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f
E
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rica
l a
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Co
m
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in
ee
ring
(
I
J
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)
Vo
l.
10
,
No
.
5
,
Octo
b
er
2
0
2
0
,
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p
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7
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.
v
1
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.
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4
6
7
1
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4
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7
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4671
J
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ur
na
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m
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e
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ttp
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co
m/in
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p
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An ef
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ith
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1
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2
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cc
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p
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n
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s
a
re
a
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ted
a
n
d
d
isc
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e
d
.
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h
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s
sh
o
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th
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ti
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d
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w
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a
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p
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d
w
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h
trad
it
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n
a
l
m
e
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o
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s
.
K
ey
w
o
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d
s
:
FP
GA
L
o
g
ar
it
h
m
g
e
n
er
ato
r
Q
u
asi
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s
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etr
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stit
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ts re
se
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C
o
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r
e
s
p
o
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A
uth
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r
:
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h
Ho
n
g
Ng
u
y
en
,
L
e
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u
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Do
n
T
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h
n
ical
U
n
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er
s
it
y
,
2
3
6
Ho
an
g
Qu
o
c
Viet
Str.
,
Ha
n
o
i,
Viet
n
am
.
E
m
ail:
n
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u
y
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h
ai
h
o
n
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2
0
0
7
@
y
ah
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o
.
co
m
.
v
n
1.
I
NT
RO
D
UCT
I
O
N
Ma
n
y
r
ea
l
-
ti
m
e
d
ig
ital
s
ig
n
al
p
r
o
ce
s
s
in
g
(
DSP
)
ap
p
licatio
n
s
s
u
ch
as
d
i
g
ital
co
m
m
u
n
ica
tio
n
s
y
s
te
m
s
,
s
p
ee
ch
r
ec
o
g
n
it
io
n
,
i
m
a
g
e
p
r
o
ce
s
s
in
g
,
etc.
r
eq
u
ir
e
lo
g
ar
ith
m
o
p
er
atio
n
s
w
i
th
h
i
g
h
s
p
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d
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o
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ate
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.
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d
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m
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m
en
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h
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w
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o
w
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l
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ar
i
th
m
co
m
p
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tatio
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at
it
c
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b
e
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s
ed
f
o
r
th
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ap
p
licati
o
n
s
w
it
h
r
ea
l
-
ti
m
e
r
eq
u
ir
e
m
en
ts
.
Ho
w
e
v
er
,
t
h
e
h
ar
d
w
ar
e
i
m
p
le
m
en
ta
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u
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h
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g
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er
h
a
r
d
w
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le
x
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m
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w
it
h
t
h
e
s
o
f
t
w
ar
e
b
ased
i
m
p
le
m
e
n
tat
io
n
.
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n
ce
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w
e
h
av
e
to
co
n
s
id
er
th
e
h
ar
d
w
ar
e
r
eso
u
r
ce
ef
f
icien
c
y
an
d
co
m
p
le
x
it
y
to
ar
ch
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e
a
g
o
o
d
tr
ad
e
-
o
f
f
b
et
wee
n
th
e
m
.
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n
ce
th
e
h
ar
d
w
ar
e
i
m
p
le
m
e
n
tatio
n
o
f
lo
g
ar
ith
m
f
u
n
ctio
n
is
n
o
r
m
al
l
y
v
er
y
co
m
p
le
x
a
n
d
r
eq
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ir
es
m
u
ch
ti
m
e
w
h
ile
r
ea
l
-
t
i
m
e
DSP
ap
p
licatio
n
s
d
o
n
o
t
r
eq
u
ir
e
ab
s
o
lu
te
p
r
ec
is
io
n
,
we
o
f
ten
u
s
e
ap
p
r
o
x
i
m
a
tio
n
m
eth
o
d
s
to
i
m
p
le
m
en
t
th
e
lo
g
ar
ith
m
g
e
n
er
ato
r
s
.
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r
eo
v
er
,
f
o
r
th
e
s
y
s
te
m
s
i
m
p
lo
y
i
n
g
th
e
lo
g
ar
ith
m
ic
n
u
m
b
er
s
y
s
te
m
(
L
N
S)
o
r
th
e
h
y
b
r
id
n
u
m
b
er
s
y
s
te
m
(
HNS)
,
it
is
d
esire
d
to
im
p
le
m
en
t
th
e
e
f
f
icien
t
lin
ea
r
b
in
a
r
y
to
lo
g
ar
it
h
m
co
n
v
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ter
s
(
L
OG
C
)
as
w
ell
a
s
th
e
lo
g
ar
it
h
m
to
li
n
ea
r
b
in
ar
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ter
s
(
AL
OG
C
:
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n
ti
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lo
g
ar
ith
m
ic
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v
er
ter
s
)
.
F
o
r
ex
a
m
p
le,
as
r
ep
o
r
ted
in
[
1
-
4
]
,
in
th
e
t
y
p
ical
i
m
p
le
m
en
tatio
n
o
f
HNS
p
r
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s
s
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r
s
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d
f
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th
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3
-
D
g
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ap
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g
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/A
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p
a
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4
%
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p
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r
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a
.
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e
r
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r
e
,
m
an
y
r
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e
a
r
ch
e
r
s
ar
e
t
r
y
in
g
t
o
r
e
d
u
c
e
t
h
e
h
a
r
d
w
a
r
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c
o
m
p
l
ex
ity
o
f
th
e
s
e
c
o
n
v
e
r
t
e
r
s
.
On
th
e
o
th
e
r
h
a
n
d
,
th
e
t
r
en
d
o
f
a
p
p
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x
im
at
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c
o
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p
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t
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g
b
e
c
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m
es
p
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l
a
r
r
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ce
n
t
ly
t
o
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e
t
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e
r
r
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q
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i
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en
t
o
f
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tim
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a
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d
a
r
t
if
i
ci
a
l
in
t
e
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en
c
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ap
p
l
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c
a
t
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s
[
5
-
1
3
]
.
T
h
e
p
u
r
p
o
s
e
o
f
th
is
r
esear
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i
s
to
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d
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p
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im
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m
et
h
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d
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th
e
i
m
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m
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o
f
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lo
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x
it
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,
h
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h
s
p
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d
w
ar
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h
m
ap
p
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m
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m
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th
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ap
p
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f
o
r
r
ea
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-
ti
m
e
DSP
ap
p
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s
w
it
h
ac
ce
p
tab
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co
m
p
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tatio
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ac
cu
r
ac
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.
T
h
e
r
em
ai
n
d
er
o
f
th
e
p
ap
er
is
o
r
g
an
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as
f
o
llo
w
s
.
Sectio
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2
in
tr
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d
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s
b
r
ief
l
y
ab
o
u
t
th
e
b
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o
f
lo
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m
h
ar
d
w
ar
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ap
p
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x
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m
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m
e
th
o
d
s
.
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3
p
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s
th
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p
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p
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m
eth
o
d
a
n
d
i
m
p
le
m
e
n
tatio
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r
esu
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s
.
F
in
a
ll
y
,
Sec
tio
n
4
co
n
clu
d
es
of
t
h
e
p
ap
er
.
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.
10
,
No
.
5
,
Octo
b
e
r
2
0
2
0
:
4
6
7
1
-
4678
4672
2.
B
I
NARY
L
O
G
AR
I
T
H
M
H
ARDWA
R
E
AP
P
RO
XIM
AT
I
O
N
Firstl
y
,
w
it
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t lo
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n
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m
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er
N
to
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p
u
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n
ar
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b
ase
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2
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lo
g
ar
ith
m
a
n
d
it c
an
b
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d
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m
p
o
s
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as:
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(
1
)
n
Nx
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ig
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1
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it
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x
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c
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w
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ar
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h
m
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ess
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o
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(
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B
y
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s
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to
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p
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h
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o
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m
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ch
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h
e
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ar
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o
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th
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2
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ed
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f
u
n
d
a
m
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tal
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e,
m
a
n
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M
itc
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ll
[
1
4
]
w
it
h
v
er
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s
i
m
p
le
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n
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ap
p
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as f
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4
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s
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0
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.
5
3
b
its
w
h
ich
is
t
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lo
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m
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t
o
f
DSP
ap
p
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s
.
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h
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e,
m
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h
o
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s
w
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ev
elo
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r
co
r
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tio
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Mitch
ell
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s
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et
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d
.
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h
er
e
ar
e
th
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m
o
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l
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m
eth
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to
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r
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t
h
is
ap
p
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m
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:
L
UT
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ased
m
eth
o
d
,
p
iece
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ter
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eth
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a
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m
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m
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th
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s
.
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n
t
h
e
L
UT
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ased
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et
h
o
d
,
a
L
UT
(
L
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k
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p
T
ab
le)
th
at
s
to
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ap
p
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m
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f
th
e
r
esid
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r
is
ad
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Mitch
ell
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s
ap
p
r
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m
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to
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ed
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ce
Mitc
h
ell
er
r
o
r
.
Ho
w
e
v
e
r
,
th
e
Mitc
h
ell
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r
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r
f
u
n
ctio
n
m
a
x
i
m
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m
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al
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e
is
v
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ig
h
,
th
is
m
eth
o
d
r
eq
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tab
le
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ize.
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o
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u
ltip
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m
et
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o
d
w
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h
w
a
s
p
r
ese
n
t
ed
i
n
[
1
5
]
.
I
n
th
is
m
et
h
o
d
,
tab
les
a
n
d
ad
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er
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ar
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til
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ased
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li
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ter
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w
a
s
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p
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S.
Pau
l
et
al.
[
1
6
]
.
I
t
r
eq
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ir
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e
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e
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ased
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ir
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n
t o
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ac
cu
r
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y
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n
th
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p
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x
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m
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m
eth
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s
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ap
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en
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n
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L
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p
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m
ated
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lin
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m
en
t
w
h
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ca
n
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ex
p
r
ess
ed
as:
ii
y
a
x
b
(
5
)
I
n
cr
ea
s
in
g
th
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n
u
m
b
er
o
f
s
eg
m
en
ts
ca
n
r
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ce
th
e
ap
p
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x
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m
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er
r
o
r
b
u
t
lead
to
h
ig
h
e
r
h
ar
d
w
ar
e
co
m
p
le
x
it
y
.
So
m
e
m
et
h
o
d
s
f
o
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d
iv
id
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e
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if
f
er
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n
t
r
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io
n
s
w
er
e
p
r
o
p
o
s
ed
in
[
1
,
1
7
-
2
4
]
.
P
ap
er
s
[
1
7
-
2
2
]
p
r
esen
ted
th
e
m
et
h
o
d
s
w
it
h
2
,
4
an
d
6
r
eg
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n
s
w
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h
d
if
f
er
en
t
v
a
lu
e
s
o
f
s
lo
p
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d
c
o
n
s
tan
t
s
bi
.
T
h
ese
v
alu
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ar
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ch
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n
b
y
“
tr
ial
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d
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”
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et
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o
d
w
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h
o
u
t
d
etail
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ti
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n
m
eth
o
d
.
Fi
g
u
r
e
1
r
ep
r
esen
ts
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e
er
r
o
r
f
u
n
ct
io
n
an
d
th
e
lin
ea
r
ap
p
r
o
x
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m
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m
et
h
o
d
u
s
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g
4
s
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m
en
t
s
an
d
a
s
m
al
l
er
r
o
r
L
UT
p
r
o
p
o
s
ed
in
[
22
]
.
I
n
[
2
3
,
2
4
]
,
au
th
o
r
s
p
r
o
p
o
s
ed
th
e
q
u
asi
-
s
y
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m
etr
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m
et
h
o
d
to
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ce
th
e
h
ar
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w
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m
p
le
x
it
y
an
d
ap
p
r
o
x
i
m
at
io
n
er
r
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r
.
Mo
r
eo
v
er
,
in
[
1
]
,
B
.
-
G.
Na
m
et
al.
p
r
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p
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s
ed
a
m
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2
4
f
o
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th
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lo
g
ar
ith
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ic
ap
p
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x
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m
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.
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w
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th
e
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m
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ap
p
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A
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is
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p
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im
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m
et
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d
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ased
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m
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iq
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r
lo
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ar
ith
m
ap
p
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m
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[
2
1
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2
4
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.
T
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s
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l
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Fig
u
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2
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p
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[
23
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3.
P
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ex
p
r
es
s
e
d
as
:
2
(
)
l
o
g
(
1
)
(
)
ii
F
x
x
D
x
a
x
b
(
6
)
I
n
w
h
ic
h
i
∈
{1
,
2
,
3
,
4
}
Mo
r
eo
v
er
,
th
e
s
lo
p
es
a
i
ar
e
ch
o
s
en
to
b
e
s
u
m
o
f
p
o
w
er
-
of
-
t
w
o
v
al
u
es
(2
k
)
s
o
th
at
w
e
ca
n
i
m
p
le
m
en
t
th
e
m
u
l
tip
licatio
n
s
b
y
s
i
m
p
le
s
h
i
f
ti
n
g
o
p
er
atio
n
s
.
T
h
e
n
,
th
e
er
r
o
r
f
u
n
ctio
n
ca
u
s
in
g
b
y
t
h
is
ap
p
r
o
x
i
m
atio
n
m
et
h
o
d
ca
n
b
e
ex
p
r
ess
ed
a
s:
2
(
)
(
)
(
)
l
o
g
(
1
)
(
)
ii
E
x
F
x
D
x
x
a
x
b
(
7
)
0
0
.
1
0
.
2
0
.
3
0
.
4
0
.
5
0
.
6
0
.
7
0
.
8
0
.
9
1
-
0
.
0
1
0
0
.
0
1
0
.
0
2
0
.
0
3
0
.
0
4
0
.
0
5
0
.
0
6
0
.
0
7
0
.
0
8
0
.
0
9
x
E
L
(x)
E
L
(1-x)
E
M
(x)
(E
M
-E
L
)(x)
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.
10
,
No
.
5
,
Octo
b
e
r
2
0
2
0
:
4
6
7
1
-
4678
4674
Fig
u
r
e
3
.
T
h
e
f
u
n
d
a
m
en
tal
f
u
n
ctio
n
F
(
x
)
=
lo
g
2
(
1
+
x
)
An
L
UT
is
u
s
ed
to
s
to
r
e
th
e
o
p
tim
ized
v
alu
e
s
o
f
th
e
er
r
o
r
f
u
n
ctio
n
E
(
x
)
.
T
h
en
,
th
e
L
UT
o
u
tp
u
t
i
s
ad
d
ed
t
o
th
e
4
-
s
eg
m
en
t
lin
ea
r
ap
p
r
o
x
i
m
atio
n
f
u
n
c
tio
n
to
f
u
r
th
er
r
ed
u
ce
th
e
r
esid
u
al
er
r
o
r
.
T
h
e
h
ig
h
er
L
UT
s
ize,
th
e
h
i
g
h
er
ac
cu
r
ac
y
le
v
e
l
o
f
t
h
e
ap
p
r
o
x
i
m
at
io
n
ca
n
b
e
ac
h
ie
v
ed
.
Ho
w
e
v
er
,
it
al
s
o
lead
s
to
th
e
h
i
g
h
er
h
ar
d
w
ar
e
co
m
p
le
x
it
y
o
f
t
h
e
ap
p
r
o
x
i
m
atio
n
cir
c
u
it.
I
n
th
is
p
ap
er
,
in
o
r
d
er
to
r
ed
u
ce
t
h
e
f
i
n
al
ap
p
r
o
x
i
m
at
io
n
er
r
o
r
w
i
th
th
e
s
m
all
e
n
o
u
g
h
L
UT
s
ize,
w
e
u
s
e
a
n
al
g
o
r
ith
m
to
f
i
n
d
o
p
tim
a
l
v
al
u
es
o
f
a
i
an
d
b
i
.
W
e
h
av
e
to
co
n
s
id
er
t
h
e
ap
p
r
o
x
i
m
atio
n
f
u
n
ctio
n
co
m
p
le
x
it
y
a
s
w
ell
as
th
e
s
ize
o
f
th
e
co
r
r
ec
tio
n
L
UT
.
T
h
er
ef
o
r
e,
w
e
p
r
o
p
o
s
ed
a
n
i
m
p
r
o
v
ed
2
-
s
tep
o
p
tim
izatio
n
al
g
o
r
ith
m
b
ased
o
n
th
e
o
n
e
i
n
[
2
3
]
to
ac
h
iev
e
a
b
etter
tr
ad
e
-
o
f
f
o
f
t
h
e
ap
p
r
o
x
i
m
atio
n
cir
c
u
it
co
m
p
l
e
x
it
y
to
t
h
e
co
r
r
ec
tio
n
L
UT
s
ize.
T
h
e
p
r
o
p
o
s
ed
o
p
ti
m
izatio
n
al
g
o
r
ith
m
ai
m
s
to
f
i
n
d
th
e
o
p
ti
m
al
v
al
u
e
s
a
i
,
b
i
f
o
r
4
li
n
ea
r
s
e
g
m
en
t
s
an
d
t
h
e
L
UT
s
ize
ca
n
b
e
r
ed
u
ce
d
as
m
u
ch
as
p
o
s
s
i
b
le
b
y
m
i
n
i
m
izin
g
th
e
m
a
x
i
m
u
m
v
alu
e
o
f
t
h
e
ab
s
o
lu
te
er
r
o
r
f
u
n
ctio
n
│
E
(
x
)
│(
Ma
x
E
r
r
o
r
)
.
T
h
e
o
p
tim
iz
atio
n
alg
o
r
ith
m
i
s
p
er
f
o
r
m
ed
b
y
Ma
tlab
s
o
f
t
w
ar
e
.
I
n
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
,
f
ir
s
tl
y
,
t
h
e
r
an
g
e
o
f
x
is
d
iv
id
ed
in
to
2
h
alv
es
an
d
th
e
al
g
o
r
ith
m
f
o
r
ea
ch
h
al
f
is
p
r
o
ce
ed
ed
in
d
ep
en
d
en
tl
y
.
T
h
e
lef
t
h
alf
(
0
≤
x
≤
0
.
5
)
is
d
iv
id
ed
in
to
t
w
o
eq
u
a
l
r
eg
io
n
s
(
0
≤
x
≤
0
.
2
5
)
an
d
(
0
.
2
5
≤
x
≤
0
.
5
)
.
Fig
u
r
e
4
d
escr
ib
es th
e
o
p
ti
m
izatio
n
al
g
o
r
ith
m
f
o
r
th
e
le
f
t
h
al
f
i
n
w
h
ic
h
2
lin
ea
r
s
eg
m
e
n
ts
ar
e
ch
o
s
en
in
d
ep
en
d
e
n
tl
y
.
I
n
s
tep
1
,
w
e
ch
o
o
s
e
t
h
e
r
a
n
g
e
s
o
f
o
ffs
et1
a
n
d
o
ffs
et2
i
n
w
h
ic
h
o
ffs
et1
a
n
d
o
ffs
et
2
r
ep
r
esen
t
t
h
e
v
alu
e
s
o
f
ap
p
r
o
x
i
m
atio
n
f
u
n
ctio
n
w
h
en
x
=
0
an
d
x
=
0
.
5
,
r
esp
ec
tiv
el
y
.
T
h
e
r
an
g
e
s
o
f
o
ffs
e
t1
an
d
o
ffs
et2
ar
e
ch
o
s
en
to
en
s
u
r
e
th
e
ac
ce
p
tab
le
ac
cu
r
ac
y
o
f
ap
p
r
o
x
i
m
atio
n
r
es
u
lts
.
T
h
en
,
a
c
o
m
p
r
eh
e
n
s
iv
e
s
ea
r
ch
in
t
h
e
r
an
g
e
s
o
f
o
ffs
et1
an
d
o
ffs
et2
is
p
er
f
o
r
m
ed
to
f
i
n
d
th
e
o
p
ti
m
al
v
al
u
e
s
o
f
a
1
an
d
a
2
th
at
m
i
n
i
m
ize
th
e
Ma
xE
r
r
o
r
.
Af
ter
th
at,
i
n
s
tep
2
,
a
1
an
d
a
2
a
r
e
r
e
-
ass
ig
n
ed
to
th
e
ad
j
ac
en
t
v
alu
es
wh
ich
ar
e
th
e
s
u
m
o
f
p
o
w
er
-
of
-
t
w
o
v
al
u
es
to
s
i
m
p
l
if
y
th
e
m
u
ltip
licatio
n
s
a
n
d
o
n
e
m
o
r
e
s
ea
r
ch
is
p
er
f
o
r
m
ed
to
f
in
d
t
h
e
o
p
ti
m
al
v
alu
e
s
o
f
b
1
a
n
d
b
2
w
h
ich
m
i
n
i
m
ize
Ma
xE
r
r
o
r
.
Fo
r
th
e
r
ig
h
t
h
al
f
(
0
.
5
≤
x
<
1
)
,
th
e
o
p
ti
m
i
za
tio
n
al
g
o
r
ith
m
i
s
i
m
p
le
m
en
ted
s
i
m
i
lar
l
y
.
Fi
g
u
r
e
5
d
ep
icts
th
e
2
-
s
tep
alg
o
r
it
h
m
f
o
r
th
e
r
ig
h
t h
a
lf
r
a
n
g
e
o
f
x
.
T
ab
le
1
s
u
m
m
ar
izes
th
e
r
es
u
lts
o
f
o
p
ti
m
izatio
n
ac
h
ie
v
e
d
b
y
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
in
ea
c
h
ap
p
r
o
x
im
a
tio
n
s
tep
f
o
r
lo
g
2
(
1
+
x
)
.
Af
ter
s
tep
2
,
Ma
xE
r
r
o
r
i
n
cr
ea
s
es
a
litt
le
b
u
t
th
e
L
UT
s
ize
is
n
o
t
c
h
a
n
g
ed
co
m
p
ar
ed
w
it
h
th
e
r
es
u
lt
s
in
s
t
ep
1
.
Hen
ce
,
th
e
ap
p
r
o
x
im
a
tio
n
f
u
n
ctio
n
ca
n
b
e
ex
p
r
ess
ed
a
s
(
8
)
.
Algorithm 1.
The improved 2
-
step optimization algorithm.
Step 1
: For {
offset
1
L
≤
offset
1 ≤
offset
1
H
and
peak_point
L
≤
peak_point
≤
peak_point
H
}:
Find the optimal values of
slope
1 and
slope
2.
St
ep
2
:
Re
-
as
si
gn
th
e
op
t
im
al
slope
1
an
d
slope
2
va
lu
es
in
st
ep
1
to
th
e
ad
j
ac
en
t
po
we
r
-
of
-
2
values and find the optimal offset values.
025
05
2
1
2
3
12
0
0
.2
5
(
2
2
2
)
0
.0
0
5
,
0
.2
5
0
.5
(
2
2
)
0
.0
6
8
4
,
l
o
g
(
1
)
0
.5
0
.7
5
(
2
2
2
)
0
.1
5
0
5
,
0
.7
5
1
(
2
2
)
0
.2
4
8
,
x
x
x
x
x
x
x
x
x
(
8
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
A
n
efficien
t h
a
r
d
w
a
r
e
lo
g
a
r
ith
m
g
en
era
to
r
w
ith
m
o
d
ified
q
u
a
s
i
-
s
ymm
etr
ica
l …
(
Min
h
Ho
n
g
N
g
u
ye
n
)
4675
Fig
u
r
e
4
.
T
h
e
im
p
r
o
v
ed
2
-
s
tep
al
g
o
r
ith
m
f
o
r
t
h
e
lef
t
h
al
f
r
an
g
e
Fig
u
r
e
5
.
T
h
e
im
p
r
o
v
ed
2
-
s
tep
alg
o
r
ith
m
f
o
r
t
h
e
r
ig
h
t h
al
f
r
a
n
g
e
T
ab
le
1
.
Op
tim
izatio
n
r
esu
l
ts
o
f
th
e
i
m
p
r
o
v
e
d
2
-
s
tep
o
p
ti
m
i
za
tio
n
alg
o
r
it
h
m
S
t
e
p
S
t
e
p
1
S
t
e
p
2
(
0
≤
x
≤
0
.
2
5
)
a
1
1
.
2
9
0
5
2
0
+
2
-
2
+
2
-
5
b
1
0
.
0
0
4
0
.
0
0
5
(
0
.
2
5
≤
x
≤
0
.
5
)
a
2
1
.
0
3
1
6
2
0
+
2
-
5
b
2
0
.
0
6
8
2
0
.
0
6
8
4
(
0
.
5
≤
x
≤
0
.
7
5
)
a
3
0
.
8
9
0
5
2
-
1
+
2
-
2
+
2
-
3
b
3
0
.
1
4
1
8
0
.
1
5
0
5
(
0
.
7
5
≤
x
<
1
)
a
4
0
.
7
6
2
1
2
-
1
+
2
-
2
b
4
0
.
2
3
7
9
0
.
2
4
8
Ma
x
Err
o
r
0
.
0
0
4
7
0
.
0
0
5
T
ab
le
2
s
h
o
w
s
th
e
r
esu
lts
o
f
th
e
er
r
o
r
an
al
y
s
i
s
w
ith
t
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
co
m
p
ar
ed
w
it
h
o
th
er
4
-
s
e
g
m
e
n
t
li
n
ea
r
ap
p
r
o
x
i
m
at
io
n
m
et
h
o
d
s
.
A
s
m
en
tio
n
p
r
ev
io
u
s
l
y
,
Ma
xE
r
r
o
r
is
t
h
e
m
ax
i
m
u
m
v
a
lu
e
o
f
th
e
ab
s
o
l
u
te
er
r
o
r
f
u
n
ctio
n
│
E
(
x
)
│.
Ma
xE
r
r
o
r
(
+)
an
d
Ma
xE
r
r
o
r
(
-
)
r
ep
r
esen
t
t
h
e
m
a
x
i
m
u
m
p
o
s
iti
v
e
v
al
u
e
an
d
th
e
m
in
i
m
u
m
n
e
g
ati
v
e
v
a
lu
es
o
f
t
h
e
er
r
o
r
f
u
n
ctio
n
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N:
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f
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k
,
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p
ly
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p
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th
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le
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e
n
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m
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p
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s
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m
f
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r
r
ea
l ti
m
e
ap
p
licatio
n
s
.
RE
F
E
R
E
NC
E
S
[1
]
B.
-
G
.
Na
m
,
H.
Kim
,
a
n
d
H.
-
J.
Y
o
o
,
"
A
lo
w
-
p
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w
e
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u
n
if
ied
a
rit
h
m
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t
ic
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n
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ro
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ra
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m
a
b
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a
n
d
h
e
l
d
3
-
D
g
ra
p
h
ics
s
y
ste
m
s,"
IEE
E
J
.
S
o
li
d
S
t
a
te Ci
r
c
u
it
s
,
v
o
l
.
4
2
,
n
o
.
8
,
p
p
.
1
7
6
7
-
1
7
7
8
,
A
u
g
.
2
0
0
7
.
[2
]
H.
Kim
,
B.
-
G
.
Na
m
,
J.
-
H.
S
o
h
n
,
J.
H.
W
o
o
,
a
n
d
H.
-
J.
Yo
o
,
"
A
2
3
1
-
M
Hz
,
2
.
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-
m
W
3
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-
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