar
e
g
r
o
u
p
ed
th
is
way
.
T
h
is
s
tu
d
y
aim
s
to
p
r
o
m
o
te
r
o
b
o
tic
au
to
n
o
m
o
u
s
s
u
r
g
er
y
b
y
r
ev
ea
lin
g
h
o
w
r
o
u
te
p
lan
n
i
n
g
a
lg
o
r
ith
m
s
im
p
r
o
v
e
s
u
r
g
ical
r
esu
lts
an
d
e
f
f
icien
cy
.
T
o
c
r
ea
te
m
o
r
e
d
e
p
e
n
d
ab
le
a
n
d
e
f
f
icien
t
r
o
b
o
tic
s
u
r
g
ical
s
y
s
tem
s
,
th
e
r
esear
ch
ad
d
r
ess
es
d
if
f
icu
lties
an
d
id
e
n
tifie
s
o
p
p
o
r
tu
n
ities
f
o
r
im
p
r
o
v
em
en
t.
Path
p
l
an
n
in
g
alg
o
r
ith
m
s
p
r
o
v
id
e
th
e
m
o
s
t
ef
f
icien
t
a
n
d
s
af
e
s
u
r
g
ical
t
o
o
l
tr
ajec
to
r
ies
d
u
r
in
g
r
o
b
o
tic
au
to
n
o
m
o
u
s
s
u
r
g
er
y
.
T
h
ese
alg
o
r
ith
m
s
tr
av
er
s
e
co
m
p
licat
ed
an
ato
m
ical
s
y
s
tem
s
u
s
in
g
m
ath
em
atica
l
m
o
d
els
an
d
o
p
t
im
izatio
n
to
av
o
id
o
b
s
tacle
s
an
d
m
i
n
im
ize
tis
s
u
e
d
am
ag
e
.
T
h
e
r
o
u
te
c
o
o
r
d
i
n
ates
s
h
o
u
ld
b
e
en
e
r
g
y
-
ef
f
icien
t,
tim
e
-
ef
f
icien
t,
an
d
co
llis
io
n
-
f
r
ee
.
T
h
e
d
esig
n
m
u
s
t
b
e
th
o
r
o
u
g
h
i
n
r
o
u
te
p
lan
n
in
g
m
eth
o
d
s
.
T
h
e
f
o
llo
wi
n
g
s
e
ctio
n
s
d
escr
ib
e
th
e
k
ey
d
esig
n
is
s
u
es
f
o
r
u
n
m
an
n
ed
ae
r
ial
v
eh
icles
(
UAV
)
r
o
u
t
e
p
lan
n
i
n
g
.
Fig
u
r
e
2
illu
s
tr
ate
s
th
e
r
estrictio
n
s
to
co
n
s
id
er
wh
ile
co
n
s
tr
u
ctin
g
p
a
th
-
o
p
tim
izatio
n
al
g
o
r
ith
m
s
.
Fig
u
r
e
2
.
C
o
n
s
tr
ain
ts
o
n
p
ath
p
lan
n
in
g
in
UAVs
Ad
v
an
ce
d
r
o
u
te
p
lan
n
i
n
g
alg
o
r
ith
m
s
in
r
o
b
o
tic
au
to
n
o
m
o
u
s
s
u
r
g
er
y
in
cr
ea
s
e
ac
cu
r
ac
y
,
o
p
er
atio
n
len
g
th
,
tis
s
u
e
d
am
ag
e,
an
d
p
atien
t
o
u
tco
m
es.
T
h
e
alg
o
r
ith
m
s
o
p
tim
ize
s
u
r
g
ical
tr
ajec
to
r
ies
an
d
elim
in
ate
s
u
p
er
f
lu
o
u
s
m
o
tio
n
s
,
im
p
r
o
v
i
n
g
s
u
r
g
ical
ef
f
icien
cy
an
d
ef
f
i
ca
cy
.
R
o
b
o
tic
au
to
n
o
m
o
u
s
s
u
r
g
er
y
p
at
h
p
lan
n
in
g
alg
o
r
ith
m
s
p
r
o
v
id
e
ap
p
r
o
p
r
ia
te
s
u
r
g
ical
to
o
l
tr
ajec
to
r
ies
b
ased
o
n
p
r
eo
p
e
r
ativ
e
im
ag
in
g
d
ata
an
d
s
u
r
g
ical
g
o
als.
T
h
ese
alg
o
r
ith
m
s
ass
ess
p
atien
t
an
ato
m
y
,
id
e
n
tify
cr
u
cial
s
ec
tio
n
s
,
an
d
c
o
m
p
u
te
t
h
e
b
est
p
ath
way
s
t
o
ta
r
g
et
s
p
o
ts
.
B
io
-
in
s
p
ir
ed
alg
o
r
ith
m
s
b
eh
av
e
lik
e
b
io
lo
g
ical
s
y
s
tem
s
b
y
an
aly
zin
g
ch
allen
g
es.
T
h
ese
m
eth
o
d
s
s
u
g
g
est
em
p
lo
y
in
g
a
p
o
wer
f
u
l
s
ea
r
ch
alg
o
r
ith
m
i
n
s
tead
o
f
co
m
p
lex
en
v
i
r
o
n
m
e
n
ta
l
m
o
d
els.
Swar
m
in
tellig
en
ce
,
ev
o
lu
tio
n
ar
y
,
b
eh
av
io
r
-
b
ased
,
an
d
m
u
lti
-
f
u
s
io
n
b
io
-
in
s
p
ir
ed
al
g
o
r
ith
m
s
ex
is
t.
Fig
u
r
e
3
s
h
o
ws
th
e
b
io
-
in
s
p
ir
ed
UAV
r
o
u
te
p
lan
n
in
g
alg
o
r
ith
m
s
'
ca
teg
o
r
izatio
n
h
ier
ar
ch
y
.
Fig
u
r
e
3
.
UAV
b
io
-
in
s
p
ir
ed
r
o
u
te
p
lan
n
in
g
alg
o
r
ith
m
class
if
icatio
n
h
ier
ar
c
h
y
Path
p
lan
n
in
g
al
g
o
r
ith
m
s
i
n
r
o
b
o
tic
a
u
to
n
o
m
o
u
s
s
u
r
g
er
y
e
n
co
u
n
ter
v
a
r
io
u
s
p
r
o
b
lem
s
d
e
s
p
ite
th
eir
p
o
ten
tial
ad
v
an
tag
es.
T
h
ese
in
clu
d
e
ad
ap
tin
g
to
ch
an
g
in
g
s
u
r
g
ical
s
ettin
g
s
,
h
an
d
lin
g
an
ato
m
ical
s
tr
u
ctu
r
al
u
n
ce
r
tain
ty
,
an
d
r
esp
o
n
d
in
g
t
o
p
r
o
ce
s
s
m
o
d
if
icatio
n
s
in
r
e
al
tim
e.
Path
p
lan
n
i
n
g
alg
o
r
ith
m
s
ar
e
u
s
ed
in
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
9
,
No
.
1
,
Ju
ly
20
25
:
2
1
4
-
2
2
4
218
m
in
im
ally
in
v
asiv
e,
tu
m
o
r
,
o
r
g
an
,
an
d
r
ec
o
n
s
tr
u
ctiv
e
r
o
b
o
ti
c
au
to
n
o
m
o
u
s
s
u
r
g
er
y
.
T
h
ese
alg
o
r
ith
m
s
ar
e
v
ital
to
r
o
b
o
tic
s
u
r
g
ical
s
y
s
tem
s
an
d
p
r
o
v
id
e
s
af
e
an
d
ac
cu
r
ate
s
u
r
g
er
y
.
I
n
ea
r
ly
o
p
tim
izatio
n
,
an
ev
o
lu
tio
n
ar
y
alg
o
r
ith
m
en
h
a
n
ce
s
th
e
lik
elih
o
o
d
o
f
in
v
esti
g
atin
g
n
ea
r
-
o
p
ti
m
al
o
u
tc
o
m
es.
I
t
ca
n
av
o
i
d
lo
ca
l
m
in
im
a
with
o
u
t
f
itn
ess
g
r
ad
ien
t
in
f
o
r
m
atio
n
an
d
b
e
h
an
d
le
d
in
p
ar
allel.
Fig
u
r
e
4
s
h
o
ws
h
o
w
ev
o
lu
tio
n
ar
y
alg
o
r
ith
m
s
u
s
e
n
atu
r
al
ev
o
l
u
tio
n
ar
y
p
r
o
ce
s
s
es
in
clu
d
in
g
r
ep
r
o
d
u
ctio
n
,
r
ec
o
m
b
in
atio
n
,
m
u
tatio
n
,
a
n
d
s
ele
ctio
n
.
Fig
u
r
e
4
.
Pro
ce
s
s
o
f
ev
o
lu
tio
n
ar
y
alg
o
r
ith
m
s
in
UAV
p
ath
p
l
an
n
in
g
Ad
v
an
ce
d
r
o
u
te
p
lan
n
in
g
alg
o
r
ith
m
s
in
r
o
b
o
tic
au
to
n
o
m
o
u
s
s
u
r
g
er
y
p
r
o
v
id
e
s
ev
er
al
b
en
ef
i
ts
.
T
h
ese
alg
o
r
ith
m
s
let
s
u
r
g
eo
n
s
co
n
d
u
ct
d
if
f
icu
lt
o
p
er
atio
n
s
m
o
r
e
ac
cu
r
ately
,
ef
f
icien
tly
,
an
d
s
af
ely
,
im
p
r
o
v
in
g
p
atien
t
o
u
tco
m
es.
T
h
ey
also
s
p
ee
d
h
ea
lin
g
an
d
r
ed
u
ce
p
o
s
to
p
er
ativ
e
p
ain
.
T
h
is
s
tu
d
y
s
h
o
u
ld
p
r
o
v
id
e
lig
h
t
o
n
r
o
b
o
tic
au
t
o
n
o
m
o
u
s
s
u
r
g
ical
r
o
u
te
p
la
n
n
in
g
alg
o
r
ith
m
s
.
T
h
e
r
esear
ch
s
ee
k
s
to
f
in
d
r
o
u
te
p
lan
n
in
g
b
est
p
r
ac
tices
b
y
ex
am
in
i
n
g
th
eir
e
f
f
icac
y
in
d
i
v
er
s
e
s
u
r
g
ical
s
ettin
g
s
an
d
th
eir
e
f
f
ec
ts
o
n
s
u
r
g
ical
o
u
tco
m
es.
T
h
e
f
u
tu
r
e
o
f
t
h
is
s
tu
d
y
in
v
o
lv
es
clin
ical
tr
ials
an
d
r
ea
l
-
wo
r
ld
im
p
lem
en
tatio
n
s
to
d
ev
elo
p
an
d
v
alid
ate
r
o
u
te
p
lan
n
in
g
a
lg
o
r
ith
m
s
.
Ad
v
an
c
es
in
ar
tific
ial
in
tellig
en
ce
an
d
m
ac
h
in
e
lear
n
in
g
m
a
y
lead
to
m
o
r
e
ad
v
an
ce
d
alg
o
r
ith
m
s
th
at
ca
n
s
o
lv
e
co
m
p
licated
s
u
r
g
ical
p
r
o
b
lem
s
an
d
ad
a
p
t
to
p
atien
t
an
ato
m
i
es.
Path
p
lan
n
in
g
r
esear
ch
an
d
d
ev
el
o
p
m
en
t
h
a
v
e
g
r
ea
t
p
r
o
m
is
e
to
ad
v
a
n
ce
r
o
b
o
t
ic
au
to
n
o
m
o
u
s
s
u
r
g
er
y
an
d
p
atien
t
ca
r
e.
Mo
b
ile
r
o
b
o
t r
o
u
te
p
lan
n
i
n
g
o
p
tim
izes
p
er
f
o
r
m
an
ce
cr
iter
ia
s
u
ch
co
m
p
u
tin
g
co
m
p
lex
ity
,
p
o
wer
co
n
s
u
m
p
tio
n
,
d
is
tan
ce
,
an
d
tim
e
to
f
in
d
a
co
llis
io
n
-
f
r
ee
p
ath
f
r
o
m
a
s
o
u
r
ce
to
a
tar
g
et.
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
4
.
1
.
Sim
ula
t
i
o
n
-
ba
s
ed
t
estin
g
Simu
latio
n
-
b
ased
test
in
g
in
v
o
lv
es
cr
ea
tin
g
v
ir
t
u
al
en
v
ir
o
n
m
en
ts
th
at
m
im
ic
r
ea
l
-
wo
r
ld
s
u
r
g
ical
s
ce
n
ar
io
s
.
Path
p
lan
n
in
g
al
g
o
r
ith
m
s
ar
e
im
p
lem
en
ted
with
in
th
ese
s
im
u
latio
n
s
[
2
6
]
to
ass
ess
th
eir
p
er
f
o
r
m
an
ce
in
n
av
ig
atin
g
t
h
r
o
u
g
h
an
at
o
m
i
ca
l
s
tr
u
ctu
r
e
s
,
av
o
id
in
g
o
b
s
tacle
s
,
an
d
ac
h
iev
in
g
s
u
r
g
ical
o
b
jectiv
es.
Simu
latio
n
s
allo
w
r
esear
ch
er
s
to
r
e
p
licate
a
wid
e
r
an
g
e
o
f
s
u
r
g
ical
s
ce
n
ar
io
s
an
d
ev
alu
ate
alg
o
r
ith
m
ic
p
er
f
o
r
m
a
n
ce
u
n
d
er
d
if
f
er
e
n
t
co
n
d
itio
n
s
wit
h
o
u
t
th
e
n
ee
d
f
o
r
p
h
y
s
ical
ex
p
er
im
en
tati
o
n
.
E
v
alu
atio
n
o
f
r
o
b
o
tic
au
to
n
o
m
o
u
s
s
u
r
g
e
r
y
p
ath
p
lan
n
in
g
alg
o
r
i
th
m
s
r
elies
h
ea
v
ily
o
n
s
im
u
latio
n
-
b
ased
test
in
g
.
T
o
ass
ess
h
o
w
well
a
n
alg
o
r
ith
m
p
er
f
o
r
m
s
,
it
is
n
ec
ess
ar
y
to
s
im
u
late
ac
tu
al
s
u
r
g
ical
p
r
o
ce
d
u
r
es
in
a
v
ir
tu
al
s
ettin
g
.
R
esear
ch
er
s
m
ay
s
tu
d
y
h
o
w
d
if
f
er
en
t
alg
o
r
ith
m
s
h
an
d
le
co
m
p
licated
an
ato
m
ical
f
ea
tu
r
es,
av
o
id
b
ar
r
ie
r
s
,
an
d
im
p
r
o
v
e
tr
ajec
to
r
ies
b
y
m
o
d
elin
g
d
iv
er
s
e
s
u
r
g
ical
tech
n
iq
u
es
an
d
s
u
r
r
o
u
n
d
i
n
g
s
[
2
7
]
.
Su
r
g
eo
n
s
m
ay
s
ee
th
e
p
atien
t'
s
m
ed
ical
h
is
to
r
y
,
p
h
y
s
io
lo
g
i
ca
l
ch
ar
ac
ter
is
tics
,
an
d
d
em
o
g
r
ap
h
ics
all
in
o
n
e
p
lace
with
th
e
h
elp
o
f
Fig
u
r
e
5
.
Fig
u
r
e
5
.
Patien
t
d
ata
a
n
d
s
u
r
g
ical
p
ar
am
eter
s
0
50
100
150
200
1
2
3
4
Pati
e
n
t's
D
ata
i
n
N
u
m
b
e
r
s
Pa
tient
'
s
N
ame
in
d
icated
in
n
u
m
b
e
r
P
a
tie
n
t's
m
ed
ic
al
h
is
t
or
y
H
e
i
g
h
t (
c
m)
We
i
g
h
t (
kg
)
A
g
e
(
y
e
ar
s)
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:
2502
-
4
7
5
2
E
n
h
a
n
ce
d
p
erfo
r
ma
n
ce
a
n
d
e
fficien
cy
o
f ro
b
o
tic
a
u
t
o
n
o
mo
u
s
p
r
o
ce
d
u
r
es
…
(
R
a
ma
n
La
th
a
)
219
C
o
m
p
r
eh
en
s
iv
e
test
in
g
m
ay
b
e
ac
co
m
p
lis
h
ed
u
s
in
g
th
is
m
e
th
o
d
with
o
u
t
en
d
an
g
e
r
in
g
p
atien
t
s
af
ety
o
r
u
s
in
g
u
p
p
r
ec
i
o
u
s
r
eso
u
r
ce
s
.
T
h
e
en
h
an
ce
m
en
t
o
f
s
u
r
g
ical
o
u
tco
m
es
m
ay
b
e
ac
h
iev
ed
b
y
th
e
r
ef
in
in
g
o
f
r
o
u
te
p
la
n
n
in
g
tech
n
i
q
u
es,
w
h
ich
in
t
u
r
n
ca
n
b
e
ac
h
ie
v
ed
th
r
o
u
g
h
th
e
d
is
co
v
er
y
o
f
al
g
o
r
ith
m
ic
f
laws
an
d
ar
ea
s
f
o
r
im
p
r
o
v
em
en
t
u
s
in
g
s
im
u
latio
n
-
b
ased
test
in
g
.
I
n
th
e
co
n
te
x
t
o
f
r
o
b
o
tic
au
to
n
o
m
o
u
s
s
u
r
g
er
y
,
its
in
clu
s
io
n
in
to
ass
ess
m
en
t
p
r
o
ce
d
u
r
es
g
u
ar
a
n
tees
th
e
r
o
b
u
s
tn
ess
an
d
r
eliab
ilit
y
o
f
r
o
u
te
p
lan
n
in
g
alg
o
r
ith
m
s
.
I
t
h
elp
s
with
au
to
n
o
m
o
u
s
r
o
b
o
tic
s
u
r
g
er
y
s
af
ety
an
d
p
atien
t
o
u
tco
m
es
b
y
allo
win
g
f
o
r
b
et
ter
d
ec
is
io
n
s
ab
o
u
t
s
u
r
g
ical
ap
p
r
o
ac
h
,
a
n
esth
etic,
an
d
p
o
s
to
p
er
ativ
e
ca
r
e.
4
.
2
.
Rea
l
-
wo
rld e
x
perim
ent
s
R
ea
l
-
wo
r
ld
ex
p
er
im
e
n
ts
in
v
o
lv
e
test
in
g
p
ath
p
lan
n
in
g
alg
o
r
ith
m
s
d
ir
ec
tly
o
n
r
o
b
o
t
ic
s
u
r
g
ical
s
y
s
tem
s
in
clin
ical
o
r
lab
o
r
ato
r
y
s
ettin
g
s
.
R
esear
ch
er
s
d
esig
n
an
d
c
o
n
d
u
ct
s
u
r
g
ical
p
r
o
c
ed
u
r
es
u
s
in
g
r
o
b
o
tic
p
latf
o
r
m
s
eq
u
ip
p
ed
with
alg
o
r
ith
m
s
u
n
d
er
ev
alu
atio
n
.
R
ea
l
-
wo
r
ld
ex
p
er
im
e
n
ts
p
r
o
v
id
e
v
a
lu
ab
le
in
s
ig
h
ts
in
to
alg
o
r
ith
m
ic
p
e
r
f
o
r
m
an
ce
i
n
a
ctu
al
s
u
r
g
ical
s
ce
n
ar
io
s
,
c
o
n
s
id
er
in
g
f
ac
t
o
r
s
s
u
ch
as
p
atien
t
v
ar
iab
ilit
y
,
tis
s
u
e
d
ef
o
r
m
atio
n
,
an
d
en
v
ir
o
n
m
e
n
tal
u
n
ce
r
tain
ties
.
W
h
en
it
co
m
es
to
test
in
g
p
ath
p
lan
n
in
g
alg
o
r
ith
m
s
f
o
r
au
to
n
o
m
o
u
s
r
o
b
o
tic
s
u
r
g
e
r
y
,
r
ea
l
-
wo
r
ld
ex
p
er
im
en
ts
ar
e
c
r
u
cial.
T
o
test
h
o
w
well
alg
o
r
ith
m
s
wo
r
k
in
r
ea
l
-
wo
r
ld
s
itu
atio
n
s
,
th
ese
in
v
esti
g
atio
n
s
u
s
e
r
o
b
o
tic
s
u
r
g
ical
s
y
s
tem
s
in
r
ea
l
-
wo
r
ld
clin
ical
s
ettin
g
s
.
R
esear
ch
er
s
m
ay
test
th
e
u
s
ef
u
l
n
ess
o
f
alg
o
r
ith
m
s
u
n
d
er
ac
tu
al
r
estrictio
n
s
,
s
u
ch
as
tis
s
u
e
d
ef
o
r
m
ati
o
n
,
eq
u
ip
m
en
t
lim
its
,
an
d
d
y
n
am
ic
s
ettin
g
s
,
b
y
p
e
r
f
o
r
m
in
g
tr
ials
in
o
p
e
r
atin
g
r
o
o
m
s
o
r
s
im
u
latio
n
lab
o
r
ato
r
ies
with
r
ea
lis
tic
cir
cu
m
s
tan
ce
s
.
B
y
s
im
u
latin
g
r
ea
l
-
wo
r
ld
s
ce
n
ar
io
s
,
r
ese
ar
ch
er
s
m
ay
b
etter
u
n
d
er
s
tan
d
h
o
w
al
g
o
r
ith
m
s
p
er
f
o
r
m
,
h
o
w
t
h
ey
r
esp
o
n
d
to
c
h
an
g
es,
an
d
h
o
w
th
e
y
f
it
in
t
o
cu
r
r
e
n
t
s
u
r
g
ical
p
r
o
ce
d
u
r
es.
Su
r
g
ical
team
s
ca
n
d
ir
ec
tly
s
ee
alg
o
r
ith
m
ic
p
er
f
o
r
m
an
ce
th
an
k
s
to
th
is
,
wh
ich
a
llo
ws
f
o
r
in
p
u
t
f
o
r
in
c
r
em
en
ta
l
im
p
r
o
v
em
e
n
t.
T
o
en
h
an
ce
p
atien
t
o
u
tco
m
es
d
u
r
in
g
r
o
b
o
tic
a
u
to
n
o
m
o
u
s
s
u
r
g
er
y
,
r
esear
ch
er
s
n
ee
d
t
o
test
th
e
s
af
ety
a
n
d
d
ep
en
d
a
b
ilit
y
o
f
r
o
u
te
p
lan
n
in
g
alg
o
r
ith
m
s
v
ia
r
ea
l
-
wo
r
ld
ex
p
er
im
en
ts
.
T
ab
le
1
d
etails
ea
c
h
p
atien
t'
s
m
ed
ical
h
is
to
r
y
,
in
clu
d
in
g
a
n
y
s
u
r
g
er
i
es,
d
r
u
g
s
,
a
n
d
aller
g
ies.
W
h
en
p
lan
n
in
g
s
u
r
g
e
r
y
a
n
d
tr
y
in
g
to
p
r
e
v
en
t
p
r
o
b
lem
s
,
it
is
e
s
s
en
tial
to
k
n
o
w
th
e
p
atien
t's
m
ed
ical
h
is
to
r
y
.
An
esth
esia
o
p
tio
n
s
,
p
o
s
to
p
er
ativ
e
ca
r
e,
an
d
s
u
r
g
ical
s
tr
ateg
y
m
ig
h
t b
e
af
f
ec
ted
b
y
aller
g
ies,
p
ast o
p
er
atio
n
s
,
an
d
m
ed
icin
es.
T
ab
le
1
.
Su
r
g
ical
h
is
to
r
y
P
a
t
i
e
n
t
I
D
P
r
e
v
i
o
u
s
s
u
r
g
e
r
i
e
s
M
e
d
i
c
a
t
i
o
n
s
A
l
l
e
r
g
i
e
s
1
A
p
p
e
n
d
e
c
t
o
m
y
A
sp
i
r
i
n
,
l
i
si
n
o
p
r
i
l
P
e
n
i
c
i
l
l
i
n
2
N
o
n
e
N
o
n
e
La
t
e
x
3
K
n
e
e
su
r
g
e
r
y
M
e
t
f
o
r
mi
n
,
si
m
v
a
st
a
t
i
n
N
o
n
e
4
G
a
l
l
b
l
a
d
d
e
r
r
e
m
o
v
a
l
I
b
u
p
r
o
f
e
n
N
o
n
e
5
N
o
n
e
W
a
r
f
a
r
i
n
,
l
o
s
a
r
t
a
n
N
o
n
e
4
.
2
.
1
.
Q
ua
ntit
a
t
i
v
e
m
e
t
rics
Qu
an
titativ
e
m
etr
ics
ar
e
u
s
ed
to
o
b
jectiv
ely
ev
alu
ate
t
h
e
p
er
f
o
r
m
a
n
ce
o
f
p
ath
p
lan
n
in
g
alg
o
r
ith
m
s
b
ased
o
n
v
ar
io
u
s
cr
iter
ia.
T
h
e
s
e
m
etr
ics
m
ay
in
clu
d
e
m
ea
s
u
r
es
s
u
ch
as
p
ath
len
g
th
,
ex
ec
u
tio
n
tim
e,
c
o
llis
io
n
av
o
id
an
ce
r
ate,
d
is
tan
ce
t
o
cr
itical
s
tr
u
ctu
r
es,
an
d
s
u
cc
ess
r
ate
in
r
ea
c
h
in
g
s
u
r
g
ical
tar
g
ets.
B
y
q
u
an
tify
in
g
alg
o
r
ith
m
ic
p
er
f
o
r
m
a
n
ce
u
s
i
n
g
s
tan
d
ar
d
ize
d
m
etr
ics,
r
esear
ch
er
s
ca
n
co
m
p
ar
e
d
if
f
er
en
t
ap
p
r
o
a
ch
es
an
d
id
en
tify
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tr
en
g
th
s
an
d
wea
k
n
ess
es.
T
h
e
ev
alu
atio
n
o
f
p
ath
p
lan
n
in
g
alg
o
r
ith
m
s
f
o
r
r
o
b
o
tic
au
t
o
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o
m
o
u
s
s
u
r
g
er
y
r
elies
h
ea
v
ily
o
n
q
u
an
titativ
e
m
etr
ics.
Me
asu
r
ab
l
e
ch
ar
ac
ter
is
tics
in
clu
d
in
g
t
r
ajec
to
r
y
ac
cu
r
ac
y
,
co
llis
io
n
f
r
eq
u
en
cy
,
ex
ec
u
tio
n
tim
e,
a
n
d
r
o
u
te
len
g
t
h
ar
e
all
p
ar
t
o
f
th
ese
m
etr
ics.
R
esear
ch
er
s
ca
n
m
o
r
e
m
eth
o
d
ically
ex
am
in
e
an
d
ev
alu
ate
th
e
ef
f
icac
y
o
f
v
ar
i
o
u
s
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o
r
ith
m
s
wh
en
th
ey
m
ea
s
u
r
e
alg
o
r
ith
m
p
er
f
o
r
m
an
ce
u
s
in
g
o
b
jectiv
e
cr
iter
ia.
W
h
en
it
co
m
es
to
tr
av
er
s
in
g
co
m
p
licated
s
u
r
g
ical
s
itu
atio
n
s
,
q
u
an
titativ
e
m
ea
s
u
r
es
g
iv
e
lig
h
t
o
n
h
o
w
e
f
f
icien
t,
s
af
e,
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d
d
ep
e
n
d
ab
le
r
o
u
te
p
lan
n
in
g
al
g
o
r
ith
m
s
a
r
e.
T
h
ey
m
ak
e
it
ea
s
y
to
f
in
d
th
e
alg
o
r
ith
m
'
s
s
tr
en
g
th
s
an
d
f
laws
an
d
allo
w
f
o
r
th
o
r
o
u
g
h
test
in
g
in
m
an
y
co
n
tex
ts
.
I
n
ad
d
itio
n
,
r
o
u
te
p
lan
n
in
g
alg
o
r
ith
m
s
m
ay
b
e
im
p
r
o
v
ed
an
d
d
ev
elo
p
e
d
with
th
e
u
s
e
o
f
q
u
an
titativ
e
m
ea
s
u
r
es
th
at
s
et
p
er
f
o
r
m
an
ce
g
o
als
an
d
b
en
ch
m
a
r
k
s
.
R
esear
ch
er
s
ca
n
im
p
r
o
v
e
th
e
ac
cu
r
ac
y
an
d
e
f
f
icien
cy
o
f
r
o
b
o
tic
au
to
n
o
m
o
u
s
s
u
r
g
er
y
b
y
u
s
in
g
q
u
an
titativ
e
in
d
icato
r
s
to
s
y
s
tem
atica
lly
test
r
o
u
te
p
lan
n
in
g
alg
o
r
ith
m
s
.
T
ab
le
2
s
h
o
ws
th
e
p
ath
p
lan
n
in
g
alg
o
r
ith
m
,
wh
ich
is
th
e
f
o
u
n
d
atio
n
an
d
im
p
r
o
v
es
r
o
b
o
tic
a
u
to
n
o
m
o
u
s
o
p
e
r
atio
n
s
b
y
ca
r
ef
u
lly
m
a
p
p
in
g
o
u
t
ef
f
ec
ti
v
e
p
ath
s
.
W
ith
th
e
h
elp
o
f
its
cr
ea
to
r
s
,
th
is
alg
o
r
ith
m
in
te
g
r
ates
f
lawless
ly
in
to
th
e
r
o
b
o
tic
s
y
s
tem
a
n
d
u
s
es
a
f
ee
d
b
ac
k
l
o
o
p
to
im
p
r
o
v
e
i
ts
elf
o
v
er
tim
e
.
Per
f
o
r
m
an
ce
an
d
ef
f
icien
cy
ar
e
g
r
ea
tl
y
en
h
an
ce
d
.
I
t
d
o
es
th
is
b
y
m
in
im
izin
g
p
o
wer
co
n
s
u
m
p
tio
n
an
d
ex
ec
u
tio
n
tim
e
wh
ile
m
ax
im
izin
g
task
ac
cu
r
ac
y
v
ia
r
o
u
te
s
elec
tio
n
an
d
o
b
s
tacle
av
o
id
in
g
.
I
n
ad
d
itio
n
,
its
f
lex
ib
ilit
y
g
u
ar
an
tees
to
p
-
n
o
tch
p
er
f
o
r
m
an
ce
e
v
en
i
n
cir
cu
m
s
tan
ce
s
th
at
ar
e
co
n
s
tan
tly
ch
an
g
in
g
.
At
th
e
e
n
d
o
f
th
e
d
ay
,
th
is
in
teg
r
ate
d
s
tr
ateg
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co
m
p
letely
c
h
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g
es r
o
b
o
tic
o
p
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,
b
r
in
g
in
g
f
o
r
th
a
n
ew
ag
e
o
f
ef
f
icien
cy
,
s
p
e
ed
,
an
d
ac
cu
r
ac
y
.
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Q
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ith
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t q
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3
.
Sens
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Sen
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v
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R
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tain
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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-
4
7
5
2
E
n
h
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ce
d
p
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ma
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a
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…
(
R
a
ma
n
La
th
a
)
221
alg
o
r
ith
m
ic
b
eh
av
io
r
an
d
g
u
id
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alg
o
r
ith
m
r
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E
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p
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r
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m
b
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R
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d
s
,
se
n
s
itiv
ity
an
aly
s
is
a
s
s
i
s
ts
in
i
d
en
tify
in
g
al
g
o
r
ith
m
lim
its
an
d
wea
k
n
ess
es.
Plu
s
,
it
lets
s
cie
n
tis
ts
ze
r
o
in
o
n
th
e
alg
o
r
ith
m
ic
f
ac
to
r
s
th
at
r
ea
lly
m
atter
f
o
r
s
u
r
g
ical
r
esu
lts
,
s
o
th
ey
ca
n
im
p
r
o
v
e
alg
o
r
ith
m
p
er
f
o
r
m
an
ce
.
W
h
en
it
co
m
es
to
r
o
b
o
tic
au
to
n
o
m
o
u
s
s
u
r
g
er
y
,
s
en
s
itiv
ity
an
aly
s
is
i
s
a
g
am
e
-
ch
an
g
er
.
I
t
m
et
h
o
d
ically
ev
alu
ates
h
o
w
r
o
u
te
p
lan
n
i
n
g
alg
o
r
ith
m
s
r
esp
o
n
d
to
d
if
f
er
e
n
t scen
ar
io
s
,
m
ak
in
g
th
em
m
o
r
e
r
eliab
le
an
d
ef
f
ec
tiv
e
.
5.
CO
NCLU
SI
O
N
An
aly
zin
g
r
o
u
te
p
lan
n
in
g
alg
o
r
ith
m
s
f
o
r
a
u
to
n
o
m
o
u
s
r
o
b
o
tic
s
u
r
g
er
y
h
ig
h
lig
h
ts
h
o
w
th
ey
m
ig
h
t
im
p
r
o
v
e
ef
f
icien
c
y
a
n
d
r
esu
lts
,
p
o
te
n
tially
tr
an
s
f
o
r
m
in
g
th
e
f
ield
o
f
s
u
r
g
e
r
y
.
T
h
er
e
ar
e
s
till
o
b
s
tacle
s
to
th
ei
r
b
r
o
ad
ac
ce
p
tan
ce
,
d
esp
ite
th
e
en
co
u
r
ag
in
g
d
ev
elo
p
m
en
ts
.
On
e
o
f
t
h
e
b
i
g
g
est
o
b
s
tacle
s
is
m
ak
in
g
s
u
r
e
alg
o
r
ith
m
s
ca
n
ad
a
p
t
to
ch
a
n
g
in
g
s
u
r
g
ical
s
ettin
g
s
with
o
u
t
co
m
p
r
o
m
is
in
g
p
atien
t
s
af
ety
.
Fu
r
t
h
er
m
o
r
e,
co
m
p
r
eh
e
n
s
iv
e
o
p
tim
izatio
n
a
n
d
v
alid
atio
n
is
r
eq
u
ir
ed
s
in
ce
alg
o
r
ith
m
ic
ch
o
ices
af
f
ec
t
s
u
r
g
ical
r
esu
lts
a
n
d
p
atien
t
r
ec
o
v
er
y
.
Op
tim
al
r
o
u
te
p
lan
n
in
g
alg
o
r
ith
m
s
h
av
e
th
e
p
o
ten
tial
to
r
ev
o
lu
tio
n
ize
r
o
b
o
tic
s
u
r
g
er
y
b
y
cu
ttin
g
d
o
wn
o
n
o
p
er
atin
g
tim
es,
in
cr
ea
s
in
g
ac
cu
r
ac
y
,
an
d
d
ec
r
ea
s
in
g
tis
s
u
e
d
am
ag
e.
T
h
er
e
is
a
n
ee
d
f
o
r
f
u
r
th
er
r
esear
c
h
an
d
in
n
o
v
atio
n
to
ad
d
r
es
s
lim
its
in
clu
d
in
g
co
m
p
u
tin
g
co
m
p
lex
ity
a
n
d
th
e
n
ec
ess
ity
f
o
r
r
ea
l
-
tim
e
ad
ap
tab
ilit
y
.
C
o
n
tin
u
in
g
r
esear
ch
an
d
m
u
ltid
is
cip
lin
ar
y
co
o
p
er
atio
n
ar
e
k
e
y
to
m
ee
tin
g
th
ese
is
s
u
es
in
th
e
f
u
tu
r
e.
T
h
e
s
ea
m
less
in
teg
r
atio
n
o
f
ad
a
p
tiv
e
alg
o
r
it
h
m
s
in
to
r
o
b
o
tic
s
y
s
tem
s
an
d
th
eir
ab
ilit
y
to
ad
ju
s
t
to
r
ea
l
-
tim
e
s
u
r
g
ical
s
itu
atio
n
s
is
o
f
th
e
u
tm
o
s
t
im
p
o
r
tan
ce
.
T
o
p
u
t
th
ese
in
n
o
v
atio
n
s
in
to
cli
n
ical
p
r
ac
tice,
th
er
e
m
u
s
t
b
e
th
o
r
o
u
g
h
r
eg
u
lato
r
y
clea
r
an
ce
a
n
d
clin
ical
v
alid
atio
n
.
Au
to
n
o
m
o
u
s
r
o
b
o
tic
s
u
r
g
er
y
,
en
a
b
led
b
y
s
o
p
h
is
tic
ated
r
o
u
te
p
lan
n
in
g
alg
o
r
ith
m
s
,
h
as
e
n
o
r
m
o
u
s
p
o
ten
tial
to
r
ev
o
lu
tio
n
ize
s
u
r
g
ical
tr
ea
tm
en
t
b
y
r
eso
lv
in
g
th
ese
is
s
u
es
an
d
co
n
s
tr
ain
ts
.
Ag
e
v
ar
ies
f
r
o
m
3
8
to
6
2
y
ea
r
s
o
ld
,
weig
h
t
f
r
o
m
6
5
to
8
5
k
g
,
h
eig
h
t
f
r
o
m
1
6
0
to
1
8
0
cm
,
b
lo
o
d
p
r
ess
u
r
e
f
r
o
m
1
1
0
to
1
4
0
/
9
0
m
m
Hg
,
h
ea
r
t
r
ate
f
r
o
m
7
0
to
8
5
b
p
m
,
h
em
o
g
lo
b
in
f
r
o
m
1
2
to
1
4
g
/DL
,
an
d
b
o
d
y
m
ass
in
d
ex
f
r
o
m
2
5
.
4
to
2
9
.
4
.
F
UNDING
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NF
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M
A
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h
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au
th
o
r
s
s
tate
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f
u
n
d
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e
d
.
AUTHO
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CO
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Na
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Ay
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Kan
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
5
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J
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3
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No
.
1
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Ju
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20
25
:
2
1
4
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2
2
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222
DATA AV
AI
L
AB
I
L
I
T
Y
Data
av
ailab
ilit
y
is
n
o
t
ap
p
li
ca
b
le
to
th
is
p
ap
er
as
n
o
n
e
w
d
ata
wer
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cr
ea
ted
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r
an
aly
ze
d
in
th
is
s
tu
d
y
.
RE
F
E
R
E
NC
E
S
[
1
]
M
.
K
u
r
t
P
e
h
l
i
v
a
n
o
ğ
l
u
,
E.
C
.
A
y
,
A
.
G
.
Ek
e
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,
N
.
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.
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l
b
a
y
r
a
k
,
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.
D
u
r
u
,
A
.
S
.
M
u
t
l
u
e
r
,
T.
T.
D
ü
n
d
a
r
,
a
n
d
I
.
D
o
ğ
a
n
,
“
A
n
e
w
su
r
g
i
c
a
l
p
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t
h
p
l
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n
n
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e
w
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n
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r
o
s
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g
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y
,
”
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h
e
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n
t
.
J
.
o
f
Me
d
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c
a
l
Ro
b
o
t
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s
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p
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v
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l
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2
0
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n
o
.
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p
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,
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o
i
:
1
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c
s.
2
5
7
6
[
2
]
L.
P
h
l
i
p
p
e
n
,
B
.
H
o
h
l
ma
n
n
,
a
n
d
K
.
R
a
d
e
r
m
a
c
h
e
r
,
“
3
D
R
e
c
o
n
st
r
u
c
t
i
o
n
o
f
F
e
mu
r
u
s
i
n
g
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l
t
r
a
s
o
u
n
d
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H
a
n
d
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u
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d
e
d
Ev
a
l
u
a
t
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o
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a
n
d
A
u
t
o
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m
o
u
s R
o
b
o
t
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c
A
p
p
r
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c
h
,
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n
Pro
c
.
o
f
t
h
e
2
2
n
d
An
n
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a
l
Me
e
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o
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0
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r
r
8
.
[
3
]
H
.
F
a
n
,
J
.
H
u
a
n
g
,
X
.
H
u
a
n
g
,
H
.
Z
h
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a
n
d
H
.
S
u
,
“
B
I
-
R
R
T*
:
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n
i
m
p
r
o
v
e
d
p
a
t
h
p
l
a
n
n
i
n
g
a
l
g
o
r
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t
h
m
f
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s
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c
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n
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t
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w
o
r
t
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mo
b
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l
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r
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o
t
s
s
y
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ms,”
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.
1
0
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p
p
.
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.
[
4
]
Y
.
H
u
a
n
g
,
L
.
Y
u
,
a
n
d
F
.
Z
h
a
n
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,
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A
su
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v
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o
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p
u
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c
t
u
r
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mo
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l
s
a
n
d
p
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h
p
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n
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a
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ms
o
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-
t
i
p
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d
f
l
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i
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d
l
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s,”
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n
,
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.
1
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p
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.
[
5
]
V
.
G
.
N
a
i
r
,
“
Ef
f
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A
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F
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l
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p
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t
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o
n
s,”
J
.
o
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R
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b
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c
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d
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o
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t
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l
(
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RC
)
,
v
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.
5
,
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o
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v
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0
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.
[
6
]
C
.
M
a
,
H
.
Z
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u
,
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.
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n
,
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m
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e
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s
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st
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lu
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T
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s,
b
i
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m
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c
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l
sig
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n
d
ima
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c
a
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o
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tac
ted
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m
a
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:
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m
a
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c
o
m
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Ms.
Ba
lu
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p
ro
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p
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rtme
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m
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l
leg
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h
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h
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p
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ter
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l
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g
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h
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n
n
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Un
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.
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r
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rti
ficia
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telli
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lo
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h
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s p
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re
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e
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e
c
a
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c
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tac
ted
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m
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:
h
b
b
s
k
2
0
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m
a
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c
o
m
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Dr
.
J
o
h
n
Be
n
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il
o
Fer
n
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n
d
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s
re
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d
th
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P
h
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D.
d
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re
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in
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fro
m
KLEF
(De
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m
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to
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Un
i
v
e
rsity
),
G
u
n
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M
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s
fro
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sta
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Un
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Ch
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n
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fr
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m
An
n
a
Un
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rsity
C
h
e
n
n
a
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re
se
a
rc
h
in
tere
sts
in
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sig
n
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l
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ss
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n
g
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AI
&
M
L,
wire
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m
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n
d
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s
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h
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s
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d
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p
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s
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ti
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jo
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ls
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re
n
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s.
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re
se
n
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y
h
e
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rk
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s
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istan
t
p
ro
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r
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th
e
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e
p
a
rtme
n
t
o
f
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Ka
las
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li
n
g
a
m
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d
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se
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rc
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),
Krish
n
a
n
k
o
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.
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c
a
n
b
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c
o
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tac
ted
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t
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m
a
il
:
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n
n
ij
0
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g
m
a
il
.
c
o
m
.
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.
Ay
a
l
a
p
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g
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Ra
tn
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Ra
j
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re
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n
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rg
ra
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u
a
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h
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n
in
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rm
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ti
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n
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g
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d
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ip
l
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e
fro
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il
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d
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2
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h
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m
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l
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wa
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rk
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s
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ss
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ro
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t
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ra
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d
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s
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ish
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c
o
p
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s,
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CI,
a
n
d
S
CIE
.
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is
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li
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e
m
e
m
b
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r
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f
IAENG
.
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re
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s
o
f
in
tere
st
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re
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l
ima
g
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ro
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ss
in
g
,
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rti
ficia
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telli
g
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n
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m
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d
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ta
sc
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c
u
rit
y
.
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c
a
n
b
e
c
o
n
tac
t
ed
at
e
m
a
il
:
a
ra
tn
a
ra
ju
_
c
se
@m
g
it
.
a
c
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in
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