Lab Manual Dsp Lab 5 Sem

8/9/2019 Lab Manual Dsp Lab 5 Sem

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Dr H.S.Prashantha, Dept of ECE, PESIT

1

DSP LABORATORY MANUAL

Prepared by

Dr H.S.Prashantha

Associate Professor

Department of Electronics Communication Engineering

PES Institute of Technology

Bangalore


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DSP LABORATORY MANUAL

CONTENTS

DSP using Matlab1. Verification of sampling theorem.

2. Impulse response of a given system.3. Linear convolution of two given sequences.4. Circular convolution of two given sequences.5. Autocorrelation of a given sequence and verification of its properties.6. Cross correlation of a given sequence and verification of its properties7. Solving a given difference equation.8. Computation of N point DFT of a given sequence and to plot magnitude and phase

Spectrum.9. Linear convolution of two given sequences using DFT and IDFT.10. Circular convolution of two given sequences using DFT and IDFT11. Design and implementation of FIR filter to meet given specification.

12. Design and implementation of IIR filter to meet given specification.

DSP Experiments using DSP processor1. Linear convolution of two given sequences.2. Circular convolution of two given sequences.3. Computation of N point DFT of a given sequence.4. Realization of an FIR filter (any type) to meet given specification. The input can be a signal

from function generator/speech signal.

5. Audio specification such as to plot time and frequency display of microphone plus a cosineusing DSP. Read a wav file and match with their respective spectrograms.

6. Noise removal: Add noise above 3KHz and then remove; Interference suppression using 400Hz ton.

7. Impulse response of a given system of a given system of first and second order.


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Cycle I

DSP using Matlab1. Verification of sampling theorem.2. Impulse response of a given system.

3. Linear convolution of two given sequences.4. Circular convolution of two given sequences.5. Autocorrelation of a given sequence and verification of its properties.6. Cross correlation of a given sequence and verification of its properties7. Solving a given difference equation.

DSP Experiments using DSP processor8. Linear convolution of two given sequences.9. Circular convolution of two given sequences.10. Impulse response of a given system of a given system of first and second order.

Cycle II

DSP using Matlab1. Computation of N point DFT of a given sequence and to plot magnitude and phase

Spectrum.2. Linear convolution of two given sequences using DFT and IDFT.3. Circular convolution of two given sequences using DFT and IDFT4. Design and implementation of FIR filter to meet given specification.5. Design and implementation of IIR filter to meet given specification.

DSP Experiments using DSP processor1. Computation of N point DFT of a given sequence.2. Realization of an FIR filter (any type) to meet given specification. The input can be a signal

from function generator/speech signal.

3. Audio specification such as to plot time and frequency display of microphone plus a cosineusing DSP. Read a wav file and match with their respective spectrograms.

4. Noise removal: Add noise above 3KHz and then remove; Interference suppression using 400Hz ton.


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Cycle 1

DSP USING MATLAB

MATLAB is a high-level language and interactive environment that enables you to perform

computationally intensive tasks faster than with traditional programming languages such as C,C++ etc. The key features of MATLAB are as follows

High-level language for technical computingDevelopment environment for managing code, files, and data

Interactive tools for iterative exploration, design, and problem solving

Mathematical functions for linear algebra, statistics, Fourier analysis, filtering,optimization, and numerical integration

2-D and 3-D graphics functions for visualizing data

Tools for building custom graphical user interfaces

• MATLAB (MATrix LABoratory) is a special purpose computer program optimized to perform

engineering and scientific calculations.• The MATLAB program implements the MATLAB language, and provides a very extensive

library of predefined functions to make technical programming tasks easier and more efficient.

• The extremely wide variety of functions makes it much easier to solve technical problems inMATLAB than in other languages such as FORTRAN or C.

• The advantages of using MATLAB for technical programming are: Ease of use Platform independence Predefined functions Device-independent plotting Graphical User Interface MATLAB Compiler

MATLAB is a huge program, with an incredibly rich variety of functions. There are morethan 1000 functions in the basic MATLAB.

The disadvantages of MATLAB are: It is interpreted language, and therefore can execute more slowly than compiled

languages.

Cost. (Student version of MATLAB is available for low cost with almost identicalfeature) full copy of MATLAB is 5 TO 10 times more expensive than a conventional C

or FORTRAN compiler.

The functional unit of data in any MATLAB program is the array. An array is acollection of data values organized into rows and columns.

When MATLAB executes, it can display several types of windows that accept commandsor display information. The three most important types of windows are command

windows, where commands may be entered: figure windows, which display plots andgraphs: and edit/debug windows which permit a user to create and modify MATLAB

programs.

The major tools within or accessible from the MATLAB desktop are: The command window: a user can enter interactive commands at the command

prompt (>>) in the command window and they will be executed on the spot.

Instead of typing commands directly in the command window, a series ofcommands may be placed in to a single file and the entire file may be executed by


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typing its name in the command window. Such files are called script files. Script

files are also called M-Files because they have file extension of “. M”. The command history window: the command history window displays a list of the

commands that a user has entered in the command window.

The launch pad: it is a special tool that collects references to the documentation,

demos, and related tools for MATLAB itself and for each toolkit that you own. The edit/debug window: used to create new M-Files, or to modify existing ones.

An edit/debug window is created automatically when you create a new M-File oropen an existing one. The edit/debug window is essentially a programming text

editor, with the MATLAB languages features highlighted in different colors.

Comments in an M-File appear in green, variables and numbers appear in black,character strings appear in red, and language keywords appear in blue.

Figure windows: used to display MATLAB graphics. A figure can be two orthree-dimensional plot of data, an image or a GUI.

The MATLAB workspace: it is a collection of all the variables and arrays that canbe used by MATLAB when a particular command, M-File, or function is

executing. All commands executed in the command window share a commonworkspace, so they can all share variables. (Clear deletes all variables from thecurrent workspace).

The workspace browser: it provides a graphic display of the same information aswhos command, and the display is dynamically updated whenever the contents ofworkspace change. The workspace browser also allows user to change the

contents of any variables in the workspace. (A list of the variables and arrays in

the current workspace can be generated with the whos command)

• Getting help: by selecting the? Help icon bar from the desktop tool bar, or by typing

helpdesk or helpwin in the command window. The other way of obtaining help is byusing lookfor command. Help command searches for an exact function name match,whereas the lookfor command searches the quick summary information in each function

for a match. Lookfor is slower than help but it improves the chances of getting useful

information.

• For new users of MATLAB, a few demonstrations may help to give you a feel for itscapabilities. To run MATLAB’s built in demonstrations, type demo in the command

window, or select “demos” from the launch pad.

• The contents of the command window can be cleared at any time using the clc command,and the contents of the current figure window can be cleared at any time using the clfcommand. The variables in the workspace can be cleared with the clear command. It is a

good idea to issue the clear command at the start of each new independent calculation.• Another important command is the abort command. If an m-file appears to be running for

too long, it might contain an infinite loop, and it will never terminate. In this case the usercan regain the control by typing control-c (^c) in the command window. When MATLAB

detects a ^c, it interrupts the running program and returns a command prompt.

• After diary command is typed, a copy of all input and most output typed in the commandwindow is echoed in the diary file. The command diary off suspends input in to the diaryfile, and the command diary on resumes input again.


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• MATLAB includes a special command (which) to help you to find out just which versionof a file is being executed. and where it is located. The format of this command is whichfunctionname, where functionname is the name of the function that you are trying to

locate.

The path related function include:

* addpath Add directory to MATLAB search path

* path Display MATLAB search path

* path2rc Add current directory to MATLAB search path

* rmpath Remove directory from MATLAB search path

• The functional unit of data in any MATLAB program is the array. An array is acollection of data values organized into rows and columns, and known by a single name.

• MATLAB variable is a region of memory containing an array, which is known by a user-specified name. MATLAB variable names must begin with a letter, followed by any

combination of letters, numbers, and the underscore( _ ) character. Only the first 31characters are significant; if more than 31 are used, the remaining characters will be

ignored. If two variables are declared with names that only differ in the 32nd

character,

MATLAB will treat them as same variable.

• Spaces cannot be used in MATLAB variable names, underscore letters can be substitutedto create meaningful names.

• It is important to include a data dictionary in the header of any program that you write. Adata dictionary lists the definition of each variable used in a program. The definitionshould include both a description of the contents of the item and the units in which it is

measured.

• MATLAB language is case-sensitive. It is customary to use lower-case letters forordinary variable names.

• The most common types of MATLAB variables are double and char.

• MATLAB is weakly typed language. Variables are not declared in a program before it isused.

•

MATLAB variables are created automatically when they are initialized. There are threecommon ways to initialize variables in MATLAB: Assign data to the variable in an assignment system. Input data into the variable from the keyboard. Read data from a file.

• The semicolon at the end of each assignment statement suppresses the automatic echoingof values that normally occurs whenever an expression is evaluated in an assignment

statement.


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How to invoke MATLAB?*) Double Click on the MATLAB icon on the desktop .

*) You will find a Command window where in which you can type the commands and see the

output. For example if you type PWD in the command window, it will print current working

directory.*)If you want to create a directory type mkdir mydir in the command window, It will create a

directory called pes.*) If you want delete a directory type rmdir mydir in the command window. How to open a file in MATLAB?

*) Go to File New M-File and clickThen type the program in the file and save the file with an extension of .m. While giving file

name we should make sure that given file name should not be a command. It is better to the file

name as myconvlution .

How to run a MATLAB file?

*) Go to Debug

run and click

1. Program to Verify Sampling Theorem

% program for verification of sampling theorem

clc; % clears the command window

clear all; % clears the variables declared

t=0:.001:0.1;

f1=input ('Enter the input freequency1 = ');f2=input ('Enter the input freequency2 = ');

y=cos(2*pi*f1*t)+cos(2*pi*f2*t);f3=max(f1,f2);

% under sampling

fs=f3; %fs = sampling freequencyts=1/fs;

tx=0:ts:0.1;

m=max(size(tx));ys=cos(2*f1*pi*tx)+cos(2*pi*f2*tx);

figure(1);

subplot(3,1,1);plot(t,y);

grid on;

title('The sinusoidal signal cos(2*pi*f1*t)+cos(2*pi*f2*t)');xlabel('Time in seconds');

ylabel('Amplitude in volts');


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subplot(3,1,2);

stem(tx,ys);grid on;

title('The sinusoidal signal sampled at fs Hz');

xlabel('Time in seconds');

ylabel('Amplitude in volts');subplot(3,1,3);

plot(tx,ys);grid on;

title('The recovered sinusidal sampled at fs Hz');

xlabel('Time in seconds');ylabel('Amplitude in volts');

% Right sampling

fs=2*f3; %fs = sampling freequencyts=1/fs;

tx=0:ts:0.1;m=max(size(tx));ys=cos(2*pi*f1*tx)+cos(2*pi*f2*tx);

figure(2);

subplot(3,1,1);plot(t,y);

grid on;

title('The sinusoidal signal cos(2*pi*f1*t)+cos(2*pi*f2*t)');


subplot(3,1,2);




subplot(3,1,3);

plot(tx,ys);

grid on;title('The recovered sinusidal sampled at fs Hz');


ylabel('Amplitude in volts');

% over sampling

fs=3*f3; %fs = sampling freequencyts=1/fs;

tx=0:ts:0.1;

m=max(size(tx));ys=cos(2*pi*f1*tx)+cos(2*pi*f2*tx);

figure(3);


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subplot(3,1,1);

plot(t,y);grid on;

title('The sinusoidal signal cos(2*pi*f1*t)+cos(2*pi*f2*t)');


ylabel('Amplitude in volts');subplot(3,1,2);




subplot(3,1,3);

plot(tx,ys);

grid on;title('The recovered sinusidal sampled at fs Hz');


OUTPUTUnder sampling

Right sampling

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1-2

0

2The sinusoidal s ignal cos(2*pi*f1*t)+cos(2*pi*f2*t)

Time in seconds

A m p l i t u d e i n v o l t s

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10

1

2The sinusoidal signal sampled at fs Hz

Time in seconds A m p l i t u d e i n v o l t s

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10

1

2The recovered sinusidal sampled at fs Hz

Time in seconds



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Over sampling

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1

-2

0

2The s inusoidal signal cos(2*pi*f1*t)+cos(2*pi*f2*t)

Time in seconds A

m p l i t u d e i n v o l t s

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1-2

0



0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1-2

0


Time in seconds

A m p l i t u

d e i n v o l t s

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1-2

0

2The sinusoidal signal cos(2*pi*f1*t)+cos(2*pi*f2*t)

Time in seconds


0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1-2

0



0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1-2

0


Time in seconds

A m p l i t u d e i n

v o l t s


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2. Program to find the Impulse response of the system

%To find the Impulse response of a given System

%a0*y(n)-a1*y(n-1)+a2*y(n-2)=b0*x(n)+b1*x(n-1)+b2*x(n-2)clc;

clear all;b=input('Enter the coefficiants of x(n) in the order x(n),x(n-1)...in the Matrix form = ');

a=input('Enter the coefficiants of y(n) in the order y(n),y(n-1)...in the Matrix form = ');n=[-5:20];

x=[(n==0)];

h=filter(b,a,x)figure(1);

subplot(2,1,1);

stem(n,x);

xlabel('n');ylabel('amplitude');

title('impulse sequence');subplot(2,1,2);stem(n,h);

xlabel('n');

ylabel('amplitude');title('impulse response');

OUTPUTEnter the coefficiants of x(n) in the order x(n),x(n-1)...in the Matrix form = [0.2066 0.41310.2066]

Enter the coefficiants of y(n) in the order y(n),y(n-1)...in the Matrix form = [1 -0.3695 0.1968]

-5 0 5 10 15 200

0.5

1

n

a m p l i t u d e

impulse sequence

-5 0 5 10 15 20-0.2

0

0.2

0.4

0.6

n

a m p l i t u d e

impulse response


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title('convolved sequence');

xlabel('signal');ylabel('time');

Input:

x1 =1 2 3 4 0 0 0

second sequencex2 =

1 2 3 4 0 0 0

convolved sequencey =

1 4 10 20 25 24 16

4. Program to perform Circular Convolution of two given sequences

%This program calculates the circular convolutionclc;

clear all;x=input('Input first sequence');

h=input('Input second sequence');

N1=length(x);N2=length(h);

N=max(N1,N2);

1 2 3 4 5 6 70

2

4first sequence

s i g n a l

time

1 2 3 4 5 6 70

2

4second sequence

s i g n a l

time

1 2 3 4 5 6 70

20

40convolved sequence

s i g n a l

time


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x=[x zeros(1,N-N1)];

h=[h zeros(1,N-N2)];

for m=1:N

y(m)=0;

for n=1:Ni=m-n+1;

if(i


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x =

1 2 3 4

second sequence

h =

1 2 3 4

convolved sequence

y =

26 28 26 20

5. Autocorrelation of a given sequencex=input('enter the sequence');

y=xcorr(x,x);

figure;

subplot(2,1,1);stem(x);

ylabel('amplitude');

1 1.5 2 2.5 3 3.5 40

2

4

first sequence

s i g n a l

time

1 1.5 2 2.5 3 3.5 40

2

4second sequence

s i g n a l

time

1 1.5 2 2.5 3 3.5 40

20


s i g n a l

time


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xlabel('n');

subplot(2,1,2);stem(fliplr(y));

y


xlabel('n');disp('the resultant signal is ');

fliplr(y);

Input:Enter the sequence[1 2 3 4]The resultant signal is

y = [ 4 11 20 30 20 11 4]

6. Cross correlation of a given sequenceclc;close all;

clear all;x=input('enter the sequence');

h=input('enter the second sequence');

y=xcorr(x,h);figure;

subplot(3,1,1);

1 1.5 2 2.5 3 3.5 40

1

2

3

4

a m p l i t u d e

n

1 2 3 4 5 6 70

10

20

30

a m p l i t u d e

n


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stem(x);

ylabel('amplitude');xlabel('n');

subplot(3,1,2);

stem(h);


subplot(3,1,3);stem(fliplr(y));

y


disp('the resultant signal is ');

fliplr(y);

Input:

enter the sequence[1 2 3 4]enter the second sequence[4 3 2 1]

y =

1.0000 4.0000 10.0000 20.0000 25.0000 24.0000 16.0000

The resultant signal is

1 1.5 2 2.5 3 3.5 40

2

4

a m p l i t u d e

n

1 1.5 2 2.5 3 3.5 40

2

4

a m p l i t u d e

n

1 2 3 4 5 6 70

10

20

30

a m p l i t u d e

n


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7. Program to Solve difference equation

%program for solution of difference equation

% y(n) -0.5 y(n-1) = x(n) with x(n)=u(n)-u(n-4)b=input('enter the coefficients of x');

a=input('enter the coefficients of y');n=[-5:50];

x=[(n>=0)]-[(n>4)];figure(1);

subplot(2,1,1);

stem(n,x);title('input sequence x(n)');

xlabel('n');

ylabel('x');

subplot(2,1,2);y=filter(b,a,x);

stem(n,y);title('output sequence y(n)');xlabel('n');

ylabel('y');

% y(n) -0.25 y(n-1) -0.125 y(n-2)= x(n) with x(n)=u(n)-u(n-10) and initial condition y(-1)=1,

y(-2)=-2

-10 0 10 20 30 40 500

0.5

1input sequence x(n)

n

x

-10 0 10 20 30 40 500

0.5

1

1.5

2output sequence y(n)

n

y


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b=input('enter the coefficients of x');

a=input('enter the coefficients of y');c=input('enter the initial conditions');

n=[-5:50];

ic=filtic(b,a,c);

x=[(n>=0)]-[(n>10)];

y=filter(b,a,x,ic);

subplot(2,1,1);

stem(n,x);title('input sequence x(n)');

xlabel('n');

ylabel('x');

subplot(2,1,2);

stem(n,y);title('output sequence y(n)');xlabel('n');

ylabel('y');

Output:

-10 0 10 20 30 40 500

0.5

1input sequence x(n)

n

x

-10 0 10 20 30 40 500

0.5

1

1.5

2output sequence y(n)

n

y


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8. Program to Calculate N point DFT and N point IDFT of the given sequence

%to compute dft of the sequence and plot magnitude and phase spectraclc;

clear all;

close all;

% input sequencexn=input('enter the input sequence');

N=input('enter the number of points');

xk=dft(xn,N);

%magnitude of fft

magxk=abs(xk);

%phase of fft

phasexk=angle(xk);

k=0:N-1;

subplot(2,1,1);

stem(k,magxk);

title('fft sequence');

xlabel('frequency');

ylabel('magnitude');

subplot(2,1,2);

stem(k,phasexk);

title('phase of fft sequence');

xlabel('frequency');

ylabel('phase');

function xk=dft(xn,N)

L=length(xn);

if(N


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%to compute idft of the sequenceclc;

clear all;

close all;

% input sequence

Xk=input('enter the input sequence');N=length(Xk)

xn=idft(Xk,N);

k=0:N-1;

stem(k,xn);

title('ifft sequence');

xlabel('time');


disp(xn)

function xn=idft(Xk,N)

for k=0:1:N-1;

for n=0:1:N-1;

p=exp(i*2*pi*n*k/N)x2(k+1,n+1)=p;

end

end

0 5 10 15 20 25 30 350

2

4

6

8fft sequence

frequency

m a g n

i t u d e

0 5 10 15 20 25 30 35-4

-2

0

2

4phase of fft sequence

frequency

p h a s e


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xn=(Xk*x2)./N;

9. Linear convolution of two sequences using DFT and IDFTclc;

clear all;

x=input('Input first sequence');h=input('Input second sequence');

n1=length(x);

n2=length(h);N=n1+n2-1;

x1=[x zeros(1,N-n1)];x2=[h zeros(1,N-n2)];

a=fft(x,N);

b=fft(h,N);c=a.*b;

d=ifft(c,N);

disp('first sequence');x

disp('second sequence');

hdisp('convolved sequence');

d

n=0:N-1;subplot(3,1,1);

stem(x);

title('first sequence');ylabel('signal');

xlabel('time');

subplot(3,1,2);stem(h);

title('second sequence');

ylabel('signal');xlabel('time');

subplot(3,1,3);stem(d);

title('convolved sequence');

ylabel('signal');

xlabel('time');Input:

x = 1 1 1 1

second sequenceh = 1 1 1 1

convolved sequence


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d = 1.0000 2.0000 3.0000 4.0000 3.0000 2.0000 1.0000

10. Circular convolution of two sequences using DFT and IDFTclc;

clear all;

x=input('Input first sequence');h=input('Input second sequence');

n1=length(x);

n2=length(h);N=max(n1,n2);

if N>n1x1=[x zeros(1,N-n1)];

end

if N>n2x2=[h zeros(1,N-n2)];

end

a=fft(x,N);

b=fft(h,N);c=a.*b;

d=ifft(c,N);

disp('first sequence');

x

disp('second sequence');hdisp('convolved sequence');

d

n=0:N-1;

1 1.5 2 2.5 3 3.5 40

0.5

1first sequence

s i g n a l

time

1 1.5 2 2.5 3 3.5 40

0.5

1second sequence

s i g n a l

time

1 2 3 4 5 6 70

2

4convolved sequence

s i g n a l

time


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subplot(3,1,1);

stem(x);title('first sequence');

ylabel('signal');

xlabel('time');

subplot(3,1,2);

stem(h);title('second sequence');

ylabel('signal');

xlabel('time');

subplot(3,1,3);

stem(d);

title('convolved sequence');ylabel('signal');

xlabel('time');

Result:Input first sequence[1 2 3]

Input second sequence[1 2 3 4]first sequence

x = 1 2 3

second sequence

h = 1 2 3 4convolved sequence

d = 18 16 10 16


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11. Design and implementation of FIR filters to meet given specificationsclear all;

close all;clc;%cutoff frequency of low pass response at 0.25 pi and a minimum stopband

%attenuation of 39 dB. transition band is 0.2 pi

wc=0.25*pi;

tw=0.2*pi;

%normalize the cutoff frequency and transition width

fcn=wc/pi;twn=tw/pi;

%the required transition width is 39 dB. Choose Hanning window%order of the filterdisp('order of the filter');

N=ceil(4/twn)

if(rem(N,2)==0);N=N+1;

end;

1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 30

2

4first sequence

s i g n a l

time

1 1.5 2 2.5 3 3.5 40

2

4second sequence

s i g n a l

time

1 1.5 2 2.5 3 3.5 40

10


s i g n a l

time


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y=hanning(N)

disp('fir filter coefficients');

b=fir1(N-1,fcn,y)

disp('frequency response');[H,f]=freqz(b,1,256)

H_mag=20*log10(abs(H));plot(f/pi,H_mag);

xlabel('frequency in rad/sec');

ylabel('gain in dB');

output:

%passband edge frequency of 2 kHz, a minimum stopband attenuation of 50 dB%transition band is 0.5 kHzat a sampling frequency of 8 kHz.

fs=8000;%normalizing frequency

fn=fs/2;

%passband and stopband frequenciesfp=2000;

tw=500;

fs=fp+tw;

%normalize the cutoff frequency and transition widthfcn=(fp+fs)/(2*fn);

twn=tw/fs;

%the required transition width is 50 dB. Choose Hamming window%order of the filter

disp('order of the filter');

N=ceil(4/twn)if(rem(N,2)==0);

N=N+1;

end;y=hamming(N)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-140

-120

-100

-80

-60

-40

-20

0

20

frequency in rad/sec

g a i n i n d B


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disp('fir filter coefficients');

b=fir1(N-1,fcn,y)disp('frequency response');

[H,f]=freqz(b,1,256)

H_mag=20*log10(abs(H));

plot(f/pi,H_mag);xlabel('frequency in rad/sec');

ylabel('gain in dB');

Output:

12. Programs to Design IIR filters

% Program to Design the Digital IIR Butterworth Low pass filter using bilinear

transformation

clc;

clear all;

pb=input('Enter the pass band edge frequency ');

sb=input('Enter the stop band edge frequency ');

pbr=input('Enter the pass band attenuation ');

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

fs= input('Enter the sampling frequency ');

pbrad=pb*2*pi;

sbrad=sb*2*pi;

[n,wn]=buttord(pbrad,sbrad,pbr,sbr,'s');

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-120

-100

-80

-60

-40

-20

0

20

frequency in rad/sec

g a i n i n d

B


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[b,a]=butter(n,wn,'s');

[z,p,k]=tf2zp(b,a);

[zd,pd,kd]=bilinear(z,p,k,fs);

[num,den]=zp2tf(zd,pd,kd);

figure(1);

freqz(num,den,512,fs);title('Butter worth freequency response');

[n,wn]=cheb1ord(pbrad,sbrad,pbr,sbr,'s');[b,a]=cheby1(n,pbr,wn,'s');

[z,p,k]=tf2zp(b,a);

[zd,pd,kd]=bilinear(z,p,k,fs);[num,den]=zp2tf(zd,pd,kd);

figure(2);freqz(num,den,512,fs);

title('Chebychev1 Freequency Response');

[n,wn]=cheb2ord(pbrad,sbrad,pbr,sbr,'s');

[b,a]=cheby2(n,sbr,wn,'s');

[z,p,k]=tf2zp(b,a);[zd,pd,kd]=bilinear(z,p,k,fs);

[num,den]=zp2tf(zd,pd,kd);

figure(3);

freqz(num,den,512,fs);

title('Chebychev 2 Freequency Response');

OUTPUTEnter the pass band edge frequency 1500

Enter the stop band edge frequency 2000Enter the pass band attenuation 0.5

Enter the stop band attenuation 30

Enter the sampling frequency 8000


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% Program to Disign the Digital IIR Butterworth Low pass filter using impulse invariance

method

clc;clear all;

pb=input('Enter the pass band edge frequency ');sb=input('Enter the stop band edge frequency ');

pbr=input('Enter the pass band attenuation in dB ');

0 500 1000 1500 2000 2500 3000 3500 4000-2000

-1000

0

Frequency (Hz) P h a s e ( d e g r e e s )

0 500 1000 1500 2000 2500 3000 3500 4000-500

0

500

Frequency (Hz) M a g n i t u d e ( d B ) Butter worth freequency response

0 500 1000 1500 2000 2500 3000 3500 4000-1000

-500

0

Frequency (Hz)

P h a s e ( d e g r e e s )

0 500 1000 1500 2000 2500 3000 3500 4000-500

0

500

Frequency (Hz)

M a g n i t u d e ( d B )

Chebychev1 Freequency Response

0 500 1000 1500 2000 2500 3000 3500 4000-500

0

500

Frequency (Hz)

P h a s e ( d e g r e e s )

0 500 1000 1500 2000 2500 3000 3500 4000-100

-50

0

Frequency (Hz)

M a g n i t u d e ( d B )

Chebychev 2 Freequency Response


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sbr=input('Enter the stop band attenuation in dB ');

fs= input('Enetr the sampling frequency ');

pbrad=pb*2*pi;

sbrad=sb*2*pi;

[n,wn]=buttord(pbrad,sbrad,pbr,sbr,'s');[b,a]=butter(n,wn,'s');

[num,den]=impinvar(b,a,fs);

figure(1);

freqz(num,den,512,fs);

title('Butter worth frequency response');


[b,a]=cheby1(n,pbr,wn,'s');[num,den]=impinvar(b,a,fs);

figure(2);

freqz(num,den,512,fs);title('Chebychev1 Frequency Response');


[b,a]=cheby2(n,sbr,wn,'s');[num,den]=impinvar(b,a,fs);

figure(3);freqz(num,den,512,fs);

title('Chebychev 2 Frequency Response');


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DSP KIT TMS306713:

Troubleshooting DSK Connectivity

If Code Composer Studio IDE fails to configure your port correctly, perform the following steps:

• Test the USB port by running DSK Port test from the start menu

Use StartProgramsTexas InstrumentsCode Composer StudioCode Composer Studio

C6713 DSK ToolsC6713 DSK Diagnostic Utilities

• The below Screen will appear

• Select Start Select 6713 DSK Diagnostic Utility Icon from Desktop

• The Screen Look like as below

• Select Start Option

• Utility Program will test the board

• After testing Diagnostic Status you will get PASS

If the board still fails to detect


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Go to CMOS setup Enable the USB Port Option

(The required Device drivers will load along with CCS Installation)

INTRODUCTION TO CODE COMPOSER STUDIO

Code Composer is the DSP industry's first fully integrated development environment (IDE) withDSP-specific functionality. With a familiar environment liked MS-based C++TM, Code

Composer lets you edit, build, debug, profile and manage projects from a single unifiedenvironment. Other unique features include graphical signal analysis, injection/extraction of data

signals via file I/O, multi-processor debugging, automated testing and customization via a C-

interpretive scripting language and much more.

CODE COMPOSER FEATURES INCLUDE:

IDE

Debug IDEAdvanced watch windowsIntegrated editor

File I/O, Probe Points, and graphical algorithm scope probes

Advanced graphical signal analysisInteractive profiling

Automated testing and customization via scripting

Visual project management system

Compile in the background while editing and debuggingMulti-processor debugging

Help on the target DSP

Procedure to work on Code Composer Studio

1. To create a New Project

• Project New (SUM.pjt)


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4. To Add rts6700.lib file & hello.cmd:

Project Add files to Project rts6700.lib

Path: c:\CCStudio\c6000\cgtools\lib\rts6700.lib


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Note: Select Object & Library in(*.o,*.l) in Type of files

Project Add files to Project hello.cmd

Path: c:\ti\tutorial\dsk6713\hello1\hello.cmd Note: Select Linker Command file(*.cmd) in Type of files

5. To Compile:Project Compile File

6. To build or Link:Project build,

Which will create the final executable (.out) file.(Eg. sum.out).

7. Procedure to Load and Run program:

Load program to DSK:File Load program sum. out

8. To execute project:

• Debug Run.

TMS320C6713 is a floating point DSP processor.Basically it is a cisc processor.It has

following features

*) It can execute eight 32-bit instructions/cycle*) Advanced Very Long Instruction Word DSP core.

*) It has eight independent functional units.In which there are 2 ALUs of fixed point, 4 ALUs

of Floating and fixed point and 2 multipliers of floating and fixed point.*) Two multi channel audio ports

*) 32 bit External memory Interface.

DSP Starter kit consists of TMS320C6713 ,CPLD,Flash Memory,SDRAM,Codec.

Code Composer Studio


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Code Composer is the Integrated Development Environment(IDE) with DSP specificfunctionality.Basically code composer studio is a cross compiler.That means the programs

written in Higher level languages like ‘C’are converted into processor specific languages.

Procedure to work with code composer studioOpen the code composer studio,make sure DSP Starter Kit(DSK) is turned on.

Create a new project as followsGo to Project->new and click on it , give project_name ,Select target as TMS320C67xx.

Then save it in C:\CCStudio\MyProjects

Then go Project -> Add Files to Project and click on it , then add the run time support library

C:\CCStudio\c6000\cgtools\lib\rts6700.lib Then go Project-> Add Files to Project and click on it , then add the linker command file

C:\CCStudio\tutorial\dsk6713\hello1\ hello.cmdThen compile the program using Project->compile->clickThen go to Project -> Rebuild All.

Then go to File->Load program -> project_name.out into the DSP Starter kit. How to view graphical output

To view the graphical output go to View Graph Time/Frequency

1

‘C’ PROGRAM TO IMPLEMENT LINEAR CONVOLUTION

/* prg to implement linear convolution */

#include

#define LENGHT1 6 /*Lenght of i/p samples sequence*/

#define LENGHT2 4 /*Lenght of impulse response Co-efficients */

int x[2*LENGHT1-1]={1,2,3,4,5,6,0,0,0,0,0}; /*Input Signal Samples*/

int h[2*LENGHT1-1]={1,2,3,4,0,0,0,0,0,0,0}; /*Impulse Response Co-efficients*/

int y[LENGHT1+LENGHT2-1];

main()

{

int i=0,j;

for(i=0;i


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PROCEDURE:

Open Code Composer Studio, make sure the DSP kit is turned on.

Start a new project using ‘Project-new ‘ pull down menu, save it in a

separate directory(c:\ti\myprojects) with name lconv.pjt.

Add the source files conv.c to the project using ‘Projectadd files to project’ pull down menu.

Add the linker command file hello.cmd .(Path: c:\ti\tutorial\dsk6713\hello1\hello.cmd)

Add the run time support library file rts6700.lib (Path: c:\ti\c6000\cgtools\lib\rts6700.lib)

Compile the program using the ‘Project-compile’ pull down menu or byclicking the shortcut icon on the left side of program window.

Build the program using the ‘Project-Build’ pull down menu or byclicking the shortcut icon on the left side of program window.

Load the program(lconv.out) in program memory of DSP chip using the‘File-load program’ pull down menu.

To View output graphicallySelect view graph time and frequency.

Configure the graphical window as shown below


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2 program to Implement Circular Convolution#include

int m,n,x[30],h[30],y[30],i,j,temp[30],k,x2[30],a[30];

void main()

{

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


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printf("%d \t",y[i]);

}

PROCEDURE:

Open Code Composer Studio, make sure the DSP kit is turned on.

Start a new project using ‘Project-new ‘ pull down menu, save it in aseparate directory(c:\ti\myprojects) with name cir conv.pjt.

Add the source files Circular Convolution.C to the project using ‘Projectadd files to project’ pull down menu.

Add the linker command file hello.cmd .(Path: c:\ti\tutorial\dsk6713\hello1\hello.cmd)

Add the run time support library file rts6700.lib (Path: c:\ti\c6000\cgtools\lib\rts6700.lib)

Compile the program using the ‘Project-compile’ pull down menu or byclicking the shortcut icon on the left side of program window.

Build the program using the ‘Project-Build’ pull down menu or byclicking the shortcut icon on the left side of program window.

Load the program(lconv.out) in program memory of DSP chip using the‘File-load program’ pull down menu.

DFT USING DSP KIT

#include

#include

float x[4]={1,3,2,5}; //input only real sequence

main()

{

float y[16]; //for 8 point DFT to store real & imaginary

float w;int n,k,k1,N=8,xlen=4;

for(k=0;k


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{

w=-2*3.14*k1*n/N; //careful about minus signy[k]=y[k]+x[n]*cos(w);

y[k+1]=y[k+1]+x[n]*sin(w);

}

printf("%f+j%f \n",y[k],y[k+1]);}

}//end of main

Procedure for Real time Programs :

1. Connect CRO to the Socket Provided for LINE OUT.

2. Connect a Signal Generator to the LINE IN Socket.

3. Switch on the Signal Generator with a sine wave of frequency 500 Hz. and Vp-p=1.5v

4. Now Switch on the DSK and Bring Up Code Composer Studio on the PC.

5. Create a new project with name codec.pjt.

6. From the File Menu new DSP/BIOS Configurationselect“dsk6713.cdb” and save it as “xyz.cdb”


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7. Add “xyz.cdb” to the current project.

8. Add the given “codec.c” file to the current project which has the main function andcalls all the other necessary routines.

9. Add the library file “dsk6713bsl.lib” to the current project

Path “C:\CCStudio\C6000\dsk6713\lib\dsk6713bsl.lib”

10. Copy files “dsk6713.h” and “dsk6713_aic23.h” fromC:\CCStudio\C6000\dsk6713\include and paste it in current project.

11. Build, Load and Run the program.

12. You can notice the input signal of 500 Hz. appearing on the CRO verifying the codecconfiguration.

13. You can also pass an audio input and hear the output signal through the speakers.

14. You can also vary the sampling frequency using the DSK6713_AIC23_setFreq Function in the “codec.c” file and repeat the above steps.


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5.0 Conclusion:The codec TLV320AIC23 successfully configured using the board support library

and verified.

codec.c

#include "xyzcfg.h"

#include "dsk6713.h"

#include "dsk6713_aic23.h"

/* Codec configuration settings */

DSK6713_AIC23_Config config = { \0x0017, /* 0 DSK6713_AIC23_LEFTINVOL Left line input channel volume */ \

0x0017, /* 1 DSK6713_AIC23_RIGHTINVOL Right line input channel volume */\

0x00d8, /* 2 DSK6713_AIC23_LEFTHPVOL Left channel headphone volume */ \

0x00d8, /* 3 DSK6713_AIC23_RIGHTHPVOL Right channel headphone volume */ \

0x0011, /* 4 DSK6713_AIC23_ANAPATH Analog audio path control */ \

0x0000, /* 5 DSK6713_AIC23_DIGPATH Digital audio path control */ \0x0000, /* 6 DSK6713_AIC23_POWERDOWN Power down control */ \

0x0043, /* 7 DSK6713_AIC23_DIGIF Digital audio interface format */ \

0x0081, /* 8 DSK6713_AIC23_SAMPLERATE Sample rate control */ \

0x0001 /* 9 DSK6713_AIC23_DIGACT Digital interface activation */ \

};

/* main() - Main code routine, initializes BSL and generates tone */

void main()

{

DSK6713_AIC23_CodecHandle hCodec;

int l_input, r_input,l_output, r_output;

/* Initialize the board support library, must be called first */

DSK6713_init();

/* Start the codec */

hCodec = DSK6713_AIC23_openCodec(0, &config);

/*set codec sampling frequency*/

DSK6713_AIC23_setFreq(hCodec, 3);

while(1)

{

/* Read a sample to the left channel */

while (!DSK6713_AIC23_read(hCodec, &l_input));

/* Read a sample to the right channel */


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while (!DSK6713_AIC23_read(hCodec, &r_input));

/* Send a sample to the left channel */

while (!DSK6713_AIC23_write(hCodec, l_input));

/* Send a sample to the right channel */

while (!DSK6713_AIC23_write(hCodec, l_input));}

/* Close the codec */

DSK6713_AIC23_closeCodec(hCodec);}

USING MATLAB TO DETERMINE FILTER COEFFICIENTS :

Using FIR1 Function on Matlab

B = FIR1(N,Wn) designs an N'th order lowpass FIR digital filter and returns the filter

coefficients in length N+1 vector B.

The cut-off frequency Wn must be between 0 < Wn < 1.0, with 1.0 corresponding to half the

sample rate. The filter B is real and has linear phase, i.e., even symmetric coefficients obeying

B(k) = B(N+2-k), k = 1,2,...,N+1.

If Wn is a two-element vector, Wn = [W1 W2], FIR1 returns an order N bandpass filter with

passband W1 < W < W2.

B = FIR1(N,Wn,'high') designs a highpass filter.

B = FIR1(N,Wn,'stop') is a bandstop filter if Wn = [W1 W2].

4. IMPLEMENTATION OF AN FIR FILTER : ALGORITHM TO IMPLEMENT :

We need to realize an advance FIR filter by implementing its difference equation as per the

specifications. A direct form I implementation approach is taken. (The filter coefficients are

taken as ai as generated by the Matlab program.)

C PROGRAM TO IMPLEMENT FIR FILTER:

fir.c


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#include "filtercfg.h"

#include "dsk6713.h"#include "dsk6713_aic23.h"

float filter_Coeff[] ={0.000000,-0.001591,-0.002423,0.000000,0.005728,0.011139,0.010502,-0.000000,-0.018003,-0.033416,-0.031505,0.000000,

0.063010,0.144802,0.220534,0.262448,0.220534,0.144802,0.063010,0.000000,-0.031505,-0.033416,-0.018003,-0.000000,0.010502,0.011139,0.005728,

0.000000,-0.002423,-0.001591,0.000000 };

static short in_buffer[100];

DSK6713_AIC23_Config config = {\

0x0017, /* 0 DSK6713_AIC23_LEFTINVOL Leftline input channel volume */\0x0017, /* 1 DSK6713_AIC23_RIGHTINVOL Right line input channel volume*/\0x00d8, /* 2 DSK6713_AIC23_LEFTHPVOL Left channel headphone volume */\

0x00d8, /* 3 DSK6713_AIC23_RIGHTHPVOL Right channel headphone volume */\0x0011, /* 4 DSK6713_AIC23_ANAPATH Analog audio path control */\

0x0000, /* 5 DSK6713_AIC23_DIGPATH Digital audio path control */\

0x0000, /* 6 DSK6713_AIC23_POWERDOWN Power down control */\0x0043, /* 7 DSK6713_AIC23_DIGIF Digital audio interface format */\

0x0081, /* 8 DSK6713_AIC23_SAMPLERATE Sample rate control */\0x0001 /* 9 DSK6713_AIC23_DIGACT Digital interface activation */ \

};

/*

* main() - Main code routine, initializes BSL and generates tone*/

void main(){

DSK6713_AIC23_CodecHandle hCodec;

Uint32 l_input, r_input,l_output, r_output;

/* Initialize the board support library, must be called first */

DSK6713_init();

/* Start the codec */hCodec = DSK6713_AIC23_openCodec(0, &config);

DSK6713_AIC23_setFreq(hCodec, 1);

while(1){ /* Read a sample to the left channel */

while (!DSK6713_AIC23_read(hCodec, &l_input));

/* Read a sample to the right channel */

while (!DSK6713_AIC23_read(hCodec, &r_input));

l_output=(Int16)FIR_FILTER(&filter_Coeff ,l_input);

r_output=l_output;

/* Send a sample to the left channel */while (!DSK6713_AIC23_write(hCodec, l_output));


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/* Send a sample to the right channel */while (!DSK6713_AIC23_write(hCodec, r_output));

}

/* Close the codec */DSK6713_AIC23_closeCodec(hCodec);

}

signed int FIR_FILTER(float * h, signed int x)

{int i=0;

signed long output=0;

in_buffer[0] = x; /* new input at buffer[0] */

for(i=30;i>0;i--)in_buffer[i] = in_buffer[i-1]; /* shuffle the buffer */

for(i=0;i

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