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k
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d
c
h
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n
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CH1
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CH4
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CH5
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a
n
d
CH
7
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s
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m
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t
sig
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8
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su
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t
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t
imp
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K
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tific
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E
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Feed
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war
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eu
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Han
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class
if
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My
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b
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s
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CC B
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C
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T
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Mic
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titu
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1.
I
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RO
D
UCT
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N
E
lectr
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m
y
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r
ap
h
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(
E
MG
)
s
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n
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p
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a
cr
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r
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in
h
u
m
an
-
co
m
p
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ter
in
te
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ac
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,
p
ar
ticu
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ly
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ap
p
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s
s
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ch
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p
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s
th
etics,
r
o
b
o
tics
,
an
d
v
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r
tu
al
r
ea
lit
y
(
VR
)
[
1
]
.
T
h
ese
s
ig
n
als
p
r
o
v
id
e
a
n
o
n
-
i
n
v
asiv
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m
ea
n
s
o
f
ca
p
t
u
r
in
g
m
u
s
cle
a
ctiv
ity
,
wh
ich
ca
n
b
e
lev
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r
a
g
ed
f
o
r
h
a
n
d
g
estu
r
e
r
ec
o
g
n
i
tio
n
.
Han
d
g
estu
r
e
class
if
icatio
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u
s
in
g
E
MG
s
i
g
n
als
h
as
b
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n
wid
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y
ex
p
l
o
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ed
f
o
r
v
ar
i
o
u
s
co
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tr
o
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ap
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licatio
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s
,
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teleo
p
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r
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ts
,
r
e
h
ab
ilit
atio
n
d
ev
ices,
an
d
v
ir
tu
al
i
n
ter
f
ac
es
[
2
]
-
[
4
]
.
Ho
wev
er
,
ac
c
u
r
ately
class
if
y
in
g
E
MG
s
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n
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ch
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in
ter
f
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e
n
ce
,
a
n
d
t
h
e
lim
itatio
n
s
o
f
av
ailab
le
h
ar
d
war
e
[
5
]
-
[
7
]
.
T
eleo
p
er
atio
n
r
o
b
o
ts
in
ter
p
r
et
E
MG
s
i
g
n
als
u
s
in
g
m
u
ltip
le
ap
p
r
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ac
h
es,
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clu
d
in
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r
u
le
-
b
a
s
ed
ex
p
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s
y
s
tem
s
,
p
atter
n
r
ec
o
g
n
itio
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,
an
d
m
ac
h
in
e
lea
r
n
in
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tec
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n
iq
u
es.
T
r
ad
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n
al
m
eth
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s
s
u
ch
as
s
u
p
p
o
r
t
v
ec
t
o
r
m
ac
h
in
es
(
SVM)
[
8
]
,
[
9
]
an
d
lin
ea
r
d
is
cr
im
in
an
t
a
n
aly
s
is
(
L
DA)
[
1
0
]
,
[
1
1
]
h
av
e
d
em
o
n
s
tr
at
ed
s
u
cc
ess
in
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estu
r
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class
if
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,
b
u
t
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ten
lac
k
th
e
r
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b
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s
tn
ess
n
ee
d
ed
f
o
r
r
ea
l
-
tim
e
ap
p
lic
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n
s
[
1
2
]
.
Mo
r
e
Evaluation Warning : The document was created with Spire.PDF for Python.
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J
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9
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Ju
ly
20
25
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59
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1
66
160
r
ec
en
tly
,
d
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p
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r
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m
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co
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C
NNs),
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t
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etwo
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task
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m
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d
s
p
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f
ea
tu
r
es
f
r
o
m
E
MG
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ig
n
als
[
1
3
]
,
[
1
4
]
.
Ho
wev
e
r
,
th
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m
o
d
els r
eq
u
ir
e
h
i
g
h
co
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r
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s
an
d
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x
ten
s
i
v
e
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ai
n
in
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atasets
[
1
5
]
.
Feed
f
o
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war
d
n
e
u
r
al
n
etwo
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k
s
(
FF
NN
s
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p
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p
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tatio
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ally
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f
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t
alter
n
ativ
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with
co
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p
etitiv
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class
if
icatio
n
ac
cu
r
ac
y
[
1
6
]
.
T
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y
h
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task
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u
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to
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p
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d
ab
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well
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I
n
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tu
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f
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My
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8
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My
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m
b
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s
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s
u
p
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ted
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-
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to
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at
en
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b
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c
o
n
tin
u
ed
ex
p
er
im
e
n
tatio
n
a
n
d
v
alid
atio
n
[
1
7
]
.
Featu
r
e
s
elec
tio
n
p
lay
s
a
cr
u
cial
r
o
le
in
im
p
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in
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th
e
p
er
f
o
r
m
an
ce
o
f
E
MG
-
b
ase
d
g
estu
r
e
class
if
icatio
n
s
y
s
tem
s
.
Var
io
u
s
tech
n
iq
u
es,
s
u
ch
as
p
r
in
ci
p
al
co
m
p
o
n
e
n
t
an
aly
s
is
(
PC
A)
[
1
8
]
,
c
o
r
r
elatio
n
an
aly
s
is
[
1
9
]
,
an
d
b
o
x
p
lo
t
an
aly
s
is
[
2
0
]
,
h
a
v
e
b
ee
n
u
s
ed
to
id
en
tif
y
th
e
m
o
s
t
r
el
ev
an
t
f
ea
tu
r
es
f
o
r
class
if
icatio
n
.
I
n
th
is
s
tu
d
y
,
we
em
p
lo
y
b
o
x
p
lo
t
a
n
aly
s
is
to
d
eter
m
in
e
th
e
m
o
s
t
s
ig
n
if
ic
an
t
E
MG
ch
an
n
els
th
at
co
n
tr
ib
u
te
to
im
p
r
o
v
e
d
g
estu
r
e
class
if
icatio
n
ac
cu
r
ac
y
[
2
1
]
.
B
y
r
ef
in
in
g
th
e
in
p
u
t
f
e
atu
r
e
s
et,
we
aim
to
en
h
an
ce
th
e
r
o
b
u
s
tn
ess
o
f
th
e
class
if
icatio
n
m
o
d
el
wh
ile
m
in
im
izin
g
co
m
p
u
tatio
n
al
c
o
m
p
l
ex
ity
.
T
h
e
k
ey
o
b
jectiv
e
o
f
th
is
s
tu
d
y
is
to
d
ev
elo
p
an
E
MG
-
b
ased
h
an
d
g
estu
r
e
class
if
icatio
n
s
y
s
tem
u
s
in
g
an
o
p
tim
ized
FF
NN
wh
ile
ev
a
lu
atin
g
th
e
ef
f
ec
tiv
en
ess
o
f
d
if
f
er
en
t
f
ea
tu
r
e
s
elec
tio
n
tech
n
iq
u
es
in
im
p
r
o
v
in
g
class
if
icatio
n
p
er
f
o
r
m
an
ce
.
A
d
d
itio
n
ally
,
it
aim
s
to
an
aly
ze
th
e
im
p
ac
t
o
f
v
ar
io
u
s
E
M
G
s
ig
n
al
p
r
o
ce
s
s
in
g
m
eth
o
d
s
o
n
g
estu
r
e
r
ec
o
g
n
itio
n
ac
c
u
r
ac
y
an
d
p
r
o
v
i
d
e
a
c
o
m
p
r
eh
e
n
s
iv
e
co
m
p
ar
is
o
n
o
f
FF
NN
with
o
th
er
n
eu
r
al
n
etwo
r
k
m
o
d
els
in
th
e
liter
atu
r
e.
B
y
ad
d
r
ess
in
g
t
h
ese
o
b
jectiv
es,
th
is
s
tu
d
y
co
n
tr
ib
u
tes
to
th
e
o
n
g
o
in
g
d
ev
elo
p
m
e
n
t
o
f
r
o
b
u
s
t
an
d
ef
f
icien
t
E
MG
-
b
ased
g
estu
r
e
r
e
co
g
n
itio
n
s
y
s
tem
s
,
with
p
o
ten
tial
ap
p
licatio
n
s
in
ass
is
tiv
e
tech
n
o
lo
g
ies,
r
eh
a
b
ilit
atio
n
,
an
d
h
u
m
a
n
-
co
m
p
u
ter
i
n
ter
ac
tio
n
.
T
h
is
p
ap
er
is
o
r
g
an
ized
as
f
o
llo
ws:
s
ec
t
io
n
2
d
etails
th
e
m
eth
o
d
o
lo
g
y
,
in
clu
d
in
g
d
ata
ac
q
u
is
itio
n
,
f
ea
tu
r
e
ex
t
r
ac
tio
n
,
an
d
n
eu
r
al
n
etwo
r
k
im
p
lem
en
tatio
n
.
Sectio
n
3
p
r
esen
ts
th
e
r
esu
lts
an
d
d
is
cu
s
s
io
n
,
h
ig
h
lig
h
tin
g
th
e
k
e
y
f
in
d
in
g
s
an
d
co
m
p
ar
in
g
th
em
with
p
r
e
v
io
u
s
s
tu
d
ies.
Sectio
n
4
co
n
cl
u
d
es
th
e
s
tu
d
y
with
in
s
ig
h
ts
in
to
f
u
tu
r
e
r
esear
ch
d
i
r
ec
tio
n
s
.
2.
M
E
T
H
O
D
2
.
1
.
Da
t
a
c
o
llect
io
n
T
h
e
s
tu
d
y
u
tili
ze
s
an
8
-
ch
a
n
n
el
My
o
Ar
m
b
a
n
d
to
co
lle
ct
E
MG
s
ig
n
als
f
r
o
m
th
e
f
o
r
ea
r
m
,
as
illu
s
tr
ated
in
Fig
u
r
e
1
.
T
h
e
My
o
Ar
m
b
an
d
,
a
wid
el
y
u
s
e
d
wea
r
ab
le
s
en
s
o
r
,
m
ea
s
u
r
es
th
e
elec
tr
ical
ac
tiv
ity
g
en
er
ated
b
y
s
k
eleta
l
m
u
s
cle
s
an
d
p
r
o
v
id
es
a
co
n
v
en
ie
n
t
m
eth
o
d
f
o
r
n
o
n
-
in
v
asiv
e
s
ig
n
al
ac
q
u
is
itio
n
[
2
2
]
.
T
o
en
s
u
r
e
co
m
p
atib
ilit
y
with
s
tan
d
ar
d
m
ac
h
in
e
lear
n
in
g
p
ip
elin
es,
an
o
p
en
-
s
o
u
r
ce
d
ata
ac
q
u
is
itio
n
lib
r
ar
y
was e
m
p
lo
y
ed
f
o
r
d
ata
c
o
llectio
n
an
d
p
r
e
p
r
o
ce
s
s
in
g
.
Data
wer
e
co
llected
f
r
o
m
3
0
p
ar
ticip
an
ts
,
ea
ch
in
s
tr
u
cted
to
p
er
f
o
r
m
th
r
ee
p
r
ed
ef
i
n
ed
h
an
d
g
estu
r
es:
a
f
is
t,
an
o
p
e
n
h
an
d
,
an
d
a
p
in
ch
.
Stan
d
a
r
d
ized
in
s
tr
u
ctio
n
s
wer
e
p
r
o
v
id
ed
to
all
p
a
r
ticip
an
ts
to
m
ain
tain
co
n
s
is
ten
cy
th
r
o
u
g
h
o
u
t
t
h
e
d
a
ta
co
llectio
n
p
r
o
ce
s
s
.
T
h
e
My
o
Ar
m
b
a
n
d
was
p
o
s
itio
n
ed
o
n
th
e
lo
wer
f
o
r
ea
r
m
,
wh
er
e
m
o
s
t
o
f
th
e
m
u
s
cles
r
esp
o
n
s
ib
le
f
o
r
f
in
g
er
m
o
v
em
en
ts
ar
e
lo
ca
ted
,
en
s
u
r
in
g
o
p
t
im
al
s
ig
n
al
ca
p
tu
r
e
[
2
3
]
.
E
ac
h
p
a
r
ticip
an
t
p
er
f
o
r
m
ed
m
u
ltip
le
r
e
p
etitio
n
s
o
f
ea
ch
g
estu
r
e
u
n
d
er
co
n
tr
o
lle
d
co
n
d
itio
n
s
,
with
s
u
f
f
icien
t
r
est
in
ter
v
als
b
etwe
e
n
tr
ials
to
m
in
im
ize
s
ig
n
al
o
v
e
r
lap
an
d
co
n
tam
i
n
atio
n
.
T
h
e
r
ec
o
r
d
ed
E
MG
d
ata
wer
e
th
en
s
to
r
ed
f
o
r
s
u
b
s
eq
u
e
n
t a
n
aly
s
is
,
in
clu
d
in
g
f
ea
tu
r
e
e
x
tr
ac
tio
n
an
d
class
if
icatio
n
.
Fig
u
r
e
1
.
My
o
A
r
m
b
an
d
with
8
-
ch
an
n
el
m
u
s
cle
s
en
s
o
r
s
2
.
2
.
Sig
na
l pro
ce
s
s
ing
a
nd
f
ea
t
ure
s
elec
t
io
n
T
o
en
h
a
n
ce
class
if
icatio
n
p
er
f
o
r
m
an
ce
,
t
h
e
E
MG
s
ig
n
als
u
n
d
er
wen
t
a
s
er
ies
o
f
p
r
ep
r
o
ce
s
s
in
g
s
tep
s
,
in
clu
d
in
g
n
o
r
m
aliza
tio
n
,
n
o
is
e
f
ilter
in
g
u
s
in
g
a
b
a
n
d
-
p
ass
f
ilter
(
2
0
-
4
5
0
Hz)
,
an
d
ar
tifa
ct
r
em
o
v
al
to
en
s
u
r
e
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
E
MG
-
b
a
s
ed
h
a
n
d
g
estu
r
e
cla
s
s
ifica
tio
n
u
s
in
g
Myo
A
r
mb
a
n
d
…
(
S
o
fea
A
n
a
s
ta
s
ia
Mo
h
d
S
a
i
d
)
161
s
ig
n
al
in
teg
r
ity
.
Featu
r
e
e
x
tr
a
ctio
n
was
th
en
p
er
f
o
r
m
e
d
to
ca
p
tu
r
e
r
elev
a
n
t
m
u
s
cle
ac
tiv
ity
ch
ar
ac
ter
is
tics
.
T
wo
p
r
im
a
r
y
f
ea
tu
r
es
wer
e
d
e
r
iv
ed
f
r
o
m
th
e
s
ig
n
als:
e
n
er
g
y
s
p
ec
tr
al
d
e
n
s
ity
(
E
SD)
an
d
p
o
wer
r
atio
(
p
R
atio
)
[
2
4
]
.
E
SD
r
ep
r
esen
ts
th
e
p
o
w
er
d
is
tr
ib
u
t
io
n
ac
r
o
s
s
d
if
f
er
en
t
f
r
eq
u
en
cies,
ef
f
ec
tiv
ely
h
ig
h
l
ig
h
tin
g
p
atter
n
s
o
f
m
u
s
cle
ac
tiv
atio
n
.
Me
an
wh
ile,
p
R
atio
m
ea
s
u
r
es
th
e
p
r
o
p
o
r
t
io
n
o
f
s
ig
n
al
en
er
g
y
in
d
is
tin
ct
f
r
eq
u
en
cy
b
an
d
s
,
p
r
o
v
id
i
n
g
a
d
d
itio
n
al
in
s
ig
h
t
i
n
to
m
u
s
cle
ac
tiv
ity
v
ar
iatio
n
s
.
T
o
f
u
r
th
er
o
p
tim
ize
class
if
icatio
n
ac
cu
r
ac
y
,
b
o
x
p
lo
t
an
aly
s
is
was
co
n
d
u
cted
to
id
en
tify
th
e
m
o
s
t
s
ig
n
if
ican
t
E
MG
ch
an
n
els
f
o
r
g
e
s
tu
r
e
r
ec
o
g
n
itio
n
.
T
h
e
an
aly
s
is
r
ev
ea
led
th
at
ch
an
n
els
C
H1
,
C
H4
,
C
H5
,
an
d
C
H7
ex
h
ib
ited
th
e
h
ig
h
est
d
is
cr
im
in
ativ
e
p
o
wer
,
m
ak
in
g
th
e
m
th
e
m
o
s
t
s
u
itab
le
f
o
r
f
u
r
th
er
p
r
o
ce
s
s
in
g
an
d
c
lass
if
icatio
n
.
Fig
u
r
e
2
s
h
o
ws
an
ex
am
p
le
o
f
th
e
b
o
x
p
l
o
t a
n
aly
s
is
u
s
ed
in
th
is
s
tu
d
y
.
Fig
u
r
e
2
.
An
ex
am
p
le
o
f
a
b
o
x
p
lo
t u
s
ed
to
s
elec
t f
ea
tu
r
es f
o
r
ac
cu
r
ate
class
if
icatio
n
2
.
3
.
F
ee
df
o
rwa
rd
neura
l
net
wo
rk
im
plem
ent
a
t
io
n
Fig
u
r
e
3
s
h
o
ws
th
e
b
lo
ck
d
ia
g
r
am
o
f
an
FF
NN.
A
FF
NN
was
im
p
lem
en
ted
to
class
if
y
E
MG
-
b
ased
h
an
d
g
estu
r
es,
in
itially
co
n
f
ig
u
r
ed
with
a
s
in
g
le
h
id
d
e
n
lay
e
r
co
n
tain
in
g
1
0
n
e
u
r
o
n
s
as
s
h
o
wn
in
Fig
u
r
e
3
(
a)
.
T
o
en
h
an
ce
p
er
f
o
r
m
an
ce
,
an
o
p
tim
i
s
ed
m
o
d
el
was
later
d
e
v
elo
p
ed
with
two
h
id
d
e
n
lay
e
r
s
,
ea
ch
c
o
m
p
r
is
in
g
2
0
n
eu
r
o
n
s
,
r
esu
ltin
g
in
a
to
t
al
o
f
4
0
n
eu
r
o
n
s
as
s
h
o
wn
i
n
Fig
u
r
e
3
(
b
)
.
T
h
e
r
ec
tifie
d
l
in
ea
r
u
n
it
(
R
eL
U)
ac
tiv
atio
n
f
u
n
ctio
n
was
ap
p
li
ed
to
th
e
h
id
d
en
lay
er
s
to
im
p
r
o
v
e
lear
n
in
g
ef
f
icien
c
y
,
wh
ile
th
e
So
f
tm
ax
ac
tiv
atio
n
f
u
n
ctio
n
was
u
s
ed
in
th
e
o
u
tp
u
t
lay
er
f
o
r
m
u
lti
-
class
cla
s
s
if
icat
io
n
.
T
h
e
m
o
d
e
l
was
tr
ain
ed
u
s
in
g
th
e
b
ac
k
p
r
o
p
ag
atio
n
alg
o
r
ith
m
with
th
e
Ad
am
o
p
tim
izer
,
wh
ich
is
well
-
s
u
ited
f
o
r
h
an
d
lin
g
n
o
n
-
s
tatio
n
ar
y
s
ig
n
als s
u
ch
as E
MG
d
ata.
T
o
ass
ess
clas
s
if
icatio
n
p
er
f
o
r
m
an
ce
,
k
ey
e
v
alu
atio
n
m
etr
i
cs,
in
clu
d
in
g
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
an
d
F1
-
s
co
r
e,
wer
e
e
m
p
lo
y
e
d
.
I
n
itially
,
th
e
FF
NN
was
tr
ai
n
ed
u
s
in
g
o
n
ly
th
e
E
SD
f
ea
t
u
r
e
with
1
0
h
i
d
d
en
n
eu
r
o
n
s
.
Ho
wev
e
r
,
t
h
is
co
n
f
i
g
u
r
atio
n
y
ield
e
d
s
u
b
o
p
tim
al
r
esu
lts
,
p
r
o
m
p
tin
g
f
u
r
th
e
r
r
ef
i
n
em
en
t.
T
h
e
m
o
d
el
was
s
u
b
s
eq
u
en
tly
o
p
tim
ized
b
y
in
cr
ea
s
in
g
th
e
n
u
m
b
er
o
f
n
eu
r
o
n
s
to
4
0
an
d
in
co
r
p
o
r
at
in
g
b
o
th
E
SD
an
d
p
R
atio
f
ea
tu
r
es
to
im
p
r
o
v
e
class
if
icatio
n
ac
cu
r
ac
y
.
T
o
en
s
u
r
e
a
r
o
b
u
s
t
ev
alu
atio
n
,
t
h
e
d
ataset
was
d
iv
id
e
d
in
to
7
0
% f
o
r
tr
ain
in
g
an
d
3
0
%
f
o
r
test
in
g
,
allo
win
g
th
e
m
o
d
e
l to
g
en
er
alize
ef
f
ec
tiv
ely
ac
r
o
s
s
u
n
s
ee
n
d
ata.
(
a)
(
b
)
Fig
u
r
e
3
.
C
o
n
f
ig
u
r
atio
n
o
f
FF
NN
f
o
r
(
a
)
i
n
itial setu
p
with
1
0
n
eu
r
o
n
s
an
d
(
b
)
o
p
tim
ized
s
etu
p
with
4
0
h
id
d
en
n
eu
r
o
n
s
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.
3
9
,
No
.
1
,
Ju
ly
20
25
:
1
59
-
1
66
162
2
.
4
.
P
er
f
o
r
m
a
nce
a
na
ly
s
is
A
co
n
f
u
s
io
n
m
atr
ix
is
em
p
l
o
y
ed
to
an
aly
z
e
th
e
p
er
f
o
r
m
an
ce
o
f
t
h
e
d
ev
el
o
p
ed
FF
N
N
f
o
r
h
a
n
d
g
estu
r
e
class
if
icatio
n
an
d
ev
alu
ate
th
is
n
etwo
r
k
’
s
tr
ain
in
g
,
test
in
g
,
an
d
v
alid
atio
n
o
u
tco
m
es.
I
t
is
a
tab
u
lar
r
ep
r
esen
tatio
n
u
tili
ze
d
to
e
v
al
u
ate
th
e
ef
f
icac
y
o
f
a
class
if
icatio
n
m
o
d
el
b
y
co
m
p
ar
in
g
th
e
p
r
ed
icted
an
d
th
e
ac
tu
al
class
es.
T
h
is
to
o
l
is
ess
en
tial
in
m
ac
h
in
e
lear
n
i
n
g
f
o
r
ass
ess
in
g
m
o
d
el
ac
cu
r
ac
y
b
y
o
f
f
er
in
g
a
co
m
p
r
eh
e
n
s
iv
e
an
aly
s
is
o
f
tr
u
e
p
o
s
itiv
es
(TP
)
,
tr
u
e
n
e
g
ativ
es
(
T
N)
,
f
alse
p
o
s
itiv
es
(
FP
)
,
an
d
f
alse
n
e
g
ativ
es
(
FN)
.
I
t
f
ac
ilit
ates
th
e
v
is
u
aliz
atio
n
o
f
an
alg
o
r
ith
m
’
s
p
er
f
o
r
m
an
ce
,
p
ar
ticu
lar
l
y
in
s
u
p
er
v
i
s
ed
lear
n
in
g
,
wh
er
e
th
e
m
o
d
el
’
s
p
r
e
d
ictio
n
s
ar
e
c
o
m
p
ar
ed
with
t
h
e
ac
tu
al
g
r
o
u
n
d
tr
u
th
.
T
h
e
f
o
r
m
u
las
f
o
r
co
m
p
u
tin
g
th
e
tr
u
e
p
o
s
itiv
e
r
ate
(
T
PR
)
o
r
r
ec
all,
tr
u
e
n
eg
ativ
e
r
ate
(
T
NR
)
o
r
p
r
ec
is
io
n
,
ac
cu
r
ac
y
,
an
d
F1
-
s
co
r
e
ar
e
p
r
esen
ted
in
(
1
)
to
(
4
)
[
2
5
]
.
Acc
u
r
ac
y
d
eter
m
in
es th
e
n
etwo
r
k
’
s
o
v
er
all
p
er
f
o
r
m
a
n
ce
in
co
r
r
ec
tly
p
r
ed
ic
tin
g
h
an
d
g
estu
r
es.
(
)
=
+
(
1
)
(
)
=
+
(
2
)
=
+
+
+
+
(
3
)
1
=
2
×
×
+
(
4
)
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
is
s
ec
tio
n
p
r
esen
ts
th
e
r
es
u
lts
o
f
f
ea
t
u
r
e
s
elec
tio
n
u
s
in
g
b
o
x
p
lo
t
an
al
y
s
is
an
d
th
e
p
er
f
o
r
m
a
n
ce
ev
alu
atio
n
o
f
FF
NN
-
b
ased
h
an
d
g
estu
r
e
class
if
icatio
n
.
T
h
e
d
is
cu
s
s
io
n
h
ig
h
lig
h
ts
k
ey
f
in
d
in
g
s
,
co
m
p
ar
es
th
em
with
p
r
io
r
s
tu
d
ies,
an
d
a
n
aly
ze
s
th
eir
im
p
licatio
n
s
.
3
.
1
.
B
o
x
plo
t
a
na
ly
s
is
f
o
r
f
e
a
t
ure
s
elec
t
io
n
T
o
id
en
tify
th
e
m
o
s
t
s
ig
n
if
ican
t
E
MG
ch
an
n
els
f
o
r
class
if
ic
atio
n
,
a
b
o
x
p
lo
t
an
aly
s
is
was
co
n
d
u
cte
d
.
T
h
e
an
aly
s
is
was
p
er
f
o
r
m
e
d
ac
r
o
s
s
all
eig
h
t
ch
an
n
els
o
f
t
h
e
My
o
Ar
m
b
an
d
,
an
d
t
h
e
r
e
s
u
lts
r
ev
ea
led
th
at
C
H1
,
C
H4
,
C
H5
,
an
d
C
H7
ex
h
ib
ited
th
e
h
ig
h
est
d
is
cr
im
in
ativ
e
p
o
wer
.
T
h
ese
ch
an
n
els
d
em
o
n
s
tr
ated
s
ig
n
if
ican
t
v
ar
iatio
n
s
in
m
u
s
c
le
ac
tiv
ity
b
etwe
en
d
i
f
f
er
en
t
h
an
d
g
estu
r
es,
m
ak
in
g
th
em
cr
u
cial
f
o
r
f
ea
tu
r
e
ex
tr
ac
tio
n
a
n
d
class
if
icatio
n
.
Fig
u
r
e
4
illu
s
tr
ates
th
e
b
o
x
p
l
o
t
an
aly
s
is
o
f
th
ese
s
elec
ted
c
h
an
n
els.
Fig
u
r
e
4
(
a)
r
ep
r
esen
ts
C
H1
,
wh
er
e
a
clea
r
d
if
f
e
r
en
ce
i
n
d
ata
d
is
tr
ib
u
ti
o
n
is
o
b
s
er
v
ed
am
o
n
g
th
e
th
r
ee
g
estu
r
e
class
es.
Fig
u
r
e
4
(
b
)
an
d
Fig
u
r
e
4
(
c)
(
C
H4
an
d
C
H5
)
s
h
o
w
a
wid
er
in
ter
q
u
ar
tile
r
a
n
g
e
(
I
QR
)
,
s
i
g
n
if
y
in
g
s
u
b
s
tan
tial
v
ar
iab
ilit
y
in
m
u
s
cle
ac
tiv
atio
n
.
T
h
is
ch
ar
ac
ter
is
tic
im
p
r
o
v
es
th
eir
ab
ilit
y
to
d
is
tin
g
u
is
h
b
etwe
en
g
estu
r
es.
Fig
u
r
e
4
(
d
)
(
C
H7
)
ex
h
ib
its
a
b
alan
ce
d
I
QR
an
d
a
n
o
ticea
b
le
m
ed
ian
s
h
if
t
ac
r
o
s
s
th
e
t
h
r
e
e
g
estu
r
e
class
es,
en
s
u
r
in
g
s
tab
le
s
ig
n
al
d
is
tr
ib
u
tio
n
.
T
h
e
r
em
ain
in
g
ch
an
n
els
(
C
H2
,
C
H3
,
C
H6
,
an
d
C
H8
)
wer
e
ex
clu
d
ed
d
u
e
to
th
eir
lo
wer
s
tatis
tical
r
elev
a
n
ce
in
g
estu
r
e
d
if
f
e
r
en
tiatio
n
.
T
h
ese
r
esu
lts
h
ig
h
lig
h
t
th
e
im
p
o
r
tan
ce
o
f
f
ea
tu
r
e
s
elec
tio
n
in
o
p
tim
i
zin
g
class
i
f
icatio
n
p
er
f
o
r
m
an
ce
.
B
y
f
o
c
u
s
in
g
o
n
th
e
m
o
s
t
r
elev
an
t
E
MG
ch
an
n
els,
th
e
co
m
p
u
tatio
n
al
c
o
m
p
lex
ity
is
r
ed
u
ce
d
wh
ile
m
ai
n
tain
in
g
h
ig
h
class
if
icatio
n
ac
cu
r
ac
y
.
(
a)
(
b
)
(
c)
(
d
)
Fig
u
r
e
4
.
B
o
x
p
lo
t a
n
al
y
s
is
o
f
th
e
m
o
s
t
s
ig
n
if
ican
t E
MG
ch
a
n
n
els (
a)
C
H1
,
(
b
)
C
H4
,
(
c)
C
H5
,
an
d
(
d
)
C
H7
,
f
o
r
FF
NN
-
b
ased
h
an
d
g
estu
r
e
class
if
icatio
n
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
E
MG
-
b
a
s
ed
h
a
n
d
g
estu
r
e
cla
s
s
ifica
tio
n
u
s
in
g
Myo
A
r
mb
a
n
d
…
(
S
o
fea
A
n
a
s
ta
s
ia
Mo
h
d
S
a
i
d
)
163
3
.
2
.
H
a
nd
g
esture
cla
s
s
if
ica
t
io
ns
us
i
ng
F
F
NN
T
h
e
in
itial
class
if
icatio
n
ex
p
er
im
en
t
u
tili
ze
d
o
n
ly
th
e
E
SD
f
ea
tu
r
e.
T
h
e
r
esu
lts
in
T
ab
le
1
s
h
o
w
th
at
u
s
in
g
1
0
h
i
d
d
en
n
e
u
r
o
n
s
led
to
s
u
b
o
p
tim
al
p
er
f
o
r
m
a
n
ce
,
p
ar
ticu
lar
ly
in
r
ec
all
a
n
d
p
r
ec
i
s
io
n
m
etr
ics.
T
h
is
lim
itatio
n
ca
n
b
e
attr
ib
u
ted
to
u
n
d
er
f
itti
n
g
,
wh
er
e
th
e
m
o
d
el
f
ailed
to
ca
p
tu
r
e
th
e
c
o
m
p
lex
v
ar
iatio
n
s
in
E
MG
s
ig
n
als.
W
h
en
th
e
n
u
m
b
er
o
f
h
id
d
en
n
eu
r
o
n
s
was
i
n
cr
ea
s
ed
to
4
0
,
p
er
f
o
r
m
a
n
ce
im
p
r
o
v
e
d
ac
r
o
s
s
all
class
es,
with
an
av
er
ag
e
F1
-
s
co
r
e
im
p
r
o
v
em
en
t
o
f
2
1
%.
Ho
wev
er
,
d
esp
ite
th
is
en
h
an
ce
m
en
t,
th
e
m
o
d
el
’
s
ab
ilit
y
to
g
en
er
alize
ac
r
o
s
s
d
if
f
er
en
t
g
e
s
tu
r
es
r
em
ain
ed
m
o
d
er
ate,
i
n
d
icatin
g
th
e
n
ee
d
f
o
r
f
u
r
th
er
f
e
atu
r
e
o
p
tim
izatio
n
.
T
h
e
r
esu
lt o
f
t
h
e
o
p
tim
ized
h
a
n
d
g
estu
r
e
class
if
icatio
n
u
s
in
g
E
SD a
n
d
p
R
atio
f
ea
tu
r
es is
s
h
o
wn
in
T
ab
le
2
.
T
ab
le
1
.
T
h
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
h
an
d
g
estu
r
e
class
if
icatio
n
u
s
in
g
1
0
a
n
d
4
0
n
e
u
r
o
n
s
f
o
r
t
h
e
E
SD f
ea
tu
r
e
alo
n
e
N
u
mb
e
r
o
f
h
i
d
d
e
n
n
e
u
r
o
n
s
C
l
a
s
s
1
(
F
i
s
t
)
C
l
a
s
s
2
(
O
p
e
n
h
a
n
d
)
C
l
a
s
s
3
(
P
i
n
c
h
)
C
l
a
s
s
4
(
D
i
s
t
u
r
b
a
n
c
e
)
10
40
10
40
10
40
10
40
PPV
(
p
r
e
c
i
si
o
n
)
Tr
a
i
n
i
n
g
0
.
6
4
0
.
7
0
0
.
7
6
0
.
8
3
0
.
5
2
0
.
7
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DATA AV
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1
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l
e
c
tro
n
ic
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n
d
c
o
m
m
u
n
ica
ti
o
n
s
e
n
g
i
n
e
e
rin
g
fr
o
m
Ne
wc
a
stle
Un
iv
e
rsity
(
2
0
0
6
)
a
n
d
P
h
.
D
.
in
e
n
g
in
e
e
rin
g
(sa
fe
ty
)
fro
m
Un
iv
e
rsity
o
f
Ab
e
rd
e
e
n
(2
0
1
2
)
.
He
h
a
s
b
e
e
n
a
UiTM
a
c
a
d
e
m
ic
st
a
ff
sin
c
e
2
0
0
6
.
He
wa
s
a
wa
rd
e
d
Ch
a
rtere
d
En
g
i
n
e
e
r
(CEn
g
)
b
y
T
h
e
In
stit
u
ti
o
n
o
f
En
g
in
e
e
r
in
g
a
n
d
Tec
h
n
o
l
o
g
y
(IE
T
UK
)
a
s
we
ll
a
s
P
ro
fe
ss
io
n
a
l
Tec
h
n
o
l
o
g
ist
fr
o
m
th
e
M
a
lay
sia
n
Bo
a
rd
o
f
Tec
h
n
o
l
o
g
ist
(M
Bo
T
)
in
2
0
1
8
.
He
h
a
s
p
u
b
li
sh
e
d
o
v
e
r
3
0
p
e
er
-
re
v
iew
e
d
jo
u
rn
a
ls
in
h
is
re
se
a
rc
h
a
re
a
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
fa
h
m
i4
7
8
@u
it
m
.
e
d
u
.
m
y
.
Ro
slin
a
Mo
h
a
m
a
d
o
b
tai
n
e
d
a
B.
En
g
.
d
e
g
re
e
in
e
lec
tri
c
a
l
e
n
g
i
n
e
e
rin
g
a
n
d
M
.
En
g
.
s
c
ien
c
e
d
e
g
re
e
fro
m
Un
iv
e
rsiti
M
a
lay
a
,
Ku
a
la
Lu
m
p
u
r
,
i
n
2
0
0
3
a
n
d
2
0
0
8
.
S
h
e
late
r
re
c
e
iv
e
d
a
P
h
.
D
.
in
a
e
ro
s
p
a
c
e
e
n
g
in
e
e
rin
g
(d
e
e
p
s
p
a
c
e
a
n
d
wire
les
s
c
o
m
m
u
n
ica
ti
o
n
s
a
lg
o
rit
h
m
s)
fro
m
Un
i
v
e
rsiti
P
u
tra
M
a
lay
sia
in
2
0
1
6
.
S
in
c
e
2
0
0
6
,
s
h
e
h
a
s
wo
rk
e
d
a
t
th
e
S
c
h
o
o
l
o
f
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
,
Co
l
leg
e
o
f
En
g
in
e
e
rin
g
,
Un
i
v
e
rsiti
Tek
n
o
l
o
g
i
M
ARA
,
a
s
a
se
n
i
o
r
lec
tu
re
r.
S
h
e
is
t
h
e
h
e
a
d
o
f
wire
l
e
ss
h
ig
h
-
sp
e
e
d
n
e
two
r
k
(W
HiS
N
e
t)
re
se
a
rc
h
in
tere
st
g
ro
u
p
.
He
r
re
se
a
rc
h
in
tere
sts
in
c
l
u
d
e
c
o
m
p
u
ti
n
g
a
lg
o
rit
h
m
s
a
n
d
d
ig
it
a
l
sig
n
a
l
p
ro
c
e
ss
in
g
f
o
r
d
e
e
p
sp
a
c
e
c
o
m
m
u
n
ica
ti
o
n
,
c
h
a
n
n
e
l
c
o
d
in
g
,
in
f
o
rm
a
ti
o
n
-
th
e
o
re
ti
c
se
c
u
rit
y
,
c
o
m
p
u
tati
o
n
th
e
o
ry
,
in
tern
e
t
o
f
th
i
n
g
s,
a
n
d
wire
l
e
ss
c
o
m
m
u
n
ica
ti
o
n
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
ro
slin
a
7
8
0
@
u
it
m
.
e
d
u
.
m
y
.
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