### Question_01:Run the Program P1_8 for M = 2 to generate the output signal with x[n] = s1[n] + s2[n] as the input. Whichcomponent of the input x[n] is suppressed by the discrete-time system simulated by this program

Program:

m=2;

n=1:100;

s1=cos(2*pi*.05*n);

s2=cos(2*pi*.47*n);

x=s1+s2;

num=ones(1,m);

y=filter(num,1,x)/m;

subplot(2,2,1);

plot(n,s1);

axis([0, 100, -2, 2]);

xlabel('Time index n'); ylabel('Amplitude');

title('Signal # 1');

subplot(2,2,2);

plot(n,s2);

axis([0, 100, -2, 2]);

xlabel('Time index n'); ylabel('Amplitude');

title('Signal # 2');

subplot(2,2,3);

plot(n,x);

axis([0, 100, -2, 2]);

xlabel('Time index n'); ylabel('Amplitude');

title('Input Signal');

subplot(2,2,4);

plot(n,y);

axis([0, 100, -2, 2]);

xlabel('Time index n'); ylabel('Amplitude');

title('Output Signal');

#### Conclusion

#### Moving average filter is used to remove signal of high frequency (act as low pass) by decreasing the order of filter(M) we infact compress the signal of high frequency,S2 is compressed.

Qn0_2

### If the LTI system is changed from y[n] = 0.5(x[n] + x[n - 1]) to y[n] = 0.5(x[n] - x[n - 1]), what would be its effect on the input x[n] = s1[n] + s2[n]?

### Program:

y[n] = 0.5(x[n] + x[n - 1])

the graph is

If Y[n]=0.5(x[n] - x[n - 1])

Then graph is

#### The graph show that if we change y[n] then x=s1+s2 after passing through filter will enrich with high frequency signal comparatly

### Qn0_3

Run Program P1_8 for other values of filter length M, and various values of the frequencies of the sinusoidal signals s1[n] and s2[n]. Comment on your results.

#### ANSWER:

#### If we increase the order of filter means we increase the value of ‘m’ then their wil be compression of signal having high frequency and we get signal having low freguency.

#### By changing frequencing there is no change in magnitude of output signal the output and input signal is compress or expand by increasing and decreasing the freguency of input signal

### Qn0_4

Use sinusoidal signals with different frequencies as the input signals and compute the output signal for each input. How do the output signals depend on the frequencies of the input signal?

### M file

function[y]=sinfun(n , f)

y=sin(2*pi*f*n);

end

in command window

n=0:100;

for i=1:5,

f=input('enter frequency');

y=sinfun(n,f);

subplot(5,1,i);

stem(n,y);

end

enter frequency.04

enter frequency .9

enter frequency .09

enter frequency .005

enter frequency .1

### Qn0_5

### Run Program P1_3 and compare y[n] obtained with weighted input with yt[n] obtained by combining the two outputs y1[n] and y2[n] with the same weights. Are these two sequences equal? Is this system linear?

>> n = 0:40;

a = 2;b = -3;

x1 = cos(2*pi*0.1*n);

x2 = cos(2*pi*0.4*n);

x = a*x1 + b*x2;

num = [2.2403 2.4908 2.2403];

den = [1 -0.4 0.75];

ic = [0 0]; % Set zero initial conditions

y1 = filter(num,den,x1,ic); % Compute the output y1[n]

y2 = filter(num,den,x2,ic); % Compute the output y2[n]

y = filter(num,den,x,ic); % Compute the output y[n]

yt = a*y1 + b*y2;

d=y-yt;

if(yt==y)

disp('they are equal ')

else

disp('they are not equal')

end

subplot(3,1,1)

stem(n,y);

ylabel('Amplitude');

title('Output Due to Weighted Input: a \cdot+ x_{1}+[n]+ b \cdot+ x_{2}+[n]');

subplot(3,1,2)

stem(n,yt);

ylabel('Amplitude');

title('Weighted Output: a \cdot+ y_{1}+[n] + b \cdot+y_{2}+[n]');

subplot(3,1,3)

stem(n,d);

xlabel('Time index n'); ylabel('Amplitude');

title('Difference Signa

they are not equal

conclusion:

they are almost equal but not exactly this system is linear

as graph show

### Qn0_6

### Consider another system described by: *y*[*n*] = *x*[*n*] *x*[*n − *1]*.*Modify Program P1_3 to compute the output sequences y1[n], y2[n], and y[n] of the above system. Compare y[n] with yt[n]. Are these two sequences equal? Is this system linear?

n = 0:40;

a = 2;b = -3;

x1 = cos(2*pi*0.1*n);

x2 = cos(2*pi*0.4*n);

x = a*x1 + b*x2;

num = [1 1];

den = [1];

ic = [0]; % Set zero initial conditions

y1 = filter(num,den,x1,ic); % Compute the output y1[n]

y2 = filter(num,den,x2,ic); % Compute the output y2[n]

y = filter(num,den,x,ic); % Compute the output y[n]

yt = a*y1 + b*y2;

d=y-yt;

if(yt==y)

disp('they are equal ')

else

disp('they are not equal')

end

subplot(3,1,1)

stem(n,y);

ylabel('Amplitude');

title('Output Due to Weighted Input: a \cdot+ x_{1}[n]+ b \cdot+ x_{2}[n]');

subplot(3,1,2)

stem(n,yt);

ylabel('Amplitude');

title('Weighted Output: a \cdot+ y_{1}[n] + b \cdot+y_{2}[n]');

subplot(3,1,3)

stem(n,d);

xlabel('Time index n'); ylabel('Amplitude');

title('Difference Signal')

àthey are not equal

### Couclusion:

### they are almost equal and they are linear as shown in graph

### Qn0_7

Run Program P1_4 and compare the output sequences y[n] and yd[n - 10]. What is the relation between these two sequences? Is this system time-invariant?

n = 0:40; D = 10;a = 3.0;b = -2;

x = a*cos(2*pi*0.1*n) + b*cos(2*pi*0.4*n);

xd = [zeros(1,D) x];

num = [2.2403 2.4908 2.2403];

den = [1 -0.4 0.75];

ic = [0 0];% Set initial conditions

% Compute the output y[n]

y = filter(num,den,x,ic);

% Compute the output yd[n]

yd = filter(num,den,xd,ic);

% Compute the difference output d[n]

d=y- yd(1+D:41+D);

if(d==0)

disp('they are equal ')

else

disp('they are not equal')

end

% Plot the outputs

subplot(3,1,1)

stem(n,y);

ylabel('Amplitude');

title('Output y[n]');grid;

subplot(3,1,2)

stem(n,yd(1:41));

ylabel('Amplitude');

title(['Output Due to Delayed Input x[n ', num2str(D),']']);grid;

subplot(3,1,3)

stem(n,d);

xlabel('Time index n'); ylabel('Amplitude');

title('Difference Signal');grid;

àthey are equal

#### Conclusion:

#### They are exactly equal with yd is shifted version of y by 10^{th} times and they are time invarient

#### Qn0_8

#### Consider another system described byY[n]=nx[n]+x[n-1]Modify Program P1_4 to simulate the above system and determine whether this system is time-invariant or not.

n = 0:40; D = 10;a = 3.0;b = -2;

x = a*cos(2*pi*0.1*n) + b*cos(2*pi*0.4*n);

xd = [zeros(1,D) x];

num = [n 1];

den = [1];

ic = 0*[0:40];% Set initial conditions

% Compute the output y[n]

y = filter(num,den,x,ic);

% Compute the output yd[n]

yd = filter(num,den,xd,ic);

% Compute the difference output d[n]

d=y- yd(1+D:41+D);

if(d==0)

disp('they are equal ')

else

disp('they are not equal')

end

% Plot the outputs

subplot(3,1,1)

stem(n,y);

ylabel('Amplitude');

title('Output y[n]');grid;

subplot(3,1,2)

stem(n,yd(1:41));

ylabel('Amplitude');

title(['Output Due to Delayed Input x[n ', num2str(D),']']);grid;

subplot(3,1,3)

stem(n,d);

xlabel('Time index n'); ylabel('Amplitude');

title('Difference Signal');grid;

they are equal

#### Conclution:

This system is time in varient as shown by graph

mian320

good