Matlab PUnjabi University

8/13/2019 Matlab PUnjabi University

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Experiment No.1

Aim:Genration of different waveforms square,sine,triangular ,exponential.

Apparatus: MATLAB Software

Program:

% Square wave

t=0:0.01:10;

x=square (t/2*pi);

% figure

subplot(2,2,1)

plot(t,x);

xlabel('Time in second');

ylabel('Amplitude');

title('Square Wave');

% Sine wave

y=sin(t);

%figure;

subplot(2,2,2)

plot(t,y);



title('Sine Wave');

%Traiangular Wave

k=1;

for i=0:pi/2:4*pi

z(k)=sin(i);


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k=k+1;

end

subplot(2,2,3)

plot(z);



title('Triangular Wave');

%Exponential Function

n=1;

for j=0:0.01:10

e(n)=exp(j);

n=n+1;

end

subplot(2,2,4)

plot(e);



title('Exponential Function');


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Experiment No.2

AIM:Program for convolution of two sequences [y(n)=x1(n)*x2(n)].


Program:

x1=input('enter the first sequence x1(n)=');

x2=input('enter the first sequence x2(n)=');

% starting point of first sequence

nx1=input('enter the range of first sequence [-n:n]=');

% starting point of second sequence

nx2=input('enter the range of second sequence [-n:n]=');

nyb=nx1(1)+nx2(1);

nye=nx1(length(x1))+nx2(length(x2));

ny=[nyb:nye];

y=conv(x1,x2)

stem(ny,y);

Output:


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Experiment No.3

AIM:Program to check the performance of linear difference equation.


Program:

%This program is used to calculate the output of a system

%which is described by linear difference equation

%Example

%Let the difference equation be

%y(n)-0.9y(n)=x(n)and

%coefficients are b=[1];a=[1,-0.9];

a=input('Enter the coefficient of input x=');

b=input('Enter the coefficient of input y=');

impz(a,b);

title('impulse Response');

xlabel('n');

ylabel('h(n)')

Output:


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Experiment No.4

AIM:Compute and plot poles and zeros of Z-transform.


Program:

%Z-transform is basically used for checking the system stability and poles and

%zeros characterizes the system performance.User has to give numerator and

%denominator polynomial.

NUM=input(‘input the numerator polynomial:’);

DEN= input(‘input the denominator polynomial:’);

TRANF=tf(NUM,DEN();

[ZEROS POLES GAIN]=tf2zp(NUM,DEN);

%disp(‘poles of transfer function are’);

%disp(Poles)-

%disp(‘Zeros of transfer function are’);

%disp(ZEROS)

%disp(Gain of transfer function is’);

%disp(GAIN)

%zplane(NUM,DEN)

Output:


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Experiment No.5

AIM:Program for finding magnitude and phase response of LTI system described by

Transfer function H(z).


Program:

LENGTH=input(‘input the length of DFT(For best result in power of 2):’);

THETA=0:pi/LENGTH:pi;

NUM=[0.05 0.033 0.008];

DEN=[0.06 4 1];

TRANSFER=tf(NUM,DEN)

[FREQ,W]=freqz(NUM,DEN,LENGTH);

grid on;

figure(1);

subplot(2,1,1)

k=0.2;

plot(abs(FREQ),’k’);

disp(abs(‘FREQ’));

title(‘Magnitude response’);

xlabel(‘FREQ index’);

ylabel(‘Magnitude’);

grid on;

subplot(2,1,2)

disp(angle(‘FREQ’));

plot(angle(FREQ),’k’);

title(‘Phase response’);

xlabel(‘Frequency index’);

ylabel(‘Phase’);


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grid on;

Output:


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Experiment No.6

AIM:Find the N point circular or linear convolution using circular convolution of the ginen two

Sequences.


Program:

%Circular convolution of given two sequences given by user.

%Convolution in time domain is simple multiplication in frequency domain

A_FIRST=input('Enter the elements of first sequence=');

B_FIRST=input('Enter the elements of Second sequence=');

LENGTH=length(A_FIRST);

FFT_A_FIRST=fft(A_FIRST);

FFT_B_FIRST=fft(B_FIRST);

TEMP1(1)=B_FIRST(1);

for i=2:LENGTH

TEMP1(i)=B_FIRST(LENGTH+2-i);

end

%CIRCULAR CONVOLUTION

for i=1:LENGTH

sum=0;

for j=1:LENGTH

sum=sum+A_FIRST(j)*TEMP1(j);

end

conv(i)=sum;

TEMP2(1)=TEMP1(LENGTH);

for k=2:LENGTH

TEMP2(k)=TEMP1(k-1);


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end

TEMP1=TEMP2;

end

disp('Convolved sequence1s:');

disp(conv);

figure(1)

subplot(3,1,1)

stem(A_FIRST,'k');

title('First input sequence');

subplot(3,1,2)

stem(B_FIRST,'k');

title('second input sequence');

subplot(3,1,3)

stem(conv,'k');

title('Convolved sequence');

Output:


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Experiment No.7

AIM: Program for designing an IIR Filter .


Program:

% This program Take the Digital filter specification

% wp digital passband frequency

% ws digital stop band frequency,Rp Passband ripple

% As Stopband attenuation

% Example:wp=0.2*pi;ws=0.3*pi;

% Rp=1;As=15;

wp=input('Enter the digital passband frequency');

ws=input('Enter the digital stopband frequency');

Rp=input('Enter the passband ripple');

As=input('Enter the stop band attenuation');

T=1;

Omegap=(2/T)*tan(wp/2); %prewarp Prototype Passband freq.

Omegas=(2/T)*tan(ws/2); %prewarp Prototype Passband freq.

% Analog Prototype Calculation

N=ceil((log10((10^(Rp/10)-1)/(10^(As/10)-1)))/(2*log10(Omegap/Omegas)));

% fprint('Butterworth filter order=%2.0f/',N);

OmegaC=Omegap/((10^(Rp/10)-1)^(1/(2*N)));

% analog BW prototype cutoff

wn=2*atan((OmegaC*T)/2);

% Digital filter design


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wn=wn/pi %Digital Butter cutoff in pi

[b,a]=butter(N,wn);

Output:


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Experiment No.8

AIM: Program for designing an low- pass FIR Filter .


Program:

% Windows:a.Hamming window.

% b.hanning window.

% The FIR filters are designed with the window functions which are the'

% empirical formulae.

% The simple example of the filter is simulated using 'SINC' function.

% The filter coefficient are calculated for respective window function i.e.

% W(n).Then point-wise multiplication of H(n)and W(n) will give Fitered response,user can

see the difference is clear from the graphical diagram itself.

N=input('Input order of the filter:');%ORDER of filter.

% TRANSFER FUNCTION EVALUATION.

THETA=-pi:(2*pi/(N-1)):pi;

Hn=sinc(THETA);

disp('Original sequence is');

disp(Hn);

% HANNING RESPONSE.

Wn_HANN=hanning(N);

disp('Filter coefficients with Hanning window are');

disp(Wn_HANN);

FILTER_RESP_HANN=(Hn').*Wn_HANN;


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disp('Filtered response with Hanning window is:');

disp(FILTER_RESP_HANN);

figure(1);

subplot(2,1,1);

plot(Hn,'k');

title('Original sequence');

subplot(2,1,2);

plot(FILTER_RESP_HANN,'k');

title('Filtered response with Hanning window');

% HAMMING RESPONSE.

Wn_HAMM=hamming(N);

disp('Filter coefficients with Hamming window are');

disp(Wn_HAMM);

FILTER_RESP_HAMM=(Hn').*Wn_HAMM;

disp('Filtered response with Hamming window is:');

disp(FILTER_RESP_HAMM);

figure(2);

subplot(2,1,1);

plot(Hn,'k');

title('Original sequence');

subplot(2,1,2);

plot(FILTER_RESP_HAMM,'k');

title('Filtered response with Hamming window');

Output:


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