T
E
L
K
O
M
N
I
K
A
T
elec
o
m
m
un
ica
t
io
n,
Co
m
pu
t
ing
,
E
lect
ro
nics
a
nd
Co
ntr
o
l
Vo
l.
19
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
1
,
p
p
.
9
6
~1
0
4
I
SS
N:
1
6
9
3
-
6
9
3
0
,
ac
cr
ed
ited
First Gr
ad
e
b
y
Kem
en
r
is
tek
d
i
k
t
i,
Dec
r
ee
No
: 2
1
/E/KPT
/2
0
1
8
DOI
: 1
0
.
1
2
9
2
8
/TE
L
KOM
NI
K
A.
v
1
9
i1
.
1
6
4
1
8
96
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//jo
u
r
n
a
l.u
a
d
.
a
c.
id
/in
d
ex
.
p
h
p
/TELK
OM
N
I
K
A
M
ea
suring
meme
tic
a
lg
o
rithm p
erf
o
rma
nce on ima
g
e
fingerprin
ts data
s
et
P
ria
t
i A
s
s
iro
j
1
,
H
.
L
.
H
.
S
.
Wa
rna
rs
2
, E
.
Abd
urra
chm
a
n
3
,
A
.
I
.
K
is
t
ij
a
nto
ro
4
,
A
.
Do
uc
et
5
1,
2,
3
Co
m
p
u
ter
S
c
ien
c
e
De
p
a
rtme
n
t
Bin
u
s G
ra
d
u
a
te P
ro
g
ra
m
,
Bi
n
a
Nu
sa
n
tara
Un
iv
e
rsit
y
Ja
k
a
rta,
In
d
o
n
e
sia
1
In
fo
rm
a
ti
o
n
S
y
ste
m
De
p
a
rtme
n
t
,
Un
iv
e
rsitas
Bu
a
n
a
P
e
rju
a
n
g
a
n
K
a
ra
wa
n
g
,
In
d
o
n
e
sia
1
P
o
li
tek
n
i
k
Im
ig
ra
si,
M
in
istry
o
f
Law
a
n
d
Hu
m
a
n
R
ig
h
t,
Re
p
u
b
l
ic
o
f
In
d
o
n
e
sia
,
In
d
o
n
e
si
a
4
S
c
h
o
o
l
o
f
El
e
c
tro
n
ics
En
g
in
e
e
rin
g
a
n
d
In
f
o
rm
a
ti
c
s,
In
stit
u
t
Te
k
n
o
l
o
g
i
Ba
n
d
u
n
g
,
I
n
d
o
n
e
sia
5
L
3
i
Lab
o
ra
to
r
y
,
L
a
Ro
c
h
e
ll
e
U
n
i
v
e
rsit
y
,
F
ra
n
c
e
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Ap
r
1
7
,
2
0
2
0
R
ev
is
ed
J
u
l 2
,
2
0
2
0
Acc
ep
ted
Au
g
2
9
,
2
0
2
0
P
e
rso
n
a
l
i
d
e
n
ti
fica
ti
o
n
h
a
s
b
e
c
o
m
e
o
n
e
o
f
th
e
m
o
st
imp
o
r
tan
t
te
rm
s
in
o
u
r
so
c
iety
re
g
a
rd
in
g
a
c
c
e
ss
c
o
n
tro
l,
c
rime
a
n
d
f
o
re
n
sic
i
d
e
n
ti
f
ica
ti
o
n
,
b
a
n
k
in
g
a
n
d
a
ls
o
c
o
m
p
u
ter
sy
ste
m
.
T
h
e
f
i
n
g
e
rp
ri
n
t
is
th
e
m
o
st
u
se
d
b
i
o
m
e
tri
c
fe
a
tu
re
c
a
u
se
d
b
y
i
ts u
n
iq
u
e
,
u
n
iv
e
rsa
li
t
y
a
n
d
sta
b
il
it
y
.
Th
e
fi
n
g
e
r
p
rin
t
is w
id
e
ly
u
se
d
a
s a
se
c
u
rit
y
fe
a
tu
re
fo
r
fo
re
n
sic
r
e
c
o
g
n
it
i
o
n
,
b
u
il
d
in
g
a
c
c
e
ss
,
a
u
to
m
a
ti
c
teller
m
a
c
h
in
e
(
ATM
)
a
u
th
e
n
t
ica
ti
o
n
o
r
p
a
y
m
e
n
t.
F
i
n
g
e
r
p
rin
t
re
c
o
g
n
it
i
o
n
c
o
u
ld
b
e
g
ro
u
p
e
d
i
n
two
v
a
ri
o
u
s
fo
rm
s,
v
e
rifi
c
a
ti
o
n
a
n
d
id
e
n
ti
fica
ti
o
n
.
Ve
rifi
c
a
ti
o
n
c
o
m
p
a
re
s
o
n
e
o
n
o
n
e
fin
g
e
rp
ri
n
t
d
a
ta.
Id
e
n
ti
fica
ti
o
n
is
m
a
tch
in
g
in
p
u
t
fin
g
e
rp
r
in
t
wi
th
d
a
ta
th
a
t
sa
v
e
d
in
th
e
d
a
tab
a
se
.
I
n
th
is
p
a
p
e
r,
we
m
e
a
su
re
th
e
p
e
rfo
rm
a
n
c
e
o
f
th
e
m
e
m
e
ti
c
a
lg
o
rit
h
m
to
p
ro
c
e
ss
th
e
ima
g
e
f
in
g
e
rp
r
in
t
s
d
a
tas
e
t.
Be
fo
re
we
ru
n
t
h
is
a
lg
o
rit
h
m
,
we
d
i
v
id
e
o
u
r
fin
g
e
rp
ri
n
t
s
in
to
f
o
u
r
g
ro
u
p
s
a
c
c
o
r
d
in
g
t
o
it
s
c
h
a
ra
c
teristics
a
n
d
m
a
k
e
1
5
sp
e
c
ime
n
s
o
f
d
a
ta,
d
o
fo
u
r
p
a
rti
a
l
tes
ts an
d
a
t
th
e
las
t
o
f
wo
r
k
we
m
e
a
su
re
a
ll
c
o
m
p
u
tati
o
n
ti
m
e
.
K
ey
w
o
r
d
s
:
B
io
m
etr
ics
F
in
g
er
p
r
in
ts
I
m
ag
e
M
em
etic
alg
o
r
ith
m
P
er
f
o
r
m
an
ce
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
:
Pria
ti Ass
ir
o
j
C
o
m
p
u
ter
Scien
ce
Dep
ar
tm
e
n
t
B
in
u
s
Gr
ad
u
ate
Pro
g
r
am
B
in
a
Nu
s
an
tar
a
Un
iv
er
s
ity
J
l.
R
ay
a
Keb
o
n
jer
u
k
,
An
g
g
r
ek
,
DKI
J
ak
ar
ta
,
I
n
d
o
n
esia
E
m
ail:
p
r
iati@
b
in
u
s
.
ac
.
id
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
in
cr
ea
s
e
o
f
d
ig
ital
cr
im
e,
s
u
ch
as
d
ata
m
an
ip
u
latio
n
an
d
im
ag
e
an
d
s
ig
n
atu
r
e
f
alsi
f
ic
atio
n
,
an
d
m
an
y
v
a
r
io
u
s
illeg
al
tr
an
s
ac
tio
n
s
,
is
eq
u
al
to
th
e
r
eq
u
est
o
f
its
s
o
lu
tio
n
s
.
C
o
m
p
u
ter
-
b
ased
b
io
m
etr
ics
v
alid
atio
n
s
ar
e
im
p
o
r
tan
t
to
o
ls
to
im
p
r
o
v
e
th
e
s
y
s
tem
'
s
s
ec
u
r
ity
.
T
h
e
m
o
s
t
im
p
o
r
tan
t
o
f
b
i
o
m
etr
ics
ar
e
m
ea
s
u
r
e
p
h
y
s
io
lo
g
y
an
d
b
e
h
av
io
r
ch
ar
ac
ter
is
tics
th
at
al
lo
w
m
ak
in
g
au
th
en
ticatio
n
to
p
e
r
s
o
n
al
id
e
n
tity
.
B
io
m
etr
ic
au
th
en
ticatio
n
i
n
th
e
co
m
p
u
ter
-
b
ased
ap
p
licatio
n
is
an
im
p
o
r
ta
n
t
th
in
g
ca
u
s
e
d
b
y
th
e
h
u
g
e
o
f
s
en
s
itiv
e
d
a
ta
in
th
e
co
m
p
u
ter
s
y
s
tem
an
d
it in
cr
ea
s
es r
ap
id
ly
.
Per
s
o
n
al
id
en
tific
atio
n
h
as
b
e
co
m
e
o
n
e
o
f
th
e
m
o
s
t
im
p
o
r
ta
n
t
ter
m
s
in
o
u
r
s
o
ciety
r
e
g
ar
d
in
g
ac
ce
s
s
co
n
tr
o
l,
cr
im
e
a
n
d
f
o
r
e
n
s
ic
id
en
tific
atio
n
,
b
a
n
k
in
g
,
a
n
d
also
co
m
p
u
ter
s
y
s
tem
[
1
]
.
B
io
m
etr
ic
f
ea
tu
r
es
th
at
ca
n
b
e
u
s
ed
to
id
en
tific
atio
n
in
clu
d
e
th
e
ir
is
,
v
o
ice,
DNA,
a
n
d
f
i
n
g
er
p
r
i
n
ts
.
Acc
o
r
d
in
g
to
[
2
]
,
t
h
e
f
in
g
er
p
r
in
t
is
th
e
m
o
s
t
u
s
ed
b
io
m
etr
ic
f
ea
tu
r
e
c
au
s
ed
b
y
its
u
n
iq
u
e,
u
n
iv
er
s
ali
ty
,
an
d
s
tab
ilit
y
.
T
h
e
f
in
g
e
r
p
r
i
n
t
is
wid
ely
u
s
ed
as
a
s
ec
u
r
ity
f
ea
t
u
r
e
f
o
r
f
o
r
e
n
s
ic
r
ec
o
g
n
itio
n
,
b
u
ild
in
g
ac
ce
s
s
,
au
to
m
atic
teller
m
ac
h
in
e
(
AT
M
)
au
th
e
n
ticatio
n
,
o
r
pa
y
m
en
t.
Au
to
m
atic
f
in
g
er
p
r
i
n
t id
en
tific
atio
n
h
as b
ec
o
m
e
a
n
in
ter
esti
n
g
r
esear
ch
to
p
ic
f
o
r
two
d
ec
a
d
es [
3
]
,
in
ac
co
r
d
an
ce
with
[
4
]
f
in
g
er
p
r
i
n
t
b
ec
au
s
e
o
f
its
u
n
iq
u
e,
s
ize,
an
d
p
ec
u
liar
ity
.
N
o
wad
ay
s
,
t
o
g
et
a
f
in
g
er
p
r
in
t
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
Mea
s
u
r
in
g
mem
etic
a
lg
o
r
ith
m
p
erfo
r
ma
n
ce
o
n
ima
g
e
fin
g
er
p
r
in
ts
d
a
ta
s
et.
.
.
(
P
r
ia
ti A
s
s
ir
o
j
)
97
r
ec
o
g
n
itio
n
to
o
l
is
v
er
y
ea
s
y
,
m
an
y
co
r
p
o
r
atio
n
s
an
d
o
r
g
an
izatio
n
s
u
s
e
it
to
id
en
tify
th
eir
m
em
b
er
[
1
]
.
Fin
g
er
p
r
in
t
r
ec
o
g
n
itio
n
co
u
ld
b
e
g
r
o
u
p
e
d
in
to
two
v
ar
io
u
s
f
o
r
m
s
,
v
er
if
icatio
n
[
5
]
an
d
id
en
tific
atio
n
[
6
]
.
Ver
if
icatio
n
co
m
p
ar
es
o
n
e
o
n
o
n
e
f
in
g
er
p
r
in
t
d
ata.
I
d
e
n
tific
atio
n
is
m
atch
in
g
in
p
u
t
f
in
g
e
r
p
r
in
t
with
d
ata
th
at
s
av
ed
in
th
e
d
atab
ase.
T
h
e
r
ef
o
r
e,
id
en
tific
atio
n
is
an
ex
ten
s
io
n
o
f
v
e
r
if
icatio
n
th
at
co
m
p
ar
e
o
n
e
f
in
g
e
r
p
r
in
t
d
ata
to
m
an
y
,
an
d
n
o
te
th
at
id
e
n
tifi
ca
tio
n
is
m
o
r
e
co
m
p
lex
th
a
n
v
er
if
icatio
n
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
2
.
1
.
M
em
e
t
ic
a
lg
o
rit
h
m
Me
m
etic
alg
o
r
ith
m
(
MA
)
is
an
alg
o
r
ith
m
b
ased
o
n
th
e
Neo
-
Dar
win
ian
E
v
o
l
u
tio
n
c
o
n
ce
p
t
an
d
Daw
k
in
s
'
o
p
in
io
n
ab
o
u
t
m
em
es
as
cu
ltu
r
al
ev
o
lu
tio
n
u
n
it
wh
ich
ca
n
m
ak
e
im
p
r
o
v
em
e
n
ts
to
its
e
lf
.
MA
is
a
h
eu
r
is
tic
m
eth
o
d
wh
ich
h
as
s
im
ilar
ch
ar
ac
ter
is
tic
to
g
en
etic
alg
o
r
ith
m
(
GA)
,
co
m
b
i
n
e
d
with
lo
ca
l
s
ea
r
ch
m
eth
o
d
,
th
at
ca
n
im
p
r
o
v
e
q
u
al
ity
s
o
lu
tio
n
s
[
7
]
.
On
MA
,
lo
ca
l sear
ch
is
u
s
ed
f
o
r
lo
ca
l im
p
r
o
v
em
en
t w
h
ich
ca
n
b
e
ap
p
lied
b
ef
o
r
e
o
r
af
ter
s
elec
tio
n
,
cr
o
s
s
o
v
er
,
a
n
d
m
u
tatio
n
p
r
o
ce
s
s
.
L
o
ca
l
s
ea
r
ch
is
also
v
er
y
u
s
ef
u
l
to
co
n
tr
o
l
s
ea
r
ch
s
p
ac
e.
MA
ca
n
ac
h
ie
v
e
a
b
etter
r
esu
lt th
an
GA
b
u
t n
e
ed
m
o
r
e
c
o
m
p
u
tatio
n
al
tim
e.
MA
is
a
s
im
p
le
alg
o
r
ith
m
b
u
t
f
lex
ib
le
a
n
d
p
o
wer
f
u
l
[
8
,
9
]
,
th
at
ca
n
f
i
n
d
q
u
ality
s
o
lu
tio
n
s
in
m
a
n
y
ch
allen
g
in
g
p
r
o
b
lem
s
[
1
0
-
12
]
.
Op
tim
izatio
n
p
r
o
b
lem
s
will in
v
o
lv
e
d
o
ze
n
s
o
f
v
ar
ia
b
les th
u
s
n
ee
d
g
o
o
d
co
d
in
g
d
ev
ices
to
o
v
e
r
co
m
e
c
o
m
p
u
t
atio
n
al
tim
e.
T
h
is
alg
o
r
ith
m
will
v
er
y
u
s
ef
u
l
in
d
ata
m
in
i
n
g
p
r
o
ce
s
s
s
u
ch
as
ag
g
lo
m
er
atio
n
an
d
tex
t
an
aly
s
i
s
[
1
3
,
1
4
]
,
b
i
o
m
ed
ical,
s
u
c
h
as
DNA
an
d
m
o
lecu
lar
s
im
u
latio
n
[
1
5
-
1
7
]
,
n
etwo
r
k
p
r
o
b
lem
s
[
1
8
,
1
9
]
,
f
ea
tu
r
e
s
elec
tio
n
tech
n
iq
u
e
[
2
0
]
,
m
o
lec
u
lar
s
im
u
latio
n
[
1
6
]
,
f
o
r
ec
ast
in
g
[
2
1
]
,
q
u
an
t
u
m
ch
em
is
tr
y
[
2
2
]
,
s
p
ec
tr
o
s
co
p
y
an
aly
s
is
[
2
3
]
,
g
eo
p
h
y
s
ics
an
aly
s
is
[
2
4
]
,
m
ed
icin
e
in
v
e
n
tio
n
[
2
5
]
,
g
en
o
m
e
s
tu
d
y
[
2
6
]
,
an
d
m
an
y
m
o
r
e.
Sp
ec
if
ica
lly
,
th
e
u
s
e
o
f
alg
o
r
ith
m
s
f
o
r
im
a
g
e
p
r
o
ce
s
s
in
g
h
as
b
ee
n
d
o
n
e
b
y
[
2
7
]
,
th
ey
h
av
e
u
s
ed
m
em
etic
alg
o
r
ith
m
f
o
r
im
ag
e
b
r
ig
h
t
n
ess
en
h
an
ce
m
e
n
t;
[
2
8
]
u
s
e
th
is
alg
o
r
ith
m
to
d
e
tect
f
ac
e
em
o
tio
n
,
wh
ile
[
2
9
]
u
s
e
th
is
alg
o
r
ith
m
f
o
r
letter
r
ec
o
g
n
itio
n
.
Ha
n
d
wr
it
ten
r
ec
o
g
n
itio
n
co
n
d
u
cted
b
y
[
3
0
]
an
d
[
3
1
]
,
r
etin
a
r
ec
o
g
n
itio
n
b
y
[
3
2
]
,
s
u
b
-
p
ix
el
m
ap
p
in
g
im
ag
er
y
b
y
[
3
3
]
,
im
a
g
e
class
if
icatio
n
b
y
[
3
4
]
,
an
d
f
i
n
g
er
p
r
i
n
t
m
atch
in
g
with
th
e
m
em
etic
alg
o
r
ith
m
b
y
[
3
5
]
,
[
3
6
]
a
n
d
[
5
]
.
2
.
2
.
B
io
m
e
t
rics
B
io
m
etr
ics
ar
e
s
p
ec
ial
ch
ar
ac
ter
is
tics
o
f
h
u
m
an
wh
ic
h
is
u
n
iq
u
e
o
n
ev
e
r
y
in
d
iv
id
u
al
an
d
ca
n
b
e
a
r
ef
er
en
ce
o
f
id
e
n
tific
atio
n
an
d
v
er
if
icatio
n
.
B
io
m
etr
ics
ch
ar
ac
ter
is
tics
ca
n
b
e
ca
teg
o
r
iz
ed
in
to
two
t
y
p
es,
p
h
y
s
ical
an
d
b
eh
av
io
r
.
W
e
all
k
n
o
w
t
h
at
p
h
y
s
ical
ty
p
es su
ch
as f
in
g
er
p
r
in
t
,
ir
is
,
r
etin
a,
an
d
f
ac
e,
also
b
eh
a
v
io
r
ty
p
e
s
u
ch
as
v
o
ice,
s
ig
n
atu
r
e,
g
ait,
p
alm
g
eo
m
etr
y
,
h
an
d
wr
itte
n
,
elec
tr
o
ca
r
d
io
g
r
ap
h
(
E
C
G)
[
3
5
]
.
T
h
e
f
in
g
er
p
r
in
t
is
o
n
e
o
f
th
e
m
o
s
t
r
eliab
le
b
i
o
m
etr
ics
f
ea
tu
r
es
ca
u
s
ed
b
y
i
ts
u
n
iq
u
e,
an
d
it
is
im
p
o
s
s
ib
l
e
to
f
in
d
th
e
s
am
e
f
in
g
er
p
r
in
t
b
etwe
en
two
d
if
f
e
r
en
t
p
eo
p
le.
T
h
is
b
i
o
m
etr
ics
f
ea
tu
r
e
ca
n
a
u
to
m
atica
lly
r
ec
o
g
n
ize
s
o
m
eo
n
e
b
y
p
atter
n
r
ec
o
g
n
itio
n
a
n
d
d
eter
m
in
e
au
th
en
ticity
p
h
y
s
io
lo
g
y
ch
ar
ac
ter
is
tics
.
2
.
2
.
F
ing
er
prints
T
h
e
f
in
g
e
r
p
r
in
t
is
a
g
r
ap
h
ic
r
i
d
g
e
an
d
v
alley
p
atter
n
at
t
h
e
ti
p
o
f
a
h
u
m
an
f
i
n
g
er
.
Hu
m
an
f
i
n
g
er
p
r
i
n
ts
ca
n
b
e
f
o
u
n
d
o
n
m
an
y
h
is
to
r
ical
o
b
jects
as
s
h
o
wn
in
F
ig
u
r
e
1
.
T
h
is
f
in
d
i
n
g
p
r
o
v
es
th
at
an
cien
t
p
eo
p
le
wer
e
awa
r
e
an
d
g
av
e
s
p
ec
ial
atten
ti
o
n
to
f
in
g
er
p
r
i
n
t
in
d
i
v
id
u
ality
,
d
esp
ite
th
ey
d
i
d
n
o
t
h
a
v
e
a
s
cien
ti
f
ic
b
asis
[
3
7
]
.
T
h
e
h
is
to
r
y
o
f
f
in
g
er
p
r
in
t
b
e
g
in
s
in
1
6
8
4
,
m
o
r
p
h
o
lo
g
is
t
f
r
o
m
E
n
g
lan
d
,
Ne
h
em
iah
Gr
ew,
p
u
b
lis
h
ed
h
is
s
cien
tific
p
ap
er
a
b
o
u
t
th
e
r
id
g
e,
g
r
o
o
v
e,
an
d
p
o
r
e
s
tr
u
ctu
r
e
o
f
f
i
n
g
er
p
r
in
t
[
3
7
]
.
I
n
1
7
8
8
Ma
y
er
h
as
d
escr
ib
ed
f
in
g
er
p
r
i
n
t
in
d
etail
[
3
8
]
.
(
a)
(
b
)
(
c)
(
d
)
Fig
u
r
e
1
.
Fin
g
e
r
p
r
in
ts
o
n
h
is
to
r
ical
o
b
jects
,
(
a)
n
e
o
lith
ic
s
cu
lp
tu
r
e
[3
8
]
,
(
b
)
s
to
n
e
h
en
g
e
at
Go
at
I
s
lan
d
[
3
7
]
,
(
c)
clay
cu
p
f
r
o
m
C
h
in
a
3
0
0
B
C
[
3
7
]
, (
d
)
tr
ac
es o
n
th
e
lig
h
t
o
f
Palest
in
ian
4
0
0
AD
[
3
8
]
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
0
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
,
Vo
l.
19
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
1
:
9
6
-
10
4
98
T
h
o
m
as
B
ewic
k
in
1
8
0
9
u
s
es
th
e
f
i
n
g
er
p
r
in
t
as
its
ch
a
r
ac
te
r
s
an
d
it
is
b
eliev
ed
as
th
e
m
i
lest
o
n
e
o
f
s
cien
tific
f
in
g
er
p
r
in
t
[
3
8
]
.
I
n
1
8
2
3
,
Pu
r
k
in
je
class
if
ies
f
in
g
e
r
p
r
in
t
as
th
e
f
ir
s
t.
He
was
class
if
ied
f
in
g
e
r
p
r
in
t
to
n
in
e
ca
teg
o
r
ies
b
ased
o
n
th
e
r
id
g
e
c
o
n
f
ig
u
r
atio
n
,
an
d
i
n
1
8
8
0
Hen
r
y
Fau
l
d
a
n
d
Her
s
ch
el
h
av
e
d
o
n
e
s
cien
tific
f
in
g
er
p
r
in
t
r
ec
o
g
n
itio
n
[
3
8
]
.
T
h
is
in
v
en
tio
n
is
t
h
e
b
asis
o
f
m
o
d
er
n
f
in
g
er
p
r
in
t
r
ec
o
g
n
itio
n
.
At
t
h
e
e
n
d
o
f
th
e
n
in
etee
n
th
ce
n
tu
r
y
,
Sir
Fra
n
ci
s
Galto
n
r
esear
ch
e
d
m
o
r
e
ab
o
u
t
f
in
g
er
p
r
i
n
ts
.
Galto
n
in
tr
o
d
u
ce
s
a
s
m
a
ll
f
ea
tu
r
e
f
o
r
f
in
g
e
r
p
r
in
t
m
atch
in
g
in
1
8
8
8
.
T
h
e
m
o
s
t
im
p
o
r
tan
t
p
r
o
g
r
es
s
in
f
in
g
er
p
r
in
t
r
ec
o
g
n
itio
n
o
cc
u
r
r
ed
in
1
8
9
9
wh
en
E
d
war
d
Hen
r
y
g
en
er
ates
"He
n
r
y
Sy
s
tem
"
[
3
7
]
.
So
at
th
e
b
eg
in
n
in
g
o
f
th
e
twen
tieth
ce
n
tu
r
y
,
th
e
f
o
r
m
o
f
f
in
g
er
p
r
in
ts
ca
n
b
e
u
n
d
e
r
s
to
o
d
w
ell.
3.
I
M
P
L
E
M
E
NT
A
T
I
O
N
M
O
D
E
L
T
h
er
e
ar
e
s
ev
e
r
al
p
r
o
ce
s
s
es
co
n
d
u
cted
t
o
im
p
lem
en
t
th
is
alg
o
r
ith
m
.
W
e
s
tar
t
th
e
p
r
o
ce
s
s
with
r
ea
d
in
g
f
o
ld
er
s
an
d
f
in
g
er
p
r
in
t
f
iles
,
w
e
will
d
o
th
e
lo
ca
l
s
ea
r
ch
p
r
o
c
ess
.
T
h
en
co
n
v
er
t
f
i
n
g
er
p
r
in
t
f
iles
to
s
tr
in
g
ar
r
ay
f
o
r
m
an
d
co
n
v
er
t
s
tr
in
g
a
r
r
ay
t
o
b
in
a
r
y
co
d
e
f
o
r
m
with
B
ase6
4
,
t
h
e
p
r
o
ce
s
s
will b
e
lo
o
p
ed
u
n
til th
e
en
tire
d
ata
co
n
v
er
ted
s
u
cc
ess
f
u
lly
.
T
h
e
n
ex
t p
r
o
ce
s
s
is
s
elec
tio
n
o
r
eliti
s
m
f
o
r
p
ar
en
t
ca
n
d
id
ates
f
r
o
m
th
e
to
tal
p
o
p
u
latio
n
th
en
m
ak
e
it
cr
o
s
s
in
g
-
o
v
er
t
o
g
et
n
ew
o
f
f
s
p
r
in
g
.
T
h
e
last
p
r
o
ce
s
s
is
a
m
u
tatio
n
o
r
cl
o
n
es
th
e
o
f
f
s
p
r
i
n
g
.
Fig
u
r
e
2
s
h
o
ws
th
e
f
lo
wch
a
r
t
o
f
th
e
p
r
o
ce
s
s
o
f
th
e
m
e
m
etic
alg
o
r
ith
m
,
an
d
a
d
etailed
p
r
o
c
ess
will
b
e
d
escr
ib
ed
in
th
e
n
ex
t
p
ar
ag
r
ap
h
.
I
n
th
is
p
ap
er
we
im
p
lem
en
t
MA
o
n
7
2
0
0
s
y
n
th
etic
im
ag
e
f
in
g
er
p
r
in
t
d
ataset
o
f
FV
C
2
0
0
6
wi
th
ch
ar
ac
ter
is
tics
as sh
o
wn
in
T
ab
le
1
.
Fig
u
r
e
2
.
Flo
wch
ar
t
o
f
m
e
m
etic
alg
o
r
ith
m
T
ab
le
1
.
Gr
o
u
p
s
o
f
f
in
g
e
r
p
r
in
t
im
ag
e
d
ata
G
r
o
u
p
s
C
o
n
t
e
n
t
s
A
10
0
%
f
u
l
l
y
-
si
z
e
d
f
i
n
g
e
r
p
r
i
n
t
s
i
ma
g
e
B
6
0
%
f
i
n
g
e
r
p
r
i
n
t
s w
i
t
h
d
a
r
k
c
o
l
o
r
b
o
u
n
d
a
r
i
e
s
C
6
0
%
f
i
n
g
e
r
p
r
i
n
t
s w
i
t
h
b
r
i
g
h
t
c
o
l
o
r
b
o
u
n
d
a
r
i
e
s
D
8
0
%
f
i
n
g
e
r
p
r
i
n
t
s w
i
t
h
b
r
i
g
h
t
c
o
l
o
r
b
o
u
n
d
a
r
i
e
s
a
n
d
u
n
c
l
e
a
r
i
m
a
g
e
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
Mea
s
u
r
in
g
mem
etic
a
lg
o
r
ith
m
p
erfo
r
ma
n
ce
o
n
ima
g
e
fin
g
er
p
r
in
ts
d
a
ta
s
et.
.
.
(
P
r
ia
ti A
s
s
ir
o
j
)
99
T
h
en
we
co
n
d
u
ct
s
ev
er
al
s
tep
s
as d
escr
ib
ed
b
elo
w:
−
L
o
ca
l
s
ea
r
ch
.
W
e
s
tar
t
th
e
lo
c
al
s
ea
r
ch
b
y
r
ea
d
in
g
th
e
f
o
ld
e
r
an
d
f
in
g
er
p
r
i
n
t
f
ile
h
id
e
n
tific
atio
n
an
d
d
ata
p
r
o
ce
s
s
in
g
.
W
e
d
iv
id
e
im
ag
e
f
in
g
er
p
r
in
t
d
ata
in
to
f
o
u
r
g
r
o
u
p
s
,
as
s
h
o
wn
in
T
ab
le
1
,
b
ased
o
n
its
ch
ar
ac
ter
is
tics
.
−
C
o
n
v
er
s
io
n
.
I
n
th
is
s
tep
is
co
n
v
er
t
f
in
g
e
r
p
r
in
ts
im
ag
e
d
ata
to
s
tr
in
g
ar
r
ay
t
h
en
co
n
v
er
t
s
tr
in
g
ar
r
ay
to
b
in
ar
y
co
d
e
u
s
e
B
ase6
4
.
I
n
t
h
is
alg
o
r
ith
m
,
we
will
d
o
s
o
m
e
s
wap
,
cr
o
s
s
o
v
er
,
an
d
m
u
tatio
n
p
r
o
ce
s
s
,
in
s
id
e
th
er
e,
ar
e
cr
o
s
s
in
g
an
d
d
ata
clo
n
i
n
g
th
at
n
ee
d
s
b
in
ar
y
in
p
u
t
an
d
o
u
tp
u
t,
s
o
we
n
ee
d
it
to
b
e
co
n
v
er
ted
to
b
in
ar
y
co
d
e.
W
e
p
r
esen
t th
e
c
o
d
e
in
jav
a
lan
g
u
a
g
e
to
c
o
n
v
e
r
t strin
g
ar
r
ay
to
b
in
ar
y
co
d
e
as sh
o
wn
in
Fig
u
r
e
3.
−
E
liti
s
m
.
I
n
th
is
s
tep
,
we
co
n
d
u
ct
s
o
m
e
s
elec
tio
n
s
tep
s
.
T
h
e
o
r
etica
lly
,
in
elitis
m
,
we
n
ee
d
a
2
0
%
r
an
d
o
m
s
am
p
lin
g
o
f
th
e
p
o
p
u
latio
n
.
I
n
th
e
p
r
elim
in
ar
y
e
x
p
er
im
e
n
t,
we
o
n
ly
u
s
e
a
2
%
r
an
d
o
m
s
am
p
lin
g
o
f
th
e
p
o
p
u
latio
n
ca
u
s
ed
b
y
t
h
e
lim
it
atio
n
o
f
o
u
r
h
ar
d
d
r
iv
e.
Fig
u
r
e
4
s
h
o
ws
th
e
elitis
m
co
d
e
with
jav
a
lan
g
u
ag
e.
At
th
e
en
d
o
f
co
d
e
,
we
f
in
is
h
with
"n
u
ll" to
clea
r
v
a
r
iab
les th
at
ar
e
n
o
t
u
s
ed
s
o
th
at
R
AM
is
n
o
t f
u
ll.
−
C
r
o
s
s
o
v
er
.
C
r
o
s
s
o
v
er
is
a
h
y
b
r
id
izatio
n
s
tep
o
f
ev
er
y
s
elec
ted
s
am
p
le
to
g
et
n
ew
o
f
f
s
p
r
in
g
.
T
h
e
s
elec
ted
s
am
p
les
d
iv
id
e
in
to
two
g
r
o
u
p
s
,
m
ale
an
d
f
em
ale,
th
e
n
th
e
y
will
b
e
s
wap
p
ed
o
n
e
t
o
th
e
wh
o
le
s
elec
ted
s
am
p
le
in
an
o
th
er
g
r
o
u
p
,
th
is
p
r
o
ce
s
s
will
b
e
lo
o
p
e
d
u
n
til
th
e
l
ast
s
elec
ted
s
am
p
le.
Fig
u
r
e
5
i
s
th
e
illu
s
tr
atio
n
o
f
th
e
cr
o
s
s
o
v
er
p
r
o
ce
s
s
in
th
is
p
ap
er
an
d
Fig
u
r
e
6
s
h
o
wn
c
r
o
s
s
o
v
er
co
d
e
i
n
jav
a
lan
g
u
ag
e.
−
Mu
tatio
n
.
T
h
is
is
th
e
last
p
r
o
ce
s
s
,
wh
er
e
n
ew
o
f
f
s
p
r
in
g
f
r
o
m
th
e
cr
o
s
s
o
v
er
p
r
o
ce
s
s
clo
n
e
d
th
en
r
e
v
er
s
ed
s
ev
er
al
b
in
ar
y
lin
es
to
g
et
n
ew
o
f
f
s
p
r
in
g
.
Fig
u
r
e
7
s
h
o
ws
th
e
co
d
e
o
f
th
e
m
u
tatio
n
p
r
o
ce
s
s
i
n
jav
a
lan
g
u
ag
e
.
All
s
tep
s
wer
e
co
d
ed
in
jav
a
lan
g
u
ag
e
with
Netb
ea
n
s
I
DE
8
.
2
an
d
E
x
p
er
im
en
ts
ca
r
r
ie
d
o
u
t
u
s
e
h
o
s
t
an
d
g
u
est
o
p
er
atin
g
s
y
s
tem
in
1
PC
with
VM
B
o
x
.
T
h
is
s
y
s
tem
r
u
n
s
in
I
n
tel
i5
-
2
5
4
0
M
with
1
6
GB
o
f
m
em
o
r
y
.
Fig
u
r
e
3
.
R
ea
d
f
ile
o
r
f
o
ld
er
a
n
d
co
n
v
er
s
io
n
c
o
d
e
in
J
av
a
T
h
is
wo
r
k
co
n
s
is
ts
o
f
s
ev
er
al
s
tep
s
.
At
th
e
f
ir
s
t step
we
co
n
d
u
ct
s
o
m
e
s
tep
s
as d
escr
ib
e
b
e
lo
w:
-
R
ea
d
f
in
g
er
p
r
in
ts
im
ag
e
f
ile
in
ev
er
y
f
o
ld
er
a
n
d
we
r
ea
d
o
n
e
b
y
o
n
e
.
-
C
o
n
v
er
t im
ag
e
d
ata
to
ar
r
a
y
s
tr
in
g
ty
p
e
u
s
in
g
B
ase6
4
.
-
C
o
n
v
er
t
ar
r
ay
s
tr
in
g
to
b
in
ar
y
co
d
e.
T
h
is
is
a
m
u
s
t
b
ec
au
s
e
we
u
s
e
b
in
ar
y
ty
p
e
in
th
e
s
w
ap
o
r
cr
o
s
s
o
v
er
p
r
o
ce
s
s
an
d
m
u
tatio
n
p
r
o
ce
s
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
0
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
,
Vo
l.
19
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
1
:
9
6
-
10
4
100
-
Po
p
u
latio
n
f
ilter
in
g
to
s
elec
t
p
ar
en
t
ca
n
d
id
ates.
I
n
th
e
f
ir
s
t
e
x
p
er
im
en
t,
we
u
s
e
2
0
%
o
f
th
e
p
o
p
u
latio
n
th
en
we
g
o
t
s
wap
o
r
c
r
o
s
s
o
v
er
r
esu
lt
was
to
o
b
ig
.
C
au
s
ed
b
y
th
e
l
im
itatio
n
o
f
o
u
r
h
ar
d
d
r
iv
e,
th
e
n
we
d
ec
i
d
ed
to
u
s
e
o
n
ly
2
% o
f
th
e
p
o
p
u
latio
n
s
o
th
at
th
e
s
wap
r
esu
lts
will n
o
t
o
v
er
wh
elm
th
e
h
a
r
d
d
r
iv
e.
-
Swap
o
r
cr
o
s
s
o
v
er
p
r
o
ce
s
s
,
m
atin
g
all
o
f
th
e
2
% o
f
th
e
p
o
p
u
latio
n
to
g
et
th
e
n
ew
o
f
f
s
p
r
in
g
.
T
h
e
p
r
o
ce
s
s
is
to
m
ate
ev
er
y
b
in
ar
y
to
ea
c
h
o
t
h
er
th
en
we
g
o
t
49
0
0
n
ew
o
f
f
s
p
r
in
g
s
.
-
T
h
e
last
p
r
o
ce
s
s
is
th
e
m
u
tatio
n
f
o
r
49
0
0
n
ew
o
f
f
s
p
r
in
g
s
f
r
o
m
th
e
cr
o
s
s
o
v
er
p
r
o
ce
s
s
.
T
h
is
is
a
b
in
ar
y
r
ev
e
r
s
e
p
r
o
ce
s
s
wh
er
e
ea
ch
v
alu
e
o
f
1
will b
e
r
ev
er
s
ed
to
0
an
d
0
wil
l b
e
r
ev
er
s
ed
t
o
1
.
T
h
ese
co
d
es
ab
o
v
e
ar
e
to
r
e
a
d
ev
er
y
f
o
ld
er
th
at
co
n
tain
s
f
i
n
g
er
p
r
i
n
ts
im
ag
e
d
ata,
th
e
n
c
o
n
v
er
ted
to
ar
r
ay
s
tr
in
g
ty
p
e
u
s
in
g
th
e
B
ase6
4
p
r
o
ce
s
s
an
d
th
e
r
esu
lt th
en
co
n
v
er
ted
to
b
i
n
ar
y
co
d
e
b
e
ca
u
s
e
cr
o
s
s
o
v
er
an
d
m
u
tatio
n
p
r
o
ce
s
s
is
m
ate
an
d
clo
n
e
p
r
o
ce
s
s
o
f
b
in
a
r
y
co
d
e
.
Fig
u
r
e
4
is
th
e
elitis
m
co
d
e.
S
elec
t r
an
d
o
m
ly
f
r
o
m
2
0
%
o
f
th
e
p
o
p
u
latio
n
t
o
f
in
d
p
ar
en
t
ca
n
d
id
ates
an
d
b
ec
au
s
e
o
f
th
e
lim
itatio
n
o
f
o
u
r
h
ar
d
d
r
iv
e
we
u
s
e
o
n
ly
2
%
o
f
th
e
p
o
p
u
latio
n
.
T
h
e
last
“n
u
ll
”
co
d
e
is
to
em
p
ty
t
h
e
v
ar
ia
b
le
wh
en
it is
n
o
t u
s
ed
s
o
th
at
R
AM
is
n
o
t f
u
ll
.
Fig
u
r
e
5
is
a
cr
o
s
s
o
v
er
f
u
n
ctio
n
co
d
e
to
m
ate
e
v
er
y
s
elec
ted
s
am
p
le
to
g
et
th
e
n
ew
o
f
f
s
p
r
in
g
s
wh
er
e
th
e
s
elec
ted
s
am
p
le
d
iv
id
ed
i
n
to
2
g
r
o
u
p
s
,
m
ale
a
n
d
f
em
al
e
th
en
s
wap
to
all
s
am
p
les
to
th
e
o
th
er
g
r
o
u
p
an
d
lo
o
p
ed
it.
T
h
e
s
wap
p
r
o
ce
s
s
is
d
ep
icted
in
Fig
u
r
e
6
s
tar
tin
g
f
r
o
m
s
am
p
le
1
in
o
n
e
g
r
o
u
p
m
at
ed
with
all
s
am
p
le
s
in
th
e
o
th
er
g
r
o
u
p
.
L
ik
ewise
with
s
am
p
le
2
an
d
o
th
er
s
.
T
h
is
p
r
o
ce
s
s
will b
e
r
ep
ea
ted
co
n
tin
u
o
u
s
ly
u
n
til all
th
e
s
am
p
les
h
av
e
b
ee
n
s
u
cc
ess
f
u
l
ly
m
ated
.
T
h
e
last
p
r
o
ce
s
s
is
th
e
p
r
o
ce
s
s
o
f
m
u
tatio
n
.
T
h
e
p
r
o
g
r
a
m
co
d
e
is
in
Fig
u
r
e
7
.
W
h
er
e
th
e
r
esu
lts
o
f
th
e
m
ar
r
iag
e
f
r
o
m
th
e
cr
o
s
s
o
v
er
p
r
o
ce
s
s
ar
e
clo
n
ed
to
th
e
n
r
ev
er
s
e
th
e
b
in
ar
y
co
d
e,
th
e
v
alu
e
o
f
1
b
ec
o
m
es
0
an
d
v
ice
v
er
s
a,
to
g
et
n
ew
o
f
f
s
p
r
in
g
.
Fig
u
r
e
4
.
E
liti
s
m
co
d
e
i
n
J
av
a
Fig
u
r
e
5
.
C
r
o
s
s
o
v
er
c
o
d
e
in
J
av
a
lan
g
u
a
g
e
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
Mea
s
u
r
in
g
mem
etic
a
lg
o
r
ith
m
p
erfo
r
ma
n
ce
o
n
ima
g
e
fin
g
er
p
r
in
ts
d
a
ta
s
et.
.
.
(
P
r
ia
ti A
s
s
ir
o
j
)
101
Fig
u
r
e
6
.
C
r
o
s
s
o
v
er
illu
s
tr
atio
n
Fig
u
r
e
7
.
Mu
tatio
n
co
d
e
in
J
av
a
4.
E
XP
E
R
I
M
E
N
T
A
L
RE
SUL
T
Acc
o
r
d
in
g
to
T
a
b
le
2
,
ex
p
er
i
m
en
ts
ca
r
r
ied
o
u
t w
ith
1
5
s
p
ec
im
en
s
o
f
d
ata.
On
ce
s
p
ec
im
en
co
n
s
is
ts
o
f
f
in
g
er
p
r
in
t d
ata
th
at
g
r
o
u
p
ed
b
ef
o
r
e.
T
h
e
f
o
llo
win
g
is
a
d
escr
ip
tio
n
o
f
ea
c
h
s
p
ec
im
en
;
Sp
ec
im
en
1
co
n
s
is
t
o
f
th
e
en
ti
r
e
f
in
g
er
p
r
in
t
d
ata
Sp
ec
im
en
2
co
n
s
is
t
s
o
f
f
in
g
er
p
r
in
t d
at
a
ty
p
e
A
Sp
ec
im
en
3
co
n
s
is
t
s
o
f
f
in
g
er
p
r
in
t d
ata
ty
p
e
B
Sp
ec
im
en
4
co
n
s
is
t
s
o
f
f
in
g
er
p
r
in
t d
ata
ty
p
e
C
Sp
ec
im
en
5
co
n
s
is
t
s
o
f
f
in
g
er
p
r
in
t d
ata
ty
p
e
D
Sp
ec
im
en
6
co
n
s
is
t
s
o
f
a
co
m
b
in
atio
n
o
f
f
in
g
er
p
r
i
n
t d
ata
ty
p
e
A
to
ty
p
e
B
Sp
ec
im
en
7
co
n
s
is
t
s
o
f
a
co
m
b
in
atio
n
o
f
f
in
g
er
p
r
i
n
t d
ata
ty
p
e
A
to
ty
p
e
C
Sp
ec
im
en
8
co
n
s
is
t
s
o
f
a
co
m
b
in
atio
n
o
f
f
in
g
er
p
r
i
n
t d
ata
ty
p
e
A
to
ty
p
e
D
Sp
ec
im
en
9
co
n
s
is
t
s
o
f
a
co
m
b
in
atio
n
o
f
f
in
g
er
p
r
i
n
t d
ata
ty
p
e
B
to
ty
p
e
C
Sp
ec
im
en
1
0
c
o
n
s
is
t
s
o
f
a
co
m
b
in
atio
n
o
f
f
in
g
er
p
r
in
t d
ata
t
y
p
e
B
to
ty
p
e
D
Sp
ec
im
en
1
1
c
o
n
s
is
t
s
o
f
a
co
m
b
in
atio
n
o
f
f
in
g
er
p
r
in
t d
ata
t
y
p
e
C
to
ty
p
e
D
Sp
ec
im
en
1
2
c
o
n
s
is
t
s
o
f
a
co
m
b
in
atio
n
o
f
f
in
g
er
p
r
in
t d
ata
t
y
p
e
A,
B
,
an
d
C
Sp
ec
im
en
1
3
c
o
n
s
is
t
s
o
f
a
co
m
b
in
atio
n
o
f
f
in
g
er
p
r
in
t d
ata
t
y
p
e
A,
B
,
an
d
D
Sp
ec
im
en
1
4
c
o
n
s
is
t
s
o
f
a
co
m
b
in
atio
n
o
f
f
in
g
er
p
r
in
t d
ata
t
y
p
e
B
,
C
,
an
d
D
Sp
ec
im
en
1
5
c
o
n
s
is
t
s
o
f
a
co
m
b
in
atio
n
o
f
f
in
g
er
p
r
in
t d
ata
t
y
p
e
A,
C
,
an
d
D
Acc
o
r
d
in
g
t
o
th
ese
s
p
ec
im
en
s,
we
co
n
d
u
ct
f
o
u
r
s
tep
s
o
f
ex
p
e
r
im
en
t,
co
n
s
is
t
s
o
f
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
0
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
,
Vo
l.
19
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
1
:
9
6
-
10
4
102
-
Par
tial
T
est
1
.
I
n
th
is
p
ar
t,
we
s
tar
t
with
r
ea
d
in
g
f
in
g
er
p
r
in
t
im
ag
e
d
ata,
co
n
v
er
t
d
ata
to
a
r
r
ay
s
tr
in
g
,
a
n
d
co
n
v
er
t a
r
r
ay
s
tr
in
g
to
b
in
a
r
y
co
d
e.
-
Par
ti
al
T
est
2
.
T
h
is
i
s
a
s
elec
ti
o
n
p
r
o
ce
s
s
wh
ich
u
s
es
eliti
s
m
p
r
in
cip
al.
W
e
s
elec
t
p
ar
en
t
ca
n
d
id
ates
2
%
o
f
th
e
en
tire
p
o
p
u
latio
n
.
-
Par
tial T
est 3
.
T
h
is
p
ar
t is
a
s
wap
o
r
cr
o
s
s
o
v
er
p
r
o
ce
s
s
to
g
en
er
ate
n
ew
o
f
f
s
p
r
in
g
.
-
Par
tial T
est 4
.
T
h
is
is
th
e
last
wh
er
e
we
d
o
m
u
tatio
n
to
ea
ch
o
f
f
s
p
r
in
g
.
W
e
m
ea
s
u
r
e
ev
er
y
p
r
o
ce
s
s
in
g
tim
e
in
m
illi
s
ec
o
n
d
f
o
r
ea
c
h
p
ar
tial
test
th
en
ad
d
th
em
u
p
.
Fo
llo
win
g
th
e
r
esu
lts
test
s
p
ec
im
en
,
2
ac
h
iev
ed
th
e
b
est
r
esu
lt
an
d
h
as
th
e
m
o
s
t
ef
f
icien
t
p
r
o
ce
s
s
in
g
tim
e
with
1
0
9
1
6
8
m
s
.
T
h
e
s
p
ec
im
en
1
is
th
e
wo
r
s
t
co
m
p
ar
ed
to
th
e
en
tire
s
p
ec
i
m
en
with
to
tal
p
r
o
ce
s
s
in
g
tim
e
4
0
3
2
2
2
0
m
s
.
T
h
is
alg
o
r
ith
m
h
as
p
r
o
ce
s
s
ed
e
v
er
y
s
p
ec
im
en
s
u
cc
ess
f
u
lly
with
an
av
er
a
g
e
o
f
to
tal
p
r
o
ce
s
s
in
g
tim
e
1
5
1
6
9
8
6
m
s
.
T
ab
le
2
.
Nu
m
e
r
ical
ex
p
er
i
m
en
t r
esu
lt
MA
Res
u
l
t
D
at
a
D
a
t
a
St
o
ra
g
e
Part
i
a
l
T
e
s
t
(
ms
)
T
o
t
a
l
T
i
me
O
r
i
g
i
n
a
l
E
l
i
t
i
s
m
Sw
a
p
Mu
t
a
t
i
o
n
1
2
3
4
Sp
e
ci
me
n
1
7
1
0
5
4
8
1
9
7
2
5
0
9
5
6
1
2
9
0
7
8
7
8
7
2
4
0
3
2
2
2
0
7
2
0
0
1
4
0
4
9
0
0
4
9
0
0
2
2
.
8
G
B
2
6
8
1
8
0
1
7
1
6
7
6
3
2
3
1
7
4
9
8
1
0
9
1
6
8
1
8
0
0
35
2
8
9
2
8
9
2
7
0
MB
3
2
4
1
6
2
3
2
1
2
1
5
3
4
3
0
4
2
6
3
9
4
6
5
6
0
7
6
5
1
8
0
0
35
2
8
9
2
8
9
4
.
8
G
B
4
2
5
6
8
4
3
2
1
5
6
5
6
4
5
3
5
0
7
0
9
6
8
5
8
8
8
1
8
1
8
0
0
35
2
8
9
2
8
9
4
.
3
G
B
5
1
7
1
7
3
1
1
2
7
9
7
8
2
7
2
4
4
4
4
1
6
2
3
7
1
1
1
5
1
8
0
0
35
2
8
9
2
8
9
2
.
4
G
B
6
3
0
1
0
2
1
4
7
2
5
3
9
2
0
7
7
4
8
3
3
9
9
9
2
1
3
2
1
3
0
1
3
6
0
0
70
1
2
2
5
1
2
2
5
8
.
9
G
B
7
2
5
9
1
0
4
3
6
3
3
7
3
1
5
8
0
6
2
2
7
9
5
7
6
1
0
6
0
1
1
6
3
6
0
0
70
1
2
2
5
1
2
2
5
7
.
9
G
B
8
2
1
1
4
3
0
2
6
1
1
1
8
1
1
5
8
0
8
2
1
2
0
5
2
8
0
0
4
0
8
3
6
0
0
70
1
2
2
5
1
2
2
5
4
.
5
G
B
9
4
6
9
3
2
5
7
4
5
5
4
2
1
5
9
7
2
5
2
4
5
1
1
9
1
6
1
9
7
1
1
3
6
0
0
70
1
2
2
5
1
2
2
5
1
1
.
4
G
B
10
4
9
9
7
4
4
6
4
6
6
6
4
1
1
0
1
4
2
1
7
6
0
3
1
1
4
3
2
5
8
2
3
6
0
0
70
1
2
2
5
1
2
2
5
8
G
B
11
3
9
0
7
2
3
5
8
7
6
0
1
1
0
9
6
4
8
1
7
5
6
3
2
1
2
6
3
6
0
7
3
6
0
0
70
1
2
2
5
1
2
2
5
7
.
6
G
B
12
5
9
7
5
0
2
1
2
2
8
7
4
0
4
1
6
6
5
1
6
8
4
1
6
7
2
9
2
7
0
6
0
5
4
0
0
1
0
5
2
7
0
4
2
7
0
4
1
7
.
4
G
B
13
4
8
5
2
5
4
1
0
0
2
5
2
9
3
0
7
5
4
5
4
5
0
9
8
9
2
2
4
6
3
1
8
5
4
0
0
1
0
5
2
7
0
4
2
7
0
4
1
3
.
1
G
B
14
6
2
0
7
4
9
1
2
7
7
4
7
1
2
4
4
3
4
1
3
7
7
1
3
5
2
5
1
9
6
9
6
5
4
0
0
1
0
5
2
7
0
4
2
7
0
4
1
5
.
8
G
B
15
3
5
5
9
7
5
8
0
5
2
5
0
2
6
4
3
9
8
4
7
6
2
9
8
1
9
0
1
9
2
3
5
4
0
0
1
0
5
2
7
0
4
2
7
0
4
1
2
.
3
G
B
5.
C
O
NCLU
SI
O
N
I
n
th
is
p
ap
e
r
,
we
d
iv
id
e
t
h
e
im
ag
e
f
in
g
e
r
p
r
in
t
d
ata
in
to
1
5
s
p
ec
im
en
s
to
d
o
f
o
u
r
p
ar
tial
test
s
.
I
n
o
r
d
er
to
k
n
o
w
th
e
p
er
f
o
r
m
an
ce
o
f
MA
,
we
m
ea
s
u
r
e
ev
er
y
s
p
ec
im
en
in
ev
er
y
p
ar
tial
test
th
en
g
en
er
ate
ea
ch
p
r
o
ce
s
s
in
g
tim
e.
Acc
o
r
d
in
g
to
th
e
r
esu
lts
,
s
p
ec
im
en
2
ac
h
iev
es th
e
b
est p
r
o
ce
s
s
in
g
tim
e
with
1
0
1
9
1
6
8
m
s
,
it h
as th
e
m
o
s
t
ef
f
icien
t p
r
o
ce
s
s
in
g
tim
e
o
f
all
s
p
ec
im
en
s
.
At
th
e
f
ea
tu
r
e,
we
will c
o
n
d
u
ct
s
o
m
e
ex
p
er
im
en
ts
to
th
is
alg
o
r
ith
m
p
er
f
o
r
m
an
ce
in
m
an
y
e
n
v
ir
o
n
m
en
tal
s
y
s
tem
s
,
s
u
ch
as in
a
co
m
p
u
ter
n
etwo
r
k
.
ACK
NO
WL
E
DG
E
M
E
NT
S
T
h
is
wo
r
k
is
s
u
p
p
o
r
ted
b
y
R
esear
ch
an
d
T
ec
h
n
o
lo
g
y
T
r
a
n
s
f
er
Of
f
ice,
B
in
a
Nu
s
an
tar
a
Un
iv
er
s
ity
as
a
p
ar
t
o
f
B
in
a
Nu
s
an
tar
a
Un
iv
er
s
ity
’
s
I
n
ter
n
atio
n
al
R
esear
ch
Gr
an
t
en
titl
ed
ME
ME
T
I
C
AL
GORIT
HM
I
N
HI
GH
-
PERF
O
R
MA
NC
E
C
O
MPUT
AT
I
ON
with
co
n
tr
ac
t
n
u
m
b
er
:
No
.
0
2
6
/VR
.
R
T
T
/I
V/2
0
2
0
an
d
c
o
n
tr
ac
t
d
ate:
6
Ap
r
il 2
0
2
0
.
RE
F
E
R
E
NC
E
S
[1
]
A.
K
.
Ja
in
.
,
e
t
a
l
., “
Bio
m
e
tri
c
s:
p
e
rso
n
a
l
i
d
e
n
ti
fi
c
a
ti
o
n
in
n
e
two
r
k
e
d
so
c
iety
,
”
S
p
rin
g
e
r In
ter
n
a
ti
o
n
a
l
,
2
0
0
6
.
[2
]
D.
M
a
lt
o
n
i,
e
t
a
l
.
,
“
Ha
n
d
b
o
o
k
o
f
fin
g
e
rp
r
in
t
re
c
o
g
n
it
io
n
,
”
S
p
ri
n
g
e
r
-
Ver
la
g
Ne
w
Y
o
rk
In
c
,
2
0
0
9
.
[3
]
An
il
K.
Ja
in
,
Jia
n
ji
a
n
g
F
e
n
g
,
“
La
ten
t
fi
n
g
e
r
p
rin
t
m
a
tch
in
g
,
”
IE
EE
T
ra
n
sa
c
ti
o
n
s
o
n
P
a
tt
e
rn
An
a
lys
is
a
n
d
M
a
c
h
in
e
In
telli
g
e
n
c
e
,
v
o
l.
3
3
,
n
o
.
1
,
Ja
n
u
a
ry
2
0
1
1
.
[4
]
S.
P
a
n
k
a
n
ti
,
S
a
li
l
P
ra
b
h
a
k
a
r,
An
il
K.
Ja
in
,
“
On
th
e
i
n
d
i
v
id
u
a
li
t
y
o
f
fin
g
e
rp
ri
n
ts
,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
P
a
tt
e
rn
An
a
lys
is
a
n
d
M
a
c
h
i
n
e
In
tell
ig
e
n
c
e
,
v
o
l
.
2
4
,
n
o
.
8
,
p
p
.
1
0
1
0
-
1
0
2
5
.
[5
]
A.
Ja
in
,
Li
n
H
o
n
g
,
“
On
-
li
n
e
fi
n
g
e
rp
ri
n
t
v
e
rifi
c
a
ti
o
n
,
IEE
E
T
r
a
n
sa
c
ti
o
n
s
o
n
P
a
tt
e
rn
A
n
a
lys
is
a
n
d
M
a
c
h
in
e
In
telli
g
e
n
c
e
,
v
o
l.
3
,
S
e
p
tem
b
e
r
1
9
9
6
.
[6
]
A.
K.
Ja
in
,
e
t
a
l
.,
“
An
id
e
n
ti
t
y
-
a
u
th
e
n
ti
c
a
ti
o
n
sy
ste
m
u
si
n
g
fi
n
g
e
rp
rin
ts,
”
Pro
c
e
e
d
in
g
s
o
f
IE
EE
,
v
o
l.
8
5
,
n
o
.
9
,
p
p
.
1
3
6
5
-
1
3
8
8
,
S
e
p
tem
b
e
r
1
9
9
7
.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
Mea
s
u
r
in
g
mem
etic
a
lg
o
r
ith
m
p
erfo
r
ma
n
ce
o
n
ima
g
e
fin
g
er
p
r
in
ts
d
a
ta
s
et.
.
.
(
P
r
ia
ti A
s
s
ir
o
j
)
103
[7
]
P
a
b
lo
M
o
sc
a
to
,
“
On
e
v
o
l
u
ti
o
n
,
se
a
rc
h
,
o
p
ti
m
iza
ti
o
n
,
g
e
n
e
ti
c
a
lg
o
rit
h
m
s
a
n
d
m
a
rti
a
l
a
rts:
to
wa
rd
m
e
m
e
ti
c
a
lg
o
rit
h
m
s
,”
T
e
c
h
n
ica
l
Rep
o
rt,
C
a
lt
e
c
h
Co
n
c
u
rr
e
n
t
C
o
mp
u
ta
ti
o
n
P
ro
g
ra
m,
Oc
t
o
b
e
r
2
0
0
0
.
[8
]
P
e
ter M
e
rs,
Be
rn
d
F
.
,
“
F
it
n
e
ss
lan
d
sc
a
p
e
s a
n
d
m
e
m
e
ti
c
a
lg
o
rit
h
m
d
e
sig
n
,
M
c
Gr
a
w
-
Hill
,
L
o
n
d
o
n
,
1
9
9
9
.
[9
]
Ye
w
-
S
o
o
n
O
n
g
,
e
t
a
l
.
,
“
Clas
sifi
c
a
ti
o
n
o
f
a
d
a
p
ti
v
e
m
e
m
e
ti
c
a
lg
o
ri
th
m
s:
a
c
o
m
p
a
ra
ti
v
e
stu
d
y
,”
IEE
E
T
ra
n
s.
S
y
st.
M
a
n
.
Cy
b
e
rn
,
v
o
l.
36
,
n
o
.
1
,
p
p
.
1
4
1
-
1
5
2
,
F
e
b
ru
a
ry
2
0
0
6
.
[1
0
]
An
d
re
a
C.
,
“
A
fa
st
a
d
a
p
ti
v
e
m
e
m
e
ti
c
a
lg
o
rit
h
m
fo
r
o
ff
-
li
n
e
a
n
d
o
n
-
li
n
e
c
o
n
tr
o
l
d
e
si
g
n
o
f
P
M
S
M
d
riv
e
rs,
”
IEE
E
T
ra
n
s.
S
y
st.
M
a
n
Cy
b
e
rn
.
Pa
rt
B
,
v
o
l.
3
7
,
n
o
.
1
,
0
0
.
2
8
-
4
1
,
2
0
0
7
.
[1
1
]
Li
c
h
e
n
g
Jia
u
,
e
t.
a
l
.,
“
Na
tu
ra
l
a
n
d
re
m
o
te
se
n
sin
g
ima
g
e
se
g
m
e
n
t
a
ti
o
n
u
sin
g
m
e
m
e
ti
c
c
o
m
p
u
ti
n
g
,
”
IEE
E
Co
m
p
u
t
.
In
tell.
M
a
g
,
v
o
l
.
5
,
n
o
.
2
,
p
p
.
7
8
-
9
1
,
M
a
y
2
0
1
0
.
[1
2
]
M
a
re
n
Urs
e
lma
n
,
e
t.
a
l
.,
“
A
m
e
m
e
ti
c
a
lg
o
rit
h
m
f
o
r
g
lo
b
a
l
o
p
ti
m
iz
a
ti
o
n
in
c
h
e
m
ica
l
p
ro
c
e
ss
sy
n
th
e
sis
p
ro
b
lem
s,
”
IEE
E
T
ra
n
s
a
c
ti
o
n
s
o
n
Evo
l
u
ti
o
n
a
ry
Co
mp
u
t
a
ti
o
n
,
v
o
l.
1
5
,
n
o
.
5
,
p
p
.
6
5
9
-
6
8
3
,
Oc
to
b
e
r
2
0
1
1
.
[1
3
]
Ya
n
p
in
g
L
u
,
e
t.
a
l.
,
“
P
a
rti
c
le
sw
a
rm
o
p
ti
m
ize
r
fo
r
v
a
riab
le
we
ig
h
ti
n
g
in
c
l
u
ste
rin
g
h
i
g
h
-
d
ime
n
sio
n
a
l
d
a
ta,
”
M
a
c
h
in
e
L
e
a
rn
in
g
,
v
o
l.
8
2
,
n
o
.
1
,
p
p
.
4
3
-
7
0
,
M
a
y
2
0
0
9
.
[1
4
]
Li
a
n
g
Ba
i,
e
t.
a
l
.,
“
A
n
o
v
e
l
a
tt
r
ib
u
te
we
ig
h
t
in
g
a
l
g
o
rit
h
m
fo
r
c
l
u
st
e
rin
g
h
ig
h
-
d
ime
n
si
o
n
a
l
c
a
teg
o
ric
a
l
d
a
ta,
”
Pa
tt
e
rn
Rec
o
g
n
it
io
n
,
v
o
l.
4
4
,
n
o
.
1
2
,
p
p
.
2
8
4
3
-
2
8
6
1
,
De
c
e
m
b
e
r
2
0
1
1
.
[1
5
]
S
tefa
n
Lan
g
,
e
t.
a
l
.
,
“
F
a
st
e
x
trac
ti
o
n
o
f
n
e
u
ro
n
m
o
rp
h
o
lo
g
ies
fro
m
larg
e
-
sc
a
le
S
BF
S
EM
ima
g
e
sta
c
k
s,
”
J
.
Co
mp
u
t.
Ne
u
ro
sc
i
,
v
o
l.
31
,
n
o
.
3
,
p
p
.
5
3
3
-
5
4
5
,
M
a
rc
h
2
0
1
1
.
[1
6
]
Yu
to
n
g
Zh
a
o
,
F
u
K.
S
h
e
o
n
g
,
“
A
fa
st
p
a
ra
lel
c
lu
ste
rin
g
a
l
g
o
ri
th
m
f
o
r
m
o
lec
u
lar
sim
u
latio
n
traje
c
to
ri
e
s,
”
J
o
u
rn
a
l
o
f
Co
mp
u
t
a
ti
o
n
a
l
C
h
e
m
istry
,
v
o
l
.
3
4
,
n
o
.
2
,
p
p
.
9
5
-
1
0
4
,
Ja
n
u
a
ry
2
0
1
3
.
[1
7
]
S
il
v
ia
Ba
h
m
a
n
n
,
Je
n
s
K
o
rtu
s,
“
EVO
—
Ev
o
l
u
ti
o
n
a
ry
a
lg
o
rit
h
m
f
o
r
c
ry
sta
l
stru
c
t
u
re
p
re
d
ictio
n
,”
Co
mp
u
t
.
P
h
y
s.
Co
mm
u
n
,
v
o
l.
1
8
4
,
n
o
.
6
,
p
p
.
1
6
1
8
-
1
6
2
5
,
Ju
n
e
2
0
1
3
.
[1
8
]
Ku
sh
R.
Va
rsh
n
e
y
,
Ala
n
S
.
Wi
ll
sk
y
,
“
Li
n
e
a
r
d
ime
n
sio
n
a
li
t
y
re
d
u
c
ti
o
n
f
o
r
m
a
rg
in
-
b
a
se
d
c
las
sifica
ti
o
n
:
Hi
g
h
-
d
ime
n
sio
n
a
l
d
a
ta an
d
se
n
so
r
n
e
tw
o
rk
s,
”
IEE
E
T
ra
n
s.
S
ig
n
a
l
Pro
c
e
s
s
,
v
o
l
.
5
9
,
n
o
.
6
,
p
p
.
2
4
9
6
-
2
5
1
2
,
Ju
n
e
2
0
1
1
.
[1
9
]
Ha
k
a
n
Erg
u
n
,
e
t.
a
l.
,
Tran
sm
issio
n
sy
ste
m
to
p
o
lo
g
y
o
p
ti
m
iza
ti
o
n
f
o
r
larg
e
-
sc
a
le
o
ffsh
o
re
win
d
i
n
te
g
ra
ti
o
n
,
”
IEE
E
T
ra
n
s.
S
u
st
.
E
n
e
rg
y
,
v
o
l.
3
,
n
o
.
4
,
p
p
.
9
0
8
-
9
1
5
,
Oc
to
b
e
r
2
0
1
2
.
[2
0
]
Jin
-
Hy
u
k
Ho
n
g
,
S
u
n
g
-
Ba
e
Ch
o
,
“
Eff
icie
n
t
h
u
g
e
-
sc
a
le
fe
a
tu
re
se
lec
ti
o
n
with
sp
e
c
iate
d
g
e
n
e
ti
c
a
lg
o
rit
h
m
,
”
P
a
tt
e
rn
Rec
o
g
n
.
L
e
tt
,
v
o
l.
2
7
,
n
o
.
2
,
p
p
.
1
4
3
-
1
5
0
,
Ja
n
u
a
ry
2
0
0
6
.
[2
1
]
Do
n
g
x
iao
Ni
u
,
e
t.
a
l
.,
“
P
o
we
r
l
o
a
d
fo
re
c
a
stin
g
u
sin
g
su
p
p
o
rt
v
e
c
to
r
m
a
c
h
in
e
a
n
d
a
n
t
c
o
l
o
n
y
o
p
ti
m
i
z
a
ti
o
n
,
”
Exp
e
rt
S
y
st.
Ap
p
l
,
v
o
l
.
37
,
n
o
.
3
,
p
p
.
2
5
3
1
-
2
5
3
9
,
M
a
rc
h
2
0
1
0
.
[2
2
]
Yih
a
n
S
h
a
o
,
e
t.
a
l
.
,
“
A
d
v
a
n
c
e
s
i
n
m
e
th
o
d
s
a
n
d
a
lg
o
rit
h
m
s
i
n
a
m
o
d
e
rn
q
u
a
n
tu
m
c
h
e
m
istry
p
r
o
g
ra
m
p
a
c
k
a
g
e
,
”
Ph
y
sic
a
l
Ch
e
mistry
Ch
e
mic
a
l
Ph
y
sic
s: P
CCP
,
v
o
l
.
8
,
n
o
.
27
,
p
p
.
3
1
7
2
–
3
1
9
1
,
J
u
ly
2
0
0
6
.
[2
3
]
Tap
ta
K.
Ro
y
,
R
o
b
e
rt
B.
G
e
rb
e
r,
“
Vib
ra
ti
o
n
a
l
se
lf
-
c
o
n
siste
n
t
fi
e
ld
c
a
lcu
latio
n
s
f
o
r
sp
e
c
tro
sc
o
p
y
o
f
b
io
l
o
g
ica
l
m
o
lec
u
les
:
n
e
w
a
l
g
o
ri
th
m
ic d
e
v
e
l
o
p
m
e
n
ts
a
n
d
a
p
p
li
c
a
ti
o
n
s,
”
P
h
y
sic
a
l
Ch
e
mistry
C
h
e
mic
a
l
P
h
y
sic
s,
v
o
l.
15
,
n
o
.
2
4
,
p
p
.
9
4
6
8
-
9
4
9
2
,
M
a
y
2
0
1
3
.
[2
4
]
Ja
c
q
u
e
s
Blu
m
,
e
t.
a
l
.,
“
Da
ta
a
ss
imilatio
n
f
o
r
g
e
o
p
h
y
sic
a
l
fl
u
id
s,
”
P.
G.
Cia
rle
t
(E
d
.
),
Ha
n
d
b
o
o
k
o
f
Nu
me
ric
a
l
An
a
lys
is,
v
o
l.
1
4
,
p
p
.
3
8
5
–
4
4
1
,
2
0
0
9
.
[2
5
]
Jo
e
l
T.
Du
d
ley
,
e
t.
a
l.
,
“
Dr
u
g
d
isc
o
v
e
ry
i
n
a
m
u
lt
i
d
ime
n
sio
n
a
l
wo
rl
d
:
s
y
ste
m
s,
p
a
tt
e
r
n
s,
a
n
d
n
e
tw
o
rk
s,
”
J
o
u
r
n
a
l
o
f
Ca
rd
io
v
a
sc
u
la
r
T
ra
n
s
l
a
ti
o
n
a
l
Re
s
e
a
rc
h
,
v
o
l.
3
,
n
o
.
5
,
p
p
.
4
3
8
-
4
4
7
,
Oc
to
b
e
r
2
0
1
0
.
[2
6
]
Weili
a
n
g
S
h
i
,
G
ra
c
e
Wah
b
a
,
Ra
fa
e
l
A.
Iriza
rry
,
“
Th
e
p
a
rti
ti
o
n
e
d
LAS
S
O
-
p
a
tt
e
rn
se
a
rc
h
a
lg
o
rit
h
m
with
a
p
p
li
c
a
ti
o
n
to
g
e
n
e
e
x
p
re
ss
io
n
d
a
ta,
”
BM
C
B
i
o
in
f
o
rm
a
ti
c
s
,
v
o
l
.
13
,
n
o
.
1
,
M
a
rc
h
2
0
1
2
.
[2
7
]
M
it
ra
M
o
n
taz
e
ri,
“
M
e
m
e
ti
c
a
lg
o
rit
h
m
ima
g
e
e
n
h
a
n
c
e
m
e
n
t
fo
r
p
r
e
se
rv
in
g
m
e
a
n
b
rig
h
t
n
e
ss
with
o
u
t
lo
sin
g
ima
g
e
fe
a
tu
re
s
,”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Ima
g
e
a
n
d
Gr
a
p
h
ics
,
v
o
l
.
1
9
,
n
o
.
4
,
Oc
to
b
e
r
2
0
1
9
.
[2
8
]
M
a
n
o
sij
G
h
o
sh
,
e
t.
a
l.
,
“
F
e
a
tu
re
se
lec
ti
o
n
fo
r
fa
c
ial
e
m
o
ti
o
n
re
c
o
g
n
it
i
o
n
u
si
n
g
late
h
il
l
-
c
li
m
b
i
n
g
b
a
se
d
m
e
m
e
ti
c
a
lg
o
rit
h
m
,”
M
u
lt
ime
d
.
T
o
o
ls A
p
p
l
,
Ju
n
e
2
0
1
9
.
[2
9
]
Ra
sh
m
i
Wele
k
a
r,
Niles
h
si
n
g
h
V.
Th
a
k
u
r,
“
An
e
n
h
a
n
c
e
d
a
p
p
r
o
a
c
h
t
o
m
e
m
e
ti
c
a
lg
o
rit
h
m
u
se
d
f
o
r
c
h
a
ra
c
ter
re
c
o
g
n
it
i
o
n
,
”
T
h
ird
I
n
ter
n
a
ti
o
n
a
l
Co
n
g
re
ss
o
n
I
n
fo
rm
a
ti
o
n
a
n
d
Co
mm
u
n
ica
ti
o
n
T
e
c
h
n
o
l
o
g
y
,
v
o
l
.
7
9
7
,
p
p
.
5
9
3
-
6
0
2
,
Ja
n
u
a
ry
2
0
1
9
.
[3
0
]
B.
W.
Hw
a
n
g
,
e
t.
a
l.
,
“
F
e
a
tu
re
se
l
e
c
ti
o
n
f
o
r
h
a
n
d
writ
ten
w
o
rd
re
c
o
g
n
it
i
o
n
u
si
n
g
m
e
m
e
ti
c
a
lg
o
rit
h
m
,”
Ad
v
a
n
c
e
s
in
In
telli
g
e
n
t
Co
m
p
u
t
in
g
,
v
o
l.
3
6
4
4
,
No
v
e
m
b
e
r
2
0
0
5
.
[3
1
]
M
a
n
o
sij
G
.
,
e
t.
a
l.
,
“
F
e
a
t
u
e
se
lec
ti
o
n
fo
r
h
a
n
d
wri
ti
n
g
wo
r
d
re
c
o
g
n
it
io
n
u
sin
g
m
e
m
e
ti
c
a
lg
o
rit
h
m
,”
Ad
v
a
n
c
e
s
in
In
telli
g
e
n
t
Co
m
p
u
t
in
g
,
v
o
l.
3
6
4
4
,
p
p
.
1
0
3
-
1
2
4
,
M
a
y
2
0
1
8
.
[3
2
]
B.
Vin
o
t
h
Ku
m
a
r,
e
t.
a
l
.,
“
E
v
o
l
u
ti
o
n
a
ry
a
l
g
o
ri
th
m
with
m
e
m
e
ti
c
se
a
rc
h
c
a
p
a
b
il
it
y
fo
r
o
p
t
ic
d
isc
lo
k
a
li
z
a
ti
o
n
in
re
ti
n
a
l
fu
n
d
u
s ima
g
e
s
,
”
Ch
a
ll
e
n
g
e
s a
n
d
S
o
l
u
ti
o
n
s I
n
telli
g
e
n
t
Da
ta
-
Ce
n
tri
c
S
y
ste
m
,
p
p
.
1
9
1
-
2
0
7
,
2
0
1
9
.
[3
3
]
Yip
e
n
g
Z
h
a
n
g
,
Ya
n
fe
i
Zh
o
n
g
,
“
S
u
b
-
p
ix
e
l
m
a
p
p
i
n
g
b
a
se
d
o
n
m
e
m
e
ti
c
a
lg
o
rit
h
m
fo
r
h
y
p
e
rsp
e
c
tral
i
m
a
g
e
ry
,
”
IEE
E
In
ter
n
a
t
io
n
a
l
Ge
o
sc
ien
c
e
a
n
d
Re
mo
te S
e
n
si
n
g
S
y
mp
o
si
u
m
,
J
u
ly
2
0
1
5
.
[3
4
]
M
in
g
y
a
n
g
Zh
a
n
g
,
e
t.
a
l
.,
“
M
e
m
e
ti
c
a
lg
o
rit
h
m
b
a
se
d
fe
a
tu
re
se
lec
ti
o
n
f
o
r
h
y
p
e
rsp
e
c
tral
ima
g
e
s
c
las
sifica
ti
o
n
,
”
2
0
1
7
IEE
E
Co
n
g
r.
Evo
l.
Co
mp
u
t.
CEC
,
Ju
n
e
2
0
1
7
.
[3
5
]
S
h
a
v
e
ta
Da
rg
a
n
,
M
u
n
is
h
K
u
m
a
r,
“
A
Co
m
p
re
h
e
n
siv
e
s
u
rv
e
y
o
n
t
h
e
b
i
o
m
e
tri
c
re
c
o
g
n
it
i
o
n
sy
s
tem
s
b
a
se
d
o
n
p
h
y
si
o
l
o
g
ica
l
a
n
d
b
e
h
a
v
i
o
ra
l
m
o
d
a
li
ti
e
s
,
”
Exp
e
rt S
y
ste
ms
W
it
h
Ap
p
li
c
a
ti
o
n
s
,
v
o
l
.
1
4
3
,
Ap
ril
2
0
2
0
.
[3
6
]
W.
S
h
e
n
g
,
G
.
Ho
we
ll
s,
M
.
F
a
ir
h
u
r
st,
a
n
d
F
.
De
ra
v
i,
“
A
m
e
m
e
ti
c
fin
g
e
rp
rin
t
m
a
tch
i
n
g
a
l
g
o
ri
th
m
,
”
IEE
E
T
ra
n
s
a
c
ti
o
n
s
o
n
I
n
fo
rm
a
ti
o
n
F
o
re
n
sic
s a
n
d
S
e
c
u
rity
,
v
o
l.
2
,
n
o
.
3
,
p
p
.
4
0
2
-
4
1
1
,
2
0
0
7
.
[3
7
]
Lee
H.C.
&
G
a
e
n
ss
len
R.
E
, “
Ad
v
a
n
c
e
s in
F
i
n
g
e
rp
ri
n
t
Tec
h
n
o
lo
g
y
,
”
2
n
d
e
d
it
i
o
n
,
El
se
v
ier
,
Ne
w
Y
o
rk
,
2
0
0
1
.
[3
8
]
An
d
re
A.,
“
F
in
g
e
rp
rin
t
Tec
h
n
iq
u
e
s,
”
Ch
il
to
n
B
o
o
k
C
o
mp
a
n
y
,
L
o
n
d
o
n
,
1
9
7
1
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
0
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
,
Vo
l.
19
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
1
:
9
6
-
10
4
104
B
I
O
G
RAP
H
I
E
S
O
F
AUTH
O
RS
Pria
ti
As
sir
o
j,
wa
s
b
o
rn
i
n
Cire
b
o
n
,
Ja
wa
Ba
ra
t,
In
d
o
n
e
sia
i
n
1
9
8
6
.
S
h
e
h
a
s
Ba
c
h
e
lo
r
a
n
d
M
a
ste
r
in
C
o
m
p
u
ter
S
c
ien
c
e
.
S
h
e
re
c
e
iv
e
d
th
e
Ba
c
h
e
lo
r
fro
m
S
TM
I
K
Ba
n
i
S
a
leh
Be
k
a
si,
i
n
2
0
1
1
a
n
d
re
c
e
iv
e
d
h
e
r
M
a
ste
r
fro
m
S
TM
IK
LIKM
I,
Ba
n
d
u
n
g
,
I
n
d
o
n
e
sia
,
in
2
0
1
6
.
F
r
o
m
2
0
1
4
to
2
0
1
6
,
s
h
e
wa
s
a
lec
tu
re
r
i
n
U
n
iv
e
rsitas
S
in
g
a
p
e
rb
a
n
g
sa
Ka
ra
wa
n
g
,
In
d
o
n
e
sia
a
n
d
fr
o
m
2
0
1
6
a
n
d
re
c
e
n
t
sh
e
is a
lec
tu
re
r
i
n
U
n
iv
e
rsitas
B
u
a
n
a
P
e
rju
a
n
g
a
n
Ka
ra
wa
n
g
in
In
f
o
rm
a
ti
o
n
S
y
ste
m
De
p
t.
S
i
n
c
e
Ja
n
u
a
ry
2
0
1
9
sh
e
is
a
lec
tu
re
r
in
P
o
li
tek
n
i
k
Im
ig
ra
si,
M
i
n
istry
o
f
Law
a
n
d
Hu
m
a
n
Rig
h
t,
Re
p
u
b
li
c
o
f
I
n
d
o
n
e
sia
.
S
in
c
e
M
a
rc
h
2
0
1
8
s
h
e
h
a
s b
e
e
n
a
sc
h
o
lar o
f
Bin
a
Nu
sa
n
tara
G
ra
d
u
a
te
P
ro
g
ra
m
,
D
o
c
to
r
o
f
C
o
m
p
u
ter
S
c
ien
c
e
,
Bi
n
a
Nu
n
sa
n
tara
Un
i
v
e
rsity
Ja
k
a
rta,
In
d
o
n
e
sia
.
He
r
re
se
a
rc
h
field
s
a
re
d
a
ta
m
in
in
g
,
h
i
g
h
p
e
rf
o
rm
a
n
c
e
c
o
m
p
u
ti
n
g
a
n
d
e
v
o
lu
ti
o
n
a
ry
a
l
g
o
rit
h
m
.
H
.
L.
H
.
S
.
W
a
r
n
a
r
s
,
re
c
e
iv
e
d
a
P
h
.
D.
d
e
g
re
e
in
Co
m
p
u
ter
S
c
ien
c
e
fro
m
M
a
n
c
h
e
ste
r
M
e
tro
p
o
li
tan
U
n
iv
e
rsit
y
.
S
i
n
c
e
S
e
p
tem
b
e
r
2
0
1
5
h
e
is
a
He
a
d
o
f
In
fo
rm
a
ti
o
n
S
y
ste
m
s
c
o
n
c
e
n
tratio
n
a
t
d
e
p
a
rtme
n
t
D
o
c
to
r
o
f
Co
m
p
u
ter
S
c
ien
c
e
Bi
n
a
Nu
s
a
n
tara
Un
iv
e
rsity
,
wo
r
k
s
so
m
e
p
ro
jec
t
re
se
a
rc
h
wi
th
m
y
d
o
c
to
ra
l
c
o
m
p
u
ter S
c
ien
c
e
stu
d
e
n
ts i
n
re
se
a
rc
h
a
re
a
su
c
h
a
s
G
a
m
e
,
Artifi
c
ial
In
telli
g
e
n
c
e
i
n
c
lu
d
i
n
g
Da
ta
M
i
n
in
g
,
M
a
c
h
in
e
Lea
rn
in
g
a
n
d
De
c
isio
n
S
u
p
p
o
rt
S
y
ste
m
a
p
p
li
c
a
ti
o
n
su
c
h
a
s DS
S
,
BI,
Da
sh
b
o
a
rd
,
Da
ta W
a
re
h
o
u
se
,
a
n
d
so
o
n
Ed
i
Abd
u
r
r
a
c
h
m
a
n
,
re
c
e
iv
e
d
B
.
S
c
a
n
d
M
a
ste
r
o
f
S
tatisti
c
s
in
Ap
p
li
e
d
S
tatisti
c
s
fro
m
Bo
g
o
r
A
g
ricu
lt
u
ra
l
Un
i
v
e
rsity
,
t
h
e
n
re
c
e
iv
e
d
M
.
S
c
a
n
d
P
h
.
D.
i
n
su
rv
e
y
sta
ti
stics
a
n
d
sta
ti
stics
fro
m
IOWA
S
tate
U
n
iv
e
rsity
,
USA.
He
is
c
u
rre
n
tl
y
a
p
r
o
fe
ss
o
r
a
n
d
d
e
a
n
o
f
th
e
Bi
n
u
s
G
ra
d
u
a
te
P
ro
g
ra
m
,
Do
c
to
r
o
f
Co
m
p
u
ter
S
c
ien
c
e
,
Bin
a
Nu
sa
n
tara
Un
iv
e
rsity
Ja
k
a
rta.
His
re
se
a
r
c
h
in
tere
st
in
c
lu
d
e
s
sta
ti
stics
,
su
rv
e
y
sta
ti
stics
,
a
n
d
a
p
p
li
e
d
sta
ti
stics
a
n
d
m
a
n
a
g
e
m
e
n
t
in
f
o
rm
a
ti
o
n
sy
ste
m
s
.
M
r.
Ab
d
u
rra
c
h
m
a
n
’s
a
wa
rd
s
a
n
d
h
o
n
o
rs
in
c
l
u
d
e
th
e
M
U
S
IG
M
A
RHO
S
o
c
iety
(
1
9
8
5
)
a
n
d
Be
st
Lec
tu
re
r
Bi
n
u
s
Un
i
v
e
rsity
(
2
0
1
2
)
.
He
is
a
lso
a
m
e
m
b
e
r
o
f
th
e
Am
e
ric
a
n
S
tatisti
c
a
l
As
so
c
iatio
n
,
In
tern
a
ti
o
n
a
l
A
ss
o
c
iatio
n
o
f
E
n
g
i
n
e
e
rs
(IAENG
),
Ga
m
m
a
S
ig
m
a
Be
ta,
a
n
d
a
s
a
Vic
e
P
re
sid
e
n
t
o
f
th
e
As
ian
F
e
d
e
ra
ti
o
n
fo
r
In
fo
rm
a
ti
o
n
Tec
h
n
o
l
o
g
y
i
n
Ag
ricu
lt
u
re
.
F
ro
m
1
9
8
0
-
2
0
1
5
a
c
ti
v
e
s
in
t
h
e
m
in
istr
y
o
f
a
g
ricu
lt
u
re
i
n
m
a
n
y
p
o
siti
o
n
s
o
f
d
irec
to
r.
He
is
a
lso
a
c
ti
v
e
a
s
a
p
u
b
li
c
sp
e
a
k
e
r
i
n
n
a
ti
o
n
a
l
a
n
d
i
n
tern
a
ti
o
n
a
l
se
m
in
a
rs.
Ac
h
m
a
d
I
.
K
istij
a
n
t
o
r
o
,
re
c
e
iv
e
d
th
e
B.
E
n
g
.
d
e
g
re
e
i
n
i
n
fo
rm
a
t
ics
fro
m
t
h
e
I
n
stit
u
te
o
f
Tec
h
n
o
l
o
g
y
Ba
n
d
u
n
g
,
(IT
B)
,
Ba
n
d
u
n
g
,
In
d
o
n
e
sia
,
th
e
m
a
ste
rs’
d
e
g
r
e
e
fro
m
TU
De
lft
,
De
lft
,
Th
e
Ne
th
e
rlan
d
s,
a
n
d
th
e
P
h
.
D.
d
e
g
re
e
fr
o
m
t
h
e
Un
iv
e
rsity
o
f
Ne
wc
a
stle
u
p
o
n
Ty
n
e
,
Ne
wc
a
stle
u
p
o
n
T
y
n
e
,
U.K.
,
His
c
u
rre
n
t
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
s
d
istri
b
u
ted
sy
ste
m
s,
p
a
ra
ll
e
l
c
o
m
p
u
tati
o
n
,
a
n
d
h
ig
h
-
p
e
rfo
rm
a
n
c
e
c
o
m
p
u
tati
o
n
.
Anto
in
e
Do
u
c
e
t
is
a
F
u
l
l
P
ro
f
e
ss
o
r
in
c
o
m
p
u
ter
sc
ien
c
e
a
t
th
e
L3
i
lab
o
ra
t
o
ry
o
f
t
h
e
Un
iv
e
rsity
o
f
La
Ro
c
h
e
ll
e
si
n
c
e
2
0
1
4
.
He
lea
d
s
th
e
re
se
a
rc
h
g
ro
u
p
in
d
o
c
u
m
e
n
t
a
n
a
ly
sis,
d
ig
it
a
l
c
o
n
te
n
ts
a
n
d
ima
g
e
s
(a
b
o
u
t
4
0
p
e
o
p
le)
a
n
d
is
a
d
d
i
ti
o
n
a
ll
y
th
e
d
irec
t
o
r
o
f
th
e
ICT
d
e
p
a
rtme
n
t
o
f
th
e
Vie
tn
a
m
-
F
ra
n
c
e
Un
i
v
e
rsity
o
f
S
c
ien
c
e
a
n
d
Tec
h
n
o
lo
g
y
o
f
Ha
n
o
i.
Ad
d
it
i
o
n
a
ll
y
,
h
e
is
th
e
p
ri
n
c
i
p
a
l
i
n
v
e
stig
a
t
o
r
o
f
t
h
e
H2
0
2
0
p
ro
jec
t
Ne
ws
Ey
e
,
ru
n
n
in
g
u
n
ti
l
2
0
2
1
a
n
d
fo
c
u
si
n
g
o
n
a
u
g
m
e
n
ti
n
g
a
c
c
e
ss
to
h
isto
rica
l
n
e
ws
p
a
p
e
rs,
a
c
ro
ss
d
o
m
a
in
s
a
n
d
lan
g
u
a
g
e
s.
He
fu
rt
h
e
r
lea
d
s
t
h
e
e
ffo
rt
o
n
se
m
a
n
ti
c
e
n
ric
h
m
e
n
t
f
o
r
l
o
w
-
re
so
u
rc
e
d
la
n
g
u
a
g
e
s
in
t
h
e
c
o
n
tex
t
o
f
th
e
H2
0
2
0
p
ro
jec
t
Emb
e
d
d
ia.
His
m
a
in
re
se
a
rc
h
in
t
e
re
sts
li
e
in
t
h
e
fiel
d
s
o
f
in
fo
rm
a
ti
o
n
re
tri
e
v
a
l,
n
a
tu
ra
l
lan
g
u
a
g
e
p
ro
c
e
ss
in
g
a
n
d
(te
x
t)
d
a
ta
m
in
in
g
.
Th
e
c
e
n
tral
f
o
c
u
s
o
f
h
is
wo
rk
is
o
n
t
h
e
d
e
v
e
lo
p
m
e
n
t
o
f
m
e
th
o
d
s th
a
t
sc
a
le
to
v
e
ry
larg
e
d
o
c
u
m
e
n
t
c
o
l
lec
ti
o
n
s
a
n
d
t
h
a
t
d
o
n
o
t
re
q
u
ire
p
r
io
r
k
n
o
wle
d
g
e
o
f
th
e
d
a
ta,
h
e
n
c
e
t
h
a
t
a
re
ro
b
u
st
t
o
n
o
ise
(e
.
g
ste
m
m
in
g
fro
m
OCR) an
d
lan
g
u
a
g
e
-
in
d
e
p
e
n
d
e
n
t.
A
n
to
i
n
e
Do
u
c
e
t
h
o
l
d
s a
P
h
D
in
c
o
m
p
u
ter
sc
ien
c
e
fro
m
th
e
Un
iv
e
rsity
i
n
He
lsin
k
i
(F
i
n
lan
d
)
sin
c
e
2
0
0
5
,
a
n
d
a
F
re
n
c
h
re
se
a
rc
h
su
p
e
rv
isi
o
n
h
a
b
il
it
a
ti
o
n
(HD
R)
si
n
c
e
2
0
1
2
.
Evaluation Warning : The document was created with Spire.PDF for Python.