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.
1
,
F
e
br
ua
r
y
2020
,
pp.
208
~
216
I
S
S
N:
1693
-
6930,
a
c
c
r
e
dit
e
d
F
ir
s
t
G
r
a
de
by
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
.
v18i1.
14750
208
Jou
r
n
al
h
omepage
:
ht
tp:
//
jour
nal.
uad
.
ac
.
id/
index
.
php/T
E
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l
Mal
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Mel
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Ar
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AB
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RA
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A
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ti
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is
tor
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:
R
e
c
e
ived
Aug
10
,
2019
R
e
vis
e
d
De
c
2
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20
19
Ac
c
e
pted
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c
23
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20
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rren
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ca
t
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o
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t
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ech
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s
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am
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am
req
u
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l
o
f
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s
Mu
s
l
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t
o
read
t
h
e
Q
u
r'an
.
T
aj
w
eed
i
s
an
i
mp
o
rt
a
n
t
b
e
ca
u
s
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i
t
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p
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h
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s
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t
h
e
a
p
p
l
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ca
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o
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f
au
g
me
n
t
e
d
real
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t
y
(A
R)
as
o
n
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f
t
h
e
mu
l
t
i
m
ed
i
a
t
ech
n
o
l
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g
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e
s
t
h
at
can
b
e
u
s
e
d
as
an
i
n
t
erac
t
i
v
e
ed
u
ca
t
i
o
n
a
l
med
i
u
m
t
o
h
el
p
s
t
u
d
y
t
h
e
t
a
j
w
ee
d
o
f
Q
u
r'an
.
T
h
e
met
h
o
d
u
s
ed
i
n
t
h
i
s
re
s
earch
i
s
Feat
u
r
es
fro
m
accel
era
t
ed
s
eg
me
n
t
t
e
s
t
(F
A
ST
)
c
o
rn
er
d
et
ec
t
i
o
n
.
T
h
e
t
es
t
i
n
g
re
s
u
l
t
w
i
t
h
3
1
t
a
j
w
ee
d
o
b
j
ect
s
s
h
o
w
t
h
at
FA
S
T
i
s
ab
l
e
t
o
reco
g
n
i
ze
al
l
T
a
j
w
ee
d
o
b
j
ect
s
an
d
d
i
s
p
l
a
y
t
h
e
i
r
A
R.
Bes
i
d
e
s
,
b
a
s
ed
o
n
a
s
u
r
v
ey
w
i
t
h
q
u
e
s
t
i
o
n
n
a
i
re
s
t
o
s
ev
eral
s
t
u
d
e
n
t
s
,
t
h
e
re
s
u
l
t
s
h
o
w
s
t
h
a
t
8
8
.
2
%
o
f
s
t
u
d
en
t
s
re
s
p
o
n
d
ed
v
er
y
w
el
l
an
d
j
u
d
g
e
d
t
h
at
i
t
w
a
s
s
u
ffi
c
i
en
t
t
o
h
el
p
s
t
u
d
y
t
h
e
t
aj
w
eed
.
K
e
y
w
o
r
d
s
:
A
ugmente
d
r
e
a
li
ty
F
AST
c
or
ne
r
de
tec
ti
on
M
ult
im
e
dia
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
:
Dia
n
S
a
’
a
dil
lah
M
a
ylaw
a
ti
,
De
pa
r
tm
e
nt
of
I
nf
or
mat
ics
,
UI
N
S
una
n
Gunung
Dja
ti
B
a
ndung,
B
a
ndung,
I
ndone
s
ia
.
E
mail:
dians
m
@
uins
gd.
a
c
.
id
1.
I
NT
RODU
C
T
I
ON
T
he
de
ve
lopm
e
nt
of
s
c
ienc
e
a
nd
tec
hnology
whic
h
is
r
a
pidl
y
incr
e
a
s
ing
ha
s
a
c
ons
ider
a
ble
in
f
luenc
e
on
the
lea
r
ning
pr
oc
e
s
s
a
nd
a
ls
o
inf
luenc
e
s
the
de
li
ve
r
y
of
mate
r
ial
in
the
tea
c
hing
a
nd
lea
r
ning
pr
oc
e
s
s
.
Anothe
r
mea
ning
o
f
lea
r
ning
ia
a
n
e
f
f
o
r
t
that
ma
de
by
tea
c
he
r
s
with
the
a
im
that
s
tudents
c
a
n
be
he
lped
in
the
lea
r
ning
pr
oc
e
s
s
e
a
s
il
y
a
nd
quickly
[
1]
.
I
nt
e
r
a
c
ti
ve
lea
r
ning
media
in
c
ur
r
e
nt
digi
tal
e
r
a
is
ne
e
de
d,
be
c
a
us
e
lea
r
ning
media
is
a
c
r
e
a
ti
ve
media
us
e
d
i
n
pr
ovidi
ng
s
ubjec
t
matte
r
to
s
tudents
s
o
that
the
lea
r
ning
pr
oc
e
s
s
is
mor
e
e
f
f
e
c
ti
ve
,
e
f
f
icie
nt,
a
nd
e
njoyable
[
2
]
.
T
he
r
e
f
or
e
,
the
us
e
of
mul
ti
media
tec
hnology
is
ve
r
y
us
e
f
ul.
T
his
is
a
ls
o
s
uppor
ted
by
the
r
e
s
e
a
r
c
hs
in
th
e
f
ield
of
e
duc
a
ti
on
that
a
ppli
e
s
mul
ti
media
tec
hno
logy
f
or
lea
r
ning
a
r
e
gr
owing
r
a
pidl
y
[
3
-
9]
.
I
n
c
ur
r
e
nt
mobi
le
tec
hnology,
a
ugmente
d
r
e
a
li
ty
(
AR
)
is
a
moder
n
a
nd
populer
mul
ti
media
tec
hnology
us
e
d
a
s
a
n
int
e
r
a
c
ti
ve
a
nd
int
e
r
e
s
t
ing
lea
r
ning
media
[
10
-
12]
.
F
or
e
xa
mpl
e
in
lea
r
ning
the
bonding
of
c
he
mi
c
a
l
c
ompounds
[
12
]
,
lea
r
ning
in
c
he
mi
s
tr
y
[1
3
-
17]
,
lea
r
ning
the
s
ola
r
s
ys
tem
[
18,
19]
,
in
b
iol
ogy
to
s
tudy
the
diges
ti
ve
or
ga
ns
[
19]
,
mor
ov
e
r
to
lea
r
n
the
c
ult
u
r
e
s
uc
h
a
s
lea
r
n
ing
t
r
a
dit
ional
mus
ica
l
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
ugme
nted
r
e
ali
ty
us
ing
featur
e
s
ac
c
e
ler
ated
s
e
gm
e
nt
tes
t
for
lear
ning
taj
w
e
e
d
(
A
d
i
P
utr
a
A
ndr
iyand
i)
209
ins
tr
u
ments
[
20]
,
lea
r
ning
tample
a
s
his
tor
ica
l
he
r
it
a
ge
[
21]
,
lea
r
ning
the
mus
e
um
objec
ts
[2
2
,
2
3
]
,
to
lea
r
n
mar
ine
mammals
[
24]
,
a
nd
s
o
on.
I
n
I
s
lamic
r
e
li
gi
on
e
duc
a
ti
on,
AR
c
a
n
a
ls
o
be
us
e
d
a
s
lea
r
ning
media
,
s
uc
h
a
s
to
lea
r
n
h
ij
aiyah
letter
(
letter
in
Qur
’
a
n
)
[
25
]
,
lea
r
ning
a
bout
w
udhu
(
a
blut
ion)
[
26]
,
a
nd
a
nothe
r
I
s
lamic
re
li
gion
e
duc
a
ti
on
s
uc
h
a
s
in
lea
r
ning
s
halat
(
pr
a
ye
r
)
[
27]
.
I
s
lam
is
one
o
f
the
lar
ge
s
t
r
e
li
gions
in
the
wo
r
ld,
r
e
a
c
hing
1.
6
bil
li
on
o
f
the
tot
a
l
wor
ld
population
o
f
a
r
ound
7.
5
bil
li
on
pe
ople
.
I
ndone
s
ia
be
c
a
me
the
c
ountr
y
with
t
he
lar
ge
s
t
M
us
li
m
population
in
the
wor
ld,
r
e
a
c
hing
222
mi
l
li
on
pe
ople.
S
o
,
it
is
v
e
r
y
ne
c
e
s
s
a
r
y
f
or
M
us
li
ms
to
s
tudy
I
s
lamic
e
duc
a
t
ion
,
one
of
whic
h
is
r
e
c
it
e
the
Qur
'a
n.
F
or
M
us
li
ms
a
ll
ove
r
the
wor
ld,
r
e
c
it
e
the
Qur
'a
n
whic
h
is
a
wa
y
of
li
f
e
is
manda
to
r
y.
One
of
the
im
por
tant
thi
ngs
in
r
e
c
it
e
th
e
Qur
'a
n
is
pa
ying
a
tt
e
nti
on
to
the
r
ule
of
taj
w
id
(
t
a
jwe
e
d
)
.
W
he
r
e
t
a
jwe
e
d
mea
ns
a
r
ti
c
ulating
the
ve
r
s
e
s
of
the
Qur
’
a
n
with
the
c
or
r
e
c
t
pr
onunc
i
a
ti
on
in
the
pr
e
s
c
r
ibed
r
u
les
.
T
his
r
e
s
e
a
r
c
h
a
im
s
to
im
pleme
nt
AR
tec
hnology
a
s
lea
r
ning
media
f
o
r
t
a
jwe
e
d
Al
-
Qur
'a
n.
T
his
lea
r
ning
media
is
e
xpe
c
ted
to
be
a
ble
to
f
a
c
il
it
a
te
s
tudents
in
lea
r
ning
the
punc
tuatio
n
a
s
we
ll
a
s
the
good
a
nd
r
ight
p
r
oc
e
dur
e
s
f
or
r
e
c
it
e
the
Qu
r
’
a
n.
M
a
ny
methods
or
a
lgor
it
hms
that
c
a
n
be
us
e
d
i
n
AR
,
s
uc
h
a
s
S
UR
F
(
s
pe
e
d
e
d
up
r
obus
t
f
e
a
tur
e
s
)
[
2
8
]
,
E
dge
De
tec
ti
on
methods
[
29
]
,
OR
B
(
o
r
ient
e
d
F
AST
(
f
e
a
tur
e
s
f
r
om
a
c
c
e
ler
a
ted
s
e
gment
tes
t
)
a
nd
r
otate
d
B
R
I
E
F
(
binar
y
r
obus
t
indepe
nde
nt
e
leme
ntar
y
f
e
a
tur
e
s
)
)
,
a
nd
s
o
on.
T
his
r
e
s
e
a
r
c
h
us
e
s
f
e
a
tur
e
f
r
om
a
c
c
e
ler
a
ted
s
e
gment
t
e
s
t
(
F
AST
)
c
or
ne
r
de
tec
ti
on
a
lgor
it
hm
be
c
a
us
e
F
AST
c
a
n
s
tr
e
a
ml
ine
the
c
a
lcula
ted
ti
me
in
r
e
a
l
-
ti
me
with
the
i
mpac
t
of
d
e
c
r
e
a
s
ing
the
a
c
c
ur
a
c
y
of
the
a
ngle
de
tec
ti
on
s
tage
s
[
22
]
.
T
he
F
AS
T
a
lgor
it
h
m
identif
ies
3D
objec
ts
by
us
ing
the
thr
e
s
hold
of
li
ght
f
r
om
a
2D
im
a
ge
o
bjec
t
whic
h
is
late
r
us
e
d
a
s
a
ma
r
ke
r
.
T
he
f
e
a
tur
e
s
dis
playe
d
a
r
e
ve
r
y
s
im
ple,
while
f
o
r
the
main
dis
play
that
is
in
the
f
o
r
m
of
a
c
a
mer
a
c
a
ptur
e
to
de
tec
t
ma
r
ke
r
s
[
30]
.
T
he
r
e
f
or
e
,
in
thi
s
s
tudy
be
s
ides
im
pleme
nti
ng
the
F
AST
c
or
n
e
r
de
tec
ti
on
a
lgor
it
hm,
the
accu
r
a
c
y
a
nd
ti
me
pr
oc
e
s
s
of
F
AST
a
lgor
it
hm
in
de
tec
ti
ng
the
t
a
jwe
e
d
will
be
in
ve
s
ti
ga
ted.
2.
RE
S
E
AR
CH
M
E
T
HO
D
Ac
ti
vit
iy
f
low
o
f
thi
s
r
e
s
e
a
r
c
h
that
de
s
c
r
ibed
in
F
i
gur
e
1
be
gin
f
r
om
c
oll
e
c
t
a
nd
unde
r
s
tand
the
r
ule
of
t
a
jwe
e
d
Qur
’
a
n
,
then
c
oll
e
c
ti
ng
the
im
a
ge
s
da
ta
of
T
a
jwe
e
d
Qur
’
a
n.
T
he
n,
c
onduc
ti
ng
mar
ke
r
le
s
s
objec
t
tr
a
c
king
a
nd
F
AS
T
c
o
r
ne
r
de
tec
ti
on
a
lgor
i
thm
.
L
a
s
t,
c
onduc
ti
ng
the
e
xpe
r
im
e
nt
a
nd
e
va
luating
the
us
a
bil
it
y
of
a
ppli
c
a
ti
on
with
que
ti
onna
ir
e
.
F
igur
e
1.
R
e
s
e
a
r
c
h
a
c
ti
vit
ies
2.
1.
Augm
e
n
t
e
d
rea
li
t
y
Augme
nted
r
e
a
li
ty
(
AR
)
is
a
r
e
volut
ion
in
c
ompu
ter
gr
a
phic
tec
hnology
that
whic
h
p
r
e
s
e
nts
vis
ua
l
im
a
ge
s
a
s
if
they
we
r
e
a
li
ve
a
nd
ve
r
y
r
e
a
l
[
31]
.
A
R
is
dif
f
e
r
e
nt
with
vir
tual
r
e
a
li
ty
(
VR
)
.
VR
is
a
c
ombi
na
ti
on
be
twe
e
n
vir
tual
r
e
a
li
ty
is
a
c
ombi
na
ti
on
o
f
the
r
e
a
l
wor
ld
a
nd
the
vi
r
tual
wor
ld
s
o
that
li
ke
f
e
e
li
ng
in
a
nother
r
e
a
l
wor
ld
,
while
AR
is
a
n
int
e
gr
a
ti
on
of
the
digi
t
a
l
wor
ld
that
c
r
e
a
ted
by
c
omput
e
r
s
with
the
r
e
a
l
wor
ld
in
r
e
a
l
ti
me
a
nd
f
oll
owing
the
e
nvi
r
onment
in
the
r
e
a
l
wor
ld.
Ac
tua
ll
y,
AR
tec
hnology
ha
d
be
e
n
int
r
o
duc
e
d
in
1957
unti
l
now
s
ti
ll
be
de
ve
loped
[
32]
.
I
n
th
is
mobi
le
e
r
a
,
a
lm
os
t
AR
tec
hnology
f
or
e
duc
a
ti
on
us
e
mobi
le
a
ppli
c
a
ti
on,
e
s
pe
s
ially
a
ndr
oid
ba
s
e
d
[
11,
33]
.
Ge
ne
r
a
ll
y,
AR
is
d
ivi
de
d
int
o
2
methods
,
na
mely
mar
ke
r
ba
s
e
d
tr
a
c
king
a
nd
mar
ke
r
les
s
a
ugmente
d
r
e
a
li
ty
[
22
]
.
M
a
r
ke
r
ba
s
e
d
tr
a
c
ti
ng
is
us
e
d
a
s
a
m
a
r
ke
r
of
a
n
objec
t
in
the
f
o
r
m
o
f
a
pa
tt
e
r
n
in
a
n
i
mage
that
c
a
n
be
r
e
a
d
by
a
c
a
mer
a
o
r
other
de
vice
,
the
mar
ke
r
ba
s
ica
ll
y
us
e
s
a
blac
k
a
nd
white
il
lum
ination
in
t
he
s
ha
pe
of
a
s
qua
r
e
a
nd
ther
e
is
a
blac
k
bor
de
r
with
a
wh
it
e
ba
c
kgr
ound.
W
hil
e
,
mar
ke
r
les
s
a
ugmente
d
r
e
a
li
ty
doe
s
not
us
e
mar
ke
r
bounda
r
ie
s
on
a
n
objec
t
o
f
de
tec
t
ion
a
nd
thi
s
method
ha
s
the
pa
tt
e
r
n
r
e
c
ognit
ion
t
e
c
hnique
s
uppor
t,
then
the
us
e
of
mar
ke
r
s
a
s
objec
t
tr
a
c
ki
ng
is
c
ha
nge
d
by
mar
king
ob
jec
t
f
ields
a
s
objec
t
tr
a
c
king
(
tr
a
c
ke
d
objec
ts
)
.
T
his
r
e
s
e
a
r
c
h
us
e
m
a
r
ke
r
les
s
met
hod
in
objec
t
tr
a
c
king
pr
oc
e
s
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
.
1
,
F
e
br
ua
r
y
2020
:
208
-
216
210
2.
2.
F
e
at
u
r
e
f
r
om
ac
c
e
ler
at
e
d
s
e
gm
e
n
t
t
e
s
t
(
F
AST
)
c
or
n
e
r
d
e
t
e
c
t
ion
C
or
ne
r
d
e
tec
ti
on
s
ymbol
ize
s
a
c
ompu
ter
s
ys
tem
a
ppr
oa
c
h
that
is
us
e
d
a
s
a
de
tec
ti
on
tool
f
or
a
ngles
on
a
n
objec
t.
C
or
ne
r
de
tec
ti
on
a
ls
o
known
a
s
int
e
r
e
s
t
point
de
tec
ti
on
.
T
his
method
is
of
ten
done
in
the
pr
oc
e
s
s
of
objec
t
de
tec
ti
on
to
de
ter
mi
ne
the
p
r
i
vil
e
ge
of
a
n
objec
t
f
or
e
xa
mpl
e
f
r
om
the
s
ha
pe
of
a
n
objec
t
a
nd
tr
a
c
king
a
n
objec
t
[
30]
.
F
AS
T
C
or
ne
r
De
tec
ti
on,
pe
r
f
or
ms
the
de
ter
mi
na
ti
on
of
the
a
ngle
point
by
c
ha
nging
the
im
a
ge
to
blac
k
a
nd
whi
te
a
f
ter
it
is
c
ha
nge
d
then
r
uns
the
a
lgor
it
hm
.
T
he
a
lgor
it
h
m
wil
l
e
ns
ur
e
the
c
or
ne
r
point
by
s
e
lec
ti
ng
the
point
that
is
p
f
r
om
the
im
a
ge
that
will
be
pr
oc
e
s
s
e
d
by
de
tec
ti
ng
16
pixels
be
s
ides
p
will
be
c
he
c
ke
d.
T
he
r
e
a
r
e
s
e
ve
r
a
l
di
f
f
e
r
e
nt
c
a
s
e
s
that
ha
ve
be
e
n
s
e
t
f
or
e
a
c
h
c
ompar
is
on
s
uc
h
a
s
in
(
1)
f
or
da
r
ke
r
,
(
2)
f
o
r
s
im
il
a
r
,
a
nd
(
3)
f
or
br
igh
ter
.
→
≤
−
(
1)
−
<
→
<
+
(
2)
+
≤
→
(
3)
w
he
r
e
,
=
I
ntens
it
y
o
f
c
e
nter
point
=
n
th
ne
ighbor
int
e
ns
it
y
point
=
thr
e
s
hold
va
lue
→
=
pixel
in
tens
it
y
of
x
.
T
he
n,
the
ne
xt
s
tep
a
f
ter
c
ompar
e
e
a
c
h
int
e
ns
it
y
point
is
c
r
e
a
ti
ng
de
c
is
ti
on
tr
e
e
that
c
a
n
c
las
s
if
y
int
e
r
e
s
t
point
in
16
loca
ti
on
.
B
a
s
e
d
on
f
lowc
ha
r
t
in
F
igur
e
2,
F
AST
C
or
ne
r
De
tec
ti
on
a
lgor
it
h
m
ha
s
a
f
oll
owing
pr
oc
e
s
s
:
1.
De
ter
mi
ne
the
point
p
in
the
im
a
ge
that
ha
s
a
s
tar
ti
ng
po
s
it
ion
(
X
p
,
Y
p
).
2.
De
ter
mi
ne
f
our
point
s
a
r
ound
the
p
point
.
T
he
f
ir
s
t
point
will
be
loca
ted
a
t
c
oor
dinate
s
(
X
p
,
Y
p
+
3
)
,
the
s
e
c
ond
point
is
loca
ted
a
t
c
oor
dinate
s
(
X
p
+
3
,
Y
p
)
,
the
thi
r
d
point
is
loca
ted
a
t
c
oor
dinate
s
(
X
p
,
Y
p
-
3
)
,
the
f
our
th
point
is
loca
ted
a
t
c
oor
dinate
s
(
X
p
-
3
,
Y
p
).
3.
C
ompar
e
the
int
e
ns
it
y
of
the
c
e
nter
point
with
t
he
f
our
s
ur
r
ounding
point
s
.
I
f
a
t
lea
s
t
th
r
e
e
poin
ts
a
r
e
obtaine
d
whic
h
a
r
e
include
d
in
the
f
oll
owing
c
ondit
ions
,
the
c
e
nter
point
is
the
ve
r
tex.
4.
T
he
c
he
c
king
pr
oc
e
s
s
is
to
a
s
c
e
r
tain
whe
ther
ther
e
a
r
e
s
ti
ll
pixels
that
ne
e
d
to
be
c
he
c
ke
d
a
ga
in.
I
f
ther
e
is
n't,
s
top
the
pr
oc
e
s
s
.
I
f
ther
e
is
,
then
c
onti
nue
the
ne
xt
p
r
oc
e
s
s
is
s
hif
ti
ng
the
p
point
to
the
ne
xt
pos
it
ion
(
+
1)
then
the
point
will
be
c
ompar
e
d
a
ga
in
with
th
e
f
our
s
ur
r
ounding
poin
ts
.
5.
R
e
pe
ti
ti
on
of
the
p
r
oc
e
s
s
will
oc
c
ur
,
unti
l
a
ll
point
s
in
the
im
a
ge
ha
ve
be
e
n
c
ompar
e
d
in
int
e
ns
it
y.
F
igur
e
1
.
F
low
c
ha
r
t
o
f
F
AS
T
c
or
ne
r
d
e
tec
ti
on
a
lg
or
it
hm
3.
RE
S
UL
T
S
A
ND
DI
S
CU
S
S
I
ON
I
n
thi
s
s
e
c
ti
on,
it
is
e
xplaine
d
the
r
e
s
ult
s
of
r
e
s
e
a
r
c
h
a
nd
a
t
the
s
a
me
ti
me
is
given
the
c
ompr
e
he
ns
ive
dis
c
us
s
ion.
B
e
gin
f
r
om
the
t
ype
s
a
nd
r
ules
of
T
a
jwe
e
d
Qur
’
a
n,
i
mpl
e
mentin
g
F
AST
c
or
ne
r
de
tec
ti
on
a
lgor
i
thm
,
unti
l
e
va
luate
the
r
e
s
ult
of
e
xpe
r
i
ment
a
n
d
que
s
ti
onna
ir
e
d
is
tr
ibut
ion.
Start
Determine point p
Determine 4 neig
hbor p
oint
Determine the intensit
y of
4 neighb
or point with point p
Has done?
Determine interest point
Have all pixels
detected?
Yes
Not Yet
End
Not Yet
Yes
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
ugme
nted
r
e
ali
ty
us
ing
featur
e
s
ac
c
e
ler
ated
s
e
gm
e
nt
tes
t
for
lear
ning
taj
w
e
e
d
(
A
d
i
P
utr
a
A
ndr
iyand
i)
211
3.
1.
Und
e
r
s
t
an
d
in
g
T
aj
we
e
d
Qu
r
’
an
a
n
d
c
oll
e
c
t
in
g
i
m
age
d
at
a
L
ingui
s
ti
c
a
ll
y,
the
ter
m
"
tajwe
e
d
"
in
Ar
a
bic
is
de
f
ined
a
s
pr
of
icie
nc
y
o
r
pr
o
f
icie
nt
in
s
howing
e
xpe
r
ti
s
e
in
doing
s
omething
,
while
in
r
e
lation
t
o
the
r
e
a
ding
of
the
Qur
’
a
n
,
tajwe
e
d
mea
ns
to
a
r
ti
c
ulate
the
ve
r
s
e
s
of
the
Qur
’
a
n
with
the
c
or
r
e
c
t
pr
onunc
i
a
ti
on
in
the
pr
e
s
c
r
ibed
r
ules
[
34,
35]
.
T
h
e
r
e
a
r
e
m
a
ny
r
ules
of
T
a
jwe
e
d
that
ne
e
d
to
be
unde
r
s
tood
by
a
r
e
a
de
r
.
Ge
ne
r
a
ll
y,
ther
e
a
r
e
7
s
e
c
ti
on
in
T
a
jwe
e
d
,
a
mong
other
s
[
34]
:
(
1
)
nun
s
uk
un
a
nd
tanw
in,
s
uc
h
a
s
idz
ar
,
idgham
bigunnah
or
idgham
naqis
ma’
a
l
gunnah
,
idgham
bil
agunnah
or
idgham
k
amil
,
iql
ab
or
qalb
un
,
a
nd
ikhfa
;
(
2)
mim
s
uk
un
,
s
uc
h
a
s
ikhfa
s
afaw
i,
idgham
mis
lai
n,
a
nd
idz
har
s
afaw
i
;
(
3)
nun
a
nd
mim
tas
y
d
id
s
uc
h
a
s
gunnah
;
(
4)
laf
adz
jal
alah
,
s
uc
h
a
s
taf
k
him
a
nd
tar
qiq
;
(
5)
lam
ta’
r
if
,
s
uc
h
a
s
ali
f
lam
w
omar
iyah
(
idz
har
qomar
i)
a
nd
ali
f
lam
s
y
ams
iyah
(
idgham
s
y
ams
i)
;
(
6)
qalqal
ah
s
uc
h
a
s
qalqal
ah
s
ugr
a
a
nd
qa
lqal
ah
qubr
o
;
las
t
is
mad
s
e
c
ti
on,
s
uc
h
a
s
mad
tabi
’
i
or
mad
as
li
,
mad
w
aji
b
muttas
il
,
mad
jai
z
munfas
il
,
mad
ar
i
d
li
s
s
uk
un,
mad
badal,
mad
‘
iw
ad,
mad
laz
im
mus
aqqol
k
ali
mi,
mad
laz
im
muk
haff
af
k
ali
mi
,
mad
laz
im
ha
r
fi
mus
y
ba,
mad
laz
im
muk
haff
af
har
fi
,
mad
li
n
,
m
ad
s
il
ah
qas
ir
ah,
mad
s
il
ah
taw
il
lah,
mad
tamk
in,
a
nd
mad
far
q
.
How
e
ve
r
,
in
thi
s
r
e
s
e
a
r
c
h
only
7
r
ules
of
t
a
jw
e
e
d
that
us
e
d
a
s
im
a
ge
da
ta
c
oll
e
c
ti
on
(
pr
ovided
in
T
a
ble
1
)
.
T
a
ble
1
.
T
a
jwe
e
d
r
u
les
that
us
e
d
in
th
is
s
tudy
T
a
jwe
e
d
L
e
tt
e
r
s
C
a
us
e
s
H
ow
t
o R
e
a
d
I
k
hf
a
,ش
,س
,ز
,
ذ
,
د
,ج
,ث
,ت
ك ,ق
,ف
,ظ
,ط
,ض,ص
ـ
ـ
ٌ
ـ
ـ
,
ـ
ـ
ٍ
ـ
ـ
,
ـ
ـ
ً
ـ
ـ
,
ْ
ن
T
he
na
s
a
l
voi
c
e
w
it
h
buz
z
e
d
I
k
hf
a Safaw
i
ب
ْ
م
N
a
s
a
l
voi
c
e
i
n
th
e
no
s
e
Q
al
qal
ah
ق
,ط
,
د
,ج
,ب
Q
al
qal
ah l
e
tt
e
r
s
t
hat
di
e
,
or
di
e
be
c
aus
e
of
fo
r
gi
v
e
ne
s
s
(
s
to
ppe
d)
R
e
f
le
c
t
or
vi
br
a
te
e
xi
s
ti
ng Q
a
lq
a
la
h
le
tt
e
r
s
I
ql
ab
ب
ـ
ـ
ٌ
ـ
ـ
,
ـ
ـ
ٍ
ـ
ـ
,
ـ
ـ
ً
ـ
ـ
,
ْ
ن
R
e
pl
a
c
e
s
th
e
s
ound with a
m
im
(
م
)
I
dgham B
ig
unnah
ي
,و
,م
,ن
ـ
ـ
ٌ
ـ
ـ
,
ـ
ـ
ٍ
ـ
ـ
,
ـ
ـ
ً
ـ
ـ
,
ْ
ن
M
e
lt
in
g de
a
d
of
nun
(
ن
)
or
ta
nw
in
(
ـ
ـ
ٍ
ـ
ـ
)
a
c
c
ompa
ni
e
d by hum
I
dgham M
imi
م
ْ
م
M
a
ke
a
dupli
c
a
t
e
or
s
in
g s
ound a
nd mu
s
t
r
e
a
d
th
e
dr
one
G
unnah
م
,ن
ّ
ن
,
ّ
م
S
ound c
omi
ng out of
th
e
nos
e
3.
2.
I
m
p
lem
e
n
t
at
ion
of
f
e
at
u
r
e
f
r
om
ac
c
e
ler
at
e
d
s
e
gm
e
n
t
t
e
s
t
(
F
AST
)
c
or
n
e
r
d
e
t
e
c
t
io
n
B
e
f
or
e
r
unning
F
AS
T
c
o
r
ne
r
de
tec
ti
on
a
lgor
it
h
m,
im
a
ge
da
ta
of
T
a
jwe
e
e
d
Qur
’
a
n
a
s
a
n
ob
jec
t
will
be
tr
a
c
ke
d
us
ing
mar
ke
r
les
s
a
ugmente
d
r
e
a
li
ty
obj
e
c
t
tr
a
c
king
method
.
M
a
r
ke
r
les
s
is
a
t
r
a
c
king
met
hod
that
us
e
s
c
olor
im
a
ge
s
a
s
mar
ke
r
s
.
T
his
method
doe
s
not
us
e
f
r
a
me
mar
ke
r
s
a
s
de
tec
ted
objec
ts
.
I
n
thi
s
AR
a
ppli
c
a
ti
on,
the
mar
ke
r
im
a
ge
is
s
our
c
e
d
f
r
om
the
Qur
'a
n
whic
h
is
made
in
to
a
n
im
a
ge
a
nd
then
m
a
de
a
s
a
mar
ke
r
.
M
a
r
ke
r
s
that
wi
ll
be
us
e
d
in
the
AR
T
a
j
we
e
d
a
ppli
c
a
ti
on
a
r
e
in
the
3D
(
thr
e
e
di
mens
ion
)
f
or
m
a
r
e
f
r
om
a
pictur
e
of
a
piec
e
o
f
Al
-
Qur
a
n
ve
r
s
e
s
in
a
c
c
or
da
nc
e
with
the
T
a
jwe
e
d
a
va
il
a
ble.
T
he
pr
oc
e
s
s
of
making
T
a
jwe
e
d
im
a
ge
s
int
o
a
mar
ke
r
is
done
by
Vuf
or
ia
.
Qua
lqom
m
whic
h
is
a
li
br
a
r
y
s
u
ppor
ti
ng
the
e
xis
tenc
e
of
AR
on
Andr
o
id
[
30]
.
T
he
im
a
ge
will
be
uploade
d
to
the
Vuf
o
r
ia
we
bs
it
e
with
the
a
im
that
the
im
a
ge
is
c
onve
r
ted
int
o
a
f
il
e
with
unit
y
pa
c
ka
ge
f
or
mat
.
T
he
f
il
e
will
late
r
be
us
e
d
a
s
a
mar
ke
r
.
B
a
s
e
d
on
the
pr
oc
e
s
s
in
Vuf
or
ia
is
in
F
igur
e
3,
the
pr
oc
e
s
s
of
mar
ke
r
f
or
mation
be
gin
f
r
om
pr
e
pa
r
ing
the
im
a
ge
than
will
be
made
a
s
ma
r
ke
r
with
f
o
r
mat
f
il
e
.
J
P
G
or
.
P
NG
with
maximal
s
ize
is
2
.
25
M
B
then
s
a
ve
or
upload
the
im
a
ge
int
o
Vu
f
or
ia.
T
he
n
,
r
e
s
ize
pr
oc
e
s
s
unti
l
t
he
mar
ke
r
f
or
mi
ng
will
be
il
lus
tr
a
ted
in
F
igur
e
4.
F
igur
e
2
.
S
tage
s
of
m
a
r
ke
r
f
or
mat
ion
B
a
s
e
d
on
the
F
AST
a
lgor
it
hm
that
e
xplaine
d
in
s
e
c
ti
on
2.
2,
a
c
tually
mar
ke
r
po
int
s
that
pr
oduc
e
d
in
F
igur
e
4
a
r
e
the
r
e
s
ult
of
F
AST
a
lgor
it
hm
.
F
or
e
xa
mpl
e
,
tajwe
e
d
of
“
q
olqol
ah
”
that
pr
ov
ided
in
F
igu
r
e
5,
f
o
r
e
a
c
h
c
or
ne
r
will
be
de
ter
mi
ne
d
with
s
e
ve
r
a
l
s
tage
s
of
li
ght
int
e
ns
it
y
c
ompar
is
on.
T
he
f
ir
s
t
s
tep
is
de
ter
mi
ne
point
(
p)
,
s
tar
ti
ng
f
r
o
m
the
uppe
r
lef
t
c
or
ne
r
to
th
e
lowe
r
r
ight
c
or
ne
r
,
the
pr
oc
e
s
s
oc
c
ur
s
unti
l
ther
e
a
r
e
no
mor
e
pixels
that
c
a
n
be
c
ompar
e
d
.
T
he
n,
de
ter
mi
ne
4
ne
ighbor
point
(
n)
with
(
1)
,
(
2)
,
a
nd
(
3
)
.
L
a
s
t,
li
ght
int
e
ns
it
y
of
point
(
p)
will
be
c
ompar
e
d
with
with
a
ll
f
ou
r
point
s
a
r
ound
n
1
,
n
2
,
n
3
a
nd
n
4
.
I
f
ther
e
a
r
e
a
t
lea
s
t
thr
e
e
point
s
in
a
c
c
or
da
nc
e
with
the
c
ondit
ions
,
t
he
cen
ter
point
(
p
)
is
a
n
a
ngle.
T
he
r
e
is
a
r
e
qui
r
e
ment
to
de
ter
mi
ne
a
n
a
ngle
that
is
the
li
ght
int
e
ns
it
y
of
th
e
c
e
nter
poin
t
(
p)
mus
t
be
br
igh
ter
or
da
r
ke
r
than
point
n,
Start
Taking Im
age
Save Im
age
Vof
oria DB
Resi
ze
Convert Grayscale
Convert Histogram
Convert Threshold
Determi
ne M
arker Point
Marki
ng
End
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
.
1
,
F
e
br
ua
r
y
2020
:
208
-
216
212
be
c
a
us
e
if
the
int
e
ns
it
y
is
the
s
a
me
then
it
c
a
nnot
be
c
a
ll
e
d
a
n
a
ngle.
F
igu
r
e
6
il
lus
tr
a
te
s
the
e
xa
mpl
e
of
c
or
ne
r
de
tec
ti
on
us
ing
F
AST
a
lgor
it
hm
f
or
s
e
ve
r
a
l
t
a
jwe
e
d
f
or
m
.
Ne
xt,
u
ti
li
z
ing
Vuf
o
r
ia
f
o
r
im
a
ge
tr
a
c
king
to
s
how
3D
objec
t
f
r
om
the
im
a
ge
that
i
ll
us
tr
a
ted
in
F
igu
r
e
7
.
T
hos
e
3D
objec
t
a
r
e
the
r
e
s
ult
of
AR
a
ppli
c
a
ti
on
to
lea
r
ning
t
a
jwe
e
d.
F
igur
e
3
.
T
he
e
xa
mpl
e
of
m
a
r
ke
r
f
or
m
ing
f
o
r
t
a
jw
e
e
d
“
q
alqal
ah
”
F
igur
e
4
.
I
l
lus
tr
a
ti
on
of
c
or
ne
r
de
tec
ti
on
us
ing
F
AST
a
lgor
it
hm
F
igur
e
5
.
T
he
e
xa
mpl
e
of
c
or
ne
r
de
tec
ti
on
us
ing
F
AST
a
lgor
it
hm
Q
ol
qol
ah
I
k
hf
a
I
dghom
M
ut
am
as
il
ai
n
G
unnah
I
k
hf
a Safaw
i
F
igur
e
6
.
T
he
e
xa
mpl
e
of
3D
objec
t
o
f
t
a
jwe
e
d
Qu
r
’
a
n
3.
3.
E
xp
e
r
im
e
n
t
an
d
q
u
e
s
t
ion
n
aire
d
is
t
r
ib
u
t
io
n
T
he
e
xpe
r
im
e
nt
is
c
onduc
ted
us
ing
Andr
oid
ope
r
a
ti
ng
s
ys
tem
9
t
h
ve
r
s
ion
(
P
ie)
with
3
Giga
B
yte
R
AM
a
nd
c
a
mer
a
s
pe
c
if
ica
ti
on
is
13
M
e
ga
P
ixel
(
5
M
e
ga
P
ixel
+
8
mega
P
ixel)
.
S
of
twa
r
e
too
ls
that
us
e
d
f
or
e
xpe
r
im
e
nt
a
mog
other
s
Uni
ty
3D
2017
.
3
64
-
bit
,
Vuf
o
r
ia
S
DK
,
Andr
o
id
S
DK
,
a
nd
Adobe
P
h
otos
hop.
B
e
s
ide
s
blac
kbox
tes
ti
ng
to
e
va
luat
e
the
f
unc
ti
ona
li
ty
of
the
a
ppli
c
a
ti
on,
t
he
e
xpe
r
im
e
nt
to
e
va
luat
e
F
AST
a
lgor
it
hm
f
o
r
de
tec
ti
ng
c
or
ne
r
a
nd
tr
a
c
king
im
a
ge
tar
ge
t
is
c
onduc
ted
us
ing
31
im
a
ge
da
ta
s
uc
h
a
s
s
h
owe
d
in
T
a
ble
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
ugme
nted
r
e
ali
ty
us
ing
featur
e
s
ac
c
e
ler
ated
s
e
gm
e
nt
tes
t
for
lear
ning
taj
w
e
e
d
(
A
d
i
P
utr
a
A
ndr
iyand
i)
213
T
a
ble
2
.
T
he
e
xa
mpl
e
of
e
xpe
r
im
e
nt
M
a
r
ke
r
I
D
I
ma
ge
T
a
jwe
e
d
T
im
e
(
s
)
A
R
R
e
s
ul
t
m_01
Q
ol
qol
ah
0.30
D
e
te
c
t
C
or
r
e
c
tl
y
m_02
I
ql
ab
0.44
D
e
te
c
t
C
or
r
e
c
tl
y
m_03
Q
ol
qol
ah and I
k
hf
a
0.28
D
e
te
c
t
C
or
r
e
c
tl
y
m_04
I
dghom M
imi
and I
k
hf
a
0.49
D
e
te
c
t
C
or
r
e
c
tl
y
m_05
Q
ol
qol
ah
0.47
D
e
te
c
t
C
or
r
e
c
tl
y
m_06
I
dghom M
imi
0.21
D
e
te
c
t
C
or
r
e
c
tl
y
...
...
...
...
...
m_11
I
khf
a
S
a
f
a
w
i
0.37
D
e
te
c
t
C
or
r
e
c
tl
y
...
...
...
...
...
m_20
G
unnah
0.35
D
e
te
c
t
C
or
r
e
c
tl
y
m_21
G
unnah
0.23
D
e
te
c
t
C
or
r
e
c
tl
y
m_22
I
k
hf
a
0.33
D
e
te
c
t
C
or
r
e
c
tl
y
m_23
G
unnah and I
dghom B
ig
unnah
0.51
D
e
te
c
t
C
or
r
e
c
tl
y
m_24
I
ql
ab
0.38
D
e
te
c
t
C
or
r
e
c
tl
y
m_25
I
ql
ab
0.37
D
e
te
c
t
C
or
r
e
c
tl
y
...
...
...
...
...
T
he
n,
que
s
ti
onna
ir
e
dis
tr
ibu
ti
on
is
c
onduc
ted
to
e
va
luate
the
us
a
bil
it
y
a
nd
be
ne
f
it
s
of
the
a
ppli
c
a
ti
on.
T
he
que
s
ti
onna
ir
e
c
ons
is
t
of
7
que
s
ti
ons
with
5
s
c
a
les
(
1
f
or
s
tr
ongly
d
is
a
gr
e
e
m
2
f
or
dis
a
gr
e
e
,
3
f
or
ne
ut
r
a
l,
4
f
or
a
gr
e
e
,
a
nd
5
f
or
s
tr
ongly
a
gr
e
e
)
that
r
e
late
d
with
us
a
bil
it
y
a
nd
be
ne
f
it
s
of
a
ppli
c
a
ti
on,
a
mong
othe
r
s
:
1.
I
s
the
t
a
jwe
e
d
Qur
’
a
n
lea
r
ning
media
on
the
a
ugm
e
nted
r
e
a
li
ty
tajwe
e
d
a
ppli
c
a
ti
on
good
f
o
r
c
hil
dr
e
n
?
2.
I
s
the
t
a
jwid
a
ugmente
d
r
e
a
li
ty
a
ppli
c
a
ti
on
indi
s
pe
ns
a
ble
in
lea
r
ning
pr
oc
e
s
s
of
t
a
jwe
e
d?
3.
Doe
s
the
tajwid
a
ugmente
d
r
e
a
li
ty
a
ppli
c
a
ti
on
mor
e
e
a
s
il
y
unde
r
s
tand
a
nd
lea
r
n
the
t
a
jwe
e
d
of
the
Qur
a
n?
4.
I
s
the
main
dis
play
go
ing
we
ll
?
5.
W
ha
t
is
the
s
uit
a
bil
it
y
of
the
ba
c
kgr
ound
/
ba
c
kg
r
ound
dis
play
de
s
ign
in
the
tajwid
a
ugmente
d
r
e
a
li
ty
a
ppli
c
a
t
ion?
6.
I
s
the
mate
r
ial
pr
e
s
e
nted
e
a
s
il
y
unde
r
s
tood?
7.
Do
the
s
ound,
e
xit
a
nd
r
e
tur
n
but
tons
f
unc
ti
on
c
o
r
r
e
c
tl
y?
T
he
r
e
s
ult
of
que
s
ti
onna
ir
e
e
va
luation
is
pr
ovided
in
the
T
a
ble
3
a
nd
F
igu
r
e
8
.
W
hil
e
,
the
e
va
luation
of
e
xpe
r
im
e
nt
a
nd
que
s
ti
nna
ir
e
a
r
e
e
xplaine
d
i
n
s
e
c
ti
on
3.
4
.
3.
4.
Re
s
u
lt
e
valu
at
ion
of
e
xp
e
r
i
m
e
n
t
a
n
d
q
u
e
s
t
ion
n
a
ire
B
a
s
e
d
on
the
e
xpe
r
im
e
nt
a
nd
que
s
ti
onna
ir
e
dis
tr
ib
uti
on,
thi
s
s
tudy
ge
ts
the
f
ol
lowing
r
e
s
ult
:
1.
E
ve
r
y
t
a
jwe
e
d
that
us
e
d
in
thi
s
s
tudy,
a
mong
ot
he
r
s
ikhfa,
ikhfa
s
afaw
i,
qalqa
lah,
idgham
bigun
nah,
idgham
mimi,
a
nd
gunnah
ha
ve
s
uc
c
e
e
de
d
in
de
tec
ti
ng
a
nd
dis
playing
3D
objec
ts
a
c
c
or
ding
to
it
s
T
a
jwe
e
d.
T
he
tes
t
r
e
s
ult
s
a
r
e
s
uc
c
e
s
s
f
ul
if
the
i
mage
s
c
a
nnin
g
is
done
on
1
o
r
2
t
a
jwe
e
d.
How
e
ve
r
,
whe
n
s
c
a
nning
im
a
ge
s
a
r
e
c
a
r
r
ied
out
thr
oughout
the
e
nti
r
e
Qur
'a
n
pa
ge
,
no
t
a
ll
r
e
s
ult
s
o
f
T
a
jwe
e
d
w
il
l
be
dis
playe
d
s
im
ult
a
ne
ous
ly.
T
his
ha
ppe
ns
b
e
c
a
us
e
of
the
li
mi
tations
of
the
Qur
'a
n
im
a
ge
da
ta
a
nd
the
i
mage
r
e
s
olu
ti
on
is
not
good
e
nough
s
o
that
the
im
a
ge
de
tec
ti
on
is
not
pe
r
f
e
c
t.
L
ikew
is
e
f
or
Qur
’
a
n
wo
r
ds
or
s
e
ntenc
e
s
that
c
ontaining
a
djac
e
nt
T
a
jwe
e
d,
the
a
ppli
c
a
ti
on
is
not
a
ble
to
de
tec
t
it
p
r
ope
r
ly,
o
r
r
e
qu
ir
e
s
a
longer
de
tec
ti
on
pr
oc
e
s
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
.
1
,
F
e
br
ua
r
y
2020
:
208
-
216
214
2.
Ge
ne
r
a
ll
y,
F
AST
c
or
ne
r
d
e
tec
ti
on
a
lgor
it
hm
is
f
a
s
t
e
nough
in
de
tec
ti
ng
the
t
a
jwe
e
d
Qur
’
a
n
,
with
a
n
a
ve
r
a
ge
va
lue
of
pr
oc
e
s
s
ing
ti
me
is
a
r
ound
0
.
335
s
e
c
onds
pe
r
objec
t
(
pr
ovided
in
F
igur
e
9)
.
Althoug
h
not
a
lwa
ys
whe
n
the
s
c
a
nne
d
t
a
jwe
e
d
objec
t
mea
ns
it
ha
s
a
longer
p
r
oc
e
s
s
ing
ti
me
.
s
uc
h
a
s
t
a
jwe
e
d
o
bjec
t
m_03
whic
h
c
ontains
2
T
a
jwe
e
d
objec
ts
ha
s
a
s
hor
ter
pr
oc
e
s
s
ing
ti
me
than
othe
r
objec
ts
that
only
c
o
ntain
1
T
a
jwe
e
d
(
f
or
e
xa
mpl
e
m_02
a
nd
m_05)
.
T
his
c
a
n
be
inf
luenc
e
d
by
im
a
ge
r
e
s
olut
ion
s
o
that
the
r
e
s
ult
ing
mar
ke
r
point
s
a
r
e
les
s
pr
e
c
is
e
s
o
it
tak
e
s
longer
pr
oc
e
s
s
ing
ti
me.
3.
B
a
s
e
d
on
the
r
e
s
ult
of
que
s
ti
onna
ir
e
with
s
e
ve
r
a
l
r
e
s
ponde
nts
s
how
that
in
a
c
c
or
da
nc
e
with
be
ne
f
it
s
of
mul
ti
media
tec
hnology
f
or
e
duc
a
ti
on
[
1
-
3]
,
thi
s
s
tudy
a
ls
o
pr
ove
s
that
AR
tec
hnology
is
int
e
r
e
s
ti
ng
a
nd
he
l
ping
s
tudent
in
lea
r
ning
,
in
thi
s
c
a
s
e
T
a
jwe
e
d
o
f
Qur
’
a
n.
I
t
is
s
uppor
ted
by
the
que
s
ti
onna
ir
e
r
e
s
ult
that
mos
t
of
r
e
s
ponde
nts
a
gr
e
e
(
with
pe
r
c
e
ntage
of
que
s
ti
onna
ir
e
da
ta
a
na
lys
is
a
r
ound
88.
28%
)
that
the
A
R
f
or
t
a
jwe
e
d
r
uns
we
ll
a
nd
c
a
n
he
lp
in
lea
r
ning
t
a
jwe
e
d
Qur
’
a
n
e
a
s
il
y.
T
a
ble
3
.
Que
s
ti
onna
ir
e
r
e
s
ult
Q
ue
s
ti
on
N
o.
T
ot
a
l
F
r
e
que
nc
y of
E
a
c
h S
c
a
le
T
ot
a
l
S
c
or
e
∑
W
e
ig
ht
*
F
r
e
que
nc
y
S
tr
ongl
y A
gr
e
e
(
5)
A
gr
e
e
(
4)
N
e
ut
r
a
l
(
3)
D
is
a
gr
e
e
(
2)
S
tr
ongl
y D
is
a
gr
e
e
(
1)
1
7
3
0
0
0
47
2
6
4
0
0
0
46
3
5
5
0
0
0
45
4
4
5
1
0
0
43
5
3
7
0
0
0
43
6
2
7
1
0
0
41
7
5
4
1
0
0
44
A
ve
r
a
ge
88,28571429 %
F
igur
e
8.
Que
s
ti
onna
ir
e
r
e
s
ult
F
igur
e
9.
T
he
e
xa
mpl
e
of
3
D
o
bjec
t
of
t
a
jwe
e
d
Qur
’
a
n
4.
CONC
L
USI
ON
L
e
a
r
ning
tajwe
e
d
Qur
’
a
n
is
im
por
tant
f
or
M
us
li
m
be
c
a
us
e
it
is
r
e
late
d
wi
th
r
e
c
it
a
ti
on
Qur
’
a
n
pr
ope
r
ly
a
nd
c
or
r
e
c
tl
y.
T
his
s
tudy
s
uc
c
e
s
s
to
uti
li
z
e
a
ugmente
d
r
e
a
li
ty
(
AR
)
tec
hnology
a
s
mul
ti
media
that
s
uppor
t
in
lea
r
ning
ta
jwe
e
d
Qur
’
a
n
.
Ge
ne
r
a
ll
y
,
AR
in
thi
s
s
tudy
that
us
e
F
AS
T
c
or
ne
r
de
tec
ti
on
a
lgor
it
hm
ha
s
be
e
n
r
un
we
ll
with
a
r
e
latively
f
a
s
t
objec
t
de
tec
ti
on
pr
oc
e
s
s
.
How
e
ve
r
,
it
s
ti
ll
lac
k
in
de
tec
ti
ng
t
a
jwe
e
d
objec
ts
s
im
ult
a
ne
ous
ly
on
one
pa
ge
of
the
Qur
'a
n.
T
he
r
e
f
o
r
e
,
f
o
r
the
f
ur
ther
s
tudy,
the
objec
t
a
nd
r
ules
of
T
a
jwe
e
d
mus
t
be
mo
r
e
c
ompl
e
te
.
T
he
n,
the
im
a
ge
da
ta
mus
t
be
ha
s
high
r
e
lot
uti
on
s
o
that
the
objec
t
c
a
n
be
de
tec
ted
be
tt
e
r
a
nd
f
a
s
ter
.
AC
KNOWL
E
DGE
M
E
NT
Author
s
wis
hing
to
a
c
knowle
dge
R
e
s
e
a
r
c
h
a
nd
P
ubli
c
a
ti
on
C
e
ntr
e
of
UI
N
S
una
n
Gunung
Dja
ti
B
a
ndung
that
s
uppor
ts
a
nd
f
unds
thi
s
r
e
s
e
a
r
c
h
publ
ica
ti
on.
RE
F
E
RE
NC
E
S
[
1]
A.
A.
I
br
a
him
,
“
E
volut
ionar
y
Na
tu
r
e
of
the
De
f
i
nit
ion
of
E
duc
a
ti
ona
l
T
e
c
hnology,
”
I
n
t.
J
.
Soc
.
S
c
i.
E
duc
.
,
vol.
5,
no.
2,
2015.
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
ugme
nted
r
e
ali
ty
us
ing
featur
e
s
ac
c
e
ler
ated
s
e
gm
e
nt
tes
t
for
lear
ning
taj
w
e
e
d
(
A
d
i
P
utr
a
A
ndr
iyand
i)
215
[
2]
R
I
S
T
E
KD
I
K
T
I
,
“
S
c
ienc
e
a
nd
T
e
c
hnology
De
ve
lopm
e
nt
a
nd
Highe
r
E
duc
a
ti
on
in
the
I
ndus
tr
ial
R
e
volut
ion
E
r
a
4
.
0
(
in
B
a
ha
s
a
:
P
e
ng
e
mbanga
n
I
ptek
da
n
P
e
ndidi
ka
n
T
inggi
di
E
r
a
R
e
volus
i
I
ndu
s
tr
i
4.
0
)
,
”
P
r
e
s
s
C
onf
e
r
e
nc
e
,
Numbe
r
:
04
/
S
P
/
HM
/
B
KK
P
/
I
/
2018
,
2018
.
[
3]
M
.
M
us
ti
ka
,
E
.
P
.
A.
S
uga
r
a
,
a
nd
M
.
P
r
a
ti
wi,
“
De
ve
lopm
e
nt
of
I
nter
a
c
ti
ve
L
e
a
r
ning
M
e
dia
Us
ing
the
M
ult
im
e
dia
De
ve
lopm
e
n
t
L
if
e
C
yc
le
M
e
thod
(
in
B
a
ha
s
a
:
P
e
nge
mbanga
n
M
e
dia
P
e
mbela
j
a
r
a
n
I
nter
a
kti
f
de
nga
n
M
e
ngguna
ka
n
M
e
tode
M
ult
im
e
dia
De
ve
lopm
e
nt
L
if
e
C
yc
le
)
,
”
J
ur
nal
Online
I
nfor
matika
,
vol
.
2
,
no
.
2
,
2018
.
[
4]
S
.
Nu
r
a
ji
z
a
h,
“
I
mpl
e
menta
ti
on
o
f
M
ult
im
e
dia
De
v
e
lopm
e
nt
L
if
e
C
yc
le
in
C
hil
dr
e
n's
S
ong
R
e
c
ognit
i
on
Applica
ti
on
(
in
B
a
ha
s
a
:
I
mpl
e
menta
s
i
M
ult
im
e
dia
De
ve
lopm
e
nt
L
if
e
C
yc
le
P
a
da
Aplikas
i
P
e
nge
n
a
lan
L
a
gu
Ana
k
-
An
a
k
)
,
”
P
r
os
is
k
o
:
J
ur
nal
P
e
nge
mbangan
R
is
e
t
dan
Obs
e
r
v
as
i
Sis
tem
K
ompute
r
,
vol
.
3,
no.
2,
pp.
14
–
19,
2016.
[
5]
I
.
B
inanto
,
“
C
ompar
is
on
of
M
ult
im
e
dia
S
o
f
twa
r
e
De
ve
lopm
e
nt
M
e
thods
C
ompar
is
on
of
M
ult
im
e
dia
S
of
twa
r
e
De
ve
lopm
e
nt
M
e
thods
(
in
B
a
ha
s
a
:
P
e
r
ba
ndingan
M
e
tode
P
e
nge
mba
nga
n
P
e
r
a
ngka
t
L
una
k
M
ult
im
e
dia
P
e
r
ba
ndingan
M
e
tode
P
e
nge
mbanga
n
P
e
r
a
ngka
t
L
una
k
M
ult
im
e
dia
)
,
”
Se
minar
N
as
io
nal
R
I
T
E
K
T
R
A
2013
,
2014.
[
6]
D.
R
.
F
i
r
manda
,
R
.
R
.
I
s
na
nto,
a
nd
I
.
P
.
W
ind
a
s
a
r
i,
“
Andr
oid
-
ba
s
e
d
Ha
jj
a
nd
Umr
a
h
P
il
gr
i
mage
L
e
a
r
ning
Applica
ti
on
(
in
B
a
ha
s
a
:
Aplikas
i
P
e
mbela
jar
a
n
M
a
na
s
ik
Ha
ji
da
n
Umr
oh
B
e
r
ba
s
is
Andr
oid
)
,
”
J
ur
nal
T
e
k
nologi
dan
Sis
tem
K
ompute
r
,
vol
.
4
,
no
.
4,
pp
.
510
-
517,
2016.
[
7]
N.
F
it
r
iyani,
D
.
T
r
e
s
na
wa
ti
,
a
nd
N.
Ha
diyanto,
“
De
ve
lopm
e
nt
of
Applica
ti
on
f
or
R
e
c
ognizing
L
e
tt
e
r
s
,
Numbe
r
s
a
nd
C
olor
s
f
or
E
a
r
ly
C
hil
dhood
B
a
s
e
d
on
Andr
oid
(
in
B
a
ha
s
a
:
P
e
nge
mbanga
n
Aplikas
i
P
e
nge
na
lan
Hur
uf
,
Angka
da
n
W
a
r
na
Untuk
Ana
k
Us
ia
Dini
B
e
r
ba
s
i
s
Andr
oid
)
,
”
J
ur
nal
A
lgor
it
ma
,
vol.
11
,
no
.
2
,
pp
.
273
-
281,
2014
.
[
8]
D.
T
r
e
s
na
wa
ti
a
nd
T
.
S
.
Nugr
a
ha
,
“
Applica
ti
on
De
ve
lopm
e
nt
f
or
I
ntr
oduc
ti
on
to
R
e
gional
Ar
t
(
i
n
B
a
ha
s
a
:
P
e
nge
mbanga
n
Aplikas
i
P
e
nge
na
lan
Ke
s
e
nian
Da
e
r
a
h
)
,
”
J
ur
nal
A
lgor
it
ma
,
vol
12,
no.
1,
pp.
156
-
165,
2015
.
[
9]
I
.
M
us
taqim,
“
Util
iza
ti
on
of
Augme
nted
R
e
a
li
ty
a
s
L
e
a
r
ning
M
e
dia
(
in
B
a
ha
s
a
:
P
e
manf
a
a
tan
Augme
nted
R
e
a
li
ty
S
e
ba
ga
i
M
e
dia
P
e
mbela
jar
a
n
)
,
”
J
ur
nal
P
e
ndidi
k
an
T
e
k
nologi
dan
K
e
jur
uan
,
vol.
13
,
no
.
2
,
pp
.
174
-
183,
201
6
.
[
10]
D.
Ninc
a
r
e
a
n,
M
.
B
.
Alia,
N.
D.
A.
Ha
li
m
,
a
nd
M
.
H
.
A.
R
a
hman,
“
M
obil
e
Augme
nted
R
e
a
l
it
y:
T
he
P
otential
f
or
E
duc
a
ti
on,
”
P
r
oc
e
dia
-
Soc
ial
an
d
B
e
hav
ior
al
Sc
ienc
e
s
,
vol.
103,
pp.
657
-
664,
201
3.
[
11]
M
.
Dunle
a
vy
a
nd
C
.
De
de
,
“
Augme
nted
r
e
a
li
ty
tea
c
hing
a
nd
lea
r
n
ing,
”
Handbook
of
R
e
s
e
ar
c
h
on
E
duc
ati
onal
C
omm
unications
and
T
e
c
hnology:
F
o
ur
th
E
dit
ion
,
Spr
inger
,
pp.
735
-
745,
2014
.
[
12]
S
.
I
r
s
ya
d,
“
Augme
nted
R
e
a
li
ty
Applica
ti
on
a
s
a
n
Andr
oid
-
B
a
s
e
d
C
he
mi
c
a
l
B
ond
S
im
ulation
M
e
dia
Us
ing
the
F
a
s
t
C
or
ne
r
De
tec
ti
on
M
e
thod
(
in
B
a
ha
s
a
:
Aplikas
i
Augme
nted
R
e
a
li
ty
S
e
ba
g
a
i
M
e
dia
S
im
ulas
i
I
ka
tan
Kimi
a
B
e
r
ba
s
is
Andr
oid
M
e
ngguna
ka
n
M
e
tode
F
a
s
t
C
or
ne
r
De
tec
ti
o
n
)
,”
Unde
r
gr
a
dua
te
thes
is
,
Unive
r
s
it
a
s
I
s
lam
Ne
ge
r
i
M
a
ulana
M
a
li
k
I
br
a
him
,
2016.
[
13]
M
.
P
.
C
he
n
a
nd
B
.
C
.
L
iao,
“
Augme
nted
r
e
a
li
ty
la
bor
a
tor
y
f
or
high
s
c
hool
e
lec
tr
oc
he
mi
s
tr
y
c
our
s
e
,
”
in
P
r
oc
e
e
dings
-
I
E
E
E
15
th
I
nter
nati
onal
C
onfer
e
nc
e
on
A
dv
anc
e
d
L
e
ar
ning
T
e
c
hnologi
e
s
:
A
dv
an
c
e
d
T
e
c
hnologi
e
s
for
Suppor
ti
ng
Ope
n
A
c
c
e
s
s
to
F
or
mal
and
I
n
for
mal
L
e
ar
ning,
I
C
A
L
T
2015
,
2015.
[
14]
F
.
S
.
I
r
wa
ns
ya
h,
Y.
M
.
Yus
uf
,
I
.
F
a
r
ida
,
a
nd
M
.
A.
R
a
mdhani,
“
Augme
nted
R
e
a
li
ty
(
AR
)
T
e
c
hnol
ogy
on
the
Andr
oid
Ope
r
a
ti
ng
S
ys
tem
in
C
he
mi
s
tr
y
L
e
a
r
ning,
”
I
O
P
C
onfer
e
nc
e
Se
r
ies
:
M
ater
ial
s
Sc
ienc
e
and
E
nginee
r
ing
,
vol
.
288
,
no
. 1
,
2018
.
[
15]
P
.
G.
C
r
a
nda
ll
e
t
a
l.
,
“
De
ve
lopm
e
nt
of
a
n
a
ugmente
d
r
e
a
li
ty
ga
me
to
tea
c
h
a
bs
tr
a
c
t
c
onc
e
pts
in
f
ood
c
he
mi
s
tr
y,
”
J
our
nal
of
F
ood
Sc
ienc
e
E
duc
ati
on
,
v
ol.
14
,
no
.
1
,
pp
.
18
-
23,
2015
.
[
16]
S
.
Ya
ng,
B
.
M
e
i,
a
nd
X.
Yue
,
“
M
obil
e
Augme
nted
R
e
a
li
ty
As
s
is
ted
C
he
mi
c
a
l
E
duc
a
ti
on:
I
ns
ight
s
f
r
om
E
leme
nts
4D,
”
J
our
nal
of
C
he
mic
al
E
duc
ati
on
,
vo
l.
95
,
no
.
6
,
pp
.
1060
-
1062
,
2018
.
[
17]
De
dyngge
go,
M
oha
mm
a
d,
a
nd
M
.
Af
f
a
n,
“
De
s
ign
of
3D
I
nter
a
c
ti
ve
L
e
a
r
ning
M
e
dia
S
olar
S
ys
tem
Us
ing
Augme
nted
R
e
a
li
ty
T
e
c
hnology
F
o
r
Gr
a
de
6
S
tudents
of
S
a
ngi
r
a
E
leme
ntar
y
S
c
hool
(
in
B
a
ha
s
a
:
P
e
r
a
nc
a
nga
n
M
e
dia
P
e
mbela
jar
a
n
I
nter
a
kti
f
3D
T
a
ta
S
ur
ya
M
e
ngguna
ka
n
T
e
knologi
Augme
nted
R
e
a
li
ty
Untuk
S
is
wa
Ke
las
6
S
e
kolah
Da
s
a
r
S
a
ngir
a
)
,
”
J
ur
nal
E
lek
tr
onik
Sis
tem
I
nfor
mas
i
dan
K
ompute
r
,
vol.
1
,
no
.
2
,
2015
.
[
18]
T
.
A
.
Ana
nda
,
N.
S
a
f
r
iadi
,
a
nd
A
.
S
.
S
uka
mt
o,
“
Applica
ti
on
of
Augme
nted
R
e
a
li
ty
a
s
a
L
e
a
r
ning
M
e
dia
to
Know
the
P
lane
ts
in
the
S
olar
S
ys
tem
(
in
B
a
ha
s
a
:
P
e
ne
r
a
pa
n
Augme
nte
d
R
e
a
li
ty
S
e
ba
ga
i
M
e
dia
P
e
mbela
jar
a
n
M
e
nge
na
l
P
lane
t
-
P
lane
t
Di
T
a
ta
S
ur
ya
)
,
”
J
ur
nal
Sis
tem
dan
T
e
k
nologi
I
nfor
mas
i
,
vol
.
4,
no.
1,
pp.
1
-
6,
2015
.
[
19]
R
.
E
.
S
a
putr
o
a
nd
D.
I
.
S
.
S
a
put
r
a
,
“
De
ve
lopm
e
nt
of
L
e
a
r
ning
M
e
dia
to
Know
Huma
n
Dige
s
t
ive
Or
ga
ns
Us
ing
Augme
nted
R
e
a
li
ty
T
e
c
hnology
(
in
B
a
ha
s
a
:
P
e
nge
mbanga
n
M
e
dia
P
e
mbela
ja
r
a
n
M
e
nge
na
l
Or
ga
n
P
e
nc
e
r
na
a
n
M
a
nus
ia
M
e
ngguna
ka
n
T
e
knologi
Augme
nted
R
e
a
li
ty
)
,
”
J
ur
nal
B
ua
na
I
nfor
matika
,
vol
.
6
,
no
.
2
,
pp
.
153
-
162,
2017
.
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
.
1
,
F
e
br
ua
r
y
2020
:
208
-
216
216
[
20]
R
.
A.
S
e
tyaw
a
n
a
nd
A
.
Dz
ikr
i
,
“
Ana
lys
is
of
the
Us
e
of
the
M
a
r
ke
r
T
r
a
c
king
M
e
thod
in
Augme
nt
e
d
R
e
a
li
ty
of
a
T
r
a
dit
ional
M
us
ica
l
I
ns
tr
ument
in
C
e
ntr
a
l
J
a
va
(
in
B
a
ha
s
a
:
Ana
li
s
is
P
e
ngguna
a
n
M
e
tode
M
a
r
ke
r
T
r
a
c
king
P
a
da
Augme
nted
R
e
a
li
ty
Ala
t
M
us
ik
T
r
a
dis
ional
J
a
wa
T
e
nga
h
)
,”
J
ur
nal
T
e
k
nik
I
ndus
tr
i,
M
e
s
in,
E
lek
tr
o
dan
I
lmu
K
ompute
r
(
Sime
t
r
is
)
,
vol.
7,
no.
1,
pp.
295
-
304,
2017.
[
21]
M
.
Avie
f
B
a
r
ka
h
a
nd
R
.
Agus
ti
na
,
“
Util
iza
ti
on
o
f
Augme
nted
R
e
a
li
ty
(
AR
)
a
s
a
n
I
nter
a
c
ti
ve
L
e
a
r
ni
ng
M
e
dia
I
ntr
oduc
ti
on
to
T
e
mpl
e
s
in
M
a
lang
R
a
ya
B
a
s
e
d
on
Andr
oid
M
obil
e
(
in
B
a
ha
s
a
:
P
e
manf
a
a
t
a
n
Augme
nt
e
d
R
e
a
li
ty
(
AR
)
S
e
ba
ga
i
M
e
dia
P
e
mbel
a
ja
r
a
n
I
nter
a
kti
f
P
e
nge
na
lan
C
a
ndi
–
C
a
ndi
di
M
a
lang
R
a
ya
B
e
r
ba
s
is
M
obil
e
Andr
oid
)
,
”
B
imas
ak
ti
,
vol.
1,
no.
5,
pp.
1
-
6,
2017
.
[
22]
H.
Vitono
,
H.
Na
s
uti
on,
a
nd
H
.
An
r
a
,
“
I
mpl
e
menta
ti
on
of
M
a
r
ke
r
les
s
Augme
nted
R
e
a
li
ty
a
s
a
n
Andr
oid
-
B
a
s
e
d
M
u
s
e
um
I
nf
or
mation
M
e
dia
C
oll
e
c
ti
on
(
in
B
a
ha
s
a
:
I
mpl
e
menta
s
i
M
a
r
ke
r
l
e
s
s
Augme
nted
R
e
a
li
ty
S
e
ba
ga
i
M
e
dia
I
nf
o
r
mas
i
Kol
e
ks
i
M
us
e
um
B
e
r
ba
s
i
s
Andr
oid
)
,
”
J
ur
nal
Sis
tem
dan
T
e
k
nologi
I
nfomar
s
i
,
v
ol
.
4
,
no
.
2
,
pp
.
1
-
7,
2016.
[
23]
Y.
A.
P
r
a
mana
,
K
.
C
.
B
r
a
ta,
a
nd
A.
H
.
B
r
a
ta,
“
De
ve
lopm
e
nt
of
Augme
nted
R
e
a
li
ty
Applica
ti
ons
f
or
Obje
c
t
R
e
c
ognit
ion
in
And
r
oid
B
a
s
e
d
M
us
e
ums
(
C
a
s
e
S
tudy:
B
lamba
nga
n
B
a
nyuwa
ngi
M
us
e
um)
(
in
B
a
ha
s
a
:
P
e
mbanguna
n
Aplika
s
i
Augme
n
ted
R
e
a
li
ty
untuk
P
e
nge
na
lan
B
e
n
da
di
M
us
e
um
B
e
r
ba
s
is
Andr
oid
(
S
tudi
Ka
s
us
:
M
us
e
um
B
lamba
nga
n
B
a
nyuwa
ngi
)
)
,
”
J
ur
nal
P
e
nge
mbangan
T
e
k
no
logi
I
nfor
mas
i
dan
K
ompute
r
,
vol.
2,
no.
5,
pp.
2034
-
2042,
2018.
[
24]
I
.
M
ulyana
,
M
.
I
.
S
u
r
ians
ya
h,
a
nd
J
.
Akba
r
,
“
I
mpl
e
m
e
ntation
of
Na
tur
a
l
F
e
a
tur
e
T
r
a
c
king
on
the
I
ntr
oduc
ti
on
of
M
a
r
ine
M
a
mm
a
ls
B
a
s
e
d
on
Au
gmente
d
R
e
a
li
ty
(
in
B
a
ha
s
a
:
I
mp
leme
ntas
i
Na
tu
r
a
l
F
e
a
tur
e
T
r
a
c
king
P
a
da
P
e
nge
na
lan
M
a
malia
L
a
u
t
B
e
r
ba
s
is
Augme
nted
R
e
a
li
ty
)
,
”
Se
minar
N
as
io
nal
T
e
k
nologi
I
nfor
mas
i
dan
M
u
lt
ime
dia
2018
,
pp
.
13
–
18,
2018.
[
25]
D.
Apr
iyani
M
e
yti
E
ka
,
Huda
M
if
takhul
,
“
Ana
lys
is
of
Us
e
of
M
a
r
ke
r
T
r
a
c
king
I
n
Augme
nted
R
e
a
li
ty
Hijaiya
h
(
in
B
a
ha
s
a
:
Ana
li
s
is
P
e
ngguna
a
n
M
a
r
ke
r
T
r
a
c
king
P
a
da
Augme
nted
R
e
a
li
ty
Hur
u
f
Hi
jaiya
h
)
,
”
J
ur
nal
I
nfor
matics
,
T
e
lec
om
munic
ati
on,
and
E
lec
t
r
onics
.
vol.
8
,
no
.
1
,
2016
.
[
26]
E
.
S
e
ti
a
wa
n,
U.
S
ya
r
ipudi
n
,
a
nd
Y.
A.
Ge
r
ha
na
,
“
I
mpl
e
menta
ti
on
of
Augme
nted
R
e
a
li
ty
T
e
c
hnology
in
the
Andr
oid
M
obil
e
B
a
s
e
d
W
udhu
Ha
ndbook
(
in
B
a
ha
s
a
:
I
mpl
e
menta
s
i
T
e
knologi
Augme
nted
R
e
a
li
ty
pa
da
B
uku
P
a
ndua
n
W
udhu
B
e
r
ba
s
is
M
obil
e
A
ndr
oid
)
,
”
J
ur
nal
Online
I
nfor
matika
,
vol
.
1,
no
.
1,
pp.
28
-
33,
2018
.
[
27]
L
.
Ha
kim
,
“
De
ve
lopm
e
nt
o
f
P
ie
B
a
s
e
d
L
e
a
r
ning
M
e
dia
B
a
s
e
d
on
Augme
nted
R
e
a
li
ty
(
in
B
a
ha
s
a
:
P
e
nge
mbanga
n
M
e
dia
P
e
mbela
jar
a
n
P
a
i
B
e
r
ba
s
is
Augme
nted
R
e
a
li
ty
)
,
”
L
e
nter
a
P
e
ndidi
k
an
J
ur
nal
I
lmu
T
ar
biyah
dan
K
e
gur
uan
,
vo
l.
21,
no.
1
,
2018
.
[
28]
H.
B
a
y,
A.
E
s
s
,
T
.
T
uytela
a
r
s
,
a
nd
L
.
Va
n
Gool,
“
S
pe
e
de
d
-
Up
R
obus
t
F
e
a
tur
e
s
(
S
UR
F
)
,
”
C
ompute
r
V
is
ion
and
I
mage
Unde
r
s
tandi
ng
,
vol.
110,
no.
3,
pp.
346
-
359
,
2008
.
[
29]
R
.
M
a
ini
a
nd
H.
Agga
r
wa
l
,
“
S
tudy
a
nd
c
ompar
is
on
of
va
r
ious
im
a
ge
e
dge
de
tec
ti
on
tec
hnique
s
,
”
I
nter
nati
onal
J
our
nal
of
I
mage
P
r
oc
e
s
s
ing
(
I
J
I
P
)
,
vol.
3
,
no
.
1
,
pp
.
1
-
11,
2009
.
[
30]
R
.
R
.
Akba
r
,
“
Augme
nted
R
e
a
li
ty
T
e
c
hnology
I
m
pleme
ntation
in
And
r
oid
-
B
a
s
e
d
I
nter
a
c
ti
ve
M
a
ga
z
ine
Us
ing
the
F
AST
C
or
ne
r
De
tec
ti
on
Algo
r
it
hm
M
e
t
hod
(
in
B
a
ha
s
a
:
I
mpl
e
menta
s
i
T
e
knologi
Augme
n
ted
R
e
a
li
ty
pa
da
M
a
jala
h
I
nte
r
a
kti
f
B
e
r
ba
s
is
Andr
o
id
M
e
ngguna
ka
n
M
e
tode
Algor
it
ma
F
AS
T
C
or
ne
r
De
tec
ti
on
)
,”
T
he
s
is
,
Unive
r
s
it
a
s
J
e
mber
,
2016.
[
31]
A.
Ha
r
dians
ya
h,
“
De
s
igni
ng
Andr
oid
B
a
s
e
d
Augme
nted
R
e
a
li
ty
L
oc
a
ti
on
-
B
a
s
e
d
S
e
r
vice
Applica
ti
on,
”
J
OI
N
(
J
ur
nal
Online
I
nfor
m
ati
k
a
,
vol.
2,
no.
2,
pp.
110
–
115,
2016
.
[
32]
M
.
A.
S
á
nc
he
z
-
Ac
e
ve
do,
B
.
A.
S
a
bino
-
M
oxo,
a
nd
J
.
A.
M
á
r
que
z
-
Domínguez
,
“
M
obil
e
Augme
n
ted
R
e
a
li
ty,
”
V
ir
tual
and
A
ugme
nted
R
e
ali
ty
,
2018.
[
33]
J
.
M
.
M
ota,
I
.
R
uiz
-
R
ube
,
J
.
M
.
Dode
r
o
,
a
nd
I
.
Ar
ne
dil
lo
-
S
á
nc
he
z
,
“
Augme
nted
r
e
a
li
ty
mobi
le
a
pp
de
ve
lopm
e
nt
f
or
a
ll
,
”
C
ompute
r
s
&
E
lec
tr
ical
E
ng
inee
r
ing
,
vol.
65
,
pp
.
250
-
260,
2018.
[
34]
A.
F
.
Al
B
a
ihaqi,
E
d
.
,
"
Al
-
Khuma
ir
a
h,
T
he
Qu
r
'a
n
T
r
a
ns
late
T
a
jwid
c
olor
17
in
One
(
in
B
a
ha
s
a
:
Al
-
Khuma
ir
a
h,
Al
-
Qur
’
a
n
T
e
r
jema
h
T
a
jwid
wa
r
na
17
in
One
)
,
"
C
V.
Alf
a
ti
h
B
e
r
ka
h
C
ipt
a
.
[
35]
S
.
A.
M
.
M
u’
a
bba
d,
"
T
a
qiya
-
C
ompl
e
te
Guide
to
T
a
jwe
e
d
S
c
ienc
e
(
in
B
a
ha
s
a
:
T
a
qiya
-
B
uku
P
a
nd
ua
n
L
e
ngka
p
I
lm
u
T
a
jwid
)
,
"
T
a
qiya
P
ubli
s
hing,
2015.
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