T
E
L
KO
M
N
I
KA
T
e
lec
om
m
u
n
icat
ion
,
Com
p
u
t
i
n
g,
E
lec
t
r
on
ics
an
d
Cont
r
ol
Vol.
18
,
No.
3
,
J
une
2020
,
pp.
12
59
~
126
7
I
S
S
N:
1693
-
6930,
a
c
c
r
e
dit
e
d
F
ir
s
t
G
r
a
de
b
y
Ke
me
nr
is
tekdikti
,
De
c
r
e
e
No:
21/E
/KP
T
/2018
DO
I
:
10.
12928/
T
E
L
KO
M
NI
KA
.
v18i3.
12960
1259
Jou
r
n
al
h
omepage
:
ht
tp:
//
jour
nal.
uad
.
ac
.
id/
index
.
php/T
E
L
K
OM
N
I
K
A
A
h
yb
r
id
al
gor
it
h
m
f
or
w
av
e
-
f
r
on
t
c
o
r
r
e
c
t
io
n
s ap
p
li
e
d
t
o sat
e
ll
ite
-
to
-
g
r
ou
n
d
l
ase
r
c
o
m
m
u
n
ic
at
io
n
M
oh
am
m
e
d
S
e
n
a
n
Al
Gob
i
1
,
Dj
am
e
l
B
e
n
at
ia
2
,
M
ou
ad
h
B
ali
3
1,
2
E
l
ect
r
o
n
i
c
D
ep
ar
t
men
t
,
L
ab
o
ra
t
o
i
re
d
'É
l
ect
r
o
n
i
q
u
e
A
v
an
cée
(L
E
A
),
Facu
l
t
y
o
f
T
ec
h
n
o
l
o
g
y
,
U
n
i
v
er
s
i
t
y
o
f
Ba
t
n
a
2
,
A
l
g
er
i
a
3
D
ep
ar
t
m
e
nt
Co
mp
u
t
er
Sc
i
en
ce,
Facu
l
t
y
o
f
E
x
act
Sc
i
en
c
es
,
U
n
i
v
er
s
i
t
é
d
’E
l
O
u
ed
,
A
l
g
er
i
a
3
L
IME
D
L
ab
o
rat
o
ry
,
Facu
l
t
y
o
f
E
x
act
Sc
i
en
ce
s
,
U
n
i
v
er
s
i
t
é
d
e
Be
j
ai
a,
A
l
g
er
i
a
Ar
t
icle
I
n
f
o
AB
S
T
RA
CT
A
r
ti
c
le
h
is
tor
y
:
R
e
c
e
ived
S
e
p
18
,
2019
R
e
vis
e
d
De
c
7
,
20
19
Ac
c
e
pted
De
c
21
,
20
19
L
as
er
co
mmu
n
i
cat
i
o
n
s
h
o
l
d
accu
ra
t
e
d
at
a
ra
t
e
fo
r
g
r
o
u
n
d
s
a
t
el
l
i
t
e
l
i
n
k
s
.
T
h
e
l
a
s
er
b
eam
i
s
t
ran
s
mi
t
t
e
d
t
h
ro
u
g
h
t
h
e
at
m
o
s
p
h
ere.
T
h
e
cl
ear
-
ai
r
t
u
r
b
u
l
en
ce
i
n
d
u
ce
s
a
n
u
mb
er
o
f
p
h
a
s
e
d
i
s
t
o
rt
i
o
n
s
t
h
at
d
ama
g
e
w
av
e
-
fro
n
t
.
A
d
ap
t
i
v
e
o
p
t
i
c
s
(A
O
)
t
reat
s
w
av
e
fro
n
t
co
rrec
t
i
o
n
.
T
h
e
n
a
t
u
re
o
f
A
O
s
y
s
t
ems
i
s
i
t
era
t
i
v
e;
i
t
can
b
e
i
n
t
eg
ra
t
ed
i
n
m
et
ah
e
u
ri
s
t
i
c
al
g
o
ri
t
h
m
s
s
u
c
h
as
g
en
et
i
c
al
g
o
r
i
t
h
m
(G
A
).
T
h
i
s
p
a
p
er
p
res
e
n
t
s
i
m
p
ro
v
ed
v
er
s
i
o
n
o
f
a
l
g
o
ri
t
h
m
fo
r
w
av
e
-
fro
n
t
co
rrec
t
i
o
n
s
.
T
h
e
i
mp
r
o
v
e
d
al
g
o
ri
t
h
m
i
s
b
as
ed
o
n
g
en
e
t
i
c
al
g
o
ri
t
h
m
(G
A
)
an
d
ad
ap
t
i
v
e
o
p
t
i
c
s
ap
p
r
o
ach
(O
A
).
It
i
s
i
m
p
l
eme
n
t
e
d
i
n
a
co
mp
u
t
er
s
i
mu
l
a
t
i
o
n
mo
d
el
cal
l
ed
o
b
j
ect
-
o
ri
e
n
t
e
d
mat
l
ab
ad
a
p
t
i
v
e
o
p
t
i
cs
(O
O
M
A
O
).
T
h
e
o
p
t
i
m
i
s
a
t
i
o
n
p
r
o
ces
s
i
n
v
o
l
v
e
s
b
es
t
p
o
s
s
i
b
l
e
G
A
p
aramet
ers
as
a
fu
n
ct
i
o
n
o
f
p
o
p
u
l
at
i
o
n
s
i
ze,
i
t
erat
i
o
n
co
u
n
t
,
an
d
t
h
e
ac
t
u
a
t
o
r
s
’
v
o
l
t
ag
e
i
n
t
erv
a
l
s
.
Res
u
l
t
s
s
h
o
w
t
h
a
t
t
h
e
ap
p
l
i
c
at
i
o
n
o
f
G
A
i
mp
ro
v
e
s
t
h
e
p
erfo
rma
n
ce
o
f
A
O
i
n
w
a
v
e
-
f
ro
n
t
co
rrect
i
o
n
s
an
d
t
h
e
co
mm
u
n
i
cat
i
o
n
b
e
t
w
ee
n
s
at
e
l
l
i
t
e
-
to
-
g
r
o
u
n
d
l
a
s
er
l
i
n
k
s
as
w
el
l
.
K
e
y
w
o
r
d
s
:
Ada
pti
ve
opti
c
s
(
AO
)
Ge
ne
ti
c
a
lgor
it
hm
Obje
c
t
-
or
iente
d
matlab
a
da
pti
ve
opti
c
s
(
OO
M
AO
)
S
a
telli
te
-
to
-
gr
ound
W
a
ve
-
f
r
ont
c
or
r
e
c
ti
on
Th
i
s
i
s
a
n
o
p
en
a
c
ces
s
a
r
t
i
c
l
e
u
n
d
e
r
t
h
e
CC
B
Y
-
SA
l
i
ce
n
s
e
.
C
or
r
e
s
pon
din
g
A
u
th
or
:
M
oha
mm
e
d
S
e
na
n
A
l
G
obi
,
E
lec
tr
onic
De
pa
r
tm
e
nt
,
L
a
bor
a
toi
r
e
d'
É
lec
tr
onique
Ava
nc
é
e
(
L
E
A)
,
F
a
c
ult
y
of
T
e
c
hnology,
Unive
r
s
it
y
of
B
a
tna
2,
B
a
tna,
Z
ip
05000
,
Alge
r
ia
.
E
mail:
moh
.
a
lgobi
@gmail.
c
om
1.
I
NT
RODU
C
T
I
ON
T
he
c
omm
unica
ti
on
s
a
telli
te
s
ys
tems
with
opti
c
a
l
l
a
s
e
r
li
nks
ha
ve
be
c
ome
pr
ior
it
y
in
c
omm
unica
ti
on
f
ields
f
or
number
of
r
e
a
s
ons
.
C
ompar
e
d
to
the
r
a
dio
c
omm
unica
ti
on
whic
h
ne
e
ds
mor
e
than
1
Gb
ps
[
1,
2]
,
the
c
omm
unica
ti
on
s
a
telli
te
s
ys
tems
with
opti
c
a
l
la
s
e
r
li
nks
da
ta
r
a
te
is
higher
(
mo
r
e
than
10
Gbps
bit
r
a
te
[
3
]
)
,
s
ignal
int
e
ns
it
y
(
s
tr
uc
tur
e
of
f
iber
las
e
r
[
4
,
5]
)
a
nd
lowe
r
e
quipm
e
nt
s
ize
[
5]
.
I
t
ha
s
made
a
s
i
gnif
ica
nt
c
ontr
ibut
ion
in
r
e
duc
ing
the
e
f
f
e
c
ts
of
a
tm
os
phe
r
ic
a
tt
e
nua
ti
on
that
is
a
ls
o
de
ter
mi
ne
d
by
the
ge
ogr
a
phic
loca
ti
on
e
s
pe
c
ially
in
the
tr
opica
l
a
nd
e
qua
tor
ial
r
e
gions
whe
r
e
t
he
r
a
in
e
f
f
e
c
t
plays
a
n
im
por
tan
t
r
ole
in
th
e
qua
li
ty
of
c
omm
unica
ti
on
[
6
-
8]
,
a
s
it
lea
ds
to
t
he
ins
tabili
ty
in
the
in
tens
it
y
a
nd
the
pha
s
e
o
f
the
r
e
c
e
ived
s
ignals
[
1,
9]
.
I
n
las
e
r
s
a
telli
te
c
omm
unica
ti
ons
,
the
e
leme
nts
of
the
a
tm
os
phe
r
e
(
wind,
r
a
in,
dus
t…)
c
a
n
a
f
f
e
c
t
the
qua
li
ty
of
c
omm
un
ica
ti
on
[
5]
.
T
he
pr
oblem
oc
c
ur
s
whe
n
the
opt
ica
l
wa
ve
pr
opa
ga
tes
in
f
r
e
e
s
pa
c
e
a
nd
is
s
ubjec
ted
to
s
e
r
ious
dis
tur
ba
nc
e
s
(
wa
ve
-
f
r
ont
s
e
n
s
or
)
whic
h
r
e
nde
r
s
the
s
ys
tem
les
s
e
f
f
e
c
ti
ve
a
nd
pr
oba
bly
los
e
s
the
inf
or
mation.
I
n
a
da
pti
ve
opti
c
s
(
AO
)
,
the
mos
t
im
por
tant
e
lem
e
nt
is
the
de
f
or
mable
mi
r
r
or
.
W
hich
c
ontr
oll
e
d
by
the
a
ppr
oxim
a
ti
on
a
lgor
it
hms
[
9]
.
T
he
r
e
a
r
e
many
a
lgor
it
hms
that
c
a
n
c
or
r
e
c
t
wa
ve
f
r
ont
s
e
ns
or
s
uc
h
a
s
:
the
s
tocha
s
ti
c
pa
r
a
ll
e
l
gr
a
dient
de
s
c
e
nt
(
S
P
G
D)
,
s
im
ulate
d
a
nne
a
li
ng
(
S
A)
,
a
nd
ge
ne
ti
c
a
lgor
it
hm
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
1693
-
6930
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
o
l
,
Vol.
18
,
No
.
3
,
J
une
2020:
12
59
-
12
67
1260
(
GA
)
[
10,
11
]
.
T
his
c
ontr
ibut
ion
pr
ovides
a
hyb
r
id
s
olut
ion
to
c
or
r
e
c
t
wa
ve
f
r
ont
s
e
ns
or
.
T
he
p
r
ovided
s
olut
ion
c
ons
is
ts
of
the
c
omb
ination
of
GA
with
AO
s
olut
i
on.
T
he
hybr
id
s
olut
ion
gives
pos
it
ive
r
e
s
ult
s
in
c
or
r
e
c
ti
ng
the
wa
ve
f
r
ont
a
be
r
r
a
ti
on
in
s
a
telli
te
las
e
r
c
omm
un
ica
ti
on.
T
he
pr
e
s
e
nt
pa
pe
r
is
divi
de
d
int
o
f
ive
s
e
c
ti
ons
.
A
br
ief
ove
r
view
of
the
e
f
f
e
c
ts
of
a
tm
os
phe
r
e
a
tt
e
nua
ti
on
on
the
r
e
f
r
a
c
ti
ve
index's
be
ha
viour
is
pr
e
s
e
nted
in
s
e
c
ti
on
two.
I
n
s
e
c
ti
on
3
p
r
ovides
the
de
s
ign
a
nd
im
pleme
ntation
of
the
ge
ne
ti
c
a
lgo
r
it
hm
(
GA
)
a
nd
the
hybr
id
(
GA
a
nd
AO
)
.
S
e
c
ti
on
4
is
de
di
c
a
ted
to
the
dis
c
us
s
ion
of
the
obtaine
d
r
e
s
ult
s
.
T
he
las
t
s
e
c
ti
on
is
de
voted
to
the
ove
r
a
ll
r
e
s
ult
s
of
ou
r
s
t
udy
a
nd
f
utur
e
wor
ks
.
2.
B
AC
KG
ROUN
D
OF
T
HE
S
T
UD
Y
W
he
n
the
opti
c
a
l
s
ignal
(
las
e
r
he
r
e
)
pa
s
s
e
s
thr
ough
the
a
tm
os
phe
r
e
,
it
ge
ts
e
xpos
e
d
to
a
tm
os
phe
r
ic
a
tt
e
nua
ti
ons
i.
e
.
c
louds
,
r
a
ins
,
winds
,
dus
t.
As
a
r
e
s
ult
,
the
s
a
telli
te
-
to
-
gr
ound
opti
c
a
l
li
nks
will
be
d
e
gr
a
de
d.
F
igur
e
1
il
lus
tr
a
tes
the
ove
r
a
ll
f
r
e
que
nc
y
be
ha
vio
r
of
the
r
e
a
l
pa
r
t
o
f
the
index
of
r
e
f
r
a
c
ti
on.
At
m
os
phe
r
ic
a
tt
e
nua
ti
ons
is
the
a
be
r
r
a
ti
on
in
wa
ve
-
f
r
ont
de
f
or
m
a
ti
on.
I
t
r
e
duc
e
s
the
int
e
ns
it
y
a
nd
the
pha
s
e
of
t
he
r
e
c
e
ived
s
ignals
.
As
r
e
s
ult
,
it
de
c
r
e
a
s
e
s
the
im
a
ge
's
r
e
s
olut
ion.
T
o
c
or
r
e
c
t
pha
s
e
a
be
r
r
a
ti
on,
wa
ve
-
f
r
ont
c
or
r
e
c
ti
on
is
im
pleme
nted
us
ing
a
da
pti
ve
opti
c
s
(
AO
)
tec
hnolog
y
[
5,
9]
.
F
igur
e
1.
B
e
ha
vior
o
f
the
index
of
r
e
f
r
a
c
ti
on
a
s
a
f
u
nc
ti
on
of
l
inea
r
f
r
e
que
nc
y
[
12]
2.
1.
T
h
e
t
h
e
or
y
of
at
m
os
p
h
e
r
ic
f
lu
c
t
u
a
t
ion
s
Atmos
phe
r
ic
f
luctua
ti
on
a
t
Ka
-
ba
nd
f
r
e
que
nc
ies
is
c
omm
only
known
a
s
a
tm
os
phe
r
ic
tur
bulenc
e
.
E
a
c
h
point
in
the
tele
s
c
ope
a
pe
r
tur
e
r
e
c
e
ivi
ng
is
c
on
s
ider
e
d
a
s
the
s
um
o
f
many
c
omponents
dis
tr
ib
uted
by
the
tur
bulent
e
ddies
.
C
ons
tr
uc
ti
ve
int
e
r
f
e
r
e
nc
e
of
las
e
r
wa
ve
s
a
ppe
a
r
a
s
s
pots
of
li
ght
.
T
he
s
e
s
pots
r
e
pr
e
s
e
nt
the
wa
ve
s
int
e
r
f
e
r
e
d
c
ons
tr
uc
ti
ve
ly
.
How
e
ve
r
,
de
s
tr
uc
ti
ve
int
e
r
f
e
r
e
nc
e
a
ppe
a
r
s
a
s
da
r
k
a
r
e
a
.
T
he
e
f
f
e
c
ts
of
a
tm
os
phe
r
ic
tur
bulenc
e
a
r
e
r
e
f
ined
by
c
ha
nging
the
tele
s
c
ope
a
pe
r
tur
e
a
nd
by
r
e
duc
ing
the
a
mpl
it
ude
s
c
int
il
lations
obs
e
r
ve
d
a
t
the
r
e
c
e
iver
s
[
13
]
.
2.
1.
1.
Clas
s
ical
at
m
os
p
h
e
r
ic
t
u
r
b
u
lence
T
wo
e
xtr
e
me
tur
bulent
s
c
a
les
(
the
int
e
r
na
l
l0,
a
nd
t
he
e
xter
na
l
s
c
a
le
L
0)
ha
ve
be
e
n
identif
ied
to
model
the
a
tm
os
phe
r
ic
tur
bulenc
e
[
5,
8
]
.
T
he
int
e
r
na
l
s
c
a
le
l0
(
r
,
t)
c
o
r
r
e
s
ponds
to
the
s
pa
ti
a
l
s
c
a
le
f
r
om
whic
h
the
kinetic
e
ne
r
gy
is
dis
s
ipate
d
in
he
a
t
by
vis
c
ous
f
r
iction
.
T
he
r
e
f
o
r
e
,
it
de
pe
nds
on
the
de
ns
it
y
of
the
a
tm
os
phe
r
e
.
l0
(
r
,
t)
c
a
n
va
r
y
f
r
o
m
a
f
e
w
mi
ll
im
e
tr
e
s
ne
a
r
the
gr
ound
to
a
f
e
w
c
e
nti
m
e
ter
s
in
the
tr
opopa
us
e
(
int
e
r
f
a
c
e
be
twe
e
n
the
tr
opos
phe
r
e
a
nd
the
s
tr
a
tos
phe
r
e
)
[
9]
.
T
he
e
xter
na
l
s
c
a
le
L
0
(
r
,
t)
(
r
a
nging
f
r
om
10
-
100
mete
r
s
)
is
de
ter
mi
ne
d
by
the
s
ize
of
the
of
a
ir
mas
s
e
s
.
I
t
c
or
r
e
s
ponds
to
the
lar
ge
s
t
mac
r
os
c
opic
phe
nomena
(
a
ir
laye
r
s
,
winds
,
w
e
a
ther
d
is
tur
ba
nc
e
s
)
.
T
he
iner
ti
a
l
domain
de
f
ines
the
s
c
a
les
f
or
whic
h
tur
bulenc
e
is
f
ul
ly
de
ve
loped.
I
t
de
ter
mi
ne
s
li
mi
ti
ng
va
lue
of
the
e
xter
na
l
s
c
a
le
of
tur
bulenc
e
L
0
a
nd
the
int
e
r
na
l
s
c
a
le
of
tur
bu
lenc
e
l0
[
9]
.
I
n
a
tur
bu
lent
a
ir
,
the
r
a
ndom
va
r
iation
s
of
r
e
f
r
a
c
ti
ve
index,
n
,
a
r
e
de
s
c
r
ibed
by
the
s
tr
uc
tur
e
f
unc
ti
on,
Dn
[
13]
.
(
)
=
⟨
|
(
)
−
(
+
)
|
2
⟩
(
1)
T
he
dif
f
e
r
e
nc
e
be
twe
e
n
the
va
lue
n
(
r
)
a
t
a
point
r
a
nd
the
va
lue
n
(
r
+
ρ)
a
t
a
point
(
r
+
ρ)
is
a
point
dis
tant
f
r
om
the
ve
c
tor
r
in
a
d
is
tanc
e
ρ.
I
t
r
e
p
r
e
s
e
nts
a
pos
it
ion
in
a
thr
e
e
-
dim
e
ns
ional
s
pa
c
e
[
14]
.
F
or
a
n
e
s
tablis
he
d
tur
bulent
r
e
gim
e
in
the
iner
ti
a
l
dom
a
in,
the
va
r
ianc
e
of
the
dif
f
e
r
e
nc
e
a
t
two
point
s
o
f
s
pa
c
e
,
or
s
tr
uc
tur
e
f
unc
ti
on,
is
given
by:
=
{
2
2
3
⁄
,
1
0
⁄
≤
≤
1
0
⁄
2
0
2
3
⁄
(
0
)
2
,
<
1
0
⁄
(
2)
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
o
l
A
hy
br
id
algor
it
hm
for
w
av
e
-
fr
ont
c
or
r
e
c
ti
ons
appli
e
d
to
…
(
M
ohamm
e
d
Se
nan
A
l
Gobi
)
1261
w
he
r
e
2
is
the
s
tr
uc
tur
e
f
unc
ti
on
c
ons
tant
e
xpr
e
s
s
e
d
in
m
-
2
/
3
a
nd
dis
plac
e
ment
(
ρ
=
|
ρ
|)
is
a
s
c
a
lar
mea
s
ur
e
d
by
m
.
2
is
typi
c
a
ll
y
va
r
y
be
twe
e
n
10
-
13
m
-
2
/
3
a
nd
10
-
15
m
-
2
/
3
f
or
s
tr
ong
tur
bu
lenc
e
a
nd
we
a
k
tu
r
bu
lenc
e
r
e
s
pe
c
ti
ve
ly
[
5,
15
]
.
2.
1.
2.
S
p
e
c
t
r
al
d
e
n
s
it
y
a
n
d
var
ian
c
e
of
r
e
f
r
ac
t
iv
e
in
d
e
x
Ac
c
or
ding
to
the
W
iene
r
-
Khinc
hine
theor
e
m,
t
he
powe
r
s
pe
c
tr
a
l
of
the
s
pa
ti
a
l
f
lu
c
tuations
is
c
a
lcula
ted
us
ing
s
im
ple
F
our
ier
tr
a
ns
f
or
m
[
14
]:
∅
(
)
=
0
.
033
2
−
11
3
⁄
(
3)
:
is
the
modul
us
of
the
s
pa
ti
a
l
f
r
e
que
nc
y
(
Kolmogor
ov
s
pe
c
tr
um)
that
is
e
xpr
e
s
s
e
d
in
m
-
1
.
T
he
Kolmogo
r
ov
s
pe
c
tr
um
(
3)
is
va
li
d
only
in
th
e
iner
ti
a
l
domain:
1/L
0
<
k
<
l/
1
0
.
I
t
r
e
pr
e
s
e
nts
a
n
e
xter
na
l
s
c
a
le
a
nd
a
n
int
e
r
na
l
s
c
a
le
of
tur
bulenc
e
r
e
s
pe
c
ti
ve
ly
[
14]
.
T
he
va
r
ianc
e
of
int
e
ns
it
y
f
luctua
ti
ons
2
(
2
)
a
t
the
ter
mi
na
l
point
of
the
r
e
c
e
iver
a
f
ter
a
n
(
ini
ti
a
ll
y
)
plane
wa
ve
with
wa
ve
-
number
k=
2
/
ha
s
pr
opa
g
a
t
e
d
thr
ough
a
de
pth
L
of
homogene
ous
tur
bule
nc
e
is
[
13
,
15
]
:
2
=
{
186
0
−
7
3
⁄
2
3
,
0
>
√
23
.
2
2
7
6
⁄
11
6
⁄
,
0
≪
√
<
0
75
.
4
〈
(
∆
)
2
〉
2
,
0
≪
√
(
4)
whe
r
e
〈
(
∆
)
2
〉
:
is
s
qua
r
e
r
e
f
r
a
c
ti
ve
index
f
luctua
ti
on
a
nd
L
n
is
the
int
e
gr
a
l
s
c
a
le
of
the
tur
bulenc
e
of
the
s
a
me
or
de
r
a
s
L
0
.
T
he
f
ir
s
t
e
xpr
e
s
s
ion
on
t
he
r
ight
s
ide
of
(
4)
r
e
f
e
r
s
to
the
opti
c
a
l
r
e
gim
e
whe
r
e
a
s
the
s
e
c
o
nd
e
xpr
e
s
s
ion
is
the
dif
f
r
a
c
ti
on
r
e
gim
e
.
T
he
latter
plays
a
n
im
por
tant
r
ole
in
mi
c
r
owa
ve
s
c
int
il
lation
that
oc
c
ur
s
on
E
a
r
th
–
s
pa
c
e
pa
ths
.
At
the
ter
m
inal
point
of
the
r
e
c
e
iver
,
the
e
xpe
c
ted
s
pe
c
tr
a
l
de
ns
it
y,
(
)
of
the
s
ignal
is
f
lat
a
t
a
low
f
r
e
que
nc
y
a
nd
r
oll
s
o
f
f
a
t
high
f
r
e
que
nc
ies
.
T
he
a
s
ympt
oti
c
be
ha
viour
is
given
by
[
15,
16]
.
(
)
→
{
2
.
765
2
,
→
0
7
.
13
2
(
)
−
8
3
⁄
,
→
∞
(
5)
whe
r
e
ω
t
=
ν
t
(
k
/L
)
1
/
2
is
the
F
r
e
s
ne
l
f
r
e
que
nc
y
a
n
d
ν
t
is
the
c
omponent
of
the
wind
ve
locity
tr
a
ns
ve
r
s
e
to
the
pr
opa
ga
ti
on
pa
th.
T
he
two
a
s
ympt
otes
int
e
r
s
e
c
t
a
t
a
c
or
ne
r
f
r
e
que
nc
y,
ω
c
,
whic
h
de
pe
nds
on
ν
t
.
F
o
r
a
thi
c
k
laye
r
of
un
if
or
m
(
homogene
ous
)
tu
r
bulenc
e
ω
c
=
1.
43
ω
t
[
13]
.
2.
2.
P
r
i
n
c
ip
al
o
f
ad
a
p
t
ive
op
t
ics
Ada
pti
ve
opti
c
s
(
AO
)
tec
hnology
he
lps
c
or
r
e
c
ti
ng
t
he
pha
s
e
a
be
r
r
a
ti
on
in
the
las
e
r
wa
ve
-
f
r
ont
c
a
us
e
d
by
a
tm
os
phe
r
e
tur
bulenc
e
in
r
e
a
l
ti
me.
AO
is
us
e
d
in
s
pa
c
e
f
ield
a
nd
in
a
number
of
f
ields
s
uc
h
a
s
:
ophthalmol
ogy
[
17]
.
R
e
c
e
ntl
y
the
a
da
pti
ve
opti
c
s
is
a
ls
o
us
e
d
in
the
two
-
photon
e
xc
it
a
ti
on
mi
c
r
os
c
opy
[
17]
.
T
o
c
o
r
r
e
c
t
the
wa
ve
-
f
r
ont
in
the
opti
c
a
l
s
ys
tems
,
AO
s
ys
tems
a
r
e
us
e
d
wi
th
a
de
f
o
r
mabl
e
mi
r
r
or
(
DM
)
[
10
,
17]
.
I
n
our
s
tudy,
opti
m
iza
ti
on
a
lgo
r
it
hm
s
indi
vidually
c
ont
r
ols
T
he
92
a
c
tuator
s
'
de
f
o
r
mabl
e
mi
r
r
or
.
T
he
y
put
e
a
c
h
a
c
tuator
in
the
r
igh
t
pos
it
ion
by
c
ha
nging
the
mi
r
r
o
r
s
ur
f
a
c
e
s
ha
pe
to
p
r
oduc
e
s
a
s
uit
a
ble
wa
ve
f
r
ont
f
or
the
s
ys
tem
[
11]
.
2.
2.
1.
Wave
-
f
r
on
t
a
n
alys
is
Unlike
in
the
r
a
dio
f
r
e
que
nc
y
f
ield
,
it
is
not
ye
t
p
os
s
ibl
e
to
mea
s
ur
e
the
pha
s
e
of
the
wa
ve
-
f
r
ont
a
t
opti
c
a
l
wa
ve
lengths
dir
e
c
tl
y.
Optica
l
de
tec
tor
is
inca
pa
ble
to
r
e
s
pond
to
tempor
a
l
f
r
e
que
nc
ies
.
T
his
pr
oblem
is
ove
r
c
ome
by
pe
r
f
or
mi
ng
indi
r
e
c
t
mea
s
ur
e
ments
i.
e
.
by
a
na
lyzing
the
im
pa
c
t
of
pha
s
e
dis
tur
ba
nc
e
s
on
the
int
e
ns
it
y
dis
tr
ibut
ion
[
9]
.
R
ous
s
e
t
c
a
r
r
ies
out
a
de
s
c
r
ipt
ion
to
a
na
lyze
r
s
f
or
a
da
pti
ve
opti
c
s
[
18]
.
T
he
S
ha
c
k
-
Ha
r
mann
a
na
lys
e
r
is
c
omm
only
us
e
d
in
a
da
pti
ve
opti
c
s
be
c
a
us
e
it
s
li
mi
tati
ons
a
r
e
the
r
e
pr
e
s
e
ntatives
of
thos
e
of
mos
t
plana
r
pupil
a
n
a
lyze
r
s
[
14]
.
2.
2.
2.
P
r
i
n
c
ip
al
o
f
t
h
e
S
h
ac
k
-
Hart
m
a
n
n
an
a
lyze
r
T
he
S
ha
c
k
-
Ha
r
tm
a
nn
wa
ve
s
ur
f
a
c
e
a
na
ly
z
e
r
(
S
H)
is
a
pupil
plane
a
na
lyze
r
a
s
s
hown
in
F
igur
e
2.
I
t
is
ba
s
e
d
on
the
ge
ometr
ica
l
opti
c
s
f
or
malis
m
[
1
9]
.
A
mi
c
r
o
-
lens
a
r
r
a
y
s
a
mpl
e
s
the
incide
nt
wa
ve
-
f
r
ont
in
the
pupil
plane
.
A
mea
s
ur
e
ment
o
f
the
pos
it
ion
of
the
im
a
ge
s
pot
f
or
med
a
t
the
f
oc
us
of
e
a
c
h
of
the
mi
c
r
o
-
lens
e
s
give
s
a
c
c
e
s
s
to
the
loca
l
s
lope
of
the
wa
ve
-
f
r
ont
in
the
pupil
plane
of
e
a
c
h
mi
c
r
o
-
lens
[
14]
.
T
he
mea
s
ur
e
ment
of
the
pos
it
ion
is
of
ten
c
a
r
r
ied
out
by
the
c
e
nter
of
g
r
a
vit
y,
but
other
pos
it
ional
e
s
ti
mator
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
1693
-
6930
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
o
l
,
Vol.
18
,
No
.
3
,
J
une
2020:
12
59
-
12
67
1262
c
a
n
a
ls
o
be
us
e
d,
s
uc
h
a
s
c
or
r
e
lation
[
19
-
21]
.
T
he
s
lop
e
mea
s
ur
e
d
by
the
c
e
nter
o
f
gr
a
vit
y
in
e
a
c
h
s
ub
-
pupil
k
is
r
e
s
pe
c
ti
ve
ly
f
or
the
dir
e
c
ti
on
in
x
a
nd
y:
=
∬
(
)
|
(
)
|
2
∬
|
(
)
|
2
(
6)
=
∬
(
)
|
(
)
|
2
∬
|
(
)
|
2
(
7)
whe
r
e
the
double
in
tegr
a
ti
on
is
c
a
r
r
ied
out
on
the
s
ur
f
a
c
e
of
the
s
ub
-
pupil
k
c
ons
ider
e
d,
φ
k
the
pha
s
e
a
nd
|
ψ
k
|
the
a
mpl
it
ude
of
the
c
ompl
e
x
f
ield.
W
he
n
the
int
e
n
s
it
y
is
c
ons
tant
in
e
a
c
h
s
ub
-
pupil
,
the
s
lope
mea
s
ur
e
ment
is
then
a
n
a
ve
r
a
ge
on
the
s
ur
f
a
c
e
of
the
s
ub
-
pupil
.
F
igur
e
2.
S
c
he
ma
o
f
a
n
a
da
pti
ve
op
ti
c
s
s
ys
tem
a
nd
the
S
ha
c
k
-
Ha
r
tm
a
nn
wa
ve
-
f
r
ont
a
na
lys
e
r
2
.3
.
Genet
ic
algorit
h
m
Ge
ne
ti
c
a
lgor
it
hm
(
GA
)
is
a
s
tocha
s
ti
c
pa
r
a
ll
e
l
a
lgor
it
hm
that
us
e
s
Da
r
win's
theor
y
of
s
pe
c
ies
e
volut
ion.
I
t
is
ba
s
e
d
on
thr
e
e
pr
inciples
:
va
r
iati
on,
a
da
ptation
a
nd
inher
it
a
nc
e
[
22,
23
]
.
Va
r
iatio
n:
e
ve
r
y
indi
vidual
withi
n
a
population
is
dif
f
e
r
e
nt.
T
he
dif
f
e
r
e
nc
e
s
,
mo
r
e
o
r
les
s
im
por
tant
,
will
be
de
c
is
ive
in
the
pr
oc
e
s
s
'
s
e
lec
ti
on.
Ada
ptation:
T
he
mos
t
a
da
pted
indi
viduals
to
their
e
nvi
r
onment
r
e
a
c
h
a
dult
ho
od
mor
e
c
omf
or
tably.
I
ndivi
dua
ls
wi
th
be
tt
e
r
s
ur
vivabili
t
y
will
ther
e
f
or
e
be
a
ble
to
r
e
pr
oduc
e
mo
r
e
.
H
e
r
e
dit
y:
T
he
c
ha
r
a
c
ter
is
ti
c
s
of
indi
viduals
mus
t
be
he
r
e
dit
a
r
y
in
or
de
r
to
be
t
r
a
ns
mi
tt
e
d
to
their
de
s
c
e
nda
nts
.
T
his
mec
ha
nis
m
will
make
it
pos
s
ibl
e
to
e
volve
t
he
s
pe
c
ies
to
s
ha
r
e
the
a
dva
ntage
ous
c
ha
r
a
c
ter
is
ti
c
s
to
it
s
s
ur
vival
[
23
-
25]
.
I
n
the
p
r
e
s
e
nt
wor
k,
populatio
n
r
e
f
e
r
s
to
the
pos
s
ibl
e
c
oll
e
c
ti
on
of
s
olut
ions
.
How
e
ve
r
,
the
indi
vidual
r
e
pr
e
s
e
nts
a
s
olut
ion.
C
hr
omos
ome,
in
the
other
ha
nd,
is
a
c
omponent
of
the
s
olut
ion
a
nd
g
e
ne
r
e
pr
e
s
e
nts
a
c
ha
r
a
c
ter
is
ti
c
(
or
a
pe
c
uli
a
r
it
y)
.
2.
4.
Ob
j
e
c
t
-
or
ient
e
d
m
at
lab
ad
a
p
t
ive
o
p
t
ics
(
OO
M
AO)
Obje
c
t
-
Or
iente
d
M
a
tl
a
b
Ada
pti
ve
Optics
(
OO
M
AO
)
is
a
li
br
a
r
y
o
f
M
a
tl
a
b
c
las
s
e
s
,
de
dica
ted
to
a
da
pti
ve
opti
c
s
(
AO
)
s
ys
tems
.
T
he
main
c
las
s
e
s
us
e
d
in
thi
s
tool
box
a
r
e
:
s
our
c
e
,
a
tm
os
phe
r
e
,
t
e
les
c
ope
,
S
ha
c
k
-
Ha
r
tm
a
nn,
de
f
or
mable
m
ir
r
o
r
[
26,
27]
.
T
he
s
our
c
e
c
las
s
is
the
li
nk
be
twe
e
n
other
c
las
s
e
s
.
T
he
a
tm
os
phe
r
e
c
las
s
c
ontains
a
ll
the
pa
r
a
mete
r
s
d
e
f
ini
ng
the
a
tm
os
phe
r
e
.
A
mul
ti
laye
r
a
tm
os
phe
r
e
i
s
c
r
e
a
ted
by
s
e
tt
ing
the
a
ppr
op
r
iate
ve
c
tor
s
of
a
lt
it
ude
s
,
wind
s
pe
e
ds
a
nd
di
r
e
c
ti
ons
a
nd
tur
bulenc
e
s
tr
e
ngths
.
An
a
tm
os
phe
r
e
objec
t
ne
e
ds
to
be
c
oupled
with
a
t
e
les
c
ope
objec
t
to
c
r
e
a
te
a
3
-
D
volum
e
of
tur
bulen
c
e
pha
s
e
s
c
r
e
e
ns
.
T
he
tele
s
c
ope
c
las
s
c
ontain
the
tele
s
c
ope
pa
r
a
mete
r
s
a
nd
the
pha
s
e
s
c
r
e
e
ns
in
the
tu
r
bulent
l
a
ye
r
s
s
e
t
by
a
n
a
tm
os
phe
r
e
objec
t.
I
n
a
c
los
e
d
-
loop
a
da
pti
ve
opti
c
s
s
ys
tem
,
the
de
f
or
mable
mi
r
r
or
is
the
f
i
r
s
t
a
c
ti
ve
c
omponent
that
e
nc
ounter
s
the
wa
ve
f
r
ont
.
T
o
c
om
plete
a
n
Ada
pti
ve
Optics
S
ys
tem,
ther
e
mus
t
be
a
w
a
ve
f
r
ont
s
e
ns
or
.
T
he
OO
M
AO
im
pleme
nts
the
wa
ve
f
r
on
t
s
e
ns
or
[
26,
28]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
o
l
A
hy
br
id
algor
it
hm
for
w
av
e
-
fr
ont
c
or
r
e
c
ti
ons
appli
e
d
to
…
(
M
ohamm
e
d
Se
nan
A
l
Gobi
)
1263
3.
OUR
P
ROP
O
S
E
D
S
L
OUT
I
ON
I
n
thi
s
s
e
c
ti
on,
we
dis
c
us
s
the
de
s
ign
of
a
hybr
id
s
olut
ion
ba
s
e
d
on
OO
M
AO
c
los
e
d
-
loop
with
the
a
s
s
is
tanc
e
of
the
ge
ne
ti
c
a
lgo
r
it
hm.
3.
1.
Genet
ic
algorit
h
m
n
o
t
at
ion
−
P
opulation:
matr
ix
P
op
(
NxM
)
of
r
e
a
l
number
s
c
on
tains
the
s
e
t
of
s
olut
ions
ge
ne
r
a
ted
by
one
it
e
r
a
ti
on
a
s
s
hown
in
T
a
ble
1
:
whe
r
e
N
is
the
number
of
s
olut
io
ns
(
or
c
hr
omos
omes
)
a
nd
M
is
the
numbe
r
of
a
c
tuator
s
in
DM
(
or
ge
ne
s
)
.
−
C
hr
omi
:
ve
c
tor
o
f
r
e
a
l
number
s
c
ontains
the
s
olut
i
on
(
i)
in
the
population.
−
Ge
ne
j:
r
e
a
l
number
,
r
e
p
r
e
s
e
nts
the
va
lue
of
the
a
s
s
igned
volt
a
ge
to
the
a
c
tuator
j
in
the
DM
T
a
ble
1.
P
opulation
c
oding
I
x J
G
e
ne
1
G
e
ne
2
….
G
e
ne
j
C
hr
om1
0.08
0.0025
…
-
0.036
C
hr
om2
…
...
…
…
…
C
hr
omi
…
3.
2.
Hyb
r
id
algorit
h
m
f
low
-
c
h
ar
t
T
he
main
s
teps
of
the
pr
oc
e
s
s
e
s
in
our
Algo
r
it
hm
a
r
e
:
−
I
nit
ializa
ti
on
:
va
lues
a
r
e
a
s
s
igned
to
GA
pa
r
a
met
e
r
s
(
population
s
ize
,
it
e
r
a
ti
ons
c
ount…)
,
a
nd
ge
ne
r
a
te
the
c
hr
omos
omes
by
a
s
s
igni
ng
r
a
ndom
va
lues
to
th
e
ir
ge
ne
s
.
−
E
va
luation
:
fi
tnes
s
f
unc
ti
on
is
de
f
ined
to
de
ter
mi
ne
the
a
da
ptation
s
c
or
e
of
c
hr
omos
omes
dur
i
ng
the
s
e
le
c
ti
on
pr
oc
e
s
s
.
W
e
de
f
ine
the
f
it
ne
s
s
(
i)
f
or
ch
r
omi
.
(
)
=
∑
=
(
8)
whe
r
e
ij
is
the
s
tanda
r
d
de
viation
f
o
r
Ga
us
s
ian
mut
a
ti
ons
of
the
s
e
ns
or
j
,
a
nd
M
is
the
number
of
the
a
c
tuator
s
.
−
S
e
lec
ti
on:
a
c
c
or
ding
to
the
r
oulette
method
of
s
e
lec
ti
on,
we
s
e
lec
t
a
s
e
t
of
be
s
t
c
hr
omos
omes
,
will
be
r
e
f
e
r
r
e
d
to
a
s
pa
r
e
nts
,
in
or
de
r
to
r
e
pr
oduc
e
the
ne
x
t
ge
ne
r
a
ti
on.
T
he
F
igur
e
3
il
lus
tr
a
tes
the
main
s
teps
of
the
pr
oc
e
s
s
e
s
in
hybr
id
a
lgor
it
hm
s
olut
ion.
F
igur
e
3.
F
l
ow
-
c
ha
r
t
of
the
hyb
r
id
s
olut
ion
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
1693
-
6930
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
o
l
,
Vol.
18
,
No
.
3
,
J
une
2020:
12
59
-
12
67
1264
4.
RE
S
UL
T
S
A
ND
DI
S
CU
S
S
I
ON
Our
e
xpe
r
im
e
ntal
wor
k
ha
s
pa
s
s
e
d
int
o
two
s
teps
.
F
ir
s
tl
y,
we
looked
f
or
the
be
s
t
c
onf
igu
r
a
ti
on
f
o
r
ge
ne
ti
c
a
lgor
it
hm
pa
r
a
mete
r
s
(
population
s
ize
-
it
e
r
a
ti
on
c
ount
-
a
c
tuator
c
ur
r
e
nt
int
e
r
va
l)
,
in
or
de
r
to
ge
t
the
be
s
t
pe
r
f
or
manc
e
o
f
the
ge
ne
ti
c
a
lgor
it
hm
.
T
he
s
e
c
ond
s
tep
is
the
e
xe
c
uti
on
of
the
OO
M
AO
c
los
e
d
loop
[
28]
a
nd
let
the
ge
ne
ti
c
a
lgor
it
hm
us
e
the
obtaine
d
r
e
s
ult
s
i
n
it
s
ini
ti
a
l
population
(
a
s
50
%
of
indi
viduals
)
.
Als
o,
i
t
us
e
s
the
be
s
t
pa
r
a
mete
r
s
f
r
om
the
f
ir
s
t
s
tep.
T
he
n
,
we
made
a
c
ompar
is
on
be
twe
e
n
OO
M
AO
c
los
e
d
loo
p
a
nd
our
hybr
id
a
lgor
it
h
m.
4.
1
.
AO
p
ar
am
e
t
e
r
s
F
or
AO
pa
r
a
mete
r
s
,
da
ta
a
r
e
a
mas
s
e
d
by
us
ing
OO
M
AO
f
ounde
r
s
in
[
26,
28]
.
W
e
pr
e
s
e
nt
them
in
T
a
ble
2
.
T
a
ble
2.
AO
pa
r
a
mete
r
s
a
s
us
e
d
in
thi
s
a
na
lys
e
P
a
r
a
me
te
r
V
a
lu
e
A
lt
it
ude
[
0, 4000, 10000]
A
tm
os
phe
r
e
f
r
a
c
ti
onna
lR
0
[
0.7, 0.25, 0.05]
W
in
d s
pe
e
d
[
5, 10, 20]
W
in
d D
ir
e
c
ti
on
[
0, pi
/4
, pi
]
S
our
c
e
W
a
ve
le
ngt
h
60
S
ha
c
k H
a
r
tm
a
nn (
w
a
ve
s
e
n
s
or
)
W
a
ve
f
r
ont
s
e
ns
in
g
700nm
le
ns
l
e
t
a
r
r
a
y
92
C
a
me
r
a
542 pixe
ls
D
ia
me
te
r
8m
T
e
le
s
c
ope
R
e
s
ol
ut
io
n
54
F
ie
ld
of
V
ie
w
2.5 a
r
c
mi
nut
e
S
a
mpl
in
g T
im
e
500Hz
4.
2
.
GA
p
ar
am
e
t
e
r
Us
ing
the
da
ta
s
hown
in
T
a
ble
2,
we
made
m
or
e
than
60
e
xpe
r
i
ments
in
dif
f
e
r
e
nts
s
it
ua
ti
ons
us
ing
a
number
of
GA
pa
r
a
mate
r
s
s
uc
h
a
s
:
population
s
ize
,
I
ter
a
ti
ons
number
a
nd
M
in
(
a
nd
M
a
x)
va
lue
of
volt
a
ge
.
T
o
obtain
the
be
s
t
GA
pa
r
a
mete
r
s
,
we
p
lot
the
r
oot
mea
n
s
qua
r
e
va
lues
in
µ
m
of
the
wa
ve
-
f
r
ont
c
or
r
e
c
ti
ons
a
s
a
f
unc
ti
on
o
f
population
s
ize
,
it
e
r
a
ti
on
a
c
c
ounts
,
a
nd
volt
a
ge
in
ter
va
ls
.
R
e
s
ult
s
a
r
e
s
hown
in
F
igur
e
s
4,
5
,
a
nd
6
r
e
s
pe
c
ti
ve
ly.
T
a
ble
3
s
umm
a
r
ize
s
the
GA
pa
r
a
mete
r
s
a
s
obtaine
d
f
r
om
t
his
s
tudy.
F
igur
e
4
r
e
pr
e
s
e
nts
the
F
it
ne
s
s
a
c
c
or
ding
to
the
s
ize
of
the
population.
I
t
is
noted
that
the
F
it
ne
s
s
va
r
ies
inver
s
e
ly
a
s
a
f
unc
ti
on
of
the
s
ize
of
the
populat
ion,
a
s
the
f
igur
e
s
hows
;
in
thi
s
c
a
s
e
we
c
a
n
s
e
e
F
it
ne
s
s
f
luctua
ti
ons
f
or
s
mall
numbe
r
o
f
population
s
ize
,
then
the
r
e
duc
ti
on
o
f
F
i
tnes
s
is
obvious
a
s
the
s
ize
of
the
population
incr
e
a
s
e
s
unti
l
a
c
e
r
tain
va
lue
of
t
he
population
s
ize
whe
r
e
the
F
it
ne
s
s
r
e
mains
s
tea
dy
whe
n
the
population
s
ize
incr
e
a
s
e
s
.
F
igur
e
5
s
hows
the
va
r
iations
of
the
f
it
ne
s
s
a
c
c
or
ding
to
the
number
o
f
it
e
r
a
ti
ons
.
F
r
om
th
is
c
ur
ve
,
we
c
a
n
s
e
e
that
f
it
ne
s
s
va
r
ies
e
nor
mous
ly
with
number
of
it
e
r
a
ti
ons
.
F
or
va
r
iable
it
e
r
a
ti
on
c
ount
va
lues
f
r
om
30
to
4000
it
e
r
a
ti
ons
,
F
it
ne
s
s
ove
r
a
ll
tr
e
nd
is
downw
a
r
d,
f
a
ll
e
n
to
a
bout
0
.
4439
μ
m
.
W
e
noti
c
e
in
thi
s
c
a
s
e
how
the
number
of
it
e
r
a
ti
on
is
s
igni
f
ica
ntl
y
e
f
f
e
c
ts
f
it
n
e
s
s
mor
e
than
the
s
ize
of
population
doe
s
.
F
igur
e
6
s
hows
the
va
r
iations
of
the
f
it
ne
s
s
a
c
c
or
ding
to
the
volt
a
ge
int
e
r
va
ls
of
the
a
c
tuator
s
.
T
his
his
togr
a
m
indi
c
a
tes
that
the
be
s
t
r
a
nge
(
int
e
r
va
l)
of
the
c
ur
r
e
nt
is
va
r
iable
f
r
om
[
-
1e
-
7
to
+
1e
-
7
]
to
obtain
the
be
s
t
F
it
ne
s
s
va
r
ies
les
s
than
0.
61
μ
m
.
T
o
c
onc
lude,
the
be
s
t
c
onf
igur
a
ti
ons
f
o
r
th
e
GA
pa
r
a
mete
r
s
that
lea
d
to
the
be
s
t
wa
ve
-
f
r
ont
c
or
r
e
c
ti
ons
a
r
e
thos
e
who
c
or
r
e
s
pond
to
population
s
ize
≥
20
00,
it
e
r
a
ti
on
c
ount
≥
2500,
a
nd
a
volt
a
ge
int
e
r
va
l
(
-
1e
-
7
…
1e
-
7
).
T
a
ble
3.
Ge
nti
c
a
lgor
it
hm
pa
r
a
mete
r
s
V
a
lu
e
D
e
s
c
r
ip
ti
on
M
in
-
c
oe
f
-
1e
-
7
M
in
im
um va
lu
e
of
vol
ta
ge
t
o be
a
ppl
ie
d on DM
a
c
tu
a
to
r
s
M
a
x
-
c
oe
f
+
1e
-
7
M
a
xi
mum
va
lu
e
of
vol
ta
ge
t
o be
a
ppl
ie
d on D
M
a
c
tu
a
to
r
s
popS
iz
e
2500
P
opul
a
ti
on s
iz
e
(
C
hr
omos
ome
s
numbe
r
)
C
hr
om S
iz
e
80
C
hr
omos
ome
s
iz
e
(
numbe
r
of
ge
ne
s
/a
c
tu
a
to
r
s
)
I
te
r
a
ti
ons
C
ount
>
3000
G
e
ne
ti
c
l
oops
c
ount
C
r
os
s
R
a
t
e
60%
C
r
os
s
ove
r
r
a
te
C
r
os
s
P
oi
nt
s
C
ount
40
C
r
os
s
ove
r
poi
nt
s
c
ount
mut
R
a
te
70%
M
ut
a
ti
on r
a
te
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
o
l
A
hy
br
id
algor
it
hm
for
w
av
e
-
fr
ont
c
or
r
e
c
ti
ons
appli
e
d
to
…
(
M
ohamm
e
d
Se
nan
A
l
Gobi
)
1265
F
igur
e
4.
W
a
ve
-
f
r
ont
c
o
r
r
e
c
ti
on
a
s
f
unc
ti
on
of
pop
s
ize
F
igur
e
5.
W
a
ve
-
f
r
ont
c
or
r
e
c
ti
on
a
s
a
f
unc
ti
on
of
it
e
r
a
ti
on
c
ounts
F
igur
e
6.
His
togr
a
ms
of
wa
ve
-
f
r
ont
c
or
r
e
c
ti
ons
a
s
a
f
unc
ti
on
o
f
the
a
c
tuator
volt
a
ge
int
e
r
va
ls
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
1693
-
6930
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
o
l
,
Vol.
18
,
No
.
3
,
J
une
2020:
12
59
-
12
67
1266
4.
3
.
Com
p
ar
is
on
b
e
t
we
e
n
OO
M
AO
an
d
h
yb
r
id
algorit
h
m
W
e
pr
opos
e
a
hybr
id
s
olut
ion
(
AO
a
nd
GA
)
.
W
h
e
n
the
ini
ti
a
l
population
of
our
GA
is
not
tot
a
ll
y
r
a
ndom,
we
took
s
ome
r
e
a
l
s
olut
ions
f
r
om
the
de
f
a
ult
OO
M
AO
c
los
e
d
loop
(
50%
a
nd
the
r
e
s
t
o
f
in
divi
dua
ls
a
r
e
r
a
ndom)
,
then
we
c
ompar
e
the
r
e
s
ult
s
.
F
igur
e
7
r
e
pr
e
s
e
nts
r
e
s
ult
s
obtaine
d
f
r
om
the
c
ompar
is
on
be
twe
e
n
the
OO
M
AO
whic
h
pr
opos
e
d
in
[
26
,
28]
a
nd
our
hybr
id
s
olut
ion
.
I
t
c
a
n
c
lea
r
ly
be
s
e
e
n
that
ther
e
ha
s
be
e
n
a
s
ha
r
p
de
c
r
e
a
s
e
in
both
gr
a
phs
be
twe
e
n
0
a
nd
20
0
it
e
r
a
ti
on
be
c
a
us
e
50%
of
s
ugge
s
ted
s
olut
ions
a
r
e
r
a
ndom.
Af
ter
300,
hyb
r
id
s
olut
ion
ha
s
only
s
hown
a
s
li
g
ht
gr
owth.
T
his
is
be
c
a
us
e
a
lgor
it
hm
e
xc
ludes
the
wr
ong
s
olut
ions
a
nd
s
tar
t
ke
e
ping
only
the
c
or
r
e
c
t
one
s
.
Af
ter
200,
the
gr
a
ph
ha
s
a
hor
izonta
l
pit
c
h.
B
e
twe
e
n
(
2500
-
3000)
,
ther
e
is
a
s
tabili
ty
in
hybr
id
s
olut
ion
gr
a
ph
whic
h
c
onti
nue
s
to
dr
op
making
a
be
tt
e
r
pe
r
f
or
manc
e
a
f
ter
3000
it
e
r
a
ti
on.
Ove
r
a
ll
,
the
g
r
a
ph
il
lus
tr
a
tes
the
c
ompar
is
on
be
twe
e
n
two
s
olut
ions
.
F
ir
s
tl
y,
we
noti
c
e
that
the
va
lues
of
hybr
id
s
olut
ion
is
bigger
than
OA
s
ol
uti
on.
T
ha
t’
s
be
c
a
us
e
the
number
of
it
e
r
a
ti
ons
is
s
mall
(
les
s
than
200)
whic
h
lea
ds
to
ve
r
y
we
a
k
r
e
s
ult
.
T
he
n
,
the
number
of
it
e
r
a
ti
ons
gr
ows
up
making
a
be
tt
e
r
r
e
s
ult
s
be
c
a
us
e
our
GA
is
ge
tt
ing
opti
mi
z
e
d
by
the
ti
me
.
F
igur
e
7.
C
ompar
is
on
be
twe
e
n
OO
M
AO
s
olut
ion
a
nd
the
pr
opos
e
d
hybr
id
s
olut
ion
5.
CONC
L
USI
ON
T
he
ge
ne
ti
c
a
lgo
r
it
hm
wa
s
a
ppli
e
d
in
a
da
pti
ve
opti
c
a
l
s
ys
tem
to
c
or
r
e
c
t
wa
ve
-
f
r
ont
s
e
ns
or
in
a
dis
tur
be
d
a
tm
os
phe
r
e
.
B
a
s
e
d
on
the
OO
M
AO
,
we
s
im
ulate
the
pe
r
f
or
manc
e
of
a
n
a
da
pti
ve
opti
c
a
l
s
ys
tem
with
tele
s
c
ope
(
r
e
s
olut
ion
54,
diame
ter
8m
)
a
nd
d
e
f
or
mable
m
ir
r
or
c
ontaining
92
a
c
tuator
s
.
W
e
us
e
d
the
be
s
t
pa
r
a
mete
r
s
of
the
p
r
e
vious
s
im
ulation
(
popS
ize
25
00,
I
ter
a
ti
ons
c
ount
>
3000
,
mi
n
-
volt
-
1e
-
7
,
max
-
volt
+
1e
-
7
)
to
c
r
e
a
te
hybr
id
s
olut
ion
(
AO
a
nd
GA
)
.
B
y
c
ompar
ing
the
obtaine
d
r
e
s
ult
s
,
it
wa
s
f
ound
that
us
ing
th
i
s
hybr
id
s
olut
ion
lea
ds
to
be
tt
e
r
e
nha
nc
e
ment
of
AO
pe
r
f
or
manc
e
than
thos
e
pr
oduc
e
d
by
a
pplyi
ng
the
OO
M
AO
with
the
dif
f
e
r
e
nc
e
o
f
0
.
0279µ
m
.
W
e
int
e
nd
in
f
utur
e
wor
ks
to
r
e
duc
e
the
r
e
s
pons
e
ti
me
of
the
GA
a
nd
de
ve
lop
mor
e
opti
mi
z
a
ti
on
a
lgor
it
h
ms
pa
r
ti
c
ula
r
ly
thos
e
r
e
late
d
to
the
im
pr
ove
ment
of
the
wa
ve
-
f
r
ont
s
e
n
s
or
li
ke
:
s
im
ulate
d
a
nne
a
li
ng
(
S
A)
,
a
lgo
r
it
hm
of
pa
tt
e
r
n
e
xtr
a
c
ti
on
(
Alope
x)
a
nd
s
tocha
s
ti
c
pa
r
a
ll
e
l
gr
a
dient
de
s
c
e
nt
(
S
P
GD
)
.
RE
F
E
RE
NC
E
S
[1
]
Z
h
u
X
.
,
K
ah
n
J
.
M.
,
“
Free
-
s
p
ace
o
p
t
i
ca
l
c
o
mmu
n
i
cat
i
o
n
t
h
r
o
u
g
h
at
m
o
s
p
h
er
i
c
t
u
rb
u
l
e
n
ce
c
h
an
n
el
s
,
”
I
E
E
E
Tr
a
n
s
a
c
t
i
o
n
s
o
n
Co
m
m
u
n
i
c
a
t
i
o
n
s
,
v
o
l
.
5
0
,
n
o
.
8
,
p
p
.
1
2
9
3
-
1
3
0
0
,
A
u
g
u
s
t
2
0
0
2
.
[2
]
Ip
p
o
l
i
t
o
L
.
J.
,
“
Rad
i
o
w
a
v
e
p
ro
p
ag
a
t
i
o
n
i
n
s
a
t
el
l
i
t
e
co
m
mu
n
i
cat
i
o
n
s
,
”
S
p
r
i
n
g
er
S
c
i
en
ce
&
B
u
s
i
n
e
s
s
M
ed
ia
,
2
0
1
2
.
[3
]
A
ri
m
o
t
o
Y
.
,
H
ay
a
n
o
Y
.
,
K
l
a
u
s
W
.,
“
H
i
g
h
-
s
p
ee
d
o
p
t
i
cal
feed
er
-
li
n
k
s
y
s
t
em
u
s
i
n
g
ad
a
p
t
i
v
e
o
p
t
i
c
s
,
”
P
r
o
cee
d
i
n
g
s
o
f
S
P
I
E
-
Th
e
In
t
e
r
n
a
t
i
o
n
a
l
S
o
ci
e
t
y
f
o
r
O
p
t
i
ca
l
E
n
g
i
n
eer
i
n
g
,
J
an
u
ary
1
9
9
7
.
[4
]
Si
l
es
G
.
A
.
,
Ri
era
J
.
M
.
,
G
arci
a
-
d
el
-
Pi
n
o
P.
,
“
A
t
mo
s
p
h
er
i
c
at
t
en
u
at
i
o
n
i
n
w
i
re
l
es
s
c
o
mmu
n
i
ca
t
i
o
n
s
y
s
t
em
s
at
mi
l
l
i
me
t
er
an
d
T
H
z
freq
u
en
c
i
es
[W
i
rel
e
s
s
Co
r
n
er],
”
IE
E
E
A
n
t
en
n
a
s
a
n
d
P
r
o
p
a
g
a
t
i
o
n
M
a
g
a
z
i
n
e
,
v
o
l
.
5
7
,
n
o
.
1
,
p
p
.
4
8
-
6
1
,
Feb
ru
ar
y
2
0
1
5
.
[5
]
Barch
ers
J
.
D
.
,
Fri
e
d
D
.
L.
,
“
O
p
t
i
mal
co
n
t
r
o
l
o
f
l
as
er
b
e
ams
fo
r
p
ro
p
a
g
a
t
i
o
n
t
h
ro
u
g
h
a
t
u
r
b
u
l
en
t
me
d
i
u
m,
”
Jo
u
r
n
a
l
o
f
t
h
e
O
p
t
i
ca
l
S
o
c
i
et
y
o
f
A
m
er
i
ca
A
(O
S
A
)
,
v
o
l
.
1
9
,
n
o
.
9
,
p
p
.
1
7
7
9
-
1
7
9
3
,
2
0
0
2
.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
o
l
A
hy
br
id
algor
it
hm
for
w
av
e
-
fr
ont
c
or
r
e
c
ti
ons
appli
e
d
to
…
(
M
ohamm
e
d
Se
nan
A
l
Gobi
)
1267
[6
]
A
b
o
zeed
M
.
I
.
,
A
l
h
i
l
al
i
M
.
,
Y
i
n
L
.
H
.
,
D
i
n
J
.
,
“
Rai
n
at
t
en
u
a
t
i
o
n
s
t
at
i
s
t
i
c
s
fo
r
mo
b
i
l
e
s
at
e
l
l
i
t
e
co
mm
u
n
i
cat
i
o
n
s
es
t
i
mat
e
d
f
ro
m
rad
ar
mea
s
u
reme
n
t
s
i
n
Ma
l
ay
s
i
a,
”
TE
L
KO
M
NIKA
Te
l
eco
m
m
u
n
i
ca
t
i
o
n
C
o
m
p
u
t
i
n
g
E
l
ect
r
o
n
i
c
s
a
n
d
Co
n
t
r
o
l
,
v
o
l
.
1
7
,
n
o
.
3
,
p
p
.
1
1
1
0
-
1
1
1
7
,
J
u
n
e
2
0
1
9
.
[7
]
A
l
h
i
l
al
i
M
.
,
L
am
H
.
,
D
i
n
J
.
,
“
Co
mp
ari
s
o
n
o
f
Rai
n
d
r
o
p
Si
ze
D
i
s
t
r
i
b
u
t
i
o
n
Ch
arac
t
eri
s
t
i
cs
acr
o
s
s
t
h
e
S
o
u
t
h
ea
s
t
A
s
i
a
Reg
i
o
n
,
”
TE
LK
O
M
NIK
A
Tel
ec
o
m
m
u
n
i
c
a
t
i
o
n
Co
m
p
u
t
i
n
g
E
l
ec
t
r
o
n
i
cs
a
n
d
Co
n
t
r
o
l
,
v
o
l
.
1
6
,
n
o
.
6
,
p
p
.
2
5
2
2
-
2
5
2
7
,
D
ecemb
er
2
0
1
8
.
[8
]
Bad
ro
n
K
.
,
Is
mai
l
A
.
F
.
,
D
i
n
J
.
,
T
h
arek
A
.
R.
,
“
Rai
n
i
n
d
u
ced
at
t
e
n
u
a
t
i
o
n
s
t
u
d
i
e
s
fo
r
V
-
b
a
n
d
s
at
e
l
l
i
t
e
c
o
mmu
n
i
ca
t
i
o
n
i
n
t
ro
p
i
ca
l
reg
i
o
n
,”
Jo
u
r
n
a
l
o
f
A
t
m
o
s
p
h
e
r
i
c
a
n
d
S
o
l
a
r
-
T
er
r
e
s
t
r
i
a
l
P
h
y
s
i
c
s
,
v
o
l
.
7
3
,
n
o
.
5
-
6
,
p
p
.
6
0
1
-
6
1
0
,
A
p
r
i
l
2
0
1
1
.
[9
]
Y
an
g
H
.
,
L
i
X
.
,
“
Co
mp
ari
s
o
n
o
f
s
ev
eral
s
t
o
c
h
as
t
i
c
p
aral
l
e
l
o
p
t
i
mi
za
t
i
o
n
al
g
o
r
i
t
h
ms
fo
r
ad
ap
t
i
v
e
o
p
t
i
c
s
s
y
s
t
em
w
i
t
h
o
u
t
a
w
av
efro
n
t
s
en
s
o
r,
”
O
p
t
i
cs
&
La
s
er
Tech
n
o
l
o
g
y
,
v
o
l
.
4
3
,
n
o
.
3
,
p
p
.
6
3
0
-
6
3
5
,
A
p
ri
l
2
0
1
1
.
[1
0
]
Ch
en
E
.
,
Ch
en
g
H
.
,
A
n
Y
.
,
L
i
X
.
,
“
T
h
e
Im
p
ro
v
emen
t
o
f
SPG
D
A
l
g
o
r
i
t
h
m
Co
n
v
er
g
en
ce
i
n
Sat
e
l
l
i
t
e
-
to
-
G
ro
u
n
d
L
as
e
r
Co
mmu
n
i
ca
t
i
o
n
L
i
n
k
s
,
”
P
r
o
ce
d
i
a
E
n
g
i
n
ee
r
i
n
g
,
v
o
l
.
2
9
,
p
p
.
4
0
9
-
4
1
4
,
2
0
12.
[1
1
]
A
v
a
n
ak
i
M
.
R
.
,
H
o
j
j
a
t
o
l
es
l
ami
S
.
,
Sarmad
i
H
.
,
E
b
ra
h
i
mp
o
u
r
R
.
,
Po
d
o
l
ea
n
u
A
.
G
.
,
ed
s
.
,
“
G
en
et
i
c
al
g
o
r
i
t
h
m
fo
r
op
t
i
mi
zat
i
o
n
o
f
o
p
t
i
cal
s
y
s
t
ems
,
”
2
0
1
0
1
8
th
Ir
a
n
i
a
n
Co
n
f
er
en
ce
o
n
E
l
ect
r
i
c
a
l
E
n
g
i
n
ee
r
i
n
g
,
p
p
.
1
7
2
-
1
7
6
,
2
0
1
0
.
[1
2
]
J
ack
s
o
n
J
.
D.
,
“
Cl
as
s
i
ca
l
el
ect
r
o
d
y
n
am
i
cs
j
o
h
n
w
i
l
e
y
&
s
o
n
s
,
”
In
c,
New
Yo
r
k
,
p
p
.
8
3
2
,
1
9
9
9
.
[1
3
]
Y
u
P
.
,
G
l
o
v
er
I
.
A
.
,
W
at
s
o
n
P
.
,
D
av
i
es
O
.
,
V
en
t
o
u
ras
S
.
,
W
ren
ch
C.
,
“
Rev
i
ew
an
d
c
o
mp
ar
i
s
o
n
o
f
t
ro
p
o
s
p
h
eri
c
s
ci
n
t
i
l
l
at
i
o
n
p
red
i
c
t
i
o
n
mo
d
e
l
s
fo
r
s
at
el
l
i
t
e
co
mmu
n
i
ca
t
i
o
n
s
,
”
In
t
e
r
n
a
t
i
o
n
a
l
Jo
u
r
n
a
l
o
f
S
a
t
el
l
i
t
e
Co
m
m
u
n
i
c
a
t
i
o
n
s
A
n
d
Net
wo
r
ki
n
g
,
v
o
l
.
2
4
,
n
o
.
4
,
May
2
0
0
6
.
[1
4
]
V
o
y
ez
J
.
,
“
Mes
u
re
s
o
p
t
i
q
u
es
d
e
p
r
o
fi
l
s
d
e
t
u
rb
u
l
e
n
ce
at
mo
s
p
h
ér
i
q
u
e
p
o
u
r
l
e
s
fu
t
u
r
s
s
y
s
t
èmes
d
'o
p
t
i
q
u
e
ad
a
p
t
a
t
i
v
e,
”
Ph
D
T
h
es
i
s
,
U
n
i
ve
r
s
i
t
é
N
i
ce
S
o
p
h
i
a
A
n
t
i
p
o
l
i
s
,
2
0
1
3
.
[1
5
]
K
o
l
m
o
g
o
r
o
v
A
.
N.
,
“
T
h
e
l
o
cal
s
t
ru
c
t
u
re
o
f
t
u
r
b
u
l
en
c
e
i
n
i
n
co
m
p
res
s
i
b
l
e
v
i
s
c
o
u
s
fl
u
i
d
fo
r
v
ery
l
ar
g
e
Rey
n
o
l
d
s
n
u
mb
ers
,
”
P
r
o
cee
d
i
n
g
s
o
f
t
h
e
R
o
y
a
l
S
o
ci
e
t
y
o
f
Lo
n
d
o
n
S
er
i
e
s
A
:
M
a
t
h
em
a
t
i
ca
l
a
n
d
P
h
ys
i
c
a
l
S
c
i
en
ce
s
,
v
o
l
.
4
3
4
,
n
o
.
1
8
9
0
,
p
p
.
9
-
1
3
,
J
u
l
y
1
9
9
1
.
[1
6
]
Bru
s
s
a
rd
G
.
an
d
W
a
t
s
o
n
P.
A
.
,
“
A
t
m
o
s
p
h
er
i
c
m
o
d
e
l
l
i
n
g
an
d
mi
l
l
i
met
re
w
av
e
p
ro
p
ag
at
i
o
n
,
”
S
p
r
i
n
g
er
Net
h
e
r
l
a
n
d
s
,
1
9
9
4
.
[1
7
]
Ch
a
J
-
W
.
,
Bal
l
es
t
a
J
.
,
So
P
.
T.
,
“
Sh
ack
-
H
art
ma
n
n
w
a
v
efro
n
t
-
s
e
n
s
o
r
-
b
a
s
ed
ad
a
p
t
i
v
e
o
p
t
i
cs
s
y
s
t
em
fo
r
mu
l
t
i
p
h
o
t
o
n
mi
cro
s
co
p
y
,
”
Jo
u
r
n
a
l
o
f
b
i
o
m
e
d
i
c
a
l
o
p
t
i
cs
,
v
o
l
.
1
5
,
n
o
.
4
,
J
u
l
y
2
0
1
0
.
[1
8
]
Ro
u
s
s
et
G
.
,
“W
av
e
-
fr
o
n
t
s
en
s
o
r
s
,
”
A
d
a
p
t
i
ve
o
p
t
i
c
s
i
n
a
s
t
r
o
n
o
m
y
,
1
9
9
9
.
[1
9
]
Po
y
n
e
er
L
.
A.
,
“
Scen
e
-
b
as
e
d
Sh
ack
-
H
art
ma
n
n
w
av
e
-
fr
o
n
t
s
en
s
i
n
g
:
an
a
l
y
s
i
s
an
d
s
i
mu
l
at
i
o
n
,
”
A
p
p
l
i
ed
O
p
t
i
c
s
,
v
o
l
.
4
2
,
n
o
.
2
9
,
p
p
.
5
8
0
7
-
5
8
1
5
,
2
0
0
3
.
[2
0
]
Rai
s
M
.
,
Mo
rel
J
-
M
.
,
T
h
i
eb
a
u
t
C
.
,
D
e
l
v
i
t
J
-
M
.
,
Facci
o
l
o
G
.
,
“
Imp
ro
v
i
n
g
w
a
v
efro
n
t
s
en
s
i
n
g
w
i
t
h
a
S
h
ack
-
H
art
m
an
n
d
ev
i
ce,
”
A
p
p
l
i
ed
O
p
t
i
cs
,
v
o
l
.
5
5
,
n
o
.
2
8
,
p
p
.
7
8
3
6
-
7
8
4
6
,
2
0
1
6
.
[2
1
]
W
ey
ra
u
ch
T
.
,
V
o
ro
n
t
s
o
v
M
.
A
.
,
Bi
fan
o
T
.
G
.
,
H
ammer
J
.
A
.
,
Co
h
en
M
.
,
Cau
w
e
n
b
er
g
h
s
G
.
,
“
Mi
cro
s
cal
e
ad
ap
t
i
v
e
o
p
t
i
c
s
:
w
av
e
-
fr
o
n
t
co
n
t
r
o
l
w
i
t
h
a
µ
-
mi
rro
r
arra
y
an
d
a
V
L
SI
s
t
o
c
h
as
t
i
c
g
rad
i
en
t
d
es
ce
n
t
c
o
n
t
ro
l
l
er,
”
A
p
p
l
i
ed
O
p
t
i
cs
,
v
o
l
.
4
0
,
n
o
.
2
4
,
p
p
.
4
2
4
3
-
4
2
5
3
,
2
0
0
1
.
[2
2
]
G
refen
s
t
e
t
t
e
J
.
J.
,
“
G
en
et
i
c
a
l
g
o
ri
t
h
m
s
an
d
t
h
ei
r
a
p
p
l
i
ca
t
i
o
n
s
,”
p
r
o
ceed
i
n
g
s
o
f
t
h
e
s
eco
n
d
i
n
t
er
n
a
t
i
o
n
a
l
co
n
f
e
r
en
ce
o
n
g
en
e
t
i
c
a
l
g
o
r
i
t
h
m
s
:
P
s
y
c
h
o
l
o
g
y
Pres
s
,
J
u
l
y
1
9
8
7
.
[2
3
]
Ch
amb
er
s
L
.
D.
,
“
T
h
e
p
ract
i
cal
h
a
n
d
b
o
o
k
o
f
g
e
n
et
i
c
al
g
o
r
i
t
h
ms
,
”
ap
p
l
i
ca
t
i
o
n
s
:
Ch
a
p
m
a
n
a
n
d
H
a
l
l
/
C
R
C
,
p
p
.
5
4
4
,
D
ecemb
er
2
0
0
0
.
[2
4
]
N
o
s
at
o
H
.
,
It
at
an
i
T
.
,
Mu
rak
aw
a
M
.
,
H
i
g
u
c
h
i
T
.
,
N
o
g
u
ch
i
H
.
,
“
A
u
t
o
mat
i
c
w
av
e
-
fro
n
t
co
rrec
t
i
o
n
o
f
a
femt
o
s
ec
o
n
d
l
as
er
u
s
i
n
g
g
e
n
et
i
c
al
g
o
r
i
t
h
m,
”
2
0
0
4
IE
E
E
In
t
er
n
a
t
i
o
n
a
l
Co
n
f
er
e
n
ce
o
n
S
y
s
t
e
m
s
,
M
a
n
a
n
d
Cyb
er
n
et
i
cs
(IE
E
E
Ca
t
.
No
.
0
4
C
H
3
7
5
8
3
)
,
T
h
e
H
a
g
u
e,
v
o
l
.
4
,
p
p
.
3
6
7
5
-
3
6
7
9
,
2
0
0
4
.
[2
5
]
Y
an
g
P
.
,
H
u
S
.
,
Ch
en
S
.
,
Y
an
g
W
.
,
X
u
B
.
,
J
i
an
g
W
.
,
“
Res
earch
o
n
t
h
e
p
h
a
s
e
ab
errat
i
o
n
co
rrect
i
o
n
w
i
t
h
a
d
efo
rma
b
l
e
mi
rro
r
co
n
t
r
o
l
l
ed
b
y
a
g
en
e
t
i
c
al
g
o
r
i
t
h
m,
”
Jo
u
r
n
a
l
o
f
P
h
y
s
i
c
s
:
Co
n
f
er
e
n
ce
S
e
r
i
e
s
,
v
o
l
.
4
8
,
n
o
.
1
,
p
p
.
1
0
1
7
-
1
0
2
4
,
O
ct
o
b
er
2
0
0
6
.
[2
6
]
Co
n
a
n
R
.
,
Co
rrei
a
C
.
,
“
O
b
j
ect
-
o
ri
e
n
t
e
d
Mat
l
ab
ad
a
p
t
i
v
e
o
p
t
i
c
s
t
o
o
l
b
o
x
,
”
in
P
r
o
ceed
i
n
g
s
o
f
S
P
IE
-
T
h
e
In
t
er
n
a
t
i
o
n
a
l
S
o
c
i
et
y
f
o
r
O
p
t
i
c
a
l
E
n
g
i
n
ee
r
i
n
g
,
p
p
.
9
1
4
8
-
9
1
4
8
6
C,
A
u
g
u
s
t
2
0
1
4
.
[2
7
]
Ch
u
l
an
i
H
.
M.
,
Ro
d
rí
g
u
ez
-
Ram
o
s
J
.
M.
,
“
Prel
i
mi
n
ary
p
erfo
rman
ce
res
u
l
t
s
o
f
t
h
e
w
e
i
g
h
t
ed
Fo
u
ri
er
p
h
a
s
e
s
l
o
p
e
cen
t
r
o
i
d
i
n
g
me
t
h
o
d
f
o
r
Sh
ac
k
–
H
art
ma
n
n
w
av
efr
o
n
t
s
e
n
s
o
rs
o
b
t
a
i
n
ed
w
i
t
h
t
h
e
O
O
MA
O
s
i
mu
l
at
o
r,
”
Co
n
f
e
r
e
n
ce:
A
d
a
p
t
i
ve
O
p
t
i
cs
f
o
r
E
xt
r
em
e
l
y
La
r
g
e
Tel
e
s
co
p
es
V
(A
O
4
E
LT5
)
,
J
u
n
e
2
0
1
7
.
[2
8
]
G
i
t
h
u
b
E
.
,
“
E
t
h
ere
u
m
rco
n
a
n
/
O
O
M
A
O
.
O
b
j
ect
-
O
ri
e
n
t
e
d
,
Ma
t
l
ab
&
A
d
ap
t
i
v
e
O
p
t
i
c
s
,
”
2
0
1
8
.
[
O
n
l
i
n
e
]
.
A
v
ai
l
a
b
l
e
:
h
t
t
p
s
:
/
/
g
i
t
h
u
b
.
co
m
/
rco
n
an
/
O
O
MA
O
.
2
0
1
8
Evaluation Warning : The document was created with Spire.PDF for Python.