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					                          Signal Processing
                             ENGD2002
                          Resit Coursework
                           Year 2008/ 2009
The module is reassessed by an examination only. The students who
receive a deferral in Signal processing coursework should resubmit reports
for the attached 8 assignments.

The following marking scheme will be used
Assignment-1     15
Assignment-2     10
Assignment-3     10
Assignment-4     15

Assignment-5       15
Assignment-6       20
Assignment-7       15

Total mark         100%


The Laboratory reports should be handed in to the Student support centre in
Gateway Building F.A.O. Dr Akil Bashagha before Friday 14th August
2009.




Dr Akil Bashagha                     1                             3/23/2011
                     School of Engineering and Technology
                               Signal Processing
                                  ENGD2002

                              Assignment-1
                     Use MATLAB for Function Evaluation

MATLAB is short for "matrix laboratory" that was invented in the late 1970s. It is a
high-level language for Engineering computation. It can solve technical computing
problems faster than with traditional programming languages, such as C, C++, and
Fortran. MATLAB is widely used in many applications such as signal processing, control
systems and communications.

In this assignment, different MATLAB methods will be used to define and evaluate
functions. Double click on MATLAB icon and you will see the prompt >>. Type the
instructions after the prompt and press the enter key to execute an instruction. If you
need some help how to use an instruction, type >> help followed by the instruction.

   I) Inline functions:
MATLAB inline constructs inline function object using the given expression and the
input arguments. For example, f= 2x +3 with an argument x can be represented as

>> f=inline ('2*x + 3', 'x')
The inline function will be f(x) = 2*x + 3
To calculate the value of f for x=5, type
>> x=5
followed by
>> f(x)
The above two steps can be rewritten as one step
>> f( 5)
Inputs should be written as vectors in order to evaluate the function for multiple values of
inputs. Let x is a variable ( -5  x 5).
>> x=(-5: 5)’ generate a vector of integers (i.e. -5, -4, …, 0, 1, .. 4, 5).
>>f(x)          calculates the function for the above values of x.
To plot the function, use the plot command
>>plot (x, f(x))
To use more points of x, use
>>x=(-5: 0.1: 5)        generate 100 points of x
>>f(x)
>>plot (x, f(x))
>> xlabel('x');
>> ylabel('f(x)');
>> grid;


Dr Akil Bashagha                             2                                    3/23/2011
Exercise:
Repeat the above steps to plot the exponentially damped sinusoid
f(t)= e-t . cos (2t) =exp(-t).*cos(2*pi*t)
for -5  t  5 using 1 and 0.1 spacing.
Repeat the plot for 0  t  5.

   II) Unit step and Pulse functions:
The unit step function is defined as
u(t)= 1 for t0 and u(t)=0 for t<0

>> u=inline('t>=0', 't')
As explained above, plot the function for -5  t  5. Use suitable spacing intervals of t so
that the function is close to a unit step.
Use the following instruction to have suitable x and y axes limits of the graph.
>> axis([-5 5 -0.2 1.2])

The pulse function p = u(t)- u(t-4) can be calculated using logical operations (e.g. & for
AND) and relational operators (e.g. >, >=) as follows
>> p=inline('(t>=0) & (t<4)', 't')
Plot the pulse function for -5  t  5

Exercise:
Plot the pulse function p = u(t)- u(t-4) as a difference between two unit step functions.
Calculate u1=inline('t>=0', 't') and u2=inline('t>=4', 't') for t=-5:0.1:5 and then find
p=u1(t)-u2(t).

   III)        Scaling and shifting of functions:
Consider the following function
f(t)= e-t . cos (2t)
g(t)=f(t).u(t)
Note that MATLAB can not multiply inline objects so construct new inline object for g(t)
and plot the function
g=inline('exp(-t).* cos (2*pi*t).*(t>=0)')

If g(t) is scaled by a and shifted by b then the new function will be g(at+b).

Exercise:
Plot g(t), g(2*t), g(t+4), g(2*t+4), g(-t+2) and g(2*t+4)+g(-t+2).



In your report, include and discuss all the results

Dr Akil Bashagha                              3                                   3/23/2011
                      School of Engineering and Technology
                                Signal Processing
                                   ENGD2002

                                  Assignment-2
                        Second Order Differential Equations

Consider the following second order differential equation

                                    d2y    dy
                                  a0 2  a1  a2 y  0
                                    dt     dt

                                      a0 y ' '  a1 y ' a 2 y  0

It is an important equation that is used in many signal processing problems. We assume
the solution to be y=Kext. The values of x can be found by writing down the characteristic
equation.

                                       a0 x 2  a1 x  a 2  0

It is a quadratic equation where the roots can be calculated as

                                            a1  a1  4a0 a2
                                                         2

                                 x1, 2 
                                                       2a 0

The solution of the differential equation is

                                 y (t )  K1e x1t  K 2 e x2t
K1 and K2 are constants that depend on the initial values y(0) and y’(0).
The Matlab roots command can be used to find the solution, x1and x2.
>>roots(A)
where A is the input vector of the equation coefficients, A= [a0 a1 a2].
    1. Assume that a0 =1, a1 = 5, a2 =4, use matalb to find the roots of the equation.
    2. Find the roots of A if a0 =1, a1 = 4, a2 =5.
At the start of each exercise, type
>> clear all
to clear all the old values.



Dr Akil Bashagha                                   4                              3/23/2011
Exercise-1:
   1. The transient current of a series RLC circuit of Fig. 1 is similar to the above
      solution with a0 =1, a1 = R/L, a2 =1/(LC). Find the current if R=400, L=20mH,
      C= 1F.
                                                      R        L
                                                           1            2

                               V u(t)                                        C




                                                      Fig. 1

   2. Change C to 0.1F and calculate the current.


Exercise-2:
Consider the operational amplifier circuit of Fig. 2. The output voltage could be
calculated from the previous equations where

                                           1 1    1  1                                  1
                   a0  1 ;         a1       (     );                    a2                ;
                                           C 2 R1 R2 R3                            R1 R2 C1C 2

                                                          R2



                                                               C1
                                            R3            R1
                                                                   -
                                                 C2
                                                                       OUT
                       input        o
                                                                   +




                                                      Fig. 2

   1. Let all the resitor values be the same, R=10k and capacitor values are the same
      C=1F. Find the roots of the output voltage.
In Matlab, define R and C as vectors, R=[1e4 1e4 1e4], C=[1e-6 1e-6]. Then calculate a1
and a2 using the vector elements. For example,
>> a2=1/(R(1)*R(2)*C(1)*C(2))

   2. Find the output voltage roots if C1 is changed to 1nF.

In your report, include and discuss all the results. Compare the Matlab values with
the theoretical values.
                      School of Engineering and Technology


Dr Akil Bashagha                                      5                                            3/23/2011
                                 Signal Processing
                                    ENGD2002

                                  Assignment-3
                          M-files (Script and Function)

Script M-Files:
The script file is a series of Matlab commands. These commands will be executed
sequentially when the file name is typed in Matlab Command window. The script file is
created using Matlab text editor: File/ New/ M-file and should be saved as filename.m.
Comments are started with % and will be ignored during the execution of the M-file.
Consider the operational amplifier circuit of Fig. 1. The output voltage could be
calculated from the previous equations where
                1 1    1  1                      1
a0  1 ; a1       (     );       a2                 ;
                C 2 R1 R2 R3                R1 R2 C1C 2
                                                    R2



                                                            C1
                                       R3           R1
                                                            -
                                            C2
                                                                 OUT

                                                            +
            Fig. 1
Exercise-1:
   1. Write the following script M-file. Start from File/ New/ M-file

%opampf.m calculate roots of 2nd order equation of Op-Amp circuit
%R, C values
R=[1e4 1e4 1e4];
C=[1e-6 1e-6];
%coefficient values
a0=1;
a1=( 1/R(1)+ 1/R(2)+ 1/R(3))/C(2);
a2=1/(R(1)*R(2)*C(1)*C(2));
A=[a0 a1 a2];
%the roots of characteristic equation
lambda=roots(A);

Save the file as opampf1.m

To execute the file, type its name opampf in the matalb command window.
>>opampf1

To view the roots, type
>>lambda


Dr Akil Bashagha                             6                               3/23/2011
  2. Find the output voltage if C1 is changed to 1nF.
Compare the roots with the previous values of the last assignment.

Function M-Files
In script files, you need to save and execute the file after changing any paramter. The
function file is similar to the script file except the first line will be

Function [output1, output2, …]=filename(input1, input2, …)

Exercise-2:
   1. Write the following function M-file. Start from File/ New/ M-file

function [lambda]=opampf2(R,C)
%opampf.m
%Function M-file calculate roots of 2nd order equation of Op-Amp
circuit
%inputs: R= length-3 vector of resistances
%inputs: C= length-2 vector of capacitances
%outputs: lambda= roots of characteristic equation

%coefficient values
a0=1;
a1=( 1/R(1)+ 1/R(2)+ 1/R(3))/C(2);
a2=1/(R(1)*R(2)*C(1)*C(2));
A=[a0 a1 a2];
%the roots of chars. equation
lambda=roots(A);

Save the file as opampf2.m
In matlab command, type the values of R and C

>> R=[1e4 1e4 1e4];
>> C=[1e-6 1e-6];

To execute the file type its name with the inputs as follows

>> lambda=opampf2(R,C)
   2. From matlab command, change C1 to 1nF and claculate the new roots
In your report, include and discuss all the results. Compare the Matlab
values with the theoretical values.




Dr Akil Bashagha                             7                                    3/23/2011
                      School of Engineering and Technology
                                Signal Processing
                                   ENGD2002

                                 Assignment-4
                      Graphical explanation of Convolution
Convolution is a complicated combination of multiplication, integration and time-
shifting. We can define the convolution between two functions, h(t) and x(t) as the
following
                                y(t)  h(t) * x(
                                               t)
                                           
                                y (t )     x( )h(t   )d
                                           
                                           
                                y (t )     x(t   )h( )d
                                           

The variable τ (greek tau) is a dummy variable for integration. For the linear time
invariant systems (LTI), x(t) is the input, y(t) is the output and h(t) is the impulse
response of the system.
The convolution operation can be difficult to perform in the time domain. Therefore,
many people prefer to use the Laplace Transform (or another transform) to convert the
convolution operation into a multiplication operation (in the frequency domain), through
the Convolution Theorem.

                                  ( y(t))  ( h(t) * x(t))
                                  Y ( s)  H ( s) X ( s)

H(s) represent the tranfer function of the LTI system and can be calculated as:

                                                     Y (s)
                                           H (s) 
                                                     X (s)
In this assignment, Matlab graphics is used to illustrate the convolution operation in the
time domain. In the next exercise, you will write a program to find the output signal of a
LTI system for an input signal
                             x(t )  1.5 sin(t )((u(t )  u(t  1))

The impulse response of the LTI system is given by
                   h(t )  1.5((u(t )  u(t  1.5))  u(t  2)  u(t  2.5)

Exercise:
   1. Open a new Matlab M-file and write down the following commands. Save the file
       as convolution1.m.


Dr Akil Bashagha                                8                                 3/23/2011
%convolution script M-file graphically demonstrates convolution
%creates new figure
figure(1)
%input x(t)=1.5sin(pi.t)(u(t)-u(t-1))
x=inline('1.5*sin(pi*t).*(t>=0&t<1)');
%impulse response h(t)=1.5(u(t)-u(t-1.5))-u(t-2)+u(t-2.5)
h=inline('1.5*(t>=0&t<1.5)-(t>=2&t<2.5)');
%limits of tau
dtau=0.005;tau=-1:dtau:4;
%limits for time
ti=0;tvec=-.25:.1:2.25;

%preallocate memory
y=NaN*zeros(1,length(tvec));
for t=tvec,
    ti=ti+1;%time index
    xh=x(t-tau).*h(tau);%integrand h(tau)*x(t-tau)
    lxh=length(xh);
    y(ti)=sum(xh.*dtau);%output, sum of area = integration
    subplot(2,1,1),plot(tau,h(tau),'b-',tau,x(t-tau),'k--',t,0,'og');
    axis([tau(1) tau(end) -2 2.5]);
    xlabel('\tau');ylabel('x,h,xh');grid;

end

      2. Use different values of the end limit of time (-0.1, 0, 0.75, 1.5, 2.25, 2.75,3.75)
         and notice the common area between the two functions.
         Try to find the area of multiplying the two functions at the above limits. The area
         represents the function of convolution y(t) for different values of time.

      3. Add the following lines to the above program (inside the for loop) and save it as
         convolution2.m. Run the new code for the above values of t.
      patch([tau(1:end-1);tau(1:end-1);tau(2:end);tau(2:end)], ...
      [zeros(1,lxh-1);xh(1:end-1);xh(2:end);zeros(1,lxh-1)],...
      [.8 .8 .8],'edgecolor','none');
      legend('h(\tau)','x(t-\tau)','t','h(\tau)x(t-\tau)','location','eastoutside');

         Try to predict the convolution function and draw it as a function of time.

      4. Add the following lines to the above program (inside the for loop) and save it as
         convolution3.m. Run the new code for the above values of t.
       subplot(2,1,2),plot(tvec, y);
       axis([tau(1) tau(end) -2 2.5]);
       xlabel('t');ylabel('y(t)=x(t)*h(t)');grid;

Compare the results with the expected convolution function from the previous step.
In your report, include and discuss all the results. Compare the Matlab values with the
theoretical values.
                       School of Engineering and Technology
                                 Signal Processing


Dr Akil Bashagha                              9                                       3/23/2011
                                            ENGD2002

                                    Assignment-5
                                Discrete Time Signals
In this assignment, some useful Matlab commands: stem, filter and conv will be used to
analyse the discrete-time systems.

Discrete Time Signals
Stem command is used to plot the discrete-time function f[n] as stems from the x-axis
terminated with circles for the data value. Options of stem command will help to use
colours and filling of the plots.

Consider the discrete-time function f[n]= e-n/5 cos(n/5) u[n] and use the following
program to plot the function for -25  n  25.

>> f=inline('exp(-n/5).*cos(pi*n/5).*(n>=0)','n')
>> n=(-25:25)';
>> stem(n,f(n));
>> xlabel('n');ylabel('f[n]');grid;


Exercise-1:
   1. Plot the functions f[n] and f[n+1] on one figure as follows

>> subplot(2,1,1);stem(n,f(n));xlabel('n');ylabel('f[n]');grid;
>> subplot(2,1,2);stem(n,f(n+1));xlabel('n');ylabel('f[n+1]');grid;

   2. Plot the functions f[n] and f[-n] on a new figure.
   3. Plot the functions f[n] and f[2n] on a new figure
   4. Plot the functions f[-2n] and f[-2n+1] on a new figure

In your report,
Q1. Use the graphs to explain the relationship between f[n] and the other functions of
steps 1, 2, and 3.
Q2. Explain the relationship between f[n], f[-2n] and f[-2n+1] of step-4. Could you
recover f[n] from f[-2n] and f[-2n+1]? Explain that.

Discrete time filtering:
The discrete-time filtering in time domain is represented by the following constant
coefficient linear difference equation:
                                 N                      M

                                a
                                k 0
                                       k   y[n  k ]   bk x[n  k ]
                                                        k 0


If we assume a0 =1 then the output filtered data y[n] is calculated as


Dr Akil Bashagha                                   10                            3/23/2011
                                     M                  N
                             y[n]   bk x[n  k ]   a k y[n  k ]
                                    k 0                k 1
Therefore, we need the input sequence x[n], the (N+1)-input coefficient vector,
B =[b0 b1 b2 b3 … bM], and the (N+1)-output coefficient vector, A=[1 a1 a2 a3 … aN], to
calculate y[n].

Exercise-2:
The following example of filtering for N=3 is used to calculate the impulse response h[n].
                              y[n]  x[n]  y[n  1]  y[n  2]
Use the filter command as follows
>> h = filter (B, A, x(n));
to calculate the impulse response h[n] for an impulse input x[n] for -25  n  25.
Use the above equations to find B and A as vectors where A=[1 a1 a2 a3 … aN]
and B =[b0 b1 b2 b3 … bM].

   1. Start with inline command to define the input function x[n] as an impulse
      (‘n==0’) and use stem to plot it.
   2. Calculate h[n] using filter command and plot it.
   3. Change the input to x[n] = cos(2n/6)u[n] and plot the input and output.
   4. Change the input to x[n] = cos(n/6)u[n] and plot the input and output.

In you report
Q1. What is the order of the system?
Q2. Is the system stable or not? Explain why?
Q3. Comment on the three inputs of the system.
Q4. Use the characteristic equation of the system to compare the impulse response and
the outputs from steps-2 and 3.

Discrete time convolution:
The output of the discrete-time system in the time domain can be obtained as a
convolution of the impulse response h[n] and the input x[n],
                                 y[n]  h[n] * x[n]
                                               
                                    y[n]     h[k ]x[n  k ]
                                             k  


The impulse response h[n] of the system
                             y[n]  x[n]  y[n  1]  y[n  2]
is given by
                                                sin(n / 3)
                          h[n]  {cos(n / 3)              }u[n]
                                                      3




Dr Akil Bashagha                                   11                           3/23/2011
Exercise-3:
   1. Use inline to define h[n] and plot it for 0  n  25.
   2. Use inline to define x[n] = cos(2n/6)u[n] as before and plot it.
   3. Calculate y[n] using conv command and plot it
   >> y=conv(h(n),x(n));
   >> stem(n,y(1:26))

   In you report
                                  sin(n / 3)
Q1. Prove that h(n)  {cos(n / 3)           }u[n] .
                                       3
Q2. Compare the above results with the results of Exercise-2.

In your report, include and discuss all the results. Answer the questions. Compare
the Matlab values with the theoretical values.




Dr Akil Bashagha                            12                             3/23/2011
                        School of Engineering and Technology
                                  Signal Processing
                                     ENGD2002

                                               Assignment-6
                                          Continuous Time Filters
The main objective of this assignment is to study analogue filters in frequency domain.
The filter is an electronic circuit used in signal processing to remove the unwanted
frequencies and to pass the wanted frequencies. The general form of a transfer function of
linear time invariant continuous time LTIC system with input X and output Y is given by
                                                                     M

                                                      Y (s)        b      k   s M k
                                         H ( s)                   k 0
                                                                     N

                                                                    a
                                                      X ( s)
                                                                           k   s N k
                                                                    k 0
The numerator and denominator polynomials are of the following form
                   M

                   b s
                   k 0
                          k
                                  M k
                                          b0 S M  b1 S M 1  b2 S M 2  ...  bM 1 S  bM
                    N

                   a s
                   k 0
                              k
                                  N k
                                           a0 S N  a1 S N 1  a2 S N 2  ...  a N 1 S  a N


To calculate H(s) in frequency domain where s=j, the vectors A and B of N+1 and M+1
coefficients are given by
                                    A  [a0 a1 a2 ... a N ]
                                    B  [b0 b1 b2 ... bM ]
In Matlab, polyval function calculates the polynomial for a range of frequencies =2f
where  in rad/sec and f in Hz. For example,
>>polyval(A,j*)
calculates the polynomial for s=j.
                    N

                   a s
                   k 0
                              k
                                  N k
                                           a0 S N  a1 S N 1  a2 S N 2  ...  a N 1 S  a N

The following Matlab commands are useful for this assignment
>> polyval(…)
>> abs( … )
>>angle(….)
>> semilogx( … )
>> log10(…)

Exercise-1:
1. Use Matlab to evaluate and plot the absolute value (using abs(H)) of the following
   first-order polynomial for 0 f 10Hz.
                                    H (s)  ( s  10) / 10


Dr Akil Bashagha                                             13                                     3/23/2011
2. Repeat the above calculation for
                                   H (s)  10 /(s  10)

3. Repeat step-1 and 2 to plot H(s) in decibels (db) against logarithmic x-axis.

Exercise-2:
There are many different types of analogue and digital filters. A simple example of an
analogue filter is the passive continuous time circuit of Fig. 1. The transfer function
H(j) and cut off frequency fc are given by
                                                   1
                                   H ( j ) 
                                              1  jRC
                                                1
                                        fc 
                                              2RC


                                        R
                         Vin                      C       Vout




                                            Fig. 1

1. Design the RC filter shown in Fig. 1 with a cut off frequency of 1 kHz and assume
   that the capacitor value is 0.1 µF.
2. Plot magnitude of transfer function in db against frequency (use log scale for x axis)
   for 1Hz f 10 kHz .
3. Plot the phase of transfer function in degrees against frequency (use log scale for x
   axis) using the Matlab command angle(…).
4. Use Matlab command subplot to plot the magnitude and phase on the same figure.
5. Calculate H(s) after swapping R with C. Follow the above four steps to design,
   calculate and plot the characteristics of the transfer function.

In your report:
    1. Calculate the theoretical values of magnitude response of Exercise-1 and compare
       them with the practical results.
    2. Derive the transfer function of the filter of Fig. 1 and its cut off frequency.
    3. Derive the transfer function and its cut off frequency after swapping R with C.
    4. What type of the filter of Fig.1 and the one after swapping R with C
    5. Calculate the theoretical values of magnitude and phase responses and cut off
       frequency of Exercise-2 and compare that with the practical results and ideal
       response.
    6. Define and comment on the cut-off frequency, -3DB points, and roll-off rate
       (DB/decade).




Dr Akil Bashagha                             14                                    3/23/2011
                      School of Engineering and Technology
                                Signal Processing
                                   ENGD2002

                                           Assignment-7
                                        Discrete Time Filters
The main objective of this assignment is to study discrete-time filters in frequency
domain. The general form of a transfer function of linear time invariant Discrete time
LTID system with input X and output Y is given by
                                         M                              M

                            Y ( z)        bk z k                      b z     k
                                                                                     k


                   H ( z)              k 0
                                          N
                                                                       k 0
                                                                            N
                                                                                                            (1)
                                         a                          1   ak z
                            X ( z)                          k                            k
                                                    k   z
                                         k 0                             k 1
The output response in frequency domain Y(z) is given by
                         Y ( z)  H ( z) X ( z)                 (2)
The output response in the time domain y[n] can be obtained as a convolution of the
impulse response h[n] and the input x[n],
                              y[n]  h[n] * x[n]
                                                           (3)
                              y[n]   h[k ]x[n  k ]
                                             k 0
The (N+1)-output coefficient vector, A, and the (M+1)-input coefficient vector, B are
given by
            A  [1 a1 a2 ... a N ]         B  [b0 b1 b2 ... bM ]      (4)
The LTID system of Eq. (1) could also be represented in the time domain as
                           N                                M
                   y[n]   ak y[n  k ]   bk x[n  k ]                                                   (5)
                          k 1                          k 0
The output y[n] is given by
                                 M                               N
                       y[n]   bk x[n  k ]   a k y[n  k ]                                        (6)
                                 k 0                            k 1


There are two types of digital filter namely finite-impulse response FIR filters and
infinite-impulse response IIR filters.
In FIR filters, the output coefficients ak (for k=1,…, N) are zeros except a0=1. The output
y[n] depends only on the present and the past input samples so that
                                                M
                                  y[n]   bk x[n  k ]                                        (7)
                                             k 0
                                                M
                                 Y ( z )   bk z k X ( z )                                   (8)
                                             k 0
The transfer function H(z)=Y(z) / X(z) is given by
                                                        M
                                     H ( z )   bk z k                                        (9)
                                                    k 0




Dr Akil Bashagha                                                 15                                               3/23/2011
The length of the impulse response h[k] of FIR filters is finite and depends on the input
coefficients bk. h[k] can be obtained from comparing Eq. (3) with Eq. (7) or by using the
inverse Z-transform of Eq. (9) as it equals the input coefficients bk.
          h[k ]  bk  B so h[0]  b0    h[1]  b1 , …, h[ M ]  bM         (10)

The infinite impulse response IIR filter has at least one of the output coefficients ak (for
k=1,…, N) is nonzero. The transfer function H(z) will be a ratio of two polynomials as in
Eq. (1). The output y[n] as given by Eq. (6) depends on the past output samples as well as
the present and the past input values. The IIR filter has a feedback from the output to the
input.


Exercise-1:
The frequency response of the filter is obtained after replacing z by ejT=ej where 
(2f) is the analogue frequency (rad/ sec) and  is the digital frequency (rad). T is the
sampling period (T=1/fs). Normally, the range of digital frequency is 0  and the
equivalent analogue frequency range is 0f  fs/2.

1. Plot the magnitude (db) and phase (°) as functions of digital frequency  (rad) for 0
    for the following FIR filter.

H(z) = 0.0101 + 0.2203 z-1 + 0.5391 z-2 + 0.2203 z-3 + 0.0101 z-4

Use the freqz Matlab command to calculate the transfer function H(z) as
>>H=freqz(B,A,wd)
where A=1; wd==2fT. Use fs=1kHz and 0f  fs/2.

2. Plot both magnitude and phase of the above step in one figure.

Exercise-2:
The command fir1could be used to design FIR filter by calculating its coefficients as

>> B=fir1(M,Wn)
M is the order of the filter and Wn is the normalized cut off frequency (cut = *Wn
rad).

The Matlab command freqz(B,A,wd) with no output arguments plots the magnitude and
phase of the filter.

1. Use fir1 to design a filter with M=4 and Wn=0.4.
2. Use freqz without H to plot the FIR filter characteristics and compare the graphs with
   Exercise-1 above.
3. Design another FIR filter with M=20 and Wn=0.4 and plot its characteristics.
   Compare the new graphs with the previous ones.




Dr Akil Bashagha                            16                                    3/23/2011
Exercise-3:
1. Plot the magnitude (db) and phase (°) of the following transfer function as functions
   of digital frequency  (rad) (0 ) for three different values of p: 0.83, 0.96, 0.99

                                                1  z 2
                                H ( z) 
                                           1  2z 1   2 z  2

2. Write an m-file to plot the magnitude (as a normal value and not in db) and phase (°)
   for the three values of p on the same graph. Use subplot to divide the figure into two
   halves. Plot the magnitude for the three values of p in the upper half and the phase in
   the lower half.

In your report, include and discuss all the results and name the types of filters used
in the above exercises.




Dr Akil Bashagha                                17                              3/23/2011

				
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