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1832
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ted
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o
m
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u
t
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h
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t
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rd
e
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trad
i
ti
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l
c
o
m
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m
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th
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sig
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ircu
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r
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f
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d
d
i
ti
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th
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sig
n
d
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it
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m
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e
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w
wa
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e
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l
with
th
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d
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n
d
p
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c
ti
c
a
l
si
m
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h
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m
.
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m
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l
re
li
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d
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th
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imp
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tatio
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se
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m
a
d
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ted
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e
sig
n
e
d
sy
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s.
Wh
ich
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h
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ra
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teriz
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ra
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t
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it
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t
h
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ra
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T
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sim
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latio
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teri
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)
to
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K
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:
Ad
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Ar
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a
rticle
u
n
d
e
r
th
e
CC BY
-
SA
li
c
e
n
se
.
C
o
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r
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s
p
o
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A
uth
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r
:
Qab
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Q.
T
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B
asra
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co
m
1.
I
NT
RO
D
UCT
I
O
N
Ma
n
y
ap
p
licatio
n
s
an
d
p
r
o
c
ess
es
ar
e
u
tili
ze
d
in
co
n
ju
n
ctio
n
with
alg
o
r
ith
m
s
to
c
o
m
p
lete
th
e
ca
lcu
latio
n
s
.
DNA
was
u
s
ed
i
n
th
e
co
m
p
u
tatio
n
s
i
n
th
e
r
ec
e
n
t
p
ast
[
1
]
.
T
h
e
v
er
y
h
ig
h
s
p
e
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in
teg
r
ated
cir
cu
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(
VHSI
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VHDL
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r
tech
n
iq
u
e
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tili
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d
in
th
is
d
o
m
ain
[
2
]
,
[
3
]
.
T
o
d
ay
o
n
e
o
f
th
e
m
o
s
t
im
p
o
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tan
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t
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iq
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es
o
f
s
o
f
t
co
m
p
u
tin
g
is
ar
tific
ial
n
eu
r
al
n
etwo
r
k
s
,
wh
ich
h
av
e
b
ee
n
r
esear
ch
ed
,
s
tu
d
ied
an
d
ap
p
li
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o
n
a
lar
g
e
s
ca
le
d
u
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in
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t
h
e
last
two
d
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I
n
r
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n
t
tim
es,
th
e
n
eu
r
al
n
etwo
r
k
h
as
b
ee
n
wid
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d
in
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f
ield
s
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m
ed
ical,
ec
o
n
o
m
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d
,
o
th
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s
[
4
]
-
[
6
]
,
b
ec
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s
e
th
e
n
eu
r
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n
etwo
r
k
is
ch
ar
ac
ter
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p
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d
ata
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its
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ize,
m
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at
it
p
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ca
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ty
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p
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in
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in
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d
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to
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way
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f
th
i
n
k
in
g
in
t
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e
h
u
m
an
m
in
d
[
7
]
.
R
esear
ch
co
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tin
u
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in
th
e
f
ield
o
f
d
esig
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i
n
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lo
g
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ates
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s
in
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th
e
n
eu
r
a
l
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etwo
r
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,
a
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d
t
h
e
m
o
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t
im
p
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r
tan
t
o
f
wh
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is
wh
at
Yellam
r
aju
et
a
l.
[
8
]
,
p
r
es
en
ted
in
ac
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iev
in
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m
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b
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c
lo
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ates.
Als
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b
asic lo
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ates (
AND,
O
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XO
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wer
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with
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ch
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e
im
p
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n
tatio
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f
an
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m
p
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n
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r
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,
w
h
er
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tr
u
th
tab
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u
s
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to
v
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if
y
th
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co
m
p
letio
n
o
f
lo
g
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cir
cu
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u
s
in
g
n
eu
r
al
n
etwo
r
k
s
in
an
ea
s
i
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way
[
9
]
.
T
h
e
n
eu
r
al
n
etwo
r
k
h
as e
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ter
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in
to
a
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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J
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)
1833
wid
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r
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p
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a
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tech
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with
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[
1
1
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[
1
5
]
,
b
u
s
in
ess
ap
p
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s
[
1
6
]
an
d
r
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g
n
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r
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wh
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in
clu
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ec
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h
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d
th
r
ee
-
d
im
en
s
io
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al
r
ec
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g
n
itio
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[
1
7
]
.
I
n
d
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in
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v
a
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io
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s
ar
ith
m
et
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esear
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clu
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b
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n
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lim
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to
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b
in
a
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y
s
y
s
tem
[
1
8
]
,
th
e
r
esid
u
al
n
u
m
b
er
[
1
9
]
,
th
e
r
ed
u
n
d
a
n
t
n
u
m
b
er
[
2
0
]
,
m
u
ltiv
alu
ed
r
a
d
ix
n
u
m
b
er
[
2
1
]
,
a
n
d
s
ig
n
e
d
d
i
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it
n
u
m
b
er
s
y
s
tem
wh
ich
g
i
v
es
two
ad
v
a
n
tag
es,
f
ir
s
t
all
o
ws
to
p
er
f
o
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m
in
g
ar
ith
m
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atio
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in
p
ar
all
el
m
an
n
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ed
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ce
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n
u
m
b
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o
f
ca
r
r
y
p
r
o
p
a
g
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n
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te
p
s
[
2
2
]
-
[
2
4
]
.
wh
ile
d
esig
n
in
g
lo
g
ic
cir
cu
its
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s
in
g
n
eu
r
al
n
etwo
r
k
s
,
it
was
m
o
s
tly
u
s
in
g
b
in
ar
y
s
y
s
tem
s
[
2
5
]
-
[
2
7
]
,
a
f
ew
p
ap
er
s
d
ea
lt
with
th
e
d
esig
n
in
a
n
eu
r
al
n
etwo
r
k
u
s
in
g
s
ig
n
d
ig
it
n
u
m
b
er
s
y
s
tem
as
co
n
s
tr
u
ct
co
u
n
ter
s
b
y
C
o
to
f
an
a
an
d
Vass
iliad
is
[
2
8
]
.
I
n
co
m
p
ar
is
o
n
to
an
alo
g
co
m
p
u
ti
n
g
,
d
ig
ital
co
m
p
u
tin
g
ap
p
r
o
ac
h
es
h
av
e
al
r
ea
d
y
g
en
er
ated
en
o
r
m
o
u
s
g
ain
s
i
n
s
p
ee
d
,
ac
cu
r
ac
y
,
a
n
d
a
d
a
p
tab
ilit
y
.
Ho
wev
er
,
d
u
e
to
t
h
e
"Vo
n
Neu
m
an
n
b
o
ttlen
ec
k
p
r
o
b
lem
,
"
d
ig
ital
e
lectr
o
n
ic
co
m
p
u
ter
s
a
r
e
u
n
ab
l
e
to
p
r
o
ce
s
s
v
ast
am
o
u
n
ts
o
f
d
ata
at
a
h
ig
h
r
ate.
T
o
o
v
er
c
o
m
e
th
is
p
r
o
b
lem
,
we
s
u
g
g
est
th
is
p
ap
er
as
o
n
e
o
f
th
e
s
o
lu
tio
n
s
,
th
e
p
r
o
p
o
s
e
m
o
d
el
h
as
b
ee
n
estab
lis
h
ed
u
s
in
g
th
e
p
ar
allel
alg
o
r
ith
m
in
o
r
d
er
t
o
p
e
r
f
o
r
m
th
e
ar
ith
m
etic
o
p
er
atio
n
s
s
u
ch
as
o
n
e
s
tep
,
two
-
s
tep
,
an
d
th
r
ee
-
s
tep
al
g
o
r
ith
m
.
I
n
th
is
wo
r
k
,
two
ar
ith
m
etic
cir
cu
its
d
esig
n
ed
f
o
r
a
d
d
itio
n
s
ig
n
ed
d
ig
it
n
u
m
b
er
s
y
s
tem
d
e
p
en
d
in
g
o
n
th
is
alg
o
r
ith
m
u
s
in
g
n
e
u
r
al
n
etwo
r
k
s
,
in
a
n
ew
way
th
at
g
av
e
an
ef
f
icien
t
s
y
s
tem
in
clu
d
e
ch
o
o
s
in
g
s
u
itab
le
alg
o
r
ith
m
an
d
m
ath
em
atic
al
f
u
n
ctio
n
th
at
p
er
f
o
r
m
its
r
u
les
in
an
ar
tific
ial
n
eu
r
al
n
etwo
r
k
.
2.
B
ACK
G
RO
UND
2
.
1
.
Art
if
ici
a
l neura
l net
wo
rk
princip
le
T
h
e
f
ir
s
t
s
im
u
latin
g
m
o
d
el
to
th
e
wo
r
k
o
f
a
b
i
o
lo
g
ical
n
eu
r
o
n
p
r
esen
ted
b
y
th
e
s
cien
tis
t
Mc
C
u
llo
ch
an
d
Pit
ts
in
1
9
4
3
,
as
co
m
p
u
tatio
n
al
m
o
d
el
f
o
r
a
b
i
n
ar
y
-
th
r
esh
o
ld
u
n
it
r
ep
r
esen
ts
an
a
r
tific
ial
n
er
v
e
ce
ll
o
p
er
atin
g
in
s
ep
ar
ate
tim
e
b
y
in
teg
r
atin
g
m
ath
e
m
atica
l
lo
g
ic
an
d
n
eu
r
o
p
h
y
s
io
lo
g
y
[
2
9
]
.
T
h
e
o
u
tp
u
t
o
f
th
e
n
eu
r
o
n
is
eq
u
als
o
n
e
wh
en
t
h
e
in
p
u
t
o
f
th
e
ac
tiv
atio
n
f
u
n
ct
io
n
is
m
o
r
e
th
a
n
o
r
eq
u
iv
alen
t
to
th
r
esh
o
ld
ter
m
o
th
er
wis
e
th
e
o
u
t
p
u
t
is
eq
u
al
to
-
1
.
T
h
e
s
im
p
lest
f
o
r
m
o
f
an
ar
tific
ial
n
eu
r
al
n
etwo
r
k
is
m
ad
e
u
p
o
f
th
e
f
o
llo
win
g
co
m
p
o
n
en
ts
:
th
e
i
n
p
u
t
lay
e
r
,
wh
ich
i
n
clu
d
es
t
h
e
g
r
o
u
p
o
f
en
t
r
ies
th
at
ar
e
s
u
m
m
ed
in
to
th
e
s
u
m
m
atio
n
f
u
n
ctio
n
,
a
n
d
th
e
n
th
e
p
r
o
ce
s
s
in
g
tak
es
p
lace
in
th
e
ac
tiv
atio
n
f
u
n
ctio
n
th
at
r
e
p
r
esen
ts
th
e
o
u
tp
u
t
lay
er
as
s
h
o
wn
in
Fig
u
r
e
1
,
th
e
s
im
p
ler
f
o
r
m
f
o
r
th
e
m
u
lti
-
lay
er
ar
tific
ial
n
etwo
r
k
,
w
h
ich
is
a
m
o
r
e
co
m
p
lex
lev
el
th
an
t
h
e
s
im
p
le
n
etwo
r
k
,
co
n
s
is
ts
o
f
an
i
n
p
u
t
la
y
er
,
h
id
d
en
lay
er
,
an
d
th
e
o
u
tp
u
t
lay
er
s
h
o
wn
in
t
h
e
Fig
u
r
e
1
[
1
1
]
.
Fig
u
r
e
1
.
Stru
ctu
r
e
o
f
an
ar
tifi
cial
n
eu
r
o
n
an
d
a
m
u
ltil
ay
er
ed
n
eu
r
al
n
etwo
r
k
[
1
1
]
:
(
a)
ar
tifi
cial
n
eu
r
o
n
an
d
(
b
)
m
u
lti lay
e
r
ar
tific
ial
n
etwo
r
k
R
o
s
en
b
latt,
an
Am
er
ican
p
s
y
c
h
o
lo
g
is
t,
p
r
o
p
o
s
ed
th
e
p
er
ce
p
t
r
o
n
as
a
c
o
m
p
u
ter
m
o
d
el
f
o
r
n
eu
r
o
n
s
in
1
9
5
8
.
T
h
e
in
tr
o
d
u
ctio
n
o
f
d
ig
i
tal
co
n
n
ec
tiv
ity
weig
h
ts
was
th
e
m
o
s
t
s
ig
n
if
ican
t
b
r
ea
k
th
r
o
u
g
h
.
T
h
e
m
o
d
el
o
f
a
p
er
ce
p
tr
o
n
a
n
e
u
r
o
n
co
n
tain
s
t
h
r
ee
f
u
n
d
am
e
n
tal
co
m
p
o
n
en
ts
as sh
o
wn
in
Fig
u
r
e
2
[
7
]
,
[
1
1
]
,
[
2
7
]
,
[
3
0
]
.
−
Sy
n
ap
s
es: wh
ich
ar
e
d
eter
m
i
n
ed
b
y
t
h
e
weig
h
ts
'
p
o
wer
.
−
An
ad
d
er
is
b
ein
g
u
s
ed
t
o
ad
d
th
e
in
p
u
t sig
n
als to
g
et
h
er
.
−
T
h
e
o
u
tp
u
t r
an
g
e
is
co
n
tr
o
lled
u
s
in
g
a
n
o
n
-
lin
ea
r
ac
tiv
atio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
24
,
No
.
3
,
Dec
em
b
er
2
0
2
1
:
1
8
3
2
-
1
8
3
9
1834
Fig
u
r
e
2
.
T
y
p
ical
p
er
ce
p
t
r
o
n
s
tr
u
ctu
r
e
[
2
3
]
2
.
2
.
B
ina
ry
s
ig
ned dig
it
nu
m
ber
s
y
s
t
em
On
e
o
f
th
e
n
u
m
b
er
r
ep
r
esen
t
atio
n
s
y
s
tem
s
is
th
e
b
in
a
r
y
m
o
d
if
ied
s
ig
n
ed
-
d
ig
it
(
B
MSD)
n
u
m
b
er
s
y
s
tem
.
B
M
SD
d
is
p
lay
s
b
o
u
n
d
ar
y
lo
a
d
g
en
er
atio
n
to
o
n
e
p
lace
o
n
th
e
lef
t
th
r
o
u
g
h
th
e
ad
d
itio
n
an
d
s
u
b
tr
ac
tio
n
o
p
e
r
atio
n
s
o
f
d
ig
i
tal
co
m
p
u
ter
s
.
T
h
e
b
in
ar
y
s
ig
n
ed
d
ig
it
n
u
m
b
er
s
allo
w
th
e
co
m
p
letio
n
o
f
th
e
ad
d
itio
n
a
n
d
s
u
b
tr
ac
tio
n
o
p
er
a
tio
n
s
s
im
u
ltan
eo
u
s
ly
,
r
eg
ar
d
le
s
s
o
f
th
e
len
g
th
o
f
th
e
n
u
m
er
ic
al
o
p
er
a
n
d
s
,
wh
ic
h
p
r
o
v
id
es
th
e
ad
v
a
n
tag
e
o
f
p
a
r
allelis
m
with
o
n
e
ex
ec
u
tio
n
tim
e.
I
n
B
MSD
n
u
m
b
er
f
o
r
m
,
th
e
d
ec
im
al
n
u
m
b
e
r
is
ex
p
r
ess
ed
as [
3
1
]
,
[
3
2
]
:
Dce
im
al=
∑
−
1
=
0
(
1
)
W
h
er
e
;
Dce
im
al:
T
h
e
n
u
m
er
al
i
n
d
ec
i
m
al
f
o
r
m
.
X
i
: is th
e
B
MS
D
n
u
m
b
er
'
s
i
-
th
d
ig
it ser
ial,
wh
er
e
{
X
i
,…
-
1
,
0
,
1
,
…,
:
≤
r
-
1
}.
r
: is th
e
f
o
r
m
o
f
B
MSD
n
u
m
b
er
s
y
s
tem
's r
ad
ix
v
alu
e,
a
n
d
in
a
B
MSD
n
u
m
b
er
,
n
is
th
e
n
u
m
b
er
o
f
d
ig
its
.
2
.
3
.
Det
a
ils
t
hree
-
s
t
ep
f
o
rm
a
lg
o
rit
hm
Dep
en
d
in
g
o
n
th
e
n
u
m
b
e
r
o
f
s
tep
s
th
er
e
ar
e
t
h
r
ee
ty
p
es
o
f
alg
o
r
ith
m
s
:
o
n
e
-
s
tep
alg
o
r
ith
m
[
3
3
]
,
two
-
s
tep
alg
o
r
ith
m
[
3
4
]
,
an
d
th
r
ee
-
s
tep
alg
o
r
ith
m
[
3
5
]
,
alg
o
r
ith
m
s
co
n
s
is
tin
g
o
f
o
n
e
an
d
two
s
tep
s
h
av
e
a
s
m
all
n
u
m
b
er
o
f
s
tep
s
,
b
u
t
th
e
d
if
f
icu
lty
o
f
im
p
lem
e
n
t
atio
n
is
in
cr
ea
s
ed
d
u
e
to
th
eir
d
ep
en
d
en
ce
o
n
p
r
ev
io
u
s
v
alu
es.
T
h
e
t
h
r
ee
-
s
tep
alg
o
r
it
h
m
is
ea
s
y
to
im
p
lem
en
t.
Ass
u
m
e
th
at
a
u
g
en
d
s
X
an
d
ad
d
e
n
d
Y
h
a
v
e
B
MSD
r
ep
r
esen
tatio
n
s
.
X
BMSD
= X
n
-
1
,
---
, X
i
,
---
,
X
0
.
Y
BMSD
= Y
n
-
1
,
---
, Y
i
,
---
, Y
0
.
T
h
e
f
o
llo
win
g
th
r
ee
s
tep
o
f
s
u
m
m
atio
n
r
u
les as e
x
p
lain
ed
in
T
ab
le
1
,
p
er
f
o
r
m
ed
ac
co
r
d
in
g
to
th
e
(
2
)
,
(
3
)
,
an
d
(
4
)
:
Step
-
o
n
e:
C
alcu
late
X
i
+Y
i
=
2
T
i+
1
+W
i
(
i=0
,
---
, n
-
1)
(
2
)
Step
-
two
: Co
m
p
u
te
T
i
+W
i
=
2
T
’
i+
1
+W’
i
(
i=
0
,
-
-
-
, n
-
1)
(
3
)
Fin
ally
,
Step
-
th
r
ee
: Calcu
late
S
i
=T
’
i
+W’
i
(
i=
0
,
-
-
-
, n
-
1)
(
4
)
T
ab
le
1
.
T
h
r
ee
-
s
tep
alg
o
r
ith
m
r
u
les
I
n
p
u
t
s
S
t
e
p
o
n
e
r
u
l
e
S
t
e
p
t
w
o
r
u
l
e
S
t
e
p
t
h
r
e
e
r
u
l
e
X
Y
T
(
C
a
r
r
y
)
W
(
S
u
m)
T'
(
C
a
r
r
y
)
W'
(
S
u
m)
F
i
n
a
l
S
u
m
(
T)
-
1
-
1
-
1
0
-
1
0
-
1
-
1
0
-
1
1
0
-
1
-
1
-
1
1
0
0
0
0
0
0
-
1
-
1
1
0
-
1
-
1
0
0
0
0
0
0
0
0
1
1
-
1
0
1
1
1
-
1
0
0
0
0
0
1
0
1
-
1
0
1
1
1
1
1
0
1
0
1
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
I
mp
leme
n
ta
tio
n
th
r
ee
-
s
tep
a
l
g
o
r
ith
m
b
a
s
ed
o
n
s
ig
n
ed
d
i
g
it n
u
mb
er sys
tem
b
y
… (
Qa
b
ee
la
Q.
Th
a
b
it
)
1835
3.
T
H
E
O
F
F
E
R
E
D
ADD
I
T
I
O
N
AL
G
O
RI
T
H
M
S
T
RUC
T
URE
3
.
1
.
Ste
p o
ne
rule/T
-
ca
rr
y
t
r
a
ns
f
o
rm
a
t
io
n
T
h
e
f
ir
s
t
r
u
le
is
,
T
-
ca
r
r
y
tr
a
n
s
f
o
r
m
atio
n
th
e
o
u
tp
u
t
eq
u
al
to
1
in
th
r
ee
ca
s
es
(
1
+1
)
,
(
1
+0
)
,
an
d
(
0
+1
)
wh
ile
it
r
esu
lts
a
(
-
1
)
in
th
e
f
o
llo
win
g
th
r
ee
ca
s
es
(
-
1+
-
1
)
,
(
-
1
+0
)
,
a
n
d
(
0
+
-
1
)
as
ex
p
lain
ed
in
T
ab
le
1
,
th
o
s
e
ar
e
d
esig
n
ed
b
y
u
s
in
g
two
f
u
n
ctio
n
s
(
s
u
m
m
atio
n
a
n
d
T
-
tr
a
n
s
f
o
r
m
atio
n
)
as
s
h
o
wn
in
Fig
u
r
e
3
.
W
h
er
e
Fu
n
.
1
in
d
icate
s
to
f
u
n
ctio
n
1
wh
ic
h
r
ep
r
esen
t th
e
o
u
tp
u
t o
f
T
-
tr
a
n
s
f
o
r
m
atio
n
as (
5
)
.
F
un
.
1
=
{
−
1
,
S
<
0
1
,
S
>
0
(
5
)
Fig
u
r
e
3
.
T
-
tr
an
s
f
o
r
m
atio
n
f
u
n
ctio
n
3
.
2
.
Ste
p o
ne
rule/W
-
s
um
t
r
a
ns
f
o
rm
a
t
io
n
Th
e
f
ir
s
t
r
u
le
is
/
W
-
s
u
m
tr
an
s
f
o
r
m
atio
n
th
e
o
u
tp
u
t
e
q
u
al
to
-
1
in
two
ca
s
es
(
1
+0
)
,
an
d
(
0
+
1
)
wh
ile
its
r
esu
lts
a
(
1
)
i
n
two
ca
s
es
(
-
1
+0
)
,
a
n
d
(
0
+
-
1
)
as
e
x
p
lain
ed
in
T
a
b
le
1
,
th
o
s
e
a
r
e
d
e
s
ig
n
ed
b
y
u
s
in
g
a
co
m
b
in
atio
n
o
f
s
u
m
m
atio
n
a
n
d
d
elta
f
u
n
ctio
n
as
s
h
o
wn
i
n
Fig
u
r
e
4
.
W
h
er
e
Fu
n
.
2
in
d
icate
s
to
f
u
n
ctio
n
2
wh
ich
r
ep
r
esen
t th
e
o
u
tp
u
t o
f
W
-
tr
an
s
f
o
r
m
atio
n
as (
6
)
.
Fu
n
.
2
=
-
δ(
S
-
1
)
+
δ(
S+1
)
(
6
)
Fig
u
r
e
4
.
W
-
tr
an
s
f
o
r
m
atio
n
f
u
n
ctio
n
3
.
3
.
Ste
p t
wo
rule/T
'
-
ca
rr
y
t
ra
ns
f
o
rm
a
t
io
n
I
n
s
tep
–
two
r
u
le/T
'
-
ca
r
r
y
tr
a
n
s
f
o
r
m
atio
n
th
e
o
u
tp
u
t
eq
u
al
to
1
in
o
n
e
ca
s
e
(
1
+1
)
wh
ile
its
r
esu
lts
a
(
-
1
)
in
o
n
e
ca
s
e
(
-
1+
-
1
)
as
e
x
p
lain
ed
in
T
ab
le
1
,
th
o
s
e
a
r
e
p
er
f
o
r
m
e
d
b
y
u
s
in
g
s
u
m
m
a
tio
n
an
d
th
r
esh
o
ld
f
u
n
ctio
n
as
s
h
o
wn
in
Fig
u
r
e
5
.
W
h
er
e
Fu
n
.
3
in
d
icat
es
to
f
u
n
ctio
n
3
wh
ich
r
ep
r
e
s
en
t
th
e
o
u
tp
u
t
o
f
T'
-
tr
an
s
f
o
r
m
atio
n
as (
7
)
.
F
un
.
3
=
δ
(
S
−
2
)
+
−
δ
(
S
+
2
)
=
{
−
1
,
S
=
−
2
1
,
S
=
2
(
7
)
Fig
u
r
e
5
.
T
'
-
tr
an
s
f
o
r
m
atio
n
f
u
n
ctio
n
3
.
4
.
Ste
p
-
t
w
o
rule
/W'
-
s
um
t
ra
ns
f
o
rm
a
t
io
n
I
n
s
tep
–
two
r
u
le
/W
'
-
s
u
m
tr
a
n
s
f
o
r
m
atio
n
th
e
o
u
tp
u
t
eq
u
al
to
1
if
two
ca
s
es
(
1
+0
)
,
o
r
(
0
+1
)
wh
ile
its
r
esu
lts
a
(
-
1
)
if
th
e
f
o
llo
win
g
two
ca
s
es
(
-
1
+0
)
,
o
r
(
0
+
-
1
)
as
ex
p
lain
ed
in
T
ab
le
1
,
th
o
s
e
ar
e
d
esig
n
ed
u
s
in
g
a
co
m
b
in
atio
n
o
f
s
u
m
m
atio
n
a
n
d
d
elta
f
u
n
ctio
n
as
s
h
o
wn
i
n
Fig
u
r
e
6
.
W
h
er
e
Fu
n
.
4
in
d
icate
s
to
f
u
n
ctio
n
4
wh
ich
r
ep
r
esen
t th
e
o
u
tp
u
t o
f
W'
-
tr
an
s
f
o
r
m
atio
n
as (
8
)
.
Fu
n
.
4
=
δ(
S
-
1
)
+
-
δ(
S+1
)
(
8
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
24
,
No
.
3
,
Dec
em
b
er
2
0
2
1
:
1
8
3
2
-
1
8
3
9
1836
Fig
u
r
e
6
.
W
'
-
tr
an
s
f
o
r
m
atio
n
f
u
n
ctio
n
3.
5
.
Ste
p
-
t
hree
I
t
in
clu
d
es
o
n
ly
T
-
tr
an
s
f
o
r
m
at
io
n
th
at
r
e
p
r
esen
ts
th
e
f
in
al
s
u
m
r
esu
lts
.
On
ly
th
e
d
if
f
er
e
n
ce
is
th
at
its
two
in
p
u
ts
ar
e
T
'
an
d
W
'
in
s
te
ad
o
f
X
an
d
Y.
T
h
e
co
llectio
n
o
f
th
e
ab
o
v
e
f
u
n
ctio
n
s
f
o
r
all
s
tep
s
r
ep
r
esen
ts
o
n
e
b
asic
ar
ith
m
etic
n
eu
r
al
n
etwo
r
k
u
n
it
(
B
ANNU
)
to
p
er
f
o
r
m
th
e
o
p
er
atio
n
o
f
ad
d
itio
n
f
o
r
o
n
e
-
b
it
o
f
ea
c
h
o
p
er
an
d
X
an
d
Y,
as g
iv
en
i
n
Fig
u
r
e
7
.
Fig
u
r
e
7
.
B
asic a
r
ith
m
etic
n
eu
r
al
n
etwo
r
k
u
n
it
4.
SI
M
UL
A
T
I
O
N
O
F
T
H
R
E
E
-
ST
E
P
ADDE
R
At
th
e
s
am
e
tim
e,
th
e
ad
d
itio
n
o
p
er
atio
n
s
ar
e
co
n
d
u
cted
s
im
u
ltan
eo
u
s
ly
f
o
r
a
s
et
o
f
n
u
m
b
e
r
p
air
s
.
As
a
r
esu
lt,
all
p
r
o
ce
s
s
es
ar
e
im
p
lem
en
ted
at
th
e
s
am
e
tim
e,
in
cr
ea
s
in
g
s
y
s
tem
p
r
o
d
u
cti
v
ity
.
Fo
r
s
im
u
latio
n
,
we
tak
e
ex
am
p
le
as
th
e
f
o
llo
win
g
ad
d
itio
n
o
p
er
ati
on
.
W
e
co
n
s
id
er
6
o
p
e
r
atio
n
s
,
ea
ch
o
p
e
r
atio
n
h
as
two
o
p
er
an
d
s
o
f
1
5
b
its
’
len
g
t
h
,
th
e
e
x
ec
u
tio
n
o
f
o
p
er
atio
n
s
is
s
h
o
wn
in
T
a
b
les
2
,
3
,
an
d
4
.
T
h
e
s
im
u
latio
n
f
o
r
th
e
T
a
b
les
2
,
3
,
an
d
4
th
r
ee
s
tep
r
esu
lts
(
s
tep
o
n
e,
s
tep
two
,
an
d
s
tep
th
r
ee
)
is
im
p
lem
en
ted
b
y
u
s
in
g
v
is
u
al
lan
g
u
a
g
e
p
r
o
g
r
a
m
m
in
g
as sh
o
wn
in
Fig
u
r
es 8
,
9
,
an
d
1
0
r
esp
ec
tiv
ely
.
T
ab
le
2
.
E
x
ec
u
tio
n
s
tep
o
n
e
f
o
r
6
o
p
er
atio
n
s
O
p
e
r
a
t
i
o
n
n
o
.
I
n
p
u
t
s
T
-
t
r
a
n
sf
o
r
ma
t
i
o
n
W
-
t
r
a
n
sf
o
r
ma
t
i
o
n
O
p
.
1
a
1
(
1
0
0
-
1
-
1
1
0
1
0
-
1
0
1
-
10
-
1)
BM
S
D
=
(
1
3
9
2
3
)
10
0
1
0
0
-
1
0
1
1
1
-
1
-
10
-
10
-
10
00
-
1
0
0
1
0
-
10
-
1
1
1
0
0
0
1
b
1
(
-
1
1
0
1
0
-
1
1
1
1
0
-
1
-
1
-
1
0
0
)
BM
S
D
=
=(
-
6
2
3
6
)
10
O
p
.
2
a
2
(
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
)
BM
S
D
=
(
3
2
7
6
7
)
10
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
b
2
(
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
)
BM
S
D
=
(
3
2
7
6
7
)
10
O
p
.
3
a
3
(
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1)
BM
S
D
=(
-
3
2
7
6
7
)
10
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
10
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
b
3
(
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1)
BM
S
D
=(
-
3
2
7
6
7
)
10
O
p
.
4
a
4
(
1
1
1
1
1
0
0
-
1
-
1
-
1
0
0
1
1
1
)
BM
S
D
=
(
3
1
5
2
7
)
10
1
1
1
1
1
1
1
-
1
-
1
-
1
-
1
-
1
0
0
0
0
0
0
0
0
0
0
-
1
-
1
1
1
1
1
1
0
0
0
b
4
(
1
1
1
1
1
1
1
0
0
0
-
1
-
1
-
1
-
1
-
1)
BM
S
D
=
(
3
2
4
8
1
)
10
O
p
.
5
a
5
(
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
)
BM
S
D
=
(
3
2
7
6
7
)
10
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
b
5
(
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
)
BM
S
D
=
(
0
)
10
O
p
.
6
a
6
(
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1)
BM
S
D
=(
-
3
2
7
6
7
)
10
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
10
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
b
6
(
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
)
BM
S
D
=
(
0
)
10
T
ab
le
3
.
E
x
ec
u
tio
n
s
tep
two
f
o
r
6
o
p
er
atio
n
s
O
p
e
r
a
t
i
o
n
n
o
.
I
n
p
u
t
s
T'
-
t
r
a
n
sf
o
r
ma
t
i
o
n
W'
-
t
r
a
n
sf
o
r
ma
t
i
o
n
1
T
1
=
0
1
0
0
-
1
0
1
1
1
-
1
-
10
-
10
-
10
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
01
-
10
-
1
1
1
0
1
0
0
1
-
10
-
11
W
1
=
00
-
1
0
0
1
0
-
10
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
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mp
leme
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ta
tio
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th
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ith
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n
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tem
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ee
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Q.
Th
a
b
it
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1837
T
ab
le
4
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x
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Fig
u
r
e
8
.
Simu
latio
n
o
f
s
tep
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e
Fig
u
r
e
9
.
Simu
latio
n
o
f
s
tep
t
wo
Fig
u
r
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1
0
.
Simu
latio
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o
f
s
tep
th
r
ee
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
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4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
24
,
No
.
3
,
Dec
em
b
er
2
0
2
1
:
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8
3
2
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1
8
3
9
1838
5.
CO
NCLU
SI
O
N
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h
e
ap
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f
co
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n
eu
r
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et
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s
p
r
o
v
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h
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a
d
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an
tag
e
o
f
p
ar
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ex
ec
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tio
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th
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lar
g
est
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o
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n
t
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f
d
ata,
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eg
ar
d
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o
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th
e
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u
m
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its
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ea
ch
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ar
a
m
eter
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e
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o
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s
u
m
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m
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licity
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o
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c
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r
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e
m
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th
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ted
in
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h
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th
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s
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o
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ith
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m
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m
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f
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s
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in
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r
al
n
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k
.
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t
is
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th
n
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at
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f
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tu
r
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,
o
th
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o
r
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s
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im
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t
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,
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o
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ith
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as
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d
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s
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l sy
s
tem
s
o
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o
th
er
wis
e.
RE
F
E
R
E
NC
E
S
[1
]
A
.
A.
Al
-
S
a
ffa
r
a
n
d
Q
.
Q.
Th
a
b
it
,
“
S
imu
latio
n
o
f
Na
n
o
sc
a
le
Op
ti
c
a
l
S
i
g
n
e
d
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g
it
Ad
d
i
ti
o
n
Ba
se
d
o
n
DNA
-
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tran
d
s
,
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p
ro
c
.
o
f
2
0
1
8
I
n
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
A
d
v
a
n
c
e
d
S
c
ien
c
e
a
n
d
E
n
g
i
n
e
e
rin
g
(
I
COAS
E)
,
2
0
1
8
,
Du
h
o
k
,
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q
,
d
o
i:
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0
.
1
1
0
9
/ICOA
S
E.
2
0
1
8
.
8
5
4
8
9
2
6
.
[2
]
B
.
A.
Iss
a
a
n
d
I
.
S
a
b
ri,
“
Hig
h
P
r
e
c
isio
n
Bi
n
a
ry
C
o
d
e
d
De
c
ima
l
(BCD)
u
n
it
fo
r
1
2
8
-
b
it
a
d
d
it
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n
,
”
Pro
c
.
o
f
th
e
2
n
d
In
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
El
e
c
trica
l,
Co
mm
u
n
ica
ti
o
n
a
n
d
Co
m
p
u
ter
En
g
i
n
e
e
rin
g
(ICECCE
)
,
12
-
1
3
Ju
n
e
2
0
2
0
,
Ista
n
b
u
l
,
T
u
rk
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y
,
d
o
i:
1
0
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1
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0
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/IC
ECCE
4
9
3
8
4
.
2
0
2
0
.
9
1
7
9
3
3
1
.
[
3
]
B
.
A
.
I
s
s
a
,
I
.
S
.
A
.
A
L
-
F
o
r
a
t
i
,
M
.
A
.
A
l
-
I
b
a
d
i
,
H
.
M
.
A
m
e
r
,
A
.
Tu
r
k
y
R
a
s
h
i
d
,
a
n
d
O
.
T
.
R
a
s
h
i
d
,
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e
s
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g
n
o
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g
h
P
r
e
c
i
s
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o
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R
a
d
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8
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A
F
U
n
i
t
w
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d
u
c
e
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L
a
t
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c
y
,
"
2
0
2
0
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
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g
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u
m
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t
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,
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p
t
i
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a
t
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a
n
d
R
o
b
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i
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p
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s
(
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)
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p
p
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,
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9
4
1
2
.
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1
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.
[
4
]
I
.
K
.
K
a
p
a
g
e
r
i
d
i
s
a
n
d
A.
G.
T
r
i
a
n
t
a
f
y
l
l
o
u
,
“
L
a
v
a
N
e
t
—
N
e
u
r
a
l
n
e
t
w
o
r
k
d
e
v
e
l
o
p
m
e
n
t
e
n
v
i
r
o
n
m
e
n
t
i
n
a
g
e
n
e
r
a
l
m
i
n
e
p
l
a
n
n
i
n
g
p
a
c
k
a
g
e
,”
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o
m
p
u
t
e
r
s
&
G
e
o
s
c
i
e
n
c
e
s
,
v
o
l
.
3
7
,
no
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4
,
p
p
.
6
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4
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p
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0
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,
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o
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1
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.
c
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o
.
2
0
1
0
.
1
0
.
0
1
7
.
[5
]
Y
.
Lu
o
,
“
Re
c
u
rre
n
t
n
e
u
ra
l
n
e
tw
o
rk
s
fo
r
c
las
sify
in
g
re
latio
n
s
in
c
li
n
ica
l
n
o
tes
,
”
J
o
u
r
n
a
l
o
f
Bi
o
me
d
ic
a
l
In
f
o
rm
a
ti
c
s
,
vol
.
7
2
,
p
p
.
8
5
-
9
5
,
2
0
1
7
,
d
o
i:
1
0
.
1
0
1
6
/j
.
jb
i
.
2
0
1
7
.
0
7
.
0
0
6
.
[6
]
N
.
M
.
Ah
m
e
d
a
n
d
A
.
O.
Ha
m
d
e
e
n
,
“
P
re
d
ictin
g
El
e
c
tri
c
P
o
we
r
En
e
rg
y
,
Us
i
n
g
Re
c
u
rre
n
t
Ne
u
ra
l
Ne
two
r
k
F
o
re
c
a
stin
g
m
o
d
e
l
,
”
J
o
u
rn
a
l
o
f
Un
ive
rs
it
y
o
f
Hu
m
a
n
De
v
e
lo
p
me
n
t
(
J
UH
D
),
v
o
l
.
4,
n
o
.
2,
pp.
53
-
6
0
,
2
0
1
8
,
d
o
i:
1
0
.
2
1
9
2
8
/
ju
h
d
.
v
4
n
2
y
2
0
1
8
.
[7
]
M
.
F
o
rss
e
ll
,
“
Ha
rd
wa
re
Im
p
lem
e
n
tatio
n
o
f
Artifi
c
ial
Ne
u
ra
l
Ne
t
wo
rk
s,”
H
a
rd
wa
re
Im
p
lem
e
n
ta
ti
o
n
o
f
Arti
fi
c
i
a
l
Ne
u
ra
l
Ne
two
rk
s
,
p
p.
1
-
4
,
2
0
1
4
,
[On
li
n
e
].
Av
a
il
a
b
le:
h
tt
p
:
//
u
se
rs.ec
e
.
c
m
u
.
e
d
u
/
~
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g
ro
v
e
r/t
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a
c
h
in
g
/fi
les
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u
ro
m
o
r
p
h
icCo
m
p
u
ti
n
g
.
p
d
f
[8
]
S
.
Ye
ll
a
m
ra
ju
,
S
.
Ku
m
a
ri,
S
.
G
iro
lk
a
r,
S
.
Ch
o
u
ra
sia
,
a
n
d
A.
D.
Tete
,
"
De
sig
n
o
f
v
a
rio
u
s
l
o
g
ic
g
a
tes
in
n
e
u
ra
l
n
e
two
rk
s,
"
2
0
1
3
A
n
n
u
a
l
IEE
E
In
d
ia
C
o
n
fer
e
n
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e
(INDICO
N),
2
0
1
3
,
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p
.
1
-
5
,
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i:
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0
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1
1
0
9
/INDCO
N.2
0
1
3
.
6
7
2
5
8
7
9
.
[9
]
P
.
Ra
m
e
sh
a
n
d
N.
Ya
d
a
iah
,
“
M
e
th
o
d
o
l
o
g
y
fo
r
De
sig
n
in
g
Lo
g
ic
Ga
tes
&
Circu
it
s
Us
in
g
M
c
c
u
ll
o
c
h
P
it
ts
Ne
u
ro
n
,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
E
lec
tro
n
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c
s E
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g
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e
e
rs
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l
.
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,
n
o
.
2
,
p
p
.
204
-
2
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3
,
2
0
1
6
.
[1
0
]
A
.
S
h
a
rm
a
a
n
d
A.
Ch
o
p
ra
,
“
Artifi
c
ial
Ne
u
ra
l
Ne
two
rk
s:
Ap
p
li
c
a
ti
o
n
s
i
n
M
a
n
a
g
e
m
e
n
t,
”
IOS
R
J
o
u
r
n
a
l
o
f
B
u
sin
e
s
s
a
n
d
M
a
n
a
g
e
me
n
t
(IO
S
R
-
J
BM
)
,
v
o
l.
1
2
,
n
o
.
5
,
pp.
32
-
4
0
,
2
0
1
3
,
d
o
i
:
1
0
.
9
7
9
0
/4
8
7
X
-
1
2
5
3
2
4
0
.
[1
1
]
K.
Ku
m
a
r
a
n
d
G
.
S
.
M
.
Th
a
k
u
r,
“
Ad
v
a
n
c
e
d
Ap
p
li
c
a
ti
o
n
s
o
f
Ne
u
ra
l
Ne
two
rk
s
a
n
d
Artifi
c
ial
In
telli
g
e
n
c
e
:
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Re
v
iew
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
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l
o
f
In
fo
rm
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io
n
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h
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g
y
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n
d
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o
mp
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ter
S
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ien
c
e
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IT
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S
)
,
v
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l.
6,
n
o
.
6
,
p
p
.
57
-
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8
,
2
0
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2
,
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i:
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0
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5
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1
5
/
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it
c
s.2
0
1
2
.
0
6
.
0
8
.
[
1
2
]
M
.
M
o
r
g
a
n
,
C
.
B
l
a
n
k
,
a
n
d
R
.
S
e
e
t
a
n
,
“
P
l
a
n
t
d
i
s
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a
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r
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d
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c
t
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f
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A
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n
t
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r
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1
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n
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1
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.
v
1
0
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i
1
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p
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5
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-
264
.
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3
]
K.
Len
in
,
“
M
in
imiz
a
ti
o
n
o
f
re
a
l
p
o
we
r
lo
ss
b
y
e
n
h
a
n
c
e
d
tea
c
h
in
g
lea
rn
in
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b
a
se
d
o
p
ti
m
iza
ti
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n
a
lg
o
rit
h
m
,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
Ro
b
o
ti
c
s
a
n
d
Au
t
o
ma
t
i
o
n
(IJ
RA
)
,
v
o
l.
9
,
n
o.
1
,
p
p
.
1
-
5
,
M
a
rc
h
2
0
2
0
,
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o
i:
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1
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5
9
1
/
ij
ra
.
v
9
i1
.
p
p
1
-
5
.
[
1
4
]
K
.
K
u
m
a
r
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t
a
l
.
,
“
S
o
f
t
c
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m
p
u
t
i
n
g
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I
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T
b
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d
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n
t
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rn
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t
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a
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ms
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J
P
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)
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v
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l
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1
2
,
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o
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3
,
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p
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8
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.
[1
5
]
A.
Ku
rn
iaw
a
n
a
n
d
E
.
S
h
in
tak
u
,
“
Two
-
ste
p
a
rti
ficia
l
n
e
u
ra
l
n
e
tw
o
r
k
to
e
stim
a
te
th
e
so
lar
ra
d
iati
o
n
a
t
Ja
v
a
Isla
n
d
,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
tric
a
l
a
n
d
Co
m
p
u
ter
En
g
in
e
e
rin
g
(I
J
ECE
)
,
v
o
l
.
1
1
,
n
o
.
4
,
p
p
.
3
5
5
9
-
3
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6
6
,
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g
u
st
2
0
2
1
,
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o
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1
0
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1
1
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9
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jec
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.
v
1
1
i
4
.
p
p
3
5
5
9
-
3
5
6
6
.
[1
6
]
K
.
A.
S
m
it
h
a
n
d
J
.
N.
D.
G
u
p
t
a
,
“
Ne
u
ra
l
n
e
two
rk
s
in
b
u
si
n
e
ss
:
tec
h
n
iq
u
e
s
a
n
d
a
p
p
li
c
a
ti
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n
s
fo
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th
e
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p
e
ra
ti
o
n
s
re
se
a
rc
h
e
r,
”
Co
mp
u
ter
s
&
Op
e
ra
ti
o
n
s
Res
e
a
rc
h
,
v
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l.
2
7
,
n
o
.
1
1
-
1
2
,
p
p
.
1
0
2
3
-
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0
4
4
,
2
0
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o
i:
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0
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1
0
1
6
/
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0
3
0
5
-
0
5
4
8
(
9
9
)
0
0
1
4
1
-
0
.
[1
7
]
O.
Aw
o
d
e
le
a
n
d
O
.
Je
g
e
d
e
,
“
N
e
u
ra
l
Ne
two
rk
s
a
n
d
Its
Ap
p
li
c
a
ti
o
n
i
n
E
n
g
i
n
e
e
rin
g
,
”
Pro
c
e
e
d
in
g
s
o
f
In
f
o
rm
in
g
S
c
ien
c
e
&
IT
Ed
u
c
a
ti
o
n
Co
n
fer
e
n
c
e
(In
S
IT
E)
,
2
0
0
9
.
[1
8
]
R
.
Ya
n
g
e
t
a
l.
,
“
CNN
-
LS
TM
d
e
e
p
lea
rn
in
g
a
rc
h
it
e
c
tu
re
fo
r
c
o
m
p
u
ter
v
isi
o
n
-
b
a
se
d
m
o
d
a
l
fre
q
u
e
n
c
y
d
e
tec
ti
o
n
,
”
M
e
c
h
a
n
ica
lS
y
ste
m a
n
d
S
i
g
n
a
l
Pr
o
c
e
ss
in
g
,
v
o
l
.
1
4
4
,
Oc
t.
2
0
2
0
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o
i
:
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1
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.
y
m
ss
p
.
2
0
2
0
.
1
0
6
8
8
5
.
[1
9
]
C.
Jia
n
g
a
n
d
S
.
Wei,
“
Re
sid
u
e
-
Weig
h
ted
Nu
m
b
e
r
C
o
n
v
e
rsi
o
n
with
M
o
d
u
li
S
e
t
{
2
p
-
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,
2
p
+
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p
}
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d
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Dig
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n
th
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ter
n
a
ti
o
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y
mp
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siu
m o
n
Distrib
u
ted
C
o
mp
u
t
in
g
a
n
d
A
p
p
l
ica
ti
o
n
to
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sin
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ss
,
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n
g
i
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,
2
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p
p
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6
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9
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3
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1
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c
a
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s.2
0
1
0
.
1
3
2
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
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J
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&
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p
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N:
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mp
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tio
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)
1839
[2
0
]
Q.
A
.
A
.
Ha
ih
a
,
Y.
Al
-
Zah
o
u
ri
,
a
n
d
M
.
Ah
m
e
d
,
“
Eff
icie
n
t
Ne
w
d
e
sig
n
a
n
d
Ve
rifi
c
a
ti
o
n
o
f
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g
n
-
Dig
it
A
d
d
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r
Two
S
y
m
m
e
tri
c
Re
d
u
n
d
a
n
t
Ra
d
ix
-
4
Nu
m
b
e
rs,”
I
n
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
n
Co
m
p
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ter
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e
a
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En
g
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g
,
v
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l.
2
,
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4
,
p
p
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9
7
2
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,
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0
1
0
.
[2
1
]
V.
P
a
tel
a
n
d
K.
S
.
G
u
ru
m
u
r
th
y
,
“
Arith
m
a
ti
c
Op
e
ra
ti
o
n
s
in
M
u
lt
i
-
Va
lu
e
d
L
o
g
ic,”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
VL
S
I
De
sig
n
&
Co
mm
u
n
ica
ti
o
n
S
y
ste
ms
(VL
S
IC
S
)
,
v
o
l.
1
,
n
o
.
1
,
p
p
.
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-
3
2
,
2
0
1
0
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d
o
i:
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0
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5
1
2
1
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lsic.
2
0
1
0
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1
1
0
3
.
[2
2
]
A.
K.
C
h
e
rri
a
n
d
H.
A.
Ka
m
a
l,
“
P
a
ra
ll
e
l
Hig
h
-
Ra
d
i
x
Ne
g
a
b
i
n
a
ry
S
ig
n
e
d
-
Dig
i
t
Arith
m
e
ti
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Op
e
ra
ti
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n
s:
On
e
-
S
tep
Tri
n
a
ry
a
n
d
O
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-
S
tep
Qu
a
tern
a
r
y
A
d
d
it
io
n
Al
g
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ri
th
m
s,”
Pr
o
c
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e
d
in
g
s
o
f
S
PIE
-
T
h
e
I
n
ter
n
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ti
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c
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fo
r
Op
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rin
g
2
0
0
4
,
d
o
i
:
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0
.
1
1
1
7
/1
2
.
5
6
8
8
4
5
.
[2
3
]
R.
Ra
n
i,
N.
S
h
a
rm
a
,
a
n
d
L.
K
.
S
in
g
h
,
"
F
a
st
c
o
m
p
u
ti
n
g
u
si
n
g
si
g
n
e
d
d
i
g
it
n
u
m
b
e
r
sy
ste
m
,
"
2
0
0
9
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Co
n
tro
l
,
A
u
to
m
a
ti
o
n
,
C
o
mm
u
n
ica
ti
o
n
a
n
d
E
n
e
rg
y
Co
n
se
rv
a
ti
o
n
,
2
0
0
9
,
p
p
.
1
-
4.
[2
4
]
Q.
Q.
Th
a
b
i
t
a
n
d
A
.
A.
Al
-
S
a
ffa
r
,
“
DN
A
-
st
ra
n
d
m
o
lec
u
lar
b
e
a
c
o
n
o
p
ti
c
a
l
p
ro
c
e
ss
o
r,
”
He
li
y
o
n
,
v
o
l.
5
,
n
o
.
9
,
2
0
1
9
,
d
o
i:
1
0
.
1
0
1
6
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.
h
e
li
y
o
n
.
2
0
1
9
.
e
0
2
3
8
9
.
[2
5
]
A.
M
.
Ha
id
a
r,
"
A
n
o
v
e
l
n
e
u
ra
l
n
e
two
rk
h
a
lf
a
d
d
e
r,
"
Pro
c
e
e
d
in
g
s.
2
0
0
4
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
In
f
o
rm
a
ti
o
n
a
n
d
C
o
mm
u
n
ic
a
ti
o
n
T
e
c
h
n
o
lo
g
ies
:
Fro
m
T
h
e
o
ry
to
Ap
p
li
c
a
t
io
n
s
,
2
0
0
4
,
p
p
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4
2
7
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8
,
d
o
i:
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1
1
0
9
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TT
A.2
0
0
4
.
1
3
0
7
8
1
4
.
[2
6
]
S
.
A.
Alz
a
e
e
m
i,
M
.
A
.
M
a
n
s
o
r,
M
.
S
h
a
re
d
u
wa
n
,
M
.
Ka
sih
m
u
d
d
i
n
,
S
.
S
a
t
h
a
siv
a
m
,
a
n
d
M
.
M
a
m
a
t,
“
Ra
d
ial
b
a
si
s
fu
n
c
ti
o
n
n
e
u
ra
l
n
e
two
rk
fo
r
2
s
a
ti
sfia
b
il
it
y
p
r
o
g
ra
m
m
in
g
,
”
In
d
o
n
e
sia
n
J
o
u
rn
a
l
o
f
E
lec
trica
l
En
g
in
e
e
rin
g
a
n
d
Co
mp
u
ter
S
c
ien
c
e
,
v
o
l.
1
8
,
n
o
.
1
,
p
p
.
4
5
9
-
4
6
9
,
Ap
r
il
2
0
2
0
,
d
o
i:
1
0
.
1
1
5
9
1
/i
jee
c
s.v
1
8
.
i
1
.
p
p
4
5
9
-
4
6
9
.
[2
7
]
E
.
S
.
I.
a
n
d
A
.
W
.
A.
,
“
Artifi
c
i
a
l
Ne
u
ro
n
Ne
two
r
k
Im
p
lem
e
n
tatio
n
o
f
Bo
o
lea
n
Lo
g
ic
G
a
tes
b
y
P
e
rc
e
p
tro
n
a
n
d
Th
re
sh
o
l
d
El
e
m
e
n
t
a
s
Ne
u
r
o
n
Ou
tp
u
t
F
u
n
c
ti
o
n
,
”
I
n
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
S
c
ien
c
e
a
n
d
Res
e
a
rc
h
(IJ
S
R)
,
v
o
l.
4
,
no.
9
,
p
p
.
6
3
7
-
6
4
1
,
2
0
1
5
,
[On
l
in
e
].
Av
a
il
a
b
le:
h
tt
p
s://
ww
w.i
jsr.
n
e
t/
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rc
h
iv
e
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4
i9
/
S
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5
7
5
8
0
.
p
d
f.
[2
8
]
S
.
Co
t
o
fa
n
a
a
n
d
S
.
Va
ss
il
iad
is,
“
S
ig
n
e
d
Dig
it
Co
u
n
ters
wit
h
Ne
u
ra
l
Ne
two
rk
s,”
Cit
e
S
eer
,
1
9
9
7
.
[2
9
]
A
.
P
.
O.
d
a
S
i
lv
a
,
C
.
R
.
M
.
Leite,
L.
M
c
M
il
lan
,
C.
A.
P
a
z
d
e
Ara
u
j
o
,
M
.
A.
C
.
F
e
rn
a
n
d
e
s,
a
n
d
A
.
M
.
G
.
G
u
e
rre
iro
,
“
Ad
a
p
ti
v
e
Bo
o
lea
n
L
o
g
ic
Us
in
g
F
e
rro
e
lec
tri
c
s
Ca
p
a
c
it
o
rs
a
s
Ba
sic
Un
it
s
o
f
Artifi
c
ial
Ne
u
ro
n
s,”
In
teg
r
a
te
d
Fer
ro
e
lec
trics
,
v
o
l.
1
5
9
,
no.
1
,
p
p
.
23
-
3
3
,
2
0
1
5
,
d
o
i:
1
0
.
1
0
8
0
/
1
0
5
8
4
5
8
7
.
2
0
1
5
.
1
0
2
9
4
0
8
.
[3
0
]
S
.
Ra
y
,
M
.
Ha
q
u
e
,
T
.
Ah
m
e
d
,
a
n
d
T
.
T
.
Na
h
i
n
,
“
Co
m
p
a
riso
n
o
f
a
rti
ficia
l
n
e
u
ra
l
n
e
tw
o
rk
(AN
N)
a
n
d
re
sp
o
n
se
su
rfa
c
e
m
e
th
o
d
o
lo
g
y
(R
S
M
)
in
p
re
d
ictin
g
th
e
c
o
m
p
re
ss
iv
e
a
n
d
sp
li
tt
in
g
te
n
sile
stre
n
g
th
o
f
c
o
n
c
re
t
e
p
re
p
a
re
d
wit
h
g
las
s
wa
ste
a
n
d
ti
n
(
S
n
)
c
a
n
fib
e
r,
”
J
o
u
rn
a
l
o
f
Kin
g
S
a
u
d
Un
ive
rs
it
y
-
En
g
in
e
e
rin
g
S
c
ien
c
e
s
,
2
0
2
1
,
d
o
i:
1
0
.
1
0
1
6
/j
.
jk
su
e
s.
2
0
2
1
.
0
3
.
0
0
6
.
[3
1
]
R.
S
.
F
y
a
th
,
A.
A.
W
.
Alsa
ffa
r,
a
n
d
M.
S
.
Ala
m
,
“
No
n
re
c
o
rd
e
d
t
rin
a
ry
si
g
n
e
d
-
d
ig
it
m
u
lt
ip
li
c
a
ti
o
n
b
a
se
d
o
n
d
i
g
it
g
ro
u
p
i
n
g
a
n
d
p
ix
e
l
a
ss
ig
n
m
e
n
t,
”
Op
ti
c
s
Co
mm
u
n
ic
a
ti
o
n
s
(El
se
v
ier
)
,
v
o
l.
2
3
0
,
n
o
.
1
-
3
,
p
p
.
3
5
-
44
,
2
0
0
4
,
d
o
i
:
1
0
.
1
0
1
6
/
j.
o
p
tco
m
.
2
0
0
3
.
1
1
.
0
3
8
.
[3
2
]
M.
S
.
Ala
m
,
K.
Ja
m
il
,
a
n
d
M
.
A.
Ka
rim,
“
Op
ti
c
a
l
Hig
h
e
r
-
Ord
e
r
Qu
a
tern
a
ry
S
i
g
n
e
d
-
Dig
it
Ari
th
m
e
ti
c
,
”
Op
ti
c
a
l
En
g
i
n
e
e
rin
g
,
v
o
l.
3
3
,
n
o
.
1
0
,
p
p
.
3
4
1
9
-
3
4
2
6
,
1
9
9
4
,
d
o
i:
1
0
.
1
3
6
4
/A
O.3
1
.
0
0
5
6
1
4
[3
3
]
M
.
S
.
Ala
m
,
“
P
a
ra
ll
e
l
Op
ti
c
a
l
Co
m
p
u
ti
n
g
U
sin
g
Re
c
o
d
e
d
Tri
n
a
ry
S
ig
n
e
d
-
Dig
it
N
u
m
b
e
rs,”
Ap
p
li
e
d
Op
ti
c
s
,
v
o
l
.
3
3
,
n
o
.
2
0
,
p
p
.
4
3
9
2
-
4
3
9
7
,
1
9
9
4
,
d
o
i:
1
0
.
1
3
6
4
/AO.
3
3
.
0
0
4
3
9
2
.
[3
4
]
M
.
S
.
Ala
m
,
“
Eff
icie
n
t
Tri
n
a
r
y
S
ig
n
e
d
-
Dig
it
S
y
m
b
o
li
c
Arith
m
e
t
ic,”
Op
ti
c
s
L
e
tt
e
rs
,
v
o
l.
1
9
,
n
o
.
5
,
p
p
.
3
5
3
-
3
5
5
,
1
9
9
4
,
d
o
i
:
1
0
.
1
3
6
4
/
o
l.
1
9
.
0
0
0
3
5
3
.
[3
5
]
A.
Aw
wa
l,
M
.
N.
Isla
m
,
a
n
d
M
.
A.
Ka
rim,
“
M
o
d
ifi
e
d
S
i
g
n
e
d
-
Dig
it
Tri
n
a
r
y
Arith
m
e
ti
c
b
y
Us
in
g
O
p
ti
c
a
l
S
y
m
b
o
li
c
S
u
b
sti
tu
ti
o
n
,
”
Ap
p
li
e
d
Op
ti
c
s
,
v
o
l
.
3
1
,
n
o
.
1
1
,
p
p
.
1
6
8
7
-
1
6
9
4
,
1
9
9
2
,
doi
:
1
0
.
1
3
6
4
/AO.
3
1
.
0
0
1
6
8
7
.
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