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Activ
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s
co
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es
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r
o
w
[
1
]
.
R
ad
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f
r
eq
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in
ter
f
er
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n
ce
ca
n
lead
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n
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ca
p
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telec
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m
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s
[
2
]
.
T
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p
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f
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eg
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(
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f
r
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[
3
]
.
On
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ap
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ac
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in
v
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lv
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T
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co
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tem
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I
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I
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N:
2088
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4683
f
r
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[
4
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A
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h
alf
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th
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)
[
5
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.
Dev
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tain
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[
6
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.
Var
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tech
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ical
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f
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,
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d
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o
f
f
ab
r
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[
7
]
.
T
h
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ar
ticle
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in
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in
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T
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b
jectiv
e
is
to
d
ev
elo
p
a
co
m
p
ac
t
SIW
f
ilter
th
at
co
m
b
in
es
h
ig
h
o
u
t
-
of
-
b
a
n
d
r
ejec
tio
n
an
d
l
o
w
in
s
er
tio
n
lo
s
s
.
T
o
ac
h
iev
e
th
is
,
m
etah
eu
r
is
tic
o
p
tim
izatio
n
alg
o
r
ith
m
s
ar
e
em
p
lo
y
ed
i
n
Fig
u
r
e
1
[
8
]
.
I
t
was
s
elec
ted
o
v
er
o
th
er
alg
o
r
ith
m
s
d
u
e
to
its
s
im
p
licity
,
r
ap
id
co
n
v
er
g
e
n
c
e,
ca
p
ab
ilit
y
to
ad
d
r
ess
c
o
m
p
lex
o
p
tim
izatio
n
c
h
allen
g
es,
an
d
r
o
b
u
s
tn
ess
in
ex
p
lo
r
in
g
a
b
r
o
ad
s
o
lu
tio
n
s
p
ac
e
wh
ile
m
itig
atin
g
p
r
em
atu
r
e
co
n
v
er
g
e
n
ce
.
T
h
is
m
eth
o
d
a
llo
ws
f
o
r
th
e
q
u
ick
an
d
ac
cu
r
ate
d
eter
m
i
n
atio
n
o
f
th
e
g
eo
m
etr
ic
p
ar
am
eter
s
co
r
r
esp
o
n
d
in
g
to
th
e
d
esire
d
f
r
e
q
u
en
cy
r
esp
o
n
s
e
o
f
th
e
SIW
s
tr
u
ctu
r
e.
Fu
r
th
er
m
o
r
e,
th
e
s
tu
d
y
g
o
es
b
ey
o
n
d
th
is
s
tep
b
y
co
m
p
ar
in
g
th
r
ee
d
if
f
er
en
t
m
etah
eu
r
is
tic
alg
o
r
ith
m
s
wid
ely
u
s
ed
in
th
e
f
ield
:
ar
tific
ial
b
ee
co
lo
n
y
alg
o
r
ith
m
(
AB
C
)
,
g
en
etic
alg
o
r
ith
m
(
GA
)
,
an
d
d
if
f
er
en
tial
ev
o
lu
tio
n
alg
o
r
it
h
m
(
DE
)
to
o
p
tim
ize
th
e
d
e
s
ig
n
.
Fin
ally
,
th
e
o
b
tain
e
d
r
esu
lts
ar
e
v
alid
ated
th
r
o
u
g
h
f
u
ll
-
wa
v
e
elec
tr
o
m
ag
n
etic
s
im
u
latio
n
u
s
in
g
h
ig
h
-
f
r
eq
u
en
cy
s
tr
u
ct
u
r
e
s
im
u
lato
r
(
HFSS
)
,
en
s
u
r
in
g
th
e
r
eliab
ilit
y
an
d
p
r
ac
tical
f
ea
s
ib
ilit
y
o
f
th
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
.
T
h
e
s
tr
u
ctu
r
e
o
f
th
e
p
a
p
er
is
a
s
f
o
llo
ws:
Sectio
n
2
p
r
esen
ts
a
co
n
cise
o
v
er
v
iew
an
d
r
e
f
er
en
ce
s
f
o
r
th
e
alg
o
r
ith
m
s
ev
alu
ated
.
Sectio
n
3
co
v
er
s
th
e
f
o
r
m
u
latio
n
o
f
th
e
co
s
t f
u
n
ctio
n
a
n
d
th
e
d
y
n
a
m
ic
weig
h
t a
llo
ca
tio
n
f
o
r
th
e
o
b
jectiv
es.
Sectio
n
4
f
o
cu
s
es
o
n
th
e
co
m
p
a
r
is
o
n
o
f
v
ar
io
u
s
o
p
tim
izatio
n
alg
o
r
it
h
m
s
with
AB
C
to
ac
h
iev
e
o
p
tim
al
p
er
f
o
r
m
a
n
ce
as
well
as
th
e
v
alid
atio
n
o
f
o
b
tain
ed
r
esu
lts
in
ANSYS
HFSS
s
o
f
twar
e
.
Fin
ally
,
Sectio
n
5
p
r
o
v
id
es a
s
u
m
m
a
r
y
o
f
th
e
r
esu
lts
.
Fig
u
r
e
1
.
T
h
e
n
u
m
b
er
o
f
r
elate
d
p
ap
er
s
o
n
g
o
o
g
le
s
ch
o
lar
f
o
r
n
ew
g
en
er
atio
n
m
etah
e
u
r
is
tics
2.
P
RO
P
O
SE
D
M
E
T
H
O
DO
L
O
G
Y
AND
M
E
T
AH
E
URI
S
T
I
C
A
L
G
O
RI
T
H
M
S
E
v
o
lu
tio
n
a
r
y
alg
o
r
ith
m
s
(
E
A
s
)
h
av
e
b
ee
n
d
ev
elo
p
e
d
o
v
er
th
e
p
ast
d
ec
a
d
e,
in
s
p
ir
e
d
b
y
Dar
win
’
s
th
eo
r
y
o
f
ev
o
lu
tio
n
an
d
n
at
u
r
al
s
elec
tio
n
[
9
]
.
T
h
e
s
tu
d
y
o
f
E
As
b
e
g
an
in
th
e
1
9
6
0
s
,
lead
in
g
to
th
e
in
d
ep
en
d
en
t
d
ev
el
o
p
m
en
t
o
f
th
r
ee
m
ain
ap
p
r
o
ac
h
es:
g
en
etic
alg
o
r
ith
m
s
,
ev
o
lu
tio
n
ar
y
p
r
o
g
r
am
m
in
g
,
an
d
ev
o
lu
tio
n
s
tr
ateg
ies
[
1
0
]
.
T
h
e
s
e
alg
o
r
ith
m
s
as
illu
s
tr
ated
in
Fig
u
r
e
2
[
1
1
]
,
ar
e
wid
ely
ap
p
lied
to
b
o
th
s
in
g
le
an
d
m
u
lti
-
o
b
jectiv
e
o
p
tim
izatio
n
p
r
o
b
lem
s
.
2
.
1
.
A
rt
if
ici
a
l
b
ee
c
o
lo
ny
a
l
g
o
rit
hm
T
h
e
ar
tific
ial
b
ee
co
lo
n
y
(
AB
C
)
alg
o
r
ith
m
was
d
ev
elo
p
ed
b
y
Oztu
r
k
an
d
Kar
a
b
o
g
a
[
1
2
]
.
I
n
s
p
ir
ed
b
y
th
e
f
o
r
ag
in
g
b
e
h
av
io
r
o
f
h
o
n
ey
b
ee
s
,
th
e
AB
C
alg
o
r
ith
m
s
im
u
lates
h
o
w
b
ee
s
s
ea
r
ch
f
o
r
n
ec
tar
a
n
d
co
m
m
u
n
icate
in
f
o
r
m
atio
n
ab
o
u
t
f
o
o
d
s
o
u
r
ce
s
to
o
th
e
r
m
e
m
b
er
s
o
f
th
e
co
l
o
n
y
[
1
3
]
.
T
h
e
q
u
ality
o
f
a
f
o
o
d
s
o
u
r
ce
,
r
ef
er
r
ed
to
as
its
“
af
f
in
ity
,
”
is
d
eter
m
in
ed
b
y
th
e
o
b
jectiv
e
f
u
n
ctio
n
.
E
s
s
en
tially
,
th
e
o
p
tim
izatio
n
p
r
o
ce
s
s
m
ir
r
o
r
s
th
e
b
ee
s
'
s
e
ar
ch
f
o
r
h
ig
h
-
q
u
ality
f
o
o
d
s
o
u
r
ce
s
,
wh
ich
is
an
alo
g
o
u
s
to
f
in
d
in
g
o
p
tim
al
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
5
,
Octo
b
e
r
20
25
:
4
6
8
2
-
4
6
9
1
4684
s
o
lu
tio
n
s
to
a
p
r
o
b
lem
[
1
4
]
.
I
n
th
e
AB
C
alg
o
r
ith
m
,
ea
ch
f
o
o
d
s
o
u
r
ce
is
lo
ca
ted
with
in
a
D
-
d
im
en
s
io
n
al
s
ea
r
ch
s
p
ac
e
an
d
r
ep
r
esen
ts
a
p
o
s
s
ib
le
s
o
lu
tio
n
to
th
e
o
p
tim
izatio
n
p
r
o
b
lem
.
T
h
e
f
itn
ess
v
alu
e
o
f
a
f
o
o
d
s
o
u
r
ce
is
eq
u
iv
alen
t
to
th
e
am
o
u
n
t
o
f
n
ec
tar
it
co
n
tain
s
.
T
y
p
ically
,
th
e
n
u
m
b
er
o
f
em
p
lo
y
e
d
b
ee
s
an
d
s
p
ec
tato
r
b
ee
s
is
eq
u
al
an
d
m
atch
es th
e
n
u
m
b
er
o
f
f
o
o
d
s
o
u
r
ce
s
.
T
h
e
in
itial so
lu
tio
n
s
ar
e
r
an
d
o
m
ly
g
en
er
ated
with
in
a
d
ef
i
n
e
d
r
an
g
e
o
f
v
ar
iab
les
(
i=1
,
2
,
..
,w
)
[
1
5
]
.
E
ac
h
em
p
lo
y
ed
b
ee
th
e
n
id
e
n
tifie
s
n
ew
s
o
u
r
ce
s
,
r
e
p
r
esen
tin
g
h
alf
o
f
t
h
e
to
tal
s
o
u
r
ce
s
.
E
q
u
atio
n
(
1
)
is
u
s
ed
to
d
eter
m
in
e
a
n
ew
s
o
u
r
ce
[
1
6
]
,
wh
er
e
k
∈
{
1
,
2
,
..
,
N}
an
d
j
∈
{1
,
2
,
…
,
D}
ar
e
r
an
d
o
m
ly
s
elec
te
d
in
d
ices.
T
h
e
v
alu
e
o
f
is
ch
o
s
en
r
an
d
o
m
l
y
b
u
t
m
u
s
t
d
if
f
er
f
r
o
m
,
an
d
is
a
r
an
d
o
m
n
u
m
b
er
b
etwe
en
0
an
d
1
,
c
o
n
tr
o
llin
g
th
e
g
en
er
atio
n
o
f
n
eig
h
b
o
r
in
g
f
o
o
d
s
o
u
r
ce
s
.
T
h
e
b
ee
v
is
u
ally
c
o
m
p
ar
es
two
f
o
o
d
p
o
s
itio
n
s
.
On
ce
a
ca
n
d
i
d
ate
s
o
u
r
ce
p
o
s
itio
n
is
g
en
e
r
ated
,
it
is
ev
alu
ate
d
.
I
f
its
n
ec
tar
q
u
ality
is
eq
u
al
to
o
r
b
etter
t
h
an
th
e
p
r
ev
io
u
s
s
o
u
r
ce
,
it
r
ep
lace
s
th
e
o
ld
o
n
e
in
m
em
o
r
y
;
o
th
e
r
wis
e,
th
e
p
r
ev
io
u
s
s
o
u
r
ce
is
r
etain
ed
.
Fin
ally
,
in
th
e
n
ex
t
p
h
ase,
o
n
lo
o
k
er
b
ee
s
s
elec
t a
f
o
o
d
s
o
u
r
ce
b
ased
o
n
th
e
p
r
o
b
a
b
ilit
y
g
iv
en
in
(
2
)
[
1
7
]
.
=
+
(
−
)
(
1
)
=
∑
=
1
(
2
)
T
h
e
ad
e
q
u
ac
y
v
alu
e
o
f
a
s
o
lu
t
io
n
i
is
p
r
o
p
o
r
tio
n
al
to
th
e
n
ec
tar
q
u
a
n
tity
at
a
f
o
o
d
s
o
u
r
ce
a
n
d
c
o
r
r
esp
o
n
d
s
to
th
e
n
u
m
b
er
o
f
em
p
lo
y
ed
b
e
es.
Sco
u
t
b
ee
s
a
r
e
r
esp
o
n
s
ib
le
f
o
r
r
an
d
o
m
s
ea
r
c
h
es
with
in
th
e
co
lo
n
y
,
o
p
er
atin
g
with
o
u
t
p
r
io
r
k
n
o
wled
g
e
.
T
h
ey
ar
e
s
elec
ted
f
r
o
m
em
p
lo
y
e
d
b
ee
s
b
ased
o
n
b
o
u
n
d
a
r
y
p
a
r
am
eter
s
.
I
f
a
f
o
o
d
s
o
u
r
ce
f
ails
to
y
ield
a
s
o
lu
tio
n
af
ter
a
s
et
n
u
m
b
er
o
f
attem
p
ts
,
it is
ab
an
d
o
n
ed
,
a
n
d
th
e
ass
o
ciate
d
b
ee
b
ec
o
m
es
a
s
co
u
t,
s
ea
r
ch
i
n
g
f
o
r
a
n
ew
s
o
u
r
ce
.
T
h
e
n
u
m
b
er
o
f
attem
p
ts
b
ef
o
r
e
ab
a
n
d
o
n
m
en
t
is
d
eter
m
in
e
d
b
y
th
e
“
lim
it
”
p
ar
am
eter
.
T
h
e
id
en
tifi
ca
tio
n
o
f
a
n
ew
s
o
u
r
ce
b
y
a
s
c
o
u
t b
ee
is
d
ef
i
n
ed
in
(
3
)
[
1
8
]
.
=
+
(
−
)
∗
(
0
,
1
)
(
3
)
wh
er
e
an
d
r
ep
r
esen
t
th
e
p
ar
am
eter
’
s
o
p
tim
izatio
n
lim
its
.
T
h
e
ter
m
in
atio
n
cr
iter
io
n
in
t
h
e
AB
C
alg
o
r
ith
m
is
ty
p
ically
b
ased
o
n
th
e
n
u
m
b
er
o
f
iter
atio
n
s
.
Fig
u
r
e
2
.
E
v
o
lu
tio
n
ar
y
alg
o
r
it
h
m
s
’
m
ain
f
am
ilies
2
.
2
.
G
enet
ic
a
lg
o
rit
h
m
s
GAs,
th
e
m
o
s
t
p
o
p
u
lar
E
As,
ar
e
in
s
p
ir
ed
b
y
Dar
win
’
s
n
atu
r
al
s
elec
tio
n
[
9
]
.
T
h
e
p
r
im
ar
y
o
p
er
ato
r
s
u
s
ed
in
GA
ar
e
s
elec
tio
n
,
c
r
o
s
s
o
v
er
,
an
d
m
u
tatio
n
.
I
n
g
en
etic
alg
o
r
ith
m
s
(
GA)
,
a
p
o
ten
tial
s
o
lu
tio
n
is
r
ep
r
esen
ted
as
a
“
ch
r
o
m
o
s
o
m
e,
”
wh
ich
is
f
u
r
t
h
er
d
iv
id
e
d
in
to
“
g
en
es.
”
T
h
e
GA
b
e
g
in
s
with
an
in
itial
p
o
p
u
latio
n
o
f
r
an
d
o
m
ly
g
e
n
er
ated
ch
r
o
m
o
s
o
m
es,
co
n
s
i
d
er
in
g
p
r
o
b
lem
co
n
s
tr
ain
ts
.
T
h
r
o
u
g
h
iter
ativ
e,
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
-
8
7
0
8
C
o
mp
a
r
a
tive
a
n
a
lysi
s
o
f m
eta
h
eu
r
is
tic
a
lg
o
r
ith
ms
…
(
S
o
u
a
d
A
kk
a
d
er
)
4685
p
r
o
b
a
b
ilis
tic
m
ec
h
an
is
m
s
,
n
e
w
p
o
p
u
latio
n
s
ar
e
cr
ea
ted
an
d
ev
alu
ate
d
u
s
in
g
f
o
u
r
k
e
y
o
p
er
ato
r
s
:
s
elec
tio
n
,
cr
o
s
s
o
v
er
,
r
e
p
lace
m
en
t,
an
d
m
u
tatio
n
.
T
h
e
o
b
jectiv
e
f
u
n
ctio
n
is
tr
a
n
s
f
o
r
m
ed
in
to
a
f
it
n
ess
f
u
n
c
tio
n
th
at
q
u
an
tifie
s
t
h
e
s
u
ita
b
ilit
y
o
f
a
ch
r
o
m
o
s
o
m
e
in
m
ee
tin
g
th
e
o
p
tim
izatio
n
g
o
al.
T
h
e
in
itial
p
o
p
u
latio
n
is
g
en
er
ated
wit
h
in
th
e
p
r
e
d
ef
in
e
d
m
in
im
u
m
an
d
m
a
x
im
u
m
lim
i
ts
f
o
r
d
ec
is
io
n
v
ar
iab
les
in
co
r
p
o
r
atin
g
b
o
th
lin
ea
r
an
d
n
o
n
lin
ea
r
co
n
s
tr
ain
ts
.
C
h
r
o
m
o
s
o
m
es
ar
e
e
v
alu
ated
b
ased
o
n
t
h
eir
f
itn
ess
v
alu
es
,
an
d
a
n
elitis
m
s
tr
ateg
y
en
s
u
r
es
th
at
th
e
b
est
ch
r
o
m
o
s
o
m
e
is
p
r
eser
v
ed
to
m
ain
tain
co
n
v
e
r
g
en
ce
.
Par
en
t
s
elec
tio
n
f
o
llo
ws
th
e
to
u
r
n
am
en
t
s
elec
tio
n
m
eth
o
d
,
af
ter
wh
ich
th
e
cr
o
s
s
o
v
er
o
p
e
r
ato
r
co
m
b
in
es
two
p
ar
en
t
ch
r
o
m
o
s
o
m
es
to
g
en
er
ate
o
f
f
s
p
r
in
g
.
E
ac
h
o
f
f
s
p
r
in
g
u
n
d
er
g
o
es
m
u
tatio
n
,
wh
er
e
a
g
e
n
e
is
r
a
n
d
o
m
l
y
alt
er
ed
.
T
h
is
iter
ativ
e
p
r
o
ce
s
s
r
e
s
u
lts
in
s
u
cc
ess
iv
e
p
o
p
u
latio
n
s
with
im
p
r
o
v
ed
f
it
n
ess
v
alu
es.
2
.
3
.
Dif
f
er
ent
ia
l
e
v
o
lutio
n
T
h
e
d
if
f
er
e
n
tial
ev
o
lu
tio
n
(
DE
)
alg
o
r
ith
m
,
in
tr
o
d
u
ce
d
b
y
Sto
r
n
an
d
Pric
e
[
1
9
]
,
is
a
p
o
p
u
latio
n
-
b
ased
ev
o
lu
tio
n
ar
y
m
eth
o
d
d
esig
n
e
d
f
o
r
o
p
tim
izin
g
co
n
tin
u
o
u
s
v
ar
iab
les
in
m
u
lti
-
d
im
e
n
s
io
n
a
l
s
p
ac
es.
Similar
to
GAs
,
DE
is
in
s
p
ir
ed
b
y
Dar
win
’
s
p
r
in
cip
les
o
f
n
atu
r
al
s
elec
tio
n
an
d
g
e
n
etic
ad
a
p
tatio
n
,
later
ap
p
lied
to
ar
tific
ial
p
r
o
b
lem
-
s
o
lv
in
g
b
y
Ho
llan
d
.
W
h
ile
DE
em
p
lo
y
s
th
e
s
am
e
ev
o
lu
tio
n
ar
y
o
p
e
r
ato
r
s
as
Gas
m
u
tatio
n
,
cr
o
s
s
o
v
er
,
an
d
s
elec
tio
n
th
ei
r
ex
ec
u
tio
n
o
r
d
er
d
if
f
er
s
.
I
n
DE
,
m
u
tatio
n
an
d
cr
o
s
s
o
v
er
m
o
d
if
y
p
ar
a
m
eter
v
ec
to
r
s
b
ef
o
r
e
s
elec
tio
n
,
u
n
lik
e
in
GAs,
wh
er
e
s
elec
tio
n
o
cc
u
r
s
f
ir
s
t.
T
h
is
ap
p
r
o
ac
h
m
itig
ates
th
e
“
d
estru
ctiv
e
”
im
p
ac
t
o
f
m
u
tat
io
n
s
ee
n
in
GAs,
as
it
is
ap
p
li
ed
at
th
e
b
eg
in
n
in
g
o
f
ea
ch
g
en
er
atio
n
in
s
tead
o
f
th
e
en
d
.
C
o
n
s
eq
u
e
n
tly
,
b
o
th
th
e
b
est
an
d
av
er
a
g
e
f
itn
es
s
v
alu
es
ev
o
l
v
e
co
n
s
is
ten
tly
with
o
u
t
r
eq
u
ir
i
n
g
ad
d
itio
n
al
m
ec
h
a
n
is
m
s
lik
e
elitis
m
.
Mo
r
eo
v
er
,
DE
en
s
u
r
e
s
ef
f
ec
tiv
e
ex
p
lo
r
atio
n
o
f
t
h
e
s
o
lu
tio
n
s
p
ac
e
b
y
tr
ea
tin
g
th
e
en
tire
p
o
p
u
latio
n
as
a
m
atin
g
p
o
o
l.
Un
lik
e
GAs
,
it
d
o
es
n
o
t
f
av
o
r
th
e
f
ittes
t
in
d
iv
id
u
als;
in
s
tead
,
m
u
tan
t
v
ec
to
r
s
ar
e
g
en
e
r
ated
u
s
in
g
r
an
d
o
m
ly
s
elec
ted
in
d
i
v
id
u
als
f
r
o
m
th
e
p
o
p
u
latio
n
,
p
r
o
m
o
tin
g
d
iv
e
r
s
ity
an
d
b
r
o
ad
s
ea
r
ch
co
v
e
r
ag
e.
3.
F
O
RM
UL
AT
I
O
N
O
F
T
H
E
O
P
T
I
M
I
Z
AT
I
O
N
P
RO
B
L
E
M
T
h
e
ar
tific
ial
b
ee
co
lo
n
y
alg
o
r
ith
m
was
u
tili
ze
d
to
o
p
tim
ize
th
e
d
esig
n
o
f
th
e
s
u
b
s
tr
ate
in
teg
r
ated
wav
eg
u
id
e
s
tr
u
ct
u
r
e.
I
n
th
is
o
p
tim
izatio
n
p
r
o
ce
s
s
,
k
ey
d
es
ig
n
p
ar
a
m
eter
s
in
clu
d
e
th
e
v
ia
d
iam
eter
(
d
)
,
th
e
s
p
ac
in
g
b
etwe
en
ad
jace
n
t
v
ias
(
p
)
,
th
e
s
u
b
s
tr
ate
h
eig
h
t
(
h
)
,
a
n
d
th
e
SIW
wid
th
as
p
r
esen
te
d
in
Fig
u
r
e
3
.
T
h
e
o
b
jectiv
e
is
to
m
in
i
m
ize
th
e
atten
u
atio
n
co
n
s
tan
t
alp
h
a0
(
4
)
,
w
h
ich
d
i
r
ec
tly
af
f
ec
ts
th
e
wav
eg
u
id
e
’
s
p
er
f
o
r
m
an
ce
an
d
it
s
u
m
m
a
r
i
ze
s
th
e
to
tal
lo
s
s
in
SIW:
c
o
n
d
u
ctiv
e
lo
s
s
(
5
)
d
ielec
tr
ic
l
o
s
s
(
6
)
an
d
r
ad
iatio
n
lo
s
s
(
7
)
[
2
0
]
.
0
=
+
+
(
4
)
(
)
=
√
0
ℎ
√
1
+
2
(
0
)
2
ℎ
√
1
−
(
0
)
2
(
5
)
(
)
=
√
√
1
−
(
0
)
2
ta
n
(
6
)
=
1
(
)
2
.
84
(
−
1
)
6
.
28
4
.
85
√
(
2
)
2
−
1
(
7
)
T
h
e
AB
C
alg
o
r
ith
m
,
i
n
s
p
ir
ed
b
y
t
h
e
f
o
r
ag
in
g
b
e
h
av
io
r
o
f
h
o
n
ey
b
ee
co
l
o
n
ies,
was
im
p
le
m
en
ted
in
MA
T
L
AB
,
lev
er
ag
in
g
its
r
o
b
u
s
t
g
lo
b
al
s
ea
r
ch
ca
p
ab
ilit
ies.
T
h
e
o
p
tim
izatio
n
p
r
o
ce
s
s
co
n
s
is
ts
o
f
th
r
ee
m
ain
p
h
ases
:
em
p
lo
y
ed
b
ee
s
ex
p
lo
r
e
th
e
s
ea
r
ch
s
p
ac
e
an
d
s
h
ar
e
i
n
f
o
r
m
atio
n
,
o
n
l
o
o
k
e
r
b
ee
s
ev
alu
ate
th
e
p
o
ten
tial
s
o
lu
tio
n
s
,
an
d
s
co
u
t
b
ee
s
in
tr
o
d
u
ce
d
iv
er
s
ity
b
y
r
ep
lacin
g
s
tag
n
atin
g
s
o
lu
tio
n
s
.
C
o
n
s
tr
ain
ts
im
p
o
s
ed
o
n
th
e
d
esig
n
p
ar
am
ete
r
s
s
tem
f
r
o
m
p
h
y
s
ical
m
an
u
f
ac
tu
r
in
g
lim
itatio
n
s
an
d
elec
tr
o
m
ag
n
etic
r
ad
iatio
n
ch
ar
ac
ter
is
tics
.
Sp
ec
if
ically
,
th
e
v
ia
d
iam
eter
is
co
n
s
tr
ain
ed
with
in
th
e
r
an
g
e
o
f
0
.
8
–
1
.
2
m
m
,
th
e
s
p
ac
in
g
(
p
)
is
s
et
b
etwe
en
1
.
5
–
2
m
m
,
th
e
wid
th
v
ar
ies f
r
o
m
1
0
–
1
1
m
m
,
an
d
th
e
s
u
b
s
tr
ate
h
eig
h
t is lim
i
ted
to
0
.
9
–
1
.
2
m
m
.
T
h
e
co
n
d
u
ctiv
ity
o
f
m
etal
is
σ
=
5
.
8
×1
0
7
S/m
an
d
th
e
m
etal
l
ay
er
h
a
v
e
a
h
ei
g
h
t
o
f
h
1
=
0
.
0
9
m
m
,
a
s
u
r
f
ac
e
r
o
u
g
h
n
ess
o
f
1
.
7
8
×1
0
−3
m
m
.
T
h
r
o
u
g
h
iter
ativ
e
o
p
tim
izatio
n
,
th
e
AB
C
alg
o
r
ith
m
ef
f
ec
tiv
ely
id
en
tifie
s
an
o
p
tim
al
s
et
o
f
d
esig
n
p
ar
am
ete
r
s
th
at
m
in
im
ize
atten
u
atio
n
w
h
ile
s
atis
f
y
in
g
all
im
p
o
s
ed
co
n
s
tr
ain
ts
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
5
,
Octo
b
e
r
20
25
:
4
6
8
2
-
4
6
9
1
4686
to
p
v
iew
later
al
v
iew
Fig
u
r
e
3.
Geo
m
etr
ic
p
ar
am
ete
r
s
o
f
th
e
SIW st
r
u
ctu
r
e
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
W
h
en
th
e
alg
o
r
ith
m
r
ea
ch
es
co
n
v
er
g
en
ce
,
it
id
en
tifie
s
th
e
b
est
p
o
s
s
ib
le
s
o
lu
tio
n
,
as
ill
u
s
tr
ated
in
Fig
u
r
e
4
.
At
t
h
is
s
tag
e,
th
e
o
p
tim
izatio
n
p
r
o
ce
s
s
h
as
ex
p
lo
r
ed
an
d
r
e
f
in
ed
t
h
e
s
ea
r
ch
s
p
ac
e,
u
ltima
tely
s
elec
tin
g
th
e
m
o
s
t
ef
f
icien
t
p
ar
am
eter
s
et.
T
h
e
atten
u
atio
n
c
o
n
s
tan
t,
a
k
ey
p
e
r
f
o
r
m
an
ce
m
etr
ic,
was
o
b
tain
ed
th
r
o
u
g
h
s
im
u
latio
n
u
s
in
g
th
e
AB
C
alg
o
r
ith
m
.
T
h
e
r
esu
lts
in
d
icate
th
at
at
a
f
r
eq
u
en
cy
o
f
ap
p
r
o
x
im
ately
1
0
GHz
,
th
e
o
p
tim
ized
SIW
s
tr
u
ctu
r
e
ac
h
iev
es
an
atten
u
atio
n
co
n
s
tan
t
o
f
0
.
1
3
9
d
B
/m
,
d
em
o
n
s
tr
atin
g
th
e
ef
f
ec
tiv
en
ess
o
f
th
e
AB
C
m
eth
o
d
in
m
in
im
izin
g
tr
a
n
s
m
is
s
io
n
lo
s
s
es.
Fig
u
r
e
4
.
Ob
jectiv
e
f
u
n
ctio
n
(
atten
u
atio
n
co
n
s
tan
t)
v
er
s
u
s
iter
atio
n
s
T
h
e
o
p
tim
ized
p
ar
a
m
eter
s
id
en
tifie
d
b
y
th
e
AB
C
alg
o
r
ith
m
ar
e
th
en
ap
p
lied
to
co
n
s
tr
u
ct
th
e
f
in
al
SIW
d
esig
n
,
en
s
u
r
in
g
th
at
th
e
s
tr
u
ctu
r
e
m
ee
ts
th
e
r
eq
u
ir
e
d
s
p
ec
if
icatio
n
s
f
o
r
p
er
f
o
r
m
a
n
ce
an
d
ef
f
icien
cy
.
T
h
ese
o
p
tim
ized
v
alu
es d
ef
in
e
cr
itical
d
im
en
s
io
n
s
s
u
ch
as su
b
s
tr
ate
th
ick
n
ess
,
v
ia
h
o
le
d
ia
m
eter
,
an
d
s
p
ac
in
g
,
wh
ich
d
ir
ec
tly
im
p
ac
t
s
th
e
wav
eg
u
id
e’
s
b
eh
av
i
o
r
a
n
d
o
v
er
al
l
f
ilter
p
er
f
o
r
m
a
n
ce
.
T
ab
le
1
p
r
esen
ts
th
e
f
in
al
s
e
t
o
f
o
p
tim
al
d
im
en
s
io
n
s
o
b
tai
n
ed
u
s
in
g
th
e
AB
C
m
eth
o
d
,
h
ig
h
lig
h
tin
g
its
ca
p
ab
ilit
y
to
f
in
e
-
tu
n
e
d
esig
n
p
ar
am
eter
s
f
o
r
im
p
r
o
v
ed
atten
u
atio
n
an
d
s
ig
n
al
in
teg
r
ity
.
T
ab
le
1
.
Par
am
eter
r
an
g
es a
n
d
th
eir
o
p
tim
al
v
al
u
es
V
a
r
i
a
b
l
e
M
i
n
i
m
u
m
(
mm
)
M
a
x
i
m
u
m
(
mm
)
O
p
t
i
mal
v
a
l
u
e
(
mm
)
d
0
.
8
1
.
2
0
.
8
p
1
.
6
2
.
2
1
.
6
4
18
20
20
h
0
.
9
1
.
2
1
.2
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
-
8
7
0
8
C
o
mp
a
r
a
tive
a
n
a
lysi
s
o
f m
eta
h
eu
r
is
tic
a
lg
o
r
ith
ms
…
(
S
o
u
a
d
A
kk
a
d
er
)
4687
4
.
1
.
P
er
f
o
r
m
a
nce
c
o
m
pa
riso
n o
f
AB
C,
G
A,
a
nd
DE
a
lg
o
rit
hm
s
New
g
en
er
atio
n
s
o
f
m
eta
h
eu
r
is
tic
tech
n
iq
u
es,
s
u
ch
as
G
A
[
2
1
]
,
[
2
2
]
,
an
d
DE
[
2
3
]
,
[
2
4
]
,
h
av
e
r
ec
en
tly
b
ee
n
in
tr
o
d
u
ce
d
in
elec
tr
o
n
ic
cir
cu
it
d
esig
n
.
I
n
th
is
co
n
tex
t,
o
u
r
s
tu
d
y
e
x
p
lo
r
es
alter
n
ativ
e
m
etah
eu
r
is
tics
f
o
r
d
esig
n
in
g
a
co
m
p
a
ct
SIW
f
ilter
,
lev
er
a
g
in
g
m
o
r
e
s
o
p
h
is
ticated
i
n
ter
ac
tio
n
m
ec
h
an
is
m
s
b
etwe
en
in
d
iv
id
u
als an
d
o
f
f
er
i
n
g
ad
d
itio
n
al
p
er
f
o
r
m
a
n
ce
b
en
ef
its
.
T
o
co
m
p
ar
e
th
e
AB
C
alg
o
r
ith
m
with
o
th
er
s
to
ch
asti
c
ap
p
r
o
ac
h
es,
we
s
elec
ted
two
wi
d
ely
u
s
ed
ev
o
lu
tio
n
ar
y
alg
o
r
ith
m
s
:
GA
an
d
DE
.
GA
was
ch
o
s
en
d
u
e
to
its
well
-
estab
lis
h
ed
r
o
le
in
o
p
tim
izatio
n
p
r
o
b
lem
s
,
p
ar
ticu
lar
ly
i
n
en
g
in
ee
r
in
g
a
p
p
licatio
n
s
,
wh
e
r
e
it
ef
f
ec
tiv
ely
e
x
p
lo
r
es
lar
g
e
s
ea
r
ch
s
p
ac
es
th
r
o
u
g
h
s
elec
tio
n
,
cr
o
s
s
o
v
er
,
an
d
m
u
ta
tio
n
.
DE
,
o
n
th
e
o
th
e
r
h
a
n
d
,
w
as
s
elec
ted
f
o
r
its
s
tr
o
n
g
ex
p
lo
itatio
n
ca
p
ab
ilit
ies
an
d
its
ab
ilit
y
to
h
an
d
le
c
o
n
ti
n
u
o
u
s
o
p
tim
izatio
n
p
r
o
b
lem
s
ef
f
icien
tly
.
B
y
co
m
p
a
r
in
g
A
B
C
wi
th
th
ese
two
ap
p
r
o
ac
h
es,
we
aim
ed
to
ev
alu
ate
h
o
w
well
ea
ch
alg
o
r
ith
m
b
alan
ce
s
e
x
p
lo
r
atio
n
an
d
ex
p
lo
itatio
n
wh
ile
o
p
tim
izin
g
th
e
s
am
e
o
b
jectiv
e
f
u
n
ctio
n
α
0
with
in
a
co
m
m
o
n
f
r
am
ewo
r
k
.
Fig
u
r
e
5
p
r
esen
ts
th
e
r
esu
lts
o
f
co
s
t
f
u
n
ctio
n
ev
o
lu
tio
n
as
a
f
u
n
ctio
n
o
f
th
e
n
u
m
b
er
o
f
iter
atio
n
s
.
T
h
e
f
in
d
in
g
s
clea
r
l
y
s
h
o
w
t
h
at
th
e
AB
C
alg
o
r
ith
m
ac
h
iev
es
th
e
b
est
o
p
tim
al
s
o
lu
tio
n
,
o
u
tp
er
f
o
r
m
in
g
th
e
o
th
er
alg
o
r
ith
m
s
in
ter
m
s
o
f
co
n
v
e
r
g
en
ce
s
p
ee
d
a
n
d
ac
c
u
r
ac
y
.
T
h
is
s
u
p
er
io
r
ity
ca
n
b
e
attr
ib
u
t
ed
to
its
ab
ilit
y
to
ef
f
ec
tiv
ely
b
alan
ce
ex
p
lo
r
atio
n
an
d
ex
p
l
o
itatio
n
o
f
th
e
s
ea
r
ch
s
p
ac
e,
as
well
a
s
its
r
o
b
u
s
tn
ess
ag
ain
s
t
lo
ca
l
m
in
im
a.
I
n
co
m
p
ar
is
o
n
,
wh
i
le
GA
an
d
DE
also
d
em
o
n
s
tr
ated
r
esp
ec
tab
le
p
er
f
o
r
m
an
ce
,
th
ey
ex
h
ib
ited
lim
itatio
n
s
in
co
n
v
er
g
en
ce
s
p
e
ed
an
d
f
in
al
ac
cu
r
ac
y
.
T
h
ese
o
b
s
er
v
atio
n
s
co
n
f
ir
m
th
e
r
elev
an
ce
o
f
AB
C
as
an
o
p
tim
izatio
n
to
o
l
f
o
r
d
esig
n
in
g
co
m
p
ac
t a
n
d
h
ig
h
-
p
er
f
o
r
m
a
n
ce
SIW f
ilter
s
.
Fig
u
r
e
5.
Ob
jectiv
e
f
u
n
ctio
n
v
s
n
u
m
b
er
o
f
iter
atio
n
s
4
.
2
.
Va
lid
a
t
i
o
n
u
s
ing
ANSY
S H
F
SS
s
im
ula
t
io
n
I
n
th
is
s
tu
d
y
,
a
d
u
al
-
b
an
d
f
ilt
er
is
d
esig
n
ed
u
s
in
g
a
to
p
o
lo
g
y
b
ased
o
n
th
r
ee
ce
n
tr
ally
p
o
s
itio
n
ed
in
d
u
ctiv
e
p
o
s
ts
in
Fig
u
r
e
6(
a
)
with
d
1
=
1
.
6
m
m
,
d
2
=
3
.
2
m
m
,
a
n
d
y
=
6
.
2
5
m
m
.
T
h
e
f
ilter
is
co
n
s
tr
u
cted
o
n
a
d
iam
o
n
d
s
u
b
s
tr
ate
with
a
r
el
ativ
e
p
er
m
itti
v
ity
o
f
1
6
.
5
,
an
d
a
le
n
g
th
o
f
2
5
m
m
.
T
h
e
c
o
p
p
er
p
late
h
as
a
th
ick
n
ess
o
f
0
.
0
9
9
m
m
.
T
h
e
v
i
as h
av
e
a
d
iam
eter
o
f
0
.
8
m
m
,
with
a
s
p
ac
in
g
o
f
1
.
6
m
m
b
et
wee
n
ad
jace
n
t v
ias,
an
d
th
e
to
tal
wid
th
is
2
0
m
m
,
an
d
a
th
ick
n
ess
s
u
b
s
tr
ate
is
1
.
2
m
m
.
T
o
en
s
u
r
e
p
r
o
p
er
in
teg
r
atio
n
b
etwe
en
SIW
an
d
m
icr
o
s
tr
ip
tech
n
o
lo
g
ies,
SIW
-
m
icr
o
s
tr
ip
tr
an
s
itio
n
s
ar
e
ess
en
tial.
T
h
e
tap
er
ed
tr
an
s
itio
n
,
d
ep
icted
in
Fig
u
r
e
6
(
b
)
,
h
as
b
ee
n
o
p
tim
ized
u
s
in
g
H
FS
S,
with
th
e
b
est
tap
er
d
im
e
n
s
io
n
s
f
o
u
n
d
to
b
e
wt
=
7
.
1
m
m
an
d
L
t
=
3
m
m
.
T
h
e
m
icr
o
s
tr
ip
lin
e
h
as
d
im
e
n
s
io
n
s
o
f
w5
0
=
1
m
m
an
d
L
5
0
=
1
m
m
.
T
h
is
tr
a
n
s
itio
n
co
n
s
is
ts
o
f
a
tap
er
e
d
m
ic
r
o
s
tr
ip
s
ec
tio
n
c
o
n
n
ec
tin
g
a
5
0
-
o
h
m
m
icr
o
s
tr
ip
lin
e
to
th
e
s
u
b
s
tr
ate
-
in
teg
r
ated
wa
v
eg
u
id
e.
T
h
e
tap
er
is
d
esig
n
ed
to
f
ac
ilit
ate
th
e
co
n
v
er
s
io
n
o
f
th
e
m
ic
r
o
s
tr
ip
lin
e'
s
q
u
asi
-
T
E
M
m
o
d
e
in
to
th
e
wav
eg
u
id
e'
s
T
E
1
0
m
o
d
e.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
5
,
Octo
b
e
r
20
25
:
4
6
8
2
-
4
6
9
1
4688
(
a)
(
b
)
Fig
u
r
e
6
.
Geo
m
etr
y
o
f
SIW f
ilter
(
a)
in
SIW +
tap
er
t
r
an
s
itio
n
,
an
d
(
b
)
with
ci
r
cu
lar
in
d
u
cti
v
e
p
o
s
ts
T
h
e
s
im
u
latio
n
r
esu
lts
d
em
o
n
s
tr
ate
ex
ce
llen
t
p
er
f
o
r
m
an
ce
in
ter
m
s
o
f
S
-
p
ar
am
eter
s
in
F
ig
u
r
e
7
.
T
h
e
SIW
f
ilter
o
f
f
er
s
k
ey
ad
v
an
ta
g
es,
in
clu
d
in
g
c
o
m
p
atib
ilit
y
with
p
lan
ar
tech
n
o
lo
g
ies,
lo
w
in
s
er
tio
n
lo
s
s
,
g
o
o
d
im
p
ed
an
ce
m
atch
in
g
,
co
m
p
a
ctn
ess
,
an
d
r
o
b
u
s
tn
ess
.
I
t
o
p
er
ates
ef
f
icien
tly
i
n
two
f
r
eq
u
en
cy
b
an
d
s
:
at
6
.
0
0
GHz
with
a
r
et
u
r
n
lo
s
s
o
f
-
4
5
d
B
an
d
an
in
s
er
tio
n
lo
s
s
o
f
-
0
.
2
4
d
B
,
an
d
at
7
.
0
0
GHz
with
a
r
etu
r
n
lo
s
s
o
f
-
2
8
.
1
2
d
B
an
d
a
n
in
s
er
tio
n
lo
s
s
o
f
-
0
.
5
6
d
B
,
en
s
u
r
in
g
o
p
tim
al
s
ig
n
al
tr
an
s
m
is
s
io
n
.
Ad
d
itio
n
ally
,
tr
an
s
m
is
s
io
n
ze
r
o
s
at
6
.
5
0
a
n
d
7
.
5
0
GH
z
s
ig
n
if
ican
tly
atten
u
ate
u
n
wan
ted
s
ig
n
als
with
-
2
3
an
d
-
12
dB
r
ejec
tio
n
r
esp
ec
tiv
ely
,
en
h
an
ci
n
g
f
ilter
s
elec
tiv
ity
,
r
ed
u
cin
g
in
ter
f
er
e
n
ce
,
an
d
im
p
r
o
v
in
g
o
v
er
all
p
er
f
o
r
m
an
ce
co
m
p
ar
ed
with
r
elate
d
wo
r
k
as
p
r
esen
t
ed
in
T
ab
le
2
.
T
h
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–
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is
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ig
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5
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n
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6
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,
th
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atch
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ir
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ir
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F a
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Fig
u
r
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p
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ed
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Per
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atch
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o
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ti
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izatio
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ality
f
ac
to
r
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b
a
n
d
wid
th
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d
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s
er
tio
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s
s
s
im
u
ltan
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s
ly
.
F
UNDING
I
NF
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M
A
T
I
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Au
th
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r
s
s
tate
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Au
th
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s
s
tate
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o
co
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f
lict o
f
in
t
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est.
DATA AV
AI
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AB
I
L
I
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Y
Der
iv
ed
d
ata
s
u
p
p
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tin
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f
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d
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th
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f
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co
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SA
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eq
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est.
AUTHO
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h
is
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C
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tr
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ax
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(
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ip
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Ab
d
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n
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ag
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Ham
id
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y
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h
f
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✓
Aziz
Dk
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ak
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Yass
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e
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ih
✓
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C
:
C
o
n
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p
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t
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:
M
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f
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RE
F
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R
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NC
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S
[
1
]
Z.
X
u
e
t
a
l
.
,
“
R
o
b
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st
w
i
d
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b
a
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d
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v
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0
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3
3
9
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/
r
s1
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1
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3
.
[
2
]
L.
W
a
n
g
,
X
.
H
u
,
a
n
d
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.
L
i
u
,
“
A
d
v
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f
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m
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ma
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a
d
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mf
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b
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d
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t
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c
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c
o
mm
u
n
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c
a
t
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s
y
st
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m,
”
El
e
c
t
r
o
n
i
c
s
(
S
w
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t
z
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rl
a
n
d
)
,
v
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o
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d
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:
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9
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/
e
l
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t
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c
s
1
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1
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0
0
.
[
3
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X
.
S
h
e
n
,
K
.
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e
,
X
.
T
a
n
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,
a
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d
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e
,
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p
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7
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.
[
4
]
M
.
G
a
o
,
Y
.
H
e
,
J
.
N
a
n
,
Z.
Y
a
n
g
,
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n
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,
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d
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d
u
a
l
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c
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b
a
n
d
s,
”
PL
o
S
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E
,
v
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l
.
1
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o
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o
h
a
m
m
e
d
ia,
Ha
ss
a
n
II
Un
iv
e
rsity
o
f
Ca
sa
b
lan
c
a
,
M
o
r
o
c
c
o
.
He
h
a
s
b
e
e
n
a
n
a
ss
istan
t
p
ro
fe
ss
o
r
wit
h
t
h
e
El
e
c
tri
c
a
l
En
g
i
n
e
e
rin
g
De
p
a
rtme
n
t.
His
re
s
e
a
rc
h
in
tere
sts
in
c
lu
d
e
e
lec
tro
n
ic
s
a
p
p
li
e
d
to
th
e
b
io
m
e
d
ica
l
d
o
m
a
in
a
n
d
a
n
a
lo
g
ICs
d
e
sig
n
,
e
lec
tro
m
a
g
n
e
ti
c
field
,
l
o
w
p
o
we
r
d
e
sig
n
,
a
n
d
BL
E
a
p
p
li
c
a
ti
o
n
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
h
a
m
id
.
b
o
u
y
g
h
f@fstm
.
a
c
.
m
a
.
Az
iz
Dki
o
u
a
k
wa
s
b
o
r
n
i
n
1
8
8
5
,
M
o
r
o
c
c
o
.
He
is
a
q
u
a
li
fied
p
h
y
sic
s
tea
c
h
e
r.
I
n
No
v
e
m
b
e
r
2
0
2
0
,
h
e
g
ra
d
u
a
ted
fr
o
m
Ab
d
e
lma
lek
Essa
a
d
i
U
n
iv
e
rs
it
y
wit
h
a
P
h
.
D.
d
e
g
re
e
in
p
h
y
sic
s,
e
lec
tro
n
ics
a
n
d
tele
c
o
m
m
u
n
ica
ti
o
n
s.
His
re
se
a
rc
h
fo
c
u
se
s
o
n
RF
,
m
icro
wa
v
e
c
ircu
it
s
a
n
d
M
IM
O
a
n
ten
n
a
s.
He
is
t
h
e
a
u
th
o
r
a
n
d
c
o
-
a
u
th
o
r
o
f
n
u
m
e
ro
u
s
a
rti
c
les
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
d
k
i
o
u
a
k
a
z
iz@
h
o
tma
il
.
fr
.
Ya
ss
in
e
G
m
ih
wa
s
b
o
rn
i
n
1
9
9
1
.
He
re
c
e
iv
e
d
th
e
m
a
ste
r
d
e
g
re
e
in
a
u
to
m
a
ti
c
,
sig
n
a
l
p
r
o
c
e
ss
in
g
,
i
n
d
u
strial
c
o
m
p
u
t
in
g
fro
m
Un
i
v
e
rsity
Ha
ss
a
n
first,
S
e
tt
a
t,
M
o
ro
c
c
o
,
i
n
2
0
1
4
.
He
is
c
u
rre
n
tl
y
a
P
h
.
D.
st
u
d
e
n
t
i
n
E
n
g
i
n
e
e
rin
g
,
I
n
d
u
str
ial
M
a
n
a
g
e
m
e
n
t
a
n
d
In
n
o
v
a
ti
o
n
re
se
a
rc
h
Lab
o
ra
to
ry
,
F
a
c
u
lt
y
o
f
S
c
ien
c
e
s
a
n
d
Tec
h
n
o
l
o
g
y
,
Ha
s
sa
n
first
Un
iv
e
rsit
y
,
with
a
th
e
sis
o
n
Co
n
tri
b
u
t
io
n
t
o
t
h
e
d
e
si
g
n
o
f
R
F
ID
a
n
ten
n
a
s.
His
re
se
a
rc
h
i
n
tere
sts
in
c
lu
d
e
a
n
ten
n
a
s,
UH
F
a
n
d
m
icro
wa
v
e
ra
d
io
fre
q
u
e
n
c
y
id
e
n
t
ifi
c
a
ti
o
n
(R
F
ID).
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
y
a
ss
in
e
.
g
m
ih
@
g
m
a
il
.
c
o
m
.
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