Case Study in Matlab
47
Transcript of Case Study in Matlab
Sta
rtin
g M
AT
LAB
So
ftw
are
�F
rom
th
e d
esk
top
—D
ou
ble
-cli
ck t
he
MA
TL
AB
ico
n
(cal
led
a s
ho
rtcu
t) t
hat
th
e in
stal
ler
crea
tes
on
yo
ur
des
kto
p.
Co
nt.
.�
Fro
m t
he
Sta
rt m
enu
—C
lick
th
e S
tart
bu
tto
n,
sele
ct
Pro
gra
ms,
an
d m
ove
th
e p
oin
ter
ove
r th
e M
AT
LA
B
entr
y. S
elec
t th
e ve
rsio
n o
f M
AT
LA
B y
ou
wan
t to
sta
rt
and
, o
n t
he
app
lica
tio
n m
enu
th
at a
pp
ears
, cl
ick
th
e M
AT
LA
B e
ntr
y.
MA
TLA
B -
WIN
DO
WS
�C
om
man
d W
ind
ow
�C
om
man
d H
isto
ry
�C
urr
ent
Dir
ecto
ry
�W
ork
Sp
ace
Co
nt.
.
MA
TLA
B-
HE
LP
Op
en
ing
M-F
ile
M-F
ILE
Exe
cuti
ng
M
-fil
e
Vie
win
g o
utp
ut
Co
nt.
.
Ed
itin
g f
igu
re
Co
mm
un
ica
tio
ns
To
olb
ox
�C
om
mu
nic
atio
ns
To
olb
ox
soft
war
e ex
ten
ds
the
MA
TL
AB
tec
hn
ical
co
mp
uti
ng
en
viro
nm
ent
wit
h
fun
ctio
ns,
plo
ts,
and
a g
rap
hic
al u
ser
inte
rfac
e fo
r ex
plo
rin
g,
des
ign
ing
, an
alyz
ing
, an
d s
imu
lati
ng
al
go
rith
ms
for
the
ph
ysic
al l
ayer
of
com
mu
nic
atio
n
syst
ems
Ke
y f
ea
ture
s
�F
un
ctio
ns
for
des
ign
ing
th
e p
hys
ical
lay
er o
f co
mm
un
icat
ion
s li
nk
s, i
ncl
ud
ing
so
urc
e co
din
g, c
han
nel
co
din
g, i
nte
rlea
vin
g, m
od
ula
tio
n, c
han
nel
mo
del
s, a
nd
eq
ual
izat
ion
�P
lots
su
ch a
s ey
e d
iag
ram
s an
d c
on
stel
lati
on
s fo
r vi
sual
izin
g c
om
mu
nic
atio
ns
sig
nal
s
�G
rap
hic
al u
ser
inte
rfac
e fo
r co
mp
arin
g t
he
bit
err
or
rate
of
you
r sy
stem
wit
h a
wid
e va
riet
y o
f p
rove
n a
nal
ytic
al
resu
lts
Mo
du
lati
on
Fe
atu
res
Ch
an
ne
l F
ea
ture
s
�ch
an =
mim
och
an(n
t, n
r, t
s, f
d)
con
stru
cts
a m
ult
iple
-in
pu
t m
ult
iple
-ou
tpu
t (M
IMO
) R
ayle
igh
fad
ing
ch
ann
el o
bje
ct w
ith
a
sin
gle
pat
h l
ink
.
�n
t is
th
e n
um
ber
of
tran
smit
an
ten
nas
.
�n
r is
th
e n
um
ber
of
rece
ive
ante
nn
as.
�n
t an
d n
r ca
n b
e in
teg
er v
alu
es f
rom
1 t
o 8
.
�ts
is
the
sam
ple
tim
e o
f th
e in
pu
t si
gn
al, i
n s
eco
nd
s.
�fd
is
the
max
imu
m D
op
ple
r sh
ift,
in
her
tz.
CA
SE
-ST
UD
Y
Co
ntr
ol
Syst
em
To
olB
ox
�It
bu
ild
s o
n t
he
fou
nd
atio
ns
of
MA
TL
AB
to
pro
vid
e fu
nct
ion
s d
esig
ned
fo
r co
ntr
ol
eng
inee
rin
g.
�It
is
a co
llec
tio
n o
f al
go
rith
ms,
wri
tten
as
M-f
iles
, th
at
imp
lem
ents
co
mm
on
co
ntr
ol
syst
em d
esig
n,
anal
ysis
, an
d m
od
elin
g t
ech
niq
ues
.�
Co
nve
nie
nt
gra
ph
ical
use
r in
terf
aces
(GU
Is)
sim
pli
fy t
ypic
al c
on
tro
l en
gin
eeri
ng
tas
ks.
Cre
ati
ng
Tra
nsf
er
Fu
nct
ion
Mo
de
ls
�T
ran
sfer
fu
nct
ion
(T
F)
mo
del
s c
an
be
crea
ted
by
spec
ifyi
ng
�
Nu
mer
ato
r co
effi
cien
ts a
nd
�D
eno
min
ato
r co
effi
cien
ts.
EXAMPLE:
�n
um
= [
1 0
];
�d
en =
[1
2 1]
;
�sy
s =
tf(
nu
m,d
en)
�T
ran
sfer
fu
nct
ion
:
s
----
----
----
-
s^2
+ 2
s +
1
Exa
mp
le o
f C
rea
tin
g Z
ero
-Po
le-
Ga
in M
od
els
�T
o c
reat
e ze
ro-p
ole
-gai
n (
ZP
K)
mo
del
s, s
pec
ify
each
o
f th
e th
ree
com
po
nen
ts i
n v
ecto
r fo
rmat
.
�sy
s =
zp
k(z
,p,k
)
�cr
eate
s a
con
tin
uo
us-
tim
e ze
ro-p
ole
-gai
n m
od
el w
ith
ze
ros
z, p
ole
s p
, an
d g
ain
(s)k
.
�T
he
ou
tpu
t sy
s is
a Z
PK
ob
ject
sto
rin
g t
he
mo
del
dat
a
Co
nt.
.�
In t
he
SIS
O c
ase,
th
e tr
ansf
er f
un
ctio
n i
s re
pre
sen
ted
as
: �z
and
p a
re t
he
vect
ors
of
real
-o
r co
mp
lex-
valu
ed z
ero
s an
d
po
les,
an
d k
is
the
real
-o
r co
mp
lex-
valu
ed s
cala
r g
ain
Exa
mp
le:
sys
= z
pk
([0
],[-
1 -1
],[1
])
Zer
o/p
ole
/gai
n:
s
----
---
(s+
1)^
2
BO
DE
PLO
T�
bo
dep
lot(
sys,
w)
�d
raw
s th
e B
od
e p
lot
for
freq
uen
cies
sp
ecif
ied
by
w.
�W
hen
w =
{w
min
,wm
ax},
th
e B
od
e p
lot
is d
raw
n f
or
freq
uen
cies
bet
wee
n w
min
an
d w
max
(in
rad
/s).
sys
= t
f(1,
[1 1
]);
h =
bo
dep
lot(
sys)
Co
nt.
.
Ne
ura
l N
etw
ork
s
Neu
ral n
etw
orks
are
com
pose
d of
sim
ple
elem
ents
ope
ratin
g in
pa
ralle
l. T
hese
ele
men
ts a
re in
spire
d by
bio
logi
cal n
ervo
us
syst
ems.
On
e –
inp
ut
Ne
uro
n:
A o
ne
-la
ye
r n
etw
ork
wit
h R
in
pu
t
ele
me
nts
an
d S
ne
uro
ns
Ima
ge
Pro
cess
ing
To
olB
ox
�T
o i
den
tify
th
e ed
ges
in
an
im
age
Ed
ge
De
tect
ors
Co
nt.
.
Co
nt.
.
Ed
ge
de
tect
ion
�I
= i
mre
ad('
circ
uit
.tif
');
�B
W1
= e
dge
(I,'p
rew
itt'
);
�im
sho
w(B
W1)
;
�fi
gu
re,
imsh
ow
(BW
2)
Re
sult
s
Sob
el
Can
ny
Alg
ori
thm
�
Ste
p 1
: Acq
uire
Imag
e
�S
tep
2: C
alcu
late
Sam
ple
Co
lors
in L
*a*b
Col
or
Sp
ace
for
Eac
h R
egio
n
�S
tep
3: C
lass
ify E
ach
Pix
el U
sin
g th
e N
eare
st N
eig
hbo
r R
ule
�S
tep
4: D
isp
lay
Res
ults
of N
eare
st N
eig
hbo
r C
lass
ific
atio
n
Ste
p 5
: Dis
pla
y 'a
' an
d 'b
' Val
ues
of t
he
Lab
eled
Co
lors
Ste
p 1
: a
cqu
ire
im
ag
e�
fab
ric
= g
etsn
apsh
ot(
vid
);
Ste
p 2
: C
alc
ula
te S
am
ple
Co
lors
in
Co
lor
Sp
ace
fo
r E
ach
Re
gio
n�
colo
rNam
es =
{
'red
','gr
een
','p
urp
le','
blu
e','y
ello
w' }
;�
nC
olo
rs =
len
gth
(co
lorN
ames
);�
sam
ple
_reg
ion
s =
fal
se([
imag
eHei
ght
imag
eWid
th n
Co
lors
]);
�%
Sel
ect
each
sam
ple
reg
ion
.�
f =
fig
ure
;�
for
cou
nt
= 1
:nC
olo
rs�
set(
f, 'n
ame'
, ['
Sel
ect
sam
ple
reg
ion
fo
r '
colo
rNam
es{c
ou
nt}
] );
�sa
mp
le_r
egio
ns(
:,:,
cou
nt)
= r
oip
oly
(fab
ric)
;�
end
�cl
ose
(f);
�%
Dis
pla
y a
sam
ple
reg
ion
.�
imsh
ow
(sam
ple
_reg
ion
s(:,
:,1)
)�
titl
e(['
sam
ple
reg
ion
fo
r ' c
olo
rNam
es{1
}]);
Re
slu
ts 1
Mnp
: 30,
per
cent
0.0
5, c
lust
er n
umbe
r 4
Mnp
: 20
, per
cent
0.0
5, c
lust
er n
umbe
r 7
Orig
inal
pic
ture
s
seg
men
ted
pict
ures
Sig
na
l P
roce
ssin
g T
oo
lbo
x�
sup
po
rts
a w
ide
ran
ge
of
sig
nal
pro
cess
ing
op
erat
ion
s,
fro
m w
avef
orm
gen
erat
ion
to
fil
ter
des
ign
an
d
imp
lem
enta
tio
n,
par
amet
ric
mo
del
ing
, an
d s
pec
tral
an
alys
is.
�p
rovi
des
tw
o c
ateg
ori
es o
f to
ols
�co
mm
and
-lin
e fu
nct
ion
s an
d
�g
rap
hic
al u
ser
inte
rfac
es
Co
mm
an
d-L
ine
Fu
nct
ion
s�
Dis
cret
e-ti
me
filt
er d
esig
n, a
nal
ysis
, an
d i
mp
lem
enta
tio
n
�A
nal
og
fil
ter
des
ign
, an
alys
is, a
nd
im
ple
men
tati
on
�L
inea
r sy
stem
tra
nsf
orm
atio
ns
�W
ind
ow
s
�S
pec
tral
an
alys
is
�S
tati
stic
al s
ign
al p
roce
ssin
g
�P
aram
etri
c m
od
elin
g
�L
inea
r p
red
icti
on
�M
ult
irat
e si
gn
al p
roce
ssin
g
�W
avef
orm
gen
erat
ion
Gra
ph
ica
l U
ser
Inte
rfa
ces
�F
ilte
r d
esig
n a
nd
an
alys
is
�W
ind
ow
des
ign
an
d a
nal
ysis
�S
ign
al p
lott
ing
an
d a
nal
ysis
,
�sp
ectr
al a
nal
ysis
, an
d f
ilte
rin
g
Fil
ter
De
sig
n a
nd
Im
ple
me
nta
tio
n�
To
des
ign
a f
ifth
-ord
er 3
0 H
z lo
wp
ass
Bu
tter
wo
rth
fi
lter
an
d a
pp
ly i
t to
th
e d
ata
in v
ecto
r x:
�[b
,a]
= b
utt
er(5
,30
/50
);
�H
d =
dfi
lt.d
f2t(
b,a
);
% D
irec
t-fo
rm I
I tr
ansp
ose
d
�y
= f
ilte
r(H
d,x
);
%
stru
ctu
re
WIN
DO
W D
ES
IGN
AN
D A
NA
LYS
IS
TO
OL
CO
NV
OLU
TIO
N &
DE
CO
NV
OLU
TIO
N
�a
= [
1 2
3];
�b
= [
4 5
6];
�c
= c
on
v(a,
b)
�c
=
�4
13
28
27
18
�d
eco
nv
to d
eco
nvo
lve
b f
rom
c:
�[q
,r]
= d
eco
nv(
c,a)
�q
=
�4
5
6
�r
=
�0
0
0
0
0
