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I
SS
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8708
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3522
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c
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1.
I
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m
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ata.
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p
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Data
in
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d
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esia
in
cr
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in
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l
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p
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d
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m
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t
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all
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ter
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o
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Go
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a
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ch
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m
a
k
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,
p
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d
o
t
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s
.
B
e
ca
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e,
b
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n
ter
in
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d
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ital
a
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all
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p
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t
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t
h
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co
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s
tr
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a
n
ap
p
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to
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in
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atab
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I
n
th
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p
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s
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m
a
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ag
in
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i
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f
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r
m
atio
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tech
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lo
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d
atab
as
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s
u
c
h
as
s
to
r
in
g
a
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d
s
ea
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ch
in
g
d
ata
th
at
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d
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t.
Data
b
ase
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esig
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s
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to
f
ac
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t
h
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m
an
a
g
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m
en
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Hu
n
d
r
ed
s
o
f
th
o
u
s
an
d
s
o
f
d
ata,
tak
e
a
lo
n
g
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i
m
e.
So
a
lo
t o
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s
ea
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i
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g
u
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g
h
a
s
h
in
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m
et
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s
to
co
m
p
lete
t
h
e
r
esea
r
ch
[
1
]
-
[
4
]
.
Sear
ch
ap
p
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ter
ac
tio
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eq
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[
5
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Su
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ca
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q
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alit
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n
a
lar
g
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n
u
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b
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o
f
d
atab
ase.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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N:
2088
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8708
P
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le
m
en
tin
g
t
h
e
h
a
s
h
s
y
s
te
m
.
T
h
e
s
ea
r
ch
p
r
o
ce
s
s
r
eq
u
ir
es
a
k
e
y
,
m
a
k
i
n
g
it
f
aster
a
n
d
m
o
r
e
ac
cu
r
ate.
T
h
e
k
e
y
u
s
ed
is
I
D
E
m
p
lo
y
ee
s
i
n
I
n
d
o
n
esia.
T
h
e
k
e
y
co
n
s
i
s
ts
o
f
1
8
n
u
m
b
er
s
o
f
u
n
i
ts
.
I
n
I
n
d
o
n
esia
I
D
E
m
p
lo
y
ee
is
a
s
y
m
b
o
l
o
f
e
m
p
lo
y
ee
d
ata.
So
f
r
o
m
t
h
e
I
D
E
m
p
lo
y
ee
s
ca
n
i
m
m
ed
iate
l
y
r
ec
o
g
n
ize
w
h
o
th
e
o
w
n
er
I
D
E
m
p
lo
y
ee
s
q
u
ic
k
l
y
.
T
h
e
ab
o
v
e
p
r
o
b
lem
s
ca
n
b
e
s
o
lv
ed
b
y
a
g
o
o
d
s
taf
f
i
n
g
ad
m
i
n
is
tr
atio
n
p
lan
.
T
h
e
au
t
h
o
r
s
s
c
h
o
s
e
th
e
m
et
h
o
d
o
f
p
r
o
g
r
ess
iv
e
o
v
er
f
lo
w
(
P
O)
an
d
lin
ea
r
q
u
o
tien
t
(
L
Q)
h
as
h
in
g
,
b
ec
au
s
e
o
f
ea
ch
m
et
h
o
d
w
ill
r
ec
u
lt
i
n
th
e
av
er
ag
e
v
a
lu
e
o
f
t
h
e
co
llis
io
n
an
d
th
e
s
p
ee
d
o
f
th
e
s
ea
r
ch
p
r
o
ce
s
s
.
C
o
llis
io
n
,
ie
th
e
o
cc
u
r
r
en
ce
o
f
co
llis
io
n
s
i
n
th
e
p
lace
m
en
t
o
f
k
e
y
v
al
u
es
o
n
t
h
e
i
n
d
ex
n
u
m
b
er
o
f
t
h
e
s
a
m
e
m
e
m
o
r
y
ad
d
r
ess
tab
le.
A
t
th
e
a
v
er
ag
e
v
al
u
e
o
f
th
e
co
lli
s
io
n
d
o
es
n
o
t
u
s
e
u
n
i
ts
an
d
f
o
r
t
h
e
le
n
g
t
h
o
f
ti
m
e
u
s
ed
i
n
t
h
e
s
ea
r
c
h
p
r
o
ce
s
s
u
s
in
g
t
h
e
s
to
p
w
atc
h
f
u
n
ctio
n
w
it
h
u
n
i
t
o
f
ti
m
e
(
m
illi
s
ec
o
n
d
)
.
B
o
th
m
eth
o
d
s
w
er
e
an
al
y
ze
d
an
d
d
eter
m
i
n
ed
th
e
m
o
s
t
e
f
f
ec
ti
v
e
i
n
ca
s
e
s
tu
d
ie
s
o
f
t
h
e
u
s
e
o
f
m
e
m
o
r
y
m
an
a
g
e
m
e
n
t.
T
h
e
d
ata
u
s
ed
am
o
u
n
ted
to
2
0
0
0
r
ec
o
r
d
s
to
d
eter
m
in
e
t
h
e
p
er
f
o
r
m
a
n
ce
o
f
b
o
th
m
e
th
o
d
s
.
I
f
th
e
2
0
0
0
r
ec
o
r
d
d
ata
h
as
n
o
t
p
r
o
d
u
ce
d
th
e
p
er
f
o
r
m
an
ce
o
f
t
h
e
tw
o
m
et
h
o
d
s
,
th
e
n
th
e
d
ata
w
il
l
b
e
ad
d
ed
u
n
til
t
h
e
m
et
h
o
d
lo
o
k
s
g
o
o
d
p
er
f
o
r
m
a
n
ce
.
So
th
e
f
u
t
u
r
e
ca
n
b
e
a
r
ef
er
en
ce
in
t
h
e
p
r
o
ce
s
s
o
f
s
ea
r
ch
i
n
g
d
ata
w
ith
a
lar
g
e
a
m
o
u
n
t.
T
h
e
au
t
h
o
r
co
n
d
u
cte
d
a
s
tu
d
y
ai
m
ed
at
th
e
i
m
p
le
m
en
tatio
n
o
g
m
e
m
o
r
y
m
a
n
ag
e
m
en
t,
p
r
ev
en
ti
n
g
th
e
s
a
m
e
d
ata
r
ep
etitio
n
,
g
o
o
d
d
atab
ase
m
a
n
ag
e
m
e
n
t,
an
d
p
r
o
v
id
e
ea
s
y
ac
ce
s
s
to
d
ata.
T
h
e
an
al
y
s
i
s
p
r
o
ce
s
s
b
y
en
ter
in
g
p
r
ed
eter
m
i
n
ed
k
e
y
w
o
r
d
s
ca
n
s
ea
r
ch
t
h
e
d
ata
an
d
d
is
p
lay
w
ith
t
h
e
r
eq
u
ir
ed
ac
cu
r
a
v
y
o
f
ti
m
e
q
u
ic
k
l
y
.
2.
B
ASI
C
T
H
E
O
RY
2
.
1
.
H
a
s
hin
g
T
h
e
s
ea
r
ch
p
r
o
ce
s
s
is
u
s
ed
f
o
r
v
ar
io
u
s
ac
tiv
ities
p
er
f
o
r
m
ed
b
o
th
o
n
li
n
e
an
d
o
f
lin
e,
m
a
n
y
alg
o
r
ith
m
s
ca
n
b
e
u
s
ed
to
p
er
f
o
r
m
t
h
e
s
ea
r
ch
p
r
o
ce
s
s
w
h
ic
h
i
s
a
h
ash
i
n
g
al
g
o
r
ith
m
[
8
]
.
Has
h
in
g
is
a
w
id
el
y
u
s
ed
tech
n
iq
u
e
f
o
r
ac
ce
s
s
i
n
g
f
iles
o
n
d
ata
s
to
r
ag
e.
Has
h
i
n
g
ca
n
b
e
f
o
u
n
d
i
n
ap
p
licatio
n
s
in
o
th
er
f
ield
s
s
u
c
h
a
s
f
u
zz
y
m
atch
in
g
,
er
r
o
r
ch
ec
k
i
n
g
,
a
u
th
e
n
ticat
io
n
,
cr
y
p
to
g
r
ap
h
y
,
a
n
d
n
et
w
o
r
k
i
n
g
.
T
h
e
h
as
h
in
g
tec
h
n
iq
u
e
h
a
s
f
o
u
n
d
t
h
e
ap
p
licatio
n
to
p
r
o
v
i
d
e
f
aster
ac
ce
s
s
to
th
e
r
o
u
ti
n
g
tab
le,
w
ith
an
in
cr
ea
s
e
i
n
t
h
e
s
ize
o
f
t
h
e
r
o
u
ti
n
g
tab
le
[
6
]
.
2
.
2
.
H
a
s
h f
un
ct
io
n
T
h
e
h
as
h
f
u
n
ct
io
n
is
th
e
m
a
p
p
in
g
o
f
t
h
e
f
u
n
c
tio
n
o
f
in
te
g
er
s
i
n
{0
,
1
,
…,
m
-
1
}
to
in
te
g
er
s
i
n
{0
,
1
,
…,
m
’
–
1
}
w
it
h
m
’
<
m
.
w
h
en
ap
p
l
y
in
g
t
h
e
h
as
h
f
u
n
c
tio
n
to
n
t
w
o
in
te
g
er
s
ca
n
b
e
m
ap
p
ed
to
th
e
s
a
m
e
v
alu
e.
T
h
is
is
ca
lled
a
co
llis
io
n
.
T
h
e
p
e
r
f
ec
t
h
as
h
f
u
n
ct
i
o
n
at
n
in
teg
er
s
is
a
h
as
h
f
u
n
ct
io
n
th
a
t
h
as
n
o
co
llis
io
n
s
f
o
r
n
i
n
teg
er
s
[
9
]
.
T
h
e
v
alu
e
r
etu
r
n
ed
f
r
o
m
t
h
e
h
ash
f
u
n
c
tio
n
is
ca
l
led
th
e
h
a
s
h
co
d
e.
I
t
is
k
n
o
w
n
h
as
h
al
g
o
r
it
h
m
w
o
r
k
s
in
o
n
e
w
a
y
a
n
d
ca
n
n
o
t
b
e
r
ev
er
s
ed
.
A
o
n
e
-
w
a
y
h
as
h
al
g
o
r
ith
m
is
d
esi
g
n
ed
u
s
in
g
t
w
o
s
tep
s
.
First,
co
n
v
er
t
t
h
e
in
p
u
t
d
ata
in
to
t
h
e
m
atr
i
x
s
y
s
te
m
b
y
u
s
i
n
g
all
th
e
co
n
v
er
s
io
n
n
ee
d
ed
to
g
en
er
ate
th
e
in
itial
h
as
h
v
al
u
e.
Seco
n
d
,
u
s
e
th
e
o
u
tp
u
t
f
r
o
m
t
h
e
f
ir
s
t
s
tep
to
cr
ea
te
a
s
u
m
m
ar
y
o
f
t
h
e
d
ata
an
d
u
lti
m
atel
y
g
en
er
ate
a
s
a
f
e
h
as
h
v
alu
e
[
1
0
]
.
A
g
o
o
d
h
ash
f
u
n
ctio
n
m
u
s
t
m
ee
t
th
r
ee
p
r
o
p
er
ties
,
n
a
m
el
y
p
r
ei
m
a
g
e
r
esis
ta
n
ce
,
s
ec
o
n
d
-
o
r
d
er
p
r
eim
a
g
e,
a
n
d
co
llis
io
n
r
e
s
is
t
an
ce
.
T
h
e
co
llis
io
n
r
esi
s
ta
n
ce
m
ea
n
s
i
t
i
s
d
if
f
ic
u
lt
to
co
m
p
u
te
g
et
t
w
o
d
i
f
f
er
e
n
t
in
p
u
t
s
th
a
t h
a
v
e
th
e
s
a
m
e
h
as
h
v
alu
e
[
1
1
]
.
T
h
e
m
ai
n
id
ea
o
f
th
e
h
as
h
v
al
u
e
f
u
n
c
tio
n
ap
p
r
o
ac
h
,
h
(
k
)
,
as
th
e
ar
r
ay
b
u
ck
et
i
n
d
ex
,
U,
is
t
h
e
s
to
r
ag
e
o
f
th
e
k
k
e
y
.
T
h
i
s
m
ea
n
s
th
a
t
lo
ck
s
to
r
ag
e
(
k
)
in
b
u
ck
e
t
U
[
h
(
k
)
]
is
a
h
a
s
h
f
u
n
c
tio
n
to
r
ed
u
ce
t
h
e
in
d
e
x
ar
r
a
y
h
an
d
li
n
g
.
T
h
e
d
iv
is
io
n
m
et
h
o
d
(
m
o
d
f
u
n
ctio
n
h
(
k
)
=
m
o
d
m
)
is
u
s
ed
to
cr
ea
te
a
h
a
s
h
f
u
n
ct
io
n
h
(
k
)
in
th
e
h
a
s
h
tab
le
[
1
2
]
.
2
.
3
.
P
ro
g
re
s
s
iv
e
o
v
er
f
lo
w
L
i
n
ea
r
P
r
o
b
in
g
o
r
Pro
g
r
ess
iv
e
Ov
er
f
lo
w
i
s
a
class
ic
i
m
p
le
m
en
tatio
n
o
f
th
e
h
as
h
tab
le.
I
t
u
s
es
t
h
e
h
as
h
h
f
u
n
ct
io
n
to
m
ap
a
s
et
o
f
n
k
e
y
s
i
n
to
a
n
m
s
ize
ar
r
a
y
[
1
3
]
.
I
n
th
e
P
o
m
et
h
o
d
,
if
th
e
k
a
y
i
s
cr
as
h
d
t
h
e
n
th
e
k
a
y
v
al
u
e
w
i
l
b
e
p
lace
d
o
n
t
h
e
n
e
x
t
in
d
e
x
t
h
at
is
s
ti
ll
e
m
p
a
t
y
.
T
h
e
n
e
x
t
lo
ca
tio
n
s
ea
r
ch
i
s
d
o
n
e
f
r
o
m
t
h
e
b
eg
in
n
i
n
g
o
f
th
e
tab
le
an
d
f
o
r
m
s
a
cir
cu
lar
in
d
ex
s
tr
u
ct
u
r
e
[
1
4
]
.
L
in
ea
r
P
r
o
b
in
g
u
s
i
n
g
h
as
h
in
g
f
u
n
ctio
n
h
(
k
,
i
)
=
(
h
’
(
k
)
+
i)
m
o
d
m
f
o
r
i=0
,
1
,
…,
m
-
1
.
T
h
is
is
a
m
aj
o
r
d
if
f
icu
l
t
y
o
f
g
r
o
u
p
in
g
e
v
e
n
t
h
o
u
g
h
it
i
s
t
h
e
e
m
p
at
y
s
lo
t state
i
f
th
e
f
u
ll sl
o
t i
s
d
o
n
e
th
e
n
e
x
t p
r
o
b
ab
ilit
y
w
it
h
(
i +
1
)
/ m
[
1
5
]
.
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
.
5
,
Octo
b
er
2
0
1
8
:
3
5
1
2
–
3
5
2
2
3514
2
.
4
.
L
inea
r
qu
o
t
ient
L
i
n
ea
r
Qu
o
tie
n
t
o
r
Do
u
b
le
Hash
i
n
g
is
a
tec
h
n
iq
u
e
u
s
ed
in
h
as
h
tab
le
to
s
o
lv
e
co
llis
io
n
s
w
h
e
n
t
w
o
v
alu
e
s
p
r
o
d
u
ce
t
w
o
id
en
t
ical
k
e
y
s
[
1
6
]
.
Do
u
b
le
h
as
h
i
n
g
b
ec
o
m
e
s
a
n
o
th
er
alter
n
ati
v
e
to
p
r
ed
ict
f
r
eq
u
en
tl
y
-
d
ef
in
ed
i
te
m
s
i
n
a
lar
g
e
n
u
m
b
er
o
f
d
ataset
s
.
W
h
i
le
t
h
e
s
q
u
a
r
e
is
to
i
n
v
esti
g
ate
th
e
lo
s
s
o
f
p
r
i
m
ar
y
clu
s
ter
i
n
g
p
r
o
b
lem
s
,
t
h
u
s
li
m
iti
n
g
th
e
n
u
m
b
er
o
f
k
e
y
s
th
a
t
ca
n
b
e
p
lace
d
o
n
th
e
f
u
ll
tab
le.
Do
u
b
l
e
h
as
h
in
g
i
s
an
o
th
er
m
et
h
o
d
f
o
r
g
ee
r
atin
g
p
r
o
b
lin
g
s
eq
u
en
ce
s
[
1
7
]
.
T
h
e
s
ec
o
n
d
h
a
s
h
i
n
g
f
u
n
ctio
n
is
a
s
tep
i
n
t
h
e
e
v
en
t
o
f
m
u
ltip
le
co
llis
io
n
s
t
h
e
id
ea
is
i
f
t
w
o
h
a
s
h
v
alu
e
s
to
t
h
e
s
a
m
e
ca
lcu
lated
f
r
o
m
th
e
in
i
tial
v
al
u
e
u
s
i
n
g
t
h
e
s
ec
o
n
d
h
a
s
h
i
n
g
f
u
n
ctio
n
.
S
o
it
ca
n
b
e
u
s
ed
to
ch
an
g
e
t
h
e
o
r
d
er
o
f
lo
ca
tio
n
s
in
th
e
tab
le,
b
u
t
s
till
,
h
av
e
ac
ce
s
s
to
th
e
e
n
tire
tab
le
[
1
5
]
.
T
h
is
tech
n
iq
u
e
u
s
es
th
e
s
ec
o
n
d
id
ea
to
ap
p
ly
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h
e
h
a
s
h
f
u
n
ct
io
n
h
’
(
k
e
y
)
to
t
h
e
k
e
y
in
t
h
e
ev
en
t
o
f
a
co
llis
io
n
.
T
h
e
r
esu
lt o
f
th
e
s
ec
o
n
d
h
as
h
f
u
n
ctio
n
i
s
to
en
ter
t
h
e
n
u
m
b
er
o
f
p
o
s
itio
n
s
f
r
o
m
t
h
e
co
llis
io
n
p
o
in
t.
T
h
er
e
ar
e
s
ev
er
al
r
eq
u
ir
em
e
n
t
s
f
o
r
th
e
s
ec
o
n
d
f
u
n
ct
io
n
:
a.
Do
n
o
t h
av
e
to
ev
a
lu
ate
to
ze
r
o
b.
Mu
s
t
m
a
k
e
s
u
r
e
th
at
all
ca
n
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e
in
v
e
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ti
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ated
T
h
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p
r
o
b
in
g
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eq
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e
n
ce
is
th
e
n
ca
lcu
lated
as
f
o
llo
w
s
[
1
8
]
:
Hi
(
x
)
=
(
h
(
x
)
+
i
h
’
(
x
)
)
m
o
d
m
.
w
h
er
e
h
(
x
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is
t
h
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ig
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u
n
ctio
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x
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ec
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d
f
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i t
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m
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er
o
f
co
llis
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s
a
n
d
m
s
i
ze
o
f
th
e
tab
le.
Fig
u
r
e
1
as
s
u
m
e
s
w
h
er
e
ea
c
h
R
r
ec
o
r
d
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n
iq
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l
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h
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K.
i
n
ad
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K
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te
R
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e
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l
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at
io
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p
ec
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f
ied
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n
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I
NFO
f
ie
ld
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Step
s
to
o
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g
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n
ize
n
o
tes
s
o
y
o
u
ca
n
q
u
i
k
l
y
f
i
n
d
r
ec
o
r
d
s
th
e
tab
le
co
llec
tio
n
.
A
ll
r
etr
ie
v
al
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eq
u
e
s
ts
a
n
d
u
p
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ates
ar
e
s
p
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if
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ed
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x
clu
s
i
v
el
y
w
i
th
th
e
r
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o
r
d
in
g
k
e
y
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n
ea
s
y
w
a
y
t
o
i
m
p
le
m
e
n
t
a
n
o
r
g
a
n
izatio
n
is
to
k
ee
p
r
ec
o
r
d
s
in
a
tab
le.
T
h
e
tab
le
en
tr
y
is
e
m
p
t
y
o
r
co
n
tain
s
o
n
e
o
f
th
e
s
tep
s
.
Sear
ch
f
o
r
r
ec
o
r
d
s
w
it
h
a
g
iv
e
n
k
e
y
b
y
ex
a
m
i
n
i
n
g
all
tab
le
en
tr
ies
in
d
ep
th
.
Si
m
ilar
l
y
,
a
n
e
w
r
ec
o
r
d
ca
n
b
e
in
s
er
ted
in
to
t
h
e
tab
le
b
y
s
ea
r
ch
i
n
g
f
o
r
e
m
p
t
y
p
o
s
i
tio
n
s
[
1
9
]
.
f
u
l
l
T
h
e
h
a
s
h
t
a
b
l
e
e
m
p
t
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f
u
l
l
e
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p
t
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C
o
l
l
i
s
i
o
n
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e
m
p
t
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T
h
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c
o
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l
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s
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o
n
R
e
s
o
l
u
t
i
o
n
s
t
r
a
t
e
g
y
T
h
e
p
r
o
b
e
p
a
t
h
m
-
1
h
h
(
k
)
0
1
2
I
N
F
O
K
E
Y
A
R
E
C
O
R
D
Fig
u
r
e
1
.
H
h
as
h
f
u
n
c
tio
n
as a
m
ap
p
in
g
[
1
9
]
Fig
u
r
e
1
o
f
h
ash
i
n
g
u
s
in
g
th
e
t
r
an
s
f
o
r
m
atio
n
o
n
k
e
y
K
g
iv
e
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ti
th
e
p
lace
w
h
er
e
th
e
r
ec
o
r
d
c
o
n
tain
i
n
g
th
e
K
k
e
y
is
lo
ca
ted
.
S
u
p
p
o
s
e
th
at
th
e
tab
le
h
as
e
n
tr
ies
o
r
p
o
s
itio
n
s
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n
u
m
b
er
s
0
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1
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…,
m
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1
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h
en
m
m
ap
s
all
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e,
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m
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1
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b
y
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lli
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t
h
e
f
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as
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d
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es it a
s
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m
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p
i
n
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I
f
j
(
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it c
a
n
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e
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aid
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at
t
h
e
k
e
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as
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o
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itio
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s
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r
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o
e
k
e
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s
m
a
y
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as
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e
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m
e
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o
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itio
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n
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er
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h
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tab
le,
i
t
m
a
y
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ap
p
en
t
h
at
t
h
e
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le
h
(
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tr
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s
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y
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cc
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ied
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y
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o
th
er
k
e
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n
th
is
ca
s
e,
w
e
n
ee
d
a
m
ec
h
an
is
m
to
i
n
v
e
s
ti
g
ate
th
e
r
est
o
f
t
h
e
tab
le
u
n
ti
l th
er
e
is
a
n
e
m
p
at
y
e
n
tr
y
.
3.
RE
S
E
ARCH
M
E
T
H
O
D
L
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G
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3
.
1
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K
ey
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1
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eq
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2
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u
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Fig
u
r
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3
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.
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
.
5
,
Octo
b
er
2
0
1
8
:
3
5
1
2
–
3
5
2
2
3518
So
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t P
O
I
f
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w
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k
e
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s
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9
6
1
0
4
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5
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9
8
3
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4
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0
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T
h
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lace
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e
n
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h
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lu
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eso
l
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tio
n
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1
1
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d
d
r
ess
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d
ex
=
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1
0
C
alcu
latio
n
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H
(
1
9
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1
9
m
o
d
1
1
=>
8
H
(
6
1
)
=
6
1
m
o
d
1
1
=>
6
H
(
0
4
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=
0
4
m
o
d
1
1
=>
4
H
(
0
5
)
=
0
5
m
o
d
1
1
=>
5
H
(
1
9
)
=
1
9
m
o
d
1
1
=>
8
(
co
ll
is
io
n
)
P
lace
d
o
n
th
e
n
ex
t e
m
p
t
y
lo
ca
tio
n
: 9
H
(
8
3
)
=
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3
m
o
d
1
1
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(
co
ll
is
io
n
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P
lace
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o
n
th
e
n
ex
t e
m
p
t
y
lo
ca
tio
n
: 7
H
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0
4
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1
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4
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co
ll
is
io
n
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P
lace
d
o
n
th
e
n
ex
t lo
ca
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n
: 5
(
co
llis
io
n
)
P
lace
d
o
n
th
e
n
ex
t lo
ca
tio
n
: 6
(
co
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io
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P
lace
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o
n
th
e
n
ex
t lo
ca
tio
n
: 7
(
co
llis
io
n
)
P
lace
d
o
n
th
e
n
ex
t lo
ca
tio
n
: 8
(
co
llis
io
n
)
P
lace
d
o
n
th
e
n
ex
t lo
ca
tio
n
: 9
(
co
llis
io
n
)
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:
2088
-
8708
P
erfo
r
ma
n
ce
A
n
a
lysi
s
o
f H
a
s
h
in
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Ma
th
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d
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o
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th
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E
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p
lo
yme
n
t o
f A
p
p
(
A
n
to
n
Yu
d
h
a
n
a
)
3519
P
lace
d
o
n
th
e
n
ex
t
e
m
p
t
y
lo
ca
tio
n
: 1
0
H
(
1
0
)
=
1
0
m
o
d
1
1
=>
1
0
(
c
o
llis
io
n
)
P
lace
d
o
n
th
e
n
ex
t e
m
p
t
y
lo
ca
tio
n
: 0
H
(
0
3
)
=
0
3
m
o
d
1
1
=>
3
T
h
e
r
esu
lts
o
f
t
h
ese
ca
lc
u
latio
n
s
ar
e
s
h
o
w
n
i
n
f
ig
u
r
e
5
.
T
h
e
av
er
ag
e
f
o
r
ac
ce
s
s
i
n
g
a
k
e
y
v
a
lu
e
is
:
Av
er
ag
e
co
lli
s
io
n
=
(
1
+
1
+
6
+
1
)
/
9
=
1
E
x
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er
i
m
e
n
t
L
Q
I
f
k
n
o
w
n
k
e
y
v
al
u
e
as
f
o
llo
w
s
:
1
9
6
1
0
4
0
5
1
9
8
3
0
4
1
0
0
3
So
th
e
p
lace
m
e
n
t o
f
t
h
ese
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e
y
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e
s
w
it
h
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h
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ea
r
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o
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m
eth
o
d
is
as
f
o
llo
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s
.
R
eso
l
u
tio
n
:
N
=
9
,
P =
1
1
,
A
d
d
r
ess
in
d
ex
=
0
to
1
0
C
alcu
latio
n
:
H
(
1
9
)
=
1
9
m
o
d
=>
8
H
(
6
1
)
=
6
1
m
o
d
=>
6
H
(
0
4
)
=
0
4
m
o
d
=>
4
H
(
0
5
)
=
0
5
m
o
d
=>
5
H
(
1
9
)
=
1
9
m
o
d
=>
8
(
c
o
llis
i
o
n
)
P
lace
d
o
n
a
n
e
w
lo
ca
tio
n
I
n
cr
e
m
e
n
t =
>
(
1
9
d
iv
1
1
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o
d
1
1
=>
1
Ne
w
lo
ca
tio
n
=>
(
8
+
1
)
m
o
d
1
1
=>
9
H
(
8
3
)
=
8
3
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o
d
1
1
=
6
(
co
lli
s
io
n
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P
lace
d
o
n
a
n
e
w
lo
ca
tio
n
I
n
cr
e
m
e
n
t =
>
(
1
9
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iv
1
1
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m
o
d
1
1
=>
7
Ne
w
lo
ca
tio
n
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(
6
+
7
)
m
o
d
1
1
=>
2
H
(
0
4
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=
0
4
m
o
d
1
1
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(
co
ll
is
io
n
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P
lace
d
o
n
a
n
e
w
lo
ca
tio
n
I
n
cr
e
m
e
n
t =
>
(
0
4
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iv
1
1
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d
1
1
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0
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et
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1
Ne
w
lo
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tio
n
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4
+
1
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m
o
d
=>
5
(
co
llis
io
n
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P
lace
d
o
n
a
n
e
w
lo
ca
tio
n
I
n
cr
e
m
e
n
t =
>
1
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w
lo
ca
tio
n
=>
(
5
+
1
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m
o
d
=>
6
(
co
llis
io
n
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P
lace
d
o
n
a
n
e
w
lo
ca
tio
n
I
n
cr
e
m
e
n
t =
>
1
Ne
w
lo
ca
tio
n
=>
(
6
+
1
)
m
o
d
=>
7
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(
1
0
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=
1
0
m
o
d
1
1
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1
0
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(
0
3
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3
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o
d
1
1
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3
T
h
e
ca
lcu
latio
n
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u
lts
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o
w
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5
.
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v
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g
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s
s
to
a
v
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e
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ar
e:
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er
ag
e
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lli
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io
n
=
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1
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.
5
5
5
6
4
.
3
.
Ana
ly
s
is
re
s
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T
h
e
au
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ater
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R
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[
1
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]
h
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3
5
1
1
3
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
.
5
,
Octo
b
er
2
0
1
8
:
3
5
1
2
–
3
5
2
2
3520
R
EG
I
S
T
R
A
TI
O
N
N
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M
B
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3
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I
n
th
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s
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ch
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th
e
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f
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s
s
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to
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al
y
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e
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v
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u
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T
h
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ed
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h
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er
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o
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a
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ce
o
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h
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e
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d
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y
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s
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n
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th
e
s
to
p
w
atch
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u
n
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n
.
Data
u
s
ed
1
0
0
0
r
ec
o
r
d
s
an
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ates
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I
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5.
CO
NCLU
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n
d
th
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,
it
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s
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d
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m
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alg
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ith
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et
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.
So
it
ca
n
b
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co
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c
lu
d
ed
t
h
a
t
h
as
h
i
n
g
m
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o
d
ca
n
r
u
n
w
ell
o
n
d
es
k
to
p
ap
p
licatio
n
s
,
an
d
b
ec
o
m
e
a
r
ef
er
en
ce
f
o
r
th
e
d
ev
elo
p
m
e
n
t
o
f
th
e
n
e
x
t
m
eth
o
d
an
d
ce
r
t
ain
l
y
ca
n
b
e
a
r
ef
er
en
ce
.
ACK
NO
WL
E
D
G
E
M
E
NT
S
I
am
g
r
ate
f
u
l to
Ah
m
ad
Dah
la
n
Un
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s
it
y
Yo
g
y
a
k
ar
ta
f
o
r
g
iv
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n
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m
e
t
h
e
o
p
p
o
r
tu
n
it
y
to
d
o
r
esear
ch
.
RE
F
E
R
E
NC
E
S
[1
]
L
.
Jia
n
w
e
i
a
n
d
C.
Hu
ij
ie,
“
A
D
y
n
a
m
ic
Ha
sh
in
g
A
l
g
o
rit
h
m
S
u
it
a
b
le
f
o
r
Em
b
e
d
d
e
d
S
y
ste
m
”
,
T
EL
KOM
NIKA
(
T
e
lec
o
mm
u
n
ica
ti
o
n
,
Co
m
p
u
t
in
g
,
El
e
c
tro
n
ics
a
n
d
Co
n
tro
l)
,
v
o
l.
1
1
,
n
o
.
6
,
p
p
.
3
2
2
0
-
3
2
2
7
,
2
0
1
3
.
[2
]
Y.
Hu
a
n
g
,
Q.
Zh
a
n
g
,
a
n
d
Z.
Yu
a
n
,
“
P
e
rc
e
p
tu
a
l
S
p
e
e
c
h
Ha
sh
in
g
A
u
th
e
n
ti
c
a
ti
o
n
A
lg
o
rit
h
m
Ba
se
d
o
n
L
in
e
a
r
P
re
d
ictio
n
A
n
a
ly
sis
”
,
T
EL
KOM
NIKA
(
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
2
,
n
o
.
4
,
p
p
.
3
2
1
4
-
3
2
2
3
,
2
0
1
4
.
[3
]
G
.
In
d
ra
w
a
n
,
e
t
a
l
.,
“
A
M
u
lt
i
-
T
h
re
a
d
e
d
F
in
g
e
rp
rin
t
Dire
c
t
-
A
c
c
e
ss
S
trate
g
y
Us
in
g
L
o
c
a
l
-
S
tar
-
S
tru
c
tu
re
-
b
a
se
d
Disc
ri
m
in
a
to
r
F
e
a
tu
re
s
”
,
T
EL
KO
M
NIKA
(
T
e
le
c
o
mm
u
n
ica
ti
o
n
,
Co
mp
u
ti
n
g
,
El
e
c
tro
n
ics
a
n
d
Co
n
tro
l
)
,
v
o
l.
1
2
,
n
o
.
5
,
p
p
.
4
0
7
9
-
4
0
9
0
,
2
0
1
4
.
[4
]
N.
Qiu
,
e
t
a
l
.,
“
Re
se
a
rc
h
o
n
Op
t
im
iz
a
ti
o
n
S
trate
g
y
to
Da
ta
Clu
ste
re
d
S
to
ra
g
e
o
f
Co
n
siste
n
t
Ha
sh
in
g
A
l
g
o
rit
h
m
”
,
T
EL
KOM
NIKA
(
T
e
lec
o
mm
u
n
ica
t
io
n
,
Co
mp
u
ti
n
g
,
El
e
c
tro
n
ics
a
n
d
Co
n
tro
l)
,
v
o
l
.
1
4
,
n
o
.
3
,
p
p
.
8
2
4
-
8
3
0
,
2
0
1
6
.
Evaluation Warning : The document was created with Spire.PDF for Python.