matlab - SKE Systems

weepingwaterpickΑσφάλεια

23 Φεβ 2014 (πριν από 3 χρόνια και 6 μήνες)

315 εμφανίσεις

Matlab

Khadarbaba.shaik@gmail.com

7842522786/9948887244


IMAGE PROCESSING


S.NO

PROJECT
CODE

PROJECT TITLE

DESCRIPTION

YEAR

1.


V
T
I
M
P
01

C
o
n
t
e
x
t
-
D
e
p
e
n
d
e
n
t

L
o
g
o

M
a
t
c
h
i
n
g

a
n
d

R
ec
o
gn
i
t
i
o
n

I
m
age

R
ec
o
g
n
i
t
i
o
n
,

L
o
go

D
e
t
ec
ti
o
n
/

R
e
c
o
g
n
i
t
i
o
n
,
S
c
al
e
I
n
v
a
r
i
a
nt

F
e
at
u
r
e
T
r
a
n
s
fo
r
m
,
Id
e
n
t
i
fy t
h
e

O
ri
g
i
n
al

o
r

F
a
k
e
P
r
o
d
u
c
t
s
/
C
er
t
i
f
ic
a
t
e
s
/
Etc

B
y
U
s
i
n
g
L
o
g
o
s
.




2013

2.


V
T
I
M
P
02

T
h
e
r
m
a
l

I
m
a
g
i
ng

a
s

a

B
i
o
m
e
t
r
i
c
s

A
pp
r
o
ac
h

t
o

F
a
c
i
a
l
S
i
g
n
a
t
u
r
e

A
u
t
h
e
n
t
i
ca
t
i
o
n

B
i
om
e
t
r
i
c

R
ec
o
g
n
i
t
i
o
n

(
F
a
c
e
)
,

T
h
e
r
m
a
l

I
m
a
g
i
ng,
A
u
t
h
e
n
t
i
c
a
t
i
o
n,
L
o
c
aliz
e
d

C
o
n
t
ou
r
i
n
g
A
l
g
o
r
i
t
h
m
s
,
I
n
fo
r
m
a
t
i
o
n
S
ec
u
r
it
y
,

L
a
w
E
n
fo
r
ce
m
e
n
t
,

S
ma
rt

ca
r
d
s
.


2013

3.


V
T
I
M
P
03

R
o
b
u
s
t

D
o
c
u
m
e
n
t

I
m
a
ge

B
i
n
a
r
i
za
t
i
o
n

T
ec
hn
i
que

f
o
r

D
e
g
r
a
d
e
d

D
o
c
u
m
e
n
t

I
m
a
g
e
s

I
m
age

R
e
s
t
o
r
at
i
o
n
/

D
e
g
r
a
d
a
t
i
o
n

D
e
g
r
a
d
e
d

D
o
c
u
m
e
nt

I
m
a
ge

B
i
n
a
r
i
z
a
t
i
o
n,

U
s
e
d
i
n
R
e
s
t
o
r
i
ng
D
o
c
u
m
e
n
t

I
m
a
g
e
s
t
h
a
t

s
u
ff
e
r

f
r
o
m
S
m
ea
r
,

H
i
s
t
o
ric
a
l

D
o
c
u
m
e
n
t
s
.




2013

4.


V
T
I
M
P
04

A

N
o
v
e
l

R
e
v
e
r
s
i
b
l
e

D
a
t
a

H
i
d
i
ng

S
c
h
e
m
e

B
a
s
e
d

o
n

T
w
o
-
D
i
m
e
n
s
i
o
n
a
l

D
i
f
f
e
r
e
n
c
e
-

H
i
s
to
g
r
a
m

M
od
i
f
i
c
a
t
i
o
n

S
t
e
ga
n
o
g
r
a
ph
y,

R
e
v
e
r
s
i
b
l
e

D
at
a

H
i
d
i
n
g
,
D
i
f
f
e
r
e
n
ce
-
P
a
i
r
-
M
a
p
p
i
n
g
,
T
w
o
-
D
im
e
n
s
i
o
n
a
l
D
i
ff
e
r
e
n
c
e
-

H
i
s
t
o
g
r
a
m
,

Mi
l
i
t
a
r
y,
M
e
d
i
ci
n
e

a
n
d
A
r
t
.


2013

5.


V
T
I
M
P
05

F
u
l
l
y

A
u
to
m
a
t
e
d

S
e
g
m
e
n
t
a
t
i
o
n

a
n
d

T
r
ac
k
i
n
g

o
f

t
h
e

I
n
t
im
a

M
e
d
i
a

T
h
i
c
k
n
e
s
s
i
n

U
l
t
r
a
s
o
u
n
d

V
i
d
e
o

S
e
qu
e
n
c
e
s

o
f
t
h
e
C
o
m
m
o
n

C
a
r
ot
i
d

A
r
t
e
r
y

B
i
o
-
Me
d
i
c
a
l
,
I
m
age
S
e
g
m
e
n
t
a
t
i
o
n
,
T
h
r
e
s
h
o
l
d
i
n
g, I
M
C

S
e
g
m
e
n
t
a
ti
o
n
a
n
d

T
r
a
c
k
i
ng

A
l
g
o
r
it
h
m
,

D
ia
g
n
o
s
i
s

a
n
d

T
r
e
a
t
m
e
nt

P
la
nn
i
n
g
,

E
a
r
l
y

D
e
t
ec
ti
o
n of

B
l
o
c
k
a
ge

t
h
us

A
vo
i
d
s

S
t
r
o
k
e
s

a
n
d

H
e
a
r
t

A
t
t
a
c
k
s
.




2013

6.


V
T
I
M
P
06

C
r
o
ss
-
S
c
a
l
e

C
o
e
ff
i
c
i
e
n
t
S
e
l
ec
t
i
o
n

f
o
r

V
o
l
u
m
e
t
r
i
c

M
e
d
i
c
a
l

I
m
a
ge

F
u
s
i
o
n

B
i
o
-
Me
d
i
c
a
l
,

M
e
d
i
c
al

I
m
age

F
u
s
i
o
n
,

3
-
D

M
e
d
i
c
a
l

I
m
a
ge

F
u
s
i
o
n,

C
r
o
s
s
-
S
c
al
e

F
u
s
i
o
n

R
u
l
e
,

F
a
c
i
l
i
t
at
e
s

I
ma
ge

P
r
o
ce
ss
i
n
g
.


2013

7.


V
T
I
M
P
07

A
u
to
m
a
t
i
c

S
e
g
m
e
n
t
a
t
i
o
n

o
f

S
c
a
l
i
ng

i
n

2
-

D

P
s
o
r
i
a
s
i
s
S
k
i
n

I
m
a
g
e
s

B
i
o
-
Me
d
i
c
a
l
,

I
m
age

S
e
g
m
e
n
t
a
t
i
o
n,

M
a
r
k
o
v

R
a
n
d
o
m

F
i
e
l
d

(
M
R
F
),

S
upp
o
r
t

V
ec
t
o
r

M
a
c
h
i
n
e (
SVM
)
,
P
i
x
e
l

L
a
b
e
li
n
g
A
l
g
o
r
i
t
h
m
, I
m
p
r
ov
e
m
e
n
t

i
n

Ps
o
r
ia
s
i
s
T
r
e
a
t
m
e
n
t
,
U
s
e
d

t
o

s
o
l
ve
a

w
i
de
r
a
nge of
S
c
al
i
n
g

P
r
ob
l
e
m
s
.


2013

8.


V
T
I
M
P
08

S
up
e
r

p
i
x
e
l

C
l
a
s
s
i
f
i
ca
t
i
o
n

b
a
s
e
d

Op
t
i
c

D
i
s
c

a
n
d

Op
t
i
c

C
up

S
e
g
m
e
n
t
a
t
i
o
n

f
o
r

B
i
o
-
Me
d
i
c
a
l
,

F
u
n
d
u
s

I
m
age

P
r
o
ce
ss
i
n
g
,

S
up
e
r

P
i
x
e
l

C
la
ss
i
f
i
c
a
t
i
o
n
,
T
h
r
e
s
h
o
l
d
i
n
g
,
H
i
s
t
o
g
r
a
m
E
qu
a
l
i
z
a
ti
o
n,

S
i
m
p
l
e
L
i
n
ea
r

I
t
e
r
a
t
i
v
e

C
l
u
s
t
e
r
i
ng
A
l
g
o
r
it
h
m

(
S
L
IC
)
,

E
a
r
l
y

T
r
e
at
m
e
nt





2013

Matlab

Khadarbaba.shaik@gmail.com

7842522786/9948887244


G
l
a
u
c
o
m
a

S
c
r
e
e
n
i
ng

c
a
n

P
r
e
v
e
n
t

P
a
t
i
e
n
t
s g
e
tt
i
ng

a
ff
e
c
t
e
d
f
r
o
m

S
e
v
e
r
e
C
o
n
d
i
t
i
o
n

a
nd p
r
og
r
e
ss
i
o
n

o
f

D
i
s
e
a
s
e
c
a
n

b
e
s
l
o
w
e
d

do
w
n
.

9.


V
T
I
M
P
09

F
i
ng
e
r
p
r
i
n
t

C
o
m
b
i
n
a
t
i
o
n

f
o
r

Pr
i
v
a
c
y

P
r
o
t
ec
t
i
o
n

B
i
om
e
t
r
i
c

R
ec
o
g
n
i
t
i
o
n
,

F
i
n
g
e
r
,

R
e
f
e
r
e
n
c
e

P
o
i
n
t
s

D
e
t
ec
ti
o
n
,

T
w
o
-

S
ta
ge
F
i
n
g
e
r
p
r
i
nt

M
at
c
h
i
n
g

(
Q
u
e
r
y
M
i
n
u
t
ia
e
D
e
t
e
r
m
i
n
ati
o
n

a
nd
M
at
c
h
i
n
g

S
c
o
r
e C
a
l
c
u
l
a
t
i
o
n
)
,
M
i
n
u
t
ia
e
-
b
a
s
e
d
F
i
n
g
e
r

P
r
i
nt
M
at
c
h
i
n
g
A
l
g
o
r
i
t
h
m
s
,
A
u
t
h
e
n
ti
ca
ti
o
n,

S
ec
ur
it
y
,
B
i
o
m
e
t
r
i
c
T
i
m
e
A
tt
e
nd
a
n
c
e

S
y
s
t
e
m
s
,

P
r
i
v
ac
y p
r
o
t
ec
ti
o
n.





2013

10.


V
T
I
M
P
10

V
e
r
t
i
c
a
l
-
E
dge
-
B
a
s
e
d
C
a
r
-
L
i
ce
n
s
e
-
P
l
a
t
e

D
e
t
e
c
t
i
o
n

M
e
t
h
o
d

I
m
age

S
e
g
m
e
n
tat
i
o
n
,

A
d
a
p
t
i
v
e

T
h
r
e
s
h
o
l
d
i
n
g (
A
T
)
,

S
ob
e
l

O
p
e
r
at
o
r,
U
n
w
a
n
t
e
d
-
Li
n
e
E
l
i
m
i
n
a
ti
o
n
A
l
g
o
r
it
h
m
(
U
L
E
A
),

V
e
r
t
i
c
a
l

E
dge
D
e
t
ec
ti
o
n

A
l
g
o
r
it
h
m (
V
E
DA
)
,
P
a
y
m
e
n
t

of
P
a
r
k
i
n
g

F
ee
s
,

H
i
g
h
w
a
y
T
o
l
l

F
ee
s
,

T
r
a
ff
i
c
D
at
a

C
o
ll
ec
ti
o
n, Cr
im
e
P
r
e
v
e
n
ti
o
n,

V
e
h
i
c
l
e

A
cce
s
s C
o
n
t
r
o
l
,

a
nd

Bo
r
d
e
r

C
o
n
t
r
o
l
.





2013

11.


V
T
I
M
P
11

A

H
i
e
r
a
r
c
h
i
c
a
l

A
pp
r
o
ac
h

to

C
h
a
n
ge

D
e
t
e
c
t
i
o
n

i
n

V
e
r
y

H
i
g
h

R
e
s
o
l
u
t
i
o
n

S
A
R
I
m
a
g
e
s

f
o
r

S
u
r
v
e
i
l
l
a
n
c
e

A
p
p
l
i
ca
t
i
o
n
s

I
m
age

F
u
s
i
o
n
,

R
ati
o

O
p
e
r
at
o
r
,

2
-
D

D
i
s
c
r
e
t
e

S
ta
t
i
o
n
a
r
y
W
a
v
e
l
e
t
T
r
a
n
s
fo
r
m

(2D
-
S
WT
),
T
h
r
e
s
h
o
l
d
i
n
g
,
T
h
e
B
a
y
e
s

D
ec
i
s
i
o
n

R
u
l
e
,
E
xp
ec
ta
t
i
o
n
-
M
a
x
i
m
i
z
a
ti
o
n (
E
M
)
A
l
g
o
r
it
h
m
,

C
h
a
nge
D
e
t
ec
ti
o
n

(
C
D
)
ma
p
s
,
R
e
m
o
t
e

S
e
n
s
i
ng,
F
r
e
i
g
ht
T
r
a
ff
i
c

S
u
r
v
e
il
la
n
c
e
.





2013

12.


V
TI
M
P
12

A

W
a
t
e
r
m
a
r
k
i
ng

B
a
s
e
d

M
e
d
i
c
a
l

I
m
a
ge

I
n
t
e
g
r
i
t
y

C
o
n
t
r
o
l

S
y
s
t
e
m

a
n
d

a
n

I
m
a
ge

M
o
m
e
n
t

S
i
g
n
a
t
u
r
e

f
o
r

T
a
m
p
e
r
i
ng

C
h
a
r
ac
t
e
r
i
z
a
t
i
o
n

In

t
h
i
s

p
a
p
e
r,

w
e

p
r
e
s
e
n
t

a

m
e
d
i
ca
l

i
m
a
ge
i
n
t
e
g
r
i
t
y v
e
r
i
f
i
c
ati
o
n

s
y
s
t
e
m
t
o
d
e
t
ec
t

a
n
d
a
p
p
r
o
x
i
m
a
t
e

l
o
c
a
l
m
a
l
e
v
o
l
e
n
t

i
ma
ge
a
lt
e
r
a
ti
o
n
s

a
s
w
e
l
l
a
s

i
d
e
n
t
i
f
y
i
n
g

t
h
e
n
a
t
u
r
e

o
f

a

g
l
ob
a
l
p
r
o
ce
ss
i
n
g

a
n

i
ma
ge

ma
y
h
a
v
e
und
e
r
g
o
n
e
.

T
h
e
p
r
o
p
o
s
e
d

i
n
t
e
g
r
it
y
a
n
a
l
y
s
i
s

p
r
o
ce
s
s
i
s

b
a
s
e
d

o
n

n
o
n
s
i
g
n
i
f
i
ca
n
t
r
e
g
i
o
n

w
at
e
r
m
a
rk
i
ng

w
i
t
h
s
i
g
n
a
t
u
r
e
s
e
x
t
r
a
c
t
e
d

f
r
o
m
d
i
f
f
e
r
e
nt p
i
x
e
l

b
l
o
c
ks

of
i
n
t
e
r
e
s
t

a
n
d

w
h
i
c
h
a
r
e
c
o
m
p
a
r
e
d

w
it
h

t
h
e

r
ec
o
m
pu
t
e
d
o
n
e
s

a
t
t
h
e

v
e
r
i
f
i
c
ati
o
n

s
t
a
g
e
.





2013

13.


V
TI
M
P
13

C
o
m
p
r
e
s
s
i
ve

F
r
a
m
e
w
o
r
k

f
o
r

D
e
m
o
s
a
i
c
i
ng

o
f

N
a
t
u
r
a
l

I
m
a
g
e
s

In

t
h
i
s

p
a
p
e
r,

w
e

p
r
e
s
e
n
t

c
o
m
p
r
e
ss
i
v
e

d
e
m
o
s
ai
c
i
n
g

(C
D
),
a
f
r
am
e
w
o
r
k

fo
r

d
e
m
o
s
ai
c
i
n
g n
a
t
u
r
a
l
i
m
a
g
e
s

b
a
s
e
d

o
n

t
h
e
t
h
e
o
r
y

of
c
o
m
p
r
e
ss
e
d

s
e
n
s
i
n
g (C
S
).

G
i
v
e
n
s
e
n
s
e
d

s
am
p
l
e
s

of
a
n

i
m
a
g
e
,

CD
e
m
p
l
o
y
s

a

CS

s
o
l
v
e
r

t
o
f
i
n
d
t
h
e
s
p
a
r
s
e

r
e
p
r
e
s
e
n
t
ati
o
n
o
f

t
h
a
t

i
m
a
ge und
e
r

a

f
i
x
e
d

s
p
a
r
s
i
f
y
i
n
g

d
i
c
ti
o
n
a
r
y Ψ.

A
s

o
pp
o
s
e
d

t
o

s
tat
e

of

t
h
e

a
r
t

C
S
-

b
a
s
e
d

d
e
m
o
s
ai
ci
n
g

a
pp
r
o
a
c
h
e
s
,

w
e

c
o
n
s
i
d
e
r

a
c
l
ea
r

d
i
s
ti
n
c
ti
o
n

b
e
t
w
e
e
n
t
h
e
i
n
t
e
r
c
h
a
n
n
e
l

(
c
o
l
o
r
)

a
nd
i
n
t
e
r
p
i
x
e
l

c
o
r
r
el
a
t
i
o
n
s

o
f
n
at
u
r
a
l
i
m
a
g
e
s
.





2013

14.


V
TI
M
P
14

C
o
n
t
e
x
t
-
B
a
s
e
d

H
i
e
r
a
r
c
h
i
c
a
l

U
n
e
qu
a
l

T
h
i
s

p
a
p
e
r

p
r
e
s
e
n
t
s

a
n

i
m
a
ge

s
e
g
m
e
n
t
a
t
i
o
n
m
et
h
o
d

n
a
m
e
d C
o
n
t
e
x
t
-



Matlab

Khadarbaba.shaik@gmail.com

7842522786/9948887244


M
e
r
g
i
n
g

f
o
r

S
A
R
I
m
a
ge

S
e
g
m
e
n
t
a
t
i
o
n

b
a
s
e
d
H
i
e
r
a
r
c
h
i
ca
l

U
n
e
qu
a
l
M
e
r
g
i
ng

fo
r

S
y
n
t
h
e
ti
c

a
p
e
r
t
u
r
e r
a
d
a
r (
SA
R
)

I
ma
ge
S
e
g
m
e
n
t
a
ti
o
n (C
H
U
MS
I
S
),

w
h
i
c
h

u
s
e
s
s
up
e
r
p
i
x
e
l
s
a
s
t
h
e
o
p
e
r
ati
o
n

u
n
it
s
i
n
s
t
ea
d

of p
i
x
e
l
s
.

B
a
s
e
d

o
n

t
h
e
G
e
s
t
a
l
t

la
w
s
,
t
h
r
e
e

r
u
l
e
s

t
h
a
t

r
e
al
i
z
e

a

n
e
w
a
nd n
at
u
r
a
l

w
a
y
t
o
ma
n
a
ge

d
i
f
f
e
r
e
nt k
i
n
ds
o
f

f
e
at
u
r
e
s

e
x
t
r
a
c
t
e
d

f
r
o
m
SA
R
ima
g
e
s

a
r
e

p
r
o
p
o
s
e
d

t
o
r
e
p
r
e
s
e
n
t

s
up
e
r
p
i
x
e
l

c
o
n
t
e
x
t
.
T
h
e
r
u
l
e
s

a
r
e
p
r
i
o
r

k
n
o
w
l
e
dge

f
r
o
m
c
o
g
n
iti
v
e

s
c
i
e
n
c
e

a
n
d

s
e
r
v
e
a
s
t
o
p
-

do
w
n

c
o
n
s
t
r
a
i
n
t
s

t
o g
l
ob
al
l
y

gu
i
de
t
h
e

s
up
e
rp
i
x
e
l
m
e
r
g
i
n
g.





2013

15.


V
TI
M
P
15

D
i
s
c
r
e
t
e

W
a
v
e
l
e
t

T
r
a
n
s
f
o
r
m

a
n
d

D
a
t
a

E
x
p
a
n
s
i
o
n

R
e
du
c
t
i
o
n

i
n

H
o
m
o
m
o
r
ph
i
c

E
n
c
r
y
p
t
e
d

D
o
m
a
i
n

In

t
h
i
s

p
a
p
e
r
,

w
e

p
r
o
p
o
s
e

a

m
e
t
h
o
d

f
o
r

im
p
l
e
m
e
n
ti
n
g

d
i
s
c
r
e
t
e
w
a
v
e
l
e
t
t
r
a
n
s
fo
r
m

(
D
W
T
)
a
nd
m
u
l
t
i
r
e
s
o
l
u
ti
o
n

a
n
a
l
y
s
i
s

(
M
R
A
)
i
n
h
o
m
o
m
o
r
p
h
i
c
e
n
c
r
y
p
t
e
d

d
o
ma
i
n
.

W
e
f
i
r
s
t

s
ugg
e
s
t a
f
r
a
m
e
w
o
r
k f
o
r p
e
r
fo
r
m
i
ng

D
W
T

a
nd

i
n
v
e
r
s
e
D
W
T (I
D
W
T
)

i
n

t
h
e
e
n
c
r
y
p
t
e
d

d
o
m
a
i
n,
t
h
e
n

c
o
n
du
c
t

a
n

a
n
al
y
s
i
s

of d
a
t
a
e
xp
a
n
s
i
o
n

a
n
d q
u
a
n
ti
z
a
t
i
o
n

e
r
r
o
r
s und
e
r

t
h
e

f
r
a
m
e
w
o
r
k.

T
o
s
o
l
ve

t
h
e
p
r
ob
l
e
m

o
f
d
at
a

e
xp
a
n
s
i
o
n
,

w
h
i
c
h
ma
y

b
e

v
e
r
y

im
p
o
r
t
a
nt

i
n
p
r
a
c
ti
ca
l
a
pp
l
i
c
a
t
i
o
n
s
,

w
e
p
r
e
s
e
nt

a
m
et
h
o
d f
o
r

r
e
du
ci
n
g d
a
t
a

e
xp
a
n
s
i
o
n

i
n

t
h
e
c
a
s
e

t
h
a
t

bo
t
h

D
W
T

a
nd

I
D
W
T
a
r
e
p
e
r
fo
r
m
e
d.

Wit
h

t
h
e
p
r
o
p
o
s
e
d
m
et
h
o
d,

m
u
l
t
i
l
e
v
e
l

D
W
T/
I
D
W
T
c
a
n
b
e
p
e
r
fo
r
m
e
d

w
it
h

l
e
s
s

d
a
t
a
e
xp
a
n
s
i
o
n

i
n
h
o
m
o
m
o
r
p
h
i
c
e
n
c
r
y
p
t
e
d

d
o
m
a
i
n.








2013

16.


V
TI
M
P
16

E
s
t
im
a
t
i
n
g

I
n
f
o
r
m
a
t
i
o
n

f
r
o
m

I
m
a
ge

C
o
l
o
r
s

An

A
p
p
l
i
ca
t
i
o
n

to

D
i
g
i
t
a
l

C
a
m
e
r
a
s
a
n
d

N
a
t
u
r
a
l

S
ce
n
e
s

T
h
e

c
o
l
o
r
s

p
r
e
s
e
n
t

i
n

a
n

i
m
a
g
e

o
f a

s
ce
n
e

p
r
o
v
i
de

i
n
f
o
r
m
a
t
i
o
n
a
b
o
ut

i
t
s
c
o
n
s
t
i
t
u
e
n
t

e
l
e
m
e
n
t
s
.
B
ut

t
h
e

a
m
o
u
n
t

o
f
i
n
f
o
r
m
a
t
i
o
n
d
e
p
e
n
d
s

o
n
t
h
e

im
a
g
i
ng
c
o
n
d
i
t
i
o
n
s
a
n
d

o
n

h
o
w
i
n
f
o
r
m
a
t
i
o
n

i
s
c
a
l
c
u
l
a
t
e
d.

T
h
i
s
w
o
r
k
h
a
d

t
w
o

a
im
s
.

T
h
e

f
i
r
s
t w
a
s
t
o

d
e
r
i
v
e

e
x
p
l
i
c
i
t
ly
e
s
t
im
a
to
r
s

o
f

t
h
e

i
n
f
o
r
m
a
t
i
o
n
a
v
a
i
l
a
b
l
e

a
n
d
t
h
e

i
n
f
o
r
m
a
t
i
o
n

r
e
t
r
i
e
v
e
d

f
r
o
m

t
h
e

c
o
l
o
r

v
a
l
u
e
s
a
t

eac
h

p
o
i
n
t

i
n
im
a
g
e
s

o
f

a
s
c
e
n
e
u
n
d
e
r

d
i
f
f
e
r
e
n
t i
l
l
u
mi
n
a
t
i
o
n
s
.

T
h
e
s
ec
o
n
d

w
a
s to

a
p
p
l
y
t
h
e
s
e

e
s
t
im
a
to
r
s

to
s
im
u
l
a
t
i
o
ns
o
f

im
a
g
e
s
o
b
t
a
i
n
e
d
w
i
t
h

f
i
ve

s
e
t
s

o
f

s
e
n
s
or
s

u
s
e
d
i
n

d
i
g
i
t
a
l

c
a
m
e
r
a
s

a
n
d

w
i
t
h
t
he
c
o
n
e

p
h
o
t
o
r
ece
p
t
o
r
s

o
f
t
h
e
h
u
m
a
n

e
y
e
.






2013

Matlab

Khadarbaba.shaik@gmail.com

7842522786/9948887244


17.


V
TI
M
P
17

G
e
n
e
r
a
l

C
o
n
s
t
r
u
c
t
i
o
ns

f
o
r

T
h
r
e
s
h
o
l
d

M
u
l
t
i
p
l
e
-
S
ec
r
e
t

V
i
s
u
a
l

C
r
y
p
to
g
r
a
ph
i
c

S
c
h
e
m
e
s

A

c
o
n
v
e
n
ti
o
n
a
l

t
h
r
e
s
h
o
l
d

(k

o
ut

o
f

n
)

v
i
s
u
a
l

s
ec
r
e
t

s
h
a
r
i
n
g

s
c
h
e
m
e
e
n
c
o
d
e
s
o
n
e

s
ec
r
e
t

ima
ge

P

i
n
t
o n
t
r
a
n
s
p
a
r
e
n
c
i
e
s

(
c
all
e
d

s
h
a
r
e
s
)
s
u
c
h
t
h
a
t

a
n
y

g
r
o
up

o
f
k

t
r
a
n
s
p
a
r
e
n
c
i
e
s
r
e
v
e
al
s

P

w
h
e
n

t
h
e
y
a
r
e
s
up
e
r
i
m
p
o
s
e
d,

w
h
i
l
e

t
h
a
t

o
f
l
e
s
s

t
h
a
n
k

o
n
e
s

ca
n
n
o
t
.

W
e d
e
f
i
n
e

a
nd

d
e
v
e
l
o
p g
e
n
e
r
a
l

c
o
n
s
t
r
u
ct
i
o
n
s

fo
r
t
h
r
e
s
h
o
l
d
m
u
lt
i
p
l
e
-
s
ec
r
e
t

v
i
s
u
a
l
c
r
y
p
t
o
g
r
a
p
h
i
c
s
c
h
e
m
e
s (
MV
C
Ss
)
t
h
a
t

a
r
e

c
a
p
a
b
l
e

o
f
e
n
c
o
d
i
n
g

s

s
ec
r
e
t
i
m
a
g
e
s

P
1
,
P
2
,
.
.
.
,
P
s

i
n
t
o

n
s
h
a
r
e
s
s
u
c
h
t
h
a
t

a
n
y

g
r
o
up

o
f

l
e
s
s

t
h
a
n

k
s
h
a
r
e
s

ob
ta
i
n
s
n
o
n
e

o
f
t
h
e

s
ec
r
e
t
s
,
w
h
il
e

1)

e
a
c
h

g
r
o
up

o
f k,

k
+
1
,
.
.
.
,

n
s
h
a
r
e
s
r
e
v
e
al
s

P
1,

P
2,

.
..
,

Ps
,
r
e
s
p
ec
ti
v
e
l
y
,

w
h
e
n
s
u
p
e
r
im
p
o
s
e
d,
r
e
f
e
r
r
e
d

t
o
a
s

(k,

n
,

s
)
-
M
V
CS

w
h
e
r
e
s
=
n
-
k
+
1
;

o
r

2)
e
a
c
h

g
r
o
up

o
f u
s
h
a
r
e
s








2013

18.


V
TI
M
P
18

G
e
n
e
r
a
l

F
r
a
m
e
w
o
r
k

to

H
i
s
t
og
r
a
m
-

S
h
i
f
t
i
ng
-
B
a
s
e
d

R
e
v
e
r
s
i
b
l
e

D
a
t
a

H
i
d
i
n
g

In

t
h
i
s

p
a
p
e
r,

w
e

r
e
v
i
s
i
t

t
h
e

H
S

t
ec
h
n
i
q
ue
a
n
d

p
r
e
s
e
nt

a

g
e
n
e
r
a
l
f
r
am
e
w
o
r
k

t
o
c
o
n
s
t
r
u
c
t

H
S
-
b
a
s
e
d
R
D
H
.

By
t
h
e

p
r
o
p
o
s
e
d

f
r
a
m
e
w
o
r
k,
o
n
e

c
a
n

g
e
t

a

R
D
H
al
g
o
r
it
h
m

by
s
im
p
l
y d
e
s
i
g
n
i
ng

t
h
e

s
o
-
c
all
e
d
s
h
i
f
t
i
ng

a
n
d
e
m
b
e
dd
i
n
g

f
u
n
ct
i
o
n
s
.
M
o
r
e
o
v
e
r
,

by

ta
k
i
n
g

s
p
ec
i
f
i
c

s
h
i
f
t
i
ng
a
n
d

e
m
b
e
dd
i
n
g

f
u
n
c
ti
o
n
s
,

w
e
s
h
ow
t
h
a
t

s
e
v
e
r
a
l

R
D
H
al
g
o
r
i
t
h
m
s
r
e
p
o
r
t
e
d

i
n

t
h
e
lit
e
r
a
t
u
r
e

a
r
e

s
p
e
c
i
a
l
c
a
s
e
s

of

t
h
i
s

g
e
n
e
r
a
l
c
o
n
s
t
r
u
c
t
i
o
n.






2013

19.


V
TI
M
P
19

H
y
p
e
r
s
p
ec
t
r
a
l

I
m
a
g
e
r
y

R
e
s
t
o
r
a
t
i
o
n

U
s
i
ng

N
o
n
l
o
c
a
l

S
p
ec
t
r
a
l
-
S
p
a
t
i
a
l

S
t
r
u
c
t
u
r
e
d

S
p
a
r
s
e

R
e
p
r
e
s
e
n
t
a
t
i
o
n

W
i
t
h

N
o
i
s
e

E
s
t
im
a
t
i
o
n

In
t
h
i
s

p
a
p
e
r
,

w
e

d
e
v
e
l
o
p

a

s
p
a
r
s
e

r
e
p
r
e
s
e
n
t
a
t
i
o
n

b
a
s
e
d
n
o
i
s
e

r
e
du
c
ti
o
n
m
et
h
o
d f
o
r

h
y
p
e
r
s
p
ec
t
r
a
l

i
m
a
g
e
r
y
,
w
h
i
c
h

i
s d
e
p
e
n
d
e
n
t

o
n

t
h
e
a
ss
u
m
p
t
i
o
n

t
h
a
t

t
h
e
n
o
n
-
n
o
i
s
e
c
o
m
p
o
n
e
n
t
i
n

a
n

ob
s
e
r
v
e
d

s
i
gn
a
l

ca
n
b
e

s
p
a
r
s
e
l
y d
e
c
o
m
p
o
s
e
d

o
v
e
r

a
r
e
d
u
n
d
a
n
t

d
i
c
ti
o
n
a
r
y

w
h
il
e

t
h
e
n
o
i
s
e
c
o
m
p
o
n
e
n
t

d
o
e
s

n
o
t

h
a
v
e

t
h
i
s
p
r
o
p
e
r
t
y
.

T
h
e

ma
i
n

c
o
n
t
r
i
b
u
ti
o
n

of

t
h
e p
a
p
e
r

i
s

i
n

t
h
e
i
n
t
r
o
du
c
ti
o
n

o
f
n
o
n
l
o
c
a
l

s
i
m
i
la
r
i
t
y
a
n
d

s
p
e
c
t
r
a
l
-

s
p
at
i
a
l

s
t
r
u
c
t
u
r
e

o
f
h
y
p
e
r
s
p
e
c
t
r
a
l
i
m
a
g
e
r
y

i
n
t
o

s
p
a
r
s
e

r
e
p
r
e
s
e
n
t
a
ti
o
n.








2013

20.


V
TI
M
P
20

I
n
t
e
r
ac
t
i
v
e

S
e
g
m
e
n
t
a
t
i
o
n

f
o
r

C
h
a
n
ge

D
e
t
e
c
t
i
o
n

i
n

M
u
l
t
i
s
p
ec
t
r
a
l

R
e
m
ot
e
-

S
e
n
s
i
ng

I
m
a
g
e
s

In

t
h
i
s

l
et
t
e
r,

w
e

p
r
o
p
o
s
e

t
o

s
o
l
v
e

t
h
e

c
h
a
nge d
e
t
ec
ti
o
n

(C
D
)

p
r
ob
l
e
m

i
n
m
u
l
tit
e
m
p
o
r
a
l
r
e
m
o
t
e
-
s
e
n
s
i
ng
i
m
a
g
e
s

u
s
i
n
g
i
n
t
e
r
a
c
ti
v
e
s
e
g
m
e
n
t
a
t
i
o
n

m
et
h
o
d
s
.

T
h
e u
s
e
r
n
ee
ds

t
o

i
n
p
u
t

ma
r
k
e
r
s
r
e
lat
e
d

t
o
c
h
a
nge

a
n
d
n
o
-
c
h
a
nge

c
la
ss
e
s

i
n

t
h
e
d
i
f
f
e
r
e
n
c
e

i
m
a
g
e
.
T
h
e
n,

t
h
e
p
i
x
e
l
s und
e
r

t
h
e
s
e

m
a
r
k
e
r
s

a
r
e

u
s
e
d

by
t
h
e
s
upp
o
r
t

v
ec
t
o
r

m
a
c
h
i
n
e
c
la
ss
i
f
i
e
r

t
o g
e
n
e
r
a
t
e

a

s
p
ec
t
r
a
l
-
c
h
a
nge

m
a
p
.








2013

21.


V
TI
M
P
21

I
n
t
r
a
-
a
n
d
-
I
n
t
e
r
-
C
o
n
s
t
r
a
i
n
t
-
B
a
s
e
d
V
i
d
e
o

In

t
h
i
s

p
a
p
e
r,

w
e

p
r
o
po
s
e

a

n
e
w

i
n
t
r
a
-
a
n
d
-
i
n
t
e
r
-
c
o
n
s
t
r
ai
n
t
-
b
a
s
e
d




Matlab

Khadarbaba.shaik@gmail.com

7842522786/9948887244


E
n
h
a
n
c
e
m
e
n
t

B
a
s
e
d

o
n

P
i
e
c
e
w
i
s
e

T
o
n
e

M
a
p
p
i
ng

v
i
d
e
o
e
n
h
a
n
ce
m
e
n
t

a
p
p
r
o
a
c
h

a
i
m
i
ng
t
o
:

1)
a
c
h
i
e
v
e

h
i
g
h

i
n
t
r
a
f
r
a
m
e qu
a
lit
y of
t
h
e

e
n
t
i
r
e

p
i
c
t
u
r
e

w
h
e
r
e

m
u
lt
i
p
l
e
r
e
g
i
o
n
s
-
of
-
i
n
t
e
r
e
s
t

(
R
O
I
s
)
c
a
n

b
e
a
d
a
p
ti
v
e
l
y
a
nd

s
i
m
u
l
t
a
n
e
o
u
s
l
y
e
n
h
a
n
ce
d,

a
n
d

2)
gu
a
r
a
n
t
e
e

t
h
e
i
n
t
e
r
f
r
a
m
e

qu
a
lit
y
c
o
n
s
i
s
t
e
n
c
i
e
s
am
o
ng
v
i
d
e
o
f
r
am
e
s
.

W
e

f
i
r
s
t
a
n
a
l
y
z
e

f
e
at
u
r
e
s
f
r
o
m

d
i
f
f
e
r
e
nt

R
O
Is
a
n
d

c
r
e
at
e

a p
i
ece
w
i
s
e

t
o
n
e

m
a
pp
i
ng
c
u
r
v
e

f
o
r

t
h
e

e
n
t
i
r
e

f
r
a
m
e

s
u
c
h

t
h
a
t
t
h
e

i
n
t
r
a
f
r
a
m
e

qu
a
l
i
t
y
c
a
n

b
e
e
n
h
a
n
c
e
d.






2013

22.


V
TI
M
P
22

L
a
t
e
n
t

F
i
n
g
e
r
p
r
i
n
t

M
a
t
c
h
i
ng

U
s
i
ng

D
e
s
c
r
i
p
to
r
-
B
a
s
e
d

H
o
ugh

T
r
a
n
s
f
o
r
m

In

t
h
i
s

p
a
p
e
r,

w
e

p
r
o
po
s
e

a

n
e
w

f
i
n
g
e
r
p
r
i
nt
m
at
c
h
i
ng

a
l
g
o
r
it
hm
w
h
i
c
h

i
s

e
s
p
ec
iall
y d
e
s
i
g
n
e
d

fo
r
m
a
t
c
h
i
n
g

l
at
e
n
t
s
.

T
h
e
p
r
o
p
o
s
e
d
al
g
o
r
i
t
hm

u
s
e
s

a
r
ob
u
s
t

ali
g
n
m
e
nt
al
g
o
r
i
t
hm (d
e
s
c
r
i
p
t
o
r
-
b
a
s
e
d

H
o
ugh
t
r
a
n
s
fo
r
m
)

t
o
al
i
g
n

f
i
n
g
e
r
p
r
i
n
t
s

a
nd
m
e
a
s
u
r
e
s

s
im
i
l
a
r
it
y b
e
t
w
e
e
n
f
i
n
g
e
r
p
r
i
n
t
s

by
c
o
n
s
i
d
e
r
i
n
g

bo
t
h
m
i
n
u
t
i
a
e
a
nd
o
r
i
e
n
t
a
ti
o
n

f
i
e
l
d
i
n
fo
r
m
a
ti
o
n.

T
o
b
e

c
o
n
s
i
s
t
e
n
t

w
i
t
h
t
h
e

c
o
mm
o
n

p
r
a
c
ti
c
e

i
n
lat
e
nt
m
a
t
c
h
i
n
g

(
i
.
e
.
,

o
n
l
y
m
i
n
u
t
i
a
e

a
r
e
m
a
r
k
e
d

by
lat
e
nt

e
x
a
mi
n
e
r
s
)
,

t
h
e
o
r
i
e
n
t
a
ti
o
n

f
i
e
l
d

i
s
r
ec
o
n
s
t
r
u
c
t
e
d
f
r
o
m

mi
n
u
t
ia
e
.

S
i
n
c
e

t
h
e
p
r
o
p
o
s
e
d
al
g
o
r
i
t
hm

r
e
li
e
s

o
n
l
y
o
n

m
a
n
u
all
y
m
a
r
k
e
d
m
i
n
u
t
i
a
e
,

i
t

ca
n

b
e

e
a
s
i
l
y u
s
e
d

i
n

l
a
w
e
n
fo
r
ce
m
e
n
t
a
pp
l
i
c
a
t
i
o
n
s
.











2013

23.


V
TI
M
P
23

L
D
F
T
-
B
a
s
e
d

W
a
t
e
r
m
a
r
k
i
n
g

R
e
s
i
l
i
e
n
t

to

L
o
c
a
l

D
e
s
y
n
c
h
r
o
n
+
i
za
t
i
o
n

A
t
t
ac
ks

In

t
h
i
s

p
a
p
e
r
,

w
e

p
r
e
s
e
n
t

a

b
li
nd

i
m
a
ge
w
at
e
r
m
a
rk
i
ng
r
e
s
y
n
c
h
r
o
n
i
z
a
ti
o
n

s
c
h
e
m
e
a
g
a
i
n
s
t
l
o
c
a
l

t
r
a
n
s
fo
r
m

a
t
ta
c
k
s
.

F
i
r
s
t
,

w
e
p
r
o
p
o
s
e

a

n
e
w

f
e
at
u
r
e

t
r
a
n
s
fo
r
m

n
am
e
d

l
o
c
a
l

d
ai
s
y f
e
at
u
r
e

t
r
a
n
s
fo
r
m
(
L
DF
T
),

w
h
i
c
h

i
s

n
o
t

o
n
l
y g
l
ob
al
l
y
b
ut

al
s
o

l
o
c
all
y
i
n
v
a
r
ia
b
l
e
.

T
h
e
n,

t
h
e
b
i
n
a
r
y

s
p
a
c
e

p
a
r
t
i
t
i
o
n
i
ng

(
B
SP
)

t
r
e
e
i
s

u
s
e
d

t
o
p
a
r
t
iti
o
n

t
h
e

g
e
o
m
e
t
r
i
c
all
y
i
n
v
a
r
i
a
nt

L
DF
T

s
p
a
ce
.

I
n

t
h
e
B
S
P
t
r
ee
,

t
h
e

l
o
c
ati
o
n

o
f
e
a
c
h

p
i
x
e
l

i
s
f
i
x
e
d u
n
d
e
r

g
l
ob
a
l

t
r
a
n
s
fo
r
m
,

l
o
c
a
l
t
r
a
n
s
fo
r
m
,

a
n
d

c
r
o
pp
i
ng.




2013

24.


V
TI
M
P
24

L
o
c
a
l

D
i
r
ec
t
i
o
n
a
l

N
u
m
b
e
r

P
a
tt
e
r
n

f
o
r

F
ac
e

A
n
a
l
y
s
i
s

F
ac
e

a
n
d

E
x
p
r
e
s
s
i
o
n

R
ec
o
gn
i
t
i
o
n

T
h
i
s

p
a
p
e
r

p
r
o
p
o
s
e
s

a

n
o
v
e
l

l
o
c
a
l

f
e
at
u
r
e

d
e
s
c
r
i
p
t
o
r
,
l
o
c
a
l

d
i
r
ec
ti
o
n
a
l
n
u
m
b
e
r

p
a
t
t
e
rn

(
L
DN
),
fo
r

f
a
c
e
a
n
a
l
y
s
i
s
,

i
.
e
.
,

f
a
c
e

a
n
d
e
x
p
r
e
ss
i
o
n
r
ec
o
g
n
i
ti
o
n.

L
D
N
e
n
c
o
d
e
s

t
h
e
d
i
r
ec
ti
o
n
a
l

i
n
fo
r
m
a
ti
o
n

of
t
h
e

f
ac
e
'
s
t
e
x
t
u
r
e
s

(
i
.
e
.
,

t
h
e

t
e
x
t
u
r
e
'
s

s
t
ru
c
t
u
r
e
)
i
n

a

c
o
m
p
a
c
t

w
a
y
,

p
r
o
du
ci
n
g

a
m
o
r
e
d
i
s
c
r
i
m
i
n
a
ti
v
e

c
o
de
t
h
a
n

c
ur
r
e
nt
m
et
h
o
d
s
.

W
e

c
o
m
pu
t
e

t
h
e

s
t
r
u
c
t
u
r
e
of

e
a
c
h

mi
c
r
o
-
p
att
e
rn

w
i
t
h

t
h
e

a
i
d

o
f a

c
o
m
p
a
s
s
ma
s
k

t
h
a
t

e
x
t
r
a
c
t
s d
i
r
ec
ti
o
n
a
l

i
n
fo
r
m
a
t
i
o
n,

a
n
d
w
e
e
n
c
o
de

s
u
c
h









2013

Matlab

Khadarbaba.shaik@gmail.com

7842522786/9948887244


i
n
fo
r
m
a
ti
o
n

u
s
i
n
g
t
h
e
p
r
o
m
i
n
e
nt
d
i
r
ec
ti
o
n

i
n
d
i
ce
s (d
i
r
ec
ti
o
n
a
l

nu
m
b
e
r
s
)
a
n
d
s
i
g
n
-

w
h
i
c
h

all
o
w
s

us
t
o

d
i
s
t
i
n
g
u
i
s
h
am
o
ng

s
i
m
i
l
a
r

s
t
r
u
c
t
u
r
a
l
p
a
tt
e
r
n
s

t
h
a
t
h
a
v
e

d
i
f
f
e
r
e
nt
i
n
t
e
n
s
it
y

t
r
a
n
s
i
t
i
o
n
s
.

25.


V
TI
M
P
25

L
o
g
-
G
a
b
o
r

F
i
l
t
e
r
s

f
o
r

I
m
a
g
e
-
B
a
s
e
d

V
e
h
i
c
l
e

V
e
r
i
f
i
c
a
t
i
o
n

T
h
i
s

p
a
p
e
r

p
r
o
p
o
s
e
s

a

n
o
v
e
l

l
o
c
a
l

f
e
at
u
r
e

d
e
s
c
r
i
p
t
o
r
,
l
o
c
a
l

d
i
r
ec
ti
o
n
a
l
n
u
m
b
e
r

p
a
t
t
e
rn

(
L
DN
),
fo
r

f
a
c
e
a
n
a
l
y
s
i
s
,

i
.
e
.
,

f
a
c
e

a
n
d
e
x
p
r
e
ss
i
o
n
r
ec
o
g
n
i
ti
o
n.

L
D
N
e
n
c
o
d
e
s

t
h
e
d
i
r
ec
ti
o
n
a
l

i
n
fo
r
m
a
ti
o
n

of
t
h
e

f
ac
e
'
s
t
e
x
t
u
r
e
s

(
i
.
e
.
,

t
h
e

t
e
x
t
u
r
e
'
s

s
t
ru
c
t
u
r
e
)
i
n

a

c
o
m
p
a
c
t

w
a
y
,

p
r
o
du
ci
n
g

a
m
o
r
e
d
i
s
c
r
i
m
i
n
a
ti
v
e

c
o
de
t
h
a
n

c
ur
r
e
nt
m
et
h
o
d
s
.

W
e

c
o
m
pu
t
e

t
h
e

s
t
r
u
c
t
u
r
e
of

e
a
c
h

mi
c
r
o
-
p
att
e
rn

w
i
t
h

t
h
e

a
i
d

o
f a

c
o
m
p
a
s
s
ma
s
k

t
h
a
t

e
x
t
r
a
c
t
s d
i
r
ec
ti
o
n
a
l

i
n
fo
r
m
a
t
i
o
n,

a
n
d
w
e
e
n
c
o
de

s
u
c
h

i
n
fo
r
m
a
ti
o
n

u
s
i
n
g
t
h
e
p
r
o
m
i
n
e
nt
d
i
r
ec
ti
o
n

i
n
d
i
ce
s (d
i
r
ec
ti
o
n
a
l

nu
m
b
e
r
s
)
a
n
d
s
i
g
n
-

w
h
i
c
h

all
o
w
s

us
t
o

d
i
s
t
i
n
g
u
i
s
h
am
o
ng

s
i
m
i
l
a
r

s
t
r
u
c
t
u
r
a
l
p
a
tt
e
r
n
s

t
h
a
t
h
a
v
e

d
i
f
f
e
r
e
nt
i
n
t
e
n
s
it
y

t
r
a
n
s
i
t
i
o
n
s
.












2013

26.


V
TI
M
P
26

N
o
i
s
e

R
e
du
c
t
i
o
n

B
a
s
e
d

o
n

P
a
r
t
i
a
l
-

R
e
f
e
r
e
n
c
e
,

Du
a
l
-
T
r
e
e

C
o
m
p
l
e
x

W
a
v
e
l
e
t

T
r
a
n
s
f
o
r
m

S
h
r
i
nk
a
ge

i
m
a
g
e
s

o
f

s
p
r
a
y
-
b
a
s
e
d

m
e
t
h
o
ds

t
e
nd

t
o

e
x
h
i
b
i
t
n
o
i
s
e

w
it
h

u
n
k
n
o
w
n
s
ta
t
i
s
ti
ca
l

d
i
s
t
r
i
b
u
t
i
o
n.

T
o
a
vo
i
d
i
n
a
p
p
r
op
r
i
at
e

a
ss
u
m
p
t
i
o
n
s
o
n

t
h
e
s
ta
t
i
s
ti
ca
l

c
h
a
r
a
c
t
e
r
i
s
ti
c
s

o
f
n
o
i
s
e
,

a
d
i
f
f
e
r
e
nt

o
n
e
i
s

ma
d
e
.

I
n

f
a
c
t
,

t
h
e
n
o
n
-
e
nh
a
n
ce
d

i
ma
ge
i
s

c
o
n
s
i
d
e
r
e
d

t
o
b
e

e
it
h
e
r

f
r
e
e

of
n
o
i
s
e

o
r

a
ff
e
c
t
e
d

b
y
n
o
n
-
p
e
r
ce
i
v
a
b
l
e

l
e
v
e
l
s

of

n
o
i
s
e
.






2013

27.


V
TI
M
P
27

Qu
e
r
y
-
A
d
a
p
t
i
v
e

I
m
a
ge

S
ea
r
c
h

W
i
t
h

H
a
s
h

C
o
d
e
s

T
h
i
s

p
a
p
e
r

i
n
t
r
o
du
ce
s

a
n

a
p
p
r
o
a
c
h

t
h
a
t

e
n
a
b
l
e
s

qu
e
r
y
-
a
d
a
p
t
i
v
e

r
a
nk
i
ng of
t
h
e

r
e
t
u
r
n
e
d

i
ma
g
e
s

w
it
h

e
qu
a
l
H
amm
i
n
g

d
i
s
t
a
n
ce
s

t
o
t
h
e

q
u
e
r
i
e
s
.
T
h
i
s

i
s

a
c
h
i
e
v
e
d

by
f
i
r
s
tl
y

of
f
li
n
e
l
ea
r
n
i
ng

b
it
w
i
s
e

w
e
i
g
h
t
s

o
f
t
h
e

h
a
s
h
c
o
d
e
s

f
o
r

a

d
i
v
e
r
s
e

s
e
t

of p
r
e
d
e
f
i
n
e
d
s
e
m
a
n
ti
c
c
o
n
ce
pt

c
la
ss
e
s
.

W
e
f
o
r
m
u
l
a
t
e

t
h
e
w
e
i
ght

l
ea
rn
i
ng p
r
o
ce
s
s

a
s a

qu
a
d
r
a
t
i
c
p
r
o
gr
a
m
m
i
ng p
r
ob
l
e
m

t
h
a
t
m
i
n
i
m
iz
e
s

i
n
t
r
a
-
c
la
s
s d
i
s
t
a
n
c
e

w
h
i
l
e
p
r
e
s
e
r
v
i
ng

i
n
t
e
r
-
c
la
s
s
r
el
ati
o
n
s
h
i
p

c
a
p
t
u
r
e
d

by

o
r
i
g
i
n
a
l r
a
w
i
m
a
ge

f
e
at
u
r
e
s
.








2013

28.


V
TI
M
P
28

R
e
v
e
a
l
i
ng

t
h
e

T
r
ace
s

o
f

J
P
E
G

C
o
m
p
r
e
s
s
i
o
n

A
n
t
i
-
F
o
r
e
n
s
i
c
s

In

t
h
i
s

p
a
p
e
r,

w
e

s
t
u
d
y

t
h
e

p
r
o
ce
ss
i
n
g

c
h
ai
n

t
h
a
t
a
r
i
s
e
s

i
n

t
h
e
c
a
s
e

of
JP
E
G

c
o
m
p
r
e
ss
i
o
n

a
n
t
i
-

f
o
r
e
n
s
i
c
s
.

W
e
ta
ke

t
h
e

p
e
r
s
p
ec
ti
v
e

of
t
h
e

fo
r
e
n
s
i
c

a
n
al
y
s
t
,

a
n
d
w
e

s
h
ow
h
o
w
i
t

i
s

p
o
ss
i
b
l
e

t
o

c
o
u
n
t
e
r

t
h
e
a
fo
r
e
m
e
n
t
i
o
n
e
d

a
n
t
i
-
fo
r
e
n
s
i
c
m
et
h
o
d
r
e
v
e
al
i
n
g

t
h
e

t
r
a
ce
s

of
JP
E
G
c
o
m
p
r
e
ss
i
o
n
,
r
e
g
a
r
d
l
e
s
s

of

t
h
e





2013

Matlab

Khadarbaba.shaik@gmail.com

7842522786/9948887244


qu
a
n
t
i
z
a
t
i
o
n

m
a
t
r
i
x

b
e
i
n
g

u
s
e
d.

29.


V
TI
M
P
29

R
e
v
e
r
s
i
b
l
e

D
a
t
a

H
i
d
i
ng

W
i
t
h

Op
t
im
a
l

V
a
l
ue

T
r
a
n
s
f
e
r

In

r
e
v
e
r
s
i
b
l
e

d
a
t
a

h
i
d
i
n
g

t
ec
h
n
i
qu
e
s
,

t
h
e

v
al
u
e
s

o
f

h
o
s
t

d
at
a
a
r
e

m
o
d
i
f
i
e
d
a
cc
o
r
d
i
n
g

t
o
s
o
m
e

p
a
r
ti
c
u
la
r

r
u
l
e
s
a
n
d

t
h
e

o
r
i
g
i
n
a
l
h
o
s
t

c
o
n
t
e
nt

ca
n

b
e
p
e
r
f
ec
t
l
y
r
e
s
t
o
r
e
d

a
f
t
e
r

e
x
t
r
a
ct
i
o
n

o
f
t
h
e h
i
dd
e
n

d
a
t
a

o
n

r
ece
i
v
e
r

s
i
d
e
.

I
n
t
h
i
s

p
a
p
e
r
,

t
h
e
o
p
t
i
ma
l
r
u
l
e of

v
al
ue
m
o
d
i
f
i
c
ati
o
n

u
n
d
e
r

a p
a
y
l
o
a
d
-

d
i
s
t
o
r
ti
o
n

c
r
i
t
e
r
i
o
n

i
s

fo
u
n
d

by u
s
i
n
g
a
n

i
t
e
r
a
t
i
v
e

p
r
o
ce
du
r
e
,

a
n
d
a
pr
a
c
ti
ca
l

r
e
v
e
r
s
i
b
l
e

d
a
t
a h
i
d
i
ng
s
c
h
e
m
e

i
s

p
r
o
p
o
s
e
d.








2013

30.


V
TI
M
P
30

R
e
v
e
r
s
i
b
l
e

W
a
t
e
r
m
a
r
k
i
ng

B
a
s
e
d

o
n

I
n
v
a
r
i
a
n
t

I
m
a
ge

C
l
a
ss
i
f
i
ca
t
i
o
n

a
n
d

D
yn
a
m
i
c

H
i
s
to
g
r
a
m

S
hi
f
t
i
n
g

In

t
h
i
s

p
a
p
e
r,

w
e

p
r
o
po
s
e

a

n
e
w

r
e
v
e
r
s
i
b
l
e

w
at
e
r
m
a
r
k
i
n
g
s
c
h
e
m
e
.
O
n
e
f
i
r
s
t

c
o
n
t
r
i
b
u
ti
o
n

i
s

a h
i
s
t
o
gr
a
m
s
h
i
f
t
i
ng
m
o
du
l
a
ti
o
n

w
h
i
c
h
a
d
a
p
ti
v
e
l
y
ta
k
e
s

ca
r
e

o
f

t
h
e

l
o
c
a
l
s
p
ec
i
f
i
c
iti
e
s

of

t
h
e

i
m
a
ge

c
o
n
t
e
n
t
.

By
a
pp
l
y
i
n
g

i
t

t
o
t
h
e

i
m
a
ge

p
r
e
d
i
c
ti
o
n
-

e
r
r
o
r
s

a
n
d

by
c
o
n
s
i
d
e
r
i
n
g

t
h
ei
r
i
m
m
e
d
i
a
t
e
n
e
i
g
h
bo
r
h
oo
d,

t
h
e
s
c
h
e
m
e

w
e

p
r
o
p
o
s
e

i
n
s
e
r
t
s d
at
a

i
n
t
e
x
t
u
r
e
d

a
r
e
a
s

w
h
e
r
e

o
t
h
e
r

m
et
h
o
ds

f
ai
l

t
o do

s
o
.

F
u
r
t
h
e
r
m
o
r
e
,

o
ur
s
c
h
e
m
e

m
a
k
e
s

u
s
e

of a

c
la
ss
i
f
i
c
ati
o
n
p
r
o
ce
s
s

f
o
r
i
d
e
n
ti
f
y
i
n
g

p
a
r
t
s

o
f

t
h
e
i
m
a
ge
t
h
a
t

ca
n

b
e

w
at
e
r
ma
r
k
e
d

w
i
t
h
t
h
e

m
o
s
t

s
u
it
e
d
r
e
v
e
r
s
i
b
l
e
m
o
du
l
a
t
i
o
n.






2013

31.


V
TI
M
P
31

R
o
b
u
s
t

F
ac
e

R
ec
o
gn
i
t
i
o
n

f
o
r

U
n
c
o
n
t
r
o
ll
e
d

P
o
s
e

a
n
d

I
ll
u
m
i
n
a
t
i
o
n

C
h
a
n
g
e
s

F
a
c
e

r
ec
o
g
n
i
t
i
o
n

h
a
s

m
a
de

s
i
g
n
i
f
i
ca
n
t

a
d
v
a
n
ce
s

i
n

t
h
e

la
s
t
d
ec
a
d
e
,

b
ut

r
ob
u
s
t

c
o
mm
e
r
c
i
a
l
a
pp
l
i
c
a
t
i
o
n
s

a
r
e

s
t
i
l
l

l
a
c
k
i
n
g
.

C
u
r
r
e
nt
a
u
t
h
e
n
t
i
c
a
t
i
o
n
/
i
d
e
n
t
i
f
i
c
ati
o
n
a
pp
l
i
c
a
t
i
o
n
s

a
r
e

l
i
m
it
e
d

t
o

c
o
n
t
r
o
ll
e
d
s
e
tt
i
n
g
s
,

e
.
g
.
,

l
i
m
i
t
e
d

p
o
s
e

a
n
d
ill
u
m
i
n
a
ti
o
n

c
h
a
ng
e
s
,

w
it
h

t
h
e

u
s
e
r u
s
u
all
y

a
w
a
r
e

of

b
e
i
n
g

s
c
r
ee
n
e
d

a
nd
c
o
lla
b
o
r
a
ti
n
g

i
n

t
h
e

p
r
o
ce
ss
.

A
m
o
n
g
o
t
h
e
r
s
,

p
o
s
e

a
n
d

il
l
u
m
i
n
a
ti
o
n
c
h
a
ng
e
s

a
r
e

l
i
m
i
t
e
d.

T
o

a
d
d
r
e
s
s
c
h
a
ll
e
n
g
e
s

f
r
o
m

l
oo
s
e
r

r
e
s
t
r
i
ct
i
o
n
s
,
t
h
i
s

p
a
p
e
r

p
r
o
p
o
s
e
s

a

n
ov
e
l
f
r
am
e
w
o
r
k

fo
r

r
ea
l
-
w
o
r
l
d

f
a
c
e
r
ec
o
g
n
i
ti
o
n

i
n

u
n
c
o
n
t
r
o
ll
e
d

s
e
tt
i
ngs n
am
e
d

F
a
c
e

A
n
a
l
y
s
i
s

f
o
r C
o
mm
e
r
ci
a
l

E
n
t
i
ti
e
s

(
FA
C
E
).






2013

32.


V
TI
M
P
32

S
ec
u
r
e

Wa
t
e
r
m
a
r
k
i
ng

f
o
r

M
u
l
t
i
m
e
d
i
a

C
o
n
t
e
n
t

P
r
o
t
e
c
t
i
o
n

T
h
e

p
a
p
e
r

i
ll
u
s
t
r
a
t
e
s

r
ece
n
t

r
e
s
u
l
t
s

r
e
g
a
rd
i
n
g

s
ec
u
r
e

w
at
e
r
m
a
r
k
i
n
g

t
o

t
h
e
s
i
g
n
a
l
p
r
o
ce
ss
i
n
g

c
o
mm
u
n
i
t
y
,
h
i
g
h
l
i
g
h
t
i
n
g
bo
t
h

b
e
n
e
f
it
s

a
n
d

s
t
i
l
l
o
p
e
n

i
ss
u
e
s
.

S
e
c
u
r
e
s
i
g
n
a
l
p
r
o
ce
ss
i
n
g,

by

w
h
i
c
h

i
nd
i
c
at
e
s

a

s
e
t
of
t
ec
h
n
i
qu
e
s

a
b
l
e

t
o

p
r
o
ce
s
s
s
e
n
s
i
t
i
v
e

s
i
gn
al
s
t
h
a
t

h
a
v
e

b
e
e
n ob
f
u
s
c
at
e
d

e
it
h
e
r

by
e
n
c
r
y
p
ti
o
n

o
r by
o
t
h
e
r

p
r
i
v
ac
y
-
p
r
e
s
e
r
v
i
ng pr
i
m
i
t
i
v
e
s
,

ma
y of
f
e
r

v
al
u
a
b
l
e
s
o
l
u
ti
o
n
s

t
o
t
h
e

a
fo
r
e
m
e
n
t
i
o
n
e
d







2013

Matlab

Khadarbaba.shaik@gmail.com

7842522786/9948887244


i
ss
u
e
s
.

33.


V
TI
M
P
33

V
i
s
u
a
l
ly

L
o
s
s
l
e
s
s

E
n
c
o
d
i
n
g

F
o
r

J
P
E
G2000

T
h
i
s

p
a
p
e
r

p
r
e
s
e
n
t
s

a

m
et
h
o
d

of

e
n
c
o
d
i
n
g

c
o
l
o
r

i
m
a
g
e
s
i
n

a

v
i
s
u
all
y
l
o
ss
l
e
s
s

m
a
n
n
e
r

u
s
i
ng
JP
E
G
200
0
.

I
n
o
r
d
e
r

t
o
h
i
de

c
o
d
i
n
g

a
r
t
i
f
a
c
t
s

c
a
u
s
e
d by

qu
a
n
t
i
z
a
t
i
o
n,

v
i
s
i
b
ilit
y
t
h
r
e
s
h
o
l
ds (
V
T
s
)

a
r
e
m
e
a
s
u
r
e
d

a
n
d

u
s
e
d

fo
r qu
a
n
t
i
z
a
t
i
o
n

o
f
s
ub
b
a
n
d

s
i
g
n
al
s

i
n
JP
E
G
2000.

T
h
e
V
T
s

a
r
e
e
xp
e
r
i
m
e
n
t
a
ll
y d
e
t
e
r
m
i
n
e
d

f
r
o
m
s
ta
t
i
s
ti
ca
ll
y
m
o
d
e
l
e
d

qu
a
n
t
i
z
a
ti
o
n
d
i
s
t
o
r
ti
o
n,

w
h
i
c
h

i
s
b
a
s
e
d

o
n

t
h
e
d
i
s
t
r
i
b
u
t
i
o
n

o
f
w
a
v
e
l
e
t

c
o
e
f
f
i
c
i
e
n
t
s
a
n
d

t
h
e d
ea
d
-
z
o
n
e

qu
a
n
t
i
z
e
r

o
f
JP
E
G
2000.









2013

34.


V
TI
M
P
34

A
u
d
i
o

W
a
t
e
r
m
a
r
k
i
n
g

V
i
a

E
M
D

In

t
h
i
s

p
a
p
e
r

a

n
e
w

a
d
a
p
t
i
v
e

a
ud
i
o

w
at
e
r
ma
r
k
i
ng

a
l
g
o
r
it
h
m

b
a
s
e
d

o
n
Em
p
i
r
i
ca
l

M
o
de

D
e
c
o
m
p
o
s
iti
o
n

(
E
MD
)

i
s

i
n
t
r
o
du
ce
d.

T
h
e

a
ud
i
o
s
i
g
n
a
l

i
s

d
i
v
i
d
e
d

i
n
t
o

f
r
a
m
e
s

a
nd
e
a
c
h

o
n
e

i
s

d
ec
o
m
p
o
s
e
d

a
d
a
p
t
i
v
e
l
y
, by

E
MD
,

i
n
t
o

i
n
t
r
i
n
s
i
c

o
s
c
illat
o
r
y
c
o
m
p
o
n
e
n
t
s

c
a
l
l
e
d

I
n
t
r
i
n
s
i
c

M
o
de
F
u
n
ct
i
o
n
s

(I
M
Fs
)
.

2013


35.


V
TI
M
P
35

A

N
o
v
e
l

A
tt
e
n
t
i
o
n
-
B
a
s
e
d

K
e
y
-
F
r
a
m
e

D
e
t
e
r
mi
n
a
t
i
o
n

M
e
t
h
o
d

In

t
h
i
s

p
a
p
e
r
,

w
e

p
r
o
p
o
s
e
d

a

n
ov
e
l

att
e
n
t
i
o
n
-
b
a
s
e
d

k
e
y

f
r
a
m
e
D
e
t
e
r
mi
n
a
ti
o
n

s
y
s
t
e
m

by

i
n
t
e
g
r
a
t
i
ng
t
h
e

o
b
j
ec
t
-
b
a
s
e
d

v
i
s
u
a
l

a
t
t
e
n
t
i
o
n
ma
ps

a
nd

t
h
e

c
o
n
t
e
x
t
u
a
l

o
n
-
g
o
i
ng g
am
e

o
u
t
c
o
m
e
s
.

T
h
e

d
ec
i
s
i
o
n

o
f

t
h
e
n
u
m
b
e
r

o
f

k
e
y
-
f
r
a
m
e
s

i
s

d
e
t
e
r
m
i
n
e
d by

u
til
i
z
i
n
g

t
h
e

c
o
n
t
e
x
t
u
a
l

a
t
t
e
n
t
i
o
n
s
c
o
r
e

2013

36.


V
TI
M
P
36

A

M
o
d
e
l
-
B
a
s
e
d

S
h
o
t

B
o
u
n
d
a
r
y

D
e
t
ec
t
i
o
n

T
ec
hn
i
que

U
s
i
ng

F
r
a
m
e

T
r
a
n
s
i
t
i
o
n

P
a
r
a
m
e
t
e
r
s

T
h
e

p
r
o
p
o
s
e
d

m
e
t
h
o
d

i
s

r
e
l
a
ti
v
e
l
y

l
e
s
s d
e
p
e
n
d
e
nt

o
n

u
s
e
r

d
e
f
i
n
e
d
t
h
r
e
s
h
o
l
ds
a
nd

i
s

f
r
e
e

f
r
o
m

s
li
d
i
ng
w
i
n
d
o
w

s
iz
e

a
s

w
i
d
e
l
y u
s
e
d

b
y
v
a
r
i
o
us
s
c
h
e
m
e
s

fo
u
n
d

i
n

t
h
e
lit
e
r
a
t
u
r
e
.

M
o
r
e
o
v
e
r
,

h
a
n
d
l
i
n
g
bo
t
h
a
b
r
u
p
t

a
n
d
g
r
a
d
u
a
l

t
r
a
n
s
iti
o
n
s

al
o
ng
w
it
h

n
o
n
-
t
r
a
n
s
i
t
i
o
n

f
r
a
m
e
s

und
e
r
a
s
i
ng
l
e
f
r
a
m
e
w
o
r
k

u
s
i
ng

m
o
d
e
l
gu
i
d
e
d

v
i
s
u
a
l

f
e
at
u
r
e

i
s
a
n
o
t
h
e
r un
i
que

a
s
p
ec
t

o
f

t
h
e

w
o
r
k.

2013

37.


V
TI
M
P
37

V
i
d
e
o

b
a
s
e
d

T
r
ac
k
i
n
g,
L
ea
r
n
i
ng, A
n
d

R
ec
o
gn
i
t
i
o
n

M
e
t
h
o
d

F
o
r

M
u
l
t
i
p
l
e

M
o
v
i
ng

Ob
j
ec
t
s

T
h
i
s

p
a
p
e
r

p
r
o
p
o
s
e
s

a

c
o
s
t

r
e
du
c
ti
o
n

m
et
h
o
d

f
o
r

t
h
e
M
C
M
C

a
p
p
r
o
a
c
h

by
ta
k
i
n
g

m
o
v
e
s
,

i
.
e
.
,

b
i
r
t
h

a
n
d

d
e
at
h,
o
ut
of

t
h
e

it
e
r
a
ti
o
n

l
oo
p

of

t
h
e
M
a
r
k
o
v
c
h
a
i
n

w
h
e
n

d
i
f
f
e
r
e
nt
m
ov
i
n
g
ob
j
ec
t
s

i
n
t
e
r
a
c
t
.

F
o
r

s
ta
b
l
e
a
n
d
r
ob
u
s
t

t
r
a
c
k
i
n
g
,

a
n

el
li
p
s
e

m
o
d
e
l
w
it
h

s
t
o
c
h
a
s
t
i
c

m
o
d
e
l

p
a
r
a
m
e
t
e
r
s
i
s u
s
e
d.

M
o
r
e
o
v
e
r
,

o
ur

H
M
M
m
e
t
h
o
d
i
n
t
e
g
r
a
t
e
s

s
e
v
e
r
a
l

d
i
ff
e
r
e
n
t

m
o
du
l
e
s
i
n

o
r
d
e
r

t
o
c
o
pe

w
it
h

m
u
lt
i
p
l
e
d
i
s
c
o
n
t
i
n
u
o
us

t
r
a
jec
t
o
r
i
e
s
.

2013

COMMUNICATION

38.


V
T
CM
01

P
i
t
c
h

A
u
to
p
i
l
o
t

D
e
s
i
g
n

f
o
r

A
g
il
e

M
i
s
s
i
l
e
s

C
o
m
m
un
ic
at
i
o
n
,

A
u
t
o
p
il
o
t

D
e
s
i
gn

F
o
r

A
g
il
e

M
i
ss
il
e
s
,

A
n
g
l
e
s

O
f


2013

Matlab

Khadarbaba.shaik@gmail.com

7842522786/9948887244


w
i
t
h

Un
ce
r
t
a
i
n

A
e
r
od
yn
a
m
i
c

C
o
e
ff
i
c
i
e
n
t
s

A
tta
c
k
,

In
t
e
g
r
a
t
o
r

B
a
c
k

S
t
e
pp
i
n
g
,

H

-
N
o
r
m

M
i
n
i
m
i
z
a
ti
o
n
,
A
e
r
o
d
y
n
a
m
i
c
s.

39.


V
T
CM
02

C
o
gn
i
t
i
v
e

R
a
d
i
o

N
e
t
w
o
r
ks

w
i
t
h

O
r
t
h
o
g
o
n
a
l

S
p
ac
e
-
T
im
e

B
l
o
c
k

C
od
i
n
g
a
n
d

M
u
l
t
i
u
s
e
r

D
i
v
e
r
s
i
t
y

C
o
m
m
un
ic
at
i
o
n
,

C
o
g
n
i
t
i
v
e
R
a
d
i
o
,
M
o
b
il
e
C
o
mm
u
n
i
c
a
t
i
o
n
,
OS
T
B
C,
T
r
a
n
s
m
i
t
A
n
t
e
n
n
a
S
e
l
ec
ti
o
n

(
T
AS
),
M
u
lt
i
u
s
e
r

S
e
l
ec
ti
o
n
.


2013

40.


V
T
CM
03

S
i
n
g
l
e
-
C
a
r
r
i
e
r

F
r
e
qu
e
n
c
y
-
D
o
m
a
in

E
qu
a
li
ze
r

w
i
t
h

M
u
l
t
i
-
A
n
t
e
n
n
a

T
r
a
n
s
m
i
t

D
i
v
e
r
s
i
t
y

C
o
m
m
un
ic
at
i
o
n
,

W
i
-
M
a
x,

A
l
a
m
o
u
t
i

S
i
g
n
a
l
i
n
g
,

M
i
n
i
m
u
m

M
ea
n
S
qu
a
r
e
E
r
r
o
r

(
MMS
E
)
,

Ze
r
o
F
o
r
ci
n
g,

C
y
c
li
c
D
e
la
y

D
i
v
e
r
s
it
y

(C
DD
)
.


2013

41.


V
T
CM
04

A

N
o
v
e
l

P
h
a
s
e

O
ff
s
e
t

S
L
M

S
c
h
e
m
e

f
o
r

P
A
P
R

R
e
du
c
t
i
o
n

i
n

A
l
a
m
o
u
t
i

M
I
M
O
-

O
F
DM
S
y
s
t
e
m
s
W
i
t
h
o
ut

S
i
de

I
n
f
o
r
m
a
t
i
o
n

C
o
m
m
un
ic
at
i
o
n
,

P
A
P
R
,

S
L
M
,

s
p
a
ce
-
f
r
e
qu
e
n
c
y

b
l
o
c
k

c
o
d
i
n
g

(
SF
B
C),

v
i
d
e
o

b
r
o
a
d
c
a
s
t
i
n
g

a
n
d

3
GPP
,

M
I
M
O

-

O
F
D
M
.


2013

42.


V
T
CM
05

An

I
n
t
e
r
f
e
r
e
n
c
e

N
u
l
l
i
ng

B
a
s
e
d

C
h
a
n
n
e
l

I
n
d
e
p
e
n
d
e
n
t

Pr
ec
o
d
i
n
g

f
o
r

M
I
M
O
-
O
F
D
M
S
y
s
t
e
m
s
w
i
t
h

I
n
s
u
f
f
i
c
i
e
n
t

C
y
c
l
i
c

Pr
e
f
i
x

C
o
m
m
un
ic
at
i
o
n
,

C
y
c
li
c

P
r
e
f
i
x

(C
P
),

In
t
e
r
f
e
r
e
n
c
e

A
li
g
n
m
e
n
t

(I
A
), CI
R
, ICI
A
n
d I
S
I,

L
T
E

o
r

M
ob
il
e
C
o
mm
u
n
i
ca
ti
o
n
,

M
I
M
O

-

O
F
D
M
.


2013

43.


V
T
CM
06

P
i
l
o
t

S
y
m
b
o
l

P
a
r
a
m
e
t
e
r

Op
t
i
m
i
za
t
i
o
n

B
a
s
e
d

o
n

I
m
p
e
r
f
ec
t

C
h
a
nn
e
l

S
t
a
t
e

Pr
e
d
i
c
t
i
o
n

f
o
r

O
F
DM
S
y
s
t
e
m
s

C
o
m
m
un
ic
at
i
o
n
,

W
I
MAX
,

Ch
a
n
n
e
l

E
s
t
i
m
a
ti
o
n,

M
i
n
i
m
u
m
M
ea
n
S
qu
a
r
e
E
r
r
o
r (
MMS
E
)
,

P
i
l
o
t

S
y
m
b
o
l
Ass
i
s
t
e
d
M
o
du
lati
o
n

(
PSAM
),
O
F
D
M
.


2013

44.


V
T
CM
07

Qu
a
n
t
i
ze
d

C
S
I
-
B
a
s
e
d

T
o
m
l
i
n
s
o
n
-

H
a
r
a
s
h
im
a

Pr
ec
o
d
i
n
g

i
n

M
u
l
t
i
u
s
e
r

M
I
M
O
S
y
s
t
e
m
s

C
o
m
m
un
ic
at
i
o
n
,

M
I
M
O

S
y
s
t
e
m
s
,

T
o
ml
i
n
s
o
n
-
H
a
r
a
s
h
i
m
a
P
r
ec
o
d
i
ng,
Q
u
a
n
t
iz
e
d

C
h
a
n
n
e
l
S
t
at
e

I
n
fo
r
m
a
t
i
o
n
(C
S
I),
Q
R

D
e
c
o
m
p
o
s
iti
o
n
,

R
a
n
d
o
m
V
ec
t
o
r

Q
u
a
n
t
i
za
t
i
o
n,
Z
e
r
o
-
F
o
r
c
i
ng,
W
i
r
e
l
e
s
s

C
o
m
m
u
n
i
ca
ti
o
n.




2013

45.


V
T
CM
08

I
n
n
e
r

B
o
u
n
d

o
n

t
h
e

G
D
OF

o
f

t
h
e

K
-
U
s
e
r

M
I
M
O

G
a
u
s
s
i
a
n

S
y
mm
e
t
r
i
c

I
n
t
e
r
f
e
r
e
n
c
e

C
h
a
n
n
e
l

C
o
m
m
un
ic
at
i
o
n
,

M
I
M
O

S
y
s
t
e
m
s
,

G
e
n
e
r
a
l
iz
e
d

D
e
g
r
ee
s

O
f
F
r
ee
d
o
m
,
In
t
e
r
f
e
r
e
n
c
e Ch
a
n
n
e
l
,

H
a
n
-

K
o
b
a
y
a
s
h
i

S
c
h
e
m
e
,
W
i
r
e
l
e
s
s C
o
mm
u
n
i
ca
ti
o
n
,
I
n
t
e
r
f
e
r
e
n
c
e
A
li
g
n
m
e
n
t
,

Z
e
r
o
-
F
o
r
ci
n
g.


2013

46.


V
T
CM
09

S
p
ec
t
r
um

S
e
n
s
i
n
g

f
o
r

D
i
g
i
t
a
l

P
r
im
a
r
y

S
i
g
n
a
l
s

i
n

C
o
gn
i
t
i
ve

R
a
d
i
o
:

A
B
a
y
e
s
i
a
n
A
pp
r
o
ac
h
f
o
r

C
o
m
m
un
ic
at
i
o
n
,

C
o
g
n
i
t
i
v
e

R
a
d
i
o
,

M
o
b
il
e

C
o
mm
u
n
i
c
a
t
i
o
n,

B
a
y
e
s
ia
n

D
e
t
ec
t
o
r
,

PS
K

m
o
du
l
a
t
i
o
n
.


2013

Matlab

Khadarbaba.shaik@gmail.com

7842522786/9948887244


M
a
xi
m
i
z
i
ng
S
p
ec
t
r
um
U
t
i
l
i
za
t
i
o
n

47.


V
T
CM
10

M
u
l
t
i
p
l
e

P
r
i
m
a
r
y

U
s
e
r

S
p
ec
t
r
u
m

S
e
n
s
i
ng

i
n

t
h
e

L
o
w

S
NR
R
e
g
im
e

C
o
m
m
un
ic
at
i
o
n
,

L
o
w

S
N
R

R
e
g
im
e
,

Sp
ec
t
r
u
m
S
e
ns
i
n
g,

M
u
l
ti
p
a
t
h
c
h
a
n
n
e
l
s
,

M
u
l
ti
p
l
e

pr
i
ma
r
y u
s
e
r
s
,
W
i
r
e
l
e
s
s

c
o
mm
u
n
i
ca
ti
o
n
.


2013

48.


V
T
CM
11

A

P
ea
k

P
o
w
e
r

E
ff
i
c
i
e
n
t

C
oo
p
e
r
a
t
i
v
e

D
i
v
e
r
s
i
t
y

u
s
i
ng

S
t
a
r
-
Q
A
M

w
i
t
h

C
o
h
e
r
e
n
t
/N
o
n
-
c
o
h
e
r
e
n
t

D
e
t
e
c
t
i
o
n

C
o
m
m
un
ic
at
i
o
n
,

P
o
w
e
r

a
m
p
l
i
f
i
e
r
,
QAM
,

A
m
p
li
fy

a
n
d
-
fo
r
w
a
r
d

(
A
F
),

P
a
i
r

w
i
s
e

e
rr
o
r

p
r
ob
a
b
ilit
y

(
P
E
P
),
S
at
e
l
l
it
e

c
o
mm
u
n
i
c
a
t
i
o
n,

DVB
-
S
H
.


2013

49.


V
T
CM
12

V
i
d
e
o
-
B
a
s
e
d

C
r
o
wd

D
e
n
s
i
t
y
E
s
t
im
a
t
i
o
n

a
n
d

Pr
e
d
i
c
t
i
o
n

S
y
s
t
e
m

f
o
r

W
i
d
e
-
A
r
e
a

S
u
r
v
e
i
l
l
a
n
c
e

C
o
m
m
un
ic
at
i
o
n
,

C
r
o
w
d

D
e
n
s
it
y

E
s
t
i
m
a
ti
o
n,
P
r
e
d
i
c
ti
o
n

S
y
s
t
e
m
,
A
cc
u
m
u
lat
e
d

M
o
s
ai
c
I
m
a
ge
D
i
f
f
e
r
e
n
c
e
(
A
M
I
D
),

GMM
,

V
i
s
u
a
l
S
u
r
v
e
il
la
n
ce
,
A
u
t
o
m
at
e
d
M
o
n
i
t
o
ri
n
g

C
r
o
w
d

M
ov
e
m
e
n
t
s
.




2013

50.


V
T
CM
13

D
e
l
a
y
-
L
i
m
i
t
e
d

S
o
u
r
c
e

a
n
d

C
h
a
n
n
e
l

C
o
d
i
n
g

o
f

Qu
a
s
i
-
S
t
a
t
i
o
n
a
r
y
S
o
u
r
ce
s

o
v
e
r
B
l
o
c
k

F
a
d
i
ng

C
h
a
n
n
e
l
s
:

D
e
s
i
g
n
a
n
d
S
c
a
li
ng

L
a
w
s

C
o
m
m
un
ic
at
i
o
n
,

M
I
M
O

s
y
s
t
e
m
s
,

O
u
ta
ge C
a
p
a
c
it
y
,

Q
u
a
s
i
-
S
t
a
ti
o
n
a
r
y
S
o
u
r
ce
,

O
u
ta
ge
D
i
s
t
o
r
ti
o
n
,

S
o
u
r
c
e
A
n
d
C
h
a
n
n
e
l

C
o
d
i
n
g,

R
at
e

A
n
d
P
o
w
e
r
A
d
a
p
t
a
ti
o
n,
W
i
r
e
l
e
s
s C
o
mm
u
n
i
ca
ti
o
n
.




2013

51.


V
T
CM
14

I
n
t
e
r
f
e
r
e
n
c
e

Al
i
g
n
m
e
n
t

T
ec
hn
i
qu
e
s

f
o
r

M
I
M
O

M
u
l
t
i
-
C
e
ll

I
n
t
e
r
f
e
r
i
n
g

B
r
o
a
d
ca
s
t

C
h
a
n
n
e
l
s

C
o
m
m
un
ic
at
i
o
n
,

C
e
ll
u
l
a
r

N
e
t
w
o
r
k,

M
e
d
i
um

A
cce
s
s C
h
a
n
n
e
l
-
B
r
o
a
d
c
a
s
t
Ch
a
n
n
e
l
(
MA
C
-
B
C),
I
n
t
e
r
f
e
r
e
n
c
e
A
li
g
n
m
e
n
t
,

B
e
am

F
o
r
m
i
n
g.


2013

52.


V
T
CM
15

S
p
ace
-
T
im
e

C
o
de

D
e
s
i
g
n

f
o
r

M
u
l
t
i
p
l
e
-

A
c
c
e
s
s

C
h
a
nn
e
l
s

W
i
t
h

Qu
a
n
t
i
ze
d

F
ee
d
b
a
c
k

C
o
m
m
un
ic
at
i
o
n
,

T
DM
A
,

S
p
a
c
e

T
i
m
e

B
l
o
c
k

C
o
d
i
n
g

(
S
T
B
C
)
,

F
u
l
l

D
i
v
e
r
s
it
y
,

M
o
b
il
e

A
d
-
H
oc
N
e
t
w
o
r
ks


2013

53.


V
T
CM
16

I
n
t
e
r
f
e
r
e
n
c
e

Al
i
g
n
m
e
n
t

W
i
t
h

D
i
ff
e
r
e
n
t
i
a
l

F
ee
d

B
ac
k

In
t
e
r
f
e
r
e
n
c
e

ali
g
n
m
e
n
t

(I
A
)

h
a
s

b
ee
n

r
ec
o
g
n
i
z
e
d

a
s

a

p
r
o
mi
s
i
ng

t
e
c
h
n
i
q
ue
t
o

o
b
ta
i
n

l
a
r
ge

m
u
lt
i
p
l
e
x
i
n
g

g
a
i
n

i
n
m
u
l
ti
p
l
e
-
i
n
p
u
t
-
m
u
l
t
i
p
l
e
-
o
u
t
p
u
t
(
M
I
MO
)

i
n
t
e
r
f
e
r
e
n
c
e

c
h
a
n
n
e
l
s
.

M
o
s
t
e
x
i
s
t
i
n
g

IA

s
c
h
e
m
e
s

r
e
qu
i
r
e

g
l
ob
a
l
c
h
a
n
n
e
l

s
t
a
t
e

i
n
fo
r
m
a
t
i
o
n

(C
S
I
)

a
t

t
h
e
t
r
a
n
s
mi
t
t
e
r

t
o

d
e
s
i
gn

p
r
ec
o
d
i
n
g
v
e
c
t
o
r
s

a
n
d,

t
hu
s
,

r
e
s
u
l
t

i
n

s
i
g
n
i
f
i
ca
n
t
c
a
p
a
c
it
y

o
v
e
rh
e
a
d

i
n

t
h
e

f
ee
d
b
a
c
k
l
i
n
k
.

2012

DIGITAL SIGNAL PROCESSING

54.


V
T
DS
P
01

W
a
v
e
l
e
t

B
a
s
e
d

E
C
G

S
t
e
g
a
n
o
g
r
a
phy

f
o
r

DS
P
,

D
at
a

S
ec
ur
it
y
,

E
n
c
r
y
p
ti
o
n

A
n
d

W
a
v
e
l
e
t

D
e
c
o
m
p
o
s
iti
o
n
,

W
at
e
r


2013

Matlab

Khadarbaba.shaik@gmail.com

7842522786/9948887244


P
r
o
t
ec
t
i
n
g

P
a
t
i
e
n
t

C
o
n
f
i
d
e
n
t
i
a
l

I
n
f
o
r
m
a
t
i
o
n

i
n

P
o
i
n
t
-
o
f
-
C
a
r
e

S
y
s
t
e
m
s

M
a
r
k
i
ng,

B
i
om
e
d
ic
a
l

App
l
ic
a
t
i
o
ns
.

55.


V
T
DS
P
02

A
u
d
i
o

W
a
t
e
r
m
a
r
k
i
n
g

V
i
a

E
M
D

DS
P
,

Wat
e
r

M
a
r
k
i
n
g,

E
m
p
i
r
i
c
a
l

M
o
de

D
ec
o
m
p
o
s
iti
o
n

(
E
MD
),

S
y
n
c
h
r
o
n
i
z
a
t
i
o
n

C
o
d
e
,

I
MF
,

S
i
g
n
a
l

S
ec
u
r
it
y

A
n
d

C
o
p
y

R
i
g
h
t
s
.


2013

56.


V
T
DS
P
03

E
m
p
i
r
i
c
a
l

M
o
de

D
ec
o
m
p
o
s
i
t
i
o
n

v
s
.

W
a
v
e
l
e
t

D
ec
o
m
p
o
s
i
t
i
o
n

f
o
r

t
h
e

E
x
t
r
a
c
t
i
o
n
o
f
R
e
s
p
i
r
a
t
o
r
y

S
i
g
n
a
l

f
r
o
m

S
i
n
g
l
e
-

C
h
a
n
n
e
l

E
C
G:

a

C
o
m
p
a
r
i
s
o
n

DS
P
,

E
n
c
r
y
p
ti
o
n

a
n
d

W
a
v
e
l
e
t

D
e
c
o
m
p
o
s
iti
o
n
,
E
m
b
e
dd
i
ng,
Em
p
i
r
i
ca
l

M
o
de
D
e
c
o
m
p
o
s
iti
o
n
,
R
e
s
p
i
r
at
o
r
y
S
e
n
s
i
n
g

S
y
s
t
e
m
,
B
i
om
e
d
ic
al

App
li
c
at
i
o
n
s
.


2013

57.


V
T
DS
P
04

C
o
o
p
e
r
a
t
i
v
e

S
ec
u
r
e

B
e
a
m

f
o
r
mi
ng

f
o
r

AF

R
e
l
a
y

N
e
t
w
o
r
ks

W
i
t
h

M
u
l
t
i
p
l
e

E
a
v
e
s
d
r
o
pp
e
r
s

DS
P
,

S
e
c
u
r
e

C
o
mm
u
n
i
ca
ti
o
n
,

A
m
p
l
i
f
y
-
A
n
d
-
F
o
r
w
a
r
d
R
e
la
y
i
n
g,
C
o
o
p
er
at
i
v
e

B
e
am
F
o
r
m
i
n
g
,
P
h
y
s
i
c
a
l
-
L
a
y
e
r

S
e
c
u
r
it
y
,
S
ec
r
e
c
y
R
at
e

M
a
x
i
m
i
z
a
ti
o
n


2013

58.


V
T
DS
P
05

P
e
r
f
o
r
m
a
n
c
e

o
f

T
wo

L
o
w
-
R
a
n
k

S
T
A
P

F
i
l
t
e
r
s

i
n

a

H
e
t
e
r
o
g
e
n
e
o
us
N
o
i
s
e

DS
P
,

S
p
a
c
e

Tim
e

A
d
a
p
t
i
v
e

P
r
o
ce
ss
i
n
g,

L
ow
-
r
a
n
k
c
l
u
t
t
e
r
,
N
o
r
m
a
l
i
z
e
d

s
a
m
p
l
e C
ov
a
r
ia
n
c
e
m
at
r
i
x
,

P
e
r
t
u
r
b
ati
o
n

m
et
h
o
d,
S
I
R
V
,
S
am
p
l
e

C
ov
a
r
ia
n
c
e

M
a
t
r
i
x
.

RADAR


2013

IMAGE PROCESSING

59.


VTIMP01

Change

Detection in Sy
n
thetic
Aperture Radar Images
b
ased
on Image Fusion and Fuzzy
Clustering


In this paper is based on unsup
e
rvised
distribution
-
free change
d
etection
approach for synthetic aperture rad
a
r
(SAR) images
based on an image fusion
stra
t
egy and a

novel fuzzy clustering alg
o
rithm.

Experiments on real SAR ima
g
es show
that the image fusion stra
t
egy
integrates the advantages of the log
-
r
a
tio operator and the mean
-
ratio
operat
o
r and gains a better
performance.

The

change detection
results obtained by the imp
r
oved fuzzy
clustering algorithm exh
i
bited lower
error

than its preexistences.
















2012

60.


VTIMP02


Image

Quality

Assessment
Based on Gradient Similari
t
y

In this proposed scheme consi
d
ers both
luminance and contrast str
u
ctural
changes to e
f
fectively assess image
qua
l
it
y
.

The

e
f
fects of the changes in lumi
n
ance and

contrast structure are int
e
grated via
an adaptive method to obtain
t
he
overall










Matlab

Khadarbaba.shaik@gmail.com

7842522786/9948887244


image quality score. Exte
n
sive
experiments conducted w
ith six publicly

available subject
-
rated databases h
a
ve
confirmed the e
f
fectiveness, robustness,
and

e
f
ficiency of the proposed scheme in
comp
a
rison with

the relevant state
-
of
-
th
e
-
art schemes.






2012

61.



VTIMP03


Removing

Boundary

Artifacts
for
Real
-
T
ime Iterated
Shrinka
g
e de convolution


In this paper we propose a solu
t
ion to
the problem of boundary artifa
c
ts
appearing in several recently published

fast de blurring

algorithms based on itera
t
ed shrinkage

thresholding in a sparse dom
a
in and
Fourier domain de convolut
i
on. Our
approach adapts an idea pro
p
osed by
Reeves for de convolution by
t
he

W
iener filte
r
.











2012

62.


VT
IMP04

Interpolation
-
Based Image
Super
-

Resolution using multi

surface Fitting

In this paper, we propose a new
interpolation
-
based method of image
super
-

resolution reconstruction. The idea
is using

Multi

surface fitting to take full advantage

of spatial structure information. Each site

of low
-
resolution pixels is fitted with one

surface, and the final estimation i
s made

by fusing the multisampling values on

these surfaces in the maximum a

Posteriori fashion.










2012

63.


VT
IMP05

A Semi

supervised
Segmentation

Model for Collections of
Images

In this paper, we consider the problem of
segmentation of large
collections of
images. We propose a semi supervised
optimization

model that determines an efficient

Segmentation of many input images. The

Advantages of the model are twofold. The

proposed model is effective for

segmentation and is computationally

efficient









2012

64.


VT
IMP06

Image Fusion Using Higher
Order Singular Value
Decomposition

In this paper proposes novel higher order
singular value decomposition (HOSVD)
-

based image fusion. This paper proposes a

novel and flexible
sigmoid
-
function
-
like

coefficient
-
combining scheme, which

incorporates the usual choose
-
max scheme

and the weighted average scheme, and

easily extends the proposed algorithm to

fuse multiple or color images.









2012

65.


VT
IMP07

Blind Separation of
Image
Sources via Adaptive
Dictionary Learning

In this paper, we address fail to
successfully recover the sources problem
and attempt to give a solution via fusing
the dictionary

Learning into the source separation. The





Matlab

Khadarbaba.shaik@gmail.com

7842522786/9948887244


proposed algorithm is designed to

ad
aptively learn the dictionaries from the

mixed images within the source separation

Process. In the proposed hierarchical

method, a local dictionary is adaptively

learned for each source along with

Separation. This process improves the

quality of source
separation even in noisy

Situations.







2012

66.


VT
IMP08

A New Method for Cross
-

Normalization and multi

temporal Visualization of SAR
Images for the Detection of
Flooded Areas

This paper is based on multi

temporal
synthetic aperture radar (SAR)
images.
Cross
-
calibration/normalization is
proposed to solve this problem. This, in
turn,

facilitates image enhancement and the

numerical comparison of different image

takes together with data fusion and

Visualization processes.









2012

67.


VT
IMP09

Human Identification Using

Finger Images


In this proposed system simultaneously
acquires the finger
-
vein and low
-
resolution
fingerprint images and combines these two

evidences using a novel score
-
level

Combination strategy. Our finger
-
vein

identification
approach utilizes peg
-
free
and

more user
-
friendly unconstrained imaging.

We develop and investigate two new
score
-

level combinations, i.e., holistic and

nonlinear fusion, and comparatively

evaluate them with more popular score
-

level fusion approaches to
ascertain their

Effectiveness in the proposed system.














2012

68.



VTIMP15


Scalable

Coding of Encrypted

images


In this paper proposes a novel scheme of

Scalable coding for encrypted images. In
the encryption phase, the original pixel
values are
masked by a modulo
-
256
addition with pseudorandom numbers
that are derived from a secret ke
y
.

After
decomposing the encrypted data into a
down sampled sub image and several
data sets with a multiple
-
resolution
construction At the receiver side, the
quantiz
ed coe
f
ficients can be used to
reconstruct the detailed content with an
iteratively

Updating procedure.














201
2

69.


VTIMP18


No stationary

Harmonic
Modeling for ECG Removal
in Surface EMG Signals

W
e present a compact approach
for mitigating the
presence of

(ECG) in surface (EMG) signals by

Means of time
-
variant harmonic
modeling of the cardiac artifact. Once
the model parameters (Polynomial







Matlab

Khadarbaba.shaik@gmail.com

7842522786/9948887244


coe
f
ficients) are estimated, the ECG
signal component is generated and
subtracted from the mixture

in order

to obtain the EMG




201
2

70.


VTIMP19

An

Ensemble
-
Based System
for Micro aneurysm
Detection and Diabetic
Retinopathy Grading

W
e propose an ensemble
-
based
framework to improve micro aneurysm
detection.

W
e provide an ensemble
creation framework

to

select the best combination.

An
exhaustive

Quantitative analysis is also given to
prove the superiority of our approach
over individual algorithms.

W
e also
investigate the grading performance of
our method, which is proven to be
competitive with

Other scre
ening systems.












201
2

DIGITAL SIGNAL PROCESSING

71.


VTDSP01


Finitely

Supported L2
-

Optimal Kernels for Digital
Signal Interpolation

In this project

W
e derive a new family
of unconstrained, finitely supported
L2
-

optimal interpolation kernels
HL(x), and compare their properties to
the previously

Known results. Our research
demonstrates

that L2
-
optimal kernels provide superior

interpolation quality








2012

72.


VTDSP02

Minimum

Euclidean
Distance Based Pre coders
for MIMO Systems Using
Rectangular QAM
Modulations

In this pape
r
, an e
f
ficient pre coder is
designed that maximizes the minimum
Distance of two received vectors is
studied. This criterion leads to a non
diagonal

Pre coding scheme and allows achieving
a

full diversity orde
r
. So
we propose
herein a

general form of minimum Euclidean

distance based Pre coders for all

Rectangular QAM modulations.










2012

73.


VTDSP03

Global

Stabilization of the
Least

Mean

Fourth

Algorithm

In this project fully deals with stability
of the least
mean fourth algorithm.

This is achieved by normalizing the
weight

vector update term by a term that is
fourth

order in the regress or and second order
in

the estimation erro
r
.







2012

74.


VTDSP04

Derivation

of the Bias of the
Normalized Sample
Covariance Matrix in a
Heterogeneous Noise
W
ith

In this project, we have developed a low
rank (LR) spatiotemporal adaptive
processing (S
T
AP) filter when the
disturbance is modeled as the sum of a





Matlab

Khadarbaba.shaik@gmail.com

7842522786/9948887244


Application to Low Rank
S
T
AP

Filter

low
rank

spherically invariant random
vector

(SI
R
V) clutter and a zero mean white

Gaussian

noise.

This LR
-
S
T
AP

filter is
built

from

the normalized sample covariance
matrix

(NSCM)

and exhibits good
robustness

properties

to secondary data contamination

by

ta
r
get
components.







2012

75.


VTDSP05

Opportunistic

Distributed
Space
-

T
ime Coding for
Decode
-
and
-

Forward
Cooperation Systems

In this pape
r
, we consider a decode
-
and
-

forward (DF) cooperation

system
consisting of two cooperative users in
sending their information to a common
destination, for which the distributed
space
-
time coding (DSTC) is applied in
an opportunistic manne
r
, called
opportunistic DSTC (O
-
DSTC),
depending on whether the two use
rs
succeed in decoding each other's
information or not.

W
e evaluate the
outage performance of the proposed O
-
DSTC as well as the conventional
selective DF (SDF) cooperation and
fixed DSTC

(F
-
DSTC) schemes.
















2012

COMMUNICATION SYSTEM

76.


VTCM01

On

the Minimum
Di
f
ferential Feedback for

T
ime
-
Correlated
Rayleigh

Block
-
Fading

Chann
els

In this pape
r
, we investigate the
di
f
ferential channel state information
(CSI) feedback problem for a general
multiple input multiple output (MIMO)
system over

time
-
correlated Rayleigh block
-
fading

channels.







2012

77.


VTCM02


Optimal

Channel and Relay
Assignment in OFDM
-
Based Multi
-
Relay Multi
-
Pair

T
wo
-
W
ay
Communication Networks


This paper considers a wireless relay
network where multiple user pairs
conduct
bidirectional communications
via multiple relays based on orthogonal
frequency
-
div multiplexing (OFDM)
transmission.







2012

78.


VTCM03

On

Performance
Improvement of
W
ireless
Push Systems via Smart
Antennas

In this pape
r
, we propose an adaptive
smart
antenna based wireless push
system where the beamwidth of each
smart antenna is

altered based on the current placement
of

clients within the system area.






2012

79.


VTCM04

Secure

Communication in
In this project Ene
r
gy e
f
ficiency is


Matlab

Khadarbaba.shaik@gmail.com

7842522786/9948887244



the

Low
-
SNR

Regime

analyzed by finding the minimum bit
ene
r
gy required for secure and reliable
communications, and the wideband
slope.



2012

80.


VTCM05

On

Optimal Front
-
End
Filter for Single
-
User
Detection in IR
-

UWB
Systems

In this pape
r
, we show that by
employing a new
front
-
end filter at the
receive
r
, the non
-

whiteness of the MAI
can be exploited to improve the
performance of the single
-
user Detector.






2012

81.


VTCM06

Cooperative

Spectrum
Sharing Protocol with
Selective Relaying System

In this pape
r
, we propose a
two
-
phase
protocol based on cooperative relaying
for a secondary system to achieve
spectrum

access along with a selective relaying

Primary system.






2012

82.


VTCM07

Diversity

Gain and Outage
Probability for MIMO Free
-
Space
Optical

Links with
Misalignment

A

novel statistical channel model for
multiple
-
input multiple
-
output (MIMO)
free
-
space optical (FSO)
communication systems impaired by
atmospheric and

Misalignment fading is developed.

A

slow
-

fading channel model is considered and
the outage probability
is derived as a

Performance measure.










2012

83.


VTCM08

Uncoordinated

Beam
forming for

Cognitive

Networks

In this pape
r
, we propose jointly
-
optimized Beam forming algorithms for
cognitive networks to maximize the
achievable rates,

where primary and
cognitive users share
the same spectrum and are equipped
with

Multiple antennas.







2012

84.


VTCM09

Asymptotic

Capacity of
La
r
ge

Relay

Networks with

Conferencing

Links

In this project, we consider a half
-

duplex la
r
ge relay network, consisting
of one
source
-
destination pair and N
relay nodes,

each of which is connected with a
subset of

the other relays via signal
-
to
-
noise ratio

(SNR)
limited out
-
of
-
band conferencing
links







2012

85.


VTCM10

T
wo Useful Bounds Related
to
W
eighted Sums of
Rayleigh Random

V
ariables
with Applications to
In this lette
r
, we derive an upper bound
on the distribution of the ratio of a
Rayleigh faded signal to a sum of
weighted Rayleigh
R
Vs plus a






Matlab

Khadarbaba.shaik@gmail.com

7842522786/9948887244


Interference Systems

nonnegative constant, dubbed the
generalized ratio(GR).

2012

86.


VTCM
1
1

Performance

Analysis over
Slow Fading Channels of a
Half
-

Duplex Single
-
Relay
Protocol: Decode or
Quantize and Forward

In this work, a static relaying protocol,
called Decode or Quantize and Forward
(DoQF), is introduced for half duplex
single
-
relay networks, and its
performance

is studied in the context of
communications

over slow fading wireless channels.








2012

87.


VTCM12

Analytic

Framework for the
E
f
fective Rate of MISO
Fading Channels

In this pape
r
, we pursue a detailed
e
f
fective
rate analysis of Nakagami
-
m,
Rician and generalized
-
K multiple input

single
-
output (MISO) fading channels
by

deriving ne
w
, analytical expressions for

their exact e
f
fective rate.







2012

88.


VTCM13

Diversity
-
Multiplexing

T
radeo
f
f of MIMO
Multiple
-
Access
Systems
with Successive
Cancellation Receivers
Having Imperfect
Cancellation

In order to analyze the asymptotic
performance of each user in a practical
multiple
-
access system, we derive the
pe
r
-

user DMT

considering error
propagation due to imperfect
canc
ellation of the SC process.








2012

89.


VTCM14

Diversity
-
Multiplexing
-
Delay
Tradeoff in Selection
Cooperation
Networks

with

ARQ

In this pape
r
, we combine the
distributed selection cooperation
protocols with

ARQ mechanism to
develop more powerful
cooperative
schemes for delay
-
tolerant

wireless networks and analyze their

performance from the perspective of
diversity multiplexing
-
delay (D
-
M
-
D)

tradeo
f
f.










2012

90.


VTCM15

An

Analysis of the
Bidirectional LMS

Algorithm over Fast
-
Fading
Channels

We

analyze the tracking performance of
the bidirectional LMS algorithm by
deriving a novel step
-
size dependent
steady
-
state MSE and optimal step
-
size
expressions over fast frequency
-

selective time
-
varying channels.









2012