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.
8
,
No
.
1
,
Feb
r
u
ar
y
201
8
,
p
p
.
3
7
9
~3
8
9
I
SS
N:
2
0
8
8
-
8708
,
DOI
: 1
0
.
1
1
5
9
1
/
i
j
ec
e
.
v8
i
1
.
p
p
3
7
9
-
3
8
9
379
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ia
e
s
co
r
e
.
co
m/
jo
u
r
n
a
ls
/in
d
ex
.
p
h
p
/
I
JE
C
E
Ant
Co
lo
ny
O
pti
m
i
z
a
tion
(
A
CO
)
b
a
sed Da
ta H
i
ding
in I
m
a
g
e
Co
m
plex
Reg
io
n
Sa
hib
K
ha
n
,
T
izia
no
B
ia
nch
i
De
p
a
rtme
n
t
o
f
El
e
c
tro
n
ics
a
n
d
T
e
lec
o
m
m
u
n
ica
ti
o
n
s,
P
o
li
tec
n
ico
Di
T
o
rin
o
,
1
0
1
2
9
I
taly
Art
icle
I
nfo
AB
ST
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Sep
9
,
2
0
1
7
R
ev
i
s
ed
Dec
2
5
,
2
0
1
7
A
cc
ep
ted
J
an
1
1
,
2
0
1
8
T
h
is
p
a
p
e
r
p
re
se
n
ts
d
a
ta
a
n
An
t
c
o
lo
n
y
o
p
ti
m
iza
ti
o
n
(A
CO)
b
a
se
d
d
a
ta
h
id
i
n
g
tec
h
n
i
q
u
e
.
A
CO
is
u
se
d
to
d
e
tec
t
c
o
m
p
lex
r
e
g
io
n
o
f
c
o
v
e
r
i
m
a
g
e
a
n
d
a
f
ter
wa
rd
,
lea
st
sig
n
if
ica
n
t
b
it
s
(L
S
B)
su
b
stit
u
ti
o
n
is
u
se
d
to
h
id
e
se
c
re
t
in
f
o
rm
a
ti
o
n
in
th
e
d
e
tec
ted
c
o
m
p
lex
re
g
io
n
s’
p
ix
e
ls.
A
CO
is
a
n
a
lg
o
rit
h
m
d
e
v
e
lo
p
e
d
i
n
sp
ire
d
b
y
th
e
i
n
b
o
r
n
m
a
n
n
e
rs
o
f
a
n
t
sp
e
c
ies
.
T
h
e
a
n
t
lea
v
e
s
p
h
e
ro
m
o
n
e
o
n
t
h
e
g
ro
u
n
d
f
o
r
se
a
rc
h
in
g
f
o
o
d
a
n
d
p
r
o
v
isio
n
s.
T
h
e
p
ro
p
o
se
d
A
CO
-
b
a
s
e
d
d
a
ta
h
id
i
n
g
in
c
o
m
p
lex
re
g
i
o
n
e
sta
b
li
sh
e
s
a
n
a
rra
y
o
f
p
h
e
ro
m
o
n
e
,
a
lso
c
a
ll
e
d
p
h
e
r
o
m
o
n
e
m
a
tri
x
,
w
h
ich
re
p
re
se
n
ts
th
e
c
o
m
p
lex
re
g
io
n
in
se
q
u
e
n
c
e
a
t
e
a
c
h
p
ix
e
l
p
o
siti
o
n
o
f
th
e
c
o
v
e
r
im
a
g
e
.
T
h
e
p
h
e
ro
m
o
n
e
m
a
tri
x
is
d
e
v
e
lo
p
e
d
a
c
c
o
rd
in
g
t
o
th
e
m
o
v
e
m
e
n
ts
o
f
a
n
ts,
d
e
term
in
e
d
b
y
lo
c
a
l
d
if
fe
re
n
c
e
s
o
f
th
e
i
m
a
g
e
e
le
m
e
n
t’s
in
ten
si
ty
.
T
h
e
lea
st
si
g
n
if
ic
a
n
t
b
it
s
o
f
c
o
m
p
lex
re
g
io
n
p
ix
e
ls
a
re
su
b
stit
u
ted
w
it
h
m
e
ss
a
g
e
b
it
s,
to
h
id
e
se
c
re
t
in
f
o
rm
a
ti
o
n
.
T
h
e
e
x
p
e
ri
m
e
n
tal
re
su
lt
s,
p
ro
v
id
e
d
,
sh
o
w
th
e
sig
n
i
f
ic
a
n
c
e
o
f
th
e
p
e
rf
o
r
m
a
n
c
e
o
f
th
e
p
ro
p
o
se
d
m
e
t
h
o
d
.
K
ey
w
o
r
d
:
An
t c
o
lo
n
y
o
p
ti
m
izatio
n
E
d
g
e
d
etec
tio
n
L
SB
Ste
g
a
n
o
g
r
ap
h
y
P
h
er
o
m
o
n
e
m
atr
i
x
Steg
a
n
al
y
s
i
s
Co
p
y
rig
h
t
©
2
0
1
8
In
stit
u
te o
f
A
d
v
a
n
c
e
d
E
n
g
i
n
e
e
rin
g
a
n
d
S
c
ien
c
e
.
Al
l
rig
h
ts
re
se
rv
e
d
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Sah
ib
K
h
an
,
Dep
ar
t
m
en
t o
f
E
lectr
o
n
ics a
n
d
T
elec
o
m
m
u
n
icatio
n
s
,
P
o
litecn
ico
d
i
T
o
r
in
o
,
C
o
r
s
o
Du
ca
d
eg
li
A
b
r
u
zz
i,
2
4
,
1
0
1
2
9
T
o
r
in
o
T
O,
I
taly
.
E
m
ail:
e
n
g
r
s
ah
ib
_
k
h
n
@
y
a
h
o
o
.
co
m
1.
I
NT
RO
D
UCT
I
O
N
I
m
ag
e
s
te
g
an
o
g
r
ap
h
y
is
i
n
f
o
r
m
atio
n
h
id
in
g
tec
h
n
iq
u
e
t
h
at
u
s
e
d
i
g
i
tal
i
m
ag
e
as
co
v
er
m
e
d
ia.
A
lo
n
g
w
it
h
s
ec
r
et
e
x
ch
a
n
g
e
o
f
in
f
o
r
m
atio
n
,
it
h
as
v
ar
io
u
s
o
th
er
ap
p
licatio
n
s
e.
g
.
co
p
y
r
i
g
h
t,
d
ata
in
teg
r
it
y
an
d
au
th
e
n
tica
tio
n
[
1
]
,
[
2
]
.
Dig
ita
l
au
d
io
,
v
id
eo
an
d
te
x
t
ca
n
al
s
o
b
e
u
s
ed
a
s
a
co
v
er
,
b
u
t
i
m
a
g
e
i
s
ad
o
p
ted
m
o
s
t
w
id
el
y
f
o
r
th
is
p
u
r
p
o
s
e
d
u
e
it
s
h
ig
h
r
ed
u
n
d
an
c
y
.
Data
h
id
in
g
tech
n
iq
u
es
ar
e
ex
p
lo
r
ed
b
y
m
a
n
y
r
esear
c
h
e
r
s
an
d
p
r
o
p
o
s
ed
v
ar
io
u
s
g
o
o
d
h
id
in
g
tech
n
iq
u
es
to
in
s
u
r
e
s
ec
u
r
it
y
o
f
h
id
d
en
i
n
f
o
r
m
atio
n
.
Ho
n
s
i
n
g
er
et
al.
’
s
a
n
d
Frid
r
ich
et
al.
’
s
p
r
o
p
o
s
ed
s
teg
a
n
o
g
r
ap
h
y
m
et
h
o
d
s
i
n
s
p
atial
d
o
m
ain
b
y
h
id
i
n
g
s
ec
r
et
i
n
f
o
r
m
atio
n
d
ir
ec
tl
y
i
n
i
m
ag
e
p
i
x
els
[
3
]
,
[
4
]
.
Sah
ib
et
al.
p
r
o
p
o
s
ed
v
ar
iab
le
least
s
ig
n
i
f
ica
n
t
b
its
(
V
L
SB
)
s
teg
a
n
o
g
r
ap
h
y
an
d
p
r
esen
ted
tech
n
iq
u
es,
lik
e
m
o
d
u
lar
d
is
tan
ce
tech
n
iq
u
e
(
MD
T
)
[
5
]
,
d
ec
r
ea
s
in
g
d
is
tan
ce
d
ec
r
ea
s
in
g
b
its
al
g
o
r
ith
m
(
DDDB
A
)
[
6
]
,
v
ar
y
i
n
g
in
d
e
x
v
ar
y
i
n
g
b
its
s
u
b
s
tit
u
tio
n
(
VI
VB
S)
alg
o
r
ith
m
[
7
]
.
Sa
h
ib
et
al.
,
in
s
p
ir
ed
f
r
o
m
c
h
ip
p
er
b
lo
ck
ch
ai
n
i
n
g
(
C
B
C
)
en
cr
y
p
tio
n
,
p
r
o
p
o
s
ed
n
e
w
te
ch
n
iq
u
es
o
f
s
teg
o
b
lo
ck
c
h
ai
n
in
g
(
SB
C
)
an
d
e
n
h
a
n
ce
d
s
t
eg
o
b
lo
ck
c
h
ain
i
n
g
(
E
SB
C
)
t
o
h
id
e
in
f
o
r
m
atio
n
i
n
d
ig
ital i
m
ag
e
s
[
8
]
.
T
h
e
ai
m
o
f
al
l
d
ata
h
id
i
n
g
te
ch
n
iq
u
es
is
to
m
ak
e
t
h
e
p
r
ese
n
ce
o
f
h
id
d
en
i
n
f
o
r
m
atio
n
u
n
d
etec
tab
le
an
d
th
i
s
attr
ac
ted
th
e
atte
n
tio
n
o
f
r
esear
ch
er
to
m
ak
e
u
s
e
o
f
HVS
l
i
m
itatio
n
.
HV
S
ca
n
v
er
y
ea
s
il
y
d
etec
t
th
e
v
ar
iatio
n
s
m
ad
e
i
n
s
m
o
o
th
ar
ea
o
f
co
v
er
i
m
a
g
e
as
co
m
p
ar
ed
to
th
e
ch
an
g
e
s
in
co
m
p
lex
r
eg
io
n
.
Du
e
to
th
i
s
ch
ar
ac
ter
is
tic
o
f
t
h
e
HV
S,
co
m
p
lex
r
e
g
io
n
o
f
co
v
er
i
m
a
g
e
is
s
u
b
j
ec
ted
to
h
id
in
g
an
d
s
m
o
o
t
h
r
e
g
io
n
i
s
n
o
t
m
o
d
i
f
ied
[
9
]
,
[
1
0
]
.
I
n
s
o
m
e
t
ec
h
n
iq
u
es,
co
m
p
lex
r
eg
io
n
is
s
u
b
j
ec
ted
to
m
o
r
e
to
d
ata
h
id
in
g
th
a
n
s
m
o
o
t
h
r
eg
io
n
.
T
h
is
ap
p
r
o
ac
h
r
esu
lts
in
h
i
g
h
q
u
alit
y
o
f
th
e
s
teg
o
-
i
m
a
g
e,
w
h
ic
h
m
ea
n
s
in
cr
ea
s
e
in
th
e
s
ec
u
r
it
y
o
f
h
id
d
en
in
f
o
r
m
a
tio
n
.
Var
io
u
s
tech
n
iq
u
es,
i
n
clu
d
i
n
g
L
SB
m
eth
o
d
s
[
1
1
]
,
P
VD
m
eth
o
d
s
,
an
d
s
id
e
-
m
atc
h
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.
8
,
No
.
1
,
Feb
r
u
ar
y
2
0
1
8
:
3
7
9
–
3
8
9
380
m
et
h
o
d
s
[
1
2
]
,
[
1
3
]
,
h
av
e
b
ee
n
p
r
o
p
o
s
ed
t
o
h
id
e
in
f
o
r
m
atio
n
in
co
m
p
le
x
ar
ea
o
f
co
v
er
;
a
d
etail
ca
n
b
e
f
o
u
n
d
in
[
1
4
]
,
[
1
5
]
.
B
u
t,
th
ese
tech
n
i
q
u
es
p
r
esen
t
a
lo
w
h
id
i
n
g
ca
p
ac
it
y
an
d
d
o
n
’
t
co
m
p
l
y
co
m
p
l
etel
y
w
it
h
th
e
r
u
les
th
at
th
e
co
m
p
le
x
r
eg
io
n
ca
n
b
ea
r
m
o
r
e
ch
a
n
g
es
th
a
n
s
m
o
o
th
r
eg
io
n
[
1
2
]
,
[
1
3
]
.
T
o
i
n
cr
ea
s
e
d
ata
h
id
i
n
g
ca
p
ac
it
y
J
u
n
g
et
al.
[
1
6
]
p
r
es
en
ted
a
n
e
w
tec
h
n
iq
u
e
t
h
at
h
id
es
d
ata
in
s
m
o
o
th
ar
ea
s
alo
n
g
w
i
th
ed
g
es,
b
u
t
r
esu
lt
s
in
m
o
r
e
d
is
to
r
tio
n
.
Ho
w
e
v
er
,
th
e
m
et
h
o
d
s
ad
o
p
ted
b
y
t
h
ese
d
ata
h
id
in
g
tec
h
n
iq
u
es
f
o
r
d
etec
tio
n
o
f
co
m
p
le
x
r
eg
io
n
ar
e
m
o
r
e
v
u
ln
er
ab
le
to
n
o
is
e.
T
h
e
p
r
o
p
o
s
ed
tech
n
iq
u
e
i
s
o
n
e
s
u
ch
e
f
f
o
r
t
to
w
ar
d
s
d
ata
h
i
d
in
g
i
n
co
m
p
lex
r
eg
io
n
o
f
co
v
er
i
m
a
g
e.
T
h
e
h
id
in
g
in
f
o
r
m
at
io
n
i
n
co
v
er
m
ed
ia
d
o
es
n
o
t
attr
ac
t
t
h
e
h
u
m
a
n
atte
n
tio
n
a
n
d
th
e
p
r
esen
ce
o
f
h
id
d
en
i
n
f
o
r
m
atio
n
is
n
o
t
p
er
ce
iv
ab
l
e
to
HVS.
T
h
is
tec
h
n
iq
u
e
m
ad
e
u
s
e
o
f
AC
O,
a
n
at
u
r
e
-
in
s
p
ir
e
d
o
p
tim
iza
tio
n
alg
o
r
ith
m
[
1
7
-
1
9
]
f
o
r
d
etec
tio
n
o
f
co
m
p
le
x
r
eg
io
n
[
2
0
]
,
an
d
s
ec
r
et
in
f
o
r
m
atio
n
ar
e
e
m
b
ed
d
ed
in
L
SB
o
f
t
h
e
co
m
p
le
x
r
eg
io
n
p
i
x
els
[
2
1
]
,
[
2
2
]
.
T
h
e
f
o
r
th
co
m
i
n
g
co
n
te
n
t
s
o
f
t
h
e
p
ap
er
ar
e
o
r
g
an
ized
a
s
f
o
llo
w
.
Sec
tio
n
2
,
p
r
esen
ts
th
e
AC
O
b
ased
d
ata
h
id
in
g
in
co
m
p
le
x
,
t
h
e
e
x
p
er
i
m
en
tal
r
es
u
lt
s
ar
e
p
r
esen
ted
i
n
Sectio
n
3
an
d
at
en
d
p
ap
er
s
is
co
n
clu
d
ed
w
ith
Sectio
n
4
.
2.
P
RO
P
O
SE
D
T
E
CH
NI
Q
U
E
T
h
e
d
etec
tio
n
o
f
co
m
p
lex
r
eg
i
o
n
in
co
v
er
i
m
ag
e
is
t
h
e
k
e
y
s
tep
in
h
id
in
g
o
f
in
f
o
r
m
atio
n
in
co
m
p
lex
r
eg
io
n
.
T
h
er
e
v
ar
io
u
s
m
e
th
o
d
s
to
d
etec
t
co
m
p
le
x
r
e
g
io
n
in
i
m
ag
e
s
.
T
h
ese
m
et
h
o
d
s
i
n
clu
d
e
ca
n
n
y
ed
g
e
d
etec
tio
n
,
d
er
ich
e,
d
if
f
er
en
tia
l,
s
o
b
el,
p
r
ew
itt,
R
o
b
er
ts
cr
o
s
s
a
n
d
o
th
er
m
et
h
o
d
s
.
T
h
ese
m
et
h
o
d
s
ar
e
v
er
y
ef
f
icien
t
to
d
etec
t
co
m
p
lex
r
e
g
io
n
i
n
d
ig
ital
i
m
a
g
es
b
u
t,
t
h
ese
m
et
h
o
d
s
d
o
n
’
t
co
m
p
l
y
co
m
p
letel
y
w
it
h
t
h
e
r
u
les
th
at
t
h
e
co
m
p
le
x
r
eg
io
n
an
d
h
id
e
d
ata
in
co
m
p
le
x
r
e
g
io
n
p
r
ev
io
u
s
m
et
h
o
d
s
h
id
e
d
ata
in
t
h
e
co
m
p
le
x
r
eg
io
n
o
f
co
v
er
i
m
a
g
e
as
m
o
s
t
o
f
th
ese
m
et
h
o
d
s
d
etec
t
w
ea
k
an
d
d
is
co
n
n
ec
ted
ed
g
e
p
ix
el
s
an
d
co
n
s
id
er
th
a
t
as
tr
u
e
co
m
p
lex
r
e
g
io
n
.
B
u
t,
t
h
ese
tec
h
n
iq
u
es
also
h
id
e
d
ata
in
th
o
s
e
p
ix
e
ls
t
h
at
d
o
esn
’
t
b
elo
n
g
to
ed
g
es
a
n
d
ar
e
m
o
r
e
v
u
l
n
er
ab
le
to
n
o
is
e.
I
n
th
i
s
p
ap
er
an
AC
O
b
ased
t
ec
h
n
iq
u
e
h
a
s
b
ee
n
u
s
ed
to
d
etec
t
co
m
p
le
x
r
eg
io
n
in
co
v
er
i
m
ag
e
[
2
3
]
,
[
2
4
]
an
d
th
en
to
tar
g
e
t th
i
s
r
eg
io
n
f
o
r
d
ata
h
id
in
g
u
s
i
n
g
L
SB
s
teg
a
n
o
g
r
ap
h
y
.
AC
O
-
b
ased
i
m
a
g
e
ed
g
e
d
etec
tio
n
ap
p
r
o
ac
h
,
co
n
s
tr
u
ct
a
p
h
er
o
m
o
n
e
m
atr
i
x
,
u
t
ilizi
n
g
m
a
n
y
a
n
ts
to
m
o
v
e
o
n
a
2
-
D
i
m
a
g
e.
T
h
e
m
o
v
e
m
e
n
t
o
f
t
h
e
a
n
ts
is
g
u
id
ed
b
y
th
e
lo
ca
l
d
i
f
f
er
e
n
ce
s
o
f
t
h
e
i
m
a
g
e
p
i
x
el
’
s
in
te
n
s
it
y
v
alu
e
s
.
T
h
e
en
tr
ies
o
f
th
e
p
h
er
o
m
o
n
e
m
atr
i
x
r
ep
r
esen
t
t
h
e
ed
g
e
in
f
o
r
m
a
tio
n
at
ea
ch
p
ix
el
lo
ca
tio
n
o
f
th
e
co
v
er
i
m
a
g
e.
T
h
e
AC
O
b
ased
tech
n
iq
u
e
is
in
itialized
f
ir
s
t
an
d
r
u
n
f
o
r
N
iter
atio
n
s
t
o
b
u
ild
p
h
er
o
m
o
n
e
m
atr
i
x
.
T
h
e
p
r
o
ce
s
s
p
er
f
o
r
m
s
b
o
th
co
n
s
tr
u
c
tio
n
a
n
d
u
p
d
ate
s
tep
s
iter
ati
v
el
y
.
A
t
th
e
en
d
d
ec
is
io
n
p
r
o
ce
s
s
i
s
u
s
ed
to
d
eter
m
in
e
t
h
e
p
ix
els
b
elo
n
g
to
co
m
p
lex
r
e
g
io
n
.
T
h
e
w
h
o
le
p
r
o
ce
s
s
is
ex
p
lai
n
ed
h
er
e
in
d
etail
as
f
o
llo
w
.
2
.
1
.
I
nitia
liza
t
io
n
A
d
i
g
ital
i
m
a
g
e
i
s
an
ar
r
a
y
o
f
p
ix
el
s
w
it
h
i
n
te
n
s
it
y
le
v
el
I
.
L
et
co
n
s
id
er
a
g
r
a
y
s
ca
le
i
m
a
g
e
o
f
s
ize
,
as
co
v
er
m
ed
i
u
m
.
A
to
ta
l
o
f
an
ts
ar
e
r
an
d
o
m
l
y
ass
ig
n
ed
o
n
an
i
m
a
g
e
.
E
ac
h
p
ix
el
o
f
th
e
co
v
er
i
m
a
g
e
is
co
n
s
id
er
ed
as
a
n
o
d
e.
T
o
in
itialize
t
h
e
co
m
p
lex
r
eg
io
n
d
etec
t
io
n
,
p
r
o
ce
s
s
th
e
i
n
itial
v
al
u
e
o
f
ea
ch
p
h
er
o
m
o
n
e
m
atr
i
x
’
s
co
m
p
o
n
en
t
is
s
et
to
a
co
n
s
ta
n
t
.
2
.
2
.
Co
ns
t
ruct
io
n
T
h
e
co
n
s
tr
u
ct
io
n
p
r
o
ce
s
s
is
c
o
m
p
o
s
ed
v
ar
io
u
s
s
tep
s
,
at
t
h
e
n
t
h
co
n
s
tr
u
ctio
n
-
s
tep
,
o
n
e
a
n
t,
f
r
o
m
a
to
tal
o
f
an
t,
is
r
a
n
d
o
m
l
y
s
ele
cted
.
T
h
e
s
elec
ted
an
t
ca
n
m
o
v
e
o
v
er
t
h
e
co
v
er
i
m
ag
e
f
o
r
m
o
v
e
m
en
t
s
tep
s
.
T
h
e
m
o
v
e
m
e
n
t
o
f
t
h
e
a
n
t
f
r
o
m
i
n
itial
n
o
d
e
to
its
n
e
ig
h
b
o
r
n
o
d
e
is
d
o
n
e
ac
co
r
d
i
n
g
to
t
h
e
tr
an
s
itio
n
p
r
o
b
ab
ilit
y
as g
i
v
e
n
b
y
E
q
u
a
tio
n
(
1
)
(
)
∑
(
)
(
1
)
W
h
er
e
P
h
er
o
m
o
n
e
v
al
u
e
at
n
o
d
e
Neig
h
b
o
r
h
o
o
d
(
4
o
r
8
-
co
n
n
ec
ted
)
n
o
d
e
o
f
th
e
n
o
d
e
Heu
r
is
tic
i
n
f
o
r
m
atio
n
at
n
o
d
e
: I
n
f
l
u
e
n
ce
o
f
p
h
er
o
m
o
n
e
m
atr
ix
: I
n
f
l
u
e
n
ce
o
f
h
e
u
r
is
tic
m
atr
i
x
T
h
e
h
eu
r
is
tic
i
n
f
o
r
m
atio
n
at
an
y
n
o
d
e
is
ca
lcu
lated
u
s
i
n
g
E
q
u
atio
n
(
2
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
A
n
t Co
lo
n
y
Op
timiz
a
tio
n
(
A
C
O)
b
a
s
e
d
Da
ta
Hid
in
g
in
.
.
.
(
S
a
h
ib
K
h
a
n
)
3
81
(
2
)
W
h
er
e
is
th
e
n
o
r
m
aliza
tio
n
f
a
cto
r
an
d
g
iv
e
n
b
y
E
q
u
atio
n
(
3
)
.
∑
∑
(
3
)
W
h
er
e
T
h
e
in
ten
s
i
t
y
le
v
el
p
ix
e
l
o
f
i
m
ag
e
C
T
h
e
d
ep
en
d
s
o
n
th
e
v
ar
iati
o
n
in
g
r
a
y
lev
el
s
o
f
s
tr
en
g
t
h
o
f
p
i
x
els
i
n
t
h
e
c
liq
u
e
is
r
ep
r
esen
ted
as b
y
E
q
u
atio
n
(
4
)
(
)
(
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
)
(
4
)
T
o
ca
lcu
late
f
(
.
)
th
er
e
ar
e
f
o
u
r
d
if
f
er
en
t
f
u
n
c
tio
n
Fla
t,
Gau
s
s
ian
,
Si
n
e
a
n
d
W
av
e
an
d
ea
ch
o
f
th
e
m
is
co
n
s
id
er
ed
in
th
i
s
p
ap
er
an
d
ar
e
g
iv
e
n
h
er
e
i
n
E
q
u
atio
n
(
5
)
to
E
q
u
atio
n
(
8
)
.
(
5
)
(
6
)
{
(
)
(
7
)
{
(
)
(
8
)
W
h
er
e
: T
h
e
s
h
ap
e
co
n
tr
o
l p
ar
am
eter
f
o
r
f
u
n
c
tio
n
s
.
2
.
3
.
Upda
t
ing
S
t
a
g
e
T
h
e
p
h
er
o
m
o
n
e
m
atr
i
x
i
s
u
p
d
ated
in
t
w
o
s
tep
s
.
T
h
e
f
ir
s
t
u
p
d
atin
g
is
p
er
f
o
r
m
ed
,
i
n
ea
c
h
c
o
n
s
tr
u
ct
io
n
s
tep
,
af
ter
th
e
m
o
v
e
m
e
n
t o
f
ea
ch
an
t,
ac
co
r
d
in
g
to
E
q
u
atio
n
(
9
)
{
(
9
)
W
h
er
e
T
h
e
ev
ap
o
r
atio
n
r
ates
: D
eter
m
i
n
ed
b
y
h
e
u
r
is
tic
m
atr
ix
is
eq
u
a
l to
W
h
en
t
h
e
en
tire
a
n
t
co
m
p
let
es
th
eir
m
o
v
e
m
en
t
in
ea
c
h
co
n
s
tr
u
c
t
io
n
s
tep
,
t
h
e
s
ec
o
n
d
u
p
d
atin
g
p
r
o
ce
s
s
is
p
er
f
o
r
m
ed
u
s
i
n
g
E
q
u
atio
n
(
1
0
)
.
(
1
0
)
W
h
er
e
T
h
e
p
h
er
o
m
o
n
e
d
ec
a
y
co
e
f
f
ici
en
t
2
.
4
.
Dec
is
io
n S
t
a
g
e
T
h
e
d
ec
is
io
n
p
r
o
ce
s
s
is
th
e
f
in
al
is
b
i
n
ar
y
d
ec
is
io
n
-
m
a
k
i
n
g
p
r
o
ce
s
s
to
d
ec
id
e
w
h
et
h
er
th
e
p
ix
el
b
elo
n
g
to
co
m
p
le
x
r
eg
io
n
o
r
s
m
o
o
t
h
r
eg
io
n
.
I
n
th
is
a
th
r
es
h
o
ld
is
ap
p
lied
o
n
th
e
f
in
al
p
h
e
r
o
m
o
n
e
m
atr
i
x
.
T
h
e
th
r
esh
o
ld
is
co
m
p
u
ted
ac
co
r
d
in
g
to
th
e
tec
h
n
iq
u
e
p
r
esen
ted
in
[
2
0
]
.
T
h
e
m
ea
n
o
f
v
al
u
e
o
f
p
h
er
o
m
o
n
e
m
atr
i
x
is
s
elec
ted
as
i
n
itia
l
th
r
es
h
o
ld
.
T
h
en
all
th
e
p
h
er
o
m
o
n
e
m
atr
i
x
en
tr
ie
s
ar
e
d
iv
id
ed
i
n
t
w
o
g
r
o
u
p
s
.
O
n
e
g
r
o
u
p
co
n
tai
n
s
al
l
th
e
v
al
u
e
s
m
a
ller
th
a
n
t
h
e
i
n
itial
th
r
es
h
o
ld
an
d
o
th
er
p
o
s
s
e
s
s
t
h
e
v
al
u
e
g
r
ea
ter
th
an
th
e
in
i
tail
t
h
r
es
h
o
l
d
.
Me
an
s
v
al
u
es
o
f
ea
ch
o
f
t
h
e
g
r
o
u
p
is
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.
8
,
No
.
1
,
Feb
r
u
ar
y
2
0
1
8
:
3
7
9
–
3
8
9
382
ca
lcu
lated
an
d
n
e
w
th
r
es
h
o
ld
is
d
ef
in
ed
as
th
e
av
er
ag
e
o
f
b
o
th
m
ea
n
s
.
T
h
e
p
r
o
ce
s
s
o
f
f
o
r
ca
lcu
latio
n
o
f
th
r
es
h
o
ld
is
r
ep
ea
ted
till
th
e
th
r
esh
o
ld
v
al
u
e
r
ea
ch
es to
a
s
tab
le
v
alu
e
i
n
ter
m
o
f
u
s
er
d
ef
i
n
e
to
ler
an
ce
.
Fin
a
l
d
ec
is
io
n
f
o
r
ea
c
h
p
ix
e
l a
t
is
m
ad
e
o
n
t
h
e
b
ases
o
f
th
e
p
h
er
o
m
o
n
e
v
ale
at
p
o
s
tio
n
co
m
p
ar
ed
w
it
h
th
e
f
i
n
al
th
r
e
s
h
o
ld
v
alu
e
as g
iv
e
n
b
y
E
q
u
atio
n
(
1
1
)
.
{
(
1
1
)
W
h
er
e
: T
h
e
b
an
iar
y
i
m
a
g
e
I
f
th
e
p
h
er
o
m
o
n
e
v
al
u
e
at
cu
r
r
en
t
p
o
s
itio
n
is
g
r
ea
ter
th
an
t
h
r
es
h
o
ld
it
is
,
co
n
s
id
er
as
a
p
ar
t
o
f
co
m
p
le
x
r
eg
io
n
an
d
o
th
er
it
s
t
r
ea
ted
as s
m
o
o
th
r
eg
io
n
p
ix
el.
2
.
5
.
Da
t
a
H
idi
ng
P
ro
ce
s
s
T
h
e
d
ata
h
id
in
g
s
tep
i
s
t
h
e
L
S
B
s
u
b
s
ti
tu
t
io
n
p
r
o
ce
s
s
.
T
h
i
s
s
t
ag
e
h
id
es
s
ec
r
et
i
n
f
o
r
m
atio
n
i
n
t
h
e
L
SB
s
o
f
th
e
co
v
er
i
m
a
g
e
o
n
t
h
e
b
ases
o
f
t
h
e
co
m
p
le
x
r
eg
io
n
d
etec
ted
.
I
n
th
is
p
r
o
ce
s
s
w
h
o
le
co
v
er
i
m
a
g
e
i
s
co
n
s
id
er
ed
an
d
i
s
p
r
o
ce
s
s
ed
p
ix
el
b
y
p
ix
e
l.
E
ac
h
p
ix
el
i
s
c
h
ec
k
w
h
e
th
er
it
b
elo
n
g
s
to
co
m
p
lex
r
eg
io
n
o
r
s
m
o
o
th
r
eg
io
n
.
I
f
t
h
e
p
i
x
el
co
r
r
esp
o
n
d
s
to
s
m
o
o
t
h
r
eg
io
n
it
is
le
f
t
u
n
a
f
f
ec
ted
an
d
a
n
o
th
er
p
ix
el
is
co
n
s
id
er
ed
.
An
d
i
f
t
h
e
p
ix
el
b
elo
n
g
s
to
co
m
p
le
x
r
e
g
io
n
t
h
e
n
it
s
L
SB
b
its
ar
e
s
u
b
s
tit
u
ed
w
it
h
t
h
e
s
ec
r
et
in
f
o
r
m
at
io
n
.
T
h
is
p
r
o
ce
s
s
co
n
ti
n
u
es
u
n
til
th
e
w
h
o
le
co
v
er
i
m
a
g
e
i
s
e
x
p
lo
r
ed
.
T
h
e
h
id
in
g
p
r
o
ce
s
s
i
s
ac
co
m
p
li
s
h
ed
i
n
f
o
llo
w
in
g
m
an
n
er
.
A
p
i
x
el
is
co
s
id
er
as
co
m
p
l
ex
r
eg
io
n
p
ix
el
i
f
i
ts
co
r
r
esp
d
in
g
=0
an
d
is
co
n
s
id
er
s
m
o
o
th
r
eg
io
n
co
m
p
o
n
a
n
t
if
.
L
ets
co
s
id
er
a
s
ec
r
et
m
es
s
a
h
e
to
b
e
h
id
d
en
in
co
m
p
le
x
r
eg
io
n
a
n
d
is
f
in
a
l
s
teg
o
i
m
ag
e
o
b
tai
n
ed
af
ter
in
f
o
r
m
at
io
n
h
id
in
g
.
T
h
e
s
teg
o
i
m
ag
e
is
g
iv
e
n
b
y
E
q
u
atio
n
(
1
2
)
.
{
(
1
2
)
Fig
u
r
e
1
.
AC
O
-
b
a
s
ed
d
ata
h
id
in
g
i
n
co
m
p
le
x
r
eg
io
n
3.
I
M
P
L
E
M
E
NT
AT
I
O
N
,
E
XP
E
RIM
E
NT
A
L
R
E
SU
L
T
S A
ND
ANAL
YSI
S
T
o
h
id
e
s
ec
r
et
d
ata
in
co
m
p
lex
r
eg
io
n
o
f
co
v
er
i
m
a
g
e
u
s
i
n
g
A
C
O
al
g
o
r
ith
m
a
n
d
g
et
e
x
p
er
im
e
n
tal
r
esu
lt
s
,
m
a
n
y
d
i
f
f
er
e
n
t
co
v
er
i
m
ag
e
s
ar
e
u
s
ed
.
T
h
ese
co
v
er
i
m
a
g
es
i
n
cl
u
d
e,
C
a
m
er
a
m
an
,
L
e
n
a,
Ho
u
s
e,
J
ell
y
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
A
n
t Co
lo
n
y
Op
timiz
a
tio
n
(
A
C
O)
b
a
s
e
d
Da
ta
Hid
in
g
in
.
.
.
(
S
a
h
ib
K
h
a
n
)
383
b
ea
n
s
,
Ma
n
d
r
ill,
P
ep
p
er
,
T
if
f
an
y
a
n
d
T
r
ee
as
p
r
esen
ted
in
Fig
u
r
e
s
2
(
a)
,
2
(
b
)
,
2
(
c)
,
2
(
d
)
,
2
(
e)
,
2
(
f
)
,
2
(
g
)
an
d
2
(
h
)
,
r
esp
ec
tiv
el
y
.
A
l
l th
e
s
e
c
o
v
er
i
m
a
g
es a
r
e
ta
k
en
f
r
o
m
i
m
ag
e
u
s
ed
ar
e
o
f
th
e
s
a
m
e
s
ize
o
f
.
E
ac
h
o
f
th
e
co
v
er
is
s
u
b
j
ec
ted
to
d
a
ta
h
id
in
g
u
s
i
n
g
t
h
e
p
r
o
p
o
s
ed
t
ec
h
n
iq
u
e.
A
s
d
is
c
u
s
s
es
ea
r
lier
AC
O
ca
n
b
e
u
s
ed
f
o
r
co
m
p
lex
r
e
g
io
n
d
etec
tio
n
u
s
i
n
g
f
o
u
r
d
if
f
er
en
t
f
u
n
ct
io
n
s
i.e
.
Flat,
Gau
s
s
ia
n
,
Si
n
e
an
d
W
av
e
as
g
iv
e
n
b
y
E
q
u
atio
n
(
5
)
to
E
q
u
at
io
n
(
8
)
,
r
esp
ec
tiv
el
y
.
Af
ter
t
h
e
co
m
p
le
x
r
eg
io
n
a
n
d
s
m
o
o
th
r
eg
io
n
’
s
p
ix
el
cla
s
s
i
f
icatio
n
,
an
L
SB
s
u
b
s
tit
u
tio
n
tec
h
n
iq
u
e
is
u
s
ed
f
o
r
d
ata
h
id
in
g
i
n
t
h
e
co
m
p
le
x
r
eg
io
n
’
s
p
ix
el
s
o
n
l
y
.
AC
O
ap
p
r
o
ac
h
is
d
ep
en
d
en
t
o
n
a
v
er
y
lar
g
e
n
u
m
b
er
o
f
p
ar
am
eter
s
.
T
h
e
p
ar
am
eter
s
s
et
f
o
r
th
e
ex
p
er
i
m
e
n
tatio
n
ar
e
g
iv
e
n
as:
T
h
e
s
h
ap
e
co
n
tr
o
l p
ar
am
eter
λ
=
1
0
T
h
e
in
f
l
u
en
ce
o
f
p
h
er
o
m
o
n
e
m
atr
i
x
α
=
1
T
h
e
in
f
l
u
en
ce
o
f
h
e
u
r
is
t
ic
m
at
r
ix
=
0
.
1
T
h
e
ev
ap
o
r
atio
n
r
ate
=
0
.
1
T
h
e
p
h
er
o
m
o
n
e
d
ec
a
y
co
e
f
f
ici
en
t
=
0
.
0
5
T
o
an
al
y
ze
th
e
p
r
o
p
o
s
ed
tech
n
iq
u
e
q
u
a
n
tita
tiv
el
y
,
t
h
e
d
ata
h
id
in
g
c
ap
ac
it
y
t
h
e
MS
E
an
d
P
S
N
R
ar
e
ca
lcu
lated
as
g
iv
e
n
b
y
E
q
u
at
io
n
(
1
3
)
to
E
q
u
atio
n
(
1
5
)
,
r
esp
ec
tiv
el
y
[
2
0
]
.
(
1
3
)
∑
∑
(
1
4
)
(
1
5
)
(
a)
(
b
)
(
c)
(
d
)
(
e)
(
f
)
(
g
)
(
h
)
Fig
u
r
e
2
.
C
o
v
er
I
m
a
g
es (
a)
C
a
m
er
a
m
a
n
,
(
b
)
L
en
a,
(
c)
Ho
u
s
e
,
(
d
)
J
elly
B
ea
n
s
,
(
e)
Ma
n
d
r
ill,
(
f
)
P
ep
p
e
r
,
(
g
)
T
if
f
an
y
,
an
d
(
h
)
T
r
ee
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.
8
,
No
.
1
,
Feb
r
u
ar
y
2
0
1
8
:
3
7
9
–
3
8
9
384
Firstl
y
,
t
h
e
p
r
o
p
o
s
ed
tech
n
iq
u
e
is
ap
p
lied
o
n
a
ll t
h
e
co
v
er
i
m
ag
e
s
s
h
o
w
n
i
n
Fig
u
r
e
2
.
F
lat
f
u
n
c
tio
n
a
s
g
iv
e
n
b
y
E
q
u
at
io
n
(
5
)
h
as
b
ee
n
u
s
ed
in
A
C
O
b
ased
co
m
p
le
x
r
eg
io
n
d
etec
tio
n
.
T
h
e
s
teg
o
im
ag
e
s
o
b
tain
ed
ar
e
s
h
o
w
n
i
n
Fi
g
u
r
e
(
3
)
.
T
h
e
h
id
in
g
ca
p
ac
it
y
,
M
SE
an
d
P
SNR
ca
lc
u
lated
f
o
r
ea
ch
co
v
e
r
i
m
ag
e
i
s
li
s
ted
in
T
ab
le
1
.
(
a)
(
b
)
(c
)
(
d
)
(
e)
(
f
)
(
g
)
(
h
)
Fig
u
r
e
3
.
Steg
o
I
m
a
g
es o
b
tain
ed
u
s
in
g
Fla
t f
u
n
ctio
n
g
iv
e
n
i
n
E
q
u
atio
n
(
5
)
(
a)
C
am
er
a
m
a
n
,
(
b
)
L
en
a,
(
c)
Ho
u
s
e,
(
d
)
J
elly
B
ea
n
s
,
(
e)
Ma
n
d
r
ill,
(
f
)
P
ep
p
er
,
(
g
)
T
if
f
a
n
y
,
an
d
(
h
)
T
r
ee
T
ab
le
1
.
Hid
in
g
C
ap
ac
it
y
,
P
S
NR
an
d
MSE
u
s
i
n
g
Flat F
u
n
ct
io
n
C
o
v
e
r
I
mag
e
P
S
N
R
(
d
B
)
M
S
E
H
i
d
i
n
g
C
a
p
a
c
i
t
y
(
%)
C
a
me
r
a
ma
n
4
5
.
7
4
1
3
1
1
.
7
3
3
6
4
.
1
2
2
9
L
e
n
a
5
0
.
2
7
6
7
9
0
.
6
1
0
1
4
.
5
1
0
5
H
o
u
se
4
6
.
4
3
2
8
8
1
.
4
7
8
4
4
.
0
3
4
4
Je
l
l
y
B
e
a
n
s
4
6
.
7
1
7
8
7
1
.
3
8
4
5
4
.
0
5
5
8
M
a
n
d
r
i
l
l
4
5
.
9
5
5
9
6
1
.
6
5
0
0
5
.
5
8
1
7
P
e
p
p
e
r
4
7
.
1
4
0
2
2
1
.
2
5
6
2
4
.
5
7
7
6
T
i
f
f
a
n
y
4
5
.
8
4
3
2
1
1
.
6
9
3
4
4
.
5
8
3
7
T
r
e
e
4
5
.
7
7
4
5
1
1
.
7
2
0
4
5
.
2
3
9
9
Seco
n
d
l
y
,
AC
O
b
ased
d
ata
h
id
in
g
in
co
m
p
lex
r
e
g
io
n
tech
n
iq
u
e
s
is
i
m
p
le
m
e
n
ted
th
e
s
a
m
e
co
v
er
i
m
a
g
es
s
h
o
w
n
i
n
Fi
g
u
r
e
2
,
b
u
t
u
s
in
g
Ga
u
s
s
ia
n
f
u
n
ctio
n
,
a
s
g
iv
e
n
b
y
E
q
u
atio
n
(
6
)
.
T
h
e
r
e
s
u
lted
s
teg
o
i
m
a
g
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
A
n
t Co
lo
n
y
Op
timiz
a
tio
n
(
A
C
O)
b
a
s
e
d
Da
ta
Hid
in
g
in
.
.
.
(
S
a
h
ib
K
h
a
n
)
385
ar
e
d
is
p
la
y
ed
h
er
e
F
ig
u
r
e
4
.
T
h
e
h
id
in
g
ca
p
ac
it
y
,
MSE
a
n
d
P
SNR
ca
lcu
la
ted
,
u
s
i
n
g
ea
ch
co
v
er
i
m
a
g
e
f
o
r
in
f
o
r
m
atio
n
h
id
i
n
g
,
i
s
lis
ted
i
n
T
a
b
le
2
.
(
a)
(
b
)
(
c)
(
d
)
(
e)
(
f
)
(
g
)
(
h
)
Fig
u
r
e
4
.
Steg
o
I
m
a
g
es o
b
tain
ed
u
s
in
g
Ga
u
s
s
ian
f
u
n
c
tio
n
g
i
v
en
i
n
E
q
u
atio
n
(
6
)
(
a)
C
am
er
a
m
an
,
(
b
)
L
e
n
a,
(
c)
Ho
u
s
e,
(
d
)
J
elly
B
ea
n
s
,
(
e)
Ma
n
d
r
ill,
(
f
)
P
ep
p
er
,
(
g
)
T
if
f
a
n
y
,
an
d
(
h
)
T
r
ee
T
ab
le
2
.
Hid
in
g
C
ap
ac
it
y
,
P
S
NR
an
d
MSE
u
s
i
n
g
Gau
s
s
ia
n
F
u
n
ct
io
n
C
o
v
e
r
I
mag
e
P
S
N
R
(
d
B
)
M
S
E
H
i
d
i
n
g
C
a
p
a
c
i
t
y
(
%)
C
a
me
r
a
ma
n
4
6
.
2
3
8
9
9
1
.
5
4
5
9
3
.
1
6
4
7
L
e
n
a
5
2
.
6
8
8
8
8
0
.
3
5
0
1
2
.
1
6
6
7
H
o
u
se
4
8
.
7
9
2
8
9
0
.
8
5
8
6
1
.
8
9
5
1
Je
l
l
y
B
e
a
n
s
4
8
.
3
6
7
3
0
.
9
4
7
0
2
.
4
7
8
0
M
a
n
d
r
i
l
l
5
0
.
3
5
9
4
4
0
.
5
9
8
6
2
.
6
4
8
9
P
e
p
p
e
r
4
8
.
2
3
9
8
7
0
.
9
7
5
2
2
.
9
7
2
4
T
i
f
f
a
n
y
4
9
.
1
6
2
2
4
0
.
7
8
8
6
2
.
0
2
9
4
T
r
e
e
4
7
.
1
2
5
0
3
1
.
2
6
0
6
4
.
3
2
7
4
Si
m
i
lar
l
y
,
i
n
th
ir
d
s
tep
all
th
e
co
v
er
i
m
ag
e
s
g
i
v
e
n
in
Fi
g
u
r
e
2
ar
e
s
u
b
j
ec
te
d
to
d
ata
h
id
in
g
u
s
i
n
g
th
e
p
r
o
p
o
s
ed
tech
n
iq
u
e.
Mo
r
eo
v
e
r
,
th
is
t
i
m
e
Si
n
e
f
u
n
ctio
n
g
iv
e
n
i
n
E
q
u
atio
n
(
7
)
is
u
s
ed
i
n
A
C
O
co
m
p
le
x
r
eg
io
n
d
etec
tio
n
.
T
h
e
o
b
tain
ed
s
teg
o
i
m
a
g
es,
w
it
h
h
id
d
en
in
f
o
r
m
atio
n
in
s
id
e
it,
ar
e
s
h
o
w
n
h
e
r
e
Fig
u
r
e
(
5
)
.
T
h
e
T
ab
le
3
co
n
tain
s
th
e
ca
lc
u
late
d
h
id
in
g
ca
p
ac
it
y
,
MSE
an
d
P
SNR
f
o
r
all
co
v
er
i
m
a
g
es.
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.
8
,
No
.
1
,
Feb
r
u
ar
y
2
0
1
8
:
3
7
9
–
3
8
9
386
(
a)
(
b
)
(c
)
(
d
)
(
e)
(
f
)
(
g
)
(
h
)
Fig
u
r
e
5
.
Steg
o
I
m
a
g
es o
b
tain
ed
u
s
in
g
Si
n
e
f
u
n
ctio
n
g
i
v
e
n
i
n
E
q
u
atio
n
(
7
)
(
a)
C
a
m
er
a
m
an
,
(
b
)
L
en
a,
(
c)
Ho
u
s
e,
(
d
)
J
elly
B
ea
n
s
,
(
e)
Ma
n
d
r
ill,
(
f
)
P
ep
p
er
,
(
g
)
T
if
f
a
n
y
,
an
d
(
h
)
T
r
ee
T
ab
le
3
.
Hid
in
g
C
ap
ac
it
y
,
P
S
NR
an
d
MSE
u
s
i
n
g
Si
n
e
Fu
n
c
tio
n
C
o
v
e
r
I
mag
e
P
S
N
R
(
d
B
)
M
S
E
H
i
d
i
n
g
C
a
p
a
c
i
t
y
(
%)
C
a
me
r
a
ma
n
4
5
.
2
3
5
8
1
1
.
9
4
7
6
4
.
4
3
1
2
L
e
n
a
5
0
.
5
4
4
7
1
0
.
5
7
3
6
4
.
3
8
5
4
H
o
u
se
4
6
.
6
3
7
3
8
1
.
4
1
0
4
3
.
7
8
4
2
Je
l
l
y
B
e
a
n
s
4
5
.
6
7
4
6
9
1
.
7
6
0
4
4
.
1
8
7
0
M
a
n
d
r
i
l
l
4
6
.
5
5
0
8
1
.
4
3
8
8
5
.
8
7
7
7
P
e
p
p
e
r
4
6
.
1
2
0
3
8
1
.
5
8
8
7
4
.
5
0
4
4
T
i
f
f
a
n
y
4
6
.
1
3
6
2
7
1
.
5
8
2
9
4
.
4
2
5
0
T
r
e
e
4
5
.
8
1
1
7
8
1
.
7
0
5
7
5
.
5
7
8
6
L
ast
l
y
,
W
av
e
f
u
n
c
tio
n
,
m
ath
e
m
atica
ll
y
g
iv
e
n
b
y
E
q
u
atio
n
(
8
)
,
is
u
s
ed
b
y
A
C
O
alg
o
r
it
h
m
to
class
i
f
y
,
th
e
co
m
p
le
x
a
n
d
s
m
o
o
th
r
eg
i
o
n
’
s
p
ix
el
s
a
n
d
th
e
co
v
er
i
m
a
g
es
o
b
tai
n
ed
af
ter
d
ata
h
id
i
n
g
in
co
m
p
lex
r
eg
io
n
ar
e
s
h
o
w
n
i
n
Fi
g
u
r
e
6
.
T
h
e
v
al
u
es o
f
h
id
i
n
g
ca
p
ac
it
y
,
M
SE
an
d
P
SNR
ar
e
lis
ted
in
T
ab
le
4
.
T
h
e
r
esu
lts
s
h
o
w
t
h
at
all
t
h
e
AC
O
b
ased
d
ata
h
id
i
n
g
i
n
co
m
p
lex
r
e
g
io
n
r
es
u
lt
s
in
s
i
g
n
if
i
ca
n
tl
y
h
i
g
h
q
u
alit
y
s
teg
o
i
m
ag
e
s
.
Ho
w
e
v
er
,
Flat
f
u
n
c
tio
n
i
n
E
q
u
atio
n
(
5
)
an
d
Sin
e
f
u
n
ctio
n
i
n
E
q
u
atio
n
(
7
)
,
ar
e
v
er
y
ef
f
icien
t b
o
th
in
ter
m
o
f
h
id
in
g
ca
p
ac
it
y
a
n
d
s
teg
o
i
m
a
g
e
q
u
alit
y
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
A
n
t Co
lo
n
y
Op
timiz
a
tio
n
(
A
C
O)
b
a
s
e
d
Da
ta
Hid
in
g
in
.
.
.
(
S
a
h
ib
K
h
a
n
)
387
(
a)
(
b
)
(
c)
(
d
)
(
e)
(
f
)
(
g
)
(
h
)
Fig
u
r
e
6
.
Steg
o
I
m
a
g
es o
b
tain
ed
u
s
in
g
W
av
e
f
u
n
ctio
n
g
i
v
e
n
in
E
q
u
atio
n
(
8
)
(
a)
C
am
er
a
m
a
n
,
(
b
)
L
en
a,
(
c)
Ho
u
s
e,
(
d
)
J
elly
B
ea
n
s
,
(
e)
Ma
n
d
r
ill,
(
f
)
P
ep
p
er
,
(
g
)
T
if
f
a
n
y
,
an
d
(
h
)
T
r
ee
T
ab
le
4
.
Hid
in
g
C
ap
ac
it
y
,
P
S
NR
an
d
MSE
u
s
i
n
g
W
av
e
F
u
n
ctio
n
C
o
v
e
r
I
mag
e
P
S
N
R
(
d
B
)
M
S
E
H
i
d
i
n
g
C
a
p
a
c
i
t
y
(
%)
C
a
me
r
a
ma
n
4
6
.
9
0
9
9
6
1
.
3
2
4
6
2
.
9
7
5
5
L
e
n
a
5
2
.
6
5
3
0
6
0.
3
5
3
0
2
.
4
2
0
0
H
o
u
se
4
8
.
5
6
2
4
0
.
9
0
5
4
2
.
0
8
7
4
Je
l
l
y
B
e
a
n
s
4
7
.
5
4
6
9
2
1
.
1
4
3
9
2
.
8
0
7
6
M
a
n
d
r
i
l
l
4
8
.
8
8
3
3
6
0
.
8
4
0
9
2
.
8
4
7
3
P
e
p
p
e
r
4
7
.
8
7
5
7
1
.
0
6
0
5
2
.
8
6
6
1
T
i
f
f
a
n
y
4
9
.
4
5
3
7
7
0
.
7
3
7
4
2
.
1
2
1
0
T
r
e
e
4
6
.
9
0
0
7
9
1
.
3
2
7
4
4
.
1
2
2
9
4.
C
O
M
P
ARIS
O
N
WI
T
H
O
T
H
E
R
T
E
CH
NI
Q
U
E
S
As
d
is
cu
s
s
ed
in
Sec
tio
n
3
,
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
r
esu
lts
i
n
a
v
er
y
h
i
g
h
q
u
alit
y
s
te
g
o
i
m
ag
es
w
it
h
P
SNR
g
r
ea
ter
t
h
an
1
0
0
d
B
f
o
r
all
i
m
a
g
es
u
s
in
g
all
f
o
u
r
f
u
n
cti
o
n
s
m
en
t
io
n
ed
in
Sectio
n
2
.
H
er
e,
th
is
r
ep
r
esen
t
s
th
e
co
m
p
ar
is
o
n
o
f
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
w
ith
d
i
f
f
er
en
t
p
r
ev
i
o
u
s
tech
n
iq
u
es.
A
s
t
h
e
p
r
o
p
o
s
ed
tech
n
iq
u
e
i
s
d
ata
h
id
in
g
m
et
h
o
d
th
at
h
id
es
s
ec
r
et
in
f
o
r
m
atio
n
in
co
m
p
lex
r
e
g
io
n
o
f
co
v
er
i
m
a
g
es.
T
h
er
ef
o
r
e,
a
co
m
p
ar
is
o
n
o
f
th
e
p
r
o
p
o
s
ed
tech
n
iq
u
e
i
s
m
a
d
e
o
n
l
y
w
it
h
t
h
e
d
ata
h
id
in
g
tech
n
iq
u
es
t
h
at
u
s
e
s
ed
g
es
o
r
co
m
p
le
x
r
eg
io
n
o
f
co
v
er
i
m
a
g
es.
T
h
e
co
m
p
ar
is
o
n
is
m
ad
e
w
it
h
Frid
r
ich
e
t
al.
[
3
]
,
Ho
n
s
in
g
er
et
a
l.
[
4
]
,
Kh
an
et
al.
[
2
4
]
,
Go
lj
an
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.
8
,
No
.
1
,
Feb
r
u
ar
y
2
0
1
8
:
3
7
9
–
3
8
9
388
et
al.
[
2
5
]
,
Ma
cq
an
d
De
w
e
y
[
2
6
]
,
an
d
Vlee
s
ch
o
u
w
er
et
al.
[
2
7
]
,
in
ter
m
o
f
P
SN
R
a
n
d
h
id
in
g
ca
p
ac
it
y
.
T
h
e
co
m
p
ar
is
o
n
m
ad
e
u
s
i
n
g
L
e
n
a
an
d
Ma
n
d
r
ill
as
co
v
e
r
i
m
a
g
es.
T
h
e
r
esu
lted
v
al
u
es
o
f
h
id
in
g
ca
p
ac
it
y
a
n
d
P
NSR
ar
e
lis
ted
in
T
ab
le
5
.
T
h
e
Ho
n
s
i
n
g
er
et
al.
a
n
d
Frid
r
ich
et
al.
tech
n
iq
u
e
s
r
es
u
lt
s
i
n
a
h
id
i
n
g
ca
p
ac
it
y
o
f
le
s
s
t
h
an
0
.
0
1
5
6
b
p
p
an
d
0
.
0
1
5
6
b
p
p
,
r
esp
ec
tiv
el
y
.
Vlee
s
c
h
o
u
r
w
er
et
al.
ac
h
iev
e
a
h
id
i
n
g
ca
p
ac
it
y
o
f
0
.
1
5
6
b
p
p
,
w
it
h
s
ig
n
i
f
ica
n
tl
y
s
teg
o
i
m
a
g
e
q
u
alit
y
o
f
3
0
d
B
in
ter
m
o
f
P
S
NR
.
Ma
cq
a
n
d
De
w
e
y
a
n
d
p
r
o
p
o
s
ed
tech
n
iq
u
e,
in
cr
ea
s
es
t
h
e
d
ata
h
id
in
g
ca
p
ac
it
y
an
d
r
esu
lts
i
n
a
h
id
in
g
ca
p
ac
it
y
m
o
r
e
th
a
n
Ho
n
s
i
n
g
er
et
al.
,
Frid
r
ich
et
al.
an
d
Vlee
s
ch
o
u
r
w
er
et
a
l.
tec
h
n
iq
u
es.
A
h
id
i
n
g
ca
p
ac
it
y
o
f
le
s
s
t
h
an
0
.
0
3
1
2
5
is
r
ec
o
r
d
ed
,
b
u
t
t
h
e
v
i
s
u
al
q
u
alit
y
o
f
t
h
e
s
teg
o
i
m
ag
e
is
af
f
ec
ted
v
er
y
m
u
c
h
.
Go
lj
an
e
t
al.
an
d
Kh
an
et
al.
p
r
esen
ted
tech
n
iq
u
e
s
clai
m
h
id
in
g
ca
p
ac
it
y
o
f
0
.
3
6
b
p
p
a
n
d
0
.
3
3
b
p
p
,
w
it
h
P
SNR
o
f
3
9
.
0
0
d
B
an
d
4
6
.
2
3
d
B
r
esp
ec
tiv
el
y
.
T
h
e
p
r
o
p
o
s
e
d
tech
n
iq
u
e
r
es
u
lted
i
n
a
h
id
i
n
g
ca
p
ac
it
y
o
f
0
.
3
6
an
d
0
.
4
4
w
ith
P
NSR
g
r
ea
ter
th
a
n
5
0
d
B
an
d
4
5
d
B
f
o
r
L
en
a
a
n
d
Ma
n
d
r
ill co
v
er
i
m
a
g
es,
co
r
r
es
p
o
n
d
in
g
l
y
.
T
h
e
T
a
b
le
5
s
h
o
w
s
t
h
at
t
h
e
P
SNR
o
f
t
h
e
p
r
o
p
o
s
ed
tech
n
iq
u
e
is
s
i
g
n
if
ica
n
tl
y
h
i
g
h
er
t
h
a
n
th
e
P
SNR
v
alu
e
s
o
f
all
t
h
e
o
t
h
er
tech
n
iq
u
es.
W
h
ile,
t
h
e
h
id
in
g
ca
p
ac
it
y
o
f
th
e
p
r
o
p
o
s
ed
tech
n
iq
u
e
i
s
also
h
i
g
h
er
th
a
n
al
l
tech
n
iq
u
es
e
x
ce
p
t
th
at
o
f
Go
lj
an
et
al.
an
d
Kh
an
et
al.
tech
n
iq
u
es.
A
C
O
b
ased
d
ata
h
id
in
g
in
co
m
p
le
x
r
eg
io
n
h
as
eq
u
al
d
at
a
h
id
in
g
ca
p
ac
it
y
as
th
a
t
o
f
Go
lj
an
et
al.
b
u
t,
th
e
q
u
alit
y
o
f
th
e
s
te
g
o
i
m
a
g
es
i
s
s
ig
n
i
f
ica
n
tl
y
b
etter
th
an
t
h
e
Go
lj
an
et
al.
T
ab
le
5
.
C
o
m
p
ar
is
o
n
o
f
p
r
o
p
o
s
ed
m
et
h
o
d
w
it
h
o
th
er
m
eth
o
d
s
T
e
c
h
n
i
q
u
e
L
e
n
a
L
e
n
a
H
i
d
i
n
g
C
a
p
a
c
i
t
y
(
b
p
p
)
P
S
N
R
(
d
B
)
H
i
d
i
n
g
C
a
p
a
c
i
t
y
(
b
p
p
)
P
S
N
R
(
d
B
)
H
o
n
si
n
g
e
r
e
t
a
l
.
<
0
.
0
1
5
6
-
<
0
.
0
1
5
6
-
F
r
i
d
r
i
c
h
e
t
a
l
.
0
.
0
1
5
6
-
0
.
0
1
5
6
-
V
l
e
e
sch
o
u
w
e
r
e
t
a
l
.
0
.
0
1
5
6
3
0
.
0
0
0
.
0
1
5
6
2
9
.
0
0
M
a
c
q
a
n
d
D
e
w
e
y
a
n
d
<
0
.
0
3
1
2
5
-
<
0
.
0
3
1
2
5
-
G
o
l
j
a
n
e
t
a
l
.
0
.
3
6
3
9
.
0
0
0
.
4
4
3
9
.
0
0
K
h
a
n
e
t
a
l
.
0
.
3
3
4
6
.
2
3
0
.
6
6
9
4
4
.
1
2
P
o
r
p
o
se
d
t
e
c
h
n
i
q
u
e
0
.
3
6
>
5
0
0
.
4
4
>
4
5
5.
C
O
NCLU
SI
O
N
AC
O
b
ased
d
ata
h
id
in
g
i
n
co
m
p
lex
r
e
g
io
n
o
f
d
i
g
ital
i
m
ag
e
s
is
an
e
f
f
icie
n
t
d
ata
h
id
in
g
te
ch
n
iq
u
e
th
a
t
s
u
cc
e
s
s
f
u
ll
y
ex
p
lo
it
s
t
h
e
H
VS
li
m
itatio
n
o
f
les
s
s
en
s
iti
v
it
y
t
o
th
e
c
h
a
n
g
e
s
i
n
co
m
p
lex
r
eg
i
o
n
s
.
T
h
is
tech
n
iq
u
e
r
esu
lt
s
in
h
ig
h
d
ata
h
id
in
g
ca
p
ac
it
y
w
ith
s
ig
n
i
f
ica
n
tl
y
g
o
o
d
q
u
alit
y
s
te
g
o
i
m
a
g
e.
T
h
e
b
ea
u
t
y
o
f
th
e
p
r
o
p
o
s
ed
tech
n
iq
u
e
l
ies
in
th
e
f
ac
t
t
h
at
h
id
in
g
ca
p
ac
it
y
ca
n
b
e
co
n
tr
o
lled
b
y
c
h
a
n
g
i
n
g
t
h
e
f
u
n
c
tio
n
in
A
C
O
class
i
f
icatio
n
s
ta
g
e
a
n
d
th
e
h
id
in
g
ca
p
ac
it
y
c
an
b
e
i
n
cr
e
ased
s
ig
n
i
f
ica
n
tl
y
w
it
h
a
f
f
ec
tin
g
t
h
e
P
SNR
b
y
ch
o
o
s
in
g
ei
th
er
Flat
f
u
n
ct
io
n
i
n
E
q
u
at
io
n
(
5
)
o
r
Si
n
e
f
u
n
ctio
n
i
n
E
q
u
a
tio
n
(
7
)
,
in
s
tead
o
f
Gau
s
s
ia
n
a
n
d
W
av
e
f
u
n
ctio
n
s
i
n
E
q
u
atio
n
(
6
)
an
d
E
q
u
atio
n
(
8
)
,
r
esp
ec
tiv
el
y
.
T
h
e
h
id
in
g
ca
p
ac
it
y
a
n
d
P
S
N
R
o
f
th
e
p
r
o
p
o
s
ed
w
o
r
k
is
h
ig
h
er
t
h
a
n
o
r
co
m
p
ar
ab
le
t
o
o
th
er
m
e
th
o
d
s
.
T
h
e
P
SN
R
o
f
t
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
r
e
m
ai
n
ab
o
v
e
1
0
0
d
B
f
o
r
all
i
m
a
g
es
as
d
is
c
u
s
s
ed
in
Sec
tio
n
I
V.
I
n
s
h
o
r
t,
th
e
A
C
O
b
ased
d
ata
h
id
in
g
in
co
m
p
le
x
r
eg
io
n
tech
n
iq
u
e
is
a
n
ef
f
icien
t
a
n
d
s
ec
u
r
e
d
ata
h
id
i
n
g
m
et
h
o
d
,
r
esu
lti
n
g
i
n
a
h
i
g
h
q
u
alit
y
s
te
g
o
-
i
m
a
g
e,
s
ig
n
i
f
ica
n
tl
y
h
ig
h
P
SN
R
a
n
d
r
ea
s
o
n
ab
le
d
ata
h
id
in
g
ca
p
ac
it
y
.
R
E
F
E
R
E
NC
E
S
[
1
]
N.
F
.
Jo
h
n
s
o
n
,
a
n
d
S
.
Ja
j
o
d
ia,
"
E
x
p
lo
rin
g
ste
g
a
n
o
g
ra
p
h
y
:
S
e
e
in
g
th
e
u
n
se
e
n
,
"
C
o
mp
u
ter
,
v
o
l.
3
1
(
2
),
p
p
.
2
6
-
3
4
,
1
9
9
8
.
[
2
]
S
.
Kh
a
n
,
M
.
A
.
Irf
a
n
,
M
.
Ism
a
il
,
T
.
Kh
a
n
,
a
n
d
N.
A
h
m
a
d
,
“
Du
a
l
l
o
ss
les
s
c
o
mp
re
ss
io
n
b
a
se
d
ima
g
e
ste
g
a
n
o
g
ra
p
h
y
fo
r
lo
w
d
a
ta
ra
te
c
h
a
n
n
e
ls,
”
In
In
tern
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
Co
m
m
u
n
ica
ti
o
n
T
e
c
h
n
o
lo
g
ies
(Co
m
T
e
c
h
),
2
0
1
7
,
p
p
.
60
-
6
4
.
[
3
]
J.
F
rid
ric
h
,
M
.
G
o
lj
a
n
,
a
n
d
R.
Du
,
"
In
v
e
rti
b
le
a
u
th
e
n
t
ica
ti
o
n
,
"
P
h
o
t
o
n
ics
W
e
st
2
0
0
1
-
E
lec
tro
n
ic
Im
a
g
in
g
.
In
tern
a
ti
o
n
a
l
S
o
c
iety
f
o
r
Op
ti
c
s an
d
P
h
o
to
n
ics
,
2
0
0
1
.
[
4
]
C.
W
.
Ho
n
sin
g
e
r,
P
.
W
.
Jo
n
e
s,
M
.
Ra
b
b
a
n
i
,
a
n
d
J.
C.
S
t
o
f
f
e
l,
"
L
o
ss
les
s
r
e
c
o
v
e
r
y
o
f
a
n
o
rig
in
a
l
im
a
g
e
c
o
n
tain
in
g
e
m
b
e
d
d
e
d
d
a
ta,"
U.S
.
P
a
ten
t
No
.
6
,
2
7
8
,
7
9
1
.
2
1
A
u
g
.
2
0
0
1
.
[
5
]
S
.
Kh
a
n
,
a
n
d
M
.
H.
Yo
u
sa
f
,
"
Im
p
le
m
e
n
tatio
n
o
f
V
L
S
B
S
teg
n
o
g
ra
p
h
y
U
sin
g
M
o
d
u
lar
Dista
n
c
e
T
e
c
h
n
iq
u
e
,
"
In
n
o
v
a
ti
o
n
s
a
n
d
A
d
v
a
n
c
e
s
in
C
o
m
p
u
ter,
In
f
o
rm
a
ti
o
n
,
S
y
ste
m
s
S
c
ien
c
e
s,
a
n
d
En
g
in
e
e
rin
g
.
S
p
ri
n
g
e
r
Ne
w
Yo
rk
,
p
p
.
5
1
1
-
5
2
5
,
2
0
1
3.
[
6
]
M
.
A
.
Ir
f
a
n
,
N.
A
h
m
a
d
,
a
n
d
S
.
Kh
a
n
,
“
A
n
a
l
y
sis
o
f
V
a
r
y
in
g
Lea
st
S
ig
n
if
i
c
a
n
t
Bit
s
DC
T
a
n
d
S
p
a
ti
a
l
Do
m
a
in
S
teg
n
o
g
ra
p
h
y
,
”
S
in
d
h
Un
iv.
Res
.
J
o
u
r.
(
S
c
i.
S
e
r.)
,
v
o
l.
4
6
(3
),
p
p
.
3
0
1
-
3
0
6
,
2
0
1
4
.
[
7
]
S
.
Kh
a
n
,
N.
A
h
m
a
d
,
a
n
d
M
.
W
a
h
id
,
“
V
a
ry
in
g
in
d
e
x
v
a
ry
in
g
b
it
s
su
b
sti
tu
ti
o
n
a
lg
o
rit
h
m
f
o
r
th
e
i
m
p
le
m
e
n
tatio
n
o
f
V
L
S
B
ste
g
a
n
o
g
ra
p
h
y
,
”
J
o
u
rn
a
l
o
f
th
e
Ch
in
e
se
In
stit
u
te
o
f
E
n
g
i
n
e
e
r
s,
v
o
l.
3
9
(
1
),
p
p
.
1
0
1
-
1
0
9
,
2
0
1
6
.
Evaluation Warning : The document was created with Spire.PDF for Python.