I
n
d
on
e
s
ian
Jou
r
n
al
o
f
E
lec
t
r
ica
l
E
n
gin
e
e
r
in
g
a
n
d
Com
p
u
t
e
r
S
c
ience
Vo
l
.
3
6
,
N
o
.
2
,
N
o
v
e
m
b
e
r
20
24
,
pp.
1
30
9
~
1
31
8
I
S
S
N:
2
502
-
4
7
52
,
DO
I
:
10
.
11591/i
j
e
e
cs
.v
3
6
.
i
2
.
pp
1
30
9
-
1
31
8
1309
Jou
r
n
al
h
o
m
e
page
:
ht
tp:
//
ij
e
e
cs
.
iaes
c
or
e
.
c
om
Op
t
i
m
i
z
i
n
g h
y
p
e
r
sp
e
c
t
r
al
c
la
ssi
f
ic
at
io
n
:
sp
e
c
t
r
al
si
m
il
ar
ity
-
b
ase
d
b
a
n
d
se
le
c
t
io
n
w
ith
c
h
o
r
d
k
-
m
e
a
n
s
Or
igan
t
i
S
u
b
h
as
h
Chan
d
e
r
Gou
d
1
,
T
h
ogar
ac
h
e
t
t
i
H
it
e
n
d
r
a
S
ar
m
a
2
,
Chi
gar
ap
all
e
S
h
ob
a
B
in
d
u
1
1
D
e
pa
r
tm
e
nt
of
C
o
mpu
te
r
S
c
ie
n
c
e
a
nd E
ngi
n
e
e
r
in
g, C
o
ll
e
g
e
of
E
ngi
ne
e
r
in
g, J
a
w
a
ha
r
la
l
N
e
hr
u
T
e
c
hn
ol
o
gi
c
a
l
U
ni
ve
r
s
it
y
(
J
N
T
U
)
,
A
na
nt
a
pur
, I
ndi
a
2
D
e
pa
r
tm
e
nt
of
I
n
f
or
ma
ti
o
n
T
e
c
hn
o
l
o
g
y
, V
a
s
a
v
i
C
ol
le
g
e
of
E
n
gi
ne
e
r
in
g,
H
y
d
e
r
a
ba
d, I
ndi
a
Ar
t
ic
l
e
I
n
f
o
AB
S
T
RA
CT
A
r
ti
c
le
h
is
tor
y
:
R
e
c
e
i
ve
d
M
a
r
15
,
202
4
R
e
vi
s
e
d
J
u
l
27
,
202
4
A
c
c
e
pt
e
d
A
ug
5
,
202
4
Ban
d
s
el
ec
t
i
o
n
i
s
c
ru
ci
al
f
o
r
a
c
h
i
e
v
i
n
g
h
i
g
h
c
l
as
s
i
f
i
c
at
i
o
n
a
ccu
ra
cy
i
n
h
y
p
e
rs
p
ec
t
ral
i
m
ag
e
(H
S
I
)
an
a
l
y
s
i
s
,
e
s
p
ec
i
a
l
l
y
w
h
en
g
ro
u
n
d
t
ru
t
h
d
at
a
ar
e
l
i
mi
t
e
d
.
W
h
i
l
e
u
n
s
u
p
e
r
v
i
s
e
d
al
g
o
r
i
t
h
m
s
ar
e
p
r
e
f
e
rr
e
d
i
n
s
u
ch
s
ce
n
ar
i
o
s
,
t
h
e
e
ff
ec
t
i
v
en
e
s
s
o
f
k
-
me
an
s
cl
u
s
t
e
ri
n
g
d
e
p
e
n
d
s
h
e
av
i
l
y
o
n
t
h
e
ch
o
i
ce
o
f
s
i
mi
l
ari
t
y
me
as
u
r
e
.
T
h
i
s
art
i
c
l
e
p
re
s
en
t
s
a
n
o
v
e
l
t
w
o
-
l
e
v
el
cl
u
s
t
e
ri
n
g
ap
p
ro
ac
h
fo
r
b
an
d
s
el
ec
t
i
o
n
.
I
n
t
h
e
f
i
rs
t
l
e
v
el
,
b
an
d
s
are
c
l
u
s
t
e
r
e
d
u
s
i
n
g
k
-
me
a
n
s
w
i
t
h
v
ari
o
u
s
s
i
m
i
l
ari
t
y
me
as
u
re
s
s
u
ch
as
E
u
c
l
i
d
e
a
n
d
i
s
t
an
ce
,
s
p
e
c
t
ral
an
g
l
e
m
ap
p
e
r
(SA
M),
a
n
d
s
p
ec
t
ral
i
n
fo
r
m
at
i
o
n
d
i
v
e
rg
en
ce
(SID
).
Su
b
s
e
q
u
en
t
l
y
,
t
h
e
s
ec
o
n
d
l
e
v
el
l
e
v
e
rag
e
s
t
h
e
ch
o
rd
me
t
ri
c
k
-
me
a
n
s
c
l
u
s
t
e
ri
n
g
t
o
f
o
r
m
cl
u
s
t
e
rs
o
f
H
SI
s
ce
n
e
s
u
p
o
n
o
p
t
i
m
a
l
b
an
d
c
l
u
s
t
e
rs
fro
m
t
h
e
f
i
rs
t
l
e
v
el
.
T
h
i
s
i
n
i
t
i
a
l
b
a
n
d
s
el
ec
t
i
o
n
r
ed
u
ce
s
d
i
men
s
i
o
n
al
i
t
y
an
d
g
u
i
d
e
s
s
u
b
s
e
q
u
e
n
t
k
-
m
e
a
n
s
c
l
u
s
t
e
ri
n
g
.
T
h
e
p
ro
p
o
s
e
d
ch
o
rd
-
b
as
e
d
c
l
u
s
t
e
ri
n
g
me
t
h
o
d
,
u
t
i
l
i
z
i
n
g
t
h
e
ch
o
r
d
me
t
ri
c,
o
u
t
p
e
rfo
r
m
s
s
t
an
d
a
rd
k
-
m
e
a
n
s
v
ar
i
an
t
s
,
d
emo
n
s
t
rat
i
n
g
s
i
g
n
i
fi
c
a
n
t
i
m
p
ro
v
eme
n
t
s
i
n
a
ccu
racy
.
E
x
p
e
ri
me
n
t
al
r
e
s
u
l
t
s
o
n
p
u
b
l
i
cl
y
a
v
ai
l
ab
l
e
h
y
p
e
rs
p
ec
t
ral
d
at
as
e
t
s
co
n
f
i
r
m
t
h
e
e
ff
ec
t
i
v
en
e
s
s
o
f
t
h
e
p
ro
p
o
s
e
d
ap
p
ro
ach
as
an
al
t
e
rn
at
i
v
e
t
o
t
rad
i
t
i
o
n
al
k
-
me
a
n
s
a
l
g
o
ri
t
h
m
s
,
s
h
o
w
c
as
i
n
g
s
i
g
n
i
fi
c
a
n
t
i
m
p
ro
v
emen
t
s
i
n
a
ccu
ra
cy
.
K
e
y
w
o
r
d
s
:
B
a
n
d
s
e
l
e
c
t
i
o
n
C
h
o
r
d
a
l
ge
b
r
a
H
y
pe
r
s
p
e
c
t
r
a
l
im
a
ge
K
-
m
e
a
n
s
S
pe
c
t
r
a
l
s
im
il
a
r
i
t
y
Th
i
s
i
s
a
n
o
p
en
a
c
ces
s
a
r
t
i
c
l
e
u
n
d
e
r
t
h
e
CC
B
Y
-
SA
l
i
cen
s
e.
C
or
r
e
s
pon
din
g
A
u
th
or
:
Or
i
ga
n
t
i
S
ubh
a
s
h
C
ha
n
de
r
Go
ud
De
pa
r
t
m
e
n
t
o
f
C
o
m
put
e
r
S
c
i
e
n
c
e
a
n
d
E
n
g
i
ne
e
r
i
n
g
,
C
o
l
l
e
g
e
o
f
E
n
g
i
ne
e
r
i
ng
J
a
wa
h
a
r
l
a
l
N
e
h
r
u
T
e
c
hn
o
l
o
gi
c
a
l
U
ni
ve
r
s
i
t
y
(
J
NT
U)
An
a
n
t
a
pur
,
A
.
P
.
,
I
n
di
a
E
m
a
i
l
:
o
r
ga
n
t
s
ubh
a
s
h@
g
m
a
il
.
c
o
m
1.
I
NT
RODU
C
T
I
ON
H
y
pe
r
s
p
e
c
t
r
a
l
i
m
a
g
i
n
g
(
HSI
)
pr
o
vi
de
s
m
o
r
e
de
t
a
i
l
s
a
b
o
ut
t
h
e
o
bj
e
c
t
s
t
h
a
t
a
r
e
c
a
p
t
ur
e
d
i
n
t
h
e
s
c
e
n
e
w
i
t
h
r
i
c
h
s
pe
c
t
r
a
l
i
nf
o
r
m
a
t
i
o
n
.
Ha
vi
ng
l
a
r
ge
s
pe
c
t
r
a
l
i
nf
o
r
m
a
t
i
o
n
i
s
v
e
r
y
u
s
e
f
u
l
t
o
c
h
a
r
a
c
t
e
r
i
z
e
t
h
e
o
bj
e
c
t
s
a
n
d
c
l
a
s
s
if
y
t
h
e
m
e
f
f
i
c
i
e
n
t
l
y
.
O
n
t
h
e
ot
h
e
r
h
a
n
d,
h
a
vi
n
g
m
o
r
e
s
pe
c
t
r
a
l
b
a
n
d
s
m
a
ke
s
r
e
s
u
l
t
s
i
n
l
e
s
s
a
c
c
ur
a
c
y
w
h
e
n
us
i
n
g
a
ny
m
a
c
hi
ne
-
l
e
a
r
ni
ng
a
l
go
r
i
t
hm
f
o
r
c
l
a
s
s
i
f
i
c
a
t
i
o
n
.
He
nc
e
,
s
pe
c
t
r
a
l
b
a
n
d
s
e
l
e
c
t
i
o
n
h
a
s
b
e
e
n
a
n
i
m
po
r
t
a
n
t
s
t
udy
i
n
HSI
c
l
a
s
s
i
f
i
c
a
t
i
o
n
[
1
]
,
[
2]
.
M
i
x
e
d
p
i
xe
l
due
to
l
o
w
s
pa
t
i
a
l
r
e
s
o
l
ut
i
o
n
de
m
a
n
d
m
o
r
e
r
o
b
us
t
b
a
n
d
s
e
l
e
c
t
i
o
n
o
r
di
m
e
n
s
i
o
n
a
li
t
y
r
e
duc
t
i
o
n
t
e
c
hni
qu
e
s
[
3]
.
HSI
b
a
n
d
s
e
l
e
c
t
i
o
n
i
s
c
r
uc
i
a
l
f
o
r
c
a
pt
u
r
i
n
g
e
s
s
e
n
t
i
a
l
gr
o
un
d
o
b
j
e
c
t
i
nf
o
r
m
a
t
i
o
n
,
r
e
duc
i
n
g
r
e
du
n
da
n
c
y
,
a
n
d
r
e
duc
i
n
g
c
o
m
put
a
t
i
o
n
a
l
c
o
s
t
s
.
W
i
t
h
c
o
n
t
i
n
uo
us
r
e
s
e
a
r
c
h
o
n
HS
I
i
m
a
ge
r
y
,
HSI
i
m
a
ge
a
n
a
ly
s
i
s
h
a
s
b
e
e
n
us
e
d
i
n
v
a
r
i
o
us
a
pp
l
i
c
a
t
i
o
ns
s
uc
h
a
s
e
nvi
r
o
nm
e
n
t
a
l
m
o
ni
t
o
r
i
n
g,
pr
e
c
i
s
i
o
n
a
gr
i
c
u
l
t
ur
e
,
m
i
ne
r
a
l
e
x
p
l
o
r
a
t
i
o
n
,
a
n
d
ur
b
a
n
p
l
a
nni
ng
[
1]
.
S
u
a
n
d
Du
[
4]
c
l
a
s
s
if
i
e
d
ba
n
d
s
e
l
e
c
t
i
o
n
m
e
t
h
o
ds
i
n
to
s
i
x
c
a
t
e
gor
i
e
s
,
w
i
t
h
a
pr
i
m
a
r
y
f
o
c
us
o
n
r
a
n
k
i
ng
-
ba
s
e
d
m
e
t
h
o
ds
t
h
a
t
pr
i
o
r
i
t
i
z
e
b
a
n
ds
b
a
s
e
d
o
n
s
pe
c
t
r
a
l
i
m
po
r
t
a
n
c
e
a
n
d
a
n
o
bj
e
c
t
i
v
e
f
u
nc
t
i
o
n
.
T
h
e
s
e
m
e
t
h
o
ds
,
wh
e
t
h
e
r
s
upe
r
vi
s
e
d
o
r
un
s
upe
r
vi
s
e
d,
de
m
o
ns
t
r
a
t
e
a
s
y
s
t
e
m
a
t
i
c
a
n
d
v
e
r
s
a
t
i
l
e
n
a
t
ur
e
,
c
r
uc
i
a
l
f
o
r
e
f
f
i
c
i
e
n
t
a
n
a
ly
s
i
s
o
r
c
l
a
s
s
if
i
c
a
t
i
o
n
t
a
s
ks
.
W
hil
e
r
e
c
o
gni
z
i
ng
t
h
e
i
r
pot
e
n
t
i
a
l
,
t
h
e
s
t
ud
y
a
c
k
n
o
w
l
e
d
ge
s
t
h
a
t
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2
5
0
2
-
4
7
52
In
do
n
e
s
i
a
n
J
E
l
e
c
E
n
g
&
C
o
m
p
S
c
i
,
Vo
l
.
3
6
,
N
o
.
2
,
N
o
v
e
m
b
e
r
20
24
:
1
30
9
-
1
31
8
1310
r
a
n
k
i
ng
-
ba
s
e
d
a
ppr
o
a
c
he
s
m
a
y
l
e
a
d
t
o
s
ub
o
pt
i
m
a
l
b
a
n
d
s
u
b
s
e
t
s
e
l
e
c
t
i
o
n
(
B
S
S
)
,
hi
g
hli
g
h
t
i
n
g
t
h
e
n
ua
n
c
e
d
de
c
i
s
i
o
n
-
m
a
k
i
ng
i
nv
o
l
ve
d
i
n
t
h
e
i
r
de
p
l
o
ym
e
n
t
a
c
r
o
s
s
d
i
v
e
r
s
e
s
c
e
n
a
r
i
o
s
.
C
h
a
n
g
a
n
d
L
i
u
[
5]
pr
o
p
o
s
e
a
s
t
a
t
i
s
t
i
c
a
l
-
ba
s
e
d
b
a
n
d
pr
i
o
r
i
t
i
z
a
t
i
o
n
m
e
t
h
o
d
f
o
r
h
y
pe
r
s
pe
c
t
r
a
l
a
n
a
ly
s
i
s
,
ut
i
li
z
i
ng
m
e
a
s
ur
e
s
l
i
k
e
v
a
r
i
a
n
c
e
a
nd
s
ke
w
n
e
s
s
f
o
r
d
i
m
e
ns
i
o
na
l
i
t
y
r
e
duc
t
i
o
n
w
hil
e
pr
e
s
e
r
vi
ng
c
r
i
t
i
c
a
l
s
pe
c
t
r
a
l
i
nf
o
r
m
a
t
i
o
n
.
De
s
p
i
t
e
i
t
s
e
f
f
i
c
i
e
n
c
y
,
pot
e
n
t
i
a
l
r
i
s
ks
o
f
i
na
c
c
ur
a
t
e
B
S
S
i
m
pa
c
t
i
n
g
c
l
a
s
s
if
i
c
a
t
i
o
n
a
c
c
ur
a
c
y
a
r
e
a
c
kn
o
w
l
e
dge
d.
I
n
a
r
e
l
a
t
e
d
c
o
n
t
e
x
t
,
P
a
l
a
n
d
F
o
o
dy
[
6]
hi
g
hl
i
g
h
t
t
h
e
s
uppo
r
t
v
e
c
t
or
m
a
c
hi
ne
(
S
VM
)
a
ppr
o
a
c
h
’
s
s
upe
r
i
o
r
pe
r
f
o
r
m
a
n
c
e
i
n
d
i
s
c
e
r
ni
ng
r
e
l
e
v
a
n
t
f
e
a
t
ur
e
s
,
w
i
t
h
c
h
a
ll
e
n
g
e
s
n
o
ted
i
n
uns
upe
r
vi
s
e
d
da
t
a
s
e
t
s
.
Yu
e
t
al.
[
7
]
i
n
t
r
oduc
e
t
h
e
li
ne
a
r
ly
c
o
ns
t
r
a
i
ne
d
m
i
n
i
m
u
m
v
a
r
i
a
n
c
e
B
S
S
(
L
C
M
V
-
B
S
S
)
a
l
go
r
i
t
hm
,
o
p
t
i
mi
z
i
ng
b
a
n
d
s
u
bs
e
t
s
i
n
hy
pe
r
s
pe
c
t
r
a
l
da
t
a
f
o
r
e
n
ha
n
c
e
d
c
l
a
s
s
i
f
i
c
a
t
i
o
n
a
c
c
ur
a
c
y
a
n
d
c
o
m
put
a
t
i
o
n
a
l
e
f
f
i
c
i
e
nc
y
.
M
e
a
n
w
hil
e
,
I
m
a
ni
a
n
d
Gh
a
s
s
e
mi
a
n
[
8]
pr
e
s
e
n
t
t
h
e
bi
na
r
y
c
o
d
i
n
g
-
b
a
s
e
d
f
e
a
t
ur
e
e
x
t
r
a
c
t
i
o
n
(
B
C
F
E
)
m
e
t
h
o
d,
s
ur
pa
s
s
i
ng
pr
i
n
c
i
pa
l
c
o
m
po
n
e
n
t
a
n
a
ly
s
i
s
(
P
C
A
)
a
n
d
l
i
ne
a
r
di
s
c
r
im
i
na
n
t
a
n
a
ly
s
i
s
(
L
D
A
)
in
d
i
m
e
ns
i
o
n
a
li
t
y
r
e
duc
t
i
o
n
f
o
r
hy
pe
r
s
pe
c
t
r
a
l
a
n
a
ly
s
i
s
,
pa
r
t
i
c
u
l
a
r
ly
e
f
f
e
c
t
i
v
e
i
n
c
a
pt
ur
i
n
g
d
i
s
c
r
im
i
na
t
i
v
e
f
e
a
t
ur
e
s
f
o
r
i
m
pr
o
v
e
d
c
l
a
s
s
if
i
c
a
t
i
o
n
a
c
c
ur
a
c
y
.
T
o
ge
t
h
e
r
,
t
h
e
s
e
s
t
udi
e
s
un
de
r
s
c
o
r
e
t
h
e
n
ua
nc
e
d
c
o
n
s
i
d
e
r
a
t
i
o
ns
a
n
d
c
h
a
ll
e
n
g
e
s
i
n
o
p
t
i
mi
z
i
ng
hy
pe
r
s
pe
c
t
r
a
l
da
t
a
a
n
a
ly
s
i
s
t
h
r
o
ugh
a
dv
a
n
c
e
d
f
e
a
t
ur
e
s
e
l
e
c
t
i
o
n
t
e
c
h
ni
qu
e
s
,
o
f
f
e
r
i
ng
pr
o
m
i
s
i
ng
a
v
e
n
ue
s
f
o
r
im
pr
o
v
e
d
d
i
m
e
ns
i
o
na
l
i
t
y
r
e
duc
t
i
o
n
a
n
d
c
l
a
s
s
if
i
c
a
t
i
o
n
a
c
c
ur
a
c
y
.
Du
a
n
d
Ya
n
g
[
9]
e
x
p
l
o
r
e
d
e
f
f
i
c
i
e
n
t
b
a
n
d
s
e
lec
t
i
o
n
i
n
hy
pe
r
s
pe
c
t
r
a
l
a
n
a
ly
s
i
s
t
h
r
o
ugh
l
i
ne
a
r
r
e
gr
e
s
s
i
o
n
-
b
a
s
e
d
(
L
P
)
a
n
d
o
r
t
h
o
g
o
n
a
l
s
u
bs
pa
c
e
pr
o
j
e
c
t
i
o
n
(
OSP
)
m
e
t
h
o
ds
,
pr
i
o
r
i
t
i
z
i
ng
b
a
n
d
s
w
i
t
h
hi
g
h
pr
e
d
i
c
t
i
o
n
e
r
r
or
s
f
o
r
di
m
e
ns
i
o
na
l
i
t
y
r
e
duc
t
i
o
n
a
n
d
e
nh
a
n
c
e
d
c
o
m
put
a
t
i
o
n
a
l
e
f
f
i
c
i
e
n
c
y
.
Q
i
a
n
e
t
al.
[
10
]
pr
o
p
o
s
e
d
a
n
o
v
e
l
f
e
a
t
ur
e
s
e
l
e
c
t
i
o
n
a
ppr
o
a
c
h
us
i
n
g
K
u
l
l
ba
c
k
-
L
e
i
b
l
e
r
d
i
v
e
r
ge
n
c
e
a
n
d
kur
to
s
i
s
-
b
a
s
e
d
s
im
il
a
r
i
t
y
m
a
t
r
i
c
e
s
,
f
o
l
l
o
we
d
by
Af
f
i
n
i
t
y
P
r
o
pa
ga
t
i
o
n
c
l
u
s
t
e
r
i
n
g,
i
m
pr
o
vi
ng
c
l
a
s
s
i
f
i
c
a
t
i
o
n
a
c
c
ur
a
c
y
by
s
e
l
e
c
t
i
n
g
i
n
f
o
r
m
a
t
i
v
e
b
a
n
ds
a
n
d
r
e
duc
i
n
g
d
im
e
n
s
i
o
n
a
li
t
y
.
K
e
s
h
a
va
[
11]
e
m
p
h
a
s
i
z
e
d
t
h
e
s
i
g
nif
i
c
a
n
c
e
o
f
d
i
s
t
a
nc
e
m
e
t
r
i
c
s
l
i
k
e
S
A
M
a
n
d
S
I
D,
c
o
upl
e
d
w
i
t
h
s
pe
c
t
r
a
l
l
i
br
a
r
i
e
s
,
f
o
r
m
a
t
e
r
i
a
l
i
de
n
t
i
f
i
c
a
t
i
o
n
i
n
hy
pe
r
s
pe
c
t
r
a
l
da
t
a
,
e
nh
a
nc
i
n
g
t
h
e
s
e
l
e
c
t
i
o
n
o
f
i
n
f
o
r
m
a
t
i
ve
ba
n
ds
a
n
d
im
pr
o
vi
n
g
d
i
s
c
r
i
mi
na
t
i
v
e
c
a
p
a
bi
li
t
i
e
s
.
R
a
j
a
ka
ni
e
t
al.
[
12
]
d
i
s
c
us
s
e
d
va
r
i
o
us
ba
n
d
s
e
l
e
c
t
i
o
n
m
e
t
h
o
ds
,
i
nc
l
u
d
i
n
g
B
C
M
,
B
DM
,
B
C
C
,
a
n
d
B
DC
,
a
i
mi
ng
t
o
mi
n
im
i
z
e
r
e
dun
da
n
c
y
a
n
d
e
nh
a
n
c
e
d
i
s
c
r
i
mi
na
t
i
v
e
po
we
r
i
n
hy
pe
r
s
pe
c
t
r
a
l
da
t
a
s
e
t
s
t
h
r
o
ugh
n
o
r
m
a
l
i
z
e
d
c
o
r
r
e
l
a
t
i
o
n
-
b
a
s
e
d
t
e
c
h
ni
que
s
.
F
i
n
a
ll
y
,
T
h
e
n
k
a
b
a
i
l
[
13]
hi
g
hli
g
h
t
e
d
b
a
n
d
s
e
l
e
c
t
i
o
n
a
s
a
di
m
e
n
s
i
o
na
l
i
t
y
r
e
duc
t
i
o
n
t
e
c
h
ni
que
t
h
a
t
s
e
l
e
c
t
i
v
e
ly
c
h
o
o
s
e
s
i
nf
o
r
m
a
t
i
ve
b
a
n
d
s
w
i
t
h
o
ut
m
a
t
h
e
m
a
t
i
c
a
l
ly
t
r
a
ns
f
o
r
m
i
ng
t
h
e
da
t
a
,
f
a
c
il
i
t
a
t
i
n
g
e
f
f
i
c
i
e
n
t
pr
o
c
e
s
s
i
n
g
a
n
d
a
n
a
ly
s
i
s
by
r
e
m
o
vi
ng
r
e
dun
da
n
t
o
r
i
r
r
e
l
e
v
a
n
t
b
a
n
d
s
w
hil
e
r
e
t
a
i
ni
ng
c
r
uc
i
a
l
s
pe
c
t
r
a
l
i
nf
o
r
m
a
t
i
o
n
.
Ov
e
r
a
l
l
,
t
h
e
s
e
s
t
u
d
i
e
s
c
o
l
l
e
c
t
i
v
e
ly
c
o
n
t
r
i
b
ut
e
to
a
dv
a
n
c
i
ng
b
a
n
d
s
e
l
e
c
t
i
o
n
m
e
t
h
o
do
l
o
g
i
e
s
f
o
r
i
m
pr
o
v
e
d
hy
pe
r
s
pe
c
t
r
a
l
a
n
a
ly
s
is
.
K
-
m
e
a
n
s
c
l
us
t
e
r
i
n
g
c
a
n
b
e
im
pr
o
v
e
d
by
e
f
f
e
c
t
i
v
e
ly
c
h
o
o
s
i
n
g
t
h
e
b
e
t
t
e
r
s
i
mi
l
a
r
i
t
y
m
e
a
s
ur
e
t
h
e
s
t
udy
by
Gupt
a
a
n
d
C
h
a
n
dr
a
[
14]
s
h
o
ws
t
h
a
t
s
i
mi
l
a
r
i
t
y
m
e
t
r
i
c
h
a
s
a
n
im
pa
c
t
o
n
c
l
us
t
e
r
s
f
o
r
m
e
d
by
k
-
m
e
a
n
s
.
Gupt
a
a
n
d
C
h
a
n
dr
a
[
15]
hi
g
hli
g
h
t
t
h
e
s
i
g
nif
i
c
a
n
t
r
o
l
e
t
h
a
t
di
s
t
a
n
c
e
/s
im
il
a
r
i
t
y
m
e
t
r
i
c
s
p
l
a
y
i
n
pa
tt
e
r
n
r
e
c
o
gni
t
i
o
n
t
a
s
ks
.
T
h
e
y
s
pe
c
if
i
c
a
ll
y
m
e
n
t
i
o
n
E
uc
li
de
a
n
,
M
a
nha
tt
a
n
,
M
a
h
a
l
a
n
o
bi
s
,
a
n
d
M
i
nko
ws
k
i
m
e
t
r
i
c
s
,
e
m
ph
a
s
i
z
i
n
g
th
e
i
r
a
d
a
p
ta
b
i
l
i
t
y
e
v
e
n
wh
e
n
d
e
a
l
i
n
g
wi
th
b
i
n
a
r
y
d
a
ta
.
I
n
a
s
i
m
i
l
a
r
v
e
i
n
,
K
on
s
ta
n
t
i
n
a
n
d
G
r
i
b
ov
[
1
6
]
de
l
v
e
i
n
t
o
c
o
v
a
r
i
a
n
c
e
m
o
de
l
s
a
n
d
e
x
p
l
o
r
e
t
h
e
us
e
o
f
kr
i
g
i
ng
a
n
d
t
h
e
c
h
o
r
da
l
m
e
t
r
i
c
f
o
r
a
n
a
ly
z
i
n
g
m
u
l
t
i
d
i
m
e
ns
i
o
n
a
l
da
t
a
p
o
i
n
t
s
,
w
i
t
h
a
s
pe
c
i
f
i
c
f
o
c
us
o
n
s
pa
t
i
a
l
a
n
a
ly
s
i
s
.
T
h
e
s
t
ud
i
e
s
by
K
a
p
il
a
n
d
C
h
a
w
l
a
[
17]
,
Gupt
a
a
n
d
C
h
a
n
dr
a
[
15]
p
r
o
vi
de
v
a
l
ua
bl
e
i
ns
i
g
h
t
s
i
n
t
o
t
h
e
a
pp
l
i
c
a
t
i
o
n
o
f
s
i
mi
l
a
r
i
t
y
/d
i
s
t
a
n
c
e
m
e
t
r
i
c
s
in
k
-
m
e
a
n
s
c
l
us
t
e
r
i
n
g
a
c
r
o
s
s
di
f
f
e
r
e
n
t
d
o
m
a
i
ns
.
T
h
e
r
e
s
e
a
r
c
h
e
m
p
h
a
s
i
z
e
s
t
h
e
s
e
n
s
i
t
i
v
i
t
y
o
f
t
h
e
k
-
m
e
a
n
s
a
l
go
r
i
t
hm
t
o
m
e
t
r
i
c
c
h
o
i
c
e
s
a
n
d
hi
g
hli
g
h
t
s
t
h
e
i
m
pa
c
t
o
f
a
l
t
e
r
i
n
g
s
im
il
a
r
i
t
y
m
e
t
r
i
c
s
o
n
c
l
us
t
e
r
f
o
r
m
a
t
i
o
n
.
T
h
e
s
e
f
i
nd
i
ngs
c
o
n
t
r
i
b
ut
e
to
a
b
e
tt
e
r
un
de
r
s
t
a
n
d
i
n
g
o
f
t
h
e
o
p
t
i
m
a
l
m
e
t
r
i
c
s
e
l
e
c
t
i
o
n
f
o
r
k
-
m
e
a
ns
c
l
us
t
e
r
i
n
g,
pa
r
t
i
c
u
l
a
r
l
y
i
n
t
h
e
c
o
n
t
e
x
t
o
f
o
nl
i
ne
u
s
e
r
da
t
a
a
n
d
I
oT
/m
u
l
t
i
m
e
d
i
a
a
pp
li
c
a
t
i
o
n
s
.
F
ur
t
h
e
r
r
e
s
e
a
r
c
h
i
n
t
hi
s
a
r
e
a
c
o
ul
d
e
x
p
l
o
r
e
a
dd
i
t
i
o
n
a
l
do
m
a
i
ns
a
n
d
pr
o
pe
r
t
i
e
s
to
a
s
c
e
r
t
a
i
n
t
h
e
b
r
o
a
de
r
a
pp
l
i
c
a
bil
i
t
y
o
f
t
h
e
s
e
i
ns
i
g
h
t
s
.
Kh
a
li
f
a
e
t
al.
[
18
]
i
nv
e
s
t
i
ga
t
e
d
t
h
e
a
pp
l
i
c
a
t
i
o
n
o
f
c
l
u
s
t
e
r
i
n
g
t
e
c
hni
que
s
w
i
t
h
d
i
ve
r
s
e
s
im
il
a
r
i
t
y
/
d
i
s
s
im
il
a
r
i
t
y
m
e
a
s
ur
e
s
t
o
s
e
l
e
c
t
c
o
m
po
un
ds
f
r
o
m
a
c
h
e
mi
c
a
l
dr
ug
r
e
po
s
i
t
o
r
y
.
T
h
e
s
t
ud
y
f
o
c
u
s
e
s
o
n
a
c
l
u
s
t
e
r
i
n
g
a
ppr
o
a
c
h
t
e
r
m
e
d
“
d
i
s
s
i
mi
l
a
r
i
t
y
-
ba
s
e
d
c
o
m
po
un
d
s
e
l
e
c
t
i
o
n
(
DB
C
S
)
,
”
a
i
m
i
ng
t
o
i
de
n
t
i
f
y
a
s
ubs
e
t
o
f
c
h
e
mi
c
a
l
m
o
l
e
c
u
l
e
s
f
r
o
m
a
dr
ug
da
t
a
b
a
s
e
t
h
r
ough
a
gg
l
o
m
e
r
a
t
i
v
e
a
n
d
hi
e
r
a
r
c
hi
c
a
l
c
l
us
t
e
r
i
n
g
m
e
t
h
o
ds
.
A
gg
l
o
m
e
r
a
t
i
v
e
c
l
u
s
t
e
r
i
n
g
e
m
p
l
o
y
e
d
t
h
e
gr
o
up
-
a
v
e
r
a
ge
t
e
c
hni
que
w
i
t
h
v
a
r
i
o
us
s
i
mi
l
a
r
i
t
y
m
e
a
s
ur
e
s
,
whi
le
hi
e
r
a
r
c
hi
c
a
l
c
l
us
t
e
r
i
n
g
ut
i
l
i
z
e
d
t
h
e
J
a
r
vi
s
-
P
a
t
r
i
c
k
m
e
t
h
o
d.
I
n
t
h
e
l
a
t
t
e
r
,
m
o
l
e
c
u
l
e
s
we
r
e
a
dde
d
t
o
a
c
l
u
s
t
e
r
i
f
t
h
e
i
r
n
e
a
r
e
s
t
n
e
i
g
hb
o
r
l
i
s
t
s
s
h
a
r
e
d
c
o
m
m
o
n
e
l
e
m
e
nt
s
wi
t
h
t
h
e
c
l
us
t
e
r
.
T
h
e
s
t
ud
i
e
s
by
va
r
i
o
us
a
ut
h
o
r
s
[
19]
-
[
22]
p
r
ovi
de
v
a
l
ua
bl
e
i
ns
i
g
h
t
s
i
n
t
o
t
h
e
a
pp
l
i
c
a
t
i
o
n
o
f
s
i
mi
l
a
r
i
t
y
/d
i
s
t
a
n
c
e
m
e
t
r
i
c
s
i
n
k
-
me
a
ns
c
l
us
t
e
r
i
n
g
a
c
r
o
s
s
di
f
f
e
r
e
n
t
do
m
a
i
ns
.
T
h
e
r
e
s
e
a
r
c
h
e
m
p
h
a
s
i
z
e
s
t
h
e
s
e
n
s
i
t
i
v
i
t
y
o
f
t
h
e
k
-
m
e
a
n
s
a
l
go
r
i
t
hm
t
o
m
e
t
r
i
c
c
h
o
i
c
e
s
,
hi
g
hli
g
h
t
i
n
g
t
h
e
im
pa
c
t
o
f
a
l
t
e
r
i
n
g
s
im
il
a
r
i
t
y
m
e
t
r
i
c
s
o
n
c
l
u
s
t
e
r
f
o
r
m
a
t
i
o
n
a
n
d
t
h
e
tr
a
de
-
o
f
f
s
a
s
s
o
c
i
a
t
e
d
wi
t
h
t
r
a
di
t
i
o
n
a
l
a
n
d
i
nn
o
va
t
i
ve
m
e
t
r
i
c
s
.
T
h
e
s
e
f
i
nd
i
n
g
s
c
o
n
t
r
i
b
ut
e
to
a
b
e
tt
e
r
un
de
r
s
t
a
n
d
i
n
g
o
f
t
h
e
o
p
ti
m
a
l
m
e
t
r
i
c
s
e
l
e
c
t
i
o
n
f
o
r
k
-
m
e
a
n
s
c
l
u
s
t
e
r
i
n
g
i
n
v
a
r
i
o
us
c
o
n
t
e
x
t
s
,
s
uc
h
a
s
o
nl
i
ne
us
e
r
da
t
a
,
i
n
t
e
r
n
e
t
o
f
t
hi
n
gs
(
I
oT
)
/m
u
l
t
i
m
e
d
i
a
a
pp
l
i
c
a
t
i
o
n
s
,
hi
g
h
-
d
i
m
e
ns
i
o
n
a
l
da
t
a
,
t
e
x
t
d
o
c
um
e
n
t
a
n
a
ly
s
i
s
,
a
n
d
hi
g
h
e
r
-
d
i
m
e
ns
i
o
na
l
s
p
a
c
e
s
.
F
ur
t
h
e
r
r
e
s
e
a
r
c
h
i
n
t
hi
s
a
r
e
a
c
o
u
l
d
e
x
p
l
o
r
e
a
dd
i
t
i
o
n
a
l
do
m
a
i
ns
a
n
d
pr
o
pe
r
t
i
e
s
to
a
s
c
e
r
t
a
i
n
t
h
e
b
r
o
a
de
r
a
ppl
i
c
a
bil
i
t
y
o
f
t
h
e
s
e
i
n
s
i
g
h
t
s
a
n
d
pot
e
n
t
i
a
ll
y
l
e
a
d
t
o
e
nh
a
nc
e
m
e
n
t
s
i
n
c
l
us
t
e
r
i
n
g
a
l
go
r
i
t
hm
s
f
o
r
i
m
pr
o
v
e
d
o
ut
c
o
m
e
s
.
T
h
e
r
e
s
e
a
r
c
h
a
r
t
i
c
l
e
s
by
Q
i
a
o
e
t
al.
[
22
]
,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
do
n
e
s
i
a
n
J
E
l
e
c
E
n
g
&
C
o
m
p
S
c
i
I
S
S
N:
2
5
0
2
-
4
7
52
Optimiz
i
ng
hy
pe
r
s
pe
c
t
r
al
c
las
s
if
ication:
s
pe
c
tr
al
s
imil
ar
it
y
-
bas
e
d
…
(
Or
igant
i
Subhas
h
C
hande
r
Go
ud
)
1311
V
i
j
a
y
e
t
al.
[
23]
s
h
e
d
li
g
h
t
o
n
t
h
e
s
i
g
nif
i
c
a
n
c
e
o
f
d
i
s
t
a
n
c
e
m
e
t
r
i
c
s
i
n
de
e
p
l
e
a
r
ni
ng
a
n
d
c
l
u
s
t
e
r
i
n
g
a
n
a
ly
s
i
s
.
Q
i
a
o
e
t
al
.
[
22
]
e
m
p
h
a
s
i
z
e
d
t
h
e
s
upe
r
i
o
r
i
t
y
o
f
c
o
s
i
ne
s
i
mi
l
a
r
i
t
y
i
n
c
l
a
s
s
if
yi
ng
HSI
,
whi
l
e
V
i
j
a
y
e
t
al.
[
23
]
hi
g
hli
g
h
t
e
d
t
h
e
n
ua
nc
e
d
a
n
d
c
o
n
t
e
x
t
-
de
pe
n
de
n
t
n
a
t
ur
e
o
f
d
i
s
t
a
n
c
e
m
e
t
r
i
c
s
e
l
e
c
t
i
o
n
i
n
c
l
us
t
e
r
i
n
g
pr
o
bl
e
m
s
.
T
h
e
i
r
f
i
nd
i
ngs
pr
o
vi
de
v
a
l
ua
bl
e
i
ns
i
g
h
t
s
f
o
r
t
h
e
e
f
f
e
c
t
i
v
e
a
pp
l
i
c
a
t
i
o
n
o
f
d
i
s
t
a
n
c
e
m
e
t
r
i
c
s
i
n
d
i
v
e
r
s
e
do
m
a
i
ns
o
f
de
e
p
l
e
a
r
ni
ng
a
n
d
c
l
u
s
t
e
r
i
n
g
a
n
a
ly
s
i
s
.
S
hi
r
k
h
o
r
s
hi
d
i
e
t
al.
[
24]
i
nve
s
t
i
ga
t
e
d
t
h
e
i
m
pa
c
t
o
f
s
i
mi
l
a
r
i
t
y
m
e
t
r
i
c
s
o
n
d
i
s
t
a
n
c
e
-
b
a
s
e
d
c
l
u
s
t
e
r
i
n
g
in
hi
g
h
e
r
-
d
i
m
e
ns
i
o
n
a
l
s
pa
c
e
s
,
i
n
t
r
o
duc
i
n
g
t
h
e
e
f
f
e
c
t
i
v
e
c
h
o
r
d
m
e
t
r
i
c
w
i
t
h
k
-
m
e
a
ns
c
l
us
t
e
r
i
n
g.
T
h
e
i
r
s
t
ud
y
hi
g
hli
g
h
t
e
d
k
-
m
e
a
n
s
’
v
e
r
s
a
t
i
li
t
y
a
c
r
o
s
s
di
v
e
r
s
e
da
t
a
s
e
t
s
wi
t
h
v
a
r
i
o
us
m
e
t
r
i
c
s
.
M
e
a
n
w
hil
e
,
Gh
a
z
a
l
e
t
al.
[
25]
e
x
t
e
ns
i
ve
ly
a
s
s
e
s
s
e
d
s
im
il
a
r
i
t
y
m
e
t
r
i
c
s
o
n
k
-
m
e
a
n
s
pe
r
f
o
r
m
a
n
c
e
,
e
m
p
h
a
s
i
z
i
ng
t
h
e
c
o
n
s
i
s
t
e
n
t
s
upe
r
i
o
r
i
t
y
o
f
t
h
e
M
a
nh
a
t
t
a
n
m
e
t
r
i
c
i
n
e
x
e
c
ut
i
o
n
t
i
m
e
a
c
r
o
s
s
d
i
v
e
r
s
e
da
t
a
s
e
t
s
a
n
d
c
l
u
s
t
e
r
s
i
z
e
s
.
B
o
t
h
s
t
udi
e
s
u
n
de
r
s
c
o
r
e
d
t
h
e
c
r
i
t
i
c
a
l
r
o
l
e
o
f
m
e
t
r
i
c
s
e
l
e
c
t
i
o
n
i
n
o
p
t
i
mi
z
i
ng
k
-
m
e
a
ns
a
n
d
s
ugge
s
t
e
d
e
x
p
l
o
r
i
n
g
a
l
t
e
r
n
a
t
i
v
e
a
l
go
r
i
t
hm
s
f
o
r
e
nh
a
nc
e
d
c
l
u
s
t
e
r
i
n
g
o
ut
c
o
m
e
s
a
c
r
o
s
s
di
f
f
e
r
e
n
t
da
t
a
s
e
t
s
.
L
u
n
d
h
o
l
m
a
n
d
S
ve
n
s
s
o
n
[
26]
pr
e
s
e
n
t
a
c
o
m
pr
e
h
e
n
s
i
ve
o
v
e
r
vi
e
w
o
f
C
l
if
f
o
r
d
ge
o
m
e
t
r
y
,
de
l
v
i
ng
i
n
t
o
i
t
s
o
r
i
g
i
ns
a
n
d
a
pp
l
i
c
a
t
i
o
ns
.
T
h
e
i
r
s
t
udy
r
i
go
r
o
us
ly
e
x
p
l
o
r
e
s
t
h
e
C
l
if
f
o
r
d
f
r
a
m
e
wo
r
k,
s
ub
s
t
a
n
t
i
a
t
i
ng
c
l
a
i
ms
w
i
t
h
pr
o
o
f
s
,
a
n
d
hi
g
hli
g
h
t
s
a
pp
l
i
c
a
t
i
o
n
s
i
n
c
y
be
r
s
e
c
ur
i
t
y
,
i
m
a
ge
pr
o
c
e
s
s
i
ng,
n
e
ur
a
l
n
e
t
wo
r
ks
,
a
n
d
ge
o
m
e
t
r
i
c
fi
e
l
ds
,
s
h
o
wc
a
s
i
ng
t
h
e
e
f
f
i
c
a
c
y
o
f
C
li
f
f
o
r
d
a
l
ge
br
a
.
S
i
m
u
l
t
a
n
e
o
us
l
y
,
H
i
t
z
e
r
e
t
al.
[
27]
e
x
t
e
n
s
i
ve
ly
e
x
p
l
o
r
e
C
l
if
f
o
r
d
a
l
ge
b
r
a
a
pp
li
c
a
t
i
o
n
s
,
e
m
p
h
a
s
i
z
i
ng
i
t
s
r
e
l
e
v
a
n
c
e
i
n
c
y
be
r
s
e
c
ur
i
t
y
f
o
r
r
o
b
us
t
da
t
a
a
n
a
l
y
s
is
a
n
d
i
n
im
a
ge
pr
o
c
e
s
s
i
n
g
f
o
r
a
dv
a
n
c
e
d
t
e
c
h
ni
que
s
.
T
h
e
i
n
t
e
gr
a
t
i
o
n
w
i
t
h
n
e
ur
a
l
n
e
t
wo
r
ks
i
n
t
r
o
duc
e
s
i
nn
o
v
a
t
i
v
e
a
l
go
r
i
t
hm
s
f
o
r
c
o
m
p
l
e
x
t
a
s
ks
i
n
m
a
c
hi
ne
l
e
a
r
ni
ng
a
n
d
pa
t
t
e
r
n
r
e
c
o
gni
t
i
o
n
.
O
v
e
r
a
ll
,
b
o
t
h
s
t
udi
e
s
u
nde
r
s
c
o
r
e
t
h
e
v
e
r
s
a
t
i
li
t
y
a
n
d
s
i
g
ni
f
i
c
a
n
c
e
o
f
C
li
f
f
o
r
d
a
l
ge
b
r
a
a
c
r
o
s
s
di
v
e
r
s
e
t
e
c
hn
o
l
o
g
i
c
a
l
do
m
a
i
ns
,
o
f
f
e
r
i
n
g
v
a
l
ua
bl
e
i
ns
i
g
h
t
s
i
n
t
o
i
t
s
pot
e
n
t
i
a
l
f
o
r
a
dv
a
n
c
i
ng
va
r
i
o
us
f
i
e
lds
,
i
nc
l
ud
i
ng
c
o
m
put
e
r
gr
a
phi
c
s
,
de
s
i
g
n
,
a
n
d
r
o
b
oti
c
s
.
As
pe
r
Dor
s
t
[
28
]
t
h
e
r
e
i
s
a
b
r
i
e
f
d
i
s
c
us
s
i
o
n
o
f
t
he
c
h
o
r
d
m
e
t
r
i
c
de
t
a
i
l
e
d
a
n
a
ly
s
i
s
pr
o
c
e
dur
e
,
wh
e
r
e
Do
r
s
t
h
a
s
m
e
n
t
i
o
n
e
d
c
h
o
r
d
m
e
t
r
i
c
a
n
d
i
t
s
r
e
l
a
t
i
o
ns
hi
p
w
i
t
h
C
li
f
f
o
r
d
a
l
ge
b
r
a
.
Do
r
s
t
de
f
i
ne
s
t
h
e
c
h
o
r
d
m
e
t
r
i
c
ut
i
li
z
a
t
i
o
n
w
hi
c
h
f
o
l
l
o
w
s
t
h
e
C
li
f
f
o
r
d
a
l
ge
b
r
a
pr
i
n
c
i
p
l
e
a
n
d
a
l
s
o
pr
o
v
e
s
h
o
w
C
l
if
f
o
r
d
a
l
ge
b
r
a
pr
i
nc
i
p
l
e
s
a
r
e
b
e
i
ng
f
o
l
l
o
we
d
by
t
h
e
c
h
o
r
d
m
e
t
r
i
c
de
f
i
ne
d
i
n
(
10)
.
A
s
pe
r
d
r
o
s
t,
t
h
e
c
h
o
r
d
m
e
t
r
i
c
f
o
l
l
o
w
s
t
h
e
C
l
if
f
o
r
d
pr
i
n
c
i
p
l
e
upo
n
s
c
a
l
a
r
s
,
v
e
c
to
r
s
pa
c
e
,
bi
-
v
e
c
to
r
s
pa
c
e
,
a
n
d
t
r
i
-
ve
c
t
or
s
pa
c
e
s
w
hi
c
h
a
r
e
de
f
i
ne
d
a
s
C
l
if
f
o
r
d
a
l
ge
b
r
a
i
c
bl
a
de
s
de
f
i
ne
d
i
n
(
1)
.
1
,
(
1
,
2
,
3
)
,
(
(
1
Λ
2
)
,
(
1
Λ
3
)
,
(
2
Λ
3
)
)
,
(
1
Λ
2
Λ
3
)
(1
)
T
h
e
f
i
r
s
t
s
e
g
m
e
n
t
o
f
(
1)
[
28
]
i
s
a
s
c
a
l
a
r
pr
o
duc
t
o
f
v
a
l
ue
s
,
a
n
d
t
h
e
s
e
c
o
n
d
pa
r
t
o
f
(
e
1,
e
2,
e
3
)
i
s
a
v
e
c
t
o
r
s
pa
c
e
r
e
pr
e
s
e
n
t
a
t
i
o
n
o
f
C
li
f
f
o
r
d
Al
ge
b
r
a
B
l
a
d
e
s
w
hi
c
h
s
e
pa
r
a
t
e
t
h
e
m
u
l
t
i
d
i
m
e
ns
i
o
n
a
l
r
e
pr
e
s
e
n
t
a
t
i
o
n
by
ut
i
li
z
i
ng
o
r
t
h
o
g
o
n
a
l
s
u
b
s
pa
c
e
s
.
T
h
e
t
hi
r
d
pa
r
t
o
f
e
qua
t
i
o
n
1
i
s
a
B
i
-
v
e
c
t
o
r
s
pa
c
e
r
e
pr
e
s
e
n
t
a
t
i
o
n
s
e
pa
r
a
t
e
d
w
i
t
h
a
c
o
m
bi
na
t
i
o
n
o
f
o
r
t
h
o
g
o
n
a
l
s
u
b
s
pa
c
e
bl
a
de
s
l
i
ke
(
e
1
^
e
2
)
whe
r
e
e
1
a
n
d
e
2
a
r
e
t
wo
or
t
h
o
g
o
n
a
l
s
ubs
pa
c
e
v
e
c
t
or
s
a
n
d
t
h
e
l
a
s
t
p
a
r
t
(
e
1
^
e
2
^
e
3
)
i
s
T
r
i
-
v
e
c
t
o
r
o
r
t
h
o
g
o
n
a
l
r
e
pr
e
s
e
n
t
a
t
i
o
n
o
f
a
v
e
c
t
o
r
w
hi
c
h
h
a
s
t
h
r
e
e
or
t
h
o
g
o
n
a
l
s
u
b
s
pa
c
e
bl
a
d
e
s
o
f
E
uc
l
i
de
a
n
s
u
b
s
p
a
c
e
r
e
pr
e
s
e
n
t
a
t
i
o
n
.
As
pe
r
R
u
h
e
e
t
al
.
[
29]
t
h
e
C
l
if
f
o
r
d
a
l
ge
b
r
a
i
s
us
e
d
f
o
r
c
o
n
s
t
r
uc
t
i
n
g
de
e
p
ne
ur
a
l
n
e
t
wor
ks
(
DN
Ns
)
,
B
r
a
n
d
s
t
e
tt
e
r
e
t
al
.
[
30]
o
f
f
e
r
a
c
o
m
pr
e
h
e
ns
i
ve
s
t
udy
o
n
t
h
e
ut
i
li
z
a
t
i
o
n
o
f
C
li
f
f
o
r
d
a
l
ge
b
r
a
,
hi
g
hli
g
h
t
i
n
g
i
t
s
a
dv
a
n
t
a
ge
s
i
n
d
e
e
p
l
e
a
r
ni
ng
.
T
h
e
r
e
s
e
a
r
c
h
s
pe
c
i
f
i
c
a
l
ly
e
m
p
h
a
s
i
z
e
s
t
h
e
a
pp
l
i
c
a
t
i
o
n
o
f
C
li
f
f
o
r
d
ge
o
m
e
t
r
y
i
n
c
o
n
s
t
r
uc
t
i
n
g
n
e
ur
a
l
ne
t
wor
ks
,
l
e
a
d
i
n
g
to
im
pr
o
v
e
d
a
c
c
ur
a
c
y
.
T
h
e
a
ut
h
o
r
s
p
r
o
p
o
s
e
C
l
if
f
o
r
d
n
e
ur
a
l
ne
t
wor
ks
a
s
a
n
a
l
t
e
r
n
a
t
i
v
e
i
m
p
l
e
m
e
n
t
a
t
i
o
n
f
o
r
s
o
l
vi
ng
pa
r
t
i
a
l
d
i
f
f
e
r
e
n
t
i
a
l
e
qu
a
t
i
o
ns
(
P
DE
s
)
,
s
h
o
wc
a
s
i
ng
pr
o
m
i
s
i
ng
r
e
s
u
l
t
s
i
n
c
o
m
pa
r
i
s
o
n
.
Dr
o
s
t
[
28]
,
B
r
a
n
d
s
t
e
tt
e
r
e
t
al.
[
30]
de
s
c
r
i
be
d
ge
o
m
e
t
r
i
c
C
li
f
f
o
r
d
a
l
g
e
b
r
a
n
e
t
wo
r
ks
(
GC
A
N
s
)
whi
c
h
a
r
e
ba
s
e
d
o
n
s
y
mm
e
t
r
i
c
gr
o
up
tr
a
n
s
f
o
r
m
a
t
i
o
n
s
us
i
ng
t
hi
s
ge
o
m
e
t
r
y
o
f
C
l
if
f
o
r
d.
T
h
e
s
e
GC
A
N
s
a
r
e
m
o
r
e
s
ui
t
a
bl
e
f
o
r
pl
a
c
e
s
t
h
a
t
r
e
qui
r
e
m
a
ni
pu
l
a
t
i
n
g
ge
o
m
e
t
r
i
c
t
r
a
n
s
f
o
r
m
a
t
i
o
n
s
f
o
r
dy
n
a
mi
c
s
y
s
t
e
m
s
.
I
n
s
u
m
m
a
r
y
,
t
h
e
r
e
s
e
a
r
c
h
e
n
de
a
v
o
r
s
d
i
s
c
u
s
s
e
d
e
n
c
o
m
pa
s
s
b
a
n
d
s
e
l
e
c
t
i
o
n
,
c
l
u
s
t
e
r
i
n
g
w
i
t
h
v
a
r
i
o
us
s
i
mi
l
a
r
i
t
y
m
e
t
r
i
c
s
,
a
n
d
c
l
us
t
e
r
i
n
g
a
l
go
r
i
t
hm
s
.
B
a
n
d
s
e
l
e
c
t
i
o
n
m
e
t
h
o
do
l
o
g
i
e
s
,
pr
e
s
e
n
t
e
d
i
n
[
6]
,
[
8]
,
[
9
]
,
[
12
]
,
p
r
o
vi
d
e
a
wi
de
r
a
n
ge
o
f
t
e
c
h
ni
que
s
f
r
o
m
l
e
v
e
r
a
g
i
ng
s
t
a
t
i
s
t
i
c
a
l
va
r
i
a
t
i
o
n
t
o
t
r
e
a
t
i
ng
b
a
n
d
s
e
l
e
c
t
i
o
n
a
s
a
r
a
n
k
i
ng
o
r
r
e
gr
e
s
s
i
o
n
pr
o
bl
e
m
,
e
m
p
h
a
s
i
z
i
ng
t
h
e
i
m
po
r
t
a
n
c
e
o
f
r
e
duc
i
n
g
da
t
a
s
e
t
di
m
e
ns
i
o
na
l
i
t
y
w
hil
e
c
a
pt
ur
i
n
g
f
u
n
da
m
e
n
t
a
l
s
pe
c
tr
a
l
i
n
f
o
r
m
a
t
i
o
n
.
F
ur
t
h
e
r
,
t
h
e
un
s
upe
r
vi
s
e
d
b
a
n
d
s
e
l
e
c
t
i
o
n
a
ppr
o
a
c
h
e
s
t
a
ke
a
dv
a
n
t
a
ge
o
f
t
h
e
a
bil
i
t
y
to
h
a
n
d
le
u
nl
a
b
e
l
e
d
da
t
a
w
i
t
h
o
ut
gr
o
un
d
tr
u
t
h
.
Ha
vi
ng
t
h
e
gr
e
a
t
e
r
a
dv
a
n
t
a
ge
o
f
li
ne
a
r
t
i
m
e
c
o
m
p
l
e
xi
t
y
a
n
d
e
a
s
e
o
f
im
p
l
e
m
e
n
t
a
t
i
o
n
,
t
h
e
k
-
m
e
a
ns
c
l
us
t
e
r
i
n
g
m
e
t
h
o
d
a
nd
i
t
s
i
m
pr
o
v
e
m
e
n
t
w
i
t
h
s
pe
c
t
r
a
l
s
im
il
a
r
i
t
i
e
s
a
r
e
r
e
vi
s
i
t
e
d
i
n
t
h
e
pr
o
p
o
s
e
d
wor
k
a
n
d
a
t
w
o
-
l
e
v
e
l
c
l
u
s
t
e
r
i
n
g
a
ppr
o
a
c
h
f
o
r
b
a
n
d
s
e
l
e
c
t
i
o
n
.
T
h
e
f
i
r
s
t
l
e
ve
l
f
o
c
us
e
s
o
n
i
de
n
t
i
f
y
i
ng
t
h
e
s
pe
c
t
r
a
l
ly
d
i
s
s
i
mi
l
a
r
ba
n
ds
a
n
d
t
h
e
s
e
c
o
n
d
l
e
v
e
l
c
l
us
t
e
r
i
n
g
l
e
v
e
r
a
ge
s
t
h
e
c
h
o
r
d
m
e
t
r
i
c
d
i
s
t
a
nc
e
to
f
ur
t
h
e
r
i
m
pr
o
v
e
t
h
e
a
c
c
ur
a
c
y
o
f
t
h
e
b
a
n
d
s
e
lec
t
i
o
n
.
T
hi
s
un
s
up
e
r
vi
s
e
d
a
ppr
o
a
c
h
e
f
f
e
c
t
i
v
e
ly
p
r
e
s
e
r
v
e
s
s
pe
c
t
r
a
l
i
n
f
o
r
m
a
t
i
o
n
,
h
a
n
d
li
ng
c
o
m
p
l
e
x
c
h
a
r
a
c
t
e
r
i
s
t
i
c
s
f
o
r
a
c
c
ur
a
t
e
b
a
n
d
s
e
l
e
c
t
i
o
n
a
n
d
i
m
pr
o
v
e
d
hy
pe
r
s
pe
c
t
r
a
l
da
t
a
a
n
a
ly
s
i
s
r
e
s
u
l
t
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2
5
0
2
-
4
7
52
In
do
n
e
s
i
a
n
J
E
l
e
c
E
n
g
&
C
o
m
p
S
c
i
,
Vo
l
.
3
6
,
N
o
.
2
,
N
o
v
e
m
b
e
r
20
24
:
1
30
9
-
1
31
8
1312
2.
M
E
T
HO
D
T
h
e
ke
y
o
bj
e
c
t
i
v
e
o
f
t
h
e
r
e
s
e
a
r
c
h
wo
r
k
pr
e
s
e
n
t
e
d
i
n
t
hi
s
pa
pe
r
i
s
to
i
de
n
t
i
f
y
a
s
ubs
e
t
o
f
ba
n
ds
us
i
ng
a
n
u
ns
upe
r
vi
s
e
d
(
c
l
us
t
e
r
i
n
g)
a
ppr
o
a
c
h
l
e
ve
r
a
g
i
n
g
s
pe
c
t
r
a
l
s
im
il
a
r
i
t
y
m
e
a
s
ur
e
a
n
d
t
o
i
n
c
o
r
por
a
t
e
c
h
o
r
d
m
e
t
r
i
c
,
im
pr
o
vi
s
i
ng
t
h
e
c
l
a
s
s
i
f
i
c
a
t
i
o
n
a
c
c
ur
a
c
y
o
f
t
h
e
tr
a
d
i
t
i
o
n
a
l
k
-
m
e
a
ns
c
l
u
s
t
e
r
i
n
g
a
l
go
r
i
t
hm
.
T
h
e
pr
o
p
o
s
e
d
m
e
t
h
o
do
l
o
g
y
r
uns
i
n
t
w
o
s
t
e
ps
:
a.
I
ni
t
i
a
l
b
a
n
d
s
e
l
e
c
t
i
o
n
by
c
l
us
t
e
r
i
n
g
t
h
e
s
pe
c
t
r
a
l
b
a
n
d
s
us
i
ng
k
-
m
e
a
n
s
w
i
t
h
s
pe
c
t
r
a
l
s
im
il
a
r
i
t
y
m
e
a
s
ur
e
(
K
M
-
S
S
M
)
.
b.
I
m
p
l
e
m
e
n
t
c
h
o
r
d
k
-
m
e
a
ns
(
C
KM
)
f
o
r
o
p
t
i
m
a
l
b
a
nd
s
e
l
e
c
t
i
o
n
.
B
a
n
d
s
e
l
e
c
t
i
o
n
i
s
pe
r
f
o
r
m
e
d
w
i
t
h
k
-
m
e
a
ns
a
s
s
h
o
wn
i
n
Al
go
r
i
t
hm
1
w
h
e
r
e
b
a
n
d
s
s
e
l
e
c
t
e
d
f
o
r
s
t
e
p
2
(
C
KM
c
l
u
s
t
e
r
i
n
g)
t
h
e
s
e
c
h
o
s
e
n
b
a
n
d
s
a
r
e
t
h
e
o
r
i
g
in
a
l
r
e
pr
e
s
e
n
t
e
d
b
a
n
d
s
o
f
t
h
e
o
r
i
g
i
na
l
HSI
da
t
a
s
e
ts
.
I
n
o
ur
pr
o
p
o
s
e
d
a
ppr
o
a
c
h
,
t
h
e
s
pe
c
t
r
a
l
s
im
il
a
r
i
t
y
m
e
a
s
ur
e
s
vi
z
.
,
s
pe
c
t
r
a
l
a
n
ge
l
m
a
ppe
r
(
S
AM
)
,
s
pe
c
t
r
a
l
i
n
f
o
r
m
a
t
i
o
n
d
i
v
e
r
ge
n
c
e
(
S
I
D)
,
hy
b
r
i
d
m
e
a
s
ur
e
w
i
t
h
s
p
e
c
t
r
a
l
a
n
g
l
e
m
a
ppe
r
(
S
I
DSA
M
)
,
J
e
f
f
e
r
y
-
M
a
t
us
i
t
a
(
J
M
)
,
JM
w
i
t
h
S
A
M
(
J
M
-
S
A
M
)
,
a
n
d
n
o
r
m
a
li
s
e
d
c
r
o
s
s
c
o
r
r
e
l
a
t
i
o
n
(
NC
C
)
a
r
e
us
e
d
i
n
p
l
a
c
e
o
f
E
uc
l
i
de
a
n
m
e
a
s
ur
e
f
o
r
sp
e
c
t
r
a
l
s
im
il
a
r
i
t
y
.
T
h
e
m
a
t
h
e
m
a
t
i
c
a
l
f
o
r
m
u
l
a
e
t
o
c
o
m
put
e
t
h
e
E
D,
S
A
M
,
S
I
D,
S
I
DSA
M
,
J
M
,
J
M
-
S
AM
,
a
n
d
NC
C
a
r
e
s
h
o
wn
i
n
(
2)
-
(
8)
.
T
h
e
s
t
e
p
2
pr
o
c
e
s
s
i
nv
o
l
ve
s
a
pp
ly
i
ng
Al
go
r
i
t
hm
2
f
o
r
b
a
n
d
s
e
l
e
c
t
i
o
n
us
i
ng
(
2)
-
(
8
)
.
T
h
e
n
,
t
h
e
b
a
n
ds
s
e
l
e
c
t
e
d
by
Al
go
r
i
t
hm
1
a
r
e
us
e
d
a
s
i
n
put
f
o
r
t
h
e
Al
go
r
i
t
hm
2
.
B
y
e
v
a
l
ua
t
i
n
g
a
n
d
f
i
n
e
-
t
uni
n
g
‘
r
’
f
o
r
e
a
c
h
s
e
t
o
f
b
a
n
d
s
,
t
h
e
go
a
l
i
s
to
i
m
pr
o
v
e
t
he
pe
r
f
o
r
m
a
n
c
e
o
f
t
h
e
c
h
o
r
d
k
-
m
e
a
ns
a
l
go
r
i
t
hm
,
pa
r
t
i
c
u
l
a
r
l
y
f
o
r
da
t
a
s
e
t
s
wi
t
h
hi
g
h
d
i
m
e
ns
i
o
n
a
li
t
y
(
m
o
r
e
t
h
a
n
2
0
b
a
n
ds
)
.
Al
go
r
i
t
hm
1
.
K
-
m
e
a
n
s
c
l
us
t
e
r
i
n
g
w
i
t
h
S
S
M
f
o
r
b
a
n
d
s
e
l
e
c
t
i
o
n
i
n
HSI
(
D
N
×
M
,
k
,
π
(
0
)
)
(
K
M
-
S
S
M
)
1.
Initialize
the
seed
cluster
center
s
K
-
means++
initialization,
denoted
by
(
0
)
=
{
1
(
0
)
,
2
(
0
)
,
.
.
.
(
0
)
}
.
2.
For
each
spectral
band
b
i
in
D
,
find
its
nearest
spectral
(
l
)
band
b
j
using
ED/SAM/SID/SIDSAM, and assign
b
i
to
C
l
j
.
3.
Co
mpute the updated mean spectral similarity of each cluster
C
l
j
.
4.
Reassign
each
spectral
band
b
i
in
D
,
find
its
nearest
spectral
band
b
j
(
l
+
1
)
u
sing
ED/SAM/SID/SIDSAM, and assign
b
i
to
C
l
j
+
1
.
5.
Repeat steps 2 through step 4 till convergence.
6.
Identify
one
optima
l
band
b
i
(
0
)
from
each
cluster
such
that
b
i
(
0
)
is
the
most
spectr
ally
similar to the center of the cluster C
i
(
0
)
.
7.
Output:
s
elected spectral bands from each cluster.
ED
(
S
i
,
S
j
)
=
√
{
∑
(
S
i
−
S
j
)
2
L
{
i
,
j
=
1
}
}
(
2)
(
,
)
=
−
1
(
)
,
=
[
{
∑
{
}
,
=
1
{
√
{
∑
{
2
}
=
1
}
}
∗
{
√
{
∑
{
2
}
=
1
}
}
}
]
(
3)
(
,
)
=
∑
∗
log
(
)
=
1
+
∑
∗
log
(
)
=
1
(
4)
(
,
)
=
(
,
)
∗
ta
n
(
(
,
)
)
(
5)
(
,
)
=
√
∑
[
√
−
√
]
2
=
1
(
6)
−
(
,
)
=
(
,
)
∗
ta
n
(
(
,
)
)
(
7)
(
,
)
=
∑
[
(
,
)
−
̅
̅
̅
]
∗
[
(
,
)
−
̅
̅
̅
]
,
√
∑
(
(
,
)
−
̅
̅
̅
)
2
,
∗
√
∑
(
(
,
)
−
̅
̅
̅
̅
)
2
,
(
8)
(
,
)
=
−
(
)
(
9)
_
(
,
)
=
,
∗
,
Θ
=
co
s
−
1
(
_
)
ℎ
(
,
)
=
2
∗
∗
s
in
Θ
2
(
10)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
do
n
e
s
i
a
n
J
E
l
e
c
E
n
g
&
C
o
m
p
S
c
i
I
S
S
N:
2
5
0
2
-
4
7
52
Optimiz
i
ng
hy
pe
r
s
pe
c
t
r
al
c
las
s
if
ication:
s
pe
c
tr
al
s
imil
ar
it
y
-
bas
e
d
…
(
Or
igant
i
Subhas
h
C
hande
r
Go
ud
)
1313
Al
g
or
i
t
hm
2
.
C
h
or
d
k
-
m
e
a
n
s
c
l
us
te
r
i
n
g
w
i
t
h
b
a
n
d
s
e
l
e
c
t
i
o
n
H
S
I
(
D
L
×
k
L
=
N
×
M
P
ixe
ls
,
K
,
L
a
b
(
1
)
)
(
c
h
o
r
d
-
K
M
)
1.
Use reduced
bands from
Algorithm 1 for each method of (2) to
(7) so that the data set is
represented with the bands of the methods as
D
L
×
k
= {
b
1
,
b
2
,
...,
b
k
}
2.
Normalise the reduced
D
L
×
k
with Z
-
score
n
o
rmalisation as (9)
3.
For
D
L
×
k
,
randomly
initialise
the
seeds
as
µ
(
0
)
= {
p
1
,
p
2
,
...
,
p
K
}
4.
For
each
point
in
D
L
×
k
find
its
nearest
point
from
µ
(
0
)
seeds
using
chord
met
ri
c
in
equation 10 and group the points according to the nearest seed as clusters
C
l
j
.
5.
Co
mpute the updated mean of each cluster
C
l
j
.
6.
Reassign each point in
D
L
×
k
, find its nearest seed updated in step 5.
7.
Repeat steps 5 and step 6 till convergence and label each point according to cluster
C
l
j
.
8.
Final
l
abels of each point in
D
L
×
k
is
compared to gr
ound truth for accuracies
.
Output:
t
he
l
abels of step 7 are
Lab
(
1
)
=
{
l
1
,
l
2
,
...,
l
k
}
He
r
e
i
n
(
2)
to
(
7)
S
i
a
n
d
S
j
a
r
e
t
h
e
pi
xe
l
v
e
c
t
o
r
s
o
f
a
n
HSI
i
m
a
ge
,
wh
e
r
e
i
n
i
t
h
o
l
d
s
t
h
e
c
o
n
di
t
i
o
n
t
h
a
t
i
,
j
<
L
w
h
e
r
e
L
i
s
t
h
e
n
u
m
be
r
o
f
b
a
n
d
s
pr
e
s
e
n
t
i
n
a
n
HSI
.
I
n
(
8)
D
i
(
x
,
y
)
,
D
j
(
x
,
y
)
r
e
pr
e
s
e
n
t
s
(
x
,
y
)
i
n
de
x
e
d
p
i
x
e
l
va
l
ue
o
f
a
n
HSI
b
a
n
d,
a
n
d
D
̅
r
e
pr
e
s
e
n
t
s
t
h
e
m
e
a
n
o
f
t
h
e
b
a
n
d
p
i
x
e
l
s
o
f
HSI
.
In
(
1
0
)
r
e
p
r
e
s
e
n
ts
th
e
c
h
or
d
m
e
tr
i
c
th
a
t
f
ol
l
ows
th
e
C
l
i
f
f
or
d
A
l
g
e
b
r
a
p
r
i
n
c
i
pl
e
s
a
s
d
e
s
c
r
i
b
e
d
b
y
D
or
s
t
[
28
]
wh
e
r
e
t
h
e
pr
i
n
c
i
p
l
e
o
f
t
h
e
c
h
o
r
d
i
s
a
pp
l
i
e
d
by
s
li
c
i
ng
t
h
e
m
u
l
t
i
d
im
e
n
s
i
o
na
l
S
phe
r
e
a
s
s
h
o
wn
i
n
F
i
gur
e
1
.
W
h
e
r
e
t
h
e
c
h
o
r
d
m
e
t
r
i
c
i
s
a
s
li
c
e
o
f
t
h
e
s
p
h
e
r
e
w
i
t
h
a
c
e
r
t
a
i
n
r
a
d
i
us
t
h
a
t
v
a
r
i
a
t
i
o
n
o
f
r
a
d
i
u
s
tr
i
e
s
to
d
i
f
f
e
r
e
n
t
i
a
t
e
t
h
e
c
l
us
t
e
r
s
f
o
r
m
e
d
by
k
-
m
e
a
ns
w
i
t
h
n
o
n
-
c
o
nv
e
x
s
h
a
pe
s
.
I
n
F
i
gur
e
1
(
10)
‘
r
’
i
s
t
h
e
r
a
di
us
o
f
t
h
e
s
phe
r
e
whi
c
h
i
s
s
li
c
e
d
a
t
a
pa
r
t
o
f
t
h
e
s
p
h
e
r
e
a
n
d
‘
Ɵ’
i
s
t
h
e
a
n
g
l
e
f
o
r
m
e
d
by
t
h
e
r
a
d
i
u
s
o
f
t
h
e
s
p
h
e
r
e
whi
c
h
i
s
s
l
i
c
e
d
,
F
i
gur
e
2
r
e
pr
e
s
e
n
t
s
t
h
e
qua
t
e
r
ni
o
n
r
e
pr
e
s
e
nt
a
t
i
o
n
o
f
C
l
if
f
o
r
d
a
l
ge
b
r
a
o
f
t
h
e
s
l
i
c
e
d
s
p
h
e
r
e
wh
e
r
e
e
1,
e
2,
e
3
ar
e
o
f
(
10)
.
F
i
gur
e
1.
C
h
o
r
d
m
e
t
r
i
c
o
f
(
10)
T
o
e
n
h
a
n
c
e
t
h
e
a
ppr
o
a
c
h
,
i
t
’
s
c
r
uc
i
a
l
to
c
o
n
s
i
de
r
t
h
e
e
f
f
e
c
t
i
v
e
n
e
s
s
o
f
b
o
t
h
t
h
e
1
a
n
d
2
a
l
go
r
i
t
hm
s
,
a
s
we
l
l
a
s
t
h
e
s
u
i
t
a
bi
li
t
y
o
f
t
h
e
e
qua
t
i
o
ns
f
o
r
b
a
nd
s
e
l
e
c
t
i
o
n
.
A
dd
i
t
i
o
n
a
ll
y
,
t
h
e
da
t
a
s
e
t
’
s
c
h
a
r
a
c
t
e
r
i
s
t
i
c
s
w
il
l
p
l
a
y
a
ke
y
r
o
l
e
i
n
de
t
e
r
m
i
n
i
ng
t
h
e
s
e
l
e
c
t
e
d
b
a
n
ds
’
r
e
l
e
v
a
nc
e
a
n
d
t
h
e
a
ppr
o
a
c
h
’
s
o
v
e
r
a
l
l
s
uc
c
e
s
s
.
F
i
gur
e
2.
Qua
t
e
r
ni
o
n
s
r
e
pr
e
s
e
n
t
a
t
i
o
n
o
f
C
li
f
f
o
r
d
a
l
ge
b
r
a
[
28]
3.
RE
S
UL
T
S
AN
D
DI
S
CU
S
S
I
ON
3.
1
.
Dat
as
e
t
s
I
n
d
i
a
n
P
i
n
e
s
we
r
e
c
o
l
l
e
c
t
e
d
by
t
h
e
a
i
r
b
o
r
n
e
v
i
s
i
b
le/
i
nf
r
a
r
e
d
im
a
g
i
ng
s
pe
c
t
r
o
m
e
t
e
r
(
A
VI
R
I
S
)
s
e
n
s
o
r
o
v
e
r
t
h
e
I
n
d
i
a
n
P
i
n
e
s
t
e
s
t
s
i
t
e
i
n
No
r
t
h
we
s
t
e
r
n
I
n
di
a
n
a
,
USA
.
T
h
e
da
t
a
s
e
t
c
o
n
t
a
i
ns
145
×
145
pi
xe
l
s
,
w
i
t
h
224
s
pe
c
t
r
a
l
b
a
n
ds
c
o
v
e
r
i
n
g
t
h
e
wa
v
e
l
e
n
gt
h
r
a
n
ge
f
r
o
m
0.
4
to
2
.
5
m
i
c
r
o
m
e
t
e
r
s
,
a
n
d
h
a
s
a
s
pa
t
i
a
l
r
e
s
o
l
ut
i
o
n
o
f
20
m
e
t
e
r
s
pe
r
p
i
x
e
l
i
n
16
d
if
f
e
r
e
n
t
c
l
a
s
s
e
s
.
S
a
l
i
na
s
w
a
s
c
o
l
l
e
c
t
e
d
by
t
h
e
A
VI
R
I
S
s
e
n
s
o
r
o
v
e
r
t
h
e
S
a
l
i
na
s
Va
l
l
e
y
i
n
C
a
li
f
o
r
ni
a
,
US
A
.
T
h
e
da
t
a
s
e
t
c
o
n
t
a
i
ns
512
×
21
7
p
i
x
e
l
s
,
w
i
t
h
224
s
pe
c
t
r
a
l
b
a
n
d
s
c
o
v
e
r
i
n
g
t
h
e
wa
v
e
l
e
n
gt
h
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2
5
0
2
-
4
7
52
In
do
n
e
s
i
a
n
J
E
l
e
c
E
n
g
&
C
o
m
p
S
c
i
,
Vo
l
.
3
6
,
N
o
.
2
,
N
o
v
e
m
b
e
r
20
24
:
1
30
9
-
1
31
8
1314
r
a
n
ge
f
r
o
m
0.
4
to
2
.
5
m
i
c
r
o
m
e
t
e
r
s
,
a
n
d
h
a
s
a
s
p
a
t
i
a
l
r
e
s
o
l
ut
i
o
n
o
f
3.
7
m
e
t
e
r
s
pe
r
pi
x
e
l
.
t
h
e
gr
o
u
n
d
t
r
u
t
h
c
o
n
t
a
i
ns
16
d
i
f
f
e
r
e
n
t
c
l
a
s
s
e
s
.
P
a
vi
a
U
ni
ve
r
i
s
t
y
hy
pe
r
s
p
e
c
t
r
a
l
da
t
a
wa
s
a
c
qu
i
r
e
d
by
t
h
e
R
OSI
S
s
e
ns
o
r
dur
i
n
g
a
f
li
g
h
t
c
a
m
p
a
i
g
n
o
v
e
r
P
a
vi
a
,
n
o
r
t
h
e
r
n
I
t
a
l
y
.
I
t
h
a
s
610*610
p
i
x
e
l
s
a
n
d
t
h
e
gr
o
un
d
tr
u
t
h
c
o
n
t
a
i
n
s
9
c
l
a
s
s
e
s
.
(
P
r
o
vi
de
d
by
P
r
o
f
.
P
a
o
l
o
Ga
m
b
a
)
.
P
a
vi
a
c
e
n
t
e
r
c
o
n
t
a
i
ns
610
s
pe
c
t
r
a
l
b
a
n
d
s
a
n
d
c
o
v
e
r
s
a
n
a
r
e
a
o
f
a
ppr
o
xim
a
t
e
l
y
610
×
34
0
pi
xe
l
s
,
w
i
t
h
e
a
c
h
p
i
xe
l
r
e
pr
e
s
e
n
t
i
n
g
a
s
qua
r
e
o
f
1.
3
met
e
r
s
.
T
hi
s
hy
pe
r
s
pe
c
t
r
a
l
da
t
a
s
e
t
h
a
s
ni
ne
c
l
a
s
s
e
s
,
whi
c
h
r
e
pr
e
s
e
n
t
d
i
f
f
e
r
e
n
t
l
a
n
d
-
c
o
v
e
r
t
y
pe
s
i
n
t
h
e
c
i
t
y
o
f
P
a
vi
a
.
3.
2
.
Re
s
u
l
t
s
T
hi
s
s
e
c
t
i
o
n
r
e
pr
e
s
e
n
t
s
t
h
e
ut
i
l
i
z
a
t
i
o
n
o
f
r
e
a
l
-
t
i
m
e
HSI
da
t
a
s
e
t
s
i
n
a
pp
lyi
ng
t
h
e
pr
o
p
o
s
e
d
m
e
t
h
o
do
l
o
g
y
.
T
h
e
r
e
s
u
l
t
s
a
r
e
pr
e
s
e
n
t
e
d
i
n
T
a
bl
e
s
1
a
n
d
2
a
s
t
h
e
o
v
e
r
a
l
l
a
c
c
ur
a
c
y
a
n
d
ka
ppa
c
o
e
f
f
i
c
i
e
n
t
,
wh
e
r
e
Al
go
r
i
t
hm
2
i
s
us
e
d
wi
t
h
a
c
h
o
r
d
m
e
t
r
i
c
r
a
n
g
i
ng
f
r
o
m
2
to
3
.
T
h
e
hi
g
hli
g
h
t
e
d
v
a
l
ue
s
i
n
d
ica
t
e
t
h
e
hi
g
h
e
s
t
a
c
c
ur
a
c
y
a
c
hi
e
ve
d
us
i
ng
t
h
e
r
e
s
pe
c
t
i
v
e
m
e
t
h
o
d
de
s
c
r
i
b
e
d
i
n
e
a
c
h
c
o
l
u
m
n.
T
a
bl
e
1
.
Ov
e
r
a
l
l
a
c
c
ur
a
c
y
o
f
P
a
vi
a
c
e
n
t
e
r
R
ED
S
I
D
JM
JM
-
S
A
M
N
C
C
S
A
M
S
I
D
-
S
A
M
2
0.97
0.95
0.97
0.98
0.97
0.97
0.95
2.05
0.97
0.95
0.96
0.98
0.96
0.97
0.94
2.1
0.97
0.97
0.97
0.98
0.97
0.97
0.97
2.15
0.96
0.94
0.97
0.98
0.97
0.97
0.96
2.2
0.97
0.95
0.97
0.98
0.97
0.97
0.97
2.25
0.97
0.97
0.97
0.97
0.97
0.97
0.95
2.3
0.96
0.96
0.97
0.98
0.97
0.98
0.95
2.35
0.97
0.97
0.96
0.98
0.97
0.98
0.97
2.4
0.97
0.95
0.97
0.98
0.97
0.97
0.97
2.45
0.96
0.96
0.97
0.98
0.96
0.97
0.95
2.5
0.95
0.96
0.96
0.98
0.97
0.97
0.95
2.55
0.96
0.9
7
0.97
0.98
0.97
0.97
0.97
2.6
0.97
0.97
0.97
0.98
0.97
0.95
0.96
2.65
0.97
0.97
0.97
0.98
0.97
0.95
0.97
2.7
0.97
0.95
0.97
0.98
0.96
0.97
0.97
2.75
0.97
0.94
0.96
0.98
0.97
0.97
0.95
2.8
0.96
0.97
0.97
0.98
0.96
0.97
0.97
2.85
0.97
0.97
0.97
0.98
0
.97
0.97
0.97
2.9
0.97
0.97
0.96
0.96
0.94
0.96
0.95
2.95
0.97
0.97
0.97
0.97
0.97
0.95
0.95
T
a
bl
e
2
.
K
a
ppa
c
o
e
f
f
i
c
i
e
n
t
o
f
P
a
vi
a
c
e
n
t
e
r
R
ED
S
I
D
JM
JM
-
S
A
M
N
C
C
S
A
M
S
I
D
-
S
A
M
2
0.91
0.86
0.91
0.93
0.90
0.91
0.86
2.05
0.91
0.86
0.
88
0.93
0.86
0.92
0.83
2.1
0.91
0.91
0.91
0.94
0.91
0.91
0.90
2.15
0.87
0.83
0.91
0.93
0.91
0.91
0.89
2.2
0.91
0.86
0.91
0.93
0.91
0.92
0.90
2.25
0.91
0.91
0.91
0.91
0.91
0.91
0.86
2.3
0.87
0.89
0.91
0.93
0.91
0.93
0.86
2.35
0.91
0.90
0.87
0.93
0.91
0.93
0.91
2.4
0.91
0.86
0.90
0.93
0.91
0.91
0.91
2.45
0.87
0.89
0.91
0.93
0.87
0.91
0.86
2.5
0.86
0.89
0.88
0.93
0.91
0.92
0.86
2.55
0.87
0.91
0.91
0.93
0.90
0.91
0.91
2.6
0.91
0.91
0.91
0.94
0.91
0.86
0.89
2.65
0.91
0.91
0.91
0.93
0.91
0.86
0.90
2.
7
0.90
0.86
0.91
0.93
0.87
0.91
0.91
2.75
0.91
0.82
0.89
0.93
0.90
0.91
0.86
2.8
0.87
0.90
0.91
0.93
0.86
0.92
0.91
2.85
0.91
0.91
0.91
0.93
0.91
0.92
0.91
2.9
0.91
0.91
0.87
0.88
0.83
0.89
0.86
2.95
0.91
0.91
0.91
0.91
0.91
0.84
0.86
F
o
r
i
n
s
t
a
n
c
e
,
T
a
bl
e
1
r
e
pr
e
s
e
n
t
s
t
h
e
s
c
e
n
e
o
f
P
a
vi
a
c
e
n
t
e
r
a
n
d
t
h
e
c
o
l
u
m
n
l
a
be
l
e
d
‘
R
’
c
o
r
r
e
s
po
n
ds
to
t
h
e
r
a
n
ge
o
f
‘
r
’
va
l
u
e
s
i
n
(
9)
.
A
dd
i
t
i
o
n
a
ll
y
,
t
h
e
c
o
l
u
m
n
s
w
hi
c
h
a
r
e
i
m
p
l
e
m
e
n
t
a
t
i
o
n
o
f
Al
go
r
i
t
hm
1
KM
-
S
S
M
b
a
n
d
s
e
l
e
c
t
i
o
n
m
e
t
h
o
ds
whi
c
h
a
r
e
de
f
i
ne
d
a
s
f
o
l
l
o
ws
:
-
E
D:
r
e
pr
e
s
e
n
t
s
t
h
e
‘
E
uc
l
i
de
a
n
d
i
s
t
a
n
c
e
’
m
e
t
h
o
d
a
pp
l
i
e
d
i
n
(
2
)
.
-
S
A
M
:
de
n
o
t
e
s
t
h
e
‘
S
pe
c
t
r
a
l
a
n
g
l
e
m
a
pp
e
r
’
a
s
de
f
i
n
e
d
i
n
(
3
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
do
n
e
s
i
a
n
J
E
l
e
c
E
n
g
&
C
o
m
p
S
c
i
I
S
S
N:
2
5
0
2
-
4
7
52
Optimiz
i
ng
hy
pe
r
s
pe
c
t
r
al
c
las
s
if
ication:
s
pe
c
tr
al
s
imil
ar
it
y
-
bas
e
d
…
(
Or
igant
i
Subhas
h
C
hande
r
Go
ud
)
1315
-
S
I
D:
de
n
ot
e
s
t
h
e
‘
S
pe
c
t
r
a
l
i
nf
o
r
m
a
t
i
o
n
d
i
v
e
r
ge
n
c
e
’
a
s
de
f
i
ne
d
i
n
(
4
)
.
-
S
I
DSA
M
:
de
n
ot
e
s
t
h
e
‘
S
pe
c
t
r
a
l
i
nf
o
r
m
a
t
i
o
n
d
i
ve
r
ge
n
c
e
’
w
i
t
h
‘
s
pe
c
t
r
a
l
a
n
g
l
e
m
a
ppe
r
’
a
s
de
f
i
ne
d
i
n
(
5
)
.
-
J
M
:
de
n
ot
e
s
t
h
e
‘
J
e
f
f
e
r
e
y
s
M
a
t
us
i
t
a
’
a
s
de
f
i
ne
d
i
n
(
6
)
.
-
JM
-
S
AM
’
:
de
n
o
t
e
s
t
h
e
‘
J
e
f
f
e
r
e
y
s
M
a
t
us
i
t
a
’
w
i
t
h
‘
s
pe
c
t
r
a
l
a
n
g
l
e
m
a
pp
e
r
’
a
s
de
f
i
ne
d
i
n
(
7
)
.
-
NC
C
:
de
n
o
t
e
s
t
h
e
‘
n
o
r
m
a
li
s
e
d
c
r
o
s
s
c
o
-
r
e
l
a
t
i
o
n’
i
n
(
8
)
.
F
i
gur
e
3
r
e
pr
e
s
e
n
t
s
t
h
e
g
r
a
phi
c
a
l
r
e
pr
e
s
e
n
t
a
t
i
o
n
o
f
Al
go
r
i
t
hm
s
1
a
n
d
2
a
n
d
t
h
e
wo
r
kf
l
o
w
o
f
t
h
e
c
o
m
p
l
e
t
e
pr
o
p
o
s
e
d
m
e
t
h
o
d.
Al
go
r
i
t
hm
s
1
a
n
d
2
a
r
e
a
pp
l
i
e
d
o
n
e
a
f
t
e
r
t
h
e
o
t
h
e
r
,
a
n
d
t
h
e
r
e
s
u
l
t
o
f
Al
g
o
r
i
t
hm
1
i
s
g
i
ve
n
a
s
i
n
put
to
Al
go
r
i
t
hm
2.
T
h
e
gr
o
un
d
tr
u
t
h
a
n
d
c
l
a
s
s
if
i
c
a
t
i
o
n
i
m
a
ge
s
o
f
t
h
e
P
a
vi
a
c
e
n
t
e
r
a
f
t
e
r
t
h
e
pr
o
p
o
s
e
d
w
o
r
k
a
r
e
r
e
pr
e
s
e
n
t
e
d
i
n
F
i
gur
e
s
3
to
5
.
T
a
bl
e
s
1
a
n
d
2
p
r
o
vi
de
i
ns
i
g
h
t
s
i
n
t
o
t
h
e
a
c
c
ur
a
c
y
a
c
hi
e
ve
d
by
t
h
e
pr
o
p
o
s
e
d
m
e
t
h
o
d,
wi
t
h
c
o
l
u
m
ns
l
i
k
e
E
D,
S
I
D,
r
e
pr
e
s
e
n
t
i
n
g
v
a
r
i
o
us
t
e
c
h
ni
que
s
f
o
r
b
a
n
d
s
e
l
e
c
t
i
o
n
f
r
o
m
Al
go
r
i
t
hm
1.
E
a
c
h
c
o
l
u
m
n
i
n
t
h
e
t
a
bl
e
s
c
o
r
r
e
s
po
n
ds
to
t
h
e
a
c
c
ur
a
c
y
a
c
hi
e
v
e
d
by
a
pp
lyi
ng
t
h
e
s
e
b
a
n
d
s
e
l
e
c
t
i
o
n
m
e
t
h
o
ds
w
i
t
h
c
h
o
r
d
m
e
t
r
i
c
-
ba
s
e
d
c
l
us
t
e
r
i
n
g
by
Al
go
r
i
t
hm
2.
T
h
e
f
i
r
s
t
c
o
l
u
m
n
,
de
n
o
t
e
d
a
s
R
,
s
i
g
nif
i
e
s
t
h
e
c
h
o
r
d
m
e
t
r
i
c
r
a
d
i
us
,
va
r
y
i
ng
b
e
t
we
e
n
2
a
n
d
3
w
i
t
h
a
n
i
n
t
e
r
v
a
l
o
f
0.
05.
N
ot
a
bl
y
,
t
h
e
b
e
s
t
a
c
c
ur
a
c
y
v
a
r
i
e
s
f
o
r
e
a
c
h
b
a
n
d
s
u
bs
e
t
ge
n
e
r
a
t
e
d
by
Al
go
r
i
t
hm
1.
J
M
-
S
A
M
s
t
a
n
d
s
o
u
t
wi
t
h
t
h
e
hi
g
h
e
s
t
a
c
c
ur
a
c
y
,
i
nd
i
c
a
t
e
d
by
a
ka
ppa
va
l
ue
o
f
0.
94
i
n
T
a
bl
e
2.
I
n
c
o
m
pa
r
i
s
o
n
to
ot
h
e
r
s
e
e
d
i
n
g
m
e
c
h
a
ni
s
m
s
l
i
k
e
R
a
n
do
m
-
S
e
e
d
i
n
g
a
n
d
Km
e
a
ns
+
+
,
“
c
h
o
r
d
k
-
m
e
a
ns
w
i
t
h
s
pe
c
t
r
a
l
s
im
il
a
r
i
t
y
m
e
a
s
ur
e
s
-
b
a
s
e
d
b
a
n
d
s
e
l
e
c
t
i
o
n
(
C
K
M
-
S
S
B
)
”
de
m
o
ns
t
r
a
t
e
s
s
upe
r
i
o
r
pe
r
f
o
r
m
a
n
c
e
.
S
pe
c
i
f
i
c
a
ll
y
,
f
o
r
t
h
e
J
M
-
S
A
M
b
a
n
d
s
u
bs
e
t
,
t
h
e
K
a
ppa
va
l
ue
s
a
r
e
0.
722
f
o
r
r
a
n
do
m
s
e
e
d
i
ng,
0.
694
f
o
r
K
M
+
+
,
a
nd
n
ot
a
bl
y
hi
g
h
e
r
a
t
0
.
9
42
f
o
r
C
K
M
-
S
S
B
,
hi
g
hli
gh
t
i
n
g
i
t
s
e
f
f
i
c
a
c
y
i
n
a
c
hi
e
vi
ng
i
m
pr
o
v
e
d
a
c
c
ur
a
c
y
.
T
h
e
K
M
-
SSM
-
C
h
o
r
d
-
K
M
a
l
go
r
i
t
hm
e
xhi
bi
t
s
s
upe
r
i
o
r
pe
r
f
o
r
m
a
n
c
e
c
o
m
pa
r
e
d
t
o
t
r
a
di
t
i
o
n
a
l
k
-
m
e
a
n
s
m
e
t
h
o
ds
,
n
a
m
e
ly
KM
-
R
S
a
n
d
KM
+
+
,
a
s
de
m
o
ns
t
r
a
t
e
d
o
n
t
h
e
P
a
vi
a
c
e
n
t
e
r
da
t
a
s
e
t
.
W
i
t
h
a
K
a
ppa
v
a
l
ue
o
f
0.
942,
K
M
-
SSM
-
C
h
o
r
d
-
K
M
o
ut
pe
r
f
o
r
m
s
o
t
h
e
r
k
-
m
e
a
n
s
a
ppr
o
a
c
he
s
(
K
a
ppa
v
a
l
ue
s
a
r
o
un
d
0.
72)
s
h
o
wn
i
n
T
a
bl
e
3
wh
e
n
c
o
upl
e
d
w
i
t
h
v
a
r
i
o
us
b
a
n
d
s
e
l
e
c
t
i
o
n
s
tr
a
t
e
gi
e
s
s
uc
h
a
s
S
I
D
a
n
d
J
M
.
T
h
e
r
e
s
u
l
t
s
a
r
e
c
on
s
i
s
t
e
n
t
a
c
r
o
s
s
di
f
f
e
r
e
n
t
da
t
a
s
e
t
s
,
hi
g
hl
i
g
h
t
i
n
g
t
h
e
a
l
go
r
i
t
hm
’
s
r
o
b
us
t
n
e
s
s
a
n
d
e
f
f
i
c
a
c
y
.
T
h
e
r
e
s
e
a
r
c
h
s
ugg
e
s
t
s
t
h
a
t
KM
-
SSM
-
C
h
o
r
d
-
K
M
h
o
l
d
s
pr
o
m
i
s
e
f
o
r
HSI
a
n
a
ly
s
i
s
,
pot
e
n
t
i
a
l
ly
s
ur
pa
s
s
i
ng
c
ur
r
e
n
t
s
t
a
t
e
-
of
-
t
h
e
-
a
r
t
c
l
u
s
t
e
r
i
n
g
a
l
go
r
i
t
hm
s
.
F
ur
t
h
e
r
e
x
p
l
o
r
a
t
i
o
n
i
n
t
o
i
t
s
ge
n
e
r
a
li
z
a
bil
i
t
y
a
n
d
a
v
e
n
ue
s
f
o
r
f
ut
ur
e
r
e
s
e
a
r
c
h
i
s
wa
r
r
a
n
t
e
d.
T
h
e
r
e
s
u
l
t
o
f
Al
go
r
i
t
hm
1
i
s
r
e
pr
e
s
e
n
t
e
d
i
n
T
a
bl
e
4
w
h
e
r
e
t
h
e
b
a
n
ds
r
e
pr
e
s
e
n
t
f
r
o
m
0
to
t
h
e
n
u
m
be
r
o
f
ba
n
ds
pr
e
s
e
n
t
i
n
t
h
e
ba
n
ds
s
e
t
,
b
a
n
d
0
r
e
pr
e
s
e
n
t
s
t
h
e
f
i
r
s
t
b
a
n
d
o
f
t
h
e
da
t
a
s
e
t.
F
i
gur
e
6
de
m
o
n
s
t
r
a
t
e
s
t
h
e
c
o
m
pa
r
a
t
i
v
e
im
pa
c
t
o
f
t
h
e
pr
o
p
o
s
e
d
a
l
go
r
i
t
hm
m
e
t
h
o
d
a
ga
i
n
s
t
tr
a
d
i
t
i
o
n
a
l
m
e
c
h
a
ni
s
m
s
li
ke
k
-
m
e
a
ns
w
i
t
h
s
t
a
n
da
r
d
r
a
n
do
m
s
e
e
d
i
n
g
a
n
d
k
-
m
e
a
n
s
+
+
s
e
e
d
i
ng.
I
t
r
e
v
e
a
l
s
a
m
a
r
g
i
na
l
de
c
r
e
a
s
e
i
n
a
c
c
ur
a
c
y
w
he
n
e
m
p
l
o
yi
ng
t
r
a
di
t
i
o
na
l
m
e
t
h
o
ds
,
wh
e
r
e
a
s
C
K
M
n
o
t
a
bl
y
e
nh
a
n
c
e
s
da
t
a
s
e
t
a
c
c
ur
a
c
y
.
I
n
t
e
r
e
s
t
i
n
g
ly
,
C
K
M
c
o
n
s
i
s
t
e
n
t
l
y
o
ut
pe
r
f
o
r
m
s
t
r
a
di
t
i
o
n
a
l
m
e
t
h
o
ds
a
c
r
o
s
s
v
a
r
i
o
us
m
e
t
r
i
c
s
,
i
n
c
l
ud
i
ng
E
D,
S
I
D
,
S
A
M
,
NC
C
,
J
M
,
a
n
d
hy
br
i
d
m
e
t
h
o
ds
l
i
ke
S
I
D
-
S
AM
a
n
d
J
M
-
S
AM
.
T
hi
s
im
pr
o
v
e
m
e
n
t
i
s
e
vi
de
n
t
e
v
e
n
w
i
t
h
m
e
t
r
i
c
v
a
r
i
a
t
i
o
n
s
i
n
t
h
e
k
-
m
e
a
n
s
a
l
go
r
i
t
hm
.
F
i
gur
e
3.
F
l
o
wc
h
a
r
t
o
f
pr
o
p
o
s
e
d
w
o
r
k
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2
5
0
2
-
4
7
52
In
do
n
e
s
i
a
n
J
E
l
e
c
E
n
g
&
C
o
m
p
S
c
i
,
Vo
l
.
3
6
,
N
o
.
2
,
N
o
v
e
m
b
e
r
20
24
:
1
30
9
-
1
31
8
1316
F
i
gur
e
4
.
C
l
a
s
s
if
i
c
a
t
i
o
n
m
a
p
o
f
P
a
vi
a
c
e
n
t
e
r
by
t
h
e
pr
o
p
o
s
e
d
m
e
t
h
o
d
wi
t
h
r
=
‘
2.
6
’
F
i
gur
e
5
.
Gr
o
un
d
t
r
u
t
h
m
a
p
o
f
P
a
vi
a
c
e
n
t
e
r
T
a
bl
e
3
.
K
a
ppa
o
f
P
a
vi
a
c
e
n
t
e
r
da
t
a
s
e
t
M
e
th
o
d
K
M
+
R
S
K
M
+
+
SSB
-
C
K
M
ED
0.544
0.725
0.914
S
I
D
0.558
0.689
0.909
S
A
M
0.628
0.701
0.932
S
I
D
S
A
M
0.558
0.689
0.907
N
C
C
0.622
0.686
0.912
JM
0.480
0.722
0.914
JM
-
S
A
M
0.722
0.694
0.942
T
a
bl
e
4
.
B
a
n
ds
s
e
l
e
c
t
e
d
us
i
n
g
t
h
e
pr
o
p
o
s
e
d
m
e
t
h
o
d
f
o
r
P
a
vi
a
c
e
n
t
e
r
D
a
ta
s
et
M
e
th
o
d
B
a
nds
s
e
le
c
t
e
d
f
r
om S
S
B
m
e
th
o
ds
P
a
v
ia
c
e
nt
e
r
ED
0/
1/
2/
3/
4/
5/
6/
7/
16/
17/
18/
28/
29/
30
/
42/
43/
44/
69/
70/
7
1/
83/
84/
85
S
A
M
0/
1/
2/
3/
11/
12/
13/
25/
26/
27/
38/
39/
40/
55
/
56/
57/
72/
73/
74/
83/
84/
85
S
I
D
0/
1/
2/
3/
4/
5/
6/
8/
9/
10/
24/
25/
26/
41
/
42/
43/
68/
69/
70/
83
/
84/
85
S
I
D
-
S
A
M
0/
1/
2/
3/
4/
5/
6/
8/
9/
10/
24/
25/
26/
41
/
42/
43/
68/
69/
70/
83
/
84/
85
N
C
C
0/
1/
2/
3/
4/
5/
6/
15/
16/
17/
31/
32/
33/
52
/5
3/
54/
72/
73/
74/
83/
84/
85
JM
0/
1/
2/
3/
4/
5/
6/
7/
8/
9/
10/
11/
12/
17
/
18/
19/
26/
27/
28/
36/
37/
38/
54/
55/
56/
71/
72/
73/
83/
84/
85
JM
-
S
A
M
2/
3/
11/
14/
21/
23/
26/
27/
29/
32/
33/
34/
36/
39
/
40/
41/
42/
4
3/
44/
45/
46/
47/
48/
49/
50/
56/
57/
62/
63/
65/
66/
67/
70/
71/
72/
74/
78/
80/
84/
85/
86/
98
F
i
gur
e
6
.
C
o
m
pa
r
i
s
o
n
o
f
t
h
e
pr
o
p
o
s
e
d
m
e
t
h
o
d
wi
t
h
d
i
f
f
e
r
e
n
t
k
-
m
e
a
ns
v
a
r
i
a
n
t
s
w
i
t
h
o
v
e
r
a
l
l
a
c
c
ur
a
c
y
4.
CONC
L
USI
ON
T
h
e
pr
o
p
o
s
e
d
a
ppr
o
a
c
h
de
m
o
n
s
t
r
a
t
e
s
a
n
ot
a
bl
e
e
nh
a
n
c
e
m
e
n
t
o
v
e
r
c
o
n
v
e
n
t
i
o
n
a
l
k
-
m
e
a
ns
c
l
u
s
t
e
r
i
n
g
t
e
c
h
ni
que
s
e
m
p
l
o
yi
ng
r
a
n
do
m
s
e
e
d
i
ng
a
n
d
k
-
m
e
a
ns
+
+
s
e
e
d
i
ng.
S
pe
c
i
f
i
c
a
ll
y
,
k
-
m
e
a
n
s
-
S
S
M
c
l
u
s
t
e
r
i
n
g
i
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
do
n
e
s
i
a
n
J
E
l
e
c
E
n
g
&
C
o
m
p
S
c
i
I
S
S
N:
2
5
0
2
-
4
7
52
Optimiz
i
ng
hy
pe
r
s
pe
c
t
r
al
c
las
s
if
ication:
s
pe
c
tr
al
s
imil
ar
it
y
-
bas
e
d
…
(
Or
igant
i
Subhas
h
C
hande
r
Go
ud
)
1317
e
x
e
c
ut
e
d
f
o
r
f
e
a
t
ur
e
s
e
l
e
c
t
i
o
n
.
I
t
b
e
c
o
m
e
s
e
vi
de
n
t
t
h
a
t
i
n
c
o
r
por
a
t
i
n
g
DR
i
n
t
o
t
h
e
c
h
o
r
d
k
-
m
e
a
n
s
f
r
a
m
e
wo
r
k
c
o
n
t
r
i
b
ut
e
s
to
i
m
pr
o
v
e
d
r
e
s
u
l
t
s
.
T
h
e
pr
o
p
o
s
e
d
c
h
o
r
d
m
e
t
r
i
c
-
b
a
s
e
d
k
-
m
e
a
n
s
(
c
h
o
r
d
-
k
-
m
e
a
n
s
)
c
lus
t
e
r
i
n
g
f
i
nd
i
n
g
t
h
e
e
x
a
c
t
‘
r
’
v
a
l
ue
w
hi
c
h
y
i
e
l
ds
t
h
e
b
e
s
t
a
c
c
ur
a
c
y
i
s
a
n
o
t
h
e
r
pr
o
b
l
e
m
pe
r
s
pe
c
t
i
v
e
.
T
hi
s
s
t
r
a
t
e
gi
c
i
n
t
e
gr
a
t
i
o
n
o
f
DR
e
nh
a
n
c
e
s
t
h
e
a
l
go
r
i
t
hm
’
s
a
bil
i
t
y
to
e
x
t
r
a
c
t
r
e
l
e
v
a
n
t
f
e
a
t
ur
e
s
,
l
e
a
d
i
ng
t
o
a
m
o
r
e
r
e
f
i
n
e
d
a
n
d
e
f
f
e
c
t
i
v
e
c
l
us
t
e
r
i
n
g
pr
o
c
e
s
s
.
F
ur
t
h
e
r
m
o
r
e
,
e
x
p
l
o
r
i
ng
t
h
e
i
m
pa
c
t
o
f
d
i
f
f
e
r
e
n
t
s
e
e
d
i
n
g
m
e
t
h
o
ds
o
n
t
h
e
e
x
e
c
ut
i
o
n
o
f
c
h
o
r
d
k
-
m
e
a
ns
pr
o
vi
de
s
pr
o
m
i
s
i
ng
r
e
s
u
l
t
s
.
RE
F
E
R
E
NC
E
S
[
1]
P
.
G
ha
mi
s
i
e
t
a
l.
,
“
A
dv
a
n
c
e
s
in
h
y
p
e
r
s
pe
c
t
r
a
l
im
a
ge
a
nd
s
ig
na
l
pr
oc
e
s
s
in
g:
a
c
o
mp
r
e
he
ns
i
ve
ove
r
v
i
e
w
of
th
e
s
ta
te
of
th
e
a
r
t,
”
I
E
E
E
G
e
os
c
ie
nc
e
and R
e
m
ot
e
Se
n
s
in
g M
agaz
in
e
, v
o
l.
5, n
o
. 4,
pp. 37
–
78, De
c
. 2017, d
oi
:
10.1109/M
G
R
S
.2017.2762087.
[
2]
J
.
M
.
B
io
uc
a
s
-
D
ia
s
,
A
.
P
la
z
a
,
G
.
C
a
mps
-
V
a
ll
s
,
P
.
S
c
h
e
unde
r
s
,
N
.
M
.
N
a
s
r
a
ba
di
,
a
nd J
.
C
ha
nu
s
s
o
t,
“
H
y
p
e
r
s
p
e
c
tr
a
l
r
e
m
o
t
e
s
e
n
s
in
g
da
ta
a
na
ly
s
is
a
nd
f
ut
ur
e
c
ha
ll
e
ng
e
s
,”
I
E
E
E
G
e
os
c
ie
nc
e
and
R
e
m
ot
e
Se
ns
in
g
M
agaz
in
e
,
v
o
l.
1,
no
.
2,
pp.
6
–
36,
J
un.
2
013,
do
i:
10.1109/M
G
R
S
.2013.2244672.
[
3]
D
.
L
a
ndgr
e
b
e
,
“
H
y
p
e
r
s
pe
c
t
r
a
l
im
a
ge
da
ta
a
na
l
y
s
is
,”
I
E
E
E
Si
gn
al
P
r
oc
e
s
s
in
g
M
agaz
in
e
,
v
o
l.
19,
n
o
.
1,
pp.
17
–
28,
2002,
do
i:
10.1109/79.974718
.
[
4]
W
.
S
un
a
nd
Q
.
D
u,
“
H
y
pe
r
s
pe
c
tr
a
l
ba
nd
s
e
l
e
c
ti
o
n:
a
r
e
v
i
e
w
,”
I
E
E
E
G
e
os
c
ie
nc
e
and
R
e
m
ot
e
Se
ns
in
g
M
aga
z
in
e
,
v
o
l.
7,
n
o
.
2,
pp. 118
–
139, J
un. 2019, do
i:
10.1109/M
G
R
S
.2019.29
11100.
[
5]
C
.
I
.
C
ha
ng
a
nd
K
.
H
.
L
iu
,
“
P
r
o
gr
e
s
s
iv
e
ba
nd
s
e
le
c
ti
o
n
of
s
pe
c
tr
a
l
unmi
x
in
g
f
or
h
y
p
e
r
s
pe
c
tr
a
l
i
ma
ge
r
y
,”
I
E
E
E
T
r
ans
ac
ti
ons
on
G
e
os
c
ie
nc
e
and R
e
m
ot
e
Se
ns
in
g
, v
o
l.
52, n
o
. 4, pp. 2002
–
2017,
A
pr
. 2014, do
i:
10.1109
/
T
G
R
S
.2013.2257604.
[
6]
M
.
P
a
l
a
nd
G
.
M
.
F
o
o
d
y
,
“
F
e
a
tu
r
e
s
e
le
c
ti
o
n
f
or
c
la
s
s
if
i
c
a
ti
o
n
of
h
y
p
e
r
s
pe
c
t
r
a
l
da
ta
b
y
S
V
M
,”
I
E
E
E
T
r
ans
ac
ti
ons
on
G
e
os
c
i
e
nc
e
and R
e
m
ot
e
Se
ns
in
g
, v
o
l.
48, n
o
. 5, pp. 2297
–
2307, M
a
y
2010,
do
i:
10.1109/
T
G
R
S
.2009.2039484.
[
7]
C
.
Y
u,
M
.
S
o
ng,
a
nd
C
.
I
.
C
h
a
ng,
“
B
a
nd
s
ub
s
e
t
s
e
le
c
ti
o
n
f
or
hy
p
e
r
s
pe
c
t
r
a
l
im
a
ge
c
la
s
s
if
i
c
a
ti
o
n,”
R
e
m
ot
e
Se
ns
in
g
,
v
ol
.
10,
no.
1,
p. 113, J
a
n. 2018, do
i:
10.3390/r
s
10010113.
[
8]
M
.
I
ma
ni
a
nd
H
.
G
ha
s
s
e
mi
a
n,
“
A
n
uns
upe
r
vi
s
e
d
f
e
a
tu
r
e
e
x
tr
a
c
ti
o
nl
m
e
th
o
d
f
or
c
la
s
s
if
i
c
a
ti
o
n
of
h
y
p
e
r
s
pe
c
t
r
a
l
im
a
g
e
s
,”
22nd I
r
ani
an C
onf
e
r
e
nc
e
on E
le
c
tr
ic
al
E
ngi
ne
e
r
in
g, I
C
E
E
2014
, pp. 1389
–
1394, 2014, do
i:
10.1109/
I
r
a
ni
a
nC
E
E
.2014.699975
0.
[
9]
Q
.
D
u
a
nd
H
.
Y
a
ng,
“
S
im
il
a
r
it
y
-
ba
s
e
d
uns
upe
r
vi
s
e
d
ba
nd
s
e
le
c
ti
o
n
f
o
r
h
y
pe
r
s
pe
c
tr
a
l
im
a
ge
a
na
l
y
s
is
,”
I
E
E
E
G
e
os
c
ie
nc
e
and
R
e
m
ot
e
Se
ns
in
g L
e
tt
e
r
s
, v
ol
. 5, n
o
. 4, pp. 564
–
568, Oc
t.
2008,
do
i:
10.1109/
L
G
R
S
.2008.2000619.
[
10]
Y
.
Q
ia
n,
F
.
Y
a
o
,
a
nd
S
.
J
ia
,
“
B
a
nd
s
e
le
c
ti
o
n
f
o
r
h
y
p
e
r
s
p
e
c
t
r
a
l
im
a
ge
r
y
us
in
g
a
f
f
in
it
y
p
r
o
pa
ga
ti
o
n,”
I
E
T
C
om
put
e
r
V
is
io
n
,
vo
l
.
3,
no
. 4, pp. 213
–
222, 2009, d
o
i:
10.1049/i
e
t
-
c
vi
.2009.0034.
[
11]
N
.
K
e
s
ha
v
a
,
“
D
is
ta
nc
e
me
t
r
i
c
s
a
n
d
ba
nd
s
e
le
c
ti
o
n
in
h
y
p
e
r
s
pe
c
tr
a
l
pr
oc
e
s
s
in
g
w
it
h
a
ppl
ic
a
ti
o
ns
to
ma
t
e
r
ia
l
id
e
nt
i
f
i
c
a
ti
o
n
a
nd
s
pe
c
t
r
a
l
li
br
a
r
i
e
s
,”
I
E
E
E
T
r
ans
ac
ti
ons
on
G
e
o
s
c
ie
nc
e
and
R
e
m
ot
e
Se
ns
in
g
,
v
ol
.
42,
no
.
7,
pp.
1552
–
1565,
J
ul
.
2
0
04,
do
i:
10.1109/
T
G
R
S
.2004.830549.
[
12]
M
.
R
a
ja
ka
ni
,
T
.
E
.
D
.,
a
nd
S
.
R
a
j
e
s
h
,
“
B
a
nd
s
e
l
e
c
t
i
o
n
f
o
r
h
y
p
e
r
s
pe
c
tr
a
l
im
a
ge
c
la
s
s
if
i
c
a
ti
o
n
us
in
g
s
ta
ti
s
ti
c
a
l
f
e
a
tu
r
e
s
,”
I
nt
e
r
nat
io
nal
J
our
nal
of
P
ur
e
and A
ppl
ie
d M
at
he
m
at
ic
s
,
v
ol
. 1
18, no
. 20, pp. 1133
–
1139, 2018
,
A
v
a
il
a
bl
e
:
59.pd
f
(
a
c
a
dpubl.e
u)
.
[
13]
P
.
S
.
T
h
e
nka
ba
il
,
“
H
y
p
e
r
s
p
e
c
tr
a
l
da
ta
p
r
o
c
e
s
s
in
g:
a
lg
o
r
it
h
m
de
s
ig
n
a
nd
a
na
l
y
s
is
,”
P
hot
ogr
am
m
e
tr
ic
E
ngi
ne
e
r
in
g
&
R
e
m
ot
e
Se
ns
in
g
, v
ol
. 81, n
o
. 6, pp. 441
–
442, J
un. 2015, do
i:
10.14358/
pe
r
s
.81.6.441.
[
14]
M
.
K
.
G
up
ta
a
nd
P
.
C
ha
ndr
a
,
“
E
f
f
e
c
ts
of
s
im
il
a
r
it
y
/d
is
ta
nc
e
m
e
tr
i
c
s
o
n
k
-
m
e
a
ns
a
lg
o
r
it
hm
w
it
h
r
e
s
pe
c
t
t
o
it
s
a
ppl
ic
a
ti
o
ns
in
I
oT
a
nd
mul
ti
me
di
a
:
a
r
e
v
i
e
w
,”
M
ul
ti
m
e
di
a
T
ool
s
and
A
p
pl
ic
at
io
ns
,
vo
l.
81,
n
o
.
26,
pp.
37007
–
37032,
N
ov
.
2
022,
do
i:
10.1007/s
11042
-
021
-
11255
-
7.
[
15]
M
.
K
.
G
upt
a
a
nd
P
.
C
ha
ndr
a
,
“
A
n
e
mpi
r
i
c
a
l
e
v
a
lu
a
ti
o
n
of
k
-
me
a
ns
c
lu
s
te
r
in
g
a
lg
or
it
hm
us
in
g
di
f
f
e
r
e
nt
di
s
ta
nc
e
/s
im
il
a
r
it
y
me
tr
i
c
s
,”
i
n
L
e
c
tu
r
e
N
ot
e
s
i
n E
le
c
tr
ic
al
E
ngi
ne
e
r
in
g
, v
ol
. 605,
2020, pp. 884
–
892.
[
16]
K
.
K
r
i
vo
r
u
c
hk
o
a
nd
A
.
G
r
ib
ov
,
“
D
is
ta
nc
e
m
e
t
r
i
c
s
f
o
r
da
ta
in
te
r
p
o
la
ti
o
n
ove
r
la
r
g
e
a
r
e
a
s
o
n
E
a
r
th
’
s
s
ur
f
a
c
e
,”
Spat
ia
l
St
at
is
t
ic
s
,
vo
l.
35, p. 100396, M
a
r
. 2020, d
o
i:
10.1016/j
.s
pa
s
ta
.2019.100396.
[
17]
S
.
K
a
pi
l
a
nd
M
.
C
ha
w
la
,
“
P
e
r
f
o
r
ma
n
c
e
e
v
a
lu
a
ti
o
n
of
K
-
me
a
n
s
c
lu
s
te
r
in
g
a
lg
o
r
it
hm
w
i
th
v
a
r
i
o
us
di
s
ta
n
c
e
m
e
tr
i
c
s
,”
in
1s
t
I
E
E
E
I
nt
e
r
nat
io
nal
C
onf
e
r
e
nc
e
on
P
ow
e
r
E
le
c
tr
oni
c
s
,
I
nt
e
ll
ig
e
nt
C
ont
r
ol
and
E
ne
r
gy
Sy
s
te
m
s
,
I
C
P
E
I
C
E
S
2016
,
J
ul
.
2017,
pp.
1
–
4,
do
i:
10.1109/
I
C
P
E
I
C
E
S
.2016.7853264.
[
18]
A
.
A
l
-
K
ha
li
f
a
,
M
.
H
a
r
a
nc
z
y
k,
a
nd
J
.
H
o
l
li
da
y
,
“
C
o
mpa
r
is
o
n
of
n
o
nbi
n
a
r
y
s
im
il
a
r
it
y
c
o
e
f
f
i
c
i
e
nt
s
f
o
r
s
im
il
a
r
it
y
s
e
a
r
c
hi
ng,
c
lu
s
te
r
in
g
a
nd
c
o
mp
o
und
s
e
l
e
c
ti
o
n,”
J
our
nal
of
C
h
e
m
ic
al
I
nf
o
r
m
at
io
n
and
M
ode
li
ng
,
vo
l.
49,
no
.
5,
pp.
1193
–
1201,
M
a
y
20
09,
do
i:
10.1021/
c
i8
004644.
[
19]
X
.
G
u,
P
.
P
.
A
nge
lo
v
,
D
.
K
a
ngi
n,
a
nd
J
. C
.
P
r
in
c
ip
e
,
“
A
ne
w
t
y
pe
of
di
s
ta
nc
e
m
e
tr
i
c
a
nd
it
s
us
e
f
o
r
c
lu
s
te
r
in
g,”
E
v
ol
v
in
g
Sy
s
te
m
s
,
vo
l.
8, n
o
. 3, pp. 167
–
177, S
e
p. 2017, d
o
i:
10.1007/s
12530
-
017
-
9195
-
7.
[
20]
A
.
S
in
gh,
A
.
Y
a
da
v
,
a
nd
A
.
R
a
na
,
“
K
-
me
a
ns
w
it
h
th
r
e
e
di
f
f
e
r
e
nt
di
s
ta
nc
e
m
e
tr
i
c
s
,”
I
nt
e
r
nat
io
nal
J
ou
r
nal
of
C
o
m
p
ut
e
r
A
ppl
ic
at
io
ns
, vo
l.
67, n
o
. 10, pp. 13
–
17, Ap
r
. 2013, d
o
i:
10.512
0/
11430
-
6785.
[
21]
F
.
A
.
A
ll
a
h,
W
.
I
.
G
r
o
s
k
y
,
a
nd
D
.
A
bo
ut
a
jd
in
e
,
“
D
oc
ume
n
t
c
l
us
te
r
in
g
ba
s
e
d
o
n
di
f
f
us
i
o
n
ma
ps
a
nd
a
c
o
mpa
r
is
o
n
of
th
e
k
-
m
e
a
ns
pe
r
f
or
ma
nc
e
s
in
v
a
r
i
o
us
s
pa
c
e
s
,”
in
P
r
oc
e
e
di
ngs
-
I
E
E
E
Sy
m
p
os
iu
m
on
C
om
put
e
r
s
and
C
om
m
uni
c
at
io
ns
,
J
ul
.
2008,
pp.
579
–
584,
do
i:
10.1109/
I
S
C
C
.2008.4625693.
[
22]
X
.
Q
ia
o
,
H
.
W
u,
S
.
K
.
R
oy
,
a
nd
W
.
H
ua
ng,
“
H
y
p
e
r
s
pe
c
t
r
a
l
im
a
ge
c
la
s
s
if
ic
a
ti
o
n
ba
s
e
d
o
n
3d
s
ha
r
pe
n
e
d
c
o
s
in
e
s
im
il
a
r
it
y
o
p
e
r
a
ti
o
n,”
in
I
nt
e
r
nat
io
nal
G
e
os
c
ie
nc
e
and R
e
m
ot
e
Se
ns
in
g Sy
m
pos
iu
m
(
I
G
A
R
SS)
, J
ul
. 2023,
vo
l.
2023
-
J
ul
y
, pp. 7669
–
7672,
do
i:
10.1109/I
G
A
R
S
S
52108.2023.10281949.
[
23]
K
.
V
ij
a
y
,
K
.
C
.
J
it
e
nd
e
r
,
a
nd
K
.
D
in
e
s
h,
“
P
e
r
f
or
ma
nc
e
e
v
a
lu
a
t
io
n
of
di
s
ta
nc
e
m
e
tr
i
c
s
in
th
e
c
lu
s
te
r
in
g
a
lg
o
r
it
h
ms
,”
I
N
F
O
C
O
M
P
J
our
nal
of
C
om
put
e
r
Sc
ie
nc
e
,
v
o
l.
1
3,
pp.
38
–
52,
2014,
[
O
nl
in
e
]
.
A
v
a
il
a
bl
e
:
ht
tp
s
:/
/i
n
f
o
c
o
mp.d
c
c
.u
f
la
.br
/i
nde
x
.php
/I
N
F
O
C
O
M
P
/a
r
ti
c
le
/
vi
e
w
/2
1.
[
24]
A
.
S
.
S
hi
r
kho
r
s
hi
di
,
S
.
A
gha
boz
o
r
gi
,
a
nd
T
.
Y
in
g
W
a
h,
“
A
c
o
mpa
r
is
o
n
s
tu
d
y
o
n
s
im
il
a
r
it
y
a
nd
di
s
s
im
il
a
r
it
y
m
e
a
s
ur
e
s
in
c
lu
s
te
r
in
g c
o
n
ti
nu
o
us
da
ta
,”
P
L
oS O
N
E
,
vo
l.
10, n
o
. 12, p.
e
014
4059, De
c
. 2015, d
oi
:
10.1371/j
o
ur
na
l.
p
o
n
e
.0144059.
[
25]
T
.
M
.
G
ha
z
a
l
e
t
al
.
,
“
P
e
r
f
or
ma
nc
e
s
of
k
-
me
a
ns
c
lu
s
t
e
r
in
g
a
lg
or
it
hm
w
it
h
di
f
f
e
r
e
n
t
di
s
ta
nc
e
m
e
tr
i
c
s
,”
I
nt
e
ll
ig
e
nt
A
ut
om
at
io
n
and
Sof
t
C
om
put
in
g
, v
o
l.
30, n
o
. 2, pp. 735
–
742, 2021, d
o
i:
10.3260
4/
ia
s
c
.2021.019067.
[
26]
D
.
L
undh
o
lm
a
nd
L
.
S
ve
ns
s
o
n,
“
C
li
f
f
o
r
d
a
lg
e
b
r
a
,
ge
o
m
e
t
r
ic
a
lg
e
br
a
,
a
nd
a
ppl
i
c
a
ti
o
ns
,”
ar
X
iv
pr
e
pr
in
t
ar
X
iv
:
0907.5356
,
2009,
[
O
nl
in
e
]
. A
v
a
il
a
bl
e
:
ht
tp
:
//
a
r
x
i
v
.
o
r
g/
a
bs
/0
907.5356.
[
27]
E
.
H
it
z
e
r
,
T
.
N
it
ta
,
a
nd
Y
.
K
ur
oe
,
“
A
ppl
ic
a
ti
o
ns
of
C
li
f
f
o
r
d
’
s
ge
o
m
e
tr
i
c
a
lg
e
b
r
a
,”
A
dv
anc
e
s
in
A
ppl
ie
d
C
li
f
f
o
r
d
A
lg
e
br
a
s
,
v
o
l.
23,
no
. 2, pp. 377
–
404, J
un. 2013, d
o
i:
10.1007/s
00006
-
013
-
0378
-
4.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2
5
0
2
-
4
7
52
In
do
n
e
s
i
a
n
J
E
l
e
c
E
n
g
&
C
o
m
p
S
c
i
,
Vo
l
.
3
6
,
N
o
.
2
,
N
o
v
e
m
b
e
r
20
24
:
1
30
9
-
1
31
8
1318
[
28]
L
.
D
o
r
s
t,
“
O
pt
im
a
l
c
o
mbi
na
ti
o
n
of
or
ie
n
ta
ti
o
n
m
e
a
s
ur
e
m
e
nt
s
unde
r
a
ngl
e
,
a
x
is
a
nd
c
hor
d
me
t
r
ic
s
,”
in
SE
M
A
SI
M
A
I
Spr
in
ge
r
Se
r
ie
s
, v
o
l.
13, 2021, pp. 47
–
88.
[
29]
D
.
R
uhe
,
J
.
K
.
G
upt
a
,
S
.
de
K
e
ni
n
c
k,
M
.
W
e
ll
in
g,
a
nd
J
.
B
r
a
nds
te
tt
e
r
,
“
G
e
ome
t
r
i
c
C
li
f
f
or
d
a
lg
e
br
a
n
e
t
w
o
r
ks
,”
P
r
oc
e
e
d
in
gs
of
M
ac
hi
ne
L
e
ar
ni
ng R
e
s
e
ar
c
h
, v
ol
. 202, pp. 29306
–
29337, 2023.
[
30]
J
.
B
r
a
nds
te
tt
e
r
,
R
.
v
a
n
de
n
B
e
r
g,
M
.
W
e
ll
in
g,
a
nd
J
.
K
.
G
upt
a
,
“
C
li
f
f
o
r
d
n
e
u
r
a
l
la
y
e
r
s
f
or
P
D
E
m
o
d
e
li
ng,”
ar
X
iv
pr
e
pr
in
t
ar
X
iv
:
2209.04934
, 2022, [
O
nl
in
e
]
. A
v
a
il
a
bl
e
:
ht
tp
:/
/a
r
x
i
v
.
o
r
g/
a
bs
/2
209.04934.
B
I
OG
RA
P
HI
E
S
OF
AU
T
HO
RS
Ori
g
a
n
ti
S
u
b
h
a
s
h
C
h
a
n
der
Go
u
d
i
s
a
r
e
s
e
ar
c
h
s
c
h
o
l
a
r
at
t
h
e
D
e
p
art
men
t
o
f
Co
m
p
u
t
e
r
S
c
i
en
ce
an
d
E
n
g
i
n
ee
ri
n
g
,
J
N
T
U
Co
l
l
eg
e
o
f
E
n
g
i
n
ee
ri
n
g
A
n
a
n
t
h
ap
u
r,
J
a
w
a
h
arl
a
l
N
eh
ru
T
ec
h
n
o
l
o
g
i
c
a
l
U
n
i
v
e
rs
i
t
y
A
n
a
n
t
h
ap
u
r,
A
n
d
h
ra
Pr
ad
e
s
h
,
I
n
d
i
a.
H
e
i
s
p
u
rs
u
i
n
g
a
Ph
.
D
.
i
n
Co
m
p
u
t
e
r
S
ci
e
n
ce
an
d
E
n
g
i
n
e
e
r
i
n
g
at
J
N
T
U
A
.
H
e
h
o
l
d
s
a
Mas
t
e
rs
’
s
d
e
g
r
ee
i
n
Co
m
p
u
t
e
r
E
n
g
i
n
ee
r
i
n
g
fro
m
J
N
T
U
H
y
d
e
rab
ad
a
n
d
Mas
t
e
rs
’
s
d
e
g
r
ee
i
n
Co
m
p
u
t
e
r
A
p
p
l
i
c
at
i
o
n
fr
o
m
O
s
m
an
i
a
U
n
i
v
e
rs
i
t
y
.
H
i
s
r
e
s
e
ar
c
h
are
as
ar
e
h
y
p
e
rs
p
ec
t
r
al
i
m
a
g
e
a
n
al
y
s
i
s
,
p
at
t
e
rn
r
e
co
g
n
i
t
i
o
n
,
an
d
m
a
ch
i
n
e
l
e
ar
n
i
n
g
.
H
e
c
an
b
e
c
o
n
t
ac
t
e
d
at
em
a
i
l
:
o
rg
an
t
s
u
b
h
as
h
@
g
m
ai
l
.
co
m
.
D
r.
T
h
o
g
a
ra
ch
etti
Hi
te
n
dra
Sa
r
m
a
o
b
t
ai
n
e
d
Ph
.
D
.
i
n
m
a
ch
i
n
e
l
e
arn
i
n
g
fr
o
m
J
N
T
U
n
i
v
e
rs
i
t
y
,
A
n
an
t
ap
u
r,
A
n
d
h
ra
Pra
d
e
s
h
,
In
d
i
a
i
n
t
h
e
y
e
ar
2
0
1
3
.
H
e
i
s
a
r
ec
i
p
i
en
t
o
f
t
h
e
T
e
ach
e
rs
A
s
s
o
c
i
at
e
s
h
i
p
f
o
r
R
e
s
e
ar
c
h
E
x
ce
l
l
e
n
ce
(T
A
R
E
)
g
ra
n
t
b
y
S
E
RB
-
D
ST
G
o
v
t
.
o
f
In
d
i
a.
H
e
h
as
p
u
b
l
i
s
h
ed
o
v
e
r
2
5
a
rt
i
cl
e
s
i
n
p
ee
r
-
r
e
v
i
ew
ed
J
o
u
rn
a
l
s
an
d
r
e
p
u
t
e
d
i
n
t
e
r
n
at
i
o
n
a
l
c
o
n
f
e
r
e
n
ce
s
l
i
k
e
I
J
CN
N
,
CE
C,
PR
e
M
I
an
d
o
t
h
e
rs
.
H
e
d
el
i
v
e
r
e
d
an
i
n
v
i
t
at
i
o
n
at
FSD
M
-
2017
i
n
T
ai
w
a
n
.
H
e
i
s
a
s
en
i
o
r
mem
b
e
r
o
f
IE
E
E
.
H
i
s
r
e
s
earc
h
ar
e
as
i
n
c
l
u
d
e
m
a
ch
i
n
e
l
e
ar
n
i
n
g
,
h
y
p
e
rs
p
ec
t
ral
i
m
a
g
e
p
ro
ce
s
s
i
n
g
,
a
n
d
d
at
a
M
i
n
i
n
g
.
H
e
c
a
n
b
e
co
n
t
a
c
t
ed
at
em
ai
l
:
t
.
h
i
t
en
d
ras
ar
m
a@
g
m
ai
l
.
c
o
m
.
P
ro
f
.
D
r
.
C
hi
g
a
ra
pa
l
l
e
Sh
o
ba
Bi
n
d
u
Ph
.
D
.
i
n
CSE
fro
m
J
N
T
U
A
,
A
n
an
t
ap
u
ra
m
u
,
A
n
d
h
ra
Prad
e
s
h
.
Sh
e
i
s
c
u
rr
en
t
l
y
w
o
r
k
i
n
g
as
a
Pro
f
e
s
s
o
r
i
n
t
h
e
D
e
p
art
me
n
t
o
f
CSE
,
J
N
T
U
A
Co
l
l
eg
e
o
f
E
n
g
i
n
ee
r
i
n
g
,
A
n
an
t
h
ap
u
ra
m
u
.
H
e
r
r
e
s
e
ar
c
h
ar
e
as
i
n
c
l
u
d
e
co
m
p
u
t
er
n
e
t
w
o
rk
s
,
n
e
t
w
o
r
k
s
ecu
ri
t
y
,
m
a
c
h
i
n
e
l
e
arn
i
n
g
,
an
d
c
l
o
u
d
c
o
m
p
u
t
i
n
g
.
S
h
e
c
an
b
e
co
n
t
a
c
t
e
d
at
em
ai
l
:
s
h
o
b
ab
i
n
d
h
u
@
g
m
ai
l
.
c
o
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.