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
.
3
,
J
u
n
e
2
0
2
1
,
p
p
.
2688
~
2
6
9
5
I
SS
N:
2
0
8
8
-
8708
,
DOI
: 1
0
.
1
1
5
9
1
/
i
j
ec
e
.
v
1
1
i
3
.
pp
2
6
8
8
-
2
6
9
5
2688
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ec
e.
ia
esco
r
e.
co
m
Bleeding
rec
o
g
nit
io
n t
echni
que in
w
ireless
caps
ule e
ndo
sco
py
i
m
a
g
es using
fu
zzy lo
g
ic and princi
pa
l co
m
po
nen
t
a
n
a
ly
sis
A.
Al
M
a
m
un
1
, P.
P
.
E
m
2
,
T
.
G
ho
s
h
3
,
M
.
M
.
H
o
s
s
a
in
4
,
M
.
G
.
H
a
s
a
n
5
,
M
.
G
.
Sa
dequ
e
6
1,
2
F
a
c
u
lt
y
o
f
En
g
in
e
e
rin
g
a
n
d
T
e
c
h
n
o
lo
g
y
,
M
u
lt
im
e
d
ia Un
iv
e
rsit
y
,
M
a
la
y
sia
3
De
p
a
rtme
n
t
o
f
El
e
c
tri
c
a
l
a
n
d
Co
m
p
u
ter E
n
g
in
e
e
rin
g
,
Un
iv
e
rsity
o
f
A
lab
a
m
a
,
Un
it
e
d
S
tate
s
4,
5,
6
De
p
a
rtm
e
n
t
o
f
EE
E,
P
a
b
n
a
Un
iv
e
rsit
y
o
f
S
c
i
e
n
c
e
a
n
d
T
e
c
h
n
o
lo
g
y
,
Ba
n
g
lad
e
sh
Art
icle
I
nfo
AB
ST
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Au
g
2
7
,
2
0
2
0
R
ev
i
s
ed
Sep
2
5
,
2
0
2
0
A
cc
ep
ted
Oct
2
8
,
2
0
2
0
W
irele
ss
c
a
p
su
le
e
n
d
o
sc
o
p
y
is
t
h
e
m
o
st
in
n
o
v
a
ti
v
e
tec
h
n
o
lo
g
y
t
o
p
e
rc
e
iv
e
th
e
e
n
ti
re
g
a
stro
in
tes
ti
n
a
l
(G
I)
t
ra
c
t
in
re
c
e
n
t
ti
m
e
s.
It
c
a
n
d
iag
n
o
se
in
n
e
r
d
ise
a
s
e
s
li
k
e
b
lee
d
in
g
,
u
lce
r,
t
u
m
o
r,
Cro
h
n
'
s
d
ise
a
se
,
a
n
d
p
o
ly
p
s
in
a
d
isc
re
ti
o
n
w
a
y
.
It
c
re
a
t
e
s
i
m
m
e
n
se
p
re
ss
u
re
a
n
d
o
n
u
s
f
o
r
c
li
n
icia
n
s
to
p
e
rc
e
iv
e
a
h
u
g
e
n
u
m
b
e
r
o
f
i
m
a
g
e
f
ra
m
e
s,
w
h
ich
is
ti
m
e
-
c
o
n
su
m
in
g
a
n
d
m
a
k
e
s
h
u
m
a
n
o
v
e
rsig
h
t
e
rro
rs.
T
h
e
re
f
o
re
a
c
o
m
p
u
ter
-
a
u
to
m
a
ted
sy
ste
m
h
a
s
b
e
e
n
in
tro
d
u
c
e
d
f
o
r
b
lee
d
in
g
d
e
tec
ti
o
n
.
A
u
n
iq
u
e
f
u
z
z
y
lo
g
ic
te
c
h
n
iq
u
e
is
p
ro
p
o
se
d
t
o
e
x
trac
t
th
e
sp
e
c
if
ied
b
lee
d
in
g
a
n
d
n
o
n
-
b
lee
d
in
g
i
n
f
o
rm
a
ti
o
n
f
ro
m
th
e
im
a
g
e
d
a
ta.
A
p
a
rti
c
u
lar
q
u
a
d
ra
ti
c
su
p
p
o
rt
v
e
c
to
r
m
a
c
h
in
e
(QSV
M
)
c
las
si
f
ier
is
e
m
p
lo
y
e
d
to
c
las
sify
th
e
o
b
tain
e
d
sta
ti
stica
l
f
e
a
tu
re
s
f
ro
m
th
e
b
lee
d
in
g
a
n
d
n
o
n
-
b
lee
d
in
g
im
a
g
e
s
in
c
o
rp
o
ra
ti
n
g
p
r
in
c
ip
a
l
c
o
m
p
o
n
e
n
t
a
n
a
ly
sis
(P
CA
).
Af
ter
e
x
ten
si
v
e
e
x
p
e
ri
m
e
n
ts
o
n
c
li
n
ica
l
d
a
ta,
9
8
%
se
n
siti
v
it
y
,
9
8
.
4
%
a
c
c
u
ra
c
y
,
9
8
%
s
p
e
c
if
icit
y
,
9
3
%
p
re
c
isio
n
,
9
5
.
4
%
F
1
-
sc
o
re
,
a
n
d
9
9
%
n
e
g
a
ti
v
e
p
re
d
icte
d
v
a
lu
e
h
a
v
e
b
e
e
n
a
c
h
iev
e
d
,
w
h
ich
o
u
tp
e
rf
o
rm
s
so
m
e
o
f
th
e
st
a
tes
o
f
a
rt
m
e
th
o
d
s
in
th
is
re
g
a
rd
.
It
is
o
p
ti
m
isti
c
th
a
t
th
e
p
ro
p
o
se
d
m
e
th
o
d
o
l
o
g
y
w
o
u
ld
sig
n
if
ica
n
tl
y
c
o
n
tri
b
u
te
t
o
b
l
e
e
d
i
n
g
d
e
t
e
c
t
i
o
n
t
e
c
h
n
i
q
u
e
s
a
n
d
d
i
m
i
n
i
s
h
t
h
e
a
d
d
i
t
i
o
n
a
l
o
n
u
s
o
f
t
h
e
p
h
y
sic
ian
s.
K
ey
w
o
r
d
s
:
B
leed
in
g
d
etec
tio
n
Fu
zz
y
lo
g
ic
Gastro
in
te
s
ti
n
al
tr
ac
t
P
r
in
cip
al
co
m
p
o
n
e
n
t a
n
al
y
s
is
QSVM
W
ir
eless
ca
p
s
u
le
e
n
d
o
s
co
p
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
:
A
b
d
u
l
lah
A
l M
a
m
u
n
Facu
lt
y
o
f
E
n
g
i
n
ee
r
i
n
g
a
n
d
T
e
ch
n
o
lo
g
y
Mu
lti
m
ed
ia
U
n
iv
er
s
it
y
Ay
er
Ker
o
h
,
Me
lak
a
-
7
5
4
0
,
M
ala
y
s
ia
E
m
ail:
m
a
m
u
n
1
3
0
2
0
3
@
g
m
ai
l
.
co
m
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
d
is
ea
s
es
o
f
th
e
Gastro
i
n
t
esti
n
a
l
tr
ac
t
i
n
t
h
e
lar
g
e
i
n
tes
tin
e,
s
m
al
l
i
n
test
i
n
e,
a
n
d
s
to
m
ac
h
h
a
v
e
tu
r
n
ed
in
to
t
h
e
m
o
s
t
ep
id
e
m
ic
p
r
o
b
lem
i
n
m
o
d
er
n
li
f
e
s
in
ce
it
in
v
o
l
v
es
d
i
f
f
er
e
n
t
d
is
ea
s
e
s
lik
e
b
le
ed
in
g
,
u
lcer
,
tu
m
o
r
,
a
n
d
ca
n
ce
r
[
1
]
.
Du
e
to
d
if
f
er
en
t
t
y
p
es
o
f
d
if
f
ic
u
lti
es
an
d
r
estrictio
n
s
,
co
n
v
en
tio
n
al
en
d
o
s
co
p
es
ar
e
u
n
ab
le
to
ex
tr
ac
t t
h
e
i
n
f
o
r
m
ati
o
n
f
r
o
m
t
h
e
in
n
er
p
o
r
tio
n
o
f
t
h
e
g
a
s
tr
o
in
tes
tin
a
l tr
ac
t [
2
]
.
A
m
aj
o
r
ity
n
u
m
b
er
o
f
p
eo
p
le
f
r
o
m
all
o
v
er
th
e
w
o
r
l
d
ar
e
u
n
d
er
g
o
in
g
th
e
s
e
v
er
e
ef
f
ec
t
o
f
GI
tr
ac
t
d
is
ea
s
es.
A
r
o
u
n
d
2
.
1
p
eo
p
le
p
e
r
th
o
u
s
an
d
s
h
a
v
e
d
ied
f
o
r
I
B
D
(
in
f
la
m
m
ato
r
y
b
o
w
el
d
is
ea
s
e
)
,
an
d
5
%
o
f
th
e
w
e
s
ter
n
p
o
p
u
latio
n
h
av
e
b
ee
n
s
u
f
f
er
in
g
f
r
o
m
t
h
e
c
d
is
ea
s
e
[
3
]
.
I
n
an
o
th
er
o
b
s
er
v
atio
n
,
ar
o
u
n
d
1
.
6
m
il
lio
n
p
eo
p
le
f
r
o
m
Am
er
ica
n
h
a
v
e
b
ee
n
af
f
e
c
ted
an
d
d
ied
f
o
r
I
B
D,
an
d
ap
p
r
o
x
i
m
atel
y
1
0
m
illi
o
n
b
ab
ies
h
av
e
b
ee
n
s
u
f
f
er
ed
f
r
o
m
C
r
o
h
n
'
s
d
is
ea
s
e
[
4
]
.
So
,
ef
f
icie
n
t
s
o
lu
t
io
n
s
n
ee
d
to
b
e
in
tr
o
d
u
ce
d
to
cu
r
e
g
a
s
tr
o
in
test
in
al
tr
ac
t
p
r
o
b
le
m
s
as
s
o
o
n
a
s
p
o
s
s
ib
le.
A
f
ter
th
e
d
ev
elo
p
m
e
n
t
a
n
d
r
ev
o
l
u
ti
o
n
o
f
o
p
tic
en
d
o
s
co
p
y
,
t
h
e
d
iag
n
o
s
is
a
n
d
th
er
ap
y
p
r
o
ce
s
s
e
s
h
av
e
b
ee
n
r
ec
eiv
ed
th
e
to
u
ch
o
f
g
r
o
w
t
h
in
th
e
g
a
s
tr
o
in
test
i
n
al
tr
ac
t
d
is
ea
s
e
s
.
B
u
t
it
is
a
m
atter
o
f
r
eg
r
et
t
h
at
o
n
l
y
a
s
m
all
p
o
r
tio
n
o
f
th
e
p
r
o
x
i
m
a
l
d
u
o
d
en
u
m
a
n
d
b
o
w
el
ca
n
b
e
e
x
a
m
in
ed
.
I
n
2
0
0
0
,
a
r
ev
o
lu
tio
n
w
as
cr
e
ated
in
t
h
e
f
ield
o
f
en
d
o
s
co
p
y
b
y
i
n
tr
o
d
u
ci
n
g
ca
p
s
u
le
e
n
d
o
s
co
p
y
f
r
o
m
th
e
g
i
v
e
n
i
m
a
g
i
n
g
co
m
p
a
n
y
.
W
C
E
h
a
s
b
ee
n
ap
p
lied
f
o
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
B
leed
in
g
r
ec
o
g
n
itio
n
tech
n
i
q
u
e
in
w
ir
eles
s
ca
p
s
u
le
en
d
o
s
co
p
y
ima
g
es u
s
in
g
.
.
.
(
A
.
A
l Ma
mu
n
)
2689
th
e
r
ea
l
-
ti
m
e
ap
p
licatio
n
i
n
2
0
0
1
.
T
h
e
ca
p
s
u
le
en
d
o
s
co
p
y
ca
n
v
i
s
u
alize
t
h
e
s
m
all
i
n
tes
t
in
e
an
d
i
n
tr
o
d
u
ce
m
o
d
er
n
tec
h
n
o
lo
g
y
to
s
n
ap
f
r
o
m
t
h
e
g
astro
i
n
test
i
n
al
tr
ac
t
b
y
a
s
w
allo
w
ed
w
ir
ele
s
s
ca
p
s
u
le.
T
h
e
FD
A
h
a
s
ce
r
tif
ied
ca
p
s
u
le
e
n
d
o
s
co
p
y
f
o
r
s
af
et
y
a
n
d
ac
ce
p
tab
ilit
y
[
5
]
.
W
ir
eless
ca
p
s
u
le
e
n
d
o
s
co
p
y
co
n
s
is
t
s
o
f
a
n
i
m
ag
e
s
en
s
o
r
,
tr
an
s
m
itter
,
b
atter
y
,
lig
h
t,
an
d
co
lo
r
ca
m
er
a.
I
t
r
eq
u
i
r
es
al
m
o
s
t
ei
g
h
t
h
o
u
r
s
an
d
s
n
ap
s
ar
o
u
n
d
5
7
K
i
m
a
g
es
to
co
m
p
lete
t
h
e
tr
av
el
th
r
o
u
g
h
o
u
t
t
h
e
w
h
o
le
g
astro
in
test
i
n
al
tr
ac
t.
T
h
e
s
n
ap
p
ed
i
m
a
g
es
ar
e
tr
an
s
m
itted
to
a
p
ar
ticu
lar
co
m
p
u
ter
t
h
at
i
s
o
p
er
ated
b
y
a
ce
r
tain
r
ad
io
-
f
r
eq
u
e
n
c
y
.
T
h
e
p
h
y
s
icia
n
s
r
eq
u
ir
e
ch
ec
k
i
n
g
o
u
t
th
e
s
e
n
t
i
m
a
g
es
to
f
i
n
d
o
u
t
th
e
d
is
ea
s
e
in
d
icatio
n
s
,
an
d
it
b
ec
o
m
e
s
t
o
o
m
u
ch
d
if
f
ic
u
lt
to
s
o
r
t
o
u
t
th
e
p
r
ec
is
e
in
f
o
r
m
atio
n
o
f
th
e
d
is
ea
s
es
f
r
o
m
t
h
e
i
m
a
g
es
m
an
u
all
y
f
o
r
th
e
s
ac
k
o
f
ir
r
eg
u
lar
d
is
tr
ib
u
tio
n
o
f
th
e
p
ar
ticu
lar
in
f
o
r
m
at
io
n
a
n
d
h
u
m
a
n
o
v
er
s
i
g
h
t
d
if
f
ic
u
lt
ies.
T
h
er
ef
o
r
e,
th
e
r
esear
ch
er
s
ar
e
tr
y
i
n
g
to
i
n
tr
o
d
u
ce
a
s
p
ec
if
ic
tec
h
n
iq
u
e
to
d
etec
t
th
e
b
leed
in
g
p
o
r
tio
n
au
to
m
atica
ll
y
[
6
]
.
2.
RE
L
AT
E
D
WO
RK
B
leed
in
g
d
etec
tio
n
is
v
ital
f
o
r
th
e
clin
ical
p
er
s
p
ec
tiv
e
b
ec
au
s
e
m
a
n
y
o
f
t
h
e
GI
tr
ac
t
d
is
ea
s
e
r
ec
o
g
n
it
io
n
s
d
ep
en
d
o
n
it
[
2
]
.
Dis
ti
n
ct
t
y
p
e
s
o
f
co
m
p
u
ter
-
a
id
ed
s
y
s
te
m
s
h
a
v
e
alr
ea
d
y
b
e
en
in
tr
o
d
u
ce
d
in
to
W
C
E
i
m
a
g
es
to
d
ed
u
ce
th
e
b
u
r
d
en
o
f
t
h
e
p
h
y
s
icia
n
s
f
o
r
d
etec
tin
g
th
e
b
leed
i
n
g
.
Ho
w
e
v
er
,
th
ese
tech
n
iq
u
e
s
p
r
o
v
id
ed
u
n
s
atis
f
ac
to
r
y
r
es
u
lt
s
in
ter
m
s
o
f
s
e
n
s
iti
v
it
y
a
n
d
s
p
ec
if
icit
y
,
s
u
ch
as
s
u
s
p
ec
ted
b
l
o
o
d
i
n
d
i
c
a
t
o
r
[
7
]
.
I
n
i
t
i
a
l
l
y
,
a
f
r
am
e
w
o
r
k
w
a
s
d
e
s
i
g
n
e
d
t
o
d
e
t
e
c
t
b
l
e
e
d
i
n
g
p
o
r
t
i
o
n
i
n
w
h
i
c
h
t
h
e
s
p
e
c
i
f
i
c
i
t
y
a
n
d
s
e
n
s
i
t
i
v
i
t
y
w
e
r
e
o
n
l
y
4
1
.
8
%
a
n
d
2
1
.
5
%
.
A
c
c
o
r
d
i
n
g
t
o
[
8
]
,
P
r
o
b
a
b
i
l
i
s
t
i
c
n
e
u
r
a
l
n
e
tw
o
r
k
i
s
a
p
p
l
i
e
d
t
o
d
e
t
e
c
t
t
h
e
b
l
e
e
d
i
n
g
p
o
r
t
i
o
n
a
n
d
a
l
s
o
i
m
p
r
o
v
e
d
t
o
r
e
c
o
g
n
i
z
e
b
l
e
e
d
i
n
g
p
a
r
t
f
o
r
a
c
h
i
e
v
i
n
g
m
o
r
e
p
r
e
c
i
s
e
r
e
s
u
l
t
s
.
A
s
u
p
e
r
p
ix
el
tec
h
n
iq
u
e
h
a
s
b
ee
n
ap
p
lied
b
y
Si
v
ak
u
m
ar
et
a
l
.
[
9
]
w
it
h
a
Naiv
e
B
a
y
es
c
lass
if
ier
to
d
etec
t
th
e
b
leed
in
g
r
eg
io
n
ac
c
u
r
atel
y
.
Ho
w
e
v
er
,
th
e
m
o
d
e
l
h
as
b
ee
n
tr
ai
n
ed
o
n
l
y
t
w
o
s
tat
is
tical
f
ea
t
u
r
es
an
d
d
id
n
o
t
v
alid
ate
w
i
th
t
h
e
o
th
er
ex
iti
n
g
tec
h
n
iq
u
e
s
.
T
h
e
in
f
o
r
m
atio
n
lo
s
s
ca
n
b
e
r
ed
u
ce
d
u
p
to
a
s
i
g
n
i
f
ica
n
ce
le
v
el
b
y
u
s
i
n
g
m
u
ltip
le
r
an
d
o
m
tr
ain
i
n
g
d
atasets
an
d
ac
h
iev
ed
h
ig
h
er
s
p
ec
i
f
icit
y
an
d
s
en
s
iti
v
i
t
y
b
y
ap
p
l
y
i
n
g
th
e
s
u
p
p
o
r
t v
ec
to
r
m
ac
h
i
n
e
.
Ko
n
s
ta
n
ti
n
et
a
l
.
h
a
v
e
i
n
tr
o
d
u
ce
d
a
b
leed
in
g
d
etec
tio
n
te
ch
n
iq
u
e
u
tili
zi
n
g
th
e
tex
t
u
r
e
an
d
co
lo
r
f
ea
t
u
r
es
t
h
at
w
o
u
ld
p
r
o
v
id
e
t
h
e
co
m
p
le
te
co
lo
r
i
n
f
o
r
m
atio
n
.
Ne
v
er
th
e
less
,
th
e
co
lo
r
in
f
o
r
m
at
io
n
tec
h
n
o
lo
g
y
p
r
o
v
id
es
lo
w
er
p
er
f
o
r
m
a
n
ce
r
esu
lt
s
co
m
p
ar
ed
to
th
e
o
t
h
er
ex
is
t
in
g
m
et
h
o
d
s
[
1
0
]
.
T
h
e
f
r
eq
u
en
c
y
s
p
ec
tr
u
m
o
f
ch
ar
ac
ter
is
tic
s
p
atter
n
is
b
ased
o
n
t
h
e
n
o
r
m
alize
d
g
r
a
y
lev
el
co
-
o
cc
u
r
r
en
ce
m
atr
i
x
a
n
d
ac
h
i
ev
ed
a
s
at
is
f
ac
to
r
y
b
leed
in
g
d
etec
tio
n
r
ate
[
1
1
]
.
A
u
n
iq
u
e
t
w
o
-
f
o
ld
s
y
s
te
m
is
in
tr
o
d
u
ce
d
to
d
etec
t
th
e
b
le
ed
in
g
p
o
r
tio
n
in
g
i
n
w
h
ic
h
K
-
m
ea
n
s
cl
u
s
ter
i
n
g
an
d
SVM
clas
s
i
f
ier
h
a
s
b
ee
n
ap
p
lied
to
ex
tr
ac
t
t
h
e
cl
u
s
ter
ce
n
ter
an
d
d
i
s
ti
n
g
u
is
h
th
e
b
leed
in
g
i
m
a
g
es
co
r
r
esp
o
n
d
en
tl
y
[
1
2
]
.
A
ch
an
g
ea
b
le
co
lo
r
d
o
m
ai
n
h
a
s
b
ee
n
i
m
p
le
m
en
ted
in
s
tead
o
f
th
e
R
GB
co
lo
r
m
o
d
el
to
r
ed
u
ce
th
e
co
m
p
u
tatio
n
al
ti
m
e.
T
h
e
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
h
as
b
ee
n
ap
p
lied
to
th
e
s
tatis
t
ical
f
ea
t
u
r
es
d
ep
en
d
i
n
g
o
n
h
i
g
h
er
a
n
d
lo
w
er
v
al
u
es
t
o
class
if
y
t
h
e
b
leed
in
g
a
n
d
n
o
n
-
b
leed
in
g
i
m
ag
e
ef
f
icien
tl
y
[
2
]
.
I
n
[
1
3
]
,
th
e
au
th
o
r
s
e
x
tr
ac
ted
d
if
f
er
e
n
t
co
l
o
r
f
ea
tu
r
es
f
r
o
m
th
e
i
m
a
g
es
b
y
e
m
p
lo
y
in
g
t
h
e
h
is
to
g
r
a
m
tec
h
n
iq
u
e,
an
d
SV
M
class
i
f
ier
ap
p
lied
to
d
is
tin
ct
i
m
a
g
es.
A
u
th
o
r
s
p
r
o
p
o
s
ed
i
n
[
1
4
]
a
co
m
p
o
u
n
d
m
o
d
el
n
a
m
ed
Y.
I
/Q
to
ex
tr
ac
t
th
e
in
f
o
r
m
atio
n
ab
o
u
t
th
e
c
h
r
o
m
in
a
n
ce
an
d
l
u
m
in
a
n
ce
f
o
r
a
cq
u
ir
in
g
th
e
r
eg
io
n
o
f
in
ter
est
o
f
t
h
e
i
m
ag
e
s
an
d
th
e
SVM
clas
s
i
f
ier
tech
n
iq
u
e
e
m
p
lo
y
ed
f
o
r
a
s
atis
f
ac
to
r
y
r
es
u
lt.
I
n
[
1
5
]
,
au
th
o
r
s
f
o
r
m
ed
a
p
ix
el
i
n
ten
s
it
y
r
atio
n
o
f
R
/G
(
R
ed
/Gr
ee
n
)
f
r
o
m
t
h
e
R
G
B
c
o
l
o
r
m
o
d
e
l
t
o
a
c
h
i
e
v
e
d
i
f
f
e
r
e
n
t
s
t
a
t
i
s
t
i
c
a
l
f
e
a
t
u
r
e
s
a
n
d
K
-
n
e
a
r
e
s
t
n
e
i
g
h
b
o
r
c
l
a
s
s
i
f
i
e
r
u
s
e
d
f
o
r
c
l
a
s
s
i
f
i
c
a
t
i
o
n
.
A
u
t
h
o
r
s
e
m
p
lo
y
ed
in
te
n
s
it
y
f
l
u
ct
u
atio
n
o
f
th
e
p
ix
els
in
t
h
e
R
GB
co
lo
r
m
o
d
el
w
it
h
s
tati
s
tical
ch
ar
ac
ter
i
s
tic
s
an
al
y
s
is
i
n
[
1
6
]
.
T
h
o
u
g
h
th
e
s
e
tech
n
iq
u
es
h
av
e
d
o
n
e
tr
e
m
en
d
o
u
s
w
o
r
k
o
n
b
leed
in
g
d
etec
tio
n
f
r
o
m
W
C
E
i
m
a
g
es,
t
h
ese
s
till
h
av
e
th
e
li
m
itatio
n
o
f
co
m
p
u
tatio
n
al
co
m
p
le
x
it
y
a
n
d
lo
w
er
p
er
f
o
r
m
a
n
ce
r
es
u
lt
s
.
C
o
lo
r
th
r
es
h
o
ld
tec
h
n
iq
u
es
o
n
d
if
f
er
en
t
co
lo
r
s
p
ac
es
h
a
v
e
b
ee
n
ap
p
lied
f
o
r
d
etec
tin
g
d
i
f
f
er
en
t
ab
n
o
r
m
alitie
s
.
Ho
w
e
v
er
,
it
co
u
ld
n
'
t
d
is
tin
g
u
is
h
t
h
e
in
f
o
r
m
ati
v
e
p
o
r
tio
n
f
r
o
m
t
h
e
b
ac
k
g
r
o
u
n
d
f
o
r
all
t
h
e
i
m
a
g
es [
1
7
-
22
].
I
n
th
is
p
ap
er
,
an
e
x
e
m
p
lar
y
ac
cu
r
ate
b
leed
in
g
d
etec
tio
n
tech
n
iq
u
e
is
i
n
tr
o
d
u
ce
d
f
r
o
m
w
ir
ele
s
s
ca
p
s
u
le
en
d
o
s
co
p
y
i
m
ag
e
s
in
t
h
e
HSV
co
lo
r
s
p
ac
e.
Firstl
y
,
t
h
e
W
C
E
i
m
a
g
es
ar
e
r
ec
eiv
ed
b
y
tr
a
n
s
f
o
r
m
i
n
g
th
e
v
id
eo
o
f
w
ir
ele
s
s
ca
p
s
u
le
en
d
o
s
co
p
y
i
n
to
i
m
a
g
e
f
r
a
m
es.
A
f
u
zz
y
lo
g
ic
tech
n
iq
u
e
a
n
d
s
tati
s
tical
f
ea
t
u
r
es
h
av
e
b
ee
n
u
s
ed
to
co
llect
t
h
e
f
ea
tu
r
es
f
r
o
m
t
h
e
ca
p
t
u
r
ed
i
m
ag
e
s
.
T
h
ese
s
tat
is
tical
f
ea
tu
r
e
s
ar
e
u
s
ed
in
a
Q
SVM
class
i
f
ier
to
d
etec
t b
leed
in
g
a
n
d
n
o
n
-
b
leed
i
n
g
i
m
a
g
es
f
r
o
m
t
h
e
w
ir
eles
s
ca
p
s
u
le
e
n
d
o
s
co
p
y
i
m
ag
e
s
.
3.
RE
S
E
ARCH
M
E
T
H
O
DO
L
O
G
Y
An
ex
e
m
p
lar
y
m
e
th
o
d
is
p
r
o
p
o
s
ed
to
d
etec
t
th
e
av
ailab
ilit
y
o
f
b
lo
o
d
p
o
r
tio
n
s
in
th
e
i
m
ag
e
ce
lls
th
at
ar
e
r
ec
eiv
ed
f
r
o
m
t
h
e
w
ir
ele
s
s
ca
p
s
u
le
en
d
o
s
co
p
y
v
id
eo
.
I
n
th
i
s
r
esear
ch
w
o
r
k
,
a
f
u
zz
y
l
o
g
ic
ed
g
e
d
etec
tio
n
tech
n
iq
u
e
h
as
b
ee
n
u
s
ed
f
o
r
id
en
ti
f
y
i
n
g
t
h
e
co
r
n
er
e
d
g
e
o
f
t
h
e
a
b
n
o
r
m
a
l
i
t
i
e
s
o
f
t
h
e
i
m
a
g
e
s
s
o
t
h
a
t
o
n
l
y
t
h
e
a
b
n
o
r
m
a
l
s
e
c
t
i
o
n
c
a
n
b
e
c
a
l
c
u
l
a
t
e
d
.
I
n
t
h
i
s
c
i
r
c
u
m
s
t
a
n
c
e
,
b
l
e
e
d
i
n
g
f
e
a
t
u
r
e
s
a
r
e
c
o
n
s
i
d
e
r
e
d
a
s
t
h
e
a
b
n
o
r
m
a
l
i
t
i
e
s
o
r
t
h
e
m
a
i
n
i
n
f
o
r
m
a
t
i
v
e
p
o
r
t
i
o
n
t
o
b
e
e
x
t
r
a
c
t
e
d
.
T
h
e
p
r
o
p
o
s
e
d
a
l
g
o
r
i
t
h
m
h
a
s
a
s
i
m
p
l
e
f
r
a
m
e
w
o
r
k
t
o
c
o
m
p
u
t
e
,
w
h
i
c
h
i
s
v
e
r
y
e
a
s
y
t
o
c
a
l
c
u
l
a
t
e
a
n
d
t
i
m
e
-
s
a
v
i
n
g
a
s
w
e
l
l
.
T
h
e
c
o
m
p
l
e
t
e
l
a
y
o
u
t
o
f
t
h
e
p
r
o
p
o
s
e
d
m
e
t
h
o
d
i
s
d
e
p
i
c
t
e
d
i
n
Fi
g
u
r
e
1
.
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
.
3
,
J
u
n
e
2
0
2
1
:
2
6
8
8
-
2695
2690
Fig
u
r
e
1
.
Sch
e
m
atic
d
iag
r
a
m
o
f
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
3
.
1
.
Acquis
it
io
n o
f
i
m
a
g
e
f
ra
m
e
T
h
e
ac
tu
al
d
ata
f
r
o
m
th
e
W
C
E
o
f
th
e
GI
tr
ac
t
is
in
th
e
v
id
eo
f
o
r
m
.
I
n
it
iall
y
,
a
r
o
b
u
s
t
d
atab
ase
o
f
i
m
a
g
es
f
o
ld
er
h
a
s
b
ee
n
cr
ea
te
d
b
y
co
n
v
er
t
in
g
t
h
e
v
id
eo
cli
p
o
b
tain
ed
f
r
o
m
th
e
W
C
E
i
n
t
o
th
e
i
m
a
g
e
f
r
a
m
e,
w
h
ic
h
d
ep
en
d
s
o
n
t
h
e
f
r
a
m
e
r
ate
s
p
ec
if
ied
b
y
W
C
E
.
T
h
e
C
E
.
P
illC
a
m
*
SB
h
as
b
ee
n
u
s
ed
as C
E
to
ta
k
e
t
h
e
GI
tr
ac
t
v
id
eo
,
w
h
o
s
e
lo
n
g
e
v
it
y
w
a
s
8
h
o
u
r
s
,
an
d
t
h
e
f
r
a
m
e
r
ate
w
as
2
,
w
h
ich
is
ad
eq
u
ate
to
s
u
p
p
l
y
a
n
e
n
r
ic
h
d
atab
ase
f
o
r
a
n
al
y
zin
g
[
2
]
.
Fro
m
t
h
e
d
atab
ase,
th
e
an
n
o
tate
d
b
leed
in
g
an
d
n
o
n
-
b
leed
i
n
g
d
ataset
h
av
e
b
ee
n
cr
ea
ted
f
o
r
cl
ass
i
f
y
i
n
g
t
h
e
b
leed
in
g
i
m
a
g
es
f
r
o
m
t
h
e
n
o
n
-
b
leed
in
g
i
m
a
g
e.
A
r
o
u
n
d
2
3
9
3
an
n
o
tated
i
m
ag
e
s
h
av
e
b
ee
n
u
s
ed
to
an
al
y
ze
t
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
,
w
h
ic
h
is
a
v
ailab
le
in
[
2
3
]
.
3
.
2
.
I
m
a
g
e
pre
-
pro
ce
s
s
ing
R
GB
is
a
co
m
p
o
u
n
d
co
lo
r
m
o
d
el
co
n
s
is
t
in
g
o
f
th
r
ee
-
co
lo
r
ch
an
n
el
s
r
ed
,
g
r
ee
n
,
an
d
b
lu
e.
T
h
e
ca
p
tu
r
ed
i
m
a
g
e
b
y
W
C
E
ca
m
er
as
is
i
n
R
GB
f
o
r
m
at.
At
f
ir
s
t,
w
e
c
u
t
o
f
f
ar
o
u
n
d
3
3
%
o
f
t
h
e
i
m
ag
e
s
o
th
a
t
w
e
ca
n
r
e
m
o
v
e
t
h
e
p
ar
tial
b
lack
i
s
h
p
o
r
tio
n
an
d
W
C
E
tag
f
r
o
m
th
e
i
m
a
g
e
f
r
a
m
e.
Af
ter
th
at,
th
is
R
GB
i
m
a
g
e
i
s
co
n
v
er
ted
i
n
to
a
g
r
a
y
s
ca
le
i
m
ag
e
s
o
t
h
at
w
e
ca
n
ta
k
e
ac
t
io
n
o
n
t
h
e
t
w
o
-
d
i
m
e
n
s
io
n
al
ar
r
a
y
in
s
tead
o
f
a
th
r
ee
-
d
i
m
en
s
io
n
al
ar
r
a
y
.
Fu
r
t
h
er
m
o
r
e,
it
is
co
n
v
er
ted
i
n
to
a
d
o
u
b
le
-
p
r
ec
is
io
n
f
r
a
m
e
w
o
r
k
as
a
f
u
zz
y
lo
g
ic
s
p
ec
u
la
t
o
r
o
n
l
y
in
a
d
o
u
b
le
-
p
r
ec
is
io
n
s
tr
u
ct
u
r
e.
T
h
e
o
u
tp
u
t i
m
ag
e
s
h
a
v
e
b
ee
n
ad
j
u
s
ted
i
n
a
r
an
g
e
o
f
1
% to
m
a
k
e
th
e
in
ten
s
it
y
lab
el
b
et
w
ee
n
lo
w
a
n
d
h
i
g
h
.
B
y
t
h
e
tech
n
iq
u
e
o
f
u
n
s
h
ar
p
m
as
k
in
g
,
t
h
e
o
u
tp
u
t
i
m
a
g
e
h
a
s
b
ee
n
tr
ied
to
m
a
k
e
s
h
ar
p
er
.
T
h
e
s
eq
u
en
tial
p
r
o
ce
s
s
o
f
i
m
a
g
e
p
r
e
-
p
r
o
ce
s
s
i
n
g
is
s
h
o
w
n
i
n
Fi
g
u
r
e
2
.
(
a)
(
b
)
(
c)
(
d
)
Fig
u
r
e
2
.
T
h
e
s
eq
u
en
tial te
c
h
n
iq
u
e
o
f
i
m
ag
e
p
r
e
-
p
r
o
ce
s
s
i
n
g
;
(
a)
p
r
im
ar
y
s
a
m
p
le
b
leed
in
g
i
m
ag
e,
(
b
)
3
3
% c
u
t o
f
i
m
a
g
e
,
(
c)
ad
j
u
s
ted
i
m
a
g
e
,
(
d
)
s
h
ar
p
en
-
i
m
a
g
e
3
.
3
.
F
uzzy
lo
g
ic
edg
e
det
ec
t
io
n
A
t
t
h
e
i
n
itial
s
tag
e
o
f
th
e
m
et
h
o
d
,
w
e
h
a
v
e
ca
lc
u
lated
th
e
g
r
ad
ien
t
o
f
th
e
i
m
a
g
e
o
n
th
e
a
x
is
o
f
x
a
n
d
y
at
w
h
er
e
i
m
a
g
es
h
a
v
e
d
i
v
er
s
if
ied
f
r
o
m
t
h
e
id
e
n
tical
r
e
g
io
n
.
Dev
iatio
n
f
r
o
m
t
h
e
u
n
i
f
o
r
m
r
eg
io
n
i
n
d
i
c
a
t
e
s
t
h
e
v
a
r
i
a
t
i
o
n
o
f
p
i
x
e
l
i
n
t
e
n
s
i
t
y
a
n
d
a
r
e
a
.
T
h
e
r
e
f
o
r
e
,
w
e
h
a
v
e
a
p
p
l
i
e
d
t
w
o
g
r
a
d
i
e
n
t
f
i
l
t
e
r
s
o
f
t
h
e
s
a
m
e
r
a
n
g
e
o
f
[
-
1
1
]
a
n
d
c
o
n
v
o
l
u
t
e
w
i
t
h
t
h
e
i
m
a
g
e
s
t
o
f
i
n
d
th
e
g
r
ad
ien
t
alo
n
g
t
h
e
t
w
o
ax
e
s
.
T
h
is
co
n
v
o
lu
tio
n
w
il
l
r
etu
r
n
a
s
u
b
s
et
f
o
llo
w
in
g
a
s
tr
u
ct
u
r
e
li
k
e
t
h
e
ce
n
ter
s
ec
tio
n
o
f
t
h
e
co
n
v
o
l
u
tio
n
w
it
h
t
h
e
s
a
m
e
s
ize
as
t
h
e
p
r
i
m
ar
y
v
ar
iab
le.
Fig
u
r
e
3
s
h
o
w
s
t
h
e
co
n
v
o
lu
t
io
n
al
r
esu
lt a
lo
n
g
th
e
a
x
es.
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
B
leed
in
g
r
ec
o
g
n
itio
n
tech
n
i
q
u
e
in
w
ir
eles
s
ca
p
s
u
le
en
d
o
s
co
p
y
ima
g
es u
s
in
g
.
.
.
(
A
.
A
l Ma
mu
n
)
2691
W
e
h
av
e
e
m
p
lo
y
ed
a
n
ed
g
e
d
etec
tio
n
f
u
zz
y
in
ter
f
ac
e
s
y
s
te
m
in
w
h
ic
h
it
r
etu
r
n
s
t
h
e
f
u
zz
y
in
ter
f
ac
e
o
f
Ma
m
d
a
n
i
w
it
h
i
n
d
icate
d
n
a
m
e.
T
h
en
,
w
e
ad
d
ed
th
e
v
ar
iab
les
i
n
to
t
h
e
i
n
ter
f
ac
e
s
y
s
te
m
b
y
i
n
cl
u
d
in
g
co
n
v
o
lu
tio
n
al
r
es
u
lt
s
alo
n
g
th
e
ax
es
w
it
h
s
p
ec
i
f
ied
b
o
u
n
d
ar
y
li
m
its
.
Af
ter
t
h
at,
w
e
h
a
v
e
c
r
ea
ted
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
as
ze
r
o
(
n
u
ll)
a
v
er
ag
e
Ga
u
s
s
ian
alg
o
r
it
h
m
f
o
r
in
d
iv
id
u
al
i
n
p
u
ts
.
I
t
i
s
c
h
ar
ac
ter
ized
as
ze
r
o
m
e
m
b
er
s
h
ip
t
y
p
e
w
i
th
d
eg
r
ee
1
w
h
e
n
t
h
e
g
r
ad
ien
t
v
a
lu
e
h
o
l
d
s
0
f
o
r
a
p
ix
el.
T
h
e
n
,
w
e
s
u
m
m
ed
t
h
is
p
ar
ticu
la
r
m
e
m
b
er
s
h
ip
f
u
n
c
tio
n
in
to
th
e
in
ter
f
ac
e
s
y
s
te
m
b
y
i
n
cl
u
d
in
g
co
n
v
o
lu
tio
n
al
r
esu
lts
alo
n
g
t
h
e
a
x
es
w
it
h
a
s
p
ec
if
ied
v
al
u
e
to
b
e
ad
d
ed
to
th
e
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
.
B
esid
es,
w
e
h
a
v
e
in
cl
u
d
ed
z
er
o
an
d
Gau
s
s
ia
n
m
e
m
b
er
s
h
i
p
f
u
n
ctio
n
s
w
it
h
s
ta
n
d
ar
d
d
ev
iatio
n
f
o
r
co
n
v
o
lu
tio
n
al
r
es
u
lts
alo
n
g
th
e
a
x
es
f
o
r
n
u
ll
m
e
m
b
er
s
h
ip
f
u
n
c
tio
n
,
w
h
ic
h
v
ar
ie
s
t
h
e
in
te
n
s
it
y
o
f
t
h
e
r
ec
o
g
n
ized
ed
g
e.
Af
ter
th
at,
w
e
f
ix
ed
[
0
1
]
o
f
th
e
in
ten
s
it
y
o
f
th
e
ed
g
e
p
er
ce
iv
ed
im
a
g
e
f
r
o
m
th
e
i
m
m
ed
iate
last
ac
tio
n
as
o
u
tp
u
t
a
n
d
also
a
m
e
m
b
er
f
u
n
ct
io
n
o
f
a
tr
ian
g
l
e
co
n
s
is
tin
g
o
f
w
h
ite
a
n
d
b
lack
v
a
lu
e
s
f
o
r
o
u
tp
u
t.
T
h
ese
tr
ian
g
u
lar
w
h
ite
an
d
b
l
ac
k
v
al
u
es
(
=
1
,
=
1
,
=
2
,
=
0
,
=
0
=
0
.
3
h
av
e
a
s
ig
n
i
f
ican
t
ef
f
ec
t
o
n
th
e
in
te
n
s
it
y
o
f
t
h
e
r
ec
o
g
n
ized
i
m
a
g
e.
L
a
s
t
o
f
all,
a
f
u
zz
y
i
n
ter
f
ac
e
s
y
s
te
m
r
u
les
h
av
e
b
ee
n
cr
ea
ted
as
m
e
n
tio
n
ed
w
h
ite
f
o
r
th
e
id
en
tical
r
eg
io
n
a
n
d
b
lack
f
o
r
t
h
e
d
is
ti
n
ct
r
eg
io
n
.
Fi
n
al
ed
g
e
r
ec
o
g
n
ized
i
m
a
g
e
s
w
er
e
e
x
tr
ac
ted
f
o
r
ev
er
y
r
o
w
f
o
r
t
h
e
p
i
x
el
i
n
d
o
u
b
le
t
y
p
e
g
r
e
y
i
m
a
g
e
e
v
al
u
ati
n
g
p
r
o
s
p
ec
tiv
e
co
n
v
o
l
u
tio
n
a
l
r
esu
lt
s
alo
n
g
t
h
e
ax
e
s
.
T
h
e
ab
o
v
e
-
s
p
ec
if
ied
Fi
g
u
r
e
4
s
h
o
w
s
th
e
f
i
n
al
ex
tr
ac
ted
ed
g
e
i
m
ag
e
f
r
o
m
th
e
s
a
m
p
le
d
ata.
T
h
e
o
u
tp
u
t
i
m
ag
e
h
as
b
ee
n
f
u
r
th
er
r
ec
o
n
s
tr
u
cted
to
th
e
R
G
B
co
lo
r
im
a
g
e.
B
esid
es,
th
e
o
u
tp
u
t
i
m
a
g
e
h
as
b
ee
n
co
n
v
er
ted
to
an
HSV
co
lo
r
im
ag
e
f
o
r
ex
tr
ac
tin
g
t
h
e
f
ea
t
u
r
es
f
r
o
m
th
e
HS
V
co
lo
r
s
p
ac
e.
T
h
e
HSV
co
lo
r
m
o
d
el
h
as
th
r
ee
co
lo
r
th
r
ee
ch
an
n
el
s
o
f
Hu
e,
Sat
u
r
atio
n
an
d
v
al
u
e,
w
h
ic
h
h
as
b
ee
n
c
o
n
v
er
ted
f
r
o
m
t
h
e
co
r
r
esp
o
n
d
i
n
g
th
r
ee
R
GB
ch
a
n
n
el
s
.
Fig
u
r
e
3
.
C
o
n
v
o
l
u
tio
n
al
v
al
u
e
s
alo
n
g
(
le
f
t)
x
-
a
x
is
a
n
d
(
r
ig
h
t
)
y
-
a
x
is
Fig
u
r
e
4
.
E
d
g
e
r
ec
o
g
n
ized
b
y
th
e
f
u
zz
y
lo
g
ic
s
y
s
te
m
(
L
e
f
t to
p
)
an
d
(
L
ef
t b
o
tto
m
)
r
ep
r
esen
t
s
s
a
m
p
le
i
m
a
g
es a
n
d
(
R
ig
h
t to
p
)
an
d
(
R
ig
h
t b
o
tto
m
)
r
ep
r
esen
ts
d
etec
t
ed
ed
g
e
i
m
ag
e
f
o
r
b
leed
in
g
a
n
d
n
o
n
-
b
leed
i
n
g
i
m
a
g
e
r
esp
ec
tf
u
ll
y
3
.
4
.
F
ea
t
ure
s
elec
t
io
n
As
t
h
e
R
GB
co
lo
r
s
p
ac
e
is
a
co
m
b
in
a
tio
n
o
f
th
r
ee
co
lo
r
c
h
an
n
el
s
,
it
ca
n
b
e
d
escr
ib
ed
w
it
h
ev
er
y
s
in
g
le
co
lo
r
ch
an
n
el.
T
h
e
in
ten
s
it
y
o
f
ev
er
y
co
lo
r
i
m
a
g
e
h
as
i
n
te
n
s
it
y
b
et
w
ee
n
0
an
d
2
5
5
o
f
R
GB
co
m
p
o
n
e
n
t
s
.
T
h
e
p
r
o
ce
s
s
o
f
f
ea
tu
r
e
ex
tr
ac
tio
n
ca
n
b
e
r
ef
er
r
ed
to
as
th
e
d
i
m
en
s
io
n
al
r
etr
e
n
ch
m
e
n
t
t
h
at
ca
n
b
e
r
ep
r
esen
ted
as
a
p
ar
ticu
lar
s
ec
tio
n
o
f
i
m
ag
e
s
e
f
f
ic
ien
t
l
y
in
a
s
p
ec
i
f
ied
v
ec
to
r
o
f
f
ea
t
u
r
es.
T
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
u
s
ed
d
is
t
in
ct
s
tati
s
tica
l
f
ea
t
u
r
es
l
ik
e
m
ea
n
,
m
o
d
e,
v
ar
ian
ce
,
en
tr
o
p
y
,
s
k
e
w
n
es
s
m
ax
i
m
a,
an
d
m
o
m
e
n
t
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
.
3
,
J
u
n
e
2
0
2
1
:
2
6
8
8
-
2695
2692
to
ex
tr
ac
t
t
h
e
f
ea
t
u
r
es
a
n
d
m
a
k
e
p
r
o
p
er
ca
lcu
latio
n
s
f
r
o
m
th
e
s
eg
m
e
n
ted
i
m
ag
e.
T
h
ese
c
h
ar
ac
ter
ized
f
ea
tu
r
es
ar
e
ex
tr
ac
ted
f
r
o
m
th
e
p
r
o
p
o
s
ed
HSV
co
lo
r
m
o
d
el
as t
h
e
HS
V
co
lo
r
m
o
d
el
g
i
v
es b
etter
p
o
s
s
ib
le
r
es
u
lt
s
o
n
t
h
e
ex
tr
ac
ted
f
ea
t
u
r
es.
T
h
e
HSV
co
lo
r
m
o
d
el
ca
n
b
e
o
b
tain
ed
b
y
co
n
v
er
tin
g
th
e
v
al
u
es
o
f
th
e
c
o
r
r
esp
o
n
d
in
g
th
r
ee
ch
an
n
el
s
o
f
th
e
R
GB
co
lo
r
m
o
d
el
[2
4
]
.
So
m
e
s
tatis
tical
f
ea
t
u
r
ed
h
a
v
e
b
ee
n
o
b
tain
ed
f
r
o
m
th
e
ab
o
v
e
-
s
e
g
m
en
ted
i
m
a
g
e
s
f
o
r
class
if
y
in
g
th
e
b
leed
in
g
i
m
a
g
es
f
r
o
m
n
o
n
-
b
l
ee
d
in
g
i
m
a
g
es.
T
h
e
f
ea
t
u
r
es
h
av
e
b
ee
n
e
x
a
m
in
ed
co
n
s
ec
u
tiv
el
y
b
y
p
r
in
cip
al
co
m
p
o
n
e
n
t
a
n
al
y
s
i
s
(
P
C
A
)
to
f
in
d
o
u
t
t
h
e
ap
p
r
o
p
r
iate
f
ea
tu
r
es
f
r
o
m
t
h
e
i
m
a
g
es.
P
C
A
w
il
l
i
n
co
r
p
o
r
ate
th
e
m
o
s
t
co
r
r
elate
d
s
tatis
tical
f
ea
t
u
r
es
f
r
o
m
t
h
e
i
m
m
e
n
s
e
p
o
o
l
o
f
f
ea
t
u
r
e
v
ec
to
r
.
I
t
w
ill
r
ed
u
ce
th
e
d
i
m
en
s
io
n
o
f
th
e
v
ec
to
r
b
y
tak
i
n
g
th
e
m
o
s
t
r
elev
an
t
co
m
b
in
at
io
n
o
f
e
le
m
en
ts
f
r
o
m
t
h
e
f
e
a
t
u
r
e
v
e
c
t
o
r
.
B
e
s
i
d
e
s
,
i
t
w
i
l
l
a
l
s
o
r
e
d
u
c
e
t
h
e
p
o
s
s
i
b
i
l
i
t
y
o
f
o
v
e
r
f
i
t
t
i
n
g
i
n
t
h
e
c
a
s
e
o
f
d
e
t
e
c
t
i
n
g
t
h
e
i
m
a
g
e
s
.
T
h
e
s
t
a
t
i
s
t
i
c
a
l
f
e
a
t
u
r
e
s
t
h
a
t
h
a
v
e
b
e
e
n
u
s
e
d
i
n
t
h
i
s
p
r
o
p
o
s
e
d
m
e
t
h
o
d
a
r
e
m
e
a
n
,
m
o
d
e
,
s
t
a
n
d
a
r
d
v
a
r
i
a
t
i
o
n
,
s
k
e
w
n
e
s
s
,
e
n
t
r
o
p
y
,
v
a
r
i
a
n
c
e
,
m
a
x
i
m
a
,
a
n
d
m
o
m
e
n
t.
3
.
5
.
Q
SVM
cla
s
s
if
ier
Su
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
is
c
o
n
s
id
er
ed
as
o
n
e
o
f
t
h
e
m
o
s
t
co
m
m
o
n
m
ac
h
i
n
e
lear
n
i
n
g
class
i
f
ier
tech
n
iq
u
es
w
h
ic
h
b
elo
n
g
to
n
o
n
p
ar
a
m
etr
ic
ar
r
an
g
e
m
e
n
t
f
o
r
th
e
s
a
k
e
o
f
e
m
p
lo
y
i
n
g
t
h
e
f
u
n
ctio
n
o
f
t
h
e
k
er
n
el.
Vap
n
ik
w
a
s
th
e
p
io
n
ee
r
w
h
o
f
ir
s
t
p
r
o
p
o
s
ed
th
is
class
i
f
ier
[
2
5
]
.
I
n
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
,
th
e
q
u
ad
r
atic
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
tec
h
n
iq
u
e
h
as
b
ee
n
u
s
ed
f
o
r
d
if
f
er
en
t
iati
n
g
th
e
b
leed
in
g
a
n
d
n
o
n
-
b
leed
i
n
g
o
b
j
ec
ts
.
Say
,
w
e
h
av
e
co
n
s
id
er
ed
n
tr
ain
in
g
d
ata
b
i
,
f
o
r
=
1
,
…
.
each
eith
er
in
u
lcer
o
r
n
o
n
-
u
lcer
,
t
h
en
as
s
ep
ar
atin
g
r
u
le.
≥
w
h
er
e,
∈
ℝ
is
a
v
ar
iab
le
w
i
th
∈
ℝ
,
∈
ℝ
is
th
e
cla
s
s
i
f
ier
p
ar
a
m
eter
s
.
I
n
th
e
p
r
o
p
o
s
ed
tech
n
iq
u
e,
a
k
er
n
el
f
u
n
ctio
n
h
a
s
b
ee
n
u
s
ed
w
h
ic
h
is
d
en
o
ted
b
y
(
,
)
w
h
er
e
x
bl
an
d
x
nbl
m
ea
n
b
leed
in
g
an
d
n
o
n
-
b
leed
in
g
,
r
esp
ec
tiv
e
l
y
.
(
,
)
=
(
.
)
W
h
er
e
S
is
a
d
eg
r
ee
o
f
th
e
co
r
r
esp
o
n
d
in
g
p
o
l
y
n
o
m
ial.
An
d
,
th
e
v
a
lu
e
s
o
f
ca
n
b
e
ca
lcu
late
d
f
r
o
m
th
e
eq
u
atio
n
(
1
)
,
min
∈
ℝ
,
∈
ℝ
,
∈
ℝ
+
1
2
[
(
,
)
(
,
)
]
−
(1
)
W
h
er
e,
=
[
(
,
)
(
,
)
]
=
−
A
v
alid
atio
n
tec
h
n
iq
u
e
i
s
e
m
p
lo
y
ed
to
f
i
n
d
o
u
t
th
e
er
r
o
r
r
ate
o
f
t
h
e
p
r
o
p
o
s
ed
m
e
th
o
d
,
w
h
ic
h
i
s
co
n
s
id
er
ed
to
b
e
clo
s
ed
to
th
e
ac
tu
al
er
r
o
r
o
f
th
e
m
et
h
o
d
.
1
0
-
f
o
ld
cr
o
s
s
-
v
alid
atio
n
h
as
b
ee
n
ex
a
m
i
n
ed
to
d
if
f
er
e
n
tiate
t
h
e
s
a
m
p
le
s
i
n
to
tr
ain
i
n
g
a
n
d
te
s
ti
n
g
s
ets.
T
h
e
tr
ain
i
n
g
a
n
d
te
s
tin
g
i
m
a
g
e
r
atio
s
ar
e
9
0
%
an
d
1
0
%,
r
esp
ec
tf
u
ll
y
co
n
s
id
er
in
g
th
e
1
0
-
f
o
ld
cr
o
s
s
-
va
lid
atio
n
.
T
h
e
o
u
tp
u
t
er
r
o
r
o
b
tain
ed
f
r
o
m
t
h
i
s
tec
h
n
iq
u
e
is
t
h
e
m
ea
n
e
r
r
o
r
r
ate
o
f
iter
atio
n
s
.
Ho
ld
o
u
t
v
alid
atio
n
h
as b
ee
n
u
tili
ze
d
i
n
QSVM
c
l
ass
i
f
ier
in
w
h
ic
h
t
h
e
v
alid
atio
n
is
p
er
f
o
r
m
ed
r
ep
ea
ted
l
y
to
g
et
th
e
o
p
ti
m
al
r
es
u
lt.
B
y
e
m
p
lo
y
in
g
th
e
la
m
in
at
io
n
tech
n
iq
u
e,
th
e
f
al
s
e
r
ate
ca
n
b
e
m
ad
e
m
o
r
e
ef
f
icie
n
t
[
2
5
]
.
T
h
e
b
leed
in
g
a
n
d
n
o
n
-
b
leed
in
g
i
m
ag
e
s
h
a
v
e
s
u
c
ce
s
s
f
u
l
l
y
s
ep
ar
ated
b
y
i
m
p
le
m
en
ti
n
g
t
h
e
Q
u
ad
r
atic
s
u
p
p
o
r
t v
ec
to
r
m
ac
h
i
n
e
.
4.
RE
SU
L
T
S A
ND
D
I
SCU
SS
I
O
N
T
h
e
p
er
f
o
r
m
a
n
ce
o
f
t
h
e
d
ia
g
n
o
s
is
p
r
o
ce
s
s
o
f
b
leed
in
g
d
et
ec
tio
n
d
ep
en
d
s
o
n
a
f
e
w
attr
i
b
u
tes.
T
h
e
s
ig
n
i
f
ica
n
t
p
o
s
s
ib
le
cir
cu
m
s
tan
ce
s
t
h
at
m
a
y
b
e
h
ap
p
en
ed
d
u
r
in
g
d
etec
ti
n
g
b
leed
i
n
g
i
m
a
g
e
s
lik
e
T
r
u
e
b
leed
in
g
d
etec
tio
n
(
T
P),
T
r
u
e
n
o
n
-
b
le
ed
in
g
d
etec
tio
n
(
T
N)
,
f
alse
b
leed
in
g
d
etec
tio
n
(
FP
)
,
an
d
f
alse
n
o
n
-
b
leed
i
n
g
d
etec
tio
n
(
FN)
.
Sin
ce
ac
c
u
r
ac
y
ca
n
n
o
t
b
e
co
n
s
id
er
ed
a
r
eli
ab
le
p
er
f
o
r
m
an
ce
p
er
i
m
eter
t
o
ev
al
u
ate
r
esear
c
h
p
er
f
o
r
m
a
n
ce
,
s
o
m
e
o
th
er
p
er
f
o
r
m
a
n
ce
m
atr
ices
ar
e
u
s
ed
.
W
e
ex
p
er
i
m
en
ted
w
it
h
s
p
ec
if
icit
y
,
s
en
s
iti
v
it
y
,
p
r
ec
is
io
n
an
d
n
e
g
ati
v
e
p
r
ed
icted
v
alu
e
F1
s
co
r
e
,
to
ev
alu
a
te
t
h
e
r
esear
ch
w
o
r
k
's p
er
f
o
r
m
a
n
c
e
.
T
h
e
p
r
o
p
o
s
ed
m
eth
o
d
ac
h
ie
v
ed
t
h
e
b
est
p
o
s
s
ib
le
r
es
u
lt
o
f
s
en
s
iti
v
it
y
9
8
%,
ac
c
u
r
a
c
y
9
8
.
2
%,
s
p
ec
if
icit
y
9
8
%,
n
eg
ati
v
e
p
r
ed
icted
v
alu
e
9
9
%,
p
r
ec
is
io
n
9
3
%,
an
d
F1
Sco
r
e
9
5
.
4
%
b
y
u
tili
zi
n
g
th
e
QSV
M
class
i
f
ier
w
i
th
u
s
ed
1
0
-
f
o
ld
cr
o
s
s
-
v
alid
atio
n
tec
h
n
iq
u
e
wh
ich
is
d
ep
icted
in
Fi
g
u
r
e
5
.
B
esid
es,
a
s
u
itab
le
co
m
b
i
n
atio
n
o
f
d
if
f
er
en
t stati
s
tical
f
ea
t
u
r
es
h
as b
e
en
g
en
er
at
ed
b
y
u
s
i
n
g
p
r
in
cip
al
co
m
p
o
n
en
t a
n
al
y
s
is
(
P
C
A
)
.
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
B
leed
in
g
r
ec
o
g
n
itio
n
tech
n
i
q
u
e
in
w
ir
eles
s
ca
p
s
u
le
en
d
o
s
co
p
y
ima
g
es u
s
in
g
.
.
.
(
A
.
A
l Ma
mu
n
)
2693
T
h
e
co
m
p
ar
is
o
n
b
et
w
ee
n
d
if
f
e
r
en
t
co
lo
r
m
o
d
els
b
y
i
m
p
le
m
e
n
ti
n
g
th
e
QSVM
clas
s
i
f
ier
is
s
h
o
w
ed
in
T
ab
le
1
.
Fro
m
T
ab
le
1
,
it
ca
n
b
e
ea
s
il
y
co
n
cl
u
d
ed
th
at
t
h
e
p
r
o
p
o
s
ed
HSV
co
lo
r
s
p
ac
e
is
m
o
r
e
s
u
p
er
io
r
to
all
o
th
er
co
lo
r
s
p
ac
es.
A
ll
th
e
p
er
f
o
r
m
a
n
ce
p
ar
am
e
ter
s
m
e
n
tio
n
ed
i
n
T
a
b
le
1
ar
e
b
etter
f
o
r
th
e
HSV
co
lo
r
m
o
d
el
th
an
t
h
e
d
if
f
er
en
t
co
lo
r
m
o
d
els.
B
esid
es,
T
ab
le
2
s
h
o
w
s
th
e
p
er
f
o
r
m
an
ce
co
m
p
ar
i
s
o
n
b
et
w
ee
n
d
is
ti
n
c
t
class
i
f
ier
s
u
s
ed
to
d
etec
t
b
leed
in
g
a
n
d
n
o
n
-
b
leed
in
g
i
m
a
g
es.
Ag
ain
,
T
ab
le
2
p
r
o
v
id
e
s
ev
id
en
ce
t
h
at
t
h
e
p
r
o
p
o
s
ed
QSVM
class
i
f
ier
h
a
s
a
b
etter
r
esu
lt th
a
n
all
o
th
er
cl
ass
i
f
ier
s
.
Fig
u
r
e
5
.
P
er
f
o
r
m
a
n
ce
r
esu
lt o
f
th
e
p
r
o
p
o
s
ed
m
e
th
o
d
T
h
o
u
g
h
r
ev
o
l
u
tio
n
ar
y
w
o
r
k
s
h
av
e
b
ee
n
i
m
p
le
m
e
n
ted
to
d
e
tect
th
e
b
leed
in
g
p
o
r
tio
n
,
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
is
u
n
iq
u
e
a
n
d
e
n
r
ich
es
t
h
e
p
er
f
o
r
m
a
n
ce
o
f
t
h
is
r
esear
ch
f
ield
.
A
co
m
p
ar
ab
ilit
y
s
t
u
d
y
b
et
w
ee
n
t
h
e
ex
is
t
in
g
p
r
o
ce
s
s
es
a
n
d
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
ar
e
p
r
esen
ted
in
T
ab
le
3
.
T
a
b
le
3
s
h
o
w
s
th
at
t
h
e
p
r
o
p
o
s
ed
tech
n
iq
u
e
is
a
n
i
m
p
r
o
v
ed
ed
iti
o
n
o
f
th
e
b
leed
in
g
d
etec
tio
n
r
esear
ch
f
ield
in
ter
m
s
o
f
ac
cu
r
ac
y
an
d
s
e
n
s
it
iv
i
t
y
co
m
p
ar
ed
w
it
h
t
h
e
e
x
is
t
in
g
w
o
r
k
.
T
h
o
u
g
h
K
u
n
d
u
et
a
l
.
[
2
6
]
ex
a
m
i
n
ed
2
3
0
0
co
lo
r
i
m
ag
es
f
r
o
m
W
C
E
f
o
r
ev
alu
a
tin
g
th
e
ir
b
leed
in
g
d
etec
tio
n
alg
o
r
ith
m
,
2
3
9
3
im
ag
e
s
h
av
e
b
ee
n
s
t
u
d
i
ed
in
th
i
s
p
r
o
p
o
s
ed
m
et
h
o
d
,
w
h
ic
h
is
a
v
ailab
le
i
n
[
2
3
]
.
A
ls
o
,
t
h
is
p
r
o
p
o
s
ed
m
et
h
o
d
ev
e
n
a
n
al
y
s
i
s
w
i
th
s
o
m
e
o
t
h
er
ad
d
itio
n
al
p
er
f
o
r
m
a
n
ce
p
ar
am
eter
s
lik
e
p
r
ec
is
io
n
,
F1
Sco
r
e
an
d
n
eg
ativ
e
-
p
r
ed
icted
v
alu
e.
I
n
a
n
u
ts
h
ell,
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
is
an
ac
cu
r
ate
m
o
d
el
o
f
b
leed
in
g
d
etec
tio
n
.
T
ab
le
1
.
P
er
f
o
r
m
a
n
ce
co
m
p
ar
i
s
o
n
w
it
h
d
is
ti
n
ct
co
lo
r
m
o
d
els
C
o
l
o
r
sp
a
c
e
s
A
c
c
u
r
a
c
y
S
e
n
si
t
i
v
i
t
y
S
p
e
c
i
f
i
c
i
t
y
P
r
e
c
i
si
o
n
F
P
V
F
1
S
c
o
r
e
HS
V
9
8
.
2
98
98
93
99
9
5
.
4
R
G
B
9
3
.
5
90
94
74
98
8
1
.
2
L
*
a
*
b
*
93
90
94
71
98
7
9
.
4
Y
C
b
C
r
9
3
.
2
92
93
70
99
7
9
.
5
T
ab
le
2
.
P
er
f
o
r
m
a
n
ce
co
m
p
ar
i
s
o
n
w
it
h
d
is
ti
n
ct
clas
s
i
f
ier
s
D
i
f
f
e
r
e
n
t
c
l
a
ssi
f
i
e
r
s
A
c
c
u
r
a
c
y
S
e
n
si
t
i
v
i
t
y
S
p
e
c
i
f
i
c
i
t
y
P
r
e
c
i
si
o
n
F
P
V
F
1
S
c
o
r
e
K
N
N
98
96
97
91
99
9
3
.
4
W
K
N
N
9
8
.
1
97
96
90
98
9
3
.
9
QS
V
M
(
Pr
o
p
o
s
e
d
)
9
8
.
2
98
98
93
99
9
5
.
4
T
ab
le
3
.
P
er
f
o
r
m
a
n
ce
an
al
y
s
is
w
it
h
d
if
f
er
e
n
t e
x
i
s
ti
n
g
tec
h
n
i
q
u
es o
f
b
leed
in
g
d
etec
tio
n
M
e
t
h
o
d
o
l
o
g
y
A
c
c
u
r
a
c
y
S
e
n
si
t
i
v
i
t
y
S
p
e
c
i
f
i
c
i
t
y
C
o
l
o
r
i
n
f
o
r
mat
i
o
n
[
1
0
]
9
7
.
7
96
9
1
.
3
U
n
i
f
o
r
m l
b
p
[
9
]
9
1
.
5
7
9
.
2
5
9
4
.
5
6
Y
i
q
s
t
a
t
[
1
9
]
9
3
.
9
0
9
3
.
5
0
9
4
.
0
0
R
/
g
s
t
a
t
e
[
2
0
]
9
4
.
9
7
8
8
.
5
0
9
6
.
6
3
R
a
t
i
o
o
f
i
n
t
e
n
s
i
t
y
[
2
1
]
9
4
.
0
9
4
.
7
8
9
3
.
5
8
Tw
o
-
f
o
l
d
s
y
st
e
m [
1
7
]
9
5
.
7
5
92
9
6
.
5
H
i
g
h
e
r
a
n
d
l
o
w
e
r
o
r
d
e
r
sat
e
[
2
]
9
7
.
9
6
9
7
.
7
5
9
7
.
9
9
Pr
o
p
o
sed
m
e
t
h
o
d
9
8
.
2
98
98
5.
CO
NCLU
SI
O
N
T
h
e
p
r
o
p
o
s
ed
m
eth
o
d
p
r
o
v
id
es
a
n
o
v
el
ap
p
r
o
ac
h
to
d
etec
t
th
e
b
leed
in
g
i
m
a
g
e
f
r
o
m
W
C
E
i
m
a
g
es.
T
h
e
b
est
p
o
s
s
ib
le
r
es
u
lt
i
s
o
b
tain
ed
f
r
o
m
t
h
e
e
x
p
er
i
m
e
n
t
u
s
in
g
a
Q
-
SV
M
clas
s
i
f
ier
u
n
d
er
th
e
1
0
-
f
o
ld
cr
o
s
s
-
v
alid
atio
n
,
F
u
zz
y
lo
g
ic
ed
g
e
d
etec
tio
n
s
y
s
te
m
w
i
th
s
o
m
e
p
r
o
ce
s
s
in
g
,
s
tat
is
tica
l
f
ea
t
u
r
e
v
e
cto
r
u
s
in
g
p
r
in
cip
a
l
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
.
3
,
J
u
n
e
2
0
2
1
:
2
6
8
8
-
2695
2694
co
m
p
o
n
e
n
t
a
n
al
y
s
i
s
(
P
C
A
)
i
n
HSV
co
lo
r
s
p
ac
e.
T
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
h
as
w
o
r
k
ed
o
n
2
3
9
3
W
C
E
an
n
o
tated
i
m
a
g
es.
T
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
o
u
tco
m
e
w
i
th
t
h
e
h
i
g
h
est
s
en
s
it
iv
i
t
y
9
8
%,
ac
cu
r
ac
y
9
8
.
2
%,
s
p
ec
if
icit
y
9
8
%,
NP
V
9
9
%,
p
r
ec
is
io
n
9
3
%,
a
n
d
F1
Sco
r
e
9
5
.
4
%.
I
t
ca
n
b
e
co
n
clu
d
ed
f
r
o
m
th
i
s
s
tat
is
ti
ca
l
r
esu
lt
t
h
at
t
h
e
p
r
o
p
o
s
ed
m
e
th
o
d
h
a
s
h
ig
h
er
ef
f
icien
c
y
to
cla
s
s
i
f
y
t
h
e
b
lee
d
in
g
a
n
d
n
o
n
-
b
leed
in
g
i
m
ag
e
s
th
a
n
t
h
e
e
x
is
tin
g
co
m
p
ar
ab
le
m
et
h
o
d
s
i
n
ter
m
s
o
f
s
e
n
s
i
tiv
it
y
,
ac
cu
r
ac
y
,
F1
Sc
o
r
e,
an
d
p
r
ec
is
io
n
.
C
o
n
s
eq
u
en
tl
y
,
it
h
a
s
f
u
l
f
illed
th
e
r
esear
c
h
o
b
j
ec
tiv
es
to
d
e
d
u
ce
th
e
p
h
y
s
icia
n
s
'
b
u
r
d
en
b
y
in
tr
o
d
u
cin
g
a
n
a
u
to
m
ated
b
leed
in
g
d
etec
t
io
n
s
y
s
te
m
.
Ho
w
ev
er
,
b
leed
i
n
g
d
e
tectio
n
ca
n
b
e
i
m
p
r
o
v
ed
b
y
e
x
a
m
in
i
n
g
d
i
f
f
er
e
n
t
ad
v
an
ce
d
f
ea
tu
r
e
l
ea
r
n
in
g
a
n
d
class
i
f
ier
tech
n
iq
u
es.
ACK
NO
WL
E
D
G
E
M
E
NT
S
T
h
is
r
esear
ch
h
as
b
ee
n
s
u
p
p
o
r
ted
b
y
M
u
lt
i
m
ed
ia
Un
i
v
er
s
i
t
y
(
MM
U)
a
n
d
th
e
Ma
la
y
s
ia
Min
i
s
tr
y
o
f
Hig
h
er
8
0
0
E
d
u
ca
tio
n
(
Gr
an
t N
o
: M
MU
E
/1
8
0
0
4
4
)
.
RE
F
E
R
E
NC
E
S
[1
]
A
.
L
.
M
.
S
il
v
e
ira,
A
.
V
.
M
.
F
e
rre
ira,
a
n
d
M
.
M
.
T
e
ix
e
ira,
"
Hig
h
-
F
ib
e
r
Die
ts
in
G
a
stro
in
tes
ti
n
a
l
T
ra
c
t
Dise
a
se
s,
"
in
Die
ta
ry
In
ter
v
e
n
ti
o
n
s in
Ga
stro
i
n
tes
ti
n
a
l
Dise
a
se
s
,
p
p
.
2
2
9
-
2
4
4
,
2
0
1
9
.
[2
]
T
.
G
h
o
sh
,
S
.
A
.
F
a
tt
a
h
,
a
n
d
K.
A
.
W
a
h
id
,
"
A
u
to
m
a
ti
c
Co
m
p
u
ter
A
id
e
d
Blee
d
in
g
De
tec
ti
o
n
S
c
h
e
m
e
f
o
r
W
ir
e
les
s
Ca
p
su
le
En
d
o
sc
o
p
y
(W
CE)
V
id
e
o
Ba
se
d
o
n
Hig
h
e
r
a
n
d
L
o
w
e
r
O
rd
e
r
S
tatisti
c
a
l
F
e
a
tu
re
s
in
a
Co
m
p
o
site
Co
lo
r,
"
J
o
u
rn
a
l
o
f
M
e
d
ica
l
a
n
d
Bi
o
l
o
g
ica
l
En
g
in
e
e
rin
g
,
v
o
l
.
3
8
,
n
o
.
2
,
p
p
.
4
8
2
-
4
9
6
,
2
0
1
8
.
[3
]
O.
Olé
n
,
e
t
a
l.
,
"
In
c
re
a
se
d
M
o
rta
li
ty
o
f
P
a
ti
e
n
ts
w
it
h
Ch
il
d
h
o
o
d
-
On
se
t
In
f
la
m
m
a
to
r
y
Bo
w
e
l
Dise
a
se
s,
Co
m
p
a
re
d
W
it
h
th
e
G
e
n
e
ra
l
P
o
p
u
latio
n
,
"
Ga
stro
e
n
ter
o
lo
g
y
, v
o
l.
1
5
6
,
n
o
.
3
,
p
p
.
6
1
4
-
6
2
2
,
2
0
1
9
.
[4
]
G
.
G
.
K
a
p
lan
a
n
d
S
.
C
.
Ng
,
"
Un
d
e
rsta
n
d
i
n
g
a
n
d
P
re
v
e
n
ti
n
g
th
e
G
lo
b
a
l
In
c
re
a
se
o
f
In
f
la
m
m
a
to
r
y
B
o
w
e
l
Dise
a
se
,
"
Ga
stro
e
n
ter
o
l
o
g
y
,
v
o
l.
1
5
2
,
n
o
.
2
,
p
p
.
3
1
3
-
3
2
1
.
e
2
,
2
0
1
7
.
[5
]
A
.
Wan
g
,
e
t
a
l.
,
"
W
irele
s
s c
a
p
su
le en
d
o
sc
o
p
y
,
"
Ga
stro
in
tes
ti
n
a
l
E
n
d
o
sc
opy
,
v
o
l
.
7
8
,
n
o
.
6
,
p
p
.
8
0
5
-
8
1
5
,
2
0
1
3
.
[6
]
G
.
P
a
n
,
G
.
Y
a
n
,
X
.
Qiu
,
a
n
d
J.
Cu
i,
"
Blee
d
in
g
d
e
tec
ti
o
n
in
w
ir
e
les
s
c
a
p
su
le
En
d
o
sc
o
p
y
b
a
se
d
o
n
p
r
o
b
a
b
i
li
stic
n
e
u
ra
l
n
e
tw
o
rk
,
"
J
o
u
rn
a
l
o
f
M
e
d
i
c
a
l
S
y
st
e
ms
,
v
o
l.
3
5
,
n
o
.
6
,
p
p
.
1
4
7
7
-
1
4
8
4
,
2
0
1
1
.
[7
]
J.
M
.
Bu
sc
a
g
li
a
,
e
t
a
l.
,
"
P
e
rf
o
r
m
a
n
c
e
Ch
a
ra
c
teristics
o
f
th
e
S
u
s
p
e
c
t
e
d
B
l
o
o
d
I
n
d
i
c
a
t
o
r
F
e
a
t
u
r
e
i
n
C
a
p
s
u
l
e
E
n
d
o
s
c
o
p
y
A
c
c
o
r
d
i
n
g
t
o
I
n
d
i
c
a
t
i
o
n
f
o
r
S
t
u
d
y
,
"
C
l
i
n
i
c
a
l
G
a
s
t
r
o
e
n
t
e
r
o
l
o
g
y
and
He
p
a
t
o
l
o
g
y
,
v
o
l
.
6
,
n
o
.
3
,
p
p
.
2
9
8
-
3
0
1
,
2
0
0
8
.
[8
]
S
.
L
ian
g
p
u
n
sa
k
u
l,
"
P
e
rf
o
rm
a
n
c
e
o
f
g
iv
e
n
su
sp
e
c
ted
b
l
o
o
d
i
n
d
ica
to
r,
"
T
h
e
Ame
ric
a
n
J
o
u
rn
a
l
o
f
Ga
stro
e
n
te
ro
l
o
g
y
,
v
o
l.
9
8
,
n
o
.
1
2
,
p
p
.
2
6
7
6
-
2
6
7
8
,
2
0
0
3
.
[9
]
P
.
S
iv
a
k
u
m
a
r
a
n
d
B.
M
.
Ku
m
a
r,
"
A
n
o
v
e
l
m
e
th
o
d
to
d
e
tec
t
b
lee
d
in
g
f
ra
m
e
a
n
d
re
g
io
n
in
w
irele
ss
c
a
p
su
le
e
n
d
o
sc
o
p
y
v
id
e
o
,
"
Clu
ste
r Co
mp
u
t
in
g
,
v
o
l
.
2
2
,
n
o
.
5
,
p
p
.
1
2
2
1
9
-
1
2
2
2
5
,
2
0
1
9
.
[1
0
]
K.
P
o
g
o
re
l
o
v
,
e
t
a
l.
,
"
Blee
d
in
g
d
e
tec
ti
o
n
in
w
ire
les
s
c
a
p
su
le
e
n
d
o
sc
o
p
y
v
id
e
o
s
-
Co
lo
r
v
e
rsu
s
te
x
tu
re
fe
a
tu
re
s,
"
J
o
u
rn
a
l
o
f
A
p
p
li
e
d
Cli
n
ica
l
M
e
d
i
c
a
l
Ph
y
sic
s
,
v
o
l.
2
0
,
n
o
.
8
,
p
p
.
1
4
1
-
1
5
4
,
2
0
1
9
.
[1
1
]
A
.
R.
Ha
ss
a
n
,
a
n
d
M
.
A
.
Ha
q
u
e
,
"
Co
m
p
u
ter
-
a
id
e
d
g
a
stro
i
n
tes
ti
n
a
l
h
e
m
o
rrh
a
g
e
d
e
tec
ti
o
n
i
n
w
irele
ss
c
a
p
su
le
e
n
d
o
sc
o
p
y
v
id
e
o
s,
"
Co
mp
u
ter
M
e
th
o
d
s
a
n
d
Pro
g
r
a
ms
in
Bi
o
me
d
icin
e
,
v
o
l.
1
2
2
,
n
o
.
3
,
p
p
.
3
4
1
-
3
5
3
,
2
0
1
5
.
[1
2
]
Y.
Yu
a
n
,
B
.
L
i,
a
n
d
M
.
Q.
H.
M
e
n
g
,
"
Blee
d
in
g
F
ra
m
e
a
n
d
Re
g
io
n
De
tec
ti
o
n
in
t
h
e
W
irele
ss
Ca
p
su
le
En
d
o
sc
o
p
y
V
id
e
o
,
"
IEE
E
J
o
u
rn
a
l
o
f
B
io
me
d
i
c
a
l
a
n
d
He
a
lt
h
I
n
f
o
rm
a
ti
c
s
,
v
o
l
.
2
0
,
n
o
.
2
,
p
p
.
6
2
4
-
63
0
,
2
0
1
6
.
[1
3
]
T
.
G
h
o
sh
,
S
.
K.
Ba
sh
a
r,
S
.
A
.
F
a
tt
a
h
,
C.
S
h
a
h
n
a
z
,
a
n
d
K.
A
.
W
a
h
id
,
"
A
n
a
u
to
m
a
ti
c
b
lee
d
in
g
d
e
tec
ti
o
n
sc
h
e
m
e
in
w
irele
ss
c
a
p
su
le
e
n
d
o
sc
o
p
y
b
a
se
d
o
n
sta
ti
stica
l
f
e
a
tu
re
s
in
h
u
e
s
p
a
c
e
,
"
2
0
1
4
1
7
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
fo
rm
a
t
io
n
T
e
c
h
n
o
lo
g
y
(
ICCIT
)
,
Dh
a
k
a
,
2
0
1
4
,
p
p
.
3
5
4
-
3
5
7
.
[1
4
]
T
.
G
h
o
sh
,
e
t
a
l.
,
"
A
n
a
u
to
m
a
ti
c
b
l
e
e
d
i
n
g
d
e
t
e
c
t
i
o
n
t
e
c
h
n
i
q
u
e
i
n
w
i
r
e
l
e
s
s
c
a
p
s
u
l
e
e
n
d
o
s
c
o
p
y
f
r
o
m
r
e
g
i
o
n
o
f
i
n
t
e
r
e
s
t
,
"
2
0
1
5
I
E
E
E
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
D
i
g
i
t
a
l
S
i
g
n
a
l
P
r
o
c
e
s
s
i
n
g
(
D
S
P
)
,
S
i
n
g
a
p
o
r
e
,
2
0
1
5
,
p
p
.
1293
-
1
2
9
7
.
[1
5
]
T
.
G
h
o
sh
,
e
t
a
l.
,
"
A
sta
ti
stica
l
f
e
a
tu
re
b
a
se
d
n
o
v
e
l
m
e
th
o
d
to
d
e
tec
t
b
lee
d
in
g
in
w
irele
ss
c
a
p
s
u
le
e
n
d
o
sc
o
p
y
im
a
g
e
s,
"
2
0
1
4
In
ter
n
a
ti
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
In
f
o
rm
a
ti
c
s,
E
lec
tro
n
ic
s
&
Vi
sio
n
(
ICIEV
)
,
Dh
a
k
a
,
2
0
1
4
,
p
p
.
1
-
4.
[1
6
]
T
.
G
h
o
sh
,
S
.
A
.
F
a
tt
a
h
,
C.
S
h
a
h
n
a
z
,
a
n
d
K.
A
.
W
a
h
id
,
"
A
n
a
u
to
m
a
ti
c
b
lee
d
in
g
d
e
tec
ti
o
n
sc
h
e
m
e
in
w
irele
ss
c
a
p
su
le
e
n
d
o
sc
o
p
y
b
a
se
d
o
n
h
isto
g
ra
m
o
f
a
n
RG
B
-
in
d
e
x
e
d
im
a
g
e
,
"
2
0
1
4
3
6
th
A
n
n
u
a
l
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
f
th
e
IE
EE
En
g
i
n
e
e
rin
g
in
M
e
d
ici
n
e
a
n
d
Bi
o
l
o
g
y
S
o
c
iety
,
Ch
ica
g
o
,
IL
,
2
0
1
4
,
p
p
.
4
6
8
3
-
4
6
8
6
.
[1
7
]
A
.
A
l
M
a
m
u
n
a
n
d
M
.
S
.
Ho
ss
a
in
,
"
Ulc
e
r
De
tec
ti
o
n
in
Im
a
g
e
Co
n
v
e
rted
f
ro
m
V
id
e
o
F
o
o
tag
e
o
f
Wi
re
les
s
Ca
p
su
le
En
d
o
sc
o
p
y
,
"
in
2
0
1
9
1
st
I
n
ter
n
a
t
io
n
a
l
Co
n
fer
e
n
c
e
o
n
A
d
v
a
n
c
e
s
in
S
c
ien
c
e
,
En
g
i
n
e
e
rin
g
a
n
d
Ro
b
o
t
i
c
s
T
e
c
h
n
o
lo
g
y
(
ICAS
ER
T
)
,
Dh
a
k
a
,
Ba
n
g
lad
e
sh
,
2
0
1
9
,
p
p
.
1
-
4.
[1
8
]
M
.
S
.
Ho
ss
a
in
,
A
.
A
l
M
a
m
u
n
,
M
.
G
.
Ha
sa
n
,
a
n
d
M
.
M
.
H
o
ss
a
in
,
"
Eas
y
S
c
h
e
m
e
f
o
r
Ulc
e
r
D
e
te
c
ti
o
n
i
n
W
irele
ss
Ca
p
su
le E
n
d
o
sc
o
p
y
I
m
a
g
e
s,
"
2
0
1
9
1
st I
n
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Ad
v
a
n
c
e
s in
S
c
ien
c
e
,
En
g
in
e
e
rin
g
a
n
d
Ro
b
o
t
ics
T
e
c
h
n
o
l
o
g
y
(
ICAS
ER
T
)
,
D
h
a
k
a
,
Ba
n
g
lad
e
sh
,
2
0
1
9
,
p
p
.
1
-
5.
[1
9
]
A
.
A
l
M
a
m
u
n
,
M
.
S
.
Ho
ss
a
in
,
M
.
M
.
Ho
ss
a
in
,
a
n
d
M
.
G
.
Ha
s
a
n
,
"
Disc
re
ti
o
n
W
a
y
f
o
r
Blee
d
i
n
g
De
tec
ti
o
n
in
W
irele
ss
Ca
p
su
le E
n
d
o
sc
o
p
y
I
m
a
g
e
s,
"
2
0
1
9
1
st I
n
ter
n
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
Ad
v
a
n
c
e
s in
S
c
ien
c
e
,
E
n
g
i
n
e
e
rin
g
a
n
d
Ro
b
o
ti
c
s
T
e
c
h
n
o
l
o
g
y
(
ICAS
ER
T
)
,
Dh
a
k
a
,
Ba
n
g
lad
e
sh
,
2
0
1
9
,
p
p
.
1
-
6.
[2
0
]
S
.
Na
rji
m
,
A
.
A
l
M
a
m
u
n
,
a
n
d
D.
Ku
n
d
u
,
"
Dia
g
n
o
sis
o
f
a
c
u
te
ly
m
p
h
o
b
las
ti
c
leu
k
e
m
ia
f
ro
m
m
i
c
ro
sc
o
p
ic
im
a
g
e
o
f
p
e
rip
h
e
ra
l
b
l
o
o
d
sm
e
a
r
u
sin
g
i
m
a
g
e
p
ro
c
e
ss
in
g
t
e
c
h
n
iq
u
e
,
"
ICONCS
2
0
2
0
:
Cy
b
e
r
S
e
c
u
rity
a
n
d
Co
m
p
u
ter
S
c
ien
c
e
,
v
o
l.
3
2
5
,
p
p
.
5
1
5
-
5
2
6
,
2
0
2
0
.
[2
1
]
M
.
S
.
Ho
ss
a
in
,
e
t
a
l.
,
"
Ulc
e
r
d
e
t
e
c
ti
o
n
in
w
irele
ss
c
a
p
su
le
e
n
d
o
sc
o
p
y
u
sin
g
lo
c
a
ll
y
c
o
m
p
u
ted
fe
a
tu
re
s,
"
ICONCS
2
0
2
0
:
Cy
b
e
r
S
e
c
u
rity a
n
d
C
o
mp
u
ter
S
c
ien
c
e
,
v
o
l.
3
2
5
,
p
p
.
4
9
1
-
5
0
2
,
2
0
2
0
.
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
B
leed
in
g
r
ec
o
g
n
itio
n
tech
n
i
q
u
e
in
w
ir
eles
s
ca
p
s
u
le
en
d
o
s
co
p
y
ima
g
es u
s
in
g
.
.
.
(
A
.
A
l Ma
mu
n
)
2695
[2
2
]
A
.
A
l
M
a
m
u
n
,
M
.
S
.
Ho
ss
a
in
,
M
.
E.
Kh
a
ll
i
l,
A
.
T
a
h
a
b
il
d
e
r,
T
.
K.
Da
s,
a
n
d
R.
Isla
m
,
"
Co
n
v
e
n
ien
t
W
a
y
to
De
te
c
t
Ulc
e
r
in
W
irele
ss
Ca
p
su
le
En
d
o
sc
o
p
y
th
ro
u
g
h
F
u
z
z
y
L
o
g
ic
T
e
c
h
n
i
q
u
e
,
"
in
2
0
2
0
IEE
E
Re
g
io
n
1
0
S
y
mp
o
si
u
m,
T
ENS
Y
M
P
2
0
2
0
,
Ju
n
.
2
0
2
0
,
p
p
.
8
8
0
–
8
8
3
,
d
o
i:
1
0
.
1
1
0
9
/T
ENS
YM
P
5
0
0
1
7
.
2
0
2
0
.
9
2
3
1
0
0
4
.
[2
3
]
V
.
Ch
a
risis,
L
.
Ha
d
ji
leo
n
ti
a
d
is,
a
n
d
G
.
S
e
rg
iad
is,
"
En
h
a
n
c
e
d
Ulc
e
r
Re
c
o
g
n
it
io
n
f
ro
m
Ca
p
su
le
En
d
o
sc
o
p
ic
Im
a
g
e
s
Us
in
g
T
e
x
tu
re
A
n
a
l
y
sis,
"
in
Ne
w
Ad
v
a
n
c
e
s i
n
th
e
Ba
sic
a
n
d
Cli
n
ica
l
Ga
str
o
e
n
ter
o
l
o
g
y
,
I
n
T
e
c
h
,
p
p
.
1
8
5
-
2
1
0
,
2
0
1
2
.
[2
4
]
P
.
Biało
ń
,
"
S
o
lv
in
g
S
u
p
p
o
r
t
Ve
c
to
r
M
a
c
h
in
e
w
it
h
M
a
n
y
Ex
a
m
p
les
,
"
J
o
u
rn
a
l
o
f
Te
lec
o
mm
u
n
ica
ti
o
n
s
a
n
d
In
fo
rm
a
t
io
n
T
e
c
h
n
o
l
ogy
,
p
p
.
6
5
-
7
0
,
2
0
1
0
.
[2
5
]
A
.
Ku
m
a
r,
"
M
a
c
h
in
e
L
e
a
rn
in
g
:
Va
li
d
a
ti
o
n
T
e
c
h
n
iq
u
e
s
,
"
DZo
n
e
AI
,
2
0
1
8
.
[2
6
]
A
.
K.
Ku
n
d
u
,
S
.
A
.
F
a
tt
a
h
,
a
n
d
M
.
N.
Rizv
e
,
"
An
a
u
to
m
a
ti
c
b
lee
d
in
g
f
ra
m
e
a
n
d
re
g
io
n
d
e
tec
ti
o
n
sc
h
e
m
e
f
o
r
w
irele
ss
c
a
p
su
le
e
n
d
o
sc
o
p
y
v
id
e
o
s
b
a
se
d
o
n
in
terp
lan
e
i
n
ten
sity
v
a
riatio
n
p
ro
f
il
e
in
n
o
rm
a
li
z
e
d
R
GB
c
o
lo
r
sp
a
c
e
,
"
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
,
2
0
1
8
.
B
I
O
G
RAP
H
I
E
S
O
F
AUTH
O
RS
Abd
u
ll
a
h
Al
M
a
m
u
n
h
a
s
re
c
e
iv
e
d
a
B.
S
c
.
d
e
g
re
e
in
E
lec
tri
c
a
l
a
n
d
El
e
c
tro
n
ic
E
n
g
in
e
e
rin
g
f
ro
m
P
a
b
n
a
Un
iv
e
rsity
o
f
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
l
o
g
y
in
2
0
1
8
.
No
w
h
e
is
p
u
rsu
i
n
g
M
.
En
g
.
S
c
.
a
t
M
u
lt
im
e
d
ia
Un
iv
e
rsit
y
(M
M
U)
in
th
e
F
a
c
u
lt
y
o
f
En
g
in
e
e
rin
g
a
n
d
T
e
c
h
n
o
lo
g
y
sin
c
e
2
0
1
9
.
He
is
a
lso
w
o
rk
in
g
a
s
a
lec
tu
re
r
(o
n
st
u
d
y
lea
v
e
)
sin
c
e
No
v
e
m
b
e
r
2
0
1
9
in
EE
E
a
t
F
e
n
i
Un
iv
e
rsity
,
F
e
n
i,
Ba
n
g
lad
e
sh
.
His
re
se
a
rc
h
in
tere
st
i
n
c
lu
d
e
s
c
o
m
p
u
ter
v
isio
n
;
im
a
g
e
p
ro
c
e
ss
in
g
,
sig
n
a
l
p
ro
c
e
ss
in
g
,
d
e
e
p
lea
rn
in
g
a
n
d
m
a
c
h
in
e
lea
rn
i
n
g
.
E
m
P
o
h
Pi
n
g
is
c
u
rre
n
tl
y
a
lec
tu
re
r
a
t
th
e
F
a
c
u
lt
y
o
f
En
g
in
e
e
rin
g
a
n
d
T
e
c
h
n
o
l
o
g
y
,
M
u
lt
im
e
d
ia
Un
iv
e
rsit
y
.
P
o
h
P
in
g
re
se
a
rc
h
e
s
v
e
h
icle
sa
f
e
t
y
,
d
a
ta
f
u
sio
n
,
f
u
z
z
y
c
o
n
tr
o
l,
a
n
d
im
a
g
e
p
ro
c
e
ss
in
g
.
His c
u
rre
n
t
p
r
o
jec
t
is
"
lan
e
d
e
p
a
rtu
re
e
stim
a
ti
o
n
u
sin
g
v
isio
n
a
n
d
v
e
h
icle
d
y
n
a
m
ica
l
'
s sta
te.
"
To
n
m
o
y
G
h
o
s
h
re
c
e
iv
e
d
th
e
B.
S
c
.
a
n
d
M
.
S
c
.
d
e
g
re
e
in
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ic
E
n
g
in
e
e
rin
g
(EE
E)
f
ro
m
th
e
B
a
n
g
lad
e
sh
U
n
iv
e
rsity
o
f
En
g
in
e
e
rin
g
a
n
d
T
e
c
h
n
o
lo
g
y
(BUE
T
),
Dh
a
k
a
,
Ba
n
g
lad
e
sh
,
in
2
0
1
2
a
n
d
2
0
1
6
,
r
e
sp
e
c
ti
v
e
l
y
.
He
j
o
in
e
d
a
s
a
L
e
c
tu
re
r
in
th
e
EE
E
d
e
p
a
rtm
e
n
t
a
t
P
a
b
n
a
Un
iv
e
rsit
y
o
f
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
l
o
g
y
(P
UST
),
P
a
b
n
a
,
Ba
n
g
lad
e
sh
.
Cu
rre
n
tl
y
,
He
is
d
o
i
n
g
h
is
P
h
D
in
th
e
d
e
p
a
rtm
e
n
t
o
f
ECE
a
t
T
h
e
Un
iv
e
rsit
y
o
f
A
lab
a
m
a
,
T
u
sc
a
lo
o
sa
,
USA
.
His
re
se
a
rc
h
in
tere
sts in
c
lu
d
e
im
a
g
e
p
ro
c
e
ss
in
g
,
p
a
tt
e
rn
re
c
o
g
n
it
io
n
,
a
n
d
d
ig
it
a
l
sig
n
a
l
p
ro
c
e
ss
in
g
.
M
d
M
o
ta
h
e
r
H
o
ss
a
in
re
c
e
iv
e
d
th
e
b
a
c
h
e
lo
r
'
s
d
e
g
re
e
in
e
lec
tri
c
a
l
a
n
d
e
lec
tro
n
ic
e
n
g
in
e
e
rin
g
f
ro
m
th
e
R
a
jsh
a
h
i
Un
iv
e
rsit
y
o
f
En
g
in
e
e
rin
g
a
n
d
T
e
c
h
n
o
lo
g
y
(R
UET
),
Ra
jsh
a
h
i,
Ba
n
g
lad
e
sh
,
in
2
0
0
7
,
t
h
e
M
.
S
c
.
d
e
g
re
e
in
Re
n
e
w
a
b
le
En
e
rg
y
f
ro
m
th
e
Ca
rl
v
o
n
Os
sie
tzk
y
Un
iv
e
rsität
Old
e
n
b
u
rg
,
Old
e
n
b
u
rg
,
G
e
r
m
a
n
y
in
2
0
1
0
.
He
is
c
u
rre
n
tl
y
p
e
ru
sin
g
a
n
o
th
e
r
M
.
S
c
.
d
e
g
re
e
f
ro
m
th
e
En
g
in
e
e
rin
g
T
e
c
h
n
o
lo
g
ies
De
p
a
rtm
e
n
t
a
t
Bo
w
li
n
g
G
r
e
e
n
S
tate
Un
iv
e
rsit
y
,
OH
,
US
A
.
He
h
a
s
b
e
e
n
w
o
rk
in
g
a
s
a
n
A
ss
istan
t
P
r
o
f
e
ss
o
r
(o
n
stu
d
y
lea
v
e
)
in
th
e
De
p
a
rtme
n
t
o
f
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ic
E
n
g
in
e
e
rin
g
a
t
P
a
b
n
a
Un
iv
e
rsit
y
o
f
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
lo
g
y
,
P
a
b
n
a
,
Ba
n
g
lad
e
sh
sin
c
e
1
1
M
a
y
2
0
1
3
.
His
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
P
o
w
e
r
S
y
st
e
m
s,
P
o
w
e
r
El
e
c
tro
n
ics
,
Re
n
e
wa
b
le E
n
e
rg
y
,
Co
n
tr
o
l
S
y
ste
m
En
g
in
e
e
rin
g
,
a
n
d
De
e
p
L
e
a
rn
in
g
.
M
d
.
G
a
li
b
H
a
sa
n
h
a
s
b
e
e
n
g
ra
d
u
a
ted
f
ro
m
Kh
u
ln
a
Un
iv
e
rsit
y
o
f
En
g
in
e
e
rin
g
a
n
d
T
e
c
h
n
o
l
o
g
y
.
His
m
a
jo
r
is
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ic
E
n
g
in
e
e
rin
g
(EE
E)
a
n
d
c
u
rre
n
tl
y
h
e
is
w
o
r
k
in
g
a
s
a
n
a
ss
istan
t
p
ro
f
e
ss
o
r
in
th
e
d
e
p
a
rt
m
e
n
t
o
f
EE
E
a
t
P
a
b
n
a
Un
iv
e
rsity
o
f
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
lo
g
y
.
His res
e
a
rc
h
in
tere
st i
n
c
lu
d
e
s
d
ig
i
tal
im
a
g
e
p
ro
c
e
ss
in
g
,
e
m
b
e
d
d
e
d
s
y
ste
m
s,
V
L
S
I,
Ch
ip
M
d
.
G
o
la
m
S
a
d
e
q
u
e
h
a
s
re
c
e
i
v
e
d
B.
S
c
.
En
g
.
d
e
g
re
e
in
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ics
En
g
in
e
e
ri
n
g
(EE
E)
f
ro
m
Ra
jsh
a
h
i
Un
iv
e
rsit
y
o
f
En
g
in
e
e
rin
g
a
n
d
T
e
c
h
n
o
l
o
g
y
(RUET
)
in
2
0
1
0
.
He
is
p
u
rsu
i
n
g
a
m
a
ste
r
'
s
o
f
e
n
g
in
e
e
rin
g
sc
ien
c
e
(M
.
En
g
.
S
c
,
)
d
e
g
re
e
u
n
d
e
r
t
h
e
f
a
c
u
lt
y
o
f
e
n
g
in
e
e
rin
g
a
t
M
u
lt
im
e
d
ia
Un
iv
e
rsity
.
He
is
a
lso
w
o
rk
in
g
a
s
a
n
A
ss
istan
t
p
ro
f
e
ss
o
r
(o
n
stu
d
y
lea
v
e
)
sin
c
e
2
3
Ju
n
e
2
0
1
5
in
t
h
e
d
e
p
a
rtm
e
n
t
o
f
EE
E
a
t
P
a
b
n
a
Un
iv
e
rsity
o
f
S
c
ie
n
c
e
a
n
d
T
e
c
h
n
o
lo
g
y
(P
UST
),
P
a
b
n
a
-
6
6
0
0
,
Ba
n
g
lad
e
sh
.
His
re
se
a
rc
h
in
tere
st
in
c
lu
d
e
s
th
e
d
e
sig
n
o
f
R
a
d
io
f
re
q
u
e
n
c
y
p
o
w
e
r
a
m
p
li
f
ier (RF
P
A
)
a
n
d
Bi
o
m
e
d
ica
l
e
n
g
in
e
e
rin
g
.
E
-
m
a
il
:
g
o
lam
sa
d
e
q
@g
m
a
il
.
c
o
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.