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lect
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1912
J
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:
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ttp
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ec
e.
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Em
bedded
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rtif
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ntelligence
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huma
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pplica
tions
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a
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ted
Dec
2
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2
0
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4
Th
is
p
a
p
e
r
p
re
se
n
ts
a
m
e
th
o
d
fo
r
c
o
ll
e
c
ti
n
g
p
re
c
ise
h
a
n
d
g
e
stu
re
(
HG
)
d
a
ta
u
sin
g
a
l
o
w
-
c
o
st
e
m
b
e
d
d
e
d
d
e
v
i
c
e
fo
r
a
n
e
m
b
e
d
d
e
d
a
rti
ficia
l
in
telli
g
e
n
c
e
(EAI)
-
b
a
se
d
h
u
m
a
n
o
i
d
r
o
b
o
ti
c
s
(
HR)
a
p
p
li
c
a
ti
o
n
.
De
sp
it
e
a
d
v
a
n
c
e
m
e
n
ts
in
th
e
field
,
c
h
a
ll
e
n
g
e
s
re
m
a
in
i
n
d
e
p
lo
y
in
g
c
o
st
-
e
ffe
c
ti
v
e
m
e
th
o
d
s
fo
r
a
c
c
u
ra
tely
c
a
p
tu
rin
g
a
n
d
re
c
o
g
n
izin
g
b
o
d
y
g
e
st
u
re
d
a
ta.
T
h
e
u
lt
ima
te
o
b
jec
ti
v
e
is
to
d
e
v
e
lo
p
h
u
m
a
n
o
id
r
o
b
o
ts
(HRS)
c
a
p
a
b
le
o
f
b
e
tt
e
r
u
n
d
e
rsta
n
d
i
n
g
h
u
m
a
n
a
c
ti
v
it
ies
a
n
d
p
ro
v
id
in
g
o
p
ti
m
a
l
d
a
il
y
l
ife
su
p
p
o
rt.
In
th
is
re
g
a
r
d
,
o
u
r
a
p
p
ro
a
c
h
u
ti
li
z
e
s
a
Ra
sp
b
e
rry
P
i
P
ico
m
icro
c
o
n
tro
l
ler
with
a
3
-
a
x
is
a
c
c
e
lero
m
e
ter
a
n
d
a
3
-
a
x
i
s
g
y
ro
sc
o
p
e
m
o
ti
o
n
se
n
s
o
r
to
c
a
p
tu
re
re
a
l
-
ti
m
e
H
G
d
a
ta,
d
e
sc
rib
in
g
ten
d
isti
n
c
t
re
a
l
-
wo
rld
tas
k
s p
e
rfo
rm
e
d
b
y
th
e
h
a
n
d
in
e
x
p
e
rime
n
tal
sc
e
n
a
rio
s.
Co
ll
e
c
ted
d
a
ta
is
sto
re
d
o
n
a
p
e
rso
n
a
l
c
o
m
p
u
ter
(P
C)
v
ia
a
m
icro
-
p
y
t
h
o
n
p
r
o
g
r
a
m
,
fo
rm
in
g
a
d
a
tas
e
t
wh
e
re
tas
k
s
a
re
c
las
sified
u
sin
g
ten
s
u
p
e
rv
ise
d
m
a
c
h
in
e
lea
rn
in
g
(S
M
L)
m
o
d
e
ls.
Two
c
las
sifica
ti
o
n
e
x
p
e
rime
n
ts
we
re
c
o
n
d
u
c
ted
:
th
e
first
i
n
v
o
lv
e
d
p
re
d
i
c
ti
n
g
ra
w
d
a
ta,
a
n
d
t
h
e
se
c
o
n
d
a
p
p
li
e
d
n
o
rm
a
li
z
a
ti
o
n
a
n
d
fe
a
tu
re
e
x
trac
ti
o
n
(F
E)
tec
h
n
iq
u
e
s
to
imp
r
o
v
e
p
re
d
ic
ti
o
n
p
e
rf
o
rm
a
n
c
e
.
Th
e
re
su
lt
s
sh
o
we
d
p
ro
m
isin
g
a
c
c
u
ra
c
y
in
t
h
e
first
p
h
a
se
(8
9
%
m
a
x
),
wit
h
f
u
rt
h
e
r
imp
r
o
v
e
m
e
n
ts
a
c
h
iev
e
d
in
t
h
e
se
c
o
n
d
p
h
a
se
(9
4
%
m
a
x
).
F
in
a
l
ly
,
b
y
e
m
p
l
o
y
i
n
g
sim
il
a
r
m
e
th
o
d
s,
we
c
a
n
i
n
teg
ra
te h
i
g
h
l
y
train
e
d
m
a
c
h
i
n
e
lea
rn
i
n
g
(M
L)
m
o
d
e
ls
i
n
t
o
e
m
b
e
d
d
e
d
h
u
m
a
n
o
id
r
o
b
o
ti
c
sy
st
e
m
s,
e
n
a
b
li
n
g
re
a
l
-
ti
m
e
h
u
m
a
n
a
s
sista
n
c
e
.
K
ey
w
o
r
d
s
:
Data
ac
q
u
is
itio
n
E
m
b
ed
d
e
d
ar
tific
ial
in
tellig
en
ce
E
m
b
ed
d
e
d
s
y
s
tem
s
Featu
r
e
ex
tr
ac
tio
n
Han
d
g
estu
r
e
r
ec
o
g
n
itio
n
Hu
m
an
o
id
r
o
b
o
tics
Su
p
er
v
is
ed
m
ac
h
i
n
e
lear
n
in
g
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
J
alal
Hd
id
L
ab
o
r
ato
r
y
o
f
E
lectr
o
n
ics an
d
Sy
s
tem
s
,
Facu
lty
o
f
Scien
ce
,
Mo
h
am
ed
1
Un
iv
er
s
ity
Mo
h
am
m
ed
VI
R
o
ad
-
P.O.
B
o
x
7
1
7
,
Ou
jd
a
6
0
0
0
0
,
Mo
r
o
cc
o
E
m
ail: j.
h
d
id
@
u
m
p
.
ac
.
m
a
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
f
ield
o
f
h
u
m
an
o
id
r
o
b
o
tics
(
HR
)
[
1
]
,
[
2
]
is
r
ap
id
ly
ad
v
an
cin
g
o
n
a
g
l
o
b
al
s
ca
le,
g
r
ea
tly
in
f
lu
en
cin
g
v
ar
io
u
s
asp
ec
ts
o
f
h
u
m
an
life
.
Hu
m
an
o
id
r
o
b
o
ts
(
HR
S)
p
o
wer
ed
b
y
ar
tific
ial
in
tellig
en
ce
(
AI
)
[
3
]
ar
e
in
cr
ea
s
in
g
ly
b
ein
g
test
ed
an
d
u
s
ed
ac
r
o
s
s
m
u
ltip
le
in
d
u
s
tr
ies,
s
u
ch
as
h
o
s
p
itality
,
ed
u
ca
tio
n
,
h
ea
lth
ca
r
e,
an
d
th
e
s
er
v
ice
in
d
u
s
tr
y
.
Ho
w
ev
er
,
d
esp
ite
th
ese
tech
n
o
lo
g
ic
al
ad
v
an
ce
m
en
ts
,
r
ea
l
-
wo
r
ld
H
R
ap
p
licatio
n
s
[
4
]
,
[
5
]
r
em
ain
lim
ited
,
p
a
r
tly
d
u
e
to
h
ig
h
d
ev
elo
p
m
en
t
co
s
ts
.
T
h
eir
d
e
p
lo
y
m
e
n
t
in
p
r
ac
tical
s
ce
n
ar
io
s
d
em
a
n
d
s
th
e
ass
o
ciatio
n
o
f
h
ig
h
-
p
er
f
o
r
m
an
ce
m
o
tio
n
ac
q
u
is
itio
n
s
y
s
tem
s
[
6
]
an
d
m
ac
h
in
e
lear
n
in
g
(
ML
)
tech
n
i
q
u
es
[
7
]
to
ac
cu
r
ately
co
llect
an
d
r
ec
o
g
n
ize
well
-
ex
p
r
ess
iv
e
h
u
m
an
b
o
d
y
g
estu
r
e
d
ata
[
8
]
.
T
h
e
ca
p
tu
r
ed
d
ata
m
u
s
t
b
e
m
eticu
lo
u
s
ly
s
tr
u
ctu
r
e
d
i
n
to
well
-
p
r
o
ce
s
s
ed
d
atasets
to
b
e
ef
f
ec
tiv
ely
ad
o
p
ted
an
d
in
ter
p
r
eted
b
y
ML
alg
o
r
ith
m
s
.
On
e
o
f
th
e
m
ain
c
h
allen
g
es
is
to
d
ev
elo
p
co
s
t
-
ef
f
ec
tiv
e,
r
ea
l
-
tim
e
d
ata
ac
q
u
is
itio
n
(
DA)
m
eth
o
d
s
th
at
ca
p
tu
r
e
h
ig
h
ly
ac
cu
r
ate
g
estu
r
e
d
ata
f
o
r
ef
f
icien
t
ML
tr
ain
in
g
.
T
h
e
u
ltima
te
g
o
al
is
to
d
ev
elo
p
HR
S
th
at
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
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&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
E
mb
ed
d
ed
s
ystems
a
n
d
a
r
tifi
cia
l in
tellig
en
ce
fo
r
en
h
a
n
ce
d
h
u
ma
n
o
id
r
o
b
o
tics
a
p
p
lica
ti
o
n
s
(
Ja
la
l H
d
id
)
1913
ca
n
im
p
r
o
v
e
r
ec
o
g
n
itio
n
an
d
r
ep
r
o
d
u
ctio
n
o
f
r
ea
l
-
life
h
u
m
a
n
ac
tiv
ities
[
9
]
,
th
er
e
b
y
o
f
f
er
i
n
g
b
etter
ass
is
tan
ce
in
d
aily
task
s
f
o
r
a
n
en
h
an
ce
d
u
s
er
ex
p
er
ien
ce
.
Var
io
u
s
s
tu
d
ies
an
d
r
elate
d
wo
r
k
s
h
a
v
e
b
ee
n
u
n
d
er
tak
en
in
an
ef
f
o
r
t
to
c
o
n
tr
ib
u
te
to
th
e
d
ev
elo
p
m
e
n
t
o
f
HR
S
f
o
r
r
ea
l
-
wo
r
ld
jo
b
s
.
T
h
ese
in
clu
d
e
h
o
s
p
itality
s
er
v
ices
s
u
ch
as
s
er
v
in
g
f
o
o
d
,
ac
tin
g
as
h
o
tel
co
n
cier
g
es
o
r
i
n
o
th
er
cu
s
to
m
er
-
f
ac
in
g
r
o
les
[
1
0
]
,
b
io
n
ic
ap
p
licatio
n
s
[
1
1
]
,
an
d
b
ec
o
m
in
g
s
en
io
r
co
m
p
an
io
n
s
[
1
2
]
.
Hu
m
an
o
id
r
o
b
o
t
p
r
o
to
t
y
p
es
ar
e
alr
ea
d
y
in
ex
is
ten
ce
to
d
ay
,
all
ar
o
u
n
d
t
h
e
wo
r
ld
.
Ma
n
y
o
f
th
em
h
av
e
ad
v
a
n
ce
d
t
o
th
e
g
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HR
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[
1
3
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6
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(
E
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[
1
7
]
eq
u
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p
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with
m
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tio
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s
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s
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d
ac
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d
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ed
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y
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AI
[
1
8
]
.
I
n
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,
th
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d
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v
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f
f
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p
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f
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d
ab
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m
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s
[
1
9
]
.
I
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p
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(
I
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)
[
2
0
]
a
n
d
c
o
n
n
ec
ted
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b
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(
C
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[
2
1
]
in
d
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s
tr
ies,
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l
-
tim
e
a
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d
au
to
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ased
AI
[
2
2
]
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r
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As
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cien
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ased
HR
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[
2
3
]
,
[
2
4
]
ca
p
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[
2
5
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a
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in
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lev
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ag
in
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th
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tech
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lo
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ies
[
2
6
]
.
T
h
u
s
,
m
icr
o
co
n
tr
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ller
s
[
2
7
]
,
[
2
8
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lik
e
th
e
Ar
d
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3
B
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s
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[
2
9
]
,
[
3
0
]
an
d
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asp
b
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Pi
Pico
[
3
1
]
,
[
3
2
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a
r
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ticu
lar
ly
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s
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r
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[
1
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.
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is
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tim
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g
estu
r
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(
HG)
d
ata.
T
h
ese
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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&
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15
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No
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2
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25
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1
9
1
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SML
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[
7
]
f
o
r
p
o
ten
tial
ad
o
p
tio
n
in
em
b
ed
d
e
d
HR
ap
p
licatio
n
s
.
T
h
e
m
eth
o
d
o
lo
g
y
in
v
o
l
v
es
co
llectin
g
m
u
ltip
le
ex
am
p
les
o
f
ea
c
h
task
’
s
h
an
d
g
estu
r
es
(
HGs)
v
ia
th
e
m
icr
o
c
o
n
tr
o
ller
an
d
s
en
s
o
r
an
d
class
if
y
in
g
th
em
with
v
ar
io
u
s
ML
m
o
d
els.
T
h
e
m
o
tio
n
s
en
s
o
r
is
a
6
-
ax
is
d
ev
ice
th
at
r
ec
o
r
d
s
3
-
ax
is
lin
ea
r
ac
ce
ler
atio
n
s
an
d
3
-
ax
is
r
o
tatio
n
al
v
elo
cities
ac
r
o
s
s
th
e
X,
Y,
a
n
d
Z
ax
es
[
3
1
]
,
[
3
3
]
.
T
h
e
d
ata
c
o
llectio
n
p
r
o
ce
s
s
co
n
s
is
ts
o
f
attac
h
in
g
th
e
m
o
tio
n
s
en
s
o
r
to
a
h
u
m
an
h
a
n
d
an
d
e
x
ec
u
tin
g
th
e
task
s
in
r
ea
l
-
wo
r
ld
s
ce
n
ar
io
s
b
y
p
er
f
o
r
m
in
g
c
o
n
tr
o
lled
m
o
v
em
en
ts
wh
ile
ca
p
tu
r
in
g
r
ea
l
-
tim
e
m
ea
s
u
r
em
en
ts
.
T
h
e
ca
p
tu
r
ed
m
ea
s
u
r
es
ar
e
s
to
r
ed
o
n
a
PC
u
s
in
g
th
e
R
asp
b
er
r
y
Pi
Pico
r
u
n
n
in
g
a
m
icr
o
-
p
y
th
o
n
p
r
o
g
r
am
.
T
h
e
co
llected
d
ata
is
th
e
n
s
av
ed
in
a
n
E
x
ce
l
f
ile,
f
o
r
m
in
g
a
d
ataset
th
at
in
clu
d
es m
u
ltip
le
in
s
tan
ce
s
o
f
ea
ch
h
an
d
task
,
all
o
f
w
h
ich
h
av
e
b
ee
n
p
er
f
o
r
m
ed
s
ev
e
r
al
tim
es.
T
h
e
p
r
im
ar
y
o
b
jectiv
e
o
f
th
is
r
esear
ch
is
to
d
ev
elo
p
a
co
s
t
-
ef
f
ec
tiv
e
m
eth
o
d
f
o
r
g
e
n
er
atin
g
a
well
-
lab
eled
an
d
r
ep
r
esen
tativ
e
HG
d
ataset
b
ein
g
ef
f
ec
tiv
ely
tr
ain
ed
an
d
in
ter
p
r
eted
b
y
ML
alg
o
r
ith
m
s
.
T
h
e
r
esu
lts
aim
to
d
em
o
n
s
tr
ate
h
o
w
well
th
e
co
llected
d
ata
d
escr
ib
e
th
e
ten
task
s
an
d
h
o
w
ac
cu
r
ately
th
e
ML
m
o
d
els
ca
n
p
r
ed
ict
th
em
f
o
r
f
u
tu
r
e
u
s
a
g
e.
T
wo
ex
p
er
im
en
tal
p
h
ases
o
f
class
if
icatio
n
wer
e
co
n
d
u
cted
.
T
h
e
f
ir
s
t
ex
p
er
im
en
t
ap
p
lied
class
if
icatio
n
to
r
aw
d
ata,
m
in
im
izin
g
r
eso
u
r
ce
u
s
e,
to
e
v
alu
ate
h
o
w
ef
f
ec
tiv
ely
th
e
r
aw
g
estu
r
e
d
ata
r
ep
r
esen
ts
th
e
ac
tiv
ities
.
T
h
e
s
ec
o
n
d
ex
p
er
im
e
n
t
em
p
lo
y
ed
d
ata
p
r
ep
r
o
ce
s
s
i
n
g
tech
n
iq
u
es
[
3
4
]
,
in
clu
d
in
g
n
o
r
m
aliza
tio
n
an
d
f
ea
tu
r
e
ex
tr
ac
tio
n
(
FE)
[
3
5
]
,
[
3
6
]
to
en
h
an
ce
class
if
icatio
n
ac
cu
r
ac
y
.
I
n
th
e
in
itial
ex
p
er
im
en
t,
f
iv
e
m
o
d
el
s
ac
h
iev
ed
ac
cu
r
ac
ies
b
etwe
en
8
0
%
–
9
0
%,
wh
ile
th
e
s
ec
o
n
d
ex
p
er
im
en
t
s
h
o
wed
en
h
an
ce
d
r
esu
lts
,
with
s
ix
m
o
d
els
ac
h
iev
in
g
ac
c
u
r
ac
ies
b
et
wee
n
8
7
%
–
9
4
%.
Fo
r
s
im
p
licity
,
th
e
R
asp
b
er
r
y
Pi
Pico
m
icr
o
co
n
tr
o
ller
will b
e
r
e
f
er
r
ed
to
as “
Pico
”
th
r
o
u
g
h
o
u
t
th
e
r
em
ain
d
e
r
o
f
th
is
ar
ticle.
T
h
e
ar
ticle
is
s
tr
u
ctu
r
ed
as
f
o
llo
ws:
s
ec
tio
n
2
d
escr
ib
es
t
h
e
ten
h
an
d
task
s
ad
o
p
te
d
f
o
r
th
e
s
tu
d
y
.
Sectio
n
3
d
etails
th
e
m
eth
o
d
o
l
o
g
y
a
n
d
m
ater
ials
u
s
ed
f
o
r
d
at
a
ca
p
tu
r
e,
with
a
p
r
esen
tatio
n
o
f
ML
m
o
d
els
a
n
d
tech
n
iq
u
es
ap
p
lied
f
o
r
class
if
icatio
n
.
Sectio
n
4
s
h
o
ws
th
e
r
e
s
u
lts
ac
h
iev
ed
with
th
e
co
llect
ed
d
ataset
an
d
th
e
ML
p
r
ed
ictio
n
p
er
f
o
r
m
an
ce
s
,
alo
n
g
with
a
n
an
aly
s
is
an
d
d
is
cu
s
s
io
n
.
Fin
ally
,
a
co
n
clu
s
io
n
s
u
m
m
ar
izes
th
e
k
ey
f
in
d
in
g
s
.
2.
ADO
P
T
E
D
H
AN
D
T
A
SK
S
F
O
R
T
H
E
S
T
UDY
I
n
th
is
s
tu
d
y
,
ten
h
u
m
a
n
h
a
n
d
task
s
wer
e
d
esig
n
ed
t
o
s
im
u
late
r
ea
l
-
wo
r
ld
HG
ex
er
cises
th
at
r
ep
r
esen
t
s
im
p
le
d
aily
ac
tiv
it
ies.
E
ac
h
task
c
o
n
s
is
ts
o
f
m
u
ltip
le
cy
cles
o
f
r
ep
ea
te
d
HG
s
,
with
m
o
v
em
en
ts
d
etec
ted
b
y
th
e
m
o
tio
n
s
en
s
o
r
an
d
ca
p
tu
r
ed
b
y
th
e
Pico
.
T
o
o
p
tim
ize
th
e
u
s
e
o
f
a
s
in
g
le
s
en
s
o
r
,
th
e
s
elec
ted
task
s
ar
e
s
im
p
le,
o
n
e
-
h
a
n
d
ed
ac
tio
n
s
illu
s
tr
ated
in
Fig
u
r
e
1
.
Du
r
in
g
th
e
ex
er
cises
,
th
e
f
is
t
an
d
f
o
r
ea
r
m
a
r
e
th
e
o
n
ly
m
o
v
i
n
g
p
ar
ts
,
wh
ile
th
e
f
in
g
er
s
r
em
ain
s
tatio
n
ar
y
.
I
n
th
is
ap
p
r
o
ac
h
,
th
e
f
is
t
is
th
e
p
r
i
m
ar
y
p
o
r
tio
n
o
f
th
e
h
an
d
u
s
ed
f
o
r
r
ec
o
r
d
in
g
m
o
tio
n
s
with
th
e
s
en
s
o
r
.
A
task
i
s
d
ef
in
ed
as st
ar
t
in
g
with
th
e
h
an
d
in
a
f
ix
ed
p
o
s
tu
r
e
(
f
in
g
er
s
an
d
f
is
t im
m
o
b
ile)
,
f
o
llo
wed
b
y
co
n
tin
u
o
u
s
m
o
v
em
e
n
t
u
n
til
th
e
cy
cle
co
m
p
letes,
at
wh
ich
p
o
in
t
th
e
h
a
n
d
r
et
u
r
n
s
b
ac
k
to
its
in
itial
p
o
s
itio
n
b
ef
o
r
e
s
tar
tin
g
t
h
e
n
e
x
t
c
y
cle.
E
ac
h
elem
en
tar
y
m
o
v
em
e
n
t
o
r
ac
tio
n
th
at
co
n
s
titu
tes
a
task
is
ca
r
ef
u
lly
tr
ac
k
ed
,
g
en
e
r
atin
g
a
r
ea
l
-
tim
e
g
estu
r
e
d
ataset
with
d
etaile
d
f
ea
tu
r
es.
T
h
e
d
ata
ca
p
tu
r
e
is
p
r
o
ce
s
s
ed
u
s
in
g
a
PC
an
d
a
m
icr
o
-
p
y
th
o
n
p
r
o
g
r
am
,
with
t
h
e
r
esu
lts
s
to
r
ed
i
n
an
E
x
ce
l
f
ile
f
o
r
f
u
r
t
h
er
an
a
ly
s
is
.
Fig
u
r
e
s
1
(
a)
to
1
(
j)
illu
s
tr
ates th
e
1
0
h
an
d
task
s
,
wh
ich
ar
e
d
escr
ib
ed
as f
o
llo
ws:
a.
Op
en
in
g
/clo
s
in
g
a
d
o
o
r
: a
h
a
n
d
r
ea
ch
es f
o
r
th
e
d
o
o
r
h
an
d
le,
tu
r
n
s
it,
an
d
eith
e
r
p
u
s
h
es o
r
p
u
lls
th
e
d
o
o
r
to
o
p
en
o
r
clo
s
e
it.
b.
L
o
ck
in
g
/u
n
lo
ck
in
g
a
lo
c
k
k
e
y
:
f
in
g
er
s
m
a
n
ip
u
late
a
k
ey
,
i
n
s
er
tin
g
it
in
to
a
l
o
ck
to
t
u
r
n
an
d
s
ec
u
r
e
t
h
e
d
o
o
r
o
r
u
n
lo
ck
it,
an
d
p
u
ll th
e
k
ey
af
ter
t
h
at.
c.
T
ap
p
in
g
k
ey
s
:
d
elica
te
in
d
ex
f
in
g
er
m
o
v
em
en
ts
p
r
ess
in
g
k
ey
s
b
u
tto
n
s
(
o
f
an
elec
tr
o
n
ic
l
o
c
k
,
f
o
r
ex
am
p
le,
en
ter
in
g
a
c
o
d
e
o
r
s
eq
u
en
ce
to
s
ec
u
r
e
o
r
g
ain
ac
ce
s
s
)
.
d.
Po
u
r
in
g
a
c
u
p
with
co
f
f
ee
:
a
h
an
d
tak
es
u
p
a
th
e
r
m
o
s
,
h
o
ld
s
it,
an
d
ca
r
ef
u
lly
p
o
u
r
s
co
f
f
ee
in
to
a
cu
p
,
co
n
tr
o
llin
g
t
h
e
f
lo
w
to
p
r
ev
e
n
t
s
p
ills
,
an
d
p
u
ts
th
e
th
er
m
o
s
d
o
wn
.
e.
Ar
r
an
g
in
g
g
lass
cu
p
s
:
f
in
g
e
r
s
o
r
a
h
an
d
ar
r
an
g
e
g
lass
cu
p
s
in
a
s
p
ec
if
ic
o
r
d
e
r
o
r
p
atter
n
,
d
em
o
n
s
tr
atin
g
d
ex
ter
ity
an
d
p
r
ec
is
io
n
.
f.
C
lean
in
g
a
win
d
o
w:
a
h
an
d
wield
s
a
clea
n
in
g
to
o
l
(
s
q
u
e
eg
ee
o
r
clo
th
)
to
clea
n
t
h
e
win
d
o
w
s
u
r
f
ac
e
s
p
r
ay
ed
with
a
clea
n
e
r
liq
u
id
f
o
llo
win
g
s
y
s
tem
atic
an
d
co
n
tr
o
lled
m
o
v
e
m
en
ts
.
g.
Pan
tin
g
a
wall:
a
h
a
n
d
d
ip
s
a
p
ain
tb
r
u
s
h
in
to
a
p
ain
t
b
u
c
k
e
t
an
d
a
p
p
lies
p
ain
t
to
a
wall
with
s
tead
y
an
d
in
ten
tio
n
al
s
tr
o
k
es.
h.
Ham
m
er
in
g
a
n
ail:
a
h
a
n
d
g
r
asp
s
a
h
am
m
e
r
a
n
d
d
r
iv
es
a
n
ail
in
to
a
s
u
r
f
ac
e
b
y
d
eliv
er
in
g
c
o
n
tr
o
lled
s
tr
ik
es.
i.
Sawin
g
a
tu
b
e:
a
h
a
n
d
g
u
id
es
a
s
aw
th
r
o
u
g
h
a
tu
b
e,
d
em
o
n
s
tr
atin
g
a
p
r
ec
is
e
cu
ttin
g
m
o
tio
n
.
j.
T
ig
h
ten
in
g
/lo
o
s
en
in
g
a
b
o
lt:
f
in
g
er
s
o
r
a
h
an
d
g
r
ip
a
wr
en
c
h
,
eith
er
tig
h
ten
in
g
o
r
lo
o
s
en
i
n
g
a
b
o
lt
with
ca
lcu
lated
tu
r
n
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
E
mb
ed
d
ed
s
ystems
a
n
d
a
r
tifi
cia
l in
tellig
en
ce
fo
r
en
h
a
n
ce
d
h
u
ma
n
o
id
r
o
b
o
tics
a
p
p
lica
ti
o
n
s
(
Ja
la
l H
d
id
)
1915
Fig
u
r
e
1
.
I
ll
u
s
tr
atio
n
s
r
ep
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h
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am
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3.
M
E
T
H
O
D
T
h
is
wo
r
k
em
p
lo
y
s
th
e
Pico
[
3
1
]
,
[
3
2
]
as
th
e
p
r
im
ar
y
elec
tr
o
n
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m
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I
MU
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le
[
3
1
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,
[
3
3
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co
n
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ased
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o
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3
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C
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m
m
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[
3
7
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pb
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P
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p
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[
2
5
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[
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8
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[
3
9
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
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2
0
8
8
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I
n
t J E
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&
C
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p
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g
,
Vo
l.
15
,
No
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2
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Ap
r
il
20
25
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1
9
1
2
-
1
9
2
3
1916
(
a)
(
b
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Fig
u
r
e
2
.
Sch
em
atics o
f
th
e
Pico
b
o
ar
d
:
(
a)
i
n
ter
n
al
co
m
p
o
n
en
ts
an
d
(
b
)
p
in
o
u
t in
ter
f
ac
es
3.
2
.
M
P
U6
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4
0
I
M
U
m
o
du
le
T
h
e
MPU6
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5
0
I
MU
m
o
d
u
le
[
3
1
]
,
[
3
3
]
as
s
h
o
wn
in
Fig
u
r
e
3
is
a
6
-
a
x
is
m
o
tio
n
d
etec
to
r
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n
i
t
co
n
s
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g
o
f
a
3
-
a
x
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ler
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m
eter
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3
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y
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o
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e,
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n
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tem
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en
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r
.
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g
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s
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g
le
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e
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o
d
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le
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ter
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ac
ed
to
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d
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o
m
m
u
n
icate
s
with
th
e
Pico
th
r
o
u
g
h
th
e
I
2
C
b
u
s
[
3
7
]
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e
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o
s
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s
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r
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t
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lar
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e
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s
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ler
o
m
eter
an
d
g
y
r
o
s
co
p
e
d
ata
en
ab
les
p
r
ec
is
e
tr
ac
k
in
g
o
f
th
e
s
en
s
o
r
’
s
m
o
v
e
m
en
t
an
d
o
r
ien
tati
o
n
,
wh
ich
c
o
n
s
titu
tes
th
e
s
tr
en
g
th
o
f
th
e
d
ata
co
llectio
n
ap
p
r
o
ac
h
e
m
p
lo
y
e
d
in
th
is
ar
ticle.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
E
mb
ed
d
ed
s
ystems
a
n
d
a
r
tifi
cia
l in
tellig
en
ce
fo
r
en
h
a
n
ce
d
h
u
ma
n
o
id
r
o
b
o
tics
a
p
p
lica
ti
o
n
s
(
Ja
la
l H
d
id
)
1917
Fig
u
r
e
3
.
I
MU
m
o
d
u
le
with
its
m
o
tio
n
o
r
ien
tatio
n
s
an
d
co
n
n
ec
tio
n
b
u
s
T
ab
le
2
.
Acc
eler
atio
n
an
d
v
el
o
city
v
alu
es in
u
n
its
an
d
r
a
n
g
e
R
a
n
g
e
A
c
c
e
l
e
r
a
t
i
o
n
v
a
l
u
e
(
+
-
g)
R
o
t
a
t
i
o
n
v
a
l
u
e
(
+
-
d
e
g
s/
s
e
c
)
0
1
2
3
2
4
8
16
2
5
0
5
0
0
1
0
0
0
2
0
0
0
3.
3
.
Dev
el
o
pm
ent
bo
a
rd
T
h
e
d
ev
el
o
p
m
en
t
b
o
ar
d
is
a
n
in
teg
r
ated
p
latf
o
r
m
d
esig
n
ed
to
f
ac
ilit
ate
p
r
ac
tical
an
d
ef
f
icien
t
co
n
n
ec
tio
n
an
d
co
m
m
u
n
icatio
n
b
etwe
en
th
e
Pico
an
d
v
ar
i
o
u
s
ex
ter
n
al
d
ev
ices,
s
u
c
h
as
s
en
s
o
r
s
an
d
m
o
to
r
s
.
As
s
h
o
wn
in
Fig
u
r
e
4
,
th
e
b
o
ar
d
in
clu
d
es
a
th
in
-
f
ilm
-
tr
a
n
s
is
to
r
liq
u
id
cr
y
s
tal
d
is
p
lay
(
T
FT
L
C
D)
,
s
ix
g
r
o
v
e
-
in
ter
f
ac
e
co
m
p
atib
le
n
etwo
r
k
s
o
ck
ets
(
lab
eled
C
N1
to
C
N6
)
[
3
1
]
,
a
n
d
a
p
in
i
n
ter
f
ac
e
f
o
r
p
lu
g
g
i
n
g
t
h
e
Pico
,
en
s
u
r
in
g
ea
s
y
ac
ce
s
s
to
th
e
GPI
O
p
in
s
.
Ad
d
itio
n
ally
,
it
f
ea
tu
r
es
th
r
ee
lig
h
t
-
em
itti
n
g
d
io
d
es
(
L
E
Ds),
th
r
ee
b
u
tto
n
s
,
a
n
d
a
b
u
zz
er
.
E
x
te
r
n
a
l
co
m
p
o
n
en
ts
ca
n
b
e
c
o
n
n
ec
te
d
to
th
e
b
o
ar
d
t
h
r
o
u
g
h
th
e
s
ix
s
o
ck
ets
v
ia
ju
m
p
er
wir
es,
allo
win
g
in
ter
ac
tio
n
wit
h
Pico
’
s
GPI
O
p
in
s
.
Fo
r
m
o
r
e
d
etailed
in
f
o
r
m
atio
n
,
r
ef
er
to
[
3
1
]
.
3
.
4
.
M
ea
s
urem
ent
pro
g
r
a
mm
ing
T
o
d
ev
elo
p
a
p
r
o
g
r
am
in
th
e
Pico
th
at
m
ea
s
u
r
es
an
d
s
to
r
es
ac
ce
ler
atio
n
an
d
v
el
o
city
d
at
a
f
r
o
m
th
e
I
MU
s
en
s
o
r
,
a
f
ew
s
tep
s
m
u
s
t
b
e
f
o
llo
wed
.
First,
th
e
Pico
is
p
lu
g
g
ed
i
n
to
th
e
d
e
v
elo
p
m
e
n
t
b
o
ar
d
b
y
alig
n
i
n
g
it with
it
s
GPI
O
p
in
h
o
les,
an
d
th
e
I
MU
s
en
s
o
r
is
co
n
n
ec
ted
to
th
e
d
ev
elo
p
m
e
n
t b
o
ar
d
th
r
o
u
g
h
th
e
co
m
p
atib
le
n
etwo
r
k
(
C
N3
)
s
o
ck
et
u
s
in
g
j
u
m
p
er
wir
es
as
s
h
o
wn
in
Fig
u
r
e
4
.
Seco
n
d
,
v
ia
its
m
icr
o
-
U
SB
-
B
p
o
r
t,
th
e
Pico
is
co
n
n
ec
ted
to
a
PC
u
s
in
g
a
m
icr
o
-
USB
-
B
/US
B
ca
b
le,
p
r
o
v
id
in
g
5
V
p
o
wer
an
d
en
ab
lin
g
d
ata
tr
an
s
m
is
s
io
n
as
s
h
o
wn
in
Fig
u
r
e
4
.
Fin
ally
,
b
y
d
o
wn
lo
ad
in
g
th
e
T
h
o
n
n
y
en
v
ir
o
n
m
en
t
s
o
f
twar
e
[
3
1
]
,
[
3
2
]
,
th
e
p
r
o
g
r
am
ca
n
b
e
d
ev
elo
p
ed
b
y
c
r
ea
tin
g
a
n
ew
f
ile
an
d
u
s
in
g
t
h
e
m
icr
o
-
p
y
th
o
n
p
r
o
g
r
am
la
n
g
u
a
g
e.
Up
o
n
r
u
n
n
in
g
t
h
e
p
r
o
g
r
a
m
,
th
e
6
-
ax
is
v
alu
es:
,
,
,
,
,
an
d
s
tar
t
b
ein
g
m
ea
s
u
r
e
d
b
y
th
e
I
MU
s
en
s
o
r
an
d
s
to
r
ed
in
an
E
x
ce
l f
ile
o
n
th
e
PC
.
Fig
u
r
e
4
.
Sch
em
a
d
escr
ib
in
g
th
e
p
r
o
p
o
s
ed
d
ata
co
llectio
n
m
eth
o
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
2
,
Ap
r
il
20
25
:
1
9
1
2
-
1
9
2
3
1918
3
.
5
.
Da
t
a
c
o
llect
io
n m
et
ho
d
o
lo
g
y
All
task
s
co
v
er
ed
in
th
is
p
ap
e
r
ar
e
o
n
e
-
h
an
d
ac
tio
n
s
(
r
ig
h
t
o
r
lef
t
h
an
d
)
th
at
h
av
e
b
ee
n
p
e
r
f
o
r
m
ed
in
a
ca
r
in
g
m
an
n
er
u
s
in
g
m
y
h
an
d
.
T
h
e
I
MU
s
en
s
o
r
was
s
ec
u
r
ely
attac
h
ed
to
th
e
b
ac
k
o
f
m
y
h
an
d
an
d
co
n
n
ec
ted
to
t
h
e
Pico
,
wh
ich
was
af
f
ix
ed
t
o
m
y
f
o
r
ea
r
m
an
d
lin
k
e
d
to
a
PC
v
ia
a
m
icr
o
-
USB
/U
SB
ca
b
le
to
p
r
o
v
id
e
b
o
t
h
p
o
wer
a
n
d
d
ata
tr
an
s
m
is
s
io
n
as
s
h
o
wn
in
Fig
u
r
e
5
.
On
ce
th
e
ac
q
u
is
itio
n
s
y
s
tem
was
s
et
u
p
,
I
lau
n
ch
e
d
th
e
p
r
o
g
r
am
an
d
b
eg
an
to
ca
r
ef
u
lly
p
er
f
o
r
m
,
f
o
r
ev
er
y
s
in
g
le
task
,
th
e
r
e
q
u
ir
e
d
h
an
d
m
o
v
em
en
ts
,
in
clu
d
in
g
d
ir
ec
tio
n
al
s
h
if
ts
a
n
d
r
o
tatio
n
s
,
th
at
d
ef
in
e
th
e
task
.
W
h
ile
d
o
i
n
g
th
at
r
ep
etitiv
ely
in
cy
cles,
ac
ce
ler
atio
n
an
d
g
y
r
o
s
co
p
e
m
ea
s
u
r
em
en
ts
wer
e
b
ein
g
r
ec
o
r
d
ed
in
r
ea
l
-
tim
e
b
y
th
e
Pico
f
r
o
m
th
e
I
MU
s
en
s
o
r
an
d
wer
e
a
u
to
m
atica
lly
b
ein
g
s
to
r
ed
an
d
s
av
ed
in
an
E
x
ce
l
f
ile.
T
h
e
m
icr
o
-
p
y
th
o
n
p
r
o
g
r
am
o
p
e
r
ated
in
a
co
n
tin
u
o
u
s
“wh
ile”
lo
o
p
,
ca
p
a
b
le
o
f
r
ec
o
r
d
i
n
g
u
p
to
9
9
9
r
o
ws
o
f
d
ata
p
e
r
s
ess
io
n
.
E
ac
h
r
o
w
co
r
r
esp
o
n
d
ed
t
o
a
s
am
p
le
co
n
s
is
tin
g
o
f
,
,
,
,
,
an
d
m
ea
s
u
r
es.
Fo
r
ea
ch
task
,
t
h
e
p
r
o
g
r
am
r
a
n
twice,
co
llectin
g
d
ata
in
two
s
ess
io
n
s
,
r
esu
ltin
g
in
1
,
9
9
8
to
tal
s
am
p
les
p
er
task
.
T
h
e
d
ata
wer
e
c
o
llected
at
a
f
r
eq
u
e
n
cy
o
f
1
0
0
Hz
with
a
tim
e
in
ter
v
al
o
f
0
.
0
1
s
ec
o
n
d
s
.
Fo
r
task
s
in
v
o
lv
in
g
th
e
tip
p
in
g
p
a
d
an
d
u
n
l
o
ck
in
g
/lo
ck
in
g
d
o
o
r
ac
tio
n
s
,
th
e
I
MU
s
en
s
o
r
was
p
lace
d
o
n
th
e
in
d
e
x
f
in
g
e
r
,
wh
i
le
f
o
r
th
e
r
est
o
f
th
e
task
s
,
it
was
p
o
s
itio
n
ed
n
ea
r
th
e
m
id
d
le
k
n
u
ck
le
o
f
th
e
h
an
d
,
as sh
o
wn
in
Fig
u
r
e
5
.
Fig
u
r
e
5
.
Pictu
r
es sh
o
win
g
th
e
h
an
d
p
er
f
o
r
m
in
g
th
e
1
0
h
an
d
task
s
in
to
r
ea
l
-
s
ce
n
ar
io
s
ex
p
er
im
en
ts
3
.
6
.
M
L
a
pp
lied in c
la
s
s
if
ica
t
io
n
T
o
e
v
a
l
u
a
t
e
t
h
e
a
c
c
u
r
a
c
y
o
f
t
h
e
a
c
q
u
i
r
e
d
d
a
t
a
s
et
i
n
r
e
p
r
e
s
e
n
ti
n
g
o
u
r
t
e
n
a
c
t
i
v
i
t
i
es
,
S
M
L
cl
as
s
i
f
i
c
a
ti
o
n
h
a
s
b
e
e
n
e
m
p
l
o
y
e
d
t
h
r
o
u
g
h
t
w
o
c
l
a
s
s
i
f
i
c
a
t
i
o
n
e
x
p
e
r
i
m
e
n
t
a
l
p
h
a
s
e
s
.
T
h
e
i
n
i
t
i
al
s
t
e
p
i
n
v
o
lv
e
s
f
o
r
e
c
a
s
t
i
n
g
t
h
e
t
a
s
k
s
b
a
s
e
d
o
n
r
a
w
d
at
a
t
o
a
s
s
e
s
s
t
h
e
ac
c
u
r
a
c
y
o
f
t
h
e
r
e
a
l
-
tim
e
d
a
t
a
ca
p
t
u
r
e
i
n
d
e
p
ic
t
i
n
g
th
e
H
G
s
.
T
h
e
s
e
c
o
n
d
p
h
a
s
e
p
r
e
p
r
o
c
e
s
s
es
t
h
e
d
a
t
as
et
[
3
4
]
,
a
p
p
l
y
i
n
g
n
o
r
m
a
l
iz
a
t
i
o
n
a
n
d
F
E
t
e
c
h
n
i
q
u
es
[
3
5
]
,
[
3
6
]
t
o
e
n
h
a
n
c
e
o
u
r
p
r
e
d
i
c
t
i
o
n
p
e
r
f
o
r
m
a
n
c
e
.
T
h
i
s
en
t
a
i
ls
e
v
e
n
l
y
d
i
v
i
d
i
n
g
t
h
e
n
o
r
m
a
l
i
z
e
d
d
a
t
as
e
t
’
s
a
x
is
s
i
g
n
a
ls
o
v
e
r
a
t
i
m
e
i
n
t
e
r
v
a
l
a
n
d
t
h
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n
u
s
i
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F
E
m
e
t
h
o
d
s
o
n
e
a
c
h
s
e
g
m
e
n
t
.
T
h
is
a
l
l
o
ws
u
s
t
o
e
x
t
r
a
c
t
a
n
e
w
,
r
e
d
u
c
e
d
,
a
n
d
m
o
r
e
i
n
f
o
r
m
a
t
i
v
e
d
a
t
a
s
et
.
T
h
e
s
i
g
n
al
s
we
r
e
s
e
g
m
e
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t
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d
i
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6
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1
.
F
ea
t
ure
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x
t
ra
ct
i
o
n
FE
[
3
5
]
,
[
3
6
]
r
e
f
er
s
to
th
e
p
r
o
ce
s
s
o
f
i
d
en
tify
in
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n
d
ex
tr
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ctin
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r
elev
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n
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ea
tu
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es
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r
o
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r
aw
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ata
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o
cr
ea
te
a
m
o
r
e
in
f
o
r
m
ativ
e
a
n
d
s
im
p
ler
d
ataset.
I
t
aim
s
to
r
ed
u
ce
d
ata
co
m
p
le
x
ity
(
o
f
t
en
k
n
o
wn
as
“d
ata
d
im
en
s
io
n
ality
”)
u
s
in
g
s
tatis
tical
m
eth
o
d
s
wh
ile
r
etain
in
g
as
m
u
ch
r
elev
an
t
in
f
o
r
m
atio
n
as
p
o
s
s
ib
le
in
th
e
o
r
ig
in
al
d
ataset.
T
h
is
h
elp
s
to
im
p
r
o
v
e
th
e
p
er
f
o
r
m
a
n
ce
an
d
ef
f
icien
cy
o
f
ML
alg
o
r
ith
m
s
an
d
s
im
p
lify
th
e
an
aly
s
is
p
r
o
ce
s
s
.
I
n
o
u
r
s
tu
d
y
,
f
o
u
r
s
tatis
tical
FE
tech
n
iq
u
es
wer
e
u
s
ed
in
c
lass
if
icatio
n
:
ar
ith
m
etic
m
ea
n
,
m
ax
–
m
in
v
alu
es
,
r
o
o
t m
ea
n
s
q
u
ar
e
(
R
MS
)
,
an
d
p
r
in
ci
p
al
co
m
p
o
n
en
t a
n
aly
s
is
(
PC
A)
[
3
4
]
–
[
3
6
]
.
T
h
es
e
tech
n
iq
u
es,
wh
en
ap
p
lied
to
g
eth
er
,
h
elp
to
f
o
r
m
a
r
o
b
u
s
t
f
ea
tu
r
e
s
et
th
at
ac
cu
r
ately
r
ep
r
esen
ts
th
e
u
n
d
er
ly
i
n
g
p
atter
n
s
o
f
h
an
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
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&
C
o
m
p
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g
I
SS
N:
2088
-
8
7
0
8
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mb
ed
d
ed
s
ystems
a
n
d
a
r
tifi
cia
l in
tellig
en
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fo
r
en
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ce
d
h
u
ma
n
o
id
r
o
b
o
tics
a
p
p
lica
ti
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n
s
(
Ja
la
l H
d
id
)
1919
m
o
v
em
en
ts
f
o
r
th
e
class
if
icatio
n
.
Utilizin
g
th
ese
f
ea
tu
r
es
en
s
u
r
es
th
at
ML
m
o
d
els
ca
n
m
o
r
e
ef
f
ec
tiv
ely
in
ter
p
r
et
th
e
d
ata
a
n
d
d
eliv
e
r
b
etter
p
er
f
o
r
m
an
ce
in
g
estu
r
e
r
e
co
g
n
itio
n
.
T
h
e
s
ig
n
if
ican
ce
o
f
th
ese
f
ea
tu
r
es a
n
d
th
eir
r
o
le
in
t
h
is
r
esear
ch
ar
e
d
escr
ib
ed
as f
o
llo
ws:
a.
Ar
ith
m
etic
m
ea
n
:
it
is
th
e
a
v
er
ag
e
v
alu
e
o
v
er
ea
ch
tim
e
in
ter
v
al,
p
r
o
v
id
in
g
in
s
ig
h
t
i
n
to
th
e
ce
n
tr
al
ten
d
en
cy
o
f
ea
ch
g
estu
r
e.
I
t g
i
v
es a
clea
r
in
d
icatio
n
o
f
th
e
o
v
er
all
m
o
v
em
en
t t
r
en
d
o
f
th
e
h
an
d
.
b.
R
MS:
i
t
p
r
o
v
id
es
in
s
ig
h
t
in
t
o
th
e
o
v
er
all
m
a
g
n
itu
d
e
a
n
d
en
er
g
y
o
f
th
e
m
o
tio
n
in
ea
c
h
d
ata
s
eg
m
en
t,
id
en
tify
in
g
its
m
o
s
t
r
ec
u
r
r
en
t
f
o
r
m
.
I
t
is
cr
itical
f
o
r
g
e
s
tu
r
e
r
ec
o
g
n
itio
n
task
s
wh
er
e
v
ar
iatio
n
s
in
m
o
v
em
en
t i
n
ten
s
ity
ar
e
s
ig
n
if
i
ca
n
t.
c.
PC
A:
i
t
o
b
s
er
v
es
co
r
r
elatio
n
s
b
etwe
en
m
o
v
em
en
t
p
atter
n
s
in
ea
ch
ax
is
b
in
an
d
r
ed
u
ce
s
in
f
o
r
m
atio
n
co
m
p
lex
ity
wh
ile
p
r
eser
v
in
g
th
e
m
o
s
t sig
n
if
ican
t p
atter
n
s
an
d
en
h
a
n
cin
g
c
o
m
p
u
tatio
n
al
ef
f
icien
cy
.
d.
Max
–
Min
v
al
u
es:
th
ey
r
ep
r
es
en
t
th
e
h
ig
h
est
an
d
lo
west
v
al
u
es
o
f
t
h
e
m
o
tio
n
with
in
ea
ch
win
d
o
w.
T
h
ey
o
f
f
er
in
s
ig
h
ts
in
to
th
e
s
p
r
ea
d
o
f
th
e
m
o
v
em
en
t,
wh
ic
h
ca
n
b
e
im
p
o
r
tan
t
f
o
r
b
o
u
n
d
ar
y
d
etec
t
io
n
in
task
s
.
3
.
6
.
2
.
M
L
m
o
dels
T
h
r
o
u
g
h
Py
th
o
n
p
r
o
g
r
a
m
m
in
g
,
ten
ML
m
o
d
els
wer
e
tr
ain
ed
o
n
th
e
d
ataset
f
o
r
b
o
th
cla
s
s
if
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n
ex
p
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s
in
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0
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s
et
an
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test
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%
test
in
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et.
Yield
in
g
v
ar
y
in
g
a
cc
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r
ac
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s
co
r
es,
th
e
r
esu
lts
will
b
e
in
ter
p
r
eted
b
y
co
m
p
ar
in
g
th
e
p
e
r
f
o
r
m
an
ce
o
f
th
e
two
e
x
p
er
im
e
n
tal
class
if
icatio
n
p
h
ases
.
Fo
r
m
o
r
e
in
-
d
ep
t
h
ev
al
u
atio
n
a
n
d
an
aly
s
is
,
a
co
n
f
u
s
io
n
m
atr
ix
[
4
3
]
was
u
tili
ze
d
to
g
ain
d
etail
ed
in
s
ig
h
ts
in
to
th
e
h
ig
h
est
-
p
er
f
o
r
m
in
g
m
o
d
el.
A
d
d
itio
n
ally
,
cr
o
s
s
-
v
alid
atio
n
[
4
4
]
was
ap
p
lied
d
u
r
in
g
th
e
t
r
ain
in
g
p
r
o
ce
s
s
to
m
itig
ate
an
y
p
o
ten
tial
o
v
er
f
itt
in
g
[
4
5
]
i
n
p
r
e
d
ictio
n
.
T
h
e
te
n
ML
class
if
ier
s
em
p
lo
y
e
d
in
clu
d
e
n
aïv
e
B
ay
es
(
NB
)
,
Ad
aBo
o
s
t
(
AB
)
,
m
u
lti
-
lay
er
p
er
ce
p
tr
o
n
(
ML
P),
r
a
n
d
o
m
f
o
r
est
(
R
F),
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SVM)
,
d
ec
is
io
n
tr
ee
(
DT
)
,
lo
g
is
tic
r
eg
r
ess
io
n
(
L
R
)
,
XGBo
o
s
t
(
X
GB
)
,
k
-
n
ea
r
est
n
eig
h
b
o
r
s
(
K
NN)
,
an
d
g
r
a
d
ien
t
b
o
o
s
tin
g
(
GB
)
[
4
6
]
.
T
h
ese
class
if
ier
s
wer
e
s
elec
ted
f
o
r
th
eir
s
u
itab
ilit
y
in
m
u
lti
-
lab
el
class
if
icatio
n
task
s
,
p
ar
ticu
lar
ly
th
o
s
e
d
ea
lin
g
with
d
atasets
th
at
co
n
tain
h
ig
h
-
d
i
m
en
s
io
n
al
clu
s
ter
s
o
r
p
atter
n
s
,
s
u
ch
as
th
e
6
-
a
x
is
HG
d
ata
u
s
ed
in
th
is
s
tu
d
y
to
class
if
y
1
0
h
an
d
task
s
.
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
4
.
1
.
T
he
o
bta
ined da
t
a
s
et
T
h
e
d
ataset
c
o
m
p
r
is
es
3
-
ax
is
ac
ce
ler
atio
n
s
(
,
,
)
an
d
3
-
a
x
is
v
elo
cities
(
,
,
)
as
i
n
p
u
t
v
ar
iab
les
an
d
th
e
class
o
f
ac
ti
v
ities
s
er
v
in
g
as
th
e
o
u
tc
o
m
e
v
ar
iab
le.
W
ith
1
,
9
9
8
s
am
p
les
allo
ca
ted
f
o
r
ea
ch
task
,
th
e
r
aw
d
ataset
co
m
p
r
is
es
1
9
,
9
8
0
s
am
p
les,
r
esu
ltin
g
in
an
in
p
u
t
m
atr
ix
X
o
f
d
im
en
s
io
n
s
(
1
9
,
9
8
0
,
6
)
an
d
an
o
u
tp
u
t
v
ec
to
r
Y
o
f
d
im
e
n
s
io
n
s
(
1
9
,
9
8
0
,
1
)
.
Po
s
t
-
p
r
ep
r
o
ce
s
s
in
g
,
th
e
d
ataset
was
r
ed
u
ce
d
to
3
1
r
o
ws
o
r
win
d
o
ws
an
d
3
0
ax
is
in
p
u
ts
(
Me
an
,
R
MS,
PC
A,
Ma
x
an
d
Min
p
er
6
-
ax
is
)
,
in
clu
d
i
n
g
9
class
es
with
3
b
in
s
ea
ch
an
d
o
n
e
class
with
4
b
in
s
.
In
T
ab
le
3
,
th
e
1
0
task
s
ar
e
d
esig
n
ated
T
0
,
T
1
,
…
,
T
9
,
an
d
f
o
r
ML
tr
ain
in
g
p
u
r
p
o
s
es,
th
ey
ar
e
en
co
d
e
d
an
d
ca
teg
o
r
ized
b
y
n
u
m
e
r
ical
v
a
lu
es r
an
g
in
g
f
r
o
m
0
to
9
.
T
ab
le
3
.
T
h
e
1
0
h
an
d
task
s
with
th
eir
n
am
es a
n
d
en
c
o
d
ed
cl
ass
es
Ta
sk
D
e
si
g
n
a
t
i
o
n
C
l
a
s
ses
O
p
e
n
i
n
g
/
C
l
o
si
n
g
a
d
o
o
r
Lo
c
k
i
n
g
/
U
n
l
o
c
k
i
n
g
a
l
o
c
k
k
e
y
Ta
p
p
i
n
g
k
e
y
s
P
o
u
r
i
n
g
c
u
p
s w
i
t
h
c
o
f
f
e
e
A
r
r
a
n
g
i
n
g
g
l
a
ss
c
u
p
s
C
l
e
a
n
i
n
g
a
w
i
n
d
o
w
P
a
n
t
i
n
g
a
w
a
l
l
H
a
mm
e
r
i
n
g
a
n
a
i
l
S
a
w
i
n
g
a
t
u
b
e
Ti
g
h
t
e
n
i
n
g
/
L
o
o
se
n
i
n
g
a
b
o
l
t
T0
T1
T2
T3
T4
T5
T6
T7
T8
T9
0
1
2
3
4
5
6
7
8
9
4
.
2
.
Cla
s
s
if
ica
t
io
n per
f
o
r
m
a
nces
I
n
th
e
f
ir
s
t
ex
p
er
im
en
t,
class
if
icatio
n
o
n
th
e
r
aw
d
ataset
ex
h
ib
ited
p
r
o
m
is
in
g
p
er
f
o
r
m
a
n
ce
f
o
r
h
alf
o
f
th
e
ten
class
if
ier
s
as
s
h
o
wn
i
n
Fig
u
r
e
6
.
Pre
d
ictio
n
r
esu
lts
s
h
o
w
h
ig
h
ac
cu
r
ac
y
s
co
r
es
(
8
0
%
–
9
0
%)
o
b
tain
ed
b
y
th
e
R
F,
ML
P,
DT
,
XGB,
a
n
d
GB
class
if
ier
s
,
with
two
o
f
th
em
n
ea
r
in
g
9
0
%.
Me
d
iu
m
s
co
r
es
(
5
3
%
–
6
2
%)
wer
e
o
b
tain
ed
b
y
th
e
NB
an
d
L
R
class
if
ier
s
,
an
d
lo
w
s
co
r
es
(
3
3
%
–
4
7
%)
b
y
AB
,
SVM,
an
d
KNN.
I
n
th
e
s
ec
o
n
d
class
if
icatio
n
t
r
ial
co
n
ce
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
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2
0
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I
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u
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lass
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f
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th
e
r
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ataset
Fig
u
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C
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er
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m
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ce
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ed
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at
aset
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h
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ictio
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d
em
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n
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tr
ate
th
at
m
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t
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f
th
e
m
o
d
els
p
er
f
o
r
m
ed
well
in
id
e
n
t
if
y
in
g
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u
r
1
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class
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wh
er
e
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ch
m
o
d
el
h
ad
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u
n
iq
u
e
in
ter
p
r
etatio
n
o
f
th
e
g
ath
e
r
ed
d
ataset.
Fiv
e
m
o
d
els
(
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ML
P,
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,
R
F,
an
d
GB
)
h
av
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alr
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s
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p
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is
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ataset
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s
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u
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,
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ile
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ile
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ab
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Ad
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d
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as
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tain
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itti
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4
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3
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M
L
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nfusi
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a
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W
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ix
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ated
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ig
h
ly
p
r
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m
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g
p
e
r
f
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r
m
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ce
s
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it
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o
r
e
p
r
ac
tica
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s
elec
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th
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est
-
p
er
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el
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ted
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Pico
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d
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ed
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o
r
f
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t
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r
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ed
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s
.
I
n
th
is
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s
e,
th
e
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ig
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4
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ich
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ier
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ts
co
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at
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[
4
3
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in
T
ab
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iew
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ic
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k
n
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as T
PR
:
tr
u
e
p
o
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itiv
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ate
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r
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all
)
[
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ch
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i
c
t
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sses
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9
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15
6
2
0
21
1
7
0
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3
5
6
8
5
.
1
6
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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&
C
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p
E
n
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I
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N:
2088
-
8
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mb
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tellig
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1921
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o
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s
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co
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f
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p
o
in
ts
b
etwe
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tain
class
es
wer
e
o
b
s
er
v
ed
,
th
e
ML
P
co
n
f
u
s
io
n
m
atr
ix
in
T
a
b
le
4
d
em
o
n
s
tr
a
tes
a
s
atis
f
ac
to
r
y
id
en
tific
atio
n
o
f
th
e
m
aj
o
r
ity
o
f
p
o
in
ts
a
cr
o
s
s
th
e
ten
task
class
es.
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h
ile
ac
co
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n
tin
g
f
o
r
n
o
n
-
lin
ea
r
tr
en
d
s
,
th
e
m
atr
ix
also
h
ig
h
lig
h
ts
a
f
ew
p
r
o
b
le
m
atic
p
o
in
ts
with
in
th
e
d
ataset.
T
h
ese
is
s
u
es
ar
o
s
e
d
u
r
i
n
g
t
h
e
ex
ec
u
tio
n
o
f
task
s
lik
e
T
0
,
T
4
,
an
d
T
9
,
wh
e
r
e
h
an
d
m
o
v
em
en
ts
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e
o
cc
asio
n
ally
u
n
s
tead
y
o
r
s
u
b
ject
to
v
ib
r
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n
s
at
s
p
ec
if
ic
in
s
tan
ce
s
.
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n
eth
eless
,
th
e
T
PR
v
alu
es
f
o
r
all
class
es
in
d
icate
th
at
th
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ML
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m
o
d
el
p
r
e
d
icted
ea
ch
task
in
d
iv
id
u
ally
with
a
h
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g
h
p
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o
n
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ate.
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task
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r
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icted
at
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n
r
ates
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g
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g
f
r
o
m
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0
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to
9
8
.
5
5
%,
with
th
e
ex
ce
p
tio
n
o
f
T
9
,
wh
ich
was
p
r
ed
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at
a
s
lig
h
tly
lo
wer
p
r
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is
io
n
r
ate
o
f
8
5
.
1
6
%.
Ov
er
all,
th
ese
r
esu
lts
u
n
d
er
s
co
r
e
th
e
ML
P’s
ab
ilit
y
to
d
is
tin
g
u
is
h
ef
f
ec
tiv
ely
b
etwe
en
th
e
ten
ac
tiv
ities
,
ac
h
iev
in
g
a
p
er
f
o
r
m
an
ce
o
f
9
4
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1
1
% a
s
th
e
T
PR
m
ea
n
.
4
.
4
.
O
utc
o
m
es a
nd
a
na
ly
s
is
T
h
e
r
esu
lts
d
em
o
n
s
tr
ate
th
at
th
e
co
llected
d
ataset
ef
f
ec
tiv
ely
r
ep
r
esen
ts
o
u
r
ten
d
is
tin
ct
ac
tiv
ities
th
at
wer
e
ac
cu
r
ately
class
if
ied
an
d
r
ec
o
g
n
ize
d
b
y
ML
m
o
d
els.
On
th
e
r
aw
d
ataset,
p
r
ed
ictio
n
ex
h
ib
ited
s
tr
o
n
g
p
er
f
o
r
m
an
ce
with
a
m
ax
im
u
m
ac
cu
r
ac
y
o
f
8
9
%,
wh
ile
p
r
ep
r
o
ce
s
s
in
g
tech
n
iq
u
es
f
u
r
th
er
en
h
an
ce
d
th
e
p
er
f
o
r
m
an
ce
to
a
m
ax
im
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m
s
co
r
e
o
f
9
4
.
1
1
%.
T
h
e
ac
h
iev
e
m
en
t
o
f
h
ig
h
o
u
tco
m
es
ca
n
b
e
attr
ib
u
ted
to
th
e
ca
r
ef
u
l
s
elec
tio
n
o
f
task
s
an
d
co
n
tr
o
lled
e
x
ec
u
tio
n
o
f
h
an
d
m
o
v
em
e
n
ts
,
with
th
e
u
s
e
o
f
s
u
itab
le
em
b
ed
d
ed
to
o
ls
alo
n
g
s
id
e
p
er
f
o
r
m
a
n
t
ML
m
eth
o
d
s
.
T
h
is
h
ig
h
lig
h
ts
th
e
s
u
cc
ess
o
f
a
lo
w
-
co
s
t
d
ata
co
llectio
n
m
eth
o
d
o
l
o
g
y
i
n
ac
cu
r
ately
ca
p
tu
r
in
g
a
n
d
p
r
ed
ictin
g
HG
d
at
a
f
o
r
HR
em
b
ed
d
ed
ML
-
b
ased
ap
p
licatio
n
s
.
5.
CO
NCLU
SI
O
N
I
n
co
n
cl
u
s
io
n
,
th
e
a
p
p
licatio
n
o
f
ML
tec
h
n
iq
u
es
in
o
u
r
lo
w
-
co
s
t
d
ata
co
llectio
n
s
tr
ate
g
y
y
ield
e
d
f
av
o
r
a
b
le
r
esu
lts
in
id
en
tify
in
g
th
e
ten
task
clas
s
es.
T
h
ese
o
u
tco
m
es
ca
n
b
e
f
u
r
t
h
er
lev
er
ag
ed
b
y
tr
ain
in
g
o
n
n
ew
d
ata
an
d
d
e
p
lo
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in
g
th
e
b
est
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p
er
f
o
r
m
in
g
m
o
d
els
o
n
th
e
Pico
as
em
b
e
d
d
ed
ML
alg
o
r
it
h
m
s
f
o
r
r
ea
l
-
wo
r
l
d
ap
p
licatio
n
s
.
T
h
is
co
u
ld
s
er
v
e
as
a
f
o
llo
w
-
u
p
to
th
is
wo
r
k
f
o
r
a
n
ew
p
r
o
ject,
d
em
o
n
s
tr
atin
g
th
e
im
p
o
r
tan
ce
o
f
af
f
o
r
d
a
b
le
em
b
e
d
d
ed
d
ev
ices
f
o
r
ef
f
icien
t
d
ata
co
llectio
n
a
n
d
r
eliab
le
ML
p
er
f
o
r
m
an
ce
.
Fu
tu
r
e
wo
r
k
co
u
ld
in
v
o
lv
e
ex
p
an
d
in
g
th
e
d
atase
t,
ex
p
lo
r
in
g
ad
d
itio
n
al
AI
f
e
atu
r
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an
d
test
in
g
d
if
f
er
e
n
t
ML
m
o
d
els
ac
r
o
s
s
v
ar
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r
ea
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-
w
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ld
s
ce
n
a
r
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s
t
o
im
p
r
o
v
e
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en
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ilit
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.
O
v
er
all,
th
is
r
esear
ch
lay
s
a
s
tr
o
n
g
f
o
u
n
d
atio
n
f
o
r
ap
p
ly
in
g
s
im
ilar
m
eth
o
d
s
in
H
R
,
in
co
r
p
o
r
atin
g
ad
v
an
ce
d
M
L
tech
n
iq
u
es
u
s
in
g
ad
d
itio
n
al
to
o
ls
.
T
h
e
ab
ilit
y
t
o
ac
cu
r
ately
r
ec
o
g
n
ize
s
p
ec
if
ic
HG
s
cr
ea
tes
o
p
p
o
r
tu
n
ities
to
in
teg
r
ate
s
u
ch
o
f
o
u
r
ten
ac
ti
v
ities
in
to
r
o
b
o
tic
s
y
s
tem
s
,
ad
v
an
cin
g
au
to
m
atio
n
in
h
u
m
a
n
-
ass
is
tiv
e
task
s
.
A
p
r
ac
tical
e
x
am
p
le
is
u
n
d
e
r
wate
r
weld
in
g
,
wh
ich
is
p
er
f
o
r
m
e
d
u
n
d
er
h
i
g
h
p
r
ess
u
r
e
in
o
ce
a
n
s
.
W
ell
-
tr
ain
ed
H
R
S
co
u
ld
ef
f
icien
tly
ex
ec
u
te
t
h
is
jo
b
,
m
itig
atin
g
r
is
k
s
to
h
u
m
an
s
s
u
ch
as e
lectr
i
c
s
h
o
ck
,
d
ec
o
m
p
r
ess
io
n
s
ick
n
ess
,
d
r
o
wn
in
g
,
h
ea
r
in
g
lo
s
s
,
an
d
h
y
p
o
th
e
r
m
ia.
ACK
NO
WL
E
DG
E
M
E
NT
S
W
e
wo
u
ld
lik
e
t
o
ex
p
r
ess
o
u
r
ap
p
r
ec
iatio
n
to
all
in
d
i
v
id
u
al
s
an
d
o
r
g
an
izatio
n
s
wh
o
co
n
t
r
ib
u
ted
t
o
th
is
p
r
o
ject.
Sp
ec
ial
t
h
an
k
s
to
th
e
p
r
o
f
ess
o
r
s
an
d
s
u
p
er
v
is
o
r
s
at
Mo
h
am
e
d
1
Un
iv
er
s
ity
f
o
r
th
eir
in
v
alu
a
b
le
s
u
p
p
o
r
t
an
d
g
u
id
an
ce
.
W
e
also
ex
ten
d
o
u
r
g
r
atitu
d
e
to
th
e
E
lectr
o
n
ics
an
d
I
n
s
tr
u
m
en
tati
o
n
T
ea
m
f
r
o
m
t
h
e
L
ab
o
r
ato
r
y
o
f
E
lectr
o
n
ics
an
d
Sy
s
tem
s
f
o
r
th
eir
co
llab
o
r
atio
n
an
d
in
p
u
t.
Ad
d
itio
n
ally
,
w
e
ac
k
n
o
wled
g
e
th
e
ef
f
o
r
ts
o
f
th
e
Op
er
atio
n
al
R
esear
ch
an
d
A
p
p
lied
Statis
tics
T
ea
m
f
r
o
m
t
h
e
L
ab
o
r
ato
r
y
o
f
A
p
p
lied
Ma
th
em
atics
o
f
th
e
Or
ie
n
tal
f
o
r
th
eir
d
e
d
icatio
n
.
Fin
ally
,
we
t
h
an
k
o
u
r
f
a
m
ilies
,
f
r
ien
d
s
,
a
n
d
c
o
lleag
u
es
f
o
r
th
eir
u
n
wav
er
in
g
s
u
p
p
o
r
t.
RE
F
E
R
E
NC
E
S
[
1
]
S
.
S
a
e
e
d
v
a
n
d
,
M
.
J
a
f
a
r
i
,
H
.
S
.
A
g
h
d
a
si
,
a
n
d
J.
B
a
l
t
e
s,
“
A
c
o
m
p
r
e
h
e
n
si
v
e
s
u
r
v
e
y
o
n
h
u
ma
n
o
i
d
r
o
b
o
t
d
e
v
e
l
o
p
m
e
n
t
,
”
T
h
e
K
n
o
w
l
e
d
g
e
En
g
i
n
e
e
ri
n
g
R
e
v
i
e
w
,
v
o
l
.
3
4
,
2
0
1
9
,
d
o
i
:
1
0
.
1
0
1
7
/
S
0
2
6
9
8
8
8
9
1
9
0
0
0
1
5
8
.
[
2
]
Y
.
T
o
n
g
,
H
.
Li
u
,
a
n
d
Z.
Z
h
a
n
g
,
“
A
d
v
a
n
c
e
me
n
t
s
i
n
h
u
m
a
n
o
i
d
r
o
b
o
t
s
:
a
c
o
mp
r
e
h
e
n
si
v
e
r
e
v
i
e
w
a
n
d
f
u
t
u
r
e
p
r
o
sp
e
c
t
s
,
”
I
EEE
/
C
A
A
J
o
u
rn
a
l
o
f
Au
t
o
m
a
t
i
c
a
S
i
n
i
c
a
,
v
o
l
.
1
1
,
n
o
.
2
,
p
p
.
3
0
1
–
3
2
8
,
F
e
b
.
2
0
2
4
,
d
o
i
:
1
0
.
1
1
0
9
/
JA
S
.
2
0
2
3
.
1
2
4
1
4
0
.
[
3
]
S
.
A
.
O
k
e
,
“
A
l
i
t
e
r
a
t
u
r
e
r
e
v
i
e
w
o
n
a
r
t
i
f
i
c
i
a
l
i
n
t
e
l
l
i
g
e
n
c
e
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
I
n
f
o
rm
a
t
i
o
n
a
n
d
M
a
n
a
g
e
m
e
n
t
S
c
i
e
n
c
e
s
,
v
o
l
.
1
9
,
n
o
.
4
,
p
p
.
5
3
5
–
5
7
0
,
2
0
0
8
.
[
4
]
A
.
Jo
s
e
p
h
,
B
.
C
h
r
i
st
i
a
n
,
A
.
A
.
A
b
i
o
d
u
n
,
a
n
d
F
.
O
y
a
w
a
l
e
,
“
A
r
e
v
i
e
w
o
n
h
u
ma
n
o
i
d
r
o
b
o
t
i
c
s
i
n
h
e
a
l
t
h
c
a
r
e
,
”
MA
T
EC
We
b
o
f
C
o
n
f
e
re
n
c
e
s
,
v
o
l
.
1
5
3
,
p
.
2
0
0
4
,
2
0
1
8
,
d
o
i
:
1
0
.
1
0
5
1
/
mat
e
c
c
o
n
f
/
2
0
1
8
1
5
3
0
2
0
0
4
.
[
5
]
J
.
F
o
x
a
n
d
A
.
G
a
m
b
i
n
o
,
“
R
e
l
a
t
i
o
n
s
h
i
p
d
e
v
e
l
o
p
m
e
n
t
w
i
t
h
h
u
m
a
n
o
i
d
s
o
c
i
a
l
r
o
b
o
t
s
:
A
p
p
l
y
i
n
g
i
n
t
e
r
p
e
r
s
o
n
a
l
t
h
e
o
r
i
e
s
t
o
h
u
m
a
n
-
r
o
b
o
t
i
n
t
e
r
a
c
t
i
o
n
,
”
C
y
b
e
r
p
s
y
c
h
o
l
o
g
y
,
B
e
h
a
v
i
o
r
,
a
n
d
S
o
c
i
a
l
N
e
t
w
o
r
k
i
n
g
,
v
o
l
.
2
4
,
n
o
.
5
,
p
p
.
2
9
4
–
2
9
9
,
2
0
2
1
,
d
o
i
:
1
0
.
1
0
8
9
/
c
y
b
e
r
.
2
0
2
0
.
0
1
8
1
.
[
6
]
K
.
Y
.
F
o
k
,
N
.
G
a
n
g
a
n
a
t
h
,
C
.
T
.
C
h
e
n
g
,
a
n
d
C
.
K
.
Tse
,
“
A
r
e
a
l
-
t
i
me
A
S
L
r
e
c
o
g
n
i
t
i
o
n
s
y
s
t
e
m
u
si
n
g
l
e
a
p
m
o
t
i
o
n
se
n
s
o
r
s,”
i
n
Pro
c
e
e
d
i
n
g
s
-
2
0
1
5
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
C
y
b
e
r
-
En
a
b
l
e
d
D
i
st
r
i
b
u
t
e
d
C
o
m
p
u
t
i
n
g
a
n
d
K
n
o
w
l
e
d
g
e
D
i
sc
o
v
e
ry,
C
y
b
e
r
C
2
0
1
5
,
S
e
p
.
2
0
1
5
,
p
p
.
4
1
1
–
4
1
4
,
d
o
i
:
1
0
.
1
1
0
9
/
C
y
b
e
r
C
.
2
0
1
5
.
8
1
.
[
7
]
V
.
N
a
st
e
s
k
i
,
“
A
n
o
v
e
r
v
i
e
w
o
f
t
h
e
su
p
e
r
v
i
se
d
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
me
t
h
o
d
s,”
H
o
r
i
zo
n
s.
B
,
v
o
l
.
4
,
p
p
.
5
1
–
6
2
,
D
e
c
.
2
0
1
7
,
d
o
i
:
1
0
.
2
0
5
4
4
/
h
o
r
i
z
o
n
s.
b
.
0
4
.
1
.
1
7
.
p
0
5
.
[
8
]
C
.
C
.
H
si
e
h
,
D
.
H
.
L
i
o
u
,
a
n
d
D
.
L
e
e
,
“
A
r
e
a
l
t
i
me
h
a
n
d
g
e
st
u
r
e
r
e
c
o
g
n
i
t
i
o
n
sy
s
t
e
m
u
si
n
g
m
o
t
i
o
n
h
i
st
o
r
y
i
ma
g
e
,
”
i
n
I
C
S
P
S
2
0
1
0
-
Pro
c
e
e
d
i
n
g
s
o
f
t
h
e
2
0
1
0
2
n
d
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
S
i
g
n
a
l
Pr
o
c
e
ss
i
n
g
S
y
st
e
m
s
,
J
u
l
.
2
0
1
0
,
v
o
l
.
2
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
S
P
S
.
2
0
1
0
.
5
5
5
5
4
6
2
.
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