Advance Communications Matlab

Advance Communications Lab Manual 1

1. Measurement of Bit Error Rate using Binary Data

n=23;

k=12;

dmin=7;

ebno=1:10;

ber_block=bercoding(ebno,'block','hard',n,k,dmin);

berfit(ebno,ber_block)

ylabel('bit error probability');

title('ber vs eb/no');

RESULT:

M.Tech DECE II Sem Dept. of ECE


2. Verification of minimum distance in Hamming Code

m=3;

n=2^m-1;

k=4;

msg=[0 0 0 0; 0 0 0 1; 0 0 1 0; 0 0 1 1; 0 1 0 0; 0 1 0 1; 0 1 1 0; 0 1 1 1];

code1 =encode(msg,n,k,'hamming/binary');

code2 =num2str(code1);

code= bin2dec(code2);

number1= [];

for i=1:8

for j=i+1:8

[number]=biterr(code(i),code(j),7);

number1=[number1 number];

end

end

minidistance = min(number1)



3. Determination of output of convolutional Encoder for a given sequence

%convolution encoder;input=1bit output=2bits with 3 memory elements,code

%rate=1/2.

function[encoded_sequence]=convlenc(message)

message=[ 1 0 1 0 1 1 1 0 0 0 1 1 0 1 1 0 0 ];

enco_mem=[ 0 0 0]; %no.of memory elments=3

encoded_sequence=zeros(1,(length(message))*2);

enco_mem(1,3)=enco_mem(1,2);


enco_mem(1,1)=message(1,1);

temp=xor(enco_mem(1),enco_mem(2));

O1=xor(temp,enco_mem(3));%gener.polynomial=111

O2=xor(enco_mem(1),enco_mem(3));%gener.polynomial=101

encoded_sequence(1,1)=O1;

encoded_sequence(1,2)=O2;

msg_len=length(message);

c=3;

for i=2:msg_len



if(i<=msg_len)

enco_mem(1,1)=message(1,i);

else

enco_mem(1,1)=0;

end

temp=xor(enco_mem(1),enco_mem(2));

O1=xor(temp,enco_mem(3));

O2=xor(enco_mem(1),enco_mem(3));

encoded_sequence(1,c)=O1;%01 generated polynomial(1,1,1)

c=c+1;

encoded_sequence(1,c)=O2;%02 generated polynomial(1,0,1)

c=c+1;

endM.Tech DECE II Sem Dept. of ECE


RESULT:

ans =

Columns 1 through 17

1 1 1 0 0 0 1 0 0 0 0 1 1 0 0 1 1


1 0 0 1 1 0 1 0 1 0 0 0 1 0 1 1 1

ans =


1 1 1 0 0 0 1 0 0 0 0 1 1 0 0 1 1


1 0 0 1 1 0 1 0 1 0 0 0 1 0 1 1 1



4. Determination of output of convolutional Decoder for a given sequence

tb=2;

t=poly2trellis([3],[7,5]);

encoded_sequence=[ 1 1 1 0 0 0 1 0 0 0 0 1 1 0 0 1 1 1 0 1 1 0 1 0 1 1 0 1 1 1 ];

decoded=vitdec(encoded_sequence,t,tb,'trunc','hard')

RESULTS:

decoded =

1 0 1 0 1 1 1 0 0 1 0 0 1 1 1



5.Efficiency of DS Spread – Spectrum Technique

%direct sequence spread spectrum

clc

clear all;

%generating the bit pattern with each bit 6 samples long

b=round(rand(1,20));

pattern=[];

for k=1:20

if b(1,k)==0

sig=zeros(1,6);

else

sig=ones(1,6)

end

pattern=[pattern sig];

end

plot(pattern);

axis([-1 130 -0.5 1.5]);

title('\bf\it original bit sequenece');

%generating the psedorandom bit pattern for spreading

spread_sig=round(rand(1,120));

figure,plot(spread_sig);

axis([-1 130 -0.5 1.5]);

title('\bf\it psedorandom bit sequenece');

%xoring the pattern with spread signal

hopped_sig=xor(pattern,spread_sig);

%modulating the hopped signal

dsss_sig=[];

t=[0:100];

fc=0.1;

c1=cos(2*pi*fc*t);

c2=cos(2*pi*fc*t+pi);

for k=1:120



if hopped_sig(1,k)==0;

dsss_sig=[dsss_sig c1]

else

dsss_sig=[dsss_sig c2]

end

end

figure,plot([1:12120],dsss_sig);

axis([-1 12120 -1.5 1.5]);

title('\bf\ it dss signal');

%plotting the fft of dsss signal

figure,plot([1:12120],abs(fft(dsss_sig)));

RESULT:







6. Simulation of Frequency Hopping (FH) system

clear all;

s=round(rand(1,20));

signal=[];

carrier=[];

t=[0:10000];

fc=.01;

for k=1:20

if s(1,k)==0

sig= -ones(1,10001);

else

sig=ones(1,10001);

end

c=cos(2*pi*fc*t);

carrier=[carrier c];

signal=[signal sig];

end

subplot(2,1,1);

plot(signal);

axis([-1 200050 -1.5 1.5]);

title('/bf/it original bit sequence');

%BPSK modulation of signal

bpsk_sig=signal.*carrier;

subplot(2,1,2);

plot(bpsk_sig);

axis([-1 200050 -1.5 1.5]);

title('/bf/it BPSK modulated signal');

%FFT plot of BPSK modulated signal

figure, plot([1:200020],abs(fft(bpsk_sig)));

title('/bf/it FFT of BPSKmodulated signal');

%preparation of six carrier frequencies

fc1=.01; fc2=.02; fc3=.03;



fc4=.04; fc5=.05; fc6=.06;

c1=cos(2*pi*fc1*t);c2=cos(2*pi*fc2*t);c3=cos(2*pi*fc3*t);

c4=cos(2*pi*fc4*t);c5=cos(2*pi*fc5*t);c6=cos(2*pi*fc6*t);

%random frequencies hoops to form a spread signal

spread_sig =[];

for n=1:20

c=randint(1,1,[1 6]);

switch(c)

case(1)

spread_sig=[spread_sig c1];

case(2)


case(3)


case(4)


case(5)


case(6)


end

end

figure,plot([1:200020],abs(fft(spread_signal)));

freq_hopped_sig=bpsk_sig.*spread_signal;

figure,plot([1:200020],abs(fft(freq_hopped_sig)));





7. Histogram of a Image

clc;

clear all;

f=imread('cameraman.tif');

figure,imshow(f);

title('Input Image');

h=imhist(f);

h1=h(1:10:256);

horz=1:10:256;

figure,bar(horz,h1);

figure,plot(horz,h1);

title('Histogram Equalized Image');

Z=adapthisteq(f,'cliplimit',0.9,'distribution','uniform');

imview(Z);

b=imhist(f);

figure,imshow(b);







8. Verification of various Transforms - FT

RGB=imread('peppers.png');

I=rgb2gray(RGB);

J=fft2(I);

k=ifft2(J);

subplot(2,2,1),imshow(RGB);

title('original image');

subplot(2,2,2),imshow(I);

title('gray scale image');

subplot(2,2,3),imshow(J);

title('DFT');

subplot(2,2,4);imshow(k,[0 255]);

title('IDFT');

RESULT:



9. Verification of various Transforms - DCT

x=imread('lena.png');

subplot(4,1,1);

imshow(x);

title('input image');

%convert rgb to BW image

a=im2bw(x);

subplot(4,1,2);

imshow(a);

title('input BW image')

%convert bw to rgb

b=bw2gray(a);

subplot(4,1,3);

imshow(b);

title('bw to rgb image');

%DCT

d=dct2(a);

subplot(4,1,4);

imshow(d);

title('DCT image');

%Inverse dct

i=idct2(d);

subplot(4,1,5);

h=imshow(i,[0 255]);

title('IDCT image');



10. Detection techniques using derivative operators - Edge

i=imread('coins.png');

imshow(i);

j=edge(i,'sobel');

figure, imshow(j)

k=edge(i,'prewitt');

figure, imshow(k)

l=edge(i,'robert');

figure, imshow(l)

h=edge(i, 'log');

figure, imshow(h)

RESULT:







Detection techniques using derivative operators - Point

%point detection%

I=imread('circuit.tif');

H=[1 1 1; 1 -8 1; 1 1 1];

B=imfilter(I,H);

subplot(1,2,1),imshow(I),title('Original image');

subplot(1,2,2),imshow(B),title('Point detection');

Detection techniques using derivative operators - Line

f= imread('coins.png');

imshow(f)

g= edge(f,'horizontal');

h= edge(f,'vertical');

figure, imshow(g)

figure, imshow(h)

k=g+h;

figure,imshow(k)

l=g-h;

figure,imshow(l)







11. Implementation of FIR filter

N=60;

R=0.5;

b=firnyquist(N,4,R,0,'nonnegative');

h=firrcos(N,0.25,R,2,'rolloff');

hfvt=fvtool(b,1,h,1);

set(hfvt,'color', [1 1 1]);

legend(hfvt,'FIR NYQUIST DESIGN','FIR RCOS DESIGN');





12. Implementation of IIR filter

clc;

N=10; %UNCONSTRAINED NUMERATOR ORDER

M=10; %UNCONSTRAINED DENOMINATOR ORDER

F=[0 0.4 0.5 1]; %FREQUENCY VECTOR

E=F; %FREQUENCY EDGES

A=[1 1 0 0]; %MAGNITUDE VECTOR

W=[1 1 100 100]; %WEIGHT VECTOR

Nc=12; %CONSTRAINED NUMERATOR ORDER

Mc=12; %CONSTRAINED DENOMINATOR ORDER

R=0.92;

[b,a,err,sos,g]=iirlpnorm(N,M,F,E,A,W);

[bc,ac,errc,sosc,gc]=iirlpnormc(Nc,Mc,F,E,A,W,R);

H(1)=dfilt.df1sos(sos,g);

H(2)=dfilt.df1sos(sosc,gc);

[z,p,k]=zpk(H(2)); %FINDS THE POLES AND ZEROS OF CONSTRAINED FILTER

sqrt(real(p).^2+imag(p).^2) %RADII OF ALL POLES

hfvt=fvtool(H);

legend(hfvt,'IIR unconstrained design','IIR constrained design');

set(hfvt,'color',[1 1 1]);


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Advance Communications Matlab