This document describes a dynamometer project involving data acquisition and filtering of force data measured by a load cell. The load cell measures the force applied by an alternator connected to an engine. Software filters are used to filter noise from the load cell data. A Butterworth low-pass filter with a 25Hz cutoff frequency and order of 5 is used. The filtered and unfiltered force data are presented on graph indicators to compare the results and evaluate engine torque.

1. Dynamometer
Project
Data
Acquisition
Filtering
Name:
Rauf
Tailony
Rocket
number:R01368594
Supervisor:
Prof.Sorin
cioc

2. 1.
Introduction:
Data
acquisition
is
used
in
this
project
to
give
an
indication
of
the
engine
power
by
extracting
data
from
a
load
cell
and
then
convert
the
load
data
into
torque
and
power
using
relevant
equations.
Data
acquisition
is
a
very
sensitive
part
of
this
project
and
any
project
because
the
more
clear
the
data
the
more
clear
the
decisions
could
be
made
related
to
the
project
success.
Collecting
data
is
usually
a
process
that
uses
sensor
of
different
types
to
collect
data
for
certain
parameter
in
order
to
compare
the
result
produced
by
these
parameters
with
the
existing
ones.
Analog
sensor
reading
are
usually
accompanied
with
a
lot
of
noise
of
different
types,
that
could
lead
to
a
non
clear
vision
for
the
parameters
that
we
are
trying
to
answer
some
questions
about,
so
in
order
to
eliminate
any
unwanted
noise
or
unwelcomed
data
its
necessary
to
use
filters
depending
on
what
kind
of
data
we
want
present
on
the
output
screen.
2.
Objective:
In
this
section
of
the
project
we
are
trying
to
do
the
following
objectives:
1. filtering
and
fine-­‐tuning
the
data
acquisitioned
from
the
sensor
;which
is
in
our
case
a
load
cell
that
measures
the
force
delivered
through
a
beam
by
the
alternator
that
is
connected
by
a
belt
with
the
engine.
2. Comparing
the
filtered
and
non
filtered
force
data
using
graph
indicators.
3. Presenting
the
torque
generated
using
graph
indicator
after
passing
the
force
data
through
related
equations.

3. 3.
Procedure:
3.1
Engine
power
delivery:
engine
power
is
delivered
to
the
alternator
through
a
belt
and
the
alternator
varies
the
load
on
the
engine
depending
on
a
lighting
system
connected
to
the
alternator
which
is
power
by
a
latching
button
,
reflected
as
a
load
on
the
engine
which
changes
engine
power
produced
relating
to
the
load,
this
load
fluctuation
leads
to
a
angular
force
produced
by
the
alternator
,
and
this
force
in
delivered
via
arm
to
be
applied
on
the
load
cell
located
at
the
end
of
the
arm,
as
shown
in
figure1.
Figure1,
connecting
arm
between
Alternator
and
Load
cell
3.2
sensor
connection
and
reading:
the
used
sensor
is
load
cell
(omegadyne),Figure
2,
and
connected
in
full
bridge
mode
through
an
NI9949
module
as
shown
in
figure
3,
and
this
module
is
connected
via
serail
port(RJ50)
through
analog
input/output
module
NI9237,Figure4,
and
fixed
on
NI
cDAQ-­‐9172,Figure
5,
and
connected
to
a
lab
view
software
of
custom
design(designed
by
Rauf
Tailony).

4.
Figure
2,Omegadyne
load
cell
Figure3,NI9949

5.
Figure4,NI9237
Figure5,NI
cDAQ-­‐9172

6. 3.3
Terminals
connection
tables
and
technical
information:
Load
cell-­‐NI9949
connections
table:
Terminal
name
Load
cell
colour
code
NI9949
pin
number
Signal
+(AL+)
Green
2
Signal
–
(AL-­‐)
White
3
Excitation
+(EX+)
Red
6
Excitation
–(EX-­‐)
Black
7
*Note:
previously
the
students
where
converting
the
wires
of
AL+
and
AL-­‐,
which
was
causing
the
data
to
be
in
minus.(fixed)
NI9949
–
NI9237
connection
table:
Device
Channel
NI9949
Single
channel
NI9237
CH0
*
Technical
data:
Load
cell
excitation
voltage
range(3-­‐10mv)
Used
excitation
voltage
(5mv)
Load
cell
Load
range(0-­‐20
LB)
Arm
length
(connecting
between
alternator
and
load
cell)
=
6
in
3.4
Filtering
and
Fine-­‐tuning:
The
filtering
procedure
we
are
using
is
software
dependent,
which
means
we
didn’t
use
any
physical
filters
or
data
conditioning
modules,
we
used
the
LV
express
filters
in
the
Block
diagram
mode
,Figure6,
to
filter
the
data
extracted
from
the
load
cell.
We
used
Butterworth
low
pass
filter
with
cutoff
frequency
of
25
HZ,
and
the
order
of
the
filter
to
be
5,
with
signals
view
mode,
and
the
data
have
been
peak
limited
using

7. mask
and
limit
testing,
and
these
filtered
signals
are
transferred
to
graph
indicators
to
be
presented
to
the
user,Figure6.
Filtered
data
are
sent
to
spectral
measurement
tools,
to
have
an
idea
about
what
kind
of
peaks
we
get
from
the
readings,
and
sent
to
a
spectral
indicator
to
be
read
by
the
user,Figure
7.
We
adjusted
the
Butterworth
filter
,
in
the
sotware
to
have
a
slider
to
control
the
cutoff
frequency
in
real
time
mode
during
the
data
presentation
on
the
graph
indicator,Figure
8,
so
we
give
a
better
control
and
understanding
of
the
signal
form.
Figure6,Block
Diagram
Mode.

8.
Figure7,Spectrum
graph
Figure8,Cutoff
frequency
Slider

9.
3.5
Lab
view
Software’s
design
detailed
information:
since
we
are
using
NI
cDaQ-­‐9172
as
interface
between
the
sensor
and
the
Lab
view
software,
we
started
the
design
in
the
block
diagram
mode
by
adding
DAQ
assistant
block,
and
then
passing
the
data
line
to
subtracter
to
apply
the
data
offset
compensation
of
15
lb,after
that
the
data
line
passed
to
a
low
pass
analogue
filter
with
cutoff
frequency
of
10HZ
and
Butterworth
type
filter
of
order
5,and
connected
a
graph
indicator
on
the
line
to
represent
the
force
with
time
and
after
that
the
data
line
passed
to
a
multiplier
with
a
magnitude
of
(6in)
which
is
the
arm
length
between
the
alternator
and
the
load
cell
in
order
to
calculate
the
torque(Lb.
In)
after
filtering
,
and
in
order
to
fine-­‐tune
the
data
more
precisely
we
passed
the
data
line
to
a
mean(averaging)
block
to
make
the
signal
more
smooth,
and
at
last
data
passed
to
a
data
recording
block(write
to
measurement
file)
to
save
the
data
on
hard
disk
depending
on
user
command,
all
the
previously
reported
blocks
are
shown
in
Figure9.
Figure9,
Block
diagram
design.

10.
The
graphical
user
interface
mode,
is
only
a
representation
of
the
output
tools
inserted
in
the
block
diagram,
which
contains
a
force-­‐time
graph,
and
Torque-­‐time
graph,
and
timer,
stop
button
and
cutoff
frequency
slider
and
sampling
rate,sampling
frequency
boxes,
as
shown
in
figure
10.
Figure10,GUI
mode

11.
*
Sensor
calibration
and
reset:
Since
our
load
cell
sensor
is
a
bridge
circuit
,
we
need
to
enter
for
the
lab
view
software
and
assign
the
first
two
values
of
calibration
provided
by
the
manufacturing
company
of
the
load
cell,
in
the
load
cell
we
are
using
we
could
find
some
calibration
data
on
the
manufacturer’s
website(Omegadyne.com),
and
we
can
find
the
place
to
calibrate
the
bridge
by
clicking
right
on
DAQ
assistant
in
block
diagram
mode
and
in
properties,
you
will
find
configure
scale,Figure11,
and
then
the
table
of
calibration
data,Figure12.
Figure11,DAQ
assistant
properties
window.

12.
Figure12,Configure
scale
window
Note:
electrical
values
are
0.0000
and
0.9668
respectively,
and
physical
values
are
0
and
2.5
respectively.
Physical
sensor
calibration:
To
make
sure
that
the
data
we
are
getting
in
the
software
are
real
and
there
is
no
error
in
the
reading
,
we
made
physical
calibration
for
the
sensor
side
by
side
with
the
sensor
software
calibration.
We
made
the
calibration
using
(0.25,0.65,1.3,5lb)
weights
,
and
loading
it
on
the
top
of
the
load
cell
after
isolating
it
from
the
system
and
then
observing
the
readings
presented
on
the
load
graph,
and
adjusting
the
subtraction
magnitude
in
order
to
make
the
load
cell
give
as
much
precise
data
as
it
is
capable
of,
as
described
in
figures
13,14
respectively.
Its
worth
it
to
mention
that
after
taking
the
data
on
the
graph
read
by
the
sensor
from
the
previous
weights,
we
found
that
there
is
an
offset
in
the
factor
of
(3.39)
which
we
could
deal
with
it
by
multiplying
the
data
line
with
this
factor
before
presenting
it
on
the
graph,
Figure
15.

13.
Figure
13,
weights
used
in
calibration.
Figure
14,
mounted
weight
on
load
cell
structure.

14.
Figure15,offset
compensation
block
3.6
Displaying
calculated
rpm
in
the
GUI
:
As
it
is
known
,
Torque(Lb.
IN)
has
a
relation
with
power(HP)
and
RPM
which
is
shown
the
relation
below:
Torque
(lb.in)
=
63,025
x
Power
(HP)
/
Speed
(RPM)……………………….(1)
and
using
this
relation
allows
us
to
show
the
calculated
engine
RPM
in
the
Labview
software
since
the
power
is
known
for
the
engine,
but
with
a
limitation
that
this
RPM
will
be
precise
only
for
the
engine
in
the
Idle
state,
but
after
coupling
the
engine
with
the
alternator
,it
will
be
very
complex
to
predict
the
RPM
in
the
calculation
torque
based
method
which
will
give
correct
data
representation
only
when
engine
have
no
load
and
running
on
high
rpm(>6000rpm)
,
Figure16.

15.
Figure16,RPM
in
the
GUI
mode
Before
we
pass
the
data
line
to
RPM
indicator
we
passed
it
to
a
formula
box
,
which
contain
the
following
formula
which
is
number
substitution
to
formula
(1)
:
(63025*0.611)/X1
………………………….(2)
Torque
data
Power(HP)
Eq.(1)
constant
We
got
the
power
value
of
0.611
,by
running
the
engine
on
RPM
=
7500,
and
coupling
it
with
the
alternator,
and
Using
the
ProCal
software
to
get
the
rpm
,
we
could
calculate
the
real
power
after
coupling
using
eq.(1),
and
you
can
trace
the
data
line
passing
through
the
formula
block
by
looking
to
figure
17.

16.
Figure17,Full
block
diagram
We
compared
the
data
we
got
from
our
design
of
labview
GUI
RPM,
with
Procal
RPM,
the
results
were
almost
the
same
in
the
same
running
conditions,
as
indicated
in
Figure18.

17.
Figure18,
Labview
RPM
VS.
Procal

18. 4.
Conclusion:
Data
filtering
can
enhance
the
data
we
extract
from
the
load
cell
sensor
even
its
not
a
physical
filtering
but
it
could
fairly
enhance
the
results
to
the
user
in
order
to
give
a
better
understanding
of
the
parameters
that
we
are
trying
to
observe
which
is
in
our
case
the
torque
and
power.
Figure
19,
Original
Data
with
noise
In
the
previous
graph
we
are
presenting
the
original
data
that
is
extracted
in
real
time
directly
from
the
sensor,
as
you
can
see
in
figure
13,
the
data
have
a
lot
of
noise
which
make
it
hard
for
the
observer
to
decide
of
the
torque
he
is
getting
from
the
engine
is
good
or
bad
,
and
following
to
that
we
have
pasted
the
filtered
force
and
torque
graphs
with
time
for
the
engine
in
the
Idle
state,
so
that
you
can
observe
the
change
happened
to
the
data
after
filtering,
and
what
kind
of
enhancement
made
to
make
the
data
more
stable
and
readable.

19.
Figure
20,
Filtered
Data
*
Mean
and
averaging:
even
we
have
implemented
the
averaging
property
to
the
block
diagram
as
you
saw
previously,
but
related
to
a
limitation
in
the
Labview
software
you
can’t
present
the
data
of
the
averaging
and
mean
or
RMS
as
a
graph
but
only
as
numbers,
as
presented
previously
in
the
GUI
screenshot.
5.
Recommendations:
I
would
recommend
for
the
coming
teams
who
will
work
on
this
project
for
the
software
side
to
use
FPGA
software
to
represent
more
filtered
and
averaged
and
stable
data
that
could
look
more
professional
for
future
use
of
the
project,
or
using
matlab
since
its
supporting
the
graphical
representation
more
than
lab
view,
and
also
have
a
lot
of
resources
that
could
help
researcher
do
a
better
design
than
the
Labview
do.

20. 6.
Index:
Filter
used
and
related
concepts:
Butterworth
Filter:
In
applications
that
use
filters
to
shape
the
frequency
spectrum
of
a
signal
such
as
in
communications
or
control
systems,
the
shape
or
width
of
the
roll-­‐off
also
called
the
“transition
band”,
for
a
simple
first-­‐order
filter
may
be
too
long
or
wide
and
so
active
filters
designed
with
more
than
one
“order”
are
required.
These
types
of
filters
are
commonly
known
as
“High-­‐order”
or
“nth-­‐order”
filters.
The
complexity
or
Filter
Type
is
defined
by
the
filters
“order”,
and
which
is
dependant
upon
the
number
of
reactive
components
such
as
capacitors
or
inductors
within
its
design.
We
also
know
that
the
rate
of
roll-­‐off
and
therefore
the
width
of
the
transition
band,
depends
upon
the
order
number
of
the
filter
and
that
for
a
simple
first-­‐order
filter
it
has
a
standard
roll-­‐off
rate
of
20dB/decade
or
6dB/octave.
Then,
for
a
filter
that
has
an
nth
number
order,
it
will
have
a
subsequent
roll-­‐off
rate
of
20n
dB/decade
or
6n
dB/octave.
So
a
first-­‐order
filter
has
a
roll-­‐off
rate
of
20dB/decade
(6dB/octave),
a
second-­‐order
filter
has
a
roll-­‐off
rate
of
40dB/decade
(12dB/octave),
and
a
fourth-­‐order
filter
has
a
roll-­‐off
rate
of
80dB/decade
(24dB/octave),
etc,
etc.
High-­‐order
filters,
such
as
third,
fourth,
and
fifth-­‐order
are
usually
formed
by
cascading
together
single
first-­‐order
and
second-­‐order
filters.
For
example,
two
second-­‐order
low
pass
filters
can
be
cascaded
together
to
produce
a
fourth-­‐order
low
pass
filter,
and
so
on.
Although
there
is
no
limit
to
the
order
of
the
filter
that
can
be
formed,
as
the
order
increases
so
does
its
size
and
cost,
also
its
accuracy
declines.
Decades
and
Octaves
One
final
comment
about
Decades
and
Octaves.
On
the
frequency
scale,
a
Decade
is
a
tenfold
increase
(multiply
by
10)
or
tenfold
decrease
(divide
by
10).
For
example,
2
to
20Hz
represents
one
decade,
whereas
50
to
5000Hz
represents
two
decades
(50
to
500Hz
and
then
500
to
5000Hz).
An
Octave
is
a
doubling
(multiply
by
2)
or
halving
(divide
by
2)
of
the
frequency
scale.
For
example,
10
to
20Hz
represents
one
octave,
while
2
to
16Hz
is
three
octaves
(2
to
4,
4
to
8
and
finally
8
to
16Hz)
doubling
the
frequency
each
time.
Either
way,
Logarithmic
scales
are
used
extensively
in
the
frequency
domain
to
denote
a
frequency
value
when
working
with
amplifiers
and
filters
so
it
is
important
to
understand
them.

21. Logarithmic
Frequency
Scale
Since
the
frequency
determining
resistors
are
all
equal,
and
as
are
the
frequency
determining
capacitors,
the
cut-­‐off
or
corner
frequency
(
ƒC
)
for
either
a
first,
second,
third
or
even
a
fourth-­‐order
filter
must
also
be
equal
and
is
found
by
using
our
now
old
familiar
equation:
As
with
the
first
and
second-­‐order
filters,
the
third
and
fourth-­‐order
high
pass
filters
are
formed
by
simply
interchanging
the
positions
of
the
frequency
determining
components
(resistors
and
capacitors)
in
the
equivalent
low
pass
filter.
High-­‐order
filters
can
be
designed
by
following
the
procedures
we
saw
previously
in
the
Low
Pass
and
High
Pass
filter
tutorials.
However,
the
overall
gain
of
high-­‐order
filters
is
fixed
because
all
the
frequency
determining
components
are
equal.
Filter
Approximations
So
far
we
have
looked
at
a
low
and
high
pass
first-­‐order
filter
circuits,
their
resultant
frequency
and
phase
responses.
An
ideal
filter
would
give
us
specifications
of
maximum
pass
band
gain
and
flatness,
minimum
stop
band
attenuation
and
also
a
very
steep
pass
band
to
stop
band
roll-­‐off
(the
transition
band)
and
it
is
therefore
apparent
that
a
large
number
of
network
responses
would
satisfy
these
requirements.
Not
surprisingly
then
that
there
are
a
number
of
“approximation
functions”
in
linear
analogue
filter
design
that
use
a
mathematical
approach
to
best
approximate
the
transfer
function
we
require
for
the
filters
design.
Such
designs
are
known
as
Elliptical,
Butterworth,
Chebyshev,
Bessel,
Cauer
as
well
as
many
others.
Of
these
five
“classic”
linear
analogue
filter
approximation
functions
only
the
Butterworth
Filter
and
especially
the
low
pass
Butterworth
filter
design
will
be
considered
here
as
its
the
most
commonly
used
function.
Low
Pass
Butterworth
Filter
Design
The
frequency
response
of
the
Butterworth
Filter
approximation
function
is
also
often
referred
to
as
“maximally
flat”
(no
ripples)
response
because
the
pass
band
is
designed
to
have
a
frequency
response
which
is
as
flat
as
mathematically
possible
from
0Hz
(DC)
until
the
cut-­‐off
frequency
at
-­‐3dB
with
no
ripples.
Higher
frequencies
beyond
the
cut-­‐off
point
rolls-­‐off
down
to
zero
in
the
stop
band
at
20dB/decade
or
6dB/octave.
This
is
because
it
has
a
“quality
factor”,
“Q”
of
just

22. 0.707.
However,
one
main
disadvantage
of
the
Butterworth
filter
is
that
it
achieves
this
pass
band
flatness
at
the
expense
of
a
wide
transition
band
as
the
filter
changes
from
the
pass
band
to
the
stop
band.
It
also
has
poor
phase
characteristics
as
well.
The
ideal
frequency
response,
referred
to
as
a
“brick
wall”
filter,
and
the
standard
Butterworth
approximations,
for
different
filter
orders
are
given
below.
Ideal
Frequency
Response
for
a
Butterworth
Filter
Where
the
generalised
equation
representing
a
“nth”
Order
Butterworth
filter,
the
frequency
response
is
given
as:
Where:
n
represents
the
filter
order,
Omega
ω
is
equal
to
2πƒ
and
Epsilon
ε
is
the
maximum
pass
band
gain,
(Amax).
If
Amax
is
defined
at
a
frequency
equal
to
the
cut-­‐
off
-­‐3dB
corner
point
(ƒc),
ε
will
then
be
equal
to
one
and
therefore
ε2
will
also
be
one.
However,
if
you
now
wish
to
define
Amax
at
a
different
voltage
gain
value,
for
example
1dB,
or
1.1220
(1dB
=
20logAmax)
then
the
new
value
of
epsilon,
ε
is
found
by:
•
Where:
•
H0
=
the
Maximum
Pass
band
Gain,
Amax.
•
H1
=
the
Minimum
Pass
band
Gain.
Transpose
the
equation
to
give:
The
Frequency
Response
of
a
filter
can
be
defined
mathematically
by
its
Transfer

23. Function
with
the
standard
Voltage
Transfer
Function
H(jω)
written
as:
•
Where:
•
Vout
=
the
output
signal
voltage.
•
Vin
=
the
input
signal
voltage.
•
j
=
to
the
square
root
of
-­‐1
(√-­‐1)
•
ω
=
the
radian
frequency
(2πƒ)
Note:
(
jω
)
can
also
be
written
as
(
s
)
to
denote
the
S-­‐domain.
and
the
resultant
transfer
function
for
a
second-­‐order
low
pass
filter
is
given
as:
Normalised
Low
Pass
Butterworth
Filter
Polynomials
To
help
in
the
design
of
his
low
pass
filters,
Butterworth
produced
standard
tables
of
normalised
second-­‐order
low
pass
polynomials
given
the
values
of
coefficient
that
correspond
to
a
cut-­‐off
corner
frequency
of
1
radian/sec.
n
Normalised
Denominator
Polynomials
in
Factored
Form
1
(1+s)
2
(1+1.414s+s2)
3
(1+s)(1+s+s2)
4
(1+0.765s+s2)(1+1.848s+s2)
5
(1+s)(1+0.618s+s2)(1+1.618s+s2)
6
(1+0.518s+s2)(1+1.414s+s2)(1+1.932s+s2)
7
(1+s)(1+0.445s+s2)(1+1.247s+s2)(1+1.802s+s2)
8
(1+0.390s+s2)(1+1.111s+s2)(1+1.663s+s2)(1+1.962s+s2)
9
(1+s)(1+0.347s+s2)(1+s+s2)(1+1.532s+s2)(1+1.879s+s2)
10
(1+0.313s+s2)(1+0.908s+s2)(1+1.414s+s2)(1+1.782s+s2)(1+1.975s+s2)
Filter
Design
–
Butterworth
Low
Pass
Find
the
order
of
an
active
low
pass
Butterworth
filter
whose
specifications
are
given
as:
Amax
=
0.5dB
at
a
pass
band
frequency
(ωp)
of
200
radian/sec
(31.8Hz),
and
Amin
=
-­‐20dB
at
a
stop
band
frequency
(ωs)
of
800
radian/sec.
Also
design
a
suitable
Butterworth
filter
circuit
to
match
these
requirements.
Firstly,
the
maximum
pass
band
gain
Amax
=
0.5dB
which
is
equal
to
a
gain
of
1.0593
(0.5dB
=
20log
A)
at
a
frequency
(ωp)
of
200
rads/s,
so
the
value
of
epsilon
ε
is
found
by:
Secondly,
the
minimum
stop
band
gain
Amin
=
-­‐20dB
which
is
equal
to
a
gain
of
-­‐10
(20dB
=
20log
A)
at
a
stop
band
frequency
(ωs)
of
800
rads/s
or
127.3Hz.
Substituting
the
values
into
the
general
equation
for
a
Butterworth
filters
frequency
response
gives
us
the
following:

24.
Since
n
must
always
be
an
integer
(
whole
number
)
then
the
next
highest
value
to
2.42
is
n
=
3,
therefore
a
“a
third-­‐order
filter
is
required”
and
to
produce
a
third-­‐
order
Butterworth
filter,
a
second-­‐order
filter
stage
cascaded
together
with
a
first-­‐
order
filter
stage
is
required.
From
the
normalised
low
pass
Butterworth
Polynomials
table
above,
the
coefficient
for
a
third-­‐order
filter
is
given
as
(1+s)(1+s+s2)
and
this
gives
us
a
gain
of
3-­‐A
=
1,
or
A
=
2.
As
A
=
1
+
(Rf/R1),
choosing
a
value
for
both
the
feedback
resistor
Rf
and
resistor
R1
gives
us
values
of
1kΩ
and
1kΩ
respectively,
(
1kΩ/1kΩ
+
1
=
2
).
We
know
that
the
cut-­‐off
corner
frequency,
the
-­‐3dB
point
(ωo)
can
be
found
using
the
formula
1/CR,
but
we
need
to
find
ωo
from
the
pass
band
frequency
ωp
then,

25.
So,
the
cut-­‐off
corner
frequency
is
given
as
284
rads/s
or
45.2Hz,
(284/2π)
and
using
the
familiar
formula
1/CR
we
can
find
the
values
of
the
resistors
and
capacitors
for
our
third-­‐order
circuit.
Note
that
the
nearest
preferred
value
to
0.352uF
would
be
0.36uF,
or
360nF.

26. Third-­‐order
Butterworth
Low
Pass
Filter
and
finally
our
circuit
of
the
third-­‐order
low
pass
Butterworth
Filter
with
a
cut-­‐off
corner
frequency
of
284
rads/s
or
45.2Hz,
a
maximum
pass
band
gain
of
0.5dB
and
a
minimum
stop
band
gain
of
20dB
is
constructed
as
follows.

27. 7.
Acknowledgment:
Very
big
thanks
for
Prof.
Sorin
Cioc,
Assistant
professor,UT,
for
giving
me
the
opportunity
to
use
his
Internal
combustion
lab,
and
giving
me
a
solid
pathway
to
use
it
in
order
to
reach
the
goal
in
this
work
using
the
shortest
road.
Special
thanks
for
Sabin
Bati,Masters
student,MIME,UT,
for
his
help
in
practical
work,
and
for
his
bright
Ideas
that
he
shared
with
me
in
order
to
make
the
data
look
and
behave
more
precise.
8.
References:
1. http://www.ni.com/community/
2. http://www.omegadyne.com/nav/entry.html
3. http://www.electronics-­‐tutorials.ws