I
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m
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I
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E
CE
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Vo
l.
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,
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.
6
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Decem
b
er
2
0
2
0
,
p
p
.
6
5
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~
6
5
4
8
I
SS
N:
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8708
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DOI
: 1
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1
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1
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.
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i
6
.
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6
5
4
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8
6541
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R
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Feb
26
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2
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2020
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1
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u
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p
re
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a
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m
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a
h
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g
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p
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c
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ss
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p
o
w
e
r
in
o
r
d
e
r
to
b
e
e
x
e
c
u
ted
in
re
a
l
ti
m
e
.
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h
e
re
f
o
re
,
it
s
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m
u
st
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s.
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it
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C6
6
7
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f
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m
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a
s
In
stru
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ts
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I).
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h
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p
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iza
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6
6
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ll
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li
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ti
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a
p
p
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s
w
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m
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le
m
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.
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f
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se
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h
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t
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re
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ter
f
a
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e
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t
h
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u
te d
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n
t
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ti
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s o
f
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a
m
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lt
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r.
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6
7
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.
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h
e
p
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p
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se
d
m
e
th
o
d
g
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s
th
e
b
e
st
p
e
rf
o
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m
a
n
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d
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s
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d
.
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s
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MP
P
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p
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stit
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ts re
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p
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uth
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r
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A
b
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a
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Klilo
u
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f
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lectr
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E
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ato
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to
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o
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v
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er
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d
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r
o
elec
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lt
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f
Sc
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ce
s
an
d
T
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h
n
o
lo
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Su
ltan
Mo
u
la
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Sli
m
a
n
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Un
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s
it
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5
2
3
,
B
en
i M
ellal,
Mo
r
o
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m
ail: a
.
k
lilo
u
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u
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m
s
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m
a
1.
I
NT
RO
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UCT
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O
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P
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ls
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co
m
p
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ess
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al
g
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ith
m
is
w
id
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y
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s
ed
in
r
ad
ar
ap
p
lic
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s
,
s
u
ch
as
p
u
l
s
e
Do
p
p
ler
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ad
ar
[
1
]
,
g
r
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d
-
m
o
v
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n
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tar
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icat
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)
[
2
]
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an
d
s
y
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th
e
tic
ap
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tu
r
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r
ad
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(
SA
R
)
[
3
]
.
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t
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ca
r
r
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o
u
t
on
th
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ac
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ir
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s
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aj
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eq
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ir
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m
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t
ca
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ld
s
it
s
p
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ce
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s
in
g
in
r
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l
-
ti
m
e.
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o
r
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o
n
e
s
o
lu
tio
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is
u
s
i
n
g
m
u
ltip
le
co
m
p
u
tin
g
co
r
es
w
o
r
k
in
g
to
g
et
h
er
;
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ch
o
n
e
o
f
th
e
m
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ec
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te
a
s
m
a
ll p
o
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tio
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o
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p
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o
ce
s
s
i
n
g
.
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h
is
p
ap
er
p
r
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ts
t
h
e
C
6
6
7
8
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f
r
o
m
T
I
as
a
p
r
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ce
s
s
in
g
p
lat
f
o
r
m
.
I
t
p
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v
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a
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i
g
h
p
er
f
o
r
m
a
n
ce
f
lo
atin
g
-
p
o
in
t
ca
lcu
latio
n
w
ith
a
lo
w
p
o
w
er
co
n
s
u
m
p
t
io
n
.
I
n
f
ac
t,
i
t
co
n
ta
in
s
eig
h
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n
d
ep
en
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en
t
C
6
6
x
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ea
ch
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a
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cisi
o
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f
lo
ati
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g
p
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t
ca
lc
u
lati
o
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[
4
]
.
I
n
ad
d
itio
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s
ev
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m
m
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n
itie
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p
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m
s
u
s
in
g
t
h
e
C
6
6
7
8
DSP
[
3
,
5
-
9
]
.
E
m
b
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ed
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s
te
m
s
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ased
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DSP
h
as
p
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r
ith
m
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I
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A
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[
1
0
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h
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tech
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W
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s
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s
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w
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f
in
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d
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
.
6
,
Decem
b
er
2020
:
6
5
4
1
-
6
5
4
8
6542
n
et
w
o
r
k
i
n
g
(
SDN)
.
A
r
s
ala
n
e
et
al.
,
[
1
1
-
1
5
]
h
av
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p
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ased
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C
6
6
7
8
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b
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f
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s
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tio
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n
o
u
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p
r
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u
s
w
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k
s
[
1
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,
w
e
p
r
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ted
a
r
ea
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p
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m
p
le
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p
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th
2
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6
6
7
8
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d
s
(
a
to
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f
1
6
p
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g
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r
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ata
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I
O)
,
w
h
ic
h
w
e
h
a
v
e
o
p
ti
m
ized
it
s
u
s
e
[
1
6
,
1
7
]
.
T
h
e
m
aj
o
r
o
b
tain
ed
r
esu
lt is
a
p
ar
all
el
ef
f
icie
n
c
y
o
f
ab
o
u
t 9
0
%.
Hu
a
n
g
et
al.
,
[
1
8
]
h
av
e
p
r
o
p
o
s
ed
a
p
ar
allel
i
m
p
le
m
e
n
tatio
n
o
f
b
ea
m
f
o
r
m
i
n
g
al
g
o
r
ith
m
o
n
T
I
-
b
ased
T
o
m
ah
a
w
k
p
lat
f
o
r
m
co
n
tain
i
n
g
s
ix
DSP
co
r
e
s
.
T
h
e
alg
o
r
ith
m
i
s
w
id
el
y
u
s
ed
in
r
ad
ar
a
p
p
licatio
n
s
.
I
n
f
ac
t
Hu
a
n
g
et
al.
,
[
1
8
]
h
av
e
u
s
ed
th
e
Op
en
MP
in
ter
f
ac
e
[
1
9
]
to
d
is
tr
ib
u
te
th
e
p
r
o
ce
s
s
i
n
g
o
v
er
th
e
s
ix
DSP
co
r
es.
R
es
u
lts
s
h
o
w
a
m
a
x
i
m
u
m
s
p
ee
d
u
p
ab
o
u
t
3
.
7
.
Me
g
o
et
al.
,
[
2
0
]
h
av
e
ev
al
u
ated
th
e
p
er
f
o
r
m
a
n
ce
o
f
p
ar
alleliza
tio
n
o
f
b
asics
s
i
g
n
a
l
p
r
o
ce
s
s
in
g
al
g
o
r
ith
m
s
,
s
u
c
h
as
f
in
i
te
i
m
p
u
ls
e
r
esp
o
n
s
e
(
FIR)
f
ilter
,
d
is
cr
ete
f
o
u
r
ier
tr
an
s
f
o
r
m
(
DFT
)
an
d
f
ast
f
o
u
r
ier
tr
a
n
s
f
o
r
m
(
FF
T
)
,
o
n
th
e
C
6
6
7
8
DSP
.
I
n
th
eir
s
tu
d
y
,
a
u
th
o
r
s
h
a
v
e
u
s
ed
t
h
e
Op
en
MP
in
ter
f
ac
e
to
d
is
tr
ib
u
te
t
h
e
p
r
o
ce
s
s
i
n
g
o
v
er
th
e
ei
g
h
t
D
SP
co
r
es.
Ob
tain
e
d
r
esu
lts
s
h
o
w
t
h
at
th
e
r
elati
v
e
s
p
ee
d
u
p
i
s
h
ig
h
l
y
d
ep
en
d
en
t
o
n
t
h
e
al
g
o
r
ith
m
an
d
th
e
a
m
o
u
n
t
o
f
p
r
o
ce
s
s
ed
d
ata.
R
esu
lts
s
h
o
w
a
m
ax
i
m
u
m
s
p
ee
d
u
p
o
f
ab
o
u
t
6
.
Yu
et
al.
,
[
2
1
]
h
av
e
i
m
p
le
m
en
ted
th
e
p
u
ls
e
Do
p
p
ler
r
ad
ar
s
ig
n
al
p
r
o
ce
s
s
in
g
ch
ain
o
n
co
m
p
u
t
in
g
p
latf
o
r
m
b
ased
o
n
th
e
C
6
6
7
8
DSP
.
T
h
e
s
tu
d
ied
alg
o
r
ith
m
i
n
clu
d
e
th
r
ee
s
tep
s
:
b
ea
m
f
o
r
m
i
n
g
,
p
u
ls
e
co
m
p
r
es
s
io
n
an
d
Do
p
p
ler
f
ilter
in
g
.
T
h
e
y
h
a
v
e
u
s
ed
Op
en
MP
f
r
a
m
e
w
o
r
k
f
o
r
p
ar
allel
i
m
p
le
m
en
ta
tio
n
.
Ob
tain
ed
r
es
u
lts
s
h
o
w
t
h
at
m
u
l
ti
-
th
r
ea
d
ed
ex
ec
u
t
io
n
is
less
t
h
an
s
i
n
g
le
-
t
h
r
ea
d
ed
.
A
cc
o
r
d
i
ng
to
au
t
h
o
r
s
,
t
h
is
d
if
f
er
e
n
ce
w
a
s
ex
p
lai
n
ed
b
y
t
h
e
h
i
g
h
l
y
n
o
n
-
l
in
ea
r
m
e
m
o
r
y
ac
ce
s
s
e
s
r
eq
u
ir
ed
b
y
t
h
e
FF
T
an
d
th
e
i
n
v
er
s
e
f
a
s
t
f
o
u
r
ier
tr
an
s
f
o
r
m
(
I
FF
T
)
.
W
an
g
et
al.
[
3
]
h
av
e
i
m
p
le
m
e
n
ted
an
d
o
p
tim
iz
ed
SA
R
al
g
o
r
ith
m
s
o
n
th
e
eig
h
t
co
r
e
o
f
th
e
C
6
6
7
8
DSP
.
T
h
e
s
tu
d
ied
alg
o
r
ith
m
in
clu
d
e
t
w
o
s
tep
s
o
f
p
u
ls
e
co
m
p
r
e
s
s
io
n
m
et
h
o
d
(
r
an
g
e
co
m
p
r
ess
io
n
an
d
az
i
m
u
th
co
m
p
r
e
s
s
io
n
)
,
r
an
g
e
ce
ll
m
i
g
r
atio
n
co
r
r
ec
tio
n
(
R
C
M
C
)
an
d
co
r
n
er
tu
r
n
.
T
h
e
Op
en
MP
f
r
am
e
w
o
r
k
w
a
s
u
s
ed
to
in
s
ta
n
tiate
i
n
d
iv
id
u
a
l
th
r
ea
d
s
ac
r
o
s
s
th
e
eig
h
t
co
r
es.
Ob
t
ain
ed
r
esu
lt
s
s
h
o
w
th
a
t
th
e
ti
m
i
n
g
r
eq
u
ir
ed
f
o
r
r
an
g
e
co
m
p
r
es
s
io
n
a
n
d
az
i
m
u
th
co
m
p
r
e
s
s
io
n
s
ca
les
v
er
y
w
el
l
w
ith
th
e
in
cr
ea
s
e
o
f
th
e
n
u
m
b
er
o
f
o
p
er
atio
n
al
co
r
es.
Ho
w
ev
er
,
th
e
o
th
er
R
C
MC
a
n
d
co
r
er
tu
r
n
s
tep
s
s
at
u
r
ates
a
t
ar
o
u
n
d
f
o
u
r
co
r
es.
Fo
r
th
e
to
t
al
ex
e
c
u
tio
n
ti
m
e,
th
e
ac
ce
ler
atio
n
f
ac
to
r
w
it
h
ei
g
h
t
co
r
es
r
elativ
e
to
a
s
i
n
g
le
co
r
e
is
eq
u
al
to
5
.
6
.
Fro
m
all
p
r
esen
ted
r
esear
ch
e
s
w
o
r
k
s
,
Op
en
MP
h
as
b
ee
n
s
u
cc
es
s
f
u
ll
y
test
ed
to
d
is
tr
ib
u
te
m
a
n
y
s
ig
n
al
-
p
r
o
ce
s
s
i
n
g
al
g
o
r
ith
m
s
o
v
er
m
u
l
ti
-
co
r
e
DSP
p
latf
o
r
m
s
.
Ho
w
e
v
e
r
,
th
e
o
b
tain
ed
p
ar
allel
ef
f
icie
n
c
y
d
o
es
n
o
t
e
x
ce
ed
7
0
%
in
t
h
e
b
est
ca
s
es.
I
n
t
h
is
p
ap
er
,
an
o
p
ti
m
ized
m
et
h
o
d
is
p
r
o
p
o
s
ed
as
an
alter
n
ativ
e
to
Op
en
MP
m
eth
o
d
in
o
r
d
er
to
im
p
r
o
v
e
th
e
p
er
f
o
r
m
a
n
ce
s
.
T
h
e
m
aj
o
r
co
n
tr
ib
u
tio
n
o
f
t
h
is
p
ap
er
is
t
h
e
d
is
tr
ib
u
t
io
n
o
f
th
e
p
u
ls
e
co
m
p
r
es
s
io
n
alg
o
r
ith
m
o
v
er
th
e
eig
h
t
p
r
o
ce
s
s
i
n
g
co
r
e
o
f
th
e
C
6
6
7
8
DSP
.
W
e
h
av
e
i
m
p
le
m
en
ted
t
w
o
p
ar
alleliza
tio
n
ap
p
r
o
ac
h
es.
T
h
e
f
ir
s
t
o
n
e,
is
b
ased
o
n
e
t
h
e
Op
e
n
M
P
,
w
h
ic
h
is
a
s
h
ar
ed
-
m
e
m
o
r
y
ap
p
licatio
n
p
r
o
g
r
a
m
m
i
n
g
i
n
t
er
f
ac
e
(
A
P
I
)
w
h
o
s
e
f
ea
t
u
r
es,
ar
e
b
ased
o
n
p
r
io
r
ef
f
o
r
ts
to
f
ac
i
litate
s
h
ar
ed
-
m
e
m
o
r
y
p
ar
allel
p
r
o
g
r
a
m
m
i
n
g
.
As
t
h
e
C
6
6
7
8
DSP
in
te
g
r
ate
s
t
w
o
lev
e
ls
o
f
m
e
m
o
r
y
s
h
ar
ed
b
et
w
ee
n
t
h
e
ei
g
h
t
co
r
es,
w
h
ic
h
ar
e
th
e
in
ter
n
al
m
u
lti
-
co
r
e
s
h
ar
ed
m
e
m
o
r
y
(
MS
M)
an
d
th
e
ex
te
r
n
al
DDR
m
e
m
o
r
y
,
t
h
e
Op
en
MP
is
f
u
ll
y
ad
ap
ted
.
T
h
e
s
ec
o
n
d
ap
p
r
o
ac
h
is
an
o
p
tim
ized
m
e
th
o
d
t
h
at
w
e
h
a
v
e
p
r
o
p
o
s
ed
to
d
is
tr
ib
u
t
e
t
h
e
p
r
o
ce
s
s
in
g
o
f
th
e
p
u
l
s
e
co
m
p
r
ess
io
n
alg
o
r
it
h
m
o
n
th
e
ei
g
h
t
co
r
e
s
.
T
h
e
p
er
f
o
r
m
an
ce
o
f
th
e
t
w
o
p
ar
alleliza
tio
n
m
eth
o
d
s
ar
e
co
m
p
ar
ed
to
e
ac
h
o
th
er
b
ased
o
n
s
p
ee
d
u
p
an
d
p
ar
allel
ef
f
ic
ien
c
y
in
d
icato
r
s.
T
h
is
p
ap
er
is
o
r
g
an
ized
as
f
o
llo
w
s
.
Sectio
n
2
p
r
esen
t
s
an
o
v
er
v
ie
w
o
f
p
u
ls
e
co
m
p
r
es
s
i
o
n
m
et
h
o
d
,
ex
p
er
i
m
e
n
tal
p
lat
f
o
r
m
,
an
d
m
etr
ics
u
s
ed
f
o
r
ev
a
lu
at
i
n
g
p
ar
allel
p
r
o
ce
s
s
i
n
g
p
er
f
o
r
m
a
n
ce
.
Mo
r
eo
v
er
,
it
p
r
esen
t
s
th
e
p
r
o
p
o
s
ed
m
eh
o
d
to
d
is
tr
ib
u
te
p
u
ls
e
co
m
p
r
ess
io
n
al
g
o
r
ith
m
o
n
m
u
ltip
le
s
co
r
es.
Sectio
n
3
p
r
o
v
id
es
th
e
e
x
p
er
i
m
e
n
tal
r
es
u
lts
o
f
p
ar
allel
i
m
p
le
m
e
n
tatio
n
o
f
p
u
l
s
e
co
m
p
r
es
s
io
n
u
s
in
g
th
e
Op
en
MP
A
P
I
an
d
th
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
.
Fin
all
y
,
a
co
n
c
lu
s
io
n
i
s
p
r
o
v
id
ed
in
s
ec
tio
n
4.
2.
RE
S
E
ARCH
M
E
T
H
O
D
2
.
1
.
P
uls
e
co
m
pres
s
io
n
a
lg
o
rit
h
m
A
co
n
v
o
l
u
tio
n
o
p
er
atio
n
b
etw
ee
n
t
h
e
tr
a
n
s
m
itted
a
n
d
t
h
e
r
ec
eiv
ed
p
u
l
s
e
is
p
er
f
o
r
m
ed
in
o
r
d
er
to
d
etec
t r
ad
ar
tar
g
ets [
2
2
]
.
I
n
f
a
ct,
t
w
o
clo
s
e
l
y
tar
g
et
s
ar
e
f
u
ll
y
m
er
g
ed
i
n
ca
s
e
w
h
er
e
t
h
e
wav
e
s
e
n
t b
y
th
e
r
ad
ar
is
a
s
in
u
s
o
id
al
s
i
g
n
al
as
s
h
o
wn
in
Fi
g
u
r
e
1
.
T
o
im
p
r
o
v
e
d
etec
tio
n
ac
cu
r
ac
y
o
f
clo
s
el
y
tar
g
ets,
t
h
e
tr
an
s
m
it
ted
w
a
v
e
u
n
d
er
g
o
es
a
l
in
ea
r
f
r
eq
u
en
c
y
m
o
d
u
lat
io
n
o
p
er
atio
n
s
h
o
w
n
in
Fig
u
r
e
2
(
b
)
.
T
h
e
o
b
tain
ed
s
i
g
n
al
i
s
ca
lled
‘
C
h
ir
p
’
s
h
o
w
n
i
n
Fig
u
r
e
2
(
a)
.
T
o
o
p
tim
ize
th
e
p
r
o
ce
s
s
in
g
o
f
th
e
p
u
l
s
e
co
m
p
r
es
s
io
n
,
t
h
e
co
n
v
o
l
u
tio
n
o
p
er
atio
n
is
r
ea
lized
i
n
th
e
f
r
eq
u
en
c
y
s
p
ac
e.
I
t
is
ca
r
r
ied
o
u
t
b
y
p
er
f
o
r
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I
n
t J
E
lec
&
C
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m
p
E
n
g
I
SS
N:
2088
-
8708
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6545
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Fig
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8
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&
C
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g
,
Vo
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10
,
No
.
6
,
Decem
b
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2020
:
6
5
4
1
-
6
5
4
8
6546
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P
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N:
2088
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8708
P
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6547
4.
CO
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5
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f
r
a
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w
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k
.
RE
F
E
R
E
NC
E
S
[1
]
A
.
Klil
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,
e
t
a
l
.
,
“
Re
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ti
m
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p
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ler
ra
d
a
r
sig
n
a
l
p
ro
c
e
ss
in
g
c
h
a
in
o
n
a
m
a
ss
i
v
e
l
y
p
a
ra
ll
e
l
m
a
c
h
in
e
b
a
se
d
o
n
m
u
lt
i
-
c
o
re
DSP
a
n
d
S
e
rial
Ra
p
id
IO
i
n
terc
o
n
n
e
c
t,
”
Eu
ra
sip
J
o
u
r
n
a
l
o
n
Ad
v
a
n
c
e
s
in
S
ig
n
a
l
Pro
c
e
ss
in
g
,
v
o
l.
1
6
1
,
2
0
1
4
.
[2
]
D.
Bu
e
n
o
,
e
t
a
l.
,
“
Op
ti
m
izin
g
Ra
p
id
IO
A
rc
h
it
e
c
tu
re
s
f
o
r
On
b
o
a
r
d
P
r
o
c
e
ss
in
g
,
”
A
CM
T
ra
n
s
a
c
ti
o
n
s
o
n
Emb
e
d
d
e
d
Co
mp
u
t
in
g
S
y
ste
ms
,
v
o
l.
9
,
n
o
.
3
,
p
p
.
1
-
3
0
,
2
0
1
0
.
[3
]
D.
W
a
n
g
a
n
d
M
.
A
li
,
“
S
y
n
th
e
ti
c
A
p
e
rtu
re
Ra
d
a
r
o
n
L
o
w
P
o
w
e
r
M
u
lt
i
-
C
o
re
Dig
it
a
l
S
ig
n
a
l
P
r
o
c
e
ss
o
r,
”
in
IEE
E
Co
n
fer
e
n
c
e
o
n
Hig
h
Per
fo
rm
a
n
c
e
Ex
tre
me
Co
mp
u
ti
n
g
(
HPEC)
,
W
a
lt
h
a
m
,
M
A
,
2012
.
[4
]
T
e
x
a
s
In
stru
m
e
n
ts,
“
T
M
S
3
2
0
C6
6
7
8
M
u
lt
ic
o
re
F
ix
e
d
a
n
d
F
lo
a
ti
n
g
-
P
o
i
n
t
Dig
it
a
l
S
ig
n
a
l
P
ro
c
e
ss
o
r
,
”
Da
ta
M
a
n
u
a
l
,
2
0
1
2
.
[5
]
M
.
Na
jo
u
i,
e
t
a
l.
,
“
V
L
IW
DSP
-
B
a
se
d
L
o
w
-
L
e
v
e
l
In
stru
c
ti
o
n
S
c
h
e
m
e
o
f
G
i
v
e
n
s
QR
De
c
o
m
p
o
s
it
io
n
f
o
r
R
e
a
l
-
T
i
m
e
P
r
o
c
e
ss
in
g
,
”
J
o
u
rn
a
l
o
f
Circ
u
i
ts S
y
ste
ms
a
n
d
Co
m
p
u
ter
s,
v
o
l
.
2
6
,
n
o
.
9
,
p
p
.
1
-
2
6
,
2
0
1
7
.
[6
]
M
.
Ba
h
tat,
e
t
a
l.
,
“
I
n
stru
c
ti
o
n
sc
h
e
d
u
li
n
g
h
e
u
risti
c
f
o
r
a
n
e
ff
icie
n
t
F
F
T
in
V
L
IW
p
ro
c
e
ss
o
r
s
w
it
h
b
a
lan
c
e
d
re
so
u
rc
e
u
sa
g
e
,
”
Eu
ra
sip
J
o
u
rn
a
l
o
n
A
d
v
a
n
c
e
s in
S
ig
n
a
l
Pro
c
e
ss
in
g
,
v
o
l.
38
,
p
p
.
1
-
2
1
,
2
0
1
6
.
[7
]
R.
Be
rg
,
e
t
a
l.
,
“
Hig
h
l
y
e
f
f
i
c
ien
t
ima
g
e
re
g
i
stra
ti
o
n
f
o
r
e
m
b
e
d
d
e
d
s
y
ste
m
s
u
sin
g
a
d
istri
b
u
ted
m
u
lt
ico
re
DSP
a
rc
h
it
e
c
tu
re
,
”
J
o
u
rn
a
l
o
f
Rea
l
-
T
i
me
Ima
g
e
Pro
c
e
ss
in
g
,
v
o
l.
1
4
,
n
o
.
2
,
p
p
.
3
4
1
-
3
6
1
,
2
0
1
8
.
[8
]
N.
Ba
h
ri
,
e
t
a
l.
,
“
Re
a
l
-
ti
m
e
H2
6
4
/A
V
C
Hig
h
De
f
in
it
io
n
v
id
e
o
e
n
c
o
d
e
r
o
n
a
M
u
lt
ico
re
DS
P
T
M
S
3
2
0
C6
6
7
8
,
”
in
In
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
C
o
mp
u
ter
Vi
si
o
n
a
n
d
Im
a
g
e
A
n
a
lys
is
Ap
p
li
c
a
ti
o
n
s,
2
0
1
5
.
[9]
A
.
Klil
o
u
,
e
t
a
l.
,
“
Re
a
l
-
ti
m
e
p
a
ra
l
lel
im
p
le
m
e
n
tatio
n
o
f
ro
a
d
traff
ic
ra
d
a
r
v
id
e
o
p
ro
c
e
ss
in
g
a
lg
o
rit
h
m
s
o
n
a
p
a
ra
ll
e
l
a
rc
h
it
e
c
tu
re
b
a
se
d
o
n
DS
P
a
n
d
A
RM
p
ro
c
e
ss
o
rs,
”
in
2
0
1
5
1
5
t
h
In
ter
n
a
t
io
n
a
l
Co
n
fer
e
n
c
e
o
n
In
te
ll
ig
e
n
t
S
y
ste
ms
De
sig
n
a
n
d
Ap
p
li
c
a
t
io
n
s,
p
p
.
1
8
3
-
1
8
8
,
2
0
1
5
.
[1
0
]
A
.
E.
A
b
d
e
lk
a
re
e
m
,
e
t
a
l.
,
“
D
e
sig
n
a
n
d
im
p
le
m
e
n
tatio
n
o
f
a
n
e
m
b
e
d
d
e
d
sy
s
te
m
f
o
r
so
f
t
w
a
r
e
d
e
f
in
e
d
ra
d
io
,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
E
lec
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
(
IJ
ECE
)
,
v
o
l.
7
,
n
o
.
6
,
p
p
.
3
4
8
4
-
3
4
9
1
,
2
0
1
7
.
[1
1
]
A
.
A
rs
a
lan
e
,
e
t
a
l.
,
“
T
h
e
a
ss
e
ss
m
e
n
t
o
f
f
r
e
sh
a
n
d
sp
o
il
e
d
b
e
e
f
m
e
a
t
u
sin
g
a
p
ro
to
ty
p
e
d
e
v
ic
e
b
a
se
d
o
n
G
ig
E
V
isio
n
c
a
m
e
ra
a
n
d
DS
P
,
”
J
o
u
rn
a
l
o
f
F
o
o
d
M
e
a
su
re
me
n
t
a
n
d
C
h
a
r
a
c
ter
iza
ti
o
n
,
v
o
l.
1
3
,
n
o
.
3
,
p
p
.
1
7
3
0
-
1
7
3
8
,
2
0
1
9
.
[1
2
]
A
.
A
rs
a
lan
e
,
e
t
a
l.
,
“
Be
e
f
a
n
d
h
o
rse
m
e
a
t
d
isc
ri
m
in
a
ti
o
n
a
n
d
sto
ra
g
e
ti
m
e
c
las
si
f
ic
a
ti
o
n
u
si
n
g
a
p
o
rtab
le
d
e
v
ice
b
a
se
d
o
n
DS
P
a
n
d
P
CA
m
e
th
o
d
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
In
telli
g
e
n
t
E
n
ter
p
rise
,
v
o
l.
4
,
n
o
.
1
-
2
,
p
p
.
5
8
-
7
5
,
2
0
1
7
.
[1
3
]
A
.
A
rsa
lan
e
,
e
t
a
l.
,
“
Bu
il
d
in
g
a
p
o
rta
b
le
d
e
v
ice
b
a
se
d
o
n
DS
P
f
o
r
m
e
a
t
d
isc
ri
m
in
a
ti
o
n
,
”
i
n
Pro
c
e
e
d
in
g
s
2
0
1
6
In
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
E
n
g
i
n
e
e
rin
g
a
n
d
M
IS
(
ICEM
IS
2
0
1
6
),
2
0
1
6
.
[1
4
]
A
.
A
rsa
lan
e
,
e
t
a
l.
,
“
A
n
e
m
b
e
d
d
e
d
sy
ste
m
b
a
se
d
o
n
DS
P
p
latf
o
rm
a
n
d
P
CA
-
S
V
M
a
lg
o
ri
th
m
s
f
o
r
ra
p
id
b
e
e
f
m
e
a
t
f
re
sh
n
e
ss
p
re
d
ictio
n
a
n
d
i
d
e
n
ti
f
ica
ti
o
n
,
”
C
o
mp
u
ter
s a
n
d
El
e
c
tro
n
ic
s in
A
g
ric
u
lt
u
re
,
v
o
l.
1
5
2
,
p
p
.
3
8
5
-
3
9
2
,
2
0
1
8
.
[1
5
]
A
.
A
rs
a
lan
e
,
e
t
a
l.
,
“
A
rti
f
icia
l
v
isi
o
n
a
n
d
e
m
b
e
d
d
e
d
sy
ste
m
s
a
s a
lt
e
r
n
a
ti
v
e
to
o
ls f
o
r
e
v
a
lu
a
ti
n
g
b
e
e
f
m
e
a
t
f
r
e
sh
n
e
ss
,
”
in
t
h
e
6
t
h
In
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
Op
t
imiza
ti
o
n
a
n
d
A
p
p
li
c
a
ti
o
n
s
,
Be
n
i
M
e
ll
a
l,
M
o
r
o
c
c
o
,
2
0
2
0
.
[1
6
]
A
.
Klil
o
u
,
e
t
a
l.
,
“
Ca
se
stu
d
ies
o
f
d
a
ta
traff
ic
m
a
n
a
g
e
m
e
n
t
o
n
a
h
ig
h
-
p
e
rf
o
rm
a
n
c
e
c
o
m
p
u
ti
n
g
sy
ste
m
b
a
se
d
o
n
m
u
lt
i
-
DSP
s
a
n
d
S
e
rial
Ra
p
id
IO
in
terc
o
n
n
e
c
t,
”
in
2
0
1
6
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
I
n
fo
rm
a
t
io
n
T
e
c
h
n
o
l
o
g
y
f
o
r
Or
g
a
n
iz
a
ti
o
n
s De
v
e
lo
p
me
n
t
(
IT
4
OD
)
,
p
p
.
1
-
6
,
2
0
1
6
.
[1
7
]
A
.
Klil
o
u
,
e
t
a
l.
,
“
P
e
rf
o
rm
a
n
c
e
o
p
ti
m
iza
ti
o
n
o
f
h
ig
h
-
sp
e
e
d
In
terc
o
n
n
e
c
t
S
e
rial
Ra
p
id
IO
f
o
r
o
n
b
o
a
rd
p
ro
c
e
ss
in
g
,
”
in
2
0
1
2
I
n
ter
n
a
t
io
n
a
l
Co
n
fer
e
n
c
e
o
n
C
o
mp
lex
S
y
ste
ms
(
ICCS
)
,
p
p
.
1
-
6
,
2
0
1
2
.
[1
8
]
L
.
Hu
a
n
g
,
e
t
a
l.
,
“
P
a
ra
ll
e
li
z
in
g
Ultras
o
u
n
d
Im
a
g
e
P
ro
c
e
ss
in
g
u
si
n
g
Op
e
n
M
P
o
n
M
u
lt
ico
re
E
m
b
e
d
d
e
d
S
y
ste
m
s,
”
in
2
0
1
2
I
E
EE
Glo
b
a
l
Hig
h
T
e
c
h
Co
n
g
re
ss
o
n
El
e
c
tro
n
ics
(
Gh
tce
),
2
0
1
2
.
[1
9
]
B.
Ch
a
p
m
a
n
,
e
t
a
l.
,
“
Us
in
g
Op
e
n
M
P
P
o
rtab
le S
h
a
re
d
M
e
m
o
ry
P
a
ra
ll
e
l
P
r
o
g
ra
m
m
in
g
,”
T
h
e
M
IT
Pre
ss
,
2
0
0
7
.
[2
0
]
R.
M
e
g
o
a
n
d
T
.
F
ry
z
a
,
“
P
e
rf
o
r
m
a
n
c
e
o
f
P
a
ra
ll
e
l
A
lg
o
rit
h
m
s
U
sin
g
Op
e
n
M
P
,
”
2
0
1
3
2
3
rd
I
n
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
Ra
d
i
o
e
lek
tro
n
ika
(
Ra
d
i
o
e
lek
tro
n
i
k
a
),
p
p
.
2
3
6
-
2
3
9
,
2
0
1
3
.
[2
1
]
X
.
N.
Yu
,
e
t
a
l.
,
“
A
n
Im
p
le
m
e
n
tat
io
n
o
f
Re
a
l
-
T
i
m
e
P
h
a
se
d
A
rra
y
R
a
d
a
r
F
u
n
d
a
m
e
n
tal
F
u
n
c
ti
o
n
s
o
n
a
DSP
-
F
o
c
u
se
d
,
Hig
h
-
P
e
rf
o
rm
a
n
c
e
,
E
m
b
e
d
d
e
d
Co
m
p
u
ti
n
g
P
latf
o
rm
,
”
Aer
o
sp
a
c
e
,
v
o
l.
3
,
n
o
.
3
,
p
p
.
2
8
-
50
,
2
0
1
6
.
[2
2
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3
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I
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10
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6
,
Decem
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2020
:
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6548
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4
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sy
ste
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s
.
Evaluation Warning : The document was created with Spire.PDF for Python.