I
nte
rna
t
io
na
l J
o
urna
l o
f
E
lect
rica
l a
nd
Co
m
p
ute
r
E
ng
in
ee
ring
(
I
J
E
CE
)
Vo
l.
11
,
No
.
2
,
A
p
r
il
2
0
2
1
,
p
p
.
1
2
5
0
~1
2
5
8
I
SS
N:
2
0
8
8
-
8708
,
DOI
: 1
0
.
1
1
5
9
1
/
i
j
ec
e
.
v
1
1
i
2
.
p
p
1
2
5
0
-
1
2
5
8
1250
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ec
e.
ia
esco
r
e.
co
m
Auto
m
a
tic
seg
m
e
ntatio
n of plan
tar
t
her
m
o
g
ra
m
s usi
ng
ada
ptive
C
m
ea
ns t
echniqu
e
M
a
dh
a
v
a
P
r
a
bh
u S
.
1
,
See
m
a
Ver
m
a
2
1
De
p
a
rtme
n
t
o
f
El
e
c
tro
n
ics
a
n
d
C
o
m
m
u
n
ica
ti
o
n
E
n
g
in
e
e
rin
g
,
M
i
d
d
le E
a
st Co
ll
e
g
e
,
M
u
sc
a
t,
S
u
lt
a
n
a
t
e
o
f
O
m
a
n
2
De
p
a
rtme
n
t
o
f
El
e
c
tro
n
ics
,
Ba
n
a
sth
a
l
i
Un
iv
e
rsity
,
Ra
jas
th
a
n
,
In
d
ia
Art
icle
I
nfo
AB
ST
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Au
g
2
8
,
2
0
1
9
R
ev
i
s
ed
A
u
g
1
7
,
2
0
2
0
A
cc
ep
ted
Oct
9
,
2
0
20
Dia
b
e
ti
c
f
o
o
t
u
lce
r
(DFU)
is
o
n
e
o
f
th
e
m
a
jo
r
c
o
n
c
e
rn
o
f
d
ia
b
e
tes
a
n
d
it
is
ra
p
id
ly
in
c
re
a
sin
g
,
in
w
o
rst
c
a
se
sc
e
n
a
r
io
th
is
m
a
y
lea
d
to
a
m
p
u
tatio
n
.
T
h
e
DFU
c
a
n
b
e
a
v
o
id
e
d
b
y
th
e
e
a
rl
y
d
e
te
c
ti
o
n
a
n
d
p
r
o
p
e
r
d
iag
n
o
sis
.
M
a
n
y
o
f
th
e
stu
d
ies
c
a
rried
o
u
t
h
ig
h
li
g
h
ts
th
a
t,
th
e
rm
o
g
ra
p
h
y
i
s
th
e
m
o
st
u
se
f
u
l
tec
h
n
iq
u
e
to
m
e
a
su
re
th
e
c
h
a
n
g
e
s
in
th
e
tem
p
e
ra
tu
re
o
f
p
lan
tar
s
u
rf
a
c
e
a
n
d
a
lerts
to
in
d
ica
te
th
e
risk
a
ss
o
c
iate
d
w
it
h
DFU.
T
h
e
d
istri
b
u
ti
o
n
o
f
tem
p
e
r
a
tu
re
d
o
e
s
n
o
t
h
a
v
e
a
f
i
x
e
d
p
a
tt
e
rn
a
c
ro
ss
t
h
e
p
a
ti
e
n
ts,
h
e
n
c
e
it
m
a
k
e
s
th
e
d
if
f
icu
lt
y
in
m
e
a
su
rin
g
th
e
a
p
p
r
o
p
riate
c
h
a
n
g
e
s.
T
h
is
g
a
p
w
il
l
p
ro
v
id
e
a
sc
o
p
e
to
im
p
ro
v
e
th
e
a
n
a
ly
sis
te
c
h
n
i
q
u
e
so
a
s
t
o
m
e
a
su
re
th
e
p
lan
t
a
r
su
rf
a
c
e
tem
p
e
r
a
tu
re
e
ff
e
c
ti
v
e
l
y
a
n
d
id
e
n
ti
fy
a
n
y
a
b
n
o
rm
a
l
c
h
a
n
g
e
s.
In
th
is
p
a
p
e
r,
th
e
se
g
m
e
n
tatio
n
a
lg
o
rit
h
m
n
a
m
e
l
y
a
d
a
p
ti
v
e
C
m
e
a
n
s
(A
CM
)
f
o
r
th
e
im
a
g
e
se
g
m
e
n
tatio
n
is
d
isc
u
ss
e
d
.
A
CM
is
b
a
se
d
o
n
th
e
s
p
a
ti
a
l
in
f
o
rm
a
ti
o
n
a
n
d
th
is
m
e
th
o
d
in
c
l
u
d
e
s
th
e
tw
o
sta
g
e
i
m
p
le
m
e
n
tatio
n
.
I
n
th
e
f
irst
sta
g
e
,
n
o
n
lo
c
a
l
sp
a
ti
a
l
in
f
o
rm
a
ti
o
n
is
a
d
d
e
d
a
n
d
i
n
t
h
e
se
c
o
n
d
sta
g
e
,
sp
a
ti
a
l
sh
a
p
e
in
f
o
rm
a
ti
o
n
is
u
se
d
in
o
rd
e
r
to
re
f
in
e
th
e
c
o
n
stra
in
t
o
f
lo
c
a
l
sp
a
ti
a
l.
Ou
tco
m
e
o
f
th
e
p
ro
p
o
se
d
m
e
th
o
d
sh
o
w
s
th
a
t
A
CM
is
v
e
r
y
m
u
c
h
e
ff
e
c
ti
v
e
a
n
d
it
o
u
t
p
e
rf
o
rm
s th
e
o
th
e
r
e
x
isti
n
g
m
e
th
o
d
s.
K
ey
w
o
r
d
s
:
A
d
ap
tiv
e
Diab
etes
I
m
ag
e
p
r
o
ce
s
s
i
n
g
Seg
m
en
tatio
n
T
h
er
m
o
g
r
ap
h
y
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
:
Ma
d
h
av
a
P
r
ab
h
u
S.
Dep
ar
t
m
en
t o
f
E
lectr
o
n
ics a
n
d
C
o
m
m
u
n
icat
io
n
E
n
g
i
n
ee
r
in
g
Mid
d
le
E
ast C
o
lleg
e
Mu
s
ca
t,
S
u
lta
n
ate
o
f
O
m
a
n
E
m
ail:
m
ad
h
a
v
@
m
ec
.
ed
u
.
o
m
1.
I
NT
RO
D
UCT
I
O
N
Diab
etes
is
s
aid
to
b
e
o
n
e
o
f
th
e
ch
r
o
n
i
c
d
is
ea
s
es,
w
h
ic
h
o
cc
u
r
s
w
h
e
n
t
h
e
h
u
m
an
p
an
cr
e
as
f
ail
s
to
p
r
o
d
u
ce
th
e
in
s
u
l
in
.
I
n
s
u
li
n
i
s
th
e
h
o
r
m
o
n
e
t
h
at
h
e
lp
s
i
n
r
eg
u
la
tin
g
t
h
e
b
lo
o
d
s
u
g
ar
.
I
n
cr
ea
s
e
in
t
h
e
b
lo
o
d
s
u
g
ar
lev
el
i
s
t
h
e
co
m
m
o
n
e
f
f
ec
t
o
b
s
er
v
ed
in
ca
s
e
o
f
u
n
co
n
tr
o
lled
d
iab
etes
an
d
th
is
l
ea
d
s
to
th
e
s
er
io
u
s
d
am
a
g
e
m
ai
n
l
y
to
t
h
e
b
lo
o
d
v
ess
els,
n
er
v
e
s
a
n
d
k
id
n
e
y
s
[
1
]
.
B
asicall
y
th
er
e
ar
e
t
w
o
t
y
p
e
o
f
d
iab
etes i.
e.
t
y
p
e
-
1
an
d
t
y
p
e
-
2
.
T
h
e
t
y
p
e
-
1
d
ia
b
etes
is
n
o
n
c
u
r
ab
le
[
2
]
.
T
h
e
co
m
m
o
n
t
y
p
e
o
f
d
iab
etes
is
t
y
p
e
-
2
t
h
at
u
s
u
a
ll
y
ex
is
t
s
i
n
ad
u
lt
s
.
T
h
e
r
ec
en
t
d
ata
r
elea
s
ed
b
y
W
o
r
ld
Hea
lth
Or
g
an
iza
tio
n
i
n
d
icate
s
t
h
at
ap
p
r
o
x
im
a
tel
y
4
2
2
m
illi
o
n
p
eo
p
le
ar
e
liv
in
g
w
it
h
d
iab
etes
w
o
r
ld
w
id
e
a
n
d
n
e
ar
l
y
1
.
6
m
illi
o
n
d
ea
t
h
s
e
v
er
y
y
ea
r
ar
e
d
ir
ec
tl
y
r
elate
d
as c
au
s
e
o
f
d
iab
etes [
3
]
.
T
h
e
co
m
m
o
n
s
y
m
p
to
m
i
s
d
ia
b
etic
f
o
o
t
u
lcer
(
DFU
)
.
DF
U
o
cc
u
r
s
w
h
en
th
e
r
ed
u
ce
d
b
lo
o
d
f
lo
w
is
co
m
b
i
n
ed
alo
n
g
w
i
th
th
e
n
e
u
r
o
p
ath
y
(
d
a
m
a
g
e
o
f
n
er
v
e)
in
f
ee
t.
DFU
ca
n
b
e
p
r
ev
en
ted
,
if
it
is
d
etec
ted
i
n
a
n
in
itial
s
ta
g
e.
C
u
r
r
en
t
l
y
th
i
s
t
y
p
e
o
f
f
o
o
t
co
m
p
lica
tio
n
i
s
id
e
n
ti
f
ied
b
y
a
n
al
y
zi
n
g
th
e
f
o
o
t
n
e
u
r
o
p
ath
y
,
p
lan
tar
f
o
o
t
p
r
ess
u
r
e
a
n
d
b
lo
o
d
cir
cu
latio
n
s
.
T
h
is
ap
p
r
o
ac
h
p
r
o
v
id
es
th
e
p
o
s
s
ib
ilit
y
to
a
n
al
y
z
e
th
e
co
n
d
itio
n
o
f
p
atien
ts
s
u
f
f
er
i
n
g
f
r
o
m
f
o
o
t
u
lcer
[
4
]
.
Ho
w
ev
er
,
th
e
s
e
tes
t
n
o
r
m
all
y
ca
u
s
e
s
th
e
p
atien
t
s
to
v
is
it
th
e
d
o
cto
r
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:
2
0
8
8
-
8708
A
u
to
ma
tic
s
eg
men
ta
tio
n
o
f p
la
n
ta
r
th
ermo
g
r
a
ms u
s
in
g
a
d
a
p
t
ive
C
mea
n
s
tech
n
iq
u
e
(
Ma
d
h
a
va
P
r
a
b
h
u
S
.
)
1251
m
o
r
e
o
f
te
n
an
d
t
h
is
is
e
x
p
en
s
i
v
e
as
w
el
l
as
i
n
tr
u
s
i
v
e.
Mo
r
eo
v
er
th
e
s
el
f
-
a
s
s
e
s
s
m
e
n
t
is
v
er
y
m
u
ch
c
h
alle
n
g
in
g
s
in
ce
it a
l
l d
ep
en
d
s
o
n
th
e
p
ati
en
t
’
s
k
n
o
w
led
g
e
r
eg
ar
d
in
g
th
e
d
is
ea
s
e
[
5
]
.
E
ar
ly
d
etec
tio
n
o
f
p
r
eu
lcer
ati
v
e
lesi
o
n
s
in
t
h
e
f
o
o
t
b
ased
o
n
t
h
e
s
ev
er
al
s
ce
n
ar
io
s
ca
n
p
r
ev
e
n
t
it
f
r
o
m
g
o
in
g
to
th
e
h
az
ar
d
o
u
s
s
tag
e
[
6
]
.
Fo
r
th
e
p
atie
n
ts
at
r
i
s
k
,
t
h
e
r
o
u
ti
n
e
as
s
es
s
m
en
t
o
f
s
u
ch
f
o
o
t
co
m
p
licatio
n
is
r
ea
ll
y
b
en
ef
ic
iar
y
as
th
er
e
is
n
o
r
ea
d
ily
av
ailab
le
in
s
tr
u
m
e
n
t
s
ar
e
av
ailab
le.
T
h
is
g
ap
ca
n
b
e
m
et
b
y
d
esi
g
n
in
g
a
th
er
m
a
l
i
m
a
g
e
p
r
o
ce
s
s
i
n
g
b
ased
m
et
h
o
d
to
d
etec
t
th
e
p
o
ten
tial
D
FUs
.
T
h
er
m
o
g
r
ap
h
y
is
n
o
t
h
i
n
g
b
u
t
th
e
t
e
c
h
n
i
q
u
e
w
h
i
c
h
i
s
u
s
e
d
f
o
r
m
e
a
s
u
r
i
n
g
t
h
e
t
e
m
p
e
r
a
t
u
r
e
d
i
s
t
r
i
b
u
t
i
o
n
t
h
a
t
o
c
c
u
r
s
i
n
t
i
s
s
u
e
s
a
s
w
e
l
l
a
s
o
r
g
a
n
s
[
7
]
.
T
h
is
te
m
p
er
atu
r
e
d
is
tr
ib
u
t
io
n
is
n
a
m
el
y
k
n
o
w
n
as
t
h
er
m
o
g
r
a
m
.
I
t
is
u
s
ed
in
v
ar
io
u
s
co
n
d
itio
n
s
o
f
p
lan
n
i
n
g
t
h
e
tr
ea
t
m
e
n
t
o
f
D
FU
[
8
]
.
T
h
er
m
o
g
r
ap
h
y
i
s
also
ca
p
ab
le
o
f
f
in
d
in
g
t
h
e
r
eg
io
n
o
f
i
n
ter
est
(
R
OI
)
.
T
h
is
tech
n
iq
u
e
is
to
tall
y
s
a
f
e
s
in
ce
it
d
o
es
n
o
t
r
eq
u
ir
e
p
h
y
s
ical
i
n
ter
ac
tio
n
o
f
h
u
m
a
n
s
an
d
n
o
in
tr
o
d
u
ct
io
n
o
f
in
s
tr
u
m
e
n
ts
i
n
to
th
e
h
u
m
an
b
o
d
y
.
Fo
r
th
er
m
o
g
r
a
m
an
al
y
s
is
,
t
h
er
e
ar
e
m
ai
n
l
y
t
w
o
ap
p
r
o
ac
h
es
i
n
v
o
l
v
ed
[
9
]
.
T
h
e
ch
ar
ac
ter
is
ti
c
p
atter
n
s
w
il
l
b
e
id
en
tifie
d
alo
n
g
w
it
h
th
er
m
a
l
v
ar
ia
tio
n
m
ea
s
u
r
e
m
e
n
t.
D
u
e
to
t
h
e
f
ac
t
th
at,
th
e
s
p
atial
p
atter
n
s
h
a
v
e
w
id
e
v
ar
iat
io
n
s
ac
r
o
s
s
t
h
e
d
iab
etic
p
atien
ts
[
1
0
]
,
w
e
p
r
o
p
o
s
ed
a
m
et
h
o
d
o
lo
g
y
n
a
m
ed
as
ad
ap
tiv
e
C
-
m
ea
n
s
(
AC
M
)
,
h
er
e
b
o
th
th
e
i
n
f
o
r
m
atio
n
i.e
.
s
p
atial
s
h
ap
e
an
d
n
o
n
lo
ca
l
s
p
atial
ar
e
u
ti
lized
,
an
d
th
is
h
elp
s
i
n
i
m
p
r
o
v
is
i
n
g
t
h
er
m
o
g
r
a
m
a
n
al
y
s
i
s
.
I
n
o
r
d
er
to
tak
e
b
en
e
f
it
o
f
n
o
n
lo
ca
l
s
p
atial
in
f
o
r
m
a
ti
o
n
,
w
ei
g
h
t
o
n
th
e
o
b
j
ec
tiv
e
f
u
n
ct
io
n
is
ad
d
ed
f
o
r
in
cr
ea
s
in
g
th
e
p
i
x
el
p
o
s
s
ib
il
ities
.
L
ater
o
n
o
u
r
m
e
th
o
d
is
co
m
p
ar
ed
w
ith
t
h
e
ex
i
s
t
in
g
s
e
g
m
e
n
tatio
n
m
e
th
o
d
s
.
T
h
e
co
m
p
ar
is
o
n
ta
k
es
p
lace
in
t
w
o
w
a
y
s
i.e
.
q
u
a
n
t
itativ
e
a
s
w
el
l
as
q
u
alitati
v
e.
T
h
e
co
n
tr
alate
r
al
co
m
p
ar
is
o
n
o
f
f
o
o
t
te
m
p
er
atu
r
e
tech
n
iq
u
e
[
1
1
]
is
n
o
t
v
er
y
m
u
c
h
u
s
e
f
u
l.
W
h
e
n
th
e
te
m
p
er
atu
r
e
c
h
an
g
e
s
in
b
o
th
t
h
e
f
ee
t
a
n
d
if
b
o
th
h
av
e
n
o
s
p
ec
if
ic
p
atter
n
,
th
e
n
o
n
e
f
o
o
t
ca
n
n
o
t
u
s
ed
a
s
a
r
ef
er
en
ce
o
f
t
h
e
a
n
o
th
er
.
T
o
o
v
er
co
m
e
t
h
i
s
,
t
h
e
p
r
o
p
o
s
ed
m
eth
o
d
tr
ies
to
co
m
p
ar
e
b
o
th
t
h
e
f
ee
t
at
a
ti
m
e.
T
h
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
w
ill
i
m
p
r
o
v
e
th
e
au
to
m
atic
clas
s
i
f
icati
o
n
an
d
ac
t
s
a
s
a
n
aid
i
n
a
s
s
is
ti
n
g
th
e
cli
n
ical
ex
p
er
ts
.
O
u
r
r
esear
ch
is
o
r
g
a
n
ized
i
n
a
w
a
y
th
at
s
ec
o
n
d
s
ec
tio
n
d
escr
ib
es
th
e
b
r
ief
d
es
cr
ip
ti
o
n
ab
o
u
t
t
h
e
ex
is
t
in
g
w
o
r
k
,
w
h
er
ea
s
t
h
ir
d
s
ec
tio
n
elab
o
r
ates
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
o
lo
g
y
,
ev
al
u
atio
n
o
f
o
u
r
alg
o
r
it
h
m
ta
k
e
s
p
lace
in
f
o
u
r
t
h
s
ec
t
io
n
,
th
is
s
e
ctio
n
also
s
h
o
w
s
t
h
e
ca
s
e
co
m
p
ar
is
o
n
o
f
o
u
r
p
r
o
p
o
s
ed
alg
o
r
i
th
m
.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
I
n
th
is
s
ec
tio
n
,
w
e
p
r
esen
t
a
b
r
ief
r
ev
ie
w
o
n
t
h
e
s
e
v
er
al
s
eg
m
e
n
tat
io
n
ap
p
r
o
ac
h
es.
T
h
e
liter
atu
r
e
r
ev
ie
w
s
h
o
w
s
th
a
t
ev
er
y
tec
h
n
iq
u
e
h
as
t
h
eir
ad
v
a
n
tag
e
s
as
w
e
ll
as
s
h
o
r
tco
m
i
n
g
s
.
T
h
is
l
ed
u
s
to
d
esi
g
n
a
n
d
d
ev
elo
p
o
u
r
o
w
n
m
e
th
o
d
o
lo
g
y
.
T
h
er
e
ar
e
li
m
ited
co
m
p
u
ter
m
et
h
o
d
s
d
ev
elo
p
ed
f
o
r
th
e
as
s
ess
m
en
t
o
f
d
iab
etic
f
o
o
t p
ath
o
lo
g
ies.
Seg
m
en
tatio
n
is
a
tec
h
n
iq
u
e
u
s
ed
f
o
r
id
en
ti
f
y
i
n
g
th
e
d
i
f
f
er
en
ce
s
b
et
w
ee
n
p
ar
ticu
lar
l
y
i
n
te
r
esti
n
g
a
n
d
u
n
i
n
ter
es
tin
g
p
ar
ts
.
I
t
i
s
al
s
o
u
s
ed
to
d
i
f
f
er
en
tia
te
t
h
e
f
o
r
eg
r
o
u
n
d
f
r
o
m
t
h
e
b
ac
k
g
r
o
u
n
d
co
n
te
n
t.
W
h
i
l
e
co
m
p
ar
i
n
g
t
h
e
s
e
v
er
al
s
e
g
m
e
n
tatio
n
tec
h
n
iq
u
e
s
[
1
2
]
,
th
e
th
r
esh
o
ld
in
g
tech
n
iq
u
e
w
a
s
r
ec
o
m
m
e
n
d
ed
.
T
h
o
u
g
h
th
is
m
et
h
o
d
is
s
i
m
p
le
to
u
s
e,
it
w
as
f
o
u
n
d
th
a
t f
o
r
ce
r
tain
i
m
a
g
es t
h
is
tec
h
n
iq
u
e
f
ail
s
s
h
o
w
t
h
e
clea
r
d
if
f
er
en
c
e
b
et
w
ee
n
b
ac
k
g
r
o
u
n
d
co
n
ten
t
an
d
f
ee
t
[
1
3
]
.
T
h
e
t
w
o
d
if
f
er
en
t
s
eg
m
e
n
tatio
n
m
e
th
o
d
s
ar
e
co
m
p
ar
ed
b
y
ev
alu
a
tin
g
th
e
p
er
f
o
r
m
a
n
ce
o
n
1
0
s
a
m
p
les
ea
ch
o
f
d
iab
etic
an
d
n
o
r
m
al
f
ee
t
[
1
4
]
,
th
e
r
es
u
lts
h
ig
h
li
g
h
ted
t
h
at
ed
g
e
d
etec
tio
n
ap
p
r
o
ac
h
i
s
co
n
s
is
ten
tl
y
g
o
o
d
in
p
er
f
o
r
m
an
ce
t
h
an
th
e
w
ater
s
h
ed
m
et
h
o
d
.
T
h
e
s
n
a
k
e
alg
o
r
ith
m
[
1
5
]
is
u
tili
ze
d
to
id
en
ti
f
y
t
h
e
R
OI
f
r
o
m
t
h
e
r
est
o
f
th
e
f
ee
t
t
h
er
m
o
g
r
a
m
.
I
t
w
as
also
co
n
clu
d
ed
th
a
t
th
e
s
n
ak
e
al
g
o
r
ith
m
is
ap
p
r
o
p
r
iate
an
d
r
elev
an
t
to
p
lan
tar
th
er
m
o
g
r
a
m
s
w
h
e
n
co
m
p
ar
ed
w
it
h
co
n
v
e
n
tio
n
al
s
eg
m
e
n
tatio
n
tec
h
n
iq
u
e
s
.
T
h
e
u
p
d
ated
v
er
s
io
n
w
it
h
b
len
d
e
d
f
ea
tu
r
e
w
as
i
n
tr
o
d
u
ce
d
[
1
6
]
,
an
d
th
is
tec
h
n
iq
u
e
s
h
o
w
ed
b
etter
r
es
u
lts
th
a
n
th
e
tr
ad
itio
n
al
s
n
a
k
e
al
g
o
r
ith
m
an
d
also
s
ev
er
al
s
e
g
m
e
n
tat
i
o
n
tech
n
iq
u
es
w
er
e
ev
alu
a
ted
to
ar
r
iv
e
at
th
e
d
ec
is
io
n
.
A
c
lass
if
ica
tio
n
b
ased
o
n
an
at
o
m
y
o
f
f
o
o
t
w
as
p
r
o
p
o
s
ed
[
1
7
]
,
b
u
t
th
i
s
m
et
h
o
d
is
v
er
y
ted
i
o
u
s
as
th
e
an
ato
m
y
o
f
in
d
i
v
id
u
al
’
s
f
ee
t
v
ar
ies
f
r
o
m
p
atie
n
t
to
p
atien
t
.
T
h
is
s
tu
d
y
is
b
ased
o
n
co
m
p
ar
in
g
t
h
e
t
h
er
m
a
l
d
is
tr
ib
u
tio
n
p
atter
n
o
f
p
lan
ta
r
r
eg
io
n
.
E
x
tr
ac
ti
n
g
th
e
h
o
tte
s
t
r
eg
io
n
i
n
p
la
n
tar
th
er
m
o
g
r
a
m
s
i
s
v
er
y
m
u
c
h
ess
e
n
tial
to
f
o
cu
s
o
n
r
elev
a
n
t
f
ea
tu
r
es.
I
t
ca
n
i
n
d
icate
s
o
m
e
ab
n
o
r
m
alitie
s
.
A
s
i
m
p
le
m
et
h
o
d
b
ased
o
n
th
r
es
h
o
ld
in
g
ca
lled
as
h
is
to
g
r
a
m
-
s
h
ap
e
w
as
u
s
ed
[
1
8
]
to
s
ep
ar
ate
th
e
h
o
tte
s
t r
eg
io
n
s
f
r
o
m
t
h
e
f
ee
t i
m
ag
e
s
.
Fo
r
p
lan
tar
i
m
ag
e
s
e
g
m
en
ta
tio
n
,
an
ac
t
iv
e
co
n
to
u
r
m
o
d
el
w
a
s
u
s
ed
,
w
h
ic
h
w
as
an
e
x
ten
d
ed
v
er
s
io
n
o
f
th
r
es
h
o
ld
in
g
to
e
x
tr
ac
t
t
h
e
h
o
t
test
r
eg
io
n
[
1
9
]
.
I
n
t
h
is
s
t
u
d
y
t
h
e
R
OI
w
as
d
iv
id
ed
i
n
to
6
p
o
r
tio
n
s
to
ex
tr
ac
t
th
e
r
elev
an
t
f
ea
t
u
r
es.
On
e
o
f
t
h
e
p
o
w
er
f
u
l
ap
p
r
o
ac
h
es
in
t
h
e
f
ield
s
e
g
m
e
n
ta
tio
n
tech
n
iq
u
e
is
ca
lled
as
atla
s
-
b
ased
.
T
h
is
ap
p
r
o
ac
h
n
ee
d
s
ex
p
er
t
k
n
o
w
l
ed
g
e
in
b
u
ild
in
g
th
e
d
atab
ase
w
h
ich
co
m
p
r
is
e
s
in
f
o
r
m
atio
n
in
th
e
f
o
r
m
a
lo
o
k
u
p
tab
le
(
L
UT
)
ca
lled
as
at
las
an
d
t
h
u
s
it
is
a
li
m
i
tatio
n
d
u
e
t
o
i
ts
co
m
p
lex
s
tr
u
ctu
r
e
[
2
0
]
.
T
h
e
f
u
zz
y
C
m
ea
n
s
(
FC
M)
tech
n
iq
u
e
p
r
o
v
id
es
m
o
r
e
ac
cu
r
ate
r
esu
lt
s
t
h
an
th
e
k
m
ea
n
s
a
n
d
m
ea
n
s
h
i
f
t a
l
g
o
r
ith
m
s
[
2
1
]
.
FC
M
i
s
o
n
e
o
f
th
e
u
n
s
u
p
er
v
i
s
ed
alg
o
r
it
h
m
,
w
h
ic
h
is
a
cl
u
s
ter
b
ased
a
n
d
n
o
t
d
ep
en
d
en
t
o
n
tr
ai
n
i
n
g
a
n
d
tr
ain
ed
d
ata.
T
h
e
o
n
e
o
f
th
e
d
r
a
w
b
ac
k
w
it
h
th
i
s
ap
p
r
o
a
ch
is
th
at,
th
e
tr
ad
itio
n
al
FC
M
tech
n
iq
u
e
o
n
l
y
u
s
e
s
p
ix
el
m
e
m
b
er
s
h
ip
in
f
o
r
m
atio
n
a
n
d
d
o
es
n
o
t
m
a
k
e
f
u
ll
u
s
e
o
f
s
p
atial
in
f
o
r
m
a
tio
n
ar
o
u
n
d
th
e
p
ix
el,
s
o
it
is
n
o
t
id
ea
l
f
o
r
n
o
is
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
11
,
No
.
2
,
A
p
r
il 2
0
2
1
:
1
2
5
0
-
1258
1252
r
ed
u
ctio
n
[
2
2
]
.
So
m
e
o
f
t
h
e
r
e
s
ea
r
ch
er
s
h
av
e
p
r
o
p
o
s
ed
s
ev
er
al
m
o
d
if
ied
tec
h
n
iq
u
e
s
f
o
r
th
e
FC
M
al
g
o
r
ith
m
to
i
m
p
r
o
v
e
th
e
p
er
f
o
r
m
an
ce
b
ase
d
o
n
th
e
in
te
n
d
ed
ap
p
licatio
n
.
On
e
o
f
t
h
e
ap
p
r
o
ac
h
to
im
p
r
o
v
e
th
e
p
er
f
o
r
m
an
ce
o
f
F
C
M
al
g
o
r
ith
m
i
s
m
a
k
in
g
i
t
s
el
f
ad
ap
tiv
e,
w
h
er
e
i
t
ca
n
c
o
m
b
i
n
e
t
h
e
n
ei
g
h
b
o
r
h
o
o
d
an
d
n
o
n
-
n
ei
g
h
b
o
r
h
o
o
d
in
f
o
r
m
atio
n
o
f
t
h
e
i
m
a
g
e.
T
h
e
co
r
r
esp
o
n
d
in
g
w
ei
g
h
t
h
as
to
b
e
ca
lcu
lated
ad
ap
tiv
el
y
alo
n
g
w
it
h
t
h
e
ad
d
itio
n
o
f
n
ei
g
h
b
o
r
h
o
o
d
s
p
atial
in
f
o
r
m
atio
n
to
th
e
cl
u
s
ter
i
n
g
m
o
d
el
[
2
3
]
.
T
h
e
co
n
ce
p
t
o
f
m
ar
k
o
v
r
an
d
o
m
f
ield
s
(
MRF
)
is
v
er
y
m
u
c
h
u
s
e
f
u
l
t
o
m
a
k
e
t
h
e
FC
M
alg
o
r
it
h
m
ad
ap
tiv
e
to
th
e
n
ei
g
h
b
o
r
h
o
o
d
s
y
s
te
m
s
[
2
4
]
.
T
h
e
MRF
is
u
s
u
all
y
d
ef
i
n
ed
th
r
o
u
g
h
a
Gib
b
s
f
u
n
c
tio
n
.
T
h
e
co
n
s
tr
ain
t
o
f
s
p
atial
co
n
te
x
t
is
ad
d
r
ess
ed
in
to
th
e
o
b
j
ec
tiv
e
f
u
n
c
tio
n
o
f
F
C
M
to
g
et
a
m
o
d
i
f
ied
FC
M
al
g
o
r
ith
m
.
W
h
en
t
h
e
liter
atu
r
e
r
ev
ie
w
i
s
an
al
y
ze
d
,
w
e
o
b
s
er
v
ed
th
at
p
r
io
r
k
n
o
w
led
g
e
o
f
s
h
ap
e
i
n
f
o
r
m
at
io
n
is
v
er
y
m
u
ch
ess
e
n
tia
l
as
i
t
h
e
l
p
s
in
ac
h
ie
v
i
n
g
th
e
s
eg
m
e
n
ta
tio
n
ac
cu
r
ac
y
h
o
w
ev
er
t
h
er
e
ar
e
s
o
m
e
i
n
s
ta
n
ce
s
w
h
er
e
it
ca
n
f
ail
w
h
e
n
o
n
l
y
s
h
ap
e
in
f
o
r
m
atio
n
is
co
n
s
id
er
ed
.
Hen
ce
to
o
v
er
co
m
e
th
ese
is
s
u
e
s
,
a
b
len
d
ed
m
et
h
o
d
o
l
o
g
y
w
h
ic
h
co
m
b
i
n
e
s
th
e
ad
v
an
ta
g
e
o
f
tr
ad
itio
n
a
l
FC
M
alo
n
g
w
it
h
MR
F
is
a
d
d
r
ess
ed
an
d
it
is
d
is
cu
s
s
ed
in
t
h
e
n
e
x
t sect
io
n
.
3.
P
RO
P
O
SE
D
M
E
T
H
O
DO
L
O
G
Y
I
n
t
h
i
s
s
e
c
t
i
o
n
w
e
p
r
e
s
e
n
t
t
h
e
p
r
o
p
o
s
e
d
m
e
t
h
o
d
o
l
o
g
y
i
.
e
.
a
d
a
p
t
i
v
e
C
-
m
e
a
n
s
(
A
C
M
)
.
T
h
i
s
m
et
h
o
d
o
lo
g
y
co
n
tain
s
t
w
o
s
ta
g
es,
f
ir
s
t
s
ta
g
e
d
ea
ls
w
it
h
i
n
cl
u
d
in
g
th
e
n
o
n
-
lo
ca
l
s
p
atia
l
in
f
o
r
m
at
io
n
w
h
er
ea
s
t
h
e
s
ec
o
n
d
s
tag
e
s
in
cl
u
d
es
t
h
e
s
p
atial
in
f
o
r
m
atio
n
.
T
h
e
Fig
u
r
e
1
s
h
o
w
s
t
h
e
f
lo
w
o
f
th
e
p
r
o
p
o
s
ed
w
o
r
k
.
A
t
f
ir
s
t
th
e
p
ar
am
eter
s
ar
e
i
n
itia
lized
,
p
ar
a
m
eter
i
n
itia
lizatio
n
is
es
s
en
t
i
al
s
i
n
ce
i
t
r
ed
u
ce
s
t
h
e
r
u
n
ti
m
e
a
n
d
t
h
is
i
n
t
u
r
n
h
elp
s
in
i
m
p
r
o
v
is
i
n
g
t
h
e
s
eg
m
en
tatio
n
o
u
tco
m
e.
L
ater
o
n
th
e
s
e
g
m
en
ta
tio
n
is
d
o
n
e.
S
p
atial
an
d
n
o
n
-
lo
ca
l
s
p
atial
i
n
f
o
r
m
atio
n
b
ased
AC
M
is
ap
p
lied
in
o
r
d
er
to
ac
h
i
ev
e
t
h
e
b
etter
r
esu
lts
.
I
n
s
e
g
m
en
tatio
n
t
h
er
e
ar
e
m
ai
n
l
y
th
r
ee
s
tep
s
.
First
s
tep
i
s
in
i
tializatio
n
,
h
er
e
t
h
e
p
ar
am
eter
s
ar
e
i
n
itialized
s
u
c
h
as
t
h
r
es
h
o
ld
p
ar
am
eter
,
f
u
zz
y
f
ac
to
r
an
d
n
u
m
b
er
o
f
cl
u
s
ter
s
.
I
n
s
ec
o
n
d
s
ta
g
e
t
h
e
m
e
m
b
er
s
h
ip
is
u
p
d
ated
,
s
i
m
ilar
l
y
i
n
t
h
e
t
h
ir
d
s
ta
g
e
clu
s
ter
ce
n
tr
e
(
CC
)
ar
e
u
p
d
ate
d
.
Fig
u
r
e
1
.
Flo
w
o
f
t
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
o
lo
g
y
3
.
1
.
ACM
ba
s
ed
o
n NSI
(
no
n
-
lo
ca
l sp
a
t
ia
l inf
o
r
m
a
t
io
n)
Her
e,
th
e
m
ain
in
ten
t
io
n
i
s
to
r
ed
u
ce
th
e
er
r
o
r
s
u
m
t
h
at
e
x
i
s
t
b
et
w
ee
n
t
h
e
ce
n
tr
o
id
o
f
ea
ch
class
a
n
d
th
e
i
n
te
n
s
i
t
y
o
f
g
i
v
en
p
ix
el
s
.
AC
M
tec
h
n
iq
u
e
r
ed
u
ce
s
t
h
e
er
r
o
r
b
et
w
ee
n
ea
c
h
c
lass
’
s
ce
n
tr
o
id
an
d
ea
ch
p
i
x
el
’
s
in
te
n
s
it
y
.
T
h
e
OF (
o
b
j
ec
t f
u
n
c
tio
n
)
o
f
FC
M
ca
n
b
e
p
r
esen
ted
as
:
=
∑
∑
‖
−
‖
2
=
1
∈
Ω
(
1
)
w
h
er
e
=
C
en
tr
o
id
=
Me
m
b
er
s
h
ip
=
P
ix
el’
s
g
r
a
y
v
al
u
e
at
p
o
in
t
Ω
=
P
ix
el
lo
ca
tio
n
o
f
th
e
i
m
a
g
e
=
Seg
m
en
ta
tio
n
v
alu
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:
2
0
8
8
-
8708
A
u
to
ma
tic
s
eg
men
ta
tio
n
o
f p
la
n
ta
r
th
ermo
g
r
a
ms u
s
in
g
a
d
a
p
t
ive
C
mea
n
s
tech
n
iq
u
e
(
Ma
d
h
a
va
P
r
a
b
h
u
S
.
)
1253
T
h
e
m
e
m
b
er
s
h
ip
f
u
n
ct
io
n
s
atis
f
ie
s
th
e
co
n
d
itio
n
g
iv
e
n
as
,
∈
[
0
,
1
]
,
∑
=
1
=
1
(
2
)
As
s
h
o
w
n
i
n
(
1
)
,
co
n
s
id
er
ati
o
n
in
A
C
M
t
h
at
l
ea
d
s
to
b
ein
g
s
e
n
s
it
iv
e
to
th
e
n
o
is
e
w
h
er
e
s
p
atial
in
f
o
r
m
atio
n
i
s
n
o
t
p
r
esen
t.
T
o
o
v
er
co
m
e
th
i
s
p
r
o
b
lem
w
e
u
s
e
n
o
n
-
lo
ca
l
SI
(
o
n
b
asis
o
f
tar
g
et
i
m
a
g
e)
.
I
n
in
f
r
ar
ed
(
I
R
)
i
m
a
g
e
s,
u
s
u
all
y
t
h
e
i
n
te
n
s
it
y
is
u
n
e
v
en
[
2
5
]
.
So
m
e
p
ix
e
ls
o
f
th
e
i
m
ag
e
m
ig
h
t
b
e
i
n
co
r
r
e
ct
w
h
il
e
s
eg
m
e
n
ted
b
y
th
e
alg
o
r
it
h
m
,
w
h
er
e
ce
n
tr
o
id
v
al
u
e
i
s
lar
g
er
th
a
n
t
h
e
g
r
a
y
v
alu
e
i
n
t
h
e
i
m
ag
e.
T
o
o
v
er
co
m
e
th
is
p
r
o
b
le
m
th
e
p
i
x
el
’
s
w
ei
g
h
t
m
ig
h
t
b
e
co
n
s
tr
u
cted
if
t
h
e
ce
n
tr
o
id
an
d
r
eg
io
n
o
f
t
h
e
tar
g
et
is
k
n
o
w
n
.
I
f
th
e
p
ix
el
is
clo
s
er
to
tar
g
et’
s
ce
n
tr
o
id
,
th
en
it
h
a
s
h
i
g
h
d
e
g
r
ee
o
f
t
h
e
m
e
m
b
er
s
h
ip
.
T
h
e
w
ei
g
h
t
o
f
ea
c
h
p
ar
t
i
s
in
v
er
s
el
y
p
r
o
p
o
r
tio
n
al
to
th
e
d
is
tan
ce
b
et
w
ee
n
ce
n
tr
o
id
an
d
p
ix
el
o
f
t
h
e
tar
g
et
ap
p
r
o
x
i
m
a
t
el
y
.
T
o
m
ea
s
u
r
e
t
h
e
d
is
tan
ce
,
t
h
e
E
u
clid
ea
n
d
is
ta
n
ce
[
2
6
]
m
e
th
o
d
i
s
u
s
ed
.
T
o
p
r
eser
v
e
t
h
e
co
r
r
ec
t
s
e
g
m
e
n
t
a
n
d
m
ast
s
d
etail
s
,
t
h
e
d
iag
o
n
al
lin
e
’
s
len
g
th
o
f
t
h
e
tar
g
et
r
eg
io
n
is
u
t
ilized
f
o
r
r
estra
in
i
n
g
t
h
e
w
ei
g
h
t
.
T
h
e
im
p
r
o
v
ed
f
u
n
ctio
n
w
it
h
w
ei
g
h
ted
ter
m
is
s
h
o
w
n
b
elo
w
:
=
∑
∑
(
)
‖
−
‖
2
=
1
∈
Ω
(
3
)
=
(
(
(
−
0
)
2
+
(
−
0
)
2
)
1
2
)
1
(
4
)
w
h
er
e,
=
W
eig
h
ted
ter
m
(
,
)
=
C
o
o
r
d
in
ate
o
f
p
ix
el
=
Diag
o
n
al
li
n
e
’
s
le
n
g
th
o
f
p
o
s
s
ib
le
r
eg
io
n
(
0
,
0
)
=
C
o
o
r
d
in
ate
o
f
ap
p
r
o
x
.
ce
n
tr
o
id
T
h
e
p
ix
el
is
lo
ca
ted
at
t
h
e
p
o
s
s
ib
le
r
eg
io
n
.
If
t
h
e
clu
s
te
r
in
g
p
r
o
ce
s
s
h
as
b
ee
n
e
x
ec
u
t
ed
at
th
e
p
o
s
s
ib
le
r
eg
io
n
,
s
o
th
e
v
alu
e
o
f
(
(
−
0
)
2
+
(
−
0
)
2
)
1
2
is
g
r
ea
ter
th
an
1
,
1
ad
j
u
s
t
s
th
e
w
ei
g
h
t
o
f
ce
r
tain
p
ix
el
’
s
d
if
f
er
e
n
t
clas
s
.
Fo
r
i
m
a
g
e
class
=
3
,
th
e
v
al
u
e
o
f
w
ei
g
h
ted
ter
m
w
ill
b
e
cu
b
e
r
o
o
t
o
f
d
is
tan
c
e
w
ei
g
h
t.
I
n
ca
s
e
o
f
clas
s
=
1
,
w
ei
g
h
ted
ter
m
’
s
v
alu
e
w
ill
b
e
s
a
m
e
as
d
is
tan
ce
w
ei
g
h
t.
Gen
er
al
l
y
t
h
e
v
al
u
e
o
f
(
(
−
0
)
2
+
(
−
0
)
2
)
1
2
is
g
r
ea
ter
th
a
n
1
,
s
o
1
>
3
.
T
h
en
,
th
e
ad
d
ed
w
e
ig
h
t
m
ig
h
t
i
n
cr
ea
s
e
th
e
m
e
m
b
er
s
h
ip
s
w
it
h
r
esp
ec
t
to
tar
g
et
i
m
ag
e.
T
h
e
ce
r
tain
p
ix
el’
s
w
e
ig
h
t
d
ep
en
d
in
g
o
n
d
if
f
er
en
t
clas
s
h
a
s
ef
f
ec
ti
v
e
u
s
e
a
n
d
n
o
n
-
li
n
ea
r
r
elatio
n
s
h
ip
w
it
h
n
o
n
-
lo
ca
l
s
p
atial
in
f
o
r
m
a
tio
n
.
T
h
e
v
alu
e
o
f
(
0
,
0
)
an
d
is
f
ix
ed
an
d
p
o
s
s
ib
le
r
eg
io
n
is
p
r
ed
ef
i
n
ed
,
th
at
m
ea
n
s
d
u
r
i
n
g
o
p
ti
m
izatio
n
p
r
o
ce
s
s
th
e
v
alu
e
o
f
w
ill b
e
f
i
x
ed
.
3
.
2
.
AC
M
ba
s
ed
o
n SSI
(
s
pa
t
ia
l s
ha
pe
info
r
m
a
t
io
n)
T
h
e
MRF
is
d
ef
i
n
ed
b
y
Gib
b
’
s
d
is
tr
ib
u
tio
n
w
h
ic
h
i
s
r
ep
r
esen
ted
as p
r
io
r
p
r
o
b
ab
ilit
y
,
(
)
=
−
(
)
(
5
)
=
∑
−
(
)
=
1
(
6
)
w
h
er
e,
z
is
th
e
N
C
(
No
r
m
aliza
tio
n
co
n
s
ta
n
t)
.
(
)
r
ep
r
esen
ts
th
e
e
n
er
g
y
t
h
at
ar
e
m
ai
n
l
y
i
n
v
o
lv
ed
i
n
th
e
d
is
tr
ib
u
tio
n
o
f
p
ix
els.
T
h
e
p
o
ten
tial e
n
er
g
y
o
f
a
g
i
v
e
n
p
ix
el
i
s
d
escr
ib
ed
as,
(
)
=
∑
,
(
|
)
(
,
)
∈
(
7
)
I
n
th
e
ab
o
v
e
eq
u
atio
n
in
d
ic
ates
t
h
e
s
ta
tis
tica
l
i
n
d
ican
t
o
f
g
iv
e
n
p
i
x
els
w
h
er
ea
s
is
s
ta
tis
tica
l
in
d
ican
t
o
f
t
h
e
p
ix
el
,
cliq
u
e
is
d
en
o
ted
b
y
.
W
h
en
v
ar
io
u
s
cliq
u
e
.
(
p
o
ten
tial
f
u
n
c
tio
n
s
)
,
(
|
)
is
s
elec
ted
,
h
u
g
e
r
a
n
g
e
o
f
t
h
e
d
i
s
tr
ib
u
tio
n
ca
n
b
e
co
m
p
u
ted
.
T
h
u
s
p
r
io
r
p
o
s
s
ib
ilit
y
o
f
g
i
v
e
n
p
ix
el
i
s
f
o
r
m
u
lated
in
th
e
b
elo
w
eq
u
atio
n
.
(
)
=
1
(
−
∑
(
1
−
,
)
∈
)
(
8
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
11
,
No
.
2
,
A
p
r
il 2
0
2
1
:
1
2
5
0
-
1258
1254
,
is
g
i
v
en
m
e
m
b
er
s
o
f
p
ix
el
,
th
i
s
b
elo
n
g
s
to
th
e
g
i
v
e
n
clas
s
,
n
eig
h
b
o
r
s
ar
e
in
d
icate
d
b
y
.
Hen
ce
(
1
−
(
)
)
in
d
ica
tes th
e
r
e
f
u
s
ab
le
lev
el,
th
is
s
h
o
w
s
t
h
at
r
esis
tan
ce
h
as b
ee
n
u
s
ed
to
ass
i
g
n
th
e
p
ix
el
to
th
e
lab
el.
A
f
ter
an
al
y
z
in
g
,
r
e
f
u
s
ab
le
lev
el
i
s
r
ep
lace
d
b
y
t
h
e
f
o
llo
w
i
n
g
eq
u
atio
n
.
(
(
1
−
(
)
)
×
(
1
−
(
)
)
)
1
/
2
(
9
)
Her
e
,
(
1
−
(
)
)
d
o
es
n
o
t
c
h
a
n
g
e
an
d
t
h
e
w
ei
g
h
t
ad
d
ed
is
(
1
−
(
)
)
,
it
i
s
ap
p
licab
le
f
o
r
r
ev
i
s
in
g
th
e
r
eu
s
ab
le
lev
el
s
.
T
h
e
en
er
g
y
in
v
o
lv
ed
in
t
h
e
d
is
tr
ib
u
tio
n
o
f
p
ix
els
ca
n
b
e
r
ep
r
esen
ted
as,
(
)
=
(
1
−
(
−
1
,
−
1
)
)
(
1
+
(
+
1
,
+
1
)
)
(
1
0
)
+
(
1
−
(
−
1
,
)
)
(
1
−
(
+
1
,
)
)
+
(
1
−
(
,
−
1
)
)
(
1
−
(
,
+
1
)
)
+
(
1
−
(
−
1
,
+
1
)
)
(
1
−
(
+
1
,
−
1
)
)
(
,
)
is
th
e
m
e
m
b
er
o
f
t
h
e
g
iv
e
n
p
i
x
el
(
k
,
i
).
Mo
r
eo
v
er
,
lo
ca
l
as
w
ell
as
n
o
n
lo
ca
l
s
p
atial
i
n
f
o
r
m
atio
n
is
u
til
ized
i
n
o
r
d
er
to
i
m
p
r
o
v
i
s
e
th
e
F
C
M
p
er
f
o
r
m
a
n
ce
.
No
w
,
th
e
O
F (
o
b
j
ec
t f
u
n
cti
o
n
)
o
f
i
m
p
r
o
v
i
s
ed
F
C
M
ca
n
b
e
p
r
esen
ted
as,
=
∑
∑
(
)
(
(
1
−
(
)
)
×
(
1
−
(
)
)
)
1
/
2
=
1
∈
Ω
(
1
1
)
×
‖
−
‖
2
I
n
ca
s
e
o
f
t
h
e
co
n
s
tr
ain
t
co
n
d
itio
n
,
to
ac
h
ie
v
e
t
h
e
u
t
m
o
s
t
o
b
j
ec
t
f
u
n
ctio
n
v
al
u
e,
a
f
u
n
ct
io
n
i
s
d
ev
elo
p
ed
u
s
in
g
t
h
e
co
n
ce
p
t o
f
L
a
g
r
a
n
g
e
m
u
ltip
lier
s
[
2
7
]
.
=
∑
∑
(
)
(
(
1
−
(
)
)
×
(
1
−
(
)
)
)
1
/
2
=
1
∈
Ω
(
1
2
)
×
‖
−
‖
2
+
(
∑
−
1
=
1
)
is
th
e
L
ag
r
a
n
g
e
m
u
ltip
lier
.
Fo
r
s
i
m
p
lify
i
n
g
th
e
o
p
ti
m
izati
o
n
is
s
u
e,
d
er
iv
ativ
e
o
f
d
er
iv
e
d
f
u
n
ct
io
n
is
tak
e
n
an
d
it
is
p
r
esen
ted
i
n
th
e
(
1
3
)
.
Me
m
b
er
s
h
ip
f
u
n
ct
io
n
is
f
o
r
m
u
lated
b
y
t
h
e
g
i
v
e
n
eq
u
atio
n
.
=
(
1
‖
−
‖
2
(
1
(
(
1
−
(
)
)
×
(
1
−
(
)
)
)
)
∑
1
‖
−
‖
2
=
1
(
1
(
(
1
−
(
)
)
×
(
1
−
(
)
)
)
)
)
2
⁄
(
1
3
)
E
ac
h
class
h
as t
h
e
ce
n
tr
o
id
f
u
n
ctio
n
t
h
at
i
s
p
r
esen
ted
in
t
h
e
(
1
4
)
.
=
∑
(
2
2
(
(
1
−
(
)
)
×
(
1
−
(
)
)
×
)
1
/
2
)
∈
Ω
∑
(
2
2
(
(
1
−
(
)
)
×
(
1
−
(
)
)
)
1
/
2
)
∈
Ω
(
1
4
)
On
ce
th
e
n
o
n
lo
ca
l
an
d
lo
ca
l
s
p
atial
in
f
o
r
m
atio
n
is
i
n
tr
o
d
u
ce
d
,
A
C
M
ca
n
ea
s
il
y
o
u
tcast
th
e
is
s
u
e
w
h
ich
i
s
ca
u
s
ed
b
y
th
e
i
n
te
n
s
i
t
y
i
n
h
o
m
o
g
en
eit
y
,
t
h
is
clea
r
l
y
i
n
d
icate
s
th
at
th
e
p
r
o
p
o
s
ed
A
C
M
m
et
h
o
d
o
lo
g
y
i
s
n
o
v
el
a
s
w
ell
a
s
ef
f
ec
ti
v
e
f
o
r
th
e
s
e
g
m
e
n
tatio
n
o
f
p
lan
tar
t
h
er
m
o
g
r
a
m
s
.
4.
RE
SU
L
T
S
I
n
th
i
s
s
ec
tio
n
w
e
e
v
alu
a
te
an
d
p
r
esen
t
th
e
r
esu
lt
s
o
f
t
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
.
A
to
tal
o
f
5
0
p
lan
tar
th
er
m
o
g
r
a
m
s
w
er
e
in
cl
u
d
ed
i
n
th
is
s
tu
d
y
.
T
h
e
to
tal
n
u
m
b
e
r
o
f
d
iab
etic
g
r
o
u
p
s
a
m
p
le
s
ar
e
4
0
an
d
r
em
ai
n
i
n
g
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:
2
0
8
8
-
8708
A
u
to
ma
tic
s
eg
men
ta
tio
n
o
f p
la
n
ta
r
th
ermo
g
r
a
ms u
s
in
g
a
d
a
p
t
ive
C
mea
n
s
tech
n
iq
u
e
(
Ma
d
h
a
va
P
r
a
b
h
u
S
.
)
1255
1
0
s
a
m
p
les
ar
e
f
r
o
m
t
h
e
co
n
t
r
o
lled
g
r
o
u
p
.
T
h
is
al
g
o
r
ith
m
w
o
r
k
ed
p
er
f
ec
tl
y
f
o
r
all
t
h
e
c
u
r
r
en
t
i
m
a
g
es
w
h
e
n
co
m
p
ar
ed
w
it
h
th
e
g
r
o
u
n
d
r
es
u
lt.
Gen
er
all
y
,
it
w
ill
b
e
p
o
s
s
ib
le
t
o
m
a
k
e
a
n
i
n
itial
d
ec
is
io
n
b
as
ed
o
n
th
e
v
is
u
al
i
n
s
p
ec
tio
n
[
2
8
]
to
v
er
if
y
if
th
e
s
e
g
m
en
ta
tio
n
co
r
r
esp
o
n
d
in
g
to
a
n
R
OI
w
a
s
e
x
tr
ac
ted
co
r
r
ec
tl
y
o
r
n
o
t.
I
n
th
is
in
s
p
ec
tio
n
,
it
w
as
f
o
u
n
d
th
at
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
,
p
er
f
ec
tl
y
d
o
es
th
e
b
ac
k
g
r
o
u
n
d
-
f
o
r
eg
r
o
u
n
d
s
eg
m
en
tatio
n
.
T
h
er
e
w
er
e
n
o
ca
s
es
o
f
m
is
s
i
n
g
o
r
ad
d
itio
n
al
f
ea
t
u
r
es
in
th
e
s
eg
m
e
n
ted
i
m
ag
e.
4
.
1
.
Ca
s
e
co
m
pa
riso
n
Un
d
er
th
is
ca
te
g
o
r
y
th
e
r
esu
l
ts
o
b
tain
ed
f
r
o
m
th
e
p
r
o
p
o
s
e
d
alg
o
r
ith
m
is
co
m
p
ar
ed
w
it
h
th
e
s
n
a
k
e
alg
o
r
ith
m
.
T
h
e
s
n
a
k
es
al
g
o
r
ith
m
s
ep
ar
ates
th
e
f
ee
t
f
r
o
m
th
e
b
ac
k
g
r
o
u
n
d
au
to
m
atica
l
l
y
.
I
t
ca
n
also
b
e
ex
ten
d
ed
to
s
ep
ar
ate
th
e
r
ig
h
t
f
o
o
t
f
r
o
m
th
e
le
f
t
f
o
r
ea
ch
o
f
th
e
i
m
a
g
e
[
1
5
]
.
T
h
e
p
r
o
p
o
s
e
d
tech
n
iq
u
e
m
a
k
es
u
s
e
o
f
ad
ap
tiv
e
m
et
h
o
d
to
ex
tr
ac
t
th
e
R
OI
f
r
o
m
t
h
e
b
ac
k
g
r
o
u
n
d
an
d
it
d
etec
ts
t
h
e
f
o
o
t
co
n
t
o
u
r
b
etter
th
an
th
e
ex
is
i
tin
g
m
et
h
o
d
.
L
ater
o
n
,
t
h
e
ex
tr
ac
ted
R
OI
ca
n
b
e
u
s
ed
f
o
r
f
u
r
th
er
p
r
o
ce
s
s
i
n
g
.
T
h
e
T
ab
le
1
p
r
o
v
id
es
th
e
ca
s
e
co
m
p
ar
is
io
n
o
f
t
h
e
p
r
o
p
o
s
ed
tech
n
iq
u
e
w
it
h
an
e
x
is
tin
g
tech
n
iq
u
e.
T
ab
le
1
.
C
o
m
p
ar
is
o
n
o
f
ex
i
s
ti
n
g
tec
h
n
iq
u
e
w
it
h
t
h
e
p
r
o
p
o
s
e
d
s
y
s
te
m
C
a
se
#
I
n
p
u
t
i
m
a
g
e
S
n
a
k
e
a
l
o
g
r
i
t
h
m
A
C
M
T
e
c
h
n
i
q
u
e
1
2
3
4
.
2
.
Q
ua
lita
t
iv
e
re
s
ults
I
n
o
r
d
er
to
ev
alu
ate
t
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
w
e
h
a
v
e
co
n
s
id
er
ed
th
e
s
ev
er
al
p
atien
t
s
ce
n
ar
io
s
.
So
m
e
o
f
th
e
s
elec
ted
ca
s
es
o
u
t
o
f
5
0
s
a
m
p
les
ar
e
lis
ted
in
t
h
is
s
e
ctio
n
.
T
h
e
ca
s
e
n
u
m
b
er
s
ar
e
i
d
en
tifie
d
u
s
i
n
g
t
h
e
n
o
m
e
n
clat
u
r
e
w
h
ic
h
is
a
n
ea
s
y
w
a
y
to
d
etec
t
th
e
ca
s
es.
T
h
e
t
w
o
letter
s
ar
e
u
s
ed
to
in
d
icate
th
e
g
r
o
u
p
.
Fo
r
co
n
tr
o
l
g
r
o
u
p
,
C
G
a
n
d
f
o
r
th
e
d
iab
etic
g
r
o
u
p
,
DM
is
u
s
ed
as
t
w
o
letter
s
co
d
e.
T
h
en
it
is
f
o
llo
w
ed
b
y
t
w
o
d
ig
its
co
r
r
esp
o
n
d
in
g
to
th
e
s
er
ial
n
u
m
b
er
s
a
n
d
th
e
las
t
letter
in
d
icate
s
t
h
e
g
e
n
d
er
o
f
th
e
s
u
b
j
ec
t.
T
h
e
letter
M
an
d
F
ar
e
u
s
ed
f
o
r
r
ep
r
esen
t
i
n
g
m
a
le
a
n
d
F
r
esp
ec
ti
v
el
y
.
I
t
is
o
b
s
er
v
ed
t
h
at
in
all
th
e
ca
s
es
t
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
p
r
o
v
id
es
th
e
c
lo
s
est
co
n
to
u
r
to
t
h
e
g
r
o
u
n
d
r
es
u
lt
s
.
T
h
e
d
etails
o
f
s
eg
m
e
n
tatio
n
r
esu
lt
s
o
b
tain
ed
b
y
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
is
p
r
o
v
id
ed
in
th
e
T
ab
le
2
in
r
ef
er
en
ce
to
f
e
w
o
f
th
e
ca
s
es tes
ted
.
4
.
3
.
P
er
f
o
r
m
a
nce
m
et
ric
ev
a
lua
t
io
n
I
n
o
r
d
er
to
an
aly
ze
t
h
e
ef
f
ici
en
c
y
o
f
a
p
ar
ticu
lar
s
e
g
m
en
tatio
n
tec
h
n
iq
u
e,
t
h
er
e
ar
e
s
ev
er
al
p
er
f
o
r
m
a
n
ce
m
etr
ic
ev
al
u
atio
n
(
P
ME
)
p
ar
am
e
ter
s
ar
e
a
v
ai
lab
le.
No
f
o
r
m
al
w
a
y
is
f
o
llo
w
ed
to
c
h
o
o
s
e
t
h
e
m
etr
ics
o
u
t
o
f
m
a
n
y
f
o
r
a
p
a
r
ticu
lar
s
e
g
m
en
tatio
n
tas
k
.
T
h
e
m
o
s
t
o
f
t
h
e
r
esear
c
h
er
s
c
h
o
o
s
e
th
e
e
v
al
u
atio
n
m
etr
ics
ac
co
r
d
in
g
to
t
h
e
p
o
p
u
lar
it
y
[
2
9
]
.
T
h
e
s
elec
tio
n
o
f
t
h
e
b
est
e
v
alu
a
tio
n
m
e
tr
ics
d
ep
en
d
s
o
n
t
h
e
s
eg
m
e
n
tatio
n
g
o
al
b
as
ed
o
n
t
h
e
tas
k
to
b
e
ac
h
ie
v
ed
.
T
h
ese
m
etr
ics
w
i
ll
h
elp
in
r
ati
n
g
th
e
v
ar
io
u
s
al
g
o
r
ith
m
s
to
s
elec
t
it
p
r
o
p
er
l
y
a
n
d
to
a
ch
iev
e
t
h
e
i
n
te
n
d
ed
p
er
f
o
r
m
a
n
ce
[
3
0
]
.
T
h
e
m
o
s
t
ap
p
r
o
p
r
ia
te
t
w
o
m
ai
n
P
ME
p
ar
am
eter
s
f
o
r
p
lan
tar
th
er
m
al
i
m
ag
e
s
e
g
m
en
tatio
n
tec
h
n
iq
u
es
ar
e
d
ice
co
ef
f
icien
t
(
D
C
)
an
d
r
o
o
t
-
m
ea
n
-
s
q
u
ar
e
d
ev
iatio
n
(
R
M
SD)
.
L
e
t’
s
u
s
e
th
e
n
o
tatio
n
s
P
f
o
r
t
h
e
m
a
n
u
al
l
y
g
e
n
er
ated
g
r
o
u
n
d
t
r
u
th
b
y
t
h
e
ex
p
er
ts
an
d
Q
f
o
r
t
h
e
r
es
u
lt
g
e
n
er
ated
b
y
t
h
e
s
eg
m
e
n
tatio
n
al
g
o
r
ith
m
.
T
h
e
p
r
o
p
o
s
ed
w
o
r
k
is
co
m
p
ar
ed
w
it
h
s
o
m
e
o
f
th
e
e
x
i
s
ti
n
g
w
o
r
k
s
i
n
t
h
e
f
ie
ld
o
f
p
la
n
tar
t
h
er
m
al
i
m
a
g
e
s
eg
m
en
tatio
n
tec
h
n
iq
u
es
an
d
th
e
r
esu
lt
s
ar
e
p
r
o
v
id
ed
in
th
e
T
ab
le
3
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
11
,
No
.
2
,
A
p
r
il 2
0
2
1
:
1
2
5
0
-
1258
1256
T
ab
le
2
.
P
lan
tar
th
er
m
o
g
r
a
m
s
o
f
p
atien
ts
a
n
d
s
e
g
m
e
n
tat
io
n
r
esu
lt
s
C
a
se
#
I
n
p
u
t
i
m
a
g
e
R
O
I
i
d
e
n
t
i
f
i
e
d
I
n
p
u
t
w
i
t
h
R
O
I
su
p
e
r
i
mp
o
se
d
D
M
2
0
M
D
M
1
8
M
D
M
3
5
F
D
M
3
6
F
Dice
co
ef
f
icie
n
t,
g
i
v
es
a
n
id
ea
ab
o
u
t
t
h
e
s
i
m
i
lar
it
y
b
et
w
e
en
t
w
o
s
ets
o
f
d
ata
.
I
t
is
a
ls
o
ca
lled
as
Sø
r
en
s
e
n
-
D
ice
in
d
e
x
,
th
e
eq
u
a
tio
n
f
o
r
m
ea
s
u
r
i
n
g
th
e
D
C
is
g
iv
en
b
y
[
3
1
]
,
=
2
|
|
∩
|
|
|
|
+
|
|
(
1
5
)
T
h
e
ca
r
d
in
alit
y
o
f
t
h
e
s
et
i
s
r
e
p
r
esen
ted
b
y
t
h
e
v
er
tical
b
ar
s
,
i.e
.
th
e
n
u
m
b
er
o
f
ele
m
e
n
ts
i
n
t
h
at
s
et,
e.
g
.
th
e
n
u
m
b
er
o
f
ele
m
e
n
t
s
in
th
e
s
et
A
is
r
ep
r
esen
ted
b
y
|
A
|
.
T
h
e
ele
m
en
t
s
t
h
at
ar
e
co
m
m
o
n
to
b
o
th
th
e
s
et
s
ar
e
r
ep
r
esen
ted
b
y
t
h
e
in
ter
s
e
ctio
n
∩
.
T
h
e
r
o
o
t
-
m
ea
n
-
s
q
u
ar
e
d
ev
iatio
n
(
R
M
SD)
i
s
a
n
i
n
d
i
ca
tio
n
o
f
d
if
f
er
e
n
ce
o
f
th
e
p
ix
e
ls
t
h
r
o
u
g
h
o
u
t th
e
i
m
ag
e
s
o
f
g
r
o
u
n
d
tr
u
th
a
n
d
r
es
u
lt g
e
n
er
ated
b
y
t
h
e
s
eg
m
e
n
tat
io
n
alg
o
r
it
h
m
.
T
h
e
eq
u
atio
n
f
o
r
ca
lcu
lati
n
g
t
h
e
R
M
SD is
g
iv
e
n
b
y
[
3
2
]
,
=
√
1
∑
∑
(
−
)
2
(
1
6
)
Her
e,
N
is
th
e
s
ize
o
f
th
e
i
m
a
g
e
.
T
h
e
D
C
h
a
s
t
h
e
r
an
g
e
b
et
w
e
en
0
(
n
o
o
v
er
lap
)
to
1
(
p
er
f
e
ct
m
atc
h
)
.
T
h
e
R
M
SD
i
s
al
wa
y
s
g
r
ea
ter
th
an
0
an
d
i
f
t
h
e
v
al
u
e
is
0
(
w
h
ich
ca
n
n
o
t
b
e
ac
h
iev
ed
i
n
p
r
ac
tice)
in
d
icate
s
a
p
er
f
ec
t
m
at
ch
b
et
w
ee
n
g
r
o
u
n
d
r
esu
lt
an
d
s
e
g
m
e
n
tat
io
n
r
esu
l
t.
I
n
g
en
er
al,
h
i
g
h
er
v
al
u
e
o
f
DC
an
d
lo
w
er
v
al
u
e
o
f
R
M
S
D
h
ig
h
li
g
h
ts
b
etter
p
er
f
o
r
m
a
n
ce
o
f
t
h
e
al
g
o
r
ith
m
.
T
h
e
T
ab
le
3
g
iv
es
a
n
i
n
d
icatio
n
o
f
b
etter
p
er
f
o
r
m
an
ce
b
y
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
.
T
ab
le
3
.
P
ME
s
co
r
e
c
o
m
p
ar
is
i
o
n
T
e
c
h
n
i
q
u
e
DC
R
M
S
D
S
n
a
k
e
[
1
5
]
0
.
8
3
4
1
5
.
8
5
7
S
n
a
k
e
+
A
t
l
a
s
[
1
6
]
0
.
9
3
4
6
.
1
9
3
P
r
o
p
o
se
d
me
t
h
o
d
o
n
A
C
M
0
.
9
4
1
5
.
9
8
6
5.
CO
NCLU
SI
O
N
I
n
th
i
s
p
ap
er
,
an
ef
f
ec
ti
v
e
as
w
ell
a
s
r
o
b
u
s
t
tech
n
iq
u
e
o
f
s
e
g
m
e
n
tat
io
n
is
p
r
esen
ted
.
T
h
e
t
ec
h
n
iq
u
e
i
s
n
a
m
ed
as
ad
ap
ti
v
e
C
m
ea
n
s
(
AC
M
)
.
T
h
is
ap
p
r
o
ac
h
is
v
er
y
ef
f
icien
t,
less
p
r
o
n
e
to
n
o
is
e
a
n
d
p
r
o
v
id
es
h
ig
h
l
y
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:
2
0
8
8
-
8708
A
u
to
ma
tic
s
eg
men
ta
tio
n
o
f p
la
n
ta
r
th
ermo
g
r
a
ms u
s
in
g
a
d
a
p
t
ive
C
mea
n
s
tech
n
iq
u
e
(
Ma
d
h
a
va
P
r
a
b
h
u
S
.
)
1257
ac
cu
r
ate
r
esu
l
ts
.
N
on
-
lo
ca
l
s
p
atial
in
f
o
r
m
atio
n
(
NSI
)
is
a
p
p
lied
b
y
u
s
in
g
t
h
e
m
e
m
b
er
s
h
ip
d
eg
r
ee
o
f
ea
c
h
g
iv
e
n
p
i
x
el
a
n
d
s
p
atia
l
s
h
ap
e
in
f
o
r
m
atio
n
(
SSI
)
i
s
h
elp
f
u
l
in
m
i
n
i
m
izi
n
g
th
e
r
u
n
ti
m
e.
W
h
en
t
h
e
r
e
s
u
l
t
is
co
m
p
ar
ed
w
it
h
th
e
ex
i
s
ti
n
g
s
i
m
ilar
m
o
d
el,
it
is
o
b
s
er
v
ed
th
at
th
e
p
r
o
p
o
s
ed
m
o
d
el
p
r
o
v
id
es
t
h
e
ab
s
o
l
u
te
R
OI
w
h
ic
h
h
elp
s
i
n
id
en
tify
i
n
g
th
e
DFU.
T
h
e
ap
p
r
o
ac
h
ca
n
b
e
ex
ten
d
ed
an
d
o
p
ti
m
ized
f
o
r
f
u
r
t
h
er
p
r
o
ce
s
s
in
g
.
T
h
is
ap
p
r
o
ac
h
is
v
er
y
m
u
c
h
ap
p
r
o
p
r
iate
co
m
p
ar
ed
to
co
n
v
en
t
io
n
al
tec
h
n
iq
u
e
s
s
u
ch
a
s
th
r
es
h
o
ld
in
g
,
ed
g
e
b
ased
an
d
w
a
ter
s
h
ed
b
ased
s
eg
m
e
n
tatio
n
s
.
T
h
e
p
r
o
p
o
s
ed
m
e
th
o
d
h
av
e
s
h
o
w
n
s
o
m
e
l
i
m
itat
io
n
s
as
it
an
u
n
s
u
p
er
v
is
ed
alg
o
r
it
h
m
.
T
o
d
etec
t
th
e
co
ld
zo
n
es
in
th
e
ed
g
es
o
f
to
es,
s
u
p
er
v
i
s
ed
alg
o
r
ith
m
s
ca
n
p
er
f
o
r
m
b
etter
as
th
e
y
ar
e
tr
ai
n
ed
.
T
h
o
u
g
h
t
h
is
i
s
j
u
s
t
a
s
tep
to
w
ar
d
s
th
e
d
etec
tio
n
o
f
DFU,
i
n
f
u
t
u
r
e
w
it
h
t
h
e
h
elp
o
f
th
is
i
m
ag
e
s
eg
m
e
n
tatio
n
tec
h
n
iq
u
e
it c
a
n
b
e
i
m
p
le
m
e
n
ted
f
o
r
f
ea
t
u
r
e
ex
tr
ac
t
io
n
a
n
d
it c
o
u
ld
h
elp
i
n
u
p
co
m
in
g
r
esear
ch
to
w
ar
d
s
t
h
e
b
etter
d
etec
tio
n
o
f
t
h
e
d
iab
etes.
ACK
NO
WL
E
D
G
E
M
E
NT
S
T
h
e
au
th
o
r
s
w
o
u
ld
l
ik
e
to
e
x
t
en
d
t
h
eir
s
p
ec
ial
th
a
n
k
s
to
Mr
.
Sh
a
h
u
l
Ha
m
ee
d
,
Fo
u
n
d
er
&
C
E
O,
DI
T
,
I
n
d
ia
f
o
r
p
r
o
v
id
in
g
t
h
e
n
ec
e
s
s
ar
y
t
h
er
m
al
i
m
ag
e
s
.
RE
F
E
R
E
NC
E
S
[1
]
S
.
M
.
P
ra
b
h
u
a
n
d
S
.
V
e
rm
a
,
“
C
o
m
p
a
ra
ti
v
e
A
n
a
l
y
sis
o
f
S
e
g
m
e
n
tatio
n
tec
h
n
i
q
u
e
s
f
o
r
P
ro
g
re
ss
iv
e
Ev
a
lu
a
ti
o
n
a
n
d
Risk
Id
e
n
ti
f
ica
ti
o
n
o
f
Dia
b
e
ti
c
F
o
o
t
Ulc
e
rs,”
2
0
1
9
4
th
M
EC
I
n
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
Bi
g
Da
t
a
a
n
d
S
m
a
rt
Cit
y
(
ICBDS
C)
,
M
u
sc
a
t,
Om
a
n
,
2
0
1
9
,
p
p
.
1
-
6
.
[2
]
K.
G
.
O
sg
o
u
ie
a
n
d
A
.
A
z
i
z
i,
"
Op
ti
m
izin
g
f
u
z
z
y
lo
g
ic
c
o
n
tro
ll
e
r
f
o
r
d
iab
e
tes
ty
p
e
I
b
y
g
e
n
e
ti
c
a
lg
o
rit
h
m
,
"
2
0
1
0
T
h
e
2
n
d
In
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
Co
mp
u
ter
a
n
d
A
u
to
m
a
ti
o
n
E
n
g
in
e
e
rin
g
(
ICCAE
)
,
S
in
g
a
p
o
re
,
2
0
1
0
,
p
p
.
4
-
8.
[3
]
W
HO
, “
Dia
b
e
tes
,
”
2
0
2
0
.
[
O
n
li
n
e
]
.
A
v
a
il
a
b
le at:
h
tt
p
s://
w
ww
.
w
h
o
.
in
t/
h
e
a
lt
h
-
to
p
ics
/d
ia
b
e
tes
#
tab
=
tab
_
1
.
[4
]
Nin
g
W
a
n
g
a
n
d
G
u
ix
ia
K
a
n
g
,
"
A
m
o
n
it
o
rin
g
s
y
ste
m
f
o
r
t
y
p
e
2
d
iab
e
tes
m
e
ll
it
u
s,"
2
0
1
2
IEE
E
1
4
th
I
n
ter
n
a
t
io
n
a
l
Co
n
fer
e
n
c
e
o
n
e
-
He
a
lt
h
Ne
two
rk
i
n
g
,
Ap
p
li
c
a
t
io
n
s a
n
d
S
e
rv
ice
s (
H
e
a
lt
h
c
o
m)
,
Be
ij
i
n
g
,
2
0
1
2
,
p
p
.
6
2
-
6
7
.
[5
]
S
.
Ra
h
a
m
a
n
,
"
Dia
b
e
tes
d
iag
n
o
sis
d
e
c
isio
n
su
p
p
o
rt
sy
ste
m
b
a
se
d
o
n
sy
m
p
to
m
s,
sig
n
s
a
n
d
risk
f
a
c
to
r
u
sin
g
s
p
e
c
ial
c
o
m
p
u
tatio
n
a
l
a
lg
o
rit
h
m
b
y
ru
l
e
b
a
se
,
"
2
0
1
2
1
5
th
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Co
mp
u
ter
a
n
d
In
f
o
rm
a
ti
o
n
T
e
c
h
n
o
l
o
g
y
(
ICCIT
),
C
h
it
tag
o
n
g
,
2
0
1
2
,
p
p
.
6
5
-
7
1
.
[6
]
M
.
A
d
a
m
,
E.
Y.
N
g
,
J.
H.
T
a
n
,
M
.
L
.
He
n
g
,
J.
W
.
T
o
n
g
,
a
n
d
U.
R.
A
c
h
a
r
y
a
,
“
Co
m
p
u
ter
a
id
e
d
d
iag
n
o
sis
o
f
d
iab
e
ti
c
f
o
o
t
u
sin
g
i
n
f
ra
re
d
th
e
rm
o
g
ra
p
h
y
:
A
re
v
i
e
w
,
”
Co
mp
u
ter
s in
Bi
o
l
o
g
y
a
n
d
M
e
d
icin
e
,
v
o
l
.
9
1
,
p
p
.
3
2
6
-
3
3
6
,
2
0
1
7
.
[7
]
S.
M
.
P
ra
b
h
u
a
n
d
S
.
V
e
rm
a
,
“
A
S
y
st
e
m
a
ti
c
L
it
e
ra
tu
re
Re
v
ie
w
f
o
r
Early
De
te
c
ti
o
n
o
f
Ty
p
e
II
Dia
b
e
tes
,
”
2
0
1
9
5
t
h
In
ter
n
a
t
io
n
a
l
Co
n
fer
e
n
c
e
o
n
A
d
v
a
n
c
e
d
Co
m
p
u
ti
n
g
&
Co
mm
u
n
ica
ti
o
n
S
y
ste
ms
(
ICACCS
)
,
Co
im
b
a
to
re
,
In
d
ia
,
2
0
1
9
,
p
p
.
2
2
0
-
224
.
[8
]
T
.
Be
rn
a
rd
,
C.
D'
El
ia,
R.
Ka
b
a
d
i
a
n
d
N.
W
o
n
g
,
"
A
n
e
a
rl
y
d
e
tec
ti
o
n
sy
ste
m
f
o
r
f
o
o
t
u
lce
ra
ti
o
n
in
d
i
a
b
e
ti
c
p
a
ti
e
n
ts,"
2
0
0
9
IE
EE
3
5
t
h
A
n
n
u
a
l
No
rth
e
a
st B
io
e
n
g
i
n
e
e
rin
g
C
o
n
fer
e
n
c
e
,
Bo
s
to
n
,
M
A
,
2
0
0
9
,
p
p
.
1
-
2.
[9
]
I.
Cru
z
-
V
e
g
a
,
D.
He
rn
a
n
d
e
z
-
Co
n
trera
s,
H.
P
e
re
g
rin
a
-
Ba
rre
to
,
J.
R
a
n
g
e
l
-
M
a
g
d
a
len
o
a
n
d
J.
Ra
m
ire
z
-
Co
rtes
,
"
De
e
p
L
e
a
rn
in
g
Clas
si
f
ica
ti
o
n
f
o
r
Dia
b
e
ti
c
F
o
o
t
T
h
e
rm
o
g
ra
m
s,"
S
e
n
so
rs
,
v
o
l.
2
0
,
n
o
.
6
,
p
.
1
7
6
2
,
2
0
2
0
.
[1
0
]
M
o
ri,
T
.
,
Na
g
a
se
,
T
.
,
T
a
k
e
h
a
ra
,
K.,
Oe
,
M
.
,
Oh
a
sh
i,
Y.
,
Am
e
m
iy
a
,
A
.
,
No
g
u
c
h
i,
H.,
Ue
k
i,
K.,
Ka
d
o
w
a
k
i,
T
.
,
S
a
n
a
d
a
,
H.
,
“
M
o
r
p
h
o
lo
g
ica
l
p
a
t
tern
c
las
sif
i
c
a
ti
o
n
sy
ste
m
f
o
r
p
lan
tar
th
e
rm
o
g
ra
p
h
y
o
f
p
a
ti
e
n
ts
w
it
h
d
iab
e
tes
,
”
J
o
u
rn
a
l
o
f
Dia
b
e
tes
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
l
ogy
,
v
o
l.
7
,
n
o
.
5
,
p
p
.
1
1
0
2
-
1
1
1
2
,
2
0
1
3
.
[1
1
]
Jo
n
e
s,
B.
F.
,
“
A
re
a
p
p
ra
isa
l
o
f
t
h
e
u
se
o
f
in
f
ra
re
d
th
e
r
m
a
l
i
m
a
g
e
a
n
a
l
y
sis
in
m
e
d
icin
e
,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
M
e
d
ica
l
Ima
g
in
g
,
v
o
l.
1
7
,
n
o
.
6
,
p
p
.
1
0
1
9
-
1
0
2
7
,
1
9
9
8
.
[1
2
]
Ka
a
b
o
u
c
h
,
N.
,
Ch
e
n
,
Y.,
Hu
,
W
.
-
C.
,
A
n
d
e
rso
n
,
J.
W
.
,
Am
e
s,
F
.
,
P
a
u
lso
n
,
R.
,
“
En
h
a
n
c
e
m
e
n
t
o
f
t
h
e
a
s
y
m
m
e
tr
y
b
a
s
e
d
o
v
e
r
l
a
p
p
i
n
g
a
n
a
l
y
s
i
s
r
o
u
g
h
f
e
a
t
u
r
e
s
e
x
t
r
a
c
t
i
o
n
,
”
J
o
u
r
n
a
l
o
f
E
l
e
c
t
r
o
n
i
c
I
m
a
g
i
n
g
,
v
o
l
.
2
0
,
n
o
.
1
,
p
.
0
1
3
0
1
2
,
2
0
1
1
.
[1
3
]
L
iu
,
C
.,
v
a
n
N
e
tt
e
n
,
J.
J.,
v
a
n
B
a
a
l,
J.
G
.
,
Bu
s,
S
.
A
.
,
v
a
n
d
e
r
H
e
ij
d
e
n
,
F
.
,
“
A
u
to
m
a
ti
c
d
e
tec
ti
o
n
o
f
d
iab
e
ti
c
f
o
o
t
c
o
m
p
li
c
a
ti
o
n
s
w
it
h
in
f
ra
re
d
th
e
r
m
o
g
r
a
p
h
y
b
y
a
s
y
m
m
e
tri
c
a
n
a
l
y
si
s,”
J
o
u
rn
a
l
o
f
Bi
o
me
d
ica
l
O
p
t
ics
,
v
o
l.
2
0
,
n
o
.
2
,
p
.
0
2
6
0
0
3
,
2
0
1
5
.
[1
4
]
G
.
L
.
Na
n
d
a
g
o
p
a
n
a
n
d
A
.
B.
Ha
ri
p
riy
a
,
"
I
m
p
le
m
e
n
tatio
n
a
n
d
c
o
m
p
a
riso
n
o
f
tw
o
ima
g
e
se
g
m
e
n
tatio
n
tec
h
n
i
q
u
e
s o
n
th
e
rm
a
l
f
o
o
t
i
m
a
g
e
s
a
n
d
d
e
tec
ti
o
n
o
f
u
lce
ra
ti
o
n
u
sin
g
a
sy
m
m
e
tr
y
,
"
2
0
1
6
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Co
mm
u
n
ica
ti
o
n
a
n
d
S
i
g
n
a
l
Pro
c
e
ss
in
g
(
ICCS
P)
,
M
e
lm
a
ru
v
a
th
u
r
,
2
0
1
6
,
p
p
.
0
3
5
6
-
0
3
6
0
.
[1
5
]
Et
e
h
a
d
tav
a
k
o
l,
M
.
,
Ng
,
E.
,
Ka
a
b
o
u
c
h
,
N.
,
“
A
u
to
m
a
ti
c
se
g
m
e
n
tatio
n
o
f
th
e
rm
a
l
i
m
a
g
e
s
o
f
d
iab
e
ti
c
-
at
-
risk
fe
e
t
u
sin
g
th
e
sn
a
k
e
a
lg
o
rit
h
m
,
”
In
fra
re
d
P
h
y
sic
s
&
T
e
c
h
n
o
lo
g
y
,
v
o
l.
8
6
,
p
p
.
6
6
-
76
,
2
0
1
7
.
[1
6
]
A
s
m
a
,
B.
,
Ha
rb
a
,
R.
,
Ca
n
a
ls,
R.
,
L
e
d
e
e
,
R.
,
Ja
b
lo
u
n
,
R.
,
“
A
jo
in
t
s
n
a
k
e
a
n
d
a
tl
a
s
-
b
a
se
d
se
g
m
e
n
tatio
n
o
f
p
lan
tar f
o
o
t
th
e
rm
a
l
i
m
a
g
e
s,”
2
0
1
7
S
e
v
e
n
th
In
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
Im
a
g
e
Pro
c
e
ss
in
g
T
h
e
o
ry
,
T
o
o
ls
a
n
d
A
p
p
li
c
a
ti
o
n
s
(
IPT
A)
,
M
o
n
trea
l,
QC,
Ca
n
a
d
a
,
2
0
1
7
,
p
p
.
1
-
6
.
[1
7
]
T
.
Na
g
a
se
,
H.
S
a
n
a
d
a
,
K.
T
a
k
e
h
a
ra
,
M
.
Oe
,
S
.
Iiza
k
a
,
Y.
Oh
a
sh
i,
M
.
Ob
a
,
T
.
Ka
d
o
w
a
k
i,
G
.
Na
k
a
g
a
m
i,
“
V
a
riatio
n
s
o
f
p
lan
tar
th
e
rm
o
g
ra
p
h
ic
p
a
tt
e
rn
s
in
n
o
rm
a
l
c
o
n
tro
ls
a
n
d
n
o
n
-
u
l
c
e
r
d
iab
e
ti
c
p
a
ti
e
n
ts:
n
o
v
e
l
c
las
sif
ica
ti
o
n
u
sin
g
a
n
g
io
so
m
e
c
o
n
c
e
p
t,
”
J
o
u
rn
a
l
o
f
P
la
stic,
Rec
o
n
stru
c
ti
v
e
a
n
d
Aes
t
h
e
t
ic
S
u
rg
e
ry
,
v
o
l
.
6
4
,
n
o
.
7
,
p
p
.
8
6
0
-
8
6
6
,
2
0
1
1
.
[1
8
]
He
rn
a
n
d
e
z
-
Co
n
t
re
ra
s,
D.,
P
e
re
g
rin
a
-
Ba
rre
to
,
H.,
Ra
n
g
e
l
-
M
a
g
d
a
le
n
o
,
J.,
Ra
m
irez
-
Co
rtes
,
J.,
R
e
n
e
ro
-
Ca
rril
lo
,
F
.
,
“
A
u
to
m
a
ti
c
c
la
ss
i
f
ica
ti
o
n
o
f
th
e
rm
a
l
p
a
tt
e
rn
s
in
d
ia
b
e
ti
c
f
o
o
t
b
a
s
e
d
o
n
m
o
rp
h
o
l
o
g
ica
l
p
a
tt
e
rn
sp
e
c
tru
m
,
”
In
fra
re
d
Ph
y
sic
s
&
T
e
c
h
n
o
lo
g
y
,
v
o
l.
7
3
,
p
p
.
1
4
9
-
1
5
7
,
2
0
1
5
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
11
,
No
.
2
,
A
p
r
il 2
0
2
1
:
1
2
5
0
-
1258
1258
[1
9
]
G
u
ru
ra
jara
o
,
S
.
B.
,
V
e
n
k
a
tap
p
a
,
U.,
S
h
iv
a
ra
m
,
J.
M.,
S
ik
k
a
n
d
a
r,
M
.
Y.,
Am
o
u
d
i
,
A
.
A
.
“
In
f
ra
re
d
T
h
e
r
m
o
g
ra
p
h
y
a
n
d
S
o
f
t
Co
m
p
u
ti
n
g
f
o
r
Dia
b
e
ti
c
F
o
o
t
A
ss
e
ss
m
e
n
t
,
”
M
a
c
h
in
e
L
e
a
rn
in
g
in
Bi
o
-
S
ig
n
a
l
An
a
lys
is
a
n
d
Dia
g
n
o
st
ic
Ima
g
i
n
g
,
p
p
.
7
3
-
97
,
2
0
1
9
.
[2
0
]
S
h
a
rm
a
N
.
,
a
n
d
Ag
g
a
r
wa
l
L
.
M.
,
“
A
u
to
m
a
ted
m
e
d
ica
l
i
m
a
g
e
se
g
m
e
n
tatio
n
tec
h
n
iq
u
e
s,”
J
o
u
rn
a
l
o
f
M
ed
ica
l
Ph
y
s
ics
,
v
o
l.
3
5
,
n
o
.
1
,
p
p
.
3
-
1
4
,
2
0
1
0
.
[2
1
]
M
.
Et
e
h
a
d
T
a
v
a
k
o
l,
S
.
S
a
d
r
i,
E
.
Y.
K.
Ng
,
“
A
p
p
li
c
a
ti
o
n
o
f
K
-
a
n
d
f
u
z
z
y
c
-
M
e
a
n
s
f
o
r
c
o
lo
r
se
g
m
e
n
tatio
n
o
f
th
e
rm
a
l
in
f
ra
re
d
b
re
a
st i
m
a
g
e
s,
”
J
o
u
rn
a
l
o
f
M
e
d
ica
l
S
y
ste
m
s
,
v
o
l.
3
4
,
n
o
.
1
,
p
p
.
3
5
-
42
,
2
0
1
0
.
[2
2
]
K.
Ch
u
a
n
g
,
"
F
u
z
z
y
c
-
m
e
a
n
s
c
lu
s
terin
g
w
it
h
sp
a
ti
a
l
in
f
o
r
m
a
ti
o
n
fo
r
im
a
g
e
se
g
m
e
n
tatio
n
,
"
Co
mp
u
t
e
rize
d
M
e
d
ica
l
Ima
g
i
n
g
a
n
d
Gr
a
p
h
ics
,
v
o
l
.
3
0
,
n
o
.
1
,
p
p
.
9
-
1
5
,
2
0
0
6
.
[2
3
]
M
.
L
i,
"
A
d
a
p
ti
v
e
S
e
g
m
e
n
tatio
n
o
f
R
e
m
o
te
S
e
n
sin
g
I
m
a
g
e
s
Ba
s
e
d
o
n
G
lo
b
a
l
S
p
a
ti
a
l
In
f
o
rm
a
ti
o
n
,"
S
e
n
so
rs
(
B
a
se
l),
v
o
l.
1
9
,
n
o
.
1
0
,
p
.
2
3
8
5
,
2
0
1
9
.
[2
4
]
Y.
Ho
n
g
Lei,
"
R
e
m
o
te
S
e
n
sin
g
Clas
sif
ic
a
ti
o
n
Us
in
g
F
u
z
z
y
C
-
m
e
a
n
s
Clu
ste
rin
g
w
it
h
S
p
a
ti
a
l
Co
n
st
ra
in
t
s
Ba
se
d
o
n
M
a
rk
o
v
Ra
n
d
o
m
F
ield
,
"
Eu
r
o
p
e
a
n
J
o
u
r
n
a
l
o
f
Rem
o
te S
e
n
sin
g
,
v
o
l.
4
6
,
n
o
.
1
,
p
p
.
3
0
5
-
3
1
6
,
2
0
1
3
.
[2
5
]
L
.
V
il
c
a
h
u
a
m
a
n
,
e
t
a
l.
,
"
De
tec
ti
o
n
o
f
d
iab
e
ti
c
f
o
o
t
h
y
p
e
rth
e
r
m
ia
b
y
in
f
r
a
re
d
i
m
a
g
in
g
,
"
2
0
1
4
3
6
t
h
An
n
u
a
l
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
f
t
h
e
I
E
E
E
E
n
g
i
n
e
e
r
i
n
g
i
n
M
e
d
i
c
i
n
e
a
n
d
B
i
o
l
o
g
y
S
o
c
i
e
t
y
,
C
h
i
c
a
g
o
,
I
L
,
2
0
1
4
,
p
p
.
4
8
3
1
-
4834.
[2
6
]
S
.
P
u
r
n
im
a
,
"
A
u
to
m
a
ted
De
t
e
c
t
io
n
o
f
Dia
b
e
ti
c
F
o
o
t
Us
in
g
T
h
e
r
m
a
l
I
m
a
g
e
s
b
y
N
e
u
ra
l
Ne
t
w
o
rk
Clas
si
f
iers
,"
In
ter
n
a
t
io
n
a
l
jo
u
rn
a
l
o
f
Eme
rg
in
g
T
re
n
d
s in
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
lo
g
y
,
v
o
l.
0
4
,
n
o
.
0
5
,
p
p
.
5
1
8
3
-
5
1
8
8
,
2
0
1
7
.
[2
7
]
V
.
V
e
la
-
Ri
n
c
ó
n
,
"
S
p
a
ti
a
l
a
l
p
h
a
-
T
ri
m
m
e
d
F
u
z
z
y
C
-
M
e
a
n
s
A
l
g
o
rit
h
m
to
Im
a
g
e
S
e
g
m
e
n
tatio
n
,
"
L
e
c
tu
re
No
tes
in
Co
mp
u
ter
S
c
ien
c
e
,
p
p
.
1
1
8
-
1
2
8
,
2
0
2
0
.
[2
8
]
J.
G
a
u
c
i,
"
A
u
to
m
a
ted
Re
g
io
n
Ex
trac
ti
o
n
f
ro
m
T
h
e
r
m
a
l
I
m
a
g
e
s
f
o
r
P
e
rip
h
e
r
a
l
V
a
sc
u
lar
Dise
a
se
M
o
n
it
o
ri
n
g
,
"
J
o
u
rn
a
l
o
f
He
a
lt
h
c
a
re
En
g
in
e
e
rin
g
,
v
o
l.
2
0
1
8
,
p
p
.
1
-
1
4
,
2
0
1
8
.
[2
9
]
A
.
A
.
T
a
h
a
,
A
.
H
a
n
b
u
ry
a
n
d
O.
A
.
J.
De
l
T
o
r
o
,
"
A
f
o
r
m
a
l
m
e
t
h
o
d
f
o
r
se
lec
ti
n
g
e
v
a
lu
a
ti
o
n
m
e
t
rics
f
o
r
i
m
a
g
e
se
g
m
e
n
tatio
n
,
"
2
0
1
4
IE
EE
I
n
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Ima
g
e
Pr
o
c
e
ss
in
g
(
ICIP)
,
P
a
ris
,
2
0
1
4
,
p
p
.
9
3
2
-
9
3
6
.
[3
0
]
S
.
Ku
m
a
r,
"
P
e
rf
o
r
m
a
n
c
e
M
e
tri
c
Ev
a
lu
a
ti
o
n
o
f
S
e
g
m
e
n
tatio
n
A
l
g
o
rit
h
m
s
f
o
r
G
o
ld
S
tan
d
a
rd
M
e
d
ica
l
I
m
a
g
e
s,"
Rec
e
n
t
Fi
n
d
in
g
s in
In
tell
ig
e
n
t
Co
mp
u
ti
n
g
T
e
c
h
n
i
q
u
e
s
,
v
o
l
.
7
0
9
,
p
p
.
4
5
7
-
4
6
9
,
2
0
1
8
.
[3
1
]
C.
M
o
o
re
,
e
t
a
l.
,
"
Dic
e
sim
il
a
r
it
y
c
o
e
ff
icie
n
t
|
Ra
d
io
lo
g
y
Re
f
e
r
e
n
c
e
A
rti
c
le
|
Ra
d
io
p
a
e
d
ia.o
rg
,
"
Ra
d
i
o
p
a
e
d
i
a
.
o
r
g
,
2
0
2
0
.
[
O
n
li
n
e
].
A
v
a
i
l
a
b
le:
h
tt
p
s://
ra
d
io
p
a
e
d
ia.
o
r
g
/article
s/d
ice
-
sim
i
larity
-
c
o
e
ff
ici
e
n
t
.
[3
2
]
S
.
G
len
,
"
RM
S
E:
Ro
o
t
M
e
a
n
S
q
u
a
re
Err
o
r
-
S
tatisti
c
s
Ho
w
T
o
,
"
S
ta
ti
stics
Ho
w
T
o
,
2
0
2
0
.
[
On
li
n
e
]
.
A
v
a
il
a
b
le
:
h
tt
p
s:/
/www
.
sta
ti
stics
h
o
w
to
.
c
o
m
/
p
ro
b
a
b
il
it
y
-
a
n
d
-
sta
ti
stics
/reg
re
ss
i
o
n
-
a
n
a
ly
sis/r
m
se
-
ro
o
t
-
mean
-
sq
u
a
re
-
e
rro
r/#
:
~
:
tex
t=Ro
o
t%
2
0
M
e
a
n
%
2
0
S
q
u
a
re
%
2
0
Err
o
r%
2
0
(RM
S
E)%
2
0
is%
2
0
th
e
%
2
0
sta
n
d
a
rd
%
2
0
d
e
v
i
a
ti
o
n
%
2
0
o
f
,
th
e
%
2
0
li
n
e
%
2
0
o
f
%
2
0
b
e
st%
2
0
f
it
.
B
I
O
G
RAP
H
I
E
S
O
F
AUTH
O
RS
M
a
d
h
a
v
a
Pr
a
b
h
u
S
.
o
b
tain
e
d
h
is
M
a
ste
r
d
e
g
re
e
in
Em
b
e
d
d
e
d
S
y
ste
m
s
f
ro
m
M
a
n
ip
a
l
Un
iv
e
rsit
y
In
d
ia,
a
n
d
Ba
c
h
e
lo
r
d
e
g
re
e
in
El
e
c
tro
n
ics
&
Co
m
m
u
n
ica
ti
o
n
En
g
in
e
e
rin
g
f
ro
m
V
isv
e
sv
a
ra
y
a
Tec
h
n
o
lo
g
ica
l
Un
iv
e
rsit
y
In
d
ia
in
2
0
0
8
a
n
d
2
0
0
5
re
sp
e
c
ti
v
e
ly
.
His
re
se
a
r
c
h
in
tere
st are
sin
g
a
l
p
ro
c
e
ss
in
g
a
n
d
e
m
b
e
d
d
e
d
sy
ste
m
s.
S
e
e
m
a
V
e
r
m
a
o
b
tain
e
d
h
e
r
M
.
T
e
c
h
a
n
d
P
h
.
D
d
e
g
re
e
f
ro
m
Ba
n
a
sth
a
li
Un
iv
e
rsity
in
2
0
0
3
a
n
d
2
0
0
6
re
sp
e
c
ti
v
e
ly
.
S
h
e
is
w
o
rk
in
g
a
s
a
P
r
o
f
e
ss
o
r
in
t
h
e
d
e
p
a
rtm
e
n
t
o
f
El
e
c
tro
n
ics
.
He
r
re
se
a
rc
h
a
re
a
s a
re
V
L
S
I
De
si
g
n
,
c
o
m
m
u
n
ica
ti
o
n
Ne
tw
o
rk
s a
n
d
sig
n
a
l
p
r
o
c
e
ss
in
g
.
Evaluation Warning : The document was created with Spire.PDF for Python.