I
nte
rna
t
io
na
l J
o
urna
l o
f
E
lect
rica
l a
nd
Co
m
p
ute
r
E
ng
in
ee
ring
(
I
J
E
CE
)
Vo
l.
6
,
No
.
6
,
Dec
em
b
er
201
6
,
p
p
.
2
7
4
2
~2
7
5
4
I
SS
N:
2
0
8
8
-
8708
,
DOI
: 1
0
.
1
1
5
9
1
/
i
j
ec
e
.
v
6i
6
.
1
0
7
6
3
2742
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ia
e
s
jo
u
r
n
a
l.c
o
m/o
n
lin
e/in
d
ex
.
p
h
p
/I
JE
C
E
I
m
pro
v
ed
D
eno
isi
ng
M
ethod for
U
l
tras
o
nic
E
cho
w
ith
M
o
ther
W
a
v
elet
O
pti
m
i
za
tion a
nd
B
est
-
B
a
sis
S
elect
io
n
M
o
ha
m
m
a
d H
o
s
s
e
in Do
o
s
t
M
o
ha
m
m
a
di
De
p
a
rt
m
e
n
t
o
f
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
,
Ha
m
e
d
a
n
Un
iv
e
rsit
y
o
f
T
e
c
h
n
o
lo
g
y
,
Ira
n
Art
icle
I
nfo
AB
ST
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Mar
,
2
0
1
6
R
ev
i
s
ed
J
u
n
2
2
,
2
0
1
6
A
cc
ep
ted
J
u
l
5
,
2
0
1
6
W
e
a
k
fe
a
tu
re
s
o
f
u
lt
ra
so
n
icn
o
n
d
e
stru
c
ti
v
e
tes
t
sig
n
a
ls
a
re
u
su
a
ll
y
i
m
m
e
rs
e
d
in
n
o
isy
sig
n
a
ls.
S
o
,
in
th
is
p
a
p
e
r,
w
e
p
ro
p
o
se
d
a
n
im
p
ro
v
e
d
s
c
h
e
m
e
f
o
r
n
o
ise
re
d
u
c
t
io
n
a
n
d
f
e
a
tu
re
e
x
tra
c
ti
o
n
b
a
se
d
o
n
d
isc
re
te
w
a
v
e
let
t
ra
n
sf
o
r
m
.
T
h
e
b
a
sis o
f
th
e
m
o
th
e
r
w
a
v
e
let
w
a
s se
le
c
ted
to
b
e
m
a
t
c
h
e
d
to
a
g
iv
e
n
sig
n
a
l.
T
h
re
e
d
iff
e
r
e
n
t
c
o
n
stra
in
ts
w
e
re
p
re
se
n
ted
to
m
in
im
ize
th
e
e
rro
r
b
e
twe
e
n
th
e
d
e
n
o
ise
d
a
n
d
t
h
e
g
iv
e
n
sig
n
a
l.
It
sh
o
u
l
d
b
e
m
e
n
ti
o
n
e
d
th
a
t
su
c
h
a
n
o
p
ti
m
u
m
w
a
v
e
let
c
a
n
re
p
re
s
e
n
t
th
e
sig
n
a
l
m
o
re
c
o
m
p
a
c
tl
y
w
it
h
a
f
e
w
larg
e
c
o
e
ff
icie
n
ts
w
h
ich
c
a
n
b
e
c
o
n
sid
e
r
e
d
a
s
th
e
sig
n
a
l
f
e
a
tu
re
s.
S
tan
d
a
rd
sig
n
a
ls
a
n
d
sim
u
late
d
u
lt
ra
so
n
ic
e
c
h
o
we
re
u
se
d
to
e
v
a
lu
a
te
th
e
p
e
r
f
o
r
m
a
n
c
e
o
f
th
e
p
re
se
n
ted
a
lg
o
rit
h
m
s.
S
ig
n
a
l
to
e
rro
r
ra
ti
o
w
a
s
u
se
d
to
c
o
m
p
a
re
th
e
d
e
sig
n
e
d
w
a
v
e
let
p
e
r
f
o
r
m
a
n
c
e
w
it
h
th
a
t
o
f
sta
n
d
a
rd
w
a
v
e
l
e
ts.
S
im
u
lat
i
o
n
re
su
l
ts
re
v
e
a
led
th
a
t
th
e
p
ro
p
o
se
d
m
e
th
o
d
o
u
tp
e
rf
o
rm
e
d
th
e
o
th
e
r
p
re
se
n
ted
m
e
th
o
d
s
a
n
d
e
v
e
n
sta
n
d
a
r
d
w
a
v
e
lets.
T
h
e
re
su
lt
s
a
lso
h
a
s
s
h
o
w
n
th
a
t
th
e
si
g
n
a
l
-
b
a
se
d
n
o
ise
re
d
u
c
ti
o
n
a
lg
o
rit
h
m
s
m
a
k
e
th
e
f
e
a
tu
re
e
x
tra
c
ti
o
n
m
o
re
re
li
a
b
le.
F
in
a
ll
y
,
th
e
p
e
rf
o
rm
a
n
c
e
o
f
th
e
p
ro
p
o
se
d
a
lg
o
rit
h
m
w
a
s
c
o
m
p
a
re
d
w
it
h
o
th
e
r
m
e
th
o
d
s f
ro
m
d
iff
e
re
n
t
li
tera
tu
re
s.
K
ey
w
o
r
d
:
Den
o
is
i
n
g
Dis
cr
ete
w
av
ele
t tr
an
s
f
o
r
m
T
h
r
esh
o
ld
Ultr
aso
n
ic_
NDT
Co
p
y
rig
h
t
©
2
0
1
6
In
stit
u
te o
f
A
d
v
a
n
c
e
d
E
n
g
i
n
e
e
rin
g
a
n
d
S
c
ien
c
e
.
Al
l
rig
h
ts
re
se
rv
e
d
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Mo
h
a
m
m
ad
Ho
s
s
ei
n
Do
o
s
t
M
o
h
a
m
m
ad
i
,
Dep
ar
t
m
en
t o
f
E
lectr
ical
E
n
g
i
n
ee
r
in
g
,
Ha
m
ed
an
U
n
i
v
er
s
it
y
o
f
T
ec
h
n
o
lo
g
y
,
Ma
r
d
o
m
St.,
S
h
a
h
id
Fah
m
id
e
h
B
lv
.
Ha
m
ed
an
,
I
r
an
.
E
m
ail:
d
o
o
s
t.
m
o
h
a
m
m
ad
i
@
h
u
t.a
c.
ir
1.
I
NT
RO
D
UCT
I
O
N
Ultr
aso
n
ic_
NDT
is
a
m
ea
s
u
r
e
u
tili
ze
d
in
m
an
y
in
d
u
s
tr
ie
s
f
o
r
id
en
tify
i
n
g
s
p
ec
ial
f
ea
tu
r
e
s
an
d
d
ef
ec
ts
in
th
e
p
r
o
ce
s
s
o
f
p
r
o
d
u
cin
g
v
ar
io
u
s
p
ar
ts
an
d
m
ater
ial
s
,
in
o
r
d
er
to
w
ar
r
a
n
t
y
t
h
e
r
eq
u
ir
ed
q
u
alit
y
an
d
r
eliab
ilit
y
o
f
th
e
f
in
is
h
ed
p
r
o
d
u
cts.
Ultr
aso
n
ic_
NDT
co
u
ld
also
b
e
u
s
ed
in
an
al
y
z
in
g
b
o
d
y
tis
s
u
e
s
an
d
d
etec
tin
g
d
is
ea
s
e
s
i
n
m
ed
ical
p
r
ac
tices.
I
t
g
o
es
w
it
h
o
u
t
s
a
y
in
g
t
h
at
ac
c
u
r
ac
y
a
n
d
p
r
ec
is
io
n
ar
e
o
f
s
alie
n
t
i
m
p
o
r
tan
ce
i
n
b
o
th
t
h
ese
f
ield
s
.
Ho
w
e
v
er
,
d
u
e
to
en
v
ir
o
n
m
e
n
tal
f
ac
to
r
s
an
d
t
h
e
s
u
b
j
ec
t
o
f
th
e
tes
t
its
el
f
,
t
h
ese
s
ig
n
al
s
u
s
u
all
y
i
n
cl
u
d
e
n
o
is
es
t
h
at
ar
e
in
t
h
e
s
a
m
e
f
r
e
q
u
en
c
y
r
a
n
g
e
as
th
e
r
ef
lec
te
d
s
ig
n
al
s
f
r
o
m
th
e
d
ef
ec
tio
n
s
.
T
h
is
co
u
ld
r
es
u
lt
i
n
a
n
i
n
ab
ilit
y
to
id
en
ti
f
y
t
h
e
w
ea
k
f
ea
t
u
r
es
a
n
d
d
e
f
ec
ts
th
a
t
w
o
u
ld
d
ec
r
ea
s
e
th
e
r
eliab
ilit
y
an
d
ac
c
u
r
ac
y
o
f
t
h
i
s
test
.
T
h
e
r
ef
o
r
e,
th
ese
s
i
g
n
a
ls
r
eq
u
ir
e
a
p
r
e
-
p
r
o
ce
s
s
in
g
s
tep
f
o
r
n
o
is
e
r
ed
u
ctio
n
.
So
f
ar
,
s
e
v
er
al
s
ig
n
al
p
r
o
ce
s
s
in
g
m
et
h
o
d
s
i
n
ti
m
e
a
n
d
f
r
eq
u
en
c
y
d
o
m
ai
n
s
h
a
v
e
b
ee
n
p
r
o
p
o
s
ed
.
Mo
h
a
m
m
ed
et
al.
p
r
esen
ted
a
n
e
w
id
ea
o
f
n
o
i
s
e
r
ed
u
ctio
n
b
ased
o
n
t
w
o
s
tag
e:
ad
ap
ti
v
e
li
n
e
en
h
a
n
ce
r
(
AL
E
)
an
d
n
o
r
m
alize
d
leas
t
m
ea
n
s
q
u
ar
e
(
NL
MS)
[
1
]
.
He
also
p
r
o
p
o
s
ed
an
ad
ap
tiv
e
n
o
is
e
c
an
ce
ller
f
o
r
r
e
m
o
te
h
ea
lt
h
m
o
n
ito
r
in
g
w
h
ich
w
a
s
a
co
m
b
i
n
atio
n
o
f
ad
ap
tiv
e
n
o
t
ch
f
i
lter
an
d
m
o
d
if
ied
L
MS
al
g
o
r
ith
m
[
2
]
.
Mo
r
e
class
ic
al
g
o
r
ith
m
s
ca
n
b
e
f
o
u
n
d
in
t
h
e
s
u
r
v
e
y
liter
at
u
r
es [
3
]
.
On
e
o
f
th
e
s
u
cc
ess
f
u
l
m
et
h
o
d
s
in
n
o
is
e
r
ed
u
ct
io
n
i
s
u
s
i
n
g
w
a
v
elet
tr
a
n
s
f
o
r
m
(
W
T
)
.
I
t
h
as
b
ee
n
illu
s
tr
ated
th
at
W
T
y
ield
s
a
m
o
r
e
p
r
ec
is
e
r
esu
lt
in
id
en
ti
f
y
in
g
th
e
s
ig
n
als
f
r
o
m
n
o
is
e
in
co
m
p
ar
i
s
o
n
to
o
th
er
m
et
h
o
d
s
,
s
u
c
h
as
co
m
m
o
n
u
s
ed
f
ilter
in
g
o
r
W
ien
er
f
ilter
in
g
m
et
h
o
d
s
.
On
e
o
f
t
h
e
m
ai
n
u
n
d
er
l
y
i
n
g
r
ea
s
o
n
s
is
th
e
co
n
ce
n
tr
atio
n
o
f
s
i
g
n
al
en
er
g
y
o
n
a
li
m
ited
n
u
m
b
er
o
f
co
ef
f
icie
n
t
s
.
Se
v
er
al
p
ar
a
m
eter
s
s
h
o
u
ld
b
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2088
-
8708
I
mp
r
o
ve
d
Den
o
is
in
g
Meth
o
d
f
o
r
Ultr
a
s
o
n
ic
E
ch
o
W
ith
Mo
th
er
W
a
ve
let
…
(
Mo
h
a
mma
d
Ho
s
s
ein
D
.
M
.
)
2743
co
n
s
id
er
ed
in
u
s
i
n
g
th
i
s
m
eth
o
d
,
p
ar
am
eter
s
s
u
ch
a
s
t
h
e
wav
elet
f
ilter
s
a
n
d
th
e
th
r
es
h
o
l
d
[
4
]
.
M
o
s
t
o
f
th
e
s
e
w
a
v
elet
b
ased
m
eth
o
d
s
u
s
e
s
tan
d
ar
d
w
av
elet
s
o
r
a
co
m
b
i
n
atio
n
o
f
s
ta
n
d
ar
d
w
av
ele
ts
a
n
d
clas
s
ic
f
il
ter
in
g
m
et
h
o
d
s
[
5
]
.
E
v
en
t
h
o
u
g
h
t
h
is
m
et
h
o
d
co
u
ld
b
e
an
ef
f
icie
n
t
w
a
y
f
o
r
r
ed
u
cin
g
n
o
is
e
s
,
b
u
t
it
is
n
o
t d
esi
g
n
ed
f
o
r
a
s
p
ec
if
ic
s
i
g
n
al
an
d
is
n
o
t c
o
m
p
letel
y
ad
j
u
s
ted
f
o
r
ea
ch
s
u
b
j
ec
t o
f
ex
p
er
im
e
n
t.
T
h
e
w
a
v
elet
d
ec
o
m
p
o
s
itio
n
i
s
d
eter
m
i
n
ed
b
y
m
o
th
er
w
av
elet
f
u
n
ctio
n
a
n
d
its
d
ilatio
n
an
d
s
h
i
f
t
v
er
s
io
n
s
[
4
]
.
Sin
ce
,
m
an
y
wo
r
k
s
h
a
v
e
b
ee
n
p
r
ese
n
ted
b
y
r
esear
ch
er
s
to
f
i
n
d
w
a
v
elets
m
at
c
h
ed
to
s
ig
n
al
s
p
r
o
v
id
in
g
th
e
b
est
r
ep
r
esen
tat
io
n
f
o
r
a
g
i
v
e
n
s
ig
n
al.
Dau
b
e
ch
ies
p
r
esen
ted
m
et
h
o
d
s
to
f
i
n
d
o
r
th
o
n
o
r
m
a
l
a
n
d
b
io
r
th
o
n
o
r
m
al
w
a
v
elet
b
ases
w
it
h
co
m
p
ac
t
s
u
p
p
o
r
t
[
6
,
7
]
.
T
h
e
r
esu
lti
n
g
w
a
v
elets
w
er
e
a
cc
ep
tab
le
ac
co
r
d
in
g
to
co
n
s
tr
ain
t
s
b
u
t
b
o
th
o
f
t
h
e
s
e
w
a
v
elet
d
esi
g
n
m
e
th
o
d
s
w
er
e
i
n
d
ep
en
d
en
t
o
f
t
h
e
s
i
g
n
als
b
ein
g
an
al
y
ze
d
.
Gu
p
ta
et
al.
p
r
esen
ted
a
m
et
h
o
d
in
th
e
ti
m
e
d
o
m
ai
n
b
ased
o
n
m
ax
i
m
izin
g
t
h
e
p
r
o
j
ec
tio
n
o
f
th
e
g
i
v
en
s
ig
n
al
in
to
a
s
u
cc
e
s
s
i
v
e
s
ca
li
n
g
s
u
b
s
p
ac
e
an
d
m
i
n
i
m
izat
io
n
in
t
h
e
w
a
v
elet
s
u
b
s
p
a
ce
[
8
-
10
]
.
T
ew
f
i
k
et
al.
p
r
esen
ted
a
m
et
h
o
d
w
h
ic
h
led
to
f
i
n
d
i
n
g
t
h
e
b
est
es
ti
m
atio
n
o
f
t
h
e
d
esi
r
ed
s
ig
n
a
l
w
it
h
i
n
te
g
er
tr
an
s
l
ates
o
f
a
v
ali
d
s
ca
lin
g
f
u
n
ctio
n
o
f
f
i
n
ite
f
i
x
e
d
s
u
p
p
o
r
t
,
d
ilated
b
y
a
g
iv
e
n
f
ac
to
r
,
at
th
e
p
r
o
p
er
s
ca
le
[
1
1
]
.
A
ct
u
all
y
in
[
1
1
]
th
e
u
p
p
er
b
o
u
n
d
o
f
er
r
o
r
n
o
r
m
h
a
s
b
ee
n
m
i
n
i
m
ize
d
in
s
tead
o
f
m
in
i
m
izi
n
g
th
e
ac
tu
al
d
is
tan
ce
b
et
w
ee
n
an
d
.
Go
p
in
ath
et
al.
,
h
as
d
o
n
e
th
e
m
i
n
i
m
izatio
n
o
f
n
o
r
m
i
n
th
e
f
r
eq
u
e
n
c
y
d
o
m
ai
n
in
s
tea
d
o
f
ti
m
e
d
o
m
ai
n
d
u
e
to
it
s
co
m
p
le
x
i
t
y
[
1
2
]
.
T
h
e
m
i
n
i
m
izatio
n
o
f
f
r
eq
u
en
c
y
d
o
m
ai
n
n
o
r
m
o
f
th
e
ap
p
r
o
x
im
a
tio
n
er
r
o
r
w
as
u
s
e
d
to
r
ea
ch
t
h
e
o
p
ti
m
alit
y
.
B
u
t
t
h
e
m
aj
o
r
p
r
o
b
lem
w
a
s
th
e
co
m
p
le
x
it
y
o
f
eq
u
atio
n
s
t
h
at
ar
e
v
er
y
d
if
f
ic
u
l
t to
s
o
lv
e.
Ma
llat
an
d
Z
h
a
n
g
p
r
o
p
o
s
ed
an
alg
o
r
it
h
m
th
at
d
ec
o
m
p
o
s
e
s
an
y
s
i
g
n
al
i
n
to
a
li
n
ea
r
ex
p
an
s
io
n
o
f
w
a
v
e
f
o
r
m
s
t
h
at
ar
e
s
elec
ted
f
r
o
m
a
r
ed
u
n
d
a
n
t
d
ictio
n
ar
y
o
f
f
u
n
ctio
n
s
[
1
3
]
.
Sim
i
lar
w
o
r
k
h
as
b
ee
n
p
r
esen
ted
b
y
Kr
i
m
b
ased
o
n
m
i
n
i
m
izati
o
n
o
f
r
ec
o
n
s
tr
u
ctio
n
er
r
o
r
o
f
th
e
u
n
d
er
l
y
i
n
g
s
i
g
n
al
[
1
4
]
.
I
n
[
1
4
]
s
elec
tin
g
a
n
o
p
tim
a
l
b
asi
s
is
d
o
n
e
a
m
o
n
g
a
f
a
m
il
y
o
f
k
n
o
w
n
b
asi
s
i
n
s
tead
o
f
co
n
s
tr
u
c
tin
g
a
w
av
e
let
m
a
tch
ed
to
t
h
e
s
tatis
t
ics o
f
t
h
e
g
iv
e
n
s
i
g
n
al.
R
ao
an
d
C
h
ap
a
h
av
e
p
r
ese
n
te
d
a
m
et
h
o
d
to
d
esig
n
a
w
a
v
el
et
th
at
lo
o
k
s
l
ik
e
a
g
iv
e
n
s
ig
n
al
f
o
r
th
e
ca
s
e
o
f
o
r
th
o
n
o
r
m
al
m
u
l
ti
r
eso
lu
tio
n
an
al
y
s
is
w
i
th
b
a
n
d
li
m
ited
w
a
v
elet
s
[
1
5
]
.
B
u
t
th
at
m
et
h
o
d
w
a
s
co
m
p
u
tatio
n
all
y
ex
p
e
n
s
i
v
e.
Si
m
ilar
w
o
r
k
h
as
b
ee
n
d
o
n
e
b
y
W
u
-
s
h
e
n
g
an
d
T
s
atsan
is
i
n
wh
ich
th
e
al
g
o
r
it
h
m
s
led
to
v
er
y
co
m
p
licated
s
o
lu
t
i
o
n
s
[
1
6
-
1
7
].
I
n
t
h
is
p
ap
er
,
w
e
p
r
ese
n
t
t
h
r
ee
m
et
h
o
d
s
f
o
r
d
esi
g
n
i
n
g
o
p
ti
m
i
ze
d
w
a
v
elet
w
h
ic
h
ar
e
b
ased
o
n
m
in
i
m
iza
tio
n
o
f
n
o
n
li
n
ea
r
esti
m
atio
n
er
r
o
r
.
P
r
esen
ted
m
e
th
o
d
s
h
a
v
e
n
o
m
aj
o
r
co
m
p
lex
it
y
an
d
co
u
ld
b
e
im
p
le
m
e
n
ted
s
i
m
p
l
y
.
T
h
ese
m
eth
o
d
s
co
u
ld
y
ield
a
co
n
s
id
er
ab
le
i
m
p
r
o
v
e
m
e
n
t
i
n
SN
R
an
d
f
ea
t
u
r
e
id
en
ti
f
ic
a
tio
n
.
Nex
t,
w
e
w
il
l
p
er
f
o
r
m
o
u
r
o
p
ti
m
ized
n
o
is
e
r
ed
u
ctio
n
m
et
h
o
d
s
in
s
i
m
u
la
t
ed
u
ltra
-
s
o
u
n
d
ec
h
o
es
a
n
d
an
al
y
ze
th
e
r
es
u
lti
n
g
lev
el
o
f
o
p
ti
m
izatio
n
i
n
id
en
t
if
y
in
g
t
h
eir
w
ea
k
f
ea
t
u
r
es a
n
d
r
elate
d
SNR
.
2.
B
ACK
G
RO
UND
AN
D
T
H
E
O
RY
2
.
1
.
Dis
cr
et
e
Wa
v
elet
T
ra
ns
f
o
r
m
As
d
escr
ib
ed
i
n
t
h
e
w
av
ele
t
liter
atu
r
e
[
1
8
]
,
b
y
u
s
i
n
g
t
h
e
d
is
cr
ete
w
a
v
elet
tr
an
s
f
o
r
m
(
DW
T
)
,
a
f
u
n
ctio
n
ca
n
b
e
ex
p
an
d
ed
as:
∑
∑
∑
(
1
)
w
h
er
e
is
ca
lled
th
e
s
ca
li
n
g
f
u
n
ctio
n
a
n
d
is
ca
lled
th
e
w
a
v
elet
f
u
n
ctio
n
.
an
d
ar
e
ca
lled
d
ilatio
n
an
d
tr
an
s
la
tio
n
p
ar
a
m
eter
s
r
esp
ec
ti
v
el
y
a
n
d
is
th
e
co
ar
s
est
s
ca
le
in
t
h
e
d
ec
o
m
p
o
s
itio
n
.
an
d
ar
e
th
e
d
etail
an
d
ap
p
r
o
x
im
atio
n
co
e
f
f
icien
ts
an
d
t
h
e
y
c
an
b
e
ca
lcu
lated
b
y
i
n
n
er
p
r
o
d
u
cts f
o
r
o
r
th
o
g
o
n
al
w
a
v
elet
s
y
s
te
m
as f
o
llo
w
[
1
]
,
[
1
4
]
:
〈
〉
(
2
)
〈
〉
(
3
)
Usi
n
g
t
h
e
b
asic r
ec
u
r
s
io
n
,
an
d
ca
n
b
e
w
r
itten
a
s
[
1
4
]
:
√
∑
[
]
(
4
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
J
E
C
E
Vo
l.
6
,
No
.
6
,
Dec
em
b
er
2
0
1
6
: 2
7
4
2
–
2
7
5
4
2744
√
∑
[
]
(
5
)
w
h
er
e
an
d
ar
e
i
m
p
u
l
s
e
r
esp
o
n
s
e
o
f
q
u
ad
r
atu
r
e
m
ir
r
o
r
f
i
lter
s
.
T
h
ese
f
ilter
s
ar
e
u
s
ed
to
i
m
p
le
m
en
t
th
e
f
a
s
t
w
a
v
elet
tr
a
n
s
f
o
r
m
an
d
s
h
o
u
ld
s
atis
f
y
th
e
co
n
d
it
io
n
s
o
f
o
r
th
o
g
o
n
alit
y
[
1
8
, 1
9
]
,
as f
o
llo
w
:
∑
[
]
√
(
6
)
∑
[
]
[
]
[
]
(
7
)
∑
[
]
(
8
)
Dec
o
m
p
o
s
itio
n
is
p
er
f
o
r
m
e
d
u
s
i
n
g
t
h
ese
f
il
ter
s
.
I
t‟
s
b
ee
n
s
h
o
w
n
th
a
t
an
d
w
ill
co
n
s
tr
u
ct
an
o
r
th
o
g
o
n
a
l b
asis
f
o
r
,
if
is
s
elec
ted
as:
[
]
[
]
(
9
)
o
r
(
1
0
)
w
h
er
e
an
d
ar
e
th
e
Fo
u
r
ier
tr
a
n
s
f
o
r
m
o
f
an
d
,
r
esp
ec
tiv
el
y
.
2
.
2
.
Wa
v
elet
E
s
t
i
m
a
t
io
n
Ou
r
g
o
al
is
d
ata
est
i
m
a
tio
n
f
r
o
m
n
o
i
s
y
s
ig
n
al
s
.
L
et
‟
s
co
n
s
id
e
r
[
]
as th
e
n
o
is
y
s
i
g
n
al,
[
]
as th
e
s
ig
n
al
to
b
e
esti
m
ated
an
d
[
]
as t
h
e
Gau
s
s
ia
n
n
o
is
e
(
w
h
i
te
o
r
co
lo
r
ed
)
.
So
,
it c
an
b
e
s
h
o
w
n
th
at
[
4
]
:
[
]
[
]
[
]
(
1
1
)
Fo
r
s
ig
n
al
est
i
m
a
tio
n
u
s
i
n
g
w
a
v
elet
s
,
s
a
m
p
les
o
f
th
e
n
o
i
s
y
s
i
g
n
al
[
]
ar
e
d
ec
o
m
p
o
s
ed
in
th
e
d
is
cr
ete
w
a
v
elet
b
asi
s
w
h
ic
h
i
s
d
ef
i
n
ed
o
v
er
[
]
[
4
]
:
[
{
}
{
}
]
(
1
2
)
I
n
th
i
s
b
asis
,
t
h
e
esti
m
ate
o
f
[
]
is
w
r
i
tten
a
s
̃
∑
∑
(
〈
〉
)
∑
(
〈
〉
)
(
1
3
)
w
h
er
e
is
ca
lled
th
e
t
h
r
es
h
o
ld
f
u
n
ct
io
n
.
B
y
m
in
i
m
izin
g
t
h
e
m
ea
n
s
q
u
ar
e
er
r
o
r
,
th
e
b
est esti
m
ate
o
f
[
]
s
o
u
ld
b
e
ac
h
iev
ed
[
4
]
:
{
‖
̃
‖
}
(
1
4
)
Usi
n
g
a
h
ar
d
th
r
es
h
o
ld
f
o
r
d
e
n
o
is
i
n
g
,
ca
n
lead
to
s
m
aller
er
r
o
r
if
a
f
e
w
d
ec
o
m
p
o
s
itio
n
co
e
f
f
icien
ts
o
f
th
e
n
o
is
y
s
ig
n
al
ar
e
ab
o
v
e
t
h
e
th
r
es
h
o
ld
.
So
,
it
ca
n
b
e
s
aid
t
h
at
a
g
o
o
d
ap
p
r
o
x
i
m
atio
n
o
f
h
as
b
ee
n
ac
h
ie
v
ed
b
y
t
h
e
m
,
as
ca
n
b
e
i
m
p
lied
f
r
o
m
[
4
]
.
T
h
u
s
w
e
m
a
y
lo
o
s
el
y
s
a
y
th
at
o
u
r
o
p
ti
m
u
m
w
av
e
let
b
ase
s
h
o
u
ld
p
r
o
v
id
e
th
e
b
est
n
o
n
l
in
ea
r
esti
m
atio
n
o
f
th
e
s
ig
n
al
.
I
n
o
th
er
w
o
r
d
s
,
th
e
er
r
o
r
o
f
s
ig
n
al
esti
m
ati
o
n
w
it
h
lar
g
est
w
a
v
elet
e
x
p
an
s
io
n
co
ef
f
icie
n
t
s
s
h
o
u
ld
b
e
m
i
n
i
m
u
m
:
[
]
‖
‖
∑
|
〈
〉
|
(
1
5
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2088
-
8708
I
mp
r
o
ve
d
Den
o
is
in
g
Meth
o
d
f
o
r
Ultr
a
s
o
n
ic
E
ch
o
W
ith
Mo
th
er
W
a
ve
let
…
(
Mo
h
a
mma
d
Ho
s
s
ein
D
.
M
.
)
2745
A
cc
o
r
d
in
g
to
th
e
n
o
tatio
n
o
f
[
4
]
,
is
th
e
s
ig
n
al
r
ec
o
n
s
tr
u
cte
d
w
ith
lar
g
est
w
a
v
elet
co
ef
f
i
cien
t
s
an
d
ar
e
th
e
b
asis
v
ec
to
r
s
o
n
w
h
ic
h
th
e
s
i
g
n
a
l
p
r
o
j
ec
tio
n
s
ar
e
lar
g
er
.
I
t
ca
n
b
e
s
aid
th
at
t
h
e
er
r
o
r
its
elf
i
s
r
elate
d
to
th
e
d
ec
a
y
s
p
ee
d
o
f
t
h
e
ex
p
a
n
s
io
n
co
ef
f
icie
n
t
s
.
So
,
th
e
r
elatio
n
co
u
ld
b
e
q
u
a
lifie
d
b
y
th
e
n
o
r
m
o
f
th
e
ex
p
a
n
s
io
n
co
ef
f
icie
n
ts
o
f
t
h
e
s
i
g
n
a
l in
t
h
e
b
asis
[
4
]
,
[
20
]:
‖
‖
(
∑
|
〈
〉
|
)
(
1
6
)
w
h
er
e
is
th
e
b
asis
o
n
w
h
ic
h
t
h
e
s
i
g
n
al
i
s
b
eg
in
e
x
p
an
d
ed
.
A
cc
o
r
d
in
g
to
th
e
n
o
tatio
n
o
f
[
4
]
,
if
‖
‖
an
d
,
th
en
w
e
h
av
e
:
[
]
‖
‖
(
1
7
)
2
.
3
.
T
here
s
ho
ldi
ng
M
et
ho
ds
As
s
h
o
w
n
i
n
F
ig
u
r
e
1
,
t
h
e
r
e
ar
e
v
ar
io
u
s
m
eth
o
d
s
o
f
b
asic
th
r
e
s
h
o
ld
i
n
g
ap
p
r
o
ac
h
es,
w
h
ich
in
cl
u
d
e
[
2
1
]
:
a.
Har
d
th
r
esh
o
ld
i
n
g
,
w
h
er
e
all
th
e
co
ef
f
icie
n
ts
b
elo
w
a
p
r
ed
ef
i
n
ed
th
r
es
h
o
ld
v
al
u
e
ar
e
s
et
to
b
e
ze
r
o
.
b.
So
f
t t
h
r
esh
o
ld
i
n
g
,
w
h
er
e
in
ad
d
itio
n
th
e
r
e
m
ain
i
n
g
v
al
u
e
ar
e
lin
ea
r
l
y
r
ed
u
ce
d
i
n
v
al
u
e.
c.
No
n
li
n
ea
r
th
r
es
h
o
ld
in
g
,
t
h
e
o
r
ig
in
al
co
e
f
f
icien
ts
ar
e
m
ap
p
ed
to
a
n
e
w
s
et,
u
s
i
n
g
a
s
m
o
o
t
h
f
u
n
ctio
n
to
av
o
id
ab
r
u
p
t v
alu
e
ch
an
g
es.
T
h
r
esh
o
ld
s
ca
n
b
e
esti
m
ated
f
r
o
m
t
h
e
w
a
v
elet
co
ef
f
icie
n
t
s
o
f
ea
ch
s
ca
le
i.
I
n
th
e
f
o
llo
w
i
n
g
,
r
ep
r
esen
ts
t
h
e
n
u
m
b
er
o
f
co
ef
f
icie
n
ts
o
f
s
ca
le
i
,
a
n
d
̂
th
e
s
t
an
d
ar
d
d
ev
iatio
n
o
f
t
h
ese
w
a
v
elet
co
e
f
f
icien
ts
.
T
h
r
esh
o
ld
s
elec
tio
n
r
u
le
u
s
e
d
in
th
i
s
w
o
r
k
is
t
h
e
Mu
ltiM
A
D
t
h
r
es
h
o
ld
w
h
ic
h
is
g
iv
e
n
b
y
√
,
[
22
-
24
]
.
Fig
u
r
e
1
.
T
h
r
esh
o
ld
Ma
p
p
in
g
Fu
n
c
tio
n
s
[
21
]
3.
M
E
T
H
O
DO
L
O
G
Y
3
.
1
.
Crit
er
ia
f
o
r
Wa
v
elet
Desig
n
I
n
t
h
ese
s
ec
tio
n
,
t
h
r
ee
cr
iter
ia
f
o
r
d
esig
n
i
n
g
w
a
v
elet
s
w
i
ll b
e
in
tr
o
d
u
ce
d
.
As e
x
p
lain
ed
in
s
ec
tio
n
2
.
2
,
th
e
d
en
o
is
i
n
g
er
r
o
r
ca
n
b
e
r
e
d
u
ce
d
b
y
r
ed
u
cin
g
‖
‖
.
So
,
th
e
m
ai
n
g
o
al
is
f
i
n
d
in
g
a
w
a
v
el
et
b
asis
th
a
t
m
i
n
i
m
izes t
h
is
n
o
r
m
:
‖
‖
(
1
8
)
w
h
er
e
is
o
u
r
d
esire
d
co
s
t f
u
n
c
tio
n
.
T
w
o
cr
iter
ia
b
ased
o
n
th
i
s
co
s
t f
u
n
c
tio
n
ca
n
b
e
p
r
esen
te
d
as f
o
llo
w
[
25]
.
C
r
ite
r
ia
1
&
2
:
A
g
u
id
i
n
g
p
r
i
n
cip
le
p
r
o
p
o
s
ed
th
er
e
w
as
to
ai
m
f
o
r
m
a
x
i
m
izatio
n
o
f
t
h
e
v
ar
ian
ce
,
eith
er
m
a
x
i
m
izatio
n
o
f
t
h
e
v
ar
ian
ce
o
f
t
h
e
ab
s
o
lu
te
v
a
lu
e
s
o
f
th
e
w
a
v
elet
co
e
f
f
icie
n
ts
,
o
r
m
ax
i
m
izatio
n
o
f
t
h
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
J
E
C
E
Vo
l.
6
,
No
.
6
,
Dec
em
b
er
2
0
1
6
: 2
7
4
2
–
2
7
5
4
2746
v
ar
ian
ce
o
f
t
h
e
s
q
u
ar
ed
w
av
e
l
et
co
ef
f
icien
ts
i.e
.
o
f
t
h
e
e
n
er
g
y
d
is
tr
ib
u
t
io
n
o
v
er
th
e
w
av
e
let
co
n
tr
ib
u
t
io
n
s
at
th
e
v
ar
io
u
s
s
ca
le
s
[
25
]
.
T
h
e
th
eo
r
em
p
r
ese
n
ted
in
[
25
]
is
as f
o
llo
w
:
L
et
|
b
e
th
e
s
eq
u
en
ce
o
f
th
e
w
a
v
elet
co
e
f
f
i
cie
n
ts
at
all
th
e
lev
els
an
d
th
e
ap
p
r
o
x
im
a
tio
n
co
e
f
f
i
cie
n
ts
at
th
e
co
ar
s
est
lev
el
r
esu
lti
n
g
f
r
o
m
th
e
p
r
o
ce
s
s
in
g
o
f
a
s
ig
n
al
b
y
m
ea
n
s
o
f
a
n
o
r
th
o
g
o
n
a
l f
il
ter
b
an
k
.
T
h
en
[
25
]:
(
a)
Ma
x
i
m
izatio
n
o
f
th
e
v
ar
ia
n
ce
o
f
th
e
s
eq
u
e
n
ce
o
f
ab
s
o
lu
t
e
v
alu
es
|
|
is
eq
u
iv
ale
n
t
to
m
i
n
i
m
i
za
tio
n
o
f
th
e
„
L
1
-
n
o
r
m
‟
∑
|
|
.
(
b
)
Ma
x
i
m
izatio
n
o
f
th
e
v
ar
ia
n
ce
o
f
th
e
s
eq
u
en
ce
o
f
e
n
er
g
ie
s
|
|
is
eq
u
iv
ale
n
t
to
m
a
x
i
m
izati
o
n
o
f
th
e
„
L
4
-
n
o
r
m
‟
∑
|
|
.
T
h
e
p
r
o
o
f
o
f
th
is
t
h
eo
r
e
m
ca
n
b
e
f
o
u
n
d
i
n
[
25
].
Fo
r
d
esig
n
i
n
g
a
w
a
v
elet,
f
ir
s
t
a
s
p
ec
if
ic
test
s
i
g
n
al
(
p
u
r
e
a
n
d
n
o
t
n
o
is
y
o
n
e)
is
ch
o
s
e
n
.
T
h
en
,
s
o
m
e
r
an
d
o
m
p
ar
a
m
eter
s
ar
e
in
itia
ll
y
s
et
a
s
w
a
v
elet
p
ar
a
m
eter
s
an
d
in
co
n
ti
n
u
e,
t
h
e
w
a
v
e
let
p
ar
am
e
ter
s
ar
e
ch
an
g
ed
to
g
iv
e
th
e
m
o
s
t
o
p
ti
m
al
L
1
-
n
o
r
m
an
d
L
4
-
n
o
r
m
r
ep
r
esen
ta
tio
n
o
f
t
h
e
test
s
ig
n
al
.
C
h
o
o
s
i
n
g
p
ar
am
eter
s
o
f
th
e
o
p
ti
m
u
m
w
a
v
elet
h
as
b
ee
n
d
o
n
e
u
s
i
n
g
o
p
ti
m
izatio
n
to
o
ls
s
u
c
h
a
s
s
i
m
u
lated
a
n
n
ea
li
n
g
,
n
eu
r
al
n
et
w
o
r
k
,
an
d
g
en
e
tic
alg
o
r
ith
m
.
I
n
t
h
is
w
o
r
k
,
w
e
u
s
e
g
e
n
etic
al
g
o
r
ith
m
to
f
i
n
d
p
ar
am
e
ter
s
o
f
th
e
o
p
tim
u
m
w
a
v
elet.
So
,
t
w
o
w
a
v
elets
h
a
v
e
b
ee
n
d
esi
g
n
ed
u
s
i
n
g
:
o
n
e
b
y
m
i
n
i
m
izi
n
g
t
h
e
L
1
-
n
o
r
m
o
f
th
e
w
a
v
elet
tr
an
s
f
o
r
m
o
f
th
e
r
ef
e
r
en
ce
s
ig
n
al
(
w
h
ic
h
w
ill
b
e
ca
ll
“
No
r
m
1
”
in
t
h
is
p
ap
er
)
,
a
n
d
an
o
th
er
o
n
e
b
y
m
ax
i
m
izin
g
it
s
L
4
-
n
o
r
m
(
w
h
i
ch
w
ill b
e
ca
ll
“
No
r
m
4
”
in
t
h
i
s
p
ap
er
)
.
C
r
ite
r
ia
3
:
Her
e
w
e
p
r
o
p
o
s
ed
a
n
e
w
m
et
h
o
d
to
d
esig
n
a
n
o
p
ti
m
u
m
w
av
ele
t.
I
n
t
h
i
s
m
eth
o
d
w
e
s
e
t
s
o
m
e
r
an
d
o
m
p
ar
a
m
eter
s
as
w
a
v
elet
p
ar
a
m
eter
s
(
)
,
an
d
th
is
w
a
v
elet
i
s
ap
p
lied
to
a
r
ef
er
en
ce
s
ig
n
al.
T
h
e
co
ef
f
icie
n
t
s
o
f
t
h
e
te
s
t
s
ig
n
al
w
il
l
b
e
u
n
d
er
th
r
es
h
o
ld
in
g
m
e
th
o
d
an
d
t
h
e
r
e
m
ai
n
ed
co
ef
f
ici
en
ts
w
ill
b
e
u
s
ed
to
r
ec
o
n
s
tr
u
ct
t
h
e
s
ig
n
al.
I
n
th
e
n
ex
t
s
tep
,
r
ec
o
n
s
tr
u
cted
s
i
g
n
a
l
is
co
m
p
ar
ed
to
th
e
o
r
ig
i
n
al
s
ig
n
al
a
n
d
th
e
er
r
o
r
s
ig
n
al
w
il
l
b
e
ex
tr
ac
ted
.
I
n
th
e
n
ex
t
s
tep
,
w
e
c
h
an
g
e
t
h
e
w
a
v
elet
p
ar
a
m
eter
s
co
n
s
id
er
in
g
t
h
at
th
e
er
r
o
r
s
ig
n
al
s
h
o
u
ld
b
e
m
i
n
i
m
ized
.
Her
e
w
e
u
s
ed
g
e
n
etic
alg
o
r
it
h
m
to
f
i
n
d
th
e
o
p
ti
m
u
m
w
a
v
elet
p
ar
a
m
eter
s
t
h
at
m
in
i
m
ize
th
e
er
r
o
r
s
ig
n
al.
T
h
is
m
et
h
o
d
is
ca
lled
“
SE
R
”.
3
.
2
.
G
enet
ic
Alg
o
rit
h
m
I
n
th
is
w
o
r
k
,
w
e
u
s
e
g
en
et
ic
alg
o
r
ith
m
s
f
o
r
s
elec
tin
g
th
e
o
p
ti
m
ized
co
e
f
f
icien
ts
.
So
m
e
s
te
p
s
s
h
o
u
l
d
b
e
co
n
s
id
er
ed
,
w
h
e
n
u
s
in
g
g
e
n
etic
al
g
o
r
ith
m
.
a.
I
n
itial seed
g
r
o
u
p
: I
n
th
i
s
w
o
r
k
w
e
ch
o
o
s
e
t
h
e
s
ee
d
g
r
o
u
p
o
f
s
ize
P
=5
0
.
b.
Seed
s
elec
tio
n
:
I
n
t
h
i
s
w
o
r
k
,
w
e
c
h
o
o
s
e
th
e
s
ee
d
s
elec
tio
n
e
q
u
al
to
2
5
.
c.
C
r
o
s
s
o
v
er
an
d
M
u
tatio
n
:
I
n
t
h
is
w
o
r
k
,
w
e
u
s
ed
t
h
e
cr
o
s
s
o
v
er
an
d
m
u
tatio
n
a
s
[
26
].
d.
Sto
p
co
n
d
itio
n
.
Oth
er
p
ar
a
m
et
er
s
to
b
e
co
n
s
id
er
ed
m
a
y
b
e
f
o
u
n
d
i
n
r
elate
d
liter
atu
r
e
[
26
].
4.
SI
M
UL
AT
I
O
N
R
E
S
UL
T
S
AND
DICU
SS
I
O
N
I
n
th
i
s
s
ec
tio
n
,
s
ta
n
d
ar
d
test
d
ata
an
d
s
i
m
u
lated
u
ltra
s
o
u
n
d
s
i
g
n
a
l
ar
e
u
s
ed
to
ev
alu
ate
t
h
e
p
er
f
o
r
m
a
n
ce
o
f
t
h
e
p
r
esen
ted
alg
o
r
ith
m
s
.
First,
w
e
p
r
esen
t
t
h
e
r
es
u
lt o
f
p
er
f
o
r
m
i
n
g
t
h
e
al
g
o
r
ith
m
o
n
s
ta
n
d
ar
d
test
d
ata,
an
d
th
en
,
s
i
m
u
late
d
u
ltra
s
o
u
n
d
s
ig
n
al
is
u
s
ed
t
o
co
m
p
ar
e
th
e
alg
o
r
it
h
m
s
p
r
esen
ted
p
r
ev
io
u
s
l
y
:
“
SE
R
”
,
“
No
r
m
1
”
,
“
No
r
m
4
”
an
d
“
DB
3
”
.
T
o
m
a
k
e
it
a
b
e
tter
co
m
p
ar
is
o
n
,
w
e
h
av
e
u
s
e
d
s
tan
d
ar
d
w
a
v
elet
Dau
b
ec
h
ie
s
3
(
DB
3
)
w
h
ic
h
s
h
o
w
ed
th
e
b
est
d
en
o
i
s
i
n
g
p
er
f
o
r
m
a
n
ce
a
m
o
n
g
all
t
h
e
o
th
er
s
tan
d
ar
d
w
a
v
ele
t
f
a
m
ilies
.
I
n
t
h
i
s
w
o
r
k
,
s
i
g
n
al
t
o
er
r
o
r
r
atio
(
SER)
is
u
s
ed
to
co
m
p
ar
e
th
e
p
er
f
o
r
m
an
ce
o
f
d
if
f
er
en
t
al
g
o
r
ith
m
s
.
A
ll
s
i
m
u
latio
n
s
ar
e
p
er
f
o
r
m
ed
in
s
o
f
t
w
ar
e
u
s
in
g
t
h
e
W
a
ve
la
b
to
o
lb
o
x
[
27
]
.
4
.
1
.
Sta
nd
a
rd
S
ig
na
ls
Her
e,
w
e
h
av
e
u
s
ed
s
tan
d
ar
d
s
ig
n
al
s
s
u
ch
a
s
Do
p
p
ler
,
B
lo
c
k
s
,
Hea
v
is
in
e
a
n
d
B
u
m
p
s
f
o
r
d
esig
n
in
g
th
e
w
a
v
elet
s
a
n
d
an
al
y
zi
n
g
t
h
eir
p
er
f
o
r
m
an
ce
.
T
h
ese
s
i
g
n
a
l
s
h
a
v
e
1
0
2
4
p
o
in
ts
a
s
s
h
o
w
n
in
F
ig
u
r
e
2
.
T
h
e
y
w
er
e
f
ir
s
t
p
r
ese
n
ted
b
y
Do
n
o
h
o
an
d
J
o
h
n
s
to
n
e
f
o
r
r
esear
c
h
i
n
w
av
elet
-
b
ased
d
e
n
o
is
i
n
g
m
et
h
o
d
s
[
23
-
24
]
.
T
h
ese
s
i
g
n
al
s
h
a
v
e
s
p
ec
ial
f
r
e
q
u
en
c
y
f
ea
t
u
r
es
a
n
d
h
av
e
m
a
n
y
s
i
m
ilar
ities
w
it
h
e
n
v
ir
o
n
m
e
n
tal
s
i
g
n
al.
T
o
h
av
e
co
m
p
ar
ab
le
r
es
u
lts
,
a
ll
t
h
e
s
i
g
n
als
ar
e
n
o
r
m
alize
d
b
e
f
o
r
e
s
i
m
u
latio
n
s
i
n
o
r
d
er
to
h
av
e
t
h
e
s
a
m
e
p
o
w
er
.
T
h
e
s
i
m
u
lat
io
n
is
p
er
f
o
r
m
ed
b
y
s
te
p
s
as f
o
llo
w
.
W
e
h
av
e
u
s
ed
a
f
i
x
ed
len
g
t
h
,
L=6
,
f
o
r
t
h
e
w
a
v
elet
f
il
ter
s
,
to
b
e
ab
le
to
co
m
p
ar
e
d
if
f
er
en
t a
lg
o
r
ith
m
s
.
Nex
t,
w
h
ite
Ga
u
s
s
ian
n
o
is
e
i
s
ad
d
ed
to
th
e
s
ig
n
a
ls
,
an
d
t
h
e
n
n
o
i
s
e
r
ed
u
ctio
n
p
r
o
ce
d
u
r
e
is
p
er
f
o
r
m
ed
o
n
t
h
e
co
r
r
u
p
ted
s
ig
n
als
u
s
i
n
g
s
tan
d
a
r
d
w
a
v
elet
(
h
er
e
Da
u
b
ec
ies
3
)
an
d
th
e
w
a
v
elet
s
d
esig
n
ed
b
ased
o
n
cr
iter
ia
f
r
o
m
th
e
p
r
ev
io
u
s
s
ec
tio
n
.
T
h
is
p
r
o
ce
s
s
i
s
p
er
f
o
r
m
ed
5
0
0
ti
m
es
o
n
n
o
is
e
s
w
it
h
d
i
f
f
er
e
n
t
p
o
w
e
r
lev
els.
T
h
e
m
ea
n
r
esu
lt i
s
co
n
s
id
er
ed
as th
e
f
i
n
a
l r
esu
lt.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2088
-
8708
I
mp
r
o
ve
d
Den
o
is
in
g
Meth
o
d
f
o
r
Ultr
a
s
o
n
ic
E
ch
o
W
ith
Mo
th
er
W
a
ve
let
…
(
Mo
h
a
mma
d
Ho
s
s
ein
D
.
M
.
)
2747
(
b
)
(
a)
(
d
)
(
c
)
Fig
u
r
e
2
.
Stan
d
ar
d
Sig
n
als U
s
ed
f
o
r
Desig
n
in
g
t
h
e
W
av
elet
s
an
d
An
al
y
z
in
g
t
h
eir
Pe
r
f
o
r
m
a
n
ce
(
a)
B
lo
ck
s
,
(
b
)
B
u
m
p
s
,
(
c)
Hea
v
is
i
n
e,
(
d
)
Do
p
p
ler
[
6
]
T
ab
le
1
.
Fre
e
P
ar
am
eter
s
an
d
th
e
V
al
u
e
o
f
t
h
e
C
o
s
t F
u
n
c
tio
n
f
o
r
Var
io
u
s
Si
g
n
al
s
.
T
h
e
Fil
t
er
L
en
g
t
h
is
L
=6
S
i
g
n
a
l
t
y
p
e
A
l
g
o
r
i
t
h
ms
sca
l
e
B
l
o
c
k
s
N
o
r
m1
_
a
l
g
o
1
.
8
6
7
2
-
2
.
8
4
3
0
7
.
3
8
1
8
9
.
9
2
0
9
8
,
9
N
o
r
m4
_
a
l
g
o
0
.
4
3
9
5
2
2
.
8
9
2
2
4
1
.
0
2
1
5
1
.
5
1
2
3
8
,
9
S
ER
_
a
l
g
o
0
.
1
7
3
4
-
1
.
5
8
1
2
1
B
u
m
p
s
N
o
r
m1
_
a
l
g
o
0
.
4
0
4
1
3
.
0
1
1
1
5
.
5
5
5
.
7
2
3
1
8
,
9
N
o
r
m4
_
a
l
g
o
3
.
4
9
1
7
3
.
0
0
3
9
0
.
3
7
7
6
0
.
4
8
3
1
8
,
9
S
ER
_
a
l
g
o
1
.
2
9
4
9
7
-
1
.
5
0
2
6
H
e
a
v
i
si
n
e
N
o
r
m1
_
a
l
g
o
-
2
.
3
0
2
9
1
.
8
4
0
4
3
.
9
7
8
3
4
.
3
4
2
4
6
,
7
,
8
,
9
N
o
r
m4
_
a
l
g
o
-
1
.
7
4
4
4
1
.
4
9
5
6
0
.
4
8
8
5
0
.
8
8
9
3
6
,
7
,
8
,
9
S
ER
_
a
l
g
o
0
.
4
0
2
5
3
.
0
1
4
3
D
o
p
p
l
e
r
N
o
r
m1
_
a
l
g
o
3
.
5
7
9
7
5
2
.
9
8
2
1
1
3
6
.
7
3
4
1
4
2
.
1
3
7
5
6
,
7
,
8
,
9
N
o
r
m4
_
a
l
g
o
0
.
8
0
3
6
5
1
.
7
9
2
0
3
.
1
6
1
4
4
.
2
3
1
5
6
,
7
,
8
,
9
S
ER
_
a
l
g
o
3
.
5
6
4
9
2
.
9
7
6
8
I
n
o
r
d
er
to
g
et
to
th
e
r
eq
u
ir
ed
s
ig
n
al,
w
e
h
a
v
e
m
i
n
i
m
ized
th
e
co
n
s
tr
ictio
n
f
r
o
m
p
r
ev
io
u
s
s
ec
tio
n
co
n
s
id
er
in
g
t
h
at
th
e
d
esi
g
n
ed
w
a
v
elet
a
n
d
s
ca
li
n
g
f
u
n
ctio
n
s
s
h
o
u
ld
m
ee
t
t
h
e
co
n
d
itio
n
s
g
iv
en
i
n
s
ec
tio
n
2
.
1
.
T
h
u
s
,
th
e
p
ar
a
m
eter
izatio
n
m
et
h
o
d
f
o
r
th
e
d
ec
o
m
p
o
s
it
i
o
n
f
il
ter
s
[
1
9
]
(
an
d
)
h
av
e
b
ee
n
u
s
ed
.
T
h
e
ad
v
an
ta
g
e
o
f
u
s
i
n
g
t
h
is
m
et
h
o
d
is
th
at
t
h
e
d
esi
g
n
o
f
w
a
v
elet
f
il
ter
w
it
h
le
n
g
th
lead
s
to
s
el
ec
tin
g
p
ar
am
eter
s
f
o
r
f
ilter
an
d
th
e
r
eq
u
ir
ed
co
n
d
itio
n
s
o
f
s
ec
t
io
n
2
.
1
w
ill
s
u
r
el
y
b
e
m
et.
T
h
e
n
,
u
s
i
n
g
eq
u
atio
n
(
9
)
,
th
e
f
ilter
ca
n
b
e
d
esig
n
ed
.
So
,
th
e
co
s
t f
u
n
ctio
n
ca
n
b
e
r
e
w
r
i
tten
as (
1
9
)
:
‖
‖
(
1
9
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
J
E
C
E
Vo
l.
6
,
No
.
6
,
Dec
em
b
er
2
0
1
6
: 2
7
4
2
–
2
7
5
4
2748
w
h
er
e
ar
e
th
e
p
a
r
am
eter
s
to
b
e
s
elec
ted
.
T
h
is
co
s
t
f
u
n
ct
io
n
s
h
o
u
ld
b
e
m
i
n
i
m
ize
f
o
r
th
e
g
iv
e
n
s
ig
n
al
s
in
th
o
s
e
s
ca
les
u
s
ed
f
o
r
d
en
o
is
in
g
o
p
er
atio
n
.
I
t
s
h
o
u
ld
b
e
m
en
t
io
n
ed
th
at
th
i
s
alg
o
r
ith
m
ca
n
b
e
u
s
ed
n
o
t
o
n
l
y
f
o
r
d
en
o
is
in
g
p
r
o
p
o
s
e,
b
u
t
al
s
o
f
o
r
p
r
o
v
id
in
g
b
etter
f
ea
t
u
r
e
ex
tr
ac
tio
n
.
D
u
e
to
p
r
o
v
id
in
g
t
h
e
i
m
p
r
o
v
ed
ap
p
r
o
x
im
a
tio
n
o
f
th
e
s
i
g
n
al,
t
h
is
m
eth
o
d
ca
n
w
ell
e
x
tr
ac
t
f
ea
tu
r
es
o
f
th
e
d
esire
d
s
i
g
n
al
b
y
p
r
o
v
id
in
g
a
f
e
w
lar
g
e
w
a
v
elet
co
ef
f
icie
n
t
s
as
t
h
e
s
i
g
n
al
‟
s
f
ea
t
u
r
es.
T
h
is
f
ea
t
u
r
es
ar
e
v
er
y
i
m
p
o
r
tan
t
b
ec
a
u
s
e
o
f
t
h
eir
r
o
les
i
n
f
au
lt
d
iag
n
o
s
is
,
d
etec
tio
n
a
n
d
co
m
p
r
ess
io
n
p
r
o
p
o
s
es.
Mo
r
e
d
etails
ab
o
u
t
p
ar
am
eter
iza
tio
n
ca
n
b
e
f
o
u
n
d
in
[
1
9
]
.
Sin
ce
w
e
h
a
v
e
u
s
ed
t
h
e
f
i
x
ed
len
g
t
h
f
i
lter
(
L
=6
)
,
w
e
h
av
e
o
n
l
y
t
w
o
f
r
ee
p
ar
a
m
eter
(
)
.
T
h
ese
t
w
o
p
ar
a
m
eter
s
,
alo
n
g
w
it
h
th
e
r
elate
d
co
s
t
f
u
n
c
tio
n
(
)
is
illu
s
tr
ated
in
th
e
T
ab
le
1
.
R
esu
lt
f
r
o
m
ap
p
l
y
i
n
g
d
if
f
er
e
n
t a
l
g
o
r
ith
m
s
o
n
s
tan
d
a
r
d
s
ig
n
al
s
ar
e
s
h
o
w
n
in
Fig
u
r
e
s
3
-
6.
E
x
a
m
in
i
n
g
Fi
g
u
r
e
3
,
th
e
“
SE
R
”
esti
m
ated
s
i
g
n
al
h
a
s
th
e
b
etter
s
h
ap
e
th
a
n
o
th
er
s
.
Fo
r
th
is
„
B
lo
ck
‟
s
ig
n
al,
th
e
“
No
r
m
1
”
m
e
th
o
d
g
en
er
all
y
p
er
f
o
r
m
s
w
ell
i
n
th
e
s
h
ar
p
co
r
n
er
s
b
u
t
n
o
t
in
th
e
f
lat
r
eg
io
n
s
(
Fig
u
r
e
3
(
d
)
)
.
A
s
s
h
o
w
n
i
n
Fi
g
u
r
e
3
(
c)
,
f
o
r
“
SE
R
”
m
et
h
o
d
,
th
e
esti
m
ated
s
ig
n
al
k
ee
p
th
e
f
lat
r
eg
io
n
s
m
o
o
t
h
er
th
an
o
th
er
s
a
n
d
th
e
co
r
n
er
s
s
h
ar
p
en
o
u
g
h
(
b
u
t
n
o
t
as
w
ell
as
“No
r
m
1
”)
.
So
,
th
e
“
No
r
m
1
”
d
o
es
a
b
etter
jo
b
th
an
o
t
h
er
m
et
h
o
d
s
at
th
e
co
r
n
er
s
,
an
d
th
e
“
SER”
m
et
h
o
d
d
o
es a
b
etter
jo
b
at
th
e
f
lat
r
eg
io
n
s
.
(
b
)
(
a)
(
d
)
(
c)
(
f
)
(
e)
Fig
u
r
e
3
.
R
esu
lts
o
f
A
p
p
l
y
in
g
Dif
f
er
en
t W
a
v
el
ets to
B
lo
ck
s
s
ig
n
al:
(
a)
P
u
r
e
B
lo
ck
s
Si
g
n
al,
(
b
)
No
is
y
Si
g
n
al
w
it
h
No
is
e
Var
ia
n
ce
,
(
c)
D
en
o
is
ed
Sig
n
al
b
y
SE
R
A
lg
o
r
it
h
m
,
(
d
)
D
en
o
is
ed
Si
g
n
al
b
y
No
r
m
1
A
l
g
o
r
ith
m
,
(
e)
D
en
o
is
ed
Si
g
n
al
b
y
DB
3
s
ta
n
d
ar
d
W
av
elets,
(
f
)
D
en
o
is
ed
Si
g
n
al
b
y
No
r
m
4
A
l
g
o
r
ith
m
I
n
Fi
g
u
r
e
4
,
f
o
r
th
e
„
B
u
m
p
s
‟
s
ig
n
al,
t
h
e
No
r
m
1
esti
m
ated
s
ig
n
al
ap
p
ea
r
to
b
e
in
b
etter
g
r
ap
h
ica
l
s
h
ap
e.
I
t
u
n
d
er
esti
m
ates
all
th
e
cu
p
s
i
n
Fi
g
u
r
e
4
(
d
)
w
h
e
n
co
m
p
ar
ed
to
th
e
o
r
ig
i
n
al
s
i
g
n
al
i
n
F
ig
u
r
e
4
(
a)
.
T
h
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2088
-
8708
I
mp
r
o
ve
d
Den
o
is
in
g
Meth
o
d
f
o
r
Ultr
a
s
o
n
ic
E
ch
o
W
ith
Mo
th
er
W
a
ve
let
…
(
Mo
h
a
mma
d
Ho
s
s
ein
D
.
M
.
)
2749
“
SE
R
”
esti
m
ated
s
ig
n
al
i
s
als
o
ac
ce
p
tab
le
an
d
cle
an
er
.
A
s
s
h
o
w
n
i
n
Fi
g
u
r
e
4
,
th
e
o
r
ig
i
n
al
s
ig
n
al
a
m
p
lit
u
d
e
is
esti
m
ated
b
etter
in
“
SE
R
”
m
e
th
o
d
r
ath
er
th
a
n
“
No
r
m
1
”.
T
h
e
“
No
r
m
4
”
a
n
d
“DB
3
”
m
eth
o
d
s
w
er
e
u
n
ab
le
t
o
ex
tr
ac
t
all
th
e
c
u
p
s
(
F
ig
u
r
es
4
(
f
)
-
4
(
e)
)
.
A
tr
e
n
d
e
m
er
g
e
s
f
r
o
m
ev
a
lu
at
in
g
t
h
ese
t
w
o
s
tan
d
ar
d
s
i
g
n
als:
(
1
)
“
SER”
m
et
h
o
d
s
ee
m
s
to
p
er
f
o
r
m
b
etter
in
s
m
o
o
t
h
r
eg
io
n
s
an
d
al
s
o
h
as
an
ac
ce
p
t
ab
le
j
o
b
s
at
s
h
ar
p
f
ea
t
u
r
es.
(
2
)
“
No
r
m
1
”
m
et
h
o
d
m
a
y
m
i
s
tak
e
n
o
is
e
f
o
r
tr
u
e
s
ig
n
a
l
in
f
la
t
an
d
s
m
o
o
th
r
e
g
io
n
s
,
b
u
t
th
e
s
h
ar
p
f
ea
t
u
r
es a
r
e
r
etain
ed
.
(
c)
(
b
)
(
a)
(
d
)
(
e)
(
f
)
Fig
u
r
e
4
.
R
esu
lts
o
f
A
p
p
l
y
in
g
Dif
f
er
en
t W
a
v
elet
s
to
B
u
m
p
s
s
ig
n
al
: (
a)
P
u
r
e
B
u
m
p
s
S
ig
n
al,
(
b
)
N
o
is
y
Si
g
n
al
w
it
h
No
is
e
Var
i
a
n
ce
,
(
c)
Den
o
is
ed
Sig
n
al
b
y
SE
R
A
lg
o
r
i
t
h
m
,
(
d
)
D
en
o
is
ed
Sig
n
al
b
y
No
r
m
1
A
l
g
o
r
ith
m
,
(
e)
D
en
o
is
ed
Si
g
n
al
b
y
DB
3
s
ta
n
d
ar
d
W
av
elets,
(
f
)
D
en
o
is
ed
Si
g
n
al
b
y
No
r
m
4
Al
g
o
r
ith
m
I
n
Fi
g
u
r
e
5
,
f
o
r
t
h
e
„
Hea
v
is
i
n
e‟
t
h
e
“
SE
R
”
e
s
ti
m
ated
s
i
g
n
al
lo
o
k
s
c
lean
er
a
n
d
s
m
o
o
th
e
r
to
g
eth
er
w
it
h
b
etter
esti
m
atio
n
o
f
t
h
e
s
h
ar
p
f
ea
tu
r
e.
T
h
e
“
No
r
m
1
”
al
s
o
h
as
a
g
o
o
d
jo
b
b
u
t
n
o
t
as
clea
n
as
“
SER”.
T
h
e
“
No
r
m
4
”
w
as
ab
le
to
e
x
tr
ac
t
th
e
s
h
ar
p
co
r
n
er
b
u
t
t
h
e
s
m
o
o
th
r
e
g
io
n
s
ar
e
n
o
t
clea
n
e
n
o
u
g
h
.
T
h
e
“
DB
3
”
h
a
s
p
r
o
b
lem
f
o
r
ex
tr
ac
ti
n
g
th
e
s
h
a
r
p
f
ea
tu
r
e,
s
o
th
e
est
i
m
a
ted
s
ig
n
al
is
n
o
t a
cc
ep
tab
le.
T
h
e
„
Do
p
p
ler
‟
s
ig
n
al
m
a
y
b
e
th
e
m
o
s
t
d
i
f
f
icu
l
t
o
n
e
to
d
en
o
is
e
o
u
t
o
f
all
th
e
s
ta
n
d
a
r
d
s
ig
n
als,
esp
ec
iall
y
i
n
th
e
h
i
g
h
l
y
o
s
cil
l
atin
g
r
eg
io
n
.
I
n
s
u
ch
r
eg
io
n
s
,
th
e
w
a
v
elet
co
ef
f
icie
n
ts
ar
e
n
o
t
s
p
ar
s
e
an
d
th
eir
a
m
p
lit
u
d
es
ar
e
s
i
m
ilar
to
th
o
s
e
o
f
n
o
is
e
at
t
h
e
f
in
e
r
eso
l
u
tio
n
lev
e
l.
T
h
u
s
,
th
is
i
s
m
o
r
e
d
if
f
ic
u
lt
to
ex
tr
ac
t
t
h
e
o
r
ig
in
al
„
Do
p
p
ler
‟
f
r
o
m
t
h
e
n
o
is
y
o
n
e.
As
s
h
o
w
n
i
n
F
ig
u
r
e
6
,
th
e
“
SER
”
m
et
h
o
d
o
u
tp
er
f
o
r
m
s
t
h
e
o
th
er
t
h
r
ee
m
et
h
o
d
s
i
n
t
h
e
s
en
s
e
th
at
t
h
e
“
SE
R
”
e
s
ti
m
ated
s
ig
n
al
s
lo
o
k
m
o
r
e
li
k
e
th
e
clea
n
s
ig
n
a
l
s
t
h
an
o
th
er
e
s
ti
m
ate
s
w
it
h
ac
ce
p
tab
le
ac
cu
r
ac
y
.
I
t
is
s
tan
d
ar
d
to
ev
alu
a
te
th
e
m
ea
n
ab
s
o
l
u
te
s
q
u
ar
e
(
MSE
)
o
r
s
ig
n
al
-
to
-
er
r
o
r
r
atio
(
SER)
o
f
ea
ch
alg
o
r
ith
m
f
o
r
m
o
r
e
ac
cu
r
ate
co
m
p
ar
is
o
n
.
T
h
e
SER is
d
ef
in
e
d
as e
q
u
atio
n
(
2
0
)
.
∑
∑
[
̂
]
(
2
0
)
T
h
e
SER
co
m
p
ar
is
o
n
o
f
ap
p
ly
in
g
p
r
ese
n
ted
m
e
th
o
d
s
to
th
e
s
tan
d
ar
d
s
i
g
n
a
ls
ar
e
ill
u
s
tr
ated
i
n
T
ab
le
2
.
E
ac
h
co
lu
m
n
is
r
elat
ed
to
th
e
s
p
ec
if
ic
n
o
is
e
v
ar
ia
n
ce
.
Hig
h
er
n
o
i
s
e
v
ar
ian
ce
lea
d
s
to
a
lo
w
er
SER
.
D
if
f
er
en
t
r
an
g
e
o
f
n
o
i
s
e
v
ar
ian
ce
is
u
s
ed
to
co
m
p
ar
e
t
h
e
p
r
esen
ted
m
eth
o
d
m
o
r
e
ac
c
u
r
ate.
T
h
ese
SE
R
s
p
r
esen
ted
in
T
ab
le
2
co
n
f
ir
m
s
th
e
f
i
n
d
in
g
s
m
en
tio
n
ed
b
ef
o
r
e.
T
h
e
b
est
m
e
th
o
d
i
n
ter
m
s
o
f
SE
R
i
s
t
h
e
“
SER
”
m
et
h
o
d
.
T
w
o
r
o
w
s
ar
e
ass
i
g
n
ed
to
th
e
“
No
r
m
1
”
a
n
d
“
No
r
m
4
”
m
et
h
o
d
s
b
ec
au
s
e
ac
co
r
d
in
g
to
T
ab
le
1
,
t
w
o
d
if
f
er
e
n
t set o
f
s
ca
les
w
er
e
s
e
l
ec
ted
to
d
esig
n
d
esire
d
w
a
v
ele
ts
f
o
r
ea
ch
m
et
h
o
d
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
J
E
C
E
Vo
l.
6
,
No
.
6
,
Dec
em
b
er
2
0
1
6
: 2
7
4
2
–
2
7
5
4
2750
(
c)
(
b
)
(
a)
(
f
)
(
e)
(
d
)
Fig
u
r
e
5
.
R
esu
lts
o
f
A
p
p
l
y
in
g
Dif
f
er
e
n
t W
a
v
ele
ts
to
Hea
v
is
i
n
e
Si
g
n
al
: (
a)
P
u
r
e
Hea
v
is
i
n
e
Sig
n
al,
(
b
)
No
is
y
Si
g
n
al
w
it
h
No
is
e
Var
ia
n
ce
,
(
c)
D
en
o
is
ed
Sig
n
al
b
y
SE
R
Alg
o
r
ith
m
,
(
d
)
D
en
o
is
ed
Si
g
n
al
b
y
No
r
m
1
A
l
g
o
r
ith
m
,
(
e)
D
en
o
is
ed
Si
g
n
al
b
y
DB
3
s
ta
n
d
ar
d
Wa
v
elets,
(
f
)
D
en
o
is
ed
Si
g
n
al
b
y
No
r
m
4
A
l
g
o
r
ith
m
T
ab
le
2
.
SER (
in
d
B
)
C
o
m
p
ar
is
o
n
o
f
Di
f
f
er
en
t M
et
h
o
d
s
A
p
p
li
ed
to
Stan
d
ar
d
Sig
n
a
ls
v
s
.
C
h
an
g
es to
T
h
e
No
is
e
Var
ian
ce
S
i
g
n
a
l
A
l
g
o
r
i
t
h
m
t
y
p
e
B
l
o
c
k
s
D
B
3
1
2
.
3
1
1
1
.
3
6
9
.
6
3
8
.
9
4
N
o
r
m1
_
a
l
g
o
2
1
.
9
1
6
.
7
9
1
2
.
2
2
1
0
.
7
5
N
o
r
m4
_
a
l
g
o
1
4
.
3
7
1
3
.
2
2
1
1
.
9
7
1
1
.
0
1
S
ER
_
a
l
g
o
1
9
.
7
1
1
6
.
9
3
1
2
.
7
1
0
.
6
2
B
u
m
p
s
D
B
3
1
2
.
4
1
1
1
.
6
3
9
.
6
8
8
.
2
0
N
o
r
m1
_
a
l
g
o
1
4
.
9
5
1
3
.
3
3
1
0
.
6
4
9
.
2
6
N
o
r
m4
_
a
l
g
o
1
4
.
4
2
1
2
.
9
2
1
0
.
5
8
9
.
1
1
S
ER
_
a
l
g
o
1
4
.
7
2
1
3
.
3
3
1
1
.
2
1
9
.
7
7
H
e
a
v
i
si
n
e
D
B
3
2
8
.
1
2
2
4
.
5
2
1
9
.
8
1
1
6
.
5
7
N
o
r
m1
_
a
l
g
o
2
7
.
7
2
2
4
.
6
2
2
0
.
1
1
1
6
.
6
5
N
o
r
m4
_
a
l
g
o
2
6
.
1
1
2
4
.
1
1
1
9
.
8
3
1
6
.
4
2
S
ER
_
a
l
g
o
2
9
.
2
3
2
5
.
5
4
2
0
.
3
3
1
6
.
9
2
D
o
p
p
l
e
r
D
B
3
2
1
.
2
7
1
7
.
4
5
1
2
.
8
7
1
0
.
5
3
N
o
r
m1
_
a
l
g
o
2
1
.
7
8
1
7
.
9
4
1
3
.
6
5
1
1
.
3
5
N
o
r
m4
_
a
l
g
o
1
9
.
8
3
1
6
.
8
5
1
3
.
0
6
1
1
.
2
3
S
ER
_
a
l
g
o
2
2
.
1
3
1
8
.
3
1
1
3
.
7
8
1
1
.
4
5
I
t
s
h
o
u
ld
b
e
n
o
ticed
th
at
n
o
i
s
e
v
ar
ian
ce
i
s
cr
u
cial
to
t
h
e
p
er
f
o
r
m
a
n
ce
o
f
d
en
o
i
s
i
n
g
m
e
th
o
d
s
.
As
s
h
o
w
n
i
n
T
ab
le
2
,
th
e
o
v
er
all
p
er
f
o
r
m
an
ce
o
f
“
SE
R
”
m
et
h
o
d
is
b
etter
t
h
an
o
th
er
s
b
u
t
t
h
e
“
No
r
m
1
”
m
et
h
o
d
o
u
tp
er
f
o
r
m
s
t
h
e
“
SER
”
m
eth
o
d
at
B
l
o
ck
s
an
d
B
u
m
p
s
s
ig
n
al
s
f
o
r
n
o
is
e
v
ar
ia
n
ce
les
s
th
a
n
0
.
2
.
A
ll
o
f
th
e
SER
s
o
f
T
ab
le
2
ar
e
p
r
esen
ted
af
ter
1
0
0
ti
m
es
r
ep
ea
t
a
n
d
ta
k
i
n
g
a
v
er
ag
e
o
f
r
es
u
lt
s
ac
h
ie
v
ed
,
d
u
e
to
r
an
d
o
m
n
at
u
r
e
o
f
th
e
n
o
is
e
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2088
-
8708
I
mp
r
o
ve
d
Den
o
is
in
g
Meth
o
d
f
o
r
Ultr
a
s
o
n
ic
E
ch
o
W
ith
Mo
th
er
W
a
ve
let
…
(
Mo
h
a
mma
d
Ho
s
s
ein
D
.
M
.
)
2751
(
c
)
(
b
)
(
a
)
(
f
)
(
e)
(
d
)
Fig
u
r
e
6
.
R
esu
lts
o
f
A
p
p
l
y
in
g
Dif
f
e
r
en
t W
a
v
elets
to
Do
p
p
ler
Si
g
n
al:
(
a)
P
u
r
e
Do
p
p
ler
Si
g
n
al,
(
b
)
N
o
is
y
Si
g
n
al
w
it
h
No
is
e
Var
ia
n
ce
,
(
c)
D
en
o
is
ed
Si
g
n
al
b
y
SE
R
A
lg
o
r
ith
m
,
(
d
)
D
en
o
is
ed
Si
g
n
al
b
y
No
r
m
1
A
l
g
o
r
ith
m
,
(
e)
D
en
o
is
ed
Si
g
n
al
b
y
DB
3
s
ta
n
d
ar
d
Wa
v
elets,
(
f
)
D
en
o
is
ed
Si
g
n
al
b
y
N
o
r
m
4
Al
g
o
r
ith
m
4
.
2
.
Si
m
ula
t
ed
Ult
ra
s
o
u
nd
Sig
na
l
I
n
th
i
s
s
ec
tio
n
,
a
m
at
h
e
m
atica
l
m
o
d
el
is
u
s
ed
to
s
i
m
u
late
u
l
tr
aso
u
n
d
s
i
g
n
als.
T
h
is
s
i
m
u
lat
ed
s
ig
n
a
l
co
u
ld
b
e
s
h
o
w
n
as e
q
u
atio
n
(
2
1
)
[
2
8
]
:
(
2
1
)
w
h
er
e
w
er
e
u
s
ed
as
r
eq
u
ir
ed
p
ar
am
eter
s
.
T
h
e
s
i
m
u
lated
ec
h
o
is
s
h
o
w
n
i
n
Fig
u
r
e
7
(
a)
.
T
h
en
,
th
is
ec
h
o
is
u
s
ed
as
a
r
ef
er
en
ce
s
i
g
n
al
f
o
r
d
esig
n
i
n
g
w
a
v
elet
s
b
ased
o
n
th
e
p
r
esen
ted
cr
iter
ia.
T
ab
le
3
illu
s
tr
ates
t
h
e
f
r
ee
p
ar
am
eter
s
(
)
ac
q
u
ir
ed
f
o
r
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
s
.
T
h
e
d
en
o
is
in
g
r
esu
lt
s
ar
e
s
h
o
w
n
i
n
Fi
g
u
r
e
7
.
Si
m
u
latio
n
i
s
p
er
f
o
r
m
ed
b
y
th
e
s
a
m
e
s
tep
s
a
s
d
escr
ib
ed
in
th
e
p
r
ev
io
u
s
s
ec
tio
n
.
T
h
e
p
r
ec
is
e
v
a
lu
e
o
f
t
h
e
s
ca
le
s
d
ep
en
d
s
o
n
p
ar
a
m
et
er
s
s
u
c
h
a
s
t
h
e
s
i
g
n
al
it
s
el
f
,
it
s
co
ar
s
e
lev
e
l,
an
d
f
ea
t
u
r
es
av
ailab
le
i
n
it.
Fo
r
s
ig
n
als
co
m
p
r
is
ed
o
f
lo
w
f
r
eq
u
e
n
c
y
f
ea
t
u
r
es,
w
e
ca
n
u
s
e
s
m
alle
r
s
ca
les,
w
h
il
s
t
f
o
r
s
h
ar
p
er
an
d
h
i
g
h
er
f
r
eq
u
e
n
c
y
f
ea
tu
r
e
s
,
a
b
r
o
ad
er
s
ca
le
s
h
o
u
ld
b
e
u
s
ed
f
o
r
o
p
ti
m
izatio
n
p
u
r
p
o
s
es.
Hen
ce
,
a
s
ca
le
o
f
6
to
9
h
av
e
b
ee
n
u
s
ed
f
o
r
th
e
s
i
m
u
lated
ec
h
o
s
ig
n
al.
I
n
all
t
h
e
s
i
m
u
la
tio
n
s
,
s
i
g
n
a
l
len
g
th
is
1
0
2
4
.
T
o
h
av
e
co
m
p
ar
ab
le
r
es
u
lts
,
all
t
h
e
s
ig
n
als ar
e
n
o
r
m
alize
d
b
ef
o
r
e
s
i
m
u
latio
n
s
i
n
o
r
d
er
to
h
av
e
th
e
s
a
m
e
p
o
w
er
.
T
ab
le
3
.
Fr
ee
P
ar
am
eter
s
a
n
d
th
e
Valu
e
o
f
th
e
C
o
s
t F
u
n
ctio
n
f
o
r
Si
m
u
lat
ed
Ultr
aso
n
ic
E
ch
o
.
T
h
e
Fil
ter
L
e
n
g
t
h
is
L
=6
A
l
g
o
r
i
t
h
m
t
y
p
e
sca
l
e
N
o
r
m1
_
a
l
g
o
1
.
1
9
8
2
8
1
.
6
7
8
0
4
6
.
1
6
3
7
2
7
.
5
6
5
3
6
,
7
,
8
,
9
3
.
5
2
4
6
6
-
0
.
1
0
6
9
3
0
.
9
7
9
2
1
.
4
1
1
4
7
,
8
,
9
N
o
r
m4
_
a
l
g
o
3
.
4
8
6
2
2
3
.
0
1
1
5
9
0
.
5
4
8
9
1
.
2
5
7
2
6
,
7
,
8
,
9
3
.
5
2
3
5
2
3
.
0
3
0
5
6
0
.
0
5
5
1
0
.
1
4
4
6
7
,
8
,
9
S
ER
_
a
l
g
o
1
.
2
1
4
3
6
1
.
6
9
3
6
2
Evaluation Warning : The document was created with Spire.PDF for Python.