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1.
I
NT
RO
D
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I
O
N
Sear
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alg
o
r
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m
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m
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tu
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ies
in
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p
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ter
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cien
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[1
-
8]
,
in
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s
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A
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-
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.
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[3
-
5,
1
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.
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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th
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r
ch
s
tr
in
g
d
ata.
Fig
u
r
e
1
.
Sis
tem
f
lo
wch
ar
t
r
u
n
n
in
g
2
.
1
.
B
o
y
er
-
M
o
o
re
a
lg
o
rit
hm
T
h
e
B
o
y
er
-
Mo
o
r
e
alg
o
r
ith
m
b
ec
o
m
es
o
n
e
o
f
th
e
m
o
s
t
f
r
eq
u
en
tly
u
s
ed
s
tr
in
g
-
lo
o
k
u
p
alg
o
r
ith
m
s
o
r
i
s
im
p
lem
en
ted
in
to
a
d
o
cu
m
e
n
t o
r
d
ata
s
ea
r
ch
f
ea
tu
r
e
in
th
e
d
atab
ase
b
ec
au
s
e
it is
co
n
s
id
er
ed
th
e
m
o
s
t e
f
f
icien
t
in
ty
p
ical
ap
p
licatio
n
s
a
n
d
is
b
est
co
m
p
ar
ed
to
o
th
er
s
tr
in
g
s
ea
r
ch
alg
o
r
ith
m
s
[
2
4
,
25]
.
T
h
e
B
o
y
er
-
Mo
o
r
e
alg
o
r
ith
m
s
tar
ts
m
atch
in
g
a
c
h
ar
ac
ter
f
r
o
m
th
e
r
ig
h
t
d
ir
ec
ti
o
n
o
f
th
e
p
atter
n
o
r
th
e
r
ig
h
t
-
to
-
lef
t
d
ir
ec
tio
n
o
f
th
e
tex
t
[
1
6
,
26
,
27]
.
Ad
eq
u
ate
ly
s
y
s
tem
atic,
th
e
s
tag
es
th
at
t
h
e
B
o
y
er
-
M
o
o
r
e
alg
o
r
ith
m
p
e
r
f
o
r
m
e
d
at
th
e
tim
e
o
f
m
atch
in
g
th
e
f
o
llo
win
g
s
tr
i
n
g
s
[
2
6
,
2
8
]
:
a.
B
o
y
er
-
Mo
o
r
e'
s
alg
o
r
ith
m
s
tar
t
s
m
atch
in
g
th
e
p
atter
n
at
th
e
b
eg
in
n
in
g
o
f
th
e
tex
t.
b.
T
h
e
B
o
y
er
-
Mo
o
r
e
alg
o
r
ith
m
will
m
atch
f
r
o
m
r
ig
h
t
-
to
-
lef
t
to
m
atch
th
e
p
atter
n
ch
a
r
ac
ter
ch
ar
ac
ter
s
with
th
e
ch
ar
ac
ter
s
in
th
e
m
atch
ed
t
ex
t u
n
til o
n
e
o
f
th
e
co
n
d
itio
n
s
is
m
et.
T
h
e
s
ea
r
ch
in
g
o
f
d
ata
in
B
o
y
er
Mo
o
r
e
alg
o
r
ith
m
ca
n
b
e
s
ee
n
o
n
th
e
p
atter
n
to
s
ea
r
ch
th
e
wo
r
d
“AL
A”
o
n
“BUR
UK
AL
A”.
a.
Alig
n
th
e
p
atter
n
o
f
AL
A,
m
at
ch
ed
with
B
UR
UK
AL
A
T
ex
t
: B U
R
U
K
A
L
A
Patter
n
: A
L
A
b.
Dete
r
m
in
e
th
e
s
h
if
t ta
b
le
B
m
B
c
an
d
B
m
Gs
T
ab
les 1
an
d
2
e
x
p
lain
t
h
e
B
o
y
er
Mo
o
r
e
alg
o
r
ith
m
in
s
ea
r
ch
in
g
d
ata
th
at
ca
n
b
e
s
ee
n
f
r
o
m
th
e
s
ea
r
ch
f
o
r
t
h
e
wo
r
d
y
o
u
wa
n
t
to
s
e
ar
ch
(
p
atter
n
s
)
.
T
h
e
B
m
B
c
v
alu
e
in
T
ab
le
1
is
o
b
tain
ed
f
r
o
m
th
e
r
esu
lts
o
f
en
u
m
er
atio
n
s
tar
tin
g
f
r
o
m
th
e
s
tr
in
g
an
d
th
en
to
th
e
in
itial
s
tr
in
g
,
s
tar
tin
g
at
th
e
0
th
in
d
ex
.
T
h
e
n
r
ec
o
r
d
th
e
ch
ar
ac
te
r
s
th
at
h
av
e
b
ee
n
f
o
u
n
d
[
2
6
,
2
8
,
2
9
]
.
T
ab
le
2
ex
p
lain
s
th
e
p
r
o
ce
s
s
o
f
f
in
d
in
g
B
m
B
C
v
alu
es.
T
h
e
en
u
m
er
ato
r
v
alu
e
will
b
e
ad
d
ed
b
y
1
f
o
u
n
d
th
is
ch
ar
a
cter
h
as
n
ev
er
b
ee
n
f
o
u
n
d
b
e
f
o
r
e.
T
h
en
b
ac
k
to
th
e
p
r
ev
i
o
u
s
p
o
s
itio
n
,
t
h
e
ch
a
r
ac
ter
"A"
b
ec
au
s
e
th
e
c
h
ar
a
cter
"A"
h
as
b
ee
n
s
ee
n
p
r
ev
i
o
u
s
ly
,
th
e
v
alu
e
o
f
th
e
tr
an
s
f
er
is
1
[
2
6
,
2
8
,
2
9
]
.
c.
Ma
k
e
th
e
i
ter
atio
n
tab
le
t
o
th
e
p
atter
n
m
atch
in
g
with
tex
t
T
ab
le
3
e
x
p
lain
s
th
e
iter
atio
n
p
atter
n
o
f
tex
t
m
atch
in
g
in
t
h
e
B
o
y
er
-
Mo
o
r
e
al
g
o
r
ith
m
.
I
t
er
atio
n
in
th
e
B
o
y
er
-
Mo
o
r
alg
o
r
ith
m
s
to
p
s
at
th
e
4
th
iter
atio
n
,
m
ea
n
in
g
th
at
th
e
s
ea
r
ch
f
o
r
th
e
wo
r
d
AL
A
in
B
UR
UK
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
C
o
mp
a
r
is
o
n
o
f sea
r
ch
a
lg
o
r
ith
ms in
Ja
va
n
ese
-
I
n
d
o
n
esia
n
d
ictio
n
a
r
y
a
p
p
lica
tio
n
(
Ya
n
a
A
d
itia
Gerh
a
n
a
)
2519
AL
A
tex
t is f
o
u
n
d
in
th
e
4
th
it
er
atio
n
.
I
ter
atio
n
is
ca
r
r
ied
o
u
t
b
ased
o
n
th
e
v
alu
e
o
f
t
h
e
s
h
if
ts
in
th
e
B
m
B
c
an
d
B
m
Gs tab
les an
d
co
m
p
ar
es wh
ich
s
h
if
t v
a
lu
e
is
h
ig
h
est b
et
wee
n
th
e
two
[
2
6
,
2
8
,
2
9
]
.
T
ab
le
1.
B
m
B
c
v
alu
e
T
ab
le
2
.
Bm
Gs
v
alu
e
Ta
b
l
e
B
m
B
c
I
n
d
e
x
0
1
2
P
a
t
t
e
r
n
A
L
A
B
mB
c
0
1
0
Ta
b
l
e
B
m
Gs
I
n
d
e
x
0
1
2
P
a
t
t
e
r
n
A
L
A
B
mB
c
3
3
1
T
ab
le
3.
B
o
y
er
Mo
o
r
e
alg
o
r
ith
m
alter
atio
n
s
ch
em
e
I
n
d
e
k
s
0
1
2
3
4
5
6
7
8
1
B
U
R
U
K
A
L
A
A
L
A
2
B
U
R
U
K
A
L
A
A
L
A
3
B
U
R
U
K
A
L
A
A
L
A
4
B
U
R
U
K
A
L
A
D
a
t
a
F
o
u
n
d
A
L
A
2
.
2
.
K
nu
t
h M
o
rr
is
P
ra
t
t
a
lg
o
rit
hm
(
K
M
P
)
T
h
e
KM
P
alg
o
r
ith
m
h
as
a
d
if
f
e
r
en
t
s
h
if
t
t
h
an
t
h
e
B
o
y
er
-
Mo
o
r
e
alg
o
r
ith
m
.
B
r
o
a
d
ly
s
p
ea
k
i
n
g
th
e
s
tag
es
in
th
e
KM
P a
lg
o
r
ith
m
,
wh
en
p
er
f
o
r
m
in
g
s
tr
in
g
m
atc
h
in
g
a
r
e
[
1
6
,
20
,
22
,
30]
:
a.
Alg
o
r
ith
m
s
KM
P st
ar
ts
m
atch
in
g
p
atter
n
at
t
h
e
b
eg
i
n
n
in
g
o
f
th
e
tex
t
b.
T
h
e
KM
P
alg
o
r
ith
m
p
er
f
o
r
m
s
a
s
h
if
t
o
r
m
atch
in
g
p
atter
n
ch
a
r
ac
ter
with
te
x
t
ch
a
r
ac
ter
s
f
r
o
m
lef
t
t
o
r
i
g
h
t
by
m
atch
in
g
ch
ar
ac
te
r
s
p
er
ch
ar
ac
ter
u
n
til o
n
e
o
f
t
h
e
co
n
d
itio
n
s
is
m
et.
T
h
e
s
ea
r
ch
in
g
o
f
d
ata
in
Kn
u
th
Mo
r
r
is
Pra
tt
alg
o
r
ith
m
ca
n
b
e
s
ee
n
o
n
th
e
p
atter
n
t
o
s
ea
r
ch
th
e
wo
r
d
“AL
A”
o
n
“BUR
UK
AL
A”.
a.
Alig
n
th
e
p
atter
n
o
f
AL
A,
m
at
ch
ed
with
B
UR
UK
AL
A
T
ex
t
: B U
R
U
K
A
L
A
Patter
n
: A
L
A
b.
Dete
r
m
in
e
th
e
v
alu
e
o
f
d
ata
f
r
in
g
e
I
n
T
ab
le
4
,
th
e
i
n
itial
b
o
u
n
d
a
r
y
v
alu
e
will
alwa
y
s
b
e
0
.
T
h
e
b
o
u
n
d
ar
y
v
alu
e
is
ca
lcu
late
d
b
ased
o
n
a
ch
ar
ac
ter
p
atter
n
o
n
l
y
.
I
f
a
n
y
o
n
e
ap
p
r
ec
iates
th
e
ch
ar
ac
ter
i
n
th
e
p
atter
n
,
th
en
th
e
e
d
g
e
v
al
u
e
1
an
d
s
o
o
n
will
in
cr
ea
s
e
in
v
alu
e
with
th
e
v
alu
e
j,
an
d
I
s
h
if
t to
th
e
n
ex
t p
atte
r
n
ch
ar
ac
ter
[
1
6
,
20
,
22
,
3
0
]
.
T
ab
le
4.
Patter
n
e
d
g
e
Ta
b
l
e
P
a
t
t
e
r
n
e
d
g
e
J
0
1
2
P
(
j
)
A
L
A
B
(
b
)
0
0
1
c.
Ma
k
e
th
e
iter
atio
n
tab
le
t
o
th
e
p
atter
n
m
atch
in
g
with
tex
t
T
ab
le
5
ex
p
lain
s
th
e
iter
atio
n
p
atter
n
m
atch
in
g
te
x
t o
n
th
e
K
MP
alg
o
r
ith
m
.
T
h
e
iter
atio
n
o
f
th
e
KM
P
alg
o
r
ith
m
is
s
im
ilar
to
th
e
B
o
y
er
Mo
o
r
e
alg
o
r
ith
m
;
th
e
iter
atio
n
s
to
p
s
at
th
e
4
th
iter
at
io
n
.
I
n
c
o
n
tr
ast
to
th
e
KM
P Alg
o
r
ith
m
,
th
e
in
ter
ac
tio
n
p
r
o
ce
s
s
is
n
o
t b
ased
o
n
B
m
B
c
an
d
B
m
Gs tab
les b
u
t b
ased
o
n
v
al
u
es
f
r
o
m
th
e
ed
g
es o
f
th
e
p
atter
n
.
T
ab
le
5.
Kn
u
th
Mo
r
r
is
p
r
att
al
g
o
r
ith
m
alter
atio
n
s
ch
em
e
I
n
d
e
k
s
0
1
2
3
4
5
6
7
8
1
B
U
R
U
K
A
L
A
A
L
A
2
B
U
R
U
K
A
L
A
A
L
A
3
B
U
R
U
K
A
L
A
A
L
A
4
B
U
R
U
K
A
L
A
D
a
t
a
F
o
u
n
d
A
L
A
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.
18
,
No
.
5
,
Octo
b
e
r
2
0
2
0
:
2
5
1
7
-
2524
2520
2
.
3
.
H
o
rsp
o
o
l a
lg
o
rit
hm
T
h
e
Ho
r
s
p
o
o
l
al
g
o
r
ith
m
is
o
n
e
o
f
t
h
e
s
tr
in
g
s
ea
r
ch
al
g
o
r
ith
m
s
,
wh
ich
is
a
s
im
p
li
f
icatio
n
o
f
th
e
B
o
y
er
-
M
o
o
r
e
alg
o
r
ith
m
[
1
5
,
1
6
,
3
1
,
32]
.
T
h
e
Ho
r
s
p
o
o
l
alg
o
r
ith
m
h
as
a
s
im
p
ler
s
h
if
tin
g
c
o
m
p
a
r
ed
t
o
th
e
B
o
y
er
-
Mo
o
r
e
alg
o
r
ith
m
.
T
h
e
B
o
y
er
-
Mo
o
r
e
alg
o
r
ith
m
h
as
two
s
h
if
tin
g
p
r
o
ce
s
s
f
u
n
ctio
n
s
,
i.e
.
,
b
ad
-
ch
a
r
ac
ter
s
h
if
t
an
d
g
o
o
d
-
s
u
f
f
i
x
s
h
if
t,
s
o
t
h
e
Ho
r
s
p
o
o
l
alg
o
r
ith
m
o
n
ly
u
s
es
o
n
e
p
an
n
in
g
th
at
is
b
ad
-
c
h
ar
ac
ter
s
h
if
tin
g
[
3
3
]
.
T
h
e
f
u
n
ctio
n
p
r
ep
r
o
ce
s
s
p
atter
n
in
th
e
Ho
r
s
p
o
o
l
alg
o
r
ith
m
is
b
y
p
er
f
o
r
m
in
g
a
ju
m
p
b
ased
o
n
"b
ad
-
ch
ar
ac
ter
"
o
r
b
ased
o
n
t
h
e
ch
ar
ac
ter
m
is
m
atch
in
th
e
p
atter
n
f
o
u
n
d
in
th
e
tex
t.
Pa
n
n
in
g
t
h
e
Ho
r
s
p
o
o
l
alg
o
r
ith
m
u
s
es
th
e
r
ig
h
tm
o
s
t
ch
ar
ac
ter
in
th
e
c
u
r
r
en
t
te
x
t
win
d
o
w
to
d
eter
m
i
n
e
th
e
s
h
if
t
d
is
tan
ce
to
b
e
p
er
f
o
r
m
ed
.
T
h
e
p
atter
n
will
s
h
if
t
to
th
e
f
a
r
r
i
g
h
t
o
f
t
h
e
win
d
o
w
u
n
til
a
m
atch
b
etwe
en
th
e
p
atter
n
ch
ar
ac
te
r
an
d
th
e
tex
t.
T
h
e
s
ea
r
ch
in
g
o
f
d
ata
in
Ho
r
s
p
o
o
l a
lg
o
r
ith
m
ca
n
b
e
s
ee
n
o
n
th
e
p
atter
n
t
o
s
ea
r
ch
t
h
e
wo
r
d
“AL
A”
o
n
“BUR
UK
AL
A”.
a.
Alig
n
th
e
p
atter
n
o
f
AL
A,
m
at
ch
ed
with
B
UR
UK
AL
A
T
ex
t
: B U
R
U
K
A
L
A
Patter
n
: A
L
A
b.
Dete
r
m
in
e
th
e
s
h
if
t ta
b
le
B
m
B
c
T
ab
le
6
ex
p
lai
n
s
th
e
i
n
itial
in
t
er
p
r
etatio
n
o
f
th
e
Ho
r
s
p
o
o
l
al
g
o
r
ith
m
.
T
h
e
m
atch
in
g
p
r
o
ce
s
s
s
tar
ts
at
th
e
0
th
in
d
ex
o
r
th
e
ch
ar
ac
ter
"A"
.
Per
f
o
r
m
th
e
p
r
ev
io
u
s
p
o
s
itio
n
,
an
d
th
e
en
u
m
e
r
ato
r
v
alu
e
will
b
e
ad
d
ed
1
if
th
is
ch
ar
ac
ter
h
as
n
ev
er
b
ee
n
f
o
u
n
d
b
e
f
o
r
e,
Step
b
ac
k
to
t
h
e
p
r
ev
io
u
s
p
o
s
itio
n
wh
ich
is
th
e
ch
ar
ac
ter
"A"
b
ec
au
s
e
th
e
ch
ar
ac
ter
"A"
h
as
b
ee
n
f
o
u
n
d
b
ef
o
r
e
th
en
t
h
e
r
ep
lace
m
en
t
v
alu
e
is
1
.
T
h
e
f
in
al
s
ch
em
e
o
f
th
e
Ho
r
s
p
o
o
l
alg
o
r
ith
m
m
atc
h
in
g
p
r
o
ce
s
s
is
ex
am
in
ed
in
T
a
b
le
7
.
T
h
e
last
iter
atio
n
d
eter
m
in
es
th
e
ch
ar
ac
ter
0
(
b
lan
k
)
in
th
e
tex
t
d
o
es
n
o
t
m
atch
th
e
ch
ar
ac
ter
A
in
th
e
p
atter
n
,
s
o
th
e
m
atch
in
g
p
r
o
ce
s
s
r
em
o
v
es
b
ec
au
s
e
0
(
em
p
ty
)
d
o
es n
o
t m
atch
.
T
ab
le
6
.
B
m
B
c
v
alu
e
Ta
b
l
e
B
m
B
c
I
n
d
e
x
0
1
2
P
a
t
t
e
r
n
A
L
A
B
mBs
0
1
0
T
ab
le
7
.
Ho
r
s
p
o
o
l a
lg
o
r
ith
m
li
ter
ac
y
s
ch
em
a
I
n
d
e
k
s
0
1
2
3
4
5
6
7
8
1
B
U
R
U
K
A
L
A
A
L
A
2
B
U
R
U
K
A
L
A
A
L
A
3
B
U
R
U
K
?
A
L
A
D
a
t
a
N
o
t
F
o
u
n
d
A
L
A
3.
RE
SU
L
T
S
A
ND
AN
AL
Y
SI
S
Data
u
s
ed
in
th
e
f
o
r
m
o
f
a
v
o
c
ab
u
lar
y
th
at
will b
e
tr
an
s
lated
in
to
th
e
J
av
a
n
ese
lan
g
u
ag
e
d
er
iv
ed
f
r
o
m
th
e
ex
is
tin
g
J
av
a
n
ese
d
ictio
n
a
r
y
.
I
n
th
e
r
esear
ch
th
at
is
b
ein
g
d
o
n
e
th
is
o
b
s
er
v
atio
n
is
d
o
n
e
i
n
th
e
f
o
r
m
o
f
d
ir
ec
t
o
b
s
er
v
atio
n
o
f
t
h
e
u
s
e
o
f
J
av
a
n
ese
lan
g
u
ag
e
in
t
h
e
co
m
m
u
n
ity
th
at
b
eg
a
n
to
b
e
s
h
if
te
d
b
a
s
ed
o
n
a
jo
u
r
n
al
o
r
ar
ticle
th
at
wr
ites
d
ir
ec
tly
ab
o
u
t
th
e
alig
n
m
en
t
T
h
e
u
s
e
o
f
J
av
an
ese
lan
g
u
ag
e
a
m
o
n
g
th
e
y
o
u
n
g
er
g
e
n
er
atio
n
an
d
s
ev
er
al
jo
u
r
n
als th
at
p
r
o
v
e
s
s
o
m
e
m
atch
in
g
s
tr
in
g
al
g
o
r
it
h
m
s
th
at
ca
n
b
e
u
s
ed
f
o
r
co
m
p
ar
is
o
n
.
Fig
u
r
e
2
ex
p
lain
s
th
e
test
r
esu
lts
o
f
th
e
th
r
ee
alg
o
r
ith
m
s
.
B
ased
o
n
th
e
r
esu
lts
o
f
test
in
g
o
f
1
5
0
0
v
o
ca
b
u
lar
ies
with
4
0
0
e
x
p
er
im
en
ts
,
th
e
a
cc
u
r
ac
y
o
f
th
e
Ho
r
s
p
o
o
l
alg
o
r
ith
m
is
lo
wer
th
an
th
e
KM
P
an
d
B
o
y
er
-
Mo
o
r
alg
o
r
ith
m
s
,
with
an
ac
cu
r
ac
y
r
ate
o
f
8
5
.
3
%,
wh
i
le
th
e
KM
P
an
d
B
o
y
er
-
Mo
o
r
a
lg
o
r
ith
m
s
ar
e
1
0
0
%
r
esp
ec
tiv
ely
.
Ma
th
em
atica
lly
t
h
e
test
r
esu
lts
ar
e
ex
p
lain
ed
as
f
o
llo
ws
:
Acc
u
r
ac
y
lev
el
=
(
N
u
m
b
er
o
f
s
u
cc
ess
f
u
l sam
p
les/
to
tal
n
u
m
b
er
o
f
s
am
p
l
es)
x
1
0
0
%
Acc
u
r
ac
y
o
f
B
o
y
er
-
Mo
o
r
e
=
(
400
400
)
100
%
=
100%
Acc
u
r
ac
y
o
f
Ak
u
r
asi KM
P
=
(
400
400
)
100
%
=
100%
Acc
u
r
ac
y
o
f
Ak
u
r
asi Ho
r
s
p
o
o
l
=
(
341
400
)
100
%
=
85
.
3%
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
C
o
mp
a
r
is
o
n
o
f sea
r
ch
a
lg
o
r
ith
ms in
Ja
va
n
ese
-
I
n
d
o
n
esia
n
d
ictio
n
a
r
y
a
p
p
lica
tio
n
(
Ya
n
a
A
d
itia
Gerh
a
n
a
)
2521
Fig
u
r
e
3
ex
p
lain
s
th
e
r
esu
lts
o
f
test
in
g
th
e
tim
e
o
f
th
e
th
i
r
d
alg
o
r
ith
m
s
ea
r
ch
p
r
o
ce
s
s
.
T
h
e
s
p
ee
d
o
f
th
e
alg
o
r
ith
m
is
test
ed
b
ased
o
n
th
e
p
r
o
ce
s
s
in
g
tim
e
i
n
s
ea
r
ch
in
g
v
o
ca
b
u
lar
y
.
B
ased
o
n
th
e
test
r
esu
lts
,
th
e
alg
o
r
ith
m
th
at
h
as
th
e
f
astes
t
s
p
ee
d
is
Kn
u
th
-
Mo
r
r
is
-
Pra
tt
with
an
av
er
ag
e
o
f
2
5
m
s
,
Ho
r
s
p
o
o
l
3
9
.
9
m
s
,
an
d
B
o
y
er
Mo
o
r
e
4
4
.
2
m
s
.
Fig
u
r
e
4
ex
p
lain
s
in
d
etail
th
e
co
m
p
ar
is
o
n
g
r
a
p
h
o
f
th
e
s
p
ee
d
o
f
ea
ch
alg
o
r
ith
m
in
s
ea
r
ch
in
g
ea
ch
J
av
an
ese
v
o
ca
b
u
lar
y
in
to
I
n
d
o
n
esian
wh
er
e
b
lu
e
in
d
icate
s
B
o
y
e
r
Mo
o
r
e'
s
alg
o
r
ith
m
,
r
ed
in
d
icate
s
Kn
u
th
Mo
r
r
is
Pra
tt'
s
alg
o
r
ith
m
,
a
n
d
g
r
ee
n
in
d
icate
s
Ho
r
s
p
o
o
l'
s
alg
o
r
ith
m
.
Fig
u
r
e
2
.
Acc
u
r
ac
y
alg
o
r
ith
m
Fig
u
r
e
3
.
Av
e
r
ag
e
v
al
u
e
o
f
co
m
p
ar
is
o
n
alg
o
r
ith
m
at
m
atc
h
s
p
ee
d
lev
el
Fig
u
r
e
4.
T
im
e
ex
ec
u
tio
n
alg
o
r
ith
m
T
esti
n
g
alg
o
r
ith
m
s
b
ased
o
n
th
e
co
m
p
le
x
ity
o
f
t
h
e
alg
o
r
ith
m
,
test
in
g
is
co
n
d
u
cted
b
ased
o
n
th
e
ef
f
icien
cy
o
f
h
o
w
m
u
ch
s
p
ac
e
(
s
p
ac
e)
an
d
th
e
tim
e
it
tak
es
f
o
r
th
e
alg
o
r
ith
m
to
r
u
n
ev
er
y
s
tep
b
y
s
tep
in
th
e
alg
o
r
ith
m
.
Af
ter
test
in
g
th
e
th
r
ee
alg
o
r
ith
m
s
,
o
b
tai
n
ed
th
e
r
esu
lt
th
at
B
o
y
er
Mo
o
r
e'
s
alg
o
r
ith
m
h
as
an
o
v
e
r
all
n
to
tal
o
f
1
1
n
,
with
t
h
e
am
o
u
n
t
o
f
n
2
as
m
u
ch
as
2
6
n
2
.
T
h
e
al
g
o
r
ith
m
o
f
Kn
u
th
M
o
r
r
is
Pra
tt
alg
o
r
ith
m
h
as
an
o
v
er
all
n
o
f
8
n
an
d
a
n
am
o
u
n
t
o
f
n
2
as
m
u
ch
as
2
0
n
2
,
an
d
th
e
H
o
r
s
p
o
o
l
alg
o
r
ith
m
h
as
a
v
alu
e
o
f
n
as
m
u
ch
as
1
0
n
an
d
v
alu
e
o
f
n
2
as
m
u
ch
as
2
0
n
2
.
Fig
u
r
e
5
ex
p
lain
s
th
at
Kn
u
th
Mo
r
r
is
Pra
tt's
alg
o
r
ith
m
h
as
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.
18
,
No
.
5
,
Octo
b
e
r
2
0
2
0
:
2
5
1
7
-
2524
2522
th
e
b
est
tim
e
ef
f
icien
cy
with
a
s
u
m
v
alu
e
o
f
N
2
an
d
th
e
least n
v
alu
es
co
m
p
ar
ed
to
th
e
Ho
r
s
p
o
o
l
alg
o
r
ith
m
an
d
B
o
y
er
Mo
o
r
e'
s
alg
o
r
ith
m
,
w
h
ich
m
ea
n
s
th
e
tim
e
th
e
alg
o
r
ith
m
p
r
o
ce
s
s
is
Has
th
e
m
o
s
t
r
ap
id
p
r
o
ce
s
s
in
g
ef
f
icien
cy
r
ates c
o
m
p
ar
ed
to
a
n
o
th
er
alg
o
r
ith
m
.
Fig
u
r
e
5.
C
o
m
p
a
r
is
o
n
o
f
alg
o
r
ith
m
co
m
p
lex
ity
test
in
g
4.
CO
NCLU
SI
O
N
B
ased
o
n
th
e
test
r
esu
lts
,
B
o
y
er
-
Mo
o
r
a
n
d
KM
P
h
av
e
a
h
ig
h
e
r
lev
el
o
f
ac
cu
r
ac
y
co
m
p
ar
ed
to
th
e
Ho
r
s
p
o
o
l
alg
o
r
ith
m
.
W
h
il
e
th
e
av
er
ag
e
p
r
o
ce
s
s
in
g
tim
e
o
f
KM
P
is
b
etter
th
an
Ho
r
s
p
o
o
l
an
d
B
o
y
er
-
Mo
o
r
.
T
h
e
p
r
o
ce
s
s
in
g
tim
e
is
d
ir
ec
tly
p
r
o
p
o
r
tio
n
al
to
th
e
ef
f
ici
en
cy
p
r
o
d
u
ce
d
b
y
KM
P,
wh
ich
is
b
etter
th
an
th
e
B
o
y
er
-
Mo
o
r
an
d
Ho
r
s
p
o
o
l
alg
o
r
ith
m
,
with
t
h
e
least n
u
m
b
er
o
f
n
2
an
d
n
v
alu
es.
RE
F
E
R
E
NC
E
S
[1]
M
.
Ab
d
u
ll
a
h
i,
M
.
A.
Ng
a
d
i
,
S
.
I
.
Dish
in
g
,
S
.
M
.
Ab
d
u
l
h
a
m
id
,
a
n
d
B.
I.
e
e
l
Ah
m
a
d
,
“
An
e
fficie
n
t
s
y
m
b
io
ti
c
o
rg
a
n
ism
s
se
a
rc
h
a
lg
o
rit
h
m
wit
h
c
h
a
o
ti
c
o
p
ti
m
iza
ti
o
n
stra
teg
y
fo
r
m
u
lt
i
-
o
b
jec
t
iv
e
tas
k
sc
h
e
d
u
li
n
g
p
ro
b
lem
s
in
c
l
o
u
d
c
o
m
p
u
ti
n
g
e
n
v
iro
n
m
e
n
t,
”
J
.
Ne
tw.
Co
mp
u
t.
Ap
p
l
.
,
v
o
l.
1
3
3
,
p
p
.
6
0
-
7
4
,
2
0
1
9
.
[2
]
G
.
Li
,
H.
Zh
o
u
,
X.
Ji
n
g
,
G
.
Ti
a
n
,
a
n
d
L.
Li
,
“
An
in
telli
g
e
n
t
wh
e
e
l
p
o
siti
o
n
se
a
rc
h
in
g
a
lg
o
r
it
h
m
f
o
r
c
u
tt
in
g
to
o
l
g
ro
o
v
e
s
with
d
iv
e
rse
m
a
c
h
in
in
g
p
re
c
isio
n
re
q
u
irem
e
n
ts,”
In
t
.
J
.
M
a
c
h
.
T
o
o
l
s M
a
n
u
f.
,
v
o
l.
1
2
2
,
p
p
.
1
4
9
-
1
6
0
,
2
0
1
7
.
[3
]
M
.
S
.
S
a
n
a
j
a
n
d
P
.
M
.
Jo
e
P
ra
t
h
a
p
,
“
Na
tu
re
in
s
p
ired
c
h
a
o
ti
c
sq
u
i
rre
l
se
a
rc
h
a
lg
o
rit
h
m
(CS
S
A)
f
o
r
m
u
lt
i
o
b
jec
ti
v
e
tas
k
sc
h
e
d
u
li
n
g
in
a
n
IAA
S
c
l
o
u
d
c
o
m
p
u
ti
n
g
a
tmo
sp
h
e
re
,
”
En
g
.
S
c
i.
T
e
c
h
n
o
l.
a
n
In
t
.
J
.
,
2
0
1
9
.
[4
]
J.
Bra
n
d
ã
o
,
“
A
m
e
m
o
ry
-
b
a
se
d
i
tera
ted
lo
c
a
l
se
a
rc
h
a
lg
o
rit
h
m
f
o
r
t
h
e
m
u
lt
i
-
d
e
p
o
t
o
p
e
n
v
e
h
icle
ro
u
ti
n
g
p
ro
b
lem
,
”
Eu
r.
J
.
Op
e
r.
Res
.
,
v
o
l.
2
8
4
,
n
o
.
2
,
p
p
.
5
5
9
-
5
7
1
,
2
0
2
0
.
[5
]
S
.
M
a
k
h
d
o
o
m
i
a
n
d
A.
As
k
a
rz
a
d
e
h
,
“
Op
ti
m
izin
g
o
p
e
ra
ti
o
n
o
f
a
p
h
o
to
v
o
lt
a
ic/
d
ies
e
l
g
e
n
e
ra
to
r
h
y
b
r
id
e
n
e
rg
y
sy
ste
m
with
p
u
m
p
e
d
h
y
d
r
o
sto
ra
g
e
b
y
a
m
o
d
ifi
e
d
c
r
o
w se
a
rc
h
a
lg
o
r
it
h
m
,
”
J
.
En
e
rg
y
S
to
r
a
g
e
,
v
o
l
.
2
7
,
2
0
2
0
.
[6
]
M
.
Ya
rlag
a
d
d
a
,
K
.
G
a
n
g
a
d
h
a
ra
Ra
o
,
a
n
d
A
.
S
r
ik
ris
h
n
a
,
“
F
re
q
u
e
n
t
it
e
m
se
t
-
b
a
se
d
f
e
a
tu
re
se
lec
ti
o
n
a
n
d
Ri
d
e
r
M
o
t
h
S
e
a
rc
h
Alg
o
ri
th
m
fo
r
d
o
c
u
m
e
n
t
c
lu
ste
rin
g
,
”
J
.
Ki
n
g
S
a
u
d
Un
iv.
-
C
o
mp
u
t
.
I
n
f.
S
c
i.
,
2
0
1
9
.
[7
]
P
.
H.
Xia
o
,
B.
W.
Li
,
a
n
d
S
.
B.
Wan
g
,
“
A
c
o
u
p
led
DEM
-
F
TS
M
a
p
p
r
o
a
c
h
wit
h
a
n
e
w
c
o
st
-
e
ff
e
c
ti
v
e
se
a
rc
h
in
g
a
lg
o
rit
h
m
o
f
p
a
rti
c
les
fo
r
g
a
s
-
so
li
d
flo
w
with
h
e
a
t
tran
sfe
r,
”
In
t.
J
.
T
h
e
rm
.
S
c
i.
,
v
o
l.
1
4
3
,
p
p
.
5
2
-
3
,
2
0
1
9
.
[8
]
E.
H.
M
o
h
a
m
e
d
a
n
d
E.
M
.
S
h
o
k
r
y
,
“
QSS
T
:
A
Qu
ra
n
ic
S
e
m
a
n
ti
c
S
e
a
rc
h
To
o
l
b
a
se
d
o
n
wo
r
d
e
m
b
e
d
d
i
n
g
,
”
J
.
Kin
g
S
a
u
d
U
n
iv.
–
C
o
mp
u
t.
I
n
f.
S
c
i.
,
2
0
2
0
.
[9
]
S
.
T.
Kle
i
n
a
n
d
D.
S
h
a
p
ira,
“
Th
e
S
tri
n
g
-
to
-
Dic
ti
o
n
a
ry
M
a
tch
in
g
P
ro
b
lem
,
"
Pro
c
e
e
d
i
n
g
s
o
f
t
h
e
D
a
t
a
C
o
mp
re
ss
io
n
Co
n
fer
e
n
c
e
,
v
o
l.
5
5
,
n
o
.
1
1
,
p
p
.
1
4
3
-
1
5
2
,
2
0
1
5
.
[1
0
]
S
.
Da
s
a
n
d
K.
Ka
p
o
o
r,
“
Weig
h
t
e
d
a
p
p
r
o
x
ima
te
p
a
ra
m
e
teriz
e
d
strin
g
m
a
tch
in
g
,
”
AKCE
In
t.
J
.
G
ra
p
h
s
Co
mb
.
,
v
o
l.
1
4
,
n
o
.
1
,
p
p
.
1
-
1
2
,
2
0
1
7
.
[
1
1
]
G.
D
i
d
i
e
r
a
n
d
L
.
T
i
c
h
i
t
,
“
D
e
s
i
g
n
i
n
g
o
p
t
i
m
a
l
-
a
n
d
f
a
s
t
-
on
-
a
v
e
r
a
g
e
p
a
t
t
e
r
n
m
a
t
c
h
i
n
g
a
l
g
o
r
i
t
h
m
s
,
”
a
r
X
i
v
:
1
6
0
4
.
0
8
8
6
0
v
4
,
2
0
1
6
.
[1
2
]
C.
Ry
u
a
n
d
K.
P
a
rk
,
“
Im
p
ro
v
e
d
p
a
tt
e
rn
-
sc
a
n
-
o
rd
e
r
a
lg
o
rit
h
m
s fo
r
s
tri
n
g
m
a
tch
in
g
,
”
J
.
Disc
re
t.
Al
g
o
r
it
h
ms
,
2
0
1
8
.
[1
3
]
M
.
Xu
,
X.
Jia
,
M
.
P
ick
e
ri
n
g
,
a
n
d
A.
J.
P
laz
a
,
“
Clo
u
d
re
m
o
v
a
l
b
a
se
d
o
n
sp
a
rse
re
p
re
se
n
tatio
n
v
i
a
m
u
lt
it
e
m
p
o
ra
l
d
ictio
n
a
ry
lea
rn
i
n
g
,
”
IEE
E
T
ra
n
s.
Ge
o
sc
i.
Rem
o
te S
e
n
s.
,
v
o
l.
5
4
,
n
o
.
5
,
p
p
.
2
9
9
8
–
3
0
0
6
,
2
0
1
6
.
[1
4
]
M
.
B
h
a
g
y
a
S
ri,
R.
Bh
a
v
sa
r,
a
n
d
P
.
Na
ro
o
k
a
,
“
S
tri
n
g
M
a
tch
i
n
g
Al
g
o
rit
h
m
s,”
In
t.
J
.
E
n
g
.
C
o
mp
u
t.
S
c
i.
,
v
o
l.
7
,
n
o
.
3
,
pp.
2
3
7
6
9
-
2
3
7
7
2
,
2
0
1
8
.
[1
5
]
C.
C.
Ho
o
n
g
a
n
d
M
.
A.
Am
e
e
d
e
e
n
,
“
Bo
y
e
r
-
m
o
o
re
h
o
rsp
o
o
l
a
lg
o
r
it
h
m
u
se
d
in
c
o
n
ten
t
m
a
n
a
g
e
m
e
n
t
sy
ste
m
o
f
d
a
ta
fa
st se
a
rc
h
in
g
,
”
Ad
v
.
S
c
i.
L
e
tt
.
,
v
o
l.
2
3
,
n
o
.
1
1
,
p
p
.
1
1
3
8
7
-
9
0
,
2
0
1
7
.
[1
6
]
D.
G
u
ru
n
g
,
U.
K.
C
h
a
k
ra
b
o
rty
,
a
n
d
P
.
S
h
a
rm
a
,
“
In
telli
g
e
n
t
P
re
d
icti
v
e
S
tri
n
g
S
e
a
rc
h
Alg
o
r
it
h
m
,
”
Pr
o
c
e
d
ia
Co
m
p
u
ter
S
c
ien
c
e
,
v
o
l.
7
9
,
p
p
.
1
6
1
-
1
6
9
,
2
0
1
6
.
[
1
7
]
W
.
R
.
K
i
n
g
,
“
K
n
o
w
l
e
d
g
e
M
a
n
a
g
e
m
e
n
t
a
n
d
O
r
g
a
n
i
z
a
t
i
o
n
a
l
L
e
a
r
n
i
n
g
,
”
A
n
n
a
l
s
o
f
I
n
f
o
r
m
a
t
i
o
n
S
y
s
t
e
m
s
4
,
p
p
.
3
-
1
3
,
2
0
0
9
.
[1
8
]
M
.
M
o
ra
les
,
S
.
S
c
h
e
re
r,
a
n
d
R.
L
e
v
it
a
n
,
“
A
Cro
ss
-
m
o
d
a
l
Re
v
iew
o
f
I
n
d
ica
to
rs
fo
r
De
p
re
ss
io
n
De
te
c
ti
o
n
S
y
ste
m
s,”
Pro
c
e
e
d
in
g
s
o
f
t
h
e
Fo
u
rth
W
o
rk
sh
o
p
o
n
Co
mp
u
t
a
ti
o
n
a
l
L
in
g
u
isti
c
s
a
n
d
Cli
n
ica
l
Psy
c
h
o
l
o
g
y
-
Fro
m
L
in
g
u
isti
c
S
ig
n
a
l
to
Cli
n
ica
l
Rea
li
ty,
2
0
1
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
C
o
mp
a
r
is
o
n
o
f sea
r
ch
a
lg
o
r
ith
ms in
Ja
va
n
ese
-
I
n
d
o
n
esia
n
d
ictio
n
a
r
y
a
p
p
lica
tio
n
(
Ya
n
a
A
d
itia
Gerh
a
n
a
)
2523
[1
9
]
A.
Ku
sn
a
d
i
a
n
d
A.
K
.
Wi
c
a
k
so
n
o
,
“
Co
m
p
a
riso
n
o
f
Ho
rsp
o
o
l
Alg
o
rit
h
m
a
n
d
Zh
u
-
Tak
a
o
k
a
Alg
o
rit
h
m
in
De
sk
to
p
-
Ba
se
d
S
tr
in
g
S
e
a
rc
h
,
”
J
.
Ulti
m.
Co
m
p
u
t
.
,
v
o
l
.
9
,
n
o
.
1
,
p
p
.
1
2
-
1
6
,
2
0
1
7
.
[2
0
]
W.
As
tu
ti
,
“
An
a
ly
sis
o
f
S
tri
n
g
M
a
tch
in
g
in
T
h
e
se
Ti
tl
e
Us
in
g
Kn
u
th
-
M
o
rris
-
P
ra
tt
(KMP
)
Al
g
o
rit
h
m
,
”
Ilk.
J
.
Ilm.
,
v
o
l.
9
,
n
o
.
2
,
p
p
.
1
6
7
-
1
7
2
,
2
0
1
7
.
[2
1
]
S
.
Vijay
a
ra
n
i,
M
.
J.
Ilam
a
th
i,
M
.
Nith
y
a
,
A.
P
r
o
fe
ss
o
r,
a
n
d
M
.
P
.
Re
se
a
rc
h
S
c
h
o
lar,
“
P
re
p
ro
c
e
ss
in
g
Tec
h
n
iq
u
e
s
fo
r
Tex
t
M
i
n
in
g
-
A
n
Ov
e
rv
iew
,
”
I
n
t.
J
.
Co
mp
u
t.
S
c
i.
Co
mm
u
n
.
Ne
two
r
k
s
,
v
o
l.
5
,
n
o
.
1
,
p
p
.
7
-
1
6
,
2
0
1
5
.
[
2
2
]
M
.
B
h
a
g
y
a
S
r
i
,
R
.
B
h
a
v
s
a
r
,
a
n
d
P
.
N
a
r
o
o
k
a
,
“
S
t
r
i
n
g
M
a
t
c
h
i
n
g
A
l
g
o
r
i
t
h
m
s
,
”
I
n
t
.
J
.
E
n
g
.
C
o
m
p
u
t
.
S
c
i
.
,
p
p
.
2
3
7
6
9
-
2
3
7
7
2
,
2
0
1
8
.
[2
3
]
C.
S
lam
e
t,
A.
R.
Atm
a
d
ja,
D.
S
.
M
a
y
law
a
t
i,
R.
S
.
Les
tari,
W.
Da
rm
a
lak
sa
n
a
,
a
n
d
M
.
A.
Ra
m
d
h
a
n
i,
“
Au
to
m
a
ted
Tex
t
S
u
m
m
a
riza
ti
o
n
f
o
r
I
n
d
o
n
e
sia
n
Ar
ti
c
le
Us
in
g
Ve
c
to
r
S
p
a
c
e
M
o
d
e
l,
”
IOP
Co
n
fer
e
n
c
e
S
e
rie
s:
M
a
ter
i
a
ls
S
c
ien
c
e
a
n
d
En
g
i
n
e
e
rin
g
,
2
0
1
8
.
[2
4
]
R.
Y.
Tsa
re
v
,
A.
S
.
C
h
e
rn
i
g
o
v
sk
i
y
,
E.
A.
Tsa
re
v
a
,
V
.
V.
B
re
z
it
sk
a
y
a
,
A.
Y.
Nik
if
o
ro
v
,
a
n
d
N.
A.
S
m
irn
o
v
,
“
Co
m
b
i
n
e
d
strin
g
se
a
rc
h
in
g
a
lg
o
ri
th
m
b
a
se
d
o
n
k
n
u
th
-
m
o
rr
is
-
p
ra
tt
a
n
d
b
o
y
e
r
-
m
o
o
re
a
lg
o
ri
th
m
s,”
IOP
Co
n
fer
e
n
c
e
S
e
rie
s:
M
a
ter
ia
ls S
c
ien
c
e
a
n
d
En
g
in
e
e
rin
g
,
2
0
1
6
.
[2
5
]
R.
F
it
ri
y
a
n
t
o
,
A.
Y
u
d
h
a
n
a
,
a
n
d
S
.
S
u
n
a
rd
i
,
“
Im
p
lem
e
n
tatio
n
S
H
A5
1
2
Ha
sh
F
u
n
c
ti
o
n
a
n
d
Bo
y
e
r
-
M
o
o
re
S
tri
n
g
M
a
tch
in
g
Alg
o
rit
h
m
fo
r
J
p
e
g
/ex
if
M
e
ss
a
g
e
Dig
e
st Co
m
p
il
a
ti
o
n
,
”
J
.
On
li
n
e
I
n
f
o
rm
.
,
v
o
l.
4
,
n
o
.
1
,
p
p
.
1
6
-
2
3
,
2
0
1
9
.
[2
6
]
F
.
T.
Waru
w
u
a
n
d
R
.
M
a
n
d
a
la,
“
Co
m
p
a
riso
n
o
f
Kn
u
th
M
o
rris
P
ra
tt
a
n
d
Bo
y
e
r
M
o
o
re
Al
g
o
rit
h
m
s
i
n
M
a
tch
in
g
S
tri
n
g
s
in
Nia
s L
a
n
g
u
a
g
e
Dic
ti
o
n
a
ry
Ap
p
li
c
a
ti
o
n
,
”
J
.
I
lm.
INFOT
EK
,
v
o
l.
1
,
n
o
.
1
,
2
0
1
6
.
[2
7
]
ra
c
h
m
a
d
fit
riy
a
n
t
o
,
A.
Yu
d
h
a
n
a
,
a
n
d
S
.
S
u
n
a
r
d
i,
“
Im
p
lem
e
n
tatio
n
S
HA
5
1
2
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sh
F
u
n
c
ti
o
n
a
n
d
B
o
y
e
r
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o
o
re
S
tr
in
g
M
a
tch
in
g
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o
rit
h
m
fo
r
J
p
e
g
/ex
if
M
e
ss
a
g
e
Dig
e
st Co
m
p
il
a
ti
o
n
,
”
J
.
On
li
n
e
I
n
f
o
rm
.
,
v
o
l.
4
,
n
o
.
1
,
p
p
.
1
6
-
2
3
,
2
0
1
9
.
[2
8
]
J.
Bh
a
n
d
a
ri
a
n
d
A.
K
u
m
a
r,
“
S
tri
n
g
M
a
tch
i
n
g
R
u
les
Us
e
d
b
y
Va
ria
n
ts
o
f
B
o
ter
-
M
o
o
re
,
”
J
.
Glo
b
.
Re
s.
Co
mp
u
t.
S
c
i
.
,
v
o
l.
5
,
n
o
.
1
,
p
p
.
8
-
1
1
,
2
0
1
4
.
[2
9
]
R.
F
.
Ra
h
m
a
t,
D.
F
.
P
ra
y
o
g
a
,
D.
G
u
n
a
wa
n
,
a
n
d
O.
S
.
S
it
o
m
p
u
l,
“
Bo
y
e
r
-
M
o
o
re
Alg
o
rit
h
m
in
Re
tri
e
v
in
g
De
lete
d
S
h
o
r
t
M
e
ss
a
g
e
S
e
rv
ice
in
An
d
ro
id
P
latf
o
rm
,
”
IOP
Co
n
f.
S
e
r.
M
a
ter
.
S
c
i
.
En
g
.
,
v
o
l.
3
0
8
,
n
o
.
1
,
2
0
1
8
.
[3
0
]
L.
S
.
Riza
,
M
.
I.
F
irma
n
s
y
a
h
,
H.
S
ireg
a
r,
D.
Bu
d
ian
a
,
a
n
d
A.
R
o
sa
les
-
P
é
re
z
,
“
D
e
term
in
in
g
stra
teg
ies
o
n
p
la
y
in
g
b
a
d
m
in
t
o
n
u
si
n
g
t
h
e
Kn
u
t
h
-
M
o
rri
s
-
P
ra
tt
a
lg
o
rit
h
m
,
”
T
EL
KOM
NIK
A
T
e
lec
o
mm
u
n
ica
ti
o
n
C
o
mp
u
ti
n
g
El
e
c
tro
n
ics
a
n
d
Co
n
tro
l,
v
o
l.
1
6
,
n
o
.
6
,
p
p
.
2
7
6
3
-
2
7
7
0
,
2
0
1
8
.
[3
1
]
R.
A.
Ba
e
z
a
-
Ya
tes
a
n
d
M
.
Ré
g
n
ier,
“
Av
e
ra
g
e
ru
n
n
i
n
g
ti
m
e
o
f
th
e
Bo
y
e
r
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M
o
o
re
-
Ho
rsp
o
o
l
a
lg
o
rit
h
m
,
”
T
h
e
o
r
.
Co
mp
u
t
.
S
c
i
.
,
v
o
l.
9
2
,
n
o
.
1
,
p
p
.
1
9
-
3
1
,
1
9
9
2
.
[3
2
]
L.
Ju
n
,
Z.
Zh
u
o
,
M
.
Ju
a
n
,
a
n
d
L.
Xin
g
fe
n
g
,
“
M
u
lt
i
-
p
a
tt
e
rn
M
a
tch
i
n
g
M
e
th
o
d
s
Ba
se
d
o
n
N
u
m
e
rica
l
Co
m
p
u
tatio
n
,
”
T
EL
KOM
NIKA
T
e
lec
o
mm
u
n
ic
a
ti
o
n
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o
mp
u
ti
n
g
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e
c
tro
n
ics
a
n
d
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n
tro
l,
v
o
l
.
1
1
,
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o
.
3
,
p
p
.
1
4
9
7
-
1
5
0
5
,
2
0
1
3
.
[3
3
]
A.
Ku
sn
a
d
i
a
n
d
A.
K.
Wi
c
a
k
so
n
o
,
“
Co
m
p
a
ris
o
n
o
f
t
h
e
Ho
rsp
o
o
l
Alg
o
ri
th
m
a
n
d
t
h
e
Zh
u
-
Tak
a
o
k
a
Alg
o
rit
h
m
in
De
sk
to
p
-
Ba
se
d
S
tri
n
g
S
e
a
rc
h
(in
Ba
h
a
sa
:
P
e
rb
a
n
d
in
g
a
n
Al
g
o
r
it
m
a
Ho
rsp
o
o
l
d
a
n
Alg
o
rit
m
a
Zh
u
-
Tak
a
o
k
a
d
a
lam
P
e
n
c
a
rian
S
tri
n
g
Be
r
b
a
sis De
sk
to
p
)
,
”
J
.
Ulti
m.
Co
mp
u
t.
,
v
o
l.
9
,
n
o
.
1
,
p
p
.
1
2
-
1
6
,
2
0
1
7
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
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RS
Ya
n
a
A
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n
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n
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d
o
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sp
e
c
ialize
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i
n
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rti
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in
telli
g
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f
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ti
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g
.
Nur
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k
m
a
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e
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ialize
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in
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fo
rm
a
ti
o
n
S
y
ste
m
a
n
d
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m
p
u
ter
S
c
ien
c
e
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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In
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s d
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in
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.
Evaluation Warning : The document was created with Spire.PDF for Python.