DIGITAL SIGNAL PROCESSING
LABORATORY MANUAL
B.TECH (IV YEAR – I SEM) – R16
Department of Electronics and Communications
SIR C.R.REDDY COLLEGE OF ENGINEERING
(Approved by AICTE, New Delhi & permanently affiliated to JNTUK, Kakinada)
Ranked as “A” grade college by Govt. of Andhra Pradesh, “AA” grade by NPTEL
ELURU- 534007, WEST GODHAVARI DIST, ANDHRA PRADESH, INDIA
Phone no.:08812-230840, Visit us at http:// www.sircrrengg.ac.in.
Course Name: Digital Signal Processing Laboratory Course code: C418.
List of the Experiments / programs
To Student has to perform at least FOUR Experiments in each part
PART-1(SIGNALS)
1) Generation of discrete time signals for discrete signals
2) To verify the Linear Convolution
a) Using MATLAB
b) Using Code Composer Studio (CCS)
3) To verify the Circular Convolution for discrete signals
a) Using MATLAB
b) Using Code Composer Studio (CCS)
4) To find the addition of Sinusoidal Signals
5) To verify Discrete Fourier Transform (DFT) and Inverse Discrete Fourier Transform(IDFT)
a) Using MATLAB
b) Using Code Composer Studio (CCS)
6) Transfer Function Stability Analysis: using pole-zero plot, bode plot, Nyquist plot, z-plane
plot.
PART-2 (FILTERS)
7) Frequency Response of IIR low pass Butterworth Filter
8) Frequency Response of IIR high pass Butterworth Filter
9) Frequency Response of IIR low pass Chebyshev Filter
10) Frequency Response of IIR high pass Chebyshev Filter
11) Frequency Response of FIR low pass Filter using Rectangle Window
12) Frequency Response of FIR low pass Filter using Triangle Window
PART – 3(IMAGE PROCESSING)
13) An image processing in a false contouring system
14) To generate the histogram equalization to the image
15) To verify the Normalized Cross Correlation to the addition of noise and removal of noise
using filters to an image.
16) Compute the edge of an image using spatial filters.
17) Perform the image motion blur and calculate PSNR to the noise image and also noise free
image.
18) To verify the PSNR to the Second order Decomposition of Discrete Wavelet transforms
and to the reconstructed image using inverse Discrete Wavelet transform
ELECTRONICS & COMMUNICATION ENGINEERING
Vision of the Department
To be a premier department in the region that nurtures individuals for acquisition of
knowledge and skills with research orientation which suits the local and global needs of
industry and society in the field of Electronics and Communication Engineering.
Mission of the Department
M1. To impart quality education and encourage research with an emphasis on application and
innovation
M2. To cater the emerging societal needs through all-round development of students
M3. To enable individuals face the global competition
M4. To inculcate the importance of ethical and moral values in students
Program Educational Objectives (PEOs)
PEO No. PEO Description
Develop a solid foundation in mathematics and sciences to solve electronics and
PEO 1
communication engineering problems
Apply core engineering concepts to develop novel products and solutions demanded
PEO 2
by modern industry
Pursue higher studies, research & development in science & technology and keep
PEO 3
abreast of latest technological developments.
Inculcate professional and ethical attitude ,effective communication skills ,Team
PEO 4
spirit & leadership qualities
Contribute to the needs of the society in solving technical problems using electronics
PEO 5
communications engineering principles, tools and practices
Program Outcomes:
PO1: Engineering knowledge: Apply the knowledge of mathematics, science,
engineering fundamentals, and an engineering specialization to the solution of complex
engineering problems.
PO2: Problem analysis: Identify, formulate, research literature, and analyse complex
engineering problems reaching substantiated conclusions using the first principles of
mathematics, natural sciences, and engineering sciences.
PO3: Design/development of solutions: Design solutions for complex engineering
problems and system components or processes that meet the specified needs with
appropriate consideration for the public health and safety, and the cultural, society,
and environmental considerations.
PO4: Conduct investigations of complex problems: Use research-based knowledge
and research methods including design of experiments, analysis and interpretation of
data, and synthesis of the information to provide valid conclusions.
PO5: Modern tool usage: Create, select, and apply appropriate techniques, resources,
and modern engineering and IT tools including prediction and modelling to complex
engineering activities with an understanding of the limitations.
PO6: The engineer and society: Apply reasoning informed by the contextual
knowledge to assess societal, health, safety, legal and cultural issues and the
consequent responsibilities relevant to the professional engineering practice.
PO7: Environment and sustainability: Understand the impact of the professional
engineering solutions in society and environmental contexts, and demonstrate the
knowledge of, and need for sustainable development.
PO8: Ethics: Apply ethical principles and commit to professional ethics and
responsibilities and norms of the engineering practice.
PO9: Individual and team work: Function effectively as an individual, and as a
member or leader in diverse teams, and in multi-disciplinary settings.
PO10: Communication: Communicate effectively on complex engineering activities
with the engineering community and with society at large, such as, being able to
comprehend and write effective reports and design documentation, make effective
presentations, and give and receive clear instructions.
PO11: Project management and finance: Demonstrate knowledge and understanding
of the engineering and management principles and apply these to one’s own work, as a
member and leader in a team, to manage projects and in multi-disciplinary
environments.
PO12: Life-long learning: Recognize the need for, and have the preparation and ability
to engage in independent and life-long learning in the broadest context of technological
change.
PART-1(SIGNALS)
GENERATION OF DISCRETE-TIME SIGNALS
AIM:
Write a MATLAB program for the generation of unit impulse, unit step,
ramp, exponential and Sinusoidal sequences.
PROGRAM:
%program for the generation of unit impulse sequence [δ (n)]
K=input ('enter the length of sequence of K :');
n= - K: 1: K;
x= [zeros (1, K), ones (1, 1), zeros (1, K)];
subplot (5, 1, 1);
stem (n, x);
xlabel('time index');
ylabel ('s(n)');
title ('unit impulse');
%program for the generation of unit step sequence [u (n)]
L=input ('enter length of sequence of L :');
n= - L: 1: L;
x= [zeros (1, L), ones (1, L+1)];
subplot (5,1,2);
stem (n,x);
xlabel('time index');
ylabel('u(n)');
title ('unit step');
%program for the generation of exponential sequence x (n) =c (a) n
M=input ('enter sequence length of M :');
n=0: M;
B=input ('enter value of B :');
d=B;
C=input ('enter a value of C :');
a=C;
x=d*(a. ^n);
subplot (5,1,3);
stem (n,x);
xlabel('time index');
ylabel('e(n)');
title ('exponential');
%program for the generation of sinusoidal sequence
N=input ('enter the length of N :');
n=0: N;
x=sin (2*pi/10*n);
subplot (5,1,4);
stem (n,x);
xlabel('time index');
ylabel('r(n)');
title ('sinusoidal');
%program for the generation of ramp sequence
O=input ('enter sequence length of O:');
l=0: O;
x=l;
subplot (5,1,5);
stem (l,x);
xlabel('time index');
ylabel('z(n)');
title ('ramp function');
RESULT:
Given sequences are generated and plotted using MATLAB.
VIVA QUESTIONS:
1. What is the use of stem command?
2. What is the use of xlabel command?
3. What is the use of ylabel command?
4. What is the use of subplot command?
5. What is the use of title command?
LINEAR CONVOLUTION OF SEQUENCES
a) Write a MATLAB program to find the linear convolution of two
sequences x (n) and h (n)
PROGRAM:
%program for the generation of first sequence [x (n)]
N=input ('enter length of the first sequence :');
n=-N: 1: N;
x=input ('enter the first sequence :');
subplot (3,1,1);
stem (n,x);
xlabel('time index');
ylabel('x(n)');
title ('1stinputsequence');
%program for the generation of second sequence [h (n)]
M=input ('enter length of the second sequence :');
k=-M: 1: M;
h=input ('enter the second sequence :');
subplot (3,1,2);
stem (k,h);
xlabel('time index');
ylabel('h(k)');
title ('2ndinputsequence');
%program for the generation of convolved sequence [y(n)=x(n)*h(n)]
p= (-N-M):1 :( M+N);
y=conv(x, h);
subplot (3,1,3);
stem (p,y);
xlabel('time index');
ylabel('y(p)');
title ('convolution');
RESULT: Linear convolution of two sequences x (n) and h (n)
are determined using MATLAB.
VIVA QUESTIONS:
1. What is the use of input command?
2. What is the use of length command?
3. What is the use of ‘conv’ function?
4. What are the uses of MATLAB?
5. What are the different toolboxes in MATLAB?
b) Linear convolution using Code Composer Studio (CCS)
#include<stdio.h>
main()
{
int m=4;
int n=4;
int i=0,j;
int x[10]={1,2,3,4,0,0,0,0};
int h[10]={1,2,3,4,0,0,0,0};
int *y;
y=(int *)0x0000100;
for(i=0;i<m+n-1;i++)
{
y[i]=0;
for(j=0;j<=i;j++)
y[i]+=x[j]*h[i-j];
}
for(i=0;i<m+n-1;i++)
printf("%d\n",y[i]);
}
OUTPUT:
RESULT:
Linear convolution of two sequences x (n) and h (n) are determined using MATLAB and Code
Composer Studio.
VIVA QUESTIONS:
1. What is the use of input command?
2. What is the use of length command?
3. What is the use of ‘conv’ function?
4. What are the uses of MATLAB?
3. CIRCULAR CONVOLUTION OF SEQUENCES
a) AIM: Write a MATLAB program to find the circular convolution of two sequences x
(n) and h (n)
PROGRAM:
%Circular Convolution
clc;
clear all;
close all;
disp('circular convolution program');
x=input('enter i/p x(n):');
m=length(x);
h=input('enter i/p sequence h(n)');
n=length(h);
subplot(2,2,1), stem(x);
title('i/p sequencce x(n)is:');
xlabel('---->n');
ylabel('---->x(n)');grid;
subplot(2,2,2), stem(h);
title('i/p sequencce h(n)is:');
xlabel('---->n');
ylabel('---->h(n)');grid;
disp('circular convolution of x(n) & h(n) is y(n):');
if(m-n~=0)
if(m>n)
h=[h,zeros(1,m-n)];
n=m;
end
x=[x,zeros(1,n-m)];
m=n;end
y=zeros(1,n);
y(1)=0;
a(1)=h(1);
for j=2:n
a(j)=h(n-j+2);
end
%ciruclar conv
for i=1:n
y(1)=y(1)+x(i)*a(i);
end
for k=2:n
y(k)=0;
% circular shift
for j=2:n
x2(j)=a(j-1);
end
x2(1)=a(n);
for i=1:n
if(i<n+1)
a(i)=x2(i);
y(k)=y(k)+x(i)*a(i);
end
end
end
disp(y);
subplot(2,2,[3,4]),stem(y);
title('Circular convolution of x(n) & h(n) is:');
xlabel('---->n');
ylabel('---->y(n)');
grid;
b) Circular Convolution for discrete signals using Code
Composer Studio (CCS)
#include<stdio.h>
intm,n,x[30],h[30],y[30],i,j,temp[30],k,x2[30],a[30];
void main()
{
int *y;
y=(int *)0x0000100;
printf(" enter the length of the first sequence\n");
scanf("%d",&m);
printf(" enter the length of the second sequence\n");
scanf("%d",&n);
printf(" enter the first sequence\n");
for(i=0;i<m;i++)
scanf("%d",&x[i]);
printf(" enter the second sequence\n");
for(j=0;j<n;j++)
scanf("%d",&h[j]);
if(m-n!=0) /*If length of both sequences are not equal*/
{
if(m>n) /* Pad the smaller sequence with zero*/
{
for(i=n;i<m;i++)
h[i]=0;
n=m;
}
for(i=m;i<n;i++)
x[i]=0;
m=n;
}
y[0]=0;
a[0]=h[0];
for(j=1;j<n;j++) /*folding h(n) to h(-n)*/
a[j]=h[n-j];
/*Circular convolution*/
for(i=0;i<n;i++)
y[0]+=x[i]*a[i];
for(k=1;k<n;k++)
{
y[k]=0;
/*circular shift*/
for(j=1;j<n;j++)
x2[j]=a[j-1];
x2[0]=a[n-1];
for(i=0;i<n;i++)
{
a[i]=x2[i];
y[k]+=x[i]*x2[i];
}}
/*displaying the result*/
printf(" the circular convolution is\n");
for(i=0;i<n;i++)
printf("%d ",y[i]);
}
OUTPUT:
4. ADDITION OF SINUSIODAL SIGNALS
Aim: To Find the addition of Sinusoidal Signals
Program:
Output:
Result:
5. To Verify Discrete Fourier Transform (DFT) and Inverse Discrete
Fourier Transform (IDFT)
PROGRAM:
clc;
close all;
clear all;
xn=input('Enter the sequence x(n)'); %Get the sequence from user
ln=length(xn); %find the length of the sequence
xk=zeros(1,ln); %initialize an array of same size as that of input sequence
ixk=zeros(1,ln); %initialize an array of same size as that of input sequence
%DFT of the sequence
%-----------------------------------------------------
for k=0:ln-1
for n=0:ln-1
xk(k+1)=xk(k+1)+(xn(n+1)*exp((i)*2*pi*k*n/ln));
end
end
%------------------------------------------------------
%Plotting input sequence
%-----------------------------------------------------
t=0:ln-1;
subplot(221);
stem(t,xn);
ylabel ('Amplitude');
xlabel ('Time Index');
title('Input Sequence');
%---------------------------------------------------------------
magnitude =abs(xk); % Find the magnitudes of individual DFT points
% plot the magnitude response
%------------------------------------------------------------
t=0:ln-1;
subplot(222);
stem(t,magnitude);
ylabel ('Amplitude');
xlabel ('K');
title('Magnitude Response');
%------------------------------------------------------------
phase=angle(xk); % Find the phases of individual DFT points % plot the magnitude
sequence
%------------------------------------------------------------
t=0:ln-1;
subplot(223);
stem(t,phase);
ylabel ('Phase');
xlabel ('K');
title ('Phase Response');
%------------------------------------------------------------
%IDFT of the sequence
%------------------------------------------------------------
for n=0:ln-1
for k=0:ln-1
ixk(n+1)=ixk(n+1)+(xk(k+1)*exp(i*2*pi*k*n/ln));
end
end
ixk=ixk./ln;
%------------------------------------------------------------
%code block to plot the input sequence
%------------------------------------------------------------
t=0:ln-1;
subplot(224);
stem(t,ixk);
ylabel ('Amplitude');
xlabel ('Time Index');
title ('IDFT sequence');
%------------------------------------------------------
Output:
Xn=[1 2 3 4 5]
Xk = 15,-2.50+3.44i,-2.50+0.81i,-2.49-0.81i,-2.49-3.44i
RESULT:
VIVA QUESTIONS:
1. What is the use of ‘dftmtx’ function?
2. What is the use of ‘dct’ function?
3. What is the use of ‘fft’ function?
4. What is the use of ‘fft2’ function?
5. What is the use of ‘fftshift’function?
PART-2(FILTERS)
7). FREQUENCY RESPONSE OF IIR LOW PASS BUTTERWORTH FILTER
AIM:
Write a MATLAB program to design a Butterworth low pass digital filter
for the given specifications and plot the Frequency response.
% To design a Butterworth low pass filter for the given specifications
clear all;
alphap=4; % Pass band attenuation in dB
alphas=30; % Stop band attenuation in dB
fp=400; % pass band frequency in Hz
fs=800; % stop band frequency in Hz
F=2000; % Sampling frequency in Hz
omp=2*fp/F; oms=2*fs/F;
% To find cutoff frequency and order of the filter
[n,wn]=buttord(omp,oms,alphap,alphas)
% system function of the filter
[b,a]=butter (n, wn)
w=0:.01:pi;
[h,om]=freqz (b,a,w,'whole');
m=20*log10(abs(h));
an=angle(h);
subplot(2,1,1);plot(om/pi,m);grid;
ylabel('Gain in dB');
xlabel('Normalised frequency');
title('magnitude response');
subplot(2,1,2);plot (om/pi,an);grid
ylabel('Phase in radians');
xlabel('Normalised frequency');
title('phase response');
RESULT:
Butterworth low pass digital filter is designed with the given
specifications and obtained the frequency response using MATLAB.
VIVA QUESTION:
1. What is the use of ‘buttord’ function?
2. What is the use of ‘butter’ function?
3. What is the use of ‘freqz’ function?
4. What is the use of ‘residuez’ function?
5. What is the use of ‘ zplane’ function?
8). FREQUENCY RESPONSE OF IIR HIGH PASS BUTTERWORTH FILTER
AIM:
Write a MATLAB program to design a Butterworth high pass digital filter
for the given specifications and plot the Frequency response.
% To design a Butterworth high pass filter for the given specifications
clear all;
alphap=4; %Pass band attenuation in dB
alphas=30; %Stop band attenuation in dB
fs=400; %pass band frequency in Hz
fp=800; %stop band frequency in Hz
F=2000; %Sampling frequency in Hz
omp=2*fp/F; oms=2*fs/F;
%To find cutoff frequency and order of the filter
[n,wn]=buttord(omp,oms,alphap,alphas)
% system function of the filter
[b,a]=butter(n,wn,'high')
w=0:.01:pi;
[h,om]=freqz(b,a,w);
m=20*log10(abs(h));
an=angle(h);
subplot(2,1,1);plot(om/pi,m);grid;
ylabel('Gain in dB');
xlabel('Normalised frequency');
subplot(2,1,2);plot(om/pi,an);grid;
ylabel('Phase in radians');
xlabel('Normalised frequency');
RESULT:
Butterworth High pass digital filter is designed with the given
specifications
using MATLAB.
VIVA QUESTIONS:
1.What is the use of ‘real’ function?
2.What is the use of ‘freqs’ function?
3.What is the use of ‘poly’ function?
4.What is the use of ‘roots’ function?
5.What is the use of ‘ ceil’ function?
9). FREQUENCY RESPONSE OF IIR BAND PASS BUTTERWORTH FILTER
AIM:
Write a MATLAB program to design a Butterworth band pass digital filter
for the given specifications and plot the Frequency response.
%To design a Butterworth Bandpass filter for the given specifications
clear all;
alphap=2; %Passband attenuation in dB
alphas=20; %Stopband attenuation in dB
wp=[.2*pi,.4*pi]; %passband frequency in radians
ws=[.1*pi,.5*pi]; %stopband frequency in radians
%To find cutoff freency and order of the filter
[n,wn]=buttord(wp/pi,ws/pi,alphap,alphas)
% system function of the filter
[b,a]=butter(n,wn)
w=0:.01:pi;
[h,ph]=freqz(b,a,w);
m=20*log(abs(h));
an=angle(h);
subplot(2,1,1);plot(ph/pi,m);grid;
ylabel('Gain in dB');
xlabel('Normalised frequency');
subplot(2,1,2);plot (ph/pi,an);grid
ylabel('Phase in radians');
xlabel('Normalised freuency');
RESULT:
Butterworth Band pass digital filter is designed with the given
specifications using MATLAB.
VIVA QUESTION:
1.What is the use of ‘fliplr’ function?
2.What is the use of ‘dfs’ function?
3.What is the use of ‘dir2cas’ function?
4.What is the use of ‘cas2dir’ function?
5.What is the use of ‘dir2par’ function?
10). FREQUENCY RESPONSE OF IIR LOWPASS CHEBYSHEV FILTER
AIM:
Write a MATLAB program to design a Chebyshev lowpass digital filter for the given
specifications and plot the Frequency response.
% To design a chebyshev type I lowpass filter for the given specifications
clear all;
alphap=1; %Passband attenuation in dB
alphas=15; %Stopband attenuation in dB
wp=.2*pi; %passband frequency in radians
ws=.3*pi; %stopband frequency in radians
%To find cutoff freency and order of the filter
[n,wn]=cheb1ord(wp/pi,ws/pi,alphap,alphas)
% system function of the filter
[b,a]=cheby1(n,alphap,wn)
w=0:.01:pi;
[h,ph]=freqz(b,a,w);
m=20*log(abs(h));
an=angle(h);
subplot(2,1,1);plot(ph/pi,m);grid;
ylabel('Gain in dB');xlabel('Normalised frequency');
subplot(2,1,2);plot(ph/pi,an);grid
ylabel('Phase in radians');xlabel('Normalised freuency');
RESULT:
chebyshev low pass digital filter is designed with the given specifications
and obtained frequency response using MATLAB.
VIVA QUESTION:
1.What is the use of ‘cheb1ord’ function?
2.What is the use of ‘cheby1’ function?
3.What is the use of ‘cheby2’ function?
4.What is the use of ‘cheb2ord’ function?
5.What is the use of ‘chebiap’ function?
11). FREQUENCY RESPONSE OF IIR HIGH PASS CHEBYSHEV FILTER
AIM:
Write a MATLAB program to design a Chebyshev high pass digital filter for the given
specifications and plot the Frequency response.
clc;
clear all;
close all;
ws=input('enter stop band freq');
wp=input('enter pass band freq');
rp=input('enter pass band ripple in db');
rs=input('enter stop band ripple in db');
r=input('enter peak to peak ripple in pass band in db');
[n,wn]=cheb1ord(wp,ws,rp,rs);
disp(n);
disp(wn);
[b,a] = cheby1(n,r,wn,'high');
disp('numerator polynomial');
disp(b);
disp('denominator polynomial');
disp(a);
w=0:0.1/pi:pi;
[h,om]=freqz(b,a,w);
m=abs(h);
gain=20*log10(m);
a=angle(h);
subplot(2,1,1)
plot(om,gain);grid;
title('frequency response')
xlabel('Normalized frequency');
ylabel('gain in db');
subplot(2,1,2)
plot(om,a);grid;
title('phase response');
xlabel('frequency');
ylabel('phase');
RESULT:
Chebyshev high pass digital filter is designed with the given specifications
and obtained frequency response using MATLAB.
12). FREQUENCY RESPONSE OF IIR BAND PASS CHEBYSHEV FILTER
AIM:
Write a MATLAB program to design a Chebyshev band pass digital filter for the given
specifications and plot the Frequency response.
% To design a chebyshev type I band pass filter for the given
specifications
clear all;
alphap=2; %Passband attenuation in dB
alphas=20; %Stopband attenuation in dB
wp=[.2*pi,.4*pi]; %passband frequency in radians
ws=[.1*pi,.5*pi]; %stopband frequency in radians
%To find cutoff freency and order of the filter
[n,wn]= cheb1ord (wp/pi,ws/pi,alphap,alphas)
% system function of the filter
[b,a]=cheby1(n,alphap,wn)
w=0:.01:pi;
[h,ph]=freqz(b,a,w);
m=20*log10(abs(h));
an=angle(h);
subplot(2,1,1);plot(ph/pi,m);grid;
ylabel('Gain in dB');xlabel('Normalised frequency');
subplot(2,1,2);plot(ph/pi,an);grid;
ylabel('Phase in radians');xlabel('Normalised freuency');
RESULT:
chebyshev band pass digital filter is designed with the given
specifications and obtained frequency response using MATLAB.
VIVA QUESTIONS:
1.What is the use of ‘cheby1(n,Rp,wn,’bandpass’)’ function?
2.What is the use of ‘cheby1(n,Rp,wn,’high’)’’ function?
3.What is the use of ‘cheby2ord’ function?
4.What is the use of ‘cheby2’ function?
5.What is the use of ‘ellipord’ function?
13). FREQUENCY RESPONSE OF FIR LOW PASS FILTER USING
RECTANGULAR WINDOW
AIM:
Write a MATLAB program to design and obtain frequency response of a Low
pass FIR filter using Rectangular and Hamming windows.
% To design a 25-tap lowpass filter with cutoff frequency of 0.5pi radians using
% rectangular and Hamming windows and plot their frequency response
clear all;
wc=.5*pi;
N=25;alpha=(N-1)/2;eps=.001;
n=0:1:N-1;
hd=sin(wc*(n-alpha+eps))./(pi*(n-alpha+eps));
wr=boxcar(N);%Rectangular window sequence
hn=hd.*wr';%Filter coeffcients
w=0:.01:pi;
h=freqz(hn,1,w);
plot(w/pi,abs(h));
hold on
wh=hamming(N);%Hamming window sequence
hn=hd.*wh';%filter coefficients
w=0:.01:pi;
h=freqz(hn,1,w);
plot(w/pi,abs(h),'-.');grid;
xlabel('normalised frequency\omega/\pi');
ylabel('Magnitude');hold off
RESULT:
Low pass FIR filter is designed using Rectangular and Hamming windows and
frequency response is obtained in MATLAB.
VIVA QUESTIONS:
1.What is the use of ‘boxcar’ function?
2.What is the use of ‘hamming’ function?
3.What is the use of ‘hanning’ function?
4.What is the use of ‘fir1’ function?
5.What is the use of ‘fir2’ function?
14). FREQUENCY RESPONSE OF FIR LOW PASS FILTER USING
TRIANGLE WINDOW
AIM:
Write a MATLAB program to design and obtain frequency response of a high pass FIR
filter using Triangle window.
clc;
clear all;
close all;
format long;
rp=input('enter pass band ripple in db');
rs=input('enter stop band ripple in db');
fs=input('enter stop band freq');
fp=input('enter pass band freq');
f=input('ente sampling freq');
wp=2*(fp/f);
ws=2*(fs/f);
num=-20*log10(sqrt(rp*rs))-13;
dem=14.6*(fs-fp)/f;
n=ceil(num/dem);
n1=n+1;
if(rem(n,2)~=0)
n1=n;
n=n-1;
end
y=triang(n1);
%lpf%
b=fir1(n,wp,y);
[h,o]=freqz(b,1,256);
m=20*log10(abs(h));
subplot(2,2,1);
plot(o/pi,m);
title('lpf');
ylabel('gain in db');
xlabel('normalized frequency');
%hpf%
b=fir1(n,wp,'high',y);
[h,o]=freqz(b,1,256);
m=20*log10(abs(h));
subplot(2,2,2);
plot(o/pi,m);
title('hpf');
ylabel('gain in db');
xlabel('normalized frequency');
%bpf%
wn=[wp,ws];
b=fir1(n,wn,'bandpass',y);
[h,o]=freqz(b,1,256);
m=20*log10(abs(h));
subplot(2,2,3);
plot(o/pi,m);
title('bpf');
ylabel('gain in db');
xlabel('normalized frequency');
%bsf%
b=fir1(n,wn,'stop',y);
[h,o]=freqz(b,1,256);
m=20*log10(abs(h));
subplot(2,2,4);
plot(o/pi,m);
title('bsf');
ylabel('gain in db');
xlabel('normalized frequency');
RESULT:
Low pass FIR filter is designed using Triagle window and frequency response is
obtained in MATLAB.
PART-3(IMAGE PROCESSING)
15. An image processing in a false contouring system
Aim: An image processing in a false contouring system
Software: MATLAB
Program:
d1 = double(imread('cameraman.tif'))./255; % Load the image, scale from 0 to 1
subplot(2, 2, 1); imshow(d1); title('d1'); % Plot the original image
d = edge(d1, 'canny', .6); % Perform Canny edge detection
subplot(2, 2, 2); imshow(d); title('d'); % Plot the edges
ds = bwareaopen(d, 40); % Remove small edge objects
subplot(2, 2, 3); imshow(ds); title('ds'); % Plot the remaining edges
iout = d1;
BW = ds;
iout(:, :, 1) = iout; % Initialize red color plane
iout(:, :, 2) = iout(:, :, 1); % Initialize green color plane
iout(:, :, 3) = iout(:, :, 1); % Initialize blue color plane
iout(:, :, 2) = min(iout(:, :, 2) + BW, 1.0); % Add edges to green color plane
iout(:, :, 3) = min(iout(:, :, 3) + BW, 1.0); % Add edges to blue color plane
subplot(2, 2, 4); imshow(iout); title('iout'); % Plot the resulting image
output:
16. To generate the histogram equalization to the image
Aim: To generate the histogram equalization to the image
Program:
clc;
clear all;
close all;
imgetfile;
u=imread(ans);
o=rgb2gray(u);
imshow(o);
figure;
imhist(o);
i=histeq(o);
figure;
imshow(i);
figure;
imhist(i);
Result: In this experiment provides for thresholding an image and the evaluation of its histogram
using Histogram equalization. This experiment illustrates the relationship among the intensities
(gray levels) of an image and its histogram.
Fig 1.1: Gray Scale Image
Fig 1.2: Histogram of Original Image
Fig 1.3: Image after Applying Histogram Equalization
Fig 1.4: Histogram equalization
Viva questions:
1. What is histogram equalization in digital image processing?
2. Why histogram equalization is needed?
3. What happens if histogram equalization is applied twice?
4. Why histogram is used in image processing?
5. What is histogram equalization in Matlab?
6. What is entropy image processing?
7. Why is a histogram useful?
8. What is the advantage of histogram?
9. What is contrast in image processing?
10. How do you make a histogram graph?
17. To verify the normalized cross correlation to the addition of
noise and removal of noise using filters to an image
A = rgb2gray(imread('onion.png'));
A_noise=imnoise(A,'gaussian');
%Prewitt_A_noise=edge(A_noise,'Prewitt');
imshow(A_noise);
%imshow(Prewitt_A_noise);
%aa=A+A_noise;
Filtered_snp=medfilt2(A_noise,[3 3]);
imshow(Filtered_snp);
aa1=normxcorr2(A_noise,Filtered_snp);
imshow(aa1)
18. Compute the Edge of an image using spatial filters
Spatial filtering term is the filtering operations that are performed directly on the pixels
of an image. The process consists simply of moving the filter mask from point to point in an
image. At each point let (x,y), the response of the filter at that point is calculated using a
predefined relationship. The spatial filtering can be mainly of two types, Linear spatial filtering
and Non-linear spatial filtering.
Aim: To compute the edge of an image using spatial filtering
clc;
clear;
x=imread('cameraman.TIF');
% --------filter coeff.-----------%
f1=[1 1 1;1 1 1;1 1 1];
f2=[1 0 -1;1 0 -1;1 0 -1];
f3=[1 1 1;0 0 0;-1 -1 -1];
% -------filtering-----------%
x1=x(1:256,1:256);
y1=conv2(x1,f1);
M1=max(max(y1));
y1=255.*(y1./M1);
y2=conv2(x1,f2);
M2=max(max(y2));
y2=255.*(y2./M2);
y3=conv2(x1,f3);
M3=max(max(y3));
y3=255.*(y2./M3);
subplot(2,2,1);imshow(x);title('IMAGE ')
subplot(2,2,2);imshow(uint8((y1)));title('f1- IMAGE ')
subplot(2,2,3);imshow(uint8((y2)));title('f2- IMAGE ')
subplot(2,2,4);imshow(uint8((y3)));title('f3-IMAGE ')
IMAGE f1- IMAGE
f2- IMAGE f3-IMAGE
