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m
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v
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u
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g
1.
I
NT
RO
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UCT
I
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N
T
h
er
e
ar
e
m
a
n
y
d
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s
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s
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io
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s
a
b
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t
th
e
o
r
ig
in
o
f
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ter
m
“v
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tu
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lit
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”.
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e
o
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m
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it
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Ger
m
an
p
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ilo
s
o
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I
m
m
a
n
u
el
Ka
n
t
[
1
-
5
]
.
T
h
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ex
ac
t d
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f
in
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tio
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at
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s
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o
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t
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ter
m
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a
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w
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en
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s
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o
t
b
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l
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as
“v
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t
u
al”
in
th
e
cu
r
r
en
t
d
ef
in
i
tio
n
[
6
-
8
]
.
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th
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id
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y
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s
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co
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p
t
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p
t
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p
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m
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w
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h
m
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f
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b
j
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alize
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m
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t c
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lo
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k
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r
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lit
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(
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m
m
ar
y
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f
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AR
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R
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m
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s
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t
h
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s
p
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AR
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a
n
d
te
le
-
o
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ar
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g
o
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to
b
e
p
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f
ec
t
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x
a
m
p
le
s
o
f
VR
[
6
-
8
]
.
T
h
e
ter
m
v
ir
t
u
al
r
ea
lit
y
i
s
q
u
it
e
co
n
tr
ad
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r
y
.
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u
r
b
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le
s
f
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d
s
a
s
o
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tio
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th
i
s
p
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b
le
m
b
y
p
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p
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s
in
g
th
e
alter
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ati
v
e
ter
m
-
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al
it
y
.
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e
m
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k
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llo
w
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s
tate
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en
t
th
at
th
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al
w
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d
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er
s
to
th
e
p
er
ce
p
tio
n
s
as
a
p
ar
t
o
f
VR
’
s
id
eo
lo
g
y
[
6
].
I
n
m
o
s
t
ca
s
e
s
,
VR
i
n
cl
u
d
es
a
v
er
y
i
m
p
o
r
ta
n
t
co
m
p
o
n
e
n
t:
i
n
ter
ac
tio
n
.
I
n
o
t
h
er
w
o
r
d
s
,
i
f
o
th
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s
en
s
o
r
s
p
ar
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p
ate
in
t
h
e
v
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t
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al
w
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r
d
.
I
f
o
n
l
y
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h
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e
y
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ar
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i
n
-
v
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lv
ed
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t
h
e
n
t
h
e
V
R
s
y
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te
m
is
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lled
an
o
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en
lo
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w
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clo
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th
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s
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d
ca
s
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th
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u
m
a
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b
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y
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a
s
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ar
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co
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m
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m
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leg
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T
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p
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v
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m
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h
ea
r
t r
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b
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
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&
C
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m
p
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n
g
I
SS
N:
2088
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8708
A
ma
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virt
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(
I
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6593
Ma
n
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s
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tis
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b
u
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4
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g
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u
r
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s
d
ata
f
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en
ter
ta
in
m
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t
[
9
-
1
6
]
.
T
h
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E
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ch
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s
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1
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2
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er
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e
an
d
an
al
y
ze
th
e
ef
f
ec
t
s
o
f
t
h
e
b
r
ain
[
1
7
]
.
W
ith
it
s
f
u
n
ctio
n
,
h
u
m
a
n
b
r
ain
g
en
er
ate
s
m
illi
o
n
s
o
f
s
m
all
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tr
ical
v
o
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g
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h
e
co
m
b
in
atio
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o
f
th
ese
f
ie
ld
s
ca
n
b
e
d
etec
ted
b
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elec
tr
o
d
es
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h
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e
s
ca
lp
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h
e
a
m
p
litu
d
e
o
f
t
h
ese
s
ig
n
al
s
v
ar
ie
s
f
r
o
m
1
μV
to
1
0
0
μV
.
E
E
G
s
h
o
w
s
w
i
d
e
v
ar
iatio
n
s
i
n
a
m
p
lit
u
d
e
d
ep
en
d
in
g
o
n
e
x
ter
n
al
s
ti
m
u
latio
n
an
d
v
ar
io
u
s
in
ter
n
a
l
m
en
tal
s
tate
s
.
Var
io
u
s
m
et
h
o
d
s
ar
e
u
s
ed
to
o
b
tain
E
E
G
s
ig
n
a
ls
.
W
e
u
s
e
th
e
n
o
n
-
i
n
v
asi
v
e
E
E
G
r
ec
o
r
d
in
g
.
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h
ese
ar
e
s
ig
n
a
ls
,
ca
p
tu
r
ed
w
it
h
elec
tr
o
d
es
attac
h
ed
to
th
e
s
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lp
.
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n
o
u
r
c
ase,
th
e
u
s
e
d
elec
tr
o
d
es
a
r
e
s
m
all
m
etal
d
is
cs.
T
h
e
y
ar
e
m
ad
e
o
f
s
tai
n
le
s
s
s
teel,
tin
,
g
o
ld
o
r
s
ilv
er
co
ated
w
it
h
a
s
ilv
er
ch
lo
r
id
e
co
atin
g
.
E
x
e
m
p
lar
y
i
n
p
u
t
d
ata
ar
e
p
r
es
en
ted
in
th
e
T
ab
le
1
[
17
-
2
2
].
T
ab
le
1
p
r
esen
ts
s
o
m
e
o
f
th
e
ee
g
s
ig
n
al
s
o
f
th
e
b
r
ain
t
h
r
o
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h
w
h
ich
w
e
co
n
t
r
o
l
th
e
v
ir
tu
al
ca
m
er
a.
T
h
r
o
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g
h
th
e
alg
o
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it
h
m
s
w
e
h
av
e
d
ev
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lo
p
ed
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w
e
ca
n
class
if
y
t
h
ese
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ig
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als an
d
m
o
v
e
th
e
ca
m
er
a
in
f
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r
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ir
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tio
n
s
.
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ab
le
1
.
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m
o
tiv
d
ata
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m
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al
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is
A
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9
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
10
,
No
.
6
,
Decem
b
er
2020
:
6
5
9
2
-
6
5
9
7
6594
3.
RE
SU
L
T
S
AND
D
I
SCU
SS
I
O
N
Ou
r
ai
m
is
b
y
u
s
i
n
g
b
r
ain
w
a
v
e
d
ata
to
b
e
a
b
le
to
m
o
v
e
i
n
th
e
v
ir
tu
al
w
o
r
ld
.
I
n
o
u
r
ex
p
er
i
m
en
t
w
e
u
s
e
th
r
ee
d
ir
ec
tio
n
s
:
f
o
r
w
ar
d
,
lef
t
an
d
r
ig
h
t
[2
1
].
I
n
Fig
u
r
es
2
a
n
d
3
w
e
p
r
esen
t
t
w
o
v
ie
w
s
i
n
a
s
y
s
te
m
o
f
v
ir
tu
a
l
r
ea
lit
y
f
r
o
m
th
e
3
D
s
o
cial
n
et
w
o
r
k
s
ec
o
n
d
lif
e.
Fig
u
r
e
2
.
Vie
w
o
f
s
ec
o
n
d
l
i
f
e
Fig
u
r
e
3
.
Vie
w
o
f
s
ec
o
n
d
l
i
f
e
W
e
ca
n
co
n
s
id
er
th
e
in
p
u
t
d
ata
as
r
an
d
o
m
s
ig
n
al
s
.
I
t
is
a
s
ig
n
al
t
h
at
is
a
f
u
n
ctio
n
o
f
ti
m
e.
T
h
eir
v
alu
e
s
ar
e
p
r
ev
io
u
s
l
y
u
n
k
n
o
w
n
.
T
h
is
t
y
p
e
o
f
s
i
g
n
al
ex
p
r
ess
es
an
ac
cid
en
tal
p
h
y
s
i
ca
l
p
h
en
o
m
e
n
o
n
o
r
p
h
y
s
ical
p
r
o
ce
s
s
.
W
h
en
r
an
d
o
m
s
i
g
n
a
l
is
r
eg
i
s
ter
ed
,
o
n
l
y
o
n
e
o
f
th
e
r
an
d
o
m
p
r
o
ce
s
s
o
u
t
p
u
ts
is
r
ea
lized
.
I
t
is
p
o
s
s
ib
le
to
d
o
th
is
o
n
l
y
af
ter
m
u
ltip
le
r
ep
etitio
n
o
f
o
b
s
er
v
atio
n
s
an
d
th
e
ca
lcu
la
tio
n
o
f
ce
r
tain
s
tati
s
tica
l
ch
ar
ac
ter
is
tic
s
o
f
t
h
e
s
i
g
n
al
co
n
v
er
s
io
n
s
et
[
2
3
]
.
T
h
e
r
an
d
o
m
s
tatio
n
ar
y
s
i
g
n
a
ls
k
ee
p
th
eir
o
w
n
s
tatis
tical
ch
a
r
ac
ter
is
tics
i
n
th
e
s
eq
u
e
n
tial
c
o
n
v
er
s
io
n
s
o
f
th
e
r
an
d
o
m
p
r
o
ce
s
s
[
2
0
]
.
T
h
e
d
ig
ital
s
i
g
n
al
i
s
t
y
p
icall
y
s
e
t
in
th
e
f
o
r
m
o
f
d
is
cr
ete
s
er
ies
o
f
n
u
m
er
ical
d
ata
:
a
n
u
m
er
ical
ar
r
ay
o
f
co
n
s
ec
u
t
iv
e
ar
ith
m
etic
v
al
u
e
s
f
o
r
Δ
=
co
n
s
t,
b
u
t
g
e
n
er
all
y
t
h
e
s
ig
n
al
ca
n
also
b
e
g
iv
en
i
n
th
e
f
o
r
m
o
f
tab
les o
f
ar
b
itra
r
y
v
alu
e
s
o
f
t
h
e
ar
g
u
m
en
t
[2
3
-
2
5
]
.
T
h
e
an
al
y
s
is
o
f
t
h
e
i
n
p
u
t
d
ata
ca
n
b
e
p
r
esen
ted
as
NP
tas
k
.
T
h
e
r
ea
s
o
n
o
f
th
i
s
is
t
h
e
h
i
g
h
v
o
latilit
y
an
d
th
e
d
if
f
er
e
n
t
ex
a
m
i
n
ed
p
eo
p
le.
T
h
ey
g
i
v
e
d
if
f
er
en
t
b
r
ai
n
s
ig
n
al
s
in
t
h
e
s
a
m
e
k
i
n
d
o
f
ex
ter
n
al
co
n
d
itio
n
s
.
Fo
r
ex
a
m
p
le,
in
o
u
r
ex
p
er
i
m
en
ts
i
n
tr
ain
i
n
g
a
s
u
b
j
ec
t
m
a
n
:
d
r
iv
in
g
th
e
c
u
r
s
o
r
in
th
r
ee
d
ir
ec
tio
n
s
-
f
o
r
w
ar
d
,
lef
t
a
n
d
r
ig
h
t.
W
h
e
n
a
w
o
m
an
ap
p
ea
r
,
th
e
d
ata
ch
an
g
es.
T
h
e
d
escr
ib
ed
E
E
G
d
ata
n
ee
d
s
a
p
r
o
p
er
an
aly
s
is
a
n
d
in
ter
p
r
etatio
n
i
n
o
r
d
er
to
s
o
lv
e
th
e
ab
o
v
e
-
m
e
n
tio
n
ed
tas
k
s
[2
5
].
T
h
e
m
et
h
o
d
o
lo
g
y
f
o
r
ex
tr
ac
tio
n
o
f
d
ep
en
d
en
cie
s
-
c
lu
s
ter
in
g
i
s
ap
p
lied
to
th
e
E
E
G
d
ata.
F
o
r
th
is
r
ea
s
o
n
,
w
e
fir
s
t t
r
a
in
o
u
r
mo
d
el
a
n
d
th
e
n
w
e
cla
s
s
if
y
th
e
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DSc
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RE
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NC
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S
[1
]
C.
Um
a
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e
t
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l.
,
“
F
e
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tu
re
Ex
trac
ti
o
n
T
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q
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[2
]
D.
F
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,
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,
“
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1
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[3
]
G
.
P
a
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t
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,
“
M
o
d
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Rec
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(
AIP
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1
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5
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2
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1
6
.
[4
]
G.
P
.
Dim
it
ro
v
,
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t
a
l.
,
“
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e
rf
o
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m
a
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n
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in
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rsity
in
f
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r
m
a
ti
o
n
s
y
ste
m
s
,”
In
ter
n
a
ti
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C
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n
fer
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[5
]
J.
D.
M
o
ss
a
n
d
E
.
R.
M
u
th
,
“
C
h
a
ra
c
teristics
o
f
h
e
a
d
-
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ted
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isp
lay
s
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n
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th
e
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r
e
f
f
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c
ts
o
n
sim
u
lato
r
sic
k
n
e
ss
,”
Hu
ma
n
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c
to
rs
,
v
o
l.
53
,
n
o
.
3
,
p
p
.
3
0
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9
,
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0
1
1
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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2
0
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8708
I
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t J
E
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&
C
o
m
p
E
n
g
,
Vo
l.
10
,
No
.
6
,
Decem
b
er
2020
:
6
5
9
2
-
6
5
9
7
6596
[6
]
K.
Ha
sh
im
o
to
,
e
t
a
l
.
,
“
Brief
d
e
m
o
n
stra
ti
o
n
o
f
o
lf
a
c
to
r
y
a
n
d
v
isu
a
l
p
re
se
n
tatio
n
u
si
n
g
w
e
a
r
a
b
le o
lf
a
c
t
o
ry
d
isp
la
y
a
n
d
h
e
a
d
m
o
u
n
ted
d
isp
lay
,”
i
n
Pro
c
e
e
d
in
g
s IE
EE
Vi
rtu
a
l
Re
a
li
ty C
o
n
fe
re
n
c
e
,
2
0
1
6
.
[7
]
M
.
T
ra
y
k
o
v
,
e
t
a
l.
,
“
A
lg
o
rit
h
m
f
o
r
p
r
o
tein
f
o
l
d
i
n
g
p
r
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b
lem
in
3
D
latt
ice
HP
m
o
d
e
l
,”
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
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o
lo
g
y
a
n
d
Bi
o
me
d
icin
e
,
v
o
l.
3
,
p
p
.
16
-
2
1
,
2
0
1
8
.
[8
]
N.
C.
Bu
rb
u
les
,
“
Re
th
in
k
in
g
th
e
v
irt
u
a
l
,”
i
n
J.
W
e
iss,
e
t
a
l.
(e
d
s)
,
T
h
e
In
ter
n
a
ti
o
n
a
l
Ha
n
d
b
o
o
k
o
f
V
irtu
a
l
L
e
a
rn
i
n
g
En
v
iro
n
me
n
ts
,
p
p
.
3
7
-
58
,
2
0
0
6
.
[9
]
A
.
F
e
re
z
li
e
v
,
e
t
a
l
.
,
“
Co
rre
latio
n
b
e
tw
e
e
n
a
v
e
ra
g
e
a
n
d
d
o
m
in
a
n
t
h
e
ig
h
t
o
f
m
id
d
le
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a
g
e
d
Do
u
g
las
f
ir
p
lan
tati
o
n
s
in
th
e
n
o
rth
-
w
e
st r
h
o
d
o
p
e
s
,”
S
il
v
a
B
a
lca
n
ica
,
v
o
l
.
19
,
n
o
.
2,
p
p
.
13
-
26
,
2
0
1
8
.
[1
0
]
N.
Na
se
e
r
a
n
d
K.
S
.
Ho
n
g
,
“
f
NIRS
-
b
a
se
d
b
ra
in
-
c
o
m
p
u
ter
in
terf
a
c
e
s:
a
re
v
iew
,”
Fro
n
ti
e
rs
i
n
H
u
ma
n
Ne
u
ro
sc
ien
c
e
,
v
o
l.
9
,
n
o
.
3
,
p
.
1
7
2
,
2
0
1
5
.
[1
1
]
O.
A
y
d
e
m
ir
,
“
Co
m
m
o
n
sp
a
ti
a
l
p
a
tt
e
rn
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b
a
se
d
f
e
a
tu
re
e
x
trac
ti
o
n
f
r
o
m
th
e
b
e
st
ti
m
e
se
g
m
e
n
t
o
f
BCI
d
a
ta,
”
T
u
rk
ish
J
o
u
rn
a
l
o
f
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e
c
trica
l
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g
in
e
e
rin
g
a
n
d
Co
m
p
er
S
c
i
e
n
c
e
s
,
v
o
l.
2
4
,
n
o
.
5
,
p
p
.
3
9
7
6
-
3
9
8
6
,
2
0
1
6
.
[1
2
]
R.
R
on
-
A
n
g
e
v
in
a
n
d
A
.
Dia
z
-
Estrella
,
“
Bra
in
c
o
m
p
u
ter
i
n
terf
a
c
e
:
Ch
a
n
g
e
s
i
n
p
e
rf
o
rm
a
n
c
e
u
sin
g
v
irt
u
a
l
re
a
li
t
y
tec
h
n
iq
u
e
s
,”
Ne
u
ro
sc
ien
c
e
L
e
tt
e
rs
,
v
o
l.
4
4
9
,
n
o
.
2
,
p
p
.
1
2
3
-
1
2
7
,
2
0
0
9
.
[1
3
]
R
.
M
a
v
re
v
sk
i
a
n
d
M
.
T
ra
y
k
o
v
,
“
V
isu
a
li
z
a
ti
o
n
so
f
tw
a
re
f
o
r
h
y
d
ro
p
h
o
b
ic
-
p
o
lar
p
r
o
tein
f
o
ld
in
g
m
o
d
e
l
,”
S
c
ien
ti
fi
c
Vi
su
a
li
z
a
ti
o
n
,
v
o
l.
11
,
n
o
.
1,
pp.
11
-
19
,
2
0
1
9
.
[1
4
]
R.
M
a
v
re
v
s
k
i
,
“
M
o
d
e
ll
i
n
g
in
f
o
o
d
tec
h
n
o
lo
g
y
,”
W
S
EA
S
T
ra
n
s
a
c
ti
o
n
s
o
n
Bi
o
l
o
g
y
a
n
d
Bi
o
me
d
icin
e
,
v
o
l.
1
6
,
p
p
.
75
-
89
,
2
0
1
9
.
[1
5
]
R.
M
a
v
re
v
sk
i
a
n
d
M
.
T
ra
y
k
o
v
,
“
Bu
il
d
i
n
g
a
n
d
S
e
lec
ti
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n
a
n
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ti
m
a
l
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a
th
e
m
a
ti
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a
l
M
o
d
e
l
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sc
rib
in
g
a
S
c
ien
ti
f
ic
o
r
En
g
in
e
e
rin
g
P
r
o
c
e
ss
,”
T
h
e
1
5
t
h
I
n
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
fo
r
I
n
fo
r
ma
ti
c
s
a
n
d
In
f
o
rm
a
ti
o
n
T
e
c
h
n
o
lo
g
y
(
CIIT
2
0
1
8
)
,
p
p
.
88
-
90
,
2
0
1
8
.
[1
6
]
S
.
G
a
o
,
e
t
a
l.
,
“
V
is
u
a
l
a
n
d
a
u
d
it
o
ry
b
ra
in
-
c
o
m
p
u
ter
i
n
terf
a
c
e
s
,”
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E
T
ra
n
s
a
c
ti
o
n
s
o
n
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o
me
d
ica
l
E
n
g
i
n
e
e
rin
g
,
v
o
l.
6
1
,
n
o
.
5
,
p
p
.
1
4
3
6
-
1
4
4
7
,
2
0
1
4
.
[1
7
]
M
irella
Ca
p
o
z
z
i.
Ka
n
t
,
“
so
e
m
m
e
rrin
g
a
n
d
th
e
im
p
o
rtan
c
e
o
f
th
e
s
e
n
se
o
f
h
e
a
rin
g
,
”
L
e
x
ico
n
P
h
il
o
s
o
p
h
icu
m
,
n
o
.
2
,
2
0
1
4
.
[
O
n
li
n
e
].
A
v
a
il
a
b
le:
h
tt
p
:/
/l
e
x
ico
n
.
c
n
r.
it
/i
n
d
e
x
.
p
h
p
/L
P
/article
/v
ie
w
/4
1
3
/
3
3
7
.
[1
8
]
S
.
T
sa
n
k
o
v
,
e
t
a
l.
,
“
Re
se
a
rc
h
o
f
t
h
e
Im
p
a
c
t
o
f
In
tera
c
ti
v
e
Ed
u
c
a
ti
o
n
a
l
M
u
lt
im
e
d
ia
o
n
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tu
d
e
n
t’s
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o
t
iv
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ti
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n
t
o
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t
u
d
y
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e
r
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n
t
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f
o
rm
a
ti
c
s
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c
ip
li
n
e
s
,
”
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n
fer
e
n
c
e
n
a
me
:
1
0
th
I
n
ter
n
a
ti
o
n
a
l
T
e
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h
n
o
lo
g
y
,
E
d
u
c
a
t
io
n
a
n
d
De
v
e
lo
p
me
n
t
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n
fer
e
n
c
e
,
p
p
.
5
7
8
5
-
5
7
9
1
,
2
0
1
6
.
[1
9
]
S
.
M
.
L
a
V
a
ll
e
,
“
V
irt
u
a
l
Re
a
li
ty
,
”
Ca
m
b
rid
g
e
u
n
iv
e
rsity
p
re
ss
,
201
6
.
[
On
li
n
e
].
A
v
a
il
a
b
le:
h
tt
p
:/
/v
r.
c
s.u
iu
c
.
e
d
u
/
.
[2
0
]
V
.
I.
P
e
tk
o
v
a
a
n
d
H.
H.
Eh
rss
o
n
,
“
If
I
W
e
r
e
Yo
u
:
P
e
rc
e
p
tu
a
l
Ill
u
sio
n
o
f
Bo
d
y
S
w
a
p
p
i
ng
,”
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o
S
ON
E
,
v
o
l.
3
,
n
o
.
1
2
,
p
p
.
e
3
8
3
2
,
2
0
0
8
.
[2
1
]
N.
Ya
n
e
v
,
e
t
a
l
.,
“
A
Ne
w
Cla
ss
i
f
ier
f
o
r
P
ro
tein
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o
l
d
Clas
s
Re
c
o
g
n
it
io
n
,”
C
o
mp
tes
R
e
n
d
u
s
d
e
1
’
Aca
d
e
’mie
Bu
lg
a
re
d
e
s
S
c
i
e
n
c
e
s
,
v
o
l.
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1
,
n
o
.
7
,
p
p
.
8
8
5
-
8
9
2
,
2
0
1
8
.
[2
2
]
S
.
M
il
a
n
o
v
,
“
Dra
w
in
g
o
f
d
e
p
e
n
d
e
n
c
ies
in
d
a
ta
f
lo
w
s
,”
P
h
D
T
h
e
sis
,
2
0
1
7
.
[
O
n
li
n
e
]
A
v
a
il
a
b
le:
h
tt
p
s:/
/fm
i.
u
n
i
-
so
f
ia.b
g
/sit
e
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fa
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lt
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il
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s/d
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ti
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n
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h
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/p
h
d
_
th
e
sis_
s
_
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il
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n
o
v
.
p
d
f
.
[2
3
]
Y.
Ak
a
tsu
k
a
a
n
d
G
.
A
.
Be
k
e
y
,
“
Co
m
p
e
n
sa
ti
o
n
f
o
r
e
n
d
t
o
e
n
d
d
e
la
y
s
in
a
V
R
sy
ste
m
,”
i
n
Pro
c
e
e
d
in
g
s
IEE
E
Vi
rt
u
a
l
Rea
li
ty A
n
n
u
a
l
I
n
ter
n
a
ti
o
n
a
l
S
y
m
p
o
siu
m
,
p
p
.
1
5
6
-
1
5
9
,
1
9
9
8
.
[2
4
]
W
.
B
o
o
t
h
b
y
,
“
A
n
In
tro
d
u
c
ti
o
n
t
o
Dif
f
e
re
n
ti
a
b
le
M
a
n
if
o
ld
s
a
n
d
Rie
m
a
n
n
ian
G
e
o
m
e
tr
y
,
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v
ise
d
,
Vo
l
u
m
e
1
2
0
,
2
n
d
Ed
it
io
n
,
”
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c
a
d
e
m
ic
P
re
ss
,
Ne
w
Yo
rk
,
2
0
0
2
.
[2
5
]
Y.
M
.
H.
Ng
a
n
d
C
.
P
.
Kw
o
n
g
,
“
Co
rre
c
ti
n
g
th
e
c
h
ro
m
a
ti
c
a
b
e
rra
ti
o
n
in
b
a
rre
l
d
ist
o
rti
o
n
o
f
e
n
d
o
s
c
o
p
ic
im
a
g
e
s
,”
J
o
u
rn
a
l
o
f
S
y
ste
mic
s,
Cy
b
e
rn
e
ti
c
s,
a
n
d
I
n
fo
rm
a
ti
c
s
,
2
0
0
3
.
B
I
O
G
RAP
H
I
E
S
O
F
AUTH
O
RS
Iv
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n
Tr
e
n
c
h
e
v
.
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ss
o
c
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ro
f
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r
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De
p
a
rtm
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t
o
f
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e
c
tri
c
a
l
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g
in
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e
rin
g
,
El
e
c
tro
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ics
a
n
d
A
u
to
m
a
ti
c
s,
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a
c
u
lt
y
o
f
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g
in
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rin
g
,
m
e
m
b
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f
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rsit
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n
ter
f
o
r
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d
v
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e
d
,
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s
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o
u
t
h
-
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e
rsit
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f
it
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sk
i"
,
6
6
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a
n
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i
h
a
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tr
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o
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ro
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