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m
eth
o
d
ac
h
iev
es
g
o
o
d
c
o
n
v
er
g
en
ce
an
d
ac
cu
r
a
cy
p
r
o
p
er
ties
.
Fu
r
th
er
,
th
e
m
eth
o
d
h
as
an
a
d
d
ed
ad
v
an
tag
e
th
at,
d
u
e
to
its
ad
ap
tiv
e
ab
ilit
y
,
it
ca
n
u
p
d
ate
th
e
m
atr
ix
in
v
er
s
e
if
th
e
s
y
s
tem
d
r
if
ts
o
v
er
tim
e,
f
o
r
ex
am
p
le,
in
n
o
n
-
s
tatio
n
ar
y
ch
an
n
el
co
n
d
itio
n
s
[
2
4
]
.
2.
T
H
E
O
RY
2
.
1
.
Sy
s
t
e
m
mo
del
C
o
n
s
id
er
a
MI
MO
s
y
s
tem
wit
h
d
im
en
s
io
n
s
×
,
with
r
ep
r
esen
tin
g
th
e
n
u
m
b
er
o
f
tr
a
n
s
m
it
n
o
d
es
an
d
th
e
n
u
m
b
er
o
f
r
ec
eiv
e
n
o
d
es,
s
u
ch
th
at
=
.
T
h
e
in
p
u
t
s
ig
n
al
v
ec
t
o
r
x
[
]
p
ass
es
th
r
o
u
g
h
th
e
ch
an
n
el
a
n
d
is
m
u
ltip
lied
b
y
a
ch
a
n
n
el
m
atr
ix
H
to
g
en
er
a
te
th
e
o
u
tp
u
t sig
n
al
v
ec
to
r
y
[
]
.
y
[
]
=
Hx
[
]
+
v
[
]
(
1
)
W
h
er
e
x
[
]
=
[
(
)
1
(
)
…
−
1
(
)
]
,
y
[
]
=
[
(
)
1
(
)
…
−
1
(
)
]
,
an
d
v
[
]
=
[
(
)
1
(
)
…
−
1
(
)
]
ar
e
co
lu
m
n
v
ec
to
r
s
o
f
d
im
en
s
io
n
×
1
,
with
(
)
an
d
(
)
r
esp
ec
tiv
ely
in
d
icatin
g
t
h
e
s
i
g
n
al
tr
a
n
s
m
itted
o
n
a
n
d
r
ec
ei
v
ed
at
th
e
-
th
n
o
d
e
at
th
e
tim
e
in
s
tan
t
.
v
[
]
r
ep
r
esen
ts
th
e
n
o
is
e
v
ec
t
o
r
w
ith
s
tatis
tica
l
ch
ar
ac
ter
izatio
n
(
0
,
2
I
)
with
I
b
ein
g
th
e
id
e
n
tity
m
a
tr
ix
.
T
h
e
ch
an
n
el
m
atr
ix
H
is
o
f
d
im
en
s
io
n
s
o
f
×
,
an
d
is
d
escr
ib
ed
as
(
2
)
:
H
=
[
ℎ
00
ℎ
01
…
ℎ
0
(
−
1
)
ℎ
10
ℎ
11
…
ℎ
1
(
−
1
)
⋮
⋮
⋱
⋮
ℎ
(
−
1
)
0
ℎ
(
−
1
)
1
…
ℎ
(
−
1
)
(
−
1
)
]
(
2
)
I
n
(
1
)
ca
n
b
e
r
ea
r
r
an
g
e
d
as
(
3
)
:
y
[
]
=
∑
[
ℎ
0
ℎ
1
⋮
ℎ
(
−
1
)
]
−
1
=
0
(
)
+
v
[
]
(
3
)
w
i
t
h
=
0
,
1
,
…
,
−
1
.
A
n
e
s
ti
m
a
t
e
o
f
(
)
c
a
n
b
e
it
e
r
a
t
i
v
el
y
o
b
t
a
i
n
e
d
f
r
o
m
y
[
]
b
y
u
s
i
n
g
a
n
a
d
a
p
t
i
v
e
f
i
l
t
e
r
(
F
i
g
u
r
e
1
)
.
s
u
c
h
a
d
a
p
t
i
v
e
f
i
l
t
e
r
b
l
o
c
k
s
c
a
n
b
e
u
s
e
d
t
o
o
b
ta
i
n
an
e
s
t
i
m
a
t
e
o
f
x
[
]
f
r
o
m
y
[
]
,
w
i
t
h
f
i
l
t
e
r
w
e
i
g
h
ts
o
f
t
h
e
b
l
o
c
k
s
m
i
n
i
m
iz
i
n
g
t
h
e
im
p
a
c
t
o
f
c
h
a
n
n
e
l
m
a
t
r
i
x
H
on
y
[
]
b
y
e
f
f
e
c
t
i
v
e
l
y
a
ct
i
n
g
a
s
a
n
i
n
v
e
r
s
e
o
f
H
.
Fig
u
r
e
1
.
T
h
e
-
th
ad
ap
tiv
e
f
ilter
b
lo
ck
f
o
r
c
o
m
p
u
tatio
n
o
f
m
a
tr
ix
in
v
er
s
e.
T
h
er
e
ar
e
s
u
ch
b
l
o
ck
s
.
r
ep
r
esen
ts
th
e
s
q
u
ar
e
m
atr
ix
s
ize
Evaluation Warning : The document was created with Spire.PDF for Python.
C
o
m
p
u
t Sci
I
n
f
T
ec
h
n
o
l
I
SS
N:
2722
-
3
2
2
1
Ma
tr
ix
in
ve
r
s
io
n
u
s
in
g
mu
ltip
le
-
in
p
u
t m
u
ltip
le
-
o
u
tp
u
t
a
d
a
p
tive
…
(
Mu
h
a
mma
d
Ya
s
ir
S
id
d
iq
u
e
A
n
ju
m
)
3
2
.
2
.
M
I
M
O
a
da
ptiv
e
f
ilte
r
Fo
r
g
iv
en
v
alu
e
o
f
,
in
o
r
d
e
r
to
o
b
tain
an
esti
m
ate
̂
(
)
o
f
(
)
,
an
ad
ap
tiv
e
f
ilter
b
lo
ck
(
Fig
u
r
e
1
)
ca
n
b
e
cr
ea
ted
with
a
weig
h
t v
ec
to
r
w
=
[
0
,
1
,
…
,
−
1
]
,
s
o
th
at
th
e
-
th
ad
ap
tiv
e
f
ilter
o
u
tp
u
t
(
)
is
:
̂
(
)
=
w
y
[
]
(
4
)
Mean
s
q
u
ar
ed
e
r
r
o
r
(
MSE
)
b
et
wee
n
th
e
f
ilter
o
u
tp
u
t
̂
(
)
an
d
th
e
d
esire
d
o
u
tp
u
t
(
)
is
d
ef
in
ed
as
(5
)
[
2
5
]
:
{
2
(
)
}
=
2
−
2
w
p
+
w
R
w
(
5
)
with
er
r
o
r
(
)
=
(
)
−
(
)
.
2
=
{
2
(
)
}
r
ep
r
esen
ts
th
e
p
o
wer
o
f
th
e
-
th
d
esire
d
s
ig
n
al
(
)
.
×
1
cr
o
s
s
-
co
r
r
elatio
n
v
ec
to
r
,
p
,
b
et
wee
n
f
ilter
in
p
u
t a
n
d
d
esire
o
u
tp
u
t is d
ef
in
ed
as
(
6
)
:
p
=
[
0
(
)
1
(
)
…
(
−
1
)
(
)
]
(
6
)
with
(
)
=
{
(
)
(
)
}
an
d
=
0
,
1
,
…
,
−
1
.
×
au
to
c
o
r
r
elatio
n
m
atr
ix
R
o
f
t
h
e
f
ilter
in
p
u
t
y
[
]
is
d
ef
in
ed
as
(
7
)
:
R
=
[
00
(
)
01
(
)
…
0
(
−
1
)
(
)
10
(
)
11
(
)
…
1
(
−
1
)
(
)
⋮
⋮
⋱
⋮
(
−
1
)
0
(
)
(
−
1
)
1
(
)
…
(
−
1
)
(
−
1
)
(
)
]
(
7
)
s
u
ch
th
at
(
)
=
{
(
)
(
)
}
.
Gr
ad
ie
n
t
o
f
MSE
t
ak
en
with
r
esp
ec
t
to
th
e
f
ilter
weig
h
ts
,
∇
,
lead
s
to
th
e
W
ien
er
-
Ho
p
f
eq
u
atio
n
[
2
5
]
:
w
o
p
t
=
R
−
1
p
(
8
)
with
∇
b
ein
g
:
∇
=
−
2
p
+
R
w
(9
)
Fo
r
=
0
,
1
,
…
,
−
1
:
W
=
R
−
1
P
(
1
0
)
s
u
ch
th
at
W
o
p
t
=
[
w
o
p
t
0
,
w
o
p
t
1
,
…
,
w
o
p
t
−
1
]
an
d
P
o
p
t
=
[
p
0
,
p
1
,
…
,
p
−
1
]
.
W
,
wh
ich
a
ttem
p
ts
to
ac
h
iev
e
a
m
in
im
u
m
MSE
esti
m
ate
o
f
x
[
]
f
r
o
m
y
[
]
b
y
m
in
im
izin
g
th
e
im
p
ac
t
o
f
H
o
n
th
e
latter
,
ef
f
ec
tiv
el
y
a
cts
as
an
in
v
er
s
e
o
f
H
.
I
ter
ativ
e
co
m
p
u
tat
io
n
o
f
W
o
p
t
is
d
is
cu
s
s
ed
in
n
ex
t sectio
n
.
2
.
3
.
I
t
er
a
t
iv
e
i
m
plem
ent
a
t
io
n
A
s
tar
tin
g
ch
o
ice
f
o
r
c
o
m
p
u
ti
n
g
W
o
p
t
iter
ativ
ely
ca
n
b
e
th
e
Steep
est
Descen
t
alg
o
r
ith
m
[
2
5
]
d
u
e
to
th
e
s
im
p
licity
o
f
th
e
f
o
r
m
:
w
[
+
1
]
=
w
[
]
−
∇
(
1
1
)
r
ep
r
esen
ts
alg
o
r
ith
m
’
s
iter
atio
n
s
tep
-
s
ize.
Su
b
s
titu
tin
g
th
e
v
alu
e
o
f
t
h
e
g
r
a
d
ien
t
f
r
o
m
(
9
)
in
(
1
1
)
,
an
d
r
ea
r
r
an
g
i
n
g
lead
s
to
:
w
[
+
1
]
=
w
[
]
+
2
y
[
]
(
)
(
1
2
)
I
n
(
1
2
)
r
ep
r
esen
ts
f
am
o
u
s
lea
s
t
m
ea
n
s
q
u
ar
es
(
L
MS)
alg
o
r
ith
m
[
2
5
]
.
A
co
n
ce
r
n
in
th
e
im
p
lem
en
tatio
n
o
f
L
MS
alg
o
r
ith
m
is
th
e
ch
o
ice
o
f
.
L
ar
g
e
v
alu
e
o
f
m
ay
lead
to
f
aster
co
n
v
er
g
en
ce
b
u
t
p
o
o
r
ac
cu
r
ac
y
.
Sm
all
v
alu
e
o
f
m
ay
lead
to
h
ig
h
er
a
cc
u
r
ac
y
b
u
t
s
lo
wer
co
n
v
er
g
en
ce
.
On
e
wa
y
to
o
v
er
co
m
e
th
e
is
s
u
e
is
to
m
ak
e
in
v
er
s
ely
p
r
o
p
o
r
tio
n
al
to
th
e
in
p
u
t
s
ig
n
al
en
er
g
y
,
wh
ich
in
th
is
ca
s
e
i
s
y
[
]
y
[
]
.
T
h
is
lead
s
to
th
e
n
o
r
m
alize
d
least m
ea
n
s
q
u
ar
es
(
NL
MS)
alg
o
r
ith
m
[
2
5
]
:
w
[
+
1
]
=
w
[
]
+
1
y
[
]
y
[
]
y
[
]
(
)
(
1
3
)
Fo
r
=
0
,
1
,
…
,
−
1
,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
7
2
2
-
3
2
2
1
C
o
m
p
u
t Sci
I
n
f
T
ec
h
n
o
l
,
Vo
l.
6
,
No
.
1
,
Ma
r
ch
2
0
2
5
:
1
-
7
4
W
[
+
1
]
=
W
[
]
+
1
y
[
]
y
[
]
y
[
]
e
[
]
(
1
4
)
with
e
[
]
=
[
0
(
)
,
1
(
)
,
…
,
−
1
(
)
]
.
3.
M
E
T
H
O
DS
Simu
latio
n
s
wer
e
p
e
r
f
o
r
m
ed
f
o
r
d
eter
m
in
in
g
th
e
ef
f
ica
cy
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
.
A
5
×
5
au
to
co
r
r
elatio
n
m
atr
ix
o
f
th
e
f
ir
s
t
-
o
r
d
e
r
au
t
o
r
eg
r
ess
iv
e
p
r
o
ce
s
s
,
AR
(
1
)
,
is
u
s
ed
f
o
r
s
i
m
u
latin
g
th
e
MI
MO
ch
an
n
el
m
atr
ix
H
.
A
5
×
5
m
atr
ix
r
esu
lts
in
f
iv
e
ad
ap
tiv
e
f
ilter
b
lo
ck
s
,
i.e
.
,
=
0
,
1
,
…
,
4
.
Fiv
e
ty
p
e
o
f
s
im
u
latio
n
s
wer
e
p
er
f
o
r
m
ed
.
I
n
th
e
f
ir
s
t stag
e,
f
ilter
weig
h
ts
w
[
]
in
(
1
3
)
,
r
ep
r
esen
tin
g
f
ir
s
t
-
r
o
w
esti
m
ates
o
f
W
,
wer
e
co
m
p
u
ted
,
o
v
er
laid
an
d
d
is
p
lay
ed
as
a
f
u
n
ctio
n
o
f
th
e
iter
atio
n
s
p
er
f
o
r
m
ed
b
y
th
e
N
L
MS
alg
o
r
ith
m
as
s
h
o
wn
in
Fig
u
r
e
2
.
I
n
t
h
e
s
ec
o
n
d
s
tag
e,
s
q
u
ar
e
o
f
th
e
er
r
o
r
ter
m
e
[
]
in
(
1
4
)
,
r
ep
r
esen
tin
g
th
e
in
d
iv
id
u
al
er
r
o
r
esti
m
ates
b
etwe
en
th
e
r
esp
ec
tiv
e
d
e
s
ir
ed
o
u
tp
u
ts
(
)
an
d
th
e
esti
m
ated
o
u
tp
u
ts
̂
(
)
,
wer
e
co
m
p
u
ted
,
o
v
e
r
laid
an
d
p
r
ese
n
ted
as
f
u
n
ctio
n
o
f
NL
MS
iter
atio
n
s
as
p
r
esen
ted
in
Fig
u
r
e
3
.
I
n
th
e
th
ir
d
s
tag
e,
elem
en
t
-
wis
e
MSE
b
etwe
en
r
esp
ec
tiv
e
r
o
ws
o
f
W
[
]
in
(
1
4
)
an
d
th
o
s
e
o
f
a
n
ex
ac
t
m
a
tr
ix
in
v
er
s
e
wer
e
co
m
p
u
ted
,
o
v
er
laid
a
n
d
d
is
p
lay
ed
as
a
f
u
n
ctio
n
o
f
n
u
m
b
er
o
f
NL
MS
iter
atio
n
s
d
is
p
lay
e
d
in
Fig
u
r
e
4
.
First,
s
ec
o
n
d
,
an
d
th
ir
d
s
tag
e
s
im
u
la
tio
n
s
wer
e
p
er
f
o
r
m
ed
at
SNR
o
f
6
0
d
B
an
d
o
n
e
Mo
n
te
C
ar
l
o
n
o
is
e
r
u
n
.
I
n
th
e
f
o
u
r
th
s
tag
e,
elem
e
n
t
-
wis
e
MSE
b
etwe
en
th
e
W
[
]
in
(
1
4
)
an
d
t
h
e
ex
ac
t
m
atr
ix
in
v
e
r
s
e
was
co
m
p
u
ted
a
n
d
d
is
p
lay
ed
as
f
u
n
ctio
n
o
f
SNR
r
an
g
in
g
f
r
o
m
0
to
1
0
0
d
B
in
th
e
in
cr
em
en
ts
o
f
u
n
ity
,
with
=
110
an
d
1
0
0
Mo
n
te
C
ar
lo
r
u
n
s
f
o
r
n
o
is
e
s
im
u
latio
n
as
s
h
o
w
n
in
Fig
u
r
e
5
.
First
f
o
u
r
s
im
u
lat
io
n
s
wer
e
ca
r
r
ied
o
u
t
with
=
0
.
4
.
I
n
th
e
f
if
th
s
tag
e,
f
o
u
r
s
im
u
l
atio
n
s
wer
e
p
er
f
o
r
m
ed
f
o
r
α
r
an
g
in
g
f
r
o
m
0
.
2
t
o
0
.
8
in
in
c
r
em
en
ts
o
f
0
.
2
;
r
esu
lts
wer
e
o
v
er
laid
a
n
d
d
is
p
lay
e
d
in
Fig
u
r
e
6
.
All
s
im
u
latio
n
s
wer
e
p
er
f
o
r
m
ed
in
MA
T
L
AB
(
Ma
th
wo
r
k
s
,
Natick
,
MA
,
US
A
)
u
s
in
g
in
-
h
o
u
s
e
wr
itten
s
cr
i
p
ts
.
Fig
u
r
e
2
.
Fil
ter
weig
h
ts
w
[
]
in
(
1
3
)
with
=
0
,
d
ep
ictin
g
f
ir
s
t
-
r
o
w
esti
m
ates o
f
in
v
er
s
e
m
atr
ix
W
Fig
u
r
e
3
.
Sq
u
ar
ed
NL
MS
-
er
r
o
r
ter
m
e
[
]
in
(
1
4
)
s
h
o
win
g
al
g
o
r
it
h
m
co
n
v
er
g
e
n
ce
p
r
o
p
er
ties
Evaluation Warning : The document was created with Spire.PDF for Python.
C
o
m
p
u
t Sci
I
n
f
T
ec
h
n
o
l
I
SS
N:
2722
-
3
2
2
1
Ma
tr
ix
in
ve
r
s
io
n
u
s
in
g
mu
ltip
le
-
in
p
u
t m
u
ltip
le
-
o
u
tp
u
t
a
d
a
p
tive
…
(
Mu
h
a
mma
d
Ya
s
ir
S
id
d
iq
u
e
A
n
ju
m
)
5
Fig
u
r
e
4
.
E
lem
e
n
t
-
wis
e
MSE
b
etwe
en
r
esp
ec
tiv
e
r
o
ws o
f
W
an
d
H
−
1
Fig
u
r
e
5
.
E
lem
e
n
t
-
wis
e
MSE
b
etwe
en
W
an
d
H
−
1
,
co
m
p
u
ted
f
o
r
a
r
an
g
e
o
f
SNR
v
alu
es
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
Fig
u
r
e
2
d
is
p
lay
s
r
ap
id
c
o
n
v
e
r
g
en
ce
p
r
o
p
er
ties
o
f
r
ep
r
esen
t
ativ
e
esti
m
ates
o
f
th
e
f
ir
s
t
ad
ap
tiv
e
f
ilter
b
lo
ck
.
Ad
a
p
tiv
e
f
ilter
esti
m
ates
co
n
v
er
g
e
t
o
ac
tu
al
v
alu
es
i
n
n
ea
r
ly
less
th
an
s
ev
en
ty
ite
r
atio
n
s
.
Als
o
,
o
n
ce
th
e
esti
m
ates
co
n
v
er
g
e,
th
e
y
d
o
n
o
t
d
iv
er
g
e
o
r
o
s
cillate
ar
o
u
n
d
t
h
e
co
n
v
er
g
e
d
v
alu
es.
F
ig
u
r
e
3
s
h
o
ws
th
e
r
o
w
-
wis
e
s
q
u
ar
ed
NL
MS
-
er
r
o
r
,
wh
ich
d
em
o
s
n
tr
ates
th
at
th
e
NL
MS,
em
p
lo
y
ed
as
an
o
p
ti
m
izatio
n
alg
o
r
ith
m
to
co
m
p
u
te
th
e
esti
m
ates,
r
em
ain
s
s
tab
le
an
d
d
o
es n
o
t d
iv
er
g
e
o
r
o
s
cillate.
T
h
is
ca
n
b
e
attr
ib
u
ted
to
th
e
ab
ilit
y
o
f
th
e
NL
MS
alg
o
r
ith
m
to
ad
ju
s
t
its
s
tep
-
s
ize
ac
co
r
d
in
g
to
th
e
in
p
u
t
s
ig
n
al
en
er
g
y
[
2
5
]
.
NL
MS
r
ed
u
ce
s
th
e
s
tep
-
s
ize
to
av
o
id
th
e
g
r
ad
ie
n
t
n
o
is
e,
if
th
e
er
r
o
r
is
s
m
all;
an
d
if
th
e
er
r
o
r
is
lar
g
e,
N
L
MS
in
cr
ea
s
es
th
e
st
ep
-
s
ize
to
av
o
id
co
n
v
er
g
e
n
c
e
lag
[
2
5
]
.
I
n
a
d
d
itio
n
,
th
e
in
s
tan
tan
eo
u
s
s
q
u
ar
e
d
er
r
o
r
r
ea
c
h
es
a
v
alu
e
o
f
1
0
-
6
at
th
e
en
d
o
f
t
h
e
eig
h
tieth
iter
at
io
n
.
T
h
is
ca
n
b
e
v
is
u
alize
d
in
Fig
u
r
e
4
,
f
o
r
all
r
o
ws
o
f
th
e
co
m
p
u
ted
i
n
v
er
s
e
m
atr
ix
,
wh
er
e
in
th
e
elem
en
t
-
wis
e
MSE
o
f
th
e
r
o
ws
r
ea
ch
e
s
th
e
th
r
esh
o
ld
o
f
1
0
-
6
,
also
at
ar
o
u
n
d
th
e
ei
g
h
tieth
iter
atio
n
.
Fig
u
r
es
2
to
4
s
h
o
w
th
e
co
n
v
er
g
en
ce
an
d
ac
cu
r
ac
y
p
r
o
p
er
ties
o
f
t
h
e
alg
o
r
ith
m
at
SNR
=6
0
d
B
,
wh
er
ea
s
Fig
u
r
e
5
s
h
o
ws
th
e
co
n
v
e
r
g
en
ce
an
d
ac
cu
r
ac
y
p
r
o
p
er
ties
o
f
th
e
m
eth
o
d
f
o
r
an
SNR
r
an
g
e
o
f
0
-
1
1
0
d
B
.
Up
to
6
0
d
B
,
th
e
a
lg
o
r
ith
m
d
is
p
lay
s
a
n
eg
ativ
e
l
o
g
-
lin
ea
r
tr
en
d
b
etwe
en
th
e
MSE
an
d
t
h
e
SNR
,
an
d
th
e
MSE
en
ter
s
a
s
tead
y
-
s
tate
at
n
ea
r
ly
a
r
o
u
n
d
6
0
d
B
,
with
o
u
t
d
is
p
lay
in
g
an
y
f
u
r
t
h
er
r
e
d
u
ctio
n
.
T
h
is
b
eh
av
io
r
b
ec
o
m
es
m
ar
k
ed
in
Fig
u
r
e
6
,
wh
er
e
th
e
r
esu
lts
ar
e
o
v
er
laid
f
o
r
d
if
f
er
en
t
co
n
d
itio
n
n
u
m
b
e
r
s
o
f
H
.
Fo
r
=0
.
2
,
w
h
er
e
t
h
e
c
o
n
d
itio
n
n
u
m
b
er
o
f
H
is
2
.
0
1
,
MSE
co
n
tin
u
es
to
d
ec
r
ea
s
e
l
o
g
-
lin
ea
r
l
y
with
in
cr
ea
s
e
in
SNR
,
r
ea
ch
in
g
n
ea
r
ly
1
0
-
10
at
1
1
0
d
B
.
On
th
e
o
th
er
h
an
d
,
f
o
r
=0
.
6
(
c
o
n
d
itio
n
n
u
m
b
er
9
.
7
4
)
,
MSE
n
o
t
o
n
l
y
r
ea
ch
es
a
s
tead
y
s
tate
co
m
p
a
r
ativ
ely
ea
r
ly
,
th
at
is
,
at
3
0
d
B
,
b
u
t
also
ac
h
ie
v
es
a
h
ig
h
er
s
tead
y
s
tate
v
alu
e
o
f
10
-
2
;
th
is
tr
en
d
co
n
tin
u
es
f
o
r
=0
.
8
,
wh
er
e
th
e
co
n
d
itio
n
n
u
m
b
er
in
cr
ea
s
es
to
2
9
.
7
6
.
At
=1
,
H
b
ec
o
m
es
s
in
g
u
lar
.
Fig
u
r
e
6
.
E
lem
e
n
t
-
wis
e
MSE
b
etwe
en
W
an
d
H
−
1
,
co
m
p
u
ted
f
o
r
r
an
g
e
o
f
m
atr
ix
c
o
n
d
itio
n
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
7
2
2
-
3
2
2
1
C
o
m
p
u
t Sci
I
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f
T
ec
h
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o
l
,
Vo
l.
6
,
No
.
1
,
Ma
r
ch
2
0
2
5
:
1
-
7
6
5.
CO
NCLU
SI
O
N
A
n
o
v
el
m
at
r
ix
in
v
er
s
io
n
m
eth
o
d
b
ased
o
n
MI
MO
ad
ap
tiv
e
f
ilter
in
g
is
p
r
esen
ted
.
M
o
n
te
C
ar
lo
s
im
u
latio
n
r
esu
lts
d
em
o
n
s
tr
ate
th
at
th
e
m
eth
o
d
h
as
g
o
o
d
co
n
v
er
g
e
n
ce
a
n
d
ac
cu
r
ac
y
p
r
o
p
e
r
ties
.
T
h
e
p
r
o
p
o
s
e
d
m
eth
o
d
h
as
th
e
a
b
ilit
y
to
ite
r
ativ
ely
ad
a
p
t
to
c
h
an
g
es
in
s
y
s
tem
co
n
d
i
tio
n
s
,
wh
ich
m
a
k
es
it
s
u
itab
le
f
o
r
p
r
ac
tical
im
p
lem
en
tatio
n
in
s
y
s
tem
s
with
tim
e
-
v
ar
y
in
g
p
r
o
p
er
ties
,
e.
g
.
,
MI
MO
wir
eless
s
y
s
tem
s
,
MI
MO
ac
o
u
s
tic
s
y
s
tem
s
,
an
d
MI
MO
co
n
tr
o
l sy
s
tem
s
.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
N
o
f
u
n
d
in
g
is
in
v
o
lv
ed
.
AUTHO
R
CO
NT
RI
B
UT
I
O
NS ST
A
T
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M
E
N
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h
is
jo
u
r
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u
s
es
th
e
C
o
n
tr
ib
u
to
r
R
o
les
T
ax
o
n
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m
y
(
C
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to
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n
ize
in
d
iv
id
u
al
au
th
o
r
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n
tr
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tio
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s
,
r
ed
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th
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h
ip
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an
d
f
ac
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llab
o
r
atio
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.
Na
m
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Aut
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Vi
Su
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h
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Yasir
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✓
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J
av
ed
I
q
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al
✓
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C
:
C
o
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c
e
p
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u
a
l
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:
M
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f
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1
6
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[
1
7
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[
1
8
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1
9
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2
3
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2
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[
2
4
]
T.
S
.
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p
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Wi
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m
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4
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[
2
5
]
B
.
F
a
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-
B
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,
A
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in
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m
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wire
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ly
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o
,
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,
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n
2
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d
2
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ti
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rsity
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y
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Isla
m
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b
a
d
,
P
a
k
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it
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ter
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t
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th
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s,
si
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g
.
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c
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n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
jav
e
d
iq
b
a
l@m
c
s.e
d
u
.
p
k
.
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