I
nte
rna
t
io
na
l J
o
urna
l o
f
E
lect
rica
l a
nd
Co
m
pu
t
er
E
ng
ineering
(
I
J
E
CE
)
Vo
l.
15
,
No
.
4
,
A
u
g
u
s
t
20
25
,
p
p
.
3
8
0
3
~
3
8
1
2
I
SS
N:
2088
-
8
7
0
8
,
DOI
: 1
0
.
1
1
5
9
1
/ijece.
v
15
i
4
.
pp
3
8
0
3
-
3
8
1
2
3803
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ec
e.
ia
esco
r
e.
co
m
Dete
c
ting sens
o
r
f
a
ults in wi
reless s
enso
r net
wo
rks
f
o
r prec
isio
n
a
g
riculture
using
lo
ng
sho
rt
-
term
memo
ry
Ya
s
s
ine A
it
a
m
a
r
1
,
J
a
m
a
l El
Abba
di
1,
2
1
S
m
a
r
t
C
o
m
m
u
n
i
c
a
t
i
o
n
s
R
e
s
e
a
r
c
h
T
e
a
m
(
E
R
S
C
)
,
M
o
h
a
m
m
a
d
i
a
S
c
h
o
o
l
o
f
E
n
g
i
n
e
e
r
s
,
M
o
h
a
m
m
e
d
V
U
n
i
v
e
r
s
i
t
y
i
n
R
a
b
a
t
,
R
a
b
a
t
,
M
o
r
o
c
c
o
2
El
e
c
t
r
i
c
a
l
D
e
p
a
r
t
me
n
t
,
M
o
h
a
mm
a
d
i
a
S
c
h
o
o
l
o
f
En
g
i
n
e
e
r
s,
M
o
h
a
mm
e
d
V
U
n
i
v
e
r
si
t
y
i
n
R
a
b
a
t
,
R
a
b
a
t
,
M
o
r
o
c
c
o
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Sep
1
,
2
0
2
4
R
ev
is
ed
Ap
r
1
5
,
2
0
2
5
Acc
ep
ted
Ma
y
2
4
,
2
0
2
5
Th
e
re
li
a
b
le
a
c
q
u
isit
i
o
n
o
f
s
o
il
d
a
ta
fro
m
wire
les
s
se
n
so
r
n
e
two
r
k
s
(W
S
Ns
)
d
e
p
lo
y
e
d
in
fa
rm
lan
d
s
is
c
rit
ica
l
fo
r
o
p
ti
m
izi
n
g
p
re
c
isio
n
a
g
ricu
l
tu
re
(P
A)
p
ra
c
ti
c
e
s.
Ho
we
v
e
r,
se
n
so
r
fa
u
l
ts
c
a
n
sig
n
ifi
c
a
n
t
ly
d
e
g
ra
d
e
d
a
t
a
q
u
a
li
ty
,
h
in
d
e
rin
g
P
A
tec
h
n
i
q
u
e
s.
O
u
r
wo
rk
p
ro
p
o
se
s
a
n
o
v
e
l
lo
n
g
sh
o
rt
-
term
m
e
m
o
ry
(LS
T
M
)
n
e
two
r
k
-
b
a
se
d
m
e
th
o
d
fo
r
fa
u
l
t
d
e
tec
ti
o
n
i
n
WS
Ns
fo
r
P
A
a
p
p
li
c
a
ti
o
n
s.
U
n
li
k
e
trad
it
i
o
n
a
l
m
e
th
o
d
s,
o
u
r
a
p
p
ro
a
c
h
u
ti
li
z
e
s
a
li
g
h
twe
ig
h
t,
tran
sfe
r
lea
rn
in
g
-
b
a
se
d
LS
TM
a
r
c
h
it
e
c
tu
re
sp
e
c
ifi
c
a
ll
y
d
e
sig
n
e
d
t
o
a
d
d
re
ss
th
e
c
h
a
ll
e
n
g
e
o
f
li
m
it
e
d
lab
e
le
d
trai
n
in
g
d
a
ta
a
v
a
il
a
b
il
it
y
in
a
g
ricu
lt
u
ra
l
se
tt
in
g
s.
Th
e
m
o
d
e
l
e
ffe
c
ti
v
e
ly
c
a
p
tu
re
s
tem
p
o
ra
l
d
e
p
e
n
d
e
n
c
i
e
s
with
in
se
n
so
r
d
a
ta
se
q
u
e
n
c
e
s,
e
n
a
b
li
n
g
a
c
c
u
ra
te
p
re
d
ictio
n
s
o
f
n
o
r
m
a
l
se
n
so
r
b
e
h
a
v
i
o
r
a
n
d
i
d
e
n
ti
f
ica
ti
o
n
o
f
a
n
o
m
a
li
e
s
in
d
ica
ti
v
e
o
f
fa
u
lt
s.
E
x
p
e
rime
n
tal
v
a
li
d
a
ti
o
n
c
o
n
firms
th
e
e
ffe
c
ti
v
e
n
e
ss
o
f
o
u
r
m
e
th
o
d
in
d
i
v
e
rse
r
e
a
l
-
wo
rld
WS
N
d
e
p
l
o
y
m
e
n
ts,
e
n
s
u
rin
g
d
a
ta
in
teg
rit
y
a
n
d
e
n
h
a
n
c
i
n
g
n
e
two
rk
re
li
a
b
il
it
y
.
T
h
is
stu
d
y
p
a
v
e
s
th
e
wa
y
fo
r
im
p
ro
v
e
d
d
e
c
isi
o
n
-
m
a
k
in
g
a
n
d
o
p
ti
m
ize
d
P
A p
ra
c
ti
c
e
s.
K
ey
w
o
r
d
s
:
Fau
lt d
etec
tio
n
L
o
n
g
s
h
o
r
t
-
ter
m
m
e
m
o
r
y
Ne
u
r
al
n
etwo
r
k
Pre
cisi
o
n
ag
r
icu
ltu
r
e
W
ir
eles
s
s
en
s
o
r
n
etwo
r
k
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
:
Yass
in
e
Aitam
ar
Sm
ar
t Co
m
m
u
n
icatio
n
s
R
esear
ch
T
ea
m
(
E
R
SC
)
,
Mo
h
am
m
a
d
ia
Sch
o
o
l o
f
E
n
g
in
ee
r
s
,
Mo
h
am
m
ed
V
Un
iv
er
s
ity
in
R
ab
at
Av
.
I
b
n
Sin
a,
Ag
d
al,
R
ab
at
1
0
0
0
0
,
M
o
r
o
cc
o
E
m
ail: y
ass
in
ea
itam
ar
@
r
esear
ch
.
em
i.a
c.
m
a
1.
I
NT
RO
D
UCT
I
O
N
Pre
cisi
o
n
ag
r
icu
ltu
r
e,
a
d
ata
-
d
r
iv
en
f
ar
m
in
g
a
p
p
r
o
ac
h
,
r
ev
o
l
u
tio
n
izes
cr
o
p
p
r
o
d
u
ctio
n
b
y
o
p
tim
izin
g
r
eso
u
r
ce
u
tili
za
tio
n
an
d
m
ax
i
m
izin
g
y
ield
s
.
T
h
is
p
a
r
ad
ig
m
s
h
if
t
n
o
t
o
n
l
y
b
o
o
s
ts
ag
r
icu
l
tu
r
al
ef
f
icien
cy
b
u
t
also
m
itig
ates
th
e
e
s
ca
latin
g
g
lo
b
al
f
o
o
d
cr
is
is
.
B
y
m
eticu
l
o
u
s
ly
m
o
n
ito
r
in
g
en
v
ir
o
n
m
en
tal
f
ac
to
r
s
an
d
cr
o
p
co
n
d
itio
n
s
,
p
r
ec
is
io
n
ag
r
ic
u
ltu
r
e
em
p
o
wer
s
f
ar
m
er
s
to
m
ak
e
in
f
o
r
m
ed
d
ec
is
io
n
s
[
1
]
,
[
2
]
,
u
ltima
tely
r
ed
u
cin
g
p
r
o
d
u
ctio
n
co
s
ts
an
d
m
in
im
izi
n
g
en
v
ir
o
n
m
e
n
tal
im
p
ac
t
[
3
]
.
C
en
tr
al
to
th
is
tr
an
s
f
o
r
m
atio
n
is
th
e
in
teg
r
atio
n
o
f
wir
e
less
s
en
s
o
r
n
etwo
r
k
s
(
W
S
Ns)
[
4
]
,
wh
ich
p
r
o
v
id
e
r
ea
l
-
ti
m
e
d
ata
o
n
c
r
u
cial
p
a
r
am
eter
s
lik
e
s
o
il
m
o
is
tu
r
e,
tem
p
er
atu
r
e,
a
n
d
ac
id
ity
.
W
SN
h
as
em
er
g
ed
as
a
c
r
i
tical
tech
n
o
lo
g
y
i
n
m
o
d
er
n
ag
r
ic
u
ltu
r
e,
e
n
ab
lin
g
p
r
ec
is
io
n
ag
r
i
cu
ltu
r
e
b
y
p
r
o
v
id
i
n
g
r
ea
l
-
tim
e
d
ata
o
n
en
v
ir
o
n
m
en
tal
co
n
d
itio
n
s
[
3
]
.
Ho
wev
er
,
th
e
d
ep
lo
y
m
en
t
o
f
W
SNs
in
h
ar
s
h
ag
r
icu
ltu
r
al
e
n
v
ir
o
n
m
en
ts
o
f
ten
lead
s
to
s
en
s
o
r
f
ailu
r
es,
co
m
p
r
o
m
is
in
g
d
ata
in
teg
r
ity
an
d
im
p
ac
tin
g
th
e
ac
cu
r
ac
y
o
f
a
g
r
icu
ltu
r
al
d
ec
is
io
n
s
[
5
]
,
[
6
]
.
E
x
is
tin
g
f
au
lt
d
etec
tio
n
tech
n
iq
u
es
f
o
r
W
SNs
h
av
e
lim
itatio
n
s
,
p
ar
ticu
lar
ly
in
h
an
d
lin
g
co
m
p
lex
d
ata
p
atter
n
s
an
d
d
y
n
am
i
c
en
v
ir
o
n
m
e
n
tal
co
n
d
itio
n
s
.
T
r
ad
itio
n
al
m
eth
o
d
s
o
f
ten
r
ely
o
n
s
tatis
tical
an
aly
s
is
o
r
r
u
le
-
b
ased
ap
p
r
o
ac
h
e
s
,
wh
ich
m
ay
n
o
t
b
e
s
u
f
f
ici
en
t
to
d
etec
t
s
u
b
tle
an
o
m
alies
[
7
]
.
T
o
a
d
d
r
ess
th
ese
ch
allen
g
es,
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es,
esp
ec
ially
d
ee
p
lear
n
in
g
,
o
f
f
er
p
r
o
m
is
in
g
s
o
lu
tio
n
s
[
8
]
.
T
h
is
p
ap
er
p
r
o
p
o
s
es
a
n
o
v
el
f
au
lt
d
etec
tio
n
ap
p
r
o
ac
h
u
s
in
g
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
L
STM
)
n
etwo
r
k
s
[
9
]
to
ac
cu
r
ately
id
en
tify
f
a
u
lty
s
en
s
o
r
n
o
d
es
in
W
SNs
,
as
we
ll
as
ai
m
s
to
im
p
r
o
v
e
t
h
e
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
.
4
,
Au
g
u
s
t
20
25
:
3
8
0
3
-
3812
3804
r
eliab
ilit
y
an
d
ac
cu
r
ac
y
o
f
W
SN
-
b
ased
ag
r
icu
ltu
r
al
s
y
s
tem
s
b
y
lev
er
ag
i
n
g
th
e
a
b
ilit
y
o
f
L
STM
s
to
ca
p
tu
r
e
tem
p
o
r
al
d
e
p
en
d
e
n
cies in
s
en
s
o
r
d
ata.
T
h
e
r
est
o
f
th
is
wo
r
k
is
o
r
g
an
ized
as
f
o
llo
ws:
Sectio
n
2
p
r
o
v
id
es
a
cr
itical
an
aly
s
is
o
f
ex
i
s
tin
g
f
au
lt
d
etec
tio
n
ap
p
r
o
ac
h
es
in
W
SN
s
,
h
ig
h
lig
h
tin
g
th
eir
s
tr
en
g
t
h
s
,
wea
k
n
ess
es,
an
d
ap
p
licab
ilit
y
to
th
e
p
r
o
p
o
s
e
d
m
eth
o
d
o
l
o
g
y
.
Sectio
n
3
d
etails
th
e
p
r
o
p
o
s
ed
f
a
u
lt
d
etec
tio
n
ap
p
r
o
ac
h
,
in
clu
d
in
g
th
e
tec
h
n
iq
u
es,
alg
o
r
ith
m
s
,
an
d
s
y
s
tem
ar
ch
itectu
r
e
em
p
l
o
y
ed
.
Sectio
n
4
f
o
cu
s
es
o
n
th
e
p
er
f
o
r
m
an
ce
an
d
ef
f
ec
tiv
en
ess
o
f
th
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
.
I
t
is
ev
alu
ated
an
d
d
is
cu
s
s
ed
in
d
etail,
s
u
p
p
o
r
te
d
b
y
r
esu
lts
an
d
c
o
m
p
a
r
ativ
e
a
n
aly
s
is
.
I
n
th
e
last
s
ec
tio
n
,
th
e
p
ap
er
is
s
u
m
m
ar
i
ze
d
,
k
ey
f
in
d
in
g
s
ar
e
r
eiter
at
ed
,
an
d
p
o
ten
tial
av
e
n
u
es
f
o
r
f
u
tu
r
e
r
esear
ch
ar
e
ex
p
lo
r
ed
.
2.
B
RI
E
F
re
v
iew
I
n
th
is
s
ec
tio
n
,
we
r
ev
iew
v
ar
i
o
u
s
ap
p
r
o
ac
h
es
to
h
an
d
lin
g
f
a
u
lt
m
ea
s
u
r
em
en
t
in
W
SNs
,
h
ig
h
lig
h
tin
g
th
eir
ad
v
an
tag
es
an
d
d
is
ad
v
a
n
tag
es.
A
d
is
tr
ib
u
ted
f
au
lt
d
e
tectio
n
ap
p
r
o
ac
h
en
ab
les
s
en
s
o
r
n
o
d
es
with
in
a
n
etwo
r
k
t
o
m
a
k
e
lo
ca
l
d
ec
i
s
io
n
s
au
to
n
o
m
o
u
s
ly
,
s
h
ar
in
g
in
f
o
r
m
atio
n
to
c
o
llectiv
ely
m
an
ag
e
f
au
lts
an
d
en
h
an
ce
n
etwo
r
k
r
esil
ien
ce
,
s
ca
lab
ilit
y
,
en
er
g
y
ef
f
icien
cy
,
an
d
ad
a
p
tab
ilit
y
.
Fo
r
in
s
tan
ce
,
a
f
au
lt
d
etec
tio
n
m
ec
h
an
is
m
b
ased
o
n
s
u
p
p
o
r
t
v
ec
to
r
r
eg
r
ess
io
n
(
SVR
)
an
d
n
eig
h
b
o
r
co
o
r
d
in
atio
n
[
1
0
]
lev
er
ag
es
r
e
d
u
n
d
an
t
m
eteo
r
o
lo
g
ical
d
ata
to
p
r
ed
ict
s
en
s
o
r
b
eh
av
io
r
an
d
g
e
n
er
ate
r
esid
u
al
s
eq
u
en
ce
s
f
o
r
f
au
lt
id
en
tific
atio
n
.
T
h
is
m
eth
o
d
e
n
h
an
ce
s
f
au
lt
d
etec
t
io
n
ac
cu
r
ac
y
a
n
d
r
e
d
u
ce
s
f
al
s
e
alar
m
s
,
esp
ec
ially
in
s
p
ar
s
e
W
SNs
with
h
ig
h
f
ailu
r
e
r
ates.
Ho
wev
er
,
it
in
tr
o
d
u
ce
s
co
m
p
u
tatio
n
al
co
m
p
le
x
ity
an
d
p
o
ten
tial
s
ca
lab
ilit
y
is
s
u
es
in
lar
g
e
-
s
ca
le
n
etwo
r
k
s
.
Similar
ly
,
Yu
an
e
t
a
l.
[
1
1
]
p
r
esen
t
a
d
is
tr
ib
u
ted
B
ay
esian
alg
o
r
ith
m
(
DB
A)
th
at
in
teg
r
ates
B
ay
esian
n
etwo
r
k
s
an
d
b
o
r
d
e
r
n
o
d
e
a
d
ju
s
tm
en
ts
to
im
p
r
o
v
e
ac
cu
r
ac
y
in
d
en
s
e
n
etwo
r
k
s
with
h
ig
h
f
au
lt
r
ates.
T
h
is
ap
p
r
o
ac
h
d
em
o
n
s
tr
ates
s
u
p
er
io
r
p
er
f
o
r
m
an
ce
c
o
m
p
ar
e
d
to
tr
ad
itio
n
al
d
is
tr
ib
u
te
d
f
au
l
t
d
etec
tio
n
m
eth
o
d
s
b
u
t m
ay
f
ac
e
ch
allen
g
es in
lar
g
e
-
s
ca
le
im
p
lem
en
tatio
n
s
.
T
r
en
d
c
o
r
r
elatio
n
an
d
s
elf
-
s
tar
tin
g
m
ec
h
a
n
is
m
s
o
f
f
er
an
o
th
er
ap
p
r
o
ac
h
.
I
n
[
1
2
]
,
a
s
tr
ateg
y
is
p
r
o
p
o
s
ed
th
at
an
aly
ze
s
d
ata
tr
en
d
s
ag
ain
s
t
n
eig
h
b
o
r
h
o
o
d
m
ed
ian
v
al
u
es
to
e
f
f
ec
tiv
ely
id
en
tify
f
a
u
lty
s
en
s
o
r
n
o
d
es.
T
h
is
m
eth
o
d
s
h
o
ws
p
r
o
m
is
in
g
r
esu
lts
in
d
etec
ti
o
n
ac
cu
r
ac
y
an
d
f
alse
alar
m
r
ates,
with
lo
w
co
m
p
u
tatio
n
al
c
o
m
p
lex
ity
a
n
d
r
ed
u
ce
d
co
m
m
u
n
icatio
n
o
v
er
h
ea
d
.
Ho
we
v
er
,
its
p
er
f
o
r
m
an
ce
u
n
d
e
r
d
y
n
am
ic
n
etwo
r
k
co
n
d
itio
n
s
an
d
c
o
m
p
l
ex
f
a
u
lt
p
atter
n
s
r
eq
u
ir
es
f
u
r
th
er
in
v
esti
g
atio
n
.
Su
p
p
o
r
t
v
ec
t
o
r
m
ac
h
in
e
(
SVM)
b
ased
class
if
icatio
n
tech
n
iq
u
e
s
[
1
3
]
u
tili
ze
k
e
r
n
el
f
u
n
ctio
n
s
to
h
an
d
le
c
o
m
p
lex
,
n
o
n
lin
e
ar
d
ata,
ac
h
ie
v
in
g
h
ig
h
d
etec
tio
n
r
ates.
Valid
atio
n
u
s
in
g
r
ea
l
-
wo
r
l
d
d
atasets
h
as
s
h
o
wn
s
u
p
er
io
r
p
er
f
o
r
m
an
ce
co
m
p
ar
e
d
to
ex
is
tin
g
m
eth
o
d
s
,
with
p
o
ten
ti
al
ex
ten
s
io
n
s
to
war
d
p
r
ed
ictiv
e
f
au
lt d
etec
tio
n
.
C
o
m
p
ar
ativ
e
s
tu
d
ies
p
r
o
v
id
e
ad
d
itio
n
al
in
s
ig
h
ts
.
I
n
[
1
4
]
,
f
a
u
lt
d
etec
tio
n
tech
n
iq
u
es
s
u
ch
as
co
n
v
ex
h
u
ll,
n
aï
v
e
B
ay
es,
a
n
d
c
o
n
v
o
lu
tio
n
al
n
e
u
r
al
n
etwo
r
k
s
(
C
NNs)
wer
e
ev
alu
ated
.
C
NNs
d
e
m
o
n
s
tr
ated
s
u
p
e
r
io
r
p
er
f
o
r
m
an
ce
in
f
au
lt
d
etec
tio
n
,
b
u
t
f
u
r
th
er
r
esear
ch
is
n
ee
d
ed
to
o
p
tim
ize
c
h
ar
g
in
g
s
tr
a
teg
ies
f
o
r
m
u
ltip
le
m
o
b
ile
ch
ar
g
in
g
u
n
its
u
s
in
g
r
ein
f
o
r
ce
m
en
t
lear
n
in
g
tec
h
n
iq
u
es.
Ne
u
r
al
n
etwo
r
k
-
b
ased
ap
p
r
o
ac
h
es
[
1
5
]
in
teg
r
ate
g
r
a
d
ien
t
d
escen
t
an
d
ev
o
lu
tio
n
ar
y
al
g
o
r
ith
m
s
to
d
etec
t,
d
iag
n
o
s
e,
an
d
is
o
late
f
au
lty
n
o
d
es.
T
h
ese
m
eth
o
d
s
,
wh
ile
s
h
o
win
g
im
p
r
o
v
ed
p
er
f
o
r
m
a
n
ce
m
et
r
ics
,
f
ac
e
ch
allen
g
es
i
n
co
m
p
u
tatio
n
al
co
m
p
lex
ity
a
n
d
o
p
tim
izatio
n
f
o
r
r
eso
u
r
ce
-
co
n
s
tr
ain
ed
W
SN e
n
v
ir
o
n
m
e
n
ts
.
Un
s
u
p
er
v
is
ed
m
ac
h
in
e
lear
n
i
n
g
tech
n
i
q
u
es
h
av
e
also
b
ee
n
ex
p
lo
r
e
d
.
A
n
etwo
r
k
a
n
o
m
al
y
d
etec
tio
n
s
y
s
tem
(
NADS)
[
1
6
]
em
p
l
o
y
s
a
d
ata
-
d
r
i
v
en
d
is
tan
ce
m
etr
ic
an
d
L
ap
lacia
n
E
ig
en
m
a
p
to
m
ap
n
etwo
r
k
co
n
n
ec
tio
n
s
in
t
o
a
f
ea
tu
r
e
s
p
a
ce
wh
er
e
a
n
o
m
a
lies
ar
e
m
o
r
e
d
is
tin
g
u
is
h
ab
le.
T
h
is
ap
p
r
o
ac
h
im
p
r
o
v
es
ac
cu
r
ac
y
in
d
etec
tin
g
n
o
r
m
al
an
d
f
alse
p
o
s
itiv
e
co
n
n
ec
tio
n
s
wh
ile
m
ain
tain
in
g
c
o
m
p
ar
a
b
le
attac
k
an
d
f
alse
n
eg
ati
v
e
r
ates,
th
o
u
g
h
it
in
tr
o
d
u
ce
s
co
m
p
u
tatio
n
al
co
m
p
lex
ity
an
d
ch
allen
g
es
in
h
an
d
lin
g
d
y
n
am
ic
n
etwo
r
k
en
v
ir
o
n
m
en
ts
.
Ad
d
itio
n
ally
,
f
au
lt
d
etec
tio
n
an
d
is
o
latio
n
(
FDI
)
m
eth
o
d
s
f
o
r
s
u
r
v
eillan
c
e
s
en
s
o
r
n
etwo
r
k
s
,
s
u
ch
as
in
[
1
7
]
em
p
h
asize
th
e
im
p
o
r
tan
ce
o
f
n
etwo
r
k
r
e
d
u
n
d
an
cy
f
o
r
ef
f
ec
tiv
e
FDI
an
d
i
n
tr
u
s
io
n
d
etec
tio
n
.
Ho
wev
er
,
th
ese
m
eth
o
d
s
r
ev
ea
l lim
itatio
n
s
in
h
an
d
lin
g
h
ig
h
f
au
lt r
ates a
n
d
s
im
u
ltan
eo
u
s
in
t
r
u
s
io
n
s
.
L
astl
y
,
o
th
er
n
o
tab
le
m
eth
o
d
s
in
clu
d
e
a
m
etr
ic
-
co
r
r
elatio
n
-
b
ased
d
is
tr
ib
u
ted
f
au
lt
d
etec
tio
n
(
MCDFD)
ap
p
r
o
ac
h
[
1
8
]
,
wh
ich
an
aly
ze
s
co
r
r
elatio
n
s
b
et
wee
n
s
en
s
o
r
n
o
d
e
s
y
s
tem
m
etr
ics
an
d
em
p
lo
y
s
a
m
o
d
if
ied
C
USUM
alg
o
r
ith
m
to
d
etec
t
p
o
ten
tial
f
ailu
r
es.
T
h
is
m
eth
o
d
'
s
s
tr
en
g
th
s
in
clu
d
e
lo
w
co
m
m
u
n
icatio
n
o
v
er
h
ea
d
an
d
r
o
b
u
s
t
p
er
f
o
r
m
a
n
ce
u
n
d
er
ch
allen
g
in
g
co
n
d
itio
n
s
,
b
u
t
its
r
elian
ce
o
n
s
y
s
tem
m
etr
ic
co
r
r
elatio
n
s
m
ay
lim
it
ap
p
licab
ilit
y
in
co
m
p
lex
f
au
lt
p
atter
n
s
.
Similar
ly
,
a
f
au
lt
d
etec
tio
n
ap
p
r
o
ac
h
u
til
izin
g
non
-
n
eg
ati
v
e
m
atr
ix
f
ac
to
r
izatio
n
(
NM
F)
f
o
r
f
ea
tu
r
e
ex
tr
ac
tio
n
[
1
9
]
de
m
o
n
s
tr
ates
p
r
o
m
is
in
g
r
esu
lts
in
d
etec
tin
g
an
o
m
alies
in
s
o
il m
o
is
tu
r
e
s
en
s
o
r
r
ea
d
i
n
g
s
,
h
ig
h
lig
h
tin
g
NM
F'
s
p
o
ten
tia
l f
o
r
f
a
u
lt d
etec
tio
n
in
W
SNs
.
I
n
lig
h
t
o
f
th
e
a
b
o
v
e
i
n
v
esti
g
atio
n
s
,
we
p
r
o
p
o
s
e
L
STM
-
b
a
s
ed
f
au
lt
d
etec
tio
n
m
eth
o
d
s
d
u
e
to
th
eir
ab
ilit
y
to
ca
p
t
u
r
e
tem
p
o
r
al
d
e
p
en
d
en
cies
with
in
s
en
s
o
r
d
ata.
T
h
ese
m
eth
o
d
s
p
r
o
v
id
e
ac
cu
r
ate
p
r
ed
ictio
n
s
o
f
n
o
r
m
al
s
en
s
o
r
b
eh
av
io
r
an
d
ef
f
ec
tiv
ely
id
en
tif
y
an
o
m
alies.
T
h
is
ap
p
r
o
ac
h
o
f
f
e
r
s
s
ig
n
if
ican
t
b
en
ef
its
f
o
r
en
er
g
y
c
o
n
s
u
m
p
tio
n
an
d
r
ea
l
-
tim
e
d
ec
is
io
n
-
m
ak
in
g
in
W
SNs
,
e
n
s
u
r
in
g
th
e
in
teg
r
ity
o
f
cr
itical
d
ata
an
d
en
h
an
cin
g
th
e
r
eliab
ilit
y
o
f
p
r
ec
is
io
n
ag
r
icu
ltu
r
e
p
r
ac
tices.
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
Dete
ctin
g
s
en
s
o
r
fa
u
lts
in
w
ir
eless
s
en
s
o
r
n
et
w
o
r
ks fo
r
p
r
ec
is
io
n
a
g
r
icu
ltu
r
e
…
(
Ya
s
s
in
e
A
ita
ma
r
)
3805
3.
SYST
E
M
M
O
D
E
L
L
I
NG
Pre
cisi
o
n
ag
r
icu
ltu
r
e
tec
h
n
o
lo
g
y
in
v
o
lv
es
o
p
tim
izin
g
ag
r
ic
u
ltu
r
al
in
p
u
ts
lik
e
wate
r
,
f
er
ti
lizer
s
,
an
d
p
esti
cid
es
b
ased
o
n
r
ea
l
-
tim
e
d
ata
to
e
n
h
an
ce
cr
o
p
y
ield
an
d
q
u
ality
.
W
SNs
p
lay
a
p
iv
o
ta
l
r
o
le
in
th
is
co
n
tex
t
b
y
p
r
o
v
id
in
g
co
n
tin
u
o
u
s
m
o
n
ito
r
in
g
o
f
en
v
ir
o
n
m
en
tal
c
o
n
d
itio
n
s
s
u
ch
as
s
o
il
m
o
is
tu
r
e
an
d
tem
p
er
at
u
r
e
[
3
]
,
[
2
0
]
.
T
h
is
d
ata
-
d
r
i
v
en
a
p
p
r
o
ac
h
e
n
ab
les
f
ar
m
er
s
to
m
ak
e
in
f
o
r
m
e
d
d
ec
is
io
n
s
r
eg
ar
d
in
g
ir
r
i
g
atio
n
,
f
er
tili
za
tio
n
,
an
d
h
ar
v
esti
n
g
,
t
h
er
eb
y
im
p
r
o
v
in
g
r
eso
u
r
ce
ef
f
icien
cy
a
n
d
r
ed
u
cin
g
waste
[
2
1
]
,
[
2
2
]
.
Ho
wev
er
,
th
e
r
eliab
ilit
y
o
f
th
ese
d
ec
is
io
n
s
is
co
n
tin
g
en
t
u
p
o
n
th
e
a
cc
u
r
ac
y
o
f
s
en
s
o
r
d
ata,
n
ec
es
s
itatin
g
r
o
b
u
s
t
f
au
lt
d
etec
tio
n
m
ec
h
a
n
is
m
s
to
id
en
t
if
y
an
d
m
itig
ate
er
r
o
n
eo
u
s
r
ea
d
in
g
s
.
3
.
1
.
WSN a
rc
hite
ct
ure
T
h
e
p
r
o
p
o
s
ed
W
SN
s
y
s
tem
co
m
p
r
is
es
f
o
u
r
s
en
s
o
r
n
o
d
es
s
tr
ateg
ically
d
ep
lo
y
ed
ac
r
o
s
s
a
f
ar
m
lan
d
ar
ea
.
T
wo
n
o
d
es
ar
e
d
ed
ica
ted
to
m
o
n
ito
r
in
g
s
o
il
tem
p
er
atu
r
e,
wh
ile
th
e
r
em
ain
i
n
g
two
m
ea
s
u
r
e
s
o
il
m
o
is
tu
r
e.
T
h
ese
s
en
s
o
r
n
o
d
es
ar
e
r
esp
o
n
s
ib
le
f
o
r
d
ata
ac
q
u
i
s
itio
n
an
d
in
itial
p
r
o
ce
s
s
in
g
b
ef
o
r
e
t
r
an
s
m
itti
n
g
it
wir
eless
ly
to
a
ce
n
tr
al
s
in
k
n
o
d
e,
Fig
u
r
e
1
s
h
o
ws th
e
m
o
d
el
o
f
W
SN a
r
ch
itectu
r
e.
Fig
u
r
e
1
.
T
h
e
m
o
d
el
o
f
W
SN a
r
ch
itectu
r
e
T
h
e
s
in
k
n
o
d
e
s
er
v
es
as
th
e
d
ata
ag
g
r
e
g
atio
n
p
o
in
t,
c
o
llectin
g
s
en
s
o
r
r
ea
d
i
n
g
s
f
r
o
m
all
n
o
d
es.
I
t
p
er
f
o
r
m
s
d
ata
p
r
e
p
r
o
ce
s
s
in
g
,
in
clu
d
in
g
f
ilter
in
g
,
n
o
r
m
aliza
t
io
n
,
an
d
p
o
te
n
tial
f
ea
tu
r
e
e
x
tr
ac
tio
n
,
to
p
r
ep
ar
e
th
e
d
ata
f
o
r
f
u
r
th
er
a
n
aly
s
is
.
On
ce
p
r
o
ce
s
s
ed
,
th
e
d
ata
i
s
tr
an
s
m
itted
to
a
r
em
o
te
s
er
v
er
a
n
d
f
o
r
war
d
ed
to
th
e
u
s
er
in
ter
f
ac
e
f
o
r
v
is
u
aliza
tio
n
,
d
ec
is
io
n
-
m
ak
i
n
g
,
an
d
p
o
te
n
tial
s
to
r
ag
e
[
2
3
]
.
T
h
is
h
ier
ar
ch
ical
ar
ch
itectu
r
e
allo
ws f
o
r
d
is
tr
ib
u
ted
d
ata
co
ll
ec
tio
n
,
ce
n
tr
alize
d
p
r
o
ce
s
s
in
g
,
an
d
r
em
o
te
ac
ce
s
s
to
th
e
co
lle
cted
in
f
o
r
m
atio
n
.
3
.
2
.
P
r
o
po
s
ed
m
et
ho
d:
f
a
ult
det
ec
t
io
n
ba
s
ed
o
n L
S
T
M
L
STM
n
etwo
r
k
s
as
s
h
o
wn
in
Fig
u
r
e
2
,
a
s
p
ec
ialized
ty
p
e
o
f
R
NN,
ex
ce
ls
at
m
o
d
elin
g
s
eq
u
en
tial
d
ata
b
y
ca
p
tu
r
in
g
in
t
r
icate
tem
p
o
r
al
d
e
p
en
d
e
n
cies.
T
h
is
ca
p
ab
ilit
y
m
ak
es
th
em
h
ig
h
ly
s
u
itab
le
f
o
r
a
wid
e
r
an
g
e
o
f
a
p
p
licatio
n
s
(
e.
g
.
,
tim
e
s
er
ies
an
aly
s
is
)
[
9
]
,
[
2
4
]
.
L
STM
n
etwo
r
k
s
ex
ce
l
in
c
ap
tu
r
in
g
in
tr
icat
e
tem
p
o
r
al
d
ep
en
d
en
cies
with
in
s
eq
u
en
tial
d
ata.
T
h
is
ca
p
ab
ili
ty
is
p
ar
ticu
lar
l
y
a
d
v
an
tag
e
o
u
s
f
o
r
f
au
lt
d
etec
tio
n
in
W
SNs
,
wh
er
e
id
en
tify
in
g
a
n
o
m
alies
o
f
ten
r
eq
u
i
r
es
u
n
d
e
r
s
tan
d
in
g
th
e
d
y
n
a
m
ic
b
eh
a
v
io
r
o
f
s
en
s
o
r
r
ea
d
i
n
g
s
o
v
er
tim
e.
L
STM
-
b
ase
d
ap
p
r
o
ac
h
es
ca
n
ac
cu
r
ately
p
r
ed
i
ct
n
o
r
m
al
s
en
s
o
r
b
eh
av
io
r
a
n
d
f
lag
d
ev
iatio
n
s
in
d
icativ
e
o
f
p
o
ten
tial
f
a
u
lts
b
y
ef
f
ec
tiv
el
y
m
o
d
elin
g
th
ese
t
em
p
o
r
al
p
atter
n
s
.
T
h
is
p
r
o
ac
ti
v
e
ap
p
r
o
ac
h
to
f
a
u
lt
d
etec
tio
n
en
ab
les tim
ely
in
ter
v
en
tio
n
s
,
en
h
an
cin
g
th
e
o
v
er
al
l r
e
liab
ilit
y
an
d
ef
f
icien
cy
o
f
W
SN
s
.
C
o
m
p
ar
ed
to
tr
ad
itio
n
al
f
au
lt
d
etec
tio
n
m
eth
o
d
s
th
at
r
ely
o
n
s
tatic
th
r
esh
o
ld
s
o
r
s
tatis
tic
al
m
o
d
els,
L
STM
n
etwo
r
k
s
o
f
f
er
s
u
p
er
io
r
p
er
f
o
r
m
a
n
ce
in
h
an
d
lin
g
co
m
p
lex
an
d
n
o
n
-
lin
ea
r
s
en
s
o
r
d
ata
p
atter
n
s
.
Fu
r
th
er
m
o
r
e
,
o
u
r
L
STM
-
b
ase
d
m
o
d
el
is
d
esig
n
ed
to
o
p
ti
m
ize
r
eso
u
r
ce
u
tili
za
tio
n
in
r
eso
u
r
ce
-
co
n
s
tr
ain
e
d
W
SN
en
v
ir
o
n
m
en
ts
,
en
s
u
r
i
n
g
ef
f
icien
t
p
r
o
ce
s
s
in
g
with
o
u
t
co
m
p
r
o
m
is
in
g
d
etec
tio
n
ac
cu
r
ac
y
.
T
h
is
m
ak
es
it
p
ar
ticu
lar
ly
well
-
s
u
ited
f
o
r
th
e
u
n
p
r
e
d
ictab
le
an
d
d
y
n
a
m
i
c
en
v
ir
o
n
m
e
n
ts
ty
p
ical
o
f
p
r
ec
is
io
n
ag
r
icu
ltu
r
e,
wh
er
e
m
ain
tain
i
n
g
th
e
i
n
teg
r
ity
o
f
c
r
itical
d
ata
an
d
co
n
s
er
v
in
g
en
e
r
g
y
is
p
ar
a
m
o
u
n
t.
T
o
f
ac
ilit
ate
u
n
d
er
s
tan
d
i
n
g
o
f
th
e
n
o
tatio
n
u
s
ed
th
r
o
u
g
h
o
u
t
th
is
p
ap
e
r
,
T
ab
le
1
p
r
o
v
id
es
a
s
u
m
m
ar
y
o
f
th
e
k
ey
s
y
m
b
o
ls
an
d
th
eir
c
o
r
r
esp
o
n
d
in
g
d
ef
in
i
tio
n
s
.
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
.
4
,
Au
g
u
s
t
20
25
:
3
8
0
3
-
3812
3806
Fig
u
r
e
2
.
B
asic L
STM
ar
ch
itectu
r
e
[
2
5
]
T
ab
le
1
.
No
tatio
n
N
o
t
a
t
i
o
n
D
e
scri
p
t
i
o
n
i
s t
h
e
i
n
p
u
t
a
t
t
i
m
e
s
t
e
p
si
g
m
o
i
d
f
u
n
c
t
i
o
n
w
e
i
g
h
t
f
o
r
t
h
e
f
o
r
g
e
t
g
a
t
e
w
e
i
g
h
t
f
o
r
i
n
p
u
t
g
a
t
e
w
e
i
g
h
t
f
o
r
c
e
l
l
st
a
t
e
w
e
i
g
h
t
f
o
r
o
u
t
p
u
t
g
a
t
e
b
i
a
s fo
r
t
h
e
f
o
r
g
e
t
g
a
t
e
B
i
a
s
f
o
r
i
n
p
u
t
g
a
t
e
B
i
a
s
f
o
r
c
e
l
l
st
a
t
e
b
i
a
s fo
r
o
u
t
p
u
t
g
a
t
e
L
STM
n
etwo
r
k
s
ar
e
d
esig
n
ed
to
ca
p
tu
r
e
lo
n
g
-
ter
m
d
ep
en
d
e
n
cies
in
s
eq
u
en
tial
d
ata
b
y
u
s
in
g
a
s
et
o
f
g
ates
to
co
n
tr
o
l
th
e
f
l
o
w
o
f
in
f
o
r
m
atio
n
,
Fig
u
r
e
2
s
h
o
ws
th
e
b
asic
L
STM
ar
ch
itectu
r
e
[
2
5
]
.
Her
e
ar
e
th
e
k
e
y
co
m
p
o
n
en
ts
an
d
e
q
u
atio
n
s
.
Fo
r
a
g
iv
en
tim
e
,
th
e
co
m
p
o
n
en
t
s
o
f
th
e
L
STM
s
tates c
an
b
e
e
x
p
r
ess
ed
as:
Fo
r
g
et
g
ate
:
=
(
[
ℎ
−
1
,
]
+
)
(
1
)
I
n
p
u
t
g
ate
:
=
(
[
ℎ
−
1
,
]
+
)
(
2
)
C
ell
s
tate
u
p
d
ate
:
=
ʘ
−
1
+
ʘ
̌
(
3
)
wh
er
e
:
̌
=
ℎ
(
[
ℎ
−
1
,
]
+
)
(
4
)
Ou
tp
u
t
g
ate
:
=
(
[
ℎ
−
1
,
]
+
)
(
5
)
Hid
d
en
s
tate
u
p
d
ate
:
ℎ
=
ʘ
ℎ
(
̌
)
(
6
)
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
Dete
ctin
g
s
en
s
o
r
fa
u
lts
in
w
ir
eless
s
en
s
o
r
n
et
w
o
r
ks fo
r
p
r
ec
is
io
n
a
g
r
icu
ltu
r
e
…
(
Ya
s
s
in
e
A
ita
ma
r
)
3807
L
STM
n
etwo
r
k
s
em
p
lo
y
s
ig
m
o
id
ac
tiv
atio
n
f
u
n
ctio
n
s
to
r
eg
u
late
th
e
f
lo
w
o
f
in
f
o
r
m
atio
n
th
r
o
u
g
h
in
p
u
t,
f
o
r
g
et,
an
d
o
u
tp
u
t
g
ates,
p
r
o
d
u
cin
g
v
al
u
es
b
etwe
en
0
an
d
1
.
T
h
ese
g
ates
m
o
d
u
late
th
e
ce
ll
s
tate,
wh
ich
ac
ts
as
th
e
n
etwo
r
k
'
s
m
em
o
r
y
co
m
p
o
n
en
t.
T
h
e
in
tr
icate
i
n
ter
p
lay
o
f
th
ese
elem
en
ts
is
lear
n
ed
th
r
o
u
g
h
a
s
p
ec
ialized
f
o
r
m
o
f
b
ac
k
p
r
o
p
a
g
atio
n
k
n
o
wn
as
b
ac
k
p
r
o
p
ag
a
tio
n
th
r
o
u
g
h
tim
e
(
B
PTT
)
[
2
6
]
.
B
PTT
iter
ativ
ely
ad
ju
s
ts
th
e
n
etwo
r
k
'
s
p
ar
am
eter
s
to
m
in
im
ize
th
e
p
r
e
d
ictio
n
er
r
o
r
,
e
n
ab
lin
g
t
h
e
L
STM
to
ef
f
ec
tiv
ely
ca
p
tu
r
e
an
d
u
tili
ze
tem
p
o
r
al
d
ep
e
n
d
en
cies w
ith
in
th
e
d
ata
[
2
7
]
.
3
.
3
.
L
ST
M
pa
ra
dig
m
Gath
er
tim
e
-
s
er
ies
d
ata
f
r
o
m
s
en
s
o
r
s
,
in
clu
d
in
g
s
o
il
tem
p
er
atu
r
e,
an
d
s
o
il
m
o
is
tu
r
e.
Af
te
r
th
at,
th
e
d
ata
is
n
o
r
m
alize
d
t
o
a
r
an
g
e
s
u
itab
le
f
o
r
n
eu
r
al
n
etwo
r
k
s
.
Fu
r
th
er
m
o
r
e,
cr
ea
te
s
eq
u
en
c
es
o
f
a
f
ix
e
d
len
g
th
f
r
o
m
th
e
n
o
r
m
alize
d
d
ata.
I
n
o
u
r
p
ap
e
r
,
a
s
eq
u
e
n
ce
len
g
th
o
f
1
0
0
m
ea
n
s
u
s
in
g
1
0
0
-
tim
e
s
tep
s
to
p
r
ed
ict
th
e
n
ex
t v
alu
e.
Fo
r
a
s
eq
u
e
n
ce
len
g
th
o
f
N
:
=
[
,
+
1
,
…
…
…
,
+
−
1
]
W
h
er
e
ar
e
th
e
n
o
r
m
alize
d
s
en
s
o
r
r
ea
d
in
g
s
.
T
h
e
n
ex
t
s
tep
is
co
m
p
u
tin
g
t
h
e
co
m
p
o
n
e
n
ts
o
f
s
u
ch
ce
ll
at
ea
ch
tim
e
s
tep
.
Af
ter
th
at,
we
m
o
v
e
to
an
o
m
aly
d
etec
tio
n
.
Fo
r
an
o
m
aly
d
etec
tio
n
,
we
co
m
p
ar
e
th
e
p
r
ed
ictio
n
er
r
o
r
s
with
a
d
y
n
am
ically
ca
lcu
lated
th
r
esh
o
ld
.
T
h
e
s
y
s
tem
f
lag
s
an
y
s
en
s
o
r
r
ea
d
in
g
s
with
er
r
o
r
s
ex
ce
ed
in
g
t
h
is
th
r
esh
o
ld
as a
n
o
m
alies.
T
h
e
p
r
ed
ictio
n
er
r
o
r
is
th
e
ab
s
o
lu
te
d
if
f
er
en
ce
b
etwe
en
th
e
r
ea
l r
ea
d
in
g
an
d
th
e
p
r
e
d
icted
r
ea
d
in
g
.
At
tim
e
s
tep
:
(
)
=
|
(
)
−
(
)
|
(
7
)
T
h
e
d
y
n
am
ic
th
r
esh
o
l
d
is
d
ef
in
ed
b
ased
o
n
th
e
m
ea
n
an
d
s
t
an
d
ar
d
d
ev
iatio
n
:
=
±
⋅
(
8
)
wh
er
e
:
=
1
∑
(
)
=
1
(
9
)
an
d
:
=
√
1
∑
(
(
)
−
)
2
=
1
(
10
)
Fo
r
ea
ch
s
en
s
o
r
r
ea
d
in
g
at
s
tep
tim
e
t
,
if
th
e
p
r
e
d
ictio
n
er
r
o
r
ex
ce
e
d
s
th
e
th
r
esh
o
ld
,
f
la
g
th
e
r
ea
d
in
g
as
an
an
o
m
aly
.
a
n
oma
l
y
(
)
=
{
0
(
−
)
<
(
)
<
(
+
)
1
ℎ
4.
SI
M
UL
A
T
I
O
N
R
E
S
UL
T
S
AND
DIS
CUSS
I
O
N
I
n
th
is
s
ec
tio
n
,
we
p
r
esen
t
a
co
m
p
r
e
h
en
s
iv
e
an
aly
s
is
o
f
th
e
p
r
o
p
o
s
ed
f
au
lt
d
etec
tio
n
s
y
s
tem
's
p
er
f
o
r
m
an
ce
b
ased
o
n
ex
p
e
r
im
en
tal
r
esu
lts
an
d
co
m
p
ar
ati
v
e
ev
alu
ati
o
n
s
.
T
h
e
ef
f
ec
tiv
e
n
ess
o
f
th
e
L
STM
m
o
d
el
in
ac
cu
r
ately
p
r
e
d
ict
in
g
n
o
r
m
al
s
en
s
o
r
b
eh
a
v
io
r
an
d
id
en
tify
in
g
a
n
o
m
alies
is
d
is
cu
s
s
ed
.
Fu
r
th
er
m
o
r
e
,
th
e
im
p
ac
t o
f
v
ar
io
u
s
s
y
s
tem
p
ar
am
eter
s
an
d
p
o
ten
tial lim
itatio
n
s
ar
e
ex
p
l
o
r
ed
.
4
.
1
.
Da
t
a
c
o
llect
io
n a
nd
prepro
ce
s
s
ing
I
n
o
u
r
r
esear
ch
,
a
W
SN
co
m
p
r
is
in
g
f
o
u
r
s
en
s
o
r
n
o
d
es
was
d
ep
lo
y
e
d
ac
r
o
s
s
a
12
×
12
f
ar
m
lan
d
ar
ea
.
T
wo
s
en
s
o
r
n
o
d
es
wer
e
d
ed
icate
d
to
m
o
n
ito
r
in
g
s
o
il
tem
p
er
atu
r
e,
wh
ile
th
e
r
em
ai
n
in
g
two
m
ea
s
u
r
ed
s
o
il m
o
is
tu
r
e
lev
els
Fig
u
r
e
3
.
C
o
llected
d
ata
was tr
an
s
m
itte
d
wir
eless
ly
v
ia
B
lu
eto
o
th
to
a
ce
n
tr
al
s
in
k
n
o
d
e
at
one
-
m
in
u
te
in
ter
v
als
o
v
er
a
s
i
m
u
latio
n
p
er
io
d
o
f
1
5
0
0
m
in
u
tes.
T
h
e
s
im
u
latio
n
s
wer
e
co
n
d
u
cted
to
ev
al
u
ate
th
e
p
r
o
p
o
s
ed
s
y
s
tem
'
s
p
er
f
o
r
m
an
ce
u
s
in
g
MA
T
L
AB
R
2
0
2
0
a
s
o
f
twar
e
.
T
h
e
h
ar
d
war
e
p
latf
o
r
m
em
p
lo
y
ed
co
n
s
is
ted
o
f
an
I
n
tel(
R
)
C
o
r
e
(
T
M)
i7
-
8
5
5
0
U
p
r
o
ce
s
s
o
r
,
a
n
I
n
tel(
R
)
UHD
6
2
0
g
r
ap
h
ics
ca
r
d
,
8
GB
o
f
R
AM
,
an
d
2
5
6
GB
o
f
R
OM
.
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
.
4
,
Au
g
u
s
t
20
25
:
3
8
0
3
-
3812
3808
Fig
u
r
e
3
.
W
SN la
y
o
u
t
T
o
estab
lis
h
a
co
m
p
r
eh
e
n
s
iv
e
d
ataset
f
o
r
f
au
lt
d
etec
tio
n
,
s
en
s
o
r
r
ea
d
i
n
g
s
wer
e
c
o
llected
f
r
o
m
th
e
d
ep
lo
y
e
d
W
SN
at
5
-
m
in
u
te
in
ter
v
als
o
v
er
1
,
5
0
0
m
in
u
tes
.
T
h
e
ac
q
u
ir
ed
d
ata
u
n
d
er
wen
t
a
p
r
ep
r
o
ce
s
s
in
g
p
h
ase
to
en
s
u
r
e
d
ata
q
u
ality
an
d
s
u
i
tab
ilit
y
f
o
r
s
u
b
s
eq
u
e
n
t
an
al
y
s
is
;
T
ab
le
2
s
h
o
ws
th
e
s
im
u
latio
n
p
a
r
am
eter
s
.
A
r
o
b
u
s
t
f
r
am
ew
o
r
k
f
o
r
f
a
u
lt
d
etec
tio
n
in
W
SNs
wa
s
estab
l
i
s
h
ed
,
an
d
th
e
L
STM
m
o
d
el
was
m
eticu
lo
u
s
ly
cr
af
ted
to
p
r
ed
ict
n
o
r
m
al
s
en
s
o
r
b
eh
av
i
o
r
.
Fo
llo
win
g
d
ata
s
et
p
r
ep
r
o
ce
s
s
in
g
an
d
p
a
r
titi
o
n
in
g
in
to
tr
ain
in
g
,
v
alid
atio
n
,
an
d
test
in
g
s
u
b
s
et
s
,
th
e
L
STM
ar
ch
itectu
r
e
(
with
its
r
ec
u
r
r
en
t
d
esig
n
an
d
m
em
o
r
y
ce
lls
)
was
tr
a
in
ed
u
s
in
g
B
PTT
.
T
h
is
iter
ativ
e
p
r
o
ce
s
s
o
p
tim
ized
m
o
d
el
p
ar
am
eter
s
b
y
m
in
im
izin
g
p
r
ed
ictio
n
er
r
o
r
s
.
T
h
e
m
o
d
el
g
e
n
er
ated
p
r
e
d
ictio
n
s
f
o
r
v
alid
atio
n
an
d
test
in
g
d
atasets
b
y
em
p
l
o
y
in
g
s
eq
u
en
ce
le
n
g
th
s
o
f
2
5
an
d
5
0
0
p
o
in
ts
,
a
d
r
o
p
o
u
t
r
ate
o
f
0
.
2
,
an
d
5
0
L
STM
u
n
its
.
T
h
ese
co
n
f
ig
u
r
atio
n
s
wer
e
s
tr
ateg
ically
ch
o
s
en
to
s
er
v
e
as
b
en
ch
m
ar
k
s
,
o
f
f
er
i
n
g
in
s
ig
h
t
s
in
to
th
e
n
u
an
ce
s
o
f
s
h
o
r
t
-
ter
m
d
ep
en
d
en
cies
ca
p
tu
r
ed
b
y
th
e
2
5
-
p
o
in
t
s
eq
u
en
ce
s
an
d
th
e
c
o
m
p
r
e
h
e
n
s
iv
e
u
n
d
er
s
tan
d
in
g
o
f
ex
ten
d
ed
tem
p
o
r
al
p
atter
n
s
en
a
b
le
d
b
y
th
e
5
00
-
p
o
i
n
t
s
eq
u
en
ce
s
.
T
h
e
d
r
o
p
o
u
t
r
ate
o
f
0
.
2
f
o
s
ter
ed
m
o
d
el
g
en
e
r
al
izatio
n
an
d
co
u
n
ter
in
g
o
v
er
f
itti
n
g
,
wh
ile
th
e
5
0
L
STM
u
n
its
f
ac
ilit
ated
ef
f
ec
ti
v
e
lear
n
in
g
f
r
o
m
th
e
s
eq
u
en
ti
al
n
atu
r
e
o
f
s
en
s
o
r
d
ata.
T
h
is
ap
p
r
o
ac
h
aim
s
to
s
ig
n
if
ican
tly
en
h
a
n
ce
f
au
lt
d
etec
t
io
n
in
W
SNs
b
y
r
o
b
u
s
tly
m
o
d
elin
g
s
en
s
o
r
b
eh
a
v
io
r
ac
r
o
s
s
d
iv
er
s
e
tim
escales,
p
av
in
g
th
e
way
f
o
r
f
u
tu
r
e
o
p
tim
izatio
n
s
an
d
ad
v
a
n
ce
m
en
ts
in
s
en
s
o
r
n
etwo
r
k
r
e
liab
ilit
y
.
T
ab
le
2
.
Simu
latio
n
p
ar
am
eter
s
P
a
r
a
me
t
e
r
V
a
l
u
e
s
A
r
e
a
1
2
m
2
N
u
mb
e
r
o
f
n
o
d
e
s
4
se
n
s
o
r
n
o
d
e
s
+
1
si
n
k
n
o
d
e
S
e
n
s
o
r
t
y
p
e
s
2
f
o
r
so
i
l
t
e
mp
e
r
a
t
u
r
e
,
2
f
o
r
so
i
l
mo
i
st
u
r
e
S
i
mu
l
a
t
i
o
n
t
i
me
1
5
0
0
m
i
n
u
t
e
s
D
a
t
a
t
r
a
n
smi
ssi
o
n
i
n
t
e
r
v
a
l
5
m
i
n
u
t
e
s
C
o
mm
u
n
i
c
a
t
i
o
n
B
l
u
e
t
o
o
t
h
B
a
n
d
w
i
d
t
h
1
0
0
K
b
p
s
P
a
c
k
e
t
si
z
e
5
0
b
y
t
e
s
Q
u
e
u
e
s
i
z
e
1
0
0
p
a
c
k
e
t
s
Fig
u
r
e
4
illu
s
tr
atin
g
th
e
co
m
p
ar
ativ
e
an
aly
s
is
b
etwe
en
r
ea
l sen
s
o
r
r
ea
d
in
g
s
an
d
p
r
ed
ictio
n
s
g
en
er
ated
b
y
th
e
L
STM
m
o
d
el
tr
ai
n
ed
o
n
n
o
n
-
f
a
u
lty
d
ata
.
Fig
u
r
e
4
(
a
)
an
d
Fig
u
r
e
4
(
b
)
co
n
tr
ast
th
e
r
ea
l
r
ea
d
in
g
s
a
n
d
m
o
d
el
p
r
ed
ictio
n
s
d
u
r
in
g
n
o
r
m
al
o
p
er
atin
g
co
n
d
i
tio
n
s
o
f
t
h
e
s
o
il
tem
p
er
atu
r
e
s
en
s
o
r
,
wh
ile
Fig
u
r
e
4
(
c)
a
n
d
Fig
u
r
e
4
(
d
)
co
m
p
ar
e
th
e
p
r
ed
icted
an
d
ac
tu
al
r
ea
d
in
g
r
esu
lts
u
n
d
er
n
o
r
m
al
o
p
er
atin
g
c
o
n
d
itio
n
s
o
f
th
e
s
o
il
m
o
is
tu
r
e
s
en
s
o
r
.
T
h
is
v
is
u
al
d
ep
ictio
n
o
f
f
er
s
a
co
m
p
r
eh
en
s
i
v
e
in
s
ig
h
t
in
to
th
e
m
o
d
el'
s
ef
f
icac
y
in
ca
p
tu
r
in
g
in
tr
icate
p
atter
n
s
an
d
d
y
n
am
i
cs
in
h
er
en
t
in
th
e
s
en
s
o
r
d
ata.
T
h
e
alig
n
m
e
n
t
b
etwe
en
p
r
ed
icted
an
d
ac
tu
a
l
s
en
s
o
r
r
ea
d
in
g
s
u
n
d
er
s
co
r
es
t
h
e
m
o
d
el'
s
ca
p
ab
ilit
y
to
s
im
u
l
ate
n
o
r
m
al
s
en
s
o
r
b
eh
av
io
r
ac
cu
r
ately
,
v
alid
atin
g
its
tr
ain
in
g
p
r
o
ce
s
s
an
d
c
o
n
f
i
g
u
r
atio
n
ch
o
ices.
Su
c
h
f
in
d
in
g
s
af
f
ir
m
t
h
e
L
STM
m
o
d
el'
s
p
o
ten
tial
as
a
r
o
b
u
s
t
to
o
l f
o
r
f
au
lt d
etec
tio
n
in
W
SNs
,
p
r
o
m
is
in
g
en
h
an
ce
d
r
eliab
ilit
y
an
d
p
er
f
o
r
m
a
n
ce
in
r
ea
l
-
wo
r
ld
ap
p
licatio
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
Dete
ctin
g
s
en
s
o
r
fa
u
lts
in
w
ir
eless
s
en
s
o
r
n
et
w
o
r
ks fo
r
p
r
ec
is
io
n
a
g
r
icu
ltu
r
e
…
(
Ya
s
s
in
e
A
ita
ma
r
)
3809
(
a)
(
b
)
(
c)
(
d
)
Fig
u
r
e
1
.
R
ea
l a
n
d
p
r
ed
icted
s
en
s
o
r
r
ea
d
in
g
s
of
(
a
)
,
(
b
)
s
o
il
t
em
p
er
atu
r
e
,
an
d
(
c)
,
(
d
)
s
o
il m
o
is
tu
r
e
4
.
1
.
Resul
t
s
dis
cu
s
s
io
n
T
o
ev
alu
ate
th
e
ef
f
ec
tiv
e
n
ess
o
f
th
e
L
STM
m
o
d
el
in
d
etec
t
in
g
s
en
s
o
r
f
au
lts
,
ab
n
o
r
m
al
v
alu
es
wer
e
in
ten
tio
n
ally
in
tr
o
d
u
ce
d
in
to
th
e
d
ataset
at
s
p
ec
if
ic
in
ter
v
als.
T
h
ese
in
ter
v
als
ar
e
as
f
o
llo
w
s
:
s
o
il
tem
p
er
atu
r
e
1
ex
p
er
ien
ce
d
f
au
lts
b
etwe
en
1
5
0
0
m
in
u
tes
an
d
1
8
0
0
m
i
n
u
tes,
wh
ile
s
o
il
m
o
is
tu
r
e
1
en
co
u
n
ter
e
d
f
au
lts
b
etwe
en
1
6
0
0
m
i
n
u
tes
an
d
1
8
0
0
m
in
u
tes.
B
y
an
aly
zin
g
th
e
m
o
d
el'
s
r
esp
o
n
s
e
to
th
ese
in
je
cted
an
o
m
alies,
we
ca
n
ass
ess
it
s
ca
p
ab
ilit
y
to
d
etec
t a
n
d
id
en
tif
y
s
en
s
o
r
f
a
u
lts
ac
cu
r
ately
.
B
y
co
m
p
ar
in
g
th
e
L
ST
M
-
p
r
e
d
icted
v
alu
es
with
th
e
ac
tu
al
s
en
s
o
r
r
ea
d
in
g
s
,
d
ev
iatio
n
s
in
d
icativ
e
o
f
f
au
lts
was
id
en
tifie
d
at
=
1500
.
Fig
u
r
e
5
(
a)
illu
s
tr
ates
a
r
ep
r
esen
tativ
e
ex
am
p
le
o
f
f
au
lt
in
je
ctio
n
,
s
h
o
win
g
th
e
d
i
v
er
g
e
n
ce
b
et
wee
n
p
r
ed
icted
an
d
ac
t
u
al
s
en
s
o
r
d
ata.
T
h
is
f
ig
u
r
e
clea
r
ly
d
em
o
n
s
tr
ates
th
e
m
o
d
el’
s
ab
ilit
y
to
m
ain
tain
ac
cu
r
ate
p
r
ed
ictio
n
s
u
n
d
er
n
o
r
m
al
co
n
d
itio
n
s
wh
ile
ef
f
ec
tiv
ely
h
ig
h
lig
h
tin
g
an
o
m
alies c
au
s
ed
b
y
th
e
in
ject
ed
f
au
lts
.
T
o
ev
al
u
ate
th
e
ef
f
ec
tiv
e
n
ess
o
f
o
u
r
p
r
o
p
o
s
ed
f
au
lt
d
etec
tio
n
a
p
p
r
o
ac
h
,
we
co
n
d
u
cte
d
a
s
er
ies
o
f
ex
p
er
im
en
ts
u
s
in
g
r
e
al
-
wo
r
ld
s
en
s
o
r
d
ata.
Fig
u
r
e
5
(
b
)
illu
s
tr
ates
a
co
m
p
a
r
is
o
n
b
etwe
en
a
h
ea
lth
y
a
n
d
a
f
a
u
lty
s
o
il
tem
p
er
atu
r
e
s
en
s
o
r
.
T
h
e
m
o
d
el
ac
cu
r
ately
p
r
ed
icted
n
o
r
m
al
b
eh
av
i
o
r
f
o
r
th
e
h
e
alth
y
s
en
s
o
r
wh
ile
co
r
r
ec
tly
id
e
n
tify
in
g
an
o
m
al
o
u
s
p
atter
n
s
in
th
e
f
a
u
lty
s
en
s
o
r
'
s
r
ea
d
in
g
s
.
Similar
ly
,
Fig
u
r
e
6
(
a)
an
d
6
(
b
)
d
ep
ict
th
e
m
o
d
el'
s
p
er
f
o
r
m
a
n
ce
in
d
etec
tin
g
f
au
lts
in
a
s
o
i
l
m
o
is
tu
r
e
s
en
s
o
r
.
B
y
in
ten
tio
n
ally
in
tr
o
d
u
cin
g
an
o
m
alies
in
to
th
e
d
at
aset,
we
as
s
ess
ed
th
e
m
o
d
el's
s
en
s
itiv
ity
to
f
a
u
lty
r
ea
d
in
g
s
.
T
h
e
m
o
d
el
s
u
cc
ess
f
u
lly
id
en
tifie
d
th
e
in
jecte
d
f
au
lts
,
d
em
o
n
s
tr
atin
g
its
ab
il
ity
to
d
is
tin
g
u
is
h
b
etwe
en
n
o
r
m
al
an
d
a
n
o
m
alo
u
s
b
eh
av
io
r
.
T
h
e
r
esu
lts
d
em
o
n
s
tr
ate
th
e
ef
f
ec
tiv
en
ess
o
f
o
u
r
p
r
o
p
o
s
ed
L
STM
-
b
ased
f
au
lt
d
etec
tio
n
m
o
d
el
i
n
ac
cu
r
ately
i
d
e
n
tify
in
g
f
au
lty
s
en
s
o
r
n
o
d
es
in
W
SNs
.
B
y
ca
p
tu
r
in
g
tem
p
o
r
al
d
ep
e
n
d
en
cies
with
in
s
en
s
o
r
d
ata
,
th
e
m
o
d
el
ca
n
ef
f
ec
tiv
ely
d
e
tect
s
u
b
tle
an
o
m
alies
th
at
m
ay
b
e
o
v
er
lo
o
k
ed
b
y
tr
ad
itio
n
al
m
eth
o
d
s
.
T
h
e
m
o
d
el'
s
ab
ilit
y
to
ac
c
u
r
ately
class
if
y
s
en
s
o
r
r
ea
d
in
g
s
as
n
o
r
m
al
o
r
f
au
lty
co
n
tr
i
b
u
tes
to
th
e
r
eliab
ilit
y
an
d
in
teg
r
ity
o
f
W
SN
-
b
ased
s
y
s
tem
s
.
W
h
ile
th
e
p
r
o
p
o
s
ed
m
o
d
el
s
h
o
ws
p
r
o
m
is
in
g
r
esu
lts
,
it
is
im
p
o
r
tan
t
to
ac
k
n
o
wled
g
e
its
lim
itatio
n
s
.
T
h
e
m
o
d
el'
s
p
er
f
o
r
m
a
n
ce
m
a
y
b
e
in
f
lu
e
n
ce
d
b
y
f
ac
to
r
s
s
u
ch
as
d
ata
q
u
ality
,
s
en
s
o
r
n
o
is
e,
an
d
t
h
e
co
m
p
lex
ity
o
f
th
e
u
n
d
e
r
ly
in
g
p
h
y
s
ical
p
r
o
ce
s
s
es
.
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
.
4
,
Au
g
u
s
t
20
25
:
3
8
0
3
-
3812
3810
(
a)
(
b
)
Fig
u
r
e
2
.
R
ea
l a
n
d
p
r
ed
icted
s
o
il tem
p
er
atu
r
e
d
ata,
(
a)
s
en
s
o
r
s
o
il tem
p
er
atu
r
e
1
; r
ea
l so
il t
em
p
er
atu
r
e
d
ata
an
d
its
p
r
ed
icted
d
ata
in
f
a
u
lty
s
itu
atio
n
an
d
(
b
)
s
en
s
o
r
s
o
il te
m
p
er
atu
r
e
2
; r
ea
l so
il tem
p
er
at
u
r
e
d
ata
an
d
in
n
o
r
m
al
co
n
d
itio
n
s
(
a)
(
b
)
Fig
u
r
e
3
.
R
ea
l a
n
d
p
r
ed
icted
s
o
il tem
p
er
atu
r
e
d
ata,
(
a)
s
en
s
o
r
s
o
il tem
p
er
atu
r
e
1
; r
ea
l so
il t
em
p
er
atu
r
e
d
ata
an
d
its
p
r
ed
icted
d
ata
in
f
a
u
lty
s
itu
atio
n
an
d
(
b
)
s
en
s
o
r
s
o
il te
m
p
er
atu
r
e
2
; r
ea
l so
il tem
p
er
at
u
r
e
d
ata
in
n
o
r
m
al
co
n
d
itio
n
s
5.
C
O
NCLU
SI
O
N
T
h
is
s
tu
d
y
p
r
esen
ts
a
n
o
v
el
L
STM
n
etwo
r
k
-
b
ased
m
eth
o
d
f
o
r
W
SNs
to
en
h
an
ce
f
a
u
lt
d
etec
tio
n
in
W
SN
s
f
o
r
p
r
ec
is
io
n
ag
r
icu
ltu
r
e
ap
p
licatio
n
s
.
B
y
lev
er
a
g
in
g
th
e
p
o
wer
o
f
d
ee
p
lea
r
n
in
g
,
s
p
ec
if
ically
L
STM
n
etwo
r
k
s
,
o
u
r
m
o
d
el
ef
f
ec
ti
v
ely
ca
p
tu
r
es
tem
p
o
r
al
d
ep
e
n
d
en
cies
with
in
s
en
s
o
r
d
ata,
en
ab
lin
g
ac
cu
r
ate
p
r
ed
ictio
n
o
f
n
o
r
m
al
s
en
s
o
r
b
eh
av
io
r
an
d
p
r
ec
is
e
d
etec
tio
n
o
f
an
o
m
alies.
T
h
is
ap
p
r
o
ac
h
s
u
r
p
ass
es
tr
ad
itio
n
al
f
au
lt
d
etec
tio
n
tech
n
i
q
u
es,
wh
ich
o
f
ten
s
tr
u
g
g
le
with
th
e
co
m
p
lex
ity
an
d
d
y
n
a
m
is
m
o
f
r
e
al
-
wo
r
ld
in
ter
n
et
o
f
th
in
g
s
(
I
o
T
)
en
v
ir
o
n
m
en
ts
.
T
h
e
ab
ilit
y
to
p
r
o
m
p
tly
id
e
n
tif
y
an
d
ad
d
r
ess
f
au
lts
is
cr
u
cial
f
o
r
m
ain
tain
in
g
th
e
in
teg
r
ity
o
f
I
o
T
s
y
s
tem
s
an
d
e
n
s
u
r
in
g
r
eliab
le
d
ata
co
llectio
n
.
Ou
r
f
in
d
in
g
s
d
em
o
n
s
tr
ate
th
e
p
o
ten
tial
o
f
th
is
ap
p
r
o
ac
h
to
im
p
r
o
v
e
t
h
e
ac
cu
r
ac
y
an
d
ef
f
icien
cy
o
f
f
au
lt
d
etec
tio
n
in
W
SNs
.
B
y
ad
d
r
ess
in
g
th
e
lim
itatio
n
s
o
f
ex
is
tin
g
m
eth
o
d
s
,
o
u
r
wo
r
k
o
p
en
s
u
p
n
ew
p
o
s
s
ib
ilit
ies
f
o
r
o
p
tim
izin
g
v
a
r
io
u
s
p
r
ec
is
io
n
ag
r
ic
u
ltu
r
e
ap
p
licatio
n
s
.
Fu
tu
r
e
r
esear
ch
d
ir
e
ctio
n
s
m
ay
in
v
o
lv
e
ex
p
lo
r
in
g
th
e
im
p
ac
t
o
f
v
ar
y
i
n
g
d
ata
q
u
ali
ty
an
d
n
o
is
e
lev
e
ls
o
n
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
p
r
o
p
o
s
ed
m
o
d
el,
as
well
as in
v
esti
g
atin
g
its
ap
p
licab
ilit
y
to
d
if
f
er
e
n
t ty
p
es o
f
s
en
s
o
r
s
an
d
en
v
ir
o
n
m
en
tal
co
n
d
itio
n
s
.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
Au
th
o
r
s
s
tate
n
o
f
u
n
d
in
g
in
v
o
lv
ed
.
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
Dete
ctin
g
s
en
s
o
r
fa
u
lts
in
w
ir
eless
s
en
s
o
r
n
et
w
o
r
ks fo
r
p
r
ec
is
io
n
a
g
r
icu
ltu
r
e
…
(
Ya
s
s
in
e
A
ita
ma
r
)
3811
AUTHO
R
CO
NT
RI
B
UT
I
O
NS ST
A
T
E
M
E
N
T
T
h
is
jo
u
r
n
al
u
s
es
th
e
C
o
n
tr
ib
u
to
r
R
o
les
T
ax
o
n
o
m
y
(
C
R
ed
iT)
to
r
ec
o
g
n
ize
in
d
iv
id
u
al
au
th
o
r
co
n
tr
ib
u
tio
n
s
,
r
ed
u
ce
au
th
o
r
s
h
ip
d
is
p
u
tes,
an
d
f
ac
ilit
ate
co
llab
o
r
atio
n
.
Na
m
e
o
f
Aut
ho
r
C
M
So
Va
Fo
I
R
D
O
E
Vi
Su
P
Fu
Yass
in
e
Aitam
ar
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
J
am
al
E
l A
b
b
ad
i
✓
✓
✓
✓
✓
✓
✓
✓
C
:
C
o
n
c
e
p
t
u
a
l
i
z
a
t
i
o
n
M
:
M
e
t
h
o
d
o
l
o
g
y
So
:
So
f
t
w
a
r
e
Va
:
Va
l
i
d
a
t
i
o
n
Fo
:
Fo
r
mal
a
n
a
l
y
s
i
s
I
:
I
n
v
e
s
t
i
g
a
t
i
o
n
R
:
R
e
so
u
r
c
e
s
D
:
D
a
t
a
C
u
r
a
t
i
o
n
O
:
W
r
i
t
i
n
g
-
O
r
i
g
i
n
a
l
D
r
a
f
t
E
:
W
r
i
t
i
n
g
-
R
e
v
i
e
w
&
E
d
i
t
i
n
g
Vi
:
Vi
su
a
l
i
z
a
t
i
o
n
Su
:
Su
p
e
r
v
i
s
i
o
n
P
:
P
r
o
j
e
c
t
a
d
mi
n
i
st
r
a
t
i
o
n
Fu
:
Fu
n
d
i
n
g
a
c
q
u
i
si
t
i
o
n
CO
NF
L
I
C
T
O
F
I
N
T
E
R
E
S
T
ST
A
T
E
M
E
NT
Au
th
o
r
s
s
tate
n
o
co
n
f
lict o
f
in
t
er
est.
DATA AV
AI
L
AB
I
L
I
T
Y
T
h
e
a
u
t
h
o
r
s
c
o
n
f
i
r
m
t
h
a
t
t
h
e
d
at
a
s
u
p
p
o
r
t
i
n
g
t
h
e
f
i
n
d
i
n
g
s
o
f
t
h
i
s
s
t
u
d
y
a
r
e
a
v
a
i
la
b
l
e
wi
t
h
i
n
t
h
e
a
r
t
i
c
le
.
RE
F
E
R
E
NC
E
S
[
1
]
T.
R
a
j
a
se
k
a
r
a
n
a
n
d
S
.
A
n
a
n
d
a
m
u
r
u
g
a
n
,
C
h
a
l
l
e
n
g
e
s
a
n
d
a
p
p
l
i
c
a
t
i
o
n
s
o
f
w
i
re
l
e
ss
se
n
s
o
r
n
e
t
w
o
rks
i
n
sm
a
rt
f
a
rm
i
n
g
—
A
s
u
rv
e
y
,
v
o
l
.
7
5
0
.
S
p
r
i
n
g
e
r
S
i
n
g
a
p
o
r
e
,
2
0
1
9
.
d
o
i
:
1
0
.
1
0
0
7
/
9
7
8
-
981
-
13
-
1
8
8
2
-
5
_
3
0
.
[
2
]
M
.
F
.
S
a
l
e
e
m
e
t
a
l
.
,
“
A
p
p
l
i
c
a
t
i
o
n
s
o
f
se
n
s
o
r
s
i
n
p
r
e
c
i
si
o
n
a
g
r
i
c
u
l
t
u
r
e
f
o
r
a
s
u
s
t
a
i
n
a
b
l
e
f
u
t
u
r
e
,
”
A
g
r
i
c
u
l
t
u
r
e
a
n
d
Aq
u
a
c
u
l
t
u
r
e
Ap
p
l
i
c
a
t
i
o
n
s
o
f
B
i
o
s
e
n
s
o
rs
a
n
d
B
i
o
e
l
e
c
t
r
o
n
i
c
s
,
n
o
.
Ja
n
u
a
r
y
,
p
p
.
1
0
9
–
1
3
7
,
2
0
2
4
,
d
o
i
:
1
0
.
4
0
1
8
/
9
7
9
-
8
-
3
6
9
3
-
2
0
6
9
-
3
.
c
h
0
0
6
.
[
3
]
A
q
e
e
l
-
Ur
-
R
e
h
ma
n
,
A
.
Z.
A
b
b
a
si
,
N
.
I
sl
a
m,
a
n
d
Z
.
A
.
S
h
a
i
k
h
,
“
A
r
e
v
i
e
w
o
f
w
i
r
e
l
e
ss
se
n
s
o
r
s
a
n
d
n
e
t
w
o
r
k
s
’
a
p
p
l
i
c
a
t
i
o
n
s
i
n
a
g
r
i
c
u
l
t
u
r
e
,
”
C
o
m
p
u
t
e
r
S
t
a
n
d
a
rd
s
a
n
d
I
n
t
e
rf
a
c
e
s
,
v
o
l
.
3
6
,
n
o
.
2
,
p
p
.
2
6
3
–
2
7
0
,
2
0
1
4
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
c
s
i
.
2
0
1
1
.
0
3
.
0
0
4
.
[
4
]
P
.
M
u
sa
,
H
.
S
u
g
e
r
u
,
a
n
d
E.
P
.
W
i
b
o
w
o
,
“
W
i
r
e
l
e
ss
se
n
s
o
r
n
e
t
w
o
r
k
s
f
o
r
p
r
e
c
i
s
i
o
n
a
g
r
i
c
u
l
t
u
r
e
:
A
r
e
v
i
e
w
o
f
N
P
K
s
e
n
so
r
i
mp
l
e
m
e
n
t
a
t
i
o
n
s,”
S
e
n
s
o
rs
,
v
o
l
.
2
4
,
n
o
.
1
,
p
p
.
1
–
1
4
,
2
0
2
4
,
d
o
i
:
1
0
.
3
3
9
0
/
s2
4
0
1
0
0
5
1
.
[
5
]
P
.
S
a
n
j
e
e
v
i
,
S
.
P
r
a
s
a
n
n
a
,
B
.
S
i
v
a
K
u
mar,
G
.
G
u
n
a
s
e
k
a
r
a
n
,
I
.
A
l
a
g
i
r
i
,
a
n
d
R
.
V
i
j
a
y
A
n
a
n
d
,
“
P
r
e
c
i
s
i
o
n
a
g
r
i
c
u
l
t
u
r
e
a
n
d
f
a
r
m
i
n
g
u
si
n
g
i
n
t
e
r
n
e
t
o
f
t
h
i
n
g
s
b
a
se
d
o
n
w
i
r
e
l
e
s
s
se
n
so
r
n
e
t
w
o
r
k
,
”
T
r
a
n
s
a
c
t
i
o
n
s
o
n
Em
e
r
g
i
n
g
T
e
l
e
c
o
m
m
u
n
i
c
a
t
i
o
n
s
T
e
c
h
n
o
l
o
g
i
e
s
,
v
o
l
.
3
1
,
n
o
.
1
2
,
p
p
.
1
–
1
4
,
2
0
2
0
,
d
o
i
:
1
0
.
1
0
0
2
/
e
t
t
.
3
9
7
8
.
[
6
]
A
.
V
.
A
g
r
a
w
a
l
,
L
.
P
.
M
a
g
u
l
u
r
,
S
.
G
.
P
r
i
y
a
,
A
.
K
a
u
r
,
G
.
S
i
n
g
h
,
a
n
d
S
.
B
o
o
p
a
t
h
i
,
“
S
mar
t
p
r
e
c
i
s
i
o
n
a
g
r
i
c
u
l
t
u
r
e
u
si
n
g
I
o
T
a
n
d
W
S
N
,
”
H
a
n
d
b
o
o
k
o
f
R
e
se
a
rc
h
o
n
D
a
t
a
S
c
i
e
n
c
e
a
n
d
C
y
b
e
rs
e
c
u
ri
t
y
I
n
n
o
v
a
t
i
o
n
s
i
n
I
n
d
u
st
r
y
4
.
0
T
e
c
h
n
o
l
o
g
i
e
s
,
n
o
.
S
e
p
t
e
mb
e
r
,
p
p
.
5
2
4
–
5
4
1
,
2
0
2
3
,
d
o
i
:
1
0
.
4
0
1
8
/
9
7
8
-
1
-
6
6
8
4
-
8
1
4
5
-
5
.
c
h
0
2
6
.
[
7
]
T.
M
u
h
a
mm
e
d
a
n
d
R
.
A
.
S
h
a
i
k
h
,
“
A
n
a
n
a
l
y
s
i
s
o
f
f
a
u
l
t
d
e
t
e
c
t
i
o
n
st
r
a
t
e
g
i
e
s
i
n
w
i
r
e
l
e
ss
s
e
n
s
o
r
n
e
t
w
o
r
k
s,
”
J
o
u
rn
a
l
o
f
N
e
t
w
o
rk
a
n
d
C
o
m
p
u
t
e
r
A
p
p
l
i
c
a
t
i
o
n
s
,
v
o
l
.
7
8
,
n
o
.
A
p
r
i
l
2
0
1
6
,
p
p
.
2
6
7
–
2
8
7
,
2
0
1
7
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
j
n
c
a
.
2
0
1
6
.
1
0
.
0
1
9
.
[
8
]
Z.
N
o
s
h
a
d
e
t
a
l
.
,
“
F
a
u
l
t
d
e
t
e
c
t
i
o
n
i
n
w
i
r
e
l
e
ss
s
e
n
s
o
r
n
e
t
w
o
r
k
s
t
h
r
o
u
g
h
t
h
e
r
a
n
d
o
m
f
o
r
e
st
c
l
a
ssi
f
i
e
r
,
”
S
e
n
s
o
rs
(
S
w
i
t
zer
l
a
n
d
)
,
v
o
l
.
1
9
,
n
o
.
7
,
p
p
.
1
–
2
1
,
2
0
1
9
,
d
o
i
:
1
0
.
3
3
9
0
/
s
1
9
0
7
1
5
6
8
.
[
9
]
S
.
H
o
c
h
r
e
i
t
e
r
a
n
d
J
.
S
c
h
m
i
d
h
u
b
e
r
,
“
Lo
n
g
sh
o
r
t
-
t
e
r
m
m
e
m
o
r
y
,
”
N
e
u
r
a
l
C
o
m
p
u
t
a
t
i
o
n
,
v
o
l
.
9
,
n
o
.
8
,
p
p
.
1
7
3
5
–
1
7
8
0
,
N
o
v
.
1
9
9
7
,
d
o
i
:
1
0
.
1
1
6
2
/
n
e
c
o
.
1
9
9
7
.
9
.
8
.
1
7
3
5
.
[
1
0
]
Y
.
C
h
e
n
g
,
Q
.
L
i
u
,
J.
W
a
n
g
,
S
.
W
a
n
,
a
n
d
T.
U
m
e
r
,
“
D
i
s
t
r
i
b
u
t
e
d
f
a
u
l
t
d
e
t
e
c
t
i
o
n
f
o
r
w
i
r
e
l
e
ss
se
n
s
o
r
n
e
t
w
o
r
k
s
b
a
s
e
d
o
n
s
u
p
p
o
r
t
v
e
c
t
o
r
r
e
g
r
e
ssi
o
n
,
”
W
i
re
l
e
ss
C
o
m
m
u
n
i
c
a
t
i
o
n
s
a
n
d
M
o
b
i
l
e
C
o
m
p
u
t
i
n
g
,
v
o
l
.
2
0
1
8
,
2
0
1
8
,
d
o
i
:
1
0
.
1
1
5
5
/
2
0
1
8
/
4
3
4
9
7
9
5
.
[
1
1
]
H
.
Y
u
a
n
,
X
.
Zh
a
o
,
a
n
d
L
.
Y
u
,
“
A
d
i
st
r
i
b
u
t
e
d
B
a
y
e
s
i
a
n
a
l
g
o
r
i
t
h
m
f
o
r
d
a
t
a
f
a
u
l
t
d
e
t
e
c
t
i
o
n
i
n
w
i
r
e
l
e
ss
s
e
n
so
r
n
e
t
w
o
r
k
s
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
I
n
f
o
rm
a
t
i
o
n
N
e
t
w
o
rk
i
n
g
,
v
o
l
.
2
0
1
5
-
Ja
n
u
a
,
p
p
.
6
3
–
6
8
,
2
0
1
5
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
O
I
N
.
2
0
1
5
.
7
0
5
7
8
5
8
.
[
1
2
]
X
.
F
u
,
Y
.
W
a
n
g
,
W
.
Li
,
Y
.
Y
a
n
g
,
a
n
d
O
.
P
o
st
o
l
a
c
h
e
,
“
L
i
g
h
t
w
e
i
g
h
t
f
a
u
l
t
d
e
t
e
c
t
i
o
n
st
r
a
t
e
g
y
f
o
r
w
i
r
e
l
e
ss
s
e
n
s
o
r
n
e
t
w
o
r
k
s
b
a
se
d
o
n
t
r
e
n
d
c
o
r
r
e
l
a
t
i
o
n
,
”
I
EEE
Ac
c
e
ss
,
v
o
l
.
9
,
p
p
.
9
0
7
3
–
9
0
8
3
,
2
0
2
1
,
d
o
i
:
1
0
.
1
1
0
9
/
A
C
C
ESS
.
2
0
2
1
.
3
0
4
9
8
3
7
.
[
1
3
]
S
.
Zi
d
i
,
T.
M
o
u
l
a
h
i
,
a
n
d
B
.
A
l
a
y
a
,
“
F
a
u
l
t
d
e
t
e
c
t
i
o
n
i
n
w
i
r
e
l
e
s
s
se
n
so
r
n
e
t
w
o
r
k
s
t
h
r
o
u
g
h
S
V
M
c
l
a
ssi
f
i
e
r
,
”
I
EEE
S
e
n
so
rs
J
o
u
r
n
a
l
,
v
o
l
.
1
8
,
n
o
.
1
,
p
p
.
3
4
0
–
3
4
7
,
2
0
1
8
,
d
o
i
:
1
0
.
1
1
0
9
/
JS
EN
.
2
0
1
7
.
2
7
7
1
2
2
6
.
[
1
4
]
R
.
R
e
g
i
n
,
S
.
S
.
R
a
j
e
st
,
a
n
d
B
.
S
i
n
g
h
,
“
F
a
u
l
t
d
e
t
e
c
t
i
o
n
i
n
w
i
r
e
l
e
ss
se
n
s
o
r
n
e
t
w
o
r
k
b
a
s
e
d
o
n
d
e
e
p
l
e
a
r
n
i
n
g
a
l
g
o
r
i
t
h
ms,
”
E
AI
En
d
o
rs
e
d
T
ra
n
s
a
c
t
i
o
n
s
o
n
S
c
a
l
a
b
l
e
I
n
f
o
rm
a
t
i
o
n
S
y
s
t
e
m
s
,
v
o
l
.
8
,
n
o
.
3
2
,
p
p
.
1
–
7
,
2
0
2
1
,
d
o
i
:
1
0
.
4
1
0
8
/
e
a
i
.
3
-
5
-
2
0
2
1
.
1
6
9
5
7
8
.
[
1
5
]
R
.
R
.
S
w
a
i
n
a
n
d
P
.
M
.
K
h
i
l
a
r
,
“
C
o
m
p
o
s
i
t
e
f
a
u
l
t
d
i
a
g
n
o
s
i
s
i
n
w
i
r
e
l
e
ss
se
n
s
o
r
n
e
t
w
o
r
k
s
u
si
n
g
n
e
u
r
a
l
n
e
t
w
o
r
k
s,”
Wi
re
l
e
ss
P
e
rso
n
a
l
C
o
m
m
u
n
i
c
a
t
i
o
n
s
,
v
o
l
.
9
5
,
n
o
.
3
,
p
p
.
2
5
0
7
–
2
5
4
8
,
2
0
1
7
,
d
o
i
:
1
0
.
1
0
0
7
/
s1
1
2
7
7
-
0
1
6
-
3
9
3
1
-
3.
[
1
6
]
R
.
A
l
i
a
k
b
a
r
i
sa
n
i
,
A
.
G
h
a
sem
i
,
a
n
d
S
.
F
e
l
i
x
W
u
,
“
A
d
a
t
a
-
d
r
i
v
e
n
me
t
r
i
c
l
e
a
r
n
i
n
g
-
b
a
s
e
d
s
c
h
e
me
f
o
r
u
n
su
p
e
r
v
i
s
e
d
n
e
t
w
o
r
k
a
n
o
m
a
l
y
d
e
t
e
c
t
i
o
n
,
”
C
o
m
p
u
t
e
rs
a
n
d
El
e
c
t
ri
c
a
l
En
g
i
n
e
e
ri
n
g
,
v
o
l
.
7
3
,
p
p
.
7
1
–
8
3
,
2
0
1
9
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
c
o
m
p
e
l
e
c
e
n
g
.
2
0
1
8
.
1
1
.
0
0
3
.
[
1
7
]
J.
M
a
r
z
a
t
,
H
.
P
i
e
t
-
La
h
a
n
i
e
r
,
a
n
d
S
.
B
e
r
t
r
a
n
d
,
“
C
o
o
p
e
r
a
t
i
v
e
f
a
u
l
t
d
e
t
e
c
t
i
o
n
a
n
d
i
so
l
a
t
i
o
n
i
n
a
s
u
r
v
e
i
l
l
a
n
c
e
se
n
so
r
n
e
t
w
o
r
k
:
a
c
a
s
e
st
u
d
y
,
”
v
o
l
.
5
1
,
n
o
.
2
4
,
p
p
.
7
9
0
–
7
9
7
,
2
0
1
8
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
i
f
a
c
o
l
.
2
0
1
8
.
0
9
.
6
6
5
.
[
1
8
]
Q
.
L
i
u
,
Y
.
Y
a
n
g
,
a
n
d
X
.
Q
i
u
,
“
A
m
e
t
r
i
c
-
c
o
r
r
e
l
a
t
i
o
n
-
b
a
se
d
d
i
st
r
i
b
u
t
e
d
f
a
u
l
t
d
e
t
e
c
t
i
o
n
a
p
p
r
o
a
c
h
i
n
w
i
r
e
l
e
ss
se
n
s
o
r
n
e
t
w
o
r
k
s
,
”
1
7
t
h
Asi
a
-
Pa
c
i
f
i
c
N
e
t
w
o
rk
O
p
e
r
a
t
i
o
n
s
a
n
d
M
a
n
a
g
e
m
e
n
t
S
y
m
p
o
s
i
u
m
:
M
a
n
a
g
i
n
g
a
Ve
r
y
C
o
n
n
e
c
t
e
d
W
o
rl
d
,
APN
O
M
S
2
0
1
5
,
n
o
.
2
0
1
1
0
0
0
5
1
1
0
0
1
1
,
p
p
.
1
8
6
–
1
9
1
,
2
0
1
5
,
d
o
i
:
1
0
.
1
1
0
9
/
A
P
N
O
M
S
.
2
0
1
5
.
7
2
7
5
4
2
4
.
[
1
9
]
J.
L
u
d
e
ñ
a
-
C
h
o
e
z
,
J.
J.
C
h
o
q
u
e
h
u
a
n
c
a
-
Ze
v
a
l
l
o
s,
a
n
d
E
.
M
a
y
h
u
a
-
L
ó
p
e
z
,
“
S
e
n
s
o
r
n
o
d
e
s
f
a
u
l
t
d
e
t
e
c
t
i
o
n
f
o
r
a
g
r
i
c
u
l
t
u
r
a
l
w
i
r
e
l
e
s
s
sen
s
o
r
n
e
t
w
o
r
k
s
b
a
s
e
d
o
n
N
M
F
,
”
C
o
m
p
u
t
e
rs
a
n
d
El
e
c
t
ro
n
i
c
s
i
n
A
g
ri
c
u
l
t
u
re
,
v
o
l
.
1
6
1
,
n
o
.
M
a
y
,
p
p
.
2
1
4
–
2
2
4
,
2
0
1
9
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
c
o
mp
a
g
.
2
0
1
8
.
0
6
.
0
3
3
.
[
2
0
]
T.
D
i
n
h
Le
a
n
d
D
.
H
.
T
a
n
,
“
D
e
si
g
n
a
n
d
d
e
p
l
o
y
a
w
i
r
e
l
e
ss
se
n
so
r
n
e
t
w
o
r
k
f
o
r
p
r
e
c
i
s
i
o
n
a
g
r
i
c
u
l
t
u
r
e
,
”
in
Pr
o
c
e
e
d
i
n
g
s
o
f
2
0
1
5
2
n
d
N
a
t
i
o
n
a
l
F
o
u
n
d
a
t
i
o
n
f
o
r
S
c
i
e
n
c
e
a
n
d
T
e
c
h
n
o
l
o
g
y
D
e
v
e
l
o
p
m
e
n
t
C
o
n
f
e
re
n
c
e
o
n
I
n
f
o
rm
a
t
i
o
n
a
n
d
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
,
N
I
C
S
2
0
1
5
,
p
p
.
2
9
4
–
2
9
9
,
2
0
1
5
,
d
o
i
:
1
0
.
1
1
0
9
/
N
I
C
S
.
2
0
1
5
.
7
3
0
2
2
1
0
.
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
.
4
,
Au
g
u
s
t
20
25
:
3
8
0
3
-
3812
3812
[2
1]
H
.
M
.
Jawa
d
,
R
.
N
o
r
d
i
n
,
S
.
K
.
G
h
a
r
g
h
a
n
,
A
.
M
.
J
a
w
a
d
,
a
n
d
M
.
I
smai
l
,
“
E
n
e
r
g
y
-
e
f
f
i
c
i
e
n
t
w
i
r
e
l
e
ss
se
n
s
o
r
n
e
t
w
o
r
k
s
f
o
r
p
r
e
c
i
s
i
o
n
a
g
r
i
c
u
l
t
u
r
e
:
A
r
e
v
i
e
w
,
”
S
e
n
so
rs
(
S
w
i
t
z
e
rl
a
n
d
)
,
v
o
l
.
1
7
,
n
o
.
8
,
2
0
1
7
,
d
o
i
:
1
0
.
3
3
9
0
/
s1
7
0
8
1
7
8
1
.
[
2
2
]
M
.
C
a
t
e
l
a
n
i
,
L.
C
i
a
n
i
,
A
.
B
a
r
t
o
l
i
n
i
,
C
.
D
e
l
R
i
o
,
G
.
G
u
i
d
i
,
a
n
d
G
.
P
a
t
r
i
z
i
,
“
R
e
l
i
a
b
i
l
i
t
y
a
n
a
l
y
s
i
s
o
f
w
i
r
e
l
e
ss
s
e
n
s
o
r
n
e
t
w
o
r
k
f
o
r
smar
t
f
a
r
mi
n
g
a
p
p
l
i
c
a
t
i
o
n
s,
”
S
e
n
s
o
rs
,
v
o
l
.
2
1
,
n
o
.
2
2
,
p
p
.
1
–
1
6
,
2
0
2
1
,
d
o
i
:
1
0
.
3
3
9
0
/
s
2
1
2
2
7
6
8
3
.
[
2
3
]
T.
O
j
h
a
,
S
.
M
i
sr
a
,
a
n
d
N
.
S
.
R
a
g
h
u
w
a
n
s
h
i
,
“
W
i
r
e
l
e
ss
se
n
so
r
n
e
t
w
o
r
k
s
f
o
r
a
g
r
i
c
u
l
t
u
r
e
:
T
h
e
st
a
t
e
-
of
-
t
h
e
-
a
r
t
i
n
p
r
a
c
t
i
c
e
a
n
d
f
u
t
u
r
e
c
h
a
l
l
e
n
g
e
s,”
C
o
m
p
u
t
e
rs
a
n
d
El
e
c
t
ro
n
i
c
s i
n
Ag
r
i
c
u
l
t
u
r
e
,
v
o
l
.
1
1
8
,
p
p
.
6
6
–
8
4
,
2
0
1
5
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
c
o
m
p
a
g
.
2
0
1
5
.
0
8
.
0
1
1
.
[
2
4
]
X
.
C
h
e
n
,
X
.
Q
i
u
,
C
.
Z
h
u
,
P
.
L
i
u
,
a
n
d
X
.
H
u
a
n
g
,
“
Lo
n
g
s
h
o
r
t
-
t
e
r
m
m
e
m
o
r
y
n
e
u
r
a
l
n
e
t
w
o
r
k
s
f
o
r
C
h
i
n
e
s
e
w
o
r
d
s
e
g
m
e
n
t
a
t
i
o
n
,
”
C
o
n
f
e
re
n
c
e
Pr
o
c
e
e
d
i
n
g
s
-
EM
N
L
P
2
0
1
5
:
C
o
n
f
e
r
e
n
c
e
o
n
Em
p
i
ri
c
a
l
Me
t
h
o
d
s
i
n
N
a
t
u
ra
l
L
a
n
g
u
a
g
e
Pr
o
c
e
ssi
n
g
,
p
p
.
1
1
9
7
–
1
2
0
6
,
2
0
1
5
,
d
o
i
:
1
0
.
1
8
6
5
3
/
v
1
/
d
1
5
-
1
1
4
1
.
[
2
5
]
A
.
S
h
e
n
f
i
e
l
d
a
n
d
M
.
H
o
w
a
r
t
h
,
“
A
n
o
v
e
l
d
e
e
p
l
e
a
r
n
i
n
g
m
o
d
e
l
f
o
r
t
h
e
d
e
t
e
c
t
i
o
n
a
n
d
i
d
e
n
t
i
f
i
c
a
t
i
o
n
o
f
r
o
l
l
i
n
g
e
l
e
me
n
t
-
b
e
a
r
i
n
g
f
a
u
l
t
s,
”
S
e
n
so
r
s (S
w
i
t
zer
l
a
n
d
)
,
v
o
l
.
2
0
,
n
o
.
1
8
,
p
p
.
1
–
2
4
,
2
0
2
0
,
d
o
i
:
1
0
.
3
3
9
0
/
s2
0
1
8
5
1
1
2
.
[
2
6
]
P
.
J.
W
e
r
b
o
s,
“
B
a
c
k
p
r
o
p
a
g
a
t
i
o
n
t
h
r
o
u
g
h
t
i
m
e
:
W
h
a
t
i
t
d
o
e
s
a
n
d
h
o
w
t
o
d
o
i
t
,
”
in
Pr
o
c
e
e
d
i
n
g
s
o
f
t
h
e
I
EEE
,
v
o
l
.
7
8
,
n
o
.
1
0
,
p
p
.
1
5
5
0
–
1
5
6
0
,
1
9
9
0
,
d
o
i
:
1
0
.
1
1
0
9
/
5
.
5
8
3
3
7
.
[
2
7
]
G
.
B
i
r
d
a
n
d
M
.
E.
P
o
l
i
v
o
d
a
,
“
B
a
c
k
p
r
o
p
a
g
a
t
i
o
n
t
h
r
o
u
g
h
t
i
me
f
o
r
n
e
t
w
o
r
k
s
w
i
t
h
l
o
n
g
-
t
e
r
m
d
e
p
e
n
d
e
n
c
i
e
s,”
a
r
Xi
v
:
2
1
0
3
.
1
5
5
8
9
,
p
p
.
1
–
9
,
Ju
n
.
2
0
2
5
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
Ya
ss
in
e
Aita
m
a
r
is
a
P
h
.
D
.
stu
d
e
n
t
i
n
c
o
m
p
u
ter
sc
ien
c
e
a
t
th
e
M
o
h
a
m
m
a
d
i
a
S
c
h
o
o
l
o
f
En
g
in
e
e
rs,
Un
i
v
e
rsity
M
o
h
a
m
e
d
V
i
n
Ra
b
a
t,
M
o
r
o
c
c
o
.
He
h
o
l
d
s
a
m
a
ste
r’s
d
e
g
re
e
in
c
o
m
p
u
tati
o
n
a
l
p
h
y
sic
s,
wh
ich
h
e
o
b
tai
n
e
d
in
2
0
1
9
fro
m
th
e
F
a
c
u
lt
y
o
f
S
c
ien
c
e
Ra
b
a
t
.
He
is
a
m
e
m
b
e
r
o
f
th
e
S
m
a
rt
Co
m
m
u
n
ica
ti
o
n
s
Re
se
a
rc
h
Tea
m
(ERS
C).
His
re
se
a
r
c
h
fo
c
u
se
s
o
n
th
e
in
ters
e
c
ti
o
n
o
f
in
tell
ig
e
n
t
se
n
so
rs
a
n
d
m
a
c
h
in
e
lea
rn
in
g
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
y
a
ss
in
e
a
it
a
m
a
r@re
se
a
rc
h
.
e
m
i.
a
c
.
m
a
.
J
a
m
a
l
El
Ab
b
a
d
i
is
a
F
u
l
l
M
o
h
a
m
m
a
d
ia
S
c
h
o
o
l
o
f
En
g
i
n
e
e
rs,
S
c
h
o
o
l
o
f
S
c
ien
c
e
,
Co
m
p
u
ti
n
g
a
n
d
E
n
g
i
n
e
e
rin
g
Tec
h
n
o
lo
g
ies
,
M
o
h
a
m
m
e
d
V
Un
iv
e
r
sity
,
i
n
Ra
b
a
t
,
M
o
ro
c
c
o
.
His
is
e
d
u
c
a
ti
o
n
a
l
c
o
o
r
d
i
n
a
to
r
o
f
th
e
El
e
c
tri
c
a
l
En
g
i
n
e
e
rin
g
De
p
a
rt
m
e
n
t
.
He
o
b
tai
n
e
d
h
is
firs
t
p
o
stg
ra
d
u
a
te
c
e
rti
fica
te
fr
o
m
t
h
e
sa
m
e
S
c
h
o
o
l
i
n
1
9
8
9
t
h
e
n
h
e
r
e
c
e
iv
e
d
h
is
P
h
.
D.
d
e
g
re
e
i
n
M
o
b
i
le
Ra
d
i
o
Tele
c
o
m
m
u
n
ica
ti
o
n
s
in
c
o
ll
a
b
o
ra
ti
o
n
with
th
e
M
o
n
tefio
re
In
sti
tu
te
o
f
Li
e
g
e
i
n
Be
lg
iu
m
.
He
is
a
m
e
m
b
e
r
o
f
th
e
S
m
a
rt
Co
m
m
u
n
ica
ti
o
n
S
y
ste
m
s
Tea
m
(ERS
C)
a
n
d
S
m
a
rt
S
y
ste
m
s
Ce
n
t
e
r.
His
c
u
rre
n
t
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
m
o
b
il
e
c
o
m
m
u
n
ica
ti
o
n
ra
d
io
4
a
n
d
5
G
c
h
a
n
n
e
l
m
o
d
if
ica
ti
o
n
a
n
d
c
h
a
ra
c
teriz
a
ti
o
n
,
wire
les
s
c
o
g
n
i
ti
v
e
n
e
t
wo
rk
s
a
n
d
wire
les
s
se
n
so
rs
a
n
d
n
e
tw
o
rk
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
jam
a
l.
e
lab
b
a
d
i@e
m
i.
u
m
5
.
a
c
.
m
a
.
Evaluation Warning : The document was created with Spire.PDF for Python.